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On Causality in Distributed Continuum Systems IEEE Internet Comput. (IF 3.2) Pub Date : 2024-04-24 Víctor Casamayor Pujol, Boris Sedlak, Praveen Kumar Donta, Schahram Dustdar
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Distributed Quantum Machine Learning: Federated and Model-Parallel Approaches IEEE Internet Comput. (IF 3.2) Pub Date : 2024-04-24 Jindi Wu, Tianjie Hu, Qun Li
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Special Issue on 6G Technologies and Applications IEEE Internet Comput. (IF 3.2) Pub Date : 2024-04-24 Arvind Narayanan, Mahesh Marina, Vijay Gopalakrishnan
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Remembering David Mills (1938–2024) IEEE Internet Comput. (IF 3.2) Pub Date : 2024-04-24 Steve Crang, Vint Cerf
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Common Metadata Framework: Integrated Framework for Trustworthy AI Pipelines IEEE Internet Comput. (IF 3.2) Pub Date : 2024-03-21 Annmary Justine Koomthanam, Sergey Serebryakov, Aalap Tripathy, Gyanaranjan Nayak, Martin Foltin, Suparna Bhattacharya
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Open Experimental Measurements of Sub-6GHz Reconfigurable Intelligent Surfaces IEEE Internet Comput. (IF 3.2) Pub Date : 2024-03-18 Marco Rossanese, Placido Mursia, Andres Garcia-Saavedra, Vincenzo Sciancalepore, Arash Asadi, Xavier Costa-Perez
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L26GC: Evolving the Low Latency Core for Future Cellular Networks IEEE Internet Comput. (IF 3.2) Pub Date : 2024-03-18 Shixiong Qi, K. K. Ramakrishnan, Jyh-Cheng Chen
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Requirements and Design Architecture for Digital Twin End-to-End Trustworthiness IEEE Internet Comput. (IF 3.2) Pub Date : 2024-03-18 Nicola Bicocchi, Mattia Fogli, Carlo Giannelli, Marco Picone, Antonio Virdis
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The Dark Crypto World! IEEE Internet Comput. (IF 3.2) Pub Date : 2024-03-07 Muhammad Abulaish, Harshita Dalal
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Hierarchical Network Data Analytics Framework for 6G Network Automation: Design and Implementation IEEE Internet Comput. (IF 3.2) Pub Date : 2024-02-27 Youbin Jeon, Sangheon Pack
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Remote Learning and Work IEEE Internet Comput. (IF 3.2) Pub Date : 2024-02-16 René Kizilcec, John Mitchell
Drawing on platforms and methods that had been developed but not widely adopted, the COVID-19 pandemic suddenly forced remote learning and remote work worldwide. While the first few months were too rushed to provide time for iteration or reflection, continuing years gave educators, students, employers, and workers an unprecedented opportunity to explore and find value in remote operation. Recognizing
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Data Management Challenges in Blockchain-Based Applications IEEE Internet Comput. (IF 3.2) Pub Date : 2024-02-16 Stanly Wilson, Kwabena Adu-Duodu, Yinhao Li, Ellis Solaiman, Omer Rana, Schahram Dustdar, Rajiv Ranjan
Effective data management is crucial to ensure the security, integrity, and efficiency of blockchain systems. This study proposes a detailed data management taxonomy specifically designed for blockchain technology. The taxonomy provides a structured framework to categorize and address various aspects of data management in blockchain networks. It covers essential aspects, such as data flow, data storage
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Call for Papers: IEEE Internet Computing Special Issue on Civilizing and Humanizing AI IEEE Internet Comput. (IF 3.2) Pub Date : 2024-02-16
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A Grateful Farewell and a Warm Welcome IEEE Internet Comput. (IF 3.2) Pub Date : 2024-02-16 George Pallis
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The EMPWR Platform: Data and Knowledge-Driven Processes for the Knowledge Graph Lifecycle IEEE Internet Comput. (IF 3.2) Pub Date : 2024-02-16 Hong Yung Yip, Amit Sheth
The unparalleled volume of data generated has heightened the need for approaches that can consume these data in a scalable and automated fashion. Although modern data-driven, deep-learning-based systems are cost-efficient and can learn complex patterns, they are black boxes in nature, and the underlying input data highly dictate their world model. Knowledge graphs (KGs), as one such technology, have
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Enabling 6G and Beyond Network Functions from Space: Challenges and Opportunities IEEE Internet Comput. (IF 3.2) Pub Date : 2024-01-30 Lixin Liu, Wei Liu, Yuanjie Li, Hewu Li
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Privacy-Preserving Recommendation based on Shuffled Federated Graph Neural Network IEEE Internet Comput. (IF 3.2) Pub Date : 2024-01-05 Qinbo Liu, Lichen Yang, Yang Liu, Jiaqi Deng, Guorui Wu
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3-D Point Cloud Map Compression for Connected Intelligent Vehicles IEEE Internet Comput. (IF 3.2) Pub Date : 2023-12-15 Youngjoon Choi, Hannah Baek, Jinseop Jeong, Kanghee Kim
In autonomous vehicles, 3-D point cloud (PCD) maps are widely employed. By matching a point cloud acquired from a 3-D ranging sensor in real time with the PCD map, the ego vehicle can be localized with high accuracy. However, the PCD maps must be compressed and customized to the vehicles because they typically have low computing power, a small memory space, and low-resolution sensors. In this study
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Adapting to Online and Remote Learning: Examining the Educational Assessment Experiences of U.S. College Students Amidst COVID-19 IEEE Internet Comput. (IF 3.2) Pub Date : 2023-11-28 Teresa M. Ober, Ying Cheng
We administered a survey to examine the impact of the COVID-19 pandemic on academic assessment in a sample of 992 U.S. college students (mean age = 22.36 years) between February and June 2021. The survey included multiple-choice and open-ended questions asking about students’ experiences before (fall 2019 to early spring 2020) and during the pandemic-affected periods (late spring 2020 to spring 2021)
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Are Remote Educational Escape Rooms Designed During the Pandemic Useful in a Postpandemic Face-to-Face Setting? IEEE Internet Comput. (IF 3.2) Pub Date : 2023-11-23 Daniel López-Fernández, Aldo Gordillo, Sonsoles López-Pernas, Edmundo Tovar
Numerous initiatives were conducted online during the COVID-19 pandemic, and today it is necessary to analyze whether it is better to continue conducting these initiatives online, or should they be done face-to-face and even readapted to this format. This article compares an educational escape room for learning software engineering conducted online during the confinement caused by the pandemic and
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Trustworthy AI and Data Lineage IEEE Internet Comput. (IF 3.2) Pub Date : 2023-11-17 Elisa Bertino, Suparna Bhattacharya, Elena Ferrari, Dejan Milojicic
AI trustworthiness properties are at the top of concerns for industry, governments, and academia. However, the AI and its models are only as good as the data used to train it. Data lineage could be tracked in many ways, including using metadata, from its generation usage, deployment, and verification. New standards, blueprints, best practices, and repositories for data are required to address requirements
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Understanding Responsible Computing via Project Management for Sustainability IEEE Internet Comput. (IF 3.2) Pub Date : 2023-11-17 Hoa Khanh Dam, Aditya Ghose, Nigel Gilbert, Munindar P. Singh
Everyone acknowledges the importance of responsible computing, but practical advice is hard to come by. Important Internet applications are ways to accomplish business processes. We investigate how they can be geared to support responsibility as illustrated via sustainability. Sustainability is not only urgent and essential but also challenging due to engagement with human and societal concerns, diverse
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Addressing the Faults Landscape in the Internet of Things: Toward Datacentric and System Resilience IEEE Internet Comput. (IF 3.2) Pub Date : 2023-11-17 Sultan Altarrazi, Tomasz Szydlo, Schahram Dustdar, Satish Narayana Srirama, Rajiv Ranjan
In the Internet of Things (IoT) context, the landscape of weaknesses in the IoT spectrum sheds light on addressing faults by researchers due to the number of IoT components that unveil immense vulnerabilities to failures. Hence, there is a need to comprehend the faults dynamics to facilitate identifying potential hazards in a developer’s design, deliver methodologies to mitigate the risks, and ensure
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Digital Transformation in Remote Learning and Work—An Externality of the COVID-19 Pandemic IEEE Internet Comput. (IF 3.2) Pub Date : 2023-11-21 Kevin K.W. Ho, Shaoyu Ye, Dickson K.W. Chiu, Takuya Sekiguchi
The COVID-19 pandemic changed the world in multiple ways. The global lockdown forced people to adopt remote learning and work, creating many examples of different organizations (government agencies, businesses, and educational institutions) adapting to this new paradigm through digital transformation. In this article, we review how educational institutions have digitally transformed teaching and learning
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Empowering Database Learning Through Remote Educational Escape Rooms IEEE Internet Comput. (IF 3.2) Pub Date : 2023-11-21 Enrique Barra, Sonsoles López-Pernas, Aldo Gordillo, Alejandro Pozo, Andres Muñoz-Arcentales, Javier Conde
Learning about databases is indispensable for individuals studying software engineering or computer science or those involved in the IT industry. We analyzed a remote educational escape room for teaching about databases in four different higher education courses in two consecutive academic years. We employed three instruments for evaluation: a pre- and posttest to assess the escape room’s effectiveness
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Remote Work and Gender Inequality: Unmasking the Challenges and Seeking Solutions IEEE Internet Comput. (IF 3.2) Pub Date : 2023-11-21 Hem Chandra Joshi, Sandeep Kumar
The COVID-19 pandemic has transformed the way we work, especially in the domain of remote work (RW). It compelled employees and organizations to embrace RW arrangements, which were previously voluntary, part time, or occasional, as a means to curtail the spread of the virus and minimize business disruptions. RW has introduced a flexible arrangement and other benefits but has also brought forth numerous
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A Tale of Two Cities: Data and Configuration Variances in Robust Deep Learning IEEE Internet Comput. (IF 3.2) Pub Date : 2023-10-06 Guanqin Zhang, Jiankun Sun, Feng Xu, Yulei Sui, H.M.N. Dilum Bandara, Shiping Chen, Tim Menzies
Deep neural networks (DNNs) have widespread applications in industries such as image recognition, supply chain, medical diagnosis, and autonomous driving. However, previous work has shown that the high accuracy of a DNN model does not imply high robustness (i.e., consistent performances on new and future datasets) because the input data and external environment (e.g., software and model configurations)
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Rethinking Certification for Trustworthy Machine-Learning-Based Applications IEEE Internet Comput. (IF 3.2) Pub Date : 2023-10-06 Marco Anisetti, Claudio A. Ardagna, Nicola Bena, Ernesto Damiani
Machine learning (ML) is increasingly used to implement advanced applications with nondeterministic behavior, which operate on the cloud–edge continuum. The pervasive adoption of ML is urgently calling for assurance solutions to assess applications’ nonfunctional properties (e.g., fairness, robustness, and privacy) with the aim of improving their trustworthiness. Certification has been clearly identified