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International Conference on Computing, engineering, and Design (ICCED)

作者： Rahmat Hidayat Achmad Solichin Adhitia Erfina Sudin Saepudin Muhamad Muslih Mohd Zainuri Saringat Master of Computer Science Budi Luhur University Jakarta Indonesia Information Systems Study Program Nusa Putra University Sukabumi Indonesia Department of Software Engineering Faculty of Computer Science and Information Technology Tun Hussein Onn Malaysia University

ISBN: (数字)9798331529376

ISBN: (纸本)9798331529383

The Plasmodium parasite, which causes malaria, is an acute fever illness that infects people when a female Anopheles mosquito bites them. It is predicted that malaria would claim 619,000 lives in 2021, with 96% of those deaths occurring in the African continent. We can achieve this by using a microscope to examine thick and thin blood smears. The proficiency of a microscope examiner is crucial for doing microscopic examinations. Consider how time-consuming, ineffective, and costly it would be to examine thousands of malaria cases. Consequently, Creating an automated method for detecting malaria parasites is the aim of this study. We employ a MobileNetV2 pretrained model with CNN technology. Because it has been trained on dozens or even millions of data points, this pretrained model is incredibly light but dependable. There are two main benefits of automatic malaria parasite detection: firstly, it can offer a more accurate diagnosis, particularly in locations with limited resources; secondly, it lowers diagnostic expenses. The optimizer utilizes Adam Weight, the criteria uses NLLLoss, and the model is trained using 32 for batch_size. In the fourteenth epoch, we obtained the maximum accuracy score of 96.26% based on the training data. The outcomes of the predictions demonstrate how excellent this score is. EfficienceNet, DenseNet, AlexNet, and other pretrained models are among the alternatives that scientists are advised to try training with.

关键词： Training Adaptation models Accuracy Costs Malaria Microscopy Data visualization Training data Cost function Data models

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DeepJDOT: Deep joint distribution optimal transport in facial recognition 2

DeepJDOT: Deep joint distribution optimal transport in facia...

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2nd International Conference on Mechatronics and Smart Systems, CONF-MSS 2024

作者： Cai, Yuchen Wen, Chenglong Chen, Haoyuan Liu, Yijia Peng, Haofei Computer Science and Software Engineer Department Rose-Hulman Institute of Technology Terre Haute47803 United States Department of Physics University of Toronto TorontoM5S 1A1 Canada Department of Physics University of Washington Seattle98195 United States Department of Water Conservancy and Civil Engineering China Agricultural University Beijing100083 China Department of Information Engineering Xi'An Jiaotong University Xi'an710000 China

This paper explores the application of deep learning in domain adaptation, with a particular focus on using the Deep Joint Distribution Optimal Transportation (DeepJDOT) method for face recognition. We test DeepJDOT on a series of visual domain adaptation tasks and find that it exhibits high accuracy and competitive advantage, especially under smaller image sizes, compared to recent state-of-the-art competitors. This research not only highlights the potential of deep learning in domain adaptation but also underscores DeepJDOT as an effective approach for improving the performance of applications like face recognition. Furthermore, we introduce a domain adaptation-tailored loss function based on optimal transport to further enhance model performance. This study provides valuable insights and methods for research in visual domain adaptation and face recognition. © Published under licence by IOP Publishing Ltd.

关键词： Face recognition

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Robust Feature engineering Techniques for Designing Motor Imagery-Based Bci Systems

SSRN

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SSRN 2025年

作者： Gardezi Ahmed, Syed Saim Khan, Mahnoor Bukhari, Syeda Muneeba Khan, Rizwan Ahmed Jawed, Soyiba Department of Biomedical Engineering Faculty of Engineering Salim Habib University Karachi Pakistan Department of Computer Software Engineering College of Electrical and Mechanical Engineering NUST Islamabad Pakistan Department of Physics Faculty of Applied Sciences Riphah International University Islamabad Pakistan Department of Biomedical Engineering Faculty of Engineering NED University Karachi Pakistan Department of Computer Science Faculty of Information Technology Salim Habib University Karachi Pakistan

A multitude of individuals across the globe grapple with motor disabilities. Neural prosthetics utilizing Brain-computer Interface (BCI) technology exhibit promise for improving motor rehabilitation outcomes. The intricate nature of EEG data poses a significant hurdle for current BCI systems. Recently, a qualitative repository of EEG signals tied to both upper and lower limb execution of motor and motor imagery tasks has been unveiled. Despite this, the productivity of the Machine Learning (ML) Models that were trained on this dataset was alarmingly deficient, and the evaluation framework seemed insufficient. To enhance outcomes, robust feature engineering (signal processing) methodologies are implemented. A collection of time domain, frequency domain, and wavelet-derived features was obtained from 16-channel EEG signals, and the Maximum Relevance Minimum Redundancy (MRMR) approach was employed to identify the four most significant features. For classification K Nearest Neighbors, Support Vector Machine, Decision Tree, and Naive Bayes models were implemented with these selected features, evaluating their effectiveness through metrics such as testing accuracy, precision, recall, and F1 Score. By leveraging SVM with a Gaussian Kernel, a remarkable maximum testing accuracy of 92.50% for motor activities and 95.48% for imagery activities is achieved. These results are notably more dependable and gratifying compared to the previous study, where the peak accuracy was recorded at 74.36%. This research work provide an in-depth analysis of the MI Limb EEG dataset and it will help in designing and developing simple, cost-effective and reliable BCI systems for neuro-rehabilitation. © 2025, The Authors. All rights reserved.

关键词： Image enhancement

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Dynamic Resource Allocation for Metaverse Applications with Deep Reinforcement Learning

arXiv

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arXiv 2023年

作者： Chu, Nam H. Nguyen, Diep N. Hoang, Dinh Thai Phan, Khoa T. Dutkiewicz, Eryk Niyato, Dusit Shu, Tao School of Electrical and Data Engineering University of Technology Sydney Australia School of Engineering and Mathematical Sciences La Trobe University Melbourne Australia School of Computer Science and Engineering Nanyang Technological University Singapore Department of Computer Science and Software Engineering Auburn University United States

This work proposes a novel framework to dynamically and effectively manage and allocate different types of resources for Metaverse applications, which are forecasted to demand massive resources of various types that have never been seen before. Specifically, by studying functions of Metaverse applications, we first propose an effective solution to divide applications into groups, namely MetaInstances, where common functions can be shared among applications to enhance resource usage efficiency. Then, to capture the real-time, dynamic, and uncertain characteristics of request arrival and application departure processes, we develop a semi-Markov decision process-based framework and propose an intelligent algorithm that can gradually learn the optimal admission policy to maximize the revenue and resource usage efficiency for the Metaverse service provider and at the same time enhance the Quality-of-Service for Metaverse users. Extensive simulation results show that our proposed approach can achieve up to 120% greater revenue for the Metaverse service providers and up to 178.9% higher acceptance probability for Metaverse application requests than those of other baselines. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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PRAD: Unsupervised KPI Anomaly Detection by Joint Prediction and Reconstruction of Multivariate Time Series 19

PRAD: Unsupervised KPI Anomaly Detection by Joint Prediction...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Xiong, Zhiying Fan, Qilin Wang, Kai Li, Xiuhua Zhang, Xu Xiong, Qingyu Chongqing University School of Big Data and Software Engineering Chongqing China Chongqing University Chongqing Key Laboratory of Digital Cinema Art Theory and Technology Chongqing China Harbin Institute of Technology School of Computer Science and Technology Weihai China Harbin Institute of Technology Research Institute of Cyberspace Security Weihai China Haihe Laboratory of Information Technology Application Innovation Tianjin China University of Exeter Department of Computer Science Exeter United Kingdom

ISBN: (纸本)9798350346558

Detecting anomalies for key performance indicator (KPI) data is of paramount importance to ensure the quality and reliability of network services. However, building the anomaly detection system for KPI is challenging due to the scarcity of abnormal labels and highly dynamic or even unseen patterns. In this paper, we propose an unsupervised KPI anomaly detection framework named PRAD by jointly optimizing prediction-based and reconstruction-based modules. Specifically, PRAD employs two parallel graph attention networks (GATs) to learn metric-oriented and time-oriented relationships. A temporal convolutional attention network (TCAN) is used to capture complex long-term dependencies. Furthermore, to tackle the overfitting issue in the variational auto-encoder (VAE), PRAD designs a reconstruction-based module that combines VAE with a generative adversarial network (GAN). The experimental results on the two public datasets demonstrate that the proposed PRAD outperforms existing anomaly detection methods. © 2022 IEEE.

关键词： Anomaly detection

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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...

引用

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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Comprehensive Evaluation of ChatGPT Reliability Through Multilingual Inquiries

arXiv

引用

arXiv 2023年

作者： Puttaparthi, Poorna Chander Reddy Deo, Soham Sanjay Gul, Hakan Tang, Yiming Shang, Weiyi Yu, Zhe Department of Software Engineering Rochester Institute of Technology RochesterNY United States School of Information Rochester Institute of Technology RochesterNY United States Department of Computer Science Rochester Institute of Technology RochesterNY United States Department of Electrical and Computer Engineering University of Waterloo WaterlooON Canada

ChatGPT is currently the most popular large language model (LLM), with over 100 million users, making a significant impact on people's lives. However, due to the presence of jailbreak vulnerabilities, ChatGPT might have negative effects on people's lives, potentially even facilitating criminal activities. Testing whether ChatGPT can cause jailbreak is crucial because it can enhance ChatGPT's security, reliability, and social responsibility. Inspired by previous research revealing the varied performance of LLMs in different language translations, we suspected that wrapping prompts in multiple languages might lead to ChatGPT jailbreak. To investigate this, we designed a study with a fuzzing testing approach to analyzing ChatGPT's cross-linguistic proficiency. Our study includes three strategies by automatically posing different formats of malicious questions to ChatGPT: (1) each malicious question involving only one language, (2) multilingual malicious questions, (3) specifying that ChatGPT responds in a language different from the questions. In addition, we also combine our strategies by utilizing prompt injection templates to wrap the three aforementioned types of questions. We examined a total of 7,892 Q&A data points, discovering that multilingual wrapping can indeed lead to ChatGPT's jailbreak, with different wrapping strategies having varying effects on jailbreak probability. Prompt injection can amplify the probability of jailbreak caused by multilingual wrapping. This study provides insights for OpenAI developers to enhance ChatGPT's support for language diversity and inclusion. © 2023, CC BY.

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Digital Human Techniques for Education Reform 24

Digital Human Techniques for Education Reform

引用

7th International Conference on Educational technology Management, ICETM 2024

作者： Guo, Peirong Zhang, Qi Tian, Chunwei Xue, Wanli Feng, Xiaocheng School of Software Northwestern Polytechnical University Shaanxi Xi’an China School of Economics and Management Harbin Institute of Technology at Weihai Shandong Weihai China Shenzhen Research Institute of Northwestern Polytechnical University Northwestern Polytechnical University Guangdong Shenzhen China School of Computer Science and Engineering Tianjin University of Technology Tianjin China Research Center for Social Computing and Information Retrieval Harbin Institute of Technology Heilongjiang Harbin China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen University Guangdong Shenzhen China

ISBN: (纸本)9798400717468

The rapid evolution of artificial intelligence, big data, and generative AI models has ushered in significant transformations across various sectors, including education. Digital Human Technique, an innovative technology grounded in advanced computer science and artificial intelligence, is reshaping educational paradigms by enabling virtual humans to simulate human behavior, express emotions, and interact with users. This paper explores the application of Digital Human Technique in education reform, focusing on creating immersive, intelligent classroom experiences that foster meaningful interactions between teachers and students. We define Digital Human Technique and delve into its key technical components such as character modeling and rendering, natural language processing, computer vision, and augmented reality technologies. Our methodology involves analyzing the role of educational digital humans created through these technologies, assessing their impact on educational processes, and examining various application scenarios in educational reform. Results indicate that Digital Human Technique significantly enhances the learning experience by enabling personalized teaching, increasing engagement, and fostering emotional connections. Educational digital humans serve as virtual teachers, interactive learning aids, and facilitators of emotional interaction, effectively addressing the challenges of traditional educational methods. They also promote a deeper understanding of complex concepts through simulated environments and interactive digital content. © 2024 Copyright held by the owner/author(s).

关键词： Digital elevation model

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How Mature is Requirements engineering for AI-based Systems? A Systematic Mapping Study on Practices, Challenges, and Future Research Directions

arXiv

引用

arXiv 2024年

作者： Umm-E-Habiba Haug, Markus Bogner, Justus Wagner, Stefan Institute of Software Engineering University of Stuttgart Stuttgart70569 Germany Department of Software Engineering University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Department of Computer Science Vrije Universiteit Amsterdam Amsterdam Netherlands TUM School of Communication Information and Technology Technical University of Munich Heilbronn Germany

Artificial intelligence (AI) permeates all fields of life, which resulted in new challenges in requirements engineering for artificial intelligence (RE4AI), e.g., the difficulty in specifying and validating requirements for AI or considering new quality requirements due to emerging ethical implications. It is currently unclear if existing RE methods are sufficient or if new ones are needed to address these challenges. Therefore, our goal is to provide a comprehensive overview of RE4AI to researchers and practitioners. What has been achieved so far, i.e., what practices are available, and what research gaps and challenges still need to be addressed? To achieve this, we conducted a systematic mapping study combining query string search and extensive snowballing. The extracted data was aggregated, and results were synthesized using thematic analysis. Our selection process led to the inclusion of 126 primary studies. Existing RE4AI research focuses mainly on requirements analysis and elicitation, with most practices applied in these areas. Furthermore, we identified requirements specification, explainability, and the gap between machine learning engineers and end-users as the most prevalent challenges, along with a few others. Additionally, we proposed seven potential research directions to address these challenges. Practitioners can use our results to identify and select suitable RE methods for working on their AI-based systems, while researchers can build on the identified gaps and research directions to push the field forward. ∗ © 2024, CC BY.

关键词： Requirements engineering

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Testing Non-Commutativity of Reduce Functions with Multi-Column Inputs

SSRN

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SSRN 2024年

作者： Zhang, Xuan Zhu, Chenlu Li, Ning Zhang, Peng Liu, Lei College of Software Jilin University Jilin China College of Computer Science and Technology Jilin University Jilin China Key Laboratory of Symbolic Computation and Knowledge Engineering Jilin University Jilin China

With the continuous development of the MapReduce programming model, it is necessary to ensure the reliability of MapReduce programs. In practice, the non-commutativity of Reduce functions seriously affects the reliability of the MapReduce program, which is difficult to debug and even causes errors. Current researches on the non-commutability detection of Reduce function consider the case that the input value is a single attribute. However, such researches ignore the situation where inputs to most reduce functions in practical applications consist of multiple columns (such as a table). To test the commutativity of reduce functions where each input record may contain several input attributes, a new testing method is proposed. This approach uses symbolic execution tools to help generate a few input records, and breaks their data dependencies to generate an original test case t0, with a dynamic program slicing technique to lessen the scale of t0. And the ultimate test suite is consisted of different permutations of records in t0. In the end, experiments demonstrate the effectiveness of our testing method and that the permutation method Gm is helpful to reduce its complexity. © 2024, The Authors. All rights reserved.

关键词： MapReduce

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