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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Zmuda, Michael A. Miami University Dept. of Computer Science and Software Engineering OxfordOH United States

ISBN: (纸本)9798350351507

This innovative practice full paper describes a new software framework based on JU nit to test student work. Automated testing is an important capability when teaching software development at the college level. Ideally, a testing system will allow the instructor to efficiently create a thorough set of tests. Also, the software should facilitate grading tasks and produce informative reports that can be distributed to the students in a timely fashion. For Java development, the well-known JUnit framework enables a test suite to be applied to a student's submission. The mutools library presented here extends the JUnit framework in novel ways to accelerate the instructor's task of creating test suites. This new framework allows the instructor to augment tests with directives to control scoring and reporting. The four main capabilities of the software include: 1) An assert statement that does not terminate the test when it has failed. Instead, statistics are maintained regarding the success or failure of each assert statement. 2) Tests that can be configured to award partial credit. This can be useful in situations where the instructor deems it appropriate to award students some credit even in the presence of incorrect asserts. 3) Tests that can be grouped into categories that match a particular rubric item. Java annotations are placed on the test suite to define these categories. For example, @TestCategory(name= 'remove', points=10.0) specifies that 10 points will be awarded for successful implementation of all tests related to removing an item from the collection class. 4) Testing reports that contain varying levels of information. With minimal details, the testing report shows each testing category with the following information: assert statistics, whether the test timed out or had abnormal termination. This software has been used for many semesters and has been found to increase the speed at which the instructor can develop test suites for grading. The framework is av

关键词： software testing

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Fake News Classification: Past, Current, and Future

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computers, Materials & Continua 2023年第11期77卷 2225-2249页

作者： Muhammad Usman Ghani Khan Abid Mehmood Mourad Elhadef Shehzad Ashraf Chaudhry Department of Computer Science University of Engineering and TechnologyLahore54890Pakistan Department of Computer Science&Information Technology Abu Dhabi UniversityAbu Dhabi59911United Arab Emirates Department of Software Engineering Faculty of Engineering and ArchitectureNisantasi UniversityIstanbulTurkey

The proliferation of deluding data such as fake news and phony audits on news web journals,online publications,and internet business apps has been aided by the availability of the web,cell phones,and social *** can quickly fabricate comments and news on social *** most difficult challenge is determining which news is real or ***,tracking down programmed techniques to recognize fake news online is *** an emphasis on false news,this study presents the evolution of artificial intelligence techniques for detecting spurious social media *** study shows past,current,and possible methods that can be used in the future for fake news *** different publicly available datasets containing political news are utilized for performing *** supervised learning algorithms are used,and their results show that conventional Machine Learning(ML)algorithms that were used in the past perform better on shorter text *** contrast,the currently used Recurrent Neural Network(RNN)and transformer-based algorithms perform better on longer ***,a brief comparison of all these techniques is provided,and it concluded that transformers have the potential to revolutionize Natural Language Processing(NLP)methods in the near future.

关键词： Supervised learning algorithms fake news classification online disinformation transformers recurrent neural network(RNN)disinformation transformers recurrent neural network(RNN)

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Part-based Person Re-Identification with Swin Transformer 6

Part-based Person Re-Identification with Swin Transformer

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6th IEEE International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Qin, Yusong Huang, Pu Zhou, Yao Huang, Zengxi Xihua University School of Computer Science and Software Engineering Chengdu China Xihua University School of Computer and Software Engineering and the Sichuan Xihua Jiaotong Forensics Center Chengdu China

ISBN: (纸本)9798350325485

Person re-identification(Re-ID) is a crucial task in computer vision, which aims to match pedestrian images captured in non-overlapping camera views. It has significant implications for public safety applications. However, current methods primarily rely on global features and thus are vulnerable to background clutters and occlusion. To address these issues, this paper proposes a part-based Re-ID method using the Swin Transformer. In this method, we first utilize human pose estimation method to detect the body keypoints of a pedestrian. We then extract local windows and patches centered at these keypoints, so as to minimize the involvement of background and occlusion information in the down-stream re-identification. These body parts in pose guided windows are fed to the Swin Transformer network. The hierarchical structure of Swin Transformer also enables the proposed method more robust to multi-scale problems. Our experimental results on both holistic and occluded datasets, i.e., Market-1501, DukeMTMC-reID, and Occluded-Duke, demonstrate that the proposed method is on par with the state-of-The-Art methods. © 2023 IEEE.

关键词： Clutter (information theory)

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State-of-the-Art in Cyber-Physical Security for Dynamic Wireless Charging of Electric Vehicles 50

State-of-the-Art in Cyber-Physical Security for Dynamic Wire...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Yazdanipour, Samaneh Oruganti, V.S.R. Varaprasad Son, Jeonggi Arani, Mohammadreza F.M. Williamson, Sheldon S. Toronto Metropolitan University Department of Electrical Computer and Biomedical Engineering Toronto Canada Ontario Tech University Department of Electrical Computer and Software Engineering Oshawa Canada

ISBN: (纸本)9781665464543

Dynamic Wireless Charging systems for electric vehicles offers a revolutionary approach to EV charging, enabling vehicles to charge while in motion and overcoming the limitations of stationary wireless charging. Despite their efficiency and convenience, dynamic wireless charging systems introduce complex security vulnerabilities that must be addressed to ensure system reliability and safety. This paper provides a comprehensive review of the cyber-physical security challenges associated with electric vehicle dynamic wireless charging systems, examining potential threats such as cyber attacks and physical tampering. It identifies specific vulnerabilities in communication protocols, hardware, and software, and discusses various mitigation strategies such as encryption and physical layer security enhancements. Furthermore, the paper highlights recent advancements in security technologies and outlines future research directions to enhance the resilience of dynamic wireless charging systems. By addressing these security challenges, stakeholders can foster a safer environment for the deployment of dynamic wireless charging, thereby promoting the broader adoption of electric vehicles and advancing sustainable transportation solutions. © 2024 IEEE.

关键词： State of charge

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Defocus blur detection using novel local directional mean patterns(LDMP)and segmentation via KNN matting

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Frontiers of computer Science 2022年第2期16卷 110-122页

作者： Awais KHAN Aun IRTAZA Ali JAVED Tahira NAZIR Hafiz MALIK Khalid Mahmood MALIK Muhammad Ammar KHAN Department of Computer Science University of Engineering and TechnologyTaxila 47050Pakistan Department of Electrical and Computer Engineering University of MichiganDearbornMI 48128USA Department of Software Engineering University of Engineering and TechnologyTaxila 47050Pakistan Department of Computer Science and Engineering Oakland UniversityMI 48309USA

Detection and segmentation of defocus blur is a challenging task in digital imaging applications as the blurry images comprise of blur and sharp regions that wrap significant information and require effective methods for information *** defocus blur detection and segmentation methods have several limitations i.e.,discriminating sharp smooth and blurred smooth regions,low recognition rate in noisy images,and high computational cost without having any prior knowledge of images i.e.,blur degree and camera ***,there exists a dire need to develop an effective method for defocus blur detection,and segmentation robust to the above-mentioned *** paper presents a novel features descriptor local directional mean patterns(LDMP)for defocus blur detection and employ KNN matting over the detected LDMP-Trimap for the robust segmentation of sharp and blur *** argue/hypothesize that most of the image fields located in blurry regions have significantly less specific local patterns than those in the sharp regions,therefore,proposed LDMP features descriptor should reliably detect the defocus blurred *** fusion of LDMP features with KNN matting provides superior performance in terms of obtaining high-quality segmented regions in the ***,the proposed LDMP features descriptor is robust to noise and successfully detects defocus blur in high-dense noisy *** results on Shi and Zhao datasets demonstrate the effectiveness of the proposed method in terms of defocus blur *** and comparative analysis signify that our method achieves superior segmentation performance and low computational cost of 15 seconds.

关键词： defocus blur detection local directional mean patterns image matting sharpness metrics blur segmentation

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A decision model for decentralized autonomous organization platform selection: Three industry case studies

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Blockchain(Research and Applications) 2023年第2期4卷 82-100页

作者： E.Baninemeh S.Farshidi S.Jansen Department of Information and Computer Science Utrecht UniversityUtrechtthe Netherlands Department of Software Engineering Lappeenranta University of TechnologyLappeenrantaFinland

Context:Decentralized autonomous organizations are a new form of smart contract-based *** autonomous organization platforms,which support the creation of such organizations,are becoming increasingly popular,such as Aragon and *** the best fitting platform is challenging for organizations,as a significant number of decision criteria,such as popularity,developer availability,governance issues and consistent documentation of such platforms,should be ***,decision-makers at the organizations are not experts in every domain,so they must continuously acquire volatile knowledge regarding such ***:Supporting decision-makers in selecting the right decentralized autonomous organization platforms by designing an effective decision model is the main objective of this *** aim to provide more insight into their selection process and reduce time and effort significantly by designing a decision ***:This study presents a decision model for the decentralized autonomous organization platform selection *** decision model captures knowledge regarding such platforms and concepts *** model is based on an existing theoretical framework that assists software engineers with a set of multi-criteria decision-making problems in software ***:We conducted three industry case studies in the context of three decentralized autonomous organizations to evaluate the effectiveness and efficiency of the decision model in assisting *** case study participants declared that the decision model provides significantly more insight into their selection process and reduces time and ***:We observe in the empirical evidence from the case studies that decision-makers can make more rational,efficient,and effective decisions with the decision ***,the reusable form of the captured knowledge regarding decentralized autonomous organization platforms can be

关键词： Decentralized autonomous organization Decision model Multi-criteria decision making Decision support system Decentralized autonomous organization platform Case study research

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Segmentation of Tissue Regions in Whole Slide Images Using Hand-Crafted Image Features 38

Segmentation of Tissue Regions in Whole Slide Images Using H...

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38th International Conference on Image and Vision Computing New Zealand, IVCNZ 2023

作者： Clark, Samuel Green, Richard University of Canterbury Computer Science and Software Engineering Christchurch New Zealand

ISBN: (纸本)9798350370515

This paper proposes a method to address the need for accurate and explainable tissue segmentation in whole slide images (WSIs) for computational pathology. The research focuses on developing a machine learning algorithm using hand-crafted image features and a random forest classifier to segment tissue regions in WSIs. Three research questions were formulated and investigated. (RQ1) Can hand-crafted image features be used as the primary features for an explainable ML algorithm to accurately segment tissue regions in WSIsƒ (RQ2) What image features are dominant for classifying whether WSI tiles are within a tissue regionƒ (RQ3) What post-processing techniques are required to improve the accuracy of the tissue segmentation algorithmƒ The proposed algorithm achieved an average accuracy of 98.05%. The results revealed the significant influence of specific image features, such as saturation channel mean and standard deviation, and grey level co-occurrence matrix measures, in accurately classifying tissue tiles within WSIs. More-over, incorporating morphological operations and thresholding as post-processing techniques improved the accuracy of tissue segmentation. The achieved accuracy of 98.05% outperformed existing solutions and demonstrated the effectiveness of the proposed algorithm in reliably segmenting tissue regions from the background of WSIs. This method presents a valuable tissue segmentation pre-processing step that can be used to support future research related to cancer region segmentation. © 2023 IEEE.

关键词： Tissue

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A Ternary-attention Capsule Feature Pyramid Network for Building Footprint Extraction from Remote Sensing Imagery

A Ternary-attention Capsule Feature Pyramid Network for Buil...

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2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023

作者： Zhang, Haiyan Li, Yazhou Wang, Meifeng Huaiyin Institute of Technology Faculty of Computer and Software Engineering Huaian China

ISBN: (纸本)9798350306293

The up-to-date and accurate building footprint database plays a significant role in a large variety of applications. Recently, remote sensing images have provided an important data source for building footprint extraction tasks. However, due to topology variations, color diversities, and complicated rooftop and environmental scenarios, it is still a challenging task to realize fully automated and highly accurate extraction of building footprints from remote sensing images. In this paper, we propose a novel ternary-attention capsule feature pyramid network (TA-CapsFPN), which is formulated with a capsule feature pyramid network architecture and integrated with context-augmentation and feature attention modules, aiming at improving the building footprint extraction accuracy by combining the superior properties of capsule representations and the powerful capability of attention mechanisms. Quantitative evaluations and comparative analyses show that the TA-CapsFPN provides a promising and competitive performance in processing buildings of varying conditions. © 2023 IEEE.

关键词： Extraction

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A Student engineering Competence Level Prediction Method Based on RNN-GRU and Dynamic Bayesian Network

A Student Engineering Competence Level Prediction Method Bas...

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2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023

作者： Xu, Xiang Li, Guanxiao Xiao, Shaozhang Zhang, Zheyi Huaiyin Institute of Technology School of Computer and Software Engineering Huai'an China

ISBN: (纸本)9798350306293

To achieve effective feedback and proficiency tracking of students in contemporary educational settings, we propose a methodology that integrates the RNN-GRU model and dynamic Bayesian networks for predicting students' engineering competence level based on their online learning behaviors. The approach employed utilizes the RNN-GRU model to analyze students' progression in algorithm development skills and algorithm implementation ability, resulting in the generation of a historical record of their learning features. These features are then employed to train a dynamic Bayesian network model, facilitating the prediction of students' engineering competence level. After the amount of data reaches a certain amount, it can compare the previous models such as RNN and LSTM to obtain higher accuracy. © 2023 IEEE.

关键词： Students

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PersianRAG: A Retrieval-Augmented Generation System For Persian Language 15

PersianRAG: A Retrieval-Augmented Generation System For Pers...

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15th International Conference on Information and Knowledge Technology, IKT 2024

作者： Hosseini, Hossein Zare, Mohammad Sobhan Mohammadi, Amir Hossein Kazemi, Arefeh Zojaji, Zahra Nematbakhsh, Mohammad Ali Department of Software Engineering University of Isfahan Isfahan Iran ADAPT Research Centre Dublin City University Dublin Ireland Department of Software Engineering Faculty of Computer Engineering University of Isfahan Isfahan Iran

ISBN: (纸本)9798331522254

Retrieval augmented generation (RAG) models, which integrate large-scale pre-trained generative models with external retrieval mechanisms, have shown significant success in various natural language processing (NLP) tasks. However, applying RAG models in Persian language as a low-resource language, poses distinct challenges. These challenges primarily involve the preprocessing, embedding, retrieval, prompt construction, language modeling, and response evaluation of the system. In this paper, we address the challenges towards implementing a real-world RAG system for Persian language called PersianRAG. We propose novel solutions to overcome these obstacles and evaluate our approach using several Persian benchmark datasets. Our experimental results demonstrate the capability of the PersianRAG framework to enhance question answering task in Persian. © 2024 IEEE.

关键词： Modeling languages

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