检索结果-内蒙古大学图书馆

arXiv 2023年

作者： Jha, Debesh Sharma, Vanshali Banik, Debapriya Bhattacharya, Debayan Roy, Kaushiki Hicks, Steven A. Tomar, Nikhil Kumar Thambawita, Vajira Krenzer, Adrian Ji, Ge-Peng Poudel, Sahadev Batchkala, George Alam, Saruar Ahmed, Awadelrahman M.A. Trinh, Quoc-Huy Khan, Zeshan Nguyen, Tien-Phat Shrestha, Shruti Nathan, Sabari Gwak, Jeonghwan Jha, Ritika K. Zhang, Zheyuan Schlaefer, Alexander Bhattacharjee, Debotosh Bhuyan, M.K. Das, Pradip K. Fan, Deng-Ping Parasa, Sravanthi Ali, Sharib Riegler, Michael A. Halvorsen, Pål de Lange, Thomas Bagci, Ulas Machine & Hybrid Intelligence Lab Department of Radiology Northwestern University Chicago United States SimulaMet Oslo Norway Oslo Metropolitan University Oslo Norway Jadavpur University Kolkata India Indian Institute of Technology Guwahati India Institute of Medical Technology and Intelligent Systems Technische Universität Hamburg Germany University of Oslo Norway Julius-Maximilian University of Würzburg Germany Faculty of Information Technology University of Science VNU-HCM Viet Nam Department of IT Convergence Engineering Gachon University Seongnam13120 Korea Republic of School of Computing University of Leeds LeedsLS2 9JT United Kingdom College of Engineering Australian National University Canberra Australia Department of Engineering Science University of Oxford Oxford United Kingdom National University of Computer and Emerging Sciences Karachi Campus Pakistan Swedish Medical Center Seattle United States Kathmandu Nepal Couger Inc Tokyo Japan Department of Software Korea National University of Transportation Chungju Korea Republic of University of Bergen Bergen Norway Department of Medicine and Emergencies Mölndal Sahlgrenska University Hospital Region Västra Götaland Sweden Department of Molecular and Clinical Medicin Sahlgrenska Academy University of Gothenburg Sweden ETH Zurich Zurich Switzerland

Automatic analysis of colonoscopy images has been an active field of research motivated by the importance of early detection of precancerous polyps. However, detecting polyps during the live examination can be challenging due to various factors such as variation of skills and experience among the endoscopists, lack of attentiveness, and fatigue leading to a high polyp miss-rate. Therefore, there is a need for an automated system that can flag missed polyps during the examination and improve patient care. Deep learning has emerged as a promising solution to this challenge as it can assist endoscopists in detecting and classifying overlooked polyps and abnormalities in real time, improving the accuracy of diagnosis and enhancing treatment. In addition to the algorithm's accuracy, transparency and interpretability are crucial to explaining the whys and hows of the algorithm's prediction. Further, conclusions based on incorrect decisions may be fatal, especially in medicine. Despite these pitfalls, most algorithms are developed in private data, closed source, or proprietary software, and methods lack reproducibility. Therefore, to promote the development of efficient and transparent methods, we have organized the "Medico automatic polyp segmentation (Medico 2020)" and "MedAI: Transparency in Medical Image Segmentation (MedAI 2021)" competitions. The Medico 2020 challenge received submissions from 17 teams, while the MedAI 2021 challenge also gathered submissions from another 17 distinct teams in the following year. We present a comprehensive summary and analyze each contribution, highlight the strength of the best-performing methods, and discuss the possibility of clinical translations of such methods into the clinic. Our analysis revealed that the participants improved dice coefficient metrics from 0.8607 in 2020 to 0.8993 in 2021 despite adding diverse and challenging frames (containing irregular, smaller, sessile, or flat polyps), which are frequently missed during a

关键词： Computer aided diagnosis

来源：评论

学校读者我要写书评

暂无评论

Sharing of Vulnerability Information Among Companies – A Survey of Swedish Companies

Sharing of Vulnerability Information Among Companies – A Su...

引用

Euromicro Conference on software engineering and Advanced Applications (SEAA)

作者： Thomas Olsson Martin Hell Martin Höst Ulrik Franke Markus Borg Software and Systems Engineering Lab RISE Research Institutes of Sweden Lund Sweden Dept. of Electrical and Information Technology Lund University Lund Sweden Lund University

software products are rarely developed from scratch and vulnerabilities in such products might reside in parts that are either open source software or provided by another organization. Hence, the total cybersecurity of a product often depends on cooperation, explicit or implicit, between several organizations. We study the attitudes and practices of companies in software ecosystems towards sharing vulnerability information. Furthermore, we compare these practices to contemporary cybersecurity recommendations. This is performed through a questionnaire-based qualitative survey. The questionnaire is divided into two parts: the providers' perspective and the acquirers' perspective. The results show that companies are willing to share information with each other regarding vulnerabilities. Sharing is not considered to be harmful neither to the cybersecurity nor their business, even though a majority of the respondents consider vulnerability information sensitive. However, the companies, despite being open to sharing, are less inclined to proactively sharing vulnerability information. Furthermore, the providers do not perceive that there is a large interest in vulnerability information from their customers. Hence, the companies' overall attitude to sharing vulnerability information is passive but open. In contrast, contemporary cybersecurity guidelines recommend active disclosure and sharing among actors in an ecosystem.

关键词： Companies software Computer security Ecosystems

来源：评论

学校读者我要写书评

暂无评论

Distributed circle formation control over directed networks with communication constraints ⁎

引用

IFAC-PapersOnLine 2019年第3期52卷 108-113页

作者： Peng Xu Jiayan Wen Chen Wang Guangming Xie College of Electrical and Information Engineering Guangxi University of Science and Technology Liuzhou 545006 China National Engineering Research Center for Software Engineering Peking University Beijing 100871 China The State Key Laboratory of Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing100871 China

This paper investigates distributed circle formation problems of multi-agent systems (MAS) subject to limited information communication under a class of weight-unbalanced directed graphs, in which the communication topology contains a directed spanning tree and each agent can only perceive the distances from itself to the nearest neighbor in counterclockwise direction as well as the counterpart in the clockwise direction. Towards this end, a novel algorithm combined with an encoder-decoder framework has been proposed. We show that, under the proposed policy, the resulting network executions can drive the states of all mobile agents to converge to some expected equilibrium point. Numerical simulation results have been given to demonstrate the effectiveness of the proposed algorithm.

关键词： Multi-agent systems Communication Bandwidth Directed Network Circle Formation Control

来源：评论

学校读者我要写书评

暂无评论

Explaining predictions of deep neural classifier via activation analysis

arXiv

引用

arXiv 2020年

作者： Stano, Martin Benesova, Wanda Marták, Lukáš Samuel Department of Informatics Informational Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology Bratislava842 16 Slovakia Department of Computer Engineering and Applied Informatics Faculty of Informatics and Information Technologies Slovak University of Technology Bratislava842 16 Slovakia Institute of Computational Perception Johannes Kepler University Linz4040 Austria LIT Artificial Intelligence Lab Johannes Kepler University Linz4040 Austria

In many practical applications, deep neural networks have been typically deployed to operate as a black box predictors. Despite high amount of work on interpretability and high demand on reliability of these systems, they typically still have to include a human actor in the loop, to validate the decisions and handle unpredictable failures and unexpected corner cases. This is true in particular for failure-critical application domains, such as medical diagnosis. We present a novel approach to explain and support an interpretation of a decision-making process to a human expert operating a deep learning system based on Convolutional Neural Network (CNN). By modeling activation statistics on selected layers of a trained CNN via Gaussian Mixture Models (GMM), we develop a novel perceptual code in binary vector space that describes how input sample is processed by the CNN. By measuring distances between pairs of samples in this perceptual encoding space, for any new input sample, we can now retrieve a set of most perceptually similar and dissimilar samples from an existing atlas of labeled samples, to support and clarify the decision made by the CNN model. Possible uses of this approach include for example Computer-Aided Diagnosis (CAD) systems working with medical imaging data, such as Magnetic Resonance Imaging (MRI) or Computed Tomography (CT) scans. We demonstrate the viability of our method in the domain of medical imaging for patient condition diagnosis, as proposed decision explanation method via similar ground truth domain examples (e.g. from existing diagnosis archives) will be interpretable by the operating medical personnel. Our results indicate that our method is capable of detecting distinct prediction strategies which enables to identify most similar predictions from an Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Using Frugal User Feedback with Closeness Analysis on Code to Improve IR-Based Traceability Recovery

Using Frugal User Feedback with Closeness Analysis on Code t...

引用

IEEE/ACM International Conference on Program Comprehension (ICPC)

作者： Hongyu Kuang Hui Gao Hao Hu Xiaoxing Ma Jian Lü Patrick Mäder Alexander Egyed State Key Lab for Novel Software Technology Nanjing University Nanjing China Fakultät für Informatik und Automatisierung Technische Universität Ilmenau Ilmenau Germany Institute for Software Systems Engineering Johannes Kepler University Linz Austria

Traceability recovery allows developers to extract and comprehend the trace links among software artifacts (e.g., requirements and code). These trace links can provide important support to software maintenance and evolution tasks. Information Retrieval (IR) is now widely accepted as the key technique of semi-automatic tools to recover candidate trace links based on textual similarities among artifacts. However, the vocabulary mismatch problem between different artifacts hinders the performance of these IR-based approaches. Thus, a growing body of enhancing strategies were proposed based on user feedback. They allow to adjust the textual similarities of candidate links after users accept or reject part of these links. Recently, several approaches successfully used this strategy to improve the performance of IR-based traceability recovery. However, these approaches require a large amount of user feedback, which is infeasible in practice. In this paper, we propose to improve IR-based traceability recovery by introducing only a small amount of user feedback into the closeness analysis on call and data dependencies in code. Specifically, our approach iteratively asks users to verify a chosen candidate link based on the quantified functional similarity for each code dependency (called closeness) and the generated IR values. The verified link is then used as the input to re-rank the unverified candidate links. An empirical evaluation based on five real-world systems shows that our approach can outperform four baseline approaches by using only a small amount of user feedback.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Deep learning driven interpretable and informed decision making model for brain tumour prediction using explainable AI

引用

Scientific reports 2025年第1期15卷 19223页

作者： Khan Muhammad Adnan Taher M Ghazal Muhammad Saleem Muhammad Sajid Farooq Chan Yeob Yeun Munir Ahmad Sang-Woong Lee Pattern Recognition and Machine Learning Lab Faculty of Artificial Intelligence and Software Gachon University Seongnam-si 13557 Republic of Korea. Department of Networks and Cybersecurity Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19111 Jordan. Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab 140401 India. Department of Cyber Security NASTP Institute of Information Technology Lahore 58810 Pakistan. Center for Secure Cyber-Physical Systems (C2PS) Computer Science Department Khalifa University Abu Dhabi United Arab Emirates. chan.yeun@ku.ac.ae. University College Korea University Seoul 02841 Republic of Korea. Pattern Recognition and Machine Learning Lab Faculty of Artificial Intelligence and Software Gachon University Seongnam-si 13557 Republic of Korea. slee@gachon.ac.kr.

Brain Tumours are highly complex, particularly when it comes to their initial and accurate diagnosis, as this determines patient prognosis. Conventional methods rely on MRI and CT scans and employ generic machine learning techniques, which are heavily dependent on feature extraction and require human intervention. These methods may fail in complex cases and do not produce human-interpretable results, making it difficult for clinicians to trust the model's predictions. Such limitations prolong the diagnostic process and can negatively impact the quality of treatment. The advent of deep learning has made it a powerful tool for complex image analysis tasks, such as detecting brain Tumours, by learning advanced patterns from images. However, deep learning models are often considered "black box" systems, where the reasoning behind predictions remains unclear. To address this issue, the present study applies Explainable AI (XAI) alongside deep learning for accurate and interpretable brain Tumour prediction. XAI enhances model interpretability by identifying key features such as Tumour size, location, and texture, which are crucial for clinicians. This helps build their confidence in the model and enables them to make better-informed decisions. In this research, a deep learning model integrated with XAI is proposed to develop an interpretable framework for brain Tumour prediction. The model is trained on an extensive dataset comprising imaging and clinical data and demonstrates high AUC while leveraging XAI for model explainability and feature selection. The study findings indicate that this approach improves predictive performance, achieving an accuracy of 92.98% and a miss rate of 7.02%. Additionally, interpretability tools such as LIME and Grad-CAM provide clinicians with a clearer understanding of the decision-making process, supporting diagnosis and treatment. This model represents a significant advancement in brain Tumour prediction, with the potential to enhance pat

关键词： Brain tumour CT scans MRI XAI

来源：评论

学校读者我要写书评

暂无评论

Hyperspectral Image Classification-Traditional to Deep Models: A Survey for Future Prospects

arXiv

引用

arXiv 2021年

作者： Ahmad, Muhammad Shabbir, Sidrah Roy, Swalpa Kumar Hong, Danfeng Wu, Xin Yao, Jing Khan, Adil Mehmood Mazzara, Manuel Distefano, Salvatore Chanussot, Jocelyn Department of Computer Science National University of Computer and Emerging Sciences Islamabad Chiniot-Faisalabad Campus Chiniot35400 Pakistan Dipartimento di Matematica e Informatica-MIFT University of Messina Messina98121 Italy Pakistan The Department of Computer Science and Engineering Jalpaiguri Government Engineering College West Bengal 735102 India The Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China The School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Fractional Signals and Systems Beijing100081 China The Institute of Data Science and Artificial Intelligence Innopolis University Innopolis 420500 Russia Institute of Software Development and Engineering Innopolis University Innopolis420500 Russia The Univ. Grenoble Alpes CNRS Grenoble INP GIPSA-Lab Grenoble38000 France

Hyperspectral Imaging (HSI) has been extensively utilized in many real-life applications because it benefits from the detailed spectral information contained in each pixel. Notably, the complex characteristics i.e., the nonlinear relation among the captured spectral information and the corresponding object of HSI data make accurate classification challenging for traditional methods. In the last few years, Deep Learning (DL) has been substantiated as a powerful feature extractor that effectively addresses the nonlinear problems that appeared in a number of computer vision tasks. This prompts the deployment of DL for HSI classification (HSIC) which revealed good performance. This survey enlists a systematic overview of DL for HSIC and compared state-of-the-art strategies of the said topic. Primarily, we will encapsulate the main challenges of traditional machine learning for HSIC and then we will acquaint the superiority of DL to address these problems. This survey breakdown the state-of-the-art DL frameworks into spectral-features, spatial-features, and together spatial-spectral features to systematically analyze the achievements (future research directions as well) of these frameworks for HSIC. Moreover, we will consider the fact that DL requires a large number of labeled training examples whereas acquiring such a number for HSIC is challenging in terms of time and cost. Therefore, this survey discusses some strategies to improve the generalization performance of DL strategies which can provide some future guidelines. © 2021, CC BY.

关键词： Surveys

来源：评论

学校读者我要写书评

暂无评论

software architecture for sensor nodes in the internet of things: A case of study in agriculture 5

Software architecture for sensor nodes in the internet of th...

引用

5th International Conference on Internet of Things: systems, Management and Security, IoTSMS 2018

作者： Mendoza, Jose Fernando Ordonez, Hugo Figueroa, Cristhian Segovia, Rocio Munoz, Diego Ordonez, Armando Research Lab in Software Development Engineering Universidad San Buenaventura Cali Colombia Department of Telematics Engineering Universidad Del Cauca Popayan Colombia Intelligent Management Systems Fundacion Universitaria de Popayan Popayan Colombia

ISBN: (纸本)9781538695852

This paper presents a software architecture for microcontrollers running on data capture cards for the Internet of Things (IoT). The software Architecture describes the structure, operation and interaction of the different components from a global perspective. This paper describes various components and subcomponents of the architecture and its communication. A case study is developed based on the proposed architecture for a cilantro crop in Colombia. © 2018 IEEE.

关键词： Microcontrollers

来源：评论

学校读者我要写书评

暂无评论

HeROS: A simulation platform for heterogeneous robotic swarms

HeROS: A simulation platform for heterogeneous robotic swarm...

引用

第三十八届中国控制会议

作者： Shuai Li Yuanfang Wan Pengzhou He Chen Wang Jinan Sun Yanling Zhang Xuyang Li Guangming Xie The State Key Laboratory of Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University School of Automation and Electrical Engineering University of Science and Technology Beijing National Engineering Research Center for Software Engineering Peking University Xi'an Bibang Intelligent Technology Co. Ltd.

This paper presents HeROS, which is a novel simulation platform for heterogeneous robotic swarms. Compared with some existing simulation platforms, our HeROS(Heterogeneous Robotic Swarms Simulation) platform is more efficient due to its special designed communication methods and framework. Besides, it's convenient for use since we provide many kinds of control functions with free API's. It also supports many external libraries, which can be used to develop different simulation tasks. Using our developed platform, a mission of target search and tracking is realized as a case study, where a UAV-UGV heterogeneous system consisting of a UAV and two UGVs is required to search for a ground target and then move close to *** implementation of the task successfully shows the effectiveness and performance of the HeROS platform.

关键词： Heterogeneous robotic swarms simulation platform UAV UGV cooperation

来源：评论

学校读者我要写书评

暂无评论

A split-and-recombine approach for follow-up query analysis

arXiv

引用

arXiv 2019年

作者： Liu, Qian Chen, Bei Liu, Haoyan Lou, Jian-Guang Fang, Lei Zhou, Bin Zhang, Dongmei State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China State Key Lab of Software Development Environment Beihang University China Microsoft Research Beijing China

Context-dependent semantic parsing has proven to be an important yet challenging task. To leverage the advances in context-independent semantic parsing, we propose to perform follow-up query analysis, aiming to restate context-dependent natural language queries with contextual information. To accomplish the task, we propose STAR, a novel approach with a well-designed two-phase process. It is parser-independent and able to handle multifarious follow-up scenarios in different domains. Experiments on the FollowUp dataset show that STAR outperforms the state-of-the-art baseline by a large margin of nearly 8%. The superiority on parsing results verifies the feasibility of follow-up query analysis. We also explore the extensibility of STAR on the SQA dataset, which is very promising. Copyright © 2019, The Authors. All rights reserved.

关键词： Semantics

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：