Tandym

tandym

Recently I came across the issue of using the module in Nuxt.js and invoking a $ in subsequent line of code in the same method. The conundrum began when the lines after the method did not execute as intended since the page was redirected to the redirect URI. auth router.push auth.loginWith It has been only a week in the Vue.js land, so I suppose this issue is something faced by many newbies. So, here goes where it all started: I have a function, whose body looks like: authenticate() { .$auth.loginWith( , { : .user }) .$router.push( .localePath({ : }))
} (e) { } try await this 'local' data this // some other line of code that shows loading msgs // ... this this path 'dashboard' catch // handling error Now, notice that once the gets invoked, the execution is handed over to the middleware of nuxt.js. Thus, using a in is redundant. line: 2 auth $router.push line:5 Before we proceed any further, let’s take a look where the auth’s configs are defined: Go to: nuxt.config.js Find the key auth auth: { : { : , : , : , : 
    }, redirect login '/login' logout '/' callback '/login' home Notice, the key. home Bingo! This is exactly where we want to tweak. Before we do any tweaking, let’s make it clear what we are trying to do and : why We want to use all the good things offered by the module, other than the thing. auth redirect The lines after the are necessary to be executed before the view can be updated. In this case it is a loading and success notification that needs to be shown before the user is redirected to the dashboard upon successful login. $auth.loginWith Now, the only thing left to do is disabling the redirect case in option auth will do the job! home: false The object in would look like this now: auth nuxt.config.js auth: { : { : , : , : , : }, redirect login '/login' logout '/' callback '/login' home false Bam! We are done. Previously published at https://medium.com/consol/using-auth-modules-redirect-in-tandem-with-router-push-in-nuxt-js-d6d703e0a85a

Using $auth Module’s Redirect in Tandem With $router.push in Nuxt.js

Every day, new developments in artificial intelligence and machine learning generate an increasing amount of data. For finance and investment professionals, navigating this data-rich environment is challenging, and the costs of mistakes are high. That’s why the industry of data analytics and visualization is expanding at an extraordinary pace. A few years ago, my approach to data analysis was completely transformed by Atmajitsinh Gohil’s R Data Visualization Cookbook, which offers over 80 “recipes” for data visualization with R programming language. Published in 2015, this book has been used by students of data analytics, journalists, designers, and policymakers alike. However, the landscape is evolving rapidly. I spoke with Atmajitsinh Gohil to discuss how technology is revolutionizing data management, particularly in finance. Data’s rapid growth With AI and ML creating new datasets that previously didn't exist, the challenge lies in continuously developing skills to visualize this data effectively. According to Gohil, the tools available often lag behind the advancements in technology. That’s why finance professionals should be innovative in their approach to data representation and analysis. AI's impact on financial applications “In the banking sector, the application of AI is still in its nascent stages,” Gohil said. “Banks are cautious about adopting new technologies due to concerns over data security and client privacy.” However, the finance industry is starting to explore AI’s potential in various areas. Gohil mentions internal AI projects aimed at text summarization, automating report generation, and predictive analysis of stock performance. Many banks are also developing in-house AI models versus relying on OpenAI or other external AI products. “The effectiveness of these models heavily depends on the quality of the data provided,” Gohil notes. “If a bank's data is biased or poorly maintained, the resulting models will likely underperform.” Use in fraud detection Data visualization plays a pivotal role in fraud detection. Gohil explains that machine learning models identify fraud by analyzing vast datasets, even though the proportion of fraudulent data is typically small. Visualization techniques help create dashboards that display login patterns, demographic information, and fraud occurrences, enabling banks to tweak and improve their fraud detection models. "Fraudsters adapt very quickly," Gohil warns, emphasizing the importance of being prepared for new threats and implementing robust security measures. Developing proprietary AI The need for concise and actionable data presentation has never been greater, said Gohil. "The rate at which we are producing data has increased," he explains. Visualization tools help make sense of this data, allowing for informed decision-making and efficient analysis. As the finance sector navigates the complexities of AI and ML, the continuous development of innovative visualization techniques is crucial in strategy and decision making. However, the biggest challenge and opportunity for banks lie in developing proprietary AI, where they face tough competition. OpenAI models benefit from training on publicly available data, from YouTube to news articles, which banks cannot use due to privacy concerns. The future of data visualization Gohil sees the data visualization industry expanding in tandem with AI and ML, development of new visualization techniques, and tools specifically designed for large-scale datasets used by AI models. "It would be interesting to see data visualization tools that explain how AI models operate at different stages," Gohil notes. Such tools would make it easier for banks to understand and adopt new technologies, potentially leading to broader acceptance and application. Photo credit: Shutterstock Every day, new developments in artificial intelligence and machine learning generate an increasing amount of data. For finance and investment professionals, navigating this data-rich environment is challenging, and the costs of mistakes are high. That’s why the industry of data analytics and visualization is expanding at an extraordinary pace. A few years ago, my approach to data analysis was completely transformed by Atmajitsinh Gohil’s R Data Visualization Cookbook , which offers over 80 “recipes” for data visualization with R programming language. R Data Visualization Cookbook Published in 2015, this book has been used by students of data analytics, journalists, designers, and policymakers alike. However, the landscape is evolving rapidly. I spoke with Atmajitsinh Gohil to discuss how technology is revolutionizing data management, particularly in finance. Data’s rapid growth Data’s rapid growth With AI and ML creating new datasets that previously didn't exist, the challenge lies in continuously developing skills to visualize this data effectively. According to Gohil, the tools available often lag behind the advancements in technology. That’s why finance professionals should be innovative in their approach to data representation and analysis. AI's impact on financial applications AI's impact on financial applications “In the banking sector, the application of AI is still in its nascent stages,” Gohil said. “Banks are cautious about adopting new technologies due to concerns over data security and client privacy.” However, the finance industry is starting to explore AI’s potential in various areas. Gohil mentions internal AI projects aimed at text summarization, automating report generation, and predictive analysis of stock performance. Many banks are also developing in-house AI models versus relying on OpenAI or other external AI products. “The effectiveness of these models heavily depends on the quality of the data provided,” Gohil notes. “If a bank's data is biased or poorly maintained, the resulting models will likely underperform.” Use in fraud detection Use in fraud detection Data visualization plays a pivotal role in fraud detection. Gohil explains that machine learning models identify fraud by analyzing vast datasets, even though the proportion of fraudulent data is typically small. Visualization techniques help create dashboards that display login patterns, demographic information, and fraud occurrences, enabling banks to tweak and improve their fraud detection models. "Fraudsters adapt very quickly," Gohil warns, emphasizing the importance of being prepared for new threats and implementing robust security measures. Developing proprietary AI Developing proprietary AI The need for concise and actionable data presentation has never been greater, said Gohil. "The rate at which we are producing data has increased," he explains. Visualization tools help make sense of this data, allowing for informed decision-making and efficient analysis. As the finance sector navigates the complexities of AI and ML, the continuous development of innovative visualization techniques is crucial in strategy and decision making. However, the biggest challenge and opportunity for banks lie in developing proprietary AI, where they face tough competition. OpenAI models benefit from training on publicly available data, from YouTube to news articles, which banks cannot use due to privacy concerns. The future of data visualization The future of data visualization Gohil sees the data visualization industry expanding in tandem with AI and ML, development of new visualization techniques, and tools specifically designed for large-scale datasets used by AI models. "It would be interesting to see data visualization tools that explain how AI models operate at different stages," Gohil notes. Such tools would make it easier for banks to understand and adopt new technologies, potentially leading to broader acceptance and application. Photo credit: Shutterstock Photo credit: Shutterstock

Revolutionizing Data Management for Strategic Decision-Making

It is safe to describe the invention of smart contracts as one of the most significant milestones in web3 and cryptography — after Bitcoin! After all, much of what is now known as the DeFi ecosystem owes its existence to smart contract functionalities, powering a host of decentralized applications (DApps). The keypoint about smart contracts is that they are self-executing agreements stored on a blockchain. They automatically enforce their terms when predefined conditions are met. Smart contracts have come a long way since they were first proposed in 1994 by the renowned American computer scientist, Nick Szabo, such that 19 years later the Ethereum blockchain emerged as the first smart contract platform popularizing the idea of a self-executing blockchain program. In a world where the mass of humanity seek to break free of centralized control, smart contract technology offers a secure and transparent way to conduct transactions without the need for intermediaries. However, despite its level of sophistication, it has remained a subject of cyber security attacks. The DAO hack of 2016, for example,is a stark reminder of what can happen when a major flaw is exploited in a smart contract. Additionally, deploying a smart on a blockchain requires a huge amount of computation and data storage. This often leads to a high transaction fee and the need to keep the network running means that miners or validators are compensated with it. But contending with a high transaction fee can create a significant barrier the adoption of this technology. This is best imagined in a scenario where people have to pay hefty fees for transactions (i.e. buying a cup of coffee with cryptocurrency or an NFT). It not only discourages mass adoption but also hinders the growth of smart contract application across various industries. This bring us to the concept of feeless smart contracts, which is often touted as a potential solution to eliminate transaction fees entirely. Can a zero-fee smart contract unlock a new era of accessibility within the blockchain ecosystem? Let’s find out! Unpacking Feeless Smart Contracts In the realm of blockchain technology, the concept of a feeless smart contract platform has attracted a significant amount of attention. This is due to the fact that it promises to not only revolutionize how we interact with decentralized applications (dApps) but also seeks to entirely reshape smart contract technology. However, the realization of a truly feeless blockchain ecosystem can be best described as a work in a progress , with blockchain experts exploring potential solutions that can help solve the current challenges hindering it. The amount of attention it continues to command in the blockchain space has not only made feeless smart contract technology an area of ongoing research and development, but it has also facilitated the emergence of   pragmatic approaches such as proof-of-work with burning, layered architectures and proof-of-stake (PoS) consensus mechanism. Proof-of-Work (PoW) With Burning: Proof-of-work with burning is a combination of the proof-of-work (PoW) consensus mechanism and the routine practice of burning of a token to drive its scarcity. The idea behind this approach is to allow miners to earn tokens that are burned (i.e. kept out of circulation) when used for transactions. However, this solution is criticized because of the deflationary effect it creates. It is also not considered a sustainable model because critics believe that miner rewards might become insufficient and the network security jeopardized, if tokens are burned too quickly. Proof-of-Stake(PoS) Consensus Mechanisms: The proof-of-stake (PoS) consensus mechanism emerged as a viable alternative to the proof-of-work (PoW) mechanism and it has gained a significant amount of attention in the industry. Leading blockchain protocols such as Solana, Cardano, Polkadot, etc leverage this approach to PoW mainly because it requires less energy consumption and it is eco-friendly. However, it is important to note that it is not entirely feeless as a very insignificant amount of transaction fee is usually required for keep the network running. Layered Architectures: In a layered architecture, the base layer is designed to handle the security and consensus mechanism of the blockchain, while the fee layer is designed to enable the execution of smart contract. This approach is often deployed by top protocols such as Cosmos and Polkadot as it creates room for a more flexible fee increasing the chances of feeless transactions on the smart contract layer. Qubic Protocol As a Case Study Qubic is an AI blockchain ecosystem pioneering innovation in feeless smart contract in a quest to eliminate the transaction fees often associated with deploying smart contract on other platforms. To achieve this feat Qubic protocol proposes a Proof-of-Work(PoW)model with a twist: burning. Similar to Proof-of-Work mechanisms, miners compete to solve complex puzzles ad secure the Qubic network. However, instead of receiving cryptocurrency, miners are rewarded with Qubic Units, the native token of the protocol recently listed on MEXC in tandem with its 2024 road map. Its proposed solution allows users who want to execute a smart contract to BURN a specific amount of QUs, instead of paying a hefty transaction fee. This burning mechanism then removes the burned QUs from circulation, fostering a potential scarcity to increase the value of the remaining tokens. However, since Proof-of-Work(PoW) mechanisms are often criticized for their high energy consumption, Qubic needs to navigate this obstacle to create a truly feeless ecosystem. Nonetheless, Qubic’s burning POW model presents a novel approach in the development of feeless smart contract. The Challenges of Feeless Smart Contracts When it comes to feeless smart contract technology, there is a thin line between hype and reality. This is why it is important to first acknowledge that it is an emerging technology and not a one-size-fit-all solution to the high transaction fee that many blockchain protocols continue to contend with. In essence, it is crucial to state that feeless smart contract technology  faces a unique set of of challenges such as security, sustainability and scalability. Sustainability Building a feeless smart contract is not enough. It must be sustainable in the long run. As a matter of fact,its sustainability lies in its ability to function on a secure and decentralized blockchain protocol. There is also the need to create and maintain sufficient incentives for miners or validators in order to run and secure  the network. Security Smart contract audit is a routine practice of checking for vulnerabilities in code. This is an industry standard procedure that is required to minimize potential cyber security risks. It is hinged on the premise that blockchain protocols are not immune from cyber attacks, which can result in substantial financial loss. In order to address a potential security risk that may arise,it is crucial for a feeless smart contract to undergo a regular smart audit  as it can help to uncover security flaw in its code. Scalability Scalability has been recognized as one of the key challenges hindering mass adoption, next only to the complex UI problem of blockchain technology. The idea behind scalability is to ensure that a blockchain protocol can meet up with the growing number of participants on its network. It stands to reason that a feeless smart contract also needs to scale efficiently to handle a growing user base and transaction volume without undermining the performance of its network. Conclusion Exploring the idea of feeless smart technology provides valuable insights into some practical solutions tailored towards reducing transaction costs and improving blockchain scalability. Despite the fact that it is currently a distant goal , it holds a lot of potential for revolutionizing the blockchain space, especially in the area of gas fee reduction. Resolved that the idea of a completely feeless blockchain protocol is bound to lead to much skepticism, the innovative approaches that have been explored are already pushing the boundaries of smart contract technology. With continuous development and research, it is going to be finally decided whether the ultimate goal of this technology is to create a feeless blockchain ecosystem or to usher in an era of improved cost-effective user experience. It is safe to describe the invention of smart contracts as one of the most significant milestones in web3 and cryptography — after Bitcoin! After all, much of what is now known as the DeFi ecosystem owes its existence to smart contract functionalities, powering a host of decentralized applications (DApps). smart contracts smart contracts The keypoint about smart contracts is that they are self-executing agreements stored on a blockchain. They automatically enforce their terms when predefined conditions are met. Smart contracts have come a long way since they were first proposed in 1994 by the renowned American computer scientist, Nick Szabo, such that 19 years later the Ethereum blockchain emerged as the first smart contract platform popularizing the idea of a self-executing blockchain program. In a world where the mass of humanity seek to break free of centralized control, smart contract technology offers a secure and transparent way to conduct transactions without the need for intermediaries. However, despite its level of sophistication, it has remained a subject of cyber security attacks . The DAO hack of 2016 , for example,is a stark reminder of what can happen when a major flaw is exploited in a smart contract. attacks attacks DAO hack of 2016 DAO hack of 2016 Additionally, deploying a smart on a blockchain requires a huge amount of computation and data storage. This often leads to a high transaction fee and the need to keep the network running means that miners or validators are compensated with it. But contending with a high transaction fee can create a significant barrier the adoption of this technology. This is best imagined in a scenario where people have to pay hefty fees for transactions (i.e. buying a cup of coffee with cryptocurrency or an NFT). It not only discourages mass adoption but also hinders the growth of smart contract application across various industries. This bring us to the concept of feeless smart contracts, which is often touted as a potential solution to eliminate transaction fees entirely. Can a zero-fee smart contract unlock a new era of accessibility within the blockchain ecosystem? Let’s find out! Unpacking Feeless Smart Contracts Unpacking Feeless Smart Contracts In the realm of blockchain technology, the concept of a feeless smart contract platform has attracted a significant amount of attention. This is due to the fact that it promises to not only revolutionize how we interact with decentralized applications (dApps) but also seeks to entirely reshape smart contract technology. However, the realization of a truly feeless blockchain ecosystem can be best described as a work in a progress , with blockchain experts exploring potential solutions that can help solve the current challenges hindering it. The amount of attention it continues to command in the blockchain space has not only made feeless smart contract technology an area of ongoing research and development, but it has also facilitated the emergence of   pragmatic approaches such as proof-of-work with burning, layered architectures and proof-of-stake (PoS) consensus mechanism. Proof-of-Work (PoW) With Burning: Proof-of-Work (PoW) With Burning: Proof-of-work with burning is a combination of the proof-of-work (PoW) consensus mechanism and the routine practice of burning of a token to drive its scarcity. The idea behind this approach is to allow miners to earn tokens that are burned (i.e. kept out of circulation) when used for transactions. However, this solution is criticized because of the deflationary effect it creates. It is also not considered a sustainable model because critics believe that miner rewards might become insufficient and the network security jeopardized, if tokens are burned too quickly. Proof-of-Stake(PoS) Consensus Mechanisms: Proof-of-Stake(PoS) Consensus Mechanisms: The proof-of-stake (PoS) consensus mechanism emerged as a viable alternative to the proof-of-work (PoW) mechanism and it has gained a significant amount of attention in the industry. Leading blockchain protocols such as Solana, Cardano, Polkadot, etc leverage this approach to PoW mainly because it requires less energy consumption and it is eco-friendly. However, it is important to note that it is not entirely feeless as a very insignificant amount of transaction fee is usually required for keep the network running. Layered Architectures: In a layered architecture, the base layer is designed to handle the security and consensus mechanism of the blockchain, while the fee layer is designed to enable the execution of smart contract. This approach is often deployed by top protocols such as Cosmos and Polkadot as it creates room for a more flexible fee increasing the chances of feeless transactions on the smart contract layer. Qubic Protocol As a Case Study Qubic is an AI blockchain ecosystem pioneering innovation in feeless smart contract in a quest to eliminate the transaction fees often associated with deploying smart contract on other platforms. To achieve this feat Qubic protocol proposes a Proof-of-Work(PoW)model with a twist: burning. Proof-of-Work(PoW)model with a twist: burning. Similar to Proof-of-Work mechanisms, miners compete to solve complex puzzles ad secure the Qubic network. However, instead of receiving cryptocurrency, miners are rewarded with Qubic Units, the native token of the protocol recently listed on MEXC in tandem with its 2024 road map. Qubic Units, Its proposed solution allows users who want to execute a smart contract to BURN a specific amount of QUs, instead of paying a hefty transaction fee. This burning mechanism then removes the burned QUs from circulation, fostering a potential scarcity to increase the value of the remaining tokens. However, since Proof-of-Work(PoW) mechanisms are often criticized for their high energy consumption, Qubic needs to navigate this obstacle to create a truly feeless ecosystem. Nonetheless, Qubic’s burning POW model presents a novel approach in the development of feeless smart contract. The Challenges of Feeless Smart Contracts When it comes to feeless smart contract technology, there is a thin line between hype and reality. This is why it is important to first acknowledge that it is an emerging technology and not a one-size-fit-all solution to the high transaction fee that many blockchain protocols continue to contend with. In essence, it is crucial to state that feeless smart contract technology  faces a unique set of of challenges such as security, sustainability and scalability. Sustainability Building a feeless smart contract is not enough. It must be sustainable in the long run. As a matter of fact,its sustainability lies in its ability to function on a secure and decentralized blockchain protocol. There is also the need to create and maintain sufficient incentives for miners or validators in order to run and secure  the network. Security Smart contract audit is a routine practice of checking for vulnerabilities in code. This is an industry standard procedure that is required to minimize potential cyber security risks. It is hinged on the premise that blockchain protocols are not immune from cyber attacks, which can result in substantial financial loss. In order to address a potential security risk that may arise,it is crucial for a feeless smart contract to undergo a regular smart audit  as it can help to uncover security flaw in its code. Scalability Scalability has been recognized as one of the key challenges hindering mass adoption, next only to the complex UI problem of blockchain technology. The idea behind scalability is to ensure that a blockchain protocol can meet up with the growing number of participants on its network. It stands to reason that a feeless smart contract also needs to scale efficiently to handle a growing user base and transaction volume without undermining the performance of its network. Conclusion Exploring the idea of feeless smart technology provides valuable insights into some practical solutions tailored towards reducing transaction costs and improving blockchain scalability. Despite the fact that it is currently a distant goal , it holds a lot of potential for revolutionizing the blockchain space, especially in the area of gas fee reduction. Resolved that the idea of a completely feeless blockchain protocol is bound to lead to much skepticism, the innovative approaches that have been explored are already pushing the boundaries of smart contract technology. With continuous development and research, it is going to be finally decided whether the ultimate goal of this technology is to create a feeless blockchain ecosystem or to usher in an era of improved cost-effective user experience.

Are Feeless Smart Contracts the Answer?

Authors:
(1) Ying Tang, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (tang@rowan.edu);
(2) Ryan Hare, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (harer6@rowan.edu). Table of Links Abstract and I. Introduction II. Overview of PING System and Gridlock III. Design Insights and Educational Impact IV. Conclusion and References Abstract This work-in-progress research-to-practice paper provides ongoing results from the development and testing of a personalized learning system integrated into a serious game. Given limited instructor resources, the use of computerized systems to help tutor students offers a way to provide higher quality education and to improve educational efficacy. Personalized learning systems like the one proposed in this paper offer an accessible solution. Furthermore, by combining such a system with a serious game, students are further engaged in interacting with the system. The proposed learning system combines expert-driven structure and lesson planning with computational intelligence methods and gamification to provide students with a fun and educational experience. As the project is ongoing from past years, numerous design iterations have been made on the system based on feedback from students and classroom observations. Using computational intelligence, the system adaptively provides support to students based on data collected from both their in-game actions and by estimating their emotional state from webcam images. For our evaluation, we focus on student data gathered from in-classroom testing in relevant courses, with both educational efficacy results and student observations. To demonstrate the effect of our proposed system, students in an early electrical engineering course were instructed to interact with the system in place of their standard lab assignments. The system would then measure and help them improve their background knowledge before allowing them to complete the lab assignment. As they played through the game, we observed their interactions with the system to gather insights for future developments, which are presented in this work. Additionally, we demonstrate the system’s educational efficacy through early prepost-test results from students who played the game with and without the personalized learning system integration. Keywords—Gamification, Educational Software, Learning Technology, Higher Education, Engineering Education I. INTRODUCTION Despite many recent advancements and paradigm shifts worldwide, classroom education still plays a pivotal role in equipping students with the skills and knowledge required to join the workforce and address real problems. However, larger classrooms can struggle to address student needs and concerns, especially when those needs heavily deviate from the standard one-size-fits-all lesson plan [1, 2]. And as classroom sizes increase, it becomes impossible for instructors to provide support to each individual student based on their needs and background [3]. As such, students often need to resort to excessive studying, self-learning, or external tutoring to gain much-needed knowledge [4]. To take some burden off the instructor while still limiting learning to class time, one possible solution is intelligent tutoring systems (ITSs). ITSs are educational systems that leverage student data to provide personalized educational support and tutoring to students [5]. Unlike human tutors, these systems are not limited by time and resource constraints and can support many students at the same time. By collecting data such as student performance, student actions when interacting with the system, or results on graded assignments, these systems can make informed decisions about what support a student needs to succeed. Furthermore, recent trends in artificial intelligence (AI) and data mining have further bolstered the accuracy and ability of these systems to provide effective support. However, while these systems can provide appropriate support, they can often struggle with engaging students. This is especially prevalent when students are interacting with systems that require them to read large sections of content, watch extended videos, or otherwise undergo non-active learning [6]. Maintaining this engagement is crucial for effective learning. [7, 8]. The solution to the issue of student engagement, as addressed in this paper, is to combine an ITS with a serious game (SG) [9]. Serious games are virtual or physical games that focus primarily on education, training, or other non-entertainment purposes [10]. Due to the gamification aspect of SGs, students are often significantly more engaged in lessons. In a well-designed game, students may not even realize that they are learning. Additionally, virtual SGs have added benefits in that they can create environments, visualizations, or interactive scenarios that students would not otherwise experience through standard lectures or videos. This paper presents results from the ongoing development of Gridlock, a serious game that focuses on binary logic, simple programming, and digital system design. Gridlock is designed to be run in tandem with a lab assignment where students are tasked with designing the logic controller for a traffic light. This task is the goal of the game, and is a common lab assignment for early students in electrical engineering, computer engineering, and computer science. Gridlock further supports student learning by combining a serious game environment with an ITS that supports students on various topics relevant to the overarching task. Using reinforcement learning, an AI method, the system automatically adjusts what support it provides based on student responses. This support then manifests as pop-up prompts, hints, adjusted content, and enabling or disabling certain areas in the game based on student performance. We refer to the combined system as the personalized instruction and need-aware gaming (PING) system, and while this paper focuses on Gridlock, the system is designed as a modular, general-purpose approach for any SG. As the contribution of this work, we provide design insights for both our ITS and our virtual game environment based on both observations of student interactions with the system and interview sessions with students where they provided direct feedback. We also provide educational results from inclassroom testing to verify the educational efficacy of our proposed system. To that end, Section II of this paper gives a more detailed overview of our ITS, Gridlock, and the support provided in the game. Section III provides our development insights and educational results, followed by conclusions in Section IV. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Ying Tang, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (tang@rowan.edu); (2) Ryan Hare, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (harer6@rowan.edu). Authors: Authors: (1) Ying Tang, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (tang@rowan.edu); Dept. of Electrical and Computer Engineering, Rowan University, (2) Ryan Hare, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (harer6@rowan.edu). Dept. of Electrical and Computer Engineering, Rowan University, Table of Links Abstract and I. Introduction Abstract and I. Introduction II. Overview of PING System and Gridlock II. Overview of PING System and Gridlock III. Design Insights and Educational Impact III. Design Insights and Educational Impact IV. Conclusion and References IV. Conclusion and References Abstract This work-in-progress research-to-practice paper provides ongoing results from the development and testing of a personalized learning system integrated into a serious game. Given limited instructor resources, the use of computerized systems to help tutor students offers a way to provide higher quality education and to improve educational efficacy. Personalized learning systems like the one proposed in this paper offer an accessible solution. Furthermore, by combining such a system with a serious game, students are further engaged in interacting with the system. The proposed learning system combines expert-driven structure and lesson planning with computational intelligence methods and gamification to provide students with a fun and educational experience. As the project is ongoing from past years, numerous design iterations have been made on the system based on feedback from students and classroom observations. Using computational intelligence, the system adaptively provides support to students based on data collected from both their in-game actions and by estimating their emotional state from webcam images. For our evaluation, we focus on student data gathered from in-classroom testing in relevant courses, with both educational efficacy results and student observations. This work-in-progress research-to-practice paper provides ongoing results from the development and testing of a personalized learning system integrated into a serious game. Given limited instructor resources, the use of computerized systems to help tutor students offers a way to provide higher quality education and to improve educational efficacy. Personalized learning systems like the one proposed in this paper offer an accessible solution. Furthermore, by combining such a system with a serious game, students are further engaged in interacting with the system. The proposed learning system combines expert-driven structure and lesson planning with computational intelligence methods and gamification to provide students with a fun and educational experience. As the project is ongoing from past years, numerous design iterations have been made on the system based on feedback from students and classroom observations. Using computational intelligence, the system adaptively provides support to students based on data collected from both their in-game actions and by estimating their emotional state from webcam images. For our evaluation, we focus on student data gathered from in-classroom testing in relevant courses, with both educational efficacy results and student observations. To demonstrate the effect of our proposed system, students in an early electrical engineering course were instructed to interact with the system in place of their standard lab assignments. The system would then measure and help them improve their background knowledge before allowing them to complete the lab assignment. As they played through the game, we observed their interactions with the system to gather insights for future developments, which are presented in this work. Additionally, we demonstrate the system’s educational efficacy through early prepost-test results from students who played the game with and without the personalized learning system integration. To demonstrate the effect of our proposed system, students in an early electrical engineering course were instructed to interact with the system in place of their standard lab assignments. The system would then measure and help them improve their background knowledge before allowing them to complete the lab assignment. As they played through the game, we observed their interactions with the system to gather insights for future developments, which are presented in this work. Additionally, we demonstrate the system’s educational efficacy through early prepost-test results from students who played the game with and without the personalized learning system integration. Keywords—Gamification, Educational Software, Learning Technology, Higher Education, Engineering Education Keywords—Gamification, Educational Software, Learning Technology, Higher Education, Engineering Education I. INTRODUCTION Despite many recent advancements and paradigm shifts worldwide, classroom education still plays a pivotal role in equipping students with the skills and knowledge required to join the workforce and address real problems. However, larger classrooms can struggle to address student needs and concerns, especially when those needs heavily deviate from the standard one-size-fits-all lesson plan [1, 2]. And as classroom sizes increase, it becomes impossible for instructors to provide support to each individual student based on their needs and background [3]. As such, students often need to resort to excessive studying, self-learning, or external tutoring to gain much-needed knowledge [4]. To take some burden off the instructor while still limiting learning to class time, one possible solution is intelligent tutoring systems (ITSs). ITSs are educational systems that leverage student data to provide personalized educational support and tutoring to students [5]. Unlike human tutors, these systems are not limited by time and resource constraints and can support many students at the same time. By collecting data such as student performance, student actions when interacting with the system, or results on graded assignments, these systems can make informed decisions about what support a student needs to succeed. Furthermore, recent trends in artificial intelligence (AI) and data mining have further bolstered the accuracy and ability of these systems to provide effective support. However, while these systems can provide appropriate support, they can often struggle with engaging students. This is especially prevalent when students are interacting with systems that require them to read large sections of content, watch extended videos, or otherwise undergo non-active learning [6]. Maintaining this engagement is crucial for effective learning. [7, 8]. The solution to the issue of student engagement, as addressed in this paper, is to combine an ITS with a serious game (SG) [9]. Serious games are virtual or physical games that focus primarily on education, training, or other non-entertainment purposes [10]. Due to the gamification aspect of SGs, students are often significantly more engaged in lessons. In a well-designed game, students may not even realize that they are learning. Additionally, virtual SGs have added benefits in that they can create environments, visualizations, or interactive scenarios that students would not otherwise experience through standard lectures or videos. This paper presents results from the ongoing development of Gridlock , a serious game that focuses on binary logic, simple programming, and digital system design. Gridlock is designed to be run in tandem with a lab assignment where students are tasked with designing the logic controller for a traffic light. This task is the goal of the game, and is a common lab assignment for early students in electrical engineering, computer engineering, and computer science. Gridlock further supports student learning by combining a serious game environment with an ITS that supports students on various topics relevant to the overarching task. Using reinforcement learning, an AI method, the system automatically adjusts what support it provides based on student responses. This support then manifests as pop-up prompts, hints, adjusted content, and enabling or disabling certain areas in the game based on student performance. We refer to the combined system as the personalized instruction and need-aware gaming (PING) system, and while this paper focuses on Gridlock, the system is designed as a modular, general-purpose approach for any SG. Gridlock Gridlock Gridlock As the contribution of this work, we provide design insights for both our ITS and our virtual game environment based on both observations of student interactions with the system and interview sessions with students where they provided direct feedback. We also provide educational results from inclassroom testing to verify the educational efficacy of our proposed system. To that end, Section II of this paper gives a more detailed overview of our ITS, Gridlock , and the support provided in the game. Section III provides our development insights and educational results, followed by conclusions in Section IV. Gridlock This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Enhancing Learning with Gamified Instruction: Abstract and Introduction

In a market as volatile as crypto, features such as stability and price protection of some sort could be very welcome for all users. They’re not always available, though, let alone if you’re looking to earn some rewards. Luckily, the token LINE deployed on the Kava network and connected to Obyte is offering this opportunity to everyone. LINE operates as a price-protected digital asset, designed to safeguard your investment through a unique borrowing system deployed on Kava. Users can acquire LINE tokens by locking up an equivalent value of GBYTE (Obyte native currency) collateral through linetoken.org. Then, if the price of LINE tokens rises, users can profitably sell them on the same platform and settle their loans. Conversely, if the price decreases, users can repay the loan with their LINE tokens and retrieve their GBYTE collateral, minus fees, ensuring that their principal investment remains almost intact. The price of LINE tokens is determined by market dynamics, and to stabilize it, mechanisms such as interest rate adjustments and origination fees are employed. Additionally, it’s important to note that LINE token loans have their own market as put options. They’re financial contracts that give the holder the right, but not the obligation, to sell a certain number of tokens at a predetermined price (known as the strike price) within a specified period. In this case, if you borrow LINE tokens with collateral, you get as well your put option in the form of a Non-Fungible Token (NFT) that can be freely traded. On the other hand, if you buy LINE tokens directly (without collateral) or sell your NFT-loan to make a profit, you’ll lose the feature of price protection. Kava Rise Program LINE was designed to work in tandem with the Counterstake Bridge in Obyte and the Kava Rise Program to offer rewards to its users. The Kava Rise Program serves as an initiative by Kava to incentivize decentralized applications (Dapps) operating on its network by rewarding their developers based on their Total Value Locked (TVL) —a measure of the total funds held within a DeFi protocol. Participating Dapps, including Counterstake and LINE, are eligible to receive a portion of the monthly rewards, amounting to 1,000,000 KAVA tokens (around $1 million), distributed in proportion to the TVL they generate on Kava. This setup creates an incentive structure where Dapps are prized for attracting and retaining users, fostering growth within the Kava ecosystem. ** ** Obyte is distributing 90% of the rewards acquired from the Kava Rise Program to participating users (50% since June). Anyone can leverage the LINE token by borrowing it against GBYTE collateral and bridging it to the Obyte network through Counterstake. Holding LINE tokens in Obyte wallets automatically qualifies users for a share of the Kava Rise rewards, proportional to twice their LINE balances. This mechanism not only encourages participation but also provides a means for users to earn rewards while minimizing risk, as the price-protected nature of the LINE token ensures that users can manage potential price fluctuations effectively. Currently, the Annual Percentage Yield (APY) of this method is around 60% (see kava.obyte.org for the actual rate). How to Use LINE to earn rewards? Besides trading with the tokens and the NFT-loans (in the “Market” section on the official webpage), you can take advantage of the Kava Rise Program or offer liquidity to its pools on Equilibre DEX. In the first case, you’ll need to use the Counterstake Bridge to move the assets across Kava and Obyte. ** ** You can follow some simple steps: Bridge your GBYTEs from Obyte to Kava using Counterstake.


Utilize your GBYTE-on-Kava to borrow LINE tokens from linetoken.org.


Bridge the borrowed LINE tokens back to your Obyte wallet using Counterstake.


Hold your LINE tokens in your Obyte wallet to automatically qualify for a share of the Kava Rise rewards, distributed monthly based on your LINE token holdings. You'll receive your rewards directly to your Obyte wallet without any additional steps required.


To reclaim your collateral, bridge your LINE tokens back to Kava, repay the loan on linetoken.org, and bridge the received GBYTEs back to Obyte. Alternatively, if the LINE token price has increased since you opened the loan, you can sell your LINE tokens on the market for a potentially higher return. In the second case, by providing liquidity to select pools trading LINE tokens, you can earn rewards in LINE tokens. Simply stake your LP (liquidity provider) tokens on the staking page to start earning rewards. ** ** Additionally, you have the option to stake your LINE tokens directly to receive a share of rewards. These rewards are sourced from fees collected from borrowers and are distributed to participants based on their contributions to liquidity. The staking section is available on the website. This way, LINE presents as a potentially profitable investment. Whether earning rewards through the Kava Rise program, leveraging liquidity provision for additional income, or simply holding LINE tokens for potential appreciation, users have diverse avenues to maximize their earnings while contributing to the growth of the LINE ecosystem. Featured Vector Image by vectorjuice / Freepik In a market as volatile as crypto, features such as stability and price protection of some sort could be very welcome for all users. They’re not always available, though, let alone if you’re looking to earn some rewards. Luckily, the token LINE deployed on the Kava network and connected to Obyte is offering this opportunity to everyone. LINE operates as a price-protected digital asset, designed to safeguard your investment through a unique borrowing system deployed on Kava. Users can acquire LINE tokens by locking up an equivalent value of GBYTE (Obyte native currency) collateral through linetoken.org. LINE operates as a price-protected digital asset, designed to safeguard your investment through a unique borrowing system deployed on Kava. LINE LINE Then, if the price of LINE tokens rises, users can profitably sell them on the same platform and settle their loans. Conversely, if the price decreases, users can repay the loan with their LINE tokens and retrieve their GBYTE collateral, minus fees, ensuring that their principal investment remains almost intact. The price of LINE tokens is determined by market dynamics, and to stabilize it, mechanisms such as interest rate adjustments and origination fees are employed. Additionally, it’s important to note that LINE token loans have their own market as put options. They’re financial contracts that give the holder the right, but not the obligation, to sell a certain number of tokens at a predetermined price (known as the strike price) within a specified period. In this case, if you borrow LINE tokens with collateral, you get as well your put option in the form of a Non-Fungible Token (NFT) that can be freely traded. Non-Fungible Token Non-Fungible Token On the other hand, if you buy LINE tokens directly (without collateral) or sell your NFT-loan to make a profit, you’ll lose the feature of price protection. Kava Rise Program Kava Rise Program LINE was designed to work in tandem with the Counterstake Bridge in Obyte and the Kava Rise Program to offer rewards to its users. The Kava Rise Program serves as an initiative by Kava to incentivize decentralized applications (Dapps) operating on its network by rewarding their developers based on their Total Value Locked (TVL) —a measure of the total funds held within a DeFi protocol. Kava Rise Program Kava Rise Program Participating Dapps, including Counterstake and LINE, are eligible to receive a portion of the monthly rewards, amounting to 1,000,000 KAVA tokens (around $1 million), distributed in proportion to the TVL they generate on Kava. This setup creates an incentive structure where Dapps are prized for attracting and retaining users, fostering growth within the Kava ecosystem. Participating Dapps, including Counterstake and LINE, are eligible to receive a portion of the monthly rewards, amounting to 1,000,000 KAVA tokens (around $1 million), Counterstake Counterstake ** ** Obyte is distributing 90% of the rewards acquired from the Kava Rise Program to participating users (50% since June). Anyone can leverage the LINE token by borrowing it against GBYTE collateral and bridging it to the Obyte network through Counterstake. Holding LINE tokens in Obyte wallets automatically qualifies users for a share of the Kava Rise rewards, proportional to twice their LINE balances. This mechanism not only encourages participation but also provides a means for users to earn rewards while minimizing risk, as the price-protected nature of the LINE token ensures that users can manage potential price fluctuations effectively. Currently, the Annual Percentage Yield (APY) of this method is around 60% (see kava.obyte.org for the actual rate). kava.obyte.org kava.obyte.org How to Use LINE to earn rewards? Besides trading with the tokens and the NFT-loans (in the “Market” section on the official webpage), you can take advantage of the Kava Rise Program or offer liquidity to its pools on Equilibre DEX. In the first case, you’ll need to use the Counterstake Bridge to move the assets across Kava and Obyte . “Market” “Market” Obyte Obyte ** ** You can follow some simple steps: Bridge your GBYTEs from Obyte to Kava using Counterstake. Utilize your GBYTE-on-Kava to borrow LINE tokens from linetoken.org. Bridge the borrowed LINE tokens back to your Obyte wallet using Counterstake. Hold your LINE tokens in your Obyte wallet to automatically qualify for a share of the Kava Rise rewards, distributed monthly based on your LINE token holdings. You'll receive your rewards directly to your Obyte wallet without any additional steps required. To reclaim your collateral, bridge your LINE tokens back to Kava, repay the loan on linetoken.org, and bridge the received GBYTEs back to Obyte. Alternatively, if the LINE token price has increased since you opened the loan, you can sell your LINE tokens on the market for a potentially higher return. Bridge your GBYTEs from Obyte to Kava using Counterstake. Bridge your GBYTEs from Obyte to Kava using Counterstake . Counterstake Counterstake Utilize your GBYTE-on-Kava to borrow LINE tokens from linetoken.org. Utilize your GBYTE-on-Kava to borrow LINE tokens from linetoken.org . linetoken.org linetoken.org Bridge the borrowed LINE tokens back to your Obyte wallet using Counterstake. Bridge the borrowed LINE tokens back to your Obyte wallet using Counterstake. Hold your LINE tokens in your Obyte wallet to automatically qualify for a share of the Kava Rise rewards, distributed monthly based on your LINE token holdings. You'll receive your rewards directly to your Obyte wallet without any additional steps required. Hold your LINE tokens in your Obyte wallet to automatically qualify for a share of the Kava Rise rewards, distributed monthly based on your LINE token holdings. You'll receive your rewards directly to your Obyte wallet without any additional steps required. Hold your LINE tokens in your Obyte wallet to automatically qualify for a share of the Kava Rise rewards, distributed monthly based on your LINE token holdings. To reclaim your collateral, bridge your LINE tokens back to Kava, repay the loan on linetoken.org, and bridge the received GBYTEs back to Obyte. Alternatively, if the LINE token price has increased since you opened the loan, you can sell your LINE tokens on the market for a potentially higher return. To reclaim your collateral, bridge your LINE tokens back to Kava, repay the loan on linetoken.org, and bridge the received GBYTEs back to Obyte. Alternatively, if the LINE token price has increased since you opened the loan, you can sell your LINE tokens on the market for a potentially higher return. In the second case, by providing liquidity to select pools trading LINE tokens, you can earn rewards in LINE tokens. Simply stake your LP (liquidity provider) tokens on the staking page to start earning rewards. staking page staking page ** ** Additionally, you have the option to stake your LINE tokens directly to receive a share of rewards. These rewards are sourced from fees collected from borrowers and are distributed to participants based on their contributions to liquidity. The staking section is available on the website. This way, LINE presents as a potentially profitable investment. Whether earning rewards through the Kava Rise program, leveraging liquidity provision for additional income, or simply holding LINE tokens for potential appreciation, users have diverse avenues to maximize their earnings while contributing to the growth of the LINE ecosystem. LINE LINE Featured Vector Image by vectorjuice / Freepik Featured Vector Image by vectorjuice / Freepik Freepik Freepik

Educational Byte: What is the Price-Protected Token LINE?

শিক্ষাগত বাইট: মূল্য-সুরক্ষিত টোকেন লাইন কি?

Bildungsbyte: Was ist der preisgeschützte Token LINE?

Byte educativo: ¿Qué es el token LINE con precio protegido?

Octet éducatif : qu'est-ce que la LIGNE de jetons à prix protégé ?

शैक्षिक बाइट: मूल्य-संरक्षित टोकन लाइन क्या है?

教育バイト: 価格保護トークン LINE とは何ですか?

교육 바이트: 가격 보호 토큰 LINE이란 무엇입니까?

Byte Educacional: O que é a LINHA de Token Protegido por Preço?

Образовательный байт: что такое защищенный от цены токен LINE?

Eğitim Baytı: Fiyat Korumalı Token HATTI Nedir?

Byte giáo dục: LINE Token được bảo vệ bằng giá là gì?

教育字节：什么是价格保护代币 LINE？

Understanding Authentication: A Guide to Cookie-Based and Session-Based Authentication

Omnichannel eCommerce is a retail strategy that can provide invaluable optimization techniques and insights for businesses to improve the experience offered to customers. Crucially, it can also unify different channels to deliver unprecedented levels of control over inventory management. The continued emergence of the cloud and Internet of Things (IoT) technology means that stores can now access unprecedented volumes of data points to inform and even automate key business decisions. What could this mean for optimizing inventory management? Let’s take a deeper look at how omnichannel eCommerce is proving to be a revolutionary force in optimizing stock and improving the customer experience (CX) for leads. Defining Omnichannel eCommerce Before we get to grips with the next generation of inventory management, let’s first define what omnichannel eCommerce is. Omnichannel eCommerce is a retail strategy that leverages a frictionless shopping experience for customers with the help of many different online channels working in tandem. By uniting different channels like online stores, social media, mobile apps, websites, and email data with brick-and-mortar insights through POS purchase data and returns information, brands can gain a comprehensive understanding of many facets of their operations. These data points work in a similar way to omnichannel retail but with a clear focus on optimizing inventory management and CX for online shoppers. The Battle for Visibility One of the biggest innovations of omnichannel eCommerce will be felt in inventory management. The difficulty that many eCommerce stores face when it comes to managing inventory stems from a lack of visibility in real-time. If you don’t know what’s happening to your stock, you have no way of effectively managing it. However, omnichannel insights can help to deliver more comprehensive inventory management. Finding Solutions in Generative AI Generative AI is forecasted to become a $1.3 trillion market by 2032, according to Bloomberg Intelligence, and the rapid growth of the industry is certain to have significant implications for digital transformation in inventory management. The technology will become a valuable component in omnichannel eCommerce, and capable of interpreting and providing automated solutions based on the masses of unstructured big data that consumers create when interacting with brands. This can help to automatically adjust stock levels and action orders based on factors that would be impossible for human decision-makers to act upon. For instance, generative AI platforms could carry out social listening to monitor sentiment toward products and increase inventories if a certain store item begins trending online to prevent possible understocking. If the AI deems that demand for a product is trending lower, for instance, it can make adjustments on the fly to avoid understocking. In-Store Data Informing eCommerce Omnichannel data can also come from in-store devices like cloud-based point of sale (POS) systems. Thanks to innovations in the wealth of insights available through customer purchasing trends and preferences, the best POS systems on the market can use purchase insights to better understand how, why, and when customers are purchasing certain products. This can help to shape seasonal forecasting for eCommerce stores on an international scale and help to balance inventories accordingly to ensure orders can be fulfilled with ease and without the danger of overstocking by misinterpreting trends. Optimizing Returns and Storage Omnichannel orders can make inventory management more complex if the technology isn’t used to streamline processes. This inventory balancing act becomes increasingly difficult throughout warehouses, distribution centers, and online stores when it comes to handling returns. However, the implementation of blockchain technology can help to break down these barriers and optimize the returns process by mapping out the entire lifecycle of products from their inception. This means that faulty batches can be actively monitored on-chain to isolate issues, and when a customer returns an item, the product can be easily reincorporated into its storage facility without becoming lost throughout siloed departments. This process can help steer next-generation storage management without the hassle of incurring additional holding costs for items or depreciation expenses. The holistic overview offered by blockchain can help strike a balance between storage costs and ensuring that returned products return to market on an efficient basis. The Future of Inventory Management The eCommerce landscape is evolving at a rapid pace thanks to immersive technologies like generative AI and blockchain. These transformative tools will pave the way for seamless control over inventory for brands that can improve the customer experience across different platforms, websites, and brick-and-mortar stores. Omnichannel eCommerce will be crucial to these fresh data-driven insights for stock and can help to ensure that overstock and understock scenarios are a thing of the past. With intelligent insights steering the way for order fulfillment, eCommerce stores can operate on a more sustainable and cost-effective basis long into the future. Omnichannel eCommerce is a retail strategy that can provide invaluable optimization techniques and insights for businesses to improve the experience offered to customers. Crucially, it can also unify different channels to deliver unprecedented levels of control over inventory management. The continued emergence of the cloud and Internet of Things (IoT) technology means that stores can now access unprecedented volumes of data points to inform and even automate key business decisions. What could this mean for optimizing inventory management? Let’s take a deeper look at how omnichannel eCommerce is proving to be a revolutionary force in optimizing stock and improving the customer experience (CX) for leads. Defining Omnichannel eCommerce Before we get to grips with the next generation of inventory management, let’s first define what omnichannel eCommerce is. Omnichannel eCommerce is a retail strategy that leverages a frictionless shopping experience for customers with the help of many different online channels working in tandem. Omnichannel eCommerce By uniting different channels like online stores, social media, mobile apps, websites, and email data with brick-and-mortar insights through POS purchase data and returns information, brands can gain a comprehensive understanding of many facets of their operations. These data points work in a similar way to omnichannel retail but with a clear focus on optimizing inventory management and CX for online shoppers. The Battle for Visibility One of the biggest innovations of omnichannel eCommerce will be felt in inventory management. The difficulty that many eCommerce stores face when it comes to managing inventory stems from a lack of visibility in real-time. If you don’t know what’s happening to your stock, you have no way of effectively managing it. However, omnichannel insights can help to deliver more comprehensive inventory management. Finding Solutions in Generative AI Generative AI is forecasted to become a $1.3 trillion market by 2032, according to Bloomberg Intelligence, and the rapid growth of the industry is certain to have significant implications for digital transformation in inventory management. become a $1.3 trillion market become a $1.3 trillion market The technology will become a valuable component in omnichannel eCommerce, and capable of interpreting and providing automated solutions based on the masses of unstructured big data that consumers create when interacting with brands. This can help to automatically adjust stock levels and action orders based on factors that would be impossible for human decision-makers to act upon. For instance, generative AI platforms could carry out social listening to monitor sentiment toward products and increase inventories if a certain store item begins trending online to prevent possible understocking . prevent possible understocking prevent possible understocking If the AI deems that demand for a product is trending lower, for instance, it can make adjustments on the fly to avoid understocking. In-Store Data Informing eCommerce Omnichannel data can also come from in-store devices like cloud-based point of sale (POS) systems. Omnichannel data Thanks to innovations in the wealth of insights available through customer purchasing trends and preferences, the best POS systems on the market can use purchase insights to better understand how, why, and when customers are purchasing certain products. This can help to shape seasonal forecasting for eCommerce stores on an international scale and help to balance inventories accordingly to ensure orders can be fulfilled with ease and without the danger of overstocking by misinterpreting trends. Optimizing Returns and Storage Omnichannel orders can make inventory management more complex if the technology isn’t used to streamline processes. This inventory balancing act becomes increasingly difficult throughout warehouses, distribution centers, and online stores when it comes to handling returns. inventory balancing act inventory balancing act However, the implementation of blockchain technology can help to break down these barriers and optimize the returns process by mapping out the entire lifecycle of products from their inception. This means that faulty batches can be actively monitored on-chain to isolate issues, and when a customer returns an item, the product can be easily reincorporated into its storage facility without becoming lost throughout siloed departments. This process can help steer next-generation storage management without the hassle of incurring additional holding costs for items or depreciation expenses. The holistic overview offered by blockchain can help strike a balance between storage costs and ensuring that returned products return to market on an efficient basis. The Future of Inventory Management The eCommerce landscape is evolving at a rapid pace thanks to immersive technologies like generative AI and blockchain. These transformative tools will pave the way for seamless control over inventory for brands that can improve the customer experience across different platforms, websites, and brick-and-mortar stores. Omnichannel eCommerce will be crucial to these fresh data-driven insights for stock and can help to ensure that overstock and understock scenarios are a thing of the past. With intelligent insights steering the way for order fulfillment, eCommerce stores can operate on a more sustainable and cost-effective basis long into the future.

From Shelves to Screens: Is Omnichannel eCommerce Transforming Inventory Management?

Load Balancing For High Performance Computing
Using Quantum Annealing: Conclusions and Outlook

Web trading is a phrase you’ve almost certainly heard before, whether casually used in conversation amongst associates or in television or film that you’ve watched. But what exactly is web trading? It is done through a web trader platform, which is an online resource or software system in and of itself in which investors can open, close, and manage market positions online. Crucially, there are two distinctly different types of trading platforms: commercial and proprietary. Commercial platforms are basically engineered for mass appeal, being easier to utilize and specifically catering to individuals. In contrast, proprietary platforms are made by financial institutions and are for those financial institutions. While commercial platforms are available to the public, proprietary platforms are not. Choosing a Platform For You These trading platforms afford investors the opportunity to open numerous different types of accounts, including margin accounts, cash accounts, retirement accounts, and self-directed accounts. Many of the most popular and successful trading platforms offer built-in features tailor-made to assist investors in making real-time decisions when it comes to the profitability of their investments. These features often utilize extensive research data and the history of given stocks to assess potential future value of such options. Other features, such as technical analysis tools or Level 2 quotes and access to market information, are all factors that should be considered when deciding on a trading platform. While many commercial platforms are readily available to the public, which one you ultimately use must be decided in conjunction with figuring out what kind of trading you’re looking to do. Considering the Potential Pitfalls In tandem with this, it’s also critical to consider the providers, requirements, and fees when using a web trader platform. You must take the reputation of the intermediary or broker (the provider) into account before deciding, as they must be someone you trust to assist you with handling your financial decisions. Also, each platform has different requirements or may insist that each trader must have a certain amount of money in equity readily available for each day of trading. All of these factors must be carefully considered so as to avoid possible fees, which could even result in expulsion from the platform. In short, there’s a lot of information to consider when it comes to web trading and the choosing of a given trading platform. Only certain sources are readily available to the public, and even those often deliberately obfuscate much of the process. Nowhere is it easier or more efficient to engage in web trading on a reliable and secure trading platform than it is on AvaTrade. AvaTrade is a platform engineered for investors who want a simplified trading experience. Instead of an independent software system, which requires costly and dense downloads, AvaTrade is able to operate directly from a web browser. This means that no installation is required and that it is readily available on any computer browser. In a business full of willful deceit and obfuscation, AvaTrade has founded its name upon transparency and honesty, making web trading truly viable for everyone. Web trading is a phrase you’ve almost certainly heard before, whether casually used in conversation amongst associates or in television or film that you’ve watched. But what exactly is web trading? It is done through a web trader platform, which is an online resource or software system in and of itself in which investors can open, close, and manage market positions online. Crucially, there are two distinctly different types of trading platforms: commercial and proprietary. Commercial platforms are basically engineered for mass appeal, being easier to utilize and specifically catering to individuals. In contrast, proprietary platforms are made by financial institutions and are for those financial institutions. While commercial platforms are available to the public, proprietary platforms are not. Choosing a Platform For You Choosing a Platform For You These trading platforms afford investors the opportunity to open numerous different types of accounts, including margin accounts, cash accounts, retirement accounts, and self-directed accounts. Many of the most popular and successful trading platforms offer built-in features tailor-made to assist investors in making real-time decisions when it comes to the profitability of their investments. These features often utilize extensive research data and the history of given stocks to assess potential future value of such options . potential future value of such options potential future value of such options Other features, such as technical analysis tools or Level 2 quotes and access to market information, are all factors that should be considered when deciding on a trading platform. While many commercial platforms are readily available to the public, which one you ultimately use must be decided in conjunction with figuring out what kind of trading you’re looking to do. Considering the Potential Pitfalls Considering the Potential Pitfalls In tandem with this, it’s also critical to consider the providers, requirements, and fees when using a web trader platform . You must take the reputation of the intermediary or broker (the provider) into account before deciding, as they must be someone you trust to assist you with handling your financial decisions. Also, each platform has different requirements or may insist that each trader must have a certain amount of money in equity readily available for each day of trading. All of these factors must be carefully considered so as to avoid possible fees, which could even result in expulsion from the platform. using a web trader platform using a web trader platform In short, there’s a lot of information to consider when it comes to web trading and the choosing of a given trading platform. Only certain sources are readily available to the public, and even those often deliberately obfuscate much of the process. Nowhere is it easier or more efficient to engage in web trading on a reliable and secure trading platform than it is on AvaTrade. AvaTrade is a platform engineered for investors who want a simplified trading experience. Instead of an independent software system, which requires costly and dense downloads, AvaTrade is able to operate directly from a web browser. This means that no installation is required and that it is readily available on any computer browser. In a business full of willful deceit and obfuscation, AvaTrade has founded its name upon transparency and honesty, making web trading truly viable for everyone.

The Basics of Web Trading: What You Need to Know

ওয়েব ট্রেডিং এর মূল বিষয়: আপনার যা জানা দরকার

Die Grundlagen des Web-Tradings: Was Sie wissen müssen

Los fundamentos del comercio web: lo que necesita saber

Les bases du trading sur le Web : ce que vous devez savoir

वेब ट्रेडिंग की मूल बातें: आपको क्या जानना चाहिए

ウェブトレーディングの基礎: 知っておくべきこと

웹 트레이딩의 기본: 알아야 할 사항

Os princípios básicos do comércio na Web: o que você precisa saber

Основы веб-трейдинга: что вам нужно знать

Web Ticaretinin Temelleri: Bilmeniz Gerekenler

Khái niệm cơ bản về giao dịch trên web: Những điều bạn cần biết

网上交易的基础知识：您需要了解的内容

When it comes to machine learning (ML), speed is the name of the game. The faster you can prepare your data, train your models, and deploy them into production, the quicker you can unlock insights and drive value for your business. Achieving this speed will require more from your company than just raw computing power. You’ll need a strategic approach to data pipeline development, cloud integration, and infrastructure planning. Your goal is to expedite the readiness of your ML models, and you can’t go wrong with some advice from an industry leader. Abhijeet Rajwade is the senior customer engineer at Google, where he spearheads the development of cloud, data, and digital workplace solutions for enterprise clients in the USA. He’s also contributed to the development of AI infrastructure and cloud technologies for years. If anyone in the industry knows how to get this right, it’s Abhijeet. The Importance of Simplifying Data Pipeline Development to Transform Data At the heart of any ML endeavor lies the data. But, preparing data for analysis and model training can be a complex and time-consuming process. That’s where you could use Google Dataflow to build a data transformation pipeline to help data readiness for Enterprise AI Workloads. Abhijeet focuses a great deal on the importance of streamlining dataflow development to enhance data engineers’ productivity. He was the product manager in charge of developing a Cloud Code Plugin for Dataflow that reduced the learning curve and ramp-up time for building dataflow streaming pipelines. This product offered several key features to enhance the developer experience, mostly accelerating the development cycle and mitigating errors more efficiently. By streamlining the creation and execution of data pipelines, organizations can accelerate the process of ingesting, transforming, and preparing data for ML tasks like feature engineering, model training, etc. Whether cleaning messy datasets, extracting relevant features, or aggregating information from multiple sources, simplified dataflow development tools empower data scientists and data engineers to focus on what they do best: analyzing data and building models. Strategic Cloud Capacity Planning: Optimizing Resources for ML Workloads In tandem with streamlined development processes, strategic cloud capacity planning plays a pivotal role in expediting ML model readiness. “Cloud capacity management is a key part of an effective IT strategy,” Abhijeet has said. “Cloud capacity planning not only ensures workloads have the required resources but also reduces the cloud bill due to overprovisioned workloads.” By evaluating capacity requirements, reviewing historical usage patterns, and strategizing capacity planning based on business needs, organizations can optimize resource allocation for ML workloads. This approach not only enhances performance but also reduces costs by ensuring optimal resource utilization. Accelerating ML Model Readiness with Integrated Solutions The convergence of dataflow development simplification, cloud code plugin integrations, and strategic cloud capacity planning offers a comprehensive solution for expediting ML model readiness. As organizations embrace these integrated solutions, they can navigate the complexities of ML model development with greater efficiency and agility. With tools and strategies designed to streamline development processes and optimize resource utilization, the journey from concept to deployment becomes a seamless and accelerated endeavor. The Industry is Changing—You Can Change With It “Reinvention is the fuel of resilience,” says Abhijeet. “But the ability to reinvent yourself ensures you’re not left stranded. You can adapt, learn new skills, and emerge stronger and more adaptable.” Is your company ready for the AI revolution? Many enterprises are on the brink of transformation, but without the right data and infrastructure strategy, they risk being left behind. This is where Abhijeet Rajwade can help. As a seasoned expert in designing solutions to transform data and leverage cloud infrastructure for AI workloads, he’s ready to design solutions that transform data and leverage cloud infrastructure to its greatest potential. The future is here, so it’s time to make sure your plans are ready for it. When it comes to machine learning (ML), speed is the name of the game. The faster you can prepare your data, train your models, and deploy them into production, the quicker you can unlock insights and drive value for your business. Achieving this speed will require more from your company than just raw computing power. You’ll need a strategic approach to data pipeline development, cloud integration, and infrastructure planning. Your goal is to expedite the readiness of your ML models, and you can’t go wrong with some advice from an industry leader. Abhijeet Rajwade is the senior customer engineer at Google, where he spearheads the development of cloud, data, and digital workplace solutions for enterprise clients in the USA. He’s also contributed to the development of AI infrastructure and cloud technologies for years. If anyone in the industry knows how to get this right, it’s Abhijeet. Abhijeet Rajwade Abhijeet Rajwade The Importance of Simplifying Data Pipeline Development to Transform Data The Importance of Simplifying Data Pipeline Development to Transform Data At the heart of any ML endeavor lies the data. But, preparing data for analysis and model training can be a complex and time-consuming process. That’s where you could use Google Dataflow to build a data transformation pipeline to help data readiness for Enterprise AI Workloads. Abhijeet focuses a great deal on the importance of streamlining dataflow development to enhance data engineers’ productivity. He was the product manager in charge of developing a Cloud Code Plugin for Dataflow that reduced the learning curve and ramp-up time for building dataflow streaming pipelines. This product offered several key features to enhance the developer experience, mostly accelerating the development cycle and mitigating errors more efficiently. By streamlining the creation and execution of data pipelines, organizations can accelerate the process of ingesting, transforming, and preparing data for ML tasks like feature engineering, model training, etc. Cloud Code Plugin for Dataflow Cloud Code Plugin for Dataflow Whether cleaning messy datasets, extracting relevant features, or aggregating information from multiple sources, simplified dataflow development tools empower data scientists and data engineers to focus on what they do best: analyzing data and building models. Strategic Cloud Capacity Planning: Optimizing Resources for ML Workloads Strategic Cloud Capacity Planning: Optimizing Resources for ML Workloads In tandem with streamlined development processes, strategic cloud capacity planning plays a pivotal role in expediting ML model readiness. “Cloud capacity management is a key part of an effective IT strategy,” Abhijeet has said. “Cloud capacity planning not only ensures workloads have the required resources but also reduces the cloud bill due to overprovisioned workloads.” By evaluating capacity requirements, reviewing historical usage patterns, and strategizing capacity planning based on business needs, organizations can optimize resource allocation for ML workloads. This approach not only enhances performance but also reduces costs by ensuring optimal resource utilization. strategic cloud capacity planning strategic cloud capacity planning Accelerating ML Model Readiness with Integrated Solutions Accelerating ML Model Readiness with Integrated Solutions The convergence of dataflow development simplification, cloud code plugin integrations, and strategic cloud capacity planning offers a comprehensive solution for expediting ML model readiness. As organizations embrace these integrated solutions, they can navigate the complexities of ML model development with greater efficiency and agility. With tools and strategies designed to streamline development processes and optimize resource utilization, the journey from concept to deployment becomes a seamless and accelerated endeavor. The Industry is Changing—You Can Change With It The Industry is Changing—You Can Change With It “Reinvention is the fuel of resilience,” says Abhijeet. “But the ability to reinvent yourself ensures you’re not left stranded. You can adapt, learn new skills, and emerge stronger and more adaptable.” Is your company ready for the AI revolution? Many enterprises are on the brink of transformation, but without the right data and infrastructure strategy, they risk being left behind. This is where Abhijeet Rajwade can help. As a seasoned expert in designing solutions to transform data and leverage cloud infrastructure for AI workloads, he’s ready to design solutions that transform data and leverage cloud infrastructure to its greatest potential. The future is here, so it’s time to make sure your plans are ready for it.

Expediting ML Model Readiness: Industry Expert Abhijeet Rajwade’s Insights

এমএল মডেল প্রস্তুতি ত্বরান্বিত করা: শিল্প বিশেষজ্ঞ অভিজিৎ রাজওয়াদের অন্তর্দৃষ্টি

Beschleunigung der ML-Modellbereitschaft: Einblicke des Branchenexperten Abhijeet Rajwade

Acelerar la preparación del modelo de aprendizaje automático: opiniones del experto de la industria Abhijeet Rajwade

Accélération de la préparation au modèle ML : les idées de l'expert du secteur Abhijeet Rajwade

एमएल मॉडल की तैयारी में तेजी लाना: उद्योग विशेषज्ञ अभिजीत राजवाड़े की अंतर्दृष्टि

ML モデルの準備の迅速化: 業界の専門家 Abhijeet Rajwade の洞察

ML 모델 준비 가속화: 업계 전문가 Abhijeet Rajwade의 통찰력

Agilizando a preparação do modelo de ML: insights do especialista do setor Abhijeet Rajwade

Ускорение готовности моделей машинного обучения: мнение отраслевого эксперта Абхиджита Раджваде

ML Modelinin Hazır Olmasını Hızlandırma: Endüstri Uzmanı Abhijeet Rajwade'in İçgörüleri

Đẩy nhanh quá trình sẵn sàng cho mô hình ML: Thông tin chi tiết của chuyên gia trong ngành Abhijeet Rajwade

加速 ML 模型准备：行业专家 Abhijeet Rajwade 的见解

Authors:
(1) Nicholas Farn, Microsoft Corporation {Microsoft Corporation {nifarn@microsoft.com};
(2) Richard Shin, Microsoft Corporation {eush@microsoft.com}. Table of Links Abstract and Intro Dataset Design Evaluation Methodology Experiments and Analysis Related Work Conclusion, Reproducibility, and References A. Complete list of tools B. Scenario Prompt C. Unrealistic Queries D. Nuances comparing prior work A COMPLETE LIST OF TOOLS We include the complete list of plugins and tools used in ToolTalk, and their corresponding descriptions. AccountTools This API contains tools for account management. • ChangePassword Changes the password of an account. • DeleteAccount Deletes a user’s account, requires user to be logged in. • GetAccountInformation Retrieves account information of logged in user. • LogoutUser Logs user out. • QueryUser Finds users given a username or email. • RegisterUser Register a new user. • ResetPassword Resets the password of a user using a verification code. • SendVerificationCode Initiates a password reset for a user by sending a verification code to a backup email. • UpdateAccountInformation Updates account information of a user. • UserLogin Logs in a user. Alarm This API contains tools for managing alarms. • AddAlarm Sets an alarm for a specific time. • DeleteAlarm Removes an alarm given an alarm id. • FindAlarms Finds alarms a user has set. Calendar This API lets a users manage events in their calendar. • CreateEvent Adds events to a user’s calendar. • DeleteEvent Deletes events from a user’s calendar. • ModifyEvent Allows modification of an existing event. • QueryCalendar Queries for events that occur in a time range. Email This API lets a user search and send emails. • SearchInbox Searches for emails matching filters returning 5 most recent results. • SendEmail Sends an email on behalf of a given user. Message This API lets a user search and send messages. • SearchMessages Searches messages matching filters returning 5 most recent results. • SendMessage Sends a message to another user. Reminder A suite of APIs for managing reminders for a TODO list. • AddReminder Add a reminder with an optional due date. • CompleteReminder Complete a reminder. • DeleteReminder Delete a reminder. • GetReminders Get a list of reminders. Weather Get weather information of a location. • CurrentWeather Get the current weather of a location. • ForecastWeather Get the 3-day weather forecast of a location. • HistoricWeather Get historic weather information of a location by month. B SCENARIO PROMPT Task You will be provided with a list of APIs. These APIs will have a description and a list of parameters and return types for each tool. Your task involves creating 3 varied, complex, and detailed user scenarios that require at least 5 API calls to complete involving at least 3 different APIs. One of these APIs will be explicitly provided and the other two will be chosen by you. For instance, given the APIs: SearchHotels, BookHotel, CancelBooking, GetNFLNews. Given that GetNFLNews is explicitly provided, your scenario should articulate something akin to: "The user wants to see if the Broncos won their last game (GetNFLNews). They then want to see if that qualifies them for the playoffs and who they will be playing against (GetNFLNews). The Broncos did make it into the playoffs, so the user wants watch the game in person. They want to look for hotels where the playoffs are occurring (GetNBANews + SearchHotels). After looking at the options, the user chooses to book a 3-day stay at the cheapest 4-star option (BookHotel)." This scenario exemplifies a scenario using 5 API calls. The scenario is complex, detailed, and concise as desired. The scenario also includes two APIs used in tandem, the required API, GetNBANews to search for the playoffs location and SearchHotels to find hotels based on the returned location. Usage of multiple APIs in tandem is highly desirable and will receive a higher score. Ideally each scenario should contain one or more instances of multiple APIs being used in tandem. Note that this scenario does not use all the APIs given and re-uses the " GetNBANews" API. Re-using APIs is allowed, but each scenario should involve at least 3 different APIs. Note that API usage is also included in the scenario, but exact parameters are not necessary. You must use a different combination of APIs for each scenario. All APIs must be used in at least one scenario. You can only use the APIs provided in the APIs section. Note that API calls are not explicitly mentioned and their uses are included in parentheses. This behaviour should be mimicked in your response. Deliver your response in this format: ‘‘‘ Scenario 1: <Scenario1>
Scenario 2: <Scenario2>
Scenario 3: <Scenario3> ‘‘‘ APIs ‘‘‘ {{API_DOCS}} ‘‘‘ Response Required API: {{REQUIRED_API}} Scenarios with >=5 API calls: ‘‘‘ - Scenario 1: C UNREALISTIC QUERIES Below are some examples of unrealistic queries gathered from various sources. These queries are useful for generating potentially complex tool interactions or unusual combinations of tools. However, as a consequence, they are unrealistic for various reasons such as forcing the usage of disparate APIs in situations a human is unlikely to ask for, explicitly asking for API endpoints which end users are unlikely to know of, or generally being unnaturally long and explicit. • “I’m working on a logistics project for my company and need to check the health of the SQUAKE API. Can you verify the API health by calling the ‘Checkhealth’ API endpoint? Additionally, I would like to retrieve the list of projects using the ‘Projects’ API endpoint.” (Qin et al., 2023b) • “How many singers have the average number of albums of singers in Beijing? Gives the square root of this number.” (Ruan et al., 2023) • “I am looking for x-large, red color women faux fur lined winter warm jacket coat, and price lower than 70.00 dollars.” Yao et al. (2022a) • “Can you retrieve the contact details of the ‘Gondrand’ customs agency in New Caledonia? I’m particularly interested in their postal code, email, name, and phone number. Also, please provide a list of all available transitaires. Begin!!!” Qin et al. (2023b) D NUANCES COMPARING PRIOR WORK Nuances comparing prior work from Table 5. ReAct and ART evaluate on hard QA tasks, but these tasks traditionally do not require the usage of tools to complete. We also note API-Bank fits all criteria. However, its level 1-2 examples are automated but simple. In comparison, its level 3 examples are complex but require manual evaluation. ToolBench’s tasks require hard to use tool, but have shallow solution paths. Their more complex environments have deeper solution paths, but re-use the existing datasets of WebShop (Yao et al., 2022a) and TableTop (Liang et al., 2023). This paper is available on arxiv under CC 4.0 license. Authors: (1) Nicholas Farn, Microsoft Corporation {Microsoft Corporation {nifarn@microsoft.com}; (2) Richard Shin, Microsoft Corporation {eush@microsoft.com}. Authors: Authors: (1) Nicholas Farn, Microsoft Corporation {Microsoft Corporation {nifarn@microsoft.com}; (2) Richard Shin, Microsoft Corporation {eush@microsoft.com}. Table of Links Abstract and Intro Abstract and Intro Dataset Design Dataset Design Evaluation Methodology Evaluation Methodology Experiments and Analysis Experiments and Analysis Related Work Related Work Conclusion, Reproducibility, and References Conclusion, Reproducibility, and References A. Complete list of tools A. Complete list of tools B. Scenario Prompt B. Scenario Prompt C. Unrealistic Queries C. Unrealistic Queries D. Nuances comparing prior work D. Nuances comparing prior work A COMPLETE LIST OF TOOLS We include the complete list of plugins and tools used in ToolTalk, and their corresponding descriptions. AccountTools This API contains tools for account management. AccountTools • ChangePassword Changes the password of an account. ChangePassword • DeleteAccount Deletes a user’s account, requires user to be logged in. DeleteAccount • GetAccountInformation Retrieves account information of logged in user. GetAccountInformation • LogoutUser Logs user out. LogoutUser • QueryUser Finds users given a username or email. QueryUser • RegisterUser Register a new user. • ResetPassword Resets the password of a user using a verification code. ResetPassword • SendVerificationCode Initiates a password reset for a user by sending a verification code to a backup email. SendVerificationCode • UpdateAccountInformation Updates account information of a user. UpdateAccountInformation • UserLogin Logs in a user. UserLogin Alarm This API contains tools for managing alarms. Alarm • AddAlarm Sets an alarm for a specific time. AddAlarm • DeleteAlarm Removes an alarm given an alarm id. DeleteAlarm • FindAlarms Finds alarms a user has set. FindAlarms Calendar This API lets a users manage events in their calendar. Calendar • CreateEvent Adds events to a user’s calendar. CreateEvent • DeleteEvent Deletes events from a user’s calendar. DeleteEvent • ModifyEvent Allows modification of an existing event. ModifyEvent • QueryCalendar Queries for events that occur in a time range. QueryCalendar Email This API lets a user search and send emails. Email • SearchInbox Searches for emails matching filters returning 5 most recent results. SearchInbox • SendEmail Sends an email on behalf of a given user. SendEmail Message This API lets a user search and send messages. Message • SearchMessages Searches messages matching filters returning 5 most recent results. SearchMessages • SendMessage Sends a message to another user. SendMessage Reminder A suite of APIs for managing reminders for a TODO list. Reminder • AddReminder Add a reminder with an optional due date. • CompleteReminder Complete a reminder. • DeleteReminder Delete a reminder. • GetReminders Get a list of reminders. AddReminder Weather Get weather information of a location. Weather • CurrentWeather Get the current weather of a location. CurrentWeather • ForecastWeather Get the 3-day weather forecast of a location. ForecastWeather • HistoricWeather Get historic weather information of a location by month. HistoricWeather B SCENARIO PROMPT Task You will be provided with a list of APIs. These APIs will have a description and a list of parameters and return types for each tool. Your task involves creating 3 varied, complex, and detailed user scenarios that require at least 5 API calls to complete involving at least 3 different APIs. One of these APIs will be explicitly provided and the other two will be chosen by you. For instance, given the APIs: SearchHotels, BookHotel, CancelBooking, GetNFLNews. Given that GetNFLNews is explicitly provided, your scenario should articulate something akin to: "The user wants to see if the Broncos won their last game (GetNFLNews). They then want to see if that qualifies them for the playoffs and who they will be playing against (GetNFLNews). The Broncos did make it into the playoffs, so the user wants watch the game in person. They want to look for hotels where the playoffs are occurring (GetNBANews + SearchHotels). After looking at the options, the user chooses to book a 3-day stay at the cheapest 4-star option (BookHotel)." This scenario exemplifies a scenario using 5 API calls. The scenario is complex, detailed, and concise as desired. The scenario also includes two APIs used in tandem, the required API, GetNBANews to search for the playoffs location and SearchHotels to find hotels based on the returned location. Usage of multiple APIs in tandem is highly desirable and will receive a higher score. Ideally each scenario should contain one or more instances of multiple APIs being used in tandem. Note that this scenario does not use all the APIs given and re-uses the " GetNBANews" API. Re-using APIs is allowed, but each scenario should involve at least 3 different APIs. Note that API usage is also included in the scenario, but exact parameters are not necessary. You must use a different combination of APIs for each scenario. All APIs must be used in at least one scenario. You can only use the APIs provided in the APIs section. Note that API calls are not explicitly mentioned and their uses are included in parentheses. This behaviour should be mimicked in your response. Deliver your response in this format: ‘‘‘ Scenario 1: <Scenario1> Scenario 2: <Scenario2> Scenario 3: <Scenario3> ‘‘‘ Scenario 1: <Scenario1> Scenario 2: <Scenario2> Scenario 3: <Scenario3> ‘‘‘ APIs ‘‘‘ {{API_DOCS}} ‘‘‘ Response Required API: {{REQUIRED_API}} Scenarios with >=5 API calls: ‘‘‘ - Scenario 1: C UNREALISTIC QUERIES Below are some examples of unrealistic queries gathered from various sources. These queries are useful for generating potentially complex tool interactions or unusual combinations of tools. However, as a consequence, they are unrealistic for various reasons such as forcing the usage of disparate APIs in situations a human is unlikely to ask for, explicitly asking for API endpoints which end users are unlikely to know of, or generally being unnaturally long and explicit. • “I’m working on a logistics project for my company and need to check the health of the SQUAKE API. Can you verify the API health by calling the ‘Checkhealth’ API endpoint? Additionally, I would like to retrieve the list of projects using the ‘Projects’ API endpoint.” (Qin et al., 2023b) • “How many singers have the average number of albums of singers in Beijing? Gives the square root of this number.” (Ruan et al., 2023) • “I am looking for x-large, red color women faux fur lined winter warm jacket coat, and price lower than 70.00 dollars.” Yao et al. (2022a) • “Can you retrieve the contact details of the ‘Gondrand’ customs agency in New Caledonia? I’m particularly interested in their postal code, email, name, and phone number. Also, please provide a list of all available transitaires. Begin!!!” Qin et al. (2023b) D NUANCES COMPARING PRIOR WORK Nuances comparing prior work from Table 5. ReAct and ART evaluate on hard QA tasks, but these tasks traditionally do not require the usage of tools to complete. We also note API-Bank fits all criteria. However, its level 1-2 examples are automated but simple. In comparison, its level 3 examples are complex but require manual evaluation. ToolBench’s tasks require hard to use tool, but have shallow solution paths. Their more complex environments have deeper solution paths, but re-use the existing datasets of WebShop (Yao et al., 2022a) and TableTop (Liang et al., 2023). This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Advancing Conversational AI with Complex Tool Orchestration

Introducing Yet Another Umpteenth Programming Language Yikes, gadzooks, and shiver me timbers! Yet another "new" programming language? Yes, Virginia, another new programming language. ZeptoN. Overview So, what the do-while is ZeptoN? Simple, ZeptoN is a simple programming language designed with two objectives... Simple learning language with common syntax style and structure.


A programming language for quick-and-dirty “throwaway programs.” My Programming History As a young boy and a teenager, I cut my teeth learning to program several programming languages. The first was the infamous and ubiquitous BASIC, and the other was Pascal. This was the 1980s, and the home computer revolution was underway. I describe how I got into software here xxx, and getting into programming led me to become a senior software engineer. Yet, one key feature of BASIC and Pascal was simplicity. Perhaps too simple in some ways, but the simplicity allowed the newbie or “noob” coder to start writing, running, and tinkering with code. I had subscriptions to “COMPUTE!” and bought programming books when I went to the mall. Yet, I could type in a program, run the code, and often, play computer games. BASIC Text Adventure I went so far as to write my own “electronic novel” which was a shameless imitation of some of the popular text adventures from Infocom, Adventure International. Mine was a murder mystery that “borrowed” from some other games, including the role-playing ones from companies like SirTech. The games were first written in BASIC, ultimately reaching GW-BASIC, and later in Turbo Pascal. This was more for the fun of creating and to impress friends to see if they could find the clues and solve the mystery. From this fascination with text adventures, I learned compiler theory concepts like lexical analysis, syntax, parsing, BNF, etc. Keep It Simple Silly Yet, there was the simplicity of BASIC and Pascal. Both programming languages were simple enough to teach important programming concepts, and often not formally. Then both programming languages were powerful enough and expressive enough to do neat and cool things, like my own text adventure. Now, when I look at introductory programming courses, powerful programming languages are often used--like Java, C++, and so forth. Great that mainstream languages are utilized that are in demand; there is only one small problem: Java, C++, C#, and Swift languages are very complex as they use the object-oriented paradigm. For a noob or newb, for a newbie coder, that is a challenging and often frustrating language to learn. I initially experienced that at university, when the computer science department switched from a Pascal curriculum to a C++ one. The big frustration was going from a structured to an object-oriented paradigm. Learn to Fly With a Jetliner The simile I use is using Java/C++/C#/Swift/etc. is like learning to fly a plane by learning to pilot a Boeing 747 or an Airbus A330. A powerful aircraft, but overloading in terms of features, controls, and operation. Kernighan [Kerni 1981] even wrote that “Comparing C and Pascal is rather like comparing a Learjet to a Piper Cub — one is meant for getting something done while the other is meant for learning.” Yet, in this case, ZeptoN is meant for learning. ZeptoN has C/C++/Java syntax, so the most ubiquitous syntax style is used and is familiar, and ZeptoN transpiles or transcompiles from ZeptoN into Java. Thus, ZeptoN leverages Java ubiquity and platform neutrality. Hence, a newb can code ZeptoN on the MacOS, Linux, or Windows platforms. Back To BASIC One problem with BASIC (and to a lesser degree Pascal) was that each platform/computer--Atari 800, Commodore 64, IBM PC, Tandy 1000EX had its own “flavor” of BASIC. Thus, one BASIC program for the TI99/4A was not quite compatible with the TRS-80 BASIC, etc. I often found myself “adapting” BASIC programs to the computer I had at the time. ZeptoN avoids this by using Java, and having a standard environment of 70+ functions and several standard constants so ZeptoN can run anywhere. All ZeptoN requires is a favorite JDK vendor, with a minimum of JDK 11 for the transcompiler or code editor, Zeptor. Moreover, ZeptoN is free (yes as in free beer) and open-sourced using the GNU GPL v3. Hence, no cost and no proprietary or secret “black box” tool to transpile ZeptoN programs into Java .class bytecode. Testing Ideas With Throwaway Programs As a senior software engineer with 30+ years of writing code, I do like to write “test” programs to try something, and then throw them away. ZeptoN’s simplicity makes this possible, so for an advanced coder to the noob coder, ZeptoN is for everyone! For more details and examples check out the ZeptoN programming language white paper, a 49-page PDF or ePub document, for more details, and some samples of ZeptoN programs. Further Reading For more details and examples check out the ZeptoN programming language white paper and some samples of ZeptoN programs. More about ZeptoN here: https://github.com/wgilreath/ZeptoN and on my home site: https://www.wfgilreath.xyz/ Also the code editor Zeptor: https://github.com/wgilreath/Zeptor Here's ZeptoN in action and in use on Zeptor on YouTube! Enjoy the zither music... 😉 More articles to follow... 😜 about ZeptoN and Zeptor. Zept to it! References [Kerni 1981] Kernighan, Brian W. "Why Pascal is Not My Favorite Programming Language,"
Computing Science Technical Report No. 100, AT&T Bell Laboratories,
April 2, 1981.
https://www.lysator.liu.se/c/bwk-on-pascal.html
https://archive.org/details/pascal-not-my-favorite/page/n2/mode/1up


[Gilre 2024] Gilreath, William F. "Begin at the Beginning...My Start in Software Engineering," PDF (60K) https://bit.ly/will_soft_start


[Gilre 2022] Gilre 2022, Gilreath, William F. "ZeptoN Programming Language," ePub (5.7Mb) https://wgilreath.github.io/ZeptonWhitePaper.epub


[Gilre 2022] Gilre 2022, Gilreath, William F. "ZeptoN Programming Language," PDF (5.yMb) https://wgilreath.github.io/ZeptonWhitePaper.pdf Introducing Yet Another Umpteenth Programming Language Yikes, gadzooks, and shiver me timbers! Yet another "new" programming language? Yes, Virginia, another new programming language. ZeptoN. Overview So, what the do-while is ZeptoN? Simple, ZeptoN is a simple programming language designed with two objectives... Simple learning language with common syntax style and structure. A programming language for quick-and-dirty “throwaway programs.” Simple learning language with common syntax style and structure. Simple learning language with common syntax style and structure. A programming language for quick-and-dirty “throwaway programs.” A programming language for quick-and-dirty “throwaway programs.” My Programming History As a young boy and a teenager, I cut my teeth learning to program several programming languages. The first was the infamous and ubiquitous BASIC, and the other was Pascal. This was the 1980s, and the home computer revolution was underway. I describe how I got into software here xxx, and getting into programming led me to become a senior software engineer. Yet, one key feature of BASIC and Pascal was simplicity. Perhaps too simple in some ways, but the simplicity allowed the newbie or “noob” coder to start writing, running, and tinkering with code. I had subscriptions to “COMPUTE!” and bought programming books when I went to the mall. Yet, I could type in a program, run the code, and often, play computer games. BASIC Text Adventure I went so far as to write my own “electronic novel” which was a shameless imitation of some of the popular text adventures from Infocom, Adventure International. Mine was a murder mystery that “borrowed” from some other games, including the role-playing ones from companies like SirTech. The games were first written in BASIC, ultimately reaching GW-BASIC, and later in Turbo Pascal. This was more for the fun of creating and to impress friends to see if they could find the clues and solve the mystery. From this fascination with text adventures, I learned compiler theory concepts like lexical analysis, syntax, parsing, BNF, etc. Keep It Simple Silly Yet, there was the simplicity of BASIC and Pascal. Both programming languages were simple enough to teach important programming concepts, and often not formally. Then both programming languages were powerful enough and expressive enough to do neat and cool things, like my own text adventure. Now, when I look at introductory programming courses, powerful programming languages are often used--like Java, C++, and so forth. Great that mainstream languages are utilized that are in demand; there is only one small problem: Java, C++, C#, and Swift languages are very complex as they use the object-oriented paradigm. For a noob or newb, for a newbie coder, that is a challenging and often frustrating language to learn. I initially experienced that at university, when the computer science department switched from a Pascal curriculum to a C++ one. The big frustration was going from a structured to an object-oriented paradigm. Learn to Fly With a Jetliner The simile I use is using Java/C++/C#/Swift/etc. is like learning to fly a plane by learning to pilot a Boeing 747 or an Airbus A330. A powerful aircraft, but overloading in terms of features, controls, and operation. Kernighan [Kerni 1981] even wrote that “Comparing C and Pascal is rather like comparing a Learjet to a Piper Cub — one is meant for getting something done while the other is meant for learning.” Yet, in this case, ZeptoN is meant for learning. ZeptoN has C/C++/Java syntax, so the most ubiquitous syntax style is used and is familiar, and ZeptoN transpiles or transcompiles from ZeptoN into Java. Thus, ZeptoN leverages Java ubiquity and platform neutrality. Hence, a newb can code ZeptoN on the MacOS, Linux, or Windows platforms. Back To BASIC One problem with BASIC (and to a lesser degree Pascal) was that each platform/computer--Atari 800, Commodore 64, IBM PC, Tandy 1000EX had its own “flavor” of BASIC. Thus, one BASIC program for the TI99/4A was not quite compatible with the TRS-80 BASIC, etc. I often found myself “adapting” BASIC programs to the computer I had at the time. ZeptoN avoids this by using Java, and having a standard environment of 70+ functions and several standard constants so ZeptoN can run anywhere. All ZeptoN requires is a favorite JDK vendor, with a minimum of JDK 11 for the transcompiler or code editor, Zeptor. Moreover, ZeptoN is free (yes as in free beer) and open-sourced using the GNU GPL v3. Hence, no cost and no proprietary or secret “black box” tool to transpile ZeptoN programs into Java .class bytecode. Testing Ideas With Throwaway Programs As a senior software engineer with 30+ years of writing code, I do like to write “test” programs to try something, and then throw them away. ZeptoN’s simplicity makes this possible, so for an advanced coder to the noob coder, ZeptoN is for everyone! For more details and examples check out the ZeptoN programming language white paper, a 49-page PDF or ePub document, for more details, and some samples of ZeptoN programs. Further Reading For more details and examples check out the ZeptoN programming language white paper and some samples of ZeptoN programs. More about ZeptoN here: https://github.com/wgilreath/ZeptoN and on my home site: https://www.wfgilreath.xyz/ Also the code editor Zeptor: https://github.com/wgilreath/Zeptor https://github.com/wgilreath/ZeptoN https://www.wfgilreath.xyz/ https://github.com/wgilreath/Zeptor Here's ZeptoN in action and in use on Zeptor on YouTube ! Enjoy the zither music... 😉 YouTube More articles to follow... 😜 about ZeptoN and Zeptor. Zept to it! References [Kerni 1981] Kernighan, Brian W. "Why Pascal is Not My Favorite Programming Language,"
Computing Science Technical Report No. 100, AT&T Bell Laboratories,
April 2, 1981.
https://www.lysator.liu.se/c/bwk-on-pascal.html
https://archive.org/details/pascal-not-my-favorite/page/n2/mode/1up [Gilre 2024] Gilreath, William F. "Begin at the Beginning...My Start in Software Engineering," PDF (60K) https://bit.ly/will_soft_start [Gilre 2022] Gilre 2022, Gilreath, William F. "ZeptoN Programming Language," ePub (5.7Mb) https://wgilreath.github.io/ZeptonWhitePaper.epub [Gilre 2022] Gilre 2022, Gilreath, William F. "ZeptoN Programming Language," PDF (5.yMb) https://wgilreath.github.io/ZeptonWhitePaper.pdf [Kerni 1981] Kernighan, Brian W. "Why Pascal is Not My Favorite Programming Language,"
Computing Science Technical Report No. 100, AT&T Bell Laboratories,
April 2, 1981.
https://www.lysator.liu.se/c/bwk-on-pascal.html
https://archive.org/details/pascal-not-my-favorite/page/n2/mode/1up [Kerni 1981] Kernighan, Brian W. "Why Pascal is Not My Favorite Programming Language,"
Computing Science Technical Report No. 100, AT&T Bell Laboratories,
April 2, 1981. https://www.lysator.liu.se/c/bwk-on-pascal.html https://archive.org/details/pascal-not-my-favorite/page/n2/mode/1up https://www.lysator.liu.se/c/bwk-on-pascal.html https://archive.org/details/pascal-not-my-favorite/page/n2/mode/1up [Gilre 2024] Gilreath, William F. "Begin at the Beginning...My Start in Software Engineering," PDF (60K) https://bit.ly/will_soft_start [Gilre 2024] Gilreath, William F. "Begin at the Beginning...My Start in Software Engineering," PDF (60K) https://bit.ly/will_soft_start https://bit.ly/will_soft_start [Gilre 2022] Gilre 2022, Gilreath, William F. "ZeptoN Programming Language," ePub (5.7Mb) https://wgilreath.github.io/ZeptonWhitePaper.epub [Gilre 2022] Gilre 2022, Gilreath, William F. "ZeptoN Programming Language," ePub (5.7Mb) https://wgilreath.github.io/ZeptonWhitePaper.epub https://wgilreath.github.io/ZeptonWhitePaper.epub [Gilre 2022] Gilre 2022, Gilreath, William F. "ZeptoN Programming Language," PDF (5.yMb) https://wgilreath.github.io/ZeptonWhitePaper.pdf [Gilre 2022] Gilre 2022, Gilreath, William F. "ZeptoN Programming Language," PDF (5.yMb) https://wgilreath.github.io/ZeptonWhitePaper.pdf https://wgilreath.github.io/ZeptonWhitePaper.pdf

What the Do-While Is ZeptoN?

First Employee: My Journey From Solopreneur to Entrepreneur

Deep Dive into Functional Programming in Javascript

The modern data landscape demands a new breed of infrastructure – one that seamlessly integrates structured and unstructured data, scales effortlessly, and empowers efficient AI/ML workloads. This is where come in, providing a central hub for all your data needs. However, building and managing an effective data lake can be complex. modern datalakes This blog post dives deep into three powerful tools that can optimize your current approach: , and MinIO. The steps below will walk you through how these services seamlessly combine to create a robust, cloud-native data lake architecture specifically optimized for AI/ML workloads. Apache Iceberg Tabular, What is Tabular? Tabular is a data platform created by the original creators of Apache Iceberg. It is designed to provide an independent, universal storage platform that connects to any compute layer, eliminating data vendor lock-in. This feature is critical to the modern data stack, it allows users to pick best-in-class compute and storage tools without being forced into a particular vendor's aged and or mismatched tool set. In an of MinIO and Iceberg and can be enhanced by Tabular. Tabular can be used to manage and query Iceberg data stored in MinIO, allowing for the storage and management of structured data in a scalable, high-performance, and cloud-native manner. These Kubernetes native, components work together smoothly with very little friction and build on each other’s capabilities to perform at scale. architecture Why S3FileIO instead of Hadoop's file-io? This implementation leverages Iceberg's S3FileIO. S3FileIO is considered better than Hadoop's file-io for several reasons. Some of which we’ve already discussed : elsewhere : Iceberg's S3FileIO is designed to work with cloud-native storage. Optimized for Cloud Storage Iceberg uses an to distribute files across multiple prefixes in a MinIO bucket, which helps to minimize throttling and maximize throughput for S3-related IO operations. Improved Throughput and Minimized Throttling: ObjectStoreLocationProvider Iceberg has been to fully leverage strict consistency by eliminating redundant consistency checks that could impact performance. Strict Consistency: updated Iceberg's S3FileIO implements a algorithm, which uploads data files parts in parallel as soon as each part is ready, reducing local disk usage and increasing upload speed. Progressive Multipart Upload: progressive multipart upload Iceberg allows for for S3 API writes to ensure the integrity of uploaded objects, which can be enabled by setting the appropriate catalog property. Checksum Verification: checksum validations Iceberg supports adding to objects during write and delete operations with the S3 API, which can be useful for cost tracking and management. Custom Tags: custom tags The FileIO interface in Iceberg does not require as strict guarantees as a Hadoop-compatible FileSystem, which allows it to that could otherwise degrade performance. Avoidance of Negative Caching: avoid negative caching In contrast, Hadoop's S3A FileSystem, which was used before S3FileIO, does not offer the same level of optimization for cloud storage. All this to say: Don’t hobble your future-facing data lake infrastructure with the trappings of the past. Prerequisites Before you begin, make sure your system meets the following requirements: Docker Docker Compose If you're starting from scratch, you can install both using the installer for your specific platform. It is often easier than downloading Docker and Docker Compose separately. Verify if Docker is installed by running the following command: Docker Desktop docker-compose --version Getting started To get started, clone or copy the YAML file in Tabular’s . You just need the YAML for this tutorial. Feel free to explore the rest of the repository at a later time. git repository Breaking it Down The provided YAML file is a Docker Compose configuration file. It defines a set of services and their configurations for a multi-container Docker application. In this case, there are two services: Spark-Iceberg and MinIO. Let's break down each section: 1. Spark-Iceberg Service: spark-iceberg:
    image: tabulario/spark-iceberg
    container_name: spark-iceberg
    build: spark/
    networks:
      iceberg_net:
    depends_on:
      - rest
      - minio
    volumes:
      - ./warehouse:/home/iceberg/warehouse
      - ./notebooks:/home/iceberg/notebooks/notebooks
    environment:
      - AWS_ACCESS_KEY_ID=admin
      - AWS_SECRET_ACCESS_KEY=password
      - AWS_REGION=us-east-1
    ports:
      - 8888:8888
      - 8080:8080
      - 10000:10000
      - 10001:10001
  rest:
    image: tabulario/iceberg-rest
    container_name: iceberg-rest
    networks:
      iceberg_net:
    ports:
      - 8181:8181
    environment:
      - AWS_ACCESS_KEY_ID=admin
      - AWS_SECRET_ACCESS_KEY=password
      - AWS_REGION=us-east-1
      - CATALOG_WAREHOUSE=s3://warehouse/
      - CATALOG_IO__IMPL=org.apache.iceberg.aws.s3.S3FileIO
      - CATALOG_S3_ENDPOINT=http://minio:9000 Specifies the Docker image to use for the spark-iceberg service. In this case, it uses the tabulario/spark-iceberg:latest image. image: Specifies that the spark-iceberg service depends on the rest and minio services. depends_on: Assigns a specific name (spark-iceberg) to the container. container_name: Sets environment variables for the container, including Spark and AWS credentials. environment: Mounts local directories (./warehouse and ./notebooks) as volumes inside the container. volumes: Maps container ports to host ports for accessing Spark UI and other services. ports: 2. Minio Service: minio:
    image: minio/minio
    container_name: minio
    environment:
      - MINIO_ROOT_USER=admin
      - MINIO_ROOT_PASSWORD=password
      - MINIO_DOMAIN=minio
    networks:
      iceberg_net:
        aliases:
          - warehouse.minio
    ports:
      - 9001:9001
      - 9000:9000
    command: ["server", "/data", "--console-address", ":9001"] Specifies the Docker image for the MinIO service. image: Assigns a specific name (MinIO) to the container. container_name: Sets environment variables for configuring MinIO, including root user credentials. environment: Maps container ports to host ports for accessing MinIO UI. ports: Specifies the command to start the MinIO server with specific parameters. command: Another aspect of the MinIO service is , MinIO’s command line tool. mc mc:
    depends_on:
      - minio
    image: minio/mc
    container_name: mc
    networks:
      iceberg_net:
    environment:
      - AWS_ACCESS_KEY_ID=admin
      - AWS_SECRET_ACCESS_KEY=password
      - AWS_REGION=us-east-1
    entrypoint: >
      /bin/sh -c "
      until (/usr/bin/mc config host add minio http://minio:9000 admin password) do echo '...waiting...' && sleep 1; done;
      /usr/bin/mc rm -r --force minio/warehouse;
      /usr/bin/mc mb minio/warehouse;
      /usr/bin/mc policy set public minio/warehouse;
      tail -f /dev/null
      " Specifies that the mc service depends on the MinIO service. depends_on: Specifies the Docker image for the mc service. image: Assigns a specific name (mc) to the container. container_name: Sets environment variables for configuring the MinIO client. environment: Defines the entry point command for the container, including setup steps for the MinIO client. entrypoint: /usr/bin/mc rm -r --force minio/warehouse;
      /usr/bin/mc mb minio/warehouse;
      /usr/bin/mc policy set public minio/warehouse;
      tail -f /dev/null
      " This sequence of commands essentially performs the following tasks: Removes the existing warehouse directory and its contents from the MinIO server. Creates a new bucket named warehouse. Sets the access policy of the warehouse bucket to public. This Docker Compose file orchestrates a multi-container environment with services for Spark, PostgreSQL, MinIO. It sets up dependencies, environment variables, and commands necessary for running the services together. The services work in tandem to create a development environment for data processing using Spark and Iceberg with MinIO as the object storage backend. Starting Up In a terminal window, cd into the tabular-spark-setup directory in the repository and run the following command: docker-compose up Login into MinIO at with the credentials to see that the warehouse bucket has been created. http://127.0.0.1:9001 admin:password Once all the containers are up and running, you can access your Jupyter Notebook server by navigating to http://localhost:8888 Run one of the sample notebooks and return to MinIO at to see your warehouse populated with data. http://127.0.0.1:9001 Building Your Modern Datalake This tutorial on building a modern datalake with Iceberg, Tabular, and MinIO is just the beginning. This powerful trio opens doors to a world of possibilities. With these tools, you can seamlessly integrate and analyze all your data, structured and unstructured, to uncover hidden patterns and drive data-driven decisions that fuel innovation. Leverage the efficiency and flexibility of this architecture in production to expedite your AI/ML initiatives and unlock the true potential of your machine learning models, accelerating your path to groundbreaking discoveries. Reach out to us at or on our channel if you have any questions as you build. hello@min.io Slack

Building Effective Modern Data Architectures with Iceberg, Tabular and MinIO

আইসবার্গ, ট্যাবুলার এবং মিনিও সহ কার্যকরী আধুনিক ডেটা আর্কিটেকচার তৈরি করা

Erstellen effektiver moderner Datenarchitekturen mit Iceberg, Tabular und MinIO

Creación de arquitecturas de datos modernas y eficaces con Iceberg, Tabular y MinIO

Construire des architectures de données modernes et efficaces avec Iceberg, Tabular et MinIO

आइसबर्ग, टेबुलर और मिनियो के साथ प्रभावी आधुनिक डेटा आर्किटेक्चर का निर्माण

Iceberg、Tabular、MinIO を使用した効果的な最新データ アーキテクチャの構築

Iceberg, Tabular 및 MinIO를 사용하여 효과적인 최신 데이터 아키텍처 구축

Construindo arquiteturas de dados modernas e eficazes com Iceberg, Tabular e MinIO

Создание эффективных современных архитектур данных с помощью Iceberg, Tabular и MinIO

Iceberg, Tabular ve MinIO ile Etkili Modern Veri Mimarileri Oluşturma

Xây dựng kiến trúc dữ liệu hiện đại hiệu quả với Iceberg, Tabular và MinIO

使用 Iceberg、Tabular 和 MinIO 构建有效的现代数据架构

Hacking and breaking into a system using different tools has become a major worry for people and organizations worldwide. Attackers today frequently utilize sophisticated data science approaches to compromise a system. Can data science be used to stop system hacking if it can be used to gain control of the system? The application of data science in cybersecurity has made it simple to anticipate system vulnerabilities, reducing the likelihood of a breach by implementing the necessary precautions. The World Economic Forum's "Global Risks Report 2023" states that assaults against technology-enabled resources and services, such as financial systems and communication infrastructure, will continue to be a risk in 2024 and that cybersecurity will remain a persistent issue. The target spectrum has expanded as cyber attackers penetrate various industries, including healthcare, finance, and retail. Cybersecurity and data science are effective tools for mitigating these losses. The article that follows will go into detail on the connection between data science and cyber security. What Does Data Science Mean in Terms of Cybersecurity? The use of data science in cybersecurity has revolutionized the way that fraudulent actions are resisted. Data science predicts the possibility of an incursion or assault by using machine learning techniques on historical data. It entails creating algorithms to infer trends from prior attacks and provide forewarnings regarding the dependability of the system under use. For instance, pinpointing unlawful entry into a facility. Only pre-registered users would be granted access by the AI model, which would check their activity to ensure that no activity beyond authorized occurs. These measures are all intended to stop data breaches and information misuse. How Cybersecurity Looked Before Data Science? In the early days of cybersecurity, fear and uncertainty were connected.  Corporations' security plans were predicated solely on conjecture, which gave rise to this particular anxiety. Certain characteristics were predicated on assumptions, such as the attack's mode of operation and the target region. The entire cyber business transformed when data science entered the picture. Since technological decisions make up the majority of cybersecurity decisions, data science predictions have helped reduce the likelihood that decisions are made incorrectly because the bulk of judgments are fact-based. By broadening their range of resources, these data-driven technologies have greatly improved the work of cybersecurity analysts and specialists, who can now create more effective security development strategies. The Connection Between Cybersecurity and Data Science Cybersecurity aims to detect and stop risks such as malware, intrusions and attacks, and fraud. Machine Learning (ML) is a tool data scientists use to identify and prevent these risks. Security experts, for instance, can go through data from several samples to find security concerns. With the least amount of false positives possible, this method seeks to identify attacks and intrusions. Data science techniques are utilized by security systems such as User and Entity Activity Analytics (UEBA) to detect anomalies in user activity. Typically, there is a connection between security breaches and unusual user activity. Cybersecurity has greatly benefited from data science. In the same way that learning data science is crucial to being competitive in any industry, we will discuss the main effects of data science on cybersecurity in this section. How Cybersecurity Is Improved by Applied Data Science and Machine Learning Together? Every day, technology advances. Cybercrimes are becoming more likely as a result. Data science for cybersecurity is the best feasible response to this query. Since an organization's sensitive data collection keeps growing daily, data science must be incorporated into every risk analysis plan. Data science can be applied in many different ways to mitigate the hazards; the following examples highlight some of these: 1. Improved Security Monitoring With the advancement of technology, hackers now utilize multiple methods to breach a system. Enhanced methodologies have rendered it more arduous for corporations to identify the routes for system penetration. Machine learning models built using historical and present attack data offer a comprehensive understanding of modeling various types of attacks. Next, these models forecast the kind of assault and the likelihood of a system breach. 2. Data Protection Every firm depends heavily on its data which must be protected at all costs. Data science uses machine learning algorithms to help develop impenetrable data channels for data transit. 3. Effective Forecasting Anticipation encompasses more than merely identifying True positives. In addition, a data science cybersecurity model must produce relatively few false positives which will aid in the fight against spam calls. Through these methods, cyber risk and threat theories can be set that are more grounded in reality than in antiquated notions. 4. Behavior Analysis It is not sufficient to just comprehend the nature of the attack or the likelihood that it will impact the system; one also needs to understand the behavioral patterns of hackers. This can be very advantageous since it will allow us to anticipate his or her next move or attack. To accomplish this behavioral analysis, several datasets are combined, network logs are examined, and correlations between systems are discovered. This allows one to identify a hacker's behavioral pattern and adjust preventive measures accordingly. What Does the Future Look Like? One of the brightest futures is that of data science. Because hackers are always looking for new ways to get into systems, data science will be a long-term answer to stop them. As tactics become more advanced, more complex attacks are appearing. Since there will be an exponential increase in data over the next few decades, data science models will perform better since they will have an increasing amount of information to connect the dots. The field of data science extends beyond the creation of models and algorithms. One of the key responsibilities in this field is the analysis and upkeep of the current data science model. Analysis facilitates the differentiation of behavior into normal and anomalous categories. Massive losses are threatening large businesses as a result of data breaches. They desperately need to figure out how to cut these losses. Wrapping Up In a short period, data science has a significant impact on cybersecurity. Every firm receives a growing volume of data every day. A data science model's capacity for prediction will rise in tandem with the amount of data. Every stage of the process flow requires cooperation between the security and data science teams. Data is vital to all businesses, regardless of size, thus safeguarding it at all costs is crucial for each of them. Data science's contribution to cybersecurity has raised the bar for security requirements. Hacking and breaking into a system using different tools has become a major worry for people and organizations worldwide. Attackers today frequently utilize sophisticated data science approaches to compromise a system. Can data science be used to stop system hacking if it can be used to gain control of the system? The application of data science in cybersecurity has made it simple to anticipate system vulnerabilities, reducing the likelihood of a breach by implementing the necessary precautions. The World Economic Forum's "Global Risks Report 2023" states that assaults against technology-enabled resources and services, such as financial systems and communication infrastructure, will continue to be a risk in 2024 and that cybersecurity will remain a persistent issue. World Economic Forum's The target spectrum has expanded as cyber attackers penetrate various industries, including healthcare, finance, and retail. Cybersecurity and data science are effective tools for mitigating these losses. The article that follows will go into detail on the connection between data science and cyber security. What Does Data Science Mean in Terms of Cybersecurity? The use of data science in cybersecurity has revolutionized the way that fraudulent actions are resisted. Data science predicts the possibility of an incursion or assault by using machine learning techniques on historical data. It entails creating algorithms to infer trends from prior attacks and provide forewarnings regarding the dependability of the system under use. For instance, pinpointing unlawful entry into a facility. Only pre-registered users would be granted access by the AI model, which would check their activity to ensure that no activity beyond authorized occurs. These measures are all intended to stop data breaches and information misuse. How Cybersecurity Looked Before Data Science? In the early days of cybersecurity, fear and uncertainty were connected.  Corporations' security plans were predicated solely on conjecture, which gave rise to this particular anxiety. Certain characteristics were predicated on assumptions, such as the attack's mode of operation and the target region. The entire cyber business transformed when data science entered the picture. Since technological decisions make up the majority of cybersecurity decisions, data science predictions have helped reduce the likelihood that decisions are made incorrectly because the bulk of judgments are fact-based. By broadening their range of resources, these data-driven technologies have greatly improved the work of cybersecurity analysts and specialists, who can now create more effective security development strategies. The Connection Between Cybersecurity and Data Science Cybersecurity aims to detect and stop risks such as malware, intrusions and attacks, and fraud. Machine Learning (ML) is a tool data scientists use to identify and prevent these risks. Security experts, for instance, can go through data from several samples to find security concerns. With the least amount of false positives possible, this method seeks to identify attacks and intrusions. Data science techniques are utilized by security systems such as User and Entity Activity Analytics (UEBA) to detect anomalies in user activity. Typically, there is a connection between security breaches and unusual user activity. Cybersecurity has greatly benefited from data science. In the same way that learning data science is crucial to being competitive in any industry, we will discuss the main effects of data science on cybersecurity in this section. learning data science How Cybersecurity Is Improved by Applied Data Science and Machine Learning Together? Every day, technology advances. Cybercrimes are becoming more likely as a result. Data science for cybersecurity is the best feasible response to this query. Since an organization's sensitive data collection keeps growing daily, data science must be incorporated into every risk analysis plan. Data science can be applied in many different ways to mitigate the hazards; the following examples highlight some of these: 1. Improved Security Monitoring With the advancement of technology, hackers now utilize multiple methods to breach a system. Enhanced methodologies have rendered it more arduous for corporations to identify the routes for system penetration. Machine learning models built using historical and present attack data offer a comprehensive understanding of modeling various types of attacks. Next, these models forecast the kind of assault and the likelihood of a system breach. 2. Data Protection Every firm depends heavily on its data which must be protected at all costs. Data science uses machine learning algorithms to help develop impenetrable data channels for data transit. 3. Effective Forecasting Anticipation encompasses more than merely identifying True positives. In addition, a data science cybersecurity model must produce relatively few false positives which will aid in the fight against spam calls. Through these methods, cyber risk and threat theories can be set that are more grounded in reality than in antiquated notions. 4. Behavior Analysis It is not sufficient to just comprehend the nature of the attack or the likelihood that it will impact the system; one also needs to understand the behavioral patterns of hackers. This can be very advantageous since it will allow us to anticipate his or her next move or attack. To accomplish this behavioral analysis, several datasets are combined, network logs are examined, and correlations between systems are discovered. This allows one to identify a hacker's behavioral pattern and adjust preventive measures accordingly. What Does the Future Look Like? One of the brightest futures is that of data science. Because hackers are always looking for new ways to get into systems, data science will be a long-term answer to stop them. As tactics become more advanced, more complex attacks are appearing. Since there will be an exponential increase in data over the next few decades, data science models will perform better since they will have an increasing amount of information to connect the dots. The field of data science extends beyond the creation of models and algorithms. One of the key responsibilities in this field is the analysis and upkeep of the current data science model. Analysis facilitates the differentiation of behavior into normal and anomalous categories. Massive losses are threatening large businesses as a result of data breaches. They desperately need to figure out how to cut these losses. Wrapping Up In a short period, data science has a significant impact on cybersecurity . Every firm receives a growing volume of data every day. A data science model's capacity for prediction will rise in tandem with the amount of data. Every stage of the process flow requires cooperation between the security and data science teams. Data is vital to all businesses, regardless of size, thus safeguarding it at all costs is crucial for each of them. Data science's contribution to cybersecurity has raised the bar for security requirements. cybersecurity

Exploring the Intersection of Data Science and Cyber Security: Insights and Applications

This paper is available on arxiv under CC 4.0 license. Authors: (1) Qin Wang, CSIRO Data61, Australia; (2) Shiping Chen, CSIRO Data61, Australia. Table of Links Abstract & Introduction Ethereum Accounts Account Abstraction Security Analysis Further Discussions Conclusion & References V. FURTHER DISCUSSIONS A. More about AA Design Aligned with the idea outlined in EIP-4337, AA introduces perfect backward compatibility, minimizing its effects on consensus layers and underlying structures. The fact is evident in security enhancements, which effectively tackle many challenges originating from the application layer rather than fundamental design flaws (refer to Tab.III). AA represents an application-level update with its primary impact centered around the application layer. Notably, separating the functions required to be executed within the smart contract and allocating a portion of them to the user’s account enhances overall efficiency. The corresponding batch processing isn’t limited to user operations but also extends to fee payments. Rather than directly merging EOA and CA, AA incorporates key functionalities and delegates subsequent tasks to related contracts in tandem. While a series of interconnected contracts work harmoniously to support the functionality of an AA account, users perceive it as effortlessly accessible as popular Web2 Apps (e.g., shopping, and banking). Complex technical jargon is relegated to the backend, and this will spare regular users from unnecessary cognitive load. The user interface offers a friendly entry point. The user experience is markedly improved when employing an AA account as opposed to an EOA. B. AA Opportunities Account abstraction not only streamlines the interaction with DApps but also significantly enhances the user experience within Web3. By embracing AA, users gain the ability to tailor their accounts to operate exclusively under specific conditions, which significantly differs from previous solutions that rely on complicated invocation across functions or contracts [32]. Users are empowered with a greater degree of control over their customized programming functions. For instance, unlike the traditional multi-sig setup, where the conditions for executing transactions are relatively standardized, account abstraction allows for a more personalized set of conditions. This adaptability ensures that the operations are only executed when predetermined criteria are met, such as a predefined number of signatories. Ease accessibility to Web3. The fundamental L2 projects revolve around alleviating the substantial computational load on L1 chains. As a result, a significant focus of various L2 solutions lies in streamlining the intricate processing logic inherent in DApps. This optimization not only reduces the overall gas cost but also enriches the user experience through the introduction of extended functionalities. To realize this goal, numerous L2 projects have embraced the notion of incorporating account abstraction, marking a pivotal step toward achieving their mission. Notable examples [7] include zkSync, which seamlessly integrates the IAccount interface, and StarkNet, with the wellregarded platform Argent. Similarly, Optimistic rollups also witness the integration of customized APIs. Promoting Layer-2 (L2).

Account Abstraction, Analysed: Further Discussions

In an exhilarating development for the decentralized finance (DeFi) community, LendFi announces the launch of its innovative app, now available for download on both the iOS Store and Google PlayStore. This groundbreaking app introduces a suite of utility features designed to enhance user experience and financial empowerment. Among these are staking, peer-to-peer (P2P) lending, swaps, integration with GameFi and non-fungible tokens (NFTs), and the pioneering use of artificial intelligence (AI). cryptocurrency A New Era of Financial Tools LendFi's app brings to the forefront a collection of services that redefine the way users interact with digital assets. The staking feature allows LendFi token holders to earn rewards by participating in network security, while the P2P lending platform offers a streamlined, intermediary-free borrowing and lending experience. The swap functionality facilitates easy and efficient token exchanges, and the GameFi integration opens up new avenues for earning through play. Additionally, the NFT marketplace leverages AI to tailor loan offers and valuations, ensuring a personalized and risk-adjusted experience for all participants. Exciting Opportunities: Private Sale and IDO In tandem with the app launch, LendFi is excited to announce its Initial DEX Offering (IDO). The private sale round one generated buzz within the DeFi community, raising $600,000 within a few hours of launch. LendFi is set to launch the round 2 of the private sale on March 30th and it's attractively priced at $0.2. Thereby offering investors the opportunity to purchase the token at a very affordable price. This will be followed by the IDO, where tokens will be available at $0.4, providing an excellent opportunity for early adopters to participate in the project's growth. In anticipation of these events, LendFi also hints at an upcoming listing on Centralized Exchanges (CEX), promising broader access and liquidity for its users. Strategic Partnerships and Future Plans LendFi's commitment to security, innovation, and growth is underscored by its strategic partnerships with industry leaders such as Certik, Hela_Labs, and Socrates. These collaborations not only enhance the platform's security posture but also bring a wealth of knowledge and resources to support LendFi's ambitious roadmap. Additionally, the recent listing on Coingecko marks a significant milestone, increasing the project's visibility and credibility within the cryptocurrency ecosystem. Over the next 12 months, LendFi is poised for aggressive expansion. With secured investments and a clear strategy, the platform aims to dominate the DeFi protocols space. Key to this strategy is the planned listing on all top-tier exchanges, further enhancing LendFi's accessibility and appeal to a global audience. In summary, LendFi's app launch marks a significant leap forward in the DeFi space, bringing an array of innovative financial tools to the fingertips of users worldwide. With its private and IDO sales underway and plans for substantial growth, LendFi is a beacon for those looking to navigate the promising yet complex waters of decentralized finance. To Learn More About LendFi | | Website Twitter Whitepaper This story was distributed as a release by BTCWire under HackerNoon’s Business Blogging Program. Learn more about the program here: https://business.hackernoon.com/

LendFi Revolutionizes DeFi Landscape with Comprehensive App Launch

LendFi ব্যাপক অ্যাপ লঞ্চের মাধ্যমে DeFi ল্যান্ডস্কেপকে বিপ্লব করে

LendFi revolutioniert die DeFi-Landschaft mit umfassender App-Einführung

LendFi revoluciona el panorama DeFi con el lanzamiento integral de una aplicación

LendFi révolutionne le paysage DeFi avec le lancement complet d'une application

व्यापक ऐप लॉन्च के साथ लेंडफाई ने डेफी लैंडस्केप में क्रांति ला दी

LendFi、包括的なアプリのリリースで DeFi 環境に革命を起こす

LendFi는 포괄적인 앱 출시로 DeFi 환경을 혁신합니다.

LendFi revoluciona o cenário DeFi com lançamento abrangente de aplicativos

LendFi революционизирует ландшафт DeFi с помощью комплексного запуска приложений

LendFi, Kapsamlı Uygulama Lansmanı ile DeFi Ortamında Devrim Yaratıyor

LendFi cách mạng hóa bối cảnh DeFi với việc ra mắt ứng dụng toàn diện

LendFi 通过全面的应用程序发布彻底改变 DeFi 格局

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