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Retracted Citations and Self-citations in Retracted Publications: A Comparative Study of Plagiarism and Fake Peer Review

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arXiv 2025年

作者： Sharma, Kiran Khurana, Parul School of Engineering & Technology BML Munjal University Haryana Gurugram122413 India Center for Advanced Data and Computational Science BML Munjal University Haryana Gurugram122413 India School of Computer Applications Lovely Professional University Punjab Phagwara144411 India

Retracted citations remain a significant concern in academia as they perpetuate misinformation and compromise the integrity of scientific literature despite their invalidation. To analyze the impact of retracted citations, we focused on two retraction categories: plagiarism and fake peer review. The data set was sourced from Scopus and the reasons for the retraction were mapped using the Retraction Watch database. The retraction trend shows a steady average growth in plagiarism cases of 1.2 times, while the fake peer review exhibits a fluctuating pattern with an average growth of 5.5 times. Although fewer papers are retracted in the plagiarism category compared to fake peer reviews, plagiarism-related papers receive 2.5 times more citations. Furthermore, the total number of retracted citations for plagiarized papers is 1.8 times higher than that for fake peer review papers. Within the plagiarism category, 46% of the retracted citations are due to plagiarism, while 53.6% of the retracted citations in the fake peer review category are attributed to the fake peer review. The results also suggest that fake peer review cases are identified and retracted more rapidly than plagiarism cases. Finally, self-citations constitute a small percentage of citations to retracted papers but are notably higher among citations that are later retracted in both the categories. © 2025, CC BY.

关键词： Inflation

Scalable Multi-view Regression Clustering for Large-scale Data

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Zhao, Xiaowei Fan, Jie Chang, Xiaojun Nie, Feiping Zhang, Qiang Guo, Jun Xidian University State Key Laboratory of Electromechanical Integrated Manufacturing of High-Performance Electronic Equipment Shaanxi Xi’an710071 China Xidian University Center for Complex Systems School of Mechano-Electronic Engineering Shaanxi Xi’an710071 China Northwest University School of Information Science and Technology Shaanxi Xi’an710127 China University of Science and Technology of China School of Information Science and Technology Hefei230026 China Mohamed bin Zayed University of Artificial Intelligence Department of Computer Vision MBZUAI Abu Dhabi United Arab Emirates Xi’an710072 China

In recent years, unsupervised linear regression has attracted attention for its ability to directly capture the mapping relationship between samples and targets. However, existing algorithms can only utilize limited information from a single view, which often leads to unsatisfactory results. To address this problem, we propose a regression clustering model based on multi-view information fusion, called Scalable Multi-view Regression Clustering. This model consists of two parts: intra-view information fusion and inter-view information fusion. In the first part, to capture the local correlations among samples, we propose constructing view-specific bipartite graphs. Unlike traditional single-view and multi-view clustering algorithms, we treat the weights of the bipartite graph as additional features of the samples, thereby directly incorporating the local manifold structure of the samples at the feature level. Furthermore, since the original features of the samples also contain valuable information, we perform unsupervised linear regression separately on the samples represented by the original features and those represented by the bipartite graph weights in each view. The results are then integrated in a weighted manner. In the second part, we propose adaptively weighting the clustering results from each view to capture complementary information across views, thereby enhancing clustering performance. This strategy not only avoids the bipartite graph alignment issue in multi-view clustering but also enables clustering with linear time complexity, making it effective for handling large-scale data. An iterative optimization algorithm is developed to update all variables alternately. Experiments conducted on benchmark datasets demonstrate the superiority of our proposed model. © 2025 IEEE. All rights reserved.

关键词： Multiple linear regression

Joint Power Allocation and Task Scheduling for Data Offloading in Non-Geostationary Orbit Satellite Networks

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IEEE Transactions on Network and Service Management 2025年

作者： He, Lijun Jia, Ziye Wang, Juncheng Lansard, Erick Han, Zhu Yuen, Chau China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China Xidian University State Key Laboratory of Integrated Services Networks Shaanxi Xi'an710071 China Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing211106 China Southeast University National Mobile Communications Research Laboratory Nanjing211111 China Hong Kong Baptist University Department of Computer Science China Nanyang Technological University School of Electrical and Electronics Engineering 639798 Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

In Non-Geostationary Orbit Satellite Networks (NGOSNs) with a large number of battery-carrying satellites, proper power allocation and task scheduling are crucial to improving data offloading efficiency. In this work, we jointly optimize power allocation and task scheduling to achieve energy-efficient data offloading in NGOSNs. Our goal is to properly balance the minimization of the total energy consumption and the maximization of the sum weights of tasks. Due to the tight coupling between power allocation and task scheduling, we first derive the optimal power allocation solution to the joint optimization problem with any given task scheduling policy. We then leverage the conflict graph model to transform the joint optimization problem into an Integer Linear Programming (ILP) problem with any given power allocation strategy. We explore the unique structure of the ILP problem to derive an efficient semidefinite relaxation-based solution. Finally, we utilize the genetic framework to combine the above special solutions as a two-layer solution for the original joint optimization problem. Simulation results demonstrate that our proposed solution can properly balance the reduction of total energy consumption and the improvement of the sum weights of tasks, thus achieving superior system performance over the current literature. © 2004-2012 IEEE.

关键词： Geostationary satellites

Improving Multi-Factor Authentication Security with Deep Learning-Based User Behaviour Analysis

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Improving Multi-Factor Authentication Security with Deep Lea...

Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Fahad Sulaiman Mohammed Alnafea Alok Singh Sengar Noor Kaylan Hamid Kuttimarks Muthu Selladurai Sharmeen Izzat Hassan R Saravanakumar Department of Accounting College of Business and Economics in Qassim University Buraida Saudi Arabia Department of Computer Science & Engineering Haridwar University Roorkee India Department of Computer Education Al Kitab University Kirkuk Iraq Shivajirao S. Jondhle College of Engineering and Technology Asangaon India Department of Computer Engineering College of Engineering Knowledge University Erbil Iraq Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

With growing frequency and sophistication of cyber threats, there is an urgent need for more adaptive, secure multi-factor authentication systems. Traditional approaches often tend to make static factor-based authentication systems that just ignore the dynamic nature of user behavior and are constantly at risk from evolved attacks against authentication mechanisms. This paper presents a novel hybrid deep learning approach - that of long short-term memory networks and convolutional neural networks combined in an attempt to address these disadvantages and enhance the security of an MFA system. The approach uses LSTM to analyze sequential data on user behavior, hence recognizing long-term dependencies like login pattern and session durations. On the other hand, CNN layers will classify the attribute by learning the spatial hierarchy thus identifying small variations that can mark emerging threats to security. This method, in contrast to others, analyzes statically or all at once, providing a dynamic check into the temporal as well as spatial aspects of user interactions; thus, there is more force in its frame for anomaly detection. Experimental tests further verify the excellent performance of the system, with results indicating an accuracy of 98.2%, precision of 99.9%, recall of 99.5%, and an F1 score of 99%. The new strategy exploits MFA frameworks more effectively in minimizing their vulnerabilities against authentication attacks, while developing a dynamic and robust solution that caters to the shift in the ever-changing landscape within cybersecurity, thus advancing the possibility of practice securely.

关键词： Deep learning Hands Multi-factor authentication Accuracy Neural networks Reflection Pattern recognition Convolutional neural networks Integrated circuit modeling Long short term memory

Multimodal Feature-Driven Deep Learning for the Prediction of Duck Body Dimensions and Weight

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arXiv 2025年

作者： Xiao, Wenbo Han, Qiannan Su, Gang Liang, Guiping Zhang, Hongyan Wang, Song Xu, Zhihao Wan, Weican Li, Chuang Jiang, Guitao Xiao, Yi College of Information and Technology Hunan Agricultural University Hunan Changsha410129 China Hunan Institute of Animal and Veterinary Science Hunan Changsha410131 China College of Animal Science and Technology Sichuan Agricultural University Sichuan Chengdu611130 China School of Computer Science and Engineering The University of New South Wales SydneyNSW2052 Australia

Accurate body dimension and weight measurements are critical for optimizing poultry management, health assessment, and economic efficiency. This study introduces an innovative deep learning-based model leveraging multimodal data—2D RGB images from different views, depth images, and 3D point clouds—for the non-invasive estimation of duck body dimensions and weight. A dataset of 1,023 Linwu ducks, comprising over 5,000 samples with diverse postures and conditions, was collected to support model training. The proposed method innovatively employs PointNet++ to extract key feature points from point clouds, extracts and computes corresponding 3D geometric features, and fuses them with multi-view convolutional 2D features. A Transformer encoder is then utilized to capture long-range dependencies and refine feature interactions, thereby enhancing prediction robustness. The model achieved a mean absolute percentage error (MAPE) of 6.33% and an R2 of 0.953 across eight morphometric parameters, demonstrating strong predictive capability. Unlike conventional manual measurements, the proposed model enables high-precision estimation while eliminating the necessity for physical handling, thereby reducing animal stress and broadening its application scope. This study marks the first application of deep learning techniques to poultry body dimension and weight estimation, providing a valuable reference for the intelligent and precise management of the livestock industry with far-reaching practical significance. Copyright © 2025, The Authors. All rights reserved.

关键词： Livestock

MP-DPCC: A Motion Proxy-Based Dynamic Point Cloud Compression Framework

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MP-DPCC: A Motion Proxy-Based Dynamic Point Cloud Compressio...

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： ZhaoYi Jiang Dong Han Cao Song FangZhe Nan Bailin Yang School of Computer Science and Technolgy Zhejiang Gongshang University School of Statistics and Mathematics Zhejiang Gongshang University Economic Forecasting and Policy Simulation Laboratory Zhejiang Gongshang University Collaborative Innovation Center of Statistical Data Engineering Technology & Application Zhejiang Gongshang University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The increasing data volume and the demand for real-time transmission highlight the necessity for efficient compression of dynamic point cloud data. Existing methods primarily focus on reducing inter-frame redundancy by calculating per-point motion information, overlooking the computational and storage costs involved. In this paper, we propose a novel motion proxy-based dynamic point cloud compression framework to enhance the efficiency and accuracy of motion information utilization. Specifically, we introduce a feature proxy module to adaptively locate proxy points, which represent the overall motion through the motion of proxy points. Additionally, a motion enhancement module is employed to refine motion details and prevent local information loss caused by dense motion trajectories. Extensive experiments demonstrate the superiority of our approach. Compared with baseline methods, it achieves an average BD-rate improvement of 12.07% (D1) and 11.90% (D2).

关键词： Point cloud compression Costs Accuracy Dynamics Termination of employment Signal processing Real-time systems Acoustics Trajectory Speech processing

Generalization of CNNs on Relational Reasoning with Bar Charts

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arXiv 2025年

作者： Cui, Zhenxing Chen, Lu Wang, Yunhai Haehn, Daniel Wang, Yong Pfister, Hanspeter School of Computer Science and Technology Shandong University China State Key Lab of CAD&CG Zhejiang University China School of Information Renmin University of China China College of Science and Mathematics University of Massachusetts Boston United States College of Computing and Data Science Nanyang Technological University Singapore School of Engineering and Applied Sciences Harvard University United States

This paper presents a systematic study of the generalization of convolutional neural networks (CNNs) and humans on relational reasoning tasks with bar charts. We first revisit previous experiments on graphical perception and update the benchmark performance of CNNs. We then test the generalization performance of CNNs on a classic relational reasoning task: estimating bar length ratios in a bar chart, by progressively perturbing the standard visualizations. We further conduct a user study to compare the performance of CNNs and humans. Our results show that CNNs outperform humans only when the training and test data have the same visual encodings. Otherwise, they may perform worse. We also find that CNNs are sensitive to perturbations in various visual encodings, regardless of their relevance to the target bars. Yet, humans are mainly influenced by bar lengths. Our study suggests that robust relational reasoning with visualizations is challenging for CNNs. Improving CNNs’ generalization performance may require training them to better recognize task-related visual properties. Copyright © 2025, The Authors. All rights reserved.

关键词： Convolutional neural networks

Energy Harvesting Techniques for Self-Powered Body-Worn Sensors in Wireless Body Area Networks

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Energy Harvesting Techniques for Self-Powered Body-Worn Sens...

Automation and Computation (AUTOCOM), International Conference on

作者： Bhupendra Ku. Gupta Shreya Ghosal Subbulakshmi Ganesan T. Sudhakar Awakash Mishra Neelesh Pandey Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India School of Sciences Chemistry and Biochemistry JAIN (Deemed to be University) Bangalore Karnataka India Department of Biomedical Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Self-powered body-worn sensors are considered one of the potential candidates for health and fitness monitoring in wireless body area networks. Sensors extract energy from the human body or its environment to power themselves, making them self-contained with no external power supply, thus improving portability and wear ability. According to this, energy harvesting methods are essential for developing selfpowered body-worn sensors in WBANs. This technical paper reviews different methods of energy harvesting used for bodyworn sensors, such as Body heat, motion, ambient light harvesting, etc. It also discusses some drawbacks and limitations, such as low energy production and inconsistent energy sources. It also features some of the latest blasting techniques and technologies concerning energy recovery. Using self-powered body-worn sensors can lead to various applications and advantages in WBANs. Such uses included continuous & non-invasive health monitoring, data collection for medical research and sports performance tracking. Self-powered sensors reduce the frequency of maintenance and minimize environmental pollution caused by battery disposal.

关键词： Wireless communication Wireless sensor networks Electric potential Reviews Body area networks Sensors Batteries Energy harvesting Monitoring Sports

Self-Citations in Academic Excellence: Analysis of the Top 1% Highly Cited India-Affiliated Research Papers

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arXiv 2025年

作者： Sharma, Kiran Khurana, Parul School of Engineering & Technology BML Munjal University Gurugram Haryana-122413 India Center for Advanced Data and Computational Science BML Munjal University Gurugram Haryana-122413 India School of Computer Applications Lovely Professional University Phagwara Punjab-144411 India

Citations demonstrate the credibility, impact, and connection of a paper with the academic community. Self-citations support research continuity, but, if excessive, may inflate metrics and raise bias concerns. The aim of the study is to examine the role of self-citations towards the research impact of India. To study this, 3.58 million papers affiliated with India from 1947 to 2024 in the Scopus database were downloaded, and 2.96 million were filtered according to document type and publication year up to 2023. Further filtering based on high citation counts identified the top 1% of highly cited papers, totaling 29,556. The results indicate that the impact of Indian research, measured by highly cited papers, has grown exponentially since 2000, reaching a peak during the 2011–2020 decade. Among the citations received by these 29,556 papers, 6% are self-citations. Papers with a high proportion of self-citations (>90%) are predominantly from recent decades and are associated with smaller team sizes. The findings also reveal that smaller teams are primarily domestic, whereas larger teams are more likely to involve international collaborations. Domestic collaborations dominate smaller team sizes in terms of both self-citations and publications, whereas international collaborations gain prominence as team sizes increase. The results indicate that while domestic collaborations produce a higher number of highly cited papers, international collaborations are more likely to generate self-citations. The top international collaborators in highly cited papers are the USA, followed by UK, and Germany. © 2025, CC BY.

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