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What ‘Five Nights At Freddy’s’ Says About The Generation Gap, Top Brands Lose Their Luster

Interface Systems is actively supporting NRF's initiative by offering retailers a set of best practices to combat the escalating retail crime wave.

Tackling The Retail Crime Challenge

Age-gap relationships work best when couples accept each other's differences and learn to handle judgment, says a therapist in an age-gap marriage.

How to make an age-gap relationship work , from a therapist in a marriage with an 18-year difference

The Gap GPS, a Zacks Rank #1 (Strong Buy), operates as an apparel retail company. GPS shares are widely outperforming the market over the past six months thanks to improved margins, resulting in a bevy of brokerage upgrades and price target hikes.

Bull of the Day: The Gap, Inc. (GPS)

(Jonathan Nackstrand/AFP/Getty Images) The Nobel economics prize was awarded Monday to Claudia Goldin, a professor at Harvard University, for advancing the understanding of the gender gap in the ...

Economist studying workplace gender gap wins Nobel prize

Despite bringing home up to $2,200 more over the course of a year, figures from the Ontario Living Wage Network (OLWN) show the gap between the minimum wage and living wage is likely to worsen in 2024. While Ontario’s lowest paid workers received a 6.8 ...

Gap between Ontario's minimum wage and living wage expected to worsen in 2024

Wells Fargo upgraded Gap stock to "overweight" Shares of Gap Inc (NYSE:GPS) are 5% higher before the opening bell, after Wells Fargo upgraded the retail stock to "overweight" from "equal weight" and hiked its price target by $5 to $16. According to the ...

Analyst: Gap Stock Has Potential "Turnaround Story"

Gap, Inc. (NYSE: GPS)’s stock price has plunge by 0.00relation to previous closing price of 11.84. Nevertheless, the company has seen a -1.74% plunge in its stock price over the last five trading sessions.

Gap, Inc. (GPS) Shares Down Despite Recent Market Volatility

NEW YORK — Sofia Coppola's latest drama is an unflinching look at two American icons. "Priscilla" (in select theaters Friday, nationwide Nov. 3), which screened for journalists Friday morning at ...

‘Priscilla’ movie doesn’t shy away from Elvis age gap: She was 'a child playing dress-up’

Today we are looking at the apparel retailer stocks, starting with Gap (NYSE:GPS). Apparel sales are not driven so much by personal need but by seasons, trends, and innovation, and over the last ...

Q2 Earnings Roundup: Gap (NYSE:GPS) And The Rest Of The Apparel Retailer Segment

The Gap, Inc. operates as an apparel retail company worldwide. The company offers apparel, accessories, and personal care products for men, women, and children under the Old Navy, Gap, Banana Republic, Athleta, Intermix, Janie and Jack, and Hill City brands. Its products include denim, tees, fleece, khakis, and other products; and fitness and lifestyle products for use in yoga, training, sports, travel, and everyday activities to women and girls. The company offers its products through company-operated stores, franchise stores, Websites, third-party arrangements, and catalogs. It has franchise agreements with unaffiliated franchisees to operate Old Navy, Gap, and Banana Republic stores in Asia, Europe, Latin America, the Middle East, and Africa. As of February 1, 2020, the company had 3,345 company-operated stores; and 574 franchise stores, as well as online. The Gap, Inc. was founded in 1969 and is headquartered in San Francisco, California.

Are emerging platforms bridging the gap or breaching trust? Blockchain used to be associated with innovation and originality, with a thrill of doing something meaningful that no one has done before. However, this linkage starts to fade away as all the platforms are using the same template of ‘bridging the gap’. What once was an authentic aspiration of platforms is becoming a trend that now everyone follows. When we look at the websites and press releases of new ICOs and fintech platforms, we see the same template of phrase: “We are bridging the gap between centralized/traditional and decentralized finance/market.” Don’t get me wrong: the idea to bridge the gap is quite good, it is that good, indeed, that it was copied and took on a life of its own. Bridging the gap became a catch phrase, like ‘social responsibility’ — somebody meant it, now everyone says it, but nobody really knows what it means. Using the phrase to highlight the aspirations of the projects became seen as a race for the spot under the sun, and the instruments to build sand castles are taken from other participants on the way to the finish line. Actions of such players undermine efforts and intentions of credible actors who truly want to make an impact. Nonetheless, we need to remain critical. When you see a stone making ripples on the water, find the stone, don’t go chasing the ripples. The only way that the Blockchain industry can settle in a niche of trust and truth, and not simply unfulfilled aspirations, is if we are critical about originality of platforms’ intentions and the credibility that they entail. We need to learn about those who lead and to distinguish them from those who follow. Dubious platforms that copy unique features of others are not likely to succeed in an industry that is based on a trust protocol. However, it is responsibility of the crypto community to ensure that such projects will not stop other stunning ICOs and platforms with trustworthy ideas and original blockchain implementation — the pioneers that have contributed to the hype of bridging the gap. As for the emerging players, here is my advice to you. If you want to be successful in the blockchain industry: be daring and innovative, but above all, honest. Do not bridge the gap, rather break through a ceiling, or better, build your own path. After all, there are only so many gaps that you can bridge, after a while, you are simply stepping over a paddle. For example, , has been bridging the gap since March 2018, and now, we are bridging the last mile between the traditional finances and the future realities of the Blockchain. Let’s see how many more miles there are left to bridge. Ternion

How Many Gaps Are There Left to Bridge?

AI/ML is a wildfire trend. It’s being integrated into just about every application you can think of. Compared to other technical innovations over the past years—like blockchain—infrastructure and tooling were being built (and still are) in parallel with discovering a meaningful application. AI/ML, on the other hand, has seemingly jumped straight to real-world application, leaving tooling and infrastructure largely lacking in comparison to adoption. We’ve built a lot of tools for developing and tuning models, but moving them into production is relatively new, and the few MLOps tools that do exist were built before AI/ML became all the rage. At some point, we have to stop and ask ourselves if we’re building long-term solutions or just bootstrapping our way to production. In my opinion, we’re standing at a crossroads—specifically, a crossroads of DevOps and MLOps. Like DevOps 10 years ago, MLOps is buzzy, and tech companies are racing to incorporate these practices into their workflows. However, the more I dig into it, the more I come to believe that MLOps is a momentary trend. I find it hard to believe that the future lies in further segmentation of XOps practices but in a return to a unified approach under the well-established (and adopted) practices of DevOps. The model handoff conundrum A major stumbling block in the current AI/ML workflow is the model handoff process. Moving a model from a development stage (typically in a Jupyter Notebook) to an ML tool, then onto a development server, and finally to a production environment like Kubernetes is…let’s say, challenging at best. Each of these transitions requires repackaging the model to suit the target environment. This is not just a pain for the team (usually DevOps) that owns everything to the right of training (see diagram below) but introduces significant risk and delay into the deployment cycle. If that’s not enough, the diversity of packaging mechanisms without a standardized procedure amplifies these issues, turning what we’ve come to enjoy as a straightforward process into a real “pain in the ops.” So we decided to do something about it. We had a crazy idea–Why not package models into a format that we’ve already standardized on? Back in 2013, a little company called Docker made it really easy to start using containers to package up applications. A big key to their success was the OCI ( ), an industry-wide initiative to have standards around how we package up our applications. Because of OCI standards, we have hundreds (maybe thousands?) of tools that can be combined to manage and deploy applications. So why aren’t we using this to package Notebooks and AI models as well? It would make deploying, sharing, and managing our models easier for everyone involved. you can learn about that here From Notebook to OCI-standard file type As I said earlier, we set out to do something about how we’re packaging up our models, and what we did was build Kit. Kit is that packages your model, datasets, code, and configuration so data scientists and developers can use their preferred tools while collaborating effortlessly. an open source MLOps project Our goal is to standardize the packaging, reproduction, deployment, and tracking of AI/ML models. We want models to be managed with the same agility as application code. Kit streamlines the deployment process and ensures models can be executed universally with minimal friction. E.g., when you hand a model to the DevOps team, it’s already good to go, meaning they can deploy it to prod! Your tools have been standardized, and best of all, you didn’t have to learn how to write a Docker or Kubernetes file. The Kit CLI packages up all of the assets/resources included in your Notebook and turns them into a ModelKit, to make handoffs between ML engineers using Jupyter Notebooks and application teams easy, letting both teams work with the tools they already use and love. . Checkout a video of the Kit CLI here KitOps' philosophy is centered around improving collaboration without requiring an overhaul of existing processes. By building on industry standards, we facilitate cross-team engagement beyond merely data scientists in the model development process. The compatibility of ModelKits and Kitfiles with current tools eliminates the need for significant modifications to established deployment pipelines or endpoints. Unmatched model traceability and reproducibility KitOps's approach to model traceability and reproducibility is different from the common practice of simply using a Docker file. To be clear, there’s nothing wrong with using Docker files, however, including your hyperparameters and data sets in a container are not a practical approach. Kit provides a unified package for everything you need to reproduce, integrate with, or test the model throughout its lifecycle in a modular package that gives users the flexibility to extract specific components of a ModelKit, such as the dataset or the model itself, or to retrieve the entire package. Furthermore, the storage of ModelKits within an organization’s registry facilitates a detailed record of state changes, crucial for auditing purposes. And, since ModelKits are OCI-compliant, you can use the registries that you’ve already adopted (Docker Hub, Amazon ECR, Artifactory, Quay, etc). In addition, ModelKits are ideally suited for secure bill-of-materials (SBOM) initiatives, addressing the growing concerns regarding security and compliance in AI/ML model deployment. Something that is becoming increasingly important. The inevitability of DevOps integration We believe that KitOps signifies a critical moment for AI/MLOps and DevOps. By addressing the issues plaguing model deployment and collaboration, KitOps paves the way for an era where the delineation between MLOps and DevOps becomes increasingly indistinct. To be clear, I’m not saying that the existing MLOps practices are not valuable; rather, I’m advocating that the foundational practices and tools being implemented in our DevOps workflows are flexible enough to incorporate the requisites of AI/ML deployment. We're still early in the development process and are really interested in collecting community feedback. You can learn more about KitOps at https://kitops.ml And check out the source code here: https://github.com/jozu-ai/kitops Also published . here

KitOps: Bridging the Gap Between AI/ML and DevOps with Standardized Packaging

Ishan Pandey: Hi Kamen, I am delighted to welcome you to our "Behind the Startup" series. Can you tell us what inspired the concept behind Nolus? Hi Ishan, I am delighted for the opportunity to be here! Let me introduce you to Nolus. The design of Nolus was inspired by traditional lease products where one can obtain an asset by providing only a fraction of the cost upfront, use the asset throughout the lease period and receive full ownership once the loan is repaid back in full. Kamen Trendafilov: Applying a similar concept in the crypto space, Nolus created the DeFi Lease: a lending solution that unlocks the full potential of crypto money markets by reducing the industry's steep over-collateralization requirements. This results in significantly improved capital efficiency and more favorable lending options for borrowers. Ishan Pandey: Could you explain the significance of Nolus' emphasis on interoperability and its ability to connect to various liquidity sources across different chains? Kamen Trendafilov: Under the hood, Nolus utilizes a semi-permissioned PoS blockchain built using the Cosmos SDK and a CosmWasm smart contract platform. Nolus emphasizes interoperability, connecting to various liquidity sources across chains without fragmenting assets. The system can swiftly swap assets on any integrated DEX, streamlining lending by negating the need for multiple pools and ensuring liquidity providers handle only stable assets. Ishan Pandey: How does Nolus navigate the complexities of the DeFi space, particularly in terms of regulatory compliance and building trust among users? Kamen Trendafilov: Nolus is a non-custodial DeFi cross-chain money market. It ultimately defines a money market between lenders looking to earn real yield on deposited stablecoins and borrowers looking to amplify holdings with more assets than their current equity. And Nolus blockchain and smart contracts connect all participants in our ecosystem. Nolus is an open-sourced protocol with multiple audits and a high appetite for simplifying the complexity of DeFi and making it more accessible to a broader user base. Ishan Pandey: How do you approach risk management, especially in ensuring a lower liquidation rate compared to other money markets? Kamen Trendafilov: Nolus boasts lower liquidation terms compared to competitors courtesy of its unique design. In Nolus, the downpayment provided by the borrower and the loan in stables from liquidity providers both are used to purchase the desired digital asset. This represents three times more exposure compared to regular crypto lenders. Because both the down payment together with the loan are locked into a DeFi Lease position and act as collateral, borrower has larger exposure to the asset bu also lower margin call risk by 40% compared to other blockchain lenders. During the amplified position with lower risk, the borrowers can utilize the assets within their active lease position in yield strategies whitelisted by the Protocol, basically creating self-repaying loans. For example, liquid staking derivatives will earn 20% APY which will create a margin of profits on the amplified position once interest is covered. The DeFi Lease comes at a never-changing interest rate and has no other related costs. Also, the price of the leveraged asset is fixed at the lease creation, thus any upside of the asset is for the borrower to keep. In short, the borrower actually gets more exposure at lower risk in a fully decentralized manner, together with yield strategies and upside appreciation. Ishan Pandey: Nolus aims to simplify and enhance cross-chain DeFi experiences. Could you share the near-term plans? Kamen Trendafilov: Nolus aims at being inclusive for beginner DeFi users, as well as proficient and experienced ones. Ultimately, Nolus aims to simplify and enhance cross-chain DeFi experiences for everyone by becoming the one-stop-shop for your crypto transaction. In the pipeline we have DEX integrations (including EVM chains), asset listings, product expansion, tired staking system. We also aim at eliminating the user’s need to perform many transactions on different chains but rather use only Nolus to interact with different liquidity hubs and assets. Ishan Pandey: Nolus is actively involved in the Cosmos ecosystem. How do you navigate the competitive landscape within Cosmos and position Nolus as a standout platform in comparison to competitors? Kamen Trendafilov: We firmly believe that Nolus’ offerings are unique and Nolus is positioned to technologically and financially become a safe harbor during crypto market’s volatility. Nolus’ rapid growth is a testament to its unique selling propositions and user demand for safe and long-term investment opportunities. In the past 8 months after launch, a total of $50m of transactional volume have been processed with more than 50 thousand wallets interacting with the blockchain. DeFi leases exceed $25m in 25 different assets, while TVL currently stands at $5m. We are certain this is just the beginning of a long road for Nolus. Ishan Pandey: With a focus on education, what strategies does Nolus plan to employ to simplify technical aspects for new users and position itself as a go-to solution within the Cosmos ecosystem? Kamen Trendafilov: First of all, Nolus has carefully crafted a simple, easy-to-grasp UI that comes from best practices of traditional financial platforms. Everything on Nolus (including swaps, buys, lending, borrowing and staking) comes at a simple one-click step. We are also actively engaged in providing simplified and elaborate explanations of every single functionality of the protocol. The main goal here is to increase crypto adoption and usage though well-known web2 practices in the world of finance. Ishan Pandey: How do you perceive the current challenges associated with the present bull run, such as increased market volatility and potential risks of liquidation, affecting the lending and borrowing market? Kamen Trendafilov: As every cycle on the crypto and regular markets, the biggest inefficiency has always been the increased leverage we observe at the very top of the bull. Market volatility in the crypto market is set to decrease as we embrace more and more the technology and core capabilities of the network. Risk of liquidation, however, is always highest when we increase the leverage in the system. This is really where we position Nolus - our solution actually cuts down the DeFi sector's high over-collateralization standards, thus decreasing the levels of liquidations and protects borrowers from getting trimmed. Furthermore, Nolus has embedded a partial liquidation model (rather than full liquidation model), thus further protecting its borrowers. If a liquidation trigger is reached, a portion of the collateral will be sold to cover part of the debt and return the lease to a healthier level. But the beauty of it is that the position is still active and potentially can recover in time thus providing yet another safeguard to the borrower. Ishan Pandey: How do you see the adoption of blockchain and DeFi transforming the traditional financial sector, and what challenges and opportunities do you anticipate in this process? Kamen Trendafilov: Absolutely. It will be a long way to go, of course, but principles and methodologies in DeFi are already transforming different aspects of traditional finance operations. The blockchain technology connects people, wallets and institutions like nothing before and it will be more than widely used in many game-changing transformations we will encounter in the following years. The opportunity is obvious - large scale transactions, cross-border, cheap and simple; a one-click to reach any point of the world, for every service and product. The challenge lies in scaling securely and safely. Ishan Pandey: In light of the increasing regulatory scrutiny in the crypto space, how does Nolus navigate compliance challenges and work towards ensuring a secure and regulatory-friendly environment for its users? Kamen Trendafilov: Regulations and scrutiny are absolutely essential for mass adoption in the crypto space. As a governance appchain, Nolus is entirely committed to embracing all regulatory developments to follow. We actually welcome such because with regulations comes further growth and prosperity in the space. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our brand-as-author program. Be it through direct compensation, media partnerships, or networking, the author has a vested interest in the company/ies mentioned in this story. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR

Nolus: Bridging the Gap in DeFi with Interoperability Across Blockchain Chains

A Revolution is Underway in Finance Decentralized finance has experienced rapid growth at an amazing pace as compared to the adoption of teleview, laptops and other technology appliances, but challenges around data and transparency still loom large if it is to reach its full potential. Issues such as ensuring reliable access to information and navigating an evolving regulatory landscape don't have straightforward solutions. Building trust also remains difficult when centralized control leaves systems open to influence. A new partnership between financial powerhouse Nomura and upstart DeFi project Pyth signals that mindsets may finally be shifting. In a surprise alliance, Laser Digital - a Nomura subsidiary - has teamed up with the Pyth Network. This is awesome for the DeFi industry. Their goal is to address precisely the problems that have held the sector back: namely, concerns related to data reliability and transparency. By providing Pyth's platform institutional-grade market data via Laser Digital, while leveraging blockchain's inherent transparency, this collaboration aims to strengthen the very foundations of the burgeoning industry. It might even help accelerate DeFi's integration with traditional finance models. Is this indication that the tide is turning in decentralized finance's favor? Some believe that with major institutional players now paying attention, DeFi could be on the brink of fundamentally upending global finance in the years ahead. While uncertainty still abounds, one thing is clear - change is most definitely underway. This partnership suggests the revolution may at last be gaining real impetus. Only time will tell where the intertwining of Wall Street and decentralized technologies ultimately leads. But it seems the future of finance is growing increasingly difficult to predict. Nomura Steps into DeFi The alliance between the organizations signals that Nomura sees real potential in the decentralized finance space to disrupt and transform the traditional banking industry in APAC and America, the company is taking a long-term view on how TradiFi and DeFi will merge together, providing better financial opportunities to retail investors. By collaborating with innovative young companies in DeFi, Nomura is aiming to get on the ground floor of this tech-driven evolution in finance and be part of the future of finance, which democratizes financial opportunities for everyone. Ensuring Reliable Data for DeFi At the core of the partnership is Pyth Network's goal of providing decentralized apps (or dapps) with more dependable market data. Using blockchain tech, Pyth believes it can deliver information with greater efficiency and stability than traditional sources. I believe this is great for the industry! This is where Laser Digital comes in. By supplying Pyth with the types of institutional-grade financial data it's accustomed to, Laser Digital acknowledges how critical trustworthy information is for the budding DeFi ecosystem to flourish. Reliable stats underpin not just dapps themselves but also build confidence in users. Gaining Traction via Transparency One of Pyth's key selling points is its focus on transparency and decentralization. This fosters a secure, verifiable environment for DeFi platforms to gain wider acceptance. Transparency also helps address past concerns about data manipulation holding the industry back. Bridging Wall Street and DeFi Innovation The tie-up between Laser Digital and Pyth Network signifies efforts to blend traditional high finance with novel decentralized models. It underscores a growing openness within the industry to DeFi's potential to revolutionize banking as we know it. Over time, the lines between Wall Street and blockchain tech may continue to blur. Overcoming Hurdles on the Horizon While alliances like this open doors, challenges around regulation and compliance still loom large on DeFi's road ahead. As adoption expands, proactively navigating these issues will be paramount. If successful, partnerships could help DeFi deliver on its promise of accessible, transparent finance for all. Don’t forget to like and share the story! This author is an independent contributor publishing via our brand-as-author program. Be it through direct compensation, media partnerships, or networking, the author has a vested interest in the company/ies mentioned in this story. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR ested Interest Disclosure:

Nomura and Pyth Pioneer a New Era in Finance: Bridging the Gap Between Traditional Banking and DeFi 

নোমুরা এবং পাইথ ফিনান্সে একটি নতুন যুগের পথপ্রদর্শক: প্রথাগত ব্যাঙ্কিং এবং ডিফাইয়ের মধ্যে ব্যবধান কমানো

Nomura und Pyth läuten eine neue Ära im Finanzwesen ein: Überbrückung der Lücke zwischen traditionellem Bankwesen und DeFi

Nomura y Pyth son pioneros en una nueva era en las finanzas: cerrar la brecha entre la banca tradicional y DeFi

Nomura et Pyth ouvrent une nouvelle ère en matière de finance : combler le fossé entre la banque traditionnelle et la DeFi

नोमुरा और पाइथ ने वित्त में एक नए युग की शुरुआत की: पारंपरिक बैंकिंग और डेफी के बीच अंतर को पाटना

野村とパイスが金融の新時代を開拓：従来の銀行業務とDeFiの間のギャップを埋める

Nomura와 Pyth는 금융의 새로운 시대를 개척합니다: 기존 은행과 DeFi 간의 격차 해소

Nomura e Pyth são os pioneiros de uma nova era nas finanças: preenchendo a lacuna entre o sistema bancário tradicional e o DeFi

Nomura и Pyth открывают новую эру в финансах: преодоление разрыва между традиционным банкингом и DeFi

Nomura ve Pyth Finansta Yeni Bir Döneme Öncülük Ediyor: Geleneksel Bankacılık ile DeFi Arasındaki Boşluğu Kapatmak

Nomura và Pyth Tiên phong kỷ nguyên mới trong lĩnh vực tài chính: Thu hẹp khoảng cách giữa ngân hàng truyền thống và DeFi

野村证券和 Pyth 开创了金融新时代：弥合传统银行与 DeFi 之间的差距

In today's rapidly changing software world, the ability to adapt and evolve the software architecture is for success. The readiness for change is a factor in ensuring a product can meet evolving business and product requirements in the high-tech industry. crucial key However, uncontrolled and pose significant risks to a product's success. These drifts can result in the loss of expected extensibility and hinder the adoption of a product as the user base grows. Identifying and addressing these drifts is often done through complex and time-consuming manual architecture reviews, relying on experienced architects to assess the software architecture. architecture drifts erosions Despite the importance of , they have limitations in terms of efficiency and coverage. Human factors can influence the identification of architectural issues, leading to inconsistencies and potentially missing critical problems. manual reviews To overcome these challenges, there is a need for automated solutions that can detect and more effectively. By leveraging automated tools and processes, organizations can achieve better outcomes in terms of identifying and preventing architectural issues. architecture drift erosion This white paper explores the concept of . It covers the impact of and on the business, the limitations of , and the benefits of automating architecture checks. Additionally, it discusses specific considerations related to , , , and more when evaluating architecture review solutions. The paper also provides use case scenarios to illustrate what automated architecture checks can look like in practice. detecting architectural gaps with automation architecture drift erosion manual reviews compliance data security DevOps By adopting solutions, organizations can improve their ability to identify and address architectural gaps, ensuring the stability, scalability, and maintainability of their software systems. automated architecture review Detecting Architectural Gaps with Automation Please read before proceeding to the next chapters. this paper You will gain insights into the following: : Learn what architecture drift and erosion are and how they can impact the business. Gain an understanding of the risks and challenges associated with uncontrolled architectural changes. Understanding architecture drift and erosion : Explore the role of dependency analysis, peer reviews, and other manual inspections in detecting architectural issues. Understand why manual reviews, while important, may not be the ideal solution due to their limitations. Manual inspections and their limitations : Discover the benefits of automating architecture checks and how they can complement manual reviews. Learn how automation can provide more efficient and comprehensive detection of architectural gaps. The need for automated architecture review : Explore specific considerations to keep in mind when evaluating architecture review solutions. Topics covered include compliance, data security, DevOps integration, and more. Considerations for evaluating architecture review solutions : Gain insights into what automating architecture checks may look like through a series of example use case scenarios. Understand how automation can be applied in practical situations to enhance architecture review processes. Use case scenarios By reading this paper, you will develop a deeper understanding of , the limitations of , the benefits of , and important considerations when evaluating architecture review solutions. architecture drift and erosion manual reviews automation Solution requirements This section outlines the requirements for implementing the solution. It is important to note that these requirements are not exhaustive and can be extended based on the specific needs of the project. The requirements are categorized into solution , solution and , and with like modifiability, extendibility, and configurability. modes and inputs integration user interaction solution output quality attributes Solution modes The proposed solution should support the following modes: : The solution should be designed to run as a standalone application, allowing it to be executed on demand without any dependencies on the target environment. This mode provides flexibility for manual execution of the solution. Standalone application : The solution should have the capability to integrate seamlessly into a CI/CD pipeline as a separate step. This mode enables regular assessment of the architecture within the automated pipeline. Integration with CI/CD pipeline In the context of the mode, the following features should be supported: CI/CD : The solution should allow setting an initial architecture as a reference point. Any deviations from this reference architecture should be identified as architecture drifts. Initial architecture reference : When a drift is detected, the solution should provide an option to approve or decline the changes with comments. The description of the change should include an impact analysis, allowing the user to understand the cost associated with the proposed change. Drift reporting and approval process : The solution should provide the ability to save the documentation that describes drifts or erosions. This documentation can be useful for future analysis and reference. It should be easily accessible for review and analysis purposes. Documenting drifts and erosion : The solution should be easily integrated with widely used CI/CD tools such as Jenkins, Bamboo, etc. This ensures compatibility and seamless integration into existing CI/CD workflows. Integration with popular CI/CD tools Solution inputs The proposed solution can accept various inputs, including: : The solution should be able to analyze and process code files from the software being reviewed. Code from the solution under review : Configuration files used by the software should be compatible with the solution to extract relevant information. Configuration from the solution under review : The solution should support importing software designs created using ( ) tools. This allows for a comprehensive analysis of the software's architectural design. Software design from UML designing tools UML Unified Modeling Language : The solution should be capable of working with database scripts and configurations, enabling analysis of the database layer in the software. Database scripts and database configuration These inputs collectively provide the necessary information for the solution to perform its analysis and evaluation of the software architecture. Solution integration Integration of the proposed solution with various tools and systems allows for a comprehensive analysis of software architecture. Here are the possibilities mentioned: integration : The solution can be integrated with offline UML designing tools like OmniGraffle and Microsoft Visio. This integration enables the import of UML diagrams created using these tools for analysis and evaluation. Offline UML designing tools : Integration with online UML designing tools such as Enterprise Architect, LucidChart, , PlantUML, and provides the ability to import UML diagrams created in these platforms. This expands the range of tools that can be used for software design and analysis. Online UML designing tools Draw.IO Gliffy.com : Integration with source control management systems like SVN and Git allows the solution to access code and configuration files directly from the repository. This ensures that the latest version of the software is analyzed, and any changes made to the codebase are taken into account. Source control management systems By integrating the solution with these tools and systems, users can leverage existing software design and version control workflows to enhance the effectiveness of the automatic review process. As an example of the input code could be the code written in using . The other example could be the code written in for . C# microservices architecture JavaScript multi-tier applications The example of configuration might be the used in deployment. Another example could be the and . description of nodes configuration of VPN networks API gateways The example of input software design can be description of interfaces saved in . Enterprise Architect User interaction The proposed solution provides a web interface that allows users to with it from a desktop computer. The interface is designed to accommodate the content and functionality required for software architecture analysis, making it unsuitable for mobile devices such as smartphones or tablets due to their smaller screen size. interact There are two types of users supported by the solution: and . have the capability to configure the system according to the intended use. They can set up various parameters and customize the solution to meet specific requirements. administrators architects Administrators , on the other hand, have the ability to run the proposed solution for analysis and review the results. They can initiate the analysis process and examine the generated analysis reports. The solution provides dashboards that display relevant metrics and software architecture status, such as the count of components and dependencies. These dashboards offer a comprehensive overview of the software architecture and assist architects in understanding its structure and characteristics. Architects By catering to both and and offering dedicated functionalities for each role, the solution ensures that users can effectively configure and utilize the system for software architecture analysis and evaluation. administrators architects Solution output The proposed solution offers two formats for presenting the output: (plain text or table) and . text-based graphic The , particularly in a table structure, provides users with detailed information about identified issues. text-based format The table typically includes the following columns: : Indicates the severity or significance of the issue. Level of issue : Specifies the priority level assigned to address the issue. Issue priority : Provides a clear description of the identified issue. Issue description : Offers recommendations or guidelines on how to resolve the issue effectively. Suggested way to fix the issue Additionally, the table format includes separate sections that list various dependencies, such as , , , , , and . Each section provides a comprehensive inventory of the associated components used in the application. This allows users to gain insights into the technologies and resources utilized and understand their impact on the architecture. frameworks libraries APIs databases technology stack (languages) application layers Furthermore, the solution enables users to request inventories of specific , including , , , and , at any time. This feature provides a convenient way to access comprehensive information about the software application's underlying components and dependencies. elements languages frameworks libraries databases The is utilized to visually represent the software architecture, including graphic format the holistic application architecture, diagrams of all services, and data flows. Both and diagramming tools are employed to save and document the extracted software architecture. This graphic format allows users to annotate and tag components and inventory items using user-defined keywords. offline online UML Here's an example of a graphic output: In the representation above, and are identified within the software architecture. The diagram clearly illustrates the relationships between these components and the direction of communication between them. graphic four services one database Modifiability and extendibility are essential attributes for the proposed solution. The solution should be designed in a way that allows for easy extension and the addition of new features, such as new checks or levels. The scope of the solution should not restrict its ability to adapt to different project needs and requirements. Modifiability and extendibility By default, the solution should include a set of checks to verify the target architecture. However, for projects that already have an established architecture governance process, users should have the flexibility to extend and customize the set of checks according to their specific needs. Given the existence of various Static Code tools like and that handle low-level checks like indentation and formatting, it would be redundant to implement those checks in the proposed solution. Instead, the solution should focus on providing value through higher-level architectural checks and analysis. Coverity SonarQube Configurability is a crucial aspect of the proposed solution, allowing users to tailor the system to their specific needs and environment. The following configuration items can be supported: Configurability : The solution should offer different modes of operation, and users can configure the preferred mode during the deployment stage. For example, it can be run as a standalone application or integrated into the CI/CD pipeline. Solution Mode : Users should be able to specify the path to read architecture documents from offline UML design tools like OmniGraffle or Microsoft Visio. Path for Architecture Documents : The solution can integrate with online UML design tools like Enterprise Architect, LucidChart, , PlantUML, or . Users need to provide the API address to access and update architecture documents online. Online UML Design Tool API Draw.IO Gliffy.com : The proposed solution should be able to integrate with version control systems like SVN or Git. Users will configure the settings to access code and configuration from these repositories. Source Control Management System Configuration : Users should provide the addresses of databases to observe the data layer and perform analysis on database scripts and configurations. Database Addresses for Data Layer Observation : The solution should seamlessly integrate with popular CI/CD tools like Jenkins or Bamboo. Configuration options should enable easy integration into the CI/CD pipeline for regular architecture assessment. CI/CD Tool Integration : Users should be able to configure report plugins, including their format and storage settings. This allows customization of the generated reports based on individual preferences and requirements. Report Plugins Configuration By providing these configurability options, the proposed solution becomes versatile and adaptable, meeting the specific needs and workflows of different projects and organizations.

Detecting Architectural Gaps with Automation - Business Context

In an enlightening conversation for the "Behind the Startup" series, Louis Bellet, the dynamic force behind Yellow Network, shares the intriguing story of its inception and its ambitious mission to redefine the cryptocurrency trading landscape. Born from the strategic merger of Openware.com and Yellow Trading, Yellow Network emerges as a groundbreaking solution, tackling the industry's pressing challenges head-on. With a focus on security, efficiency, and decentralization, Bellet details the innovative mechanisms and future-forward technologies that make Yellow Network a beacon of progress in the digital asset ecosystem. Revolutionizing Crypto Trade: Louis Bellet Unveils Yellow Network's Vision Ishan Pandey: Hi Louis, great to have you here for our "Behind the Startup" series. Can you share the story behind the founding of Yellow Network and what inspired you to start this venture? Louis Bellet: Yellow Network was born as a result of a collaboration of two established players in the industry. My journey began with Openware.com, where we built over 150 trading platforms, gaining unique insights into the pains and gains of both institutional and retail crypto businesses. Meanwhile, my partner Alexis, one of the founders of GSR and Yellow Trading, brought an in-depth perspective from the trading side. Our combined understanding of the industry's challenges led us to create Yellow Network. It represents our collaborative effort to innovate and solve persistent issues in the digital asset ecosystem, aiming to revolutionize the way digital assets are traded. Ishan Pandey: Can you elaborate on how Yellow Network aims to make cryptocurrency trading leaner, more efficient, and more secure in the current blockchain landscape? Louis Bellet: One of the main issues of the crypto industry today is the absence of the proper trading infrastructure. Yellow Network solves this by introducing a decentralized clearing system. It is designed to minimize reliance on a single market player with no separation of responsibilities, thus greatly reducing counterparty risks and removing potential points of failure. This approach connects brokers and exchanges across blockchains, enabling efficient and secure cross-chain trading and clearing. Yellow Network optimizes capital management through position netting, and streamlined settlement processes that consolidate a batch of trades into a single transaction. Ishan Pandey: How does Yellow Network utilize a P2P network and state channels to connect brokers across various blockchains? Louis Bellet: The P2P approach enables brokers to instantly connect to other network participants to match orders and execute trades directly with each other in a decentralized manner, bypassing the traditional centralized framework. State channels facilitate private, secure off-chain transactions between parties, ensuring privacy, scalability and efficiency without compromising the blockchain's integrity. In this way, we can bring ultra-high-frequency trading and minimize transaction costs, while also being able to trade assets cross-chain. Ishan Pandey: Could you explain in more detail how Yellow Network's automated smart clearing protocol and its significance in the context of cryptocurrency trading? Louis Bellet: Our core component is called ClearSyncTM; it is a protocol that automates the post-trade process, including the clearing and settlement of trades, through smart contracts. It handles all activities from the moment a transaction commitment is made until it is settled, similar to traditional finance clearing processes​​. Key features of ClearSyncTM include the ability to open and close trading channels, deposit collateral in various cryptocurrencies, and send high-frequency margin-calls requests off-chain. In addition, it offers the flexibility to request settlements using different methods, including atomic cross chain swaps, or even third-party custodians. It also handles disputes on a portofolio state to unlock the collateral, enhancing the security and reliability of the trading process​​. Essentially, ClearSyncTM reduces counterparty risk and eliminates the need for intermediaries, leading to a more streamlined and less costly trading process. Ishan Pandey: What features make Yellow Network an all-in-one Web3 solution, and how is it tailored for the global retail market? Louis Bellet: Yellow Network offers a comprehensive suite of tools and services tailored to the needs and expectations of a global crypto market. Features such as cross-chain liquidity access, decentralized identity management, and integrated wallet services serve well for both beginners and experienced traders. Among other things, the platform also features: Decentralized trading, allowing participants to trade using their nearest node; Scalable infrastructure: with the use of state channels to enable swift, efficient off-chain trading and clearing suitable for high-frequency trading​​; Enhanced liquidity access as opposed to traditional silos, offering seamless liquidity exchange across platforms and chains​​; Minimized counterparty risks with the automated netting and operates collateral and safety deposits for secure settlements​​; Capital efficiency for consolidating trades and lowering margin requirements, optimizing capital usage​​; A broker-dealer model that aligns with regulatory preferences and streamlines broker operations​​. Ishan Pandey: Can you provide insights into the decision to merge Openware and Yellow Trading to form Yellow Network? How has this merger contributed to the company's overall strategy? Louis Bellet: The decision to merge was strategic in nature and aimed at uniting our expertise in blockchain development and financial markets. When we first started working together with Alexis, we quickly realized that we have an aligned vision of the future of crypto and a similar outlook on how the main problems of the industry can be tackled. We have leveraged Openware's technical expertise in building infrastructure for exchanges with Yellow Trading's market insights, which created Yellow Network as we know it now. Ishan Pandey: Can you elaborate on Yellow Trading's role as a market maker and startup incubator within the Yellow Network ecosystem? Louis Bellet: Yellow Trading plays a dual role within Yellow Network. As a market maker, it provides liquidity, ensuring efficient and stable market operations. This is crucial in creating a fruitful trading environment, especially in emerging or less liquid markets. As a startup incubator, Yellow Trading nurtures innovative blockchain projects, providing them with the necessary resources, mentorship, and network support to succeed, further enriching and integrating them into the Yellow Network ecosystem. Ishan Pandey: How do you perceive the current state of blockchain adoption globally, and what role does Yellow Network play in driving adoption? Louis Bellet: The global adoption of blockchain is primarily focused on trading and speculation. However, most traders don't fully utilize the benefits of the blockchain; they simply can’t; due to the isolated nature of exchanges and scalability issues. Blockchains are not the complete solution for decentralizing trading, but rather a component of the infrastructure. Yellow Network bridges the gap between isolated trading platforms and chains, facilitating efficient, real-time cross-chain transactions. This innovative approach progresses blockchain usage beyond current limitations, positioning Yellow Network at the forefront of promoting broader blockchain adoption and transforming the cryptocurrency trading landscape. Ishan Pandey: Looking ahead, how do you anticipate the regulatory landscape for blockchain and cryptocurrency evolving, and what preparations is Yellow Network making for potential regulatory changes? Louis Bellet: The regulatory framework for blockchain and cryptocurrency is expected to evolve towards greater clarity and structure, much like in traditional finance. In traditional finance, separating responsibilities like custody, exchange, and clearing is standard practice for risk mitigation and regulatory compliance. We are anticipating and aligning with this trend by distributing these functions. Our broker-dealer model, part of the network's design, separates these roles, reducing the risk of having all operations centralized in one entity. Our goal is to remain adaptable and responsive to regulatory changes, ensuring the longevity and legitimacy of our platform. Don’t forget to like and share the story! This author is an independent contributor publishing via our brand-as-author program. Be it through direct compensation, media partnerships, or networking, the author has a vested interest in the company/ies mentioned in this story. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure:

Bridging Gaps in Crypto Trading: The Strategic Vision of Yellow Network

Authors: (1) JunJie Wee, Department of Mathematics, Michigan State University; (2) Jiahui Chen, Department of Mathematical Sciences, University of Arkansas; (3) Kelin Xia, Division of Mathematical Sciences, School of Physical and Mathematical Sciences Nanyang Technological University & xiakelin@ntu.edu.sg; (4)Guo-Wei Wei, 1Department of Mathematics, Michigan State University, Department of Biochemistry and Molecular Biology, Michigan State University, Department of Electrical and Computer Engineering, Michigan State University & xiakelin@ntu.edu.sg. Table of Links Abstract & Introduction Results Discussion Conclusion Materials and Methods Software and resources, Code and Data Availability Supporting Information, Acknowledgments & References 4 Conclusion In the multifaceted quest to understand mutation-induced solubility changes, various scientific domains including quantum mechanics, molecular mechanics, biochemistry, biophysics, and molecular biology have made significant contributions. Despite these concerted efforts, state of-art models have limitations, as evidenced by their normalized CPR value of 0.656 even after employing feature selection methods. Persistent homology (PH) has emerged as a powerful tool for capturing the complexity of biomolecular structures and has achieved noteworthy success in drug discovery applications. However, its inability to capture the nuances of homotopic shape evolution, crucial for delineating molecular interactions in proteins, presents a critical shortcoming. Our study introduces TopLapGBT, a novel model that integrates persistent Laplacian (PL) features with pretrained transformer features, thereby bridging the gap in capturing both topology and homotopic shape evolution. This innovative fusion leads to significant advancements in classification performance. Specifically, TopLapGBT achieves normalized CPR and GC2 scores of 0.688 and 0.361, respectively, marking improvements of 4.88% and 15.71% over the state-of-the-art PON-Sol2. These findings are further corroborated by an independent blind test, where TopLapGBT continues to outperform existing models. In summary, our proposed TopLapGBT model not only achieves superior performance over existing state-of-the-art methods but also introduces a more nuanced approach for the classification of protein solubility changes upon mutation. These results underscore the transformative potential of integrating geometric and topological features with machine learning in advancing the field of molecular biology. This paper is under CC 4.0 license. available on arxiv

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