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Variational Neural Inference Enhanced Text Semantic Communication System

China Communications 2024年第7期21卷 50-64页

作者： Zhang Xi Zhang Yiqian Li Congduan Ma Xiao School of Electronics and Communication Engineering Sun Yat-Sen UniversityShenzhen 518107China Guizhou University State Key Laboratory of Public Big Data Guiyang 550025China Shenzhen Key Laboratory of Navigation and Communication Integration Shenzhen 518107China School of Computer Science and Engineering Sun Yat-Sen UniversityGuangzhou 510006China

Recently,deep learning-based semantic communication has garnered widespread attention,with numerous systems designed for transmitting diverse data sources,including text,image,and speech,*** efforts have been directed toward improving system performance,many studies have concentrated on enhancing the structure of the encoder and ***,this often overlooks the resulting increase in model complexity,imposing additional storage and computational burdens on smart ***,existing work tends to prioritize explicit semantics,neglecting the potential of implicit *** paper aims to easily and effectively enhance the receiver's decoding capability without modifying the encoder and decoder *** propose a novel semantic communication system with variational neural inference for text ***,we introduce a simple but effective variational neural inferer at the receiver to infer the latent semantic information within the received *** information is then utilized to assist in the decoding *** simulation results show a significant enhancement in system performance and improved robustness.

关键词： deep learning semantic communication variational neural inference

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Improving Event Coreference Resolution Using Prompt Learning and U-shaped Module 4

Improving Event Coreference Resolution Using Prompt Learning...

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4th International Conference on computer Science and Blockchain, CCSB 2024

作者： Zhang, Mengyao Long, Jun Huang, Wenti School of Software Xinjiang University Urumqi China Institute of Big Data Central South University Changsha China School of Computer Science and Engineering Hunan University of Science and Technology Changsha China

ISBN: (纸本)9798350356502

Event Coreference Resolution (ECR) focuses on clustering event mentions that allude to identical actual events. Previous research primarily focuses on encoding event mentions without incorporating human interpretations, making it difficult to integrate human-summarized rules for guiding the ECR task. Furthermore, the semantic relations between event mentions have not been thoroughly explored, which impacts coreference judgment. To address these issues, we first utilize the powerful Longformer to acquire event representations at the document level. In addition, we introduce a prompt learning template to better emphasize event types and parameters, enhancing the interaction between event modeling and coreference judgment. Ultimately, we create matching vectors for event mentions using tensor matching and incorporate a U-shaped module to effectively identify the semantic relationships among these mentions, which enhances coreference discrimination. DPTU surpasses other ECR models on the KBP2017 dataset in cause of some experiments. © 2024 IEEE.

关键词： Semantics

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Distributionally Robust Deep Neural Network for Malicious App Software Dynamic Detection 6

Distributionally Robust Deep Neural Network for Malicious Ap...

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6th International Conference on Frontier Technologies of Information and computer, ICFTIC 2024

作者： Li, Jie Tang, Yunle Yang, Linfeng Chen, Shifei The College of Big Data Guangxi Vocational & Technical College Nanning China The School of Computer Electronics and Information Guangxi University Nanning China The School of Electrical Engineering Guangxi University Nanning China

ISBN: (纸本)9798331541750

Malicious App Software Detection (MASD) is a crucial step in ensuring device security, as it helps users identify and eliminate malicious software that may pose a threat to the system or data security. Based on the distributionally robust optimization theory, this paper constructs an ambiguity set by using Wasserstein distance, and furtherly proposes a distributionally robust Deep Neural Networks (DNN) model to deal with unseen data for the MASD problem. A computationally-efficient upper approximation of the proposed model is given based on Lipchitz constants of the DNN network. Various test results reveal that the proposed model has better performances than the corresponding DNN. And our model possesses outstanding robustness when the training data set is reduced. © 2024 IEEE.

关键词： distributionally robust optimization Lipchitz constant Malicious App detection Wasserstein distance

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HOLMES: An Efficient and Lightweight Semantic Based Anomalous Email Detector 19

HOLMES: An Efficient and Lightweight Semantic Based Anomalou...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Wu, Peilun Guo, Hui Data Security & Compliance Cdo Data & Cloud PwC Cn China School of Computer Science and Engineering Australia

ISBN: (纸本)9798350346558

Email threat is a serious issue for enterprise security. The threat can be in various malicious forms, such as phishing, fraud, blackmail and malvertisement. The traditional anti-spam gateway often maintains a greylist to filter out unexpected emails based on suspicious vocabularies present in the email's subject and contents. However, this type of signature-based approach cannot effectively discover novel and unknown suspicious emails that utilize various evolving malicious payloads. To address the problem, in this paper, we present HOLMES, an efficient and lightweight semantic based engine for anomalous email detection. HOLMES can convert each email event log into a sentence through word embedding and then identify abnormalities that deviate from a historical baseline based on those translated sentences. We have evaluated the performance of HOLMES in a real-world enterprise environment, where around 5,000 emails are sent/received each day. In our experiments, HOLMES shows a high capability to detect email threats, especially those that cannot be handled by the enterprise anti-spam gateway. It is also demonstrated through our experiment that HOLMES can discover more concealed malicious emails that are immune from several commercial detection tools. © 2022 IEEE.

关键词： Intrusion detection

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Design, Mathematical Modeling, and Control of an Underactuated 3-DOF Experimental Helicopter

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IEEE Access 2024年 12卷 55568-55586页

作者： Makki, Osamah Talal Moosapour, Seyyed Sajjad Mobayen, Saleh Nobari, Jafar Heyrani Shahid Chamran University of Ahvaz Faculty of Engineering Ahvaz*** Iran National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu Yunlin640301 Taiwan K. N. Toosi University of Technology Department of Electrical and Computer Engineering Tehran19697 Iran

This article covers the design, implementation, mathematical modelling, and control of a multivariable, underactuated, low-cost, three-degrees-of-freedom experimental helicopter system (namely a 3-DOF helicopter). The system is considered an essential benchmark for the development and evaluation of control algorithms and identification methods as it mimics real helicopters and UAVs. The system is multivariable and difficult to control due to its underactuation characteristics. In addition, it presents further challenges in modelling and control analysis in terms of high nonlinearity, coupling, uncertainty, and unmodeled dynamics. In this scene, the system is designed, developed, and operated virtually in SolidWorks using the necessary hardware and software. A new accurate multivariable coupled nonlinear mathematical model is derived and proposed. The model takes into account the complex dynamics and interactions between the system components, providing a more comprehensive representation of the real system compared to previous models. System parameters are then identified using the Sum of the Square Error (SSE) algorithm, and the mathematical model is compared to the practical results in many different scenarios via Simulink. Experimental tests confirmed the mathematical model and robust performance of the implemented experimental 3-DOF helicopter system, as well as the ability of the proposed controller to stabilize the helicopter and track different trajectories in the presence of uncertainties and cross-coupling effects. © 2013 IEEE.

关键词： Iron

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CIFD: A Distance for Complex Intuitionistic Fuzzy Set 4th

CIFD: A Distance for Complex Intuitionistic Fuzzy Set

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Zhao, Yangyang Xiao, Fuyuan School of Computer and Information Science Southwest University Chongqing400715 China School of Big Data and Software Engineering Chongqing University Chongqing401331 China

ISBN: (纸本)9783031200953

Intuitionistic fuzzy set (IFS) has attracted much attention because it can deal with fuzziness and uncertainty more flexibly than traditional fuzzy set. Complex intuitionistic fuzzy set (CIFS) extends intuitionistic fuzzy to the complex plane, which can better express and process uncertain information. In this paper, a novel distance measure complex intuitionistic fuzzy distance (CIFD) is proposed for CIFSs. Firstly, inspired by Tanimoto coefficient, a new similarity measure between CIFSs is proposed. Then, based on the similarity measure, the CIFD is proposed and its non-negativity, non-degeneracy, and symmetry are analyzed. Moreover, when CIFS degenerates into classical IFS, CIFD is also applicable to measure the differences between IFSs. Finally, to illustrate the effectiveness of CIFD, an example is given at the end. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Fuzzy sets

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Probabilistic Reliability via Subsystem Structures of Arrangement Graph Networks

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IEEE Transactions on Reliability 2024年第1期73卷 279-289页

作者： Huang, Yanze Lin, Limei Xu, Li Hsieh, Sun-Yuan Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications School of Computer Science and Mathematics Fuzhou350118 China Fujian Normal University College of Computer and Cyber Security Key Laboratory of Network Security and Cryptology Fuzhou350117 China National Cheng Kung University Department of Computer Science and Information Engineering Tainan701 Taiwan

With the rapid growth of the number of processors in a multiprocessor system, faulty processors occur in it with a probability that rises quickly. The probability of a subsystem with an appropriate size being fault-free in a definite time interval is a significant and practical measure of the reliability for a multiprocessor system, which characterizes the functionality of a multiprocessor system well. Motivated by the study of subgraph reliability, as well as the attractive structure and fault tolerance properties of (n, k)-arrangement graph A n, k, we focus on the subgraph reliability for A n, k under the probabilistic fault model in this article. First, we investigate intersections of no more than four subgraphs in A n, k, and classify all the intersecting modes. Second, we focus on the probability P(q, A n, k n-1, k-1) with which at least one (n-1, k-1)-subarrangement graph is fault-free in A n, k, when given a uniform probability q with which a single vertex is fault-free, and we establish the P(q, A n, kn-1, k-1) by adopting the principle of inclusion-exclusion under the probabilistic fault model. Finally, we study the probabilistic fault model involving a nonuniform probability with which a single vertex is fault-free, and we prove that the P(q, A n, kn-1, k-1) under both models is very close to the asymptotic value by both theoretical arguments and experimental results. © 1963-2012 IEEE.

关键词： Fault tolerance

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Image super-resolution algorithm based on multi-dimensional Swin Transformers 3

Image super-resolution algorithm based on multi-dimensional ...

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3rd International Conference on Electronic Information engineering and computer Communication, EIECC 2023

作者： Zheng, Qinan Xu, Huahu Bian, Minjie School of Computer Engineering and Science Shanghai University Shanghai China Shanghai Data Group Co. Ltd Shanghai China

ISBN: (纸本)9798350359961

Numerous studies have demonstrated the Swin Transformer performs well in image super-resolution tasks. However, Swin Transformer typically divides the input image into fixed size blocks (such as 48x48) and independently processes each block, causing it to overlook a wider area. Furthermore, many deep learning super-resolution technologies face the challenge of computationally intensive operations. To address these issues, we propose a new NSTSR model consisting of four parts: data preprocessing layer, primary feature extraction layer, advanced feature extraction layer, and image generation layer. The advanced feature extraction layer consists of multiple STNBs (Swin Transformer N-segment blocks). We have introduced a technique called N-Segments, which applies the N-Gram contextual method for natural language processing tasks to low-level visual tasks. In our work, N-Segments refers to neighboring local windows in Swin, distinct from the traditional text analysis definition where N-Segments represents a continuous picture segment. Leveraging sliding-WSA, the N-Segments interact with each other, enabling the expansion of the regions seen to restore degraded pixels effectively. The experimental results demonstrate that the NSTSR model exhibits excellent performance compared to existing advanced methods while maintaining the computational efficiency structure. © 2023 IEEE.

关键词： component data Augmentation Image Super-resolution N-Segments Swin Transformer

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Multicomponent Similarity Graphs for Cross-Network Node Classification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1411-1424页

作者： Zhang, Yuhong Shi, Congmei Li, Xinzheng Zhang, Zan Hu, Xuegang School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230088 China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei230009 China

Cross-network node classification aims to train a classifier for an unlabeled target network using a source network with rich labels. In applications, the degree of nodes mostly conforms to the long-tail distribution, i.e., most nodes in the network are tail nodes with sparse neighborhoods. The established methods focus on either the discrepancy cross network or the long tail in a single network. As for the cross-network node classification under long tail, the coexistence of sparsity of tail nodes and the discrepancy cross-network challenges existing methods for long tail or methods for the cross-network node classification. To this end, a multicomponent similarity graphs for cross-network node classification (MS-CNC) is proposed in this article. Specifically, in order to address the sparsity of the tail nodes, multiple component similarity graphs, including attribute and structure similarity graphs, are constructed for each network to enrich the neighborhoods of the tail nodes and alleviate the long-tail phenomenon. Then, multiple representations are learned from the multiple similarity graphs separately. Based on the multicomponent representations, a two-level adversarial model is designed to address the distribution difference across networks. One level is used to learn the invariant representations cross network in view of structure and attribute components separately, and the other level is used to learn the invariant representations in view of the fused structure and attribute graphs. Extensive experimental results show that the MS-CNC outperforms the state-of-the-art methods. Impact Statement-Node classification is an important task in graph mining. With the unavailability of labels, some researchers propose cross-network node classification, using one labeled network to assist the node classification of another unlabeled network. However, the long-tail of nodes leads to unsatisfactory performance and challenges the recent cross-network node classification m

关键词： Graphic methods

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Multiclass Classification and Defect Detection of Steel Tube Using Modified YOLO 30th

Multiclass Classification and Defect Detection of Steel Tu...

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30th International Conference on Neural Information Processing, ICONIP 2023

作者： G, Deepti Raj B, Prabadevi Gadekallu, Thippa Reddy Bhatia Khan, Surbhi Saraee, Mohammed School of Computer Science Engineering and Information Systems VIT University Vellore India Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon College of Information Science and Engineering Jiaxing University Jiaxing314001 China Department of Data Science School of Science Engineering and Environement University of Salford Manchester United Kingdom School of science engineering and environment University of Salford Salford United Kingdom

ISBN: (纸本)9789819981830

Steel tubes are widely used in hazardous high pressure environments such as petroleum, chemicals, natural gas and shale gas. Defects in steel tubes have serious negative consequences. Using deep learning object recognition to identify and detect defects can greatly improve inspection efficiency and drive industrial automation. In this work, we use a well-known YOLOv7(You Only Look Once version7) deep learning model and propose to improve it to achieve accurate defects detection of steel tube images. First, the classification of the dataset is checked using a sequential model and AlexNet. A Coordinate Attention (CA) mechanism is then integrated into the YOLOv7 backbone network to improve the expressive power of the feature graph. Additionally, the SIoU (SCYLLA-Intersection over Union) loss function is used to speed up convergence due to class imbalance in the dataset. Experimental results show that the evaluation index of the optimized and modified YOLOv7 algorithm outperforms other models. This study demonstrates the effectiveness of using this method in improving the model’s detection performance and providing a more effective solution to steel tube defects. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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