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IEEE International Conference on Progress in Informatics and Computing (PIC)

作者： Dan Wu Bo Liu Zhijian Chen Wenyan Xie Xiang Huang Shuwei Pei Bin Sheng Donghan Feng Information Center China Southern Power Grid Co. Ltd. Dept. of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Network & Information Branch Guangdong Electric Power Design Institute Guangzhou China Chinese Academy of Sciences Institute of Software Beijing China Dept. of Electrical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781479920327

Charging of large-scale electric vehicles will have a significant impact on the grid so that their charging schedule needs to be optimized. The traditional centralized scheduling method requires high performance in information exchange and computing. Based on the analysis of the features of electric vehicle charging load and the research of cloud computing, we propose a cloud computing based schedule platform for optimal charging of electric vehicles. In addition, the function and implementation of the three basic modules of the platform (the data collection module, the cloud computing center and the control center) are exposed. The platform performs well in optimal scheduling for electric vehicles. In addition, the platform can also make full use of the computing resource and reduce the communication congestion.

关键词： Electric vehicles Cloud computing Optimal scheduling Power grids

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Revisiting Token Dropping Strategy in Efficient BERT Pretraining

arXiv

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arXiv 2023年

作者： Zhong, Qihuang Ding, Liang Liu, Juhua Liu, Xuebo Zhang, Min Du, Bo Tao, Dacheng National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China JD Explore Academy China Research Center for Graphic Communication Printing and Packaging Institute of Artificial Intelligence Wuhan University China Institute of Computing and Intelligence Harbin Institute of Technology China University of Sydney Australia

Token dropping is a recently-proposed strategy to speed up the pretraining of masked language models, such as BERT, by skipping the computation of a subset of the input tokens at several middle layers. It can effectively reduce the training time without degrading much performance on downstream tasks. However, we empirically find that token dropping is prone to a semantic loss problem and falls short in handling semantic-intense tasks (§2). Motivated by this, we propose a simple yet effective semantic-consistent learning method (SCTD) to improve the token dropping. SCTD aims to encourage the model to learn how to preserve the semantic information in the representation space. Extensive experiments on 12 tasks show that, with the help of our SCTD, token dropping can achieve consistent and significant performance gains across all task types and model sizes. More encouragingly, SCTD saves up to 57% of pretraining time and brings up to +1.56% average improvement over the vanilla token dropping. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Research on the transformer model in computer vision

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AIP Conference Proceedings 2024年第1期3194卷

作者： Kaipeng Li Yizhen Song College of Computer Science and Software Engineering Shenzhen University Shenzhen China Reading Academy Nanjing University of Information Science & Technology Nanjing China

In the realm of computer vision, the visual Transformer model is commonly utilized as researchers have improved the Transformer model. The purpose of this paper is to study the transformer model in the field of computer vision, to summarise the visual transformer model, and to introduce the applications in the field of computer vision. This paper firstly introduces the idea and structure of the transformer model, including the Self-Attention Mechanism, Multi-Head Attention and secondly gives an overview of the research progress of Transformer model the second is a review of the research progress of the Transformer model, including the Vision Transformer, Swin Transformer and other improved models, selecting representative models and their applications to be introduced, laying a foundation for the subsequent research work. As the field of computer vision continues to evolve, the Transformer model needs to be further developed to handle different types of data and tasks to meet the needs of increasingly complex applications.

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An Efficient Framework for Unsupervised Anomaly Detection over Edge-Assisted Internet of Things

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ACM Transactions on Sensor Networks 1000年

作者： Yumeng Liu Hongan Wang Xu Zheng Ling Tian Beijing Key Laboratory of Human-Computer Interaction Institute of Software Chinese Academy of Sciences China School of Computer Science and Technology University of Chinese Academy of Sciences China School of Computer Science and Engineering University of Electronic Science and Technology of China China

The detection of anomaly status plays a pivotal role in the maintenance of public transportation and facilities in smart cities. Owing to the pervasively deployed sensing devices, one can collect and apply multi-dimensional sensing data to detect and analyze potential anomalies and react promptly. Current efforts concentrate on offline manners and fail to fit the situation in smart cities, where efficient and online solutions are expected. In this paper, a novel framework is designed for anomaly detection over edge-assisted Internet-of-Things (IoTs). The framework allows periodical data collection from sensors and continuous anomaly detection at the edge node. A novel efficient and unsupervised deep learning model is designed to balance the resource consumption and accuracy for anomaly detection, based on the combination of a convolutional autoencoder and adversarial training. Meanwhile, the proposed framework also adopts an adaptive strategy for continuous anomaly detection to reduce the overall resource consumption. According to theoretical analysis and evaluation on several real-world datasets, the proposed framework can discover the potential correlation features among multi-dimensional sensing data, and efficiently detect the abnormality of public transportation and facilities in smart cities.

关键词： Anomaly detection Smart city Internet of Thing Unsupervised learning

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Entertainment for Education. Digital Techniques and Systems 1

引用

丛书名： Lecture Notes in computer science

1000年

作者： Xiaopeng Zhang Shaochun Zhong Zhigeng Pan Kevin Wong Ruwei Yun

ISBN: (数字)9783642145339

ISBN: (纸本)9783642145322

With the technical advancement of digital media and the medium of communication in recent years, there is a widespread interest in digital entertainment. An emerging te- nical research area edutainment, or educational entertainment, has been accepted as education using digital entertainment. Edutainment has been recognized as an eff- tive way of learning using modern digital media tools, like computers, games, mobile phones, televisions, or other virtual reality applications, which emphasizes the use of entertainment with application to the education domain. The Edutainment conference series was established in 2006 and subsequently - ganized as a special event for researchers working in this new interest area of e-learning and digital entertainment. The main purpose of Edutainment conferences is to facilitate the discussion, presentation, and information exchange of the scientific and technological development in the new community. The Edutainment conference series becomes a valuable opportunity for researchers, engineers, and graduate s- dents to communicate at these international annual events. The conference series - cludes plenary invited talks, workshops, tutorials, paper presentation tracks, and panel discussions. The Edutainment conference series was initiated in Hangzhou, China in 2006. Following the success of the first event, the second (Edutainment 2007 in Hong Kong, China), third (Edutainment 2008 in Nanjing, China), and fourth editions (Edutainment 2009 in Banff, Canada) were organized. Edutainment 2010 was held during August 16–18, 2010 in Changchun, China. Two workshops were jointly org- ized together with Edutainment 2010.

关键词： computers and Education

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Sliding Mode Control in Finite Time Stabilization for Synchronization of Chaotic Systems

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International Scholarly Research Notices 2013年第1期2013卷

作者： Zhan-Shan Zhao Jing Zhang Lian-Kun Sun School of Computer Science and Software Engineering Tianjin Polytechnic University Tianjin 300387 *** State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing 100190 *** Tianjin Vocational Institute Tianjin 300410 ***

An adaptive sliding mode control for chaotic systems synchronization is considered. The design of robust finite time convergent controller is based on geometric homogeneity and integral sliding mode manifold. The knowledge of the upper bound of the system uncertainties is not prior required. The chaos synchronization is presented to system stability based on the Lyapunov stability theory. The simulation results show the effectiveness of the proposed method.

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PATCH: Empowering Large Language Model with Programmer-Intent Guidance and Collaborative-Behavior Simulation for Automatic Bug Fixing

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ACM Transactions on software engineering and Methodology 1000年

作者： Yuwei Zhang Zhi Jin Ying Xing Ge Li Fang Liu Jiaxin Zhu Wensheng Dou Jun Wei Key Laboratory of System Software (Chinese Academy of Sciences) Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences China Key Laboratory of High Confidence Software Technologies (Peking University) Ministry of Education School of Computer Science Peking University School of Computer Science Wuhan University China School of Intelligent Engineering and Automation Beijing University of Posts and Telecommunications China School of Computer Science Peking University China State Key Laboratory of Complex & Critical Software Environment School of Computer Science and Engineering Beihang University China

Bug fixing holds significant importance in software development and maintenance. Recent research has made substantial strides in exploring the potential of large language models (LLMs) for automatically resolving software bugs. However, a noticeable gap in existing approaches lies in the oversight of collaborative facets intrinsic to bug resolution, treating the process as a single-stage endeavor. Moreover, most approaches solely take the buggy code snippet as input for LLMs during the patch generation stage. To mitigate the aforementioned limitations, we introduce a novel stage-wise framework named PATCH. Specifically, we first augment the buggy code snippet with corresponding dependence context and intent information to better guide LLMs in generating the correct candidate patches. Additionally, by taking inspiration from bug management practices, we decompose the bug-fixing task into four distinct stages: bug reporting, bug diagnosis, patch generation, and patch verification. These stages are performed interactively by LLMs, aiming to simulate the collaborative behavior of programmers during the resolution of software bugs. By harnessing these collective contributions, PATCH effectively enhances the bug-fixing capability of LLMs. We implement PATCH by employing the powerful dialogue-based LLM ChatGPT. Our evaluation on the widely used bug-fixing benchmark BFP demonstrates that PATCH has achieved better performance than state-of-the-art LLMs.

关键词： Bug Fixing Large Language Model Bug Management Multi-Agent Collaboration

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A new Technique in Protein Structure Quantitative Identification

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Procedia computer science 2022年 214卷 1546-1553页

作者： Mengjiao Guo Haobin Cao Jing He Tengfei Ji Department of Computer Science and Software Engineering Swinburne University of Technology Melbourne 3122 Australia Department of mechanical engineering University College London London WC1E 6BT United Kingdom Department of Neuroscience University of Oxford Oxford OX39DU United Kingdom State Key Laboratory of Bioactive Substance and Function of Natural Medicines Institute of Materia Medica Peking Union Medical College and Chinese Academy of Medical Sciences Beijing 100050 China

Graph distance is also named approximate graph isomorphism, error-tolerant graph matching, it is a measure of similarity (or dissimilarity) between two graphs. The graph distance function is an elusive question remaining a pressing problem in a wide range of fields. In general, graphs are usually enriched with node and edge attributes, namely, heterogeneous graphs, our focus is encapsulating node and edge identities in a graph. In addition, an adequate representation of the graph-adjacency matrix can capture the connectivity of the graph structure, while an adjacency matrix bears high-order combinatorial nature. Solving these challenges in the effective analysis of these graphs is of utmost importance. In this paper, structural and semantic features are both preserved by graphs, so the analysis of row sum and eigenspectra for vertex and edge adjacency matrix has captured the affinity interactions within graphs locally and globally. Therefore, we estimate the geometrical and semantic dissimilarities/distances between graphs extensively and systemically. Several illustrative chemical compound cases implement in practical scenarios, thus, the theory could be easily understood. Our method defines a quantitative distance (or dissimilarity) measure for comparing graphs, it has a good measure of expressiveness and a suited polynomial computation complexity, which paves the way for graph analysis.

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A review of uncertainty quantification in deep learning: Techniques, applications and challenges

arXiv

引用

arXiv 2020年

作者： Abdar, Moloud Pourpanah, Farhad Hussain, Sadiq Rezazadegan, Dana Liu, Li Ghavamzadeh, Mohammad Fieguth, Paul Cao, Xiaochun Khosravi, Abbas Rajendra Acharya, U. Makarenkov, Vladimir Nahavandi, Saeid Deakin University Australia College of Mathematics and Statistics Guangdong Key Lab. of Intelligent Information Processing Shenzhen University Shenzhen518060 China Dibrugarh University Dibrugarh India Department of Computer Science and Software Engineering Swinburne University of Technology Melbourne Australia Center for Machine Vision and Signal Analysis University of Oulu Oulu Finland Google research United States Department of Systems Design Engineering University of Waterloo Waterloo Canada State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing China Department of Electronics and Computer Engineering Ngee Ann Polytechnic Clementi Singapore Department of Computer Science University of Quebec in Montreal MontrealQC Canada

—Uncertainty quantification (UQ) plays a pivotal role in the reduction of uncertainties during both optimization and decision making, applied to solve a variety of real-world applications in science and engineering. Bayesian approximation and ensemble learning techniques are two of the most widely-used UQ methods in the literature. In this regard, researchers have proposed different UQ methods and examined their performance in a variety of applications such as computer vision (e.g., self-driving cars and object detection), image processing (e.g., image restoration), medical image analysis (e.g., medical image classification and segmentation), natural language processing (e.g., text classification, social media texts and recidivism risk-scoring), bioinformatics, etc. This study reviews recent advances in UQ methods used in deep learning, investigates the application of these methods in reinforcement learning, and highlight the fundamental research challenges and directions associated with the UQ field. Copyright © 2020, The Authors. All rights reserved.

关键词： Image segmentation

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Feedback Recommendation System Based on Structured Feedback Acquisition

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Journal of Physics: Conference Series 2020年第1期1447卷

作者： E Hassan N Sherief W M Abdelmoez A A Saad Department of Computer Engineering College of Engineering and Technology Arab Academy for Science Technology and Maritime Transport - Egypt Department of Software Engineering College of Computing and Information Technology Arab Academy for Science Technology and Maritime Transport - Egypt

Users' feedback are increasingly becoming a vital factor during the software evolution process. Users' feedback are widely considered to be an indication of how satisfied are the system's users. It was found that large percentage of feedback represents problems reported by users who face problems in using the system. Users' feedback are usually communicated in an ad-hoc manner with no defined structure. The ad-hoc nature of the feedback makes it difficult to study and analyse feedback. Also, it usually happens that similar problems are reported by different users, so engineers spend wasted time and effort in duplicate issues. In this paper we propose a recommendation system which recommends to the user reported problems that are similar to his from prevoius feedback threads to reuse existing solutions. We based our work on a structured feedback system to ensure we get better results. Structured feedback ensures minimum level of useful and meanigful information that helps enhance the analysis results. Initial evaluation was conducted and the results are promising.

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