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Personalized Review Recommendation based on Implicit dimension mining

Personalized Review Recommendation based on Implicit dimensi...

2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024

作者： Xu, Bei Xu, Yifan School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing210023 China

Users usually browse product reviews before buying products from e-commerce websites. Lots of e-commerce websites can recommend reviews. However, existing research on review recommendation mainly focuses on the general usefulness of reviews and ignores personalized and implicit requirements. To address the issue, we propose a Large language model driven Personalized Review Recommendation model based on Implicit dimension mining (PRR-LI). The model mines implicit dimensions from reviews and requirements, and encodes them in the form of "text + dimension". The experiments show that our model significantly outperforms other state-of-the-art textual models on the Amazon-MRHP dataset, with some of the metrics outperforming the state-of-the-art multimodal models. And we prove that encoding "text + dimension" is better than encoding "text" and "dimension" separately in review recommendation. © 2024 Association for Computational Linguistics.

关键词： Signal encoding

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Detecting Vulnerabilities in Ethereum Smart Contracts with Deep Learning 4

Detecting Vulnerabilities in Ethereum Smart Contracts with D...

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4th International Conference on data Intelligence and security, ICDIS 2022

作者： Wu, Zhendong Li, Shan Wang, Bin Liu, Tianjian Zhu, Yongsheng Zhu, Chenming Hu, Mingqing National Key Laboratory of Science and Tecknology on Information System Security China Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong University China Zhejiang Electronic Information Products Inspection and Research Institute China IFLYTEK Co. Ltd China

ISBN: (数字)9781665459686

ISBN: (纸本)9781665459686

Decentralized applications and smart contracts running on Ethereum have been widely applied in many fields, such as finance and logistics. On the one hand, the number of smart contracts surges. On the other hand, smart contracts often carry million dollars. Once there is a severe vulnerability in a smart contract, huge losses may occur. Detection tools like Oyente and Mythril were developed for vulnerabilities in smart contracts. However, many of these tools many detection tools do not use intrinsic features in the contract, resulting in ineffectiveness in the detection. In addition, many tools rely on symbolic execution and lack automation in the detection, resulting in inefficiency and in incapacity in vetting huge number of emerging smart contract on Ethereum. In our work, we are motivated to enhance the effectiveness and efficiency of vulnerability detection in contracts. First, we use opcodes as static features and use bigram to build the opcode feature space that is further optimized with Mtfidf. Second, We use deep learning algorithms namely, CNN, LSTM, CNN-BiLSTM, and ResNets, for the smart contract vulnerability detection. Extensive experimental results based on real-world smart contracts show that our methods are promising. Mtfidf significantly improves the detection performance. The best detection performance with ResNets reaches 82% in terms of Macro-F1. © 2022 IEEE.

关键词： Smart contract

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An Embedding-Based Multi-Modal Few-Shot Learning Method for Network Intrusion Detection 21

An Embedding-Based Multi-Modal Few-Shot Learning Method for ...

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21st International Computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Qiulin, He Jinquan, Zhang Yuanyuan, Huang School of Cybersecurity Xin Gu Industrial College Chengdu University of Information Technology Chengdu610225 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610225 China Dazhou Key Laboratory Government Data Security Dazhou635000 China

ISBN: (纸本)9798331519254

In this paper, we introduce an embedding-based few-shot learning framework, aiming to achieve classification by calculating the similarity measure between unknown samples and known samples in the embedding space. This framework mainly consists of two parts: embedding space and similarity calculation stage. In the embedding space, semantic information and visual features are embedded into a common space to achieve multi-modal embedding. In the similarity calculation stage, the classification task is achieved by calculating multiple score indicators between vectors. The experimental results show that when the number of support sets is 5, the accuracy on the CICIDS2017 and KDDCUP99 datasets reaches 98.89% and 99.85%, demonstrating excellent performance in the domain of few-shot learning for intrusion detection. © 2024 IEEE.

关键词： Network intrusion

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Secure Cross-domain Medical data Sharing based on Distributed Cloud and Blockchain Services

Secure Cross-domain Medical Data Sharing based on Distribute...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Cui, Jiasheng Duan, Li Ni, Wei Li, Chao Beijing Jiaotong University Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing100044 China Data61 Csiro MarsfieldNSW2122 Australia

ISBN: (纸本)9798350304855

Electronic Health Record (EHR) provides critical medical data. Sharing EHRs can add services and value for hospitals and patients and effectively improve medical services' quality. In the process of cross-hospital diagnosis, EHRs cannot be shared across domains because they are separately stored in various hospitals, which may prevent timely diagnosis and lead to data isolation in medical data. Insecure storage and sharing also endanger the privacy of patients' data, which likely leads to the disclosure of privacy-sensitive information or unauthorized access. To address these challenges, we propose a secure, cross-domain medical data-sharing scheme with data confidence protection. Specifically, we use a distributed cloud to realize cross-domain sharing of medical data, addressing the data isolation caused by the difference in hospital geographical locations. We combine a symmetric encryption algorithm with attribute encryption based on ciphertext policy (CP-ABE) to ensure the confidentiality and privacy of shared data, delivering fine-grained access control of shared medical data. Further, we introduce a key blockchain to store the ciphertext of the decryption key, the storage index of the data ciphertext, and store the ciphertext of the original data off-blockchain. Experiments show that the proposed scheme can perform better than existing cloud server-based data-sharing schemes. © 2023 IEEE.

关键词： Distributed cloud

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Multi-source Infection Pattern Mining Algorithms over Moving Objects 25

Multi-source Infection Pattern Mining Algorithms over Moving...

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25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Chen, Yu Dai, Hua Yang, Geng Chen, Yanli Nanjing University of Posts and Telecommunications School of Computer Science Nanjing210003 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing210003 China

ISBN: (纸本)9781665405270

Using the trajectory data of moving objects to analyze and study the infection mode of viruses or germs has practical application value. The definition of infection pattern in existing works only considers one-to-one infection mode rather than many-to-one mode, and thus some infection events could be ignored. This paper presents multi-source infection pattern mining algorithms oriented to moving objects for the first time. The multi-source infection event (MSIE) is defined. On the basis of MSIE, the multi-source infection pattern mining algorithm (MIPM) is proposed, which uses sliding window mechanism. The sliding window is used to record the set of candidate infection source that may infect each normal object at each time point, and then the determined infection events are mined. To improve performance, an optimized mining algorithm (MIPM+) based on the R-tree index is proposed, which can reduce the number of objects to be detected at each time point. The experimental results show that our proposed multi-source infection pattern mining algorithms can mine more potential infection events. © 2022 IEEE.

关键词： Decision trees

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A semantic and emotion-based dual latent variable generation model for a dialogue system

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CAAI Transactions on Intelligence Technology 2023年第2期8卷 319-330页

作者： Ming Yan Xingrui Lou Chien Aun Chan Yan Wang Wei Jiang State Key Laboratory of Media Convergence and Communication Communication University of ChinaBeijingChina School of Information and Communications Engineering Communication University of ChinaBeijingChina Key Laboratory of Acoustic Visual Technology and Intelligent Control System Communication University of ChinaBeijingChina Department of Electrical and Electronic Engineering The University of MelbourneMelbourneVictoriaAustralia School of Data Science and Intelligent Media Communication University of ChinaBeijingChina

With the development of intelligent agents pursuing humanisation,artificial intelligence must consider emotion,the most basic spiritual need in human *** emotional dialogue systems usually use an external emotional dictionary to select appropriate emotional words to add to the response or concatenate emotional tags and semantic features in the decoding step to generate appropriate ***,selecting emotional words from a fixed emotional dictionary may result in loss of the diversity and consistency of the *** propose a semantic and emotion-based dual latent variable generation model(Dual-LVG)for dialogue systems,which is able to generate appropriate emotional responses without an emotional *** from previous work,the conditional variational autoencoder(CVAE)adopts the standard transformer ***,Dual-LVG regularises the CVAE latent space by introducing a dual latent space of semantics and *** content diversity and emotional accuracy of the generated responses are improved by learning emotion and semantic features ***,the average attention mechanism is adopted to better extract semantic features at the sequence level,and the semi-supervised attention mechanism is used in the decoding step to strengthen the fusion of emotional features of the *** results show that Dual-LVG can successfully achieve the effect of generating different content by controlling emotional factors.

关键词： conditional variational autoencoder dual latent space emotional responses latent variable generation

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E2E-MFERC:AMulti-Face Expression Recognition Model for Group Emotion Assessment

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Computers, Materials & Continua 2024年第4期79卷 1105-1135页

作者： Lin Wang Juan Zhao Hu Song Xiaolong Xu Jiangsu Key Laboratory of Big Data Security&Intelligent Processing Nanjing University of Posts and TelecommunicationsNanjing210042China School of Network Security Jinling Institute of TechnologyNanjing211169China State Grid Jiangsu Electric Power Company Limited Nanjing210000China School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing210042China

In smart classrooms, conducting multi-face expression recognition based on existing hardware devices to assessstudents’ group emotions can provide educators with more comprehensive and intuitive classroom effect analysis,thereby continuouslypromotingthe improvementof teaching ***,most existingmulti-face expressionrecognition methods adopt a multi-stage approach, with an overall complex process, poor real-time performance,and insufficient generalization ability. In addition, the existing facial expression datasets are mostly single faceimages, which are of low quality and lack specificity, also restricting the development of this research. This paperaims to propose an end-to-end high-performance multi-face expression recognition algorithm model suitable forsmart classrooms, construct a high-quality multi-face expression dataset to support algorithm research, and applythe model to group emotion assessment to expand its application value. To this end, we propose an end-to-endmulti-face expression recognition algorithm model for smart classrooms (E2E-MFERC). In order to provide highqualityand highly targeted data support for model research, we constructed a multi-face expression dataset inreal classrooms (MFED), containing 2,385 images and a total of 18,712 expression labels, collected from smartclassrooms. In constructing E2E-MFERC, by introducing Re-parameterization visual geometry group (RepVGG)block and symmetric positive definite convolution (SPD-Conv) modules to enhance representational capability;combined with the cross stage partial network fusion module optimized by attention mechanism (C2f_Attention),it strengthens the ability to extract key information;adopts asymptotic feature pyramid network (AFPN) featurefusion tailored to classroomscenes and optimizes the head prediction output size;achieves high-performance endto-end multi-face expression detection. Finally, we apply the model to smart classroom group emotion assessmentand provide design refe

关键词： Multi-face expression recognition smart classroom end-to-end detection group emotion assessment

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Oscillation resolution for massive cell phone traffic data 1

Oscillation resolution for massive cell phone traffic data

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1st Workshop on Mobile data, Mobidata 2016, co-located with MobiSys 2016

作者： Qi, Ling Qiao, Yuanyuan Abdesslem, Fehmi Ben Ma, Zhanyu Yang, Jie Center for Data Science Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Network BUPT Beijing China Decisions Networks and Analytics Laboratory SICS Stockholm Sweden Center for Data Science Pattern Recognition and Intelligent System Lab BUPT Beijing China

ISBN: (纸本)9781450343275

Cellular towers capture logs of mobile subscribers whenever their devices connect to the network. When the logs show data traffic at a cell tower generated by a device, it reveals that this device is close to the tower. The logs can then be used to trace the locations of mobile subscribers for different applications, such as studying customer behaviour, improving location-based services, or helping urban planning. However, the logs often suffer from an oscillation phenomenon. Oscillations may happen when a device, even when not moving, does not only connect to the nearest cell tower, but is instead unpredictably switching between multiple cell towers because of random noise, load balancing, or simply dynamic changes in signal strength. Detecting and removing oscillations are a challenge when analyzing location data collected from the cellular network. In this paper, we propose an algorithm called SOL (Stable, Oscillation, Leap periods) aimed at discovering and reducing oscillations in the collected logs. We apply our algorithm on real datasets which contain about 18.9 TB of traffic logs generated by more than 3 million mobile subscribers covering about 21000 cell towers and collected during 27 days from both GSM and UMTS networks in northern China. Experimental results demonstrate the ability and effectiveness of SOL to reduce oscillations in cellular network logs. © 2016 ACM.

关键词： Location based services

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An economic dispatch model based on scenario tree in industrial micro-grid with wind power 27

An economic dispatch model based on scenario tree in industr...

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27th Chinese Control and Decision Conference, CCDC 2015

作者： Ding, Hao Gao, Feng Liu, Kun Guan, Xiaohong State Key Laboratory for Manufacturing System Engineering Xi'An Jiaotong University Xi'an China Ministry of Education Key Lab for Intelligent Networks and Networks Security Xi'An Jiaotong University Xi'an China

ISBN: (纸本)9781479970179

Wind power is a kind of clean and renewable energy, and it is of great benefit to utilize it in an industrial micro-grid. This paper establishes an economic dispatch model of industrial micro-grid with wind power that aims at minimizing energy costs based on scenario tree. The uncertainty of wind power and load are managed by adjusting the production schedule and unit generation schedule properly. A numerical test is taken with data of an industrial micro-grid in the paper, and the result indicates that the fluctuation of wind power and load can be mitigated by production and power generation schedule effectively and utilizing wind power can help decrease the energy costs largely. The model is also compared with the deterministic model, and some conclusions are obtained. © 2015 IEEE.

关键词： economic dispatch industrial micro-grid scenario tree wind power Wind energy Energy costs Dispatching Industrial production control LOAD

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Spiderweb Protector: Biomimetic Defense Against Targeted Attacks on Graph Neural Networks 21

Spiderweb Protector: Biomimetic Defense Against Targeted Att...

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21st International Computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Zhou, Zeyu Huang, Yuanyuan Wang, Yongli Li, Siyu Lu, Jiazhong He, Peilin Chengdu University of Information Technology Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610225 China Dazhou Key Laboratory Government Data Security Dazhou635000 China University of Pittsburgh Department of Informatics and Networked Systems Pittsburgh15260 United States

ISBN: (纸本)9798331519254

Graph Neural Networks (GNNs) have emerged as powerful tools across diverse applications, including social networks, bioinformatics, and recommendation systems. However, their susceptibility to adversarial attacks, particularly targeted attacks, poses a significant challenge. Targeted attacks exploit the message-passing mechanism of GNNs to induce severe performance degradation, even with minimal perturbations. Inspired by the self-repair and adaptive behaviors of spider webs, this paper introduces SpiderWeb Protector, a biomimetic defense strategy designed to enhance GNN robustness. The proposed method integrates three core components: Community-Centric Graph Update, which selectively reinforces key graph structures;Topology Feature Merging, which improves feature representation by incorporating structural insights;and Edge Pruning, which dynamically removes adversarial connections. Comprehensive experiments on three datasets (Cora, Citeseer and Cora_ml) and two GNN models (GCN and GIN) demonstrate the superior performance of SpiderWeb Protector against both direct and influence-targeted attacks, achieving state-of-the-art results. © 2024 IEEE.

关键词： Graph neural networks

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