Article
Computer Science, Software Engineering
Virgilio Almeida, Fernando Filgueiras, Ricardo Fabrino Mendonca
Summary: Societies are undergoing a transformation where automation and algorithms are becoming increasingly important in daily activities, determining employment, social benefits, immigration, information exposure, transportation, and relationship choices. Digital technologies and algorithms play a central role in reshaping society.
IEEE INTERNET COMPUTING
(2022)
Article
Computer Science, Interdisciplinary Applications
Irum Saeed Abbasi, Jayson L. Dibble
Summary: The study found a positive relationship between mental illness and social media intrusion, with SNS related infidelity behaviors partially mediating this relationship. Therefore, partners should exercise caution in making friends online to avoid engaging in infidelity behaviors.
SOCIAL SCIENCE COMPUTER REVIEW
(2021)
Article
Computer Science, Cybernetics
Roxanne Leitao
Summary: This article investigates data from three online discussion forums for victims and survivors of domestic abuse, focusing on technology-facilitated abuse. The findings cover discussions on forms of technology-facilitated abuse, the use of technology by forum members in the context of intimate partner abuse, and the exchange of digital privacy and security advice. The article concludes with a discussion on the dual role of digital technologies in intimate partner abuse, challenges and advantages of digital ubiquity, issues surrounding digital evidence of abuse, and the labor of managing digital privacy and security.
HUMAN-COMPUTER INTERACTION
(2021)
Article
Health Care Sciences & Services
Seigo Mitsutake, Yoshimitsu Takahashi, Aki Otsuki, Jun Umezawa, Akiko Yaguchi-Saito, Junko Saito, Maiko Fujimori, Taichi Shimazu, INFORM Study Grp
Summary: This study aims to improve strategies to provide reliable self-management information for chronic diseases online and identify populations facing barriers to internet use for health. The study found that patients with cancer and depression or anxiety disorder were more likely to seek online health information, while patients with chronic lung diseases were more likely to watch health-related videos on YouTube.
JOURNAL OF MEDICAL INTERNET RESEARCH
(2023)
Article
Mathematics, Interdisciplinary Applications
Saima Rashid, Rehana Ashraf, Ebenezer Bonyah
Summary: Excessive use of social media is a developing concern that needs attention. Advertisements and awareness campaigns are used to raise awareness about the negative impact of digital technologies. This study uses novel mathematical techniques to investigate social media addiction and proposes a more effective methodology. Numerical simulations show that the newly developed differential operators produce remarkable results compared to the classical model.
Article
Engineering, Civil
Neetesh Kumar, Rashmi Chaudhry, Omprakash Kaiwartya, Neeraj Kumar, Syed Hassan Ahmed
Summary: The Social Internet of Vehicles (SIoV) is a framework that integrates smart devices with vehicular communications, focusing on green traffic data dissemination under disruptive vehicular environments. A meta-heuristic solution called Two-Way Particle Swarm Optimization (TWPSO) is proposed for this problem, and extensive simulation experiments demonstrate its advantages in real SIoV environments.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2021)
Editorial Material
Engineering, Multidisciplinary
Ching-Hsien Hsu, Carlos Enrique Montenegro Marin, Ruben Gonzalez Crespo, Hassan Fouad Mohamed El-sayed
Summary: The papers in this special section focus on social computing and the social Internet of Things (SIoT), which is a new paradigm that extends Internet of Things and allows for beneficial interactions among groups and communities.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2022)
Article
Engineering, Multidisciplinary
Zhiwei Guo, Keping Yu, Yu Li, Gautam Srivastava, Jerry Chun-Wei Lin
Summary: With the increasing demand for personalized social services, researchers propose a deep learning-embedded social Internet of Things (IoT) solution to address the data management and preference ambiguity issues in social recommendation. Experimental results show that the proposed solution outperforms benchmark methods and exhibits good robustness.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Yushan Lin, Yasser Alshehri, Noha Alnazzawi, Mohammad Abid, Samina A. Khan, Fouzia Jabeen, Ibrahim Elwarfalli
Summary: The usage of social media is rapidly increasing and has a significant impact on various aspects of life, including healthcare and education systems during the COVID-19 pandemic. Social media has revolutionized healthcare research and practice, providing new ways for professionals to connect and share knowledge. Social media analytics play a crucial role in health interventions, benefiting both patients and clinicians.
Article
Computer Science, Cybernetics
Tuja Khaund, Baris Kirdemir, Nitin Agarwal, Huan Liu, Fred Morstatter
Summary: Online social networks (OSNs) have become a crucial aspect of societal digitalization, altering communication, decision-making, and beliefs, with impacts on social groups, financial systems, and political communication. Social media platforms are home to social bots, which mimic human social behaviors and automate sociotechnical actions.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2022)
Article
Psychology, Multidisciplinary
Yan Yu
Summary: This study investigates the impact of online social networking (OSN) engagement on university students' learning outcomes. The results show that OSN engagement helps students build self-efficacy, achieve social acceptance, and adapt to the environment, leading to positive learning outcomes such as self-esteem development, satisfaction with university life, and academic performance. This research contributes to the understanding of the role of OSN in whole person development and the mediating role of social learning theory.
FRONTIERS IN PSYCHOLOGY
(2022)
Article
Information Science & Library Science
Chuang Wang, Jun Zhang, Matthew K. O. Lee
Summary: Research on excessive use of mobile instant messaging (MIM) has focused on social learning in facilitating technology overuse. The highly interactive nature of MIM creates a technology-based social structure that encourages excessive MIM use. Perceived interactivity of MIM influences excessive MIM use through contextualized social learning factors, which are moderated by MIM use experience.
INFORMATION TECHNOLOGY & PEOPLE
(2022)
Article
Computer Science, Hardware & Architecture
Guangchi Liu, Qing Yang, Honggang Wang, Alex X. Liu
Summary: This paper proposes the three-valued subjective logic (3VSL) model to assess trust in online social networks, and further designs the AssessTrust (AT) algorithm to accurately compute trust between any two users. Experimental results validate that 3VSL can accurately model trust between indirectly connected users.
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
(2021)
Article
Computer Science, Information Systems
Xiangjian Zuo, Lixiang Li, Shoushan Luo, Haipeng Peng, Yixian Yang, Linming Gong
Summary: This article presents an efficient and privacy-preserving verifiable graph intersection scheme with cryptographic accumulators in social networks, offering secure verifiable graph intersection operation in an untrusted cloud. The scheme is shown to be secure and feasible through detailed correctness proof and performance analysis, providing strong protection for data owners' graph data privacy and user authentication.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Psychology, Clinical
Rae D. Drach, Natalia C. Orloff, Julia M. Hormes
Summary: Correlational research suggests that emotion regulation deficits, known risk factors for addictions, may also contribute to problematic behaviors like excessive use of social networking sites. This study experimentally assessed the emotion regulatory function of SNS use, finding that individuals with disordered SNS use reported greater deficits in emotion regulation. Exposure to SNS resulted in increased positive affect, indicating that interventions targeting problematic SNS use should focus on improving emotion regulation strategies.
ADDICTIVE BEHAVIORS
(2021)
Article
Biochemical Research Methods
Ramon Vinas, Helena Andres-Terre, Pietro Lio, Kevin Bryson
Summary: The study developed a method based on conditional generative adversarial networks to generate realistic transcriptomics data for Escherichia coli and humans. Results showed that the approach performed better in preserving gene expression properties compared to existing simulators, maintaining tissue- and cancer-specific attributes, and exhibiting real gene clusters and ontologies at different scales.
Article
Biochemical Research Methods
Paul Scherer, Maja Trebacz, Nikola Simidjievski, Ramon Vinas, Zohreh Shams, Helena Andres Terre, Mateja Jamnik, Pietro Lio
Summary: Gene expression data is often high dimensional, noisy, and has a low number of samples, making it challenging for learning algorithms. In this article, a method called Gene Interaction Network Constrained Construction (GINCCo) is proposed to construct computational graph models for gene expression data by incorporating the structure of gene interaction networks. The results of a case study on cancer phenotype prediction tasks show that GINCCo outperforms other models while greatly reducing model complexity.
Article
Biology
Phasit Charoenkwan, Nalini Schaduangrat, Mohammad Ali Moni, Pietro Lio, Balachandran Manavalan, Watshara Shoombuatong
Summary: This study presents a novel computational method, SAPPHIRE, for accurately identifying thermophilic proteins (TPPs) using sequence information. The method combines different feature encodings and machine learning algorithms to train baseline models and extract key information of TPPs. SAPPHIRE outperforms existing methods in terms of predictive performance and achieves higher accuracy and correlation coefficient.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Biology
Phasit Charoenkwan, Nalini Schaduangrat, Pietro Lio, Mohammad Ali Moni, Balachandran Manavalan, Watshara Shoombuatong
Summary: This study proposes a novel computational approach, NEPTUNE, for the accurate and large-scale identification of Tumor Homing Peptides (THPs) from sequence information. The results demonstrate that NEPTUNE achieves superior performance in THP prediction and improves interpretability using the SHapley additive explanations method.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Computer Science, Artificial Intelligence
Tiago Azevedo, Alexander Campbell, Rafael Romero-Garcia, Luca Passamonti, Richard A. I. Bethlehem, Pietro Lio, Nicola Toschi
Summary: In this paper, a novel deep neural network architecture is proposed that combines graph neural networks and temporal convolutional networks for learning from both the spatial and temporal components of resting-state functional magnetic resonance imaging (rs-fMRI) data. The model is evaluated using samples from the UK Biobank and Human Connectome Project datasets, showing effectiveness and explainability-related features. This approach lays the groundwork for future deep learning architectures focused on the spatio-temporal nature of rs-fMRI data.
MEDICAL IMAGE ANALYSIS
(2022)
Article
Oncology
Yanan Wang, Yu Guang Wang, Changyuan Hu, Ming Li, Yanan Fan, Nina Otter, Ikuan Sam, Hongquan Gou, Yiqun Hu, Terry Kwok, John Zalcberg, Alex Boussioutas, Roger J. Daly, Guido Montufar, Pietro Lio, Dakang Xu, Geoffrey I. Webb, Jiangning Song
Summary: This study proposes an AI-powered digital staging system that analyzes spatial patterns in the tumor microenvironment to accurately predict survival rates and staging of gastric cancer patients. The results show outstanding model performance and significant improvement over traditional staging systems.
NPJ PRECISION ONCOLOGY
(2022)
Article
Biochemical Research Methods
Pietro Bongini, Franco Scarselli, Monica Bianchini, Giovanna Maria Dimitri, Niccolo Pancino, Pietro Lio
Summary: Drug side-effects have a significant impact on public health, care system costs, and drug discovery processes. Predicting the probability of side-effects before their occurrence is crucial to reduce this impact, especially in drug discovery. By integrating heterogeneous data into a graph dataset, this study successfully utilizes Graph Neural Networks (GNNs) to predict drug side-effects, showing promising results. The experimental results highlight the significance of utilizing relationships between data entities and suggest potential future developments in this field.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Engineering, Multidisciplinary
Jianxin Zhao, Yanhao Feng, Xinyu Chang, Peng Xu, Shilin Li, Chi Harold Liu, Wenke Yu, Jian Tang, Jon Crowcroft
Summary: Recently, there has been a growing interest in the sixth generation network, which aims to support a wider range of applications with higher capacity and greater coverage than existing 5G connections. One promising application is Decentralised Federated Learning, which preserves privacy in machine learning and relies on peer-to-peer mobile connections among devices. However, the heterogeneity of data and devices, as well as the dynamic nature of mobile networks, pose challenges to the performance of federated learning. In this paper, the authors propose a data redistribution phase to balance data distribution on participating devices, improving system performance in the training phase. They model the problem as a bargaining game and propose centralised and decentralised algorithms to solve it, with the latter being more energy efficient. The proposed system is evaluated through simulations and DNN training tasks on large scale FEMNIST-based datasets.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2023)
Article
Computer Science, Information Systems
Ella Peltonen, Nitinder Mohan, Peter Zdankin, Tanya Shreedhar, Tri Nguyen, Suzan Bayhan, Jon Crowcroft, Jussi Kangasharju, Daniela Nicklas
Summary: Not every research effort yields positive results. Trying new approaches can sometimes surpass state-of-the-art, but there are instances when hypotheses are unproven, improvements are insignificant, or systems fail due to design errors in previous works. In pervasive computing, errors can arise from various sources such as hardware, communication channels, and software environments. However, failure does not equate to lack of progress. It is crucial to establish platforms for sharing insights, experiences, and lessons learned to prevent repetition of the same mistakes in pervasive computing research. Moreover, problems can serve as indicators for discovering new research challenges. This article presents a comprehensive discussion on perspectives regarding the publication of negative results, valuable failures, and lessons learned in pervasive computing, drawing on the collective input from the First International Workshop on Negative Results in Pervasive Computing (PerFail 2022) co-located with the 20th International Conference on Pervasive Computing and Communications (PerCom 2022).
IEEE PERVASIVE COMPUTING
(2023)
Article
Computer Science, Information Systems
Yuetai Li, Yixuan Fan, Lei Zhang, Jon Crowcroft
Summary: The centralized system becomes less efficient, secure, and resilient as the network size and heterogeneity increase. Distributed consensus mechanisms characterized by decentralization, autonomy, parallelism, and fault-tolerance can meet the increasing demands of safety and security. This article establishes a Node and Link probabilistic failure model for the RAFT protocol and proposes reliability performance indicators for quick deployment. The impact of failures in a distributed consensus network is evaluated.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Information Systems
Nuruzzaman Faruqui, Mohammad Abu Yousuf, Md Whaiduzzaman, A. K. M. Azad, Salem A. Alyami, Pietro Lio, Muhammad Ashad Kabir, Mohammad Ali Moni
Summary: The Internet of Medical Things (IoMT) has become an attractive target for cybercriminals due to its market value and rapid growth. However, IoMT devices have limited computational capabilities, making them vulnerable to cyber-attacks. To address this, a novel Intrusion Detection System (IDS) called SafetyMed is proposed, which combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to defend against intrusion from sequential and grid data. SafetyMed has shown high detection rates and accuracy, making it a potential game-changer in vulnerable sectors like the medical industry.
Article
Health Care Sciences & Services
Md. Martuza Ahamad, Sakifa Aktar, Md. Jamal Uddin, Md. Rashed-Al-Mahfuz, A. K. M. Azad, Shahadat Uddin, Salem A. Alyami, Iqbal H. Sarker, Asaduzzaman Khan, Pietro Lio, Julian M. W. Quinn, Mohammad Ali Moni
Summary: Good vaccine safety and reliability are crucial for countering infectious diseases effectively. This study aims to reduce adverse reactions to COVID-19 vaccines by identifying common factors through patient data analysis and classification. Patient medical histories and postvaccination effects were examined, and statistical and machine learning approaches were used. The analysis revealed that prior illnesses, hospital admissions, and SARS-CoV-2 reinfection were significantly associated with poor patient reactions.
Article
Engineering, Electrical & Electronic
Jianxin Zhao, Pierre Vandenhove, Peng Xu, Hao Tao, Liang Wang, Chi Harold Liu, Jon Crowcroft
Summary: In this paper, a parallel training method is proposed using operators as scheduling units, and a pebble-game-based memory-efficient optimization in training is discussed. Experiments show the flexibility and good performance of the proposed method.
IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING
(2023)
Proceedings Paper
Acoustics
Alexander Campbell, Lorena Qendro, Pietro Lio, Cecilia Mascolo
Summary: This article proposes an approach for estimating predictive uncertainty using early exit ensembles. Empirical evaluation shows that this method performs well in terms of accuracy and uncertainty metrics, while also providing significant computational speed-up and memory reduction compared to single model baselines.
2022 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)
(2022)
Review
Biology
Phasit Charoenkwan, Nalini Schaduangrat, Md Mehedi Hasan, Mohammad Ali Moni, Pietro Lio, Watshara Shoombuatong
Summary: This article comprehensively investigates 14 state-of-the-art TPP predictors and summarizes their characteristics and advantages and disadvantages. Through comparative analysis, it provides future perspectives for the development of more accurate and efficient TPP predictors.
