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

作者： Zhijie Shen Bowen Liu Xiaolong Xu Lianyong Qi Fei Dai Wanchun Dou State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen China College of Computer Science and Technology China University of Petroleum (East China) Qingdao China School of Computer Science Nanjing University of Information Science and Technology Nanjing China College of Big Data and Intelligent Engineering Southwest Forestry University Kunming China

As a standardized and systematic management method, business process management plays an important role in the industrial Internet of Things (IIoT) scenario. With the rapid development of mobile Internet and the increasing amount of data generated by the Internet of Things devices, the traditional storage scheme can no longer meet the requirements of managing the IIoT mobile business data. The cloud-edge collaborative data storage scheme provides mass storage capability by the cloud data center, and meanwhile takes access delay into account by storing data in the edge servers near to user, making it an effective solution to manage mobile business data in the IIoT scenario. However, the storage capacity of edge servers is relatively limited. How to design a reasonable data placement strategy to maximize the speed of data reading and tasks execution in IIoT business process has become an urgent problem to be solved. In order to shorten the overall running time, reduce data transmission costs and enhance users experience, we propose a data placement strategy based on Bayesian optimization algorithm, which can reasonably allocate storage capacity in the edge side, and further determine which edge server the data should be placed on. The results of the experiment show that the strategy based on our proposed algorithm performs better in a variety of different scenarios than the ones that based on other kind of optimization algorithms which have been adopted in similar studies.

关键词： Cloud computing Systematics Memory User experience Bayes methods Servers Task analysis

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Explicit Unsupervised Feature Selection Based on Structured Graph and Locally Linear Embedding

SSRN

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SSRN 2023年

作者： Miao, Jianyu Zhao, Jingjing Yang, Tiejun Fan, Chao Tian, Yingjie Shi, Yong Xu, Mingliang School of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou450001 China College of Information Science and Engineering Henan University of Technology Zhengzhou450001 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450001 China

Numerous redundant and irrelevant features, and outliers are usually contained in high-dimensional data whose presence, if ignored, can bring detrimental effects on the performance of data processing tasks. Feature selection as a trending topic in a great deal of domains has been greatly concerned, which can effectively deal with noise and outlier. In this work, we propose a novel unsupervised method that unifies the explicit feature selective matrix learning and structured graph learning into a framework. An explicit selection matrix only consisting of 0 and 1 are designed, which can be integrated into locally linear embedding, and can be effectively and efficiently solved with closed-form solution. Furthermore, we take into account the local consistency between the samples so that the low dimensional manifold structures of samples can be preserved to a better degree. Instead of using the pre-defined similarity matrix to construct graph, we make use of the way of learning to obtain the adaptive graph. Besides, for the purpose of exploiting the cluster structure of data, we impose a rank constraint on the graph. Notably, both explicit selection matrix and intrinsic structure are learned in low-dimensional feature subspace, and thus the proposed methodology is more straightforward. We present an effective and efficient algorithm for the resulting problem based on alternative optimization strategy, and establish its convergence and computational complexity analysis results. Extensive experiments verify that our proposed approach can significantly outperform advanced methods. © 2023, The Authors. All rights reserved.

关键词： Matrix algebra

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Dynamic multi-key FHE in asymmetric key setting from LWE

TechRxiv

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TechRxiv 2021年

作者： Dong, Sen Chen, Yuling Li, Tao Wang, Yilei Zhou, Huiyu State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China Guangxi Key Laboratory of Cryptography and Information Security Guilin University Of Electronic Technology Guilin541004 China school of Qufu Normal University Qufu273165 China school of Informatics University of Leicester LeicesterLE1 7RH United Kingdom

Multi-key Fully homomorphic encryption (MFHE) schemes allow computation on the encrypted data under different keys. However, traditional multi-key FHE schemes based on Learning with errors (LWE) have the undesirable property that is the number of keys has to be fixed in advance. A dynamic multi-key FHE scheme is the most versatile variant which the information about the participants is not required before key generation. To support further homomorphic computation on extended ciphertexts and ciphertexts encrypted under additional keys, Peikert and Shiehian (TCC’16) proposed a leveled dynamic multi-key FHE scheme. Nevertheless, it introduces the circular-security assumption for the LWE parameters to ensure its security, which provides weaker security to a certain extent. The problem of how to construct a LWE-based dynamic multi-key FHE scheme is still open. To address the above problem, in this work, we present a dynamic multi-key FHE scheme based on the LWE assumption in public key setting. The ciphertext can be extended and performed homomorphic evaluation with the ciphertexts encrypted under additional keys. Compared with current constructions, our proposed method requires fewer "local" memory and the process of ciphertext extension is distributed. Our proposed method provides a new way to extend the ciphertext such that the ciphertext homomorphism computation is more efficient. Our scheme is proven to be secure under standard LWE assumptions without using the circular-security assumption. © 2021, CC BY-NC-SA.

关键词： Security of data

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A Node-collaboration-informed Graph Convolutional Network for Precise Representation to Undirected Weighted Graphs

arXiv

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arXiv 2022年

作者： Wang, Ying Yuan, Ye Luo, Xin The School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China The Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Engineering Research Center of Big Data Application for Smart Cities Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China The College of Computer and Information Science Southwest University Chongqing400715 China

An undirected weighted graph (UWG) is frequently adopted to describe the interactions among a solo set of nodes from real applications, such as the user contact frequency from a social network services system. A graph convolutional network (GCN) is widely adopted to perform representation learning to a UWG for subsequent pattern analysis tasks such as clustering or missing data estimation. However, existing GCNs mostly neglects the latent collaborative information hidden in its connected node pairs. To address this issue, this study proposes to model the node collaborations via a symmetric latent factor analysis model, and then regards it as a node-collaboration module for supplementing the collaboration loss in a GCN. Based on this idea, a Node-collaboration-informed Graph Convolutional Network (NGCN) is proposed with three-fold ideas: a) Learning latent collaborative information from the interaction of node pairs via a node-collaboration module;b) Building the residual connection and weighted representation propagation to obtain high representation capacity;and c) Implementing the model optimization in an end-to-end fashion to achieve precise representation to the target UWG. Empirical studies on UWGs emerging from real applications demonstrate that owing to its efficient incorporation of node-collaborations, the proposed NGCN significantly outperforms state-of-the-art GCNs in addressing the task of missing weight estimation. Meanwhile, its good scalability ensures its compatibility with more advanced GCN extensions, which will be further investigated in our future studies. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolution

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A Stepwise Path Selection Scheme Based on Multiple QoS Parameters Evaluation in SDN 17th

A Stepwise Path Selection Scheme Based on Multiple QoS Param...

引用

17th EAI International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2021

作者： Liu, Lin Zhou, Jian-Tao Xing, Hai-Feng Guo, Xiao-Yong College of Computer Science Ecological Big Data Engineering Research Center of the Ministry of Education Cloud Computing and Service Software Engineering Laboratory of Inner Mongolia Autonomous Region National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Social Computing and Data Processing Key Laboratory of Inner Mongolia Autonomous Region Big Data Analysis Technology Engineering Research Center of Inner Mongolia Autonomous Region Inner Mongolia University Inner Mongolia Hohhot China College of Computer Science and Technology Inner Mongolia Normal University Inner Mongolia Hohhot China College of Computer Information and Management Inner Mongolia University of Finance and Economics Inner Mongolia Hohhot China

ISBN: (纸本)9783030926342

Nowadays, the best-effort service can not guarantee the quality of service (QoS) for all kinds of services. QoS routing is an important method to guarantee QoS requirements. It involves path selection for flows based on the current network status and the performance criteria of the service requirements. However, it is difficult for proposed solutions to obtain all the available paths owing to not fully considering all the QoS parameters of paths. In this paper, we propose SWQoS, a novel, universal, and stepwise QoS guarantee scheme based on multiple QoS parameter evaluation for selecting the available paths including preferred paths, satisfied paths and reluctant paths in SDN. The experiments show that SWQoS can select all the available paths that meet the performance criteria of the service requirements and have better QoS parameter performance compared with other path selection methods. © 2021, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Quality of service

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CellAgent: An LLM-driven Multi-Agent Framework for Automated Single-cell data Analysis

arXiv

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arXiv 2024年

作者： Xiao, Yihang Liu, Jinyi Zheng, Yan Xie, Xiaohan Hao, Jianye Li, Mingzhi Wang, Ruitao Ni, Fei Li, Yuxiao Luo, Jintian Jiao, Shaoqing Peng, Jiajie AI for Science Interdisciplinary Research Center School of Computer Science Northwestern Polytechnical University No.1 Dongxiang Road Xi’an China College of Intelligence and Computing Tianjin University No.92 Weijin Road Tianjin China Key Laboratory of Big Data Storage and Management Northwestern Polytechnical University Ministry of Industry and Information Technology No.1 Dongxiang Road Xi’an China

Single-cell RNA sequencing (scRNA-seq) data analysis is crucial for biological research, as it enables the precise characterization of cellular heterogeneity. However, manual manipulation of various tools to achieve desired outcomes can be labor-intensive for researchers. To address this, we introduce CellAgent (http://***/), an LLM-driven multi-agent framework, specifically designed for the automatic processing and execution of scRNA-seq data analysis tasks, providing high-quality results with no human intervention. Firstly, to adapt general LLMs to the biological field, CellAgent constructs LLM-driven biological expert roles—planner, executor, and evaluator—each with specific responsibilities. Then, CellAgent introduces a hierarchical decision-making mechanism to coordinate these biological experts, effectively driving the planning and step-by-step execution of complex data analysis tasks. Furthermore, we propose a self-iterative optimization mechanism, enabling CellAgent to autonomously evaluate and optimize solutions, thereby guaranteeing output quality. We evaluate CellAgent on a comprehensive benchmark dataset encompassing dozens of tissues and hundreds of distinct cell types. Evaluation results consistently show that CellAgent effectively identifies the most suitable tools and hyperparameters for single-cell analysis tasks, achieving optimal performance. This automated framework dramatically reduces the workload for science data analyses, bringing us into the "Agent for science" era. Copyright © 2024, The Authors. All rights reserved.

关键词： Cells

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An Intelligent Resource Scheduling Method with Edge Channel Deployment for BPM

An Intelligent Resource Scheduling Method with Edge Channel ...

引用

Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Bowen Liu Wanchun Dou Xiaokang Zhou Xuyun Zhang Lianyong Qi Fei Dai Chaochao Chen State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Faculty of Data Science Shiga University Hikone Japan Department of Computing Macquarie University Sydney Australia College of Computer and Software China University of Petroleum (East China) Qingdao China College of Big Data and Intelligent Engineering Southwest Forestry University Kunming China College of Computer Science and Technology Zhejiang University Hangzhou China

Edge computing is a novel computing paradigm that offers kinds of resources at the network edge. In edge computing, terminal users are connected to edge servers via the wireless network and there are various channels in each wireless link. These wireless channels are limited resource while different channel has different cost and service ability. The dynamic changes of users’ status make it harder to find an appropriate method to satisfy the BPM requirements of channel deployment. With this observation, it is a tricky challenge to make a trade-off between the system cost(rental price) and the service ability(number of users). In view of this challenge, an intelligent resource scheduling method, named EdgeIRS, is proposed in this paper. In the technical sense, the EdgeIRS method can accommodate most users at the edge with a minimum cost of deploying channel resources in an online way. Its performance is analyzed theoretically and the experiments verify the superiority of the method.

关键词：

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Unsupervised Social Bot Detection via Structural Information Theory

arXiv

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arXiv 2024年

作者： Peng, Hao Zhang, Jingyun Huang, Xiang Hao, Zhifeng Li, Angsheng Yu, Zhengtao Yu, Philip S. School of Cyber Science and Technology Beihang University No. 37 Xue Yuan Road Haidian District Beijing100191 China State Key Laboratory of Public Big Data Guizhou University No. 2708 South Section of Huaxi Avenue Huaxi District Guizhou Province Guiyang City550025 China Guangxi Key Lab of Multi-source Information Mining & Security Guangxi Normal University Guilin541004 China Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology Kunming650500 China College of Science University of Shantou No. 243 University Road Shantou515063 China School of Computer Science and Engineering Beihang University No. 37 Xue Yuan Road Haidian District Beijing100191 China Faculty of Information Engineering and Automation Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology Kunming650500 China Department of Computer Science University of Illinois at Chicago ChicagoIL60607 United States

Research on social bot detection plays a crucial role in maintaining the order and reliability of information dissemination while increasing trust in social interactions. The current mainstream social bot detection models rely on black-box neural network technology, e.g., Graph Neural Network, Transformer, etc., which lacks interpretability. In this work, we present UnDBot, a novel unsupervised, interpretable, yet effective and practical framework for detecting social bots. This framework is built upon structural information theory. We begin by designing three social relationship metrics that capture various aspects of social bot behaviors: Posting Type Distribution, Posting Influence, and Follow-to-follower Ratio. Three new relationships are utilized to construct a new, unified, and weighted social multi-relational graph, aiming to model the relevance of social user behaviors and discover long-distance correlations between users. Second, we introduce a novel method for optimizing heterogeneous structural entropy. This method involves the personalized aggregation of edge information from the social multi-relational graph to generate a two-dimensional encoding tree. The heterogeneous structural entropy facilitates decoding of the substantial structure of the social bots network and enables hierarchical clustering of social bots. Thirdly, a new community labeling method is presented to distinguish social bot communities by computing the user's stationary distribution, measuring user contributions to network structure, and counting the intensity of user aggregation within the community. Compared with ten representative social bot detection approaches, comprehensive experiments demonstrate the advantages of effectiveness and interpretability of UnDBot on four real social network datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Information theory

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Synthetic Instance Segmentation from Semantic Image Segmentation Masks

arXiv

引用

arXiv 2023年

作者： Shen, Yuchen Zhang, Dong Zhang, Zhao Fu, Liyong Ye, Qiaolin College of Information Science and Technology & Artificial Intelligence Nanjing Forestry University Jiangsu Nanjing210037 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Hefei230009 China College of Information Science and Technology Nanjing Forestry University Jiangsu Nanjing210037 China Institute of Forest Resource Information Techniques Chinese Academy of Forestry Beijing100091 China

In recent years, instance segmentation has garnered significant attention across various applications. However, training a fully-supervised instance segmentation model requires costly both instance-level and pixel-level annotations. In contrast, weakly-supervised instance segmentation methods, such as those using image-level class labels or point labels, often struggle to satisfy the accuracy and recall requirements of practical scenarios. In this paper, we propose a novel paradigm called Synthetic Instance Segmentation (SISeg). SISeg achieves instance segmentation results by leveraging image masks generated by existing semantic segmentation models, and it is highly efficient as we do not require additional training for semantic segmentation or the use of instance-level image annotations. In other words, the proposed model does not need extra manpower or higher computational expenses. Specifically, we first obtain a semantic segmentation mask of the input image via an existent semantic segmentation model. Then, we calculate a displacement field vector for each pixel based on the segmentation mask, which can indicate representations belonging to the same class but different instances, i.e., obtaining the instance-level object information. Finally, the instance segmentation results are refined by a learnable category-agnostic object boundary branch. Extensive experimental results on two challenging datasets highlight the effectiveness of SISeg in achieving competitive results when compared to state-of-the-art methods, especially fully-supervised methods. The code will be released at: SISeg Copyright © 2023, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Towards Making More Reliable Cardiotocogram data Prediction with Limited Expert Knowledge: Exploiting Unlabeled data with Semi-supervised Boosting Method 6th

Towards Making More Reliable Cardiotocogram Data Prediction ...

引用

6th International Conference on data Mining and big data, DMBD 2021

作者： Hong, Jiaming Qin, Chen Huang, Yun Zhou, Yin School of Medical Information Engineering Guangzhou University of Chinese Medicine Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Software College Jishou University Zhangjiajie China School of Computer Science Shenzhen Institute of Information Technology Shenzhen China

ISBN: (纸本)9789811674754

Cardiotocography (CTG) is frequently used as a method of diagnosing fetal distress during pregnancy and delivery, including listening to Fetal Heart Rate (FHR) and monitoring Uterine Contractions (UC). Many scholars have contributed to classify CTG data through machine learning methods, intending to reduce the consuming-time and mistakes during obstetricians’ identification. In this study, we used repeated holdout cross-validation for data pre-processing and classified the CTG dataset as a normal class and pathological class. Sensitivity, specificity, accuracy, and AUC were involved to measure the performance of the models. According to the results of ASSEMBLE AdaBoost and AdaBoost, we can conclude that ASSEMBLE AdaBoost is robust in the classification of CTG data. Training with few labeled data and amounts of unlabeled data, ASSEMBLE AdaBoost achieved an accuracy of over 90%, which was superior to AdaBoost. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Adaptive boosting

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