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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yuanming Zheng Jiaxuan Yao Xiangyu Deng Yuhong Yang Ruiqi Liao Weiping Tu Cedar Lin National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan China Guangdong OPPO Mobile Telecommunications Corp. China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Spatial audio quality evaluation is essential for applications like virtual and augmented reality, where accurate sound reproduction enhances user immersion. While subjective listening tests are the gold standard, they are costly and time-consuming. To address this, we propose HAPG-SAQAM, an objective metric for assessing timbre quality, spatial quality, and overall quality of binaural audio, guided by human auditory perception. Our contributions include: (1) the Multi-scale Auditory Guided Feature Extraction (MAGFE) module, incorporating gammatone frequency cepstral coefficients for better alignment with human perception; (2) Perceptual Weighted Loss (PWL), optimizing the weighting of timbre quality (TQ) and spatial quality (SQ) loss based on subjective test data; and (3) data augmentation techniques to enhance robustness by amplifying perceptual distortions. Experimental results show HAPG-SAQAM improves correlation with subjective scores by 10%, with ablation studies confirming the contributions of its components to enhanced spatial and overall audio quality.

关键词： Measurement Accuracy Spatial audio Performance gain Feature extraction Robustness Quality assessment Timbre Speech processing Optimization

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A BiLSTM cardinality estimator in complex database systems based on attention mechanism

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CAAI Transactions on Intelligence Technology 2022年第3期7卷 537-546页

作者： Qiang Zhou Guoping Yang Haiquan Song Jin Guo Yadong Zhang Shengjie Wei Lulu Qu Louis Alberto Gutierrez Shaojie Qiao School of Information Science and Technology Southwest Jiaotong UniversityChengduChina School of Software Engineering Chengdu University of Information TechnologyChengduChina School of Energy Power and Mechanical Engineering North China Electric Power UniversityBaodingChina Digital Media Art Key Laboratory of Sichuan ProvinceSichuan Conservatory of MusicChengduChina Department of Computer Science Rensselaer Polytechnic InstituteNew YorkNew YorkUSA

An excellent cardinality estimation can make the query optimiser produce a good execution *** there are some studies on cardinality estimation,the prediction results of existing cardinality estimators are inaccurate and the query efficiency cannot be guaranteed as *** particular,they are difficult to accurately obtain the complex relationships between multiple tables in complex database *** dealing with complex queries,the existing cardinality estimators cannot achieve good *** this study,a novel cardinality estimator is *** uses the core techniques with the BiLSTM network structure and adds the attention ***,the columns involved in the query statements in the training set are sampled and compressed into ***,the Word2vec model is used to embed the word vectors about the query ***,the BiLSTM network and attention mechanism are employed to deal with word *** proposed model takes into consideration not only the correlation between tables but also the processing of complex *** experiments and the evaluation of BiLSTM-Attention Cardinality Estimator(BACE)on the IMDB datasets are *** results show that the deep learning model can significantly improve the quality of cardinality estimation,which is a vital role in query optimisation for complex databases.

关键词： attention BiLSTM cardinality estimation complex database systems query optimiser Word2vec

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Siguard: Detecting Signature-Related Vulnerabilities in Smart Contracts

Siguard: Detecting Signature-Related Vulnerabilities in Smar...

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International Conference on software engineering - Companion, ICSE, Companion

作者： Jiashuo Zhang Yue Li Jianbo Gao Zhi Guan Zhong Chen Key Laboratory of High Confidence Software Technologies MoE Peking University Beijing China School of Computer Science Peking University Beijing China National Engineering Research Center for Software Engineering Peking University Beijing China

Ethereum smart contract enables developers to en-force access control policies of critical functions using built-in signature verification interfaces, i.e., ecrecover. However, due to the lack of best practices for these interfaces, improper verifications commonly exist in deployed smart contracts, leaving potential unauthorized access and financial losses. Even worse, the attack surface is ignored by both developers and existing smart contract security analyzers. In this paper, we take a close look at signature-related vulnerabilities and de-mystify them with clear classification and characterization. We present Siguard, the first automatic tool to detect these vulnerabilities in real-world smart contracts. Specifically, Siguard explores signature-related paths in the smart contract and extracts data dependencies based on symbolic execution and taint analysis. Then, it conducts vulnerability detection based on a systematic search for violations of standard patterns including EIP-712 and EIP-2621. The preliminary evaluation validated the efficacy of Siguard by reporting previously unknown vulnerabilities in deployed smart contracts on Ethereum. A video of Siguard is available at https://***/xXAEhqXWOu0

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Design and Implementation of Links Generation for Inter Domain Routing System 12

Design and Implementation of Links Generation for Inter Doma...

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12th International Conference on Information Science and Technology, ICIST 2022

作者： Wang, Yu Qiao, Zhi Yin, Junru Zhang, Mingliang College of Software Henan University of Engineering Zhengzhou China College of Information and Electrical Engineering China Agriculture University Beijing China College of Computer and Communication Engineering Zhengzhou University of Light Industry Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Network Research Department Zhengzhou China

ISBN: (数字)9781665485821

ISBN: (纸本)9781665485821

Due to the inherent flaws of BGP protocol, the security of inter domain routing system has been regularly threatened. Since it is difficult to achieve collaborative defense against existing resources and strategies of multiple autonomous systems, this paper designs and organizes them into autonomous system community. Within the community, a precomputing multi-link generation mechanism is proposed based on genetic algorithm. Before the inter domain routing system is attacked, the link set can be generated as complete as possible in advance. Once an attack occurs, the community will quickly select proper precomputing links to replace the failed ones, in order to ensure the stability of inter domain communication. The effectiveness of the mechanism has been proved in function and performance through simulation experiments. © 2022 IEEE.

关键词： Genetic algorithms

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DIDAPPER: A Practical and Auditable On-Chain Identity Service for Decentralized Applications

DIDAPPER: A Practical and Auditable On-Chain Identity Servic...

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IEEE International Conference on Decentralized Applications and Infrastructures

作者： Libin Xia Jiashuo Zhang Xihan Zhang Yue Li Jianbo Gao Zhi Guan Zhong Chen Key Laboratory of High Confidence Software Technologies Peking University MoE Beijing China School of Computer Science Peking University Beijing China National Engineering Research Center for Software Engineering Peking University Beijing China

The popularity of decentralized applications brings increasing on-chain audit concerns. Since on-chain accounts do not link to real-world identities, decentralized applications have been wildly used for illegal activities like money laundering. Decentralized identity (DID) is one of the most promising solutions to guarantee on-chain auditability in a privacy-preserving way. However, the existing DID systems suffer from two limitations, making them rarely adopted in on-chain decentralized applications. First, most previous systems only provide partial auditability, meaning that their regulators cannot trace users' real-world identities after users' malicious behaviors occur. Second, current solutions are usually isolated systems, causing a lack of interoperability with on-chain decentralized applications. To address the problems, we propose Didapper, a practical and auditable on-chain identity service with both privacy and auditability for decentralized applications. We introduce group signatures to provide anonymous and traceable credentials for on-chain users and decentralized applications. In order to meet the efficiency and flexibility requirements, we design credential structure and workflow mostly compatible with W3C standards (a DID recommendation that enables verifiable, decentralized digital identity), and realize credential verification service with updatable policies. We implement Didapper and evaluate it on Ethereum, and the results show that the performance of Didapper is acceptable in practice.

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TD3-Based Collaborative Computation Offloading and Charging Scheduling in Multi-UAV-Assisted MEC Networks

TD3-Based Collaborative Computation Offloading and Charging ...

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IEEE Conference on Wireless Communications and Networking

作者： Liang Zhao Yujun Yao Huan Zhou Hao Wang Victor C. M. Leung Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering College of Computer and Information Technology China Three Gorges University Yichang China School of Computer Science Northwestern Polytechnical University Xi'an China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Computation offloading, resource allocation, and endurance issues in unmanned aerial vehicle (UAV)-aided mobile edge computing (MEC) networks have always been a research focus. UAV-aided MEC allows mobile users (MUs)' tasks to be offloaded to drones for processing in special scenarios, such as natural disasters or military attacks. However, as the number and size of offloaded tasks increase, a single UAV is difficult to meet all computational demands, result in the decline of QoS. To address this issue, this paper presents a collaborative computation offloading scheme where multiple UAVs can cooperate to handle massive tasks. Firstly, considering that battery-limited UAVs cannot complete all tasks and sustain flight without charging, we incorporate charging stations (CS) into multi-UAV-assisted MEC networks. Subsequently, we design a price-based incentive mechanism to maximize the total revenue obtained from UAVs' collaborative computation. Then, we formulate the joint optimization problem of computation offloading, resource allocation and charging scheduling as a Markov Decision Process (MDP), and propose a Twin Delayed Deep Deterministic policy gradient (TD3) algorithm to find optimal strategies. Finally, extensive simulations demonstrate that the proposed TD3 algorithm outperforms other benchmark methods, achieving the highest overall system utility under different scenarios.

关键词： Processor scheduling Computational modeling Inductive charging Collaboration Charging stations Scheduling Resource management

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Federated Distributed Deep Reinforcement Learning for Recommendation-enabled Edge Caching

Federated Distributed Deep Reinforcement Learning for Recomm...

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IEEE Conference on computer Communications Workshops, INFOCOM Wksps

作者： Hao Wang Huan Zhou Mingze Lit Liang Zhao Victor C.M. Leung Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering College of Computer and Information Technology China Three Gorges University Yichang China School of Computer Science Northwestern Polytechnical University Xi'an China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9798350384475

ISBN: (纸本)9798350384482

This paper investigates a content recommendation-based edge caching method in multi-tier edge-cloud networks while considering content delivery and cache replacement decisions as well as bandwidth allocation strategies. First, we formu-late the optimization problem with the goal of minimizing long-term content delivery delay. Second, we model the optimization problem as a Partially Observable Markov Decision Process, and propose a Federated Distributed Deep Deterministic Policy Gradient-based method (FD3PG) to solve the corresponding problem. In conclusion, simulation results demonstrate that the proposed FD3PG achieves not only a much lower content delivery delay but also a higher cache hit rate compared with other baselines in various scenarios.

关键词： Simulation Markov decision processes Conferences Channel allocation Deep reinforcement learning Delays Optimization

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A Fine-Grained Anomaly Detection Method Fusing Isolation Forest and Knowledge Graph Reasoning 19th

A Fine-Grained Anomaly Detection Method Fusing Isolation For...

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19th International Conference on Web Information Systems and Applications, WISA 2022

作者： Xu, Jie Zhou, Jiantao College of Computer Science Engineering Research Center of Ecological Big Data Ministry of Education National and Local Joint Engineering Research Center of Mongolian Intelligent Information Processing Technology Inner Mongolia Cloud Computing and Service Software Engineering Laboratory Inner Mongolia Social Computing and Data Processing Key Laboratory Inner Mongolia Discipline Inspection and Supervision Big Data Key Laboratory Inner Mongolia Big Data Analysis Technology Engineering Laboratory Inner Mongolia University Hohhot China

ISBN: (纸本)9783031203084

Anomaly detection aims to find outliers data that do not conform to expected behaviors in a specific scenario, which is indispensable and critical in current safety environments related studies. However, when performing outlier detection on a large-scale multidimensional dataset, most of the traditional methods make no distinctions between classes of outliers and lack the reasoning ability and the explainability of the analysis results, which leads to the low accuracy of global outlier detection and the inability to effectively identify local outliers. In this paper, we propose a fine-grained anomaly detection method which well utilizes isolation forest and knowledge graph reasoning tactics. First, a better extracting data feature method and a more reasonable weighted strategy are used to find global outliers based on the traditional isolation forest algorithm, and then we construct a custom rule base according to the in-depth research and analysis of global abnormal data, and at last the ontology knowledge is reasoned based on such rule base to achieve the detection of local outliers. Through analysis of extensive experiment results, our method could effectively discover more abnormal data without big loss of time costs, has strong generalization, and correspondingly improves the performance for abnormal detection. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Anomaly detection

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A Survey of Recommender Systems Based on Hypergraph Neural Networks 1

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7th International Conference on Smart Computing and Communication, SmartCom 2022

作者： Liu, Canwei He, Tingqin Zhu, Hangyu Li, Yanlu Xie, Songyou Hosam, Osama School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China Guangdong Financial High-tech Zone "Blockchain +" Fintech Research Institute Guangzhou510623 China Hunan Key Laboratory for Service computing and Novel Software Technology Xiangtan411201 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China Higher Colleges of Technology Abu Dhabi United Arab Emirates

ISBN: (数字)9783031281242

ISBN: (纸本)9783031281235

Unlike highly purposeful search, a recommender system tends to uncover the user’s potential interests and is a personalized information filtering system. Recently, the performance of hypergraph neural networks in classification tasks has attracted much attention. Compared with traditional recommender systems, hypergraph neural network-based recommender systems have better mining higher-order associations, accurate modeling of multivariate relationships, handling of multimodal and heterogeneous data, and clustering advantages. This fact drives the development of recommendation algorithms based on hypergraph neural networks. To this end, we 1) define generic links of recommender systems, and systematically analyze the challenges of hypergraph neural network-based recommender systems in different research directions. 2) present some new perspectives on existing weaknesses and future developments. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Recommender systems

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Contrastive Learning Guided Latent Diffusion Model for Image-to-Image Translation

arXiv

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arXiv 2025年

作者： Si, Qi Wang, Bo Zhang, Zhao Zhao, Mingbo Jin, Xiaojie Xu, Mingliang Wang, Meng School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Yunnan Key Laboratory of Software Engineering Yunan China School of Computer Science and Engineering Donghua University Shanghai China ByteDance Culver City United States School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China

The diffusion model has demonstrated superior performance in synthesizing diverse and high-quality images for text-guided image translation. However, there remains room for improvement in both the formulation of text prompts and the preservation of reference image content. First, variations in target text prompts can significantly influence the quality of the generated images, and it is often challenging for users to craft an optimal prompt that fully captures the content of the input image. Second, while existing models can introduce desired modifications to specific regions of the reference image, they frequently induce unintended alterations in areas that should remain unchanged. To address these challenges, we propose pix2pix-zeroCon, a zero-shot diffusion-based method that eliminates the need for additional training by leveraging patch-wise contrastive loss. Specifically, we automatically determine the editing direction in the text embedding space based on the reference image and target prompts. Furthermore, to ensure precise content and structural preservation in the edited image, we introduce cross-attention guiding loss and patch-wise contrastive loss between the generated and original image embeddings within a pre-trained diffusion model. Notably, our approach requires no additional training and operates directly on a pre-trained text-to-image diffusion model. Extensive experiments demonstrate that our method surpasses existing models in image-to-image translation, achieving enhanced fidelity and controllability. Copyright © 2025, The Authors. All rights reserved.

关键词： Photointerpretation

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