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arXiv 2024年

作者： Si, Jiasheng Zhao, Yibo Zhu, Yingjie Zhu, Haiyang Lu, Wenpeng Zhou, Deyu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences China School of Computer Science and Engineering Southeast University China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science China

With the growing complexity of fact verification tasks, the concern with "thoughtful" reasoning capabilities is increasing. However, recent fact verification benchmarks mainly focus on checking a narrow scope of semantic factoids within claims and lack an explicit logical reasoning process. In this paper, we introduce CHECKWHY, a challenging dataset tailored to a novel causal fact verification task: checking the truthfulness of the causal relation within claims through rigorous reasoning steps. CHECKWHY consists of over 19K "why" claim-evidence-argument structure triplets with supports, refutes, and not enough info labels. Each argument structure is composed of connected evidence, representing the reasoning process that begins with foundational evidence and progresses toward claim establishment. Through extensive experiments on state-of-the-art models, we validate the importance of incorporating the argument structure for causal fact verification. Moreover, the automated and human evaluation of argument structure generation reveals the difficulty in producing satisfying argument structure by fine-tuned models or Chain-of-Thought prompted LLMs, leaving considerable room for future improvements1 Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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NextSeg: Automatic COVID-19 Lung Infection Segmentation from CT Images Based on Next-ViT

NextSeg: Automatic COVID-19 Lung Infection Segmentation from...

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International Joint Conference on Neural networks (IJCNN)

作者： Aimei Dong Ruixin Wang Xuening Zhang Jian Liu 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) Jinan China Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

The spread of COVID-19 has brought great disaster to the world, and automatic segmentation of the infected region can help doctors make a quick diagnosis and reduce workload. However, automatic segmentation of infected regions in computed tomography (CT) images still faces many challenges. The most central problem at the moment is that when capturing low-frequency signals and high-frequency signals, the use of Transformer blocks for capturing low-frequency signals may deteriorate the high-frequency signals (local texture information) to some extent. For this reason, in this paper, we propose a new network for automatic segmentation of COVID-19 lung infections from CT images, called NextSeg. NextSeg follows an encoder-decoder architecture, where the encoder part uses NCB and NTB convolutional attention modules to extract better features while reducing the deterioration of texture profile information. The decoder part uses an attention fusion module to learn the weight relationship between features and retain more important feature information. In addition to the difficulty of extracting the boundary details of the COVID-19 infected region as well as the loss of details the deeper the network is, we designed a new hybrid strategy. Traditional and state-of-the-art approaches to the COVID-19 segmentation domain are compared. Extensive experiments show that our proposed framework outperforms existing competitors in both qualitative and quantitative results.

关键词： COVID-19 Image segmentation Convolution Computed tomography Neural networks Lung Medical services

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Burst Sequence Based Graph Neural network for Video Traffic Identification

Burst Sequence Based Graph Neural Network for Video Traffic...

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15th EAI International Conference on Digital Forensics and Cyber Crime, ICDF2C 2024

作者： Xiong, Jian Bao, YingShuo Li, ShengBao Zhao, Kun Peng, LiZhi Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan250022 China Quancheng Laboratory Jinan China School of Cyber Security University of Chinese Academy of Sciences Beijing China Shandong Branch of National Computer Network Emergency Response Technical Team/Coordination Center of China Jinan China Inspur Computer Technology Co. Ltd. Jinan China

ISBN: (纸本)9783031893599

With the vigorous development of video applications and the comprehensive upgrade of encryption protocols, video content review is becoming increasingly difficult. As a practical solution, video traffic identification has attracted more and more attention in recent years. In order to achieve more refined video traffic recognition, a primary problem to be solved is that the extracted traffic features lack higher-level semantic information to reflect changes in video content. Fortunately, under the same network conditions, similar types of video clips or traffic data generated by live video often have the same traffic change pattern, which allows different types of video traffic to be distinguished. Therefore, this paper extracts burst sequences from video traffic to represent video changes and constructs a graph structure based on burst sequences to extract high-level semantic information. With the powerful performance of a graph neural network, we transform video traffic classification into a graph classification problem and introduce GraphBurst, a model based on burst sequences and graph neural networks. We collected real-world traffic data from different network environments with five video types, including on-demand live streaming, with more than one hundred thousand streams. We launch a set of comparing experiments on the dataset, and compared with the existing methods, the experimental results show that our method has higher accuracy and interpretability. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Graph neural networks

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HiCoCS: High Concurrency Cross-Sharding on Permissioned Blockchains

arXiv

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arXiv 2025年

作者： Yang, Lingxiao Dong, Xuewen Wan, Zhiguo Lu, Di Zhang, Yushu Shen, Yulong School of Computer Science and Technology Xidian University Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education Shaanxi Key Laboratory of Blockchain and Secure Computing Xi’an710071 China Zhejiang Lab Zhejiang Hangzhou311121 China School of Computer Science and Technology Xidian University Shaanxi Key Laboratory of Network and System Security Xi’an710071 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing210016 China

As the foundation of the Web3 trust system, blockchain technology faces increasing demands for scalability. Sharding emerges as a promising solution, but it struggles to handle highly concurrent cross-shard transactions (CSTxs), primarily due to simultaneous ledger operations on the same account. Hyperledger Fabric, a permissioned blockchain, employs multi-version concurrency control for parallel processing. Existing solutions use channels and intermediaries to achieve cross-sharding in Hyperledger Fabric. However, the conflict problem caused by highly concurrent CSTxs has not been adequately resolved. To fill this gap, we propose HiCoCS, a high concurrency cross-shard scheme for permissioned blockchains. HiCoCS creates a unique virtual sub-broker for each CSTx by introducing a composite key structure, enabling conflict-free concurrent transaction processing while reducing resource overhead. The challenge lies in managing large numbers of composite keys and mitigating intermediary privacy risks. HiCoCS utilizes virtual sub-brokers to receive and process CSTxs concurrently while maintaining a transaction pool. Batch processing is employed to merge multiple CSTxs in the pool, improving efficiency. We explore composite key reuse to reduce the number of virtual sub-brokers and lower system overhead. Privacy preservation is enhanced using homomorphic encryption. Evaluations show that HiCoCS improves cross-shard transaction throughput by 3.5-20.2 times compared to the baselines. Copyright © 2025, The Authors. All rights reserved.

关键词： Concurrency control

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Research on the Path of Industrial Intelligence Promoting the Upgrading of Manufacturing Industry

Research on the Path of Industrial Intelligence Promoting th...

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作者： Wenhao Liu Delong Han Mengmeng Sun Qianlong Liu 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) Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Qilu University of Technology (Shandong Academy of Sciences)

With the deepening development of a new round of global technological revolution and industrial transformation, the new generation of information technology is constantly breaking through. At the same time, the international environment is becoming increasingly complex, and competition in technology and industry is becoming more intense. Currently, China has shifted to a stage of high-quality development and is in a critical period of transforming its development mode, optimizing its economic structure,and transforming its growth momentum. As an important manufacturing province in China,Shandong has a complete range of industries and a complete structure. However, there are also constraints such as outdated traditional industries, surplus low-end manufacturing, and insufficient technological innovation. Therefore, promoting the digital and intelligent transformation and upgrading of the manufacturing industry has become an inevitable choice for industrial development. This article studies the current situation and practical difficulties of industrial intelligence empowering the development of the manufacturing industry, explores paths suitable for the upgrading of Shandong's manufacturing industry,and has certain reference value for promoting the intelligent transformation and highquality development of Shandong's manufacturing industry, and accelerating the construction of an advanced manufacturing strong province.

关键词： Industrial Intelligence Manufacturing Industry Industrial Upgrading Shandong Province

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Proceedings Research on the Path of High-Quality Development of Big Data Industry in Shandong Province

Proceedings Research on the Path of High-Quality Development...

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作者： Yu Wang Mingle Zhou Chunjing Wang Xingheng Mei Yumei Li 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) Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Qilu University of Technology (Shandong Academy of Sciences)

The big data industry is a major opportunity to promote the development of the digital economy. This paper analyzes the status quo and problems of the big data industry in Shandong Province, combines the connotation of the big data industry chain,and puts forward countermeasure suggestions for the high-quality development of the big data industry in Shandong Province in four aspects, namely, infrastructure construction,data factor market cultivation, and data application development.

关键词： Big data industry Shandong province Big data industry chain High-quality development

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Speed Master: Quick or Slow Play to Attack Speaker Recognition 39

Speed Master: Quick or Slow Play to Attack Speaker Recogniti...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Ye, Zhe Zhang, Wenjie Ren, Ying Kang, Xiangui Yan, Diqun Ma, Bin Wang, Shiqi Guangdong Key Lab of Information Security School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences Jinan China Department of Computer Science City University of Hong Kong Hong Kong

ISBN: (纸本)157735897X

Backdoor attacks pose a significant threat during the model’s training phase. Attackers craft pre-defined triggers to break deep neural networks, ensuring the model accurately classifies clean samples during inference yet erroneously classifies samples added with these triggers. Recent studies have shown that speaker recognition systems trained on large-scale data are susceptible to backdoor attacks. Existing attackers employ unnoticed ambient sounds as triggers. However, these sounds are not inherently part of the training samples themselves. In essence, triggers can be designed to maintain an intrinsic connection with the original speech to enhance stealthiness. Our paper presents a novel attack methodology named Speed Master, which undermines deep neural networks by manipulating the speed of speech samples. Specifically, we execute poison-only backdoor attacks using speed or tempo adjustment. Changes in speech rate have become a common occurrence, as seen on platforms that allow users to adjust playback speed. In real-world scenarios, people naturally adjust their speaking rate depending on the context. As a result, changes in a speaker’s speech rate are typically perceived as normal and are unlikely to raise suspicion. Furthermore, detecting such subtle adjustments becomes challenging for users without reference speech. Our comprehensive experiments demonstrate that Speed Master can achieve an ASR over 99% in the digital domain, with only a 0.6% poisoning rate. Additionally, we validate the feasibility of Speed Master in the real world and its resistance to typical defensive measures. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Deep neural networks

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Ultra-high definition video quality assessment method based on deep learning

Ultra-high definition video quality assessment method based ...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Wei Li Can He MingLe Zhou HengYu Zhang WenLong Liu Hisense Video Technology Co. Ltd Qing Dao China 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) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

With the development of display technology, people’s demand for high-definition video is increasing, and the requirements for video quality are also increasing. Nowadays, a large number of videos have emerged, and how to distinguish the quality of videos faces many challenges. Nowadays, deep learning is developing rapidly, and many video quality assessment (VQA) methods have emerged. These VQA methods can efficiently evaluate video quality and save a lot of manpower and material resources. To achieve the evaluation of UHD video quality, we propose an inter-frame downsampling UHD video quality assessment model (IFDU-VQA) based on deep learning. This model is a no-reference (NR) evaluation model that adopts a dual-branch structure: one branch uses a recursive network to deeply extract video inter-frame features, and combines inter-frame downsampling technology with a multi-information fusion (MIF) module to achieve different time and frequency information. Effective fusion; the other branch applies convolutional neural networks (CNN) to extract features from individual video frames. By fusing the outputs of the two branches, regression and pooling modules are used to calculate the final quality score of the video. Compared with multiple existing models, experimental results show that IFDU-VQA demonstrates superior performance on multiple standard data sets.

关键词： Deep learning Ultra-high definition video Time-frequency analysis Feature extraction Data models Quality assessment Convolutional neural networks Data mining Video recording Standards

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Topic-based Multi-layer Knowledge Filtering for Emotion Recognition in Conversation

Topic-based Multi-layer Knowledge Filtering for Emotion Reco...

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International Conference on Computer Supported Cooperative Work in Design

作者： Xiaoyang Li Zhenyu Yang Yiwen Li Zhijun Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Emotion recognition in conversation (ERC) is a prominent research topic in natural language processing, widely applicable in various scenarios. However, accessing external commonsense knowledge and related dialogue topics, along with the complexity of fusing utterances, presents significant challenges. In this paper, we introduce a Topic-based Multilayer Knowledge Filtering (TMKF) model to enhance dialog emotion recognition accuracy. TMKF acquires commonsense knowledge for each utterance and employs two Variational Autoencoder (VAE) modules to extract global and speaker-level dialogue topics. We utilize Global Knowledge Filtering (GKF) and Local Knowledge Filtering (LKF) to obtain topic-specific knowledge representations after filtering commonsense information through global and local topic hierarchies. Subsequently, we leverage relational graphs to convolve commonsense knowledge with utterances, resulting in knowledge-constrained utterance representations for classification. Our proposed model is evaluated through extensive experiments on four widely used benchmark datasets for conversational emotion recognition. The results underscore the effectiveness of TMKF, which significantly outperforms other methods in standard metric evaluations.

关键词： Measurement Emotion recognition Filtering Convolution Oral communication Benchmark testing Nonhomogeneous media

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Intelligent Contract Vulnerability Detection Method Based on Bic-RL

Intelligent Contract Vulnerability Detection Method Based on...

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Data security and Privacy Protection (DSPP), International Conference on

作者： Shuhui Zhang Changdong Hu Tian Lan LianHai Wang Shujiang Xu Wei Shao 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) Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

Blockchain technology is praised for its decentralization, anonymity, and tamper-proof nature, but its complex architecture has led to numerous attacks, particularly against smart contracts. These attacks have caused significant economic losses and hindered the development of blockchain technology. Therefore, research on smart contract vulnerability detection methods has significant practical and application value. Existing detection technologies (such as symbolic execution, formal verification, and so on) have limitations, including heavy reliance on contract source code and incomplete feature extraction. This paper proposes a fully automated smart contract vulnerability detection method based on Bic-RL(Smart contracts Detection Based on Bicubic interpolation and Deep Residual Neural networks - Long short-term memory) to address these issues. This method not only simplifies the data processing process but also realizes the complete feature extraction and ensures the high efficiency of detection performance. Performance evaluation shows this method has higher detection accuracy and generalization ability, with an accuracy rate of 84.5%.

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