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IEEE International Conference on Cyber Security and Cloud computing (CSCloud)

作者： Tao Huang Xiang Chen Li Peng School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan Hunan China Hunan Key Laboratory for Service computing and Novel Software Technology

The rapid development of single-cell RNA sequencing (scRNA-seq) technology has enabled researchers to explore gene expression differences at the level of individual cells, revealing more refined cell types and states. However, due to the low expression and high noise of scRNA-seq data, feature selection has become particularly important in the analysis of single-cell data. Here, we introduce the Entropy Stepwise Regression (ESR) method for feature selection. This method utilizes the correlation between genes and the entropy values of each feature to filter out genes that are conducive to downstream analysis. In mouse kidney samples, we compared the performance of three methods in terms of Adjusted Rand Index and achieved good results. This indicates that the method can improve the accuracy of downstream analysis.

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scIAMC:Single-Cell Imputation via adaptive matrix completion

scIAMC:Single-Cell Imputation via adaptive matrix completion

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IEEE International Conference on Cyber Security and Cloud computing (CSCloud)

作者： Shuai Zhang Xiang Chen Li Peng School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan Hunan China Hunan Key Laboratory for Service computing and Novel Software Technology

Single-cell sequencing techniques are often impacted by technical noise, leading to the generation of very sparse expression matrices. This technical noise is referred to as dropouts and poses as a major challenge for downstream analysis. In this study, we introduce scIAMC (single-cell imputation via adaptive parameter matrix completion), which is based on matrix completion theory to recover missing values in expression matrices. To expedite the algorithm's running time and avoid any parameter tuning on data, we formulated an optimization problem. Our approach led to an enhanced cell population identification and minimal errors, while also restoring biological landscapes that were damaged by these dropouts.

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SyNet:Medical image anomaly detection with noise synthesis network

SyNet:Medical image anomaly detection with noise synthesis n...

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IEEE International Conference on Big Data Security on Cloud (BigDataSecurity), High Performance and Smart computing (HPSC) and Intelligent Data and Security (IDS)

作者： Jingnian Liu Wei Liang Yuxiang Chen Kai Jin School of Computer Science and Engineering Hunan Key Laboratory for Service Computing and Novel Software Technology Hunan University of Science and Technology Xiangtang China

ISBN: (数字)9798350389524

ISBN: (纸本)9798350389531

Medical image anomaly detection refers to machine learning techniques to analyze and identify lesions and abnormalities in them. However, in medical images, anomaly samples are usually sparse, which can lead to supervised learning not being able to obtain sufficient training. While existing unsupervised learning is mainly based on reconstruction and generation as well as feature embedding, these methods do not take into account the problem of data type bias during domain migration and often face the problem of loose judgment boundaries. To this end, this paper proposes a novel unsupervised learning method, SyNet, based on noisy anomaly synthesis. First, the problem at domain migration is well solved by aggregating the range of features within the feature map, fusing the multiscale features at the intermediate level, and adding feature adapters. Then, anomaly samples are synthesized by adding noise in the feature space, which is more efficient and stable than generating samples in the image space. Finally, normal and abnormal data are distinguished by training the discriminator. In this paper, experiments are conducted on ISIC and brain MRI datasets, and SyNet achieves nearly 20% improvement in AUC, ACC and other metrics compared with the current mainstream methods, and also has good inference efficiency.

关键词： Training Magnetic resonance imaging Noise Supervised learning Security Noise measurement Lesions

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A Short Review of Truck and Drone Collaborative Delivery Problem

A Short Review of Truck and Drone Collaborative Delivery Pro...

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IEEE International Conference on Cyber Security and Cloud computing (CSCloud)

作者： Yena Zhu Min Liu Dongyao Jin School of Computer Science and Engineering Hunan University of Science and Technology Hunan Key Laboratory for Service computing and Novel Software Technology Xiangtan China

With the emergence of efficient logistics models and the development of drone technology, the last-mile delivery problem has become a focus of attention for scholars in various fields. The advantages of drones, such as low cost, energy saving, and independence from road conditions, have made them increasingly popular in the delivery industry. However, drones have shortcomings such as short endurance, small payload capacity, and susceptibility to interference. Therefore, the cooperative delivery model of vehicles and drones has important theoretical research significance and practical application value. Existing literature reviews focus on static truck-drone delivery problems (TDDPs) and methods such as exact algorithms, heuristics, and metaheuristics. However, real-life truck-drone delivery problems are easily affected by dynamic environmental changes. Accordingly, this article introduces some new dynamic variants of TDDPs proposed in recent years, which consider variable drone flight endurance, urban crowded road conditions, stochastic customer demands, and the influence of weather changes. In recent years, some new methods for solving TDDPs have been introduced, such as the increasingly popular machine learning and reinforcement learning methods. Finally, the possible future research directions and development trends of TDDPs are presented.

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Energy-saving Processors Two-phases Frequency Reduction Algorithm on Heterogeneous Embedded Systems

Energy-saving Processors Two-phases Frequency Reduction Algo...

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IEEE International Conference on Cyber Security and Cloud computing (CSCloud)

作者： Weihong Huang Kuan Jiang Jing Huang Li Lisi Yufeng Xiao Zihao Deng School of Computer Science and Engineering Hunan University of Science and Technology Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan China

Energy saving has become a key issue for heterogeneous embedded systems. Previous energy-saving methods attempt to minimize the energy consumption of applications in heterogeneous embedded systems subject to deadline constraints by reducing the processor frequency. However, good scheduling strategy can also minimize energy consumption to some extent. This paper proposes a novel energy-saving scheduling algorithm, called the Energy-saving Processors Two-phases Frequency Reduction (EPTFR) Algorithm. In the first stage, within the deadline constraints, the maximum working frequency of each processor is reasonably and synchronously reduced; in the second phase, when running sub-applications on the processor, under the constraints of the earliest start time and latest end time of sub-applications, the actual operating frequency of the processor is reasonably reduced. Finally, the effectiveness of the EPTFR algorithm is verified through numerical experiments, and the results show that the proposed EPTFR algorithm can achieve a significant energy-saving effect.

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A Comprehensive Overview of Object Detection Based on Deep Learning

A Comprehensive Overview of Object Detection Based on Deep L...

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Intelligent Data and Security (IDS), IEEE International Conference on

作者： Gaojing Yuan Linshu Chen Jiahong Cai Chaoyi Yang Jinnian Liu Hunan Key Laboratory for Service Computing and Novel Software Technology School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China

ISBN: (数字)9798350389487

ISBN: (纸本)9798350389494

Target detection is an important task in the field of computer vision and its application has been extended to the medical field. In the medical field, target detection techniques can be used for diagnosis, treatment and research. For example, by performing target detection on medical images, lesion regions or foci can be automatically identified and localized to help doctors perform accurate diagnosis and treatment. In traditional machine learning methods, commonly used features include morphological features, texture features, and histogram features. And in deep learning methods, the commonly used structures include Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), etc. In addition, there are many special problems and challenges for medical images, such as image quality, variation between modalities, and lack of data. Therefore, target detection in the medical field requires special techniques and algorithms to address these issues. By integrating expertise in the medical field, combining traditional and deep learning techniques, and developing improved and optimized algorithms for the special problems of medical images, the accuracy and efficiency of medical target detection can be improved, thus contributing to the advancement of medical research and clinical practice.

关键词： Deep learning Image quality Recurrent neural networks Accuracy Object detection Medical services Convolutional neural networks

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Evolution of Web API Cooperation Network via Exploring Community Structure and Popularity

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6659-6671页

作者： Kang, Guosheng Wang, Yang Liu, Jianxun Cao, Buqing Xiao, Yong Xu, Yu Hunan University of Science and Technology Hunan Provincial Key Laboratory for Services Computing and Novel Software Technology School of Computer Science and Engineering Xiangtan411201 China University of Cambridge Cambridge Baker Systems Genomics Initiative British Heart Foundation Cardiovascular Epidemiology Unit Department of Public Health and Primary Care CambridgeCB1 8RN United Kingdom

With the growing popularity of the Internet, Web applications have become increasingly essential in our daily lives. Web application programming interfaces (Web APIs) play a crucial role in facilitating interaction between applications. However, most Web service platforms are suffering from the imbalance of Web services now, many services of good quality but low popularity are difficult to be invoked even once and do not create direct connections with the users. Some graph-based Web service recommendation methods also often present a long-tailed distribution of recommended Web services due to limited Mashup-API invocation relationships. To relieve this problem and promote service recommendation, in this article, we propose a community structure and popularity-based approach by constructing an evolving cooperation network for Web APIs. We leverage the Louvain algorithm in community detection to assign community structure to each Web API and consider both the popularity and community structure in constructing the network. By optimizing the Barabαsi-Albert (BA) evolving network model, we demonstrate that our approach outperforms the BA, Bianconi-Barabαsi (BB), and popularity-similarity optimization (PSO) models in Web service clustering. Based on our proposed evolutionary network model for the evolutionary extension of API cooperation network and used for downstream Web service recommendation tasks, the experimental results also show that our recommended approach outperforms some other baseline models for Web service recommendation. © 2020 IEEE.

关键词： Web services

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Towards Planning Package Delivery Paths by Exploiting the Crowd of Private Cars

Towards Planning Package Delivery Paths by Exploiting the Cr...

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

作者： Hejing Nie Yiping Wen Tiancai Li Sipeng OuYang Muyang Tian School of Computer Science and Engineering Hunan University of Science and Technology China Hunan Key Laboratory for Service computing and Novel Software Technology Hunan University of Science & Technology

Crowdsourced delivery can provide cost-effective deliveries and it is becoming increasingly important in practice. This paper considers a package delivery problem which exploits relays of private cars to transport packages via their commuter routes. Specifically, we propose a method of planning package delivery paths based on the commuter routes of private cars. Using the commuter routes and time constraints as the reference, it can find optimal delivery paths and assign drivers of private cars to transport packages. The simulation experiment results show the validity and effectiveness of such method.

关键词： Product delivery Planning Automobiles Time factors Relays Task analysis

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Explainable AI: A Way to Achieve Trustworthy AI

Explainable AI: A Way to Achieve Trustworthy AI

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IEEE International Conference on Big Data Security on Cloud (BigDataSecurity), High Performance and Smart computing (HPSC) and Intelligent Data and Security (IDS)

作者： Yanlu Li Yufeng Xiao Yinyan Gong Ronglin Zhang Yingzi Huo Yanbing Wu Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China

ISBN: (数字)9798350389524

ISBN: (纸本)9798350389531

AI is black-box and non-explainable, in other words, due to the complexity of the decision-making process of AI, people are unable to know why and how AI makes the decision. For these reasons, people will question and worry about AI’s decision-making. Against this background, researchers have proposed the concept of Explainable Artificial Intelligence (XAI). The aim of this paper is to help readers be able to better understand XAI through extensive surveys. In this paper, we discuss the concept of XAI, the motivation of the research, the current status of the research, and the difficulties in the research process. At the same time, we also discuss the specific methods to realize AI explainability by dividing AI explainability methods into two ways: transparent design and ex post facto interpretation. In addition, we select the three fields of healthcare, finance, and autonomous driving, discuss the application of XAI in these three fields, and select some representative results as examples through surveys. Finally, we summarize this article and present our outlook on the future development of XAI.

关键词： Surveys Explainable AI Decision making Finance Closed box Medical services Big Data

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PRIVACY-PRESERVING ONLINE SHARING CHARGING PILE SCHEME WITH DIFFERENT NEEDS MATCHING

arXiv

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arXiv 2023年

作者： Huang, Zhiyu School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan China

With the development of electric vehicles, more and more electric vehicles have difficulties in parking and charging. One of the reasons is that the number of charging piles is difficult to support the energy supply of electric vehicles, and a large number of private charging piles have a long idle time, so the energy supply problem of electric vehicles can be solved by sharing charging piles. The shared charging pile scheme uses Paillier encryption scheme and improved scheme to effectively protect user data. The scheme has homomorphism of addition and subtraction, and can process information without decryption. However, considering that different users have different needs, the matching is carried out after calculating the needs put forward by users. This scheme can effectively protect users' privacy and provide matching mechanisms with different requirements, so that users can better match the appropriate charging piles. The final result shows that its efficiency is better than the original Paillier scheme, and it can also meet the security requirements. © 2023, CC BY.

关键词： Piles

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