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

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

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

arXiv

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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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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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The OPS-SAT benchmark for detecting anomalies in satellite telemetry

arXiv

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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

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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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A Mobile Cloud-Based eHealth Scheme

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computers, Materials & Continua 2020年第4期63卷 31-39页

作者： Yihe Liu Aaqif Afzaal Abbasi Atefeh Aghaei Almas Abbasi Amir Mosavi Shahaboddin Shamshirband Mohammed A.A.Al-qaness Data Recovery Key Laboratory of Sichuan Province College of Computer Science and AINeijiang Normal UniversityNeijiang641100China Department of Software Engineering Foundation UniversityIslamabad44000Pakistan Department of Communication Faculty of Social SciencesUniversity of TehranTehran***Iran Department of Computer Science International Islamic UniversityIslamabad46000Pakistan Institute of Research and Development Duy Tan UniversityDa Nang550000Vietnam Kalman Kando Faculty of Electrical Engineering Obuda UniversityBudapest1034Hungary School of the Built Environment Oxford Brookes UniversityOxfordOX30BPUK Department for Management of Science and Technology Development Ton Duc Thang UniversityHo Chi Minh CityVietnam Faculty of Information Technology Ton Duc Thang UniversityHo Chi Minh CityVietnam School of Computer Science Wuhan UniversityWuhan430072China

Mobile cloud computing is an emerging field that is gaining popularity across borders at a rapid ***,the field of health informatics is also considered as an extremely important *** work observes the collaboration between these two fields to solve the traditional problem of extracting Electrocardiogram signals from trace reports and then performing *** developed system has two front ends,the first dedicated for the user to perform the photographing of the trace *** the photographing is complete,mobile computing is used to extract the *** the signal is extracted,it is uploaded into the server and further analysis is performed on the signal in the *** this is done,the second interface,intended for the use of the physician,can download and view the trace from the *** data is securely held using a password-based authentication *** system presented here is one of the first attempts at delivering the total solution,and after further upgrades,it will be possible to deploy the system in a commercial setting.

关键词： Cloud computing electrocardiograms health-care signal analysis signal processing

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Local Gradient Difference Features for Classification of 2D-3D Natural Scene Text Images

Local Gradient Difference Features for Classification of 2D-...

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International Conference on Pattern Recognition

作者： Lokesh Nandanwar Palaiahnakote Shivakumara Ramachandra Raghavendra Tong Lu Umapada Pal Daniel Lopresti Nor Badrul Anuar Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Faculty of Information Technology and Electrical Engineering IIK NTNU Norway National Key Lab for Novel Software Technology Nanjing University Nanjing China Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Computer Science & Engineering Lehigh University Bethlehem PA USA

Methods developed for normal 2D text detection do not work well for text that is rendered using decorative, 3D effects, etc. This paper proposes a new method for classification of 2D and 3D natural scene text images so that an appropriate recognition method can be chosen accordingly based on the classification results for better performance. The proposed method explores local gradient differences for obtaining candidate pixels, which represent a stroke. To study the spatial distribution of candidate pixels, we propose a measure, called COLD, which is denser for pixels toward the center of strokes and scattered for non-stroke pixels. This observation leads us to introduce mass features for extracting the regular spatial pattern of COLD, which indicates a 2D text image. The extracted features are fed into a Neural Network (NN) for classification. The proposed method is tested on (i) a new dataset introduced in this work (ii) a second dataset assembled from standard natural scene datasets (iii) Non-Text Image datasets which does not contain text, rather it contains objects. Experimental results of the proposed method on images with text and non-text show that the proposed method is independent of text. The proposed approach improves text detection and recognition performance significantly after classification.

关键词： Three-dimensional displays Image recognition Graphical models Text recognition Artificial neural networks Feature extraction Standards

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A deep learning based wearable healthcare iot device for ai-enabled hearing assistance automation 19

A deep learning based wearable healthcare iot device for ai-...

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19th International Conference on Machine Learning and Cybernetics, ICMLC 2020

作者： Young, Fraser Zhang, Li Jiang, Richard Liu, Han Wall, Conor Northumbria University Department of Computer and Information Sciences NewcastleNE1 8ST United Kingdom Lancaster University School of Computing and Communications Faculty of Science and Technology Lancaster United Kingdom Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China

ISBN: (纸本)9780738124261

With the recent booming of artificial intelligence (AI), particularly deep learning techniques, digital healthcare is one of the prevalent areas that could gain benefits from AI-enabled functionality. This research presents a novel AI-enabled Internet of Things (IoT) device operating from the ESP-8266 platform capable of assisting those who suffer from impairment of hearing or deafness to communicate with others in conversations. In the proposed solution, a server application is created that leverages Google's online speech recognition service to convert the received conversations into texts, then deployed to a micro-display attached to the glasses to display the conversation contents to deaf people, to enable and assist conversation as normal with the general population. Furthermore, in order to raise alert of traffic or dangerous scenarios, an 'urban-emergency' classifier is developed using a deep learning model, Inception-v4, with transfer learning to detect/recognize alerting/alarming sounds, such as a horn sound or a fire alarm, with texts generated to alert the prospective user. The training of Inception-v4 was carried out on a consumer desktop PC and then implemented into the AI-based IoT application. The empirical results indicate that the developed prototype system achieves an accuracy rate of 92% for sound recognition and classification with real-time performance. © 2020 IEEE.

关键词： Internet of things

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Erratum to “Effective Prediction of Bug-Fixing Priority via Weighted Graph Convolutional Networks”

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IEEE Transactions on Reliability 2024年第4期73卷 1994-1994页

作者： Sen Fang You-shuai Tan Tao Zhang Zhou Xu Hui Liu Faculty of Information Technology Macau University of Science and Technology Macau China School of BigData and Software Engineering Chongqing University Chongqing China School of Computer Science and Technology Beijing Institute of Technology Beijing China

In [1], the affiliation of the primary authors should be as follows:

关键词： Graph convolutional networks computer science

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