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检索条件"机构=Computer Engineering and Informatics"

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Evaluation of Pseudorandom Pixel Placement in Enhancing Face Recognition using VGG-Face 5

Evaluation of Pseudorandom Pixel Placement in Enhancing Face...

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5th International Conference on Image, Video Processing, and Artificial Intelligence, IVPAI 2023

作者： Hadis, Muhammad Sabirin Akita, Junichi Toda, Masashi Dept. of Electrical Engineering and Computer Science Kanazawa University Kakuma-machi Ishikawa Kanazawa920-1192 Japan Research and Education Institute for Semiconductors and Informatics Kumamoto University Kumamoto860-8555 Japan

ISBN: (纸本)9781510674660

In efforts to enhance face recognition performance, techniques ranging from super-resolution methods to the use of Local Binary Pattern (LBP) and deep learning have been explored. Among these, the pseudorandom pixel placement (PSE) technique has demonstrated potential in face recognition. Nevertheless, its testing was previously limited to just 8 subjects. This study undertakes a comprehensive evaluation of the PSE technique with a larger sample, utilizing 2000 subjects from the DigiFACE1M dataset and leveraging the state-of-the-art VGG-Face deep learning model. Through experiments involving 10 different PSE patterns on 144.000 face images, our findings indicate that, compared to Regular Pixel Placement (REG), PSE achieved an improvement in average accuracy by 1.05%, reduced the standard deviation by 1.47%, and resulted in 31 additional subjects achieving 100% accuracy. We conclude that PSE consistently outperforms REG in face recognition tasks using the VGG-Face model across the majority of tested scenarios. © 2024 SPIE. All rights reserved.

关键词： Pixels

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Classifying Upper Extremity Motor Function Using Deep Learning and Third-Person Video Data

Classifying Upper Extremity Motor Function Using Deep Learni...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Tran, Tan Chang, Lin-Ching Lum, Peter The Catholic University of America Department of Electrical Engineering and Computer Science WashingtonDC United States New Jersey Institute of Technology Department of Informatics NewarkNJ United States The Catholic University of America Department of Biomedical Engineering WashingtonDC United States

ISBN: (纸本)9798350362480

Post-stroke rehabilitation of upper extremity (UE) motor function is essential. Despite the widespread use of UE rehabilitation in clinical settings, assessing the success of these treatments is challenging. Methods for evaluating UE motor function include task performance under clinical supervision, patient self-reports, and data analysis from wearable devices equipped with accelerometers and gyroscopes. In prior research, we demonstrated that machine learning and deep learning models using data from a single wrist-worn accelerometer sensor could accurately differentiate between functional and non-functional UE movements in stroke patients. To overcome the limitations of single wrist-worn accelerometer sensors - challenges in capturing the full context of functional movements - this study presents a new deep learning (DL) based framework designed to classify functional and non-functional arm movements in videos captured from a conventional camera. The system is entirely automated and comprises two DL networks. The first network performs human pose estimation, extracting 2D pose key points of the paretic arm(s) and torso on 2-second sequences of the frames. The second network then uses these 2D pose landmarks to classify the movement sequence as functional or non-functional. This system offers two key benefits for rehabilitation. First, it can automatically generate initial annotations for video frames, significantly reducing the time needed for manual labeling. Second, analyzing the effectiveness of UE rehabilitation through third-person videos allows for objective outcome measurement of UE treatments in stroke survivors' home environments. © 2024 IEEE.

关键词： Accelerometers

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eCeLLM: Generalizing Large Language Models for E-commerce from Large-scale, High-quality Instruction Data 41

eCeLLM: Generalizing Large Language Models for E-commerce fr...

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41st International Conference on Machine Learning, ICML 2024

作者： Peng, Bo Ling, Xinyi Chen, Ziru Sun, Huan Ning, Xia Department of Computer Science and Engineering The Ohio State University United States Translational Data Analytics Institute The Ohio State University United States Department of Biomedical Informatics The Ohio State University United States

With tremendous efforts in developing effective e-commerce models, conventional e-commerce models show limited success in generalist e-commerce modeling, and suffer from unsatisfactory performance on new users and new products - a typical out-of-domain generalization challenge. Meanwhile, large language models (LLMs) demonstrate outstanding performance in generalist modeling and out-of-domain generalizability in many fields. Toward fully unleashing their power for e-commerce, in this paper, we construct ECInstruct, the first open-sourced, large-scale, and high-quality benchmark instruction dataset for e-commerce. Leveraging ECInstruct, we develop eCeLLM, a series of e-commerce LLMs, by instruction-tuning general-purpose LLMs. Our comprehensive experiments and evaluation demonstrate that eCeLLM models substantially outperform baseline models, including the most advanced GPT-4, and the state-of-the-art task-specific models in in-domain evaluation. Moreover, eCeLLM exhibits excellent generalizability to out-of-domain settings, including unseen products and unseen instructions, highlighting its superiority as a generalist e-commerce model. Both the ECInstruct dataset and the eCeLLM models show great potential in empowering versatile and effective LLMs for e-commerce. ECInstruct and eCeLLM models are publicly accessible through https://***/eCeLLM/. Copyright 2024 by the author(s)

关键词： Marketplaces

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Sentiment Analysis of Thesis Policy by Instagram Users Using Logistic Regression

Sentiment Analysis of Thesis Policy by Instagram Users Using...

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International Conference on Electrical engineering, computer Science and informatics (EECSI)

作者： Fathia Dwi Astuti Widodo Bambang Prasetya Adhi Dept. of Informatics and Computer Engineering Education Universitas Negeri Jakarta Jakarta Indonesia

ISBN: (数字)9798350355314

ISBN: (纸本)9798350355321

The requirements for university graduation were changed by the Minister of Education, Culture, Research and Technology in August 2023, where a thesis is no longer required and can be replaced with another form. The existence of this regulation has caused discussion in the community with various responses visible through social media Instagram. Instagram has become a forum for sharing the latest topics for the public, causing the emergence of online media accounts such as Folkative. This research aims to analyze sentiment regarding the policy of eliminating thesis obligations at universities through data collected from comments on Folkative's Instagram account posts. Sentiment analysis was carried out using Logistic Regression for classification, K-Fold Cross Validation for data validation, Confusion Matrix for evaluation, and ADASYN to resolve data imbalances. The data collected from the scraping process was 4,172 comments. After carrying out the classification and performance evaluation process, the best method produced was a combination of Logistic Regression and ADASYN achieving results of 89.6% accuracy, 91.5% precision, 87.7% recall, and f-1 score 89.5%. This shows that the performance of the Logistic Regression method is good and suitable for use as a classification model in this research.

关键词： Support vector machines Performance evaluation Logistic regression Sentiment analysis Accuracy Social networking (online) Media Regulation Web sites Multimedia communication

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Detection of Tuberculosis Based on X-Ray Image Using the Vision Transformer Method

Detection of Tuberculosis Based on X-Ray Image Using the Vis...

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International Conference on Information and Communications Technology (ICOIACT)

作者： Raihan Ahnaf Nafisaputra Tessy Badriyah Prasetyo Wibowo Fitri Setyorini Informatics and Computer Engineering Department Politeknik Elektronika Negeri Surabaya (PENS) Surabaya INDONESIA

ISBN: (数字)9798331536206

ISBN: (纸本)9798331536213

Tuberculosis, or TB, is an infectious disease caused by the Mycobacterium tuberculosis bacteria, which can attack the human body regardless of age and gender. This bacterium is a very strong bacillus, requiring a long time to treat. The types of radiological examinations commonly used today to detect tuberculosis are chest X-rays and CT scans. However, not all hospitals have CT scan machines due to their higher cost, while almost every hospital has an X-ray machine. This research aims to develop an assistive device with a high level of accuracy in detecting the disease through the analysis of X-ray images. The solution is to develop a tuberculosis detection assistive device based on X-ray images using the Vision Transformer classification model. The dataset used is obtained from four references and contains chest X-ray images categorized into four classes: Normal, Tuberculosis, Pneumonia, and Covid-19. This Vision Transformer model is designed to classify chest X-ray images to predict lung diseases according to the specified labels. From this research, the model can predict with an accuracy of up to 89%. The result of this research can help doctors in making decisions and also serve as a learning tool for detecting diseases in X-ray images.

关键词： Training computer vision Accuracy Tuberculosis Hospitals Computed tomography Predictive models Transformers X-ray imaging Biomedical imaging

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CORRECTIVE MAINTENANCE TIME MODEL OF THE 2-PARALLEL CONFIGURATION OF THE EARTH STATION SYSTEM USING CURVE FITTING 51

CORRECTIVE MAINTENANCE TIME MODEL OF THE 2-PARALLEL CONFIGUR...

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51st International Conference on computers and Industrial engineering, CIE 2024

作者： Kamaruddin, Nur Fatin Syamimi Rahim, Nadirah Abdul Mansor, Mahayaudin M. Department of Electrical and Computer Engineering International Islamic University Malaysia Jalan Gombak Kuala Lumpur Malaysia School of Mathematical Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam Malaysia

An Earth station system serves as a key hub in the ever-changing environment of satellite communication technology, allowing uninterrupted data interchange between the Earth and orbiting satellites. These systems are essential for various applications like telecommunications, remote sensing, and global positioning. In this research, a 2-parallel configuration is chosen for the Earth station system design because the literature suggests that it has the lowest operational cost and is reliably acceptable if compared to other parallel configurations. However, the 2-parallel configuration comes with the issue of care and upkeep, especially in the context of corrective maintenance time, which is the aim of this study. Corrective maintenance, or repairing components and systems after failure, is critical to the Earth station system operation. This study uses curve fitting techniques by using the Monte Carlo method from MATLAB software to foresee system component failures and predict their behavior over time. The useful insights can be extracted from previous maintenance data by using curve fitting, allowing one to make informed decisions regarding when and how to do maintenance. The results demonstrate the curve fitting of the combination of the corrective maintenance for seven subsystems with the mean time between failures (MTBF) of 1, 3, 5, 7, and 10 years, and it is shown that the first-degree polynomial has the lowest Root Mean Square Error (RMSE), which is 1.35. Furthermore, the corrective maintenance time model is developed not only to analyse component failures but also to assist in deriving appropriate maintenance guidelines in the future that can help system engineers repair the failed subsystem. © 2024 computers and Industrial engineering. All rights reserved.

关键词： MATLAB

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Analyzing Bloom's Taxonomy to Interactions in Virtual Reality Application for Education

Analyzing Bloom's Taxonomy to Interactions in Virtual Realit...

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International Electronics Symposium (IES)

作者： Hestiasari Rante Muhammad Agus Zainuddin Zacky Maulana Achmad Khairunnisa’ Sekar Ramadhanti Cahyo Arissabarno Dept. of Informatics and Computer Engineering Politeknik Elektronika Negeri Surabaya Surabaya Indonesia

ISBN: (数字)9798350391992

ISBN: (纸本)9798350392005

Virtual Reality (VR) technology enables users to engage with a three-dimensional, computer-simulated environment that mimics the real world. This immersive technology allows users to fully interact with 3D content as if they were present in that environment. VR has the potential for development in numerous areas, including medical education. Integrating VR into education, particularly in the medical field, can align with Bloom's Taxonomy to create more effective and meaningful interactions. Consequently, interaction design in VR applications for medical education can be optimized to improve student learning outcomes according to Bloom's Taxonomy principles. This research leverages an immersive interactive experience that enables users to actively engage in simulations and practical tasks. By incorporating the principles of Bloom's Taxonomy, the system enhances learning engagement and motivation, ensuring users can recall, apply, analyze, and evaluate information in depth. The purpose of analyzing VR interactions is to evaluate whether the interactions used in the VR Childbirth Surgery application have been implemented correctly through the lens of Bloom's Taxonomy. The interactions analyzed are Grab, Touch, and Click in the VR Childbirth Surgery application, based on journals, applications, and demo videos reviewed. The Click interaction is straightforward and user-friendly, the Grab interaction enhances realism and engagement but requires precise implementation and advanced hardware, and the Touch interaction provides high immersion but is dependent on the capabilities of the VR hardware.

关键词： Solid modeling Three-dimensional displays Taxonomy Education MIMICs Surgery Hardware

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Machine Speech Chain with Emotion Recognition 11

Machine Speech Chain with Emotion Recognition

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11th International Conference on Advanced informatics: Concept, Theory and Application, ICAICTA 2024

作者： Naufal, Akeyla Pradia Lestari, Dessi Puji Purwarianti, Ayu Azizah, Kurniawati Tanaya, Dipta Sakti, Sakriani School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia University of Indonesia Faculty of Computer Science Depok Indonesia Graduate School of Information Science Nara Institute of Science and Technology Nara Japan

ISBN: (纸本)9798331520311

Developing natural speech recognition and speech synthesis systems requires speech data that authentically represents real emotions. However, this type of data is often challenging to obtain. Machine speech chain offers a solution to this challenge by using unpaired data to continue training models initially trained with paired data. Given the relative abundance of unpaired data compared to paired data, machine speech chain can be instrumental in recognizing emotions in speech where training data is limited. This study investigates the application of machine speech chain in speech emotion recognition and speech recognition of emotional speech. Our findings indicate that a model trained with 50% paired neutral emotion speech data and 22% paired non-neutral emotional speech data shows a reduction in Character Error Rate (CER) from 37.55% to 34.52% when further trained with unpaired neutral emotion speech data. The CER further decreases to 33.75% when additionally trained with combined unpaired speech data. The accuracy of recognizing non-neutral emotions ranged from 2.18% to 53.51 %, though the F1 score fluctuated, increasing by up to 20.6% and decreasing by up to 23.4%. These results suggest that the model demonstrates a bias towards the majority class, as reflected by the values of the two metrics. © 2024 IEEE.

关键词： Chains

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Enhancing E-Voting Security with Multi-Factor Authentication Using Fingerprint and Cryptography Protocols in India

Enhancing E-Voting Security with Multi-Factor Authentication...

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2024 International Conference on Progressive Innovations in Intelligent Systems and Data Science, ICPIDS 2024

作者： Thakur, Gaurav Chouksey, Pradeep Chopra, Mayank Sadotra, Parveen Central University of Jammu Department of Computer Science and Engineering Jammu and Kashmir Samba 181143 India Central University of Himachal Pradesh Department of Computer Science and Informatics Himachal Pradesh Kangra 176206 India

ISBN: (纸本)9798331534691

The integrity and security of e-voting systems are the core to ensure free and fair elections in any country. As the process involves an enormous, highly diversified population in India, e-voting systems are open to a hugely diversified variety of imminent threats, therefore, extraordinary security measures are incumbent on safeguarding them. This paper deals with the implementation of multi-factor authentication in a manner intended to improve the security of e-voting systems by integrating fingerprint biometrics with visual cryptography and also the state-of-the-art cryptographic protocols. It is proposed that some of these vulnerabilities associated with conventional methods of authentication should be tackled by the use of biometric verification, which can provide each voter with a unique and immutable identity. A detailed survey is presented on the current challenges to e-voting security in India and the battle-hardened limitations of existing systems, providing a compelling case for robust MFA solutions. The proposed system uses fingerprint authentication as its primary factor and further fortifies this with cryptographic methods that ensure confidentiality, integrity, and authenticity of the entire process of voting, right from the voter's end to the final tallying of votes. Finally, it provides end-to-end security from voter authentication to vote-tallying using symmetric and asymmetric encryption, as well as secure hash functions. The approach is tested against common security threats with simulations and practical implementation on voter impersonation, data tampering, and unauthorized access. Our results show that a high degree of enhancement in the security and reliability of e-voting schemes can be achieved by integrating fingerprint-based MFA with cryptographic protocols. This work is, therefore, a part of the modernization and security effort within the electoral process in India and is hopefully going to be one prospective solution for protecting the

关键词： E-Voting Security Homomorphic Encryption (HHE) Multi-Factor Authentication (MFA) PyCryptodome Zero-Knowledge Proofs (ZKP)

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Anomaly Detection With Quantified Explanations for Multivariate Time Series Networking Data

Anomaly Detection With Quantified Explanations for Multivari...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Marantis, Panagiotis Ramantas, Kostas Verikoukis, Christos Iquadrat Informatica S.L Barcelona Spain University of Patras Dept. of Computer Engineering and Informatics Patras Greece Athena Research Center Athens Greece Industrial Systems Institute Patras Greece

ISBN: (纸本)9798350351255

Next-generation telecommunication networks provide vast amounts of Multivariate Time Series monitoring data during their operation. Using these data to automatically detect anomalous system behavior is of critical importance to operators and Internet Service Providers, in order to analyze the network state and take mitigating actions to resolve possible faults. For the output of this Anomaly Detection process to be trusted, comprehensive and accurate explanations must be provided. These explanations are essential to make critical decisions that affect the network state. In this paper, we present a framework that utilizes reconstruction-based Deep Neural Network models to perform the anomaly detection task, while also using an explainability module that provides explanations of the results. The quality of the explanations is quantified through the use of interpretation labels. In addition, we provide a comparative analysis, performing Anomaly Detection on three different open-source datasets from the mobile networking domain, while testing multiple Deep Learning models and comparing their performance. The presented results show that our framework accurately detects various types of anomalies that can be present in mobile networks, while offering the flexibility to identify and select the model that offers the best performance for each dataset, in both the Anomaly Detection and explainability tasks. © 2024 IEEE.

关键词： DNNs Explainable Anomaly Detection Machine Learning Multivariate Time Series Next-Generation Networks Reconstruction-based Models

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