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In the exciting journey of developing a RISC-V assembler using C#, we delve into the intricate processes that bring assembly code to life. In this article, we focus on the Label Processing phase and discuss the essential aspects of handling labels, calculating addresses, and dealing with offsets. Phase 1: Label Processing The Label Processing phase is crucial for creating a robust assembler. Labels provide a symbolic representation of memory addresses and play a pivotal role in facilitating jumps and branches in the code. Let's break down the steps involved in processing labels. Identifying Labels The first step is to iterate through the assembly code and identify labels. Labels in assembly code typically end with a colon “:”. We use a simple algorithm to extract and store these labels along with their corresponding addresses. public static void ProcessLabels(string[] code)
{
    foreach (var assemblyLine in code)
    {
        var line = assemblyLine.Trim();
        if (string.IsNullOrEmpty(line)) continue;
        if (line.StartsWith(".")) continue;
        if (line.EndsWith(":"))
        {
            string label = line.Substring(0, line.Length - 1);
            AddressLookupTable.Add(label, Address.CurrentAddress);
            continue;
        }
        Address.CurrentAddress += 4;
    }
} Calculating Addresses To maintain the coherence of memory addresses, we increment the address by 4 (decimal) for each instruction, excluding empty lines, labels, and directives. In RISC (Reduced Instruction Set Computing) architectures, all instructions are of the same fixed size. Each instruction is 32 bits (4 bytes) long. By incrementing the address by 4 for each instruction, the assembler ensures that subsequent instructions are stored in consecutive memory locations. This sequential arrangement facilitates easier memory access during the execution phase, as the processor can fetch instructions efficiently without having to perform complex calculations to determine the next instruction's location. Addressing Offsets The concept of offsets is crucial for resolving the actual memory addresses of labels during execution. Since the assembler does not know the final memory layout, we use offsets to bridge the gap. The offset is calculated by subtracting the current address ( ) from the label's address. This difference represents the distance between the current instruction and the target label in terms of memory locations. Address.CurrentAddress The calculated offset is then utilized in the machine code generation process. It allows the assembler to generate machine code instructions that incorporate the correct memory addresses, accounting for the relative positions of labels within the code. For example, if the current address is 28 and a label is at 10, the offset is -18. During execution at address , the location of the label is ( ). 0x00FF28 0x00FF06 0x00FF28 - 0x12 Machine Code Generation During the generation of machine code, we encounter the challenge of handling labels, especially when they represent memory addresses. If an immediate value is not an integer, it implies a label reference. In such cases, we retrieve the address from the lookup table. int labelAddress = AddressLookupTable[assemblyLine];
int offset = labelAddress - Address.CurrentAddress; As we progress in developing the assembler, understanding labels, addresses, and offsets lays a solid foundation for the subsequent phases. SharpRISCV The repository serves as the starting point for enthusiasts eager to explore and contribute to the project. SharpRISCV If you find the project helpful and informative, don’t forget to give it a star on GitHub to show your support. In the previous parts, we delved into i , and now, with a grasp of addressing modes, labels, and offsets, we eagerly anticipate the exploration of directives in the upcoming part. Stay tuned for the next installment in our journey through the world of RISC-V assembly and C# magic. nstruction types, parsing instructions, and converting to machine code

Understanding RISC-V Assembly Language by Building an Assembler in C# - Part 2

Introduction: In the ever-evolving landscape of computer architecture, RISC-V stands out as an open-source instruction set architecture that offers flexibility and adaptability. To facilitate learning and exploration of RISC-V assembly language, a powerful tool has emerged—SharpRISCV. This browser-based RISC-V assembler supports building Linux ELF, Windows PE, Intel Hex, and RAW output, providing a comprehensive platform for enthusiasts and learners. Features: SharpRISCV supports the assembly of code for both Linux ELF and Windows PE formats. This versatility enables users to explore RISC-V on different operating systems, broadening the scope of learning and experimentation. Cross-Platform Support: The assembler generates output in multiple formats, including Linux ELF, Windows PE, Intel Hex, and RAW. This diversity caters to users with varied preferences and requirements, ensuring compatibility with different tools and environments. Output Formats: For Linux ELF and Windows PE outputs, SharpRISCV goes beyond basic assembly. It provides a hex dump visualization of the generated code, allowing users to inspect the machine code at a granular level. This feature aids in understanding the correspondence between assembly instructions and machine code. Hex Dump Visualization: SharpRISCV enhances the learning experience by offering interactive byte descriptions. Users can hover over specific bytes in the hex dump, and the tool provides detailed descriptions of the corresponding instructions. This interactive feature facilitates a deeper understanding of the RISC-V assembly language. Interactive Byte Descriptions: Supported Instruction Types: R Type U Type I Type B Type S Type J Type Assembler Directives: : Specifies the beginning of the text section. .text : Marks the beginning of the data section. .data and : Define string literals by adding a null terminator. .string .asciz .asciz : Reserves space for a word (32 bits). .word and : Handle high and low parts of a value, respectively. .%hi .%lo Accessing SharpRISCV: To start your RISC-V assembly journey, visit the RISC-V web interface at . The user-friendly interface makes it easy to write, assemble, and explore RISC-V code directly from your web browser. SharpRISCV Contribute to the Project: SharpRISCV is an open-source project available on GitHub at . If you find this tool valuable and helpful, consider giving it a star on GitHub. Your support encourages the continuous improvement of SharpRISCV and contributes to the growth of the RISC-V community. github.com/rizwan3d/SharpRISCV Embark on your RISC-V assembly journey today with SharpRISCV, where learning meets exploration in the world of open architecture!

SharpRISCV Overview: A Browser-Based RISC-V Assembler for Seamless Learning and Exploration

RISC-V is an open-source instruction set architecture (ISA) that has gained popularity due to its simplicity and flexibility. In this article, we’ll explore the fundamentals of RISC-V assembly language by building an assembler in C#. Our goal is to read RISC-V assembly code, identify the instruction type, and convert it into machine code. We’ll use Visual Studio as our development environment for this project. Setting Up the Project Let’s begin by creating a new project in Visual Studio. We’ll build our RISC-V assembler step by step. We mention that we will be building the assembler incrementally, and the first task is to set up a loop to read a file containing RISC-V assembly code and iterate over each line. This loop will serve as the foundation for processing the assembly code. string[] lines = File.ReadAllLines(filePath);

foreach (string line in lines)
{
  // Process the 'line' here, e.g., identify instruction type, parse, and convert to machine code
} Identifying Instruction Types RISC-V instructions are categorized into different types: R, U, I, B, S, and J. To determine the type of instruction, we’ll use a lookup table for opcodes, func2, and func7. You can find the lookup table in this . file Here’s an example of how to identify the instruction type: switch (opCode)
{
    case (OpCode)0b0110011:
        return InstructionType.R;
    case (OpCode)0b0010111:
        return InstructionType.U;
    case (OpCode)0b0110111:
        return InstructionType.U;
    case (OpCode)0b0010011:
        return InstructionType.I;
    case (OpCode)0b1100011:
        return InstructionType.B;
    case (OpCode)0b0000011:
        return InstructionType.I;
    case (OpCode)0b0100011:
        return InstructionType.S;
    case (OpCode)0b1101111:
        return InstructionType.J;
    default:
        return InstructionType.Unknown;
} You can find the implementation of this function in the file. RiscVAssembler.cs Parsing Instructions Now that we can identify the instruction type, let’s parse each instruction based on its type. We’ll start with the R-type instructions, which have the syntax: . op rd, rs1, rs2 For example, the instruction can be parsed as follows: add x10, x1, x2 Regex rTypeRegex = new Regex(@"^(\w+)\s+(\w+),\s+(\w+),\s+(\w+)$");
Match rTypeMatch = rTypeRegex.Match(instruction);
if (rTypeMatch.Success)
{
    return new RiscVInstruction
    {
        Instruction = instruction,
        Opcode = rTypeMatch.Groups[1].Value,
        Rd = rTypeMatch.Groups[2].Value,
        Rs1 = rTypeMatch.Groups[3].Value,
        Rs2 = rTypeMatch.Groups[4].Value,
        Immediate = null,
        InstructionType = InstructionType.R
    };
} You can find the complete implementation of the R-type instruction parser in the file. R_Parser.cs Converting to Machine Code Once we’ve parsed an instruction, we can convert it into machine code. Each instruction type has its own format. For R-type instructions, the format is as follows: R type: .insn r opcode6, func3, func7, rd, rs1, rs2
+-------+-----+-----+-------+----+---------+
| func7 | rs2 | rs1 | func3 | rd | opcode6 |
+-------+-----+-----+-------+----+---------+
31      25    20    15      12   7        0 For example, the instruction is translated into , where: add x10, x1, x2 00000000001000001000010100110011 Opcode 6: 0110011 Rd = 01010 Func 3 = 000 Rs1 = 00001 Rs2 = 00010 Func7 = 0000000 Here’s an example of how to convert the parsed instruction into machine code: string opcode = ((int)instruction.OpcodeBin).ToBinary(7);
string rdBinary = Convert.ToString(int.Parse(instruction.Rd.Substring(1)), 2).PadLeft(5, '0');
string func3 = ((int)instruction.Funct3).ToBinary(3);
string rs1Binary = Convert.ToString(int.Parse(instruction.Rs1.Substring(1)), 2).PadLeft(5, '0');
string rs2Binary = Convert.ToString(int.Parse(instruction.Rs2.Substring(1)), 2).PadLeft(5, '0');
string func7 = ((int)instruction.Funct7).ToBinary(7);

return new MachineCode($"{func7}{rs2Binary}{rs1Binary}{func3}{rdBinary}{opcode}", instruction.Instruction); You can find the complete implementation of machine code generation in the file. R_MachineCode.cs Conclusion In this article, we’ve embarked on a journey to learn RISC-V assembly language by building an assembler in C#. We’ve covered the basics of reading RISC-V assembly code, identifying instruction types, parsing instructions, and converting them into machine code. This project serves as a valuable learning experience for understanding the inner workings of RISC-V assembly language and its translation into machine code. To delve deeper into the RISC-V architecture, refer to the . RISC-V Specification Here’s the GitHub repository link for the project where you can find the code for building a RISC-V assembler in C#: . SharpRISCV GitHub Repository If you find the project helpful and informative, don’t forget to give it a star on GitHub to show your support. In the of our RISC-V assembly language learning series, we will explore , which are essential concepts for understanding and writing more complex assembly programs. Stay tuned for the next installment! next part addressing modes, labels, and offsets Also published . here

Understanding RISC-V Assembly Language by Building an Assembler in C#

Andre’s tutor hastily assembled the chief Hungarian lords

Paris is known for its romance, fashion, and art, but for a week in March it was also the epicenter of the blockchain world. Paris Blockchain Week 2023 (PBW 2023) brought together some of the brightest minds and influential voices in the crypto industry to discuss and debate the current state and future prospects of the decentralized ecosystem. The Avengers of Crypto on Stage One of the highlights of PBW 2023 was the panel discussion titled “State of the Chain 2023” moderated by Cointelegraph’s editor-in-chief Kristina Lucrezia Cornèr. The panel featured five experts from different sectors of the blockchain space: Ryan Nitz, the head of solutions architecture at Coinbase; Matthew Savarese, the head of strategy at Nasdaq Digital Assets; Rich Widmann, the head of strategy for Web3 at Google; Dante Disparte, the chief strategy officer and head of global policy at Circle; and Denelle Dixon, the CEO of Stellar Development Foundation. Takeaways from the Panel Discussion The panelists shared their insights on the current state of the Web3 space and their predictions on how 2023 would evolve. Here are some of the key takeaways from the panel discussion: The state of blockchain is not strong, but innovation is happening. Disparte expressed his belief that the chain’s current state is “not strong” due to the recent challenges and failures in the industry, such as the FTX bankruptcy, the USDC collapse, and the bank crisis. However, people will continue building and developing amid these challenges. He compared the current situation to past tech and financial industry failures that led to innovation and reform. Web2 brands are getting involved in Web3 and crypto. Nitz shared that Coinbase is trying its best to help onboard Web2 companies and eliminate the complexity barrier. Web2 brands are evolving and starting getting involved in Web3 and crypto, such as Twitter, Reddit, Shopify, and Spotify. Web3 is the next evolution of the internet. Widmann emphasized that Google is supporting Web3 as a way to build value for users in the next evolution of the internet. Google is providing cloud infrastructure, data analytics, machine learning, and artificial intelligence for Web3 applications. Additionally, Google is exploring how to leverage blockchain technology for its own products and services. Nasdaq is using blockchain to enhance its core business. Savarese revealed that Nasdaq is using blockchain technology as a way to enhance its core business of providing market infrastructure, data, and analytics for capital markets. It is integrating blockchain technology into its existing platforms, such as Nasdaq Private Market and Nasdaq Linq. Nasdaq is collaborating with other industry players to create standards and best practices for blockchain adoption. In Conclusion The future of the blockchain is bright, despite the current challenges and setbacks. The panelists showed that there is a lot of innovation, collaboration, and value creation happening in the Web3 space, across different sectors and domains. Blockchain technology is not only transforming the way we exchange value, but also . the way we build and interact with the internet Blockchain technology is . Blockchain technology can offer more efficiency, transparency, security, and inclusion for both institutions and individuals. not a threat to the traditional financial system, but a complement and an enhancement We’ve got a glimpse of what to expect in 2023, such as more Web2 involvement, more Web3 applications, more blockchain integration, and more cross-border payments. Blockchain technology is still evolving and maturing, and . we need to be patient and resilient in the face of adversity We need to , to shape the future of finance and the Web3 economy. keep learning and building together, as a community and as an industry If you’re feeling inspired by PBW 2023, you can also check out some of the upcoming events in the crypto industry, such as Consensus 2023, EthCC 2023, or Devcon 7. 🔥If you've been reading this far, you are an absolute legend 😎 and I sincerely appreciate it 🫰. Consider leaving a like or a comment for more content like this. Follow me on twitter: @stephabauva 👨‍🚀 See ya 👋

PBW 2023: The Avengers of Crypto Assemble 

PBW 2023: Los Vengadores de Crypto Assemble

PBW 2023 : les vengeurs de la crypto se rassemblent

PBW 2023: द एवेंजर्स ऑफ क्रिप्टो असेंबल

PBW 2023: クリプト アセンブルのアベンジャーズ

PBW 2023: Os Vingadores do Crypto Assemble

PBW 2023: The Avengers of Crypto Assemble

PBW 2023：加密复仇者集结

The Opt Out Writing Contest: Round 1 Results Announced!

What Does It Take to Get a Medical Device to Market?

At Hummus Said in Acre, Israel, I always order hummus with Foul, don’t think much about it, just add a bit of salt and digg in with the onion. At Hummus Said, a legendary restaurant in Akko, Israel, I savor their signature dish: creamy hummus with a side of Foul. The taste is always perfect, but on one visit, I brought a giant container, joking, "Can bulk hummus match this experience?" Said, the owner and hummus maestro, chuckled, "The flavor might be the same, but the experience suffers.” Just as the bulk hummus misses the essence of Hummus Said, many leaders get lost in the sheer volume of data.  They dream of overflowing databases, perfect metrics for drag-and-drop dashboards, and magically solved problems.- Lior Barak Forget the data dreamland, leaders! You envision overflowing databases, perfect metrics for drag-and-drop dashboards, and magically solved problems. Think again. Simple KPIs like sessions and revenue are just appetizers, not the full course to success. The harsh reality? Most companies choke on their data because they focus on the wrong ingredients. Data strategy isn't a gourmet buffet; it's a clear, actionable recipe. Time to ditch the data daydream and start cooking! Set Clear KPIs! Many leaders envision a data strategy as a readily available buffet - a vast collection of metrics that can be easily assembled into insights. This approach, however, overlooks a critical first step: defining clear and actionable Key Performance Indicators (KPIs). Just as a chef wouldn't haphazardly throw ingredients together and expect a culinary masterpiece, a data strategy requires a well-defined recipe. Your recipe, in this case, consists of the 3-5 key performance indicators (KPIs) that truly capture the essence of your company's success. These KPIs serve as the foundation for your data strategy, guiding all stakeholders towards the metrics that matter most. Think of KPIs as the flavor profile of your data. In my book, "Data is Like a Plate of Hummus" I promote a funnel methodology for defining KPIs: Identify key questions: What are the most critical questions you need answered about your business? For example: What are the revenues from ad-free subscriptions?
How many users bought more than one item in the same basket? Refine your questions: Focus on the most essential ones. Understand which questions are critical for your company goals and strategy, and which answers will help you evaluate the company's health. Define KPIs: Which metrics can effectively provide answers to those refined questions? Understand the KPIs that will give you these answers following the five W's principles: Who is the controller of the KPI? Who owns the data that will be used for the KPI, and who is responsible for ensuring its accuracy and stability?
What action will it drive me to take? If I see a positive or negative change in the KPI.
How well will it give me answers for the company’s health? What else am I missing?
Where is the issue? Can I receive an answer about which company section the issue is?
When do I need to see it? How frequently are these measurements needed? (For example, a weekly "time-on-site" KPI might be sufficient to gauge the effectiveness of UX changes, not requiring daily monitoring.) Building Scalable and Iterative Infrastructure Now that you know what flavors you need, let's talk kitchen setup. Think fast, efficient deployment. Forget a one-size-fits-all approach. Start with a basic setup that can be easily enhanced. Treat it like a recipe collection, evolving with each new dish. Each iteration focuses on either enhancing the kitchen tools or optimizing costs. As a chef, you care if your kitchen equipment is top-of-the-line or basic, but you will know how to work with both. What matters is delivering a delicious, well-presented dish. Your team needs to think about how to maximize the buy/build approach for the first iteration because they will learn all the time about what enhances the flavors and what doesn't, and where the issues lie. The main purpose of picking kitchen tools should be to enhance the Secret Ingredients you selected and allow for further experimentation by the chefs. It needs to be something that will be easy to switch or optimize. But for me, the more important part of the setup is to launch something I can taste-test with the diners and learn how it complements the dish, whether they notice enhancements or not. So, don’t deploy and forget until it breaks. The data chefs need to go around and collect feedback, understand the flavor profiles of diners, and prioritize them in the recipe book. Building trust is essential. Inconsistent flavors in a dish quickly erode a diner's trust in the chef. Similarly, unreliable or inconsistent data undermine the credibility of your data-driven insights. Establish clear data governance guidelines – outlining data availability timelines, ownership, and issue resolution protocols – to ensure data quality and consistency. Imagine the wasted effort required to validate data if its reliability is questionable constantly! Embrace the Journey, Share the Feast Data strategy is about crafting flavorful dishes for a successful restaurant. By focusing on clear recipes (KPIs), strong governance, and a scalable kitchen setup (infrastructure), you can create your culinary oasis – one that delights your diners (customers) and fuels your restaurant's (company's) growth. Ready to take a bite out of your data woes? Read my book, "Data is Like a Plate of Hummus” Buy My Book On Amazon Dive deeper into the five Ws of effective KPIs, explore data governance best practices, and guide you through building a scalable data infrastructure. Learn how to transform your data into a strategic advantage and unlock the full flavor of your company's success! At Hummus Said in Acre, Israel, I always order hummus with Foul, don’t think much about it, just add a bit of salt and digg in with the onion. At Hummus Said, a legendary restaurant in Akko, Israel, I savor their signature dish: creamy hummus with a side of Foul. The taste is always perfect, but on one visit, I brought a giant container, joking, "Can bulk hummus match this experience?" Said, the owner and hummus maestro, chuckled, "The flavor might be the same, but the experience suffers.” Just as the bulk hummus misses the essence of Hummus Said, many leaders get lost in the sheer volume of data.  They dream of overflowing databases, perfect metrics for drag-and-drop dashboards, and magically solved problems.- Lior Barak Just as the bulk hummus misses the essence of Hummus Said, many leaders get lost in the sheer volume of data.  They dream of overflowing databases, perfect metrics for drag-and-drop dashboards, and magically solved problems. - Lior Barak Forget the data dreamland, leaders! You envision overflowing databases, perfect metrics for drag-and-drop dashboards, and magically solved problems. Think again. Simple KPIs like sessions and revenue are just appetizers, not the full course to success. Forget the data dreamland, leaders! Think again. The harsh reality? Most companies choke on their data because they focus on the wrong ingredients. Data strategy isn't a gourmet buffet; it's a clear, actionable recipe. Time to ditch the data daydream and start cooking! The harsh reality? Time to ditch the data daydream and start cooking! Set Clear KPIs! Set Clear KPIs! Many leaders envision a data strategy as a readily available buffet - a vast collection of metrics that can be easily assembled into insights. This approach, however, overlooks a critical first step: defining clear and actionable Key Performance Indicators (KPIs). Just as a chef wouldn't haphazardly throw ingredients together and expect a culinary masterpiece, a data strategy requires a well-defined recipe. Your recipe, in this case, consists of the 3-5 key performance indicators (KPIs) that truly capture the essence of your company's success. These KPIs serve as the foundation for your data strategy, guiding all stakeholders towards the metrics that matter most. key performance indicators (KPIs) Think of KPIs as the flavor profile of your data. In my book, " Data is Like a Plate of Hummus " I promote a funnel methodology for defining KPIs: flavor profile Data is Like a Plate of Hummus funnel methodology Identify key questions: What are the most critical questions you need answered about your business? For example: What are the revenues from ad-free subscriptions? How many users bought more than one item in the same basket? What are the revenues from ad-free subscriptions? How many users bought more than one item in the same basket? Refine your questions: Focus on the most essential ones. Understand which questions are critical for your company goals and strategy, and which answers will help you evaluate the company's health. Define KPIs: Which metrics can effectively provide answers to those refined questions? Understand the KPIs that will give you these answers following the five W's principles: Who is the controller of the KPI? Who owns the data that will be used for the KPI, and who is responsible for ensuring its accuracy and stability? What action will it drive me to take? If I see a positive or negative change in the KPI. How well will it give me answers for the company’s health? What else am I missing? Where is the issue? Can I receive an answer about which company section the issue is? When do I need to see it? How frequently are these measurements needed? (For example, a weekly "time-on-site" KPI might be sufficient to gauge the effectiveness of UX changes, not requiring daily monitoring.) Who is the controller of the KPI? Who owns the data that will be used for the KPI, and who is responsible for ensuring its accuracy and stability? Who is the controller of the KPI? What action will it drive me to take? If I see a positive or negative change in the KPI. What action will it drive me to take? How well will it give me answers for the company’s health? What else am I missing? How well will it give me answers for the company’s health? Where is the issue? Can I receive an answer about which company section the issue is? Where is the issue? When do I need to see it? How frequently are these measurements needed? (For example, a weekly "time-on-site" KPI might be sufficient to gauge the effectiveness of UX changes, not requiring daily monitoring.) When do I need to see it? Building Scalable and Iterative Infrastructure Building Scalable and Iterative Infrastructure Now that you know what flavors you need, let's talk kitchen setup. Think fast, efficient deployment. Forget a one-size-fits-all approach. Start with a basic setup that can be easily enhanced. Treat it like a recipe collection, evolving with each new dish. Each iteration focuses on either enhancing the kitchen tools or optimizing costs. As a chef, you care if your kitchen equipment is top-of-the-line or basic, but you will know how to work with both. What matters is delivering a delicious, well-presented dish. Your team needs to think about how to maximize the buy/build approach for the first iteration because they will learn all the time about what enhances the flavors and what doesn't, and where the issues lie. The main purpose of picking kitchen tools should be to enhance the Secret Ingredients you selected and allow for further experimentation by the chefs. It needs to be something that will be easy to switch or optimize. But for me, the more important part of the setup is to launch something I can taste-test with the diners and learn how it complements the dish, whether they notice enhancements or not. So, don’t deploy and forget until it breaks. The data chefs need to go around and collect feedback, understand the flavor profiles of diners, and prioritize them in the recipe book. Building trust is essential. Inconsistent flavors in a dish quickly erode a diner's trust in the chef. Similarly, unreliable or inconsistent data undermine the credibility of your data-driven insights. Establish clear data governance guidelines – outlining data availability timelines, ownership, and issue resolution protocols – to ensure data quality and consistency. Imagine the wasted effort required to validate data if its reliability is questionable constantly! Building trust is essential. Embrace the Journey, Share the Feast Embrace the Journey, Share the Feast Data strategy is about crafting flavorful dishes for a successful restaurant. By focusing on clear recipes (KPIs), strong governance, and a scalable kitchen setup (infrastructure), you can create your culinary oasis – one that delights your diners (customers) and fuels your restaurant's (company's) growth. Ready to take a bite out of your data woes? Read my book, "Data is Like a Plate of Hummus” Ready to take a bite out of your data woes? Buy My Book On Amazon Buy My Book On Amazon Dive deeper into the five Ws of effective KPIs, explore data governance best practices, and guide you through building a scalable data infrastructure. Learn how to transform your data into a strategic advantage and unlock the full flavor of your company's success!

The Five W's for Setting KPIs

The words "venture" and "venture capital investment" are heard more and more often nowadays, but many beginner investors experience fear of uncertainty and doubt when choosing this method of financing. In order to better understand this topic, I asked Aleksandr Belov, co-founder and CEO of the Prosto VC club*.* First of all, it is important to understand that angel investors are very eager to reach out to the top startups investments in which can multiply your invested money many times over. But for a regular angel investor, this is virtually impossible. Especially remotely, it is impossible to reach the best venture deals that take place, for example, in Silicon Valley - there are thousands of funds there, claiming your place in the startup. Therefore, the only working business model for getting into such deals is the model of community and co-investment with those who already know how to get into such deals. Why do people go into venture capital? And what is it, anyway? Venture means nothing less than investment. It’s neither philanthropy, nor sponsorship of startups and not just the financing of some technologies. In abstract terms, venture is a field of financial engineering. Simply speaking, it is one of the ways to multiply your own capital. Not to save, but to multiply, because venture capital is a high-risk tool, so it is much less suitable for saving than classical banking and stock exchange options, which means that people come to venture capital to earn money. It is important for both beginner investors and startups to understand this - a venture capitalist is the same as an entrepreneur who is looking for a way to make a super profit, but in a more passive way. That is, if a start-up founder's contribution consists of 90% of his time and 10% of his personal funds, an investor, on the contrary, wants to devote 10% of his time, but is ready to compensate 90% of his "involvement" with money. This is what the investor's partnership with the startup is based on. In Kiyosaki's Cash Flow Quadrant, this type of employment is called "Investor". It turns out that those who aim at this part of the quadrant go into venture capital. They can be former entrepreneurs, top managers, celebrities who have the initial capital to make the first steps towards this role. Venture is, obviously, not quick, you have to wait several years for your share in the startup to grow in monetary terms. And during these years you have to build a whole portfolio of projects, i.e. invest in other startups to reduce the risk of losses and increase the chances that several portfolio projects will shoot up and bring super profits - 10, 100, in rare cases 1000 times more than you invested in them. This is why people come to venture capital when there is no longer a goal of making money "here and now". Why do they go into venture capital - from a purely practical point of view, to be able to earn much more than with any other tool. In real estate the average income yield is 8-10% per annum, the average annual yield of the US stock market is 15%,the investments into metals in some cases can reach 20%. In venture capital it is historically 30-40%. But it is important to realize that not every startup will show such profitability, and the portfolio as a whole - you should be prepared that out of 10 startups 5 will pass away, a few more will bring nothing, and only 1-2 will show such growth that the earnings from them will cover the losses from all the others. To sum it up - people go into venture capital with the aim to improve their standard of living; to satisfy their own motivation to provide for the future, for their tom orrow better due to strategically higher profitability than can be obtained in classical financial instruments. Is venture capital complicated, is it knowledge available only to a few? After talking to the founders of the Venture Club, I can say that, in fact, no - this opinion about venture capital is not accurate - it comes from ignorance, from the lack of completeness of information. Like when you first go to the gym and you think that losing weight and gaining muscle is some kind of rocket science. Or like when we all thought that about math in first grade. But as you dive in, the subject gets easier. Venturing is an extension of entrepreneurial or management experience. Decision making is very similar, the same questions about market, team, strategy are relevant as in any other non-venture project. Of course, there are nuances - for example, in venture capital you have to be a little more immersed in technological trends; you have to do market analytics a little differently. But there is already a lot of information on how to do this. The main rule of thumb is that if you start today, in six months you will have a good understanding of venture capital and an idea of what kind of portfolio you want to have. If you don't start - you will be six months off your own startup earning schedule. Do venture capitalists need large amounts of capital? It's no secret that previously an investor's check to a startup was several tens or hundreds of thousands of dollars. Even 5 years ago, the minimum check was $25,000-30,000. But the industry is evolving. The business model of syndicates and venture clubs has emerged - where several investors can throw together smaller checks, and eventually score one substantial one. There are already deals where the minimum check is $10,000, and there are even deals where the minimum check is $5,000. But at the same time, all club members together can raise up to $500,000, which is even more than the checks of many funds at the early stage of a startup's development. In this case, the investor has no commitments, they independently decide whether to join the deal and in what amount. To do this, the club provides all the information required to make the decision. In addition, there are online platforms where you can invest as little as $1,000 in your favorite startup according to the same principle. All this makes it possible, even with a relatively small amount of capital allocated for high-risk venture investments, to assemble a balanced portfolio of projects in different industries, in different markets and with different levels of risk. Moreover, Aleksandr told me that even those club members who have large venture budgets should not be in a hurry to put $100,000 into each project. From the point of view of statistics and mathematical expectations, it is better to distribute these $100,000 into a dozen of projects. Strategically, this will give a better return on the entire portfolio. And if you see that some startup is rapidly taking off, you will have the opportunity to put a bigger check into the startup at the next round, thus greatly reducing risks by investing substantial checks only in the most successful projects in your portfolio. So, small initial checks are a boon for the investor, it is his option to "take a place" in a promising project without risking all his capital at once. So if you are ready to allocate $5,000-10,000 several times a year to your favorite startups, join venture clubs - they give you the opportunity to get a small check into interesting and often exclusive deals that are simply impossible to discover without a club. The words "venture" and "venture capital investment" are heard more and more often nowadays, but many beginner investors experience fear of uncertainty and doubt when choosing this method of financing. The words "venture" and "venture capital investment" are heard more and more often nowadays, but many beginner investors experience fear of uncertainty and doubt when choosing this method of financing. In order to better understand this topic, I asked Aleksandr Belov, co-founder and CEO of the Prosto VC club *.* In order to better understand this topic, I asked Aleksandr Belov, co-founder and CEO of the Aleksandr Belov, co-founder and CEO of the Prosto VC club Prosto VC club First of all, it is important to understand that angel investors are very eager to reach out to the top startups investments in which can multiply your invested money many times over. But for a regular angel investor, this is virtually impossible. Especially remotely, it is impossible to reach the best venture deals that take place, for example, in Silicon Valley - there are thousands of funds there, claiming your place in the startup. Therefore, the only working business model for getting into such deals is the model of community and co-investment with those who already know how to get into such deals. Why do people go into venture capital? And what is it, anyway? Why do people go into venture capital? And what is it, anyway? Venture means nothing less than investment. It’s neither philanthropy, nor sponsorship of startups and not just the financing of some technologies. In abstract terms, venture is a field of financial engineering. Simply speaking, it is one of the ways to multiply your own capital. Not to save, but to multiply, because venture capital is a high-risk tool, so it is much less suitable for saving than classical banking and stock exchange options, which means that people come to venture capital to earn money. It is important for both beginner investors and startups to understand this - a venture capitalist is the same as an entrepreneur who is looking for a way to make a super profit, but in a more passive way. That is, if a start-up founder's contribution consists of 90% of his time and 10% of his personal funds, an investor, on the contrary, wants to devote 10% of his time, but is ready to compensate 90% of his "involvement" with money. This is what the investor's partnership with the startup is based on. In Kiyosaki's Cash Flow Quadrant , this type of employment is called "Investor". It turns out that those who aim at this part of the quadrant go into venture capital. They can be former entrepreneurs, top managers, celebrities who have the initial capital to make the first steps towards this role. Cash Flow Quadrant Venture is, obviously, not quick, you have to wait several years for your share in the startup to grow in monetary terms. And during these years you have to build a whole portfolio of projects, i.e. invest in other startups to reduce the risk of losses and increase the chances that several portfolio projects will shoot up and bring super profits - 10, 100, in rare cases 1000 times more than you invested in them. This is why people come to venture capital when there is no longer a goal of making money "here and now". Why do they go into venture capital - from a purely practical point of view, to be able to earn much more than with any other tool. In real estate the average income yield is 8-10% per annum, the average annual yield of the US stock market is 15%,the investments into metals in some cases can reach 20%. In venture capital it is historically 30-40%. But it is important to realize that not every startup will show such profitability, and the portfolio as a whole - you should be prepared that out of 10 startups 5 will pass away, a few more will bring nothing, and only 1-2 will show such growth that the earnings from them will cover the losses from all the others. Why do they go into venture capital Why do they go into venture capital To sum it up - people go into venture capital with the aim to improve their standard of living; to satisfy their own motivation to provide for the future, for their tom orrow better due to strategically higher profitability than can be obtained in classical financial instruments. Is venture capital complicated, is it knowledge available only to a few? Is venture capital complicated, is it knowledge available only to a few? After talking to the founders of the Venture Club, I can say that, in fact, no - this opinion about venture capital is not accurate - it comes from ignorance, from the lack of completeness of information. Like when you first go to the gym and you think that losing weight and gaining muscle is some kind of rocket science. Or like when we all thought that about math in first grade. But as you dive in, the subject gets easier. Venturing is an extension of entrepreneurial or management experience. Decision making is very similar, the same questions about market, team, strategy are relevant as in any other non-venture project. Of course, there are nuances - for example, in venture capital you have to be a little more immersed in technological trends; you have to do market analytics a little differently. But there is already a lot of information on how to do this. The main rule of thumb is that if you start today, in six months you will have a good understanding of venture capital and an idea of what kind of portfolio you want to have. If you don't start - you will be six months off your own startup earning schedule. Do venture capitalists need large amounts of capital? Do venture capitalists need large amounts of capital? It's no secret that previously an investor's check to a startup was several tens or hundreds of thousands of dollars. Even 5 years ago, the minimum check was $25,000-30,000. But the industry is evolving. The business model of syndicates and venture clubs has emerged - where several investors can throw together smaller checks, and eventually score one substantial one. There are already deals where the minimum check is $10,000, and there are even deals where the minimum check is $5,000. But at the same time, all club members together can raise up to $500,000, which is even more than the checks of many funds at the early stage of a startup's development. In this case, the investor has no commitments, they independently decide whether to join the deal and in what amount. To do this, the club provides all the information required to make the decision. In addition, there are online platforms where you can invest as little as $1,000 in your favorite startup according to the same principle. All this makes it possible, even with a relatively small amount of capital allocated for high-risk venture investments, to assemble a balanced portfolio of projects in different industries, in different markets and with different levels of risk. Moreover, Aleksandr told me that even those club members who have large venture budgets should not be in a hurry to put $100,000 into each project. From the point of view of statistics and mathematical expectations, it is better to distribute these $100,000 into a dozen of projects. Strategically, this will give a better return on the entire portfolio. And if you see that some startup is rapidly taking off, you will have the opportunity to put a bigger check into the startup at the next round, thus greatly reducing risks by investing substantial checks only in the most successful projects in your portfolio. So, small initial checks are a boon for the investor, it is his option to "take a place" in a promising project without risking all his capital at once. So if you are ready to allocate $5,000-10,000 several times a year to your favorite startups, join venture clubs - they give you the opportunity to get a small check into interesting and often exclusive deals that are simply impossible to discover without a club.

Breaking into Venture Capital: Overcoming Obstacles and Embracing Potential 

Talks On Productivity: Evgeniya Malina - Head of Processes and Automation at Whizz 

When James Cameron first teased Terminator 2 back in the day, it was through a 90-second teaser that showed an automated robot factory pumping out endless rows of robots, ending with a shot of Arnold as the build process completed. Pure pulp fiction. Or was it? Fast forward to 2024, and we are now entering the era of the hyperfactory, where the main body of cars is autonomously assembled every 76 seconds. No, that’s not a mistype because Xaoimi, the smartphone OEM, has entered the electric vehicle arena with the SU7 and with it, a factory setup that is 91% automated with 400 robots armed with HD cameras and can operate in the dark. It reminds me of the time I spent on stage at the Big Data Expo in China with the chairman of Foxconn, and we debated the merits of running a smartphone factory with the lights off — because who needs lights when it’s just the robots running the show? The factory of the future will have only two employees, a man and a dog. The man will be there to feed the dog. The dog will be there to keep the man from touching the equipment.
— Warren Bennis Watching the keynote reveal is an eye-opening vision of the future of manufacturing but not in the way you think. “We have 269 welding machines to achieve 100% automated production and have the 36 robots to achieve 0.5 millimeters of assembly precision” “…industry leading seven axis coating robots with 70,000 RPM, it can make the paint coverage extremely fine balanced. This produces a natural flat and smooth finish..” “AMR robots that can replace forklifts/internal transport around the factory…” The best part is that the cars can then drive themselves off the factory line with the autopilot software that the company has introduced. In all, it’s a fascinating look at the future of car manufacturing. But there’s something else to consider. We are looking at the precursor of mass-produced, highly automated robot and drone production, something that I can’t help but compare to that dystopian movie. Because, let’s face it, everything is starting to point towards it. We have major AI players all rushing to give their hyperbolic algorithms some form of physical embodiment to contextually understand the world around them in a new dimension. The idea that military, DARPA, or even startup funding won’t find its way to a hyperfactory of this kind is ludicrous because they’ll be eyeing this up as a way to mass-produce killer drones and robotic tech in the blink of an eye if it means flooding the market with robotic slaves. One of the best (read: accurate) dystopian movies of the future to this day is Elysium by Neill Blomkamp, but even then, his vision of a robotic factory is forcing human labor to manufacture the military machines that take the piss on a daily basis. It just won’t be like that. It’s time to pay close attention to other industries for clues as to how stuff like Terminator or The Matrix could feasibly come true — our hubris in automating the human out of every job role and process will ultimately lead us to a path where automation can be easily retooled and reprogrammed to manufacture something else. And it won’t be a Roomba. When James Cameron first teased Terminator 2 back in the day, it was through a 90-second teaser that showed an automated robot factory pumping out endless rows of robots, ending with a shot of Arnold as the build process completed. Pure pulp fiction. Or was it? Fast forward to 2024, and we are now entering the era of the hyperfactory, where the main body of cars is autonomously assembled every 76 seconds. Fast forward to 2024, and we are now entering the era of the hyperfactory , where the main body of cars is autonomously assembled every 76 seconds. hyperfactory No, that’s not a mistype because Xaoimi, the smartphone OEM, has entered the electric vehicle arena with the SU7 and with it, a factory setup that is 91% automated with 400 robots armed with HD cameras and can operate in the dark. It reminds me of the time I spent on stage at the Big Data Expo in China with the chairman of Foxconn, and we debated the merits of running a smartphone factory with the lights off — because who needs lights when it’s just the robots running the show? It reminds me of the time I spent on stage at the Big Data Expo in China with the chairman of Foxconn, The factory of the future will have only two employees, a man and a dog. The man will be there to feed the dog. The dog will be there to keep the man from touching the equipment. — Warren Bennis The factory of the future will have only two employees, a man and a dog. The man will be there to feed the dog. The dog will be there to keep the man from touching the equipment. The factory of the future will have only two employees, a man and a dog. The man will be there to feed the dog. The dog will be there to keep the man from touching the equipment. — Warren Bennis — Warren Bennis Watching the keynote reveal is an eye-opening vision of the future of manufacturing but not in the way you think. keynote reveal keynote reveal “We have 269 welding machines to achieve 100% automated production and have the 36 robots to achieve 0.5 millimeters of assembly precision” “…industry leading seven axis coating robots with 70,000 RPM, it can make the paint coverage extremely fine balanced. This produces a natural flat and smooth finish..” “AMR robots that can replace forklifts/internal transport around the factory…” The best part is that the cars can then drive themselves off the factory line with the autopilot software that the company has introduced. In all, it’s a fascinating look at the future of car manufacturing. But there’s something else to consider. We are looking at the precursor of mass-produced, highly automated robot and drone production, something that I can’t help but compare to that dystopian movie. We are looking at the precursor of mass-produced, highly automated robot and drone production, something that I can’t help but compare to that dystopian movie. that dystopian movie. Because, let’s face it, everything is starting to point towards it. We have major AI players all rushing to give their hyperbolic algorithms some form of physical embodiment to contextually understand the world around them in a new dimension. The idea that military, DARPA, or even startup funding won’t find its way to a hyperfactory of this kind is ludicrous because they’ll be eyeing this up as a way to mass-produce killer drones and robotic tech in the blink of an eye if it means flooding the market with robotic slaves. The idea that military, DARPA, or even startup funding won’t find its way to a hyperfactory of this kind is ludicrous because they’ll be eyeing this up as a way to mass-produce killer drones and robotic tech in the blink of an eye if it means flooding the market with robotic slaves. One of the best ( read: accurate ) dystopian movies of the future to this day is Elysium by Neill Blomkamp, but even then, his vision of a robotic factory is forcing human labor to manufacture the military machines that take the piss on a daily basis. It just won’t be like that. read: accurate It’s time to pay close attention to other industries for clues as to how stuff like Terminator or The Matrix could feasibly come true — our hubris in automating the human out of every job role and process will ultimately lead us to a path where automation can be easily retooled and reprogrammed to manufacture something else. And it won’t be a Roomba.

Skynet Is Not the One You Should Be Fearing - Fear Your Favourite Car Manufacturer Instead

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Jihoon Chung;
(2)  Zhenyu (James) Kong. Table of Links Abstract & Introduction
Review of Related Work
Proposed Research Methodology
Numerical Case Studies
Real-World Simulation Case Studies
Conclusion
References V. REAL-WORLD SIMULATION CASE STUDIES An assembly operation from an actual auto-body assembly process is used as a real-world case study. Fig. 5 describes a car’s floor pan, which is the assembled product from this process. The assembled product consists of four parts, including the right bracket, left bracket, right floor pan, and left floor pan. Fig. 6 shows the process assembly procedure consisting of three stations. During the assembly process, the parts are held by fixtures, which are the KCCs in this process [7]. KPCs are measured from four points, namely, M1, M2, M3, and M4, respectively, as shown in Fig. 5. Every part has a designated location for measuring the KPCs. For instance, part 1 has M1, part 2 has M2, part 3 has M3, and part 4 has M4. These measurements can be taken at each station once the relevant part has been assembled in the preceding stations. For example, M3 on part 3 cannot be measured in station 1 because part 3 has not yet been assembled at station 1 [7]. In the designated location, KPCs are measured in three directions (X, Y, and Z). In this assembly process, there exists a total of 33 KCCs, which are dimensional errors of fixture locators [8]. The fault pattern matrix Φ is established based on the literature [7], [11], [47] and provided in Appendix F. Since the number of measurements (12) is less than that of KCCs (33), it causes an underdetermined system in the fault quality linear model. Therefore, sparse estimation is required to identify process faults. In this assembly process, two lists of correlated KCCs exist; the rest are assumed to be independent [8]. The first list comprises six KCCs, including KCC8, KCC9, KCC10, KCC11, KCC12, and KCC13. The second list is composed of KCC31, KCC32, and KCC33. The correlation structures of the two groups are described (for simplification, the lower triangular terms are removed) as follows [8]: To provide nonstationary process faults along the KPCs samples collected over time, two groups are provided. In the first group, the first correlated list is provided as process faults. For the process faults of the second group, all the correlated lists and one independent KCCs are included. Therefore, the case study represents the situation when the process faults evolve over time since the variation of certain KCCs will be propagated to other KCCs if the process faults are not mitigated immediately during the process. 50 KPCs samples are generated from each group. The variances of process faults, non-process faults, and noise of KPCs are determined to be the same value as in Section IV. In addition, performance evaluation measures and benchmark methods used in Section IV are still utilized in Section V. Table III shows the performance comparison in AUC and NMSE by varying the correlation coefficients. For convenience, the correlation coefficients k1 and k2 are equal and are selected from {0.1, 0.3, 0.6, 0.9, 0.95} in the case studies. Specifically, the AUC of the proposed method is close to 1.0 at every correlations levels, representing the perfect classification between the process faults and non-process faults in terms of estimated variance. The effectiveness of the proposed method is significant in NMSE when a high correlation exists. Specifically, the NMSE of the proposed method is less than 10% of that of all the benchmark methods when the correlation coefficient is 0.9 and 0.95. To demonstrate the generalizability of the proposed method, the correlated KCCs are randomly selected in further case studies. Assuming there exist two correlated lists with the size of three, respectively. Therefore, six KCCs are randomly selected from 33 KCCs for two correlated lists in each trial. As in the previous study, one correlated list is provided as process faults in the first group, while two correlated lists and two independent KCCs are used as process faults in the remaining second group. Fig. 7 shows the performances of all the methods in AUC and NMSE results in ten different spatial correlations (i.e., k1 = k2). From Fig. 7 (a), it is prominent that the proposed method successfully differentiates between process faults and non-process faults. Specifically, the AUC of the proposed method is nearly 1.0, while those of most benchmark methods are below 0.9 in the highly correlated case studies. In addition, Fig. 7 (b) represents that the proposed method provides valuable information to the practitioners to determine when to stop the operations to maintain the process when a high correlation exists between KCCs. All the benchmark methods illustrate similar trends compared to Section IV since the methods cannot consider both the spatial correlation of KCCs and the nonstationary process faults. This paper is available on arxiv under CC 4.0 license. Authors: (1) Jihoon Chung; (2)  Zhenyu (James) Kong. This paper is available on arxiv under CC 4.0 license. Authors: (1) Jihoon Chung; (2)  Zhenyu (James) Kong. Table of Links Abstract & Introduction Review of Related Work Proposed Research Methodology Numerical Case Studies Real-World Simulation Case Studies Conclusion References Abstract & Introduction Abstract & Introduction Review of Related Work Review of Related Work Proposed Research Methodology Proposed Research Methodology Numerical Case Studies Numerical Case Studies Real-World Simulation Case Studies Real-World Simulation Case Studies Conclusion Conclusion References References V. REAL-WORLD SIMULATION CASE STUDIES An assembly operation from an actual auto-body assembly process is used as a real-world case study. Fig. 5 describes a car’s floor pan, which is the assembled product from this process. The assembled product consists of four parts, including the right bracket, left bracket, right floor pan, and left floor pan. Fig. 6 shows the process assembly procedure consisting of three stations. During the assembly process, the parts are held by fixtures, which are the KCCs in this process [7]. KPCs are measured from four points, namely, M1, M2, M3, and M4, respectively, as shown in Fig. 5. Every part has a designated location for measuring the KPCs. For instance, part 1 has M1, part 2 has M2, part 3 has M3, and part 4 has M4. These measurements can be taken at each station once the relevant part has been assembled in the preceding stations. For example, M3 on part 3 cannot be measured in station 1 because part 3 has not yet been assembled at station 1 [7]. In the designated location, KPCs are measured in three directions (X, Y, and Z). In this assembly process, there exists a total of 33 KCCs, which are dimensional errors of fixture locators [8]. The fault pattern matrix Φ is established based on the literature [7], [11], [47] and provided in Appendix F. Since the number of measurements (12) is less than that of KCCs (33), it causes an underdetermined system in the fault quality linear model. Therefore, sparse estimation is required to identify process faults. In this assembly process, two lists of correlated KCCs exist; the rest are assumed to be independent [8]. The first list comprises six KCCs, including KCC8, KCC9, KCC10, KCC11, KCC12, and KCC13. The second list is composed of KCC31, KCC32, and KCC33. The correlation structures of the two groups are described (for simplification, the lower triangular terms are removed) as follows [8]: To provide nonstationary process faults along the KPCs samples collected over time, two groups are provided. In the first group, the first correlated list is provided as process faults. For the process faults of the second group, all the correlated lists and one independent KCCs are included. Therefore, the case study represents the situation when the process faults evolve over time since the variation of certain KCCs will be propagated to other KCCs if the process faults are not mitigated immediately during the process. 50 KPCs samples are generated from each group. The variances of process faults, non-process faults, and noise of KPCs are determined to be the same value as in Section IV. In addition, performance evaluation measures and benchmark methods used in Section IV are still utilized in Section V. Table III shows the performance comparison in AUC and NMSE by varying the correlation coefficients. For convenience, the correlation coefficients k1 and k2 are equal and are selected from {0.1, 0.3, 0.6, 0.9, 0.95} in the case studies. Specifically, the AUC of the proposed method is close to 1.0 at every correlations levels, representing the perfect classification between the process faults and non-process faults in terms of estimated variance. The effectiveness of the proposed method is significant in NMSE when a high correlation exists. Specifically, the NMSE of the proposed method is less than 10% of that of all the benchmark methods when the correlation coefficient is 0.9 and 0.95. To demonstrate the generalizability of the proposed method, the correlated KCCs are randomly selected in further case studies. Assuming there exist two correlated lists with the size of three, respectively. Therefore, six KCCs are randomly selected from 33 KCCs for two correlated lists in each trial. As in the previous study, one correlated list is provided as process faults in the first group, while two correlated lists and two independent KCCs are used as process faults in the remaining second group. Fig. 7 shows the performances of all the methods in AUC and NMSE results in ten different spatial correlations (i.e., k1 = k2). From Fig. 7 (a), it is prominent that the proposed method successfully differentiates between process faults and non-process faults. Specifically, the AUC of the proposed method is nearly 1.0, while those of most benchmark methods are below 0.9 in the highly correlated case studies. In addition, Fig. 7 (b) represents that the proposed method provides valuable information to the practitioners to determine when to stop the operations to maintain the process when a high correlation exists between KCCs. All the benchmark methods illustrate similar trends compared to Section IV since the methods cannot consider both the spatial correlation of KCCs and the nonstationary process faults.

Sparse Bayesian Learning: Real-World Simulation Case Studies

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Jihoon Chung;
(2)  Zhenyu (James) Kong. Table of Links Abstract & Introduction
Review of Related Work
Proposed Research Methodology
Numerical Case Studies
Real-World Simulation Case Studies
Conclusion
References Abstract Sensor technology developments provide a basis for effective fault diagnosis in manufacturing systems. However, the limited number of sensors due to physical constraints or undue costs hinders the accurate diagnosis in the actual process. In addition, time-varying operational conditions that generate nonstationary process faults and the correlation information in the process require to consider for accurate fault diagnosis in the manufacturing systems. This article proposes a novel fault diagnosis method: clustering spatially correlated sparse Bayesian learning (CSSBL), and explicitly demonstrates its applicability in a multistation assembly system that is vulnerable to the above challenges. Specifically, the method is based on a practical  assumption that it will likely have a few process faults (sparse). In addition, the hierarchical structure of CSSBL has several parameterized prior distributions to address the above challenges. As posterior distributions of process faults do not have closed form, this paper derives approximate posterior distributions through Variational Bayes inference. The proposed method’s efficacy is provided through numerical and real-world case studies utilizing an actual autobody assembly system. The generalizability of the proposed method allows the technique to be applied in fault diagnosis in other domains, including communication and healthcare systems. Note to Practitioners—This article proposes a new process fault diagnosis method: clustering spatially correlated sparse Bayesian learning. This method effectively diagnoses time-varying defects by leveraging the correlation structures in the process when sensor measurements are insufficient. The actual autobody assembly process is utilized to show the proposed method’s effectiveness. The proposed method performs superior to the benchmark methods in fault detection capability. In addition, the proposed method accurately estimates the severity of the process faults, providing significant information to the practitioners for their decision-making in the maintenance schedule. Specifically, the error between the estimation from the proposed method and the actual severity of the process faults achieves less than 10% of error of all the benchmark methods when there exists a high correlation between the variations of the fixture locators in the autobody assembly system. Index Terms—Sparse Bayesian Learning, Spatially Correlated Faults, Nonstationary Faults, Variational Bayes Inference, MultiStage Assembly Systems. I. INTRODUCTION The sparse estimation has received considerable attention in signal processing due to its ability to reconstruct a high-dimensional sparse source signal from a low-dimensional measurement [1]. Specifically, sparse estimation has broad applications in a wide range of industries in identifying the sources of sensor measurements. These applications include channel estimation in wireless systems [2], Electroencephalography (EEG) source localization in neuroimaging [3], radar detection [4], and fault diagnosis in manufacturing systems [5]. However, time-varying operational conditions in the manufacturing systems cause nonstationary process faults, hindering the accurate sparse estimation. For example, the component degradation (e.g., fixture wearing in assembly systems) will vary over time, violating the stationary process faults assumptions of most existing sparse estimation methods [6], [7]. In addition, the correlation information that occurred due to the structure of the manufacturing system should consider for effective fault diagnosis [8], but it is often neglected in sparse estimation [9], [10]. One motivating example to address the above challenges is the process fault diagnosis in the multistation assembly systems. The systems perform assembly operations from multiple stations to assemble a final product. The final product’s quality relies on several factors known as key control characteristics (KCCs) [8]. The positioning accuracy of fixture locators is KCCs in the multistation assembly [5]. Fixture locators carry out the clamping of parts during the assembly process. Therefore, any deviations from their nominal positions can lead to dimensional quality issues in the final product. Hence, fault diagnosis in multistation assemblies estimates the mean and variance of KCCs, namely, the variations of fixture locators [9]. This article focuses on process faults due to excessive variance, which is a more challenging task to diagnose than mean shifts [7]. Since monitoring the dimensional variation of KCCs is not feasible due to physical constraints in the process [10], the key product characteristics (KPCs), which are essentially measurements obtained from the final product, can be utilized to estimate the variance of KCCs. Specifically, a fault-quality linear model represents the relationship between KPCs and KCCs as follows [11], [12]: Numerous studies have employed Eq. (1) on fault diagnosis in multistation assembly systems [13], [14]. However, they assume that the number of measurements (M) is greater than the number of KCCs (N) which may not hold in actual manufacturing applications. This is because using an excessive number of sensors (measurements) can result in undue costs [8]. If this assumption is violated, Eq. (1) becomes an underdetermined system that has a nonunique solution. To address this challenge, the sparse solution assumption [15] that x in Eq. (1) has a minimal number of non zero elements is required. In the fault diagnosis problem, sparsity refers to the small number of process faults in the fault-quality linear model, which is a reasonable assumption since there are typically only a few process faults in practice [5]. Among the several sparse estimation methods, the Bayesian approach called sparse Bayesian learning has received much attention recently because of its superior estimation performance guaranteed from the several theoretical properties [16]–[18]. Sparse Bayesian learning has been applied for fault diagnosis in multistation assembly systems [10], [19]. These studies successfully identified process faults by providing prior distribution of KCCs (i.e., x in Eq. (1)) to encourage the sparsity of process faults. Especially the work in [10] applied Bayesian learning to diagnose the process faults based on the following multiple measurements vectors (MMV) model in sparse Bayesian learning [20]: Beyond the spatial correlation between KCCs, the non stationarity of process faults also needs to be considered for accurate fault diagnosis. In practice, the external time-varying operational condition, including temperature and noise, may cause nonstationary process faults over time in the manufacturing systems [6]. For example, the fixture locators in the assembly process can be deviated because of the thermal expansions of pins caused by the ambient temperature of the process that varies over time [22]. In addition, the variation of some locators will be propagated to other locators over time if the process faults are not mitigated immediately [23]. It results in nonstationary process faults along the KPCs samples collected over time. In other words, the KCCs with excessive variance differ depending on the columns of X in Eq. (2). Therefore, the stationary process faults assumption applied in the previous studies [8], [10] needs to be addressed. To consider both the spatial correlation of KCCs and the nonstationary process faults along the KPCs samples, this paper proposes a novel sparse Bayesian learning method, namely, clustering and spatially correlated sparse Bayesian learning (CSSBL). Given KPCs samples, the proposed method clusters the samples into groups sharing the same process faults. At the same time, our method estimates the variance of KCCs of each group to identify the process faults accurately. The following summarizes the contributions of this study: • From the methodological point of view, this paper proposes a novel sparse Bayesian learning that can consider both the spatial correlation of KCCs and the nonstationary process faults. Since the posterior distribution of the sparse solution in the proposed method is computationally intractable, this article derives the approximate posterior distribution of the sparse solution via Variational Bayes inference [24]. • From the application perspective, the proposed method is applied to fault diagnosis in the actual auto-body assembly process. The method performs better for process fault detection capability than the benchmark methods. Furthermore, the proposed method accurately estimates locators’ variance representing the severity of the process faults to assist practitioners’ decision-making in their maintenance policy. The subsequent sections of this paper are structured in the following manner. Section II presents an overview of relevant literature, while Section III introduces the proposed methodology. The methodology’s effectiveness is evaluated through numerical case studies in Section IV. Section V provides realworld case studies which are fault diagnosis problems in the multistation assembly system. Finally, Section VI discusses conclusions and future work. This paper is available on arxiv under CC 4.0 license. Authors: (1) Jihoon Chung; (2)  Zhenyu (James) Kong. This paper is available on arxiv under CC 4.0 license. Authors: (1) Jihoon Chung; (2)  Zhenyu (James) Kong. Table of Links Abstract & Introduction Review of Related Work Proposed Research Methodology Numerical Case Studies Real-World Simulation Case Studies Conclusion References Abstract & Introduction Abstract & Introduction Review of Related Work Review of Related Work Proposed Research Methodology Proposed Research Methodology Numerical Case Studies Numerical Case Studies Real-World Simulation Case Studies Real-World Simulation Case Studies Conclusion Conclusion References References Abstract Sensor technology developments provide a basis for effective fault diagnosis in manufacturing systems. However, the limited number of sensors due to physical constraints or undue costs hinders the accurate diagnosis in the actual process. In addition, time-varying operational conditions that generate nonstationary process faults and the correlation information in the process require to consider for accurate fault diagnosis in the manufacturing systems. This article proposes a novel fault diagnosis method: clustering spatially correlated sparse Bayesian learning (CSSBL), and explicitly demonstrates its applicability in a multistation assembly system that is vulnerable to the above challenges. Specifically, the method is based on a practical  assumption that it will likely have a few process faults (sparse). In addition, the hierarchical structure of CSSBL has several parameterized prior distributions to address the above challenges. As posterior distributions of process faults do not have closed form, this paper derives approximate posterior distributions through Variational Bayes inference. The proposed method’s efficacy is provided through numerical and real-world case studies utilizing an actual autobody assembly system. The generalizability of the proposed method allows the technique to be applied in fault diagnosis in other domains, including communication and healthcare systems. Note to Practitioners —This article proposes a new process fault diagnosis method: clustering spatially correlated sparse Bayesian learning. This method effectively diagnoses time-varying defects by leveraging the correlation structures in the process when sensor measurements are insufficient. The actual autobody assembly process is utilized to show the proposed method’s effectiveness. The proposed method performs superior to the benchmark methods in fault detection capability. In addition, the proposed method accurately estimates the severity of the process faults, providing significant information to the practitioners for their decision-making in the maintenance schedule. Specifically, the error between the estimation from the proposed method and the actual severity of the process faults achieves less than 10% of error of all the benchmark methods when there exists a high correlation between the variations of the fixture locators in the autobody assembly system. Note to Practitioners Index Terms —Sparse Bayesian Learning, Spatially Correlated Faults, Nonstationary Faults, Variational Bayes Inference, MultiStage Assembly Systems. Index Terms I. INTRODUCTION The sparse estimation has received considerable attention in signal processing due to its ability to reconstruct a high-dimensional sparse source signal from a low-dimensional measurement [1]. Specifically, sparse estimation has broad applications in a wide range of industries in identifying the sources of sensor measurements. These applications include channel estimation in wireless systems [2], Electroencephalography (EEG) source localization in neuroimaging [3], radar detection [4], and fault diagnosis in manufacturing systems [5]. However, time-varying operational conditions in the manufacturing systems cause nonstationary process faults, hindering the accurate sparse estimation. For example, the component degradation (e.g., fixture wearing in assembly systems) will vary over time, violating the stationary process faults assumptions of most existing sparse estimation methods [6], [7]. In addition, the correlation information that occurred due to the structure of the manufacturing system should consider for effective fault diagnosis [8], but it is often neglected in sparse estimation [9], [10]. One motivating example to address the above challenges is the process fault diagnosis in the multistation assembly systems. The systems perform assembly operations from multiple stations to assemble a final product. The final product’s quality relies on several factors known as key control characteristics (KCCs) [8]. The positioning accuracy of fixture locators is KCCs in the multistation assembly [5]. Fixture locators carry out the clamping of parts during the assembly process. Therefore, any deviations from their nominal positions can lead to dimensional quality issues in the final product. Hence, fault diagnosis in multistation assemblies estimates the mean and variance of KCCs, namely, the variations of fixture locators [9]. This article focuses on process faults due to excessive variance, which is a more challenging task to diagnose than mean shifts [7]. Since monitoring the dimensional variation of KCCs is not feasible due to physical constraints in the process [10], the key product characteristics (KPCs), which are essentially measurements obtained from the final product, can be utilized to estimate the variance of KCCs. Specifically, a fault-quality linear model represents the relationship between KPCs and KCCs as follows [11], [12]: Numerous studies have employed Eq. (1) on fault diagnosis in multistation assembly systems [13], [14]. However, they assume that the number of measurements (M) is greater than the number of KCCs (N) which may not hold in actual manufacturing applications. This is because using an excessive number of sensors (measurements) can result in undue costs [8]. If this assumption is violated, Eq. (1) becomes an underdetermined system that has a nonunique solution. To address this challenge, the sparse solution assumption [15] that x in Eq. (1) has a minimal number of non zero elements is required. In the fault diagnosis problem, sparsity refers to the small number of process faults in the fault-quality linear model, which is a reasonable assumption since there are typically only a few process faults in practice [5]. Among the several sparse estimation methods, the Bayesian approach called sparse Bayesian learning has received much attention recently because of its superior estimation performance guaranteed from the several theoretical properties [16]–[18]. Sparse Bayesian learning has been applied for fault diagnosis in multistation assembly systems [10], [19]. These studies successfully identified process faults by providing prior distribution of KCCs (i.e., x in Eq. (1)) to encourage the sparsity of process faults. Especially the work in [10] applied Bayesian learning to diagnose the process faults based on the following multiple measurements vectors (MMV) model in sparse Bayesian learning [20]: Beyond the spatial correlation between KCCs, the non stationarity of process faults also needs to be considered for accurate fault diagnosis. In practice, the external time-varying operational condition, including temperature and noise, may cause nonstationary process faults over time in the manufacturing systems [6]. For example, the fixture locators in the assembly process can be deviated because of the thermal expansions of pins caused by the ambient temperature of the process that varies over time [22]. In addition, the variation of some locators will be propagated to other locators over time if the process faults are not mitigated immediately [23]. It results in nonstationary process faults along the KPCs samples collected over time. In other words, the KCCs with excessive variance differ depending on the columns of X in Eq. (2). Therefore, the stationary process faults assumption applied in the previous studies [8], [10] needs to be addressed. To consider both the spatial correlation of KCCs and the nonstationary process faults along the KPCs samples, this paper proposes a novel sparse Bayesian learning method, namely, clustering and spatially correlated sparse Bayesian learning (CSSBL). Given KPCs samples, the proposed method clusters the samples into groups sharing the same process faults. At the same time, our method estimates the variance of KCCs of each group to identify the process faults accurately. The following summarizes the contributions of this study: • From the methodological point of view, this paper proposes a novel sparse Bayesian learning that can consider both the spatial correlation of KCCs and the nonstationary process faults. Since the posterior distribution of the sparse solution in the proposed method is computationally intractable, this article derives the approximate posterior distribution of the sparse solution via Variational Bayes inference [24]. • From the application perspective, the proposed method is applied to fault diagnosis in the actual auto-body assembly process. The method performs better for process fault detection capability than the benchmark methods. Furthermore, the proposed method accurately estimates locators’ variance representing the severity of the process faults to assist practitioners’ decision-making in their maintenance policy. The subsequent sections of this paper are structured in the following manner. Section II presents an overview of relevant literature, while Section III introduces the proposed methodology. The methodology’s effectiveness is evaluated through numerical case studies in Section IV. Section V provides realworld case studies which are fault diagnosis problems in the multistation assembly system. Finally, Section VI discusses conclusions and future work.

Sparse Bayesian Learning: Abstract & Introduction

Empathizing with your customers is the first step in creating a successful e-commerce user experience (UX). In our case, we consider the role of empathy in the context of e-commerce in a narrower niche – baby carrier for newborn children. Baby K'tan is a solution that will help parents be closer to their children during the first years of their lives. Baby wearing allows caregivers to keep the baby close and safe while keeping both hands free. Focusing on the target audience and adhering to the authenticity of the Baby K'tan brand, the updated regional online store helps parents focus on what is most important to them - the safety and benefits for the child, while reducing the time spent searching for goods and choosing it. A particular challenge for in this case is maximum responsibility, since we are talking about the health of a newborn child and his mother. We know that during the postpartum period, all the mother's attention is focused only on the child. And this fragile balance must not be disturbed. The goal was to make navigation as simple as possible. And at the same time, maintain consistency in presenting relevant information throughout the user journey. The complexity of the product lies in the need to accompany it with a large amount of information describing its properties and confirming its reliability. At the stage of initial communication and further testing with a focus group, it was possible to divide this information into three levels of importance and develop a system for its presentation. At the initial stages of developing the project concept, there was a low point due to the difficulty of combining two goals: important information should be transmitted to the user quickly and easily accessible, but without losing the quality of the information and important aspects. The key was empathy and immersion in the lifestyle and needs of modern parents after the birth of a child. After prototype testing when we saw that this puzzle finally came together, there were emotions that can be compared to an “aha! moment.” To make the most empathic UX design, you need to try to understand in what conditions, at what time and under what circumstances your user will interact with your design. The main users are mothers who are preparing for the birth of a child and mothers of newborn babies and are looking for devices to ease the postpartum period. The main problem she faces is the lack of time that she can spend studying the issue and the solution should be easy and obvious The main task is to emphasize expert opinion. The period after birth is extremely important to all mothers, it is full of vivid emotions, tender moments. We understand that our goal is to help focus on these moments. Anxiety and daily routine can be very distracting. Focusing on the target audience and relying on the brand identity of Baby K'TAN, i have developed an online store redesign for the regional branch. The research was conducted in several stages and for each a target focus group was assembled – women preparing to become mothers (45%), mothers with children aged 1-2 years (45%) and fathers. We believe this proportion most accurately reflects the user. Based on the Baby K’Tan data, in addition to working with the focus group, the main principles of the design concept were highlighted: minimalism, gender neutrality, maximum ease of navigation and simple but relevant animation of element. What is the essence of the product or service? What are the core benefits for the target group? Your little one depends on you for everything, and at times this can feel overwhelming. But for the last nine months, you were her whole world, and in a few short years, she’ll want down instead of “up, up!” The truth is, for right now just the beat of your heart under hers is enough. Relish it while you can with an intuitive carrier that wraps her up tightly, close against you. The user journey begins with the moment the purchase decision is made and ends with the moment when the mother can truly say that her hands are free and her child is comfortable. The essence of the project is that the process of choosing a carrier, selecting the right size, understanding how to use it correctly, it is quite complicated, because we are talking about the health of the child. The difficulty also lies in the fact that users (mothers) are faced with one of the most serious changes in their lives, which also affects them at different levels. In this regard, it was decided to move in the direction of simplifying navigation as much as possible. Also, according to our research, mothers who decide to purchase a baby carrier after the birth of their child often conduct their research at night, when the child is already sleeping and this is the only time free for them for tasks of this kind. In this regard, a color concept was developed so as not to increase the load on the girl’s tired body. In conclusion I can said that heartfelt approach as important as data and metrics. While technological prowess and design innovation remain integral, it is the sincere approach that truly resonates with users. Empathizing with your customers is the first step in creating a successful e-commerce user experience (UX). In our case, we consider the role of empathy in the context of e-commerce in a narrower niche – baby carrier for newborn children. Baby K'tan is a solution that will help parents be closer to their children during the first years of their lives. Baby wearing allows caregivers to keep the baby close and safe while keeping both hands free. Focusing on the target audience and adhering to the authenticity of the Baby K'tan brand, the updated regional online store helps parents focus on what is most important to them - the safety and benefits for the child, while reducing the time spent searching for goods and choosing it. A particular challenge for in this case is maximum responsibility, since we are talking about the health of a newborn child and his mother. We know that during the postpartum period, all the mother's attention is focused only on the child. And this fragile balance must not be disturbed. The goal was to make navigation as simple as possible. And at the same time, maintain consistency in presenting relevant information throughout the user journey. The complexity of the product lies in the need to accompany it with a large amount of information describing its properties and confirming its reliability. At the stage of initial communication and further testing with a focus group, it was possible to divide this information into three levels of importance and develop a system for its presentation. At the initial stages of developing the project concept, there was a low point due to the difficulty of combining two goals: important information should be transmitted to the user quickly and easily accessible, but without losing the quality of the information and important aspects. The key was empathy and immersion in the lifestyle and needs of modern parents after the birth of a child. After prototype testing when we saw that this puzzle finally came together, there were emotions that can be compared to an “aha! moment.” To make the most empathic UX design, you need to try to understand in what conditions, at what time and under what circumstances your user will interact with your design. The main users are mothers who are preparing for the birth of a child and mothers of newborn babies and are looking for devices to ease the postpartum period. The main problem she faces is the lack of time that she can spend studying the issue and the solution should be easy and obvious The main task is to emphasize expert opinion. The period after birth is extremely important to all mothers, it is full of vivid emotions, tender moments. We understand that our goal is to help focus on these moments. Anxiety and daily routine can be very distracting. Focusing on the target audience and relying on the brand identity of Baby K'TAN, i have developed an online store redesign for the regional branch. The research was conducted in several stages and for each a target focus group was assembled – women preparing to become mothers (45%), mothers with children aged 1-2 years (45%) and fathers. We believe this proportion most accurately reflects the user. Based on the Baby K’Tan data, in addition to working with the focus group, the main principles of the design concept were highlighted: minimalism, gender neutrality, maximum ease of navigation and simple but relevant animation of element. What is the essence of the product or service? What are the core benefits for the target group? Your little one depends on you for everything, and at times this can feel overwhelming. But for the last nine months, you were her whole world, and in a few short years, she’ll want down instead of “up, up!” The truth is, for right now just the beat of your heart under hers is enough. Relish it while you can with an intuitive carrier that wraps her up tightly, close against you. The user journey begins with the moment the purchase decision is made and ends with the moment when the mother can truly say that her hands are free and her child is comfortable. The essence of the project is that the process of choosing a carrier, selecting the right size, understanding how to use it correctly, it is quite complicated, because we are talking about the health of the child. The difficulty also lies in the fact that users (mothers) are faced with one of the most serious changes in their lives, which also affects them at different levels. In this regard, it was decided to move in the direction of simplifying navigation as much as possible. Also, according to our research, mothers who decide to purchase a baby carrier after the birth of their child often conduct their research at night, when the child is already sleeping and this is the only time free for them for tasks of this kind. In this regard, a color concept was developed so as not to increase the load on the girl’s tired body. In conclusion I can said that heartfelt approach as important as data and metrics. While technological prowess and design innovation remain integral, it is the sincere approach that truly resonates with users.

Empathy in UX Design for E-commerce

Web3 Gaming is developing fast, but limitations such as isolated in-game economies and single-use NFTs restrict its full potential. Eldarune — a multi-faceted gaming ecosystem with average monthly transactions of over 100,000 — seems to break down these barriers and promote a more all-encompassing experience. Eldarune is the biggest Turkish Web3 gaming project that has the most advanced technology in the Web3 gaming market to date. The studio was conceived in Seedify, a Web3 gaming-focused incubator and launchpad, and has garnered the support of BNB Chain, Bybit, Decubate, Hacken, Immutable, Layer One X, and other The team behind Eldarune is based in Turkey and consists of 28 individuals, each bringing their expertise to the table.Can Picak, the CEO and project spokesperson, is a prominent industry leader with a background in Web3 gaming and blockchain marketing. He has practical experience in integrating gaming with blockchain and leveraging AI and IoT in startups. Building a Gaming Studio Powerhouse: The DIGA Labs Legacy Eldarune is a creation of DIGA Labs, a cutting-edge blockchain gaming solutions provider. Founded in 2021 by a team of passionate gamers and blockchain enthusiasts, DIGA Labs has a proven gaming industry track record and brings in software development expertise. This expertise shows that, technologically speaking, Eldarune titles are state-of-the-art. The company breaks the mold of traditional Web3 gaming, letting the players use their digital assets across multiple games. ELDA tokens and NFTs are like universal game currency, working in all their titles. This creates a connected ecosystem where all in-game efforts have real value and can be applied to different gaming experiences. ELDA Token The ELDA token is the lifeblood of the Eldarune ecosystem, powering a diverse range of in-game experiences. Players can use ELDA to access exclusive features and content, and also NFT purchases and rentals to expand their arsenals. ELDA unlocks participation in competitive activities like arena battles and tournaments – a juicy chance to win even more tokens and prestigious rewards. Players can also stake their ELDA for passive income through farming and burning mechanisms or directly invest it in powerful in-game items and character upgrades. For those eager to join the Eldarune adventure, ELDA is readily available for purchase on popular DEXs and CEXs like Bybit, MEXC, and PancakeSwap. A Universe of Games: Eldarune's Multi-Faceted Approach Eldarune is building a sprawling gaming universe that is not confined to a single blockchain. This multi-chain approach leverages the strengths of BNB Chain, Polygon, and Ethereum, creating a versatile and expansive player experience. On the 8th of April, Eldarune will also integrate Avalanche, and later, Solana will join the party. So, what is the multi-chain approach all about? Imagine playing different games where your cool sword or rare character skin follows you from one adventure to the next. That's the magic of Layer One X tech – Eldarune’s partner. It lets you trade your digital assets (tokens, NFTs, and customization items) seamlessly between their games, even if they’re on different blockchains. Simply put, you can jump between games without losing your hard-earned loot, creating a more connected experience and opening doors to potentially sweet rewards. So far, Eldarune’s universe includes four titles. World of Elymnias (WoE), an open beta idle game, lets players build empires and collect loot with minimal effort, perfect for casual sessions. Strategic minds can test their skills in the closed beta of Heroes of Eldarune (HoE), a TCG where they can build their ultimate deck and outwit opponents. Journey (open Beta) offers a captivating narrative adventure through text-based choices, allowing players to craft their own story. For the competitive crowd, ELDA RUSH, a closed alpha MOBA, is on the waitlist, promising fast-paced brawls. Eldarune's Diverse Gaming Universe: World of Elymnias (WoE) This idle game attracts over 10,000 daily active users. WoE has PvP modes where you can test your strategic prowess against other players in the arena. It is currently in open Beta, and players can download the game’s APK through the Eldarune Discord channel. Heroes of Eldarune (HoE) HoE is a TCG (Trading Card Game) with simple mechanics and user-friendly graphics. The game’s continuous development and new features are decided through collaboration with the Eldarune community and DAO. HoE is currently in the Beta stage, expected to launch in Q3 2024. Journey It is a text-based adventure game in which players explore a narrative-driven world, with over 50,000 users actively engaging in the storyline. You'll send your troops on dangerous journeys, following the narrative's twists and turns as you make choices that shape your path. Completing quests and progressing through the story unlocks valuable rewards. Eldarune Journey is in closed Beta. ELDA Rush It is a multiplayer online battle arena (MOBA) game akin to popular titles like Dota 2 and League of Legends. Assemble your team of powerful heroes, each with distinct abilities, and dive into the battlefield. ELDA Rush is currently in a closed Alpha stage with a limited player base. Players can join Eldarune's live streams to experience the game firsthand and potentially participate in the testing phase. Interoperability, Multi-Chain Dominance – and More Eldarune's true innovation lies in its interoperable NFTs and multi-chain functionality, setting it apart in the crowded Web3 gaming space. As of 2024, it delivers the most advanced technology in the market: Interoperable NFTs Players can use their acquired NFTs across all Eldarune titles. In-game NFTs in Eldarune transcend individual games, boosting their value and utility. The ability to rent or sell these NFTs on marketplaces creates a highly liquid in-game economy, allowing players to earn real-world value from their assets. Multi-Chain Gameplay Eldarune is currently operational on BNB, Polygon, and Ethereum, which is a great chance for players from different ecosystems to connect and compete. This creates a more expansive and inclusive gaming environment, attracting a wider player base. AI-powered Eldarune elevates gameplay through its AI that tailors experiences to individual players. By learning from your choices and actions, the AI personalizes the in-game world and adjusts difficulty for continued engagement. Players can train the AI and make NPCs fight alongside them in battles; AI-powered NPCs can create unique missions, and trained AI champions can be sold as NFTs. Community-focused Eldarune creates a space where players can contribute to the game's evolution. Further boosting engagement, a system of 21 seasons keeps players hooked, contributing to a sustainable and dynamic in-game economy. Gameplay Versatility Each title offers multiple modes, from conquering campaigns and exploring dungeons to battling clan bosses and testing skills in PvP arenas. This diversity ensures there's something for everyone. Accessibility is paramount, with all Eldarune games playable across browsers, mobile devices, PCs, and even consoles, allowing players to jump in anytime, anywhere. Deep Submersion Eldarune goes beyond captivating gameplay. Each title has its own immersive soundtrack album, setting a new standard for atmospheric experiences. Web3 Gaming Ecosystem That Constantly Evolves With over 20,000 unique NFT holders and the ELDA token reaching an all-time high of $0.062 in January 2024, the Eldarune ecosystem appears to be thriving. The project’s roadmap outlines a continuous development process with exciting prospects – Eldarune gears up for a busy March and April. Public Beta Testnets for both Heroes of Eldarune and Journey kick off in March, followed by exciting Public Beta Testnet Tournaments for both games in March and April. 2024 brings even more action with a free mint event on Avalanche (April 15th), Avalanche integration, L1X integration, and an Avalanche HoE Tournament. Eldarune's commitment to interoperability, multi-chain functionality, and a diverse gaming portfolio positions it as a strong contender in the Web3 gaming landscape. Even though the project is still under development, with Elda Rush and HoE yet to be fully launched, the initial games offer a glimpse into Eldarune's potential. The long-term success will hinge on the execution of the ambitious roadmap and the continuous delivery of engaging and vanward gaming experiences. This story was distributed under HackerNoon’s Brand As An Author Program. Learn more about the program here. Web3 Gaming is developing fast, but limitations such as isolated in-game economies and single-use NFTs restrict its full potential. Eldarune — a multi-faceted gaming ecosystem with average monthly transactions of over 100,000 — seems to break down these barriers and promote a more all-encompassing experience. Eldarune Eldarune Eldarune is the biggest Turkish Web3 gaming project that has the most advanced technology in the Web3 gaming market to date. The studio was conceived in Seedify , a Web3 gaming-focused incubator and launchpad, and has garnered the support of BNB Chain, Bybit, Decubate, Hacken, Immutable, Layer One X, and other Web3 gaming Seedify Seedify The team behind Eldarune is based in Turkey and consists of 28 individuals, each bringing their expertise to the table. Can Picak , the CEO and project spokesperson, is a prominent industry leader with a background in Web3 gaming and blockchain marketing. He has practical experience in integrating gaming with blockchain and leveraging AI and IoT in startups. Can Picak Can Picak Building a Gaming Studio Powerhouse: The DIGA Labs Legacy Eldarune is a creation of DIGA Labs , a cutting-edge blockchain gaming solutions provider. Founded in 2021 by a team of passionate gamers and blockchain enthusiasts, DIGA Labs has a proven gaming industry track record and brings in software development expertise. DIGA Labs DIGA Labs This expertise shows that, technologically speaking, Eldarune titles are state-of-the-art. The company breaks the mold of traditional Web3 gaming, letting the players use their digital assets across multiple games. ELDA tokens and NFTs are like universal game currency, working in all their titles. This creates a connected ecosystem where all in-game efforts have real value and can be applied to different gaming experiences. ELDA Token The ELDA token is the lifeblood of the Eldarune ecosystem, powering a diverse range of in-game experiences. Players can use ELDA to access exclusive features and content, and also NFT purchases and rentals to expand their arsenals. ELDA token ELDA token ELDA unlocks participation in competitive activities like arena battles and tournaments – a juicy chance to win even more tokens and prestigious rewards. Players can also stake their ELDA for passive income through farming and burning mechanisms or directly invest it in powerful in-game items and character upgrades. For those eager to join the Eldarune adventure, ELDA is readily available for purchase on popular DEXs and CEXs like Bybit , MEXC , and PancakeSwap . Bybit Bybit MEXC MEXC PancakeSwap PancakeSwap A Universe of Games: Eldarune's Multi-Faceted Approach Eldarune is building a sprawling gaming universe that is not confined to a single blockchain. This multi-chain approach leverages the strengths of BNB Chain, Polygon, and Ethereum, creating a versatile and expansive player experience. On the 8th of April, Eldarune will also integrate Avalanche, and later, Solana will join the party. So, what is the multi-chain approach all about? Imagine playing different games where your cool sword or rare character skin follows you from one adventure to the next. That's the magic of Layer One X tech – Eldarune’s partner . It lets you trade your digital assets (tokens, NFTs, and customization items) seamlessly between their games, even if they’re on different blockchains. Simply put, you can jump between games without losing your hard-earned loot, creating a more connected experience and opening doors to potentially sweet rewards. partner partner So far, Eldarune’s universe includes four titles. World of Elymnias (WoE), an open beta idle game, lets players build empires and collect loot with minimal effort, perfect for casual sessions. Strategic minds can test their skills in the closed beta of Heroes of Eldarune (HoE), a TCG where they can build their ultimate deck and outwit opponents. Journey (open Beta) offers a captivating narrative adventure through text-based choices, allowing players to craft their own story. For the competitive crowd, ELDA RUSH, a closed alpha MOBA, is on the waitlist, promising fast-paced brawls. Eldarune's Diverse Gaming Universe: World of Elymnias (WoE) World of Elymnias (WoE) This idle game attracts over 10,000 daily active users. WoE has PvP modes where you can test your strategic prowess against other players in the arena. It is currently in open Beta, and players can download the game’s APK through the Eldarune Discord channel . Eldarune Discord channel Eldarune Discord channel Heroes of Eldarune (HoE) Heroes of Eldarune (HoE) HoE is a TCG (Trading Card Game) with simple mechanics and user-friendly graphics. The game’s continuous development and new features are decided through collaboration with the Eldarune community and DAO. HoE is currently in the Beta stage, expected to launch in Q3 2024. Journey Journey It is a text-based adventure game in which players explore a narrative-driven world, with over 50,000 users actively engaging in the storyline. You'll send your troops on dangerous journeys, following the narrative's twists and turns as you make choices that shape your path. Completing quests and progressing through the story unlocks valuable rewards. Eldarune Journey is in closed Beta. ELDA Rush ELDA Rush It is a multiplayer online battle arena (MOBA) game akin to popular titles like Dota 2 and League of Legends. Assemble your team of powerful heroes, each with distinct abilities, and dive into the battlefield. ELDA Rush is currently in a closed Alpha stage with a limited player base. Players can join Eldarune's live streams to experience the game firsthand and potentially participate in the testing phase. Interoperability, Multi-Chain Dominance – and More Eldarune's true innovation lies in its interoperable NFTs and multi-chain functionality, setting it apart in the crowded Web3 gaming space. As of 2024, it delivers the most advanced technology in the market: Interoperable NFTs Interoperable NFTs Players can use their acquired NFTs across all Eldarune titles. In-game NFTs in Eldarune transcend individual games, boosting their value and utility. The ability to rent or sell these NFTs on marketplaces creates a highly liquid in-game economy, allowing players to earn real-world value from their assets. Multi-Chain Gameplay Multi-Chain Gameplay Eldarune is currently operational on BNB, Polygon, and Ethereum, which is a great chance for players from different ecosystems to connect and compete. This creates a more expansive and inclusive gaming environment, attracting a wider player base. AI-powered AI-powered Eldarune elevates gameplay through its AI that tailors experiences to individual players. By learning from your choices and actions, the AI personalizes the in-game world and adjusts difficulty for continued engagement. Players can train the AI and make NPCs fight alongside them in battles; AI-powered NPCs can create unique missions, and trained AI champions can be sold as NFTs. Community-focused Community-focused Eldarune creates a space where players can contribute to the game's evolution. Further boosting engagement, a system of 21 seasons keeps players hooked, contributing to a sustainable and dynamic in-game economy. Gameplay Versatility Gameplay Versatility Each title offers multiple modes, from conquering campaigns and exploring dungeons to battling clan bosses and testing skills in PvP arenas. This diversity ensures there's something for everyone. Accessibility is paramount, with all Eldarune games playable across browsers, mobile devices, PCs, and even consoles, allowing players to jump in anytime, anywhere. Deep Submersion Deep Submersion Eldarune goes beyond captivating gameplay. Each title has its own immersive soundtrack album, setting a new standard for atmospheric experiences. Web3 Gaming Ecosystem That Constantly Evolves With over 20,000 unique NFT holders and the ELDA token reaching an all-time high of $0.062 in January 2024, the Eldarune ecosystem appears to be thriving. reaching an all-time high reaching an all-time high The project’s roadmap outlines a continuous development process with exciting prospects – Eldarune gears up for a busy March and April. Public Beta Testnets for both Heroes of Eldarune and Journey kick off in March, followed by exciting Public Beta Testnet Tournaments for both games in March and April. 2024 brings even more action with a free mint event on Avalanche (April 15th), Avalanche integration, L1X integration, and an Avalanche HoE Tournament. Eldarune's commitment to interoperability, multi-chain functionality, and a diverse gaming portfolio positions it as a strong contender in the Web3 gaming landscape. Even though the project is still under development, with Elda Rush and HoE yet to be fully launched, the initial games offer a glimpse into Eldarune's potential. The long-term success will hinge on the execution of the ambitious roadmap and the continuous delivery of engaging and vanward gaming experiences. This story was distributed under HackerNoon’s Brand As An Author Program. Learn more about the program here. This story was distributed under HackerNoon’s Brand As An Author Program. Learn more about the program here . here

Eldarune: Revolutionizing Web3 Gaming with Interoperability and Multi-Chain Dominance

Eldarune: ইন্টারঅপারেবিলিটি এবং মাল্টি-চেইন আধিপত্য সহ ওয়েব3 গেমিংকে বিপ্লবী করা

Eldarune: Revolutionierung des Web3-Gamings durch Interoperabilität und Multi-Chain-Dominanz

Eldarune: revolucionando los juegos Web3 con interoperabilidad y dominio de múltiples cadenas

Eldarune : révolutionner le jeu Web3 avec l'interopérabilité et la domination multi-chaînes

एल्डारून: इंटरऑपरेबिलिटी और मल्टी-चेन प्रभुत्व के साथ वेब3 गेमिंग में क्रांति लाना

Eldarune: 相互運用性とマルチチェーン優位性で Web3 ゲームに革命を起こす

Eldarune: 상호 운용성과 다중 체인 지배력을 통해 Web3 게임에 혁명을 일으키다

Eldarune: Revolucionando os jogos Web3 com interoperabilidade e domínio multi-chain

Eldarune: революция в Web3-играх благодаря совместимости и доминированию нескольких цепочек

Eldarune: Birlikte Çalışabilirlik ve Çoklu Zincir Hakimiyeti ile Web3 Oyunlarında Devrim Yaratıyor

Eldarune: Cách mạng hóa trò chơi Web3 với khả năng tương tác và thống trị đa chuỗi

Eldarune：通过互操作性和多链主导性彻底改变 Web3 游戏

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Andrey Zhmoginov, Google Research & {azhmogin,sandler,mxv}@google.com;
(2) Mark Sandler, Google Research & {azhmogin,sandler,mxv}@google.com;
(3) Max Vladymyrov, Google Research & {azhmogin,sandler,mxv}@google.com. Table of Links Abstract and Introduction
Problem Setup and Related Work
HyperTransformer
Experiments
Conclusion and References
A Example of a Self-Attention Mechanism For Supervised Learning
B Model Parameters
C Additional Supervised Experiments
D Dependence On Parameters and Ablation Studies
E Attention Maps of Learned Transformer Models
F Visualization of The Generated CNN Weights
G Additional Tables and Figures 3 HYPERTRANSFORMER In this section, we describe our approach to few-shot learning that we call a HYPERTRANSFORMER (HT) and justify the choice of the self-attention mechanism as its basis. 3.1 FEW-SHOT LEARNING MODEL Along with the input samples, the sequence passed to the transformer was also populated with special learnable placeholder tokens, each associated with a particular slice of the to-be-generated weight tensor. Each such token was a learnable d-dimensional vector padded with zeros to the size of the input sample token. After the entire input sequence was processed by the transformer, we read out model outputs associated with the weight slice placeholder tokens and assembled output weight slices into the final weight tensors (see Fig. 2). Training the model. The weight generation model uses the support set to produce the weights of some or all CNN model layers. Then, the cross-entropy loss is computed for the query set samples that are passed through the generated CNN model. The weight generation parameters φ (including the transformer model and shared/local feature extractor weights) are learned by optimizing this loss function using stochastic gradient descent. 3.2 REASONING BEHIND THE SELF-ATTENTION MECHANISM The choice of self-attention mechanism for the weight generator is not arbitrary. One motivating reason behind this choice is that the output produced by generator with the basic self-attention is by design invariant to input permutations, i.e., permutations of samples in the training dataset. This also makes it suitable for processing unbalanced batches and batches with a variable number of samples (see Sec. 4.3). Now we show that the calculation performed by a self-attention model with properly chosen parameters can mimic basic few-shot learning algorithms further motivating its utility. Supervised learning. Self-attention in its rudimentary form can implement a method similar to cosine-similarity-based sample weighting encoded in the logits layer[3] with weights W: [3] here we assume that the embeddings e are unbiased, i.e., heii = 0 [4] in other words, the self-attention layer should match tokens (µ(i), 0) with (ξ(i), . . .). This paper is available on arxiv under CC 4.0 license. Authors: (1) Andrey Zhmoginov, Google Research & {azhmogin,sandler,mxv}@google.com; (2) Mark Sandler, Google Research & {azhmogin,sandler,mxv}@google.com; (3) Max Vladymyrov, Google Research & {azhmogin,sandler,mxv}@google.com. This paper is available on arxiv under CC 4.0 license. Authors: (1) Andrey Zhmoginov, Google Research & {azhmogin,sandler,mxv}@google.com; (2) Mark Sandler, Google Research & {azhmogin,sandler,mxv}@google.com; (3) Max Vladymyrov, Google Research & {azhmogin,sandler,mxv}@google.com. Table of Links Abstract and Introduction Problem Setup and Related Work HyperTransformer Experiments Conclusion and References A Example of a Self-Attention Mechanism For Supervised Learning B Model Parameters C Additional Supervised Experiments D Dependence On Parameters and Ablation Studies E Attention Maps of Learned Transformer Models F Visualization of The Generated CNN Weights G Additional Tables and Figures Abstract and Introduction Abstract and Introduction Problem Setup and Related Work Problem Setup and Related Work HyperTransformer HyperTransformer Experiments Experiments Conclusion and References Conclusion and References A Example of a Self-Attention Mechanism For Supervised Learning A Example of a Self-Attention Mechanism For Supervised Learning B Model Parameters B Model Parameters C Additional Supervised Experiments C Additional Supervised Experiments D Dependence On Parameters and Ablation Studies D Dependence On Parameters and Ablation Studies E Attention Maps of Learned Transformer Models E Attention Maps of Learned Transformer Models F Visualization of The Generated CNN Weights F Visualization of The Generated CNN Weights G Additional Tables and Figures G Additional Tables and Figures 3 HYPERTRANSFORMER In this section, we describe our approach to few-shot learning that we call a HYPERTRANSFORMER (HT) and justify the choice of the self-attention mechanism as its basis. 3.1 FEW-SHOT LEARNING MODEL Along with the input samples, the sequence passed to the transformer was also populated with special learnable placeholder tokens, each associated with a particular slice of the to-be-generated weight tensor. Each such token was a learnable d-dimensional vector padded with zeros to the size of the input sample token. After the entire input sequence was processed by the transformer, we read out model outputs associated with the weight slice placeholder tokens and assembled output weight slices into the final weight tensors (see Fig. 2). Training the model. The weight generation model uses the support set to produce the weights of some or all CNN model layers. Then, the cross-entropy loss is computed for the query set samples that are passed through the generated CNN model. The weight generation parameters φ (including the transformer model and shared/local feature extractor weights) are learned by optimizing this loss function using stochastic gradient descent. Training the model. 3.2 REASONING BEHIND THE SELF-ATTENTION MECHANISM The choice of self-attention mechanism for the weight generator is not arbitrary. One motivating reason behind this choice is that the output produced by generator with the basic self-attention is by design invariant to input permutations, i.e., permutations of samples in the training dataset. This also makes it suitable for processing unbalanced batches and batches with a variable number of samples (see Sec. 4.3). Now we show that the calculation performed by a self-attention model with properly chosen parameters can mimic basic few-shot learning algorithms further motivating its utility. Supervised learning. Self-attention in its rudimentary form can implement a method similar to cosine-similarity-based sample weighting encoded in the logits layer[3] with weights W: Supervised learning. [3] here we assume that the embeddings e are unbiased, i.e., heii = 0 [4] in other words, the self-attention layer should match tokens (µ(i), 0) with (ξ(i), . . .).

HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning

Job Scam Alert: The "Data Provider" Pyramid Scheme

চাকরির স্ক্যাম সতর্কতা: "ডেটা প্রদানকারী" পিরামিড স্কিম

Job-Betrugswarnung: Das Pyramidensystem des „Datenanbieters“

Alerta de estafa laboral: el esquema piramidal del "proveedor de datos"

Alerte à l'arnaque à l'emploi : le système pyramidal du "fournisseur de données"

नौकरी घोटाला चेतावनी: "डेटा प्रदाता" पिरामिड योजना

求人詐欺警告: 「データプロバイダー」ねずみ講

직업 사기 경고: "데이터 제공자" 피라미드 계획

Alerta de golpe de trabalho: o esquema de pirâmide do "provedor de dados"

Предупреждение о мошенничестве с вакансиями: финансовая пирамида «Поставщик данных»

İş Dolandırıcılığı Uyarısı: "Veri Sağlayıcı" Piramit Şeması

Cảnh báo lừa đảo việc làm: Lược đồ kim tự tháp "Nhà cung cấp dữ liệu"

求职诈骗警报：“数据提供商”金字塔骗局

Decentralization Levels: Bitcoin's Proof-of-Work vs. Obyte's DAG Approach

বিকেন্দ্রীকরণ স্তর: বিটকয়েনের কাজের প্রমাণ বনাম ওবাইটের ডিএজি পদ্ধতি

Dezentralisierungsebenen: Bitcoins Proof-of-Work vs. Obytes DAG-Ansatz

Niveles de descentralización: prueba de trabajo de Bitcoin frente al enfoque DAG de Obyte

Niveaux de décentralisation : la preuve de travail de Bitcoin par rapport à l'approche DAG d'Obyte

विकेंद्रीकरण स्तर: बिटकॉइन का प्रूफ-ऑफ-वर्क बनाम ओबाइट का DAG दृष्टिकोण

分散化レベル: ビットコインのプルーフ・オブ・ワーク vs. Obyte の DAG アプローチ

분산화 수준: 비트코인의 작업 증명과 Obyte의 DAG 접근 방식

Níveis de descentralização: Prova de Trabalho do Bitcoin vs. Abordagem DAG da Obyte

Уровни децентрализации: доказательство работы Биткойна против подхода Obyte DAG

Merkezi Olmama Düzeyleri: Bitcoin'in Proof-of-Work'ü ve Obyte'nin DAG Yaklaşımı

Cấp độ phi tập trung: Bằng chứng công việc của Bitcoin so với phương pháp tiếp cận DAG của Obyte

去中心化程度：比特币的工作量证明与 Obyte 的 DAG 方法

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