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How Microsoft plans to improve the low-code landscape

#artificialintelligence

Taking on the challenges head-on that stand in the way of their low-code platforms growing, Microsoft's series of new product announcements this week at Build 2022 gives organizations new options for achieving low-code development goals. Microsoft's series of low-code announcements made this week include Power Pages, the latest Microsoft Power Platform addition for creating integrated, scalable and secure websites. Lured by the promises of democratizing app development with visual, declarative, drag and drop interfaces often bundled with enterprise-wide platforms like Microsoft, Salesforce, ServiceNow and others, enterprises have been quick to jump in and experiment. They're learning that support for a low-code platform can get expensive fast once app development moves from small department coding projects to larger-scale, enterprise-wide apps. Low-code platforms' hidden costs include limited process workflow support that further adds to the challenge of scaling them enterprise-wide.


InfoQ Mobile and IoT Trends Report 2022

#artificialintelligence

One of the most compelling InfoQ features are our topic graphs, which synthesizes our understanding of how different topics stack up in the technology adoption curve. They are immensely useful as a guide to prioritize different and competing interests when it's time to decide what we want to cover from an editorial perspective, but we also believe that sharing them can help our readers to better understand the current and future tech landscape and help inform their decision process. Topic graphs build upon the well-known framework Geoffrey Moore developed in his book "Crossing the Chasm." Moore's framework describes five stages that describe how technology adoption evolves in time, through the "innovators", "early adopters", "early majority", "late majority", and "laggard" stages. InfoQ has a leaning towards identifying those ideas and technologies that belong to the innovators, early adopters, and early majority stages. We also strive to acknowledge topics that we consider as having already crossed into late majority. You will generally find plenty of content on InfoQ about the late majority and laggards phases, as artifacts of our previous coverage.


Technology Ethics in Action: Critical and Interdisciplinary Perspectives

arXiv.org Artificial Intelligence

This special issue interrogates the meaning and impacts of "tech ethics": the embedding of ethics into digital technology research, development, use, and governance. In response to concerns about the social harms associated with digital technologies, many individuals and institutions have articulated the need for a greater emphasis on ethics in digital technology. Yet as more groups embrace the concept of ethics, critical discourses have emerged questioning whose ethics are being centered, whether "ethics" is the appropriate frame for improving technology, and what it means to develop "ethical" technology in practice. This interdisciplinary issue takes up these questions, interrogating the relationships among ethics, technology, and society in action. This special issue engages with the normative and contested notions of ethics itself, how ethics has been integrated with technology across domains, and potential paths forward to support more just and egalitarian technology. Rather than starting from philosophical theories, the authors in this issue orient their articles around the real-world discourses and impacts of tech ethics--i.e., tech ethics in action.


Forecasting: theory and practice

arXiv.org Machine Learning

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.


WebFed: Cross-platform Federated Learning Framework Based on Web Browser with Local Differential Privacy

arXiv.org Artificial Intelligence

For data isolated islands and privacy issues, federated learning has been extensively invoking much interest since it allows clients to collaborate on training a global model using their local data without sharing any with a third party. However, the existing federated learning frameworks always need sophisticated condition configurations (e.g., sophisticated driver configuration of standalone graphics card like NVIDIA, compile environment) that bring much inconvenience for large-scale development and deployment. To facilitate the deployment of federated learning and the implementation of related applications, we innovatively propose WebFed, a novel browser-based federated learning framework that takes advantage of the browser's features (e.g., Cross-platform, JavaScript Programming Features) and enhances the privacy protection via local differential privacy mechanism. Finally, We conduct experiments on heterogeneous devices to evaluate the performance of the proposed WebFed framework.


An Empirical Cybersecurity Evaluation of GitHub Copilot's Code Contributions

arXiv.org Artificial Intelligence

There is burgeoning interest in designing AI-based systems to assist humans in designing computing systems, including tools that automatically generate computer code. The most notable of these comes in the form of the first self-described `AI pair programmer', GitHub Copilot, a language model trained over open-source GitHub code. However, code often contains bugs - and so, given the vast quantity of unvetted code that Copilot has processed, it is certain that the language model will have learned from exploitable, buggy code. This raises concerns on the security of Copilot's code contributions. In this work, we systematically investigate the prevalence and conditions that can cause GitHub Copilot to recommend insecure code. To perform this analysis we prompt Copilot to generate code in scenarios relevant to high-risk CWEs (e.g. those from MITRE's "Top 25" list). We explore Copilot's performance on three distinct code generation axes -- examining how it performs given diversity of weaknesses, diversity of prompts, and diversity of domains. In total, we produce 89 different scenarios for Copilot to complete, producing 1,692 programs. Of these, we found approximately 40% to be vulnerable.


A Survey on Data-driven Software Vulnerability Assessment and Prioritization

arXiv.org Artificial Intelligence

Software Vulnerabilities (SVs) are increasing in complexity and scale, posing great security risks to many software systems. Given the limited resources in practice, SV assessment and prioritization help practitioners devise optimal SV mitigation plans based on various SV characteristics. The surge in SV data sources and data-driven techniques such as Machine Learning and Deep Learning have taken SV assessment and prioritization to the next level. Our survey provides a taxonomy of the past research efforts and highlights the best practices for data-driven SV assessment and prioritization. We also discuss the current limitations and propose potential solutions to address such issues.


The Top 100 Software Companies of 2021

#artificialintelligence

The Software Report is pleased to announce The Top 100 Software Companies of 2021. This year's awardee list is comprised of a wide range of companies from the most well-known such as Microsoft, Adobe, and Salesforce to the relatively newer but rapidly growing - Qualtrics, Atlassian, and Asana. A good number of awardees may be new names to some but that should be no surprise given software has always been an industry of startups that seemingly came out of nowhere to create and dominate a new space. Software has become the backbone of our economy. From large enterprises to small businesses, most all rely on software whether for accounting, marketing, sales, supply chain, or a myriad of other functions. Software has become the dominant industry of our time and as such, we place a significance on highlighting the best companies leading the industry forward. The following awardees were nominated and selected based on a thorough evaluation process. Among the key criteria considered were ...


Announcing new Power Platform capabilities at Microsoft Ignite - Microsoft Power Platform Blog

#artificialintelligence

This year at Microsoft Ignite, we are announcing a set of new capabilities across Power Platform that enable business users (Citizen Developers), business analysts, IT admins, and professional developers to build and deliver applications faster and more cost-effectively. Be sure to watch our featured sessions--What's next for the Microsoft Power Platform and Driving a Data Culture with Power BI for a deep dive into these capabilities, as well the news story and featured session for Dynamics 365 for a comprehensive view of innovation across Microsoft Business Applications that can drive innovation and customer excellence across the organization. Since first introducing RPA in Microsoft Power Automate at Ignite in 2019, hundreds of thousands of organizations have adopted Power Automate and are now automating billions of actions each month. In 2020, we introduced Power Automate Desktop, which extended automation capabilities in Power Automate to on-premises processes and local desktop tasks. Today, we are continuing this momentum by announcing that Power Automate Desktop, which offers RPA capabilities that easily automate time-consuming manual work, will be available to Windows 10 users at no additional cost.


Abolish the #TechToPrisonPipeline

#artificialintelligence

The authors of the Harrisburg University study make explicit their desire to provide "a significant advantage for law enforcement agencies and other intelligence agencies to prevent crime" as a co-author and former NYPD police officer outlined in the original press release.[38] At a time when the legitimacy of the carceral state, and policing in particular, is being challenged on fundamental grounds in the United States, there is high demand in law enforcement for research of this nature, research which erases historical violence and manufactures fear through the so-called prediction of criminality. Publishers and funding agencies serve a crucial role in feeding this ravenous maw by providing platforms and incentives for such research. The circulation of this work by a major publisher like Springer would represent a significant step towards the legitimation and application of repeatedly debunked, socially harmful research in the real world. To reiterate our demands, the review committee must publicly rescind the offer for publication of this specific study, along with an explanation of the criteria used to evaluate it. Springer must issue a statement condemning the use of criminal justice statistics to predict criminality and acknowledging their role in incentivizing such harmful scholarship in the past. Finally, all publishers must refrain from publishing similar studies in the future.