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information Technology: IoT and Smart City (ITIoTSC), International Conference on

作者： Ruixue Kuang Lianhai Wang Shuhui Zhang Shujiang Xu Wei Shao Qizheng Wang 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 Shandong China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan Shandong China

ISBN: (数字)9798350328349

ISBN: (纸本)9798350328356

In order to improve the detection accuracy and improve the detection speed, this paper designs a detection method of trusted blockchain link flood attack based on the trusted execution environment. First, above all, analyze the trusted block chain structure, then design the trusted block chain link flood attack detection algorithm, and finally the trusted execution environment is used to realize the trusted block chain link flood attack detection. The experimental results prove that the false alarm rate and leakage detection rate of the detection method of the flood flood attack designed based on the trusted execution environment are both low, with the lowest value only 3% and 0.98%. The detection accuracy is high, the detection time is short, and the detection can be completed in at least 13s. It can be proved that the performance of this design method is better and has certain application value.

关键词： Accuracy information resources Smart cities Computer hacking Design methodology Blockchains Floods

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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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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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CHECKWHY: Causal Fact Verification via Argument Structure

arXiv

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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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AoI Optimization for UAV-Assisted Wireless Sensor networks

AoI Optimization for UAV-Assisted Wireless Sensor Networks

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IEEE International Conference on Communications Workshops, ICC

作者： Aijing Sun Chi Sun Jianbo Du Chen Chen Chengbin Huang Jisheng Sui Shaanxi Key Laboratory of Information Communication Network and Security School of Communications and Information Engineering Xi'an University of Posts and Telecommunications Xi'an China School of Telecommunication Engineering Xidian University Xi'an China State Grid Jilin Province Electric Power Company Limited Information Communication Company Changchun China

ISBN: (数字)9798350304053

ISBN: (纸本)9798350304060

The age of information (AoI) and real-time delay optimization become critical with the increasing use of unmanned aerial vehicles (UAV) in wireless sensor networks (WSN). With two distinct components, this paper offers a comprehensive method to reduce the AoI in UAV-assisted WSN. First, the possibilistic fuzzy c-means (PFCM) algorithm is improved and the honey badger algorithm (HBA) is implemented to achieve an effective clustering. This entails choosing the best hovering locations for the UAV and related sensor nodes, improving the clustering procedure as a whole, and boosting data gathering efficiency. Second, we concentrate on path planning between UAV hovering places using the proximal policy optimization 2 (PPO2) reinforcement learning technique. Our goals are to reduce the total AoI and improve communication dependability by optimizing PPO2. As a result, the UAV's course planning becomes more clever and flexible, improving its ability to carry out missions in challenging circumstances. Comprehensive simulation tests are carried out to compare the suggested algorithm with current techniques, showing that it is highly effective in lowering AoI in UAV-assisted WSN.

关键词： Wireless communication Wireless sensor networks Conferences Clustering algorithms Reinforcement learning Autonomous aerial vehicles Delays

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Multi-Channel Hypergraph Convolution Group Recommendation with Member information Enhancement 25

Multi-Channel Hypergraph Convolution Group Recommendation wi...

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25th IEEE International Conferences on High Performance computing and Communications, 9th International Conference on Data Science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Chen, Tianhao Gao, Qian Fan, Jun Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Shandong Jinan250014 China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Shandong Jinan250353 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Shandong Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd Shandong Jinan250101 China

ISBN: (纸本)9798350330014

Group recommendation involves comprehensively considering various aspects, including members and items, to predict the overall interests of a group and recommend suitable items through a recommendation system. With the rapid evolution of the Internet, online group activities have become increasingly prevalent, making group recommendation a highly discussed topic within the realm of recommendation systems. However, current research in group recommendation still confronts the following challenges. Firstly, a predominant portion of group recommendation research focuses solely on aggregating group-level information, neglecting the valuable contribution of higher-order member-level insights to group recommendation. Moreover, some aggregation strategies overly prioritize fairness, thereby disregarding common real-world patterns. Secondly, previous studies have primarily relied on aggregating individual member interests to establish group preferences, lacking a holistic consideration of group dynamics. To tackle these challenges, this study introduces an innovative approach by implementing multi-channel hypergraph convolution within the member perspective. This approach aims to effectively extract higher-order insights from members by utilizing a member information enhancement module. By establishing group interconnections based on similarity and employing an adaptive fusion network to amalgamate multiple viewpoints, a final representation is derived. Experimental results demonstrate that our proposed model surpasses baseline models by 3% to 4% in terms of hit rate and accuracy, validating the efficacy of our approach. © 2023 IEEE.

关键词： Recommender systems

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information Protection of International Students Based on network security 4

Information Protection of International Students Based on Ne...

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4th International Conference on Computer network, Electronic and Automation, ICCNEA 2021

作者： Wang, Tianma Zhao, Dongmei Zheng, Le International Education Institute North China Electric Power University Beijing China College Of Computer And Cyber Security Hebei Normal University Shijiazhuang China Key Laboratory Of Network And Information Security Hebei Province Shijiazhuang China

ISBN: (纸本)9781665444866

With China's overall national strength, the education of studying in China has entered a period of rapid development, and China has become one of the important destination countries for international student mobility. With political stability, rapid economic development, and continuous improvement in the quality of higher education, the educational value of studying in China is increasingly recognized by international students. International students study and live in the same way as domestic students. While the development of the Internet has brought convenience to people, it has also created many security risks. How to protect the information security of international students is the focus of this paper. This paper introduces the classification, characteristics and security risks of international students' personal information. In order to protect the private data of international students from being leaked, filtering rules are set in the campus network through WinRoute firewall to effectively prevent information from being leaked, tampered or deleted, which can be used for reference by other universities. © 2021 IEEE.

关键词： network security

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Federated Semantic Learning for Privacy-preserving Cross-domain Recommendation

arXiv

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arXiv 2025年

作者： Lu, Ziang Guo, Lei Yu, Xu Cheng, Zhiyong Han, Xiaohui Zhu, Lei Shandong Normal University Shandong Jinan250358 China Shandong Qingdao266580 China Hefei University of Technology Anhui Hefei230009 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences Shandong Jinan250014 China Tongji University Shanghai Jinan200092 China

In the evolving landscape of recommender systems, the challenge of effectively conducting privacy-preserving Cross-Domain Recommendation (CDR), especially under strict non-overlapping constraints, has emerged as a key focus. Despite extensive research has made significant progress, several limitations still exist: 1) Previous semantic-based methods fail to deeply exploit rich textual information, since they quantize the text into codes, losing its original rich semantics. 2) The current solution solely relies on the text-modality, while the synergistic effects with the ID-modality are ignored. 3) Existing studies do not consider the impact of irrelevant semantic features, leading to inaccurate semantic representation. To address these challenges, we introduce federated semantic learning and devise FFMSR as our solution. For Limitation 1, we locally learn items’ semantic encodings from their original texts by a multi-layer semantic encoder, and then cluster them on the server to facilitate the transfer of semantic knowledge between domains. To tackle Limitation 2, we integrate both ID and Text modalities on the clients, and utilize them to learn different aspects of items. To handle Limitation 3, a Fast Fourier Transform (FFT)-based filter and a gating mechanism are developed to alleviate the impact of irrelevant semantic information in the local model. We conduct extensive experiments on two real-world datasets, and the results demonstrate the superiority of our FFMSR method over other SOTA methods. Our source codes are publicly available at: https://***/Sapphire-star/FFMSR. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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Prediction of exosomal piRNAs based on deep learning for sequence embedding with attention mechanism

Prediction of exosomal piRNAs based on deep learning for seq...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Liu, Yajun Ding, Yulian Li, Aimin Fei, Rong Guo, Xie Wu, Fangxiang Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China University of Saskatchewan Division of Biomedical Engineering Saskatoon Canada Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an China University of Saskatchewan Division of Biomedical Engineering Department of Computer Science Department of Mechanical Engineering Saskatoon Canada

ISBN: (纸本)9781665468190

PIWI-interacting RNAs (piRNAs) are a type of small non-coding RNAs which bind with the PIWI proteins to exert biological effects in various regulatory mechanisms. A growing amount of evidence reveals that exosomal piRNAs are potential biomarkers for diagnosis and treatment of complex diseases. Effective methods for the prediction of exosomal piRNAs are the foundation of piRNA functional research. In this study, we propose an end-to-end deep network for identifying exosomal piRNAs based on features learned from natural language processing (NLP) models for sequence embedding with attention mechanism. First, a benchmark dataset is constructed by processing piRNA subcellular localization annotated data and sequence data. Moreover, bagging positive unlabeled learning is applied to get the reliable negative set. Finally, we treat a piRNA sequence as a sentence and its k-mer subsequence as a token. Sequence embedding models with self-attention mechanism is designed to extract features from exosome piRNA sequences, which are used for the prediction task. Compared with three competing methods, our model achieves the best performance and reveals the key factors of exosomal piRNA sequences by the attention mechanism. Our model characterizes exosomal piRNAs and could be beneficial for researchers to investigate exosomal piRNAs' functions. © 2022 IEEE.

关键词： Embeddings

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A Scientific Workflow Engine Design and Implementation

A Scientific Workflow Engine Design and Implementation

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第四届公共管理与智能社会国际学术会议

作者： Zhichao Zhang Meng Guo Tingting Guo Key Laboratory of' Computing Power Network and Information Security of Ministry of Education Shandong Computer Science Center (National Supercomputing 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 Inspur General Software Co.Ltd. Shandong Carbon Manager Group Co.Ltd.

This paper designs and implements a scientific workflow engine that follows the BPMN 2.0, enriching existing research on scientific workflow engine specification and theory. The engine is implemented using the Java language and adopts a layered architecture, including a process virtual machine, a BPMN model engine and a scientific workflow model engine from the bottom up, which has good scalability and is able to provide powerful support for the design and operation of scientific workflows so as to process scientific research data more efficiently.

关键词： Scientific workflow workflow workflow engine BPMN 2.0

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