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International Research Conference on Smart Computing and Systems engineering (SCSE)

作者： G.H.A.U. Hewawitharana U.M.L.A. Nawarathne A.S.F. Hassan Lochana M. Wijerathna G.D. Sinniah Samitha P. Vidhanaarachchi Jagath Wickramarathne Janaka L. Wijekoon Department of Information Technology Sri Lanka Institute of Information Technology Malabe Sri Lanka Plant Pathology Division Tea Research Institute of Sri Lanka Talawakelle Sri Lanka Department of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Systems Engineering Department Military Technological College Muscat Oman

Ceylon tea industry faces a major challenge in the form of pathogen-induced crop loss, with Blister Blight (BB) caused by Exobasidium vexans posing the greatest threat, leading to harvest losses of over 30%. This fungus attacks the tender tea shoots, resulting in a direct negative impact on the tea harvest. This paper presents a system to identify the suspicious tea leaves and BB disease at its early stages along with an assessment of severity, offering a potential solution to this critical issue. By utilizing real-time object detection, the system filters out non-tea leaves from the captured initial image of a segment of a tea plant. The identified tea leaves are then subjected to BB identification and severity assessment based on differing visual symptoms of the BB stages. This approach enables the system to accurately identify BB in the initial stage and severity stage, allowing for timely and targeted intervention to minimize crop losses. The YOLOv8 model has been able to correctly identify 98% of the objects it has detected as relevant (precision), and it has been able to correctly identify 96% of all the relevant objects present in the scene (recall). The Residual Network 50 (Resnet50) convolutional neural network (CNN) model was selected as the final model, achieving an accuracy of 89.90% during the training phase and an accuracy of 88.26% during the testing phase.

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Scalable analysis of stop-and-go waves

arXiv

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arXiv 2024年

作者： Ji, Junyi Gloudemans, Derek Wang, Yanbing Zachár, Gergely Barbour, William Sprinkle, Jonathan Piccoli, Benedetto Work, Daniel B. Department of Civil and Environmental Engineering Vanderbilt University United States Institute for Software Integrated Systems Vanderbilt University United States Department of Computer Science Vanderbilt University United States The Transportation and Power Systems Division Argonne National Laboratory United States School of Sustainable Engineering and the Built Environment Arizona State University United States Department of Mathematical Sciences Rutgers University Camden United States

Analyzing stop-and-go waves at the scale of miles and hours of data is an emerging challenge in traffic research. In the past, datasets were of limited scale and could be easily analyzed by hand or with rudimentary methods to identify a very limited set of traffic waves present within the data. The past 5 years have seen an explosion in the availability of large-scale traffic data containing traffic waves and complex congestion patterns, making existing approaches unsuitable for repeatable and scalable analysis of traffic waves in this data. This paper makes a first step towards addressing this shortcoming by introducing an automatic and scalable stop-and-go wave identification method capable of capturing wave generation, propagation, dissipation, as well as bifurcation and merging, which have previously been observed only very rarely. Using a concise and simple critical-speed based definition of a stop-and-go wave, the proposed method identifies all wave boundaries that encompass spatio-temporal points where vehicle speed is below a chosen critical speed. The method is built upon a graph-based representation of the spatio-temporal points associated with stop-and-go waves, specifically wave front (start) points and wave tail (end) points, and approaches the solution as a graph component identification problem. The method is implemented in Python and demonstrated on a large-scale dataset, I-24 MOTION INCEPTION. New insights revealed from this demonstration with emerging phenomena include: (a) we demonstrate that waves do generate, propagate, and dissipate at a scale (miles and hours) and ubiquity never observed before;(b) wave fronts and tails travels at a consistent speed for a critical speed between 10-20 mph, with propagation variation across lanes, where wave speed on the outer lane are less consistent compared to those on the inner lane;(c) wave fronts and tails propagate at different speeds;(d) wave boundaries capture rich and non-trivial wave topologies (with

关键词： Traffic congestion

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"I Don't Want People to Look At Me Differently": Designing User-Defined Above-the-Neck Gestures for People with Upper Body Motor Impairments

arXiv

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arXiv 2022年

作者： Zhao, Xuan Fan, Mingming Han, Teng School of Information Rochester Institute of Technology United States The Hong Kong University of Science and Technology Guangzhou China Division of Integrative Systems and Design Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Institute of Software Chinese Academy of Sciences China

Recent research proposed eyelid gestures for people with upper-body motor impairments (UMI) to interact with smartphones without finger touch. However, such eyelid gestures were designed by researchers. It remains unknown what eyelid gestures people with UMI would want and be able to perform. Moreover, other above-the-neck body parts (e.g., mouth, head) could be used to form more gestures. We conducted a user study in which 17 people with UMI designed above-the-neck gestures for 26 common commands on smartphones. We collected a total of 442 user-defined gestures involving the eyes, the mouth, and the head. Participants were more likely to make gestures with their eyes and preferred gestures that were simple, easy-to-remember, and less likely to draw attention from others. We further conducted a survey (N=24) to validate the usability and acceptance of these user-defined gestures. Results show that user-defined gestures were acceptable to both people with and without motor impairments. © 2022, CC BY-NC-ND.

关键词： Smartphones

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Gated CNN-Transformer Network for Automatic Cardiovascular Diagnosis using 12-lead Electrocardiogram

Gated CNN-Transformer Network for Automatic Cardiovascular D...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Yuanlin Liu Haiying Li Jie Lin Hairui Li Haijun Lei Chunmei Xia Chunlun Xiao Baiying Lei School of Biomedical Engineering Health Science Center Shenzhen University National-Regional Key Technology Engineering Laboratory for Medical Ultrasound Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging Shenzhen China Division of Cardiac Arrhythmia Cardiac and Vascular Center The University of Hong Kong Shenzhen Hospital Shenzhen China College of Computer Science and Software Engineering Shenzhen University Guangdong Province Key Laboratory of Popular High Performance Computers Shenzhen China

12-lead electrocardiogram (ECG) is a widely used method in the diagnosis of cardiovascular disease (CVD). With the increase in the number of CVD patients, the study of accurate automatic diagnosis methods via ECG has become a research hotspot. The use of deep learning-based methods can reduce the influence of human subjectivity and improve the diagnosis accuracy. In this paper, we propose a 12-lead ECG automatic diagnosis method based on channel features and temporal features fusion. Specifically, we design a gated CNN-Transformer network, in which the CNN block is used to extract signal embeddings to reduce data complexity. The dual-branch transformer structure is used to effectively extract channel and temporal features in low-dimensional embeddings, respectively. Finally, the features from the two branches are fused by the gating unit to achieve automatic CVD diagnosis from 12-lead ECG. The proposed end-to-end approach has more competitive performance than other deep learning algorithms, which achieves an overall diagnostic accuracy of 85.3% in the 12-lead ECG dataset of CPSC-2018.

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Imploding between the facts and concerns: analysing human–AI musical interaction

Humanities and Social Sciences Communications

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Humanities and Social sciences Communications 2025年第1期12卷 1-20页

作者： Kelsey Cotton Kıvanç Tatar Anna-Kaisa Kaila Petra Jääskeläinen André Holzapfel Data Science and AI Division Computer Science and Software Engineering Chalmers Institute of Technology and University of Gothenburg Gothenburg Sweden Division of Media Technology and Interaction Design KTH Royal Institute of Technology Stockholm Sweden

The advancement of AI-tools for musical performance has inspired exciting opportunities for interaction with musical-AI-agents. Interactions between humans and AI-agents in musical settings entail dynamic exchanges of control and power, and framings of AI-agents’ roles by human performers. We probe these framings and power-control exchanges through qualitative thematic lenses, drawing from post-phenomenology,matters of fact and concernand feminist science and technology studies. We contribute with a novel interdisciplinary analytical method as a tool for developers and designers of AI systems to help visibilise and examine theimplicit, the wider connections and entangled filaments in Human–AI musical interactions.

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Multitask Deep Learning for Accurate Risk Stratification and Prediction of Next Steps for Coronary CT Angiography Patients

arXiv

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arXiv 2023年

作者： Lu, Juan Bennamoun, Mohammed Stewart, Jonathon Eshraghian, Jason K. Liu, Yanbin Chow, Benjamin Sanfilippo, Frank M. Dwivedi, Girish The Medical school and Harry Perkins Institute of Medical Research School of Computer Science and Software Engineering The University of Western Australia PerthWA6009 Australia The School of Computer Science and Software Engineering The University of Western Australia PerthWA6009 Australia The Medical School Harry Perkins Institute of Medical Research The University of Western Australia PerthWA6009 Australia School of Computer Science and Software Engineering The University of Western Australia PerthWA6009 Australia The Department of Electrical and Computer Engineering University of California Santa CruzCA95064 United States The School of Computing Australian National University CanberraACT2601 Australia The Division of Cardiology Departments of Medicine University of Ottawa Heart Institute OttawaONK1Y 4W7 Canada The School of Population and Global Health The University of Western Australia PerthWA6009 Australia The Medical School Harry Perkins Institute of Medical Research The University of Western Australia Australia The Department of Cardiology Fiona Stanley Hospital PerthWA6154 Australia

Diagnostic investigation has an important role in risk stratification and clinical decision making of patients with suspected and documented Coronary Artery Disease (CAD). However, the majority of existing tools are primarily focused on the selection of gatekeeper tests, whereas only a handful of systems contain information regarding the downstream testing or treatment. We propose a multi-task deep learning model to support risk stratification and down-stream test selection for patients undergoing Coronary Computed Tomography Angiography (CCTA). The analysis included 14,021 patients who underwent CCTA between 2006 and 2017. Our novel multitask deep learning framework extends the state-of-the art Perceiver model to deal with real-world CCTA report data. Our model achieved an Area Under the receiver operating characteristic Curve (AUC) of 0.76 in CAD risk stratification, and 0.72 AUC in predicting downstream tests. Our proposed deep learning model can accurately estimate the likelihood of CAD and provide recommended downstream tests based on prior CCTA data. In clinical practice, the utilization of such an approach could bring a paradigm shift in risk stratification and downstream management. Despite significant progress using deep learning models for tabular data, they do not outperform gradient boosting decision trees, and further research is required in this area. However, neural networks appear to benefit more readily from multi-task learning than tree-based models. This could offset the shortcomings of using single task learning approach when working with tabular data. © 2023, CC BY.

关键词： Decision trees

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A Taxonomy of Information Attributes for Test Case Prioritisation: Applicability, Machine Learning

arXiv

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arXiv 2022年

作者： Ramírez, Aurora Feldt, Robert Romero, José Raúl Chalmers University of Technology Sweden Campus de Rabanales Edificio Marie Curie CórdobaE-14071 Spain Software Engineering Division Department of Computer Science and Engineering Chalmers University of Technology Jupiter Building Lindholmen Campus GothenburgSE-412 96 Sweden

Most software companies have extensive test suites and re-run parts of them continuously to ensure recent changes have no adverse effects. Since test suites are costly to execute, industry needs methods for test case prioritisation (TCP). Recently, TCP methods use machine learning (ML) to exploit the information known about the system under test (SUT) and its test cases. However, the value added by ML-based TCP methods should be critically assessed with respect to the cost of collecting the information. This paper analyses two decades of TCP research, and presents a taxonomy of 91 information attributes that have been used. The attributes are classified with respect to their information sources and the characteristics of their extraction process. Based on this taxonomy, TCP methods validated with industrial data and those applying ML are analysed in terms of information availability, attribute combination and definition of data features suitable for ML. Relying on a high number of information attributes, assuming easy access to SUT code and simplified testing environments are identified as factors that might hamper industrial applicability of ML-based TCP. The TePIA taxonomy provides a reference framework to unify terminology and evaluate alternatives considering the cost-benefit of the information attributes. © 2022, CC BY-NC-ND.

关键词： software testing

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Unsupervised Learning Based Anomaly Detection Using Object Tracking and Density Clustering in Road Traffic

SSRN

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SSRN 2023年

作者： Cho, Seongun Yoo, Hyun Chung, Kyungyong Division of AI Computer Science and Engineering Kyonggi University 154-42 Gwanggyosan-ro Yeongtong-gu Gyeonggi-do Suwon-si16227 Korea Republic of Contents Convergence Software Research Institute Kyonggi University 154-42 Gwanggyosan-ro Yeongtong-gu Gyeonggi-do Suwon-si16227 Korea Republic of

In the last few years, real-time object detection and tracking technology has been attracting attention as deep learning technology has been making progress. In particular, such technology is considered very important for control systems using CCTVs. However, since anomaly detection in the current field of image surveillance is a technology that mostly uses supervised learning, it lacks versatility and expandability, which leads to the problem that each CCTV system requires different learning. In addition, the one-class-classification method has limitations in that it recognizes a data slightly different from the normal data as an abnormal data and in that it is unable to flexibly respond to environmental changes. To resolve these problems, in this study, an unsupervised learning-based anomaly detection using object tracking and density clustering in road traffic is proposed. The proposed method extracts information such as object location and direction, removes outliers through clustering, and constructs feature extraction techniques and normal distribution forms to arithmetically express abnormal situations. Through such process, data obtained from an image input system is analyzed, and abnormal situations can be automatically classified without having to pre-define abnormal situations through unsupervised learning. Since the classified outliers are classified as dangerous situations, it becomes possible to promptly respond to them. © 2023, The Authors. All rights reserved.

关键词： Object detection

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A performance comparison of graph analytic methods for supporting enterprise architecture model maintenance

A performance comparison of graph analytic methods for suppo...

引用

2019 Practice of Enterprise Modelling Conference Forum, PoEM 2019 Forum

作者： Rajashekar, Nikhitha Hacks, Simon Silva, Nuno Research Group Software Construction RWTH Aachen University Germany Division of Network and Systems Engineering KTH Royal Institute of Technology Sweden Department of Computer Science and Engineering Technical University of Lisbon Lisbon Portugal

Enterprise Architecture (EA) models help enterprise architects to make business decisions and to support organisations to understand and analyse its structure. Creating and maintaining such an EA model is expensive and time-consuming in large organisations. In this paper, the authors provide a review of how graph analytic methods can be used to support EA model maintenance. To achieve the best results for the duplicate identification, it is necessary to evaluate and optimise different graph analytic and Machine Learning (ML) algorithms to support the maintenance process. Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Machine learning

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Physical Adversarial Attack Meets computer Vision: A Decade Survey

arXiv

引用

arXiv 2022年

作者： Wei, Hui Tang, Hao Jia, Xuemei Wang, Zhixiang Yu, Hanxun Li, Zhubo Satoh, Shin'ichi Van Gool, Luc Wang, Zheng School of Computer Science National Engineering Research Center for Multimedia Software Wuhan University Wuhan China National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University Beijing100871 China Colleage of Software and Technology Zhejiang University Hangzhou China School of Cyber Science and Engineering Wuhan University Wuhan China Digital Content and Media Sciences Research Division National Institute of Informatics Japan Department of Information and Communication Engineering Graduate School of Information Science and Technology The University of Tokyo Japan Computer Vision Lab of ETH Zurich Zürich8092 Switzerland KU Leuven Leuven3000 Belgium INSAIT Sofia Bulgaria

Despite the impressive achievements of Deep Neural Networks (DNNs) in computer vision, their vulnerability to adversarial attacks remains a critical concern. Extensive research has demonstrated that incorporating sophisticated perturbations into input images can lead to a catastrophic degradation in DNNs’ performance. This perplexing phenomenon not only exists in the digital space but also in the physical world. Consequently, it becomes imperative to evaluate the security of DNNs-based systems to ensure their safe deployment in real-world scenarios, particularly in security-sensitive applications. To facilitate a profound understanding of this topic, this paper presents a comprehensive overview of physical adversarial attacks. Firstly, we distill four general steps for launching physical adversarial attacks. Building upon this foundation, we uncover the pervasive role of artifacts carrying adversarial perturbations in the physical world. These artifacts influence each step. To denote them, we introduce a new term: adversarial medium. Then, we take the first step to systematically evaluate the performance of physical adversarial attacks, taking the adversarial medium as a first attempt. Our proposed evaluation metric, hiPAA, comprises six perspectives: Effectiveness, Stealthiness, Robustness, Practicability, Aesthetics, and Economics. We also provide comparative results across task categories, together with insightful observations and suggestions for future research directions. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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