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1st ACM Virtual Global Computing Education Conference V. 1, SIGCSE Virtual 2024

作者： Zilles, Craig Zhao, Chenyan Chen, Yuxuan Matthews, Evan Michael West, Matthew Computer Science University of Illinois UrbanaIL United States Mechanical Science and Engineering University of Illinois UrbanaIL United States

ISBN: (纸本)9798400705984

Academic integrity continues to be an issue in education. Students' grades are often computed using a collection of evidence that varies in its trustworthiness (e.g., a proctored exam can be trusted more than an out-of-class programming project) due to practical constraints. When a student cheats, their trusted and less trustworthy scores are inconsistent, which presents instructors a choice between rewarding the cheating behavior and the burden of investigating / making cheating allegations. In this position paper, we propose that Bayesian inference might be a useful tool in assigning grades derived from trusted and less trusted evidence. Rather than compute grades by performing arithmetic on both trusted and untrusted assessments, we instead try to infer a latent variable, the student's mastery of the course material, from these observed performances and their potential for cheating. Key to this approach is that grades can be assigned that discount suspicious work without needing to explicitly make a cheating allegation. A logical conclusion of this approach is that the needed amount of trusted assessments for a given student depends on how inconsistent are their trusted and untrusted assessments. © 2024 Owner/Author.

关键词： Students

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Performance Assessment of Data Centric and Model Centric Approach for Premature Detection of Leaf Disease

Performance Assessment of Data Centric and Model Centric App...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Prabhu, A. Kumar, Sanjay Pundeer, Shivali Department Of Computer Science And Information Technology Bangalore India Noida Institute Of Engineering And Technology Department Of Mechanical Engineering Uttar Pradesh Greater Noida India Tula's Institute Department Of Computer Science And Engineering Dehradun India

ISBN: (纸本)9798350366846

After rice and maize, cassava is the third most common source of carbohydrates in the world. Recognizing the critical relationship between agricultural productivity and a country's economy, specialists have made significant investments in the early diagnosis of leaf diseases in order to protect the entire plant from degeneration. The abundance of data, improved resource availability, and improvements in learning algorithms are all credited with the recent achievements of Machine Learning (ML) models. In order to improve system efficiency, the traditional method for detecting leaf diseases uses a Model-Centric approach that optimizes hyperparameters. The emphasis has recently switched from model-entered to data-centered techniques, in which the model is kept constant, but dataset quality is increased by addressing issues like label consistency and systematic sampling of training data while choosing the right batches. The current paper comprises using confidence learning to locate and remove noisy labels from the Cassava Leaf Disease Classification dataset. The model is then fed with newly produced high-quality data, and the performance of the model-centric and data-centric approaches are contrasted. This paper intends to highlight the underappreciated potential for increasing system functionality using a data-centric approach rather than to minimize the significance of model-centric approaches. The findings indicate that the data-centric method leads to a performance enhancement of 7.84%. © 2024 IEEE.

关键词： Learning algorithms

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Rule-based Explaining Module: Enhancing the Interpretability of Recurrent Relational Network in Sudoku Solving

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Machine Graphics and Vision 2023年第3-4期32卷 125-145页

作者： Cheewaprakobkit, Pimpa Shih, Timothy K. Lau, Timothy Lin, Yu-Cheng Lin, Chih-Yang Department of Information Technology Asia-Pacific International University Saraburi Thailand Department of Computer Science and Information Engineering National Central University Taoyuan Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan Department of Mechanical Engineering National Central University Taoyuan Taiwan

computer vision has gained significant attention in the field of information technology due to its widespread application that addresses real-world challenges, surpassing human intelligence in tasks such as image recognition, classification, natural language processing, and even game playing. Sudoku, a challenging puzzle that has captivated many people, exhibits a complexity that has attracted researchers to leverage deep learning techniques for its solution. However, the reliance on black-box neural networks has raised concerns about transparency and explainability. In response to this challenge, we present the Rule-based Explaining Module (REM), which is designed to provide explanations of the decision-making processes using Recurrent Relational Networks (RRN). Our proposed methodology is to bridge the gap between complex RRN models and human understanding by unveiling the specific rules applied by the model at each stage of the Sudoku puzzle solving process. Evaluating REM on the Minimum Sudoku dataset, we achieved an accuracy of over 98.00%. © 2023 Institute of Information Technology, Warsaw University of Life sciences - SGGW. All rights reserved.

关键词： Image recognition

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High‑Order ADER Discontinuous Galerkin Schemes for a Symmetric Hyperbolic Model of Compressible Barotropic Two‑Fluid Flows

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Communications on Applied Mathematics and Computation 2024年第4期6卷 2119-2154页

作者： Laura Río‑Martín Michael Dumbser Department of Civil Environmental and Mechanical EngineeringUniversity of TrentoVia Mesiano 77Trento 38123Italy Department of Information Engineering and Computer Science University of Trentovia Sommarive 9PovoTrento 38123Italy

This paper presents a high-order discontinuous Galerkin(DG)finite-element method to solve the barotropic version of the conservative symmetric hyperbolic and thermodynamically compatible(SHTC)model of compressible two-phase flow,introduced by Romenski et ***[59,62],in multiple space *** the absence of algebraic source terms,the model is endowed with a curl constraint on the relative velocity *** this paper,the hyperbolicity of the system is studied for the first time in the multidimensional case,showing that the original model is only weakly hyperbolic in multiple space *** restore the strong hyperbolicity,two different methodologies are used:(i)the explicit symmetrization of the system,which can be achieved by adding terms that contain linear combinations of the curl involution,similar to the Godunov-Powell terms in the MHD equations;(ii)the use of the hyperbolic generalized Lagrangian multiplier(GLM)curl-cleaning approach *** PDE system is solved using a high-order ADER-DG method with a posteriori subcell finite-volume limiter to deal with shock waves and the steep gradients in the volume fraction commonly appearing in the solutions of this type of *** illustrate the performance of the method,several different test cases and benchmark problems have been run,showing the high order of the scheme and the good agreement when compared to reference solutions computed with other well-known methods.

关键词： Compressible two-fluid flows Symmetric hyperbolic and thermodynamically compatible(SHTC)systems Hyperbolic systems with curl involutions High-order ADER discontinuous Galerkin(DG)schemes with subcell finite-volume limiter Conservative form of hyperbolic models

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EVGPT - Fine-Tuned Large Language Model for Electric Vehicle Operational Assistance 4

EVGPT - Fine-Tuned Large Language Model for Electric Vehicle...

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4th IEEE International Conference on Emerging Frontiers in Electrical and Electronic Technologies, ICEFEET 2024

作者： Kanimozhi, S. Prithvi, G. Barath, M. Vaikash, K.G. Mathew, Abel Manoj Gukhan, V.B.S. School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Mechanical Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331528614

The optimization of electric vehicle (EV) utilization and efficiency is becoming increasingly essential in the ever-changing landscape of sustainable transportation. Consulting user manuals, online resources, and seeking expert advice for EV maintenance and operation is a significant obstacle to productivity. Electric Vehicle GPT (EVGPT) is a specialized Large Language Model (LLM) specifically designed to address queries related to EV maintenance, operation, and troubleshooting, filling the gaps that conventional LLMs may miss. This model serves as a comprehensive digital assistant for technicians, operators, and EV enthusiasts, providing timely and precise information while upholding response quality. This study offers an overview of the EVGPT model architecture and presents an operational evaluation based on its implementation in a specific EV model. ©2024 IEEE.

关键词： Electric vehicles

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Enhancing mechanical performance of kenaf fiber reinforced polymer composites: influence of fiber characteristics and processing techniques

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Interactions 2024年第1期245卷 1-20页

作者： Chanderasekaran, J. H. Rakini Naidu, B. Vishnu Vardhana Palanisamy, Satishkumar Vallinayagam, Sugumari Saminathan, Rajasekaran Arignar Anna Government Arts College Tamilnadu Musiri India Department of Mechanical Engineering Sree Vidyanikethan Engineering College Mohan Babu University Andhra Pradesh Tirupati India Department of Mechanical Engineering Rathinam Technical Campus Tamilnadu Coimbatore India Department of Biotechnology Vel Tech Rangarajan Dr. Sagunthala R & ampD Institute of Science and Technology Tamilnadu Chennai India Department of Mechanical Engineering College of Engineering and Computer Science Jazan University Jizan Saudi Arabia

The inherent fiber strength of kenaf is decent because of the high cellulose content and low micro fibrillar angle of the fiber. During the hand layup process of creating the epoxy matrix composite, it will be used as reinforcement in the proposed project. Composite testing has looked at how different fiber types, orientations, and content affect mechanical properties. Different types of fibers (short length and long length fibers), orientations (0°, 90°, and 60°), and fiber contents (6, 12, and 18) were altered to prepare the samples. At 18 wt% fiber content, the mechanical characterization results show that the short fiber composite achieves its maximum tensile strength of 24.90 MPa and its ultimate fiber strength of 45.94 MPa. Nevertheless, the highest tensile and fracture strengths of long fiber reinforced composites containing 5% fiber are 38.96 MPa and 59.43 MPa, respectively. The composite showed an impact strength of 25.30 J m− 2 and a highest flexural strength of 56.32 MPa with fibres orientated at 0°. A maximum water absorption value of 1.25% was recorded for long fiber reinforced composites and 1.74% for short fiber reinforced composites. At last, the morphological analysis was performed to identify the most common causes of composite failure: debonding, splitting, and fiber pull-out. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Kenaf fibers

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Robust Indoor Positioning of Automated Guided Vehicles in Internet of Things Networks With Deep Convolution Neural Network Considering Adversarial Attacks

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IEEE Transactions on Vehicular Technology 2024年第6期73卷 7748-7757页

作者： Elsisi, Mahmoud Rusidi, Akhmad Lutfi Tran, Minh-Quang Su, Chun-Lien Ali, Mahmoud N. National Kaohsiung University of Science and Technology Department of Electrical Engineering Kaohsiung807618 Taiwan Cairo11629 Egypt National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei106 Taiwan Tuetech University Department of Mechanical Engineering Thai Nguyen250000 Viet Nam

The effectiveness of positioning techniques that utilize the receiver signal strength (RSS) is highly dependent on the instability of the received signal strength indicator (RSSI). Up to now, there is no strategy that effectively lowers the influence of such instability on the accuracy of positioning. Moreover, recent studies showed that indoor positioning techniques are vulnerable to noise in RSSI data and cyber-attacks, which make them more expensive. In this study, a new Internet of Things (IoT) paradigm is proposed for the indoor positioning of automated guided vehicles (AGVs) using a deep convolution neural network (CNN). The proposed method handles signal processing by converting the RSSI signal into an image. In which, the 1-D RSSI signal is converted into 2-D image data in order to generate the new features based on continuous wavelet transform (CWT), and then the proposed deep CNN is implemented for the indoor positioning system. The test results show that the proposed model can outperform other state-of-the-art positioning techniques with small position errors. Furthermore, the robustness of the proposed model is validated against various adversarial attacks. In addition, the proposed method can have a lower impact on RSSI change compared with other methods. © 1967-2012 IEEE.

关键词： Feature extraction

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Implementation of Neural Networks for the Prediction of CHF Location 1st

Implementation of Neural Networks for the Prediction of CHF ...

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1st International Conference on Fluid, Thermal and Energy Systems, ICFTES 2022

作者： Vishnu, Kumar Kohli, Rishika Gupta, Shaifu Pothukuchi, Harish Department of Mechanical Engineering IIT Jammu Jammu181221 India Department of Computer Science and Engineering IIT Jammu Jammu181221 India

ISBN: (纸本)9789819959891

Prediction of critical heat flux (CHF) location is a challenging issue in power generation industries. A variety of factors influence the occurrence of CHF and different mechanisms are proposed to understand the physical phenomena of CHF. Due to the nonlinear and complex behavior of CHF, these mechanisms cannot include all the factors simultaneously, as a result, some of them remain unresolved. Considering the above issues, this study employs a neural network (NN)-based machine learning method to model and predict CHF location. Becker et al. (An experimental investigation of post dryout heat transfer, 1983) data bank was selected for both training and testing. Hyperparameter tuning is applied to optimize the performance of the model. The model is found to be performing well with the training dataset as well as the testing dataset. The proposed model is helpful for the design engineers and analysts in the power plants for design optimization and to reduce the computational time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Heat flux

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Discrimination of Vibrotactile Stimuli: Effects of Frequency Variation

Discrimination of Vibrotactile Stimuli: Effects of Frequency...

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ASME 2023 International mechanical engineering Congress and Exposition, IMECE 2023

作者： Yeganeh, Nashmin Makarov, Ivan Kristjánsson, Árni Unnthorsson, Runar The Faculty of Industrial Engineering Mechanical Engineering and Computer Science University of Iceland Reykjavik Iceland The Faculty of Industrial Engineering Mechanical Engineering and Computer Science The Faculty of Psychology University of Iceland Reykjavik Iceland The Faculty of Psychology University of Iceland Reykjavik Iceland

ISBN: (纸本)9780791887585

This paper reports on an empirical study that investigated the effect of frequency on the acuity of spatial localization on the skin of the forearm, as well as differences in localizability at different locations on the forearm. Three actuators were positioned in a 3 × 1 array along the forearm, and two adjacent actuators were sequentially activated with a 100 ms interstimulus interval, followed by activation of one of the three actuators in random order. Participants were asked about the direction of the second stimulation relative to the first one, and whether it was perceived in the same location as the first, below, or above it. Three frequencies — 100Hz, 200Hz, and 250Hz — were used for the stimuli. Results show that the frequency of the stimulus has a negligible influence on accuracy when the amplitude is kept constant. However, significant differences in the localization accuracy were found at different parts of the forearm. This study provides valuable insights into the design of tactile displays for conveying information on the skin. It suggests that spatial resolution may be optimized by positioning the actuators appropriately, rather than by manipulating the frequency of the stimulus. Copyright © 2023 by ASME.

关键词： Actuators

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Emotion Recognition Through Speech 4th

Emotion Recognition Through Speech

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4th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2023

作者： Shashank, Valandas Sai Bhaskar, Nuthanakanti Srujan Raju, K. Raji Reddy, A. Department of Computer Science and Engineering CMR Technical Campus Hyderabad India Department of Mechanical Engineering CMR Technical Campus Hyderabad India

ISBN: (纸本)9789819994410

Speech is individuals’ primary contact form, and vocabulary is their primary tool. In social connections, emotion is essential. Given that we encounter interaction between humans and machines, it is crucial and difficult to identify the emotions in a speech. The way a person expresses their emotions changes depending on the feeling they are experiencing. When somebody expresses their emotions, each will have a distinctive intensity, pitch, and tone fluctuation that can be put together based on the matter. Therefore, a future objective of computer vision is the identification of spoken emotions. This paper aims to create an intelligent speech that recognizes emotions using a convolutional neural network. Multiple sections are utilized to identify feelings, and a classification algorithm is distinguished between happiness, sadness, anger, and amazement. The machine will transform the mathematical representation of human voice signals, go through its routine, and then show emotion. The data is a spoken specimen, and the Librosa library extracts the attributes from the voice sample. As a research dataset, we are employing the RAVDESS collection. This study demonstrates that all classifications for the data we used have an accuracy level of 68%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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