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Research Square 2025年

作者： Batoure Bamana, Apollinaire Sokdou Bila Lamou, Yannick Fotsa-Mbogne, David Jaures Shafiee Kamalabad, Mahdi Department of Computer Engineering University Institute of Technology The University of Ngaoundere Cameroon Department of Robotic and Industrial Computing School of Chemical Engineering and Mineral Industries The University of Ngaoundere Cameroon Department of Mathematics and Computer Science Faculty of Science The University of Ngaoundere Cameroon Laboratory of Mathematics Computer Science and Applications Department of Mathematics and Computer Science National Higher School of Agro-Industrial Sciences The University of Ngaoundéré Cameroon Laboratory of Mathematics and Computer Science Department of Mathematics Statistics and Computer Science Faculty of Science The University of Bertoua Cameroon Department of Methodology and Statistics Faculty of Social and Behavioural Sciences Utrecht University Utrecht Netherlands Centre for Complex Systems Studies Utrecht University Utrecht Netherlands

Stationarity in time series is a key property for practical data analysis, inferences, and predictions particularly in biosciences. Stationarity can be either deterministic or stochastic. If a time series data is not stationary, it can be rendered stationary through detrending or differentiation techniques. Differentiation can be performed in either an integer or fractional form. This paper investigates non-stationarity in time series data, focusing specifically on stochastic trends characterised by a unit root. It hypothesises that fractional differentiation can enhance forecast accuracy for datasets with limited volume. For experiments, 24 series corresponding to weekly Malaria and Typhoid Fever from Adamawa Region (Cameroon) are analysed and forecasted. Each series comprises 156 observations covering January 2021 to December 2023. After collecting, checking and classifying the data, four forecasting models, Auto Regressive Integrated Moving Average (ARIMA), Fractional ARIMA (ARFIMA), Long Short-Term Memory (LSTM), and the proposed Fractional Differencing and LSTM (FD-LSTM), are implemented. The models' performances are evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of determination (R2) metrics, which inform further analysis and recommendations. Results reveal that ARFIMA outperformed the ARIMA by 93% in training and 100% in testing. Similarly, FD-LSTM achieved a 100% improvement over LSTM in training and testing. These findings underscore the value of achieving stationarity through fractional differentiation, enhancing model performance and providing a robust framework for forecasting in datasets with limited observations. © 2025, CC BY.

关键词： Time series

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Towards Gradient Equalization and Feature Diversification for Long-Tailed Multi-Label Image Recognition

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IEEE Transactions on Multimedia 2025年

作者： Chen, Zhao-Min Cui, Quan Zhang, Xiaoqin Deng, Ruoxi Xia, Chaoqun Lu, Shijian Nanjing University National Key Laboratory for Novel Software Technology Nanjing210093 China Waseda University Graduate School of Information Production and Systems Fukuoka169-8050 Japan Wenzhou University College of Computer Science and Artificial Intelligence Wenzhou325060 China Nanyang Technological University School of Computer Science and Engineering 639798 Singapore

Multi-label image recognition with convolutional neural networks has achieved remarkable progress in the past few years. However, most existing multi-label image recognition methods suffer from the long-tailed data distribution problem, i.e., head categories occupy most training samples, while tailed classes have few samples. This work firstly studies the influence of long-tailed data distribution on existing multi-label image recognition methods. Based on this, two crucial issues of the existing methods are identified: 1) severe gradient imbalance between head and tailed categories, even though re-balancing strategies are adopted;2) the lack of diversity of tail category training samples. To tackle the first issue, this paper proposes a group sampling strategy to create group- wise balanced data distribution. Meanwhile, a dynamic gradient balancing loss is proposed to equalize the gradient for all categories. To tackle the second issue, this paper proposes a diversity enhancement module to fuse the information across all categories, preventing the network from overfitting tail classes. Furthermore, it also balances the gradient, promoting the discriminability of learned classifiers. Our method significantly outperforms the baseline method and achieves competitive performance with state-of-the-art methods on VOC-LT and COCO-LT datasets. Extensive ablation studies are conducted to verify the effectiveness of the essential proposals. © 1999-2012 IEEE.

关键词： Labeled data

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LEARNING EFFICIENT POSITIONAL ENCODINGS WITH GRAPH NEURAL NETWORKS

arXiv

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arXiv 2025年

作者： Kanatsoulis, Charilaos I. Choi, Evelyn Jegelka, Stephanie Leskovec, Jure Ribeiro, Alejandro Dept. of Computer Science Stanford University United States School of Computation Information and Technology Technical University of Munich Computer Science & Artificial Intelligence Laboratory Massachusetts Institute of Technology United States Dept. of Electrical and Systems Engineering University of Pennsylvania United States

Positional encodings (PEs) are essential for effective graph representation learning because they provide position awareness in inherently position-agnostic transformer architectures and increase the expressive capacity of Graph Neural Networks (GNNs). However, designing powerful and efficient PEs for graphs poses significant challenges due to the absence of canonical node ordering and the scale of the graph. In this work, we identify four key properties that graph PEs should satisfy: stability, expressive power, scalability, and genericness. We find that existing eigenvector-based PE methods often fall short of jointly satisfying these criteria. To address this gap, we introduce PEARL, a novel framework of learnable PEs for graphs. Our primary insight is that message-passing GNNs function as nonlinear mappings of eigenvectors, enabling the design of GNN architectures for generating powerful and efficient PEs. A crucial challenge lies in initializing node attributes in a manner that is both expressive and permutation equivariant. We tackle this by initializing GNNs with random node inputs or standard basis vectors, thereby unlocking the expressive power of message-passing operations, while employing statistical pooling functions to maintain permutation equivariance. Our analysis demonstrates that PEARL approximates equivariant functions of eigenvectors with linear complexity, while rigorously establishing its stability and high expressive power. Experimental evaluations show that PEARL outperforms lightweight versions of eigenvector-based PEs and achieves comparable performance to full eigenvector-based PEs, but with one or two orders of magnitude lower complexity. Our code is available at https://***/ehejin/Pearl-PE. Copyright © 2025, The Authors. All rights reserved.

关键词： Encoding (symbols)

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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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Correction: Materials laboratories of the future for alloys, amorphous, and composite materials (MRS Bulletin, (2025), 50, 2, (190-207), 10.1557/s43577-024-00846-y)

引用

MRS Bulletin 2025年 1-2页

作者： Banerjee, Sarbajit Meng, Y. Shirley Minor, Andrew M. Zhang, Minghao Zaluzec, Nestor J. Chan, Maria K.Y. Seidler, Gerald McComb, David W. Agar, Joshua Mukherjee, Partha P. Melot, Brent Chapman, Karena Guiton, Beth S. Klie, Robert F. McCue, Ian D. Voyles, Paul M. Robertson, Ian Li, Ling Chi, Miaofang Destino, Joel F. Devaraj, Arun Marquis, Emmanuelle A. Segre, Carlo U. Liu, Huinan H. Yang, Judith C. Momeni, Kasra Misra, Amit Abdolrahim, Niaz Medvedeva, Julia E. Cai, Wenjun Sehirlioglu, Alp Dizbay-Onat, Melike Mehta, Apurva Graham-Brady, Lori Maruyama, Benji Rajan, Krishna Warner, Jamie H. Taheri, Mitra L. Kalinin, Sergei V. Reeja-Jayan, B. Schwarz, Udo D. Simon, Sindee L. Brown, Craig M. Department of Chemistry and Department of Materials Science & Engineering Texas A & M University College Station United States Pritzker School of Molecular Engineering The University of Chicago Chicago United States Department of Materials Science & Engineering University of California Berkeley Berkeley United States National Center for Electron Microscopy Molecular Foundry Lawrence Berkeley National Laboratory Berkeley United States Center for Nanoscale Materials Argonne National Laboratory Lemont United States Department of Physics University of Washington Seattle United States Department of Materials Science & Engineering The Ohio State University Columbus United States Department of Mechanical Engineering and Mechanics Drexel University Philadelphia United States School of Mechanical Engineering Purdue University West Lafayette United States Department of Chemistry University of Southern California Los Angeles United States Department of Chemistry Stony Brook University The State University of New York Stony Brook United States Department of Chemistry University of Kentucky Lexington United States Department of Physics University of Illinois at Chicago Chicago United States Department of Materials Science & Engineering Northwestern University Evanston Bangladesh Department of Materials Science & Engineering University of Wisconsin–Madison Madison United States Department of Materials Science and Engineering University of Pennsylvania Philadelphia United States Department of Mechanical Engineering and Materials Science Duke University Durham United States Department of Chemistry Creighton University Omaha United States Physical and Computational Sciences Directorate Pacific Northwest National Laboratory Richland United States Department of Materials Science & Engineering University of Michigan–Ann Arbor Ann Arbor United States Department of Physics Illinois Institute of Technology Chicago United States Department of Bioengineering and the Mater

This article was updated to correct Minghao Zhang’s affiliation from "Pritzker School of Molecular engineering, The University of Chicago, Chicago, USA" to "Pritzker School of Molecular engineering, The University of Chicago, Chicago, USA;Argonne National laboratory, Lemont, USA." Argonne National laboratory was left off the original publication. © The Author(s) 2025.

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CASI: Context-aware Automatic Semantic Inference by fusing video and network traffic information in industrial control systems

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Information Fusion 2025年 122卷

作者： Hetu, Zheqiu Zhang, Zhenyong Wang, Mufeng Xv, Zidong Meng, Jie State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China China Industrial Control Systems Cyber Emergency Response Team Beijing100040 China State Key Laboratory of Industrial Control Technology and College of Control Science and Engineering Zhejiang University Hangzhou310027 China

The increasing adoption of Internet of Things devices in Industrial Control systems (ICS) has provided both attackers and security professionals with new perspectives on these traditionally safety-critical systems. Our observations reveal that video captured by field-deployed cameras contains process variables (PVs) essential for both ICS attack and defense. Based on this insight, we propose a Context-Aware Semantic Inference (CASI) method. CASI integrates video information from cameras with network traffic to infer the physical semantics of PVs in network packets, serving as a tactical tool for attackers or security professionals. A key advantage of CASI is its non-intrusive nature, deriving PVs location in the packets and semantics solely from video and network traffic without interacting with the runtime ICS, which is both covert and ensures the uninterrupted operation of the system. We constructed an ICS testbed using real devices to replicate a simplified industrial process, collecting traffic from 2 industrial protocols and creating 4 datasets. We evaluated CASI's precision and recall in detecting PVs within this context, comparing it with similar tools. Experimental results demonstrate that CASI achieves a recall rate of 1.00 in PV semantic extraction, significantly outperforming other tools. Furthermore, leveraging CASI-derived semantic information for data tampering attacks successfully disrupted monitoring and data acquisition programs, posing a severe security threat to the ICS. © 2025 Elsevier B.V.

关键词： Reverse engineering

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Distributed Prescribed-Time Observer for Nonlinear systems

arXiv

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arXiv 2025年

作者： de Heij, Vincent Niazi, M.B. Umar Johansson, Karl H. Ahmed, Saeed Faculty of Science and Engineering University of Groningen Groningen9747 AG Netherlands Division of Decision and Control Systems Digital Futures KTH Royal Institute of Technology StockholmSE-100 44 Sweden Laboratory for Information and Decision Systems Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States

This paper proposes a distributed prescribed-time observer for nonlinear systems representable in a block-triangular observable canonical form. Using a weighted average of neighbor estimates exchanged over a strongly connected digraph, each observer estimates the system state despite limited local sensor measurements. The proposed design guarantees that distributed state estimation errors converge to zero at a user-specified convergence time, irrespective of observers’ initial conditions. To achieve this prescribed-time convergence, distributed observers implement time-varying local output injection gains that asymptotically approach infinity as the prescribed time is approached. The theoretical convergence is rigorously proven and validated through numerical simulations. Copyright © 2025, The Authors. All rights reserved.

关键词： Nonlinear simulations

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Hierarchical Recording Architecture for Three-Dimensional Magnetic Recording

arXiv

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arXiv 2025年

作者： Jian, Yugen Luo, Ke Chen, Jincai Fong, Xuanyao Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan430074 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore School of Computer of Science and Technology Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China Wuhan430074 China

Three-dimensional magnetic recording (3DMR) is a highly promising approach to achieving ultra-large data storage capacity in hard disk drives. One of the greatest challenges for 3DMR lies in performing sequential and correct writing of bits into the multi-layer recording medium. In this work, we have proposed a hierarchical recording architecture based on layered heat-assisted writing with a multi-head array. The feasibility of the architecture is validated in a dual-layer 3DMR system with FePt-based thin films via micromagnetic simulation. Our results reveal the magnetization reversal mechanism of the grains, ultimately attaining appreciable switching probability and medium signal-to-noise ratio (SNR) for each layer. In particular, an optimal head-to-head distance is identified as the one that maximizes the medium SNR. Optimizing the system’s noise resistance will improve the overall SNR and allow for a smaller optimal head-to-head distance, which can pave the way for scaling 3DMR to more recording layers. Copyright © 2025, The Authors. All rights reserved.

关键词： Hard disk storage

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Dual Distillation Fusion for Weakly Supervised Anomaly Detection in Surveillance Videos

引用

IEEE Transactions on Circuits and systems for Video Technology 2025年

作者： Zhang, Sijia Gong, Maoguo Lin, Yiming Li, Hao Gao, Yuan Zhang, Yihong Shaanxi Normal University School of Computer Science Xi'an China Xidian University School of Electronic Engineering Key Laboratory of Collaborative Intelligence Systems Ministry of Education Xi'an China Inner Mongolia Normal University Academy of Artificial Intelligence College of Mathematics Science Hohhot China

Anomaly detection in surveillance videos aims to differentiate anomalies from regular events by discriminative representations, which has gathered considerable attention due to its significant effect to public security. However, most existing works are limited in the lack of annotated samples, and lots of approaches find it challenging to avoid the well-reconstruction of anomalous data. To alleviate these issues, we propose a dual distillation fusion framework for weakly supervised anomaly detection. We reformulate the anomaly detection problem into two steps, namely filtering anomalies and inpainting normal patterns. Each step corresponds to one branch of the dual distillation. Specifically, the dual distillation comprises the contrastive distillation module and the inpainting distillation module. The contrastive distillation optimizes the encoder to filter out abnormal features and capture key normal features, while the inpainting distillation refines the decoder to inpaint normal patterns on the encoded features. The contrastive distillation module and the inpainting distillation module are optimized iteratively in a self-training manner with video-level labeled data. Moreover, a joint optimization module is devised to effectively fuse the distilled encoder and decoder, thereby collectively improving the anomaly detection performance. During the training phase, we take into account the diversity of normal samples by selecting pseudo normal and abnormal samples with high confidence from abnormal videos. These selected samples, along with original normal frames, are then fed into the subsequent training iterations to enhance the distinguishing ability of the model. Experimental results show that our proposed method performs competitively on five benchmark datasets. © 1991-2012 IEEE.

关键词： Iterative decoding

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Fine-Tuning the BERT Model to Predict Depression and Anxiety Using Multi-Labeled Twitter Data

Fine-Tuning the BERT Model to Predict Depression and Anxiety...

引用

Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Lamia Bendebane Zakaria Laboudi Asma Saighi Seif Eddine Bouziane Research Laboratory on Computer Science's Complex Systems (ReLa(CS)2) University of Oum El Bouaghi Algeria Laboratory of Artificial Intelligence and Autonomous Things (LIAOA) University of Oum El Bouaghi Algeria Department of Intelligent Systems Engineering National School of Artificial Intelligence Algiers Algeria

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Studying mental health through social media data has become an emerging area of research, notably for the detection of depression and anxiety. In this regard, many researches have been conducted, yielding very satisfactory results (e.g., [1]–[3]). However, most of these studies have addressed each of these two mental disorders separately. This is due to the overlap of symptoms associated with depression and anxiety that makes distinguishing between them significantly challenging. Based on this context, this work leverages pretrained large language models (LLMs) to develop efficient multi-class models for predicting both depression and anxiety. For this purpose, a small, multi-labeled Twitter dataset is first constructed. Then, a pre-trained BERT-based uncased model is fine-tuned for six epochs on the dataset, resulting in a model referred to as DAC-BERT. Finally, the DAC-BERT model is evaluated and compared against both hybrid deep learning models and other LLM-based models. The obtained results show that DAC-BERT model outperforms existing approaches, achieving accuracies of up to 97.20% on a multi-labeled dataset containing normal, depressive, and anxious tweets, and up to 96.50% on a dataset with only normal and depressive tweets. These findings highlight the promising potential of fine-tuning LLMs for predicting mental health disorders, particularly depression and anxiety.

关键词： Deep learning Sentiment analysis Social networking (online) Large language models Anxiety disorders Blogs Mental health Predictive models Depression Data models

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