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arXiv 2021年

作者： Liu, Jiakun Huang, Qiao Xia, Xin Shihab, Emad Lo, David Li, Shanping College of Computer Science and Technology Zhejiang University China PengCheng Laboratory China College of Computer Science and Technology Zhejiang University China Faculty of Information Technology Monash University Melbourne Australia Department of Computer Science and Software Engineering Concordia University Canada School of Information Systems Singapore Management University Singapore

To complete tasks faster, developers often have to sacrifice the quality of the software. Such compromised practice results in the increasing burden to developers in future development. The metaphor, technical debt, describes such practice. Prior research has illustrated the negative impact of technical debt, and many researchers investigated how developers deal with a certain type of technical debt. However, few studies focused on the removal of different types of technical debt in practice. To fill this gap, we use the introduction and removal of different types of self-admitted technical debt (i.e., SATD) in 7 deep learning frameworks as an example. This is because deep learning frameworks are some of the most important software systems today due to their prevalent use in life-impacting deep learning applications. Moreover, the field of the development of different deep learning frameworks is the same, which enables us to find common behaviors on the removal of different types of technical debt across projects. By mining the file history of these frameworks, we find that design debt is introduced the most along the development process. As for the removal of technical debt, we find that requirement debt is removed the most, and design debt is removed the fastest. Most of test debt, design debt, and requirement debt are removed by the developers who introduced them. Based on the introduction and removal of different types of technical debt, we discuss the evolution of the frequencies of different types of technical debt to depict the unresolved sub-optimal trade-offs or decisions that are confronted by developers along the development process. We also discuss the removal patterns of different types of technical debt, highlight future research directions, and provide recommendations for practitioners. © 2021, CC BY.

关键词： Economic and social effects

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A Review on Type of Attacks on Fingerprint Image and Watermarking Techniques

A Review on Type of Attacks on Fingerprint Image and Waterma...

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International Conference on Green engineering Technology and Applied Computing 2019, IConGETech2 019 and International Conference on Applied Computing 2019, ICAC 2019

作者： Lebcir, Mohamed Awang, Suryanti Faculty of Computer Systems and Software Engineering University Malaysia Pahang UMP Lebuhraya Tun Razak Kuantan Pahang 26300 Malaysia

Fingerprint image watermarking techniques have several advantages over traditional security systems that based on token or knowledge. However, they are also vulnerable to attacks that can decrease their security. In this regard, this paper presents a study of a number of research studies on the attacks of fingerprint images watermarking techniques. The attacks can be categorized into intentional and unintentional. We study several attacks based on that category and several fingerprint image watermarking methods. We produce a comprehensive comparison of those methods to show how the methods able to prevent the attacks. We also discuss the advantages and disadvantages of the methods. Therefore, based on that, we can see what are the common attacks on the fingerprint image and what are the methods that efficient in preventing the attacks. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Image watermarking

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Multi-dimensional K-Means Algorithm for Student Clustering 2nd

Multi-dimensional K-Means Algorithm for Student Clustering

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2nd International Conference on Advanced Data and Information engineering, DaEng 2015

作者： Mohd, Wan Maseri Wan Beg, A.H. Herawan, Tutut Noraziah, Ahmad Chiroma, Haruna Faculty of Computer Systems & Software Engineering University Malaysia Pahang Lebuhraya Tun Razak Gambang KuantanPahang26300 Malaysia

ISBN: (纸本)9789811317972

K-Means is one of the popular methods for generating clusters. It is very well-known and commonly used for its convenience and fastness. The main disadvantage of these criteria is that user should specify the number of cluster in enhance. As a repetitive clustering strategy, a K-Means criterion is very delicate to the preliminary beginning circumstances. In this paper, has been proposed a clustering strategy known as Multi-dimensional K-Means clustering criteria. This algorithm auto generates preliminary k (the preferred variety of cluster) without asking input from the user. It also used a novel strategy of establishing the preliminary centroids. The experiment of the proposed strategy has been conducted using synthetic data, which is taken form LIyod’s K-means experiments. The algorithm is suited for higher education for calculating the student’s CGPA and extracurricular activities with graphs. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Data mining

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Corrections to “An Ensemble Hybrid Framework: A Comparative Analysis of Metaheuristic Algorithms for Ensemble Hybrid CNN Features for Plants Disease Classification”

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IEEE Access 2025年 13卷 31659-31659页

作者： Khaoula Taji Ali Sohail Tariq Shahzad Bilal Shoaib Khan Muhammad Adnan Khan Khmaies Ouahada Electronic Systems Information Processing Mechanics and Energy Laboratory Kenitra Faculty of Sciences Ibn Tofail University Kenitra Morocco Department of Computer Science Minhaj University Lahore Lahore Pakistan Department of Electrical and Electronic Engineering Science University of Johannesburg Johannesburg South Africa Department of Computer Science Green International University Lahore Pakistan Department of Software Faculty of Artificial Intelligence and Software Gachon University Seongnam-si Republic of Korea

Presents corrections to the paper, Corrections to “An Ensemble Hybrid Framework: A Comparative Analysis of Metaheuristic Algorithms for Ensemble Hybrid CNN Features for Plants Disease Classification”.

关键词： Plant diseases Metaheuristics

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Reasoning like human: Hierarchical reinforcement learning for knowledge graph reasoning 29

Reasoning like human: Hierarchical reinforcement learning fo...

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Wan, Guojia Pan, Shirui Gong, Chen Zhou, Chuan Haffari, Gholamreza School of Computer Science Institute of Artificial Intelligence National Engineering Research Center for Multimedia Software Wuhan University China Faculty of Information Technology Monash University Australia School of Computer Science and Engineering Nanjing University of Science and Technology China Department of Computing Hong Kong Polytechnic University Hong Kong Academy of Mathematics and Systems Science Chinese Academy of Sciences China

ISBN: (纸本)9780999241165

Knowledge Graphs typically suffer from incompleteness. A popular approach to knowledge graph completion is to infer missing knowledge by multi-hop reasoning over the information found along other paths connecting a pair of entities. However, multi-hop reasoning is still challenging because the reasoning process usually experiences multiple semantic issue that a relation or an entity has multiple meanings. In order to deal with the situation, we propose a novel Hierarchical Reinforcement Learning framework to learn chains of reasoning from a Knowledge Graph automatically. Our framework is inspired by the hierarchical structure through which a human being handles cognitionally ambiguous cases. The whole reasoning process is decomposed into a hierarchy of two-level Reinforcement Learning policies for encoding historical information and learning structured action space. As a consequence, it is more feasible and natural for dealing with the multiple semantic issue. Experimental results show that our proposed model achieves substantial improvements in ambiguous relation tasks. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Knowledge graph

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A Brief Review of Explainable Artificial Intelligence in Healthcare

SSRN

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SSRN 2023年

作者： Sadeghi, Zahra Alizadehsani, Roohallah Cifci, Mehmet Akif Kausar, Samina Rehman, Rizwan Mahanta, Priyakshi Bora, Pranjal Kumar Almasri, Ammar Alkhawaldeh, Rami S. Hussain, Sadiq Alatas, Bilal Shoeibi, Afshin Moosaei, Hossein Hladík, Milan Nahavandi, Saeid Pardalo, Panos M. Institute for Big Data Analytics Faculty of Computer Science Dalhousie University Canada Deakin University Geelong Australia The Institute of Computer Technology Tu Wien University Vienna1040 Austria University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Centre for Computer Science and Applications Dibrugarh University Assam India Department of Management Information Sys Al-Balqa Applied University Salt19117 Jordan Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Examination Branch Dibrugarh University Assam Dibrugarh India Department of Software Eng. Firat University Elazig23100 Turkey Data Science and Computational Intelligence Institute University of Granada Spain Department of Informatics Faculty of Science Jan Evangelista Purkyně University in Ústí nad Labem Czech Republic Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Swinburne University of Technology HawthornVIC3122 Australia Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville32611 United States

XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such as medical domain have increased the demand for transparency and explainability since wrong predictions may have severe consequences. Model explainability and interpretability are vital successful deployment of AI models in healthcare practices. AI applications' underlying reasoning needs to be transparent to clinicians in order to gain their trust. This paper presents a systematic review of XAI aspects and challenges in the healthcare domain. The primary goals of this study are to review various XAI methods, their challenges, and related machine learning models in healthcare. The methods are discussed under six categories: Features-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric methods. Most importantly, the paper explores XAI role in healthcare problems to clarify its necessity in safety-critical applications. The paper intends to establish a comprehensive understanding of XAI-related applications in the healthcare field by reviewing the related experimental results. To facilitate future research for filling research gaps, the importance of XAI models from different viewpoints and their limitations are investigated. © 2023, The Authors. All rights reserved.

关键词： Decision making

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Mobile-based Intelligent Skin Diseases Diagnosis System 1

Mobile-based Intelligent Skin Diseases Diagnosis System

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1st International Conference of Intelligent Computing and engineering, ICOICE 2019

作者： Sallam, Amer Ba Alawi, Abdulfattah E. Al-Saced Faculty of Engineering IT Taiz University Computer Networks and Distributed Systems Dept Taiz Yemen Al-Saecd Faculty of Engineering IT Taiz University Software Engineering Dept Taiz Yemen

ISBN: (纸本)9781728144870

Skin diseases are the most common diseases in humans. The inherent variability in the appearance of skin diseases makes it hard even for medical experts to detect disease type from dermoscopic images. Recent advances in image processing using the Convolution Neural Networks have led to better results in diagnosing systems. We aim to develop an advanced diagnostic system in a manner that meets the requirements of real-Time and extensibility of medical services for skin disease detection. The proposed system provides offline diagnosis for the users who have not Internet connection or online diagnosing uses on-cloud service. The user captures the affected area and get the offline immediate report. The schema offers the users a communication window with dermatologists to get medical recommendations in addition to an online accurate diagnosis service. The new images that are labeled by dermatologists are used to retrain the model to enhance model accuracy. To maximize the number of users, the system is implemented in a mobile-based environment. With the growing numbers of portable apps, it becomes easy for people to obtain up-To-date data. Users are familiar with looking for answers from the virtual globe including health issues. The following experimental results demonstrate the feasibility of the proposed method. The average obtained accuracy is 83% in testing cases. © 2019 IEEE.

关键词： Diagnosis

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Risk Assessment for Power Circuit Breaker by Using Failure Modes, Effects and Criticality Analysis

Risk Assessment for Power Circuit Breaker by Using Failure M...

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2020 International Conference on Power, Energy and Innovations, ICPEI 2020

作者： Promseela, Lineman Suwanasri, Cattareeya Saribut, Surapol Suwanasri, Thanapong Phadungthin, Rattanakorn King Mongkut's University of Technology North Bangkok Electrical and Computer Engineering Faculty of Engineering Bangkok Thailand King Mongkut's University of Technology North Bangkok Electrical and Software Systems Engineering The Sirindhorn International Thai-German School of Engineering Bangkok Thailand King Mongkut's University of Technology North Bangkok Electronics Engineering Technology College of Industrial Technology Bangkok Thailand

ISBN: (纸本)9781728172408

This paper explains risk assessment for circuit breaker utilization by using failure modes, effects, and criticality analysis (FMECA). The circuit breaker components are divided into three main parts such as live part, operating mechanism and insulation, and control device. FMECA is analyzed to 4 groups such as finance, safety, environment and efficiency. Finally, criticality is categorized into 4 levels such as low level, medium level, high level and highest risk level. Data collection and analysis were performed. The results of the criticality analysis will assist in making maintenance resource management decisions in order to avoid damage to the system with efficient usage of available or limited resources. © 2020 IEEE.

关键词： Failure modes

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Multi-Resolution Localization of Individual Logs in Wooden Piles Utilizing YOLO with Tiling on Client/Server Architectures 33

Multi-Resolution Localization of Individual Logs in Wooden P...

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33rd European Modeling and Simulation Symposium, EMSS 2021

作者： Praschl, Christoph Auersperg-Castell, Philipp Forster-Heinlein, Brigitte Zwettler, Gerald Adam Research Group Advanced Information Systems and Technology Research and Development Department University of Applied Sciences Upper Austria Softwarepark 11 Hagenberg4232 Austria Department of Software Engineering School of Informatics Communications and Media University of Applied Sciences Upper Austria Softwarepark 11 Hagenberg4232 Austria Bluedynamics Auersperg-Castell KG Kritzing 31 Freinberg4785 Austria Faculty of Computer Science and Mathematics University of Passau Innstraße 43 Passau94032 Germany

In industrial domains with time and cost intensive manual or semi-automated inspection the demand for automation is high. Utilizing state of the art deep learning models for localization in vision-based domains such as wood log analysis, the precision can be increased while reducing the demand for manual inspection. In this paper a YOLO network is trained on wood log images to allow for detection of single wood piles in images with hundreds and thousands instances. Due to the high variability in scale and large amount of wood logs within the images, common YOLO architectures are not applicable. Thus, tiling is necessitated to implicitly form a multi-resolution image pyramid. Due to lack in training data, besides common data augmentation modelling of different seasonal and weather conditions is applied. The wood log detection process can be run on a client/server architecture to allow for both, preview and refined results. Evaluation on real-world data sets shows an log detection accuracy of 82,9% utilizing a tiny YOLO model and 94,1% with a fully connected YOLO model, respectively. © 2021 The Authors.

关键词： Deep learning

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The personification of the user’s interface: classification vs. Clusterization of users of online courses 12

The personification of the user’s interface: classification...

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12th International Scientific and Practical Conference on Environment. Technology. Resources

作者： Matrosova, Nataliia D. Shtennikov, Dmitry G. Faculty of Software Engineering and Computer Systems Saint-Petersburg National Research University of Information Technologies Mechanics and Optics Saint-Petersburg Russia

Researchers compared the classification and the clusterization of users of online course for the personification of the users’ information system interface. When interacting with control and information systems, users may manifest individual features, including implicit characteristics that may affect one’s results within the system. At the same time due to information system building peculiarities one of the most comprehensive statistics can be collected via e-learning systems. When using a course, the user leaves a wide trail of activity that may contain different information depending on the learning environment structure. Online blended learning courses draw the researcher’s attention to the impact of digital teaching models on students as well as its ability to adjust distant learning courses to individual students’ needs and differences. Information personalization is a highly relevant content presentation at the most individual level. Therefore, the task of personalization is to show users information that meets their needs and interests. Personalization gives the opportunity to focus on points that have real value for users. © 2019 Nataliia D. Matrosova, Dmitry G. Shtennikov.

关键词： Machine learning

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