检索结果-内蒙古大学图书馆

arXiv 2023年

作者： Ge, Jidong Liu, Yuxiang Gui, Jie Fang, Lanting Lin, Ming Kwok, James Tin-Yau Huang, LiGuo Luo, Bin State Key Laboratory for Novel Software Technology Software Institute Nanjing University Nanjing210093 China The School of Cyber Science and Engineering Southeast University Purple Mountain Laboratories Nanjing210000 China *** L.L.C. United States Alibaba Group China The Department of Computer Science and Engineering The Hong Kong University of Science and Technology 999077 Hong Kong The Department of Computer Science Southern Methodist University DallasTX75205 United States

Self-supervised learning enables networks to learn discriminative features from massive data itself. Most state-of-the-art methods maximize the similarity between two augmentations of one image based on contrastive learning. By utilizing the consistency of two augmentations, the burden of manual annotations can be freed. Contrastive learning exploits instance-level information to learn robust features. However, the learned information is probably confined to different views of the same instance. In this paper, we attempt to leverage the similarity between two distinct images to boost representation in self-supervised learning. In contrast to instance-level information, the similarity between two distinct images may provide more useful information. Besides, we analyze the relation between similarity loss and feature-level cross-entropy loss. These two losses are essential for most deep learning methods. However, the relation between these two losses is not clear. Similarity loss helps obtain instance-level representation, while feature-level cross-entropy loss helps mine the similarity between two distinct images. We provide theoretical analyses and experiments to show that a suitable combination of these two losses can get state-of-the-art results. Code is available at https://***/guijiejie/ICCL. Copyright © 2023, The Authors. All rights reserved.

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

Optimal Resource Utilization in Hyperledger Fabric: A Comprehensive SPN-Based Performance Evaluation Paradigm

Optimal Resource Utilization in Hyperledger Fabric: A Compre...

引用

IEEE Symposium on Network Operations and Management

作者： Carlos Melo Glauber Gonçalves Francisco A. Silva Leonel Feitosa Iure Fé André Soares Eunmi Choi Tuan Anh Nguyen Dugki Min Applied Research on Distributed Systems Lab (PASID) Federal University of Piauí (UFPI) Brazil School of Software College of Computer Science Kookmin University Seoul South Korea Konkuk Aerospace Design-Airworlhiness Research Inslilule (KADA) Konkuk Universily Seoul Soulh Korea Department of Computer Science and Engineering Konkuk University Seoul South Korea

ISBN: (数字)9798350327939

ISBN: (纸本)9798350327946

Hyperledger Fabric stands as a leading framework for permissioned block-chain systems, ensuring data security and audit-ability for enterprise applications. As applications on this platform grow, understanding its complex configuration concerning various block-chain parameters becomes vital. These configurations significantly affect the system’s performance and cost. In this research, we introduce a Stochastic Petri Net (SPN) model to analyze Hyper-ledger Fabric’s performance, considering variations in block-chain parameters, computational resources, and transaction rates. We provide case studies to validate the utility of our model, aiding block-chain administrators in determining optimal configurations for their applications. A key observation from our model highlights the block size’s role in system response time. We noted an increased mean response time, between 1 to 25 seconds, due to variations in transaction arrival rates.

关键词： Performance evaluation Distributed ledger Computational modeling Data security Petri nets Stochastic processes Fabrics

来源：评论

学校读者我要写书评

暂无评论

software development processes in ocean system modeling

arXiv

引用

arXiv 2021年

作者： Jung, Reiner Gundlach, Sven Hasselbring, Wilhelm Software Engineering Group Department of Computer Science Kiel University Kiel Germany

Scientific modeling provides mathematical abstractions of real-world systems and builds software as implementations of these mathematical abstractions. Ocean science is a multidisciplinary discipline developing scientific models and simulations as ocean system models that are an essential research asset. In software engineering and information systems research, modeling is also an essential activity. In particular, business process modeling for business process management and systems engineering is the activity of representing processes of an enterprise, so that the current process may be analyzed, improved, and automated. In this paper, we employ process modeling for analyzing scientific software development in ocean science to advance the state in engineering of ocean system models and to better understand how ocean system models are developed and maintained in ocean science. We interviewed domain experts in semi-structured interviews, analyzed the results via thematic analysis, and modeled the results via the business process modeling notation BPMN. The processes modeled as a result describe an aspired state of software development in the domain, which are often not (yet) implemented. This enables existing processes in simulation-based system engineering to be improved with the help of these process models. Copyright © 2021, The Authors. All rights reserved.

关键词： Process engineering

来源：评论

学校读者我要写书评

暂无评论

A Framework for Autonomous Vehicle Testing Using Semantic Models

A Framework for Autonomous Vehicle Testing Using Semantic Mo...

引用

IEEE International Conference on Artificial Intelligence Testing (AITest)

作者： Yejun He Muslim Razi Jerry Gao Chuanqi Tao Ministry Key Laboratory for Safety-Critical Software Development and Verification Nanjing University of Aeronautics and Astronautics Nanjing Jiangsu China Computer Engineering Department and Applied Data Science Department San Jose State University San Jose USA

With the rapid development of big data and artificial intelligence, autonomous vehicles (AV) have achieved great success in diverse application domains. Although the current development of autonomous driving technology has been greatly improved, the accident rate of driverless vehicles still rises. Therefore, how to ensure the quality of AVs using different testing technologies has become an important issue. Traditional testing approaches such as decision tables and state charts struggle with diversified test inputs. Hence, there is a strong demand for new test models for AV testing. In this paper, we present a model-based testing framework that utilizes semantic trees and 3-dimensional (3D) test tables to model driving scenarios into individual test cases. Using the proposed framework, we generate fine-grained examples corresponding to the real world. Also, we evaluate the behavior of the Apollo AV system in 30 scenarios in SVL simulator. The results show that the scenarios generated by our framework can cover most conditions occurring, thus addressing the challenge of testing AVs for safety and reliability.

关键词：

来源：评论

学校读者我要写书评

暂无评论

UX Design and Evaluation: Holism versus Reductionism Approaches - Which is Better?

UX Design and Evaluation: Holism versus Reductionism Approac...

引用

Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Emmanuel O.C. Mkpojiogu Monday Jubrin Abdullahi Obianuju E. Okeke-Uzodike Universiti Utara Malaysia 06010 UUM Sintok Malaysia Department of Software Engineering Veritas University Abuja Nigeria Department of Computer Science Airforce Institute of Technology Kaduna Kaduna State Nigeria West Africa Department of Applied Management Durban University of Technology Pietermaritzburg KwaZulu-Natal South Africa

A literature content analysis strategy was used to synthesize extant literature sources to comprehend and capture the approaches to the design, evaluation and modeling of user experience. From the analysis, two main methods were identified for the UX research. These paradigms are holism and reductionism. The holist approach stresses on sense making and interpretative approaches to user experience, leading to construction and reconstruction of users’ quality perceptions about their experiences with technological artifacts. This paradigm is design based. Conversely, the reductionism approach is concerned with the deconstruction of users’ experiences. This approach conceives experiences as a model that can be broken down into aspects and dimensions that facilitates understanding of user experience. The debate among researchers on these differing viewpoints conceived and drove this study. The findings of the research show that the two paradigms are insightful, rich and unique and can be independently used in UX research, nevertheless, as both approaches a limited and address the peculiar concerns of the two respective paradigms, this paper advocates and endorses the use of a mixed paradigm especially in longitudinal studies to amalgamate the rich components of both varying approaches, thus implying the interdependent use of the two paradigms in UX research.

关键词：

来源：评论

学校读者我要写书评

暂无评论

PC-based Intelligent Traffic Monitoring System with Real-time Analysis for Smart Cities 14

PC-based Intelligent Traffic Monitoring System with Real-tim...

引用

14th International Conference on COMmunication Systems and NETworkS, COMSNETS 2022

作者： Lau, Chi-Yat Yuen, Man-Ching Yueng, Ka-Ho Fan, Cheuk-Pan Ko, On-Yi Ngan, Lit-Wang Tam, Wing-Chun Yeung, Wai-Nam Vocational Training Council Department of Information Technology Hong Kong Hong Kong Hong Kong Shue Yan University IFREE GROUP Innovation and Research Centre Department of Applied Data Science Hong Kong Hong Kong Limited Hong Kong Hong Kong City University of Hong Kong Department of Computer Science Hong Kong Hong Kong Chinese University of Hong Kong Department of Statistics Hong Kong Hong Kong National Taipei University of Technology Department of Computer Science and Information Engineering Taiwan Taiwan University of the West of England Bristol Department of Computer Science and Creative Technologies Bristol United Kingdom

ISBN: (纸本)9781665421041

In recent years, Hong Kong has been experiencing severe traffic problems and especially traffic congestion. In this paper, we proposed a traffic monitoring system which is PC-based, no infrastructure requirement but suitable for the complex road networks in Hong Kong. Besides, our system is designed to recognize vehicle type and plate number for vehicles commonly used in Hong Kong. By analyzing the data collected on road usage, we present our findings for decision-making regarding traffic policies. © 2022 IEEE.

关键词： Smart city

来源：评论

学校读者我要写书评

暂无评论

Classification Models and Variable Selection for SME Credit Risk Assessment in Balance the Data Set 10

Classification Models and Variable Selection for SME Credit ...

引用

10th International Conference on Digital Arts, Media and Technology and 8th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2025

作者： Intharasompong, Sornthun Jitpattanakul, Anuchit Mekruksavanich, Sakorn Wongwiwat, Piyada Phaphan, Wikanda King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Applied Statistics Bangkok10800 Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Mathematics Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Thailand University of Phayao School of Information and Communication Department of Computer Engineering 56000 Thailand Suansunandha Rajabhat University Faculty of Science and Technology Bangkok10800 Thailand Research Group in Statistical Learning and Inference Thailand

ISBN: (纸本)9798331543273

Small and medium-sized enterprises (SMEs) are frequently considered high credit risk, making it difficult to obtain loans from banks. A viable substitute for assessing borrower creditworthiness more impartially is using machine learning-based credit risk assessment (CRA) algorithms. This study evaluated the performance of five machine learning techniques: decision tree, support vector machine, gradient boosting, K-nearest neighbor, and Naïve Bayes. We evaluated each model with K-fold cross-validation and utilized the Stepwise selection method for the variable selection. To address the underlying data imbalance, we balanced the dataset with the SMOTE technique, which showed that normal loans were more widespread than Non-Performing Loans (NPLs). After balancing the data, the Gradient Boosting model displayed the best accuracy, F-measure, and robustness without variable selection, making it the most effective model for predicting SME loans. © 2025 IEEE.

关键词： Risk assessment

来源：评论

学校读者我要写书评

暂无评论

An explainable deep learning approach for stock market trend prediction

引用

Heliyon 2024年第21期10卷 e40095页

作者： Muhammad, Dost Ahmed, Iftikhar Naveed, Khwaja Bendechache, Malika CRT-AI and ADAPT Research Centres School of Computer Science University of Galway Ireland Department of Software Engineering University of Europe for Applied Sciences Potsdam Germany Swedish Center for Digital Innovation University of Gothenburg Sweden ADAPT Research Centre School of Computer Science University of Galway Ireland

Given the intricate nature of stock forecasting as well as the inherent risks and uncertainties, analysis of market trends is necessary to capitalize on optimal investment opportunities for profit maximization and timely disinvestment for loss minimization. In this work, we propose a deep learning model for predicting five distinct stock market trends: upward, downward, double top, rounded bottom, and rounded top. The proposed model surpasses common benchmarks, including support vector machine, random forest, and logistic regression, achieving an average accuracy of 94.9%, compared to 85.7% for random forest, 60.07% for support vector machine, and 52.45% for logistic regression. Furthermore the proposed model excels in F1-score, with a 94.85% performance, compared to 77.95% for random forest, 21.02% for support vector machine and 46.23% for logistic regression, across four real world diverse datasets. Additionally, we employ explainable AI (XAI) techniques, SHAP and LIME, to enhance interpretability, enabling stakeholders to understand the key factors driving predictions. The SHAP analysis reveals the top 10 most important/influential features, enabling feature reduction while maintaining performance. Interestingly, while accuracy slightly decreases with top 10 features, precision, recall, and F1-score improve, suggesting a trade-off between comprehensiveness and performance. These results demonstrate the potential for practical application in financial decision-making, providing a balance between interpretability and predictive power that can support investors in risk management and strategic planning. © 2024 The Author(s)

关键词： Deep neural network Explainable AI Machine learning Stock market trend prediction Time series prediction

来源：评论

学校读者我要写书评

暂无评论

Classifying Psychiatric Patients Using Machine Learning

Classifying Psychiatric Patients Using Machine Learning

引用

Information Technology (ACIT)

作者： Hmmam M. Alrjoob Raed Alazaidah Radwan Batyha Hayel Khafajeh Esraa Abu Elsoud Ala'a Saeb Al-Sherideh Ali Elrashidi Muhyeeddin Alqaraleh Waleed Amer Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Department of Data Science and AI Faculty of Information Technology Zarqa University Zarqa Jordan Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan Department Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Department of Cyper Security Faculty of Information Technology Zarqa University Zarqa Jordan Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia Department of Software Engineering Faculty of Information Technology Zarqa University Zarqa Jordan Department of Cyber Security Faculty on Information Technology Zarqa University Zarqa Jordan

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Psychological disorders are considered chronic illnesses that affect a wide range of populations. Some studies in the United States indicate that one in every eight individuals is affected by a psychological disorder. Additionally, individuals with a psychological disorder often have an impact on the nature of the lives of those around them. It is beneficial to detect the illness in its early stages to increase the likelihood of recovery. In this research, thirteen classification algorithms are used. Each algorithm has an interesting approach. It can be used to analyze several datasets where three datasets specific to Psychiatric patients were selected. The datasets have been chosen from UCI Machine Learning Repository. The datasets were re-analyzed after only 50% of the characteristics were approved after being evaluated by the several feature selection techniques. The main objective of this research is to find the best classifier and feature selection strategy in analyzing datasets of Psychiatric patients. After conducting numerous comparisons, it became evident that, the best classifier for the Autism-Adult-Data dataset are Bagging and Random Forest. As for the best learning strategy, it was found to be Functions and Trees, the best feature selection was obtained using Symmetrical Uncourt Attribute Evaluation. Both IBK and KStar classifier proved to be the best classifier for the Parkinson's dataset, Lazy demonstrated to be the most effective learning strategy, Correlation Attribute Evaluation technique yielded the best feature selection. Logistic emerged as the best classifier for planning-relax dataset, the best learning strategy was Bayes, the best feature selection was found to be Symmetrical Uncert Feature Selection.

关键词： Machine learning algorithms Correlation Psychology Feature extraction Classification algorithms Bayes methods Information technology Random forests Bagging Logistics

来源：评论

学校读者我要写书评

暂无评论

Framework of software design patterns for energy-aware embedded systems 15

Framework of software design patterns for energy-aware embed...

引用

15th International Conference on Evaluation of Novel Approaches to software engineering, ENASE 2020

作者： Schaarschmidt, Marco Uelschen, Michael Pulvermuller, Elke Westerkamp, Clemens Faculty of Engineering and Computer Science University of Applied Sciences Osnabrück Germany Software Engineering Research Group University of Osnabrück Germany

ISBN: (纸本)9789897584213

With the increasing size and complexity of embedded systems, the impact of software on energy consumption is becoming more important. Previous research focused mainly on energy optimization at the hardware level. However, little research has been carried out regarding energy optimization at the software design level. This paper focuses on the software design level and addresses the gap between software and hardware design for embedded systems. This is achieved by proposing a framework for software design patterns, which takes aspects of power consumption and time behavior of the hardware level into account. We evaluate the expressiveness of the framework by applying it to well-known and novel design patterns. Furthermore, we introduce a dimensionless numerical efficiency factor to make possible energy savings quantifiable. © Copyright 2020 by SCITEPRESS - science and Technology Publications, Lda. All rights reserved.

关键词： Embedded systems

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：