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检索条件"任意字段=7th International Conference on the Art, Science, and Engineering of Programming, Programming 2023"

共 1136 条记录，以下是561-570 订阅

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Casual Discovery for Complex Mission Planning with Subtask Dependencies 7

Casual Discovery for Complex Mission Planning with Subtask D...

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7th international conference on Big Data and Information Analytics, BigDIA 2021

作者： Liang, Wenqian Zhang, Zhuxin Xiao, Wenhua Zhang, Dayu Niu, Liyuan Tao, Jingjing College of Systems Engineering National University of Defense Technology Changsha China North Automatic Control Technology Institute Taiyuan China Academy of Military Sciences Beijing China

ISBN: (纸本)9781665424660

Discovering causal structure among a set of previously-unseen subtasks which have inner dependencies is a fundamental problem in real-world environments. Unlike existing hierarchical multitask RL approaches that focus on task descriptions such as natural language or formal language that describe what the agent should do at a high level, we develop a Casual Inference Solver with Subtask Graph (CISG), which performs complex reasoning with subtasks, properties and relationships among them to find the optimal subtask to execute. To overcome the difficulty of efficient exploration, upper confidence bound (UCB) is adopted to produce the intrinsic reward. Meanwhile, a novel inductive logic programming (ILP), which utilizes the classification and regression tree (Cart) to infer the precondition function for subtasks with Gini impurity, is utilized to pretrain our CISG agent to achieve the optimal subtask to execute. Our experimental results on StarCraft II domains show that our method is able to perform complex reasoning to accurately find the casual relationship among subtasks and make a mission planning more efficiently than existing hierarchical RL methods. © 2021 IEEE.

关键词： Formal languages

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A Real-Time Pedestrian Tracking Method Based on Enhanced Tracktor 23

A Real-Time Pedestrian Tracking Method Based on Enhanced Tra...

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Proceedings of the 2023 7th international conference on Electronic Information Technology and Computer engineering

作者： Huawei Song Jian Li Fangjie Wan Jiahao Hong School of Cyber Science and Engineering Zhengzhou University China School of Computer and Artificial Intelligence Zhengzhou University China

ISBN: (纸本)9798400708305

In the domain of multi-object video tracking, the issue of target identity loss, which results from detection biases caused by interactive occlusions and other influences, is addressed in this paper. We propose AUTracktor, a pedestrian multi-object tracking algorithm based on an enhanced Tracktor. Initially, a dynamic updating module is designed for bounding box regression, leveraging a Siamese network for further refinement and localization of the proposed bounding boxes. Subsequently, a temporal data optimization unit is employed to update a more suitable template for the current frame, establishing a connection between frames. Feature fusion is conducted through pixel correlation, thereby enhancing the edge and scale information of the target. Experimental evaluations on the public MOT16 dataset demonstrate that our proposed approach exhibits superior tracking accuracy compared to the state-of-the-art methods, with commendable robustness and a stable frame rate of 24 FPS.

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Cyber-Physical Systems Design in An Uncertain Environment with Time Uncertainty Concern

Cyber-Physical Systems Design in An Uncertain Environment wi...

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international conference on Parallel and Distributed Systems (ICPADS)

作者： Si Chen Lida Huang Xiongren Xiao Yan Liu Guoqi Xie Renfa Li College of Computer Science and Electronic Engineering Key Laboratory for Embedded and Network Computing of Hunan Province Hunan University Changsha China Center for Convergence of Automobile and Cyberspace Research Institute of Hunan University in Chongqing Chongqing China

Multiple processors system on chip (MPSoC) has been the trend in cyber-physical systems (CPSs), and reasonable partitioning for MPSoC resources is a critical step in CPSs design. the uncertainties of environment and time are both important factors that need to be considered in the design, but none of the previous work pays attention to two uncertainties at the same time. the state-of-the-art work presented a detailed process of applying uncertain programming to solve the partitioning problem, which provides a solution for designing in an uncertain environment. However, this work only considers the bipartition scenario which cannot be directly applied to MPSoC, and it does not focus specifically on time uncertainty. In this paper, we propose a method for modeling the MPSoC partitioning problem in an uncertain environment, with the time uncertainty concern. We present the uncertain model that can be applied to the multiple optional resources scenario. We build the optimization model with the objective of minimizing time, analyze two different cases of minimizing the uncertain time, and finally prove a unified deterministic model to solve. We come up with three algorithms, including the heuristic algorithm, the genetic algorithm, and the exact algorithm, and experiments show that the heuristic algorithm and the genetic algorithm can obtain good approximate solutions compared with the exact algorithm.

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Practical and Efficient Model Extraction of Sentiment Analysis APIs

Practical and Efficient Model Extraction of Sentiment Analys...

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international conference on Software engineering (ICSE)

作者： Weibin Wu Jianping Zhang Victor Junqiu Wei Xixian Chen Zibin Zheng Irwin King Michael R. Lyu School of Software Engineering Sun Yat-sen University Department of Computer Science and Engineering The Chinese University of Hong Kong Department of Computing The Hong Kong Polytechnic University Tencent

Despite their stunning performance, developing deep learning models from scratch is a formidable task. therefore, it popularizes Machine-Learning-as-a-Service (MLaaS), where general users can access the trained models of MLaaS providers via Application programming Interfaces (APIs) on a pay-per-query basis. Unfortunately, the success of MLaaS is under threat from model extraction attacks, where attackers intend to extract a local model of equivalent functionality to the target MLaaS model. However, existing studies on model extraction of text analytics APIs frequently assume adversaries have strong knowledge about the victim model, like its architecture and parameters, which hardly holds in practice. Besides, since the attacker's and the victim's training data can be considerably discrepant, it is non-trivial to perform efficient model extraction. In this paper, to advance the understanding of such attacks, we propose a framework, PEEP, for practical and efficient model extraction of sentiment analysis APIs with only query access. Specifically, PEEP features a learning-based scheme, which employs out-of-domain public corpora and a novel query strategy to construct proxy training data for model extraction. Besides, PEEP introduces a greedy search algorithm to settle an appropriate architecture for the extracted model. We conducted extensive experiments with two victim models across three datasets and two real-life commercial sentiment analysis APIs. Experimental results corroborate that PEEP can consistently outperform the state-of-the-art baselines in terms of effectiveness and efficiency.

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CovDNet: A Hybrid Multi - Level Densely Connected Neural Network for Improvised Analysis and Classification of COVID-19 Using Chest CT-Scans

CovDNet: A Hybrid Multi - Level Densely Connected Neural Net...

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international conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Deepa S Praveenkumar G D R Suguna Vivek Duraivelu C Jamunadevi S G Sundhar Department of Computer Technology-PG Kongu Engineering College Perundurai Erode Tamilnadu Department of computer science Vet institute of Arts and Science (Co-Education) College Erode Tamilnadu Department of Computer science and Engineering Bannari Amman Institute of Technology Sathyamangalam Department of Computer Science and Engineering B V Raju Institute of Technology Narsapur Telangana Department of Computer Technology PG Kongu Engineering College Perundurai Erode Tamilnadu Final M.Sc(Software Systems) Kongu Engineering College Perundurai Erode Tamilnadu

COVID-19, caused by the new coronavirus SARS-Co V-2, has turned into a worldwide health emergency, needing speedy and precise diagnostic techniques. this abstract provides a thorough evaluation of research works focusing on COVID-19 identification using DenseNet, a cutting-edge convolutional neural network architecture. DenseNet is notable for its innovative design, which fosters feature reuse while relieving the vanishing gradient problem and enhancing network information flow. Researchers have created novel ways for COVID-19 identification using medical imaging, such as chest X-rays and CT scans, by harnessing the capabilities of DenseNet. the analyzed studies demonstrate DenseNet's efficiency in recognizing COVID-19-specific patterns and distinguishing them from other lung diseases. these investigations have shown remarkable accuracy, sensitivity, and precision, exceeding established machine learning approaches and even professional radiologists. this research study also highlights the difficulties and constraints experienced while using DenseNet to identify COVID-19. Issues such as dataset quantity, class imbalance, and model decision-making process interpretability are addressed. Furthermore, future research paths and prospective enhancements, such as multi-modal data integration and the development of explainable AI systems, are investigated. the experimental results indicate that the proposed approach achieves high accuracy, sensitivity, and precision in the identification of COVID - 19. the proposed methodology achieved an accuracy of 89.74%, sensitivity of 87.25 % , and precision of 79.56%, which outperforms the existing state-of-the-art methods. the proposed approach is robust and can effectively differentiate between COVID-19 positive and negative cases, which is essential for early detection and prompt treatment.

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A Hybrid Deep Neural Network with Attention Mechanism for Human Activity Recognition Based on Smartphone Sensors

A Hybrid Deep Neural Network with Attention Mechanism for Hu...

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international conference on Information Technology (INCIT)

作者： Sakorn Mekruksavanich Datchakorn Tancharoen Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Faculty of Engineering and Technology Panyapiwat Institute of Management Nonthaburi Thailand Department of Mathematics Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand

Human activity recognition (HAR) is an area of study that seeks to automatically and precisely detect an individual’s behavior by analyzing bio-signal data. Bio-signal data can be acquired using sensing technology integrated into smartphones and other worn intelligent gadgets. Nevertheless, collecting sensor data tagged with activity information employing individual smartphones can result in data being analyzed in varying contexts, potentially compromising the accuracy of machine learning prediction methodologies. this paper introduces a novel HAR approach via smartphone sensors. the proposed method incorporates a hybrid deep neural network architecture enhanced with an attention mechanism. the deep learning model under consideration is referred to as the Att-CNN-BLSTM network. this particular network can autonomously extract significant features from smartphone sensor data. this feature extraction aims to effectively classify different human actions with a high degree of accuracy. In order to assess the efficacy of the hybrid model, a series of investigations were undertaken utilizing a publically accessible HAR dataset. this dataset was employed for training and testing purposes, employing a 5-fold cross-validation approach. We additionally carried out an analytical comparison utilizing state-of-the-art deep learning models from prior research. the findings from our experimentation demonstrate superior performance of the Att-CNN-BLSTM model over other advanced AI algorithms, with the highest accuracy rate of 90.54% and top F1-score of 86.88% attained.

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Optimal Design of Group Orthogonal MIMO Radar Waveforms Bases on Quantum Genetic Algorithm

Optimal Design of Group Orthogonal MIMO Radar Waveforms Base...

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IEEE international conference on Information Communication and Signal Processing (ICICSP)

作者： Zhimin Qu Hongbo Zhang Tianqu Liu Jinping Sun School of Electronic and Information Engineering Beihang University Beijing China School of Automation SouthEast University Nanjing China AVIC Leihua Electronic Technology Research Institute Wuxi China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

Digital radio frequency memory (DRFM) devices can quickly intercept radar waveforms and has become the main technique to achieve advanced radar jamming. Multiple-input multiple-output (MIMO) radars can counter advanced DRFM jamming based on waveform agility technology by emitting waveform sets which are orthogonal between groups. To design multiple groups of orthogonal waveform sets simultaneously, an optimization design model for group agile orthogonal waveform sets is developed in this paper. the correlation function peak of intergroup waveforms is multiplied by a deviation weight, serving as the objective function. In order to solve the proposed optimization model, we propose an optimization design algorithm for group agile orthogonal waveform sets. the quantum genetic algorithm (QGA), employing a partial search strategy and having no special requirements on the properties of the objective function, is capable of solving such complex non-convex optimization problems. Numerical simulation results demonstrate that the proposed algorithm yields waveform sets with good intra- and intergroup orthogonality performance, adjustable via changing weight coefficients.

关键词： Correlation MIMO radar Simulation Signal processing algorithms Radar Signal processing Linear programming Jamming Optimization Genetic algorithms

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A Method for Calculating and Realising the Dynamic Characteristics of an Electromagnetic Contactor

A Method for Calculating and Realising the Dynamic Character...

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international conference on Electric Power Equipment - Switching Technology (ICEPE-ST)

作者： Lingzhi Tang Hao Sun Zifeng Wang Chunping Niu Yi Wu Shuwu Wei Zhihua Wu School of Electrical Engineering Xi'an Jiaotong University Xi'an China Beijing Yundao Zhiyan Technology Co. Ltd Beijing China

ISBN: (数字)9798350388947

ISBN: (纸本)9798350388954

A contactor is a low-voltage switchgear device widely used in the power system. this study provides a method for the calculation and implementation of the dynamic characteristics of contactors through self-developed manual programming. the computation is divided into two calculations: the current and the motion. In the current calculation, the input parameters include the voltage waveform and other necessary parameters of the main circuit. the program then calculates and outputs the magnitude of the current at each time point; the motion calculation program is used to determine the position of the contactor's moving iron core at each time step by iterating the position and velocity. Combining the current results, the electromagnetic force acting on the moving iron core is calculated, and displacement and velocity are determined and outputted. through experimental measurements, the start-up time and closing time of the moving iron core in both experimental and simulated scenarios differed by less than 5%, which falls within an acceptable range. therefore, employing our method as a general numerical computation method for analyzing the dynamic characteristics of contactors is feasible.

关键词： Switchgear Power system dynamics Dynamics Switches Iron Software Time measurement Real-time systems Velocity measurement Electromagnetics

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Intelligent Chatbot For Educational Institutions

Intelligent Chatbot For Educational Institutions

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international conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： Nandigam Srikar Reddy Naini Pranay Chaitanya Panthangi Sai Varshitha Rudhraraju Chaitanya Varma Patlolla Karthik Reddy Jagadeesh Dandu Computer Science and Engineering B V Raju Institute of Technology Narsapur Telangana India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

Our project introduces an "Intelligent Chatbot for Educational Institutions". Responding to the dynamic landscape of educational technology, we unveil an advanced full-stack chatbot initiative Designed to overhaul academic administration and student information access. Our chatbot leverages sophisticated rule based systems and traditional programming paradigms to ensure optimal efficiency. this chatbot excels at processing natural language queries and furnishing real-time updates on students’ academic progress and accomplishments. A notable application of its capabilities lies in swiftly identifying and presenting information about the top performing student in a given academic year, such as the college valedictorian of 2022. this functionality is made possible by a meticulously designed algorithm that systematically analyses academic records, discerns query context, and extracts pertinent data to deliver precise and valuable insights to users, including educators, students, and parents. With a user-friendly interface, the chatbot ensures accessibility for users of all technical proficiencies. Our approach places a premium on accuracy in information retrieval and continuous refinement through iterative enhancements, enabling the chatbot to adapt seamlessly to the evolving educational landscape. this initiative aims to establish a new standard in educational technology by providing an innovative solution that streamlines information dissemination and enriches the academic journey for stakeholders in the educational realm, all achieved with reliance on ML and DL technologies.

关键词： Ethics Technological innovation Educational technology Chatbots Information retrieval Real-time systems Iterative methods

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Air Quality Prediction and Classification using Machine Learning

Air Quality Prediction and Classification using Machine Lear...

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international conference on Computing Methodologies and Communication (ICCMC)

作者： Dinesh Komarasamy Nandhidha G C Nandhinidevi S Nanthini P L Mohanasaranya Kousalya K Computer Science and Engineering Kongu Engineering College Perundurai India

Air is one of the finest resources in the present environment which contains various mixtures of gases. the various compositions may be Nitrogen, Oxygen, Sulphur Dioxide (SO2), Ozone (O3), Carbon Dioxide (CO2), Nitrogen Dioxide (NO2). the concentration of components may be varied because of air pollution. Numerous actions contribute to air pollution, which in turn makes people sick. thus, air quality monitoring is a required task across many industrial and urban regions across the world. thus, the researchers introduced various technologies like Internet of things (IOT), Deep learning and Machine learning to forecast the air quality. From the state of art, the existing work considers only limited attributes which results in less accuracy in predicting air quality. As a result, this work proposes Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm along with Linear Regression which gives great classification performance when selecting the most contaminated sectors. Air Quality Operational Centers (AQOCs) are developed in places where there is severe air pollution. Experiment, which approximate the complicated physicochemical processes involved, have been the major foundation for modelling air pollution events. Even if these models have become more intricate and complicated over time, their use for real-time air quality monitoring seems unsuitable in terms of performance, input data requirements, and respect to the problem's time constraints.

关键词： Atmospheric modeling Air pollution Prediction algorithms Real-time systems Nitrogen Internet of things Time factors

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