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International Conference on Computing and Networking Technology (ICCNT)

作者： Sachin C. Patil Sairam Madasu Krishna J Rolla Ketan Gupta N. Yuvaraj Senior Software Engineer USA Senior Cloud Software Engineer Microsoft Charlotte North Carolina USA Department of Information Technology University of The Cumberlands Williamsburg KY USA Computer Science and Engineering Sri Shakthi Institute of Engineering and Technology Coimbatore Tamil Nadu India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The ability of the system to gain knowledge for lowering prescription drug prices has been increasingly studied in recent years. Gadget-gaining knowledge is a branch of synthetic intelligence that enables machines and PC systems to enhance their overall performance on specific obligations by means of mastering beyond facts. Within the context of prescription drug costs, gadget learning may be used to find patterns and developments in drug pricing and usage, decide disparities in getting admission to and usage, and have a look at pricing strategies hired by pharmaceutical businesses. The use of gadgets to gain knowledge can help to discover elements using prescription drug costs, inclusive of the pricing and utilization of individual capsules or the relationships among drug costs and effects. Moreover, AI-primarily based algorithms may be used to use massive datasets in an effort to apprehend the role that healthcare vendors, insurers, and pharmaceutical organizations have on prescription drug ***, AI-based total fashions can also be used to become aware of and degree cost elements associated with specific drug combinations. Sooner or later, machine studying may be leveraged to examine the effects of policy changes and healthcare reforms on prescription drug costs and to become aware of goal populations with high prescription drug prices. By combining gadget studying with conventional healthcare price analysis gear, researchers can gain insights into the drivers of prescription drug charges and make public coverage decisions. In sum, a device gaining knowledge can be a powerful tool.

关键词： Drugs Costs Accuracy Machine learning algorithms software algorithms Pricing Medical services Organizations Machine learning software

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A Novel Outlier-Tolerable and Predictive Approach to Web Service Composition 1

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29th International Conference on Web Services, ICWS 2022 Held as Part of the Services Conference Federation, SCF 2022

作者： Sun, Xiaoning Chen, Peng Dai, Mengxuan Xia, Yunni Zheng, Wanbo Li, Jianqi Xie, Hong Fu, Xiaodong Peng, Kai Niu, Xianhua Chen, Juan School of Computer and Information Science Chongqing Normal University Chongqing401331 China School of Computer and Software Engineering Xihua University Chengdu610039 China School of Computer and Information Engineering Jiangxi Normal University Nanchang330022 China College of Computer Science Chongqing University Chongqing400030 China Data Science Research Center Faculty of Science Kunming University of Science and Technology Kunming650031 China Global Energy Interconnection Research Institute Co. Ltd. Beijing102209 China Engineering college Huaqiao University Quanzhou362021 China

ISBN: (数字)9783031235795

ISBN: (纸本)9783031235788

The Quality-of-Service (QoS) aspects of Web service has gained popularity in the field of service computing. QoS-oriented Web service composition is a distributed model to construct new web service on top of existing primitive or other composite web services with QoS guarantees. A major challenge in this field is that the QoS data of candidate services are with run-time fluctuations and thus difficult to predict. Traditional approaches in this direction tended to address the challenge by statistics, prediction and neural network-based models. A major limitation of these methods lies in that they ignore outliers data in the historical QoS data, in terms of inconsistencies, errors, shifts, corruptions, etc. In this work, instead, we consider outliers in QoS series to be non-neglectable, and propose an outlier-tolerable and predictive approach to service composition through leveraging a joint estimation-based outlier detection method and a niched genetic algorithm. To validate the effectiveness of our proposed method, we conduct extensive case studies based on different outlier conditions, and the experimental results show that our method is superior to existing ones. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Quality of service

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Searching for an Accurate Robot Calibration via Improved Levenberg–Marquardt and Radial Basis Function System

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Journal of Field Robotics 2025年第0期

作者： Li, Zhibin Deng, Xun Chen, Tinghui Yang, Yuhang Chen, Linlin Yang, Xiwen Hu, Zhenzhen Hu, Lun Hu, Pengwei Li, Shuai Luo, Xin Xinjiang Technical Institute of Physics & Chemistry Chinese Academy of Sciences Urumqi China School of Software Engineering Chengdu University of Information Technology Chengdu China Dazhou Key Laboratory of Government Data Security Sichuan University of Arts and Science Dazhou China School of Software Engineering Xinjiang University Urumqi China Faculty of Information Technology and Electrical Engineering University of Oulu Oulu Finland VTT-Technical Research Centre of Finland Oulu Finland College of Computer and Information Science Southwest University Chongqing China

Robots are frequently utilized in manufacturing, aviation, and other industries, which enhance industrial production efficiency and quality. Specifically, robots perform high-precision tasks like welding, assembly and material handling, which reduce the intensity of manual labor in factories. In the logistics field, robots automatically sort and deliver goods, thereby speeding up supply chain operations. However, the prolonged operation of robots suffers from a decline in positioning accuracy, which makes them unable to satisfy task requirements. To address this challenging issue, this study designs an efficient calibration system integrating the Levenberg–Marquardt algorithm with fuzzy proportion integration differentiation controller and radial basis function neural network. The innovations of this method include: (1) integrating the fuzzy proportion integration differentiation controller into the updating rules of Levenberg–Marquardt algorithm, which further enhances the identification performance of kinematic errors;(2) adopting the radial basis function neural network to handle the robot dynamic errors, which addresses the complexity of dynamic error sources. Extensively experimental robot positioning points are gathered on an HSR JR680 robot, and then experimental validations are conducted by using the designed calibration system. The experiments indicate that the developed algorithm outperforms these existing advanced algorithms. © 2025 Wiley Periodicals LLC.

关键词： Radial basis function networks

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Unsupervised Rail Surface Defect Detection Method

Unsupervised Rail Surface Defect Detection Method

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International Symposium on Environmentally Conscious Design and Inverse Manufacturing (EcoDesign)

作者： ChenYu Zhao Yi Han Ningkang Ouyang YiFan Zhao School of Artificial Intelligence Zhengzhou Software Vocational and Technical College Zhengzhou China Department of Computer Science and Information Anyang Institute of Technology Engineering Anyang China School of Information Engineering North China University of Water Resources and Electric Power Zhengzhou China

ISBN: (数字)9798350363043

ISBN: (纸本)9798350363050

Railroad travel has become an essential part of modern life, therefore, rail surface defect detection has become a problem that cannot be ignored. However, most of the currently existing methods often require a large number of defect samples for learning, which are labor-intensive and very difficult to obtain. In order to alleviate the data dependence of the detection model, this paper proposes an unsupervised method. Specifically, we use Transformer’s attention mechanism to construct the encoder-decoder structure and train it only on normal samples with the purpose of reconstruction, so that those regions that cannot be reconstructed will be regarded as defects at the time of detection. To prevent the model from generalizing its generalization ability to those unknown defective samples, i.e., to avoid overgeneralization, we introduce a noise perturbation in the training so that the model focuses on the reconstruction of normal features. Experiments show that the proposed unsupervised approach outperforms a host of supervised models with low data requirements and can effectively address the current challenges of track defect detection.

关键词： Rails Surface reconstruction Perturbation methods Noise Transformers Data models Decoding Image reconstruction Unsupervised learning Defect detection

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Model for Recognition of Sign Language and Hand Gesture by Hard Hearing People

Model for Recognition of Sign Language and Hand Gesture by H...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Manjula R Jagadeesan S Venkadeshan Ramalingam Vetriselvi T Johnpeter T Department of Computing Technologies College of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Tamilnadu India Information Technology Department University of Technology and Applied Sciences Sultanate of Oman Department of IOT School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamilnadu India Department of Computer Science and Engineering K.Ramakrishnan College of Engineering Trichy India

Hard hearing and deaf people are communicating with other people using the sign language. This is the only way of communication of such people to convey Since this is the only means by which these individuals can communicate, it is imperative that others be able to understand their language. Sign language recognition by computer begins with the acquisition of sign gestures and ends with recognition and conversion to text or voice. Static and dynamic are the two ways of sign gestures used. This study aims at focusing on the dynamic gesture recognition and the steps for recognizing sign language are discussed. The collected data are preprocessed and feature are extracted for classification. Finally the results are all investigated and developed for single user.

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Learning Transferable Negative Prompts for Out-of-Distribution Detection

arXiv

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arXiv 2024年

作者： Li, Tianqi Pang, Guansong Bai, Xiao Miao, Wenjun Zheng, Jin School of Computer Science and Engineering State Key Laboratory of Complex & Critical Software Environment Jiangxi Research Institute Beihang University China School of Computing and Information Systems Singapore Management University Singapore

Existing prompt learning methods have shown certain capabilities in Out-of-Distribution (OOD) detection, but the lack of OOD images in the target dataset in their training can lead to mismatches between OOD images and In-Distribution (ID) categories, resulting in a high false positive rate. To address this issue, we introduce a novel OOD detection method, named ‘NegPrompt’, to learn a set of negative prompts, each representing a negative connotation of a given class label, for delineating the boundaries between ID and OOD images. It learns such negative prompts with ID data only, without any reliance on external outlier data. Further, current methods assume the availability of samples of all ID classes, rendering them ineffective in open-vocabulary learning scenarios where the inference stage can contain novel ID classes not present during training. In contrast, our learned negative prompts are transferable to novel class labels. Experiments on various ImageNet benchmarks show that NegPrompt surpasses state-of-the-art prompt-learning-based OOD detection methods and maintains a consistent lead in hard OOD detection in closed- and open-vocabulary classification scenarios. Code is available at https://***/mala-lab/negprompt. © 2024, CC BY.

关键词： Chemical detection

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Self-Supervised Signed Graph Attention Network for Social Recommendation

Self-Supervised Signed Graph Attention Network for Social Re...

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International Joint Conference on Neural Networks (IJCNN)

作者： Qin Zhao Gang Liu Fuli Yang Ru Yang Zuliang Kou Dong Wang Department of Computer Science and Technology Shanghai Normal University Shanghai China Shanghai Newtouch Software Company Limited Shanghai China School of Computer Science and Information Engineering Shanghai Institute of Technology Shanghai China

In recent times, social recommendation has become a popular technique in recommender systems due to its ability to enhance the accuracy of recommendations by leveraging the social relationships among users. Despite its widespread use, the prevalent social recommendation methods are often marred by sparsity and noise issues that negatively impact their practicality. Additionally, these methods fail to consider complex user interactions, which could potentially provide additional information. To address these limitations, this paper proposes a novel self-supervised signed graph attention network (SSAN) that incorporates user attitudes in the construction of higher-order user relations. This approach integrates signed networks in the formation of reasonable higher-order local neighborhood relations and aggregates user interests in different social relations through graph convolution and balance theory. Furthermore, two self-supervised signals, derived from social theory, are designed and incorporated into the recommendation framework to better exploit the rich structural and semantic information in social relationship graphs. Empirical results on three publicly available datasets demonstrate that SSAN outperforms existing state-of-the-art social recommendation methods.

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Contrastive Learning Based Human Activity Recognition Using Body Sensors 9

Contrastive Learning Based Human Activity Recognition Using ...

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9th International Conference on Big Data Computing and Communications, BigCom 2023

作者： Xie, Chuanshi Zhou, Yu Yu, Dandan Sun, Shilong Zhang, Xiao Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The First Affiliated Hospital of Dalian Medical University Information Center Dalian China Harbin Institute of Technology School of Mechanical Engineering and Automation Shenzhen518055 China South-Central Minzu University Department of Computer Science China South-Central Minzu University Key Laboratory of Cyber-Physical Fusion Intelligent Computing State Ethnic Affairs Commission Wuhan430074 China

ISBN: (纸本)9798350331240

Pervasive sensing and wearable sensor techniques have been increasingly employed to monitor and recognize human activities through body sensors in areas of smart healthcare and manufacturing. However, conventional machine learning or deep learning based human activity recognition (HAR) requires a large amount of labeled data, which is cost-expensive in real-world scenarios. To tackle this issue, we propose a contrastive learning framework for HAR (CL-HAR), which utilizes the generated unlabeled data as the input and explores the supervised features of the unlabeled data under the principle of self-supervised learning. A simple yet effective backbone network as a feature extractor for subsequent activity recognition is proposed. By using a small portion of the labeled samples as the training set which is fed into our learned feature extractor, we build a classifier and use the rest of the data to verify the feasibility and effectiveness of our CL method. Extensive experiments on three benchamark datasets and one real-world dataset demonstrate that CL-HAR can achieve better classification accuracy than compared supervised and semi-supervised methods with less labelled samples, which is of practical use. © 2023 IEEE.

关键词： Wearable sensors

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Llm-Based Automated Modeling in Symbolic Execution for Securing Medical software

SSRN

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SSRN 2024年

作者： Chen, Jingxue Deng, Liangjun Qiu, Yao Zhao, Pengbiao Lei, Hang Song, Jingcheng Wang, Xiaopei School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu610054 China Institute of Building Information Modeling Ningbo Polytechnic College Zhejiang Ningbo315800 China College of Information Science and Engineering Linyi University Shandong Linyi276000 China Department of Computer Science and Engineering University of California RiversideCA92521 United States

There are lots of sensitive information in medical consumer devices. Since Consumer electronics products in the healthcare mainly focus on health monitoring and personal health management, such as wearable ECG monitoring devices, smart weight scales and so on. It is paramount to ensure the security and reliability of software in healthcare delivery, especially for sensitive medical data of integration with artificial intelligence (AI). Therefore, it is necessitating strict security verification and continuous vigilance against potential software vulnerabilities. WebAssembly, known for its security, portability, and speed, has emerged as a crucial technology for medical device software, significantly impacting the healthcare electronics sector, particularly in AI-generated content. There is a novel machine learning-based approach to address the challenges of symbolic execution in WebAssembly by automatically modeling unknown functions using a fine-tuned large language model (LLM) with 20 billion parameters, enhancing the execution's integrity and efficiency. The method, applicable across multiple programming languages and extendable to WebAssembly-compiled programs, shows promising experimental results in model performance and efficiency. This ensures enhanced security and reliability of software for CE, setting the stage for more flexible WebAssembly standards in medical CE and AI-driven healthcare devices. © 2024, The Authors. All rights reserved.

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Achieving Efficient and Accurate Segmentation for Autonomous Driving

Achieving Efficient and Accurate Segmentation for Autonomous...

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International Symposium on Environmentally Conscious Design and Inverse Manufacturing (EcoDesign)

作者： Ningkang Ouyang Jie Dong Yi Han ChenYu Zhao School of Information Engineering North China University of Water Resources and Electric Power Zhengzhou China Department of Computer Science and Information Anyang Institute of Technology Engineering Anyang China School of Artificial Intelligence Zhengzhou Software Vocational and Technical College Zhengzhou China

ISBN: (数字)9798350363043

ISBN: (纸本)9798350363050

Autonomous driving technology, as a mainstream trend in today’s technological development, holds significant commercial value. Semantic segmentation, a core technology in this field, faces challenges with current mainstream models due to their complex structures, large numbers of parameters, and the need for high-performance hardware. These issues hinder their application in mobile devices. Therefore, this paper proposes a road scene semantic segmentation algorithm based on dynamic asymmetric convolution and capsule networks, named Asymmetric Capsule Convolutional Networks (ACCN). Specifically, the algorithm uses dynamic asymmetric convolution to construct a baseline network that combines dynamic convolution and capsule dynamic routing algorithms to enhance the model’s representational capabilities. Additionally, a dispersed attention module is built to strengthen the dependency between features to achieve more adaptable network weights. Experiments demonstrate that ACCN achieves a good balance between segmentation accuracy and detection speed. On the CamVid and Cityscapes test datasets, the proposed method in this paper achieved an average Intersection over Union (mIoU) of $\mathbf{8 0. 8\%}$ at 55.2 fps, showcasing better trade-offs in segmentation accuracy and efficiency. The experimental results indicate the effectiveness of our method.

关键词： Accuracy Convolution Heuristic algorithms Semantic segmentation Roads Routing Hardware Mobile handsets Vehicle dynamics Autonomous vehicles

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