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14th International Conference on Electronics, Communications and Networks, CECNet 2024

作者： Xie, Guoli Yang, Lei Tian, Liwei Li, Sijin Lai, Jiacheng Guangdong University of Science and Technology Dongguan China Dongguan Key Laboratory of Industrial Software for Intelligent Manufacturing China

ISBN: (纸本)9781643685632

This research focuses on the predictive analysis of ChiNext and blue-chip stocks in the Chinese stock market, employing the random forest model to conduct in-depth research on the stock trends of these two sectors. In the experimental process, we randomly selected six stocks from each sector as research samples, aiming to explore whether there are significant differences in prediction results between the two. Through rigorous experimental verification and data analysis, we found no significant difference in the predictive effect of random forest models between ChiNext and blue-chip stocks. © 2024 The Authors.

关键词： Prediction models

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Unsupervised Multimodal Graph Contrastive Semantic Anchor Space Dynamic Knowledge Distillation Network for Cross-Media Hash Retrieval 40

Unsupervised Multimodal Graph Contrastive Semantic Anchor Sp...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Yu, Yang Liang, Meiyu Yin, Mengran Lu, Kangkang Du, Junping Xue, Zhe Beijing University of Posts and Telecommunications Beijing Key Laboratory of Intelligent Communication Software and Multimedia Beijing100876 China

ISBN: (纸本)9798350317152

Cross-media hash retrieval are efficient and effective techniques for retrieval on multi-media database. The success of the Multimodal Large Models (MLM) provides a valuable direction to enhance the accuracy of multimodal hash retrieval, which achieves decent retrieval accuracy with finetuning the pretrained multimodal large models, but their massive model parameters significantly reduce retrieval efficiency. Knowledge Distillation (KD) methods enable small models to learn from the knowledge of larger models, achieving a reduction in model parameter count while ensuring a certain level of accuracy. However, current KD methods face challenges when applied in the multimodal domain, as it requires preserving the multimodal semantic information while minimizing accuracy degradation. To address these challenges, we propose a novel unsupervised multimodal graph contrastive semantic anchor space dynamic knowledge distillation network for cross-media hash retrieval (GASKN). Firstly, to obtain a multimodal semantic anchor space, we construct a large multimodal fusion teacher model using the BEiT-3 model as the backbone. This teacher model is capable of encoding data from different modalities, such as images and text, using the same multimodal encoder to acquire multimodal hash codes that contain rich information from both modalities simultaneously. Secondly, to ensure efficient retrieval capabilities for the student model, we utilize the ALBERT text encoding model and the BiFormer image encoding model as the compact student model's backbones. This allows us to build a lightweight student model with only a twentieth of the parameter count of the teacher model. We propose a dynamic knowledge distillation technique to transfer the multimodal semantic anchor space knowledge embedded in the multimodal large teacher model to the lightweight student model as much as possible. Thirdly, to further distill the structural knowledge of the semantic anchor space from the teacher model to

关键词： Semantics

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Defect detection methods for smart contracts based on multimodality

Defect detection methods for smart contracts based on multim...

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2024 International Conference on Image, Signal Processing, and Pattern Recognition, ISPP 2024

作者： Tambadou, Sidy Zhang, Xueyuan Wang, Guanghui Zuo, Fang School of Software Henan International Joint Laboratory of Intelligent Network Theory and Key Technology Henan University China

ISBN: (纸本)9781510680425

With the popularization of blockchain technology, the application of smart contracts in various scenarios is becoming increasingly ***, due to its inherent complexity and dynamism, the security issues of smart contracts are gradually becoming *** order to effectively detect and repair defects in smart contracts, this paper proposes a multimodal smart contract defect detection *** method integrates multimodal data information, including visual, semantic, and inheritance relationship structural *** combines the Transform method to comprehensively analyze and identify potential defects in the operational status of smart *** with other mainstream methods, it has been proven that our method can more comprehensively identify and locate defects and improve detection accuracy and coverage. © 2024 SPIE.

关键词： Smart manufacturing

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Blockchain-Enabled Secure, Fair and Scalable Data Sharing in Zero-Trust Edge-End Environment

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 2056-2069页

作者： Xu, Xiaolong Meng, Ke Xiang, Haolong Cui, Guangming Xia, Xiaoyu Dou, Wanchun Nanjing210044 China Nanjing University of Information Science and Technology School of Software and the Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Nanjing210044 China RMIT University School of Computing Technologies MelbourneVIC3000 Australia Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China

In edge computing, the Zero-Trust Security Model (ZTSM), as a key enabling technology for next-generation networks, plays a crucial role in providing authentication for addressing data sharing concerns, such as frequent data breaches, data misuse, and cyberattacks. However, due to the complexity and diversity of edge environments, ZTSM struggles to meet the security requirements of data sharing frameworks solely through enhanced authentication. Consequently, such frameworks with ZTSM still face challenges in ensuring data integrity, evaluating various node behaviors, and coping with the increasing complexity of node attributes. To address these issues, we propose a blockchain-enabled secure, fair and scalable data sharing framework in a zero-trust edge-end environment in this paper. Specifically, we first propose a Merkle forest-based data storage model for the classified storage of loosely coupled data, consequently enhancing the scalability of the model. Then, we design a node behavior-based reputation assessment mechanism to ensure fairness during data sharing. Moreover, a data sharing protocol supervised by smart contract is proposed, working with the aforementioned storage and assessment schemes, to ensure the security of data sharing. Finally, comprehensive security analysis validates the security, fairness and scalability of the proposed framework. Extensive experimental results show that, as transaction volume grows, the time cost of data traversal in the storage model becomes progressively more efficient. Additionally, when the size of the smart contract is increased tenfold, the maximum time cost of the data sharing protocol rises by only 4.98 times. © 1983-2012 IEEE.

关键词： Data integrity

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Decision Tree Prediction Based on Preceding Feature engineering

Decision Tree Prediction Based on Preceding Feature Engineer...

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2024 International Conference on Optical Communication and Optoelectronic Technology, OCOT 2024

作者： Xie, Guoli Yang, Lei Li, Sijin Lai, Jiacheng Guangdong University of Science and Technology Dongguan China Dongguan Key Laboratory of Industrial Software for Intelligent Manufacturing China

ISBN: (纸本)9781510683266

This research employed feature engineering techniques to preprocess an original stock dataset, followed by the introduction of a decision tree prediction model for forecasting the dataset. Experimental results demonstrate an enhancement in predictive performance, offering a more effective analytical tool for forecasting stock market trends. This approach also serves as an inspiration in fields such as optoelectronic signal processing, optical image recognition, and optical computation and processing. © 2024 SPIE.

关键词： Prediction models

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MLDA:a multi-level k-degree anonymity scheme on directed social network graphs

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Frontiers of Computer Science 2024年第2期18卷 199-215页

作者： Yuanjing HAO Long LI Liang CHANG Tianlong GU Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin 541004China Engineering Research Center of Trustworthy AI(Ministry of Education) Jinan UniversityGuangzhou 510632China

With the emergence of network-centric data,social network graph publishing is conducive to data analysts to mine the value of social networks,analyze the social behavior of individuals or groups,implement personalized recommendations,and so ***,published social network graphs are often subject to re-identification attacks from adversaries,which results in the leakage of users’***-anonymity technology is widely used in the field of graph publishing,which is quite effective to resist re-identification ***,the current researches still exist some issues to be solved:the protection of directed graphs is less concerned than that of undirected graphs;the protection of graph structure is often ignored while achieving the protection of nodes’identities;the same protection is performed for different users,which doesn’t meet the different privacy requirements of ***,to address the above issues,a multi-level-degree anonymity(MLDA)scheme on directed social network graphs is proposed in this ***,node sets with different importance are divided by the firefly algorithm and constrained connectedness upper approximation,and they are performed different-degree anonymity protection to meet the different privacy requirements of ***,a new graph anonymity method is proposed,which achieves the addition and removal of edges with the help of fake *** addition,to improve the utility of the anonymized graph,a new edge cost criterion is proposed,which is used to select the most appropriate edge to be ***,to protect the community structure of the original graph as much as possible,fake nodes contained in a same community are merged prior to fake nodes contained in different *** results on real datasets show that the newly proposed MLDA scheme is effective to balance the privacy and utility of the anonymized graph.

关键词： directed social network graph graph publishing k-degree anonymity community structure graph utility

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A Novel Locomotion Rule Rmbedding Long Short-Term Memory Network with Attention for Human Locomotor Intent Classification Using Multi-Sensors Signals

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Computers, Materials & Continua 2024年第6期79卷 4349-4370页

作者： Jiajie Shen Yan Wang Dongxu Zhang Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of EducationColleague of Computer Science and TechnologyJilin UniversityChangchun130012China College of Software and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of EducationJilin UniversityChangchun130012China

Locomotor intent classification has become a research hotspot due to its importance to the development of assistive robotics and wearable *** work have achieved impressive performance in classifying steady locomotion ***,it remains challenging for these methods to attain high accuracy when facing transitions between steady locomotion *** to the similarities between the information of the transitions and their adjacent steady ***,most of these methods rely solely on data and overlook the objective laws between physical activities,resulting in lower accuracy,particularly when encountering complex locomotion modes such as *** address the existing deficiencies,we propose the locomotion rule embedding long short-term memory(LSTM)network with Attention(LREAL)for human locomotor intent classification,with a particular focus on transitions,using data from fewer sensors(two inertial measurement units and four goniometers).The LREAL network consists of two levels:One responsible for distinguishing between steady states and transitions,and the other for the accurate identification of locomotor *** classifier in these levels is composed of multiple-LSTM layers and an attention *** introduce real-world motion rules and apply constraints to the network,a prior knowledge was added to the network via a rule-modulating *** method was tested on the ENABL3S dataset,which contains continuous locomotion date for seven steady and twelve transitions *** results showed that the LREAL network could recognize locomotor intents with an average accuracy of 99.03%and 96.52%for the steady and transitions states,*** is worth noting that the LREAL network accuracy for transition-state recognition improved by 0.18%compared to other state-of-the-art network,while using data from fewer sensors.

关键词： Lower-limb prosthetics deep neural networks motion classification

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Multimodal Interactive Network for Sequential Recommendation

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Journal of Computer Science & Technology 2023年第4期38卷 911-926页

作者：韩滕跃王鹏飞牛少彰 Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia Beijing University of Posts and TelecommunicationsBeijing 100876China School of Computer Science Beijing University of Posts and TelecommunicationsBeijing 100876China

Building an effective sequential recommendation system is still a challenging task due to limited interactions among users and *** work has shown the effectiveness of incorporating textual or visual information into sequential recommendation to alleviate the data sparse *** data sparse problem now is attracting a lot of attention in both industry and academic ***,considering interactions among modalities on a sequential scenario is an interesting yet challenging task because of multimodal *** this paper,we introduce a novel recommendation approach of considering both textual and visual information,namely Multimodal Interactive Network(MIN).The advantage of MIN lies in designing a learning framework to leverage the interactions among modalities from both the item level and the sequence level for building an efficient ***,an item-wise interactive layer based on the encoder-decoder mechanism is utilized to model the item-level interactions among modalities to select the informative ***,a sequence interactive layer based on the attention strategy is designed to capture the sequence-level preference of each *** seamlessly incorporates interactions among modalities from both the item level and the sequence level for sequential *** is the first time that interactions in each modality have been explicitly discussed and utilized in sequential *** results on four real-world datasets show that our approach can significantly outperform all the baselines in sequential recommendation task.

关键词： multi-modality interactive network sequential recommendation

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Research on the application of a hybrid model based on deep learning and cluster analysis in anomaly detection of cloud platform access 4

Research on the application of a hybrid model based on deep ...

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4th International Conference on Computer Vision and Pattern Analysis, ICCPA 2024

作者： Li, Guohua Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou China Guangxi Colleges and Universities Key Laboratory of Intelligent Industry Software Wuzhou University Wuzhou China

ISBN: (纸本)9781510682528

With the rapid development of digitalization, cloud platforms have become a key technology supporting remote networking. However, the accompanying cybersecurity issues, particularly the detection of anomalous access activities, pose significant challenges in maintaining platform stability and user data security. This paper proposes a hybrid model that integrates a one-dimensional convolutional neural network (1D-CNN) with the DBSCAN clustering algorithm, aimed at enhancing the detection accuracy of anomalous access behaviors in cloud platforms. Through in-depth analysis and feature extraction of network traffic data, combined with the effective identification of abnormal patterns by the clustering algorithm, this study not only improves detection efficiency but also enhances the model's adaptability to novel anomalous behaviors. Experimental results demonstrate that this model surpasses traditional detection methods in multiple performance metrics such as accuracy, precision, recall, and F1 score, confirming its feasibility and effectiveness in practical application scenarios. © 2024 SPIE.

关键词： Cloud platforms

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Expression Recognition and intelligent Classroom State Mining in Teaching Videos Based on Semi-Supervised Learning Generative Adversarial Network 5

Expression Recognition and Intelligent Classroom State Minin...

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5th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2023

作者： Fan, Ruoyu Liang, Meiyu Yin, Mengran Du, Junping Multimedia School of Computer Science National Pilot Software Engineering School Beijing University of Posts and Telecommunications Beijing Key Laboratory of Intelligent Communication Software Beijing China

ISBN: (纸本)9798350393521

We constructed a student classroom teaching video dataset, constructed a student classroom expression dataset through video frame extraction, face detection, face alignment, and facial extraction, and constructed a student classroom behavior dataset through object detection algorithm. Aiming at the characteristics that the annotation information of the students' classroom expression dataset constructed in this paper is limited and the data of some categories are less, an expression recognition method based on semi-supervised learning generative adversarial network (SSLGAN) is proposed. The problem of limited annotation information is solved through semi-supervised learning, and the problem of less data of some categories is solved through data enhancement of generative adversarial network. An intelligent classroom state mining method that integrates student expressions and behaviors has been proposed, achieving intelligent classroom state mining of teaching videos and automatic evaluation of classroom listening status for individual and overall students in different types of classrooms, such as interactive classes, discussion classes, and self-study classes. Compared with CNN, RMN, Temporal Ensembling and Mean Teacher, the proposed SSLGAN has achieved the highest accuracy in the benchmark dataset FER2013, JAFFE, RaFD and self-built student classroom expression dataset. In the case where there are few labels in the self-built student classroom expression dataset, the accuracy of student expression recognition results still reaches 76%, and the proposed intelligent classroom state mining method that integrates student expressions and behaviors for different types of classrooms also has high accuracy on the self-built student classroom teaching video dataset. © 2023 IEEE.

关键词： Students

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