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3rd International Conference on Cloud Computing, Big Data Application and Software engineering, CBASE 2024

作者： Zhang, Tingting Wang, Yongli Liu, Dongmei School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798331506582

Multimodel knowledge graph completion (MKGC) aims to predict missing entities in MKGs. Differing from prior Single-model knowledge graph completion efforts, MKGC leverages rich textual descriptions, images, and other multi-model information associated with entities. Traditional MKGC methods often use simple concatennation, weighted fusion, or tensor-based approaches for multi-modal information fusion, but they fail to capture the in-tra-modality and inter-modality dynamics. Moreover, traditional models like TransE and TransH, based on translational operations, struggle with complex relationships such as 1-N, N-N. To address these issue, this paper proposes a multi-modal information fusion enhanced by multi-level attention mechanism and relation-specific graph attention network method for MKGC. Firstly, obtaining text, attribute, and image embeddings of entities by leveraging pre-trained modals such as BERT and Vision Transformer. Secondly, using low-rank tensors for multimodal fusion and introducing self-attention and cross-attention blocks to capture complementary information within and across modalities respectively. Finally, constructing a multi-source node graph and a relation graph attention network is employed to capture the structural information of the graph. The ConvE modal is used as a decoder, and the performance is evaluated through link prediction tasks on real datasets. Experimental results show that new method out-performs other baselines, such as TransE, R-GCN, RSME, KG-BERT. © 2024 IEEE.

关键词： Knowledge graph

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Reasonable Gradients for Online Training Algorithms in Spiking Neural Networks 27

Reasonable Gradients for Online Training Algorithms in Spiki...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Xue, Lang Liu, HanWen Wang, Jing Qu, Hong School of Computer Science and Engineering University of Electronic Science and Technology of China China

ISBN: (纸本)9781643685489

Spiking neural networks (SNNs) have the potential to simulate sparse and spatio-temporal dynamics observed in biological neurons, making them promising for achieving energy-efficient artificial general intelligence. While backpropagation through time (BPTT) ensures reliable precision for training SNNs, it is hampered by high computation and storage complexity and does not conform to the instantaneous learning mechanism in brains. On the contrary, online training algorithms, which are biologically interpretable, offer low latency and memory efficiency, and are well-suited for on-chip learning applications. However, recent research exhibit a deficiency in the scientific comprehension of online gradients, which leads to certain limitations. To address this issue, we conduct an in-depth analysis of the calculation deviation in chain derivations induced by weight update and find two pivotal factors that affect the accuracy of online gradients: completeness and timeliness. To further enhance the performance of online training leveraging these findings, we propose spatio-temporal online learning (STOL), which substantially ameliorates the accuracy of the online gradients and demonstrates superior computation and memory efficiency. Our experiments on CIFAR-10, CIFAR-100, ImageNet, CIFAR10-DVS, and DVS128-Gesture datasets demonstrate that our method achieves state-of-the-art performance across most of these tasks. Besides, it shows a great improvement compared with existing online training algorithms. © 2024 The Authors.

关键词： Contrastive Learning

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A Multi-Modal Rumor Detection Method Based on Topic Features and Multi-Level Fusion 24

A Multi-Modal Rumor Detection Method Based on Topic Features...

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2024 7th International Conference on computer Information science and Artificial Intelligence, CISAI 2024

作者： Yang, Xiaolu Ge, Jike Wang, Zining Yu, Wencheng Zhang, Yifan Xiang, Hao School of Computer Science and Engineering Chongqing University of Science and Technology Chongqing China

ISBN: (纸本)9798400707254

Rumors can have a negative impact on social life, and multimodal rumors, which combine text and images, are more misleading and spread more widely than text-only rumors. Therefore, detecting multimodal rumors is particularly important. Existing multimodal rumor detection methods primarily focus on contextual information in the text and image information, neglecting other feature information, and simply concatenate features during fusion, which fails to fully utilize the multimodal information. To address these issues, we propose a multimodal rumor detection method based on topic features and multi-level fusion. First, contrastive learning is used to optimize the sentence embeddings of the pre-trained BERT model, enabling the model to learn high-quality embeddings that capture the semantics of sentences. Then, the optimized BERT model is employed to extract contextual semantic features from the text, while the LDA topic model is used to extract topic semantic features from the text. At the same time, the pre-trained ViT model is used to extract features from images, overcoming the limitation of small training samples. Next, a multi-level fusion of various features is performed to fully capture the potential relationships between different features. Finally, the obtained multimodal fused features are fed into the rumor detection module for classification. Experimental results show that our model outperforms mainstream multimodal baseline models on both Twitter and Weibo datasets, enhancing the effectiveness of multimodal rumor detection. © 2024 Copyright held by the owner/author(s).

关键词： Semantics

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Swin-DCIR based for transmission image reconstruction of TGS nuclear waste drums

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European Physical Journal Plus 2025年第6期140卷 1-17页

作者： Wei, Yuhong Shi, Rui Xu, Xinhua Yang, Guang Gou, Rui Tuo, Xianguo School of Computer Science and Engineering Sichuan University of Science and Engineering Yibin 644005 China School of Physics and Electrical Engineering Sichuan University of Science and Engineering Yibin 644005 China School of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu 610059 China

In the process of nuclear waste disposal and nuclear facility decommissioning, the amount of low-and-intermediate level radioactive waste will increase significantly. If it is not handled properly, it will threaten the development of the nuclear industry and human safety. Tomographic gamma scanning (TGS) technology is an effective method for non-destructive detection of low-and-intermediate level radioactive waste in sealed spaces, of which transmission imaging is an important part of TGS. The TGS transmission image reconstruction methods based on algebraic iterative algorithms and residual neural networks have made significant progress in improving image clarity and reconstruction speed, but they still face the problems of blurred details at the image edges, large noise bias, and poor objective evaluation indexes. Moreover, the quality of transmission image reconstruction directly impacts the reconstruction quality of subsequent emission images. Therefore, it becomes especially important to investigate a method that can preserve edge details, reduce noise, and improve reconstruction quality more effectively. In this paper, we propose an algorithmic model Swin-DCIR (Image Restoration Network with Swin Transformer and Dynamic Cross-Attention) for TGS transmission image reconstruction. The model introduces linear variable kernel convolution module and dynamic cross-attention network module in the shallow feature extraction module and deep feature extraction module, respectively. The introduced modules enhance the model's attention to global features and local details and improve the ability to capture details. In order to verify its significant advantages in TGS transmission image reconstruction, we conducted evaluation experiments on a homemade TGS transmission image dataset, and the experimental results show that the Swin-DCIR model has higher reconstruction accuracy, lower noise, and fewer parametric quantities in the dataset task, and the reconstructed image is vi

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Prolonging The Range of Low-power Visible Light Communication Systems with M-FSK 30

Prolonging The Range of Low-power Visible Light Communicatio...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Kang, Mengyu Wu, Jiyi Li, Jacheng Zhou, Yi Zhao, Zhiwei Zhou, Yaxin University of Electronic Science and Technology of China School of Computer Science and Engineering China

ISBN: (纸本)9798331515966

Visible light communication (VLC) has become a powerful and complementary technology to radio frequency (RF) communication thanks to its unrestricted bandwidth resources and high transmission rates. However, current VLC applications are limited to the short-ranged and interference-free scenarios because of the attenuation characteristics of light and the interference of ambient light. In order to extend VLC to various IoT applications, this paper tries to prolong the communication range of VLC systems based on M-frequency shift keying (MFSK), where M denotes the modulation index. Specifically, 1) we modulate the luminous intensity of the LEDs to form sine waves instead of square waves and tune the sine wave frequency to transmit data. Compared to light intensity modulation, it can effectively resist the ambient light interference. Meanwhile, 2) we propose a demodulation method based on gradient thresholding to increase its fault tolerance in decoding. In addition, 3) we built a real VLC platform and conducted comprehensive experiments to verify the communication distance and reliability of the low-power VLC systems (with 1W/3W LEDs). The experimental results show that the proposed work successfully prolongs the communication distance to 5.8 meters with 1 W LED and to 10 meters with 3 W LED, extending the VLC range by nearly 2. 5 x compared to existing work. © 2024 IEEE.

关键词： Light interference

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Leveraging user's preference and social circle for personalized recommendation via matrix factorization with sub-linear convergence rate

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Journal of Intelligent and Fuzzy Systems 2024年第1-2期47卷 1-13页

作者： Qiu, Laixiang Zhou, Wang Tian, Ying Ul Haq, Amin Ahmad, Sultan School of Computer and Software Engineering Xihua University Chengdu China Intelligent Policing Key Laboratory of Sichuan Province Sichuan Police College LuZhou China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj Saudi Arabia

Recommender systems aim to filter information effectively and recommend useful sources to match users' requirements. However, the exponential growth of information in recent social networks may cause low prediction accuracy for recommendation systems. This article proposes a unified personalized recommendation architecture referred to as PSRec, which incorporates user preference and social relationship into matrix factorization framework. Specifically, PSRec generates two collections for textual reviews and contextual information respectively, and performs preference learning for each user via the Latent Dirichlet Allocation topic model. Moreover, PSRec exploits the inner relations within the social circle for recommendation, including direct trust relationship and indirect trust relationship. Additionally, it's certificated that PSRec can converge at a sub-linear rate via theoretical analysis. Experimental results over DoubanMovie, CiaoDVDs and Yelp demonstrate the superiority of the proposed PSRec, which can achieve significant improvements and provide much better user experience while compared with other benchmark models. © 2024 - IOS Press. All rights reserved.

关键词： Matrix factorization

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Anti-Noise and Cross-Domain CSI Gesture Recognition with Multi-Features Fusion Transformer

Anti-Noise and Cross-Domain CSI Gesture Recognition with Mul...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Li, Xiaolong Yang, Heng Yi, Changyan Deng, Ruiting Wu, Xin Hunan University Of Technology and Business School of Computer Science Changsha China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing China

ISBN: (纸本)9798350351255

Human-machine interaction has sparked significant interest, leading to the rise of Channel State Information (CSI)-based gesture recognition systems. However, these systems often struggle with accuracy due to high noise levels and limited cross-domain performance. This paper presents a robust approach that enhances CSI-based gesture recognition by addressing these challenges. We introduce key concepts such as the CSI ratio and phase matrix and develop a robust data preprocessing method that reduces environmental noise while retaining dynamic components crucial for gesture recognition. Our method, WiMT, leverages a robust multi-features fusion transformer with spatiotemporal partitioning and a multiscale spatiotemporal self-attention mechanism to effectively capture both local spatial and global temporal features of gestures. Evaluations on the Widar3 dataset demonstrate that our model surpasses existing methods in in-domain and cross-domain gesture recognition tasks. © 2024 IEEE.

关键词： Channel state information

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A Hessian Recovery Based Linear Finite Element Method for Molecular Beam Epitaxy Growth Model with Slope Selection

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Advances in Applied Mathematics and Mechanics 2024年第1期16卷 1-23页

作者： Minqiang Xu Qingsong Zou College of Science Zhejiang University of TechnologyHangzhouZhejiang 310023China School of Computer Science and Engineering Sun Yat-Sen UniversityGuangzhouGuangdong 510275China School of Computer Science and Engineering and Guangdong Province Key Laboratory of Computational ScienceSun Yat-Sen UniversityGuangzhouGuangdong 510275China

In this paper,we present a Hessian recovery based linear finite element method to simulate the molecular beam epitaxy growth model with slope *** the time discretization,we apply a first-order convex splitting method and secondorder Crank-Nicolson *** the space discretization,we utilize the Hessian recovery operator to approximate second-order derivatives of a C^(0)linear finite element function and hence the weak formulation of the fourth-order differential operator can be discretized in the linear finite element *** energy-decay property of our proposed fully discrete schemes is rigorously *** robustness and the optimal-order convergence of the proposed algorithm are numerically *** a large spatial domain for a long period,we simulate coarsening dynamics,where 1/3-power-law is observed.

关键词： Molecular beam epitaxy Hessian recovery linear finite element method superconvergence.

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A Holistic Secure Communication Mechanism Using a Multilayered Cryptographic Protocol to Enhanced Security

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computers, Materials & Continua 2024年第3期78卷 4417-4452页

作者： Fauziyah Zhaoshun Wang Mujahid Tabassum School of Computer and Communication Engineering University of Science and TechnologyBeijing100000China Noroff School of Technology and Digital Media(Noroff Accelerate) Noroff University CollegeKristiansand4612Norway

In an era characterized by digital pervasiveness and rapidly expanding datasets,ensuring the integrity and reliability of information is *** cyber threats evolve in complexity,traditional cryptographic methods face increasingly sophisticated *** article initiates an exploration into these challenges,focusing on key exchanges(encompassing their variety and subtleties),scalability,and the time metrics associated with various cryptographic *** propose a novel cryptographic approach underpinned by theoretical frameworks and practical *** to this approach is a thorough analysis of the interplay between Confidentiality and Integrity,foundational pillars of information *** method employs a phased strategy,beginning with a detailed examination of traditional cryptographic processes,including Elliptic Curve Diffie-Hellman(ECDH)key *** also delve into encrypt/decrypt paradigms,signature generation modes,and the hashes used for Message Authentication Codes(MACs).Each process is rigorously evaluated for performance and *** gain a comprehensive understanding,a meticulously designed simulation was conducted,revealing the strengths and potential improvement areas of various ***,our cryptographic protocol achieved a confidentiality metric of 9.13 in comprehensive simulation runs,marking a significant advancement over existing ***,with integrity metrics at 9.35,the protocol’s resilience is further *** metrics,derived from stringent testing,underscore the protocol’s efficacy in enhancing data security.

关键词： Cryptographic security privacy preservation decryption integrity

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Enhanced Cybercrime Detection on Twitter Using Aho-Corasick Algorithm and Machine Learning Techniques

Informatica (Slovenia)

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Informatica (Slovenia) 2024年第18期48卷 97-108页

作者： Rawat, Romil Raj, A. Samson Arun Chakrawarti, Rajesh Kumar Sankaran, Krishnan Sakthidasan Sarangi, Sanjaya Kumar Rawat, Hitesh Rawat, Anjali Department of Computer Science Engineering Shri Vaishnav Institute of Information Technology Shri Vaishnav Vidyapeeth Vishwavidyalaya Indore India Division of Computer Science and Engineering School of Computer Science and Technology Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore India MP Indore India Department od Electronics and Communication Engeneering Hindustan Institute of Technology and Science Chennai India Department of Computer Science and Engineering Utkal University Bhubaneswar India Department of Business Management and Economics University of Extremadura Spain Department of Computer and Communication Technology University of Extremadura Spain

The proposed work objective is to adapt Online social networking (OSN) is a type of interactive computer-mediated technology that allows people to share information through virtual networks. The microblogging feature of Twitter makes cyberspace prominent (usually accessed via the dark web). The work used the datasets and considered the Scrape Twitter Data (Tweets) in Python using the SN-Scrape module and Twitter 4j API in JAVA to extract social data based on hashtags, which is used to select and access tweets for dataset design from a profile on the Twitter platform based on locations, keywords, and hashtags. The experiments contain two datasets. The first dataset has over 1700 tweets with a focus on location as a keypoint (hacking-for-fun data, cyber-violence data, and vulnerability injector data), whereas the second dataset only comprises 370 tweets with a focus on reposting of tweet status as a keypoint. The method used is focused on a new system model for analysing Twitter data and detecting terrorist attacks. The weights of susceptible keywords are found using a ternary search by the Aho-Corasick algorithm (ACA) for conducting signature and pattern matching. The result represents the ACA used to perform signature matching for assigning weights to extracted words of tweet. ML is used to evaluate Twitter data for classifying patterns and determining the behaviour to identify if a person is a terrorist. SVM (Support Vector Machine) proved to be a more accurate classifier for predicting terrorist attacks compared to other classifiers (KNN- K-Nearest Neighbour and NB-Naïve Bayes). The 1st dataset shows the KNN-Acc. -98.38% and SVM Accuracy as 98.85%, whereas the 2nd dataset shows the KNN-Acc. -91.68% and SVM Accuracy as 93.97%. The proposed work concludes that the generated weights are classified (cyber-violence, vulnerability injector, and hacking-for-fun) for further feature classification. Machine learning (ML) [KNN and SVM] is used to predict the occurrence and

关键词： Tweets

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