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Piecewise combination of hyper-sphere support vector machine for multi-class classification problems

International Journal of Performability engineering 2019年第6期15卷 1611-1619页

作者： Liu, Shuang Chen, Peng Li, Jiayi Yang, Hui Lukač, Niko School of Computer Science and Engineering Dalian Minzu University Dalian116600 China Department of Software Engineering Dalian Neusoft University of Information Dalian116023 China Faculty of Electrical Engineering and Computer Science University of Maribor MariborSI-2000 Slovenia

Hyper-sphere Support vector machine (SVM) is a widely used machine learning method for multi-class classification problems such as image recognition, text classification, or handwriting recognition. In most cases, only one hyper-sphere optimization problem is computed to solve the problem. However, there are many complex applications with complicated data distributions. In these cases, the computation cost will be increased with unsatisfied classification results if only one support vector machine is adopted as the classification decision rule. To achieve good classification performance, a piecewise combination of the hyper-sphere support vector machine is put forward in this paper based on the analysis of the data sample distribution. First, statistical analysis is adopted for the original data. Then, the kmeans cluster algorithm is introduced to compute cluster centers for different classes of the data. For the n classes classification problem, m (m > n) hyper-spheres are computed to solve the objective problems based on the number of data centers. For simple sphere-distribution and locally linearly separable distribution cases, the minimum enclosing and maximum excluding support vector machine and the combination of hyper-sphere support vector machine are defined. Experimental results show that different support vector machines for different data distributions will improve the final classification performance. © 2019 Totem Publisher, Inc. All rights reserved.

关键词： Support vector machines

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A Brief Review of Explainable Artificial Intelligence in Healthcare

arXiv

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arXiv 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 Pardalos, 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 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, CC BY-NC-ND.

关键词： Decision making

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

Reasoning like human: Hierarchical reinforcement learning fo...

引用

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 Principled Design of Image Representation: Towards Forensic Tasks

arXiv

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arXiv 2022年

作者： Qi, Shuren Zhang, Yushu Wang, Chao Zhou, Jiantao Cao, Xiaochun The College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China The Institute of Information Engineering Chinese Academy of Sciences Beijing China The Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China The State Key Laboratory of Internet of Things for Smart City China The Department of Computer and Information Science Faculty of Science and Technology University of Macau China The School of Cyber Science and Technology Shenzhen Campus of Sun Yat-sen University Shenzhen China

Image forensics is a rising topic as the trustworthy multimedia content is critical for modern society. Like other vision-related applications, forensic analysis relies heavily on the proper image representation. Despite the importance, current theoretical understanding for such representation remains limited, with varying degrees of neglect for its key role. For this gap, we attempt to investigate the forensic-oriented image representation as a distinct problem, from the perspectives of theory, implementation, and application. Our work starts from the abstraction of basic principles that the representation for forensics should satisfy, especially revealing the criticality of robustness, interpretability, and coverage. At the theoretical level, we propose a new representation framework for forensics, called dense invariant representation (DIR), which is characterized by stable description with mathematical guarantees. At the implementation level, the discrete calculation problems of DIR are discussed, and the corresponding accurate and fast solutions are designed with generic nature and constant complexity. We demonstrate the above arguments on the dense-domain pattern detection and matching experiments, providing comparison results with state-of-the-art descriptors. Also, at the application level, the proposed DIR is initially explored in passive and active forensics, namely copy-move forgery detection and perceptual hashing, exhibiting the benefits in fulfilling the requirements of such forensic tasks. Copyright © 2022, The Authors. All rights reserved.

关键词： Image representation

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Guided pattern mining for API misuse detection by change-based code analysis

arXiv

引用

arXiv 2020年

作者： Nielebock, Sebastian Heumüller, Robert Schott, Kevin Michael Ortmeier, Frank Otto-von-Guericke University Magdeburg Faculty of Computer Science Chair of Software Engineering

Lack of experience, inadequate documentation, and sub-optimal API design frequently cause developers to make mistakes when re-using third-party implementations. Such API misuses can result in unintended behavior, performance losses, or software crashes. Therefore, current research aims to automatically detect such misuses by comparing the way a developer used an API to previously inferred patterns of the correct API usage. While research has made significant progress, these techniques have not yet been adopted in practice. In part, this is due to the lack of a process capable of seamlessly integrating with software development processes. Particularly, existing approaches do not consider how to collect relevant source code samples from which to infer patterns. In fact, an inadequate collection can cause API usage pattern miners to infer irrelevant patterns which leads to false alarms instead of finding true API misuses. In this paper, we target this problem (a) by providing a method that increases the likelihood of finding relevant and true-positive patterns concerning a given set of code changes and agnostic to a concrete static, intra-procedural mining technique and (b) by introducing a concept for just-in-time API misuse detection which analyzes changes at the time of commit. Particularly, we introduce different, lightweight code search and filtering strategies and evaluate them on two real-world API misuse datasets to determine their usefulness in finding relevant intra-procedural API usage patterns. Our main results are (1) commit-based search with subsequent filtering effectively decreases the amount of code to be analyzed, (2) in particular method-level filtering is superior to file-level filtering, (3) project-internal and project-external code search find solutions for different types of misuses and thus are complementary, (4) incorporating prior knowledge of the misused API into the search has a negligible effect. Copyright © 2020, The Authors. All rights r

关键词： Data mining

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Cheating Detection in Browser-based Online Exams through Eye Gaze Tracking

Cheating Detection in Browser-based Online Exams through Eye...

引用

International Conference on Information Technology Research (ICITR)

作者： Nimesha Dilini Asara Senaratne Tharindu Yasarathna Nalin Warnajith Leelanga Seneviratne Software Engineering Teaching Unit University of Kelaniya Kelaniya Sri Lanka College of Engineering and Computer Science The Australian National University Canberra Australia Faculty of Information Technology University of Moratuwa Moratuwa Sri Lanka

ISBN: (纸本)9781665420013

Eye-tracking can detect and examine human visual attention, emotional conditions, latent cognitive processes such as efforts to recall a concept or the fear of running out of time, and so on. Hence, we can use eye-tracking to identify deviant behavior patterns in learning and problem-solving. At present, given the existence of a global pandemic, online exams are widely used by educational institutions to evaluate students' performance. However, identifying cheating is challenging due to the absence of a human (invigilator) monitoring students' behavior as done in exams held in a physical location. In an online environment, students' behavior, and attempts to cheat, can only be captured via a computer, thus requiring a mechanism for online proctoring with capabilities for cheating detection. In this research paper, we present a browser based cheating detection approach in online examinations through eye gaze tracking. We developed a browser plugin to track the eye gaze movements through the in-built web camera. Using the plugin, we generate an eye gaze dataset while a student faces an online examination. We then process and analyze this dataset to detect any misbehavior during an online examination. The underlying research work of this paper identifies different eye gaze patterns during online examinations and present a cheating detection mechanism. For anomaly detection in the eye gaze data, we use a One-Class Support Vector Machine (OCSVM). We then use these identified anomalies to predict cheating behaviors of the test takers. The given approach can be used for any web-based quiz examination such as academic institutions' exams, company recruitment exams, and overseas testing exams to detect any anomalous behaviors of the test takers during the examination period. The given eye tracking approach can also be applied to other research domains such as online gaming, and web usability studies to capture information related to user behaviors.

关键词： Support vector machines Visualization Tracking Gaze tracking Browsers Problem-solving Usability

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Students Online Exam Proctoring: A Case Study Using 360 Degree Security Cameras

Students Online Exam Proctoring: A Case Study Using 360 Degr...

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2020 International Conference on Emerging Technology in Computing, Communication and Electronics, ETCCE 2020

作者： Turani, Aiman A Alkhateeb, Jawad H Alsewari, AbdulRahman A. Taibah University Information System Department AlMadinah Almunawarah Saudi Arabia Taibah University Computer Science Department AlMadinah Almunawarah Saudi Arabia Faculty of Computing Software Engineering Department Pekan Pahang Malaysia

ISBN: (纸本)9781665419628

Online courses, online exams and online certificates are conducted by various universities and Information Technology (IT) institutes worldwide. Delivery tools have been created for conducting the exams from any place. Applying this will lead saving time and travelling cost. Nowadays, due to the COVID-19 pandemic, there is a big demand on the online courses and exams. This paper introduces a new approach for exam proctoring using 360-degree security camera. Mainly, online exams' security is a major concern. Thus, a delivery tools must not only ensure the identity of a test-taker but also the overall test integrity. In this paper, the usage of the 360-degree security camera over the traditional webcam was investigated in order to enhance the exam security and to minimize the stressful restrictions. To verify this goal, a case study on a group of volunteer students within the college of computer science and engineering was made. In addition, an automated proctoring model that will eliminate the need for a real-time proctoring and remove any scheduling constraints in order to prevent cheating is proposed in this paper. The machine learning algorithms is exploited to enrich the proposed system. A secure frame work using the biometric is applied in order to ensure authentication and running the online exam smoothly. © 2020 IEEE.

关键词： Students

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Enriched DNA strands classification using CGR images and convolutional neural network 8

Enriched DNA strands classification using CGR images and con...

引用

8th International Conference on Bioinformatics and Biomedical science, ICBBS 2019

作者： Safoury, Sarah Hussein, Walid Software Engineering Department Faculty of Informatics and Computer Science British University in Egypt Egypt Computer Science Department Faculty of Informatics and Computer Science British University in Egypt Egypt

ISBN: (纸本)9781450372510

Bioinformatics is the biological study which applies programming techniques for more understanding and analysis of living objects such as the study of genome structure. The genome structure could be represented in the form of an image. Chaos Game Representation (CGR) is the practice of converting the DNA sequence (i.e., genomes) into images, where each image is a graphical appearance for an individual DNA strand's signature. CGR is a method of converting a long one-dimensional DNA sequence into a graphical form. This method provides a visual image of a DNA sequence different from the traditional manual linear arrangement of nucleotides polymerase chain reaction. In the recent years, CGR was introduced to automatically classify genomes not only by archival references but also through its' unique signature. In this paper, a novel CGR classification approach is developed combining the advances of image processing and pattern recognition approaches. The approach starts by declaring the genome and using the CGR technique to map it to the graphical interface (i.e., 16x16 signature images). Then, an image processing procedure is prepared to handle complex geometric shapes, analyze structured and visualized genome sequences and fractal point the included nucleotides of these images. Finally, the convolutional neural network was designed and well-Trained by those signatures to classify each genome tested. © 2019 Association for Computing Machinery.

关键词： Genome

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The OPS-SAT benchmark for detecting anomalies in satellite telemetry

arXiv

引用

arXiv 2024年

作者： Ruszczak, Bogdan Kotowski, Krzysztof Evans, David Nalepa, Jakub Faculty of Electrical Engineering Automatic Control and Informatics Department of Informatics Opole University of Technology Prószkowska Str. 76 Opole45-758 Poland Faculty of Automatic Control Electronics and Computer Science Department of Algorithmics and Software Silesian University of Technology Akademicka Str. 16 Gliwice44-100 Poland KP Labs Bojkowska Str. 37J Gliwice44-100 Poland European Space Agency ESOC Robert-Bosch-Str. 5 Darmstadt64293 Germany

Detecting anomalous events in satellite telemetry is a critical task in space operations. This task, however, is extremely time-consuming, error-prone and human dependent, thus automated data-driven anomaly detection algorithms have been emerging at a steady pace. However, there are no publicly available datasets of real satellite telemetry accompanied with the ground-truth annotations that could be used to train and verify anomaly detection supervised models. In this article, we address this research gap and introduce the AI-ready benchmark dataset (OPSSAT-AD) containing the telemetry data acquired on board OPS-SAT—a CubeSat mission which has been operated by the European Space Agency which has come to an end during the night of 22–23 May 2024 (CEST). The dataset is accompanied with the baseline results obtained using 30 supervised and unsupervised classic and deep machine learning algorithms for anomaly detection. They were trained and validated using the training-test dataset split introduced in this work, and we present a suggested set of quality metrics which should be always calculated to confront the new algorithms for anomaly detection while exploiting OPSSAT-AD. We believe that this work may become an important step toward building a fair, reproducible and objective validation procedure that can be used to quantify the capabilities of the emerging anomaly detection techniques in an unbiased and fully transparent way. © 2024, CC BY.

关键词： Anomaly detection

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Framework for understanding quantum computing use cases from a multidisciplinary perspective and future research directions

arXiv

引用

arXiv 2022年

作者： Ukpabi, Dandison Karjaluoto, Heikki Bötticher, Astrid Nikiforova, Anastasija Petrescu, Dragoş Schindler, Paulina Valtenbergs, Visvaldis Lehmann, Lennard Yakaryilmaz, Abuzer University of Jyväskylä Jyväskylä University School of Business and Economics Finland Friedrich-Schiller-University Jena Department of Political Science Jena Germany University of Tartu Faculty of Science and Technology Institute of Computer Science Chair of Software Engineering Tartu Estonia University of Bucharest Faculty of Political Science Bucharest Romania Friedrich-Schiller-University Jena Project "Spheres of Freedom and Protection of Liberty in the Digital State" Jena Germany University of Latvia Faculty of Social Sciences Riga Latvia University of Latvia Faculty of Computing Riga Latvia QWorld Association Tallinn Estonia

Recently, there has been increasing awareness of the tremendous opportunities inherent in quantum computing (QC). Specifically, the speed and efficiency of QC will significantly impact the Internet of Things, cryptography, finance, and marketing. Accordingly, there has been increased QC research funding from national and regional governments and private firms. However, critical concerns regarding legal, political, and business-related policies germane to QC adoption exist. Since this is an emerging and highly technical domain, most of the existing studies focus heavily on the technical aspects of QC, but our study highlights its practical and social uses cases, which are needed for the increased interest of governments. Thus, this study offers a multidisciplinary review of QC, drawing on the expertise of scholars from a wide range of disciplines whose insights coalesce into a framework that simplifies the understanding of QC, identifies possible areas of market disruption and offer empirically based recommendations that are critical for forecasting, planning, and strategically positioning QCs for accelerated diffusion. © 2022, CC BY.

关键词： Commerce

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