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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Min Li Delong Han Rui Shao Jinghui He Faculty of Data Science City University of Macau China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) 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 Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

In the process of large-scale industrial production, complex defects of different shapes and sizes can occur in industrial products and their components, and these defects seriously affect the performance of industrial products and their components, and being able to quickly and accurately identify defective industrial products and their components is an important task. Memory-based methods are usually used for anomaly detection, and these methods often rely on pre-trained models for feature extraction, but have high requirements on feature quality. In addition, patch-based methods mainly focus on local differences in feature embedding when detecting anomalies without explicitly exploiting broader contextual information, thus greatly affecting the robustness and accuracy of anomaly detection. Aiming at the above problems and requirements, this paper proposes a Feature Aggregation Network for Memory-Based Industrial Anomaly Detection (FANet), which is based on the pre-extraction of anomalous features, attentional attention to key features, information aggregation of multi-level and multi-scale features, and the FANet provides advanced performance in industrial image anomaly detection and localisation by pre-extraction of anomalous features, attentional attention to key features, information aggregation of multi-layered and multi-scale features, and introduction of environmental information in the anomaly score computation to perform industrial anomaly detection tasks, and using core set subsampling to reduce storage requirements. On the MVTec AD, FANet has an image-level AUROC score of 99.1% and a pixel-level AUROC score of 98.2%. Similarly high detection results were achieved on the ViSA dataset, further demonstrating the robustness of FANet.

关键词： Training Runtime Shape Memory management Training data Production Feature extraction Robustness Anomaly detection Testing

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Performance Analysis for Intrusion Target Detection in Wireless Sensor networks

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Chinese Journal of Electronics 2023年第4期20卷 725-729页

作者： FAN Gaojuan WANG Ruchuan HUANG Haiping SUN Lijuan XIAO Fu College of Computer Nanjing University of Posts and Telecommunications Nanjing China School of Computer and Information Engineering Henan University Kaifeng China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing China Key Laboratory of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Ministry of Education of Jiangsu Province Nanjing China

Target detection and field surveillance are among the most prominent applications of wireless sensor networks. The quality of detection achieved by a sensor network can be quantified by evaluating the probability of detecting a mobile target crossing a sensing field. Detection probability of sensor nodes has been studied in sensor networks for many purposes such as quality of service and decision making. However, the sensing capabilities of sensors are affected by environmental factors in real deployment. This paper investigates the problem of detecting probability in a log-normal shadow fading environment. It presents an analytic method to evaluate the detection probability by at least $k$ sensors under practical considerations. Furthermore, we also shows that shadow fading makes significant influence in detection probability compared to unit disk sensing model through extensive simulation experiments.

关键词： Industries Wireless communication Fading channels Wireless sensor networks Surveillance Object detection Quality of service

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Integrated high-performance error correction for continuous-variable quantum key distribution

arXiv

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arXiv 2024年

作者： Zhou, Chuang Li, Yang Ma, Li Yang, Jie Huang, Wei Sun, Ao Wang, Heng Luo, Yujie Li, Yong Chen, Ziyang Lau, Francis C.M. Zhang, Yichen Yu, Song Guo, Hong Xu, Bingjie National Key Laboratory of Security Communication Institute of Southwestern Communication Chengdu610041 China College of Computer Science Chongqing University Chongqing400044 China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Center for Quantum Information Technology Peking University Beijing100871 China Department of Electronic and Information Engineering Hong Kong Polytechnic University Hong Kong State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing100876 China

An integrated error-correction scheme with high throughput, low frame errors rate (FER) and high reconciliation efficiency under low signal to noise ratio (SNR) is one of the major bottlenecks to realize high-performance and low-cost continuous variable quantum key distribution (CV-QKD). To solve this long-standing problem, a novel two-stage error correction method with limited precision that is suitable for integration given limited on-chip hardware resource while maintaining excellent decoding performance is proposed, and experimentally verified on a commercial FPGA. Compared to state-of-art results, the error-correction throughput can be improved more than one order of magnitude given FER © 2024, CC BY.

关键词： Error correction

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BAT: Mining Binary-API Topic for Multi-service Application Development

BAT: Mining Binary-API Topic for Multi-service Application D...

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International Conference on computer Supported Cooperative Work in Design

作者： Pengfei He Linlin Liu Dianlong You Limin Shen Zhen Chen School of Information Science and Engineering Yanshan University Qinhuangdao China National Science Libraries Chinese Academy of Sciences Beijing China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China

With the growing popularity of API-driven multiservice application (mashup) development, the burgeoning web APIs have left developers drowning in the sea of web API selections. Matching developers with the most appropriate APIs is the key to improving user satisfaction and promoting more popular web applications. As a result, more and more researchers pay attention to web API recommender systems based on collaborative filtering. However, employing collaborative filtering to recommend APIs is challenging due to the severe sparsity of mashup and API interactions. To address this problem, we propose a probabilistic generative model, called the Binary-API Topic model (BAT), to parameterize mashups and APIs. Technically, BAT is equipped with a mechanism to extract binary-APIs and predict unknown pairwise interactions. To improve generality and capture more relevance from a limited number of interactions, we learn binary-API topics by directly modeling the generation of API co-occurrence patterns across the repository (all mashup collections from ***). The main advantage of BAT is that it preserves API co-occurrence patterns in model learning and exploits the rich global relevance. Finally, through extensive experiments, we demonstrate that BAT can achieve the highest performance on the sparse real-world data set.

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Photonic integrated spiking neuron chip based on a self-pulsating DFB laser with a saturable absorber

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Photonics Research 2023年第8期11卷 1382-1389页

作者： YUECHUN SHI SHUIYING XIANG XINGXING GUO YAHUI ZHANG HONGJI WANG DIANZHUANG ZHENG YUNA ZHANG YANAN HAN YONG ZHAO XIAOJUN ZHU XIANGFEI CHEN XUN LI YUE HAO Yongjiang Laboratory Ningbo 315202China State Key Laboratory of Integrated Service Networks State Key Discipline Laboratory of Wide Bandgap Semiconductor TechnologyXidian UniversityXi’an 710071China Key Laboratory of Intelligent Optical Sensing and Manipulation Ministry of EducationNational Laboratory of Solid State MicrostructuresCollege of Engineering and Applied SciencesInstitute of Optical Communication EngineeringNanjing UniversityNanjing 210023China School of Science Jiangnan UniversityWuxi 214122China School of Information Science and Technology Nantong UniversityNantong 226019China Department of Electrical and Computer Engineering McMaster UniversityHamiltonOantario L8S 4K1Canada

We proposed and experimentally demonstrated a simple and novel photonic spiking neuron based on a distributed feedback(DFB)laser chip with an intracavity saturable absorber(SA).The DFB laser with an intracavity SA(DFBSA)contains a gain region and an SA *** gain region is designed and fabricated by the asymmetric equivalentπ-phase shift based on the reconstruction-equivalent-chirp *** properly injected current in the gain region and reversely biased voltage in the SA region,periodic self-pulsation was experimentally observed due to the Q-switching *** self-pulsation frequency increases with the increase of the bias current and is within the range of several *** the bias current is below the self-pulsation threshold,neuronlike spiking responses appear when external optical stimulus pulses are *** results show that the spike threshold,temporal integration,and refractory period can all be observed in the fabricated DFB-SA *** numerically verify the experimental findings,a time-dependent coupled-wave equation model was developed,which described the physics processes inside the gain and SA *** numerical results agree well with the experimental *** further experimentally demonstrated that the weighted sum output can readily be encoded into the self-pulsation frequency of the DFB-SA *** also benchmarked the handwritten digit classification task with a simple single-layer fully connected neural *** using the experimentally measured dependence of the self-pulsation frequency on the bias current in the gain region as an activation function,we can achieve a recognition accuracy of 92.2%,which bridges the gap between the continuous valued artificial neural networks and spike-based neuromorphic *** the best of our knowledge,this is the first experimental demonstration of a photonic integrated spiking neuron based on a DFB-SA,which shows great potential to realizing large-scale mult

关键词： technique laser saturable

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DH-Based Updatable Private Set Intersection

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International Journal of Network Security 2024年第6期26卷 1092-1101页

作者： Zhang, Yingbo Liu, Momeng Li, Yunyun Networking and Intelligent Information Services School of Computer Science Xi’an Polytechnic University State Key Laboratory of Integrated Services Networks Xidian University Shaanxi Key Laboratory of Clothing Intelligence State and Local Joint Engineering Research Center for Advanced No. 58 Shan-gu Road Lintong Xi’an710600 China

The rapid development of information technology provides convenience for online shopping, and many merchants try to increase their income through advertising conversion rates. However, in a virtual network environment, the transaction carries risks that may lead to private data leakage. Fortunately, private set intersection (PSI) provides an effective private data protection solution, allowing two parties to compute the intersection of their private sets without revealing any additional information beyond the intersection itself. This paper proposes an updatable PSI with a Diffie-Hellman-based key agreement paradigm. Our scheme allows dynamic updating of private sets of advertisers or merchants for computation, breaking the limitations of static scenarios. At the same time, we choose Rank and Select based Quotient Filter (RSQF) to process the data sets of participants in the scenario, it only needs small storage space to filter advertising data than Cuckoo filter. In addition, it allows merge operations between filters, and the filter is rebuilt when it reaches its maximum capacity without generating a new filter from scratch. Finally, we conducted comparison experiments and found that our solution has lower communication complexity and running time than related works. © (2024), (International Journal of Network Security). All rights reserved.

关键词： information leakage

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Graph Contrastive Learning with Learnable Graph Augmentation

Graph Contrastive Learning with Learnable Graph Augmentation

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

作者： Xinyan Pu Ke Zhang Huazhong Shu Jean Louis Coatrieux Youyong Kong Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China LTSI Universit de Rennes 1 Rennes France Centre de Recherche en Information BioMdicale Sino-Franais Rennes France

Graph contrastive learning has gained popularity due to its success in self-supervised graph representation learning. Augmented views in contrastive learning greatly determine the quality of the learned representations. Handcrafted data augmentations in previous work require tedious trial-and- errors per dataset, which is time-consuming and resource-intensive. Here, we propose a Graph Contrastive learning framework with Learnable graph Augmentation called GraphCLA. Specifically, learnable graph augmentation trains augmented views by minimizing the mutual information (MI) between the input graphs and their augmented graphs. This paper designs a kernel contrastive loss function based on an end-to-end differentiable graph kernel to learn augmented views. In addition, this paper utilizes a min-max optimization strategy to learn challenging augmented graphs and to learn discriminative representations adversarially. Finally, we compared GraphCLA with state-of-the-art self-supervised learning baselines and experimentally validate the effectiveness of GraphCLA.

关键词： Representation learning Self-supervised learning Signal processing Benchmark testing Acoustics Kernel Speech processing

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An Efficient Energy Consumption Prediction Framework for High Performance Computing Cluster Jobs

An Efficient Energy Consumption Prediction Framework for Hig...

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International Conference on computer Engineering and Applications (ICCEA)

作者： Yantao Lou Jibin Wang Shoupeng Feng Xian Yu 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 Department of Public Security of Shandong Province Shandong Intelligent Public Security Technology Innovation Center Jinan China

ISBN: (数字)9798350386776

ISBN: (纸本)9798350386783

Energy consumption prediction is a crucial approach to enhance the operational efficiency of HPC (High Performance Computing) clusters. Existing predicting methods for the energy consumption mainly rely on time series mode, focusing on the instantaneous power during job execution. However, in practical applications, due to the large scale of HPC clusters, the prediction models need to process a vast amount of data, often resulting in poor performance. Moreover, it's challenging to provide effective predictions for jobs with significant power fluctuations. Thus we propose a more efficient energy consumption prediction framework for HPC cluster jobs, achieving two main objectives: On one hand, it uses a data encoding module to pre-encode the sampled data of energy consumption related indicators, simplifying the data that the time series prediction model needs to process and improving prediction efficiency. On the other hand, the prediction target is set to the power distribution range in a future period of time, rather than the instantaneous power at some specific time points, which improves the usability of the prediction results. Test results show that the proposed prediction framework can achieve more markable performance improvement while ensuring the effectiveness of the predictions.

关键词： Energy consumption Computational modeling High performance computing Time series analysis Power distribution Predictive models Feature extraction

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Multi-Channel Hypergraph Network for Sequential Diagnosis Prediction in Healthcare

Multi-Channel Hypergraph Network for Sequential Diagnosis Pr...

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International Conference on computer Supported Cooperative Work in Design

作者： Xin Zhang Xueping Peng Weimin Chen Weiyu Zhang Xiaoqiang Ren Wenpeng Luv 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 Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia Shandong Mental Health Center Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Sequential diagnosis prediction (SDP) is a complex and challenging task, aming to predict future diagnoses of patients by analyzing their historical medical records. Although graph neural networks(GNNs) has been applied to successfully address the challenge of heterogeneous data integration in electronic health records, relatively limited work has been done on GNNs for sequential diagnosis prediction. Graph neural network-based methods, aimed at capturing structural and relational patterns of EHR data for sequential diagnosis prediction, explore code-code pairwise relationships, resulting in an inability to learn fine-grained, higher-order interaction relationships among different types medical codes. As a result, they are difficult to effectively model complex, multi-dimensional interactions among different types of medical codes necessary for accurate and nuanced diagnosis predictions. To address these challenges, this paper proposes a novel approach called Multi-Channel Hypergraph Network (MCHN) predictive framework for sequential diagnosis prediction. The proposed method aims to explore the fine-grained higher-order interactions between different types of medical codes via multi-channel hypergraphs. Specifically, MCHN learns two levels of code embeddings from multi-channel hypergraph learning module and line graph learning module, respectively: (i) multi-channel hypergraph learning module, which is to learn multi-channel hypergraph level code embeddings by modeling the higher-order relationships between medical codes in different hypergraphs; and (ii) line graph learning module, which is to learn the line graph level code embeddings by modeling code-code pairwise relationships. In MCHN, we propose a novel channel-level attention mechanism to help our model attend to the informativeness of the different channel for forecasting future patient diagnoses. We also design a code-level attention mechanism, which can to pay more attention to the medical codes that are m

关键词： Codes Attention mechanisms Federated learning Medical services Predictive models Benchmark testing Sequential diagnosis

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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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