Readings


Towards multi-modal causability with Graph Neural Networks enabling information fusion for explainable AI

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Title: Towards multi-modal causability with Graph Neural Networks enabling information fusion for explainable AI
Authors: Holzinger Andreas, Malle Bernd, Saranti Anna, Pfeifer Bastian. (2021)
Journal: Information Fusion
Publisher: Elsevier

The authors describe a novel, holistic approach to an automated medical decision pipeline, building on state-of-the-art Machine Learning research, yet integrating the human-in-the-loop via an innovative, interactive & exploration-based explainability technique called counterfactual graphs. They outline the necessity of computing a joint multi-modal representation space in a decentralized fashion, for the reasons of scalability and performance as well as ever-evolving data protection regulations. This effort is indented as a motivation for the international research community and a launchpad for further work in the fields of multi-modal embeddings, interactive explainability, counterfactuals, causability, as well as necessary foundations for effective future human–AI interfaces.

More: https://featurecloud.eu/wp-content/uploads/2021/03/Holzinger-et-al_2021_Towards-multi-model-causability.pdf

Sustainable computational science: the ReScience initiative

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Title: Sustainable computational science: the ReScience initiative
Authors: Nicolas Rougier,  Hinsen Konrad and others
Journal: PeerJ Computer Science
Publisher: PeerJ Inc.

Computer science offers a large set of tools for prototyping, writing, running, testing, validating, sharing and reproducing results; however, computational science lags behind. In the best case, authors may provide their source code as a compressed archive and they may feel confident their research is reproducible. But this is not exactly true.
James Buckheit and David Donoho proposed more than two decades ago that an article about computational results is advertising, not scholarship. The actual scholarship is the full software environment, code, and data that produced the result. This implies new workflows, in particular in peer-reviews.  Existing journals have been slow to adapt: source codes are rarely requested and are hardly ever actually executed to check that they produce the results advertised in the article.
ReScience is a peer-reviewed journal that targets computational research and encourages the explicit replication of already published research, promoting new and open-source implementations in order to ensure that the original research can be replicated from its description. To achieve this goal, the whole publishing chain is radically different from other traditional scientific journals. ReScience resides on GitHub where each new implementation of a computational study is made available together with comments, explanations, and software tests.

More: https://www.labri.fr/perso/nrougier/papers/10.7717.peerj-cs.142.pdf

Hardware Versus Software Fault Injection of Modern Undervolted SRAMs

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Researchers from Barcelona Supercomputing Center (Spain) and Abdullah Gul University in Kayseri (Turkey) are sharing an approach to apply real under-volting SRAM fault maps to a simulated system and observe the resiliency of the applications.
They compare the hardware guided fault injection approach with a random guided fault injection approach. Significant differences appears in the coarse categorization of the resiliency of the application, which become more obvious as the number of faulty bits increases. There are also differences when inspecting the quality of the output among the two techniques. This is because in an realisticsystem  not all fault locations have the same probability to  present faults, therefore from the software  perspective the faults can propagate to a limited number of software structures.

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Corrective Commit Probability Code Quality Metric

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An article signed by Idan Amit and Dror G. Feitelson from the Department of Computer Science at the Hebrew University of Jerusalem, presents a code quality metric, the Corrective Commit Probability (CCP).

This metric measures the probability that a commit reflects corrective maintenance. The authors think that this metric agrees with developers’ concept of quality, informative, and stable. Corrective commits are identified by applying a linguistic model to the commit messages. The  team compute the CCP of all large active GitHub projects (7,557 projects with 200+ com-mits in 2019). This leads to the creation of a quality scale, suggesting that the bottom 10% of quality projects spend at least 6 times more effort on fixing bugs than the top 10%. Analysis of project attributes shows that lower CCP (higher quality) is associated with smaller files, lower coupling, use of languages like JavaScript and C# as opposed to PHP and C++, fewer developers, lower developer churn, better on boarding, and  better  productivity. Among  other  things these results support the “Quality is Free” claim, and suggest that achieving higher quality need not require higher expenses.

MongoDB, A Database For Document Stores

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A potential prey for Oracle or Microsoft, MongoDB leads the document store market, and is now ranked #5 among all DBMS (source: DB Engines). It is at the heart of the DECODER PKM and also of multiple one-page websites based on the MEAN stack (Angular, MongoDB, Express, NodeJS).

In a recent article, Eric Weiss, Analyst at several large banks, sees MongoDB as the clear-cut leader within the high-growth, non-relational database SaaS sector. "MongoDB has been and will continue to be an indirect beneficiary of high-growth megatrends such as AI, Machine Learning, IoT (Internet-of-the-Things) and digitalization. Each of these trends have sparked an exponential growth in supply of unstructured data resulting in an increasing demand for (NoSQL) non-relational database solutions. Such databases can much more efficiently handle this new flow of data workloads compared to more traditional relational, SQL-based solutions".

Big Code has a direct impact on the business outcomes

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For developers, code releases are "emotional" events. Many have fear and anxiety at the moment they release code or submit it for review and fear breaking dependencies.

Indeed, managing large and complex code bases (Big Code) can become laborious, time consuming and costly. Joe McKendrick article refers to a 2020 survey of 500 north American professional developers compiled by Dimensional Data and underwritten by Sourcegraph. The Emergence of Big Code survey highlights a dramatic growth in the volume and complexity of software code.

It's almost unanimous: 99% of respondents report that big code has a direct impact on the business outcomes of software development efforts. Challenges include less time for new hires to be productive (62%), code breaking due to a lack of understanding of dependencies (57%), and difficulties managing changes to code (50%).

Read the full article in ZDnet: https://www.zdnet.com/article/low-and-no-code-are-wonderful-but-a-big-code-world-lurks-underneath/

Machine Learning for Cybersecurity

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Automated Vulnerability Detection in Source Code Using Minimum Intermediate Representation

Vulnerability is one of the root causes of network intrusion. An effective way to mitigate security threats is to discover and patch vulnerabilities before an attack. Traditional vulnerability detection methods rely on manual participation and incur a high false positive rate. The intelligent vulnerability detection methods suffer from the problems of long-term dependence, out of vocabulary, coarse detection granularity and lack of vulnerable samples.
This paper proposes an automated and intelligent vulnerability detection method in source code based on the minimum intermediate representation learning.
More..

MLOps Demystified

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As Machine Learning at an organization matures from research to applied enterprise solutions, there comes the need for automated Machine Learning operations that can efficiently handle the end-to-end ML Lifecycle.

The goal of level 1 MLOps (see figure) is to perform continuous training of the model by automating the entire machine learning pipeline which in turn leads to continuous delivery of prediction service. The underlying concept which empowers the continuous model training is the ability to do data version control along with efficient tracking of training/evaluation events. 

DevOps Market to reach $15 billion by 2026

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The global DevOps market size is projected to reach $14,969.6 million by 2026, a compound annual growth rate of 19.1%, according to a Fortune Business Insights report. The report highlighted the significance of this increase, noting nearly +404% in eight years, as that market was only worth $3,708.1 million in 2018. Containerization, PaaS (Platform as a Service) and hybrid cloud are three major enablers in DevOps growth.

For more information:

Agile Testing + DevOps = DevTestOps

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"DevOps is now really DevTestOps and for teams to be truly agile, test management is the vital link in the success of DevOps. You require TestOps to match the pace of DevOps and testing early and often — breaking the silos."

In fact, the World Quality Report 2019-2020 led by Capgemini shows that there is increased investment in the QA and Test function reported by 90% of US and 69% percent of Canadian survey participants in the past four years.

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The Global Embedded Systems Market Expected to Grow +5% CAGR by 2024

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An embedded system is a combination of software and hardware which together facilitate the accurate functioning of a target device. Embedded system market is expected to mark significant growth over 2019 to 2024 owing to increasing consumers spending on smart phones, providing high application- specified integrated circuit and high speed operating systems applications and technological advancement. 

A recent Advance Market Analytics market study is being classified by Type (Normal Phase HPLC and Reverse Phase HPLC), by Application (Automotive, Telecommunication, Healthcare, Industrial, Consumer Electronics and Military & Aerospace) and major geographies with country level break-up. According to this study, the Global Embedded Systems market is expected to see growth rate of 5.28% and may see market size of USD536.2 Million by 2024.

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Distinct AI Techniques Bring Different Business Values

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Machine learning and deep learning are often conflated by business decision makers. Machine Learning can involve a wide variety of techniques for building analytics models or decision engines that don't involve neural networks, the mechanism for deep learning. And there is a whole range of AI techniques outside of machine learning as well that can be applied to solve business problems.

Do you leverage these techniques or do you prefer computer vision and natural language processing applications to solve your business problems?

Read George Lawton article in TechTarget

TESTAR test results extracted while executing MyThaiStar as web system under test

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Authors: Fernando Pastor Ricos and Tanja E. Vos from Universitat Politècnica de València

TESTAR test results datasets extracted with TESTAR tool using MyThaiStar web application as System Under Test (SUT). These datasets have been generated to be used as an example to be automatically generated and introduced locally in DECODER PKM, from H2020 DECODER Project.

TESTAR tool is an open source tool for automated testing through graphical user interface (GUI) currently  being  developed  by  the Universitat Politecnica de Valencia and the Open University of the Netherlands.

MyThaiStar is the reference application that Capgemini uses internally to promote best programming practices and the correct use of last technologies. It’s is developed with Devon Framework, the standard tool for development at the company. More...

An MLOps approach to bring models to production

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Machine Learning Open Studio and Model as a Service (MaaS) from Activeeon helps data scientists and IT operations work together in an MLOps approach allowing to bring ML models to production. Machine Learning Open Studio includes automatic data drift detection mechanisms and allows traceability and audit over model performance to retrain it when necessary.

Only a small percentage of ML projects make it to production because of deployment complexity, lack of governance tools and many other reasons. Once in production, ML models often fail to adapt to the changes in the environment and its dynamic data which results in performance degradation.

To maintain the prediction accuracy of ML models in production, an active monitoring of model performance is mandatory. This allows to know when to retrain it using the most recent data and the newest implementation techniques, then redeploy in production. More...

Algorithm and Data Structure Visualization

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Visualizations can help us understand how data structures and algorithms work. 

The visualgo.net website provides great visualization and animations on advanced algorithms. Most of them are discussed in 'Competitive Programming', co-authored by two brothers Dr Steven Halim and Dr Felix Halim. Today, some of these advanced algorithms visualization/animation can only be found in VisuAlgo. 

An online quiz system has been added that allows students to test their knowledge of basic data structures and algorithms. It generates questions and check the student answers automatically.

Covid-19 infection in Italy: when AI provides vital insights

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Thanks to mathematical models and predictions, Gianluca Malato - a Data Scientist, fiction author and software developer - compared logistic and exponential models applied to Covid-19 virus infection in Italy. Both models help to better understand the evolution of the infection. The data preparation and python coding are detailed in an article posted in Towards Data Science on 8 March 2020. At that time, the main projections - now checked regularly by this Covid-19 Italian infection collaborative research - were:

Clear Linux OS automates the creation of RPM packaging

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Designed by Intel and open source contributors, the Clear Linux OS delivers a secure, hardware optimized OS. Its updates ensure that software dependencies remain mutually compatible. 

The autospec tool is used to assist with the automated creation and maintenance of RPM packaging in Clear Linux OS. Where a standard RPM build process using rpmbuild requires a tarball and .spec file to start, autospec requires only a tarball and package name to start.

Recent reviews confirm the performance an stability improvements of Clear Linux OS. However, software that are packaged in other formats for other Linux distributions are not guaranteed to work on Clear Linux OS and may be impacted by Clear Linux OS updates. 

The Twelve-Factor App, a Methodology for Building Web Apps

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Suggested by the designers of the Heroku PaaS platform, the twelve-factor methodology can be applied to apps written in any programming language, and which use any combination of backing services (database, queue, memory cache, etc). It is aimed at building Software-as-a-Service apps that:

  1. Use declarative formats for setup automation, to minimize time and cost for new developers joining the project;
  2. Have a clean contract with the underlying operating system, offering maximum portability between execution environments;
  3. Are suitable for deployment on modern cloud platforms, obviating the need for servers and systems administration;
  4. Minimize divergence between development and production, enabling continuous deployment for maximum agility;
  5. And can scale up without significant changes to tooling, architecture, or development practices.

More about the Twelve-Factor App

A New Model-Based Approach for API Testing

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Keeping Pace with Agile Development, Visualizing Complex Dependencies, and Orchestrating for Completeness of Testing are three good reasons to select a Model-Based approach for API testing, according to Collin Chau, a DevOps test expert. 

"With the proliferation and complexity in microservices development that the Internet of Things brings, development teams are struggling to embrace API testing for more effective QA testing in-sprint. Learn how a model-based testing approach makes the difference in your API tests."

Read Collin Chau full article in Continuous Testing

NLP Search Paves the Way for Augmented Data Discovery

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Combining natural language understanding and natural language generation will result in dynamic, bi-directional human-machine communication that will take several forms: text, voice and images. In text and voice scenarios, the BI or analytics solution can converse with the user to render the desired result - regardless of data-related and query-related search complexity.

Data visualizations also will become more interactive, if not immersive, along the lines of Busby from Oblong Industries. This product focuses on immersive interfaces, not specifically BI or analytics. However, its concepts could have a ripple effect on how people interact with data and thus, augmented data discovery.

"I think the future of BI is no BI. Don't ask me to search and look for things anymore. Give me that piece of information when I need it and if I need it. Come to me when there's something I need to know", foresees Erick Brethenoux, senior director analyst at Gartner.

For more information, read Lisa Morgan TechTarget article entitled NLP makes augmented data discovery a reality in analytics

Is BERT a Game Changer in NLP?

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BERT  (Bidirectional Encoder Representations from Transformers) is an open-sourced NLP pre-training model developed by researchers at Google in 2018. It has inspired multiple NLP architectures, training approaches and language models, including Google’s TransformerXL, OpenAI’s GPT-2, ERNIE2.0, XLNet, and RoBERTa. 

For instance, BERT is now used by Google Search to provide more relevant results. And it can also be used in smarter chatbots with conversational AI applications, expects Bharat S Raj. 

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