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Enhancing Learning and Therapy for Children with Autism: Innovative Activities with the NAO Robot

Enhancing Learning and Therapy for Children with Autism: Inn...

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E-Health and Bioengineering Conference (EHB)

作者： Iustina-Bianca Bulai Otilia Zvorişteanu Dept. of Computer Science and Engineering Faculty of Automatic Control and Computer Engineering “Gheorghe Asachi” Technical University of Iaşi Iaşi Romania

ISBN: (数字)9798331532147

ISBN: (纸本)9798331532154

Worldwide, autism affects 1 in 100 children, with diagnoses steadily rising since the 2000s. Romania lacks official statistics on autism prevalence, but global estimates suggest similarity. Children with autism struggle with social interactions and verbal communication, prompting interest in social robots for therapy and education. Tailored robot-assisted sessions offer potential for cognitive development, vocabulary enhancement, social interaction, and behavioral stimulation like concentration and attention. The NAO robot's user-friendly design, technical capabilities, and integrated features make it well-suited for these purposes. This paper presents activities that exemplify the concept of “learning through play,” allowing children to engage in exploration, experimentation, and effective learning in an enjoyable manner.

关键词： Autism Vocabulary Hospitals Social robots Education Biomedical engineering

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On Detecting Low-Pass Graph Signals Under Partial Observations

On Detecting Low-Pass Graph Signals Under Partial Observatio...

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Sensor Array and Multichannel Signal Processing Workshop

作者： Hoang-Son Nguyen Hoi-To Wai Dept. of Computer Science & Engineering The Chinese University of Hong Kong Dept. of Systems Engineering & Engineering Management The Chinese University of Hong Kong

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

The application of graph signal processing (GSP) on partially observed graph signals with missing nodes has gained attention recently. This is because processing data from large graphs are difficult, if not impossible due to the lack of availability of full observations. Many prior works have been developed using the assumption that the generated graph signals are smooth or low pass filtered. This paper treats a blind graph filter detection problem under this context. We propose a detector that certifies whether the partially observed graph signals are low pass filtered, without requiring the graph topology knowledge. As an example application, our detector leads to a pre-screening method to filter out non low pass signals and thus robustify the prior GSP algorithms. We also bound the sample complexity of our detector in terms of the class of filters, number of observed nodes, etc. Numerical experiments verify the efficacy of our method.

关键词： Array signal processing Conferences Low-pass filters Signal processing algorithms Detectors Filtering algorithms Complexity theory

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Validation and evaluation of models in the study of characteristic descriptors based on patients' gender

Validation and evaluation of models in the study of characte...

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2022 International Conference Automatics and Informatics, ICAI 2022

作者： Marinova, Ginka Todorova, Maya Technical University of Varna Dept. of Computer Science and Engineering Varna Bulgaria Technical University of Varna Dept. of Software and Internet Technologies Varna Bulgaria

ISBN: (数字)9781665476256

ISBN: (纸本)9781665476256

Cancer-related distress is the result of the psyche's reaction to the diagnosis given and to the various stressors in the development of the type of malignant disease. The spreading of the psychological distress and depression may change depending on the stage, gender, and age of the patient. Identifying gender-based distress is important for improving the work of health professionals. The results of the study of models of characteristic descriptors based on patients' sex evaluated distress by applying three methods of machine learning. This report evaluates the effectiveness of the metrics accuracy and classification error. It implemented validation of models of descriptors and the calculated correlation coefficients. The results obtained for the two metrics - accuracy and classification error - were high. © 2022 IEEE.

关键词： Machine learning

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Enhancing programming Language Models for C++ Code Clone Detection

Enhancing Programming Language Models for C++ Code Clone Det...

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International Conference on Platform Technology and Service (PlatCon)

作者： Hyonjun Kang Haeun Chun Mucheol Kim School of Computer Sceince and Engineering Chung-Ang University Seoul Korea dept. of Computer Science and Engineering Chung-Ang University Seoul Korea

ISBN: (数字)9798350367874

ISBN: (纸本)9798350367881

This paper examines the fine-tuning methods of two Code Language Models, namely Code-Reviewer and GraphCodeBERT, with the objective of enhancing code clone detection in C++ code. Two experiments are conducted: sentence embeddings and sentence classification with the BM25L algorithm for enhanced pair selection. The Euclidean distance method demonstrated superior performance among the five similarity methods in the sentence embedding comparison. The combination of sentence classification with BM25L as a sentence pairing algorithm achieves superior performance, indicating a promising avenue for further development. The results show that GraphCodeBERT outperforms Code-Reviewer in every task, and sentence classification outperforms sentence embedding in a large margin. This paper highlights the potential of these models for clone detection and emphasizes the benefits of methodological enhancements for improved accuracy, providing valuable insights for software developers and future research.

关键词： Codes Sensitivity Semantics Software algorithms Cloning Termination of employment C++ languages Syntactics Software Classification algorithms

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Sustainable Smart Agriculture Advisor through Artificial Intelligence Approach

Sustainable Smart Agriculture Advisor through Artificial Int...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Hariharan, Shanmugasundaram Srivani, Pogula Gogula, Sreenivasulu Venkateswaran, N. Kukreja, Vinay Jose, P. Vardhaman College of Engineering Dept. of Artificial Intelligence and Data Science Hyderabad India Cmr Technical Campus Department of H&s Hyderabad India Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Jyothishmathi Institute of Technology & Science Nustulapur Department of Computer Science and Engineering Karimnagar India Centre for Research Impact & Outcome Chitkara University Rajpura India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Cse Chennai India

ISBN: (纸本)9798350375442

Agriculture, constituting the backbone of India's economy and employing 70% of its population, faces critical challenges such as water scarcity, soil degradation, and unpredictable crop yields. Traditional farming methods lack efficiency, contributing to environmental impact and resource inefficiency. To address these issues on smart crop cultivation with artificial intelligence enabled crop advisor sustainable agriculture generation integrates machine learning and data analytic to offer real-time insights and recommendations. This advanced agricultural approach aims to optimize resource utilization, minimize environmental impact, and adapt to changing climatic conditions. By empowering farmers with actionable insights, Smart Crop seeks to revolutionize agriculture, promoting sustainable practices for increased resource efficiency and enhanced resilience against challenges like limited precision farming, climate variability, and yield maximization. This innovative solution endeavors to bridge the gap between traditional farming methods and modern challenges, fostering a more sustainable and productive future for Indian agriculture. The methods analyzed leads to promising results as compared with well known metrics. © 2024 IEEE.

关键词： Smart agriculture

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Virtual Field Trip of the Central Heating and Chiller Plant as an Instructional Tool for Thermodynamics Education 53

Virtual Field Trip of the Central Heating and Chiller Plant ...

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Ha, Oai Berrier, Seth Oss, Keif University of Wisconsin-Stout Dept. of Engineering and Technology MenomonieWI United States Dept. of Math Stats and Computer Science University of Wisconsin-Stout MenomonieWI United States University of Wisconsin-Stout Dept. of Design MenomonieWI United States

ISBN: (纸本)9798350336429

This work in progress introduces a framework for developing a virtual tour of the central heating and chiller plant (the Plant) at a university in the US Midwest for teaching and learning Thermodynamics at the campus. Traditionally, this theory-heavy and abstract course is taught using textbooks, lectures, problem solving, and some laboratory experiments. The majority of course content lacks the tangible connections between engineering devices and systems in a close real-world application, making the course challenging and uninteresting for many students. In this project, 360-degree panoramic photos and proper authoring software are employed to render an immersive virtual environment of the Plant that students can visit at any time and from anywhere. The virtual field trip would provide students with tangible insights into thermodynamic and heat transfer processes from over 25 main engineering equipment, devices, and systems at the Plant. Among them are boilers, chillers, a steam turbine, heat exchangers, cooling towers, blowers, and economizers. The follow-up quizzes and assessments are designed to contribute to the course learning outcomes, such as an understanding of phase transitions, thermodynamic properties of steam, and thermal efficiencies of engineering devices. The virtual immersive experiences can help students enhance their learning experiences and engagement in the course's materials without the costs and dangers inherent in a physical tour of the Plant facility. A preliminary result of the virtual tour's evaluation with a group of students and faculty is also included to provide inputs to the next phase of the project. © 2023 IEEE.

关键词： 360-degree panoramic images immersive education mechanical engineering education Thermodynamics education virtual field trip

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Person Re-Identification using Deep Learning 4

Person Re-Identification using Deep Learning

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Saini, Taruna Gandhi, Ridhima Roy, Sanu Diwakar, Manoj Singh, Prabhishek Mishra, Amit Kumar Graphic Era Deemed to Be University Department of Cse Dehradun India Bennett University School of Computer Science Engineering and Technology Greater Noida India Graphic Era Hill University Dept. of Cse Dehradun India

ISBN: (纸本)9798350364729

Re-identification of person (re-ID) is a computer vision task, which is trying to find out the same individual's pictures through a single camera scenario. For addressing Re-ID, we have proposed a deep learning architecture i.e. ResNet, and open Visual Inference and Neural Optimization (Open VINO) toolkit from intel. In this paper, we have utilized the probable feature of learning in the ResNet model that is instructed on enormous datasets and fine-turned on strenuous market-1501 re-ID standard dataset. This process implicates data preprocessing, model selection, training, and optimization steps that are important for acceptable results. We have used data augmentation and triplet loss function to construct implanting more selective as a final step in this pipeline and our developed ResNet model is supported by OpenVINO for hardware-specific optimizations and model conversion. Our Solution on ResNet delivers real-time processing speeds using GPU. This approach shows that is possible and easy to deploy precise person Re-Id for surveillance, forensic identification, and security monitoring. © 2024 IEEE.

关键词： Deep learning

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Comparative Analysis of LSTM and BiLSTM for Sexual Harassment Classification on Indonesian Social Media

Comparative Analysis of LSTM and BiLSTM for Sexual Harassmen...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Azani Cempaka Sari Karto Iskandar Amrita Prasad Risma Yulistiani Aditya Kurniawan Nora Fitriawati Rifqi Alfinnur Charisma Ayu Maulina Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Dept. of Mechanical Engineering Jadavpur University Kolkata India

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

Sexual harassment on social media is a growing concern, especially on platforms that allow unrestricted user interactions. This study aims to compare the performance of two deep learning models, Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (BiLSTM), in classifying sexually harassing comments on Indonesian-language social media. The comparison is intended to identify which model performs better in accurately detecting such harmful content. To address the class imbalance, the dataset was processed using the Synthetic Minority Over-sampling Technique (SMOTE). Both models were then evaluated based on accuracy, precision, recall, and F1-score metrics. The results indicate that, while both models achieved high classification accuracy, the LSTM model slightly outperformed BiLSTM with an accuracy of 94%, precision of 96%, recall of 94%, and F1-score of 94%. In contrast, the BiLSTM model attained an accuracy of 93%, precision of 95%, recall of 93%, and F1-score of 94%. These findings suggest that LSTM has a marginal advantage over BiLSTM in this classification task, making it a potentially more effective choice for detecting sexually harassing comments. This comparison provides valuable insights into model selection for automated detection of harmful content on Indonesian-language social media, contributing to efforts to create a safer digital environment.

关键词： Deep learning Seminars Measurement Analytical models Accuracy Social networking (online) Bidirectional long short term memory Robustness Intelligent systems Information technology

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A Web-based Application for Snake Species Identification using Vision Transformer and CNN-based Ensemble Meta Classifier

A Web-based Application for Snake Species Identification usi...

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2022 IEEE Pune Section International Conference, PuneCon 2022

作者： Dandeniya, D.D.K.R.W. Wickramasinghe, B.C.T. Dasanayaka, C. University of Peradeniya Dept. of Electrical and Electronic Engineering Peradeniya Sri Lanka University of Peradeniya Dept. of Statistics and Computer Science Peradeniya Sri Lanka

ISBN: (纸本)9781665498975

Being a tropical country, Sri Lanka has one of the highest snakebite rates in the world. In 2019, 50 snake bite fatalities have been reported in Sri Lanka. Therefore, the accurate identification of the snake category is crucial for healthcare workers to diagnose and treat the victims as well as to save the snake from being killed. In this paper, we present a web-based application based on Convolutional Neural Network and Vision Transformer architectures to classify between the Russell's viper and the Indian Rock Python. Five different image classification models were trained using the pre-trained architectures ResNet-50, ResNet-100, EfficientNet B0, EfficientNet B7 and Data-Efficient Image Transformers. We were able to gain a testing accuracy of 94.5% by using an ensemble approach for the mentioned classifiers. Furthermore, this study presents the first web-based application in Sri Lanka enabling the automatic identification between Russell's Viper and the Indian Rock Python. © 2022 IEEE.

关键词： Python

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Analysis of Common Mode Currents and Harmonic Pollution at Supplying Induction Motors from Static Converters with Variable Modulation Frequency

Analysis of Common Mode Currents and Harmonic Pollution at S...

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2023 Power Quality and Electromagnetic Compatibility at Low-Frequency, PQEMC-LF 2023

作者： Nuca, Iurie Wan, Lu Nicolae, Petre - Marian Nicolae, Ileana - Diana Grassi, Flavia Wu, Xinglong University of Craiova Electrical Engineering Energetic and Aeronautics Dept. Craiova Romania Politecnico di Milano Department of Electronics Information and Bioengineering Milan Italy University of Craiova Computer Science and Information Technology Dept. Craiova Romania

ISBN: (纸本)9798350341331

The problems of Electromagnetic Compatibility (EMC) in three-phase systems are less studied than single-phase or direct current, due to the smaller ratio of emissions relative to the rated power of such systems. This paper is focused on the problem of emissions in three-phase variable frequency drive systems in the low-frequency EMC range, specifically on the common mode current through the connected ground wire between the static converter and power grid. The common mode current is determined to be variable depending on switching frequency. For a deeper understanding Fast Fourier Transform was used to decompose the signal. Main parameter to understand the magnitude of common mode current was the RMS of the current. The decomposition showed a significant DC component which cannot be picked up by the used RF probe, thus presenting a noise offset necessary to be considered when calculating the total effective value of common mode current. The analysis was made on the time-domain representation and the frequency behavior of the converter. The paper suggests that the standard measurement in the low-frequency range and the picked-up signal are investigated enough, as there is a disparity in the impact of emissions in the low-frequency range compared to what off-the-shelf devices have to offer, which are standard compliant, but still present considerable emissions. © 2023 IEEE.

关键词： Static converters

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