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Exploring the Power of Entangled Data in Quantum Machine Learning

Wuhan University Journal of Natural sciences 2024年第3期29卷 193-194页

作者： WANG Xinbiao DU Yuxuan TU Zhuozhuo LUO Yong YUAN Xiao TAO Dacheng Institute of Artificial Intelligence School of Computer ScienceWuhan UniversityWuhan 430072HubeiChina National Engineering Research Center for Multimedia Software Wuhan UniversityWuhan 430072HubeiChina JD Explore Academy Beijing 101111China School of Computer Science and Engineering Nanyang Technological UniversitySingapore 639798Singapore School of Computer Science Faculty of EngineeringUniversity of SydneyNSW 2008Australia Center on Frontiers of Computing Studies Peking UniversityBeijing 100871China School of Computer Science Peking UniversityBeijing 100871China

Quantum entanglement is a key resource for achieving superiority of quantum ***,scientists are extensively focusing on how to integrate quantum entanglement into various components of quantum machine learning(QML)models,aiming to surpass the performance of traditional machine learning *** successes include the use of entangled measurements^([1-3])and entangled channels^([4]),which have been shown to reduce query complexity or improve the prediction precision for specified QML *** entangled data,capable of encoding more information compared to classical data of the same size,is recognized for its potential to achieve quantum ***,the impact of the entanglement degree in quantum data on model performance remains a challenging and unresolved research question.

关键词： quantum specified integrate

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A Hybrid Approach for Developer Recommendation Based on Social Network 2

A Hybrid Approach for Developer Recommendation Based on Soci...

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2nd International Conference on Electronic Information Technology, EIT 2023

作者： Liang, Huilin Wei, Qingjie School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China

ISBN: (数字)9781510666627

ISBN: (纸本)9781510666610

Bug fixing requires collaboration among developers, however, most of current studies recommend developers based on the textual content and similarity of bug reports, ignoring the implicit social relationships generated by developers when they collaborate on fixing tasks. This paper proposes a hybrid approach for bug developer recommendation to address this problem. The method constructs a developer social network, extracts developer social relationships implicitly modeled in bug report history records and comment, and uses Graph Convolutional Neural Network (GCN) to learn the closeness between developers. In addition, Convolutional Neural Networks (CNN) are used to learn the bug report text content, suitable developers are recommended by fusing the collaborative relationships between developers and the textual content of bug reports. According to experimental findings, the method's accuracy is significantly better than that of other methods. © 2023 SPIE.

关键词： Convolutional neural networks

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Cable: Design of a Transmission-Type Snake Rescue Robot 4

Cable: Design of a Transmission-Type Snake Rescue Robot

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4th International Symposium on Intelligent Robotics and Systems, ISoIRS 2024

作者： Zhang, Kefeng Wang, Jiawei Zhang, Hao Jiang, Jichao Dou, Xinrui Zhao, Wenyi Tian Gong University School Of Electronics&Information Engineering China Tian Gong University School Of Aeronautics And Astronautics China Tian Gong University School of Computer Science and Technology China Tian Gong University School of Software China

ISBN: (纸本)9798350364798

The advancement of robotics holds great promise for enhancing rescue missions. Existing rescue robots often suffer from poor collaboration. In response to this challenge, we present the design of Cable, a snake rescue robot with docking capabilities. The docking mechanism enhances the robot's redundancy, enabling it to perform more intricate movements and provide essential services, such as sound, light, and power, in confined spaces for the rescued individuals. One notable feature is the integration of a visual system that collaborates seamlessly with rescue personnel, facilitating comprehensive surveys of the rescue area. This visual feedback not only aids in environmental assessment but also empowers the robot to autonomously select optimal movement modes. This paper begins by introducing the system architecture design and visual algorithms, followed by an analysis of docking strategies and kinematic processes. Finally, the feasibility of the robot design is validated through experiments. The results demonstrate the robot's collaborative advantages in complex rescue environments. © 2024 IEEE.

关键词： Machine design

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Stress detection system for social media users 7

Stress detection system for social media users

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7th International Conference on computer science and Computational Intelligence, CSCI 2022

作者： Febriansyah, Mochamad Rizky Nicholas Yunanda, Rezki Suhartono, Derwin Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Software Engineering Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Stress detection is a growing topic in the field of natural language processing. The study of stress detection for mental health prediction has been proven to benefit the development of recommender systems and automated mental health assessments in previous studies. Additionally, the widespread usage of social media has served as a potential data source for developing such models. Our research tried to detect whether the users of social media were under stress or not. We used a dataset from Dreaddit consisting of posts from one of the popular social media platforms, Reddit. We propose a machine learning model consisting of Support Vector Machine (SVM), Naïve Bayes, Decision Tree, Random Forest, Bag of Words, and Term Frequency - inverse document frequency (TF-IDF) for stress detection. The final evaluation of the model achieved an 80.00% F-1 Score and 75.00% accuracy, and both were scored by SVM. © 2022 Elsevier B.V.. All rights reserved.

关键词： Support vector machines

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Vision-Language Models are Strong Noisy Label Detectors 38

Vision-Language Models are Strong Noisy Label Detectors

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wei, Tong Li, Hao-Tian Li, Chun-Shu Shi, Jiang-Xin Li, Yu-Feng Zhang, Min-Ling School of Computer Science and Engineering Southeast University Nanjing China Ministry of Education China National Key Laboratory for Novel Software Technology Nanjing University China School of Artificial Intelligence Nanjing University China

Recent research on fine-tuning vision-language models has demonstrated impressive performance in various downstream tasks. However, the challenge of obtaining accurately labeled data in real-world applications poses a significant obstacle during the fine-tuning process. To address this challenge, this paper presents a Denoising Fine-Tuning framework, called DEFT, for adapting vision-language models. DEFT utilizes the robust alignment of textual and visual features pretrained on millions of auxiliary image-text pairs to sieve out noisy labels. The proposed framework establishes a noisy label detector by learning positive and negative textual prompts for each class. The positive prompt seeks to reveal distinctive features of the class, while the negative prompt serves as a learnable threshold for separating clean and noisy samples. We employ parameter-efficient fine-tuning for the adaptation of a pre-trained visual encoder to promote its alignment with the learned textual prompts. As a general framework, DEFT can seamlessly fine-tune many pre-trained models to downstream tasks by utilizing carefully selected clean samples. Experimental results on seven synthetic and real-world noisy datasets validate the effectiveness of DEFT in both noisy label detection and image classification tasks. Our source code is available at https://***/HotanLee/DeFT. © 2024 Neural information processing systems foundation. All rights reserved.

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Container-based data-intensive application scheduling in hybrid cloud-edge collaborative environment

Container-based data-intensive application scheduling in hyb...

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作者： Tang, Bing Luo, Jincheng Zhang, Jiaming School of Computer Science and Engineering Hunan University of Science and Technology Hunan Xiangtan China Hunan Key Laboratory for Service Computing and Novel Software Technology Hunan University of Science and Technology Hunan Xiangtan China School of Information and Communication Engineering Guangzhou Maritime University Guangdong Guangzhou China School of Information Engineering Wuhan University of Technology Hubei Wuhan China

Container virtualization technology represented by Docker has been widely used in the industry due to its advantages of lightweight, fast deployment, and easy portability. It can bring convenience to system deployment, operation, and maintenance. This article considers the implementation of AI-based IoT applications based on Docker and container technology in a cloud-edge collaborative environment. Three typical data-intensive applications, preprocessing, training, and inference in machine learning, are deployed using Docker containers. For these three types of tasks, this article proposes a container-based data-intensive application scheduling framework. Using an attribute-based data-driven method, a new scheduling approach considering multiple weighting factors is proposed, which is called DICS-OPT. The cloud nodes and edge nodes are scored and sorted uniformly. The container is scheduled to the node with the highest score, and then the task data is transmitted to the node to execute the task. NSGA-III-based genetic algorithm has been proposed to search for the optimal weighting factors. This article introduces the implementation of the prototype system and builds a cloud-edge collaborative testbed consisting of Raspberry Pis and PCs. The performance evaluation results indicates that the proposed scheduling approach outperforms existing approaches. Compared with existing approaches, DICS-OPT improves the average edge resource utilization by 10.05% to 69.04%, and saves the cloud resource cost by 16.02% to 36.68%. © 2023 John Wiley & Sons Ltd.

关键词： Edge computing

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Enhancing Energy Efficiency in WSNs with Hybrid LEACH-D and ANN

Enhancing Energy Efficiency in WSNs with Hybrid LEACH-D and ...

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2024 Multimedia University engineering Conference, MECON 2024

作者： Qamar, Muhammad Salman Haq, Ihsan Ul Munir, Muhammad Fahad Roslee, Mardeni Bin Awan, Adnan Anwar Waseem, Athar International Islamic University Department of Electrical and Computer Engineering Islamabad Pakistan Multimedia University Centre for Wireless Technology Faculty of Engineering Cyberjaya63100 Malaysia School of Engineering California Polytechnic State University Department of Computer Science and Software Engineering San Luis ObispoCA United States

ISBN: (纸本)9798331530747

This paper explores strategies to improve the energy efficiency and operational longevity of Wireless Sensor Networks (WSNs) through the optimization of clustering techniques and energy efficient routing protocols. The proposed methodology is precisely crafted to optimize energy consumption (EC) while preserving the overall effectiveness of WSNs. LEACH (Low Energy Adaptive Cluster Hierarchy) is a cluster-based protocol where non-cluster head nodes deactivate their RF until their allocated time slots. Nevertheless, a drawback of traditional LEACH is its random rotation of local Cluster Heads (CH), leading to unequal cluster distribution. To overcome this issue, we proposed a novel hybrid technique called LEACH-D protocol and Artificial Neural Network (ANN). The LEACH-D algorithm enhances the duration of transmission tasks while ensuring uniform battery energy consumption. On the other hand, the ANN plays a pivotal role in coordinating optimal routing decisions and CH placement, refining data aggregation and transmission mechanisms to restrain idle listening. The findings indicate that the suggested approach manifests as substantial reductions in energy consumption and prolonged network lifespan of WSN. © 2024 IEEE.

关键词： Sensor nodes

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Building Modeling Tools Based on Metamodeling and Product Line Technologies

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Chinese Journal of Electronics 2025年第2期23卷 219-226页

作者： Zhiyi Ma Xiao He Software Institute School of Electronics Engineering and Computer Science Peking University Beijing China Key Laboratory of High Confidence Software Technologies (Peking University) Ministry of Education Beijing China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China

With the evolution of existing modeling languages and the emergence of more and more new modeling languages, it is necessary to rapidly build the corresponding software modeling tools with good quality. However, modeling tools for larger modeling languages are usually diversity in function and complexity in implementation technology. Taking building modeling tools as a domain, this paper presents an approach to building software modeling tools based on metamodeling and product line technologies. The paper provides the concept system of the approach and a feature model from diverse functions of modeling tools in order to specify the commonality and variability of the tools by deeply making the domain analysis, discusses the design and implementation of a general tool framework that provides the conveniences for reusing components and generating code for components, and specifies the mapping between the feature model and the components for modeling tools.

关键词： Analytical models Codes Buildings Metamodeling software Complexity theory Research and development

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The application of image Style Conversion Network in the design of Zhuang brocade pattern derivation 3

The application of image Style Conversion Network in the des...

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3rd International Conference on Artificial Intelligence, Virtual Reality, and Visualization, AIVRV 2023

作者： Meng, Kailin Shi, Zhuo Zhang, Mengxue Xiong, Biao Su, Qianli School of Art and Design Guilin University of Electronic Technology Guangxi Guilin541004 China School of Computer Science and Information Security School of Software Engineering Guangxi Guilin541004 China

ISBN: (纸本)9781510671485

Object: With the help of style conversion Network, the design of the Zhuang brocade pattern is derived to explore the new direction of inheritance and development of the Zhuang brocade pattern in the current era. Methods: The cultural connotation and design value of Zhuang brocade patterns are analyzed, and the selected Zhuang brocade patterns are used as content maps and different style maps to conduct style conversion Network experiments. The experimental results are analyzed and evaluated to find the optimal conversion solution. Investigate the effect of the conversed Zhuang brocade pattern in the derivative design. Result: By using style conversion Network, different styles of patterns can be generated quickly and can produce good derivative design effects in many products, thus achieving the purpose of integrating Zhuang brocade patterns with modern products. Conclusion: The addition of the style conversion Network algorithm makes the style simulation process significantly reduced, saving time for subsequent derivative designs and providing the possibility of mass production. The designs produced by the style conversion Network retain the original pattern characteristics of Zhuang brocade, with both auspicious symbolic meaning and national spirit, and realize the interaction between traditional crafts and the art field, in which the cultural heritage can also be shown, promoting Zhuang culture and providing new possibilities for the modern development of Zhuang brocade patterns. © 2023 SPIE.

关键词： Product design

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Towards Attention-Aware Interactive 360-Degree Video Streaming on Smartphones

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IEEE Network 2025年

作者： Zhang, Lei Zhou, Haobin Shen, Linfeng Liu, Jiangchuan Cui, Laizhong Shenzhen University College of Computer Science and Software Engineering China Simon Fraser University School of Computing Science Canada

Browsing 360-degree videos on smartphones is becoming increasingly popular, which is challenging due to heavy user interactions, limited hardware capabilities, and constrained batteries. To improve resource efficiency and user experience, we propose a framework for attention-aware interactive 360-degree video streaming for smartphone users. We first explore the built-in capabilities of off-the-shelf smartphones to capture user attention during video browsing. Using the information of user attention to predict viewport and analyze user-perceptive quality, we then investigate the approaches for improving video encoding, transmission, and rendering in 360-degree video streaming. Further, we discuss the opportunities for enhancing user interactive experience with error tolerance, protocol design, and on-device computation. Finally, our prototype implementation is presented as a case study to validate our design principles. Our framework integrates and coordinates advanced techniques for different components of 360-degree video streaming systems to achieve better user experience, which can also serve as a foundation for leveraging modern attention-based multi-modal learning architectures. © 2025 IEEE.

关键词： Video streaming

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