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3rd International conference on computer, Big Data and Artificial Intelligence, ICCBDAI 2022

作者： Liu, Shilong Liu, Yang Zhang, Xiaotong Xu, Cheng He, Jie Qi, Yue School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China

One crucial challenge in the recommendation research field is the cold-start problem. Meta-learning is a feasible algorithm to reduce the error of cold-start recommendation because it can adjust to new tasks rapidly through relatively few updates. However, meta-learning does not take the diverse interests of users into account, which limits the performance improvement in cold-start scenarios. We proposed a recommendation model called attentional meta-learned user preference estimator that combines attention network and meta-learning. This method enhances the ability of modelling the personalized user interest by learning the weights between users and items based on attention mechanism, then improves the performance of cold-start recommendation. We validated the model with two publicly available datasets in the recommendation field. Compared with the three benchmark methods, the proposed model reduces the mean absolute error by at least 2.3% and the root mean square error of 2.5%. © Published under licence by IOP Publishing Ltd.

关键词： Errors

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Online education course recommendation algorithm based on multimodal graph attention network 3

Online education course recommendation algorithm based on mu...

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3rd International Symposium on computer Applications and Information Systems, ISCAIS 2024

作者： Li, Gang Hou, Yuchen School of Computer and Communication Engineering Beijing University of Science and Technolgy Beijing100083 China

ISBN: (纸本)9781510681316

With the rapid development of online learning platforms, learners have more opportunities to choose courses. However, many mainstream education platforms currently lack personalized recommendation process, and due to the diversity of course quality and content, many students find it difficult to choose more suitable courses for themselves. Therefore, personalized course recommendation plays a core role in online education platforms. The existing art education curriculum has a variety of attributes, and these attributes also include a variety of modal forms (such as course video, course pictures and course introduction);Learners themselves also have a variety of attributes, their age, educational background have an impact on the recommendation results;There is also information exchange between learners and the course, such as rating on the course, which can reflect the degree of learners' interest in the course and connect them to each other. Therefore, this paper constructs a knowledge graph, which regards learners and courses as entities and the multi-modal form of courses as course attributes. Taking learners' personal information as learner attributes and different information exchanges between learners and courses as relationships .We use the applicability of graph networks to graph structures to capture learners' preferences for different modal attributes, so as to make more accurate personalized recommendations. In this paper, MOOCCube is used as the main data set, and experiments show that the proposed algorithm is better than the baseline. © 2024 SPIE.

关键词： E-learning

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Research on Intelligent Signal Detection in the Internet of Things Based on Deep Learning Algorithms 24

Research on Intelligent Signal Detection in the Internet of ...

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3rd International Conference on Algorithms, Data Mining, and Information technology, ADMIT 2024

作者： Liu, Xingcen School of Information and Communication Engineering Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9798400718120

With the rapid development of Internet of Things technology, intelligent signal detection has become a highly challenging key issue. Traditional manual feature extraction methods are no longer able to cope with complex and ever-changing practical environments. In response to this issue, this study proposes an intelligent signal detection model for the Internet of Things based on deep learning. This model adopts a convolutional neural network structure, which can automatically learn local pattern features in signals, thereby improving its adaptability to complex environments. Research a large-scale wireless signal dataset constructed based on real IoT scenarios, and adopt reasonable data preprocessing and augmentation strategies. Through scientific model training and multi-dimensional evaluation index settings, a comprehensive evaluation of the proposed model was conducted on the test set. The experimental results show that the model has achieved excellent performance over traditional methods in key indicators such as accuracy, recall, and F1 score, especially in complex signal recognition scenarios. © 2024 Copyright held by the owner/author(s).

关键词： Deep learning algorithms

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DeepOCL:A deep neural network for Object Constraint Language generation from unrestricted nature language

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CAAI Transactions on Intelligence technology 2024年第1期9卷 250-263页

作者： Yilong Yang Yibo Liu Tianshu Bao Weiru Wang Nan Niu Yongfeng Yin School of Software Beihang UniversityBeijingChina College of Computer Science and Technology Guizhou UniversityGuiyangGuizhouChina Faculty of Information Technology Beijing University of TechnologyBeijingChina Department of Electrical Engineering and Computer Sciences University of CincinnatiCincinnatiOhioUSA

Object Constraint Language(OCL)is one kind of lightweight formal specification,which is widely used for software verification and validation in NASA and Object Management Group *** OCL provides a simple expressive syntax,it is hard for the developers to write correctly due to lacking knowledge of the mathematical foundations of the first-order logic,which is approximately half accurate at the first stage of devel-opment.A deep neural network named DeepOCL is proposed,which takes the unre-stricted natural language as inputs and automatically outputs the best-scored OCL candidates without requiring a domain conceptual model that is compulsively required in existing rule-based generation *** demonstrate the validity of our proposed approach,ablation experiments were conducted on a new sentence-aligned dataset named *** experiments show that the proposed DeepOCL can achieve state of the art for OCL statement generation,scored 74.30 on BLEU,and greatly outperformed experienced developers by 35.19%.The proposed approach is the first deep learning approach to generate the OCL expression from the natural *** can be further developed as a CASE tool for the software industry.

关键词： deep learning OCL software engineering

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Towards Blockchain-Based Secure BGP Routing,Challenges and Future Research Directions

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computers, Materials & Continua 2024年第5期79卷 2035-2062页

作者： Qiong Yang Li Ma Shanshan Tu Sami Ullah MuhammadWaqas Hisham Alasmary Faculty of Information Technology Beijing University of TechnologyBeijing100124China School of Information Science and Technology North China University of TechnologyBeijing100144China Department of Computer Science Shaheed Benazir Bhutto UniversitySheringal18050Pakistan School of Computing and Mathematical Science Faculty of Engineering and ScienceUniversity of GreenwichLondonSE109LSUK School of Engineering Edith Cowan UniversityPerth6027Australia Department of Computer Science King Khalid UniversityAbha61421Saudi Arabia

Border Gateway Protocol(BGP)is a standard inter-domain routing protocol for the Internet that conveys network layer reachability information and establishes routes to different *** BGP protocol exhibits security design defects,such as an unconditional trust mechanism and the default acceptance of BGP route announcements from peers by BGP neighboring nodes,easily triggering prefix hijacking,path forgery,route leakage,and other BGP security ***,the traditional BGP security mechanism,relying on a public key infrastructure,faces issues like a single point of failure and a single point of *** decentralization,anti-tampering,and traceability advantages of blockchain offer new solution ideas for constructing secure and trusted inter-domain routing *** this paper,we summarize the characteristics of BGP protocol in detail,sort out the BGP security threats and their ***,we analyze the shortcomings of the traditional BGP security mechanism and comprehensively evaluate existing blockchain-based solutions to address the above problems and validate the reliability and effectiveness of blockchain-based BGP security methods in mitigating BGP security ***,we discuss the challenges posed by BGP security problems and outline prospects for future research.

关键词： BGP security blockchain prefix hijacking trust model secure routing

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Frequency-enhanced and decomposed transformer for multivariate time series anomaly detection

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Applied Intelligence 2025年第6期55卷 1-18页

作者： Li, Shijiang Wang, Zhihai Wang, Xiaokang Yin, Zihao Yao, Muyun School of Computer Science and Technology Beijing Jiaotong University Beijing China School of Automation and Intelligence Beijing Jiaotong University Beijing China Engineering Center on Computer Network Management in Hi-Speed Railway Ministry of Education Beijing Jiaotong University Beijing China

With the widespread adoption of the Internet of Things (IoT), vast amounts of multivariate time series data are generated, which reflect the operational status of systems. Accurate and efficient anomaly detection in these data is crucial for maintaining system stability. However, data from unstable environments often exhibit high volatility, data drift, and complex patterns of anomalies. Unsupervised anomaly detection models are typically designed for stable data and lack generalizability, leading to a high rate of false positives when applied to unstable data. This paper introduces the frequency-enhanced and decomposed transformer for anomaly detection (FDTAD), which is a novel anomaly detection model based on a transformer that is enhanced with frequency and time series decomposition. FDTAD addresses data drift by decomposing time series and leverages both time-domain and frequency-domain information to improve the generalization ability of the model. The model preserves major amplitudes in the frequency domain to extract primary periodic patterns, uses spectral residuals to capture detailed variations, and incorporates a frequency-domain correlation attention mechanism to extract dependencies in frequency-domain data in a sparse representation. Additionally, a spatiotemporal module is designed to extract the temporal correlations in the data and spatial correlations among the data with different attributes. FDTAD combines a data periodic pattern reconstructor and a data detailed pattern reconstructor through an adversarial mechanism to achieve maximum accuracy in reconstructing normal data. Extensive experiments on 10 public datasets demonstrate that FDTAD outperforms state-of-the-art baseline methods, with a 4.1% improvement in the F1 score and a 4.7% improvement in precision. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Time series

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Align vision-language semantics by multi-task learning for multi-modal summarization

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Neural Computing and Applications 2024年第25期36卷 15653-15666页

作者： Cui, Chenhao Liang, Xinnian Wu, Shuangzhi Li, Zhoujun School of Cyber Science and Technology Beihang University Beijing100191 China School of Computer Science and Engineering Beihang University Beijing100191 China Cloud Xiaowei Tencent Beijing100089 China

Most current multi-modal summarization methods follow a cascaded manner, where an off-the-shelf object detector is first used to extract visual features. After that, these visual features are fused with language representations for the decoder to generate the text summary. However, the cascaded way employs separate encoders for different modalities, which makes it hard to learn the joint vision and language representation. In addition, they also ignore the semantics alignment between paragraphs and images for multi-modal summarization tasks, which are crucial to a precise summary. To tackle these issues, in this paper, we propose ViL-Sum to jointly model paragraph-level Vision-Language Semantic Alignment and Multi-Modal Summarization. Our ViL-Sum contains two components for better learning multi-modal semantics and aims to align them. The first one is a joint multi-modal encoder. The other one is two well-designed tasks for multi-task learning, including image reordering and image selection. Specifically, the joint multi-modal encoder converts images into visual embeddings and attaches them with text embedding as the input of the encoder. The reordering task guides the model to learn paragraph-level semantic alignment, and the selection task guides the model to select summary-related images in the final summary. Experimental results show that our proposed ViL-Sum outperforms current state-of-the-art methods on most automatic and manual evaluation metrics. In further analysis, we find that two well-designed tasks and a joint multi-modal encoder can effectively guide the model to learn reasonable paragraph-image and summary-image relations. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Embeddings

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Digital twin empowered cooperative trajectory planning of platoon vehicles for collision avoidance with unexpected obstacles

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Digital communications and Networks 2024年第6期10卷 1666-1676页

作者： Hao Du Supeng Leng Jianhua He Kai Xiong Longyu Zhou School of Information&Communication Engineering University of Electronic Science and Technology of ChinaChengdu 611731China School of Computer Science and Electronic Engineering University of EssexColchester CO43SQUnited Kingdom Shenzhen Institute for Advanced Study University of Electronic Science and Technology of ChinaShenzhen 518110China

Road obstacles that unexpectedly appear due to vehicle breakdowns and accidents are major causes of fatal road *** Autonomous Vehicles(CAVs)can be used to avoid collisions to ensure road safety through cooperative sensing and ***,the collision avoidance performance of CAVs with unexpected obstacles has not been studied in the existing *** this paper,we first design a platoon-based collision avoidance framework for *** this framework,we deploy a Digital Twin(DT)system at the head vehicle in a platoon to reduce communication overhead and decision-making delay based on a proposed trajectory planning *** addition,a DT-assistant system is deployed on the assistant vehicle to monitor vehicles out of the sensing range of the head vehicle for the maintenance of the DT *** this case,the transmission frequency of kinetic states of platoon members can be reduced to ensure low-overhead ***,we design a variable resource reservation interval that can ensure DT synchronization between DT and the assistant system with high *** further improve road safety,an urgency level-based trajectory planning algorithm is proposed to avoid unexpected obstacles considering different levels of emergency *** results show that our DT system-based scheme can achieve significant performance gains in unexpected obstacle *** to the existing schemes,it can reduce collisions by 95%and is faster by about 10%passing by the unexpected obstacle.

关键词： Platoon driving Cooperative trajectory planning Digital twin Obstacle avoidance

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Edge-Enabled Metaverse:The Convergence of Metaverseand Mobile Edge Computing

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Tsinghua science and technology 2024年第3期29卷 795-805页

作者： Nyothiri Aung Sahraoui Dhelim Liming Chen Huansheng Ning Luigi Atzori Tahar Kechadi Information Engineering College Xinjiang Institute of EngineeringUrumqi 830023Chinaand also with School of Computer ScienceUniversity College DublinDublinD04 V1W8Ireland School of Computing Ulster UniversityBelfastBT151EDUK School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Department of Electrical and Electronic Engineering University of CagliariSardinia 09124Italy School of Computer Science University College DublinDublinD04 V1W8Ireland

Metaverse is a virtual environment where users are represented by their avatars to navigate a virtual world having strong links with its physical *** state-of-the-art Metaverse architectures rely on a cloud-based approach for avatar physics emulation and graphics rendering *** current centralized architecture of such systems is unfavorable as it suffers from several drawbacks caused by the long latency of cloud access,such as low-quality *** this end,we propose a Fog-Edge hybrid computing architecture for Metaverse applications that leverage an edge-enabled distributed computing *** applications leverage edge devices’computing power to perform the required computations for heavy tasks,such as collision detection in the virtual universe and high-computational 3D physics in virtual *** computational costs of a Metaverse entity,such as collision detection or physics emulation,are performed at the device of the associated physical *** validate the effectiveness of the proposed architecture,we simulate a distributed social Metaverse *** simulation results show that the proposed architecture can reduce the latency by 50%when compared with cloud-based Metaverse applications.

关键词： industrial Metaverse edge computing Blockchain virtual environment task offloading fog computing

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Dynamic Routing of Multiple QoS-Required Flows in Cloud-Edge Autonomous Multi-Domain Data Center Networks

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computers, Materials & Continua 2024年第2期78卷 2287-2308页

作者： Shiyan Zhang Ruohan Xu Zhangbo Xu Cenhua Yu Yuyang Jiang Yuting Zhao School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing100876China College of Computer Science and Technology Tianjin UniversityTianjin300072China Institute of Artificial Intelligence Nankai UniversityTianjin300071China College of Information Science and Engineering East China University of Science and TechnologyShanghai200237China

The 6th generation mobile networks(6G)network is a kind of multi-network interconnection and multi-scenario coexistence network,where multiple network domains break the original fixed boundaries to form connections and *** this paper,with the optimization objective of maximizing network utility while ensuring flows performance-centric weighted fairness,this paper designs a reinforcement learning-based cloud-edge autonomous multi-domain data center network architecture that achieves single-domain autonomy and multi-domain *** to the conflict between the utility of different flows,the bandwidth fairness allocation problem for various types of flows is formulated by considering different defined reward *** the tradeoff between fairness and utility,this paper deals with the corresponding reward functions for the cases where the flows undergo abrupt changes and smooth changes in the *** addition,to accommodate the Quality of Service(QoS)requirements for multiple types of flows,this paper proposes a multi-domain autonomous routing algorithm called LSTM+*** a Long Short-Term Memory(LSTM)layer in the actor and critic networks,more information about temporal continuity is added,further enhancing the adaptive ability changes in the dynamic network *** LSTM+MADDPG algorithm is compared with the latest reinforcement learning algorithm by conducting experiments on real network topology and traffic traces,and the experimental results show that LSTM+MADDPG improves the delay convergence speed by 14.6%and delays the start moment of packet loss by 18.2%compared with other algorithms.

关键词： Multi-Domain data center networks autonomous routing

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