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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Tianhao Chen Qian Gao Jun Fan 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 Shandong 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 Shandong China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan Shandong China China Telecom Digital Intelligence Techonology Co Ltd Jinan Shandong China

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.

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Wireless Powered Backscatter Communications with Spatially Random Tags

Wireless Powered Backscatter Communications with Spatially R...

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IEEE International Conference on Communications in China (ICCC)

作者： Yingting Liu Yinghui Ye Xingwang Li Zhongyu Ma Zhiyang Zhou School of Electronic and Information Engineering Lanzhou Jiaotong University Lanzhou China State Grid Gansu Electric Power Research Institute Lanzhou China School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing China Shaanxi Key Laboratory of Information Communication Network and Security Xi’an University of Posts & Telecommunications Xi’an China School of Physics and Electronic Information Engineering Henan Polytechnic University Jiaozuo China College of Computer Science and Engineering Northwest Normal University Lanzhou China

In this paper, a wireless powered backscatter communication (BackCom) system is considered, which consists of one dedicated radio-frequency source node (S), one destination node and multiple tags randomly located in the coverage region of S. The consumed power by tags is considered and an optimal dynamic reflection coefficient (RC) is used to maximize the power of the backscattered signals while just satisfying the tag’s power requirement. The random tag selection (RTS) and nearest tag selection (NTS) schemes are proposed and the corresponding outage probabilities and diversity orders are derived. Numerical results verify the theoretical analysis and show that the NTS scheme has the best performance compared to the RTS and farthest tag selection schemes. Moreover, numerical results also show that the performance of the adopted optimal dynamic RC is superior to that of the fixed RC.

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Signature Scheme Based on The SM2 Algorithm in Fabric

Signature Scheme Based on The SM2 Algorithm in Fabric

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International Conference on networking and network Applications (NaNA)

作者： Yaling Zhang Hongyun Wang Yichuan Wang Wenjiang Ji Lei Zhu School of computer science and engineering Xi’an University of technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an University of technology Xi’an China

Based on the analysis and research of the widely used Hyperledger Fabric alliance chain structure, this paper proposes a digital signature scheme based on the national cryptographic algorithm SM2 which can be applied to Hyperledger Fabric. Firstly, two elliptic curve public-key cryptography algorithms are analyzed, and the feasibility of the SM2 in Fabric system is studied. Secondly, the Fabric system using the SM2 algorithm is designed and implemented, and the generation process of the Fabric chain address using the SM2 algorithm is given. Finally, experimental verification of the availability and performance of the Fabric system after the replacement of national cryptography algorithms is carried out. The experimental results show that the SM2 interface of the new Fabric alliance chain has good usability, and the average latency is reduced by 51.8%, and the transaction throughput is increased by 33.1%.

关键词： Distributed ledger Elliptic curves Public key cryptography Throughput Fabrics Blockchains Usability

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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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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

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.

关键词： Proteins Location awareness RNA Biological system modeling Predictive models Feature extraction Natural language processing

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SAR Image Scene Classification Based on Metric Learning and CNNs

SAR Image Scene Classification Based on Metric Learning and ...

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Information Communication and Software engineering (ICICSE), IEEE International Conference on

作者： Yaozhong Wang Zhiqiang Zhao Lei Wang School of Computer Science and Engineering and the Shaanxi Key Laboratory of Network Computing and Security Technology Xi‘an University of Technology Xi'an China School of Mathematics and Computer Science Shaanxi University of Technology Hanzhong China

ISBN: (纸本)9781665430302

With the development of synthetic aperture radar (SAR) imaging techniques, many high-quality images are acquired for exactly earth observation. As a result of heavy speckle, it is difficult to extract effective representation of different scene images to distinguish them accurately. In this paper, a metric-learning based convolutional neural network (CNN) is proposed to characterize SAR images. Specifically, a regularization term is proposed, which is constrained by the Mahalanobis distance. The regularization term learns a new metric space based on CNN, where the similar images should be close and those of dissimilar images should be separated. Comparing with the other approaches, experimental results of the proposed approach illustrate a superior classification for different scenes.

关键词： Earth Image analysis Conferences Speckle Extraterrestrial measurements Radar polarimetry Convolutional neural networks

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A Deep Learning-Based Framework for the Normalization of WOS Institutional Names

A Deep Learning-Based Framework for the Normalization of WOS...

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Artificial Intelligence, Internet and Digital Economy (ICAID), International Conference on

作者： Lin Shi Yan Lu Chuan Zhou Weiliu Chenxi Yi Zhou School of Cyber Science and Engineering Southeast University Nanjing China School of Microelectronics Tianjin University Tianjin China Minzu University of China Beijing China Inspur software Co. Ltd. Jinan 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 Jinan China

ISBN: (数字)9798331510664

ISBN: (纸本)9798331510671

Academic evaluation assesses the contributions and influence of researchers, institutions, or disciplines, determining their reputation and standing within a specific field. As a leading citation database, Web of Science (WOS) is a crucial source for academic evaluation data. However, ambiguities in institutional names due to name changes, multilingual translations, transcription errors, and author preferences significantly impact the accuracy of evaluations. To make data more reliable in academic assessment, this paper addresses deficiencies in deep learning-based techniques for normalizing institutional names in literature. It proposes a framework that combines multiple granular embeddings and multiple context information for synonym detection of WOS institutional names.

关键词： Accuracy Databases Neural networks Organizations Multilingual Internet Reliability Artificial intelligence

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Algorithm Application of Speech Recognition Technology Based on Multimodal Information in Human-computer Interaction

Algorithm Application of Speech Recognition Technology Based...

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Artificial Intelligence, Internet and Digital Economy (ICAID), International Conference on

作者： Lin Shi Yan Lu Yi Zhou Weiliu Chenxi Chuan Zhou School of Cyber Science and Engineering Southeast University Nanjing 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 Jinan China Inspur software Co. Ltd. Jinan China School of Microelectronics Tianjin University Tianjin China Beijing Minzu University of China Beijing China

ISBN: (数字)9798331510664

ISBN: (纸本)9798331510671

Multimodal human-computer interaction (MMHCI) has been considered a key direction during the evolution of the human-computer interaction (HCI) domain. To speed up the technical advancement of HCI systems, this investigation initially combines attention mechanism (AM) with the Connectionist Temporal Classification (CTC) algorithm for developing a speech recognition (SR) framework. The framework is further enhanced by applying an optimized artificial swarming algorithm to the end-to-end SR framework, resulting in an SR model appropriate for the MMHCI system. The experimental outcomes indicate that the proposed improved swarm algorithm achieves better convergence, solving the solution set with less difference from the real solution set, the hypervolume metric converges at 0.861, and the inverse generation distance converges at 0.194. The SR model surpasses others in terms of evaluation parameters containing accuracy, precision, and recall, while achieving the lowest error rates in various aspects as the sentence level, word, and character, which are correspondingly $0.035,0.037$, and 0.036. This ensures better accuracy of recognition while maintaining a balanced on-access processing rate. As to a system of multimodal interaction, the relatively higher scores of mean opinion and objective grading for voice quality were received in the experimental group (EG), instead of the control group (CG). The speech processing technology developed in such investigation is quite essential for elevating interaction efficiency together with user experience, offering valuable insights and guidance for research in the areas of HCI and SR.

关键词： Human computer interaction Measurement Accuracy Speech recognition User experience Classification algorithms Internet Speech processing Optimization Convergence

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Density-aware and Depth-aware Visual Representation for Zero-Shot Object Counting

Density-aware and Depth-aware Visual Representation for Zero...

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

作者： Fang Nan Feng Tian Ni Zhang Nian Liu Haonan Miao Guang Dai Mengmeng Wang Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an China School of Automation Northwestern Polytechnical University Xi’an China Computer Vision Department Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi UAE SGIT AI Lab State Grid Corporation of China Zhejiang University of Technology Hang Zhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Previous methods often utilize CLIP semantic classifiers with class names for zero-shot object counting. However, they ignore crucial density and depth knowledge for counting tasks. Thus, we propose a density-aware and depth-aware prompt counting model, which captures density information via learning density-aware prompts based on density-aware contrastive loss and incorporates depth guidance with predefined depth-aware prompts. To facilitate the training process, we design two strategies for standard counting loss and the contrastive loss, where the former prioritizes larger and sparser objects initially, gradually focusing on smaller and denser objects, and the latter adopts coarse-to-fine density learning. Besides, we construct a dataset named LVIS-372 with more real-world scenarios and balanced instance distribution compared to existing ones. Finally, the experimental results demonstrate the effectiveness of our proposed method.

关键词： Training Visualization Semantics Focusing Signal processing Acoustics Power capacitors Speech processing Standards

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A Review of Multimodal Explainable Artificial Intelligence: Past, Present and Future

arXiv

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arXiv 2024年

作者： Sun, Shilin An, Wenbin Tian, Feng Nan, Fang Liu, Qidong Liu, Jun Shah, Nazaraf Chen, Ping School of Computer Science and Technology Xi’an Jiaotong University Xi’an710049 China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an710049 China Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an710049 China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University Xi’an710049 China Institute for Future Transport and Cities Coventry University Priory Street CoventryCV1 5FB United Kingdom Department of Engineering University of Massachusetts Boston BostonMA02125 United States

Artificial intelligence (AI) has rapidly developed through advancements in computational power and the growth of massive datasets. However, this progress has also heightened challenges in interpreting the "black-box" nature of AI models. To address these concerns, eXplainable AI (XAI) has emerged with a focus on transparency and interpretability to enhance human understanding and trust in AI decision-making processes. In the context of multimodal data fusion and complex reasoning scenarios, the proposal of Multimodal eXplainable AI (MXAI) integrates multiple modalities for prediction and explanation tasks. Meanwhile, the advent of Large Language Models (LLMs) has led to remarkable breakthroughs in natural language processing, yet their complexity has further exacerbated the issue of MXAI. To gain key insights into the development of MXAI methods and provide crucial guidance for building more transparent, fair, and trustworthy AI systems, we review the MXAI methods from a historical perspective and categorize them across four eras: traditional machine learning, deep learning, discriminative foundation models, and generative LLMs. We also review evaluation metrics and datasets used in MXAI research, concluding with a discussion of future challenges and directions. A project related to this review has been created at https://***/ShilinSun/mxai_review. Copyright © 2024, The Authors. All rights reserved.

关键词： Data fusion

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Cell Manipulation Attack Against Onion Services

Cell Manipulation Attack Against Onion Services

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Jianjun Lin Yinliang Yue Yijing Zhang Xiaoyu Liu XiaoDu Yang Weilin Gai Bo Sun Chinese Academy of Sciences Institute of Information Engineering Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Zhongguancun Laboratory Beijing China Chinese Academy of Sciences Institute of Software Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China National Computer Network Emergency Response Technical Team/Coordination Center of China (CNCERT/CC) Beijing China

In this study, we proposed OREA-OS, a cell manipulation attack devised to compromise Onion Services. Through the manipulation of cells, two collaborating nodes within a circuit exchange information to unveil the anonymity of the targeted Onion Service. We conducted an analysis of existing de-anonymization scenarios and effectively addressed the associated challenges, thereby achieving successful de-anonymization of Onion Services. To assess the efficacy of our attacks, we conducted experiments on both private and public Tor networks. The experimental results validate the effectiveness of our approach, as we accomplished 100% accurate identification of the relationship between domain names and Internet Protocol addresses, with no false positives. Within our simulated private network, the recall rate neared 100 %. On the public Tor network, the de-anonymization rate (98.6 %) exceeded the successful download rate (98.2 %). Furthermore, 99.2 % of success attacks took less than 10 seconds to confirm.

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