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International Conference on Computer Supported Cooperative Work in Design

作者： Yang Liu Xiaohui Han Wenbo Zuo Haiqing Lv Jing Guo 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 Quan Cheng Laboratory Jinan China Information Research Institute Qilu University of Technology(Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Advanced Persistent Threat (APT) pose an increasingly serious challenge to the security of businesses and organizations. In order to cope with these threats more effectively, effective utilization of cybers threat intelligence becomes crucial. Cyber Threat Intelligence(CTI) is characterized by fragmentation and voluminous, and information extraction can help cybersecurity researchers extract valuable information from massive cyber threat intelligence and understand the full picture of attack events. We propose a threat intelligence joint extraction model CTI-JE, which models the joint extraction problem as a table-filling task. CTI-JE uses SecBERT to generate embedding representations of words and then learns different representations for NER and RE tasks to avoid feature confusion. We construct a shared feature to enhance the interaction between subtasks, using dilated convolution and channel attention mechanism to extract fine-grained features. Experimental results on a cyber threat intelligence dataset show that our proposed model achieves better performance than existing baseline models.

关键词： Convolution Transforms Organizations Knowledge graphs Feature extraction information retrieval Vectors

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Research on Virtual 3D Remote Sensing Technology of power Grid Station Based on 2D Vision 5

Research on Virtual 3D Remote Sensing Technology of Power Gr...

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5th International Conference on information Technologies and Electrical Engineering, ICITEE 2022

作者： Yu, Hai Tang, Shiyang Peng, Lin Yu, Zhen Qian, Kun School of Automation Southeast University Jiangsu Nanjing210096 China Power Grid Digitization Technology Research Institute State Grid Smart Grid Research Institute Jiangsu Nanjing210003 China State Grid Key Laboratory of Information & Network Security Nanjing210003 China

ISBN: (纸本)9781450396806

Aiming at the production safety management and control problems of substations, the digital twin 3D volume of substations is constructed based on 3D twinning technology, and the 2D/3D space fusion positioning perception technology based on existing multi-band surveillance cameras is studied. The automatic safety risk monitoring algorithm of common monitoring equipment, such as the automatic safety risk monitoring algorithm, realizes the high-precision perception analysis of the three-dimensional space of personnel/equipment in the substation, and can be applied to the safety management and control of personnel, equipment and perimeter of the substation, and improves the safety management level of the power grid. © 2022 ACM.

关键词： Risk assessment

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Research on Dynamic Technology of Digital Benefit Intelligent Quantitative Control 11

Research on Dynamic Technology of Digital Benefit Intelligen...

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11th International Conference of information and Communication Technology, ICTech 2022

作者： He, Zhimin Dong, Aidi Pan, Jianhong Global Energy Interconnection Research Institute Co. Ltd Jiangsu Nanjing210003 China State Grid Key Laboratory of Information & Network Security Beiqijia Beijing100000 China State Grid Jilin Electric Power Co. Ltd Changchun130021 China

ISBN: (数字)9781665496940

ISBN: (纸本)9781665496940

With the rapid development of the digital economy, combined with changes in the company's internal and external development situations, the digitization of grid companies is extending from supporting management digitization to serving energy Internet digitization applications, extending from serving internally to internally and externally, and helping to improve quality and efficiency. To empower emerging industries to upgrade and extend, the digital construction of power grid companies will become an important core task for the construction of the energy Internet. This article is based on the quantitative control dynamic technology of digital economic benefit evaluation, through the analysis of business types, project cycles., new business development, etc., using big data analysis., data governance, machine learning and other technologies to capture, integrate, and analyze project data, To form an architectural feature model, to screen and effectively correlate digital economic benefit evaluation indicators, and finally complete the construction of a dynamic analysis model for digital economic benefit evaluation intelligent quantitative control. © 2022 IEEE.

关键词： Economic and social effects

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Filter-Enhanced Hypergraph Transformer for Multi-Behavior Sequential Recommendation

Filter-Enhanced Hypergraph Transformer for Multi-Behavior Se...

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

作者： Zhufeng Shao Shoujin Wang Wenpeng Lu Weiyu Zhang Hongjiao Guan Long Zhao 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 Data Science Institute University of Technology Sydney Sydney Australia

Sequential recommendation has been developed to predict the next item in which users are most interested by capturing user behavior patterns embedded in their historical interaction sequences. However, most existing methods appear to exhibit limitations in modeling fine-grained dependencies embedded in users’ various periodic behavior patterns and heterogeneous dependencies across multi-behaviors. Towards this end, we propose a Filter-enhanced Hypergraph Transformer framework for Multi-Behavior Sequential Recommendation (FHT-MB) to address the above challenges. Specifically, a multi-scale filter layer equipped with multi-learnable filters is devised to encode behavior-aware sequential patterns emerging from different periodic trends (e.g., daily or weekly routines), and then a hypergraph structure is devised to extract heterogeneous dependencies across users’ multiple types of behaviors. Extensive experiments on two real-world e-commerce datasets show the superiority of our proposed FHT-MB over various state-of-the-art methods. 1

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MemADet: A Representative Memory Bank Approach for Industrial Image Anomaly Detection

MemADet: A Representative Memory Bank Approach for Industria...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Min Li Jinghui He Zuobin Ying Gang Li Mingle Zhou 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 Faculty of Data Science City University of Macau Macau China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In the field of industrial production, anomaly detection is crucial for ensuring product quality and maintaining production efficiency. With the continuous advancement of computer vision technology, it has shown tremendous potential in industrial applications. However, the scarcity of labeled anomaly samples in real-world operating environments poses significant challenges for traditional anomaly detection techniques. To address this, we propose MemADet, a novel anomaly detection model that employs an unsupervised approach and leverages a representative memory bank. It employs a dynamic decision mechanism to control the representativeness of the features stored in the memory bank, and employs a weighted anomaly score calculation mechanism to further enhance the performance of image anomaly detection. Our evaluations indicate that MemADet performs robustly in industrial image anomaly detection across three datasets, with particularly no-table detection accuracy on the MVTec AD dataset. Its efficacy is further validated by competitive results on two additional datasets, highlighting its consistent and effective performance in various settings.

关键词： Accuracy Computational modeling Memory management Production Feature extraction Real-time systems Product design Quality assessment Reliability Anomaly detection

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Type-aware Enhanced Cylinder Embedding for Multi-hop Reasoning over Knowledge Graphs

Type-aware Enhanced Cylinder Embedding for Multi-hop Reasoni...

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International Joint Conference on Neural networks (IJCNN)

作者： Yong Shang Xiaoding Zhou Yan Ming Yuyan Zheng Huiting Li Weiyu Zhang 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 Shcool of Business Shandong Normal University Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Query embedding (QE) is designed to embed entities and first-order logic (FOL) queries in low-dimensional spaces, it has demonstrated considerable effectiveness in multi-hop reasoning over knowledge graphs. Recently, the practice of embedding entities and queries using geometric shapes has emerged as a promising approach, given the natural ability of these shapes to represent query answer sets and the logical relationships between them. However, existing geometry-based models, such as those employing point and box embeddings, cannot handle logical negation operations. Additionally, these models typically focus only on entities and relations, overlooking the rich semantic information available in knowledge graphs. To address these limitations, we propose the Type-aware Enhanced Cylinder Embedding (TECE) model. This model leverages three-dimensional geometric representations, integrating entity relationship type information. TECE covers a spectrum of essential First-Order Logic (FOL) operations, such as conjunctive, disjunctive, and negative. TECE improves the representation of entities and relationships in queries by incorporating semantic information. Moreover, it strengthens the model’s ability in generalization, along with deductive and inductive reasoning. Our experiment results demonstrate that TECE achieves significantly superior performance compared with existing SOTA methods on standard datasets.

关键词： Solid modeling Shape Semantics Neural networks Knowledge graphs Spread spectrum communication Cognition

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Research on Multi-dimensional Sensitivity Economic Evaluation Method of Digital Technology in power Grid Enterprises 1

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5th International Conference on Wireless Communications and Applications, ICWCA 2021

作者： Zhu, Liang Dong, Aidi Pan, Jianhong Global Energy Interconnection Research Institute Co. Ltd. Jiangsu Nanjing China State Grid Key Laboratory of Information and Network Security Jiangsu Nanjing China State Grid Jilin Electric Power Co. Ltd. Changchun130021 China

ISBN: (数字)9789811934865

ISBN: (纸本)9789811934858

The participation of power grid enterprises is essential to meet the requirements of new energy reform for carbon neutralization. Aiming at the multi-dimensional sensitivity of power grid enterprises, this paper proposes a digital technical and economic analysis model for power business needs and a digital benefit technical and economic evaluation method based on multi-dimensional index sensitivity analysis, which can meet the requirements of safe and stable operation of power grid, intelligent operation management of enterprises, construction of energy Internet ecosystem, economic accounting of energy Internet industry Driving the demand for the benefit evaluation of digital construction from the aspects of integration economic impact, innovating the production mode of power grid enterprises and the construction of energy Internet service system has important practical significance to improve the economic benefits of power grid enterprises, so as to provide an economic evaluation method for realizing carbon peak. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sensitivity analysis

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Anomaly-Free Prior Guided Knowledge Distillation for Industrial Anomaly Detection

Anomaly-Free Prior Guided Knowledge Distillation for Industr...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Gang Li Tianjiao Chen Min Li Delong Han MingLe Zhou 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 Faculty of Data Science City University of Macau Macau China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In industrial manufacturing, visual anomaly detection is critical for maintaining product quality by detecting and preventing production anomalies. Anomaly detection methods based on knowledge distillation demonstrate promising performance in addressing the unpredictability and diversity of anomalies. However, they suffer from a lack of effective guidance from anomaly-free priors when handling anomalous features and underutilize multi-scale features during the segmentation scoring stage, yielding suboptimal detection results. To alleviate these issues, we propose an Anomaly-free Prior Guided knowledge distillation (APG) for industrial anomaly detection. Firstly, it filters the abnormal features by training the de-noising target network with knowledge distillation structure. Concurrently, we propose the Prior Perception Propagation Module (P 3 M), which extracts more efficient anomaly-free features by imposing constraints on anomalous features. Secondly, we propose the Multi-scale Prior Guided Fusion Module (MPGFM) to improve anomaly detection accuracy by utilizing anomaly-free features from the target network as priors to guide the generation and fusion of cross-scale differential features. Finally, the Global Perception Enhancement Module (GPEM) is proposed to construct an anomaly scoring network, leveraging comprehensive scene features to enhance the detection and localization performance of numerous small-target anomalies in industrial manufacturing. Extensive experiments on the MVTecAD and BTAD datasets show that the proposed method demonstrates a consistent and significant outperformance against competing methods.

关键词： Training Location awareness Visualization Noise reduction Production Feature extraction Product design Manufacturing Quality assessment Anomaly detection

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Fairness of Large Music Models: From a Culturally Diverse Perspective

Fairness of Large Music Models: From a Culturally Diverse Pe...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Qinyuan Wang Bruce Gu He Zhang Yunfeng Li School of Music University of Sydney NSW Australia 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 Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China CNPIEC KEXIN Ltd Beijing China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

This paper explores the fairness of large music models in generating culturally diverse musical compositions, emphasizing the need for inclusivity and equity in AI-generated music. By analyzing popular datasets such as the Million Song Dataset and the Lakh MIDI Dataset, we identify a significant underrepresentation of non-Western musical elements. To address this, we adapt existing models to incorporate non-Western scales, rhythms, and instruments. The adapted models demonstrate a substantial improvement in generating culturally diverse music. Using AI-based synthetic listeners, we evaluate the impact of these adaptations on listener perception and engagement. Results indicate that music from the adapted models scores higher in terms of enjoyment, novelty, cultural resonance, and overall engagement compared to the original models. Our findings highlight the importance of cultural diversity in enhancing the user experience and ensuring ethical AI practices. The study also discusses challenges, such as dataset limitations and model adaptation complexity, and suggests directions for future research to further promote fairness and inclusivity in music AI. This work contributes to the development of more culturally aware and representative music generation systems, aiming to enrich the global musical landscape and foster greater appreciation of diverse musical traditions.

关键词： Adaptation models Ethics Instruments Data science Rhythm Data models User experience Resonance Cultural differences Artificial intelligence

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A SUPERVISED information ENHANCED MULTI-GRANULARITY CONTRASTIVE LEARNING FRAMEWORK FOR EEG BASED EMOTION RECOGNITION

arXiv

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arXiv 2024年

作者： Li, Xiang Song, Jian Zhao, Zhigang Wang, Chunxiao Song, Dawei Hu, Bin 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 Institute of Engineering Medicine Beijing Institute of Technology Beijing China

This study introduces a novel Supervised Info-enhanced Contrastive Learning framework for EEG based Emotion Recognition (SI-CLEER). SI-CLEER employs multi-granularity contrastive learning to create robust EEG contextual representations, potentially improving emotion recognition effectiveness. Unlike existing methods solely guided by classification loss, we propose a joint learning model combining self-supervised contrastive learning loss and supervised classification loss. This model optimizes both loss functions, capturing subtle EEG signal differences specific to emotion detection. Extensive experiments demonstrate SI-CLEER's robustness and superior accuracy on the SEED dataset compared to state-of-the-art methods. Furthermore, we analyze electrode performance, highlighting the significance of central frontal and temporal brain region EEGs in emotion detection. This study offers an universally applicable approach with potential benefits for diverse EEG classification tasks. © 2024, CC0.

关键词： Emotion Recognition

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