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SSRN 2024年

作者： Levron, Yoash Belikov, Juri Weiss, George The Andrew and Erna Viterbi Faculty of Electrical & Computer Engineering Technion—Israel Institute of Technology Haifa3200003 Israel Department of Software Science Tallinn University of Technology Tallinn12618 Estonia School of Electrical Engineering Tel-Aviv University Tel-Aviv69978 Israel

This theoretical paper continues a long line of research works about the stability of power systems represented by reduced order models. We provide a small-signal stability criterion for a dynamic model that uses time-varying phasors, and is characterized by a lossless transmission network and constant power loads located anywhere in the network. A key concept in this study is the matrix H of the partial derivatives of the generator powers with respect to their relative angles. Our main result is that the local stability of any equilibrium point is completely determined by properties of this matrix H. More specifically, we show that an equilibrium point is asymptotically stable if H is positive definite, and is unstable if H is full-rank and not positive definite. In addition, we also prove several relations between the eigenvalues of H and the eigenvalues of the system's Jacobian matrix. These results are demonstrated on a number of case-studies, and compared with related earlier ones from the literature. © 2024, The Authors. All rights reserved.

关键词： Electric power transmission networks

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BMRMIA:A Platform for Radiologists to Systematically Learn Automated Medical Image Analysis by Three Dimensional Medical Decision Support System

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computer Modeling in engineering & Sciences 2022年第5期131卷 851-863页

作者： Yankun Cao Lina Xu Zhi Liu Xiaoyan Xiao Mingyu Wang Qin Li Hongji Xu Geng Yang School of Software Shandong UniversityJinan250101China School of Information Science and Engineering Shandong UniversityQingdao266237China School of Computer Science and Technology Shandong Jianzhu UniversityJinan250012China Department of Nephrology Qilu Hospital of Shandong UniversityJinan250012China State Key Laboratory of ASIC&Systems The School of MicroelectronicsFudan UniversityShanghai200433China Shenzhen Institute of Information Technology Shenzhen518172China

Contribution:This paper designs a learning and training platform that can systematically help radiologists learn automated medical image analysis *** platform can help radiologists master deep learning theories and medical applications such as the three-dimensional medical decision support system,and strengthen the teaching practice of deep learning related courses in hospitals,so as to help doctors better understand deep learning knowledge and improve the efficiency of auxiliary ***:In recent years,deep learning has been widely used in academia,industry,*** increasing number of companies are starting to recruit a large number of professionals in the field of deep *** numbers of colleges and universities also offer courses related to deep learning to help radiologists learn automated medical image analysis *** now,however,there is no practical training platform that can help radiologists learn automated medical image analysis ***:The platform proposes the basic learning,model combat,business application(BMR)concept,including the learning guidance system and the assessment training system,which constitutes a closed-loop learning guidance mode of“learning-assessment-training-learning”.Findings:The survey results show that most of radiologists met their learning expectations by using this *** platform can help radiologists master deep learning techniques quickly,comprehensively and firmly.

关键词： BMR deep learning three dimensional medical decision support system deep learning engineer standard

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Attention-Based Knowledge-aware Multi-Interest Intelligent Model for Sequential Recommendation 19

Attention-Based Knowledge-aware Multi-Interest Intelligent M...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Li, Yang Li, Qianmu Meng, Shunmei Hou, Jun Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China Wuyi University Intelligent Manufacturing Department Jiangmen China Nanjing University State Key Lab. for Novel Software Technology Nanjing China Nanjing Vocational University of Industry Technology School of Social Science Nanjing China

ISBN: (纸本)9798350346558

As a fundamental means of intelligent service, recommender systems have shown a trend of diversification. Sequential recommendation, a class of time-aware methods, aims to predict the user's next click. Multi-interest representation is a hot topic in recommender systems, but the research on how to apply multi-interest into sequences is still in the minority. And many methods will introduce knowledge graph to improve the representation of items, but how to consider both knowledge graph and user's multi-interest in sequential recommendation has become a temporary blank. In this paper, we propose an attentive sequential recommendation with knowledge-aware multi-interest module (AKAMI). Items are embedded from the sequence's structure and user's interests. Specifically, we represent items by stacking self-attention layers to mine the internal correlation of the sequence. Meanwhile, we segment the sequence via the common neighbors in the knowledge graph and cast the user's multi-interest based on the aggregation of subsequences. Multi-interests are integrated into item representation by the gating network adaptively. Comprehensive experiments on two real-world datasets show that the proposed model performs better than various representative methods under different evaluation metrics. Additionally, we also visually explain the model through a case study. © 2022 IEEE.

关键词： Recommender systems

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Rnntcs: A Test Case Selection Method for Recurrent Neural Networks

SSRN

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SSRN 2023年

作者： Wu, Xiaoxue Shen, Jinjin Zheng, Wei Lin, Lidan Sui, Yulei Semasaba, Abubakar Omari Abdallah School of Information Engineering Yangzhou University Yangzhou China School of Software Engineering Northwestern Polytechnical University Xi’an China School of Computer Science University of Technology Sydney Sydney Australia

Recurrent Neural Network (RNN) is a typical feedback neural network, which is particularly effective in processing time-series data tasks such as image description, text generation and classification, etc. However, its safety and quality issues have aroused strong concern in academia and industry. One of the most commonly used methods to ensure RNN-driven software quality is testing and optimising RNN. Its core idea is to introduce human efforts to label test cases so that many labelled test cases can be used to trigger faults in RNNs. But this brings a huge overhead. Therefore, it is of great importance to study the test case selection method for RNN. Current research on test case selection for RNN has achieved some results from the stateful view of RNN, but there are still some problems, such as a low bug detection rate, poor inclusiveness, and lack of diversity. In this paper, according to the structural characteristics of RNNs, we propose an RNN test case selection method, RNNtcs, which combines clustering and uncertainty to select test cases that can touch RNN bugs as much as possible, thus reducing the cost of labeling. To evaluate RNNtcs, we conduct experiments on popular image datasets, text datasets and RNN models. The experimental results show that the RNNtcs method is superior to DeepState in bug detection effectiveness, inclusiveness and diversity, and the test cases selected by the RNNtcs method can effectively improve the robustness of RNN models. © 2023, The Authors. All rights reserved.

关键词： Recurrent neural networks

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Task Scheduling Strategy Based on Resource Constraint in Edge Computing System

Task Scheduling Strategy Based on Resource Constraint in Edg...

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Industrial Cyber-Physical Systems (ICPS)

作者： Ren Qing Huanle Rao Gangyong Jia Youqing Xu Wang Wei GuoJie Xie Department of Computer Science Hangzhou Dianzi University Hangzhou China Department of Automation Hangzhou Dianzi University Hangzhou China Department of Software Engineering University of Science and Technology of China Hefei China Department of Computer Science Dalian University of Technology Dalian China Department of Computer Science Fuyang Normal University Fuyang China

ISBN: (数字)9798350363012

ISBN: (纸本)9798350363029

With the advent of the era of the Internet of Everything, edge computing, characterized by its high reliability and low latency, has been widely applied in various intelligent systems. This paper addresses the challenge of optimizing task execution in edge systems, which differ from traditional cloud computing with their heterogeneous nodes and varied task demands. We introduce a task-scheduling approach focused on resource constraints. Our method involves analyzing the effects of network IO and CPU resources under soft constraints, developing a task performance prediction model based on single resource pressure testing, and implementing a throughput maximization scheduling (MTSched) strategy. Our experimental analysis on a custom-built edge heterogeneous cluster demonstrates that MTSched significantly outperforms three benchmark algorithms (random, round-robin, and weighted round-robin scheduling) in system throughput and task execution delay.

关键词： Job shop scheduling Runtime Processor scheduling Computational modeling Predictive models Throughput Reliability

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CasCIFF: A Cross-Domain Information Fusion Framework Tailored for Cascade Prediction in Social Networks

arXiv

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arXiv 2023年

作者： Zhu, Hongjun Yuan, Shun Liu, Xin Chen, Kuo Jia, Chaolong Qian, Ying School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Engineering Research Center of Software Quality Assurance Testing and Assessment Chongqing400065 China School of Engineering Computer and Mathematical Sciences Auckland University of Technology Auckland1010 New Zealand

The explosive growth of the Internet has elevated social networks to a pivotal role in information propagation, reshaping conventional paradigms of information distribution. Utilizing the vast amount of data available online to model and predict this diffusion is of great importance in various fields. Existing approaches for information cascade prediction fall into three main categories: feature-driven methods, point process-based methods, and deep learning-based methods. Among them, deep learning-based methods, characterized by their superior learning and representation capabilities, mitigate the shortcomings inherent in the other methods. However, current deep learning methods still face several persistent challenges. In particular, accurate representation of user attributes remains problematic due to factors such as fake followers and complex network configurations. Previous algorithms that focused on the sequential order of user activations often neglected the rich insights offered by activation timing. Furthermore, these techniques often fail to holistically integrate temporal and structural aspects, thus missing the nuanced propagation trends inherent in information cascades. To address these issues, we propose the Cross-Domain Information Fusion Framework (CasCIFF), which is tailored for information cascade prediction. This framework exploits multi-hop neighborhood information to make user embeddings robust. When embedding cascades, the framework intentionally incorporates timestamps, endowing it with the ability to capture evolving patterns of information diffusion. In particular, CasCIFF seamlessly integrates the tasks of user classification and cascade prediction into a consolidated framework, thereby allowing the extraction of common features that prove useful for all tasks, a strategy anchored in the principles of multitask learning. After extensive experiments conducted on publicly available datasets, the results demonstrate CasCIFF’s superiority over e

关键词： Forecasting

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Semantic recommendation through Reinforcement Learning and weighted meta-paths

Semantic recommendation through Reinforcement Learning and w...

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2022 International Conference on Electronic Information engineering, Big Data, and computer technology, EIBDCT 2022

作者： Yu, Jing Lang, Yue Li, Xuewen Zhang, Jin Zheng, Shaojie Gong, Jibing 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 Key Laboratory for Software Engineering of Hebei Province Yanshan University Qinhuangdao066004 China

ISBN: (纸本)9781510655133

Explainable recommendation systems, which can produce high-accuracy recommendations and help users make quick decisions, have become a hotspot in research field. Most of existing research algorithms committed to improving the accuracy of recommendation, but ignored the interpretability of recommendation. We propose a semantic recommendation algorithm through Reinforcement Learning and weighted meta-paths which analyzes and selects the meta-path, and uses Reinforcement Learning network to train the weight of meta-path. This method can effectively solve data sparsity, improve the accuracy of recommendation, and increase interpretability. Compared with the baseline method, indicators of the recommended methods for integrating Reinforcement Learning with Heterogeneous Information Networks have been greatly improved, as verified and compared on a real MovieLens 1M dataset. Experimental results prove that the algorithm can effectively improve the accuracy and interpretability of the recommendation. © 2022 SPIE.

关键词： Reinforcement learning

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Graph-based Multi-view Clustering for Web services

Graph-based Multi-view Clustering for Web services

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Wang Yang Yuan Zhenzhen Kang Guosheng Cao Buqing Liu JianXun Xiao Yong HNUST Xiangtan China Hunan Provincial Key Lab. for Services Computing and Novel Software Technology Hunan University of Science and Technology Xiangtan China School of Computer Science and Engineering

The number of Web services on the Internet has been steadily increasing in recent years due to their growing popularity. Under the big data environment, how to effectively manage Web services is of significance for service discovery, service recommendation, etc. It is widely studied that Web services clustering is an effective way for service management. However, most of the current Web service clustering only extracts the information of Web services for clustering from one view, such as Web service content descriptions, networks in which Web services participate, and so on. Extracting information from Web services only unilaterally will not be able to provide a three-dimensional and comprehensive description of Web services, which may diminish the effect of Web service clustering. In addition, some Web service resources will be wasted if other information of Web services is not used at the same time. We find that multi-view clustering can simultaneously consider multiple information of a data at the same time, and multiple information can complement and enhance each other according to the characteristics of multiview clustering. Therefore, in this paper, we apply Web services to graph-based multi-view clustering in multi-view clustering to improve the performance of Web service clustering by simultaneously considering multiple feature information about Web services and distributing different weights to different information in the clustering process.

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Weakly-Supervised Log-Based Anomaly Detection with Inexact Labels via Multi-Instance Learning

Weakly-Supervised Log-Based Anomaly Detection with Inexact L...

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International Conference on software engineering (ICSE)

作者： Minghua He Tong Jia Chiming Duan Huaqian Cai Ying Li Gang Huang School of Software and Microelectronics Peking University Beijing China Institute for Artificial Intelligence Peking University Beijing China National Key Laboratory of Data Space Technology and System Beijing China School of Computer Science Peking University Beijing China National Engineering Research Center for Software Engineering Peking University Beijing China

ISBN: (数字)9798331505691

ISBN: (纸本)9798331505707

Log-based anomaly detection is essential for maintaining software availability. However, existing log-based anomaly detection approaches heavily rely on fine-grained exact labels of log entries which are very hard to obtain in real-world systems. This brings a key problem that anomaly detection models require supervision signals while labeled log entries are unavailable. Facing this problem, we propose a new labeling strategy called inexact labeling that instead of labeling an log entry, system experts can label a bag of log entries in a time span. Furthermore, we propose MIDLog, a weakly supervised log-based anomaly detection approach with inexact labels. We leverage the multiinstance learning paradigm to achieve explicit separation of anomalous log entries from the inexact labeled anomalous log set so as to deduce exact anomalous log labels from inexact labeled log sets. Extensive evaluation on three public datasets shows that our approach achieves an F1 score of over 85% with inexact labels.

关键词： software Labeling Anomaly detection software engineering

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ROS package search for robot software development: a knowledge graph-based approach

arXiv

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arXiv 2023年

作者： Wang, Shuo Mao, Xinjun Yang, Shuo Wu, Menghan Zhang, Zhang College of Computer National University of Defense Technology Changsha410000 China Key Laboratory of Software Engineering for Complex Systems Changsha410000 China College of Systems Engineering National University of Defense Technology Changsha410000 China College of Computer Science and Technology Zhejiang University Hangzhou310058 China

In recent years, ROS (Robot Operating System) packages have become increasingly popular as a type of software artifact that can be effectively reused in robotic software development. Indeed, finding suitable ROS packages that closely match the software’s functional requirements from the vast number of available packages is a nontrivial task using current search methods. The traditional search methods for ROS packages often involve inputting keywords related to robotic tasks into general-purpose search engines (e.g., Google) or code hosting platforms (e.g., Github) to obtain approximate results of all potentially suitable ROS packages. However, the accuracy of these search methods remains relatively low because the task-related keywords may not precisely match the functionalities offered by the ROS packages. To improve the search accuracy of ROS packages, this paper presents a novel semantic-based search approach that relies on the semantic-level ROS Package Knowledge Graph (RPKG) to automatically retrieve the most suitable ROS packages. Firstly, to construct the RPKG, we employ multi-dimensional feature extraction techniques to extract semantic concepts, including code file name, category, hardware device, characteristics, and function, from the dataset of ROS package text descriptions. The semantic features extracted from this process result in a substantial number of entities (32,294) and relationships (54,698). Subsequently, we create a robot domain-specific small corpus and further fine-tune a pre-trained language model, BERT-ROS, to generate embeddings that effectively represent the semantics of the extracted features. These embeddings play a crucial role in facilitating semantic-level understanding and comparisons during the ROS package search process within the RPKG. Secondly, we introduce a novel semantic matching-based search algorithm that incorporates the weighted similarities of multiple features from user search queries, which searches out more accurate

关键词： Robots

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