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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Lee, Chanwoo Lee, Hyunjeong Kim, Minsang Kim, Hyun Jang, Haneol Park, Cheoneum Seoul Woman's University Dept. of Software Convergence Seoul Korea Republic of Hanbat National University Dept. of Computer Engineering Daejeon Korea Republic of Korea University Dept. of Computer Science and Engineering Seoul Korea Republic of Electronics and Telecommunications Research Institute Superintelligence Creative Research Lab. Daejeon Korea Republic of

ISBN: (纸本)9798350364637

In the field of NLP, Large Language Models (LLMs) have recently achieved significant advancements, leading to the development of various benchmarks for their evaluation. Along-side NLP, Vision Language Models (VLMs) have also VLM have also significantly progressed, similar to LLMs. However, benchmarks for VLMs are still relatively underdeveloped compared to those for NLP, and their construction is often costly. In this work, we propose an automatically generated benchmark for evaluating VLMs based on LLMs and conduct a visual question answering task to assess this benchmark. The benchmark includes multiple-choice questions that not only distinguish between animate and inanimate objects but also generate these distinctions automatically, along with entity and object information within images. We evaluate the performance of open VLM using the generated multiple-choice questions, demonstrating the model's capabilities and the significance of the automatically generated benchmark. Finally, we discuss the necessity and future directions for benchmark research in this area. © 2024 IEEE.

关键词： Visual languages

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Automated Romberg Test: Leveraging a CNN and Centre of Mass Analysis for Sensory Ataxia Diagnosis

arXiv

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arXiv 2024年

作者： Haskins, Reilly Green, Richard Dept. of Computer Science and Software Engineering University of Canterbury Christchurch New Zealand

This paper proposes a novel method to diagnose sensory ataxia via an automated Romberg Test - the current de facto medical procedure used to diagnose this condition. It utilizes a convolutional neural network to predict joint locations, used for the calculation of various bio-mechanical markers such as the center of mass of the subject and various joint angles. This information is used in combination with data filtering techniques such as Kalman Filters, and center of mass analysis which helped make accurate inferences about the relative weight distribution in the lateral and anterior-posterior axes, and provide an objective, mathematically based diagnosis of this condition. In order to evaluate the performance of this method, testing was performed using dual weight scales and pre-annotated diagnosis videos taken from medical settings. These two methods both quantified the veritable weight distribution upon the ground surface with a ground truth and provided a real-world estimate of accuracy for the proposed method. A mean absolute error of 0.2912% was found for the calculated relative weight distribution difference, and an accuracy of 83.33% was achieved on diagnoses. © 2024, CC BY.

关键词： Convolutional neural networks

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GreenLight: Green Traffic Signal Control using Attention-based Reinforcement Learning on Fog Computing Network

GreenLight: Green Traffic Signal Control using Attention-bas...

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International Green Computing Conference

作者： Chengyu Tang Sanjeev Baskiyar Dept. of Computer Science and Software Engineering Auburn University Auburn Alabama USA

ISBN: (数字)9798331507862

ISBN: (纸本)9798331507879

Traffic congestion is a pervasive problem causing severe environmental and economic issues. In recent years, traffic signal control using reinforcement learning (RL) has come a long way. Most existing studies focus on using distributed agents with data exchange among neighbors, which, however, increases network complexity and usage and still suffers from the lack of broader coordination. Meanwhile, the attention mechanism has achieved tremendous success, and advances in vehicle-to-infrastructure (V2I) communications have enabled real-time collections of granular data. However, integrating these technologies into traffic signal control remains under-explored. Therefore, we present GreenLight, a forward-thinking and eco-friendly traffic signal control framework that can be applied to V2I-equipped fog computing environments. For a large urban area, traffic signals are divided into clusters, each coordinated by a fog node with an RL agent. Intra-cluster indexed self-attention is applied to extract context-aware features that the fog-residing RL agent utilizes to determine the proper signal control command. Results of simulation experiments using both synthetic and real-world scenarios show that the presented framework yields lower waiting time, emissions, and fuel consumption compared to baseline methods, indicating its potential for next-generation transportation systems.

关键词： Vehicle-to-infrastructure Green products Urban areas Transportation Reinforcement learning Feature extraction Real-time systems Fuels Traffic congestion Edge computing

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A software Framework Based on JUnit for Automated software Testing in computer science Courses

A Software Framework Based on JUnit for Automated Software T...

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Frontiers in Education (FIE) Conference

作者： Michael A. Zmuda Dept. of Computer Science and Software Engineering Miami University Oxford OH USA

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

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 computer science Java Annotations Education Life estimation software Libraries software development management

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Decision Support System: Analysis and Design Methodology 2

Decision Support System: Analysis and Design Methodology

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2nd IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer engineering, MI-STA 2022

作者： Darbi, Huweida A. Saleh, Eiman M. University of Derna Dept. Computer Science Derna Libya Benghazi University Dept. Software Engineering Benghazi Libya

ISBN: (数字)9781665479189

ISBN: (纸本)9781665479189

The success of any organization depends on the quality of the decisions made, and good decisions can be made by analyzing all the available data and knowledge of the organization. Decision Support Systems (DSSs) are used to analyze this data and aid in decision-making in the formulation of well-studied decisions based on knowledge. However, the analysis and design of decision support systems (DSS) is a complex process that involves the use of appropriate methodologies, methods, and tools to model decision-making processes. This paper presents an architectural design of DSS framework. The proposed approach helps developers develop decision-making systems through models that detail the analysis and design. © 2022 IEEE.

关键词： Decision support systems

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XAI for Understanding the Success in Curbing COVID-19 6

XAI for Understanding the Success in Curbing COVID-19

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6th International Conference on Intelligent Systems and computer Vision, ISCV 2024

作者： Bouktif, Salah Kunnath, Nimmi Ouni, Ali CIT United Arab Emirates University Dept. of Computer Science and Software Engineering Al Ain United Arab Emirates University of Quebec École de Technologie Supérieure MontrealQC Canada

ISBN: (纸本)9798350350180

The Covid-19 pandemic has prompted governments worldwide to implement various non-pharmaceutical interventions (NPIs) in an effort to curb the pandemic to attenuate the harmness of the pandemic. However, there is a debate about how to assess the effectiveness of these interventions. A set of indicators has been used to monitor the outbreak such as case counts, infection rates, death rate, hospitalization. In this study, we contribute to the debate by firstly proposing a definition of the success in managing the pandemic in terms of mortality rate, socio-demographic and epidemiological factors. Secondly, we propose a modeling framework based on (1) analysing the time series pandemic data and (2) employing eXplainable Artificial Intelligence (XAI) to provide interpretability of the successfulness of countries in responding to the pandemic. By using a rich dataset collected by Oxford COVID-19 Government Response Tracker (OxCGRT), we built a success model based on Random Forest Regressor for time series analysis and we identified the major factors/practices/measures/protocols. Our findings indicate a significant relationship between policy compliance levels and death counts, highlighting the importance of considering mortality outcomes in evaluating the efficacy of interventions. Additionally, we discovered that socio-demographic and epidemiological factors such as elderly populations aged (65+), prevalent cardiovascular disease, high diabetes prevalence, increased smoking rates, and reduced life expectancy based on the person correlation coefficient are key determinants of success in managing the pandemic. © 2024 IEEE.

关键词： COVID-19

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Adaptive and Responsive Traffic Signal Control Using Reinforcement Learning and Fog Computing

Adaptive and Responsive Traffic Signal Control Using Reinfor...

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IEEE Cloud Summit

作者： Chengyu Tang Sanjeev Baskiyar Dept. of Computer Science and Software Engineering Auburn University Auburn Alabama USA

ISBN: (数字)9798350370065

ISBN: (纸本)9798350370072

As urban traffic becomes increasingly complex, conventional actuated traffic signal control methods are showing their limitations in mitigating congestion, and Deep Reinforcement Learning (DRL) is considered a promising solution. Existing studies mainly focus on using distributed agents, each intersection being controlled by a separate agent that can communicate with its neighbors to facilitate local coordination, which subsequently increases network complexity and usage. This article presents a DRL-based intelligent traffic signal control framework that leverages the fog computing paradigm. Traffic signals are divided into clusters, each controlled by a shared DRL agent in a fog node based on the collective information of the regional traffic situation. We have conducted simulation experiments in multiple scenarios, and the experimental results show that the proposed method yields lower average waiting times compared to existing methods.

关键词： Deep reinforcement learning Complexity theory Edge computing

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Unsupervised Insider Threat Detection Using Multi-Head Self-Attention Mechanisms

Unsupervised Insider Threat Detection Using Multi-Head Self-...

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Intelligent Cybersecurity Conference (ICSC)

作者： Simon Bertrand Pascal Germain Nadia Tawbi dept. of Computer Science & Software Engineering Laval University Québec Canada

ISBN: (数字)9798350354775

ISBN: (纸本)9798350354782

We propose an unsupervised insider threat detection system that learns normal user behaviors through audit data using neural networks equipped with multi-head self-attention mechanisms. The attention mechanisms learn precise normal user behaviors using large event windows. The key idea is to consider a sequence abnormal when it exhibits some events that are not likely to happen given the preceding events sequence. In addition, our method provides insights into detected threats through an event-based scoring system to facilitate threat understanding by security experts. Furthermore, the proposed solution does not require intensive audit logs pre-processing, such as manual domain knowledge-rich features extraction and data balancing. Thus, the proposed approach is easy to implement and use across organizations regardless of their expertise level. The proposed solution also provides the ability to detect threats in real-time. On the benchmark dataset CERT version 4.2, our solution, combined with hyperparameter search by Bayesian optimization, outperforms the previous state-of-the-art approaches with an area under the ROC curve of 97.23%, a recall of 91.51%, an accuracy of 93.13% and a false positive rate of 6.8%.

关键词： Neural networks Manuals Feature extraction Vectors Threat assessment Data models Real-time systems Bayes methods Optimization Tuning

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Measuring Influencing Factors of API Usability

Measuring Influencing Factors of API Usability

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2022 International Conference on Computational science and Computational Intelligence, CSCI 2022

作者： Tian, Jeff Alanazy, Sultan Bokhary, Abdullah Alharthi, Sultan Ghanem, Sakher Southern Methodist University Dept of Computer Science DallasTX United States College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia College of Computer Science and Engineering University of Jeddah Dept of Software Engineering Jeddah Saudi Arabia College of Computer Science and Engineering University of Jeddah Dept of Computer Science and Artificial Intelligence Jeddah Saudi Arabia

ISBN: (纸本)9798350320282

In this paper, we identify the influencing factors of API usability by extending a comprehensive framework for measuring API usability we proposed earlier, through a systematic examination of entities involved and artifacts produced in API development and usage. Existing research in software metrics are adapted to define metrics capturing relevant metrics dimensions and at the appropriate levels. Our current set of identified influencing factors includes metrics defined on API documentation and other entities/artifacts, in addition to the code metrics. Some preliminary results from actual measurement of such influencing factors are included to demonstrate the viability of our approach. © 2022 IEEE.

关键词： Application programs

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Report on the 28th Foundation of software engineering Workshop (FOSE2021)

Report on the 28th Foundation of Software Engineering Worksh...

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作者： Nagura, Masataka Sawada, Atsushi Sekizawa, Toshifusa Dept. of Software Engineering Fuculty of Sciense and Technology Nanzan University Japan Dept. of Computer Science College of Engineering Nihon University Japan

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