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36th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2024

作者： Luo, Ying Luo, Xudong Chen, Guibin School of Computer Science and Engineering Guangxi Normal University Guangxi Key Lab of Multi-source Information Mining and Security Guangxi Key Lab of Education Blockchain and Intelligent Technology of Ministry of Education Guilin541004 China

ISBN: (纸本)9798331527235

This paper presents a novel multi-choice question answering model tailored for the legal domain, which integrates a DeBERTa-based framework with bilinear attention mechanisms. The model is designed to enhance the interpretation of complex legal texts, utilising vector similarity searches to align questions with pertinent statutes and a neural network to analyse this data effectively. It aims to improve the accuracy of legal question answering by employing advanced techniques of natural language processing. A binary classifier is also incorporated to assess answer options and generate conclusive responses. Experimental results demonstrate the model's superiority over traditional methods, particularly in handling intricate legal questions. This study underscores the model's potential to advance legal AI technology significantly, offering substantial benefits for judicial examinations and legal professionals. © 2024 IEEE.

关键词： Bilinear attention mechanism Legal article matching Legal question answering Natural language processing Neural network

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Fuzzy Concession Strategy for Emotional Human-computer Negotiation 36

Fuzzy Concession Strategy for Emotional Human-Computer Negot...

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36th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2024

作者： Luo, Xudong Luo, Ying Sun, Kaili Li, Yanling School of Computer Science and Engineering Guangxi Normal University Guangxi Key Lab of Multi-source Information Mining and Security Guangxi Key Lab of Education Blockchain and Intelligent Technology of Ministry of Education Guilin541004 China

ISBN: (纸本)9798331527235

This paper proposes an emotion-based human-computer negotiation system. Existing systems handle basic dialogues but struggle with complex negotiations involving human emotions. To address this, we design a fuzzy concession strategy that adjusts tactics through sentiment analysis, managing states like anger, happiness, and anxiety. Experiments show significant improvement in negotiation success and overall gains when considering emotional factors. Results demonstrate that this model enhances customer satisfaction and user experience by achieving agreements that satisfy users. This research highlights the potential of integrating fuzzy reasoning, sentiment analysis, and natural language processing into AI systems, laying the foundation for more intelligent, human-like dialogue agents and improving human-computer interaction and e-commerce engagement. © 2024 IEEE.

关键词： Natural language processing systems

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Cascaded Sliding-Window-Based Relativistic GAN Fusion for Perceptual and Consistent Video Super-Resolution 6th

Cascaded Sliding-Window-Based Relativistic GAN Fusion for P...

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6th IFIP TC 12 International Conference on Intelligence Science, ICIS 2024

作者： Li, Dingyi PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

ISBN: (纸本)9783031712524

Perceptual video super-resolution aims at converting low-resolution videos to visually appealing high-resolution ones. It may lead to temporal inconsistency due to the drastically changing outputs. In this paper, we propose cascaded sliding-window-based relativistic GAN (Generative Adversarial network) fusion for perceptual and consistent video super-resolution (PC-VSR). Firstly, cascaded sliding-window-based relativistic GAN is designed to extract more useful information. It enlarges the temporal receptive field of sliding-window-based model in each step. It is able to enhance perceptual quality and compensate temporal consistency progressively and sufficiently. The trained separate refinement generator networks are fused into a final refinement generator. The final refinement generator can be calculated recursively at the testing stage. With our generator fusion, the parameter number is reduced and good quality is maintained. Extensive experimental results demonstrate that our approach outperforms state-of-the-art super-resolution methods in terms of perceptual quality. Our method also achieves good temporal consistency and per-pixel accuracy, compared with other perceptual approaches. © IFIP International Federation for information Processing 2025.

关键词： Generative adversarial networks

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Towards Public Integrity Audition for Cloud-IoT Data Based on Blockchain

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computer Systems Science & Engineering 2022年第6期41卷 1129-1142页

作者： Hao Yan Yanan Liu Shuo Qiu Shengzhou Hu Weijian Zhang Jinyue Xia School of Network Security Jinling Institute of TechnologyNanjing211169China Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Normal UniversityFuzhou350007China Department of Mathematics and Computer Science Gannan Normal UniversityGanzhou341000China Network Security and Information Office Hohai UniversityNanjing210098China International Business Machines Corporation(IBM) New YorkNYUSA

With the rapidly developing of Internet of Things (IoT), the volume ofdata generated by IoT systems is increasing quickly. To release the pressure ofdata management and storage, more and more enterprises and individuals preferto integrate cloud service with IoT systems, in which the IoT data can be outsourced to cloud server. Since cloud service provider (CSP) is not fully trusted,a variety of methods have been proposed to deal with the problem of data integritychecking. In traditional data integrity audition schemes, the task of data auditing isusually performed by Third Party Auditor (TPA) which is assumed to be ***, in real-life TPA is not trusted as people thought. Therefore, theseschemes suffer from the underlying problem of single-point failure. Moreover,most of the traditional schemes are designed by RSA or bilinear map techniqueswhich consume heavy computation and communication cost. To overcome theseshortcomings, we propose a novel data integrity checking scheme for cloud-IoTdata based on blockchain technique and homomorphic hash. In our scheme, thetags of all data blocks are computed by a homomorphic hash function and storedin blockchain. Moreover, each step within the process of data integrity checking issigned by the performer, and the signatures are stored in blockchain through smartcontracts. As a result, each behavior for data integrity checking in our scheme canbe traced and audited which improves the security of the scheme greatly. Furthermore, batch-audition for multiple data challenges is also supported in our *** formalize the system model of our scheme and give the concrete *** performance analyses demonstrate that our proposed scheme is efficientand practical without the trust-assumption of TPA.

关键词： Blockchain cloud-IoT data integrity checking homomorphic hash function batch audition

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Robust Voice Activity Detection using Locality-Sensitive Hashing and Residual Frequency-Temporal Attention 25

Robust Voice Activity Detection using Locality-Sensitive Has...

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25th Interspeech Conferece 2024

作者： Li, Shu Zhang, Peng Li, Ye Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science China

For voice activity detection (VAD), recent works focus on learning the attention distribution on contextual information of speech to reduce the impact of irrelevant noise. However, contextual frames selected with specific steps may not be relevant, and these attention mechanisms can not fully discover the structure and characteristics of speech. In this paper, we explore a self-attention-inspired locality-sensitive hashing algorithm (SALSH) for dynamic and efficient contextual frame selection to enrich the frame-level features into a 2D partial spectrogram. Then, we propose a residual frequency-temporal attention model (FTAM) for VAD, consisting of an interval branch, an analogous hourglass structure with channel attention, and an attention learning mechanism for speech based on frequency-temporal attention. On the LibriSpeech and TIMIT datasets, the proposed method outperforms the others in terms of area under the curve (AUC), even under extremely low signal-to-noise ratio of -15dB. © 2024 International Speech Communication Association. All rights reserved.

关键词： Speech recognition

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Hyper-Relational Knowledge Representation Learning with Multi-Hypergraph Disentanglement 25

Hyper-Relational Knowledge Representation Learning with Mult...

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34th ACM Web Conference, WWW 2025

作者： Li, Jiecheng Luo, Xudong Lu, Guangquan Zhang, Shichao School of Computer Science and Engineering Key Lab of Education Blockchain and Intelligent Technology of the Ministry of Education Guangxi Key Lab of Multi-source Information Mining & Security Guangxi Normal University Guilin China

ISBN: (纸本)9798400712746

Hyper-relational knowledge graphs (HKGs) extend the traditional triplet-based knowledge graph by adding qualifiers to the relationships, making HKGs particularly useful for tasks that require more profound understanding and inference from relationships between entities. However, existing hyper-relational knowledge representation learning methods (HKRL) focus on direct neighbourhood information of entities only by neglecting the relational similarity of the main triple in hyper-relational facts and the attribute details in the qualifiers. In addition, few works extract common and private information across multiple views to minimize noise and interference. This paper proposes a multi-hypergraph disentanglement method for HKRL to address the above issues. Specifically, we first construct four hypergraphs to mine and utilise the inherent structure information of HKGs, and then propose to extract common representations among hypergraphs and private representations within individual hypergraphs to mine the semantic information and the task-relevant information, respectively. Experiment results on four real datasets demonstrate the effectiveness of the proposed method compared to SOTA methods in link prediction tasks on HKGs. © 2025 Copyright held by the owner/author(s).

关键词： hyper-relational knowledge graph

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Research on Setting of Two Firewall Rules Based on Ubuntu Linux System 5

Research on Setting of Two Firewall Rules Based on Ubuntu Li...

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5th International Conference on computer network, Electronic and Automation, ICCNEA 2022

作者： Ma, Limei Zhao, Dongmei Hebei Normal University College of Computer and Cyber Security Shijiazhuang China Key Laboratory of Network and Information Security in Hebei Province Shijiazhuang China

ISBN: (数字)9781665491099

ISBN: (纸本)9781665491099

'security first' is the most concerned issue of Linux administrators. security refers to the integrity of data. The authentication security and integrity of data are higher than the privacy security of data. Firewall is used to realize the function of access control under Linux. It is divided into hardware or software firewall. No matter in which network, the firewall must work at the edge of the network. Our task is to define how the firewall works. This is the firewall's policies and rules, so that it can detect the IP and data in and out of the network. At present, there are three or four layers of firewalls on the market, which are called network layer firewalls, and seven layers of firewalls, which are actually the gateway of the agent layer. But for the seven layer firewall, no matter what your source port or target port, source address or target address is, it will check all your things. Therefore, the seven layer firewall is more secure, but it brings lower efficiency. Therefore, the usual firewall schemes on the market are a combination of the two. And because we all need to access from the port controlled by the firewall, the work efficiency of the firewall has become the most important control of how much data users can access. This paper introduces two types of firewalls iptables and TCP_Wrappers. What are the differences between the use policies, rules and structures of the two firewalls? This is the problem to be discussed in this paper. © 2022 IEEE.

关键词： network layers

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8th International Congress on Edge Computing, EDGE 2024, Held as Part of the Services Conference Federation, SCF 2024

作者： Tian, Yun Wu, Bin Shi, Jiaoli Zhang, Caicai Xu, Du School of Computer and Big Data Science Jiujiang University Jiujiang332005 China Jiujiang Key Laboratory of Network and Information Security Jiujiang332005 China School of Modern Information Technology Zhejiang Polytechnic University of Mechanical and Electrical Engineering Hangzhou310053 China

ISBN: (纸本)9783031770685

The development of cloud computing and the widespread application of cloud services have made outsourcing services more convenient. The need for individuals and businesses to store and manipulate the graph data they generate is growing rapidly. The unreliability and insecurity of cloud servers make outsourcing graph data a great risk of information leakage. To effectively protect data security, encrypting outsourced data is a useful method. The adjacent vertex query is a very commonly used and fundamental operation, and similarity search is a widely used and powerful tool to improve the scope and functionality of queries. After outsourcing encrypted sparse graph data to cloud servers, it becomes very inconvenient to use and manipulate the data. In this work, we present a scheme to realize the adjacent vertex query supporting similarity search on sparse graph data in cloud environment (SSAQ), which also protects the security of the information. This work uses edit distance and the searchable encryption principle to construct query index, and next implement the similar adjacent vertex query on cloud server. This work provides a formal security analysis, and also gives the experimental comparison and analysis. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： information leakage

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A Novel 3D Medical Image Segmentation Model Using Improved SAM

A Novel 3D Medical Image Segmentation Model Using Improved S...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Kuang, Yuansen Ma, Xitong Zhao, Jing Wang, Guangchen Zeng, Yijie Liu, Song Key Laboratory of Computing Power Network and Information Security Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9781665410205

3D medical image segmentation is an essential task in the medical image field, which aims to segment organs or tumours into different labels. A number of issues exist with the current 3D medical image segmentation task: existing models cannot simultaneously obtain the space correlation and depth correlation of 3D slices;previous models suffer from local detail loss of positional embedding in 3D images;previous approaches often have blurring of boundaries in segmenting 3D images. To solve these shortcomings, we propose a 3D medical image segmentation model named TPM-SAM. In our model, we design a twinchannel image encoder to simultaneously capture the space correlation and depth correlation of 3D slices through a multi-head attention mechanism and improved adapters. Furthermore, we design a prompt encoding generator, which divides the volumetric image into small blocks and better captures the local detail information. In addition, we introduce a multi-layer aggregation decoder by employing U-Net with multi-level skip connection to solve the blurring of boundaries in processing 3D images. Finally, we experimented and evaluated our model on KiTS21 and LiTS17 datasets to compare with other baseline models. © 2024 IEEE.

关键词： Image segmentation

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A Novel Meta-Hierarchical Active Inference Reinforcement Learning Approach for QoS-Driven Resource Allocation in Dynamic Clouds

A Novel Meta-Hierarchical Active Inference Reinforcement Lea...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Xian, Peijie He, Ying Yu, F. Richard Du, Jianbo Shenzhen Univ. College of Computer Science and Software Engineering Shenzhen China Xi'an Univ. of Posts & Telecomm. Shaanxi Key Lab. of Information Comm. Network and Security Shaanxi Xi'an China Carleton Univ. School of Information Technology OttawaON Canada

ISBN: (纸本)9798350351255

The cloud computing environment is highly dynamic due to a variety of external factors such as seasonal changes, market trends, and social events. In this case, tenant behavior patterns exhibit significant variability. Therefore, the cloud computing resource allocation model must continuously adapt to these changes. To this end, we propose an adaptive algorithm for dynamic cloud resource allocation based on meta-hierarchical active inference reinforcement learning (MHAIRL). The algorithm combines active inference with meta-hierarchical reinforcement learning. It improves the overall performance of the algorithm, as well as quickly adapts to environmental changes, and improves the generalization performance. In addition, we design a novel polling scheduling framework combined with long short-term memory (LSTM) network. The framework ensures scheduling fairness and flexibility while greatly reducing the state and action space dimensions of the agent. Extensive simulation results show that our method outperforms baseline algorithms in quality of service (QoS) metrics and significantly improves system performance in highly dynamic cloud resource allocation. © 2024 IEEE.

关键词： Active learning

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