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

作者： Khan, A.Z. Panwar, Neeraj Rayaguru, N.K. Shelke, Priya M. Giri, Rakesh Kumar Jaishankar, P. Yeshwantrao Chavan College of Engineering Applied Physics Department Nagpur India University Gehu Dehradun Department of School of Computing India Vel Tech Rangarajan Dr sagunthala R & D Institute of Science and Technology Department of Eee India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Sunrise University Department of Computer Science & Engineering Alwar India Sastra Deemed to be University Department of School of Civil Engineering India

ISBN: (纸本)9798350370249

Measuring the electromagnetic overall performance and configuration of networks in high-interference environments can be challenging. In those environments, the indicators of hobby can be infected with unwanted heritage indicators, i.e., noise, leading to degraded overall performance and misguided size readings. This paper investigates the most reliable techniques for measuring networks' electromagnetic performance and configuration in these problematic environments, utilizing introducing a unique multistage framework that combines sign-processing strategies with some community parameters. In particular, the framework includes four degrees: sign reception, sensitivity analysis, pass-correlation, and community configuration optimization-an analysis of the results of varying environmental conditions on sign reception and sensitivity change into conduct. A cross-correlation approach has evolved to decide the extent of correlation among one-of-a-kind alerts and to locate the specific configurations of the particular networks. Sooner or later, an optimization set of rules was carried out to locate the network's optimum configuration. The final result of this research is a reliable method for measuring the electromagnetic performance and configuration of networks in high-interference environments. The proposed technique is anticipated to help researchers and engineers better understand the electromagnetic behaviors of complicated networks in actual-world scenarios. © 2024 IEEE.

关键词： Configuration Electromagnetic Environmental Performance Technique

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From LLMs to LLM-based Agents for Software engineering: A Survey of Current, Challenges and Future

arXiv

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arXiv 2024年

作者： Jin, Haolin Huang, Linghan Cai, Haipeng Yan, Jun Li, Bo Chen, Huaming The School of Electrical and Computer Engineering The University of Sydney Sydney2006 Australia The School of Electrical Engineering and Computer Science Washington State University United States The School of Computing and Information Technology University of Wollongong Australia The Computer Science Department The University of Chicago United States

With the rise of large language models (LLMs), researchers are increasingly exploring their applications in various vertical domains, such as software engineering. LLMs have achieved remarkable success in areas including code generation and vulnerability detection. However, they also exhibit numerous limitations and shortcomings. LLM-based agents, a novel technology with the potential for Artificial General Intelligence (AGI), combine LLMs as the core for decision-making and action-taking, addressing some of the inherent limitations of LLMs such as lack of autonomy and self-improvement. Despite numerous studies and surveys exploring the possibility of using LLMs in software engineering, it lacks a clear distinction between LLMs and LLM-based agents. It is still in its early stage for a unified standard and benchmarking to qualify an LLM solution as an LLM-based agent in its domain. In this survey, we broadly investigate the current practice and solutions for LLMs and LLM-based agents for software engineering. In particular we summarise six key topics: requirement engineering, code generation, autonomous decision-making, software design, test generation, and software maintenance. We review and differentiate the work of LLMs and LLM-based agents from these six topics, examining their differences and similarities in tasks, benchmarks, and evaluation metrics. Finally, we discuss the models and benchmarks used, providing a comprehensive analysis of their applications and effectiveness in software engineering. We anticipate this work will shed some lights on pushing the boundaries of LLM-based agents in software engineering for future research. © 2024, CC BY.

关键词： Application programs

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Globally Stable Neural Imitation Policies

Globally Stable Neural Imitation Policies

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Amin Abyaneh Mariana Sosa Guzmán Hsiu-Chin Lin Department of Electrical and Computer Engineering McGill University Montreal Canada Department of Electrical and Computer Engineering Universidad Veracruzana Veracruz Mexico Department of Electrical and Computer Engineering School of Computer Science McGill University Montreal Canada

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Imitation learning mitigates the resource-intensive nature of learning policies from scratch by mimicking expert behavior. While existing methods can accurately replicate expert demonstrations, they often exhibit unpredictability in unexplored regions of the state space, thereby raising major safety concerns when facing perturbations. We propose SNDS, an imitation learning approach aimed at efficient training of scalable neural policies while formally ensuring global stability. SNDS leverages a neural architecture that enables the joint training of the policy and its associated Lyapunov candidate to ensure global stability throughout the learning process. We validate our approach through extensive simulations and deploy the trained policies on a real-world manipulator arm. The results confirm SNDS’s ability to address instability, accuracy, and computational intensity challenges highlighted in the literature, positioning it as a promising solution for scalable and stable policy learning in complex environments.

关键词： Training Perturbation methods Imitation learning computer architecture Reliability theory Manipulators Stability analysis

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Noncrossing Longest Paths and Cycles 32

Noncrossing Longest Paths and Cycles

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32nd International Symposium on Graph Drawing and Network Visualization, GD 2024

作者： Aloupis, Greg Biniaz, Ahmad Bose, Prosenjit De Carufel, Jean-Lou Eppstein, David Maheshwari, Anil Odak, Saeed Smid, Michiel Tóth, Csaba D. Valtr, Pavel Khoury College of Computer Sciences Northeastern University BostonMA United States School of Computer Science University of Windsor Canada School of Computer Science Carleton University Ottawa Canada School of Electrical Engineering and Computer Science University of Ottawa Canada Computer Science Department University of California IrvineCA United States Department of Mathematics California State University Northridge Los AngelesCA United States Department of Computer Science Tufts University Medford United States Department of Applied Mathematics Charles University Prague Czech Republic

ISBN: (纸本)9783959773430

Edge crossings in geometric graphs are sometimes undesirable as they could lead to unwanted situations such as collisions in motion planning and inconsistency in VLSI layout. Short geometric structures such as shortest perfect matchings, shortest spanning trees, shortest spanning paths, and shortest spanning cycles on a given point set are inherently noncrossing. However, the longest such structures need not be noncrossing. In fact, it is intuitive to expect many edge crossings in various geometric graphs that are longest. Recently, Álvarez-Rebollar, Cravioto-Lagos, Marín, Solé-Pi, and Urrutia (Graphs and Combinatorics, 2024) constructed a set of points for which the longest perfect matching is noncrossing. They raised several challenging questions in this direction. In particular, they asked whether the longest spanning path, on any finite set of points in the plane, must have a pair of crossing edges. They also conjectured that the longest spanning cycle must have a pair of crossing edges. In this paper, we give a negative answer to the question and also refute the conjecture. We present a framework for constructing arbitrarily large point sets for which the longest perfect matchings, the longest spanning paths, and the longest spanning cycles are noncrossing. © Greg Aloupis, Ahmad Biniaz, Prosenjit Bose, Jean-Lou De Carufel, David Eppstein, Anil Maheshwari, Saeed Odak, Michiel Smid, Csaba D. Tóth, and Pavel Valtr.

关键词： Trees (mathematics)

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REMOTELY CONTROLLABLE DNA MICROFLOW 26

REMOTELY CONTROLLABLE DNA MICROFLOW

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26th International Conference on Miniaturized Systems for Chemistry and Life sciences, MicroTAS 2022

作者： Udono, Hirotake Nomura, Shin-Ichiro M. Takinoue, Masahiro Department of Computer Science School of Computing Tokyo Institute of Technology Japan Department of Robotics Graduate School of Engineering Tohoku University Japan

ISBN: (纸本)9781733419048

Despite an emerging interest in fluid manipulation within confined environment such as biological cells and micro-scale biomimics, remotely controllable micro-scale flow remains unexplored. However, intracellular microfluidics is not compatible with established top-down microfluidic methods because of limited accessibility. Here, we demonstrate photoresponsive DNA fluid, condensate of DNA nanostructures (DNA motifs), as a versatile mechanistic tool for the remote control of microflow. Insertion site of azobenzene, introduced for photocontrollability, determines an extent of dynamic shift in binding enthalpy between DNA motifs, providing the underpinning mechanism for drastic and facile flow-mode switching. © 2022 MicroTAS 2022 - 26th International Conference on Miniaturized Systems for Chemistry and Life sciences. All rights reserved.

关键词： Remote control

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Transforming High-Frequency Trading Predictions with CNN-LSTM Networks

Transforming High-Frequency Trading Predictions with CNN-LST...

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Advances in electrical, computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： S. Baghavathi Priya Priyanga S Sri Surya Shobith Kamisetty Venkata Sai Vinay Neelapala Nithila G Department of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Electronics & Communication Engineering School of Electronics Engineering (SENSE) Vellore Institute of Technology Vellore India Department of Computer Science & Engineering School of Computer Science and Engineering (SCOPE) Vellore Institute of technology Vellore India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

High-Frequency Trading (HFT) has transformed financial markets by enabling rapid and automated trading. However, the prediction of critical market metrics, such as trading volumes, remains a challenge due to the noisy and complex nature of financial data. This paper presents a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model designed specifically for predicting trading volumes in the NIFTY50 index using high-resolution, minutelevel Yahoo Finance data. By combining CNN's spatial feature extraction capabilities with LSTM's temporal modeling strengths, the proposed approach effectively captures the intricate patterns and sequential dependencies inherent in high-frequency trading datasets. Comprehensive data preprocessing, including noise reduction and temporal segmentation, coupled with advanced cross-validation techniques, ensures robust performance across market conditions. Comprehensive evaluations using metrics such as Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R2) demonstrate the model's efficacy in capturing intricate financial dynamics. This work underscores the potential of hybrid deep learning models in enhancing predictive analytics for high-frequency trading applications in emerging markets.

关键词： Fluctuations Noise reduction Finance Predictive models Feature extraction Convolutional neural networks Indexes Noise measurement Predictive analytics Long short term memory

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SynchroLearn: Master Deadlock and Synchronization Through Interactive Learning

SynchroLearn: Master Deadlock and Synchronization Through In...

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International Conference on computing and Networking Technology (ICCNT)

作者： G Bhavya Shreya Bhanot M Srinidhi Samreen Sidhu Sajitha Krishnan Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science & Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Today, educational tools have become necessary for learners to learn well and improve their knowledge. These tools complement knowledge as they can make it more vivid and add some simulation or game into the learning process. It is self-learning since the students can learn from any place and it also enables the students to go further on content that may interest them, and which may help them in the understanding of the concept. This paper takes the idea of developing SynchroLearn, which is an educational visualization tool developed with the help of HTML, CSS, and JavaScript that enables the teaching of deadlock handling techniques effectively for both the developers and the students. To expound on techniques such as the Producer-Consumer problem, Peterson’s method, Test-and-Set Lock, and the Dining Philosophers problem, an online tool known as SynchroLearn helps in providing the learner an actual time visualization. These strategies present several ways of handling resources, and with that, the management of their utilization process, so that the processes could be controlled in terms of what resources they require, which in turn, helps to solve and prevent such cases of deadlocks. This approach helps to learn complex relationships as well as to understand the interactions between the different processes, identify the deadlock situations, and use the essential methods for their resolution. They bring out the idea linking theoretical proficiencies and practical application by demonstrating them as simple-to-follow graphics which are therefore easier to implement.

关键词： Visualization Education Process control Games System recovery Synchronization

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Constrained reinforcement learning under model mismatch 24

Constrained reinforcement learning under model mismatch

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Proceedings of the 41st International Conference on Machine Learning

作者： Zhongchang Sun Sihong He Fei Miao Shaofeng Zou Department of Electrical Engineering University at Buffalo New York School of Computing University of Connecticut Storrs Department of Electrical Engineering University at Buffalo New York and Department of Computer Science & Engineering University at Buffalo New York

Existing studies on constrained reinforcement learning (RL) may obtain a well-performing policy in the training environment. However, when deployed in a real environment, it may easily violate constraints that were originally satisfied during training because there might be model mismatch between the training and real environments. To address this challenge, we formulate the problem as constrained RL under model uncertainty, where the goal is to learn a policy that optimizes the reward and at the same time satisfies the constraint under model mismatch. We develop a Robust Constrained Policy Optimization (RCPO) algorithm, which is the first algorithm that applies to large/continuous state space and has theoretical guarantees on worst-case reward improvement and constraint violation at each iteration during the training. We show the effectiveness of our algorithm on a set of RL tasks with constraints.

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Machine Learning Techniques for Non-Intrusive Load Monitoring for Enhanced Energy Predictions 14

Machine Learning Techniques for Non-Intrusive Load Monitorin...

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2024 IEEE 14th Annual computing and Communication Workshop and Conference, CCWC 2024

作者： Chugh, Tushar Tyagi, Kanishka Seth, Rolly Carnegie Mellon University School of Computer Science Pittsburgh United States The University of Texas at Arlington Department of Electrical Engineering ArlingtonTX United States Microsoft Redmond United States

ISBN: (纸本)9798350360134

We explore the application of diverse machine learning methodologies within the domain of electricity load disaggregation. This process involves discerning and categorizing electrical appliances based on their distinctive electrical signatures, a practice commonly called Non-intrusive Load Monitoring (NILM). NILM encompasses the analysis of time series data containing voltage, current, and power inputs to a building, facilitating the identification of various appliance types and their respective energy consumption patterns. The ability to disaggregate individual appliance activities holds significant potential across multiple domains. Researchers can harness this information for many applications, including but not limited to occupancy detection, refined energy management strategies, fault diagnosis, anomaly detection, and predictive maintenance. The current study emphasizes evaluating and enhancing the efficacy of machine learning algorithms tailored to execute NILM tasks proficiently. The broad objective lies in contributing to the significant goals of energy efficiency and intelligent energy management through the accurate and efficient performance of NILM processes. © 2024 IEEE.

关键词： Anomaly detection

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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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