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21st International Conference on Information technology Based Higher Education and Training, ITHET 2024

作者： Ward, Anthony Manoharan, Sathiamoorthy Ye, Xinfeng School of Physics and Engineering Technology University of York York United Kingdom School of Computer Science University of Auckland Auckland New Zealand

ISBN: (纸本)9798331516635

Traditionally, statistics suggest that around 30% of students resort to cheating when presented with the opportunity. This alarming trend often goes unnoticed by instructors, as many do not actively monitor for such misconduct. However, the onset of the pandemic saw a surge in cheating incidents, mainly due to the shift to online learning environments where supervision was more challenging. Despite this, instances of cheating remained detectable, albeit with some effort. As we transition out of the pandemic era, a new challenge has emerged in the form of generative AI technology, which has dramatically altered the landscape of academic dishonesty. With the advent of sophisticated generative AI models, it has become virtually impossible to discern between authentic student work and AI-generated content in unsupervised settings. Consequently, there has been a noticeable decline in detected cheating cases compared to pre-pandemic levels, largely attributed to the widespread use of generative AI tools. Traditional plagiarism detection software such as Turnitin and MOSS, once considered reliable safeguards against academic dishonesty, have become obsolete in the face of these advanced AI systems. The unique solutions produced by generative AI render such detection methods ineffective, posing a significant challenge to maintaining academic integrity in the modern educational landscape. This paper explores the nature of academic misconduct incidents in the context of two universities across the globe: one in the United Kingdom and the other in New Zealand. © 2024 IEEE.

关键词： Generative adversarial networks

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CannabisO: The Ontology-based Knowledge Model for Safe Cannabis Consumption in Thailand 20

CannabisO: The Ontology-based Knowledge Model for Safe Canna...

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20th International Joint Conference on computer science and Software engineering, JCSSE 2023

作者： Samasutthi, Pranpreya Anutariya, Chutiporn School of Engineering and Technology Asian Institute of Technology Computer Science & Information Management Ict Department Thailand

ISBN: (纸本)9798350300505

In June 2022, the Ministry of Public Health of Thailand announced that cannabis can be used in healthcare, medical, research, and commerce. This announcement led to a wide discussion in social media, TV broadcasts, and public media about how to use cannabis safely, the symptoms that cannabis can relieve, side effects, and precautions of cannabis. Prior to the Ministry's announcement, cannabis was illegal and forbidden to consume since it was considered as a kind of drugs and was dangerous to consume, even privately. Most people still lack of proper knowledge about safe cannabis consumption. Also, information provided on the Internet and social media about cannabis is now often redundant, inconsistent, and unreliable. Therefore, this study gathers official and reliable published knowledge about cannabis consumption specifically for general users. CannabisO is modeled as a formal, shareable, and reusable ontology-based knowledge model to be used for a Q&A system that can help answer questions about safe edible consumption of cannabis, precautions and side effects of cannabis consumption, and lastly, symptoms that can be alleviated by cannabis. Several interesting Q&As are demonstrated in this paper. © 2023 IEEE.

关键词： Ontology

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Adaptive Interference Detection and Removal in SAR Images Based on the Order-2 Tracy-Widom Distribution 2

Adaptive Interference Detection and Removal in SAR Images Ba...

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Yang, Huizhang Chen, Chengzhi Xi, Feng Lang, Ping Yin, Junjun Yang, Jian Nanjing University of Science and Technology Department of Electronic Engineering Nanjing China Tsinghua University Department of Electronic Engineering Beijing China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China

ISBN: (纸本)9798331515669

This paper proposes a novel eigenvalue-based detector for adaptive detection of radio interference in synthetic aperture radar (SAR) images. The proposed method leverages the increased eigenvalues caused by interference in single-look-complex SAR image blocks, where the interference is expected to have a small set of eigenvalues, particularly with a dominating one. Specifically, the method segments the image into multiple blocks, computes the eigenvalues of each block's covariance matrix, and compares the largest eigenvalue with a threshold to determine the presence of interference under the criteria of constant false alarm rate. The threshold is determined by the order-2 Tracy-Widom distribution of the largest eigenvalue for each image block under the assumption of image homogeneity. The proposed detector is integrated with our previously proposed block subspace filter for adaptive interference detection and removal. Experimental results on Sentinel-1 double-polarization and Gaofen-3 HV-polarization images demonstrate the effectiveness of the proposed method on adaptive detection and suppression of interference artefacts in SAR images. © 2024 IEEE.

关键词： Eigenvalues and eigenfunctions

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Transformer oil temperature prediction based on PSO-SVR 6

Transformer oil temperature prediction based on PSO-SVR

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6th IEEE International Conference on Civil Aviation Safety and Information technology, ICCASIT 2024

作者： Zhou, Hao Zhu, Jinyi Liu, Yuheng Shanghai University School of Computer Engineering and Science Shanghai China Xi'an University of Posts & Telecommunications School of Communications and Engineering School of Artificial Intelligence Xi'an China Beijing University of Chemical Technology College of Information Science and Technology Beijing China

ISBN: (纸本)9798350389418

Transformer oil temperature prediction is crucial to ensure power system stability and transformer selfmaintenance. In view of the limitations of traditional prediction models in terms of accuracy and real-time performance, as well as the advantages of deep learning techniques in dealing with complex datasets, this study proposes a particle swarm optimization support vector regression (PSO-SVR)-based transformer oil temperature prediction model, which significantly improves the prediction accuracy by optimizing the key parameters of SVR. In this paper, the theoretical foundations of SVR and PSO are first outlined, followed by a detailed description of the construction of the PSO-SVR model and the process of model training and validation with actual transformer oil temperature data. Through the iterative calculation of the PSO algorithm, when the penalty parameter is taken as 0.2201 and the radial kernel parameter is taken as 0.1864, the assessment indexes such as the mean square error of the model prediction results reach the lowest, which is much lower than the traditional SVR model, showing high prediction accuracy and good applicability, and providing a new technical way for intelligent management of the transformer oil temperature and the power system. © 2024 IEEE.

关键词： Support vector regression

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Enhancing Siamese Neural Network Performance with Custom Local Binary Patterns for Face Matching 8

Enhancing Siamese Neural Network Performance with Custom Loc...

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8th IEEE International Conference on Information and Communication technology, CICT 2024

作者： Koranga, Pawan Singh Indian Institute of Technology Kharagpur Dept. of Computer Science and Engineering Kharagpur India School of Computer Sciences Upes Dehradun248001 India

ISBN: (纸本)9798331531539

In this study, we investigate the efficacy of incorporating custom local binary patterns (LBP) as a preprocessing technique to enhance the performance of Siamese neural networks for face matching tasks. Our research focuses on training Siamese models using the DC Face dataset and comparing their performance when trained with and without LBP integration. The custom LBP function is designed to capture fine-grained texture details from facial images, providing robust feature representations that are invariant to variations in lighting conditions and facial expressions. Through rigorous experimentation and evaluation, we analyze the impact of LBP on the threshold point for image differentiation and compare the matching accuracy of Siamese models trained with and without LBP. Our findings reveal significant improvements in matching accuracy and robustness when utilizing the texture features extracted by the custom LBP function. By encoding local pixel structures and capturing subtle facial characteristics, LBP-enhanced Siamese models demonstrate enhanced performance in accurately matching faces across different images. Further-more, our study highlights the importance of using domain-specific preprocessing techniques, such as LBP, to optimize the capabilities of Siamese neural networks for face matching tasks. The results underscore the effectiveness of customizing feature extraction methods to suit the characteristics of the dataset, leading to improved matching accuracy and robustness. Overall, our research contributes valuable insights into the role of LBP in enhancing the performance of Siamese neural networks for image-based similarity tasks, with implications for various real-world applications in face recognition and biometric authentication. © 2024 IEEE.

关键词： Local binary pattern

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An Approach to Analyze the Permanent Magnet Motor Torque Ripple for Improving Torque Power Curves used in Electric Vehicles 15

An Approach to Analyze the Permanent Magnet Motor Torque Rip...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Neelakandan, Varatharaj Prabakaran, B. Balaji, D. Suresh, P. Maheswaran, S. Indhumathi, N. Hindustan Institute of Technology and Science Chennai India Vellore Institute of Technology School of Computer Science and Engineering TamilNadu Vellore India Kongu Engineering College Department of Electronics and Communication Engineering TamilNadu Erode India

ISBN: (纸本)9798350370249

An electric car's permanent magnet synchronous motor (PMSM) will be designed and evaluated for performance in this research article. In order to satisfy the unique needs of the electric car and improve its overall performance, the goal is to achieve the highest torque density and the lowest torque ripple. The study's goal is to increase the electric vehicle's power density to do this, which calls for the use of a high-power-density motor. This is addressed by conducting an optimization procedure to increase the generated torque while making sure that the chosen motor stays within the defined size and volume restrictions. The efficient use of permanent magnet (PM) materials with strong thermal limiting capacity, which can enhance the power, is a critical component of this improvement. The efficient use of permanent magnet (PM) materials with high thermal limiting capacity, which may boost the power output under both peak and continuous loads, is a vital component of this optimization. The most important motor design parameters are carefully chosen throughout the optimization process to have the greatest influence on motor performance. Using the finite element software program 'CAE Software,' the research team tests and executes the suggested motor design. The overall goal of this project is to build a PMSM that is highly effective and adapted to the needs of electric vehicles. The goal of the study is to optimize critical motor parameters and use modern PM materials with higher thermal properties in order to increase torque density and reduce torque ripple of the motor. © 2024 IEEE.

关键词： Permanent magnets

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HIT-MI &T Lab’s Submission to CCMT 2023 Automatic Post-editing Task 19th

HIT-MI &T Lab’s Submission to CCMT 2023 Automatic Post-edi...

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19th China Conference on Machine Translation, CCMT 2023

作者： Zhang, Rui Yuan, Jinghao Huang, Hui Yang, Muyun Zhao, Tiejun Research Center on Language Technology School of Computer Science and Engineering Harbin Institute of Technology Harbin China

ISBN: (纸本)9789819978939

Automatic post-editing (APE) aims to automatically correct the outputs of the machine translation system by learning from manual correction examples. We present the system developed by HIT-MI &T Lab for the CCMT 2023 APE task of Chinese-English direction. We use mBART as the backbone model, and explore different techniques to create synthetic data, including domain selection, forward translation and data augmentation via large language model. Multi-model ensemble is also adopted in our final system. The experiment results on the development set demonstrate the effectiveness of our proposed method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine translation

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Deep Learning Image Classification Abiotic Plant Disease Detection Classification Using MobileNet 4

Deep Learning Image Classification Abiotic Plant Disease Det...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Aggarwal, Ritu Aggarwal, Eshaan Jain, Anurag Aluvalu, Rajanikanth Maheswari, Uma Choudhury, Tanupriya Department of Computer Science and Engineering India School of Computer Science and Engineering Iilm University Haryana Gurugram India Symbiosis Institute of Technology Hyderabad Campus Hyderabad India Symbiosis International University Pune India Chaitanya Bharathi Institute of Technology Department of Computer Science and Engineering Hyderabad India School of Computer Sciences University of Petroleum and Energy Studies Upes Uttarakhand Dehradun248007 India

ISBN: (纸本)9798350387490

In the field of agriculture sector the key role of feature food which has a major role in the emergent population with their economy. In the food production for plant disease, it may cause significant loss for the era of diversity in species and the crop production rate in the plant disease detection and diagnosis the accurate detecting techniques could increase the superiority in food productivity and reduce the economic loss for the image categorization and object recognition. As per computed results and analysis of the various techniques, deep learning is as best approach to get the accuracy means and classify the images. In this proposed work to tune the parameters required to train load balancing overload forecasting load balancing overload forecasting the models using CNN. The model performance is evaluated by confusion matrix parameters such as accolade balancing overload forecasting accuracy, precision, Recall, sensitivity, specificity, f-measure, etc. In the plants, the stress will destroy the crop's growth, and yields are affected negatively due to the water supply's suboptimal quantity, soil erection, and abnormal temperature due to other physiological conditions and changes in biochemicals. In this paper, the current model Mobile, which is used for image detection and classification in disease, achieves an accuracy of 99.8%. © 2024 IEEE.

关键词： Climate change Crop disease production Drought Heat stress Plant responses Stress management

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Time-Series Cryptocurrency Forecasting Using Ensemble Deep Learning

Time-Series Cryptocurrency Forecasting Using Ensemble Deep L...

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2023 International Conference on Circuit Power and Computing Technologies, ICCPCT 2023

作者： Rao, K Rama Prasad, M Lakshmi Kumar, G Ravi Natchadalingam, R. Hussain, Mohammad Manzoor Reddy, Pundru Chandra Shaker Geethanjali College of Engineering and Technology Electronics and Communication Engineering Telangana Hyderabad India Institute of Aeronautical Engineering Computer Science and Engineering Telangana Dundigal India CMR College of Engineering & Technology Computer Science and Engineering Hyderabad India SRM Madurai College for Engineering and Technology Computer Science and Engineering Tamilnadu Madurai India B v Raju Institute of Technology Computer Science and Engineering Telangana Narsapur India School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798350333244

Cryptocurrency is now widely accepted as a payment and exchange method, permeating nearly every aspect of the financial sector. Similar to the non-stationary and very erratic price movements of traditional stocks, cryptocurrency price swings are highly unpredictable. The rising popularity of cryptocurrencies has prompted an increase in the number of studies conducted to predict their future prices. The importance of cryptocurrency forecasting has grown significantly with the advent of deep learning. portfolio optimisation and decision making cannot be achieved without the creation of a smart forecasting model. The primary contribution of this study is the utilize of traditional deep learning (DL) models in combination with the three most popular ensemble learning algorithms (ensemble-averaging, bagging, and stacking) to predict the hourly values of major cryptocurrencies. Traditional DL strategies consisting of combinations of long short-term memory (LSTM), Bi-directional (BiLSTM), and convolutional layers were utilized to assess the suggested ensemble methods. The ensemble techniques were tested on their capacity to forecast the price of a cryptocurrency an hour basis (regression) and to determine whether the price will rise or fall relative to the present (classification). Our in-depth experimental research shows that combining ensemble learning with deep learning can produce robust, stable, and trustworthy forecasting strategies. © 2023 IEEE.

关键词： Long short-term memory

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ILRM: Imitation Learning Based Resource Management for Integrated CPU-GPU Edge Systems with Renewable Energy Sources

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2024年第6期44卷 2392-2397页

作者： Hou, Xiangpeng Zhou, Junlong Li, Liying Zhao, Mingzhou Cong, Peijin Wu, Zebin Hu, Shiyan Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China Southeast University National Mobile Communications Research Laboratory Nanjing211111 China University of Hong Kong Department of Data and Systems Engineering Hong Kong Hong Kong

This paper focuses on integrated CPU-GPU edge systems with renewable energy sources and studies the resource management problem to minimize the energy consumption of real-time tasks while ensuring temperature and reliability *** propose an imitation learning (IL)-based resource management scheme, ILRM, implemented in two phases: offline Oracle generation and online imitation learning. In the offline phase, we design a fast-converging heuristic to generate near optimal solutions (i.e., Oracles) for training an online prediction model. In the online phase, we realize imitation learning using the trained model that predicts the resource configuration policies for the incoming task sets to be scheduled. A data aggregation method is also developed to enhance the robustness of the prediction model. We validate ILRM through extensive experiments on both simulated and real integrated CPU-GPU edge platforms. © 1982-2012 IEEE.

关键词： Graphics processing units Reliability Central Processing Unit Renewable energy sources Real-time systems Integrated circuit modeling Imitation learning Generators Energy consumption Edge computing

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