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Self‐training maximum classifier discrepancy for EEG emotion recognition

CAAI Transactions on Intelligence technology 2023年第4期8卷 1480-1491页

作者： Xu Zhang Dengbing Huang Hanyu Li Youjia Zhang Ying Xia Jinzhuo Liu School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqingChina Key Laboratory of Tourism Multisource Data Perception and Decision Ministry of Culture and TourismChongqingChina School of Electrical and Computer Engineering Inha UniversityIncheonSouth Korea School of Software Yunnan UniversityYunnanChina

Even with an unprecedented breakthrough of deep learning in electroencephalography(EEG),collecting adequate labelled samples is a critical problem due to laborious and time‐consuming *** study proposed to solve the limited label problem via domain adaptation ***,they mainly focus on reducing domain discrepancy without considering task‐specific decision boundaries,which may lead to feature distribution overmatching and therefore make it hard to match within a large domain gap completely.A novel self‐training maximum classifier discrepancy method for EEG classification is proposed in this *** proposed approach detects samples from a new subject beyond the support of the existing source subjects by maximising the discrepancies between two classifiers'***,a self‐training method that uses unlabelled test data to fully use knowledge from the new subject and further reduce the domain gap is ***,a 3D Cube that incorporates the spatial and frequency information of the EEG data to create input features of a Convolutional Neural Network(CNN)is *** experiments on SEED and SEED‐IV are *** experimental evaluations exhibit that the proposed method can effectively deal with domain transfer problems and achieve better performance.

关键词： artificial intelligence bioinformatics domain adaptation EEG neural network pattern classification

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Less Traces Are All It Takes: Efficient Side-Channel Analysis on AES

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2024年第6期44卷 2080-2092页

作者： Xiao, Zhiyuan Wang, Chen Shen, Jian Wu, Q. M. Jonathan He, Debiao Zhejiang Sci-Tech University School of Information Science and Engineering School of Cyber Science and Technology the Zhejiang Provincial Key Laboratory of Digital Fashion and Data Governance Zhejiang Provincial International Cooperation Base for Science and Technology on Cloud Computing Security and Data Aggregation Hangzhou310018 China University of Windsor Department of Electrical and Computer Engineering WindsorON Canada Wuhan University State Key Laboratory of Software Engineering Computer School Wuhan430072 China

In cryptography, side-channel analysis (SCA) is a technique used to recover cryptographic keys by examining the physical leakages that occur during the operation of cryptographic devices. Recent advancements in Deep Learning (DL) have greatly enhanced the extraction of crucial information from intricate leakage patterns. A considerable amount of research is dedicated to studying the SubByte (SB) operations of the advanced encryption standard (AES). This is because the SB process, which generates numerous transitions between 0s and 1s during encryption, results in significant energy leakage. However, traditional analysis models primarily focus on the initial round of SB operations in AES, which are less effective on mobile terminals where it is difficult to collect enough signals. These models often neglect additional operations and subsequent rounds, thus providing limited insights from small datasets. Consequently, this limitation has a direct impact on the accuracy and efficiency of key recovery. Our study uses ρ-test analysis to show that significant leakage occurs not only during the S-box operation but also during the AddRoundKey (AR) phase of AES. To address these challenges, we propose a new SCA method that is optimized for small sample sizes. This method includes a new comprehensive round trace labeling algorithm, which simultaneously analyzes the SB and AR stages of each AES round. Additionally, we introduce the peak precise localization algorithm to accurately identify the points of energy leakage during each encryption round. Our experiments, conducted with power and Electromagnetic (EM) datasets from the STM32F303 microcontroller, demonstrate that our method can reliably recover keys with as few as 20 traces. These results highlight the enhanced capability of our method in handling the complexities of small sample datasets in cryptographic analysis. © 2024 IEEE.

关键词： Information leakage

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Generalized Image Embedding for Multi-Domain Image Retrieval 26

Generalized Image Embedding for Multi-Domain Image Retrieval

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Xiao, Boao Wu, Siyuan He, Xin Dou, Wanchun Nanjing University State Key Laboratory for Novel Software Technology China Nanjing University of Posts and Telecommunications School of Computer Science China

ISBN: (纸本)9798350331684

Image embedding, being a fundamental task in computer vision, plays a crucial role in various downstream tasks such as image retrieval. Widely adopted in e-commerce and social media collaboration, image retrieval benefits greatly from representations learned by the embedding model. However, conventional embedding models are often trained on a single domain, leading to inadequate performance in the multi-domain scenario. To address this challenge, we introduce a generalized image embedding model designed for multi-domain image retrieval. The proposed method employs a contrastively learned Vision Transformer and a carefully crafted training scheme to enhance domain generalization capability. Our theoretical analysis and experimental results, conducted on a large-scale, real-world multi-domain image retrieval dataset, demonstrate the superiority of the proposed method over existing embedding models in terms of both accuracy and domain generalization capability. © 2023 IEEE.

关键词： Image retrieval

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Sequence generation model of traditional Chinese medicine 12

Sequence generation model of traditional Chinese medicine

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12th International Conference on Information technology in Medicine and Education, ITME 2022

作者： Duan, Chengzhi Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China

ISBN: (纸本)9798350310153

The application of artificial intelligence in the field of traditional Chinese medicine is more and more extensive, and the related research is also more and more. Increasingly, the electronic processing of TCM textual information is becoming more and more important. In this paper, we propose a TCM text-based prescription generation task, intending to be able to implement automatic prescription generation functions based on the sequence-to-sequence (seq2seq) model. Traditional TCM prescriptions are given by the attending physicians, and the prescription recommendation system proposed in this paper achieves a certain degree of accuracy compared to real doctors. After a series of system data training, we improved the accuracy rate to 76%. For FI the value reached the best result of 74% at the existing stage. © 2022 IEEE.

关键词： Diagnosis

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Honeytoken-Detector: A Symbolic Execution-Based Honeypot Token Detection Tool 26

Honeytoken-Detector: A Symbolic Execution-Based Honeypot Tok...

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26th ACIS International Winter Conference on software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD-Winter 2023

作者： Liu, Yi Cai, Lizhi School of Information Science and Engineering East China University of Science and Technology Shanghai China Shanghai Development Center of Computer Software Technology Shanghai Key Laboratory of Computer Testing & Evaluating Shanghai China

ISBN: (纸本)9798350345865

Smart contracts have emerged as one of the most successful applications in the blockchain domain, playing a significant role in various blockchain ecosystems. Inspired by smart contracts, a multitude of cryptographic assets have been created. To standardize these assets, industry standards such as ERC20 (Ethereum Request for Comments 20), ERC721, and ERC1155 have been proposed. In recent years, smart contracts have frequently fallen victim to attacks. Honeypot contracts, disguised as ERC20-compliant tokens, are widely prevalent on the blockchain, enticing victims to make purchases. Such malicious smart contracts exhibiting deceptive behavior are collectively referred to as honeypot tokens. This paper focuses on ERC20-compliant smart contracts and defines six common types of honeypot issues. Building upon advancements in smart contract vulnerability detection, we propose an enhanced symbolic execution-based detection tool called Honeytoken-Detector. We conduct experiments on both contracts known to have similar issues and actual token contracts from the real world. The experimental results demonstrate the effectiveness of our tool in identifying vulnerabilities. © 2023 IEEE.

关键词： Smart contract

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GridDroid--An Effective and Efficient Approach for Android Repackaging Detection Based on Runtime Graphical User Interface

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Journal of computer Science & technology 2022年第1期37卷 147-181页

作者： Jun Ma Qing-Wei Sun Chang Xu Xian-Ping Tao State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China Department of Computer Science and Technology Nanjing UniversityNanjing 210023China Huatai Securities Co. Ltd.Nanjing 210019China

Repackaging brings serious threats to Android *** birthmark techniques are typically applied to detect repackaged *** based on apps'runtime graphical user interfaces(GUI)are effective,especially for obfuscated or encrypted ***,existing studies are time-consuming and not suitable for handling apps in large *** this paper,we propose an effective yet efficient dynamic GUI birthmark for Android ***,we run an app with automatically generated GUI events and dump its layout after each *** divide each dumped layout into a grid,count in each grid cell the vertices of boundary rectangles corresponding to widgets within the layout,and generate a feature vector to encode the *** layouts are merged at runtime,and finally we obtain a graph as the birthmark of the *** a pair of apps to be compared,we build a weighted bipartite graph from their birthmarks and apply a modified version of the maximum-weight-bipartite-matching algorithm to determine whether they form a repackaging pair(RP)or *** implement the proposed technique in a prototype,GridDroid,and apply it to detect RPs in three datasets involving 527 *** reports only six false negatives and seven false positives,and it takes GridDroid merely 20 microseconds on average to compare a pair of birthmarks.

关键词： Android apps dynamic software birthmark graphical user interface(GUI) repackaging detection

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General Category Network: Handwritten Mathematical Expression Recognition with Coarse-Grained Recognition Task 48

General Category Network: Handwritten Mathematical Expressio...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zhang, Xinyu Ying, Han Tao, Ye Xing, Youlu Feng, Guihuan Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China Nanjing University Software Institute Nanjing210023 China Nanjing University School of Computer Science and Technology Nanjing210023 China

ISBN: (纸本)9781728163277

Handwritten Mathematical Expression Recognition (HMER) is an important task in pattern recognition. It is a challenging task due to symbols resembling each other in appearance("z/2", "B/β") and the complex mathematical syntax. The encoder-decoder architecture has been widely used in recent HMER methods. Several works introduce HMER-related tasks that enhance the performance of HMER. We propose the General Category Recognition Task (GCRT) and design the General Category Network(GCN) to perform HMER and GCRT in parallel. GCRT alleviates the symbol confusion problem and also helps the model generate results conforming to mathematical syntax. Compared with SOTA methods, the experimental results show that Expression Recognition Rates(ExpRate) of our method are increased by 1.12%, 2.45% and 1.84% on CROHME 2014, 2016 and 2019 test set respectively. In addition, current works explore the effect of a single auxiliary task on HMER. We investigated the effect of multiple tasks on HMER. We performed many experiments and drew multiple valuable *** will publish all codes in the future. © 2023 IEEE.

关键词： Character recognition

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Security and Privacy in Solar Insecticidal Lamps Internet of Things:Requirements and Challenges

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 58-73页

作者： Qingsong Zhao Lei Shu Kailiang Li Mohamed Amine Ferrag Ximeng Liu Yanbin Li the College of Artificial Intelligence Nanjing Agricultural UniversityNanjing 210031 the State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023 the State Key Laboratory of Cryptology Beijing 100878China IEEE the School of Engineering University of LincolnLincoln LN67TSUK the Artificial Intelligence and Digital Science Research Center Technology Innovation InstituteMasdar City 9639UAE the College of Computer and Data Science Fuzhou UniversityFuzhou 350108China

Solar insecticidal lamps(SIL) can effectively control pests and reduce the use of pesticides. Combining SIL and Internet of Things(IoT) has formed a new type of agricultural IoT,known as SIL-IoT, which can improve the effectiveness of migratory phototropic pest control. However, since the SIL is connected to the Internet, it is vulnerable to various security *** issues can lead to serious consequences, such as tampering with the parameters of SIL, illegally starting and stopping SIL,etc. In this paper, we describe the overall security requirements of SIL-IoT and present an extensive survey of security and privacy solutions for SIL-IoT. We investigate the background and logical architecture of SIL-IoT, discuss SIL-IoT security scenarios, and analyze potential attacks. Starting from the security requirements of SIL-IoT we divide them into six categories, namely privacy, authentication, confidentiality, access control, availability,and integrity. Next, we describe the SIL-IoT privacy and security solutions, as well as the blockchain-based solutions. Based on the current survey, we finally discuss the challenges and future research directions of SIL-IoT.

关键词： Challenges Internet of Things(IoT) privacy and security security requirements solar insecticidal lamps(SIL)

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CPDG: A Contrastive Pre-Training Method for Dynamic Graph Neural Networks 40

CPDG: A Contrastive Pre-Training Method for Dynamic Graph Ne...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Bei, Yuanchen Xu, Hao Zhou, Sheng Chi, Huixuan Wang, Haishuai Zhang, Mengdi Li, Zhao Bu, Jiajun Meituan China College of Computer Science and Technology Zhejiang Provincial Key Laboratory of Service Robot Zhejiang University China School of Software Technology Zhejiang University China Institute of Computing Technology Chinese Academy of Science China

ISBN: (纸本)9798350317152

Dynamic graph data mining has gained popularity in recent years due to the rich information contained in dynamic graphs and their widespread use in the real world. Despite the advances in dynamic graph neural networks (DGNNs), the rich information and diverse downstream tasks have posed significant difficulties for the practical application of DGNNs in industrial scenarios. To this end, in this paper, we propose to address them by pre-training and present the Contrastive Pre-Training Method for Dynamic Graph Neural Networks (CPDG). CPDG tackles the challenges of pre-training for DGNNs, including generalization capability and long-short term modeling capability, through a flexible structural-temporal subgraph sampler along with structural-temporal contrastive pre-training schemes. Extensive experiments conducted on both large-scale research and industrial dynamic graph datasets show that CPDG outperforms existing methods in dynamic graph pre-training for various downstream tasks under three transfer settings. © 2024 IEEE.

关键词： Graph neural networks

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Monitorability for Runtime Verification 23rd

Monitorability for Runtime Verification

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23rd International Conference on Runtime Verification, RV 2023

作者： Havelund, Klaud Peled, Doron Laboratory for Reliable Software Jet Propulsion Laboratory California Institute of Technology Pasadena United States Department of Computer Science Bar Ilan University Ramat Gan Israel

ISBN: (纸本)9783031442667

Runtime verification (RV) facilitates the formal analysis of execution traces. In particular, it permits monitoring the execution of a system and checking it against a temporal specification. Online RV observes, at any moment, a prefix of the complete monitored execution and is required to provide a verdict whether all the complete executions that share that prefix satisfy or falsify the specification. Not every property (and for every kind of verdict) lends itself to obtaining such an early verdict. Monitorability of a temporal property is defined as the ability to provide positive (success) or negative (failure) verdicts after observing a finite prefix of the execution. We classify temporal properties based on their monitorability and present related monitoring algorithms. A common practice in runtime verification is to concentrate on the class of safety properties, where a failure to satisfy the specification can always be detected in finite time. In the second part of the paper we concentrate on monitoring safety properties and their place among the other classes of properties in terms of algorithms and complexity. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Specifications

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