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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Zhou, Ziqi Song, Yufei Li, Minghui Hu, Shengshan Wang, Xianlong Zhang, Leo Yu Yao, Dezhong Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Computer Science and Technology Huazhong University of Science and Technology China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

Segment Anything Model (SAM) has recently gained much attention for its outstanding generalization to unseen data and tasks. Despite its promising prospect, the vulnerabilities of SAM, especially to universal adversarial perturbation (UAP) have not been thoroughly investigated yet. In this paper, we propose DarkSAM, the first prompt-free universal attack framework against SAM, including a semantic decoupling-based spatial attack and a texture distortion-based frequency attack. We first divide the output of SAM into foreground and background. Then, we design a shadow target strategy to obtain the semantic blueprint of the image as the attack target. DarkSAM is dedicated to fooling SAM by extracting and destroying crucial object features from images in both spatial and frequency domains. In the spatial domain, we disrupt the semantics of both the foreground and background in the image to confuse SAM. In the frequency domain, we further enhance the attack effectiveness by distorting the high-frequency components (i.e., texture information) of the image. Consequently, with a single UAP, DarkSAM renders SAM incapable of segmenting objects across diverse images with varying prompts. Experimental results on four datasets for SAM and its two variant models demonstrate the powerful attack capability and transferability of DarkSAM. Our codes are available at: https://***/CGCL-codes/DarkSAM. © 2024 Neural information processing systems foundation. All rights reserved.

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Linear Distribution system State Estimation with Synchrophasor Measurements and Voltage-Dependent Load Model

Linear Distribution System State Estimation with Synchrophas...

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Zargar, Behzad Ferdowsi, Mohsen Su, Jinshun Li, Yifu Dehghanian, Payman Hibberts-Caswell, Richard Fichtner GmbH & Co. KG Electrical System Studies Department Stuttgart70191 Germany Siemens Grid Software Nurnberg90459 Germany The George Washington University Department of Electrical & Computer Engineering WashingtonDC20025 United States

ISBN: (纸本)9798331541125

Distribution system state estimation (DSSE) is a critical enabler for distribution system operators (DSOs) to manage active distribution grids efficiently. Timely detection of state changes requires fast-executing state estimation techniques, yet the prevalent nonlinear weighted least squares approach, though effective, presents computational challenges. Linearizing inherent nonlinearities is a way to expedite DSSE solutions. This paper proposes a linear DSSE method that leverages synchrophasor measurements and a linear voltage-dependent load model for pseudo-measurements. This approach provides a more accurate representation of load response to voltage variations, yielding more realistic estimation results than methods using conventional constant active and reactive load models. © 2025 IEEE.

关键词： DC distribution systems

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CASKA-CRT: Chinese Remainder Theorem Empowered Certificateless Aggregate Signcryption Scheme with Key Agreement in IoVs

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-16页

作者： Hou, Yingzhe Cao, Yue Xiong, Hu Sun, Zhili Mumtaz, Shahid Tian, Daxin School of Cyber Science and Engineering Wuhan University Wuhan China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Institute of Communication Systems University of Surrey Guildford U.K Department of Applied Informatics Silesian University of Technology Akademicka Gliwice Poland State Key Laboratory of Vehicle-Infrastructure Integrated ITS the State Key Laboratory of Intelligent Transportation System and the School of Transportation Science and Engineering Beihang University Beijing China

To enhance the privacy of messages in Internet of Vehicles (IoVs), it is critical to preserve the communication security between Road Side Unit (RSU) and numerous vehicles. The primitive of signcryption is introduced to guarantee the confidentiality, integrity and unforgeability of transmitted messages. Nevertheless, the existing signcryption schemes fail to achieve a balance between security and efficiency. In this paper, we construct a Chinese remainder theorem (CRT) empowered certificateless aggregate signcryption scheme with key agreement (CASKA-CRT). As the vehicle joins and leaves dynamically, the proposed scheme can ensure the dynamic security via one modulo division operation. Besides, the certificateless aggregate signcryption and key agreement mechanisms are embedded. The former idea can both address the certificate management and key escrow problems, while the latter technology can create authentication as a premise of secure communication. Based on this construction, the hash-to-group operation and bilinear pairing are avoided, to realize a faster verification with the increased number of messages. Moreover, the security of proposed scheme is proved under the random oracle model. Finally, the performance analysis demonstrates the advantage of CASKA-CRT in terms of security and reliability over related works. IEEE

关键词： Public key cryptography

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An Adaptive Trust Evaluation Model for IOT Nodes 4

An Adaptive Trust Evaluation Model for IOT Nodes

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4th International Conference on Frontiers Technology of Information and Computer, ICFTIC 2022

作者： Ren, Xiaohui Li, Da Guo, Ting Cui, Jiayang School of Software Beijing University of Technology Beijing100124 China Beijing Internets of Things Software and System Engineering Technology Research Center Beijing University of Technology Beijing100124 China

ISBN: (纸本)9798350321951

Aiming at the shortcomings of the existing trust evaluation model of IOT nodes, such as poor effect, low accuracy, and difficulty in dealing with malicious node fraud, a dynamic trust evaluation model DATEM based on IOT nodes is proposed. In the direct trust calculation, considering the fault-tolerant and consistent data transmission of adjacent nodes and the impact of node energy on transmission, the dynamic reward and punishment factor is used to accelerate the change rate of trust value when malicious acts occur, and improve the competitiveness of normal nodes;In the calculation of recommendation trust, K-means clustering algorithm is used to filter recommendation nodes and shield false recommendations from malicious nodes;In the calculation of comprehensive trust degree, the weight of trust value is determined according to the sufficiency of node trust queue and the dispersion of direct trust value in the queue;The simulation results show that DATEM has a faster response to data attacks and a better ability to identify and constrain fraud. © 2022 IEEE.

关键词： Internet of things

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Human Resource Application Development using Person-Job Matching for Placement of Prospective Employees 5

Human Resource Application Development using Person-Job Matc...

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5th International Conference on Advancement in Data Science, E-Learning and Information systems, ICADEIS 2023

作者： Putri, Alifiah Suakanto, Sinung Siswanto, Joko Arifin, Farah Fitriavida Darmawan, Irfan Angela, Dina Telkom University Faculty of Software Engineering Department of Information System Bandung Indonesia Bandung of Institute Technology Industrial Engineering Research Group Bandung Indonesia Faculty of Industrial Engineering Institut Teknologi Harapan Bangsa Information Techology Research Group Bandung Indonesia

ISBN: (纸本)9798350303414

The recruitment process presents a significant challenge for Human Resource (HR) staff, particularly in their ability to objectively assess candidate competencies and align them with job requirements. While qualitative comparisons between resumes and position requirements may yield candidates who appear suitable, this approach proves ineffective in terms of time and cost. Furthermore, many recruiters lack sufficient knowledge of the positions they are hiring for, making it difficult to justify their selection decisions. This research aims to address this challenge by developing an HR person-job matching application for the recruitment of prospective employees. The study focuses on analyzing and designing applications that can quantitatively measure the competencies of candidates and compare them with job requirements using a person-job matching method. The effectiveness of this approach was evaluated by testing it on a sample of employees from small and medium-sized enterprises (SMEs). The study found that out of the 16 employees across three job positions, the person-job matching application achieved an accuracy rate of 75%. This accuracy percentage was determined by comparing the fit value results with the actual conditions of the employees in their respective workplaces. The findings highlight the potential of the HR person-job matching application to enhance the efficiency and effectiveness of recruitment efforts. © 2023 IEEE.

关键词： Job satisfaction

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Efficiency of Adding a New Template to Network Management system and Zabbix Application

Efficiency of Adding a New Template to Network Management Sy...

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Human-Computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Mehmet Kara Gökhan Tuğrul Software Engineering Kocaeli University of Health and Technology Kocaeli Turkey Information System Engineering Kocaeli University of Health and Technology Kocaeli Turkey

ISBN: (数字)9798331510886

ISBN: (纸本)9798331510893

In today's world, where institutional business processes are transferred to electronic environments, the real-time monitoring and management of software and hardware assets in institutional information systems are of great importance for ensuring business continuity. Information system software and hardware are managed using network management software. Due to the presence of numerous different brands of network devices, software, IoT devices, mobile computing devices, and cloud computing devices, network management software cannot recognize every type of device. Therefore, there is a need for methods that can efficiently and quickly introduce new and different devices to network management *** the scope of this article, approaches to adding software and hardware to the open-source network management system Zabbix are analyzed. Templator tool was analyzed. The study examines how software and hardware can be quickly introduced by creating templates and provides an application of this process.

关键词： Temperature sensors Temperature measurement software Hardware Business continuity Reliability Monitoring Robots Information systems Business

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Hadiyyissa Automatic Speech Recognition Using Deep Learning Approach 5

Hadiyyissa Automatic Speech Recognition Using Deep Learning ...

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5th International Conference on Information and Communication Technology for Development for Africa, ICT4DA 2023

作者： Shamore, Selamu Gure, Amin Tuni Yimer, Mohammed Abebe Wachemo University Department of Computer Science Hossana Ethiopia Arsi University Department of Information System Arsi Ethiopia Arba Minch University Faculty of Computing and Software Engineering Arba Minch Ethiopia

ISBN: (纸本)9798350328486

Speech Recognition focuses on developing advanced technologies that can recognize and translate spoken language into text. Automatic Speech Recognition (ASR) is an Artificial Intelligence (AI) technology that captures human voice from a mi-crophone and analyzes it based on spoken words to comprehend natural language. In Ethiopia, ASR models have been developed for specific local languages such as Amharic, Tigrigna, Wolaytta, and Afaan Oromo, but there has been a lack of research on ASR models for the Hadiyyisa language, which is also spoken in Ethiopia. The research study designed and developed an ASR model for the Hadiyyisa language. The researchers collected data from native speakers of the Hadiyyisa language and used Python programming to design and develop the deep learning model for the Hadiyissa ASR model. Different methods and techniques were used in the study to achieve the research objectives and complete the entire research process. The proposed model utilized deep learning algorithms including RNN, LSTM, BiLSTM, and CNN, along with suitable feature extraction techniques such as Mel Frequency Cepstral Coefficients (MFCC). The accuracy of the proposed ASR model was evaluated through experimentation, which demonstrated that the BiLSTM model outperformed the other deep learning models with an overall accuracy of 90.95 % Based on the experimental evidence, the proposed model was found to be better suited for ASR for the Hadiyyisa language. © 2023 IEEE.

关键词： Speech recognition

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PERIODICITY DECOUPLING FRAMEWORK FOR LONG-TERM SERIES FORECASTING 12

PERIODICITY DECOUPLING FRAMEWORK FOR LONG-TERM SERIES FORECA...

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12th International Conference on Learning Representations, ICLR 2024

作者： Dai, Tao Wu, Beiliang Liu, Peiyuan Li, Naiqi Bao, Jigang Jiang, Yong Xia, Shu-Tao College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China WeBank Institute of Financial Technology Shenzhen University China

Convolutional neural network (CNN)-based and Transformer-based methods have recently made significant strides in time series forecasting, which excel at modeling local temporal variations or capturing long-term dependencies. However, real-world time series usually contain intricate temporal patterns, thus making it challenging for existing methods that mainly focus on temporal variations modeling from the 1D time series directly. Based on the intrinsic periodicity of time series, we propose a novel Periodicity Decoupling Framework (PDF) to capture 2D temporal variations of decoupled series for long-term series forecasting. Our PDF mainly consists of three components: multi-periodic decoupling block (MDB), dual variations modeling block (DVMB), and variations aggregation block (VAB). Unlike the previous methods that model 1D temporal variations, our PDF mainly models 2D temporal variations, decoupled from 1D time series by MDB. After that, DVMB attempts to further capture short-term and long-term variations, followed by VAB to make final predictions. Extensive experimental results across seven real-world long-term time series datasets demonstrate the superiority of our method over other state-of-the-art methods, in terms of both forecasting performance and computational efficiency. Code is available at https://***/Hank0626/PDF. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Time series

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Processing of Nonlinear Heterogeneous Data in Information and Control systems

Processing of Nonlinear Heterogeneous Data in Information an...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Yu.S. Shevnina S. F. Tsarapkin Si Thu Thant Sin Institute of System and Software Engineering and Information Technologies National Research University MIET Zelenograd Moscow Russia

The article presents a method for cluster analysis of heterogeneous data using the provisions of fuzzy logic. Heterogeneous are data of various formats and collected from various sources. In addition, such data is usually incomplete and inaccurate, which makes it difficult to process and cluster them. The article presents mathematical models for representing a candidate for a vacant position, which is characterized by heterogeneous data. The apparatus of algebraic systems was used to develop mathematical models. A method for determining the membership function of fuzzy sets using a probabilistic approach, as the most effective when working with heterogeneous data, is described in detail. An example of the formation of a base of logical rules for the formation and selection of classification features in a set of heterogeneous data of the personnel reserve of a manufacturing enterprise is given. The selected classification features allow for further accurate and efficient verification and evaluation of information about candidates for a vacant position. In addition, the proposed method of cluster analysis of heterogeneous data can be applied in various subject areas that involve the use of incomplete and inaccurate data, for example, socio-economic, technical, biological systems.

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Attention Enhanced Feature Fusion for Mental Stress Level Classification 21

Attention Enhanced Feature Fusion for Mental Stress Level Cl...

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21st International Computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Sahlu, Ameneshewa Abush Yohannes, Gobena Mulugeta Dawit, Bekalu Nigus Teshome, Molla Woretaw School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu China School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu China

ISBN: (纸本)9798331519254

Mental stress poses significant health risks, manifesting in various psychological and physical issues such as depression, anxiety, and cardiovascular complications. Establishing a reliable method for swiftly and accurately classifying mental stress levels is crucial due to its widespread impact. Existing classification approaches predominantly rely on either data-intensive traditional machine learning algorithms or opaque deep learning models, hindering real-world use and interpretability. This study addresses these limitations by proposing a novel approach for stress level classification using the WESAD dataset. Our method combines an attention-based ResN et with CBAM. This innovative approach synergistically integrates shallow and deep features, harnessing both low-level details and high-level semantic information to effectively classify stress levels into four distinct states: baseline, stress, amusement, and meditation, or three states excluding the meditation state. The experimental result shows 92.74% accuracy and 91.19% F1 score for the 4-class setup, and 97.58% accuracy and 97.51 % F1 score for the 3-class setup. Our approach offers a more robust and interpretable framework for mental stress classification, with potential applications in health care and stress management. © 2024 IEEE.

关键词： Deep learning

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