AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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2,751 papers
Feature Usage Explorer can be reused in any HTML5 based applications where an understanding of how users interact with the system is required (i.e. user experience and usability studies, human computer interaction field, or requirement prioritization area).
An empirical study on the impact of affective states on software developers’ performance while programming and the value of applying psychometrics in Software Engineering studies is demonstrated and a call to valorize the human, individualized aspects of software developers is echoed.
An approach and an associated tool to automate feature identification for web applications are presented and it is indicated that there is a good potential for automating feature identification in web applications.
It is claimed that consumers' technology foresight horizon is limited by the existing technological base and the idea database should be an interesting source of ideas for service developers.
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The ongoing work building a Raspberry Pi cluster consisting of 300 nodes is presented, with potential use cases being an inexpensive and green test bed for cloud computing research and a robust and mobile data center for operating in adverse environments.
This paper proposes a research design towards a comparative analysis framework of JSF, which merges researcher needs and practitioner needs and is a call for action to the critical needs.
It is shown that high article processing charges are not sufficiently justified by the publishers, which often lack transparency and may prevent authors from adopting OA.
In this study, the authors characterize their context and identify common software development startup practices.
This state-of-practice investigation was performed using a literature review followed by a multiple-case study approach and presents how inconsistency between managerial strategies and execution can lead to failure by means of a behavioral framework.
A study with 42 participants investigates the relationship between the affective states, creativity, and analytical problem-solving skills of software developers and offers support for the claim that happy developers are indeed better problem solvers in terms of their analytical abilities.
The results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.
The results of this study show that the Ball ecosystem has the potential to improve the productivity of software development, however, it should produce smaller and more reasonable software systems, leading to a better reusability and a shorter learning phase for new developers.
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
By rethinking how large cloud computing systems operate, Associate Professor Christina Delimitrou seeks to make data centers more energy efficient.
An ongoing survey tracks the latest AI accelerator systems to keep hardware relevant for Lincoln Laboratory staff and sponsors.
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
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