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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Shukla, Tushar Dhar Bharadwaja, A.V. Bhattacharya, Chaitali Chary Ch, Narasimha Bhushan, P. Vinay Thulasimani, T. University Institute Physical Education & Sports Chandigarh University Punjab Mohali India Autonomous College Department of ECE Visakhapatnam India Tecnia Institute of Advanced Studies CDL New Delhi India School of Engineering & Technology Dept of CSE Telangana Hyderabad India Malla Reddy University School of Engineering Computer Science and Engineering Hyderabad India Bannari Amman Institute of Technology Department of Mathematics Erode India

ISBN: (纸本)9798331534950

Teachers are no different from everyone else when it comes to the value of helpful criticism. Because the system teach so often, teachers have plenty of chances to perfect these skills. However, improving one's abilities via unstructured exercise alone isn't enough;targeted practice, usually with the help of a coach, may be necessary for mastery. The ideal coach would provide instructors with realistic yet demanding training goals, provide them with helpful feedback, and help them find ways to advance their careers. The proposed approach incorporates preprocessing, feature selection, and training of the model. Data cleaning, conversion, and formatting are all part of data preparation, which is preparing a dataset for analysis. Data gain and principal component analysis make up feature selection. The ARIMA-LSTM model was consistently used throughout the training. With a 90.86% average accuracy, this innovative method surpasses ARIMA and LSTM. © 2024 IEEE.

关键词： Contrastive Learning

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How Do Hackathons Foster Creativity? Towards Automated Evaluation of Creativity at Scale 25

How Do Hackathons Foster Creativity? Towards Automated Evalu...

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2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Falk, Jeanette Chen, Yiyi Rafner, Janet Zhang, Mike Bjerva, Johannes Nolte, Alexander Computer Science Aalborg University Copenhagen Denmark Department of Computer Science Aalborg University Copenhagen Denmark Center for Hybrid Intelligence Department of Management Aarhus University Aarhus Denmark Aarhus Institute for Advanced Studies Aarhus University Aarhus Denmark Software Engineering and Technology Eindhoven University of Technology Eindhoven Netherlands

ISBN: (纸本)9798400713941

Hackathons have become popular collaborative events for accelerating the development of creative ideas and prototypes. There are several case studies showcasing creative outcomes across domains such as industry, education, and research. However, there are no large-scale studies on creativity in hackathons which can advance theory on how hackathon formats lead to creative outcomes. We conducted a computational analysis of 193,353 hackathon projects. By operationalizing creativity through usefulness and novelty, we refined our dataset to 10,363 projects, allowing us to analyze how participant characteristics, collaboration patterns, and hackathon setups influence the development of creative projects. The contribution of our paper is twofold: We identified means for organizers to foster creativity in hackathons. We also explore the use of large language models (LLMs) to augment the evaluation of creative outcomes and discuss challenges and opportunities of doing this, which has implications for creativity research at large. © 2025 Copyright held by the owner/author(s).

关键词： Hackathons

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Optimal Design of Distributed Network Based on Power Quality to Minimize the Flicker of Wind Turbines

Optimal Design of Distributed Network Based on Power Quality...

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Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Abolfazl Ghaffari Alireza Askarzadeh Roohollah Fadaeinedjad Gerry Moschopoulos Department of Electrical and Computer Engineering Graduate University of Advanced Technology Kerman Iran Department of Energy Management and Optimization Institute of Science and High Technology and Environmental Sciences Graduate University of Advanced Technology Kerman Iran Department of Electrical and Computer Engineering Western University London Ontario Canada

The use of wind energy as an energy resource in distribution networks is increasing. A disadvantage to using wind energy, however, is the emission of flicker in an electrical distribution network (EDN), which may adversely affect power quality during system operation. An effective way to improve power quality is to include the allocation of wind turbines (WTs) in the planning phase. The location and size of WTs can significantly improve the network power quality and electrical performance in an EDN. This paper considers modeling for flicker emission by using a mixed integer non-linear programming (MINLP) model to determine the optimal location and size of WTs. The optimal design of the network associated with WTs generation profile patterns and the variation of load demand during the planning horizon 24-h is also determined. The proposed model is implemented in GAMS and tested on a standard IEEE 33-bus system.

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Recent Advancements in Hyperspectral Face Recognition: A Review 13

Recent Advancements in Hyperspectral Face Recognition: A Rev...

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13th International Conference on Computing Communication and Networking Technologies, ICCCNT 2022

作者： Das, Samiran Chatterjee, Sujoy JIS University Centre for Data Science JIS Institute for Advanced Studies and Research Kolkata India Amity University Department of Computer Science and Engineering Kolkata India

ISBN: (数字)9781665452625

ISBN: (纸本)9781665452625

The increasing demand for high-accuracy face recognition methods and the rapid advancement in instrumentation has made hyperspectral face recognition a viable alternative to traditional face recognition. The high-security biometric application scenarios, which demand high accuracy of recognition are switching to hyperspectral imaging due to its robustness against spoofing, illumination, pose variation, etc. The hundreds of visible and near infrared (NIR) images contained in a hyperspectral face cube provide sufficient information content, leading to more accurate recognition. However, the gigantic volume of information and the computational challenges call for efficient learning strategies for the recognition task. Researchers have introduced several feature extraction, supervised learning, deep learning, and pattern recognition approaches to carry out face recognition from hyperspectral face cubes. This work summarized the state-of-the-art approaches for hyperspectral face recognition in detail and highlight their pros and cons. The work also discusses future directions and challenges in this domain. © 2022 IEEE.

关键词： Biometrics

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A Bayesian approach to online planning 24

A Bayesian approach to online planning

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Proceedings of the 41st International Conference on Machine Learning

作者： Nir Greshler David Ben Eli Carmel Rabinovitz Gabi Guetta Liran Gispan Guy Zohar Aviv Tamar General Motors Advanced Technical Center Israel Department of Electrical and Computer Engineering Technion - Israel Institute of Technology Haifa Israel

The combination of Monte Carlo tree search and neural networks has revolutionized online planning. As neural network approximations are often imperfect, we ask whether uncertainty estimates about the network outputs could be used to improve planning. We develop a Bayesian planning approach that facilitates such uncertainty quantification, inspired by classical ideas from the meta-reasoning literature. We propose a Thompson sampling based algorithm for searching the tree of possible actions, for which we prove the first (to our knowledge) finite time Bayesian regret bound, and propose an efficient implementation for a restricted family of posterior distributions. In addition we propose a variant of the Bayes-UCB method applied to trees. Empirically, we demonstrate that on the ProcGen Maze and Leaper environments, when the uncertainty estimates are accurate but the neural network output is inaccurate, our Bayesian approach searches the tree much more effectively. In addition, we investigate whether popular uncertainty estimation methods are accurate enough to yield significant gains in planning. Our code is available at: https://***/nirgreshler/bayesian-online-planning.

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A Fast Algorithm for Finding the Roots of Polynomials over Finite Fields 15

A Fast Algorithm for Finding the Roots of Polynomials over F...

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15th IEEE International Conference on Wireless Communications and Signal Processing, WCSP 2023

作者： Li, Pingping Yu, Leilei Song, Linqi Hou, Hanxu Dongguan University of Technology School of Computer Science & Technology Dongguan China University of Electronic Science & Technology of China Shenzhen Institute for Advanced Study China City University of Hong Kong Department of Computer Science Hong Kong Dongguan University of Technology School of Electrical Engineering & Intelligentization Dongguan China

ISBN: (纸本)9798350324662

Finding all roots of polynomials over finite fields is a crucial problem in algebraic coding techniques, such as decoding BCH codes and Reed-Solomon codes. The current commonly used methods are Chien search or fast Fourier transform (FFT), which evaluates all elements in a finite field and results in field multiplications whose number increases linearly with the field size. In this paper, we propose a new approach that enables the multiplicative complexity to grow sub-linearly with the field size. To our knowledge, this is the currently lowest multiplicative complexity for this problem. Additionally, the proposed approach maintains the additive complexity similar to that of FFT-based method. © 2023 IEEE.

关键词： error-correction codes error-locator polynomial fast Fourier transforms (FFT) Polynomial evaluation

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Graphene Bridge Heterostructure Devices for Negative Differential Transconductance Circuit Applications

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Nano-Micro Letters 2023年第2期15卷 161-171页

作者： Minjong Lee Tae Wook Kim Chang Yong Park Kimoon Lee Takashi Taniguchi Kenji Watanabe Min‑gu Kim Do Kyung Hwang Young Tack Lee Department of Electrical and Computer Engineering Inha UniversityIncheon 22212Republic of Korea Department of Materials Science and Engineering The University of Texas at DallasRichardsonTX 75080USA Center for Opto‑Electronic Materials and Devices Post‑Silicon Semiconductor InstituteKorea Institute of Science and Technology(KIST)Seoul 02792Republic of Korea Department of Physics Kunsan National UniversityGunsan 54150Republic of Korea Advanced Materials Laboratory National Institute for Materials ScienceTsukuba 305‑0044Japan Department of Information and Communication Engineering Inha UniversityIncheon 22212Republic of Korea Division of Nanoscience and Technology KIST SchoolUniversity of Science and Technology(UST)Seoul 02792Republic of Korea Department of Electronic Engineering Inha UniversityIncheon 22212Republic of Korea

Two-dimensional van der Waals(2D vdW)material-based heterostructure devices have been widely studied for high-end electronic applications owing to their heterojunction *** this study,we demonstrate graphene(Gr)-bridge heterostructure devices consisting of laterally series-connected ambipolar semiconductor/Gr-bridge/n-type molybdenum disulfide as a channel material for field-effect transistors(FET).Unlike conventional FET operation,our Gr-bridge devices exhibit nonclassical transfer characteristics(humped transfer curve),thus possessing a negative differential *** phenomena are interpreted as the operating behavior in two series-connected FETs,and they result from the gate-tunable contact capacity of the Gr-bridge ***-value logic inverters and frequency tripler circuits are successfully demonstrated using ambipolar semiconductors with narrow-and wide-bandgap materials as more advanced circuit applications based on non-classical transfer ***,we believe that our innovative and straightforward device structure engineering will be a promising technique for future multi-functional circuit applications of 2D nanoelectronics.

关键词： Graphene bridge Heterostructure device Non-classical transfer characteristics Multi-value logic inverter Frequency tripler

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Evasion Attacks in Smart Power Grids: A Deep Reinforcement Learning Approach

Evasion Attacks in Smart Power Grids: A Deep Reinforcement L...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Ahmed T. El-Toukhy Mohamed Mahmoud Atef H. Bondok Mostafa M. Foudal Maazen Alsabaan Department of Electrical and Computer Engineering Tennessee Tech. University Cookeville TN USA Department of Electrical Engineering Faculty of Engineering Al-Azhar University Cairo Egypt Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Center for Advanced Energy Studies (CAES) Idaho Falls ID USA Department of Computer Engineering King Saud University Riyadh Saudi Arabia

In smart power grids, certain customers are motivated by financial gains to manipulate electricity consumption data, aiming to reduce their bills. Despite the development of machine learning-based detectors, these systems remain vulnerable to evasion attacks. This paper investigates the susceptibility of deep reinforcement learning (DRL)-based detectors to evasion attacks. We propose an evasion attack model that employs the double deep Q learning (DDQN) algorithm for a black-box attack scenario. Our model generates adversarial evasion samples by altering malicious consumption data, tricking detectors into classifying them as benign. Leveraging the unique attributes of reinforcement learning (RL), our model determines optimal actions for manipulating malicious data. For comparative analysis, we compare our DRL-based model with an FGSM-based attack model. Our experiments consistently demonstrate the effectiveness of our DRL-based attack model, achieving an impressive attack success rate (ASR) ranging from 92.92% to 99.96%, outperforming the FGSM-based attack model.

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Enhancing Mobile Robot Speed Control: PID Controller Optimization with Bio-Inspired Algorithms 4

Enhancing Mobile Robot Speed Control: PID Controller Optimiz...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Singh, Sangeeta Uma, H.R. Bhargav, H.K. Arya, Sujata Singh, Praveen Natrayan, L. JSS Academy of Technical Education Department of Electrical Engineering Uttar Pradesh Noida201 301 India BGS Institute of Technology Adichunchanagiri University Department of Computer Science and Engineering Karnataka Mandya571 448 India Basaveshwar Engineering College Department of Computer Science and Engineering Karnataka Bagalkot587 102 India Faculty of Law Viman Nagar Maharashtra Pune411 014 India Graphic Era Deemed to be University Department of Management Studies Uttarakhand Dehradun248002 India Saveetha School of Engineering SIMATS Department of Mechanical Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350385793

This research study presents an investigation into the optimization of PID controllers using bio-inspired algorithms for controlling the speed of mobile robots. The study begins by identifying a mathematical model that accurately represents the dynamics of the DC motor used in robots. Three different tuning methods, namely Ziegler-Nichols (ZN), Flower Pollination Algorithm (FPA), and Fish Swarm Optimization (FSO) are then utilized to design the PID controllers. The primary objective is to enhance the speed control performance of mobile robots. To evaluate the effectiveness of the optimization methods, the set speed is given to each of the three controllers, and their performance is compared. The results demonstrate that the optimization techniques outperform the conventional ZN method in achieving the desired speed control. Among the two bio-inspired algorithms, FPA yields exceptional outcomes in terms of time characteristics. The comparison of time characteristics serves as a benchmark for evaluating the performance of the controllers. It reveals that bio-inspired optimization methods, especially FPA, significantly improve the speed control of mobile robots. The time characteristics, such as rise time, settling time, overshoot, peak time, and peak value, illustrate the superior performance achieved through the optimization process. © 2024 IEEE.

关键词： DC motors

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A Logical Design of Internet of Things Assisted Digital Health Record Monitoring System using Deep Learning Principles

A Logical Design of Internet of Things Assisted Digital Heal...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Gayathri, S. Vijayanandh Thirumoorthy, P. Valarmathy, A.S. Pandi, V. Samuthira Shobana, D. R.M.K. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Dhanalakshmi College of Engineering Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Tamil Nadu Chennai India Rajalakshmi Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350308167

Intelligent systems for monitoring health have been made possible by the IoT and the mental/physical health of an individual may be monitored using such a system. Determinants of numerous diseases, both physical and mental include tension, worry and high blood pressure. Problems associated with aging including anxiety, depression, stress and pressure require specialized care in this context. The prompt identification of health issues by monitoring of nervousness tension, stress, and heart rate is a key to avoiding irreversible harm. This will improve health outcomes;lessen the burden on caregivers, and save money. Find innovative technological answers for continuous monitoring of stress, anxiety, and blood pressure through the use of covert embedded sensors as well as deep learning methods. As a result of the suggested smart health monitoring system, health monitoring services may now be provided quickly, cheaply, and reliably from remote places, whereas this was not previously achievable with conventional health care systems. The implementation of a blockchain-based framework has increased the safety and privacy of patients' private information and reduced the likelihood of it being misused. Using Deep Learning and related technologies to evaluate health data for numerous goals has aided in the achievement of both preventative healthcare and fatality control. This has allowed for the early diagnosis of chronic illnesses, which was previously impossible. The combination of computing resources in the cloud and electronic storage has been introduced in order to render operations cheaper and instantaneously. This research provides a comprehensive overview of the state of health monitoring technology, including the most up-to-date developments and the most pressing problems. © 2023 IEEE.

关键词： Deep Learning Health Record Monitoring Healthcare Internet of Things IoT Sensors

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