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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

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

arXiv

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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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Information-Theoretic Analysis of Multimodal Image Translation

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Liu, Ruihao Li, Yudu Li, Yao Du, Yiping P. Liang, Zhi-Pei Shanghai Jiao Tong University School of Biomedical Engineering Shanghai200030 China University of Illinois at Urbana-Champaign Beckman Institute for Advanced Science and Technology UrbanaIL61801 United States University of Illinois at Urbana-Champaign National Center for Supercomputing Applications Department of Bioengineering Grainger College of Engineering Beckman Institute for Advanced Science and Technology UrbanaIL61801 United States Shanghai Jiao Tong University School of Biomedical Engineering Institute for Medical Imaging Technology Shanghai200030 China University of Illinois at Urbana-Champaign Beckman Institute for Advanced Science and Technology Department of Electrical and Computer Engineering Grainger College of Engineering UrbanaIL61801 United States

Multimodal image translation has found useful applications in solving several medical imaging problems. In this paper, we presented a systematic analysis of multimodal images and machine learning-based image translation from an information-theoretic perspective. Specifically, we analyzed the amount of mutual information that exists in some commonly used multimodal images. This analysis revealed varying structural correlation across modalities and tissue-dependence of mutual information. We also analyzed the amount of information transferred and gained in multimodal image translation and provided an upper bound on the information gain. Information-theoretic measures were also proposed to assess the effectiveness of an image translator, and the uncertainty associated with image translation. Numerical results were presented to demonstrate the information gain in practical multimodal image translation, and to validate the proposed upper bound on information gain and the translation error predictor. Finally, several potential applications of our analysis results were discussed, including the image denoising and reconstruction using side information generated by image translation. The findings from this study may prove useful for guiding the further development and application of multimodal image translation. © 1982-2012 IEEE.

关键词： Machine translation

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How Do Hackathons Foster Creativity? Towards AI Collaborative Evaluation of Creativity at Scale

arXiv

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arXiv 2025年

作者： Falk, Jeanette Chen, Yiyi Rafner, Janet Zhang, Mike Bjerva, Johannes Nolte, Alexander Aalborg University Copenhagen Department of Computer Science Copenhagen Denmark Center for Hybrid Intelligence Department of Management and Aarhus Institute for Advanced Studies Aarhus University Aarhus Denmark Aalborg University Copenhagen Department of Computer Science Pioneer Centre for Artificial Intelligence Copenhagen Denmark Software Engineering and Technology Eindhoven University of Technology Eindhoven Netherlands and Software and Societal Systems Carnegie Mellon University PittsburghPA United States

Hackathons have become popular collaborative events for accelerating the development of creative ideas and prototypes. There are several case studies showcasing creative outcomes across domains such as industry, education, and research. However, there are no large-scale studies on creativity in hackathons which can advance theory on how hackathon formats lead to creative outcomes. We conducted a computational analysis of 193,353 hackathon projects. By operationalizing creativity through usefulness and novelty, we refined our dataset to 10,363 projects, allowing us to analyze how participant characteristics, collaboration patterns, and hackathon setups influence the development of creative projects. The contribution of our paper is twofold: We identified means for organizers to foster creativity in hackathons. We also explore the use of large language models (LLMs) to augment the evaluation of creative outcomes and discuss challenges and opportunities of doing this, which has implications for creativity research at large. © 2025, CC BY.

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MLFuse: Multi-Scenario Feature Joint Learning for Multi-Modality Image Fusion

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IEEE Transactions on Multimedia 2025年

作者： Lei, Jia Li, Jiawei Liu, Jinyuan Wang, Bin Zhou, Shihua Zhang, Qiang Wei, Xiaopeng Kasabov, Nikola K. Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Dalian University of Technology School of Mechanical Engineering Dalian116024 China Dalian University of Technology School of Computer Science and Technology Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1010 New Zealand Ulster University Intelligent Systems Research Center LondonderryBT52 1SA United Kingdom

Multi-modality image fusion (MMIF) entails synthesizing images with detailed textures and prominent objects. Existing methods tend to use general feature extraction to handle different fusion tasks. However, these methods have difficulty breaking fusion barriers across various modalities owing to the lack of targeted learning routes. In this work, we propose a multi-scenario feature joint learning architecture, MLFuse, that employs the commonalities of multi-modality images to deconstruct the fusion progress. Specifically, we construct a cross-modal knowledge reinforcing network that adopts a multipath calibration strategy to promote information communication between different images. In addition, two professional networks are developed to maintain the salient and textural information of fusion results. The spatial-spectral domain optimizing network can learn the vital relationship of the source image context with the help of spatial attention and spectral attention. The edge-guided learning network utilizes the convolution operations of various receptive fields to capture image texture information. The desired fusion results are obtained by aggregating the outputs from the three networks. Extensive experiments demonstrate the superiority of MLFuse for infrared-visible image fusion and medical image fusion. The excellent results of downstream tasks (i.e., object detection and semantic segmentation) further verify the high-quality fusion performance of our method. The code is publicly available at https://***/jialei-sc/MLFuse. © 1999-2012 IEEE.

关键词： Semantic Segmentation

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SG-GAN: Fine Stereoscopic-Aware Generation for 3D Brain Point Cloud Up-Sampling From a Single Image

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IEEE Transactions on Emerging Topics in Computational Intelligence 2025年

作者： Hu, Bowen Yao, Weiheng Qiao, Sibo Pham, Hieu Wang, Shuqiang Ng, Michael Kwok-Po Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology Shenzhen518055 China Tiangong University School of Software Tianjin300387 China VinUniversity College of Engineering and Computer Science VinUni-Illinois Smart Health Center Hanoi100000 Viet Nam Hong Kong Baptist University Department of Mathematics Hong Kong

In minimally-invasive brain surgeries with indirect and narrow operating environments, 3D brain reconstruction is crucial. However, as requirements of accuracy for some new minimally-invasive surgeries (such as brain-computer interface surgery) are higher and higher, the outputs of conventional 3D reconstruction, such as point cloud (PC), are facing the challenges that sample points are too sparse and the precision is insufficient. On the other hand, there is a scarcity of high-density point cloud datasets, which makes it challenging to train models for direct reconstruction of high-density brain point clouds. In this work, a novel model named stereoscopic-aware graph generative adversarial network (SG-GAN) with two stages is proposed to generate fine high-density PC conditioned on a single image. The Stage-I GAN sketches the primitive shape and basic structure of the organ based on the given image, yielding Stage-I point clouds. The Stage-II GAN takes the results from Stage-I and generates high-density point clouds with detailed features. The Stage-II GAN is capable of correcting defects and restoring the detailed features of the region of interest (ROI) through the up-sampling process. Furthermore, a parameter-free-attention-based free-transforming module is developed to learn the efficient features of input, while upholding a promising performance. Comparing with the existing methods, the SG-GAN model shows superior performance in terms of visual quality, objective measurements, and performance in classification, as demonstrated by comprehensive results measured by several evaluation metrics including PC-to-PC error and Chamfer distance. © 2017 IEEE.

关键词： 3D reconstruction

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Constraint Boundary Wandering Framework: Enhancing Constrained Optimization with Deep Neural Networks

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年 PP卷 PP页

作者： Wu, Shuang Chen, Shixiang Shen, Li Zhang, Lefei Tao, Dacheng Wuhan University National Engineering Research Center for Multimedia Software School of Computer Science Wuhan430072 China University of Science and Technology of China Key Laboratory of the Ministry of Education for Mathematical Foundations and Applications of Digital Technology School of Mathematical Sciences China Shenzhen Campus of Sun Yat-sen University School of Cyber Science and Technology Shenzhen518107 China Nanyang Technological University College of Computing and Data Science 639798 Singapore

Constrained optimization problems are pervasive in various fields, and while conventional techniques offer solutions, they often struggle with scalability. Leveraging the power of deep neural networks (DNNs) in optimization, we present a novel learning-based approach, the Constraint Boundary Wandering Framework (CBWF), to address these challenges. Our contributions include introducing a boundary wandering strategy inspired by the active-set method, enhancing equality constraint feasibility, and treating the Lipschitz constant as a learnable parameter. Additionally, we evaluate the regularization term, illustrating that the nonsmooth L2 norm yields superior results. Extensive testing on synthetic datasets and the ACOPT dataset demonstrates CBWF's superiority, outperforming existing deep learning-based solvers in terms of both objective and constraint loss. © 1979-2012 IEEE.

关键词： Constrained optimization

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Reversible Data Hiding With Secret Encrypted Image Sharing and Adaptive Coding

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IEEE Internet of Things Journal 2025年

作者： Fang, Guangtian Wang, Feng Zhao, Chenbin Qin, Chuan Chang, Ching-Chun Chang, Chin-Chen Fujian University of Technology College of Computer Science and Mathematics Fuzhou350118 China Fujian University of Technology College of Computer Science and Mathematics Key Laboratory of Big Data Mining and Applications Fuzhou350118 China Monash University Faculty of IT Department of Software Systems and Cybersecurity VIC3800 Australia Ministry of Education School of Cyber Science and Engineering Key Laboratory of Aerospace Information Security and Trusted Computing Wuhan430072 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Feng Chia University Information and Communication Security Research Center Taichuang 40724 Taiwan Feng Chia University Department of Information Engineering and Computer Science Taichung40724 Taiwan

To ensure the security of image information and facilitate efficient management in the cloud, the utilization of reversible data hiding in encrypted images (RDHEI) has emerged as pivotal. However, most existing RDHEI schemes suffer from lower security and limited embedding capacity. To tackle these challenges, we propose a reversible data hiding with secret encrypted image sharing and adaptive coding scheme. Specifically, in the encryption phase, we introduce an improved secret sharing (SS) encryption method based on the chinese remainder theorem for polynomials (CRTP). This method not only improves the security of encrypted images but also vacates a larger room for embedding. In the embedding phase, we introduce an adaptive coding embedding approach using XOR preservation (XORP) and huffman coding, which provides high embedding capacity. Experimental results and security analysis demonstrate that our proposed encryption method achieves optimal values in security indicators for encrypted images, such as information entropy, histograms, NPCR and UACI. The proposed embedding method is superior to some state-of-the-art schemes in terms of embedding capacity. Furthermore, in datasets BOSSBase and BOWS2, the average embedding rates of the proposed embedding approach can reach 2.1745 bpp and 2.0656 bpp, respectively. © 2025 IEEE.

关键词： adaptive coding chinese remainder theorem for polynomials encrypted images Reversible data hiding secret sharing

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ERetinex: Event Camera Meets Retinex Theory for Low-Light Image Enhancement

arXiv

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arXiv 2025年

作者： Guo, Xuejian Tian, Zhiqiang Wang, Yuehang Li, Siqi Jiang, Yu Du, Shaoyi Gao, Yue the School of Software Engineering Xi’an Jiaotong University Xi’an710049 China the College of Computer Science and Technology Jilin University Changchun130012 China the National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an710049 China BNRist THUIBCS KLISS BLBCI School of Software Tsinghua University Beijing100084 China

Low-light image enhancement aims to restore the under-exposure image captured in dark scenarios. Under such scenarios, traditional frame-based cameras may fail to capture the structure and color information due to the exposure time limitation. Event cameras are bio-inspired vision sensors that respond to pixel-wise brightness changes asynchronously. Event cameras’ high dynamic range is pivotal for visual perception in extreme low-light scenarios, surpassing traditional cameras and enabling applications in challenging dark environments. In this paper, inspired by the success of the retinex theory for traditional frame-based low-light image restoration, we introduce the first methods that combine the retinex theory with event cameras and propose a novel retinex-based low-light image restoration framework named ERetinex. Among our contributions, the first is developing a new approach that leverages the high temporal resolution data from event cameras with traditional image information to estimate scene illumination accurately. This method outperforms traditional image-only techniques, especially in low-light environments, by providing more precise lighting information. Additionally, we propose an effective fusion strategy that combines the high dynamic range data from event cameras with the color information of traditional images to enhance image quality. Through this fusion, we can generate clearer and more detail-rich images, maintaining the integrity of visual information even under extreme lighting conditions. The experimental results indicate that our proposed method outperforms state-of-the-art (SOTA) methods, achieving a gain of 1.0613 dB in PSNR while reducing FLOPS by 84.28%. The code is available at https://***/lodew920/ERetinex. © 2025, CC BY.

关键词： Cameras

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A Near-Optimal Category Information Sampling in RFID Systems

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IEEE Transactions on Mobile Computing 2025年

作者： Wang, Xiujun Liu, Zhi Zhou, Xiaokang Liao, Yong Hu, Han Zheng, Xiao Li, Jie Anhui University of Technology School of Computer Science Ma'anshan243032 China Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Ma'anshan243032 China University of Electro-Communications Graduate School of Informatics and Engineering Tokyo182- 8585 Japan Shiga University Faculty of Data Science Hikone522-8522 Japan RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan University of Science and Technology of China School of Cyber Science and Technology Hefei China Beijing Institute of Technology School of Information and Electronics Beijing China School of Electronic Information and Electrical Engineering Shanghai200240 China

In many RFID-enabled applications, objects are classified into different categories, and the information associated with each object's category (called category information) is written into the attached tag, allowing the reader to access it later. The category information sampling in such RFID systems, which is to randomly choose (sample) a few tags from each category and collect their category information, is fundamental for providing real-time monitoring and analysis in RFID. However, to the best of our knowledge, two technical challenges, i.e., how to guarantee a minimized execution time and reduce collection failure caused by missing tags, remain unsolved for this problem. In this paper, we address these two limitations by considering how to use the shortest possible time to sample a different number of random tags from each category and collect their category information sequentially in small batches. In particular, we first obtain a lower bound on the execution time of any protocol that can solve this problem. Subsequently, we present a near-OPTimal Category information sampling protocol (OPT-C) that solves the problem with an execution time close to the lower bound. Finally, extensive simulation results demonstrate the superiority of OPT-C over existing protocols, while real-world experiments further validate its practicality. © 2002-2012 IEEE.

关键词： category information sampling execution time lower bound near-optimal protocol RFID systems

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