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arXiv 2025年

作者： Chacko, George Park, Minhyuk Ramavarapu, Vikram Grama, Ananth Robles Granda, Pablo Warnow, Tandy Siebel School of Computing and Data Science Grainger College of Engineering University of Illinois Urbana-ChampaignIL United States Department of Computer Science Purdue University United States

Whether citations can be objectively and reliably used to measure productivity and scientific quality of articles and researchers can, and should, be vigorously questioned. However, citations are widely used to estimate the productivity of researchers and institutions, effectively creating a ‘grubby’ motivation to be well-cited. We model citation growth, and this grubby interest using an agent-based model (ABM) of network growth. In this model, each new node (article) in a citation network is an autonomous agent that cites other nodes based on a ‘citation personality’ consisting of a composite bias for locality, preferential attachment, recency, and fitness. We ask whether strategic citation behavior (reference selection) by the author of a scientific article can boost subsequent citations to it. Our study suggests that fitness and, to a lesser extent, out degree and locality effects are influential in capturing citations, which raises questions about similar effects in the real world. © 2025, CC BY.

关键词： Autonomous agents

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Completed Feature Disentanglement Learning for Multimodal MRIs Analysis

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Liu, Tianling Liu, Hongying Shang, Fanhua Yu, Lequan Han, Tong Wan, Liang Tianjin University College of Intelligence and Computing Tianjin China Medical School of Tianjin University Tianjin China The University of Hong Kong Department of Statistics and Actuarial Science School of Computing and Data Science Hong Kong Brain Medical Center of Tianjin University Huanhu Hospital Tianjin China

Multimodal MRIs play a crucial role in clinical diagnosis and treatment. Feature disentanglement (FD)-based methods, aiming at learning superior feature representations for multimodal data analysis, have achieved significant success in multimodal learning (MML). Typically, existing FD-based methods separate multimodal data into modality-shared and modality-specific features, and employ concatenation or attention mechanisms to integrate these features. However, our preliminary experiments indicate that these methods could lead to a loss of shared information among subsets of modalities when the inputs contain more than two modalities, and such information is critical for prediction accuracy. Furthermore, these methods do not adequately interpret the relationships between the decoupled features at the fusion stage. To address these limitations, we propose a novel Complete Feature Disentanglement (CFD) strategy that recovers the lost information during feature decoupling. Specifically, the CFD strategy not only identifies modality-shared and modality-specific features, but also decouples shared features among subsets of multimodal inputs, termed as modality-partial-shared features. We further introduce a new Dynamic Mixture-of-Experts Fusion (DMF) module that dynamically integrates these decoupled features, by explicitly learning the local-global relationships among the features. The effectiveness of our approach is validated through classification tasks on three multimodal MRI datasets. Extensive experimental results demonstrate that our approach outperforms other state-of-the-art MML methods with obvious margins, showcasing its superior performance. © 2025 IEEE.

关键词： Contrastive Learning

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Bayesian classifier based on cancer prognostic markers using accelerated failure time model with frailty effect

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Quality and Quantity 2025年 1-27页

作者： Vishwakarma, Gajendra K. Kumari, Pragya Bhattacharjee, Atanu Ong, Seng Huat Department of Mathematics & Computing Indian Institute of Technology Dhanbad Jharkhand Dhanbad 826004 India Division of Population Health and Genomics School of Medicine University of Dundee Dundee United Kingdom Institute of Actuarial Science and Data Analytics UCSI University Kuala Lumpur 56000 Malaysia Institute of Mathematical Sciences University of Malaya Kuala Lumpur 50603 Malaysia

This work presents a Bayesian classifier technique to categorize patients based on predictive biomarkers of time-to-event data utilizing the Accelerated Failure Time (AFT) model incorporating the frailty effect. Before classification, efficient and significant markers from a high-dimensional gene expression dataset need to be identified. Currently, it is an emerging area in oncology. A conventional three-step feature selection approach is introduced to select the most relevant markers from high-dimensional data. A threshold value for each selected marker is obtained using the proposed Bayesian classification procedure incorporating the AFT model with the frailty effect. The frailty effect is incorporated to account for unobserved heterogeneity in the expression values of subjects, allowing for a more accurate investigation of the risk of cancer-related outcomes. A simulation study is performed to validate the proposed classification approach, and the classification’s performance is evaluated using the Brier score, and the result indicates that it is relatively high. Application of the proposed classification technique is provided for two high-dimensional TCGA datasets of lung cancer patients. Our results provide evidence about the effect of classified gene expression values on the survival risk of lung cancer patients. © The Author(s), under exclusive licence to Springer Nature B.V. 2025.

关键词： Accelerated failure time model Bayesian Cancer’s biomarker Classification Survival analysis

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Decision Systems in Cybersecurity: A Machine Learning Perspective 12th

Decision Systems in Cybersecurity: A Machine Learning Perspe...

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12th International Conference on Frontiers of Intelligent computing: Theory and Applications, FICTA 2024

作者： Dhablia, Anishkumar Ganesh, R. Senthil Rizvi, Nuzhat Arora, Himanshu Borade, Monali Ravindra Bhujbal, Pallavi Altimetrik India Pvt. Ltd Maharashtra Pune India Department of Electronics and Communication Engineering Sri Krishna College of Engineering and Technology Tamilnadu Coimbatore India Nagpur Campus Pune India Computer Science and Engineering Arya College of Engineering Jaipur India Artificial Intelligence and Data Science VIIT Pune India Department of Information Technology School of Computing MIT ADT University Loni Kalbhor Pune India

ISBN: (纸本)9789819601462

The evolving digital environment requires sophisticated decision systems to protect important information assets from the growing complexity and variety of cyber threats. This review paper examines the impact of machine learning (ML) on cybersecurity decision systems, with a specific focus on analyzing ML and deep learning (DL) methods. The study employs the UNSW-NB15 dataset, which is a widely used benchmark dataset in the field of network security. The paper starts with an overview of the present cybersecurity environment, emphasizing the difficulties presented by advanced and changing cyber threats. It highlights the importance of adaptive and intelligent decision systems that can efficiently identify and reduce cyberattacks as they occur. A significant part of the paper focuses on a thorough analysis of different machine learning and deep learning techniques used in cybersecurity. The review discusses conventional machine learning algorithms like decision trees, support vector machines, and ensemble methods, along with sophisticated deep learning architectures such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Each approach is thoroughly assessed for its strengths and limitations based on factors like accuracy, interpretability, and scalability. The performance of various ML and DL models is evaluated using the UNSW-NB15 dataset as a benchmark. The dataset covers various cyberattack scenarios, enabling a thorough assessment of algorithms’ ability to detect anomalies and classify malicious activities. The paper explores the incorporation of machine learning (ML) and deep learning (DL) into decision support systems, highlighting the significance of explainability and interpretability in the realm of cybersecurity. The conversation covers the difficulties of implementing machine learning (ML) and deep learning (DL) models in practical situations, such as concerns about data privacy, adversarial attacks, and model resilience. This rev

关键词： Adversarial machine learning

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TrumorGPT: Graph-Based Retrieval-Augmented Large Language Model for Fact-Checking

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Hang, Ching Nam Yu, Pei-Duo Tan, Chee Wei Saint Francis University Yam Pak Charitable Foundation School of Computing and Information Sciences Hong Kong Hong Kong Chung Yuan Christian University Department of Applied Mathematics Taiwan Nanyang Technological University College of Computing and Data Science Singapore

In the age of social media, the rapid spread of misinformation and rumors has led to the emergence of infodemics, where false information poses a significant threat to society. To combat this issue, we introduce TrumorGPT, a novel generative artificial intelligence solution designed for fact-checking in the health domain. TrumorGPT aims to distinguish "trumors", which are health-related rumors that turn out to be true, providing a crucial tool in differentiating between mere speculation and verified facts. This framework leverages a large language model (LLM) with few-shot learning for semantic health knowledge graph construction and semantic reasoning. TrumorGPT incorporates graph-based retrieval-augmented generation (GraphRAG) to address the hallucination issue common in LLMs and the limitations of static training data. GraphRAG involves accessing and utilizing information from regularly updated semantic health knowledge graphs that consist of the latest medical news and health information, ensuring that fact-checking by TrumorGPT is based on the most recent data. Evaluating with extensive healthcare datasets, TrumorGPT demonstrates superior performance in fact-checking for public health claims. Its ability to effectively conduct fact-checking across various platforms marks a critical step forward in the fight against health-related misinformation, enhancing trust and accuracy in the digital information age. © 2020 IEEE.

关键词： fact-checking Graph-based retrieval-augmented generation health informatics knowledge graph large language models semantic reasoning

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RE-POSE: Synergizing Reinforcement Learning-Based Partitioning and Offloading for Edge Object Detection

arXiv

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arXiv 2025年

作者： Shi, Jianrui Zhao, Yong Cui, Zeyang Shen, Xiaoming Zeng, Minhang Liu, Xiaojie Department of Electrical and Computer Engineering National University of Singapore Singapore Department of Computing The Hong Kong Polytechnic University Hong Kong School of Computing and Data Science The University of Hong Kong Hong Kong Pengcheng Laboratory Shenzhen China

Object detection plays a crucial role in smart video analysis, with applications ranging from autonomous driving and security to smart cities. However, achieving real-time object detection on edge devices presents significant challenges due to their limited computational resources and the high demands of deep neural network (DNN)-based detection models, particularly when processing high-resolution video. Conventional strategies, such as input down-sampling and network up-scaling, often compromise detection accuracy for faster performance or lead to higher inference latency. To address these issues, this paper introduces RE-POSE, a Reinforcement Learning (RL)Driven Partitioning and Edge Offloading framework designed to optimize the accuracy-latency trade-off in resource-constrained edge environments. Our approach features an RL-Based Dynamic Clustering Algorithm (RL-DCA) that partitions video frames into non-uniform blocks based on object distribution and the computational characteristics of DNNs. Furthermore, a parallel edge offloading scheme is implemented to distribute these blocks across multiple edge servers for concurrent processing. Experimental evaluations show that RE-POSE significantly enhances detection accuracy and reduces inference latency, surpassing existing methods. © 2025, CC BY.

关键词： Computation offloading

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RIS-Assisted Localization: A Novel Conditional Sample Mean Approach without CSI

arXiv

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arXiv 2025年

作者： Yao, Jiawei Mao, Yijie Chen, Mingzhe School of Information Science and Technology ShanghaiTech University Shanghai China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL United States

Reconfigurable intelligent surface (RIS) has been recognized as a promising solution for enhancing localization accuracy. Traditional RIS-based localization methods typically rely on prior channel knowledge, beam scanning, and pilot-based assistance. These approaches often result in substantial energy and computational overhead, and require real-time coordination between the base station (BS) and the RIS. In this work, we propose a novel multiple RISs aided localization approach to address these challenges. The proposed method first estimates the angle-of-directions (AoDs) between the RISs and the user using the conditional sample mean approach, and then uses the estimated multiple AoD pairs to determine the user’s position. This approach only requires measuring the received signal strength at the BS for a set of randomly generated phase shifts across all RISs, thereby eliminating the need for real-time RIS phase shift design or user-to-BS pilot transmissions. Numerical results show that the proposed localization approach improves localization accuracy while significantly reducing energy and signaling overhead compared to conventional methods. Copyright © 2025, The Authors. All rights reserved.

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Phantom Events: Demystifying the Issues of Log Forgery in Blockchain

arXiv

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arXiv 2025年

作者： Liu, Yixuan Dong, Yuxin Liu, Ye Luo, Xiapu Li, Yi College of Computing and Data Science Nanyang Technological University Singapore School of Software and Microelectronics Peking University Beijing China School of Computing and Information Systems Singapore Management University Singapore Department of Computing The Hong Kong Polytechnic University Hong Kong

With the rapid development of blockchain technology, transaction logs play a central role in various applications, including decentralized exchanges, wallets, cross-chain bridges, and other third-party services. However, these logs, particularly those based on smart contract events, are highly susceptible to manipulation and forgery, creating substantial security risks across the ecosystem. To address this issue, we present the first in-depth security analysis of transaction log forgery in EVM-based blockchains, a phenomenon we term Phantom Events. We systematically model five types of attacks and propose a tool designed to detect event forgery vulnerabilities in smart contracts. Our evaluation demonstrates that our approach outperforms existing tools in identifying potential phantom events. Furthermore, we have successfully identified real-world instances for all five types of attacks across multiple decentralized applications. Finally, we call on community developers to take proactive steps to address these critical security vulnerabilities. Copyright © 2025, The Authors. All rights reserved.

关键词： Phantoms

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LLMs as Historical Actors: How AI Systems Influence the Web's Evolution 25

LLMs as Historical Actors: How AI Systems Influence the Web'...

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Companion Proceedings of the ACM on Web Conference 2025

作者： Usman Naseem Junaid Rashid Junguen Kim Amin Beheshti Jian Yang Mark Dras School of Computing Macquarie University Sydney Australia Department of AI and Data Science Sejong University Seoul Republic of Korea Department of CSE Inha University Incheon Republic of Korea

ISBN: (纸本)9798400713316

The history of the web has been defined by a series of revolutionary developments, from the advent of HTML and web browsers to the explosion of social media and cloud computing. Throughout this evolution, human actors have played pivotal roles, serving as developers, technologists, and innovators who have guided the trajectory of the Internet. However, the emergence of Large Language Models (LLMs), such as OpenAI's GPT series, represents a paradigm shift. These AI systems are not merely tools for humans but are increasingly becoming autonomous agents that shape the flow of information on the web. In this paper, we argue that LLMs can be framed as historical actors, meaning they are not only facilitators of human endeavors but also independent forces that influence the web's evolution. We explore how these AI systems direct the flow of information, shape public discourse, and may influence future interpretations of web-based history.

关键词： actors

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AUTHFi: Cross-Technology Device Authentication via Commodity WiFi

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IEEE Transactions on Mobile computing 2025年

作者： Wang, Weizheng Niyato, Dusit Xiong, Zehui Yin, Zhimeng City University of Hong Kong Department of Computer Science SAR Hong Kong Nanyang Technological University College of Computing and Data Science Singapore Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore

The explosive growth of the Internet of Things (IoT) has dramatically increased the demand for secure mechanisms to protect against unauthorized access and attacks. Traditionally, expensive Software-Defined Radios (SDRs) have been utilized to gather IoT physical features, which are critical for reliable authentication. However, the high cost of SDRs makes them impractical for widespread deployment across the vast and diverse IoT ecosystem. In contrast, this paper presents AUTHFi, a novel cross-technology device authentication framework that transforms the SDR approach for collecting and authenticating IoT device signals (e.g., ZigBee and Bluetooth) by utilizing commercial WiFi devices. Specifically, AUTHFi leverages the recent advances in Cross-Technology Communication (CTC) to reconstruct the partial waveform of IoT transmission, thus eliminating the requirement for expensive SDRs. AUTHFi requires us to address several unique challenges. First, AUTHFi compensates for signal losses of the partial waveform to get more signal information. Then, it introduces an enhanced Carrier Frequency Offset (CFO) estimation and a fusion neural network that combines CFO and the reconstructed waveform for accurate device authentication. We implement AUTHFi based on RTL8812au (commodity WiFi) and CC2652P (commodity ZigBee/Bluetooth). Our thorough evaluation confirms that AUTHFi offers reliable authentication under various settings, achieving a maximum accuracy of 94.2%. © 2025 IEEE. All rights reserved.

关键词： Wireless local area networks (WLAN)

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