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Companies are using machine learning to solve real-world problems and are developing hundreds to thousands of features in the process. They are building feature engineering pipelines as part of MLOps life cycle to transform data from…

Machine Learning · Computer Science 2023-06-01 Anya Li , Bhala Ranganathan , Feng Pan , Mickey Zhang , Qianjun Xu , Runhan Li , Sethu Raman , Shail Paragbhai Shah , Vivienne Tang

Clouds gather a vast volume of telemetry from their networked systems which contain valuable information that can help solve many of the problems that continue to plague them. However, it is hard to extract useful information from such raw…

Networking and Internet Architecture · Computer Science 2020-04-28 Behnaz Arzani , Bita Rouhani

The growing use of Machine Learning and Artificial Intelligence (AI), particularly Large Language Models (LLMs) like OpenAI's GPT series, leads to disruptive changes across organizations. At the same time, there is a growing concern about…

While AI is often introduced into organizations to drive innovation and efficiency, many adoption efforts fail as workers resist and struggle to integrate these systems. These failures point to a deeper issue: workers, the very people…

Artificial Intelligence · Computer Science 2026-05-06 Christine P. Lee , Min Kyung Lee , Bilge Mutlu

DevOps is a collaborative and multidisciplinary organizational effort to automate continuous delivery of new software updates while guaranteeing their correctness and reliability. The present survey investigates and discusses DevOps…

Software Engineering · Computer Science 2019-11-20 Leonardo Leite , Carla Rocha , Fabio Kon , Dejan Milojicic , Paulo Meirelles

Developments in Artificial Intelligence (AI) and Distributed Ledger Technology (DLT) currently lead to lively debates in academia and practice. AI processes data to perform tasks that were previously thought possible only for humans. DLT…

Cryptography and Security · Computer Science 2020-02-06 Konstantin D. Pandl , Scott Thiebes , Manuel Schmidt-Kraepelin , Ali Sunyaev

Knowledge silos emerge when structural properties of organizational interaction networks limit the diffusion of information. These structural barriers are known to take many forms at different scales - hubs in otherwise sparse…

Physics and Society · Physics 2022-03-03 Laurent Hébert-Dufresne , Guillaume St-Onge , John Meluso , James Bagrow , Antoine Allard

Recently software development companies started to embrace Machine Learning (ML) techniques for introducing a series of advanced functionality in their products such as personalisation of the user experience, improved search, content…

Human-Computer Interaction · Computer Science 2017-08-09 Ilias Flaounas

The integration of machine learning (ML) into complex software systems has increased challenges in collaboration and communication (CoCo) of the teams building these systems. ML engineering (MLE) teams often involve diverse roles, ML…

Software Engineering · Computer Science 2026-05-11 A. Azamnouri , M. Haug , L. Woltmann , M. Fritz , J. Bogner , S. Wagner

Security Operations Centers (SOCs) face growing challenges in managing cybersecurity threats due to an overwhelming volume of alerts, a shortage of skilled analysts, and poorly integrated tools. Human-AI collaboration offers a promising…

Cryptography and Security · Computer Science 2025-05-13 Massimiliano Albanese , Xinming Ou , Kevin Lybarger , Daniel Lende , Dmitry Goldgof

Language models (LMs) are machine learning models designed to predict linguistic patterns by estimating the probability of word sequences based on large-scale datasets, such as text. LMs have a wide range of applications in natural language…

There is an increasing need for organizations to collaborate with internal and external partners on a global scale for creating software-based products and services. Many aspects and risks need to be addressed when setting up such global…

Software Engineering · Computer Science 2014-10-07 Inna Smirnova

Systems are growing into more complex ones for developing and maintaining. Existing systems which do not have much in common on the first look are connected, due to the technical progress, even if it was never intended that way. It is an…

Software Engineering · Computer Science 2018-09-17 Tobias Eckl

Deploying machine learning applications on edge devices can bring clear benefits such as improved reliability, latency and privacy but it also introduces its own set of challenges. Most works focus on the limited computational resources of…

Machine Learning · Computer Science 2022-03-29 Sam Leroux , Pieter Simoens , Meelis Lootus , Kartik Thakore , Akshay Sharma

The increasing deployment of Artificial Intelligence (AI) and other autonomous algorithmic systems presents the world with new systemic risks. While focus often lies on the function of individual algorithms, a critical and underestimated…

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Automating cloud configuration and deployment remains a critical challenge due to evolving infrastructures, heterogeneous hardware, and fluctuating workloads. Existing solutions lack adaptability and require extensive manual tuning, leading…

Modern networked systems are increasingly reconfigurable, enabling demand-aware infrastructures whose resources can be adjusted according to the workload they currently serve. Such dynamic adjustments can be exploited to improve network…

Data Structures and Algorithms · Computer Science 2019-04-12 Monika Henzinger , Stefan Neumann , Stefan Schmid

Edge computing enables real-time data processing closer to its source, thus improving the latency and performance of edge-enabled AI applications. However, traditional AI models often fall short when dealing with complex, dynamic tasks that…

Networking and Internet Architecture · Computer Science 2025-07-02 Haoxiang Luo , Yinqiu Liu , Ruichen Zhang , Jiacheng Wang , Gang Sun , Dusit Niyato , Hongfang Yu , Zehui Xiong , Xianbin Wang , Xuemin Shen

The rapid rise of Large Language Models (LLMs) has revolutionized various artificial intelligence (AI) applications, from natural language processing to code generation. However, the computational demands of these models, particularly in…

Machine learning (ML) techniques are enjoying rapidly increasing adoption. However, designing and implementing the systems that support ML models in real-world deployments remains a significant obstacle, in large part due to the radically…

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