检索结果-内蒙古大学图书馆

International Conference on Computing, Engineering, and Design (ICCED)

作者： Arief Agus Sukmandhani Yulyani Arifin Muhammad Zarlis Widodo Budiharto Computer Science Department Binus Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Management Department BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Text summarization is a natural language processing (NLP) technique in artificial intelligence that has been studied in recent years. Every document containing text is tested to get a good summary result. In producing a good summary, proper accuracy is required; therefore, various techniques and methods are used, and another challenge is the use of language in the documents being tested. Various types of documents have been used as research for text summarization, including scientific documents, so researchers are interested in studying them more intensely to get the state of the art in research. This research was conducted to review previous research using literature studies from various publications that published research on text summarization in scientific documents. The researcher collected data from several publishers who had published this type of research from 1988 to 2023 quarter 1 and obtained three hundred and eleven articles, then the researcher analyzed the data using the Kitchenham method to get the right literature to answer the research question. Thirty-seven articles were selected for further in-depth analysis. In addition, the researcher found some interesting findings beyond the research question, including other techniques used to solve the text summarization problem and other types of output that are not commonly produced so that it can increase knowledge in researching text summarization.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Mixed Attention Densely Residual Network for Single Image Super-Resolution

引用

computer Systems science & Engineering 2021年第10期39卷 133-146页

作者： Jingjun Zhou Jing Liu Jingbing Li Mengxing Huang Jieren Cheng Yen-Wei Chen Yingying Xu Saqib Ali Nawaz School of Information and Communication Engineering Hainan UniversityHaikou570228China State Key Laboratory of Marine Resource Utilization in the South China Sea Hainan UniversityHaikou570228China Research Center for Healthcare Data Science Zhejiang LabHangzhou311121China School of Computer Science and Cyberspace Security Hainan UniversityHaikou570228China Graduate School of Information Science and Engineering Ritsumeikan University5258577Japan College of Computer Science and Technology Zhejiang UniversityHangzhou311100China

Recent applications of convolutional neural networks(CNNs)in single image super-resolution(SISR)have achieved unprecedented ***,existing CNN-based SISR network structure design consider mostly only channel or spatial information,and cannot make full use of both channel and spatial information to improve SISR performance *** present work addresses this problem by proposing a mixed attention densely residual network architecture that can make full and simultaneous use of both channel and spatial ***,we propose a residual in dense network structure composed of dense connections between multiple dense residual groups to form a very deep *** structure allows each dense residual group to apply a local residual skip connection and enables the cascading of multiple residual blocks to reuse previous features.A mixed attention module is inserted into each dense residual group,to enable the algorithm to fuse channel attention with laplacian spatial attention effectively,and thereby more adaptively focus on valuable feature *** qualitative and quantitative results of extensive experiments have demonstrate that the proposed method has a comparable performance with other stateof-the-art methods.

关键词： Channel attention Laplacian spatial attention residual in dense mixed attention

来源：评论

学校读者我要写书评

暂无评论

Visual Evaluative AI: A Hypothesis-Driven Tool with Concept-Based Explanations and Weight of Evidence

arXiv

引用

arXiv 2024年

作者： Le, Thao Miller, Tim Zhang, Ruihan Sonenberg, Liz Singh, Ronal School of Computing and Information Systems The University of Melbourne Australia School of Electrical Engineering and Computer Science The University of Queensland Australia CSIRO/Data61 Australia

This paper presents Visual Evaluative AI, a decision aid that provides positive and negative evidence from image data for a given hypothesis. This tool finds high-level human concepts in an image and generates the Weight of Evidence (WoE) for each hypothesis in the decision-making process. We apply and evaluate this tool in the skin cancer domain by building a web-based application that allows users to upload a dermatoscopic image, select a hypothesis and analyse their decisions by evaluating the provided evidence. Further, we demonstrate the effectiveness of Visual Evaluative AI on different concept-based explanation approaches. Copyright © 2024, The Authors. All rights reserved.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

Organizational Intent, Organizational Structures, and Reviewer Mental Models Influence Mortality Review Processes

引用

Mayo Clinic Proceedings: Innovations, Quality and Outcomes 2023年第6期7卷 515-523页

作者： Khayal, Inas S. Butcher, Rebecca L. McLeish, Colin H. Shentu, Yujia Barnato, Amber E. Dartmouth Institute for Health Policy and Clinical Practice Geisel School of Medicine at Dartmouth Lebanon NH United States Department of Biomedical Data Science Geisel School of Medicine at Dartmouth Lebanon NH United States Center for Program Design and Evaluation Geisel School of Medicine at Dartmouth Lebanon NH United States Department of Computer Science Dartmouth College Hanover NH United States Section of Palliative Care Department of Medicine Dartmouth Hitchcock Medical Center Lebanon NH United States Geisel School of Medicine at Dartmouth Hanover NH United States Tuck School of Business Hanover NH United States Department of Neurosurgery The University of Texas McGovern Medical School Houston

Objective: To identify the factors that influence the mortality review process at health systems, including how mortality review is conducted, cases are adjudicated, and results are used. Methods: We conducted a qualitative analysis of the mortality review processes of 6 US health systems from February 1, 2021 to June 31, 2021. The data sources included individual and small-group semi-structured interviews with mortality review team members and a content analysis of site artifacts (eg, guiding principles, chart abstraction forms, review workflows, and clinical pathways developed from past mortality reviews). We analyzed each site's mortality review process, goals and incentives for mortality review, historical and evolving aspects of mortality review, personnel involved, and post-review use of findings. Results: Across the 6 systems, we interviewed a total of 24 mortality review experts and analyzed 26 site documents. We identified 3 thematic factors that influence mortality review processes: organizational intent, organizational structures for mortality review, and the mental models of individuals involved in the review process. Two subthemes emerged within organizational intent: (1) identifying preventable deaths to lower (clinical or financial) risk and (2) using death cases to guide system improvement. Sites varied in governance and decision rights concerning mortality review and adjudication, with 2 subthemes within organizational structures: (1) centralized-hierarchical and (2) decentralized or multidisciplinary. The analysis of mental models of participating reviewers revealed 2 themes: (1) confirmation of preventability and (2) identification of patterns or “signals.” Conclusion: Understanding the factors that influence mortality review allows health systems to better leverage mortality review for institutional improvement and to develop training that builds shared mental models to enhance the review process. © 2023 The Authors

关键词： AHRQ Agency for Healthcare Research and Quality COREQ COnsolidated criteria for REporting Qualitative research MR mortality review QI quality improvement

来源：评论

学校读者我要写书评

暂无评论

Decision Systems in Cybersecurity: A Machine Learning Perspective 12th

Decision Systems in Cybersecurity: A Machine Learning Perspe...

引用

12th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Dhablia, Anishkumar Ganesh, R. Senthil Rizvi, Nuzhat Arora, Himanshu Borade, Monali Ravindra Bhujbal, Pallavi Altimetrik India Pvt. Ltd Maharashtra Pune India Department of Electronics and Communication Engineering Sri Krishna College of Engineering and Technology Tamilnadu Coimbatore India Nagpur Campus Pune India Computer Science and Engineering Arya College of Engineering Jaipur India Artificial Intelligence and Data Science VIIT Pune India Department of Information Technology School of Computing MIT ADT University Loni Kalbhor Pune India

ISBN: (纸本)9789819601462

The evolving digital environment requires sophisticated decision systems to protect important information assets from the growing complexity and variety of cyber threats. This review paper examines the impact of machine learning (ML) on cybersecurity decision systems, with a specific focus on analyzing ML and deep learning (DL) methods. The study employs the UNSW-NB15 dataset, which is a widely used benchmark dataset in the field of network security. The paper starts with an overview of the present cybersecurity environment, emphasizing the difficulties presented by advanced and changing cyber threats. It highlights the importance of adaptive and intelligent decision systems that can efficiently identify and reduce cyberattacks as they occur. A significant part of the paper focuses on a thorough analysis of different machine learning and deep learning techniques used in cybersecurity. The review discusses conventional machine learning algorithms like decision trees, support vector machines, and ensemble methods, along with sophisticated deep learning architectures such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Each approach is thoroughly assessed for its strengths and limitations based on factors like accuracy, interpretability, and scalability. The performance of various ML and DL models is evaluated using the UNSW-NB15 dataset as a benchmark. The dataset covers various cyberattack scenarios, enabling a thorough assessment of algorithms’ ability to detect anomalies and classify malicious activities. The paper explores the incorporation of machine learning (ML) and deep learning (DL) into decision support systems, highlighting the significance of explainability and interpretability in the realm of cybersecurity. The conversation covers the difficulties of implementing machine learning (ML) and deep learning (DL) models in practical situations, such as concerns about data privacy, adversarial attacks, and model resilience. This rev

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Mixture Gaussian Distribution-Based Collaborative Reinforcement Learning for 3D UAV Localization Optimization Against Jamming Attacks

Mixture Gaussian Distribution-Based Collaborative Reinforcem...

引用

2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zhu, Yujiao Chen, Mingzhe Wang, Sihua Liu, Yuchen Li, Gaolei Yin, Changchuan Quek, Tony Q.S. Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Network Beijing100876 China Singapore University of Technology and Design 487372 Singapore University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL33146 United States North Carolina State University Department of Computer Science RaleighNC27695 United States Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai200240 China

ISBN: (纸本)9798350351255

In this paper, the optimization of unmanned aerial vehicle (UAV) localization under jamming attacks is studied. In the considered network, a base station (BS) collaborates with an active UAV to localize a target UAV. During this positioning process, a jamming UAV transmits discontinuous signals to passive UAVs to interfere the distance information measurement. To localize the target UAV under jamming attacks, the BS jointly use two localization methods: 1) generative adversarial network (GAN)-based positioning method and 2) time difference of arrival (TDOA)-based positioning method. Since GAN-based positioning method cannot defense in a strong jamming signal while TDOA-based positioning method may consume more energy and sacrifice localization accuracy, the BS must select an appropriate positioning method (GAN-based or TDOA-based methods) and four distance measurement information of passive UAVs to estimate the position of the target UAV. This problem is formulated as an optimization problem whose goal is to minimize the positioning error between the estimated and the ground truth positions of the target UAV while considering jamming attacks and the trajectory of passive UAVs. To solve this problem, we propose a mixture Gaussian distribution model-based collaborative reinforcement learning (RL) method which enables the active UAV to determine its transmit power and trajectory, and enables the BS to select the most appropriate subsets of distance measurement information and the optimal positioning method according to the movement of passive UAVs and the unknown jamming attack pattern of the jamming UAV. Simulation results show the proposed method can reduce the positioning error of the target UAV by up to 36.5% compared to the method that does not consider the GAN-based positioning method. © 2024 IEEE.

关键词： Gaussian distribution

来源：评论

学校读者我要写书评

暂无评论

Towards a SSIM based Imperceptible Adversarial Attack Approach for Object Detection 4

Towards a SSIM based Imperceptible Adversarial Attack Approa...

引用

4th International Conference on Artificial Intelligence and computer Engineering, ICAICE 2023

作者： Ma, Zhicheng Zhao, Jinxiong Zhao, Hong Yin, Bangguo Yu, Jun Geng, Jiangyi Digital and Communication Technology Research Institute State Grid Gansu Electric Power Company Electric Power Science Research Institute Lanzhou China School of Computer Science Wuhan University Wuhan China Co. Ltd Baoding China Hubei Intelligent Linked Open Data Technology Co. Ltd Wuhan China

ISBN: (纸本)9798400708831

To address the problem of poor imperceptibility in the current adversarial attack methods, a Structure Similarity Index Metric (SSIM) based imperceptibility oriented adversarial attack method for object detection is proposed. This method is an iterative white box adversarial attack process based on sensitivity analysis. In each iteration, the vanishing loss and the structural similarity value between the adversarial sample and the original image are calculated and gradient backpropagation is performed. The gradient direction contains components that are beneficial for improving image structure similarity index. The experimental results show that under the condition of white box attack Faster RCNN, compared to RPAttack, aDPAttack, gDPAttack and PGD, the reduction rates of this method in LPAD, TV distance and Wasserstein distance exceed 29%, 1.6% and 27.2% respectively. © 2023 ACM.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Visual-Semantic Graph Matching Net for Zero-Shot Learning

arXiv

引用

arXiv 2024年

作者： Duan, Bowen Chen, Shiming Guo, Yufei Xie, Guo-Sen Ding, Weiping Wang, Yisong The School of Computer Science and Technology Guizhou University Guizhou550025 China The Institute for Artificial Intelligence Guizhou University Guiyang China The Mohamed bin Zayed University of AI Abu Dhabi19282 United Arab Emirates The Intelligent Science & Technology Academy of CASIC Beijing China The School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China The School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China The Faculty of Data Science City University of Macau 999078 China

Zero-shot learning (ZSL) aims to leverage additional semantic information to recognize unseen classes. To transfer knowledge from seen to unseen classes, most ZSL methods often learn a shared embedding space by simply aligning visual embeddings with semantic prototypes. However, methods trained under this paradigm often struggle to learn robust embedding space because they align the two modalities in an isolated manner among classes, which ignore the crucial class relationship during the alignment process. To address the aforementioned challenges, this paper proposes a Visual-Semantic Graph Matching Net, termed as VSGMN, which leverages semantic relationships among classes to aid in visual-semantic embedding. VSGMN employs a Graph Build Network (GBN) and a Graph Matching Network (GMN) to achieve two-stage visual-semantic alignment. Specifically, GBN first utilizes an embedding-based approach to build visual and semantic graphs in the semantic space and align the embedding with its prototype for first-stage alignment. Additionally, to supplement unseen class relations in these graphs, GBN also build the unseen class nodes based on semantic relationships. In the second stage, GMN continuously integrates neighbor and cross-graph information into the constructed graph nodes, and aligns the node relationships between the two graphs under the class relationship constraint. Extensive experiments on three benchmark datasets demonstrate that VSGMN achieves superior performance in both conventional and generalized ZSL scenarios. The implementation of our VSGMN and experimental results are available at github: https://***/dbwfd/VSGMN. Copyright © 2024, The Authors. All rights reserved.

关键词： Zero-shot learning

来源：评论

学校读者我要写书评

暂无评论

Searching with Extended Guard and Pivot Loop

Searching with Extended Guard and Pivot Loop

引用

2021 Prague Stringology Conference, PSC 2021

作者： Pakalén, Waltteri Tarhio, Jorma Watson, Bruce W. Department of Computer Science Aalto University Finland Information Science Centre for AI Research School for Data-Science & Computational Thinking Stellenbosch University South Africa

ISBN: (纸本)9788001068694

We explore practical optimizations on comparison-based exact string matching algorithms. We present a guard test that compares q-grams between the pattern and the text before entering the match loop, and evaluate experimentally the benefit of optimization of this kind. As a result, the Brute Force algorithm gained most from the guard test, and it became faster than many other algorithms for short patterns. In addition, we present variations of a recent algorithm that uses a special skip loop where a pivot, a selected position of the pattern, is tested at each alignment of the pattern and in case of failure;the pattern is shifted based on the last character of the alignment. The variations include alternatives for the pivot and the shift function. We show the competitiveness of the new algorithm variations by practical experiments. © Czech Technical University in Prague, Czech Republic.

关键词： String searching algorithms

来源：评论

学校读者我要写书评

暂无评论

A Deep Graph Convolutional Network based Web API Classification Approach for Mashup Creation

A Deep Graph Convolutional Network based Web API Classificat...

引用

IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Lianyong Qi Boyuan Yan Jiaqi Zheng Xiaolong Xu Wanchun Dou Xiaokang Zhou Jie Zhang School of Computer Science Qufu Normal University China College of Computer Science and Technology China University of Petroleum (East China) China Department of Computer Science and Technology Nanjing University China School of Computer and Software Nanjing University of Information Science and Technology China Faculty of Data Science Shiga University Japan RIKEN Center for Advanced Intelligence Project Japan

Software developers create mashups of their interest by searching, selecting, and combining web Application programming Interfaces (APIs) with diverse functionalities. However, achieving the selection of APIs relies on a crucial prerequisite: accurate classification of APIs. However, the traditional web API classification methods are often based on a single piece of API function's information such as manual annotations and description documents which decreases the accuracy of API classification results significantly, especially for the web API sharing communities with sparse data (e.g., ***). To tackle this issue, we propose BDGCN which improves some existing web API classification approaches by adding isomorphic graph of strong correlation among different web APIs on the basis of API description documents. Concretely, at the first, we integrate the information of multiple dimensions of web APIs as embedded inputs in which we then build an API-API strongly correlated isomorphic network to strengthen the association among API individuals, and to initialize the embedding vector from the description of each API by tuning the pre-trained Bidi-rectional Encoder Representation from Transformers (BERT) model. Besides, in order to smooth the impact of high-order features between neighboring nodes, a deep graph convolutional network is introduced in this paper that is consistent with the problem. In fact, we set up a categorization layer and utilize cross-entropy function to achieve accurate classification of APIs. Experimental results demonstrate the superiority of our method compared to the existing methods.

关键词：

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：