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19th CCF Conference on computer Supported Cooperative Work and Social Computing, ChineseCSCW 2024

作者： Ma, Xinhong Zeng, Yehong Qi, Mingyang Si, Yali Chen, Zhen School of Information Science and Engineering Yanshan University Qinhuangdao066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao066004 China Changshu Institute of Technology Jiangsu Changshu215500 China

ISBN: (纸本)9789819623754

In the era of open cloud, cloud API is an important component and key enabling technology to achieve efficient data transmission, artificial intelligence algorithm empowerment, and software development cost reduction and efficiency. With the proliferation of cloud API with similar functionality, quality of service (QoS) becomes essential for differentiating cloud API performance, and QoS-driven prediction for cloud API has emerged as a critical topic for high quality cloud API selection. However, existing methods often inadequately utilize contextual information in resource-constrained scenario, limiting the improvement of cloud API prediction performance. We in this paper propose a geo-enhanced high-order feature interaction network (GHFIN) for cloud API QoS prediction. Firstly, a correlation analysis was conducted on real-world QoS dataset to explore the relationship between QoS data and geospatial context. Then, we design a geo-enhanced strategy that effectively utilizes the hierarchical relationships of geospatial entities (country, province, city, and district) through an offline real-time coordinate conversion module. On this basis, a geo-enhanced cloud API QoS prediction network GHFIN was designed, which aims to learn the high-order feature interactions through layer-wise feature stacking. Experimental results on two real-world QoS datasets illustrate the effectiveness of GHFIN compared to the classic and state-of-the-art methods, and our extensive analysis shows how the geospatial context and high-order feature interaction positively impact the performance of cloud API QoS prediction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Quality of service

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A short text clustering study integrating context-awareness and contrast learning

A short text clustering study integrating context-awareness ...

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International Conference on Information Systems Engineering (ICISE)

作者： Qiaonan Zhang Yuan Liu School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China Jiangsu Province Key Laboratory of Media Design and Software Technology Jiangnan University Wuxi China

Traditional text representation techniques will produce high-dimensional and sparse vectors for short texts since they have fewer words, which increases the likelihood of noise interference and lowers the accuracy of short text clustering. An alternative model, CASA-Bert-K, is investigated to overcome the aforementioned issues. First, In order to add contextual features into the text representation, context-awareness self-attention networks are combined with Bert. Secondly, To de-optimize the representation of short texts, a simple and effective data augmentation approach to contrast learning is introduced. The text representation module and the clustering module are jointly trained to optimize both text representation and clustering in order to further improve the clustering accuracy. The experimental findings show the researched model's effectiveness in raising the accuracy rate through the use of three datasets in the tests.

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An Exploration of Online Missing Value Imputation in Non-stationary Data Stream

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SN computer Science 2021年第2期2卷 1-11页

作者： Dong, Wenlu Gao, Shang Yang, Xibei Yu, Hualong School of Computer Jiangsu University of Science and Technology No.2 Mengxi Road Jiangsu Zhenjiang 212003 China Artificial Intelligence Key Laboratory of Sichuan Province Sichuan University of Science and Engineering Yibin 644000 China

Missing value imputation (MVI) is an important data preprocessing technique. In previous decades, MVI technique has been widely studied as well as most MVI approaches have been proposed by means of either statistics or machine learning techniques. However, all previous methods only focus on the static data, but ignore the imputation for the dynamic online data. It is intuitionistic that the imputation errors may be significantly increased when there exists concept drifts in the data stream. In this paper, we investigate the impact of adopting the conventional MVI methods in non-stationary data stream. Meanwhile, two slide time window-based strategies are proposed to alleviate this impact, where one is the plain average strategy, and the other is the logarithmic weighted average strategy that gradually adds the weights of instances along the time axis. Combining with the proposed strategies, three popular MVI techniques, mean imputation (MI), KNN imputation (KNNI) and the Bayesian principal component analysis imputation (BPCAI) are adopted, to indicate the effect of the strategies are irrelevant to the specific MVI technique. The experimental results on three different types' concept drift synthetic data sets and two real-world drifting data sets have presented the effectiveness and feasibility of the proposed strategies. Moreover, the impact of time window size has also been investigated for guiding the parameter settings in future practical applications. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. part of Springer Nature.

关键词： Concept drift Data stream Missing value imputation Slide time window Weighting

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Multi-stage Objective Function Optimized Hand-Eye Self-calibration of Robot in Autonomous Environment 1

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17th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2022

作者： Liu, Kaibo Zheng, Wangli Min, Huasong Lin, Yunhan School of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Hubei Wuhan430065 China State Grid Electric Power Research Institute Jiangsu Nanjing211106 China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Hubei Wuhan430081 China

ISBN: (数字)9789819915491

ISBN: (纸本)9789819915484

In the application scenario of robot autonomous tasks, the robot needs to be able to complete calibration online and automatically to achieve self-maintenance, which differs from traditional robot hand-eye calibration in that the traditional robot hand-eye calibration requires a dedicated calibration board to assist offline completion. Aiming at the problem that the existing self-calibration methods cannot be optimized as a whole, which leads to low accuracy and instability of the solution, a multi-stage objective function optimization self-calibration algorithm is proposed, which describes the solution of hand-eye self-calibration as a minimization objective function problem involving multiple stages. An optimization method based on the minimization of re-projection error is designed to compensate for the results, which uses an efficient Oriented fast and rotated brief (ORB) feature extraction algorithm and introduces a scoring mechanism to retain more correct matching points in the feature matching stage. Two different types of experiments were designed to validate our method. One is a single camera dataset experiment, which shows that our method is more accurate and robust than the existing self-calibration method;the other is an application platform experiment, which verifies the feasibility and availability of our method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Calibration

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Application of conditional generative adversarial network in image super-resolution reconstruction

Application of conditional generative adversarial network in...

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International Conference on Application of Intelligent Systems in Multi-modal Information Analytics, MMIA 2020

作者： Wei, Xiang Wang, Feng Chen, Xi Yan, Yongjie Chen, Ping Liu, Sheng State Key Laboratory of Air Traffic Management System and Technology NanjingJiangsu Province China College of Information and Computer Science Taiyuan University of Technology TaiyuanShanxi Province China Taigu County Meteorological Bureau JinzhongShanxi Province China

ISBN: (纸本)9783030515553

In the process of image processing, the higher the resolution of the image, the richer the data information contained in the image. Aiming at the problem of low image resolution, through the research of generating countermeasure network, the condition generating countermeasure network is used to solve this problem. We use a symmetric convolutional neural network as the generator network, each of which has 6 layers. At the same time, by entering the labels into the discriminator, the connection between the original image and the model-generated image can be derived. In the loss function during the experiment, perceptual loss and mean square error are added, and good results are obtained in the actual experiment, and the visual effect of the generated image is more ideal. Compared with other algorithms, the proposed algorithm achieves the visual effect of SRGAN, and the effect on some pictures has been greatly improved. © Springer Nature Switzerland AG 2021.

关键词： Generative adversarial networks

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Multimodal Spatiotemporal Drug Assessment Platform Based on a Microelectrode Neural Network Chip in Alzheimer's Disease

SSRN

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SSRN 2024年

作者： Gao, Fan Xu, Wei Xiao, Yan Jiang, Wei Chen, Mu Liu, Zekai Zhang, Peng Yang, Tianyi Qian, Zhiyu Gao, Keqiang Department of Biomedical Engineering Key Laboratory of Multi-modal Brain-Computer Precision Drive Ministry of Industry and Information Technology Key Laboratory of Digital Medical Equipment and Technology of Jiangsu Province Nanjing University of Aeronautics and Astronautics Nanjing210016 China School of Pharmacy Nanjing Medical University Nanjing211166 China

The pathogenesis of Alzheimer's disease (AD) remains elusive. The development of new drugs is protracted, costly, and fraught with failure. There is a dearth of in vitro high-throughput assay platforms capable of observing the therapeutic effects of AD drugs at the level of cellular and network. Consequently, an in vitro multimodal spatiotemporal drug assessment platform has devised based on a neuronal network chip. It has a bi-directional distribution of electrode points of different sizes, which are more suitable for the detection of neuronal connections. The multi-level neurophysiological properties, including single neuron firing pattern, intercellular connection, transmission speed, and neural network communication, were detected in real time. The results demonstrated that the differences between experimental groups in electrophysiological properties could significantly distinguish the effects of AD representative drug treatment after different levels of neurological injury. The administration of the drug at the initial stage of β-amyloid oligomers toxicity was found to effectively impede the deterioration of neurons and network. Furthermore, the neurophysiological mechanism of drug interaction was elucidated, which may provide the guidance for the optimal timing and dosage of drug administration. It is anticipated that this will become a novel platform for the preclinical evaluation and screening of AD drugs. © 2024, The Authors. All rights reserved.

关键词： Neural networks

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Predicting Genotype Information Related to COVID-19 for Molecular Mechanism Based on Computational Methods

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computer Modeling in Engineering & Sciences 2021年第10期129卷 31-45页

作者： Lejun Gong Xingxing Zhang Li Zhang Zhihong Gao Jiangsu Key Laboratory of Big Data Security&Intelligent Processing School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing210046China Smart Health Big Data Analysis and Location Services Engineering Laboratory of Jiangsu Province Nanjing210046China College of Computer Science and Technology Nanjing Forestry UniversityNanjing210037China Zhejiang Engineering Research Center of Intelligent Medicine Wenzhou325035China

Novel coronavirus disease 2019(COVID-19)is an ongoing health *** studies are related to ***,its molecular mechanism remains *** rapid publication of COVID-19 provides a new way to elucidate its mechanism through computational *** paper proposes a prediction method for mining genotype information related to COVID-19 from the perspective of molecular mechanisms based on machine *** method obtains seed genes based on prior *** genes are mined from biomedical *** candidate genes are scored by machine learning based on the similarities measured between the seed and candidate ***,the results of the scores are used to perform functional enrichment analyses,including KEGG,interaction network,and Gene Ontology,for exploring the molecular mechanism of *** results show that the method is promising for mining genotype information to explore the molecular mechanism related to COVID-19.

关键词： COVID-19 SARS-CoV-2 computational method bioinformatics genotype machine learning

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MDSDroid: A Multi-level Detection System for Android Repackaged Applications

MDSDroid: A Multi-level Detection System for Android Repacka...

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IEEE International Conference on Signal and Image Processing (ICSIP)

作者： Xujian Liu Zihan Yu Zhiyong Song Liquan Chen Zhongyuan Qin Key Laboratory of Computer Networking Technology of Jiangsu Province School of Cyber Science and Engineering Southeast University Nanjing China

ISBN: (纸本)9781665446006

The repackaged Android applications released in the app markets have posed huge threats to users' privacy and security. Prior detection algorithms based on code similarity can hardly detect obfuscated and encrypted applications. UI features are resistant to code reinforcement, but they may lack scalability if dynamically obtained when an application is executing, therefore they can be affected by noise through static methods. In this paper, MDSDroid (Multi-level Detection System for Android Apps) is proposed, which combines the fast scalability of static methods with the accuracy of dynamic methods into the extraction of layout features. A novel tree structure similarity comparison algorithm is presented to measure applications' similarity on the multi-dimensional sequence. Our method is evaluated on the AndroZoo dataset. Experiments show that it has better performance than the well-known repackaging detection tool FSquaDRA, in terms of accuracy, scalability, and resistance to code reinforcement.

关键词： Resistance Privacy Codes Scalability Heuristic algorithms Layout Resists

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Information Hiding of Specific Image Region Using Adversarial Examples

Information Hiding of Specific Image Region Using Adversaria...

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IEEE International Conference on Signal and Image Processing (ICSIP)

作者： Jiarui Cui Tian Yao Changlong Yan Ge Wu Liquan Chen Zhongyuan Qin Key Laboratory of Computer Networking Technology of Jiangsu Province School of Cyber Science and Engineering Southeast University Nanjing China

ISBN: (纸本)9781665446006

Deep neural networks have been widely used in our daily life. However, malicious organizations may collect image data and identify users for commercial purposes. Even worse, criminals may steal private information through image recognition to achieve illegal purposes. As well known, deep neural networks are easily attacked by adversarial examples. We propose a range oriented adversarial examples generation algorithm to fool AlexNet by improving the non-directional DeepFool algorithm. Furthmore we combine it with object detection and propose an information hiding algorithm of specific image region. Experimental results show that compared with traditional methods such as mosaic, blur, and partial occlusion, our scheme is more concealed while ensuring the usability of the image.

关键词： Deep learning Analytical models Image recognition Conferences Neural networks Organizations Object detection

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Adversarial Samples Generation Based on RMSProp

Adversarial Samples Generation Based on RMSProp

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IEEE International Conference on Signal and Image Processing (ICSIP)

作者： Yacong Yu Lei Zhang Liquan Chen Zhongyuan Qin Key Laboratory of Computer Networking Technology of Jiangsu Province School of Cyber Science and Engineering Southeast University Nanjing China

ISBN: (纸本)9781665446006

Adversarial sample attacks seriously threaten the security and robustness of deep learning models. There are three problems in state-of-the-art adversarial sample generation schemes: the gradient update step size needs to be manually selected, inaccurate gradient update direction and uncontrollable times of iterations. In order to solve these problems, Root Mean Square Prop optimization algorithm (RMSProp) is proposed, which is integrated with IFGSM and IFGM. This algorithm can be easily extended to other attacks, and to a certain extent it can alleviate the trade-off between white box attacks and deliverability. The algorithm proposed in this paper can generate non-targeted adversarial samples more efficiently and quickly. Experiments show that it can generate effective and robust adversarial samples against current mainstream convolutional neural network (CNN).

关键词： Deep learning Image processing Conferences Robustness Convolutional neural networks Security Iterative methods

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