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International Conference on computer and Information Technology (ICCIT)

作者： Israt Jahan Khan Md. Fahim Bin Amin Kamrul Islam Shahin Huu-Hoa Nguyen Dewan Md. Farid Department of Computer Science and Engineering United International University Dhaka Bangladesh Software Engineering The Maersk Mc-Kinney Moller Institute University of Southern Denmark Odense M Denmark College of Information and Communication Technology Can Tho University Can Tho City Vietnam

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Multi-class classification presents a significant challenge in supervised machine learning, and it is frequently applied across various real-world domains. Random Forest (RF) stands out as a popular ensemble technique for multi-class classification tasks that utilizes attribute bagging to construct random trees and employs a majority-voting scheme for classification. However, the existing Random Forest approach often faces limitations in accuracy and efficiency, particularly for more critical tasks. In this study, we have addressed this gap by introducing a novel methodology that integrates the K-Nearest Neighbor (KNN) classifier and Decision Trees into the Random Forest framework to enhance performance. The proposed method has involved training a K-Nearest Neighbor and a Decision Tree (DT) classifier on the extracted features. The output probabilities from these classifiers have been used as meta-features, forming an enriched feature vector fed into a Random Forest as the meta-learner for final classification. Through extensive experimentation on 5 datasets, the proposed approach has demonstrated superior performance in accuracy and efficiency compared to the traditional method. By integrating Random Forest and K-Nearest Neighbor techniques, this research has offered a versatile solution applicable across diverse domains where precise multi-class classification is essential.

关键词： Training Accuracy Nearest neighbor methods Feature extraction Transformers Vectors Diabetes Random forests Classification tree analysis Diseases

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A Three-Step Procedural Paradigm for Domain-Specific Social Media Slang Analytics

A Three-Step Procedural Paradigm for Domain-Specific Social ...

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Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： Aishwarya Sundaram Hema Subramaniam Siti Hafizah Ab Hamid Azmawaty Mohamad Nor Institute for Advanced Studies Universiti Malaya Kuala Lumpur Malaysia Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia Department of Educational Psychology and Counselling Faculty of Education Universiti Malaya Kuala Lumpur Malaysia

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

Social media is a crucial aspect of modern society, shaping global communication and information exchange. Social media slang encompasses informal language expressions, abbreviations, and unconventional words on these platforms, serving purposes such as concise communication and identity establishment. Data analytics from social media slang plays a vital role in providing real-time trends, enhancing sentiment analysis, and improving the accuracy of predictive models by capturing evolving linguistic trends. This study aims to address gaps in existing social media slang analytics by proposing a three-step process for extracting and validating domain-specific social media slang terms, specifically focusing on the domain of anxiety prediction in the context of mental health. The proposed approach involves the identification of relevant users, extraction of social media slang terms, and validation by subject matter experts. A pilot study is conducted in the domain of anxiety prediction, employing a self-prepared questionnaire and involving participants aged 13-14 from schools in Selangor, Malaysia. The pilot study identifies ten social media slang terms associated with anxiety, validated by subject matter experts who are licensed mental health practitioners. The slang terms exhibit varying prevalence levels among adolescents, with some terms absent from popular slang dictionaries like Urban Dictionary. The findings highlight the need for extracting slang terms from pertinent users and subject matter expert validation in incorporating social media slang into more appropriate predictive analytics.

关键词： Sentiment analysis Dictionaries Social networking (online) Soft sensors Anxiety disorders Mental health Predictive models

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Conventional/Phase Shift Dual Drive Mach-Zehnder Modulation Measured Type Based Radio over Fiber Systems

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Journal of Optical Communications 2024年第s1期45卷 s123-s131页

作者： Amiri, I.S. Rashed, Ahmed Nabih Zaki Rahman, Ziaur Paul, Bikash Kumar Ahmed, Kawsar Computational Optics Research Group Advanced Institute of Materials Science Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Applied Sciences Ton Duc Thang University Ho Chi Minh City Viet Nam Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf32951 Egypt School of Computer Science and Software Engineering RMIT University 414-418 Swanston Street MelbourneVIC3000 Australia Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Santosh Tangail1902 Bangladesh Group of Bio-photomatiχ Mawlana Bhashani Science and Technology University Santosh Tangail1902 Bangladesh Department of Software Engineering Daffodil International University Sukrabad Dhanmondi Dhaka1207 Bangladesh

This work clarified the conventional/phase shift dual Mach-Zehnder modulation measured technique for upgrading radio over fiber (ROF) systems. Four levels of pulse amplitude modulation and non-return to zero code scheme are employed in different previous studies for upgrading ROF systems. The possible transmission distance is extended to 150 km and a bit rate of 40 Gb/s for achieved acceptable max. Q factor of 13.7498 and minimum bit error rates of 1.82×10-43. The total optical received power is measured at fiber cable length for maximum distance. Also, in the same way, the signal power levels are measured after fiber optic cable and avalanche photo detector optical receiver. The proposed model has presented the enhancement percentage ratio of 13.654% over the previous models in signal quality enhancement. © 2020 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Q factor measurement

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A Small-Signal Stability Criterion for a Power System Dynamic Model with Constant Power Loads

SSRN

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SSRN 2024年

作者： Levron, Yoash Belikov, Juri Weiss, George The Andrew and Erna Viterbi Faculty of Electrical & Computer Engineering Technion—Israel Institute of Technology Haifa3200003 Israel Department of Software Science Tallinn University of Technology Tallinn12618 Estonia School of Electrical Engineering Tel-Aviv University Tel-Aviv69978 Israel

This theoretical paper continues a long line of research works about the stability of power systems represented by reduced order models. We provide a small-signal stability criterion for a dynamic model that uses time-varying phasors, and is characterized by a lossless transmission network and constant power loads located anywhere in the network. A key concept in this study is the matrix H of the partial derivatives of the generator powers with respect to their relative angles. Our main result is that the local stability of any equilibrium point is completely determined by properties of this matrix H. More specifically, we show that an equilibrium point is asymptotically stable if H is positive definite, and is unstable if H is full-rank and not positive definite. In addition, we also prove several relations between the eigenvalues of H and the eigenvalues of the system's Jacobian matrix. These results are demonstrated on a number of case-studies, and compared with related earlier ones from the literature. © 2024, The Authors. All rights reserved.

关键词： Electric power transmission networks

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Server configuration optimization in mobile edge computing: A cost-performance tradeoff perspective

Server configuration optimization in mobile edge computing: ...

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作者： He, Zhenli Li, Kenli Li, Keqin Zhou, Wei College of Computer Science and Electronic Engineering & National Supercomputing Center in Changsha Hunan University Changsha China College of Software Yunnan University Kunming China Department of Computer Science State University of New York New PaltzNY United States

Before service providers build up an mobile edge computing (MEC) platform, an important issue that needs to be considered is the configuration of computing resources on edge servers. Since the computing resources on an edge server are limited compared with a cloud server and the service provider's deployment budget is limited, it would be unrealistic to equip all edge servers with abundant computing resources. In addition, the edge servers have different computation demands due to their different geographies. Therefore, this article investigates the problem of server configuration optimization in an MEC environment based on a given computation demand statistics of the selected deployment locations. Our strategy is to treat each edge server as an M/M/m queueing model, and then establish the performance and cost models for the system. Two optimization problems, including cost constrained performance optimization, and performance constrained cost optimization are formulated based on our models and solved by a series of fast numerical algorithms. We also conduct extensive numerical simulation examples to show the effectiveness of the proposed algorithms. MEC service providers can use our strategy to get the appropriate type of processor and obtain the optimal processor number for each edge server to achieve two different goals: (1) deliver the highest-quality services with a given cost constraint;(2) minimize the investment cost with a service-quality guarantee. Our research is of great significance for service providers to control the tradeoff between investment cost and service quality. © 2021 John Wiley & Sons, Ltd.

关键词： Mobile edge computing

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Automated Test Case Repair Using Language Models

arXiv

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arXiv 2024年

作者： Yaraghi, Ahmadreza Saboor Holden, Darren Kahani, Nafiseh Briand, Lionel School of Electrical Engineering and Computer Science University of Ottawa OttawaONK1N 5N6 Canada Lero The Research Ireland Centre for Software University of Limerick Ireland Department of Systems and Computer Engineering Carleton University OttawaONK1S 5B6 Canada

Ensuring the quality of software systems through testing is essential, yet maintaining test cases poses significant challenges and costs. The need for frequent updates to align with the evolving system under test often entails high complexity and cost for maintaining these test cases. Further, unrepaired broken test cases can degrade test suite quality and disrupt the software development process, wasting developers’ time. To address this challenge, we present TARGET (TEST REPAIR GENERATOR), a novel approach leveraging pre-trained code language models for automated test case repair. TARGET treats test repair as a language translation task, employing a two-step process to fine-tune a language model based on essential context data characterizing the test breakage. To evaluate our approach, we introduce TARBENCH, a comprehensive benchmark we developed covering 45,373 broken test repairs across 59 open-source projects. Our results demonstrate TARGET’s effectiveness, achieving a 66.1% exact match accuracy. Furthermore, our study examines the effectiveness of TARGET across different test repair scenarios. We provide a practical guide to predict situations where the generated test repairs might be less reliable. We also explore whether project-specific data is always necessary for fine-tuning and if our approach can be effective on new projects. © 2024, CC BY.

关键词： software design

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Leveraging Digital Twins and Demand Side Recommender Chatbot for Optimizing Smart Grid Energy Efficiency

Leveraging Digital Twins and Demand Side Recommender Chatbot...

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IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia)

作者： Abiodun E. Onile Juri Belikov Eduard Petlenkov Yoash Levron Department of Software Science Tallinn University of Technology Akadeemia tee 15a Tallinn Department of Computer Systems Tallinn University of Technology Akadeemia tee 15a Tallinn Faculty of Electrical & Computer Engineering Technion—Israel Institute of Technology Haifa Israel

Electricity consumers often face the challenge of selecting an optimal plan for saving energy. Strategic energy management and monitoring plays a key role in overcoming these challenges. Developments around Industry 5.0 powered smart grid proffers adequate solutions which allows end-consumers to monitor their energy performance towards effecting demand side recommendation services. Specific problems where end-users are likely to ignore recommended advice exists, thereby contributing to widening ‘knowledge-action gap’. An ensemble of hybrid digital twins (DT) asset modelling based on ordinary differential equation (ODE) physics engine and data driven recurrent neural network (RNN) prediction approach alongside PageRank based asset behavior scoring algorithm deployed for demand side recommender and generative pre-trained transformers (GPT) based conversational chatbot technology show effectiveness in engaging and extending end-consumers interests in recommended advice. The novelty of the study lies in extending current scope of demand side recommender scheme via conversational chatbot interface for DT of electricity grid assets that better engage and monitors end-user’s energy behavior while offering appropriate energy efficiency advice towards achieving energy conservation goals of smart grid consumers. Extensive experiments, including evaluation of end-user studies, revealed the effectiveness of proposed approach in terms of improved recommendation quality and user engagement towards net electricity demand reduction.

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Providing Technical Support to Sustain Student Motivation and Engagement in software engineering Project-Based Learning

Providing Technical Support to Sustain Student Motivation an...

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Frontiers in Education (FIE) Conference

作者： Ahmad D. Suleiman David Shepherd Jan DeWaters Yu Liu Daqing Hou Computing and Information Sciences Rochester Institute of Technology Rochester NY USA Department of Computer Science Louisiana State University Baton Rouge LA USA Institute for STEM Education Clarkson University Potsdam NY USA Electrical & Computer Engineering Clarkson University Potsdam NY USA Software Engineering Department Rochester Institute of Technology Rochester NY USA

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

In this research paper, drawing from our own and other computing instructors' experiences, we highlight common technical challenges faced by students in software engineering project-based learning (PjBL) and discuss ways in which instructors can support students in overcoming them so that motivation is summoned and sustained. Through the use of practical hands-on experiences, PjBL has been shown to be an effective educational approach. However, unless projects are intentionally designed and supported in a way that summons and sustains student motivation, PjBL is likely to fail to accomplish its goals. Several factors influence student motivation, including their perception of the project's value and how confident they are in their ability to complete it. In particular, challenges that students perceive as insurmountable during the project can significantly weaken their motivation. On the other hand, supporting students to overcome such hurdles can be troublesome, especially in large classes as well as classes with diversity in student backgrounds. To generalize from our own experience, we designed a questionnaire targeted at PjBL computing instructors that contained closed and open questions on technical challenges faced by students, support instructors provided to overcome such challenges, and lessons learned by instructors on the effectiveness of their support. A total of 47 responses were collected from instructors with diverse backgrounds in terms of courses taught, students' years, and class sizes. We categorized the technical challenges into three main categories, namely (a) challenges in installing and configuring software packaged tools, (b) lack of prerequisite knowledge, and (c) challenges while completing project tasks. In this paper, we present the survey results from the three categories of technical challenges, their frequencies, importance, and effective support strategies instructors use to alleviate them.

关键词： Surveys Hands Time-frequency analysis software software engineering

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Compromising Rechargeable Sensor Networks in Marine Environment

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IEEE Transactions on Mobile Computing 2025年

作者： Wang, Qiwei Lin, Chi Dai, Haipeng Obaidat, Mohammad S. Hsiao, Kuei-Fang Fan, Xin Dalian University of Technology School of Software Technology & DUT-RU International School of Information Science and Engineering China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Dalian116620 China Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China SRM University Department of Computational Intelligence School of Computing SRM Nagar TN Kattankulathur603203 India The Amity University School of Engineering UP Noida201301 India The King Abdullah II School of Information Technology The University of Jordan Amman11942 Jordan

Marine Wireless Rechargeable Sensor Networks (MWRSNs), enhanced by recent Wireless Power Transfer (WPT) technology, present a significant advancement in extending network life. Traditional methods improve network performance through algorithm optimization, but neglect charging security, exposing networks to potential attacks. This paper addresses this problem from an adversarial view and develops a novel attack for MWRSN through Denying of Charge (DoC) to maximize network destructiveness. We start by establishing a generalized on-demand charging model, essential for developing DoC tactics. Subsequently, we unveil the Collaborative DoC (CoDoC) algorithm, capable of manipulating and falsifying charging requests. Central to CoDoC is the Request Prediction Method (RPM), which forecasts the initiation of charging requests and facilitates rapid request surges to enhance the attack's efficacy. CoDoC is able to disguise the presence of the attack, which is able to escape from being detected by the base station. Theoretical analyses are provided to explore the features of the proposed scheme. To demonstrate the outperformed features of the proposed schemes, extensive simulations and test-bed experiments are conducted. Our analysis and extensive simulations demonstrate that CoDoC increases sensor node failures by 20% to 142% compared to traditional methods, highlighting its effectiveness in marine environments. © 2002-2012 IEEE.

关键词： Charging stations

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Detecting Multimedia Generated by Large AI Models: A Survey

arXiv

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arXiv 2024年

作者： Lin, Li Gupta, Neeraj Zhang, Yue Ren, Hainan Liu, Chun-Hao Ding, Feng Wang, Xin Li, Xin Verdoliva, Luisa Hu, Shu Department of Computer and Information Technology Purdue University IN46202 United States School of Software Nanchang University Jiangxi Nanchang330031 China Amazon Prime Video SunnyvaleCA94089 United States Department of Epidemiology and Biostatistics School of Public Health Department of Computer Science College of Nanotechnology Science and Engineering both at University at Albany SUNY AlbanyNY12222 United States Department of Electrical Engineering and Information Technology University Federico II of Naples Naples80125 Italy

The rapid advancement of Large AI Models (LAIMs), particularly diffusion models and large language models, has marked a new era where AI-generated multimedia is increasingly integrated into various aspects of daily life. Although beneficial in numerous fields, this content presents significant risks, including potential misuse, societal disruptions, and ethical concerns. Consequently, detecting multimedia generated by LAIMs has become crucial, with a marked rise in related research. Despite this, there remains a notable gap in systematic surveys that focus specifically on detecting LAIM-generated multimedia. Addressing this, we provide the first survey to comprehensively cover existing research on detecting multimedia (such as text, images, videos, audio, and multimodal content) created by LAIMs. Specifically, we introduce a novel taxonomy for detection methods, categorized by media modality, and aligned with two perspectives: pure detection (aiming to enhance detection performance) and beyond detection (adding attributes like generalizability, robustness, and interpretability to detectors). Additionally, we have presented a brief overview of generation mechanisms, public datasets, and online detection tools to provide a valuable resource for researchers and practitioners in this field. Furthermore, we identify current challenges in detection and propose directions for future research that address unexplored, ongoing, and emerging issues in detecting multimedia generated by LAIMs. Our aim for this survey is to fill an academic gap and contribute to global AI security efforts, helping to ensure the integrity of information in the digital realm. The project link is https://***/Purdue-M2/Detect-LAIM-generated-Multimedia-Survey. Copyright © 2024, The Authors. All rights reserved.

关键词： Computational linguistics

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