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9th International Conference on Cloud computing and Internet of Things, CCIOT 2024

作者： Jiao, Zhilong Bai, Fenhua Zhang, Chi Zhang, Xiaohui Gong, Bei Faculty of Information Engineering and Automation Kunming University of Science and Technology China Kunming China Department of Computing Beijing University of Technology China Beijing China

ISBN: (纸本)9798400717161

Since most communications in vehicular networks rely on wireless transmissions, information broadcast by vehicles is highly susceptible to interception and eavesdropping by third parties, making it vulnerable to various attacks. This can result in communication messages being tampered with and the leakage of vehicle locations and identity privacy. From a security perspective, this paper introduces Yao's garbled circuit mechanism, combining random bytes with the solutions of linear equations as identity information assigned to vehicle nodes. By employing dual authentication, the proposed approach addresses issues such as privacy protection, anonymity, replay attacks, and collusion attacks in vehicular networks, thereby enhancing security. Moreover, considering the post-authentication access control scenario, this paper proposes a novel LSSS matrix transformation algorithm for ciphertext hidden access control strategy. It realizes a partially hidden policy, which makes matrix generation more efficient by supporting both AND and OR gates, thus improving the access control efficiency while ensuring security. Performance analysis indicates that in terms of identity authentication, the scheme reduces computational costs by an average of 96.25%, and in access control, the scheme reduces key generation time by an average of 80%. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Ciphertext

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Enhancing Classification Algorithm Recommendation in Automated Machine Learning: A Meta-Learning Approach Using Multivariate Sparse Group Lasso

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Computer Modeling in engineering & Sciences 2025年第2期142卷 1611-1636页

作者： Irfan Khan Xianchao Zhang Ramesh Kumar Ayyasamy Saadat M.Alhashmi Azizur Rahim School of Software Dalian University of TechnologyDalian116620China Faculty of Information and Communication Technology Universiti Tunku Abdul RahmanKampar31900Malaysia College of Computing and Informatics University of SharjahSharjah27272United Arab Emirates Department of Computer Systems Engineering University of Engineering and Applied SciencesSwat19200Pakistan

The rapid growth of machine learning(ML)across fields has intensified the challenge of selecting the right algorithm for specific tasks,known as the Algorithm Selection Problem(ASP).Traditional trial-and-error methods have become impractical due to their resource *** Machine Learning(AutoML)systems automate this process,but often neglect the group structures and sparsity in meta-features,leading to inefficiencies in algorithm recommendations for classification *** paper proposes a meta-learning approach using Multivariate Sparse Group Lasso(MSGL)to address these *** method models both within-group and across-group sparsity among meta-features to manage high-dimensional data and reduce multicollinearity across eight meta-feature *** Fast Iterative Shrinkage-Thresholding Algorithm(FISTA)with adaptive restart efficiently solves the non-smooth optimization *** validation on 145 classification datasets with 17 classification algorithms shows that our meta-learning method outperforms four state-of-the-art approaches,achieving 77.18%classification accuracy,86.07%recommendation accuracy and 88.83%normalized discounted cumulative gain.

关键词： Meta-learning machine learning automated machine learning classification meta-features

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Sizing and Implementation of Phase-Shifting Transformer for Congestion Management in Renewable-Rich Regions: A Case Study of the Croatian Power Grid

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IEEE Transactions on Power Systems 2025年

作者： Hrgović, Ivana Pavić, Ivica University of Zagreb Faculty of Electrical Engineering and Computing Zagreb10000 Croatia

Phase shifting transformers (PSTs) are a cost-efficient solution for controlling power flow without additional operating expenses. They can enhance power distribution in transmission systems and increase system capacity, particularly for renewable energy integration. However, existing methods for PST sizing and allocation often rely on DC power flow models, which may lack accuracy, and are typically tested on simplified networks that don't fully capture real-world conditions. Furthermore, these methods often overlook practical challenges, such as space limitations in substations. This paper presents a new methodology for PST sizing and allocation in real power networks. Unlike previous approaches, our method is based on AC power flow calculations and takes into account system requirements, space constraints, and economic factors. The goal is to determine the optimal type, size, phase angle regulation range, and location of PSTs to prevent system overloading under normal and N-1 system conditions. © 1969-2012 IEEE.

关键词： congestion management phase-shifting transformer PSDF

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Exploring information hiding in images – problems and measures: a survey

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Multimedia Tools and Applications 2025年 1-63页

作者： Khan, Ahmed Sarfaraz, Aaliya School of Computer Science Taylor’s University Subang Jaya Malaysia School of Computing and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Selangor Darul Ehsan Bandar Sunway47500 Malaysia

The widespread adoption of digital communication technologies across various domains has led to a significant shift towards digitalization. However, this evolution has also introduced vulnerabilities, including unauthorized access, data manipulation, and fabrication by malicious entities, which severely impact the confidentiality and integrity of information. The proliferation of digital images on the internet has facilitated the use of these media for embedding confidential data in a way that prevents detection or extraction by unauthorized third parties. Despite numerous efforts, existing image steganography and watermarking techniques often lack robustness. This paper proposes several novel strategies to enhance the dynamism and imperceptibility of these techniques, integrating cryptographic methods by manipulating the entropy of encrypted images using minimal pixel value differences and applying unique image transformations. Additionally, the study highlights that implementing multi-layered security mechanisms can protect confidential information from modern statistical steganalysis attacks, thereby preserving both the integrity of hidden data and image quality. A comprehensive review of information hiding, specifically focusing on image watermarking and steganography, is provided. Furthermore, this survey outlines key problem statements and offers quantitative metrics for future research in this area. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Steganography

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Level Up with ML Vulnerability Identification: Leveraging Domain Constraints in Feature Space for Robust Android Malware Detection

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ACM Transactions on Privacy and Security 2025年第2期28卷 1-32页

作者： Bostani, Hamid Zhao, Zhengyu Liu, Zhuoran Moonsamy, Veelasha Digital Security Group Institute for Computing and Information Sciences Radboud University Nijmegen Netherlands Faculty of Electronic and Information Engineering Xi'an Jiaotong University Xi'an China Horst Görtz Institute for It Security Ruhr University Bochum Bochum Germany

Machine Learning (ML) promises to enhance the efficacy of Android Malware Detection (AMD);however, ML models are vulnerable to realistic evasion attacks - crafting realizable Adversarial Examples (AEs) that satisfy Android malware domain constraints. To eliminate ML vulnerabilities, defenders aim to identify susceptible regions in the feature space where ML models are prone to deception. The primary approach to identifying vulnerable regions involves investigating realizable AEs, but generating these feasible apps poses a challenge. For instance, previous work has relied on generating either feature-space norm-bounded AEs or problem-space realizable AEs in adversarial hardening. The former is efficient but lacks full coverage of vulnerable regions, whereas the latter can uncover these regions by satisfying domain constraints but is known to be time consuming. To address these limitations, we propose an approach to facilitate the identification of vulnerable regions. Specifically, we introduce a new interpretation of Android domain constraints in the feature space, followed by a novel technique that learns them. Our empirical evaluations across various evasion attacks indicate effective detection of AEs using learned domain constraints, with an average of 89.6%. Furthermore, extensive experiments on different Android malware detectors demonstrate that utilizing our learned domain constraints in adversarial training outperforms other adversarial training based defenses that rely on norm-bounded AEs or state-of-the-art non-uniform perturbations. Finally, we show that retraining a malware detector with a wide variety of feature-space realizable AEs results in a 77.9% robustness improvement against realizable AEs generated by unknown problem-space transformations, with up to 70× faster training than using problem-space realizable AEs. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Android malware

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A New Benchmark and Algorithm for Clothes-Changing Video Person Re-Identification

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IEEE Transactions on Information Forensics and Security 2025年 20卷 1993-2005页

作者： Wang, Likai Zhang, Xiangqun Han, Ruize Wei, Yanjie Wang, Song Feng, Wei Tianjin University College of Intelligence and Computing Tianjin300350 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518107 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

Person re-identification (Re-ID) is a classical computer vision task and has significant applications for public security and information forensics. Recently, long-term Re-ID with clothes-changing has attracted increasing attention. However, existing methods mainly focus on image-based setting, where richer temporal information is overlooked. In this paper, we focus on the relatively new yet practical problem of Clothes-Changing Video-based Re-ID (CCVReID), which is less studied. First, given the dataset shortage, we build two new benchmark datasets for CCVReID problem, including a large-scale synthetic video dataset and a real-world one, both containing human sequences with various clothing changes. Moreover, we systematically study this problem by simultaneously considering the classical appearance feature and temporal feature contained in the video. We develop a dual-branch fusion framework that makes use of the information from both clothes-aware appearance feature and clothes-free gait feature. For better information fusion, a confidence-guided re-ranking strategy is proposed to adaptively balance the weight of these two categories of features. We have released the benchmark and code proposed in this work to the public at https://***/kkw98/CCVReID. © 2025 IEEE.

关键词： Reamers

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Dialect recognition based on improved GFCC with VMD and Teager energy operator cepstral coefficients 16

Dialect recognition based on improved GFCC with VMD and Teag...

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16th International Conference on Signal Processing Systems, ICSPS 2024

作者： Zhang, Liyan Qi, Chao Liu, Zengli Zhao, Xuanzhi Rong, Shaowei Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Kunming University of Science and Technology Computing Center Kunming650500 China

ISBN: (纸本)9781510689251

Aiming at the problems of low recognition rate of Chinese dialects and poor robustness in noisy environment, this paper proposes an improved feature extraction algorithm, which combines the Sparrow Search Algorithm (SSA) and Variational Mode Decomposition (VMD) with the improved Gamma-tone Frequency Cepstral Coefficients (GFCC) and Teager Energy Operator Cepstral Coefficient (TEOCC). Firstly, the VMD parameters are optimized by SSA, the dialect signal is decomposed into different modal components, and the correlation coefficients with the original signal are calculated. Then, the component of the dominant signal is screened according to the correlation, which is used to calculate the GFCC characteristics. Subsequently, the improved GFCC features are fused with TEOCC, and Principal Component Analysis (PCA) is used to reduce the dimension of the fusion features. Finally, the Gaussian mixture model-universal background model (GMM-UBM) is used for dialect recognition. The experimental results show that the proposed method can improve the recognition rate compared with the traditional GFCC and single feature in both noise-free and noise-free environments, and verify the effectiveness of the improved algorithm in dialect recognition. © 2025 SPIE.

关键词： Variational mode decomposition

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Participation of electric vehicle charging station aggregators in the day-ahead energy market using demand forecasting and uncertainty-based pricing

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Energy 2025年 328卷

作者： Matkovic, Daria Pilski, Terezija Matijasevic Capuder, Tomislav University of Zagreb Faculty of Electrical Engineering and Computing Croatia

This paper introduces a novel approach to the smart management of public EV charging infrastructure, combining day-ahead energy bidding with a dynamic end-user pricing model. It addresses critical challenges such as demand fluctuations and uncertainties in the day-ahead market while minimizing waiting times and maximizing profit and load distribution. Although charging prices do not need to directly mirror wholesale day-ahead market prices, they are based on these prices due to their availability and market relevance. Day-ahead energy procurement offers advantages such as liquidity and price stability;however, forecast errors can lead to overprocurement, negatively impacting profitability. To mitigate this, a pricing model that accounts for forecast uncertainty is proposed, ensuring profitability during demand fluctuations by setting higher prices during periods of greater uncertainty. Additionally, when a preferred station is occupied, the model offers lower prices at underutilized stations, improving load distribution and reducing waiting times. The proposed approach is compared to benchmark models, demonstrating improvements in load distribution (7.79%), reduced waiting times (83.02%), and increased profitability (27.81%). These results contribute to an enhanced user experience and more efficient use of public infrastructure, showcasing the effectiveness of the strategy in optimizing energy procurement and pricing for smart public charging infrastructure. © 2025 Elsevier Ltd

关键词： Electric rates

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How Much Do Activity Patterns Change With Age? 6th

How Much Do Activity Patterns Change With Age?

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6th International Conference on Biomedical and Health Informatics, ICBHI 2024

作者： Jurčić, Krunoslav Gómez, Andrea Juliana Ruiz Zequera-Diaz, Martha Magjarević, Ratko Faculty of Electrical Engineering and Computing University of Zagreb Unska 3 Zagreb10000 Croatia School of Engineering BASPI/FootLab Pontificia Universidad Javeriana Cra. 7 #40–62 Bogotá Colombia

ISBN: (纸本)9783031863226

This paper presents a comparison of features derived from various biomedical signals from both younger as well as elderly subjects. The dataset consists of accelerometer, gyroscope and altimeter signals acquired from wearable sensors, describing three different activities of daily living: walking, running and sitting on a chair. The features of all three signal data were processed, and later analysed to determine whether significant differences between signals recorded by younger subjects and elder subjects are present. Results of this comparison could prove valuable in the field of signal processing for various applications (activity recognition, fall detection, elderly care etc.) in terms of rehabilitation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Aneroid altimeters

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SV-data2vec: Guiding Video Representation Learning with Latent Skeleton Targets

SV-data2vec: Guiding Video Representation Learning with Late...

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Zorana Doždor Tomislav Hrkać Zoran Kalafatić Faculty of Electrical Engineering and Computing University of Zagreb Croatia

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Recent advancements in action recognition leverage both skeleton and video modalities to achieve state-of-the-art performance. However, due to the challenges of early fusion, which tends to underutilize the strengths of each modality, existing methods often resort to late fusion, consequently leading to more complex designs. Additionally, self-supervised learning approaches utilizing both modalities remain underexplored. In this paper, we introduce SV-data2vec, a novel self-supervised framework for learning from skeleton and video data. Our approach employs a student-teacher architecture, where the teacher network generates contextualized targets based on skeleton data. The student network per-forms a masked prediction task using both skeleton and visual data. Remarkably, after pretraining with both modalities, our method allows for fine-tuning with RGB data alone, achieving results on par with multimodal approaches by effectively learning video representations through skeleton data guidance. Extensive experiments on benchmark datasets NTU RGB+D 60, NTU RGB+D 120, and Toyota Smarthome confirm that our method outperforms existing RGB based state-of-the-art techniques. The code is available at github. com/zoranadozdor/SVdata2vec.

关键词： Representation learning Visualization Computer vision Codes Self-supervised learning Computer architecture Benchmark testing Skeleton Software development management

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