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2021 IEEE Conference on Telecommunications, Optics and Computer Science, TOCS 2021

作者： Li, Zhuolun Kang, Fei Yu, Pu Shu, Hui Zhengzhou University School of Software Zhengzhou China Meac State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665424981

Field text analysis is the process of using natural language processing technology to understand the Text and extract information from unstructured text data. In recent years, due to the increasing maturity of machine learning technology, higher requirements have been put forward for field text analysis. This review first introduces the process of Text preprocessing and the development of text representation. Secondly, it summarizes the four typical techniques of text analysis, including named entity recognition, relation extraction, extractive text summarization, and text classification. Finally, for each typical technology, it introduces the commonly used methods and part of the researcher's exploration of this technology. © 2021 IEEE.

关键词： Text processing

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Hyperbolic Geometric Latent Diffusion Model for Graph Generation 41

Hyperbolic Geometric Latent Diffusion Model for Graph Genera...

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41st International Conference on Machine Learning, ICML 2024

作者： Fu, Xingcheng Gao, Yisen Wei, Yuecen Sun, Qingyun Peng, Hao Li, Jianxin Li, Xianxian Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Institute of Artificial Intelligence Beihang University Beijing China School of Software Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and Engineering Beihang University Beijing China

Diffusion models have made significant contributions to computer vision, sparking a growing interest in the community recently regarding the application of them to graph generation. Existing discrete graph diffusion models exhibit heightened computational complexity and diminished training efficiency. A preferable and natural way is to directly diffuse the graph within the latent space. However, due to the non-Euclidean structure of graphs is not isotropic in the latent space, the existing latent diffusion models effectively make it difficult to capture and preserve the topological information of graphs. To address the above challenges, we propose a novel geometrically latent diffusion framework HypDiff. Specifically, we first establish a geometrically latent space with interpretability measures based on hyperbolic geometry, to define anisotropic latent diffusion processes for graphs. Then, we propose a geometrically latent diffusion process that is constrained by both radial and angular geometric properties, thereby ensuring the preservation of the original topological properties in the generative graphs. Extensive experimental results demonstrate the superior effectiveness of HypDiff for graph generation with various topologies. Copyright 2024 by the author(s)

关键词： Graph theory

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A Review on IoT Botnet 2021

A Review on IoT Botnet

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2nd International Conference on computing and Data Science, CONF-CDS 2021

作者： Zhao, Hao Shu, Hui Xing, Ying State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Software College Zhongyuan University of Technology Zhengzhou China

ISBN: (纸本)9781450389570

The rapid development of the Internet of Things (IoT) also generates security threats that cannot be ignored. This paper lists the development history of botnets, introduces representative IoT botnets, and reveals their new characteristics. The botnet mechanism and characteristics of IoT botnets are analyzed in depth from three aspects: composition structure, life cycle, and attack behavior, and are compared with traditional botnets. Networks for comparison. The analysis discusses the details of IoT botnet technologies such as scanning discovery, covert communication and survival residency. Summarizes the research status of IoT botnet detection technologies and gives future directions. Finally, the importance of preventing IoT botnets and solving IoT security problems is discussed. © 2021 ACM.

关键词： Internet of things

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Impact of Data Breadth and Depth on Performance of Siamese Neural Network Model: Experiments with Three Keystroke Dynamic Datasets

arXiv

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arXiv 2025年

作者： Wahab, Ahmed Anu Hou, Daqing Cheng, Nadia Huntley, Parker Devlen, Charles Electrical and Computer Engineering Clarkson University PotsdamNY United States Statistics University of Virginia CharlottesvilleVA United States College of Computing Georgia Tech AtlantaGA United States Software Engineering RIT RochesterNY United States Computing and Information Science RIT RochesterNY United States

Deep learning models, such as the Siamese Neural Networks (SNN), have shown great potential in capturing the intricate patterns in behavioral data. However, the impacts of dataset breadth (i.e., the number of subjects) and depth (e.g., the amount of training samples per subject) on the performance of these models is often informally assumed, and remains under-explored. To this end, we have conducted extensive experiments using the concepts of "feature space" and "density" to guide and gain deeper understanding on the impact of dataset breadth and depth on three publicly available keystroke datasets (Aalto, CMU and Clarkson II). Through varying the number of training subjects, number of samples per subject, amount of data in each sample, and number of triplets used in training, we found that when feasible, increasing dataset breadth enables the training of a well-trained model that effectively captures more inter-subject variability. In contrast, we find that the extent of depth’s impact from a dataset depends on the nature of the dataset. Free-text datasets are influenced by all three depth-wise factors;inadequate samples per subject, sequence length, training triplets and gallery sample size, which may all lead to an under-trained model. Fixed-text datasets are less affected by these factors, and as such make it easier to create a well-trained model. These findings shed light on the importance of dataset breadth and depth in training deep learning models for behavioral biometrics and provide valuable insights for designing more effective authentication systems. © 2025, CC BY.

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Defect Screening on Nuclear Power Plant Concrete Structures: A Two-staged Method Based on Contrastive Representation Learning

Defect Screening on Nuclear Power Plant Concrete Structures:...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Wenlian Huang Guanming Zhu Qiang Huang Zhuangzhuang Chen Jie Chen Jianqiang Li College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

Intelligent defect detection methods are important for the surface of the containment of nuclear power plants and face many challenges in the field of computer vision. Due to the irregular shapes and large variation of defects, as well as the similarity of the features between some defects and the background. Most existing deep learning-based defect detection networks suffer from insufficient feature extraction capabilities, making it difficult to detect defects from the background. Inspired by discrete representation learning methods, we propose a two-stage defect detection model called ConVQVAE, which uses a semi-supervised method for representation learning to achieve binary classification for defect detection. In addition, contrastive learning is performed in two stages to enhance the model's diversity representation ability and inter-class disentanglement representation ability. Finally, we experimentally verify that the proposed method can obtain good classification results.

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Early Diagnosis of Lung Tumors for Extending Patients’ Life Using Deep Neural Networks

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Computers, Materials & Continua 2023年第7期76卷 993-1007页

作者： A.Manju R.Kaladevi Shanmugasundaram Hariharan Shih-Yu Chen Vinay Kukreja Pradip Kumar Sharma Fayez Alqahtani Amr Tolba Jin Wang Department of Computer Science and Engineering SRM Institute of Science and TechnologyRamapuramChennaiIndia Department of Computer Science and Engineering Saveetha Engineering CollegeChennaiIndia Department of Computer Science and Engineering Vardhaman College of EngineeringHyderabadIndia Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin 64002Taiwan Intelligence Recognition Industry Service Research Center National Yunlin University of Science and TechnologyYunlin 64002Taiwan Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjabIndia Department of Computing Science University of AberdeenAberdeenUK Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh 12372Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadh 11437Saudi Arabia School of Computer&Communication Engineering Changsha University of Science&TechnologyChangshaChina

The medical community has more concern on lung cancer *** experts’physical segmentation of lung cancers is time-consuming and needs to be *** research study’s objective is to diagnose lung tumors at an early stage to extend the life of humans using deep learning ***-Aided Diagnostic(CAD)system aids in the diagnosis and shortens the time necessary to detect the tumor *** application of Deep Neural Networks(DNN)has also been exhibited as an excellent and effective method in classification and segmentation *** research aims to separate lung cancers from images of Magnetic Resonance Imaging(MRI)with threshold *** Honey hook process categorizes lung cancer based on characteristics retrieved using several *** this principle,the work presents a solution for image compression utilizing a Deep Wave Auto-Encoder(DWAE).The combination of the two approaches significantly reduces the overall size of the feature set required for any future classification process performed using *** proposed DWAE-DNN image classifier is applied to a lung imaging dataset with Radial Basis Function(RBF)*** study reported promising results with an accuracy of 97.34%,whereas using the Decision Tree(DT)classifier has an accuracy of 94.24%.The proposed approach(DWAE-DNN)is found to classify the images with an accuracy of 98.67%,either as malignant or normal *** contrast to the accuracy requirements,the work also uses the benchmark standards like specificity,sensitivity,and precision to evaluate the efficiency of the *** is found from an investigation that the DT classifier provides the maximum performance in the DWAE-DNN depending on the network’s performance on image testing,as shown by the data acquired by the categorizers themselves.

关键词： Lung tumor deep wave auto encoder decision tree classifier deep neural networks extraction techniques

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Detecting Poisoning Attacks in Collaborative IDSs of Vehicular Networks Using XAI and Shapley Value

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ACM Journal on Autonomous Transportation Systems 2024年第3期2卷 1-21页

作者： Ahmed Saleh Bataineh Mohammad Zulkernine Adel Abusitta Talal Halabi School of Computing Queen's University Kingston Canada and Computer Science Al Hussein Technical University Amman Jordan Queen's University Kingston Canada Department of Computer Engineering and Software Engineering Polytechnique Montréal Montreal Canada Laval University Quebec Canada

Machine learning-based Intrusion Detection Systems (IDSs) for vehicle networks can collaborate to enhance their performance by sharing crucial decisions when individual datasets lack diversity, which hinders effective model training. However, such collaborative coalitions are vulnerable to poisoning attacks, where certain members tamper with training data, leading to wrong predictions by the IDSs. The current solutions to this problem have the following issues: (1) They require accessing the training dataset of IDSs, which raises critical privacy concerns; (2) their heavy reliance on voting mechanisms may exclude clients and fail to detect malicious coalition members when attackers form the majority coalition; and (3) the verification process can potentially expose sensitive information, posing privacy risks. To address these issues and improve the resilience of collaborative IDSs against poisoning attacks, we propose a novel approach that combines XAI (Explainable AI) technology with Shapley value from cooperative game theory. XAI justifies IDS decisions, while the Shapley value identifies poisoning attacks in training datasets by capturing contradictions between explanations and decisions. We tested our implementation using a publicly available dataset and various machine learning models (e.g., RFC, SVM, and LSTM). Our results prove highly promising in detecting poisoning attacks and overcoming the flaws in existing solutions.

关键词： XAI shaply value

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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年第13期12卷 23933-23945页

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： Cryptography Encryption Security Data mining Adaptive coding Correlation Cloud computing Polynomials Image coding Streaming media

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Why not Just Look for Answers? Using a More Direct Way for API Recommendation

Why not Just Look for Answers? Using a More Direct Way for A...

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Asia-Pacific Conference on software engineering

作者： Changxin Liu Ling Xu Wenhan Mu Rui Qin Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University) Ministry of Education China School of Big Data and Software Engineering Chongqing University Chongqing China

ISBN: (数字)9798331534011

ISBN: (纸本)9798331534028

Automated API recommendation technology can help developers quickly find target APIs that meet their require-ments. Some retrieval-based API recommendation approaches, such as BIKER and CLEAR, adopt an indirect API recommen-dation strategy. Although these approaches have shown good performance, we observed two limitations in these approaches: (1) These approaches heavily relies on the coverage of the collected question titles proposed by developers; (2) These approaches ignore the API source code information. To overcome the two limitations, we propose an approach named AnsAPIRec (Answer- directed API Recommendation), which leverages a pre-trained model and joint-attention mechanism in this paper. In addition to the fully qualified name of the API, AnsAPIRec extracts the corresponding source code from the JDK as an additional feature. AnsAPIRec adopts a direct API recommendation strategy, which directly calculates the similarity between user queries and API features, i.e., API fully qualified name and API source code, to find the target API, alleviating the reliance on question titles. To further enhance AnsAPIRec's ability to understand the semantics of the inputs, we propose a joint-attention mechanism to learn the interdependent representations between the queries and API features. This deep semantic fusion mechanism enables AnsAPIRec to perform well in understanding user intent and API functionality. We reused the APIBench-Q dataset, containing 5885 queries for training and 653 queries for testing. The experimental result shows that AnsAPIRec achieved an MRR of 0.473, significantly outperforming the baseline approaches, BIKER and CLEAR.

关键词： Training Source coding Semantics Feature extraction Testing software engineering

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$\mathrm{E}_{\gamma}$-Mixing Time

$\mathrm{E}_{\gamma}$-Mixing Time

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IEEE International Symposium on Information Theory

作者： Behnoosh Zamanlooy Shahab Asoodeh Mario Diaz Flavio P. Calmon Department of Computing and Software McMaster University Canada IIMAS Universidad Nacional Autónoma de México Mexico School of Engineering and Applied Sciences Harvard University USA

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

We investigate the mixing times of Markov kernels under $\mathsf{E}_{\gamma}-\mathbf{divergence}$ . We demonstrate that the zero-error $\mathsf{E}_{\gamma}- \mathbf{mixing}$ time, for any $\gamma > 1$ , of irreducible and aperiodic Markov chains, is bounded, a property that is not shared by the TV-mixing time. We further obtain upper bounds on the $\mathsf{E}_{\gamma}-\mathbf{mixing}$ times for a broad family of contractive Markov kernels via a new non-linear strong data processing inequality for the $\mathsf{E}_{\gamma}- \mathbf{divergence}$ . We apply our results to derive new bounds for the local differential privacy guarantees offered by the sequential application of a privacy mechanism to data.

关键词： Differential privacy Upper bound Data processing Kernel Information theory

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