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A Binary Code Similarity Detection Method Based on Multi-source Contrastive Learning 20th

A Binary Code Similarity Detection Method Based on Multi-sou...

引用

20th International conference on Information Security and Cryptology, Inscrypt 2024

作者： Yang, Hongyu Wang, Yunlong Hu, Ze Cheng, Xiang School of Computer Science and Technology Civil Aviation University of China Tianjin300300 China School of Safety Science and Engineering Civil Aviation University of China Tianjin300300 China School of Information Engineering Yangzhou University Yangzhou225127 China Information Security Evaluation Center of Civil Aviation Civil Aviation University of China Tianjin300300 China

ISBN: (纸本)9789819647309

Existing binary code similarity detection (BCSD) methods often overlook the actual execution information and local semantic details of programs, leading to suboptimal performance in assembly code semantic representation learning, high training resource consumption, and poor similarity detection performance. To address these issues, this paper proposes a Multi-Source Coordinated Representation Learning (MSRL) method for binary code similarity detection. First, we extract the semantic correspondence between assembly instruction sequences and programming language fragments to construct a contrastive learning dataset. We then propose an assembly code and source code semantic alignment (ACSA) method, which uses the high-level semantics of source code as supervisory information. Through contrastive learning tasks, we align the feature representations of the ACSA-Asm encoder and the programming language encoder in the semantic space, thereby enhancing the semantic representation learning capability of ACSA-Asm for assembly instructions. Next, we design a graph-based binary function embedding (GBFE) method, which uses a semantic structure-aware network to fuse the semantic information extracted by ACSA-Asm with the actual execution information of the program, generating function embedding vectors for similarity detection. Experimental results show that, compared to existing methods, MSRL improves the Recall@1 metric for binary code similarity detection by 8%–33%. Additionally, in the context of code obfuscation, MSRL exhibits stronger resilience, with less degradation in the Recall@1 metric. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep neural networks

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A high-performance Grid-Tied Three-Level T-Type Converter for Off-Board Battery Chargers under AC-Side Voltage Distortions and Unbalances 19

A High-Performance Grid-Tied Three-Level T-Type Converter fo...

引用

19th International conference on Electrical Engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON 2022

作者： Thanakam, Thidarat Kumsuwan, Yuttana Chiang Mai University Graduate Program in Electrical Engineering Department of Electrical Engineering Faculty of Engineering Chiang Mai Thailand Chiang Mai University Faculty of Engineering Department of Electrical Engineering Chiang Mai Thailand

ISBN: (纸本)9781665485845

The conversions are a key factor in the battery charging of electric vehicles. The off-board battery chargers, which are composed of converters and controller units, are needed to fast charge by transferring high-power. The converters should serve applications even with unusual grid systems where non-sinusoidal grid currents occur. This paper describes a high-performance, three-level T-type converter for off-board battery chargers. It can operate under conditions of grid voltage distortions and grid voltage unbalances. The proposed control algorithm provides the three-phase sinusoidal currents to prevent their effect on the grid system and the equipment, which is an improvement from the conventional control algorithm. The constant current and constant voltage modes are applied for charging applications. Moreover, the power factor correction can be obtained. The simulation results are provided to investigate the performance of the off-board battery charger based on the three-level T-type converter. © 2022 IEEE.

关键词： Charging (batteries)

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performance Analysis of Space-Air-Ground Integrated Network (SAGIN): UAV Altitude and Position Angle

Performance Analysis of Space-Air-Ground Integrated Network ...

引用

2023 IEEE/CIC International conference on Communications in China, ICCC 2023

作者： Tan, Jingjing Tang, Fengxiao Zhao, Ming Kato, Nei Central South University School of Computer Science and Engineering Changsha410075 China Tohoku University The Graduate School of Information Sciences Sendai98008459 Japan

ISBN: (纸本)9798350345384

With the explosive growth of mobile data traffic, the Space-Air-Ground integrated network (SAGIN) has attracted the attention of academia and industry. As the communication infrastructure in the air network of the SAGIN system, Unmanned Aerial Vehicles (UAVs) are considered the air relay nodes to access ground and space networks. Due to the mobility of satellites in the space network, the formed variable position angle between the UAV and the satellite and the fixed UAV altitude can reduce the air-to-space transmission capability. Moreover, a UAV's battery is usually very limited, which may cause SAGIN transmission energy interruptions when the battery runs out. To address these issues in the SAGIN environment, we propose an integrated UAV channel fading and UAV energy consumption model to analyze the transmission performance. Based on the network performance analysis results, we derive the optimal UAV altitude expression. The numerical results verify the accuracy of the derived expressions and evaluate the impact of some key parameters on the optimal UAV altitude in the SAGIN uplink. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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A Distributed Learning Method for Deep Echo State Network Based on Improved SVD and ADMM 20

A Distributed Learning Method for Deep Echo State Network Ba...

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20th IEEE International conference on Mobile Ad Hoc and Smart Systems, MASS 2023

作者： Zhu, Meifeng Zhou, Jian Cai, Hui He, Xin Xiao, Fu Nanjing University of Posts and Telecommunications College of Computer Nanjing210003 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing210003 China

ISBN: (纸本)9798350324334

In recent years, echo state network (ESN) has emerged as an effective way in processing the classification and regression of time series. As a combination of deep learning and ESN, deep echo state network (DESN) naturally improves the performance of shallow ESN. However, two key challenges have to be addressed to achieve the training of DESN in edge environments. On the one hand, a high-dimensional reservoir state matrix is generated when training DESN with a large amount of data, which causes to the high computation cost. On the other hand, it probably produces the delay, privacy concerns when transmitting data from distributed edge nodes to a centralized location. To solve the above challenges, we propose a distributed learning method for DESN based on improved singular value decomposition (SVD) and alternating direction method of multipliers (ADMM). First, a decomposition algorithm for reservoir state matrix based on improved SVD is proposed to train DESN only using the main features of reservoir state matrix, thus reduces the computation cost. Second, a distributed training algorithm for DESN based on improved ADMM is proposed by using dispersed data to train DESN fast, which produces the less communication cost. Finally, the experimental results in theoretical and real-world datasets show that the proposed method can effectively reduce the computation and communication costs in the learning process of DESN while suffering an acceptable performance loss, compared with other distributed learning methods. © 2023 IEEE.

关键词： Singular value decomposition

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A Loop Optimization Method for Dataflow Architecture 24

A Loop Optimization Method for Dataflow Architecture

引用

24th IEEE International conference on high performance Computing and Communications, 8th IEEE International conference on Data Science and Systems, 20th IEEE International conference on Smart City and 8th IEEE International conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Fan, Zhihua Li, Wenming Liu, Tianyu Tang, Shengzhong Wang, Zhen An, Xuejun Ye, Xiaochun Fan, Dongrui State Key Lab of Processors Institute of Computing Technology Cas Beijing100049 China School of Computer and Control Engineering Ucas Beijing Beijing100190 China

ISBN: (纸本)9798350319934

Dataflow architecture is a promising parallel computing platform that provides high performance, high efficiency and flexibility. Since the execution of a loop is usually the most time-consuming in a program, extracting sufficient loop-level parallelism is an essential task of dataflow architecture. However, existing methods often result in low execution performance and poor hardware utilization due to the imbalance among dataflow graph nodes and the non-preemptive mechanism. To tackle these problems, this paper makes three contributions: 1) A highly effective dataflow graph balancing method is developed to improve the utilization. 2) An enhanced dataflow execution model is proposed, which adapts a novel preemptive mechanism to further improve the hardware utilization and performance. 3) A decoupled dataflow architecture is designed to efficiently support our proposed dataflow execution model. Experiment shows that our methods can achieve an average of 2.61 ×performance improvement and 2.5 ×utilization improvement compared to the state-of-the-art methods with acceptable overhead. © 2022 IEEE.

关键词： computer architecture

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ViT-based Classification of Oracle Bones for Periodization of the Shang Dynasty

ViT-based Classification of Oracle Bones for Periodization o...

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2023 International conference on Algorithms, high performance Computing, and Artificial Intelligence, AHPCAI 2023

作者： Li, Bang Ding, Zengmao Zheng, Rongxin Han, Shengwei Zhang, Han Zhang, Zhan Guo, An Wang, Nan Gao, Feng Liu, Yongge School of Computer & Information Engineering Anyang Normal University Henan Anyang455000 China Key Laboratory of Oracle Bone Inscriptions Information Processing Ministry of Education of China Henan Anyang455000 China International Joint Research Laboratory for Perception Data Intelligent Processing of Henan Anyang Normal University Henan Anyang455000 China

ISBN: (纸本)9781510671881

Oracle bone inscriptions are the oldest systematically written characters in China, and their periodization and chronology are essential tasks in historical research. However, current deep-learning models face significant challenges when they are used to recognize oracle bone images directly. These models can only achieve an accuracy of no more than 45%. As the research on the periodization of oracle bones mainly relies on the characters inscribed on the bones, a domain knowledge-based oracle bone classification model is proposed that improves the recognition accuracy of Vision Transformer (ViT) by 5.28%. The proposed approach incorporates results obtained from oracle bone character detection to provide a more effective recognition object. Moreover, a novel model with a style network and category embedding is proposed in this paper that focuses on characters, resulting in a notable improvement in recognition accuracy of 16.01%. Our study reveals the importance of domain knowledge in the classification of historical documents, and it presents new insights into using artificial intelligence for assisting in historical document organization tasks. © 2023 SPIE. All rights reserved.

关键词： Domain Knowledge

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An Automated Calibration Scheme for PLL in Time-to-Digital Converters 5

An Automated Calibration Scheme for PLL in Time-to-Digital C...

引用

5th International conference on Circuits and Systems, ICCS 2023

作者： Xu, Jiayuan Yu, Hang Li, Yan Xi, Daoming Ge, Binjie Liu, Hongzhao College of Computer Science and Software Engineering Shenzhen University Shenzhen Laboratory of Ic Design for Iot Shenzhen China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Shenzhen MSU-BIT University Dept. of Electronics and Computer Engineering Shenzhen China Quality and Standards Academy Shenzhen Technology University Shenzhen China

ISBN: (纸本)9798350308273

In high performance Time-to-Digital Converters (TDCs), PLL can be adopted to generate the required reference clock, which is in ∼ 100 MHz range. In order to improve the phase noise performance, the full VCO frequency range in such a PLL is generally divided into multiple sectors, which can be selected by digital code. This paper proposes an automated calibration scheme to set the digital code for optimized VCO performance. Primarily based on successive approximation algorithm, the proposed scheme completes the automated calibration with only minimal steps. By co-simulating with a typical PLL, the efficacy of the proposed scheme is fully validated. Tentatively synthesized using CMOS 180nm technology, the automated calibration scheme only occupies 100× 100um2 of silicon area, and consists of 144 logical gates. The power consumption is only ∼ 100 uW. © 2023 IEEE.

关键词： Approximation algorithms

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An Adversarial Attack Method Against Financial Fraud Detection Model Beta Wavelet Graph Neural Network via Node Injection 11

An Adversarial Attack Method Against Financial Fraud Detecti...

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11th IEEE International conference on Cyber Security and Cloud Computing, CSCloud 2024

作者： Yang, Shuhan Guo, Hengqi Du, Xiaozheng Yang, Jirui Lu, Zhihui School of Computer Science Fudan University Shanghai China Business Analysis BU GienTech Technology Co. Ltd School of Computer Science Fudan University Shanghai China

ISBN: (纸本)9798350376982

Financial fraud detection plays a crucial role in maintaining financial security and risk control. Many types of financial data, such as transaction networks and entity relationship networks, can be represented as graph-structured data. Graph neural networks, exemplified by the Beta Wavelet Graph Neural Network (BWGNN), are instrumental in financial fraud detection. However, current research on adversarial attacks against graph neural networks primarily focuses on vanilla GNNs, with limited exploration into adversarial attacks targeting models like BWGNN used in financial fraud detection. This paper presents effective adversarial attack methods tailored to BWGNN. Leveraging node injection as an adversarial attack method, we construct surrogate models that closely resemble the structure of BWGNN, significantly enhancing the attack performance. Additionally, by incorporating dropout layers after the input layer of the surrogate model, we further enhance the attack effectiveness. This paper reveals the adversarial vulnerabilities of financial fraud detection models represented by BWGNN, which holds significant implications for enhancing the security of fraud detection models applied in critical financial security domains. © 2024 IEEE.

关键词： Decentralized finance

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EEG-Based Tension Recognition Annotated with Electrodermal Activity 46

EEG-Based Tension Recognition Annotated with Electrodermal A...

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46th Annual International conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Gu, Wei Li, Yun-Huan Zhao, Li-Ming Zheng, Wei-Long Lu, Bao-Liang Shanghai Jiao Tong University Department of Computer Science and Engineering China Shanghai Emotionhelper Technology Co. Ltd China Shanghai Jiao Tong University School of Medicine RuiJin-Mihoyo Laboratory Clinical Neuroscience Center RuiJin Hospital China

ISBN: (纸本)9798350371499

The precise annotation of emotions is crucial for constructing emotion EEG datasets, where videos are the dominant emotion-inducing tools. However, existing annotation methodologies are often limited, assigning a uniform label to the entire video and neglecting subjects' emotional arousal variations during viewing. This paper proposes a novel approach to address this issue by integrating electrodermal activity (EDA), a psychophysiological marker of arousal, with EEG data. We introduce a new dataset that captures both tension and calmness, utilizing EDA to annotate EEG data with high and low arousal. The method is systematically tested in subject-specific paradigms, employing a suite of machine learning and deep learning algorithms. Our results demonstrate that models trained solely on highly induced EEG data, comprising 71.75% of the initial training set, yield equal or superior performance on test sets, regardless of their arousal levels. This underscores the potential of EDA in enhancing emotion recognition accuracy in EEG studies. © 2024 IEEE.

关键词： Emotion Recognition

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Real-time traffic intrusion detection based on CNN-LSTM deep neural networks

Real-time traffic intrusion detection based on CNN-LSTM deep...

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2024 International conference on computer Network Security and Software Engineering, CNSSE 2024

作者： Liu, Runjie Ma, Yinpu Gao, Xu Zhang, Lianji National Supercomputing Center In Zhengzhou Zhengzhou University No. 123 Changchun Road Henan Zhengzhou450001 China School of Computer and Artificial Intelligence of Zhengzhou University No. 100 Science Avenue Henan Zhengzhou450001 China

ISBN: (纸本)9781510680234

As Internet technology is developed and applied, the problems of large amounts of real-time traffic data and many unknown attacks are becoming increasingly serious, and intrusion detection systems have increased in efficiency and effectiveness. In this paper, a real-time traffic intrusion detection method based on Inception-LSTM deep neural network combining CNN and LSTM is proposed for improving label-based intrusion detection performance. Network traffic records are converted into 2D gray scale graphs. It extracts network traffic features using image processing techniques with high generalization ability. Experimental validation is performed on the publicly available CIC-IDS-2017 dataset, and the results show that the proposed Inception-LSTM neural network improves the detection accuracy and F1-score by 0.5% and 0.7%, respectively;the results of the comparison between the detection done on real-time captured traffic data and the network security devices show that the method is effective and feasible. © 2024 SPIE.

关键词： Intrusion detection

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