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作者： Chandra Shit, Rathin Sharma, Suraj Watters, Paul Yelamarthi, Kumar Pradhan, Biswajeet Davison, Richard Morgan, Graham Puthal, Deepak Department of Computer Science and Engineering International Institute of Information Technology Bhubaneswar India Department of Computer Science and Information Technology La Trobe University MelbourneVIC Australia School of Engineering and Technology Central Michigan University Mount PleasantMI United States School of Information Systems and Modelling University of Technology Sydney UltimoNSW Australia School of Computing Newcastle University Newcastle upon Tyne United Kingdom

Advancement of computing and communication techniques transforms the traditional transport system into the intelligent transportation system (ITS). The development of distributed computing in a vehicular network platform also called Vehicular Edge Computing (VEC) promise to address most of the challenges faced by the ITS. Localization is important in these vehicular networks because of its key contribution in autonomous driving, smart traffic monitoring, and collision avoidance services. For localization, current GPS and hybrid methods are in-efficient because of GPS outage in urban infrastructure and dynamic nature of the vehicular networks. The cooperative localization approaches, on the other hand, use dedicated short range communication to broadcast messages and estimate location. However, these messages are un-encrypted and periodic which gives a privacy risk for vehicles. This article presents a privacy-preserving cooperative localization in vehicular network based upon dynamic pseudonym changing strategy. First, the localization delay is addressed with the implementation of dynamic vehicular edge assignment for computational task management. In the next step, the localization is estimated from the neighbor and road side unit ranging measurement followed by a real-time prediction of the vehicle. The performance of the proposed algorithms is analyzed in terms of localization accuracy and privacy preservation strength. Furthermore, the proposed method is simulated in a real city scenario followed by localization accuracy and privacy analysis. Finally, the localization accuracy and privacy strength of the proposed approach are compared with the state-of-the-art methods. © 2020 John Wiley & Sons, Ltd.

关键词： Edge computing

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Evolutionary Computing based Analysis of Diversity in Grammatical Evolution

Evolutionary Computing based Analysis of Diversity in Gramma...

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2021 International Conference on Artificial Intelligence and Smart systems, ICAIS 2021

作者： Youssef, Ayman Gupt, Krishn Kumar Raja, Muhammad Adil Murphy, Aidan Ryan, Conor Research Group Department of Computer Science and Information Systems Limerick Ireland Limerick Institute of Technology Bds and Department of Electrical and Electronic Engineering Limerick Ireland

ISBN: (纸本)9781728195377

Diversity is a much sought after aspect of any evolutionary system. More diversity means a cornucopia of diverse behaviors and traits among the individuals of a population. Lack of diversity, on the other hand, leads to a stagnant population whose individuals are more or less similar to each other. Subsequently, they fail to produce a variety of offspring. Grammatical Evolution (GE), being an Evolutionary Algorithm (EA), is also an aspirant of diversity. It allows a GE system to maintain a dynamic population over multiple *** this paper, we present our reflections about diversity estimates in a (large) number of experiments. We performed evolutionary experiments to estimate a bunch of well-known benchmark polynomials. We also employed hybrid optimization in our experiments. Our results are insightful. In this paper, we also test the effect of hybrid optimization algorithms integrated with GE on the diversity of the population. © 2021 IEEE.

关键词： Computational grammars

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Consistent and Asymptotically Statistically-Efficient Solution to Camera Motion Estimation

arXiv

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arXiv 2024年

作者： Zeng, Guangyang Zeng, Qingcheng Li, Xinghan Mu, Biqiang Chen, Jiming Shi, Ling Wu, Junfeng The School of Data Science Chinese University of Hong Kong Shenzhen Shenzhen518172 China The College of Control Science and Engineering The State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 China Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China The Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong

Given 2D point correspondences between an image pair, inferring the camera motion is a fundamental issue in the computer vision community. The existing works generally set out from the epipolar constraint and estimate the essential matrix, which is not optimal in the maximum likelihood (ML) sense. In this paper, we dive into the original measurement model with respect to the rotation matrix and normalized translation vector and formulate the ML problem. We then propose a two-step algorithm to solve it: In the first step, we estimate the variance of measurement noises and devise a consistent estimator based on bias elimination;In the second step, we execute a one-step Gauss-Newton iteration on manifold to refine the consistent estimate. We prove that the proposed estimate owns the same asymptotic statistical properties as the ML estimate: The first is consistency, i.e., the estimate converges to the ground truth as the point number increases;The second is asymptotic efficiency, i.e., the mean squared error of the estimate converges to the theoretical lower bound - Cramer-Rao bound. In addition, we show that our algorithm has linear time complexity. These appealing characteristics endow our estimator with a great advantage in the case of dense point correspondences. Experiments on both synthetic data and real images demonstrate that when the point number reaches the order of hundreds, our estimator outperforms the state-of-the-art ones in terms of estimation accuracy and CPU time. Copyright © 2024, The Authors. All rights reserved.

关键词： Maximum likelihood estimation

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Access-Redundancy Tradeoffs in Quantized Linear Computations

Access-Redundancy Tradeoffs in Quantized Linear Computations

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IEEE International Symposium on Information Theory

作者： Vinayak Ramkumar Netanel Raviv Itzhak Tamo Department of Electrical Engineering–Systems Tel Aviv University Tel Aviv Israel Department of Computer Science and Engineering Washington University in St. Louis St. Louis MO USA

Linear real-valued computations over distributed datasets are common in many applications, most notably as part of machine learning inference. In particular, linear computations which are quantized, i.e., where the coefficients are restricted to a predetermined set of values (such as ±1), gained increasing interest lately due to their role in efficient, robust, or private machine learning models. Given a dataset to store in a distributed system, we wish to encode it so that all such computations could be conducted by accessing a small number of servers, called the access parameter of the system. Doing so relieves the remaining servers to execute other tasks, and reduces the overall communication in the system. Minimizing the access parameter gives rise to an access-redundancy tradeoff, where smaller access parameter requires more redundancy in the system, and vice versa. In this paper we study this tradeoff, and provide several explicit code constructions based on covering codes in a novel way. While the connection to covering codes has been observed in the past, our results strictly outperform the state-of-the-art, and extend the framework to new families of computations.

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Real-Time Detection of Traffic Anomalies Near Roundabouts 5th

Real-Time Detection of Traffic Anomalies Near Roundabouts

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5th Mediterranean Conference on Pattern Recognition and Artificial Intelligence, MedPRAI 2021

作者： Pramanik, Anima Sarkar, Sobhan Djeddi, Chawki Maiti, J. Department of Industrial and Systems Engineering IIT Kharagpur Kharagpur721302 India Information Systems and Business Analytics IIM Ranchi Jharkhand Ranchi834008 India Department of Mathematics and Computer Science Larbi Tebessi University Tebessa Algeria LITIS Lab University of Rouen Rouen France Centre of Excellence on Safety Engineering and Analytics IIT Kharagpur Kharagpur721302 India

ISBN: (纸本)9783031041112

Visual analytics can bridge the gap between computational and human approaches for detecting traffic anomalies near the round-about, making the data analysis process more transparent. The main problem with anomaly detection is the unavailability of anomalous data as they do not occur frequently. Moreover, there is a variety in the characteristics of the same real-time scenario. A solution is proposed to handle these issues using unsupervised learning which mainly encloses the detection of vehicles, road, roundabout violations, and traffic jams near the round-about. Initially, vehicles, roads, and round-about are detected based on Histogram of Gradient (HoG) feature descriptor and color information. Then, optical flow is applied to the detected vehicles for obtaining their trajectory information. Finally, the round-about violation is detected based on the position and angle between the centre of the round-about and the vehicle trajectory. Additionally, the number of vehicles near the round-about is computed for detecting the traffic jam. The effectiveness of the proposed algorithm is demonstrated over 30 real-time traffic videos. Some additional comparative studies are done over the benchmark data YouTube8M. Through an extensive study, it can be concluded that our proposed algorithm is superior to some state-of-the-arts. © 2022, Springer Nature Switzerland AG.

关键词： Anomaly detection

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A study on pokémon GO: Exploring the potential of location-based mobile exergames in connecting players with nature 21

A study on pokémon GO: Exploring the potential of location-...

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8th ACM SIGCHI Annual Symposium on computer-Human Interaction in Play, CHI PLAY 2021

作者： Saaty, Morva Haqq, Derek Toms, Devin B Eltahir, Ibrahim McCrickard, D. Scott Department of Computer Science Virginia Tech BlacksburgVA United States Department of Industrial and Systems Engineering Virginia Tech BlacksburgVA United States

ISBN: (纸本)9781450383561

Exergaming incorporates exercising into video games, with the purpose of physically engaging users in the gameplay. Location-based games have gained the attention of exergame designers as they have been able to motivate people to exercise and spend time in outdoors. Studies have shown the impact of current exergames to intervene with sedentariness by containing some elements of nature and outdoor activities. However, it is unclear how location-based mobile exergames can engage users with nature throughout playing and exercising. This paper presents insights from an exploratory self-study of experiences provided by the most popular location-based mobile game, Pokémon GO, concerning its potential to engage players in outdoors. We also provide design considerations for location-based mobile exergames to engage players in nature while becoming physically active. © 2021 Owner/Author.

关键词： Human computer interaction

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Efficient Hybrid Multi-Population Genetic Algorithm for Multi-UAV Task Assignment in Consumer Electronics Applications

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IEEE Transactions on Consumer Electronics 2025年

作者： Bai, Xiaoshan Jiang, Haoyu Li, Chao Ullah, Inam Dabel, Maryam M. Al Bashir, Ali Kashif Wu, Zongze Sam, Shuzhi Shenzhen University College of Mechatronics and Control Engineering National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Shenzhen University Shenzhen City Joint Laboratory of Autonomous Unmanned Systems and Intelligent Manipulation Shenzhen518060 China Tashkent State University of Economics Department of Artificial Intelligence Tashkent100066 Uzbekistan University of Hafr Al Batin Department of Computer Science and Engineering College of Computer Science and Engineering Saudi Arabia Manchester Metropolitan University Manchester United Kingdom Shenzhen518060 China National University of Singapore Department of Electrical and Computer Engineering Singapore117576 Singapore

In recent years, as people’s living standards have improved and consumption concepts have been transformed, the demand for purchasing consumer electronics online has continued to grow, further stimulating the development of the logistics industry. Consequently, how to deliver consumer electronics to households at minimal cost has become a crucial factor that limits the development of the consumer technology industry. To tackle this problem, this paper studies the task assignment problem for multiple initially dispersed UAVs to deliver products to target locations while minimizing their total operation time. Each UAV can continuously provide delivery services to multiple target locations within its limited loading capacity and operation time. To solve this problem, we propose several hybrid multipopulation genetic algorithms. First, a novel crossover operator for the genetic algorithms is designed, through which a single parent chromosome can generate offspring individually. Second, two mutation mechanisms are performed to increase gene diversity. Third, multiple local search strategies are employed to enhance the populations’ fitness during each iteration of evolution. An improved 2-opt local search strategy is applied to optimize individual chromosomes when their similarity with the current best chromosome falls below a prescribed threshold. Alternatively, local search strategies are utilized for 1-opt, 2h-opt and interchange processes. Combining local search strategies, genetic operators, and the multi-population mechanism leads to several hybrid multi-population genetic algorithms. Numerical simulations and experimental tests demonstrate that the hybrid multi-population genetic algorithm, integrated with the improved 2-opt and 1-opt local search strategies, exhibits superior performance among the designed hybrid genetic algorithms, the minimum marginal cost algorithm (MMA), and the existing popular Co-evolutionary Multi-population Genetic Algorithm (CMGA). In exp

关键词： Genetic algorithms

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CKTGNN: CIRCUIT GRAPH NEURAL NETWORK FOR ELECTRONIC DESIGN AUTOMATION

arXiv

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arXiv 2023年

作者： Dong, Zehao Cao, Weidong Zhang, Muhan Tao, Dacheng Chen, Yixin Zhang, Xuan Department of Computer Science & Engineering Washington University St. Louis United States Department of Electrical & Systems Engineering Washington University St. Louis United States Institute for Artificial Intelligence Peking University China School of Computer Science The University of Sydney Australia

The electronic design automation of analog circuits has been a longstanding challenge in the integrated circuit field due to the huge design space and complex design trade-offs among circuit specifications. In the past decades, intensive research efforts have mostly been paid to automate the transistor sizing with a given circuit topology. By recognizing the graph nature of circuits, this paper presents a Circuit Graph Neural Network (CktGNN) that simultaneously automates the circuit topology generation and device sizing based on the encoder-dependent optimization subroutines. Particularly, CktGNN encodes circuit graphs using a two-level GNN framework (of nested GNN) where circuits are represented as combinations of subgraphs in a known subgraph basis. In this way, it significantly improves design efficiency by reducing the number of subgraphs to perform message passing. Nonetheless, another critical roadblock to advancing learning-assisted circuit design automation is a lack of public benchmarks to perform canonical assessment and reproducible research. To tackle the challenge, we introduce Open Circuit Benchmark (OCB), an open-sourced dataset that contains 10K distinct operational amplifiers with carefully-extracted circuit specifications. OCB is also equipped with communicative circuit generation and evaluation capabilities such that it can help to generalize CktGNN to design various analog circuits by producing corresponding datasets. Experiments on OCB show the extraordinary advantages of CktGNN through representation-based optimization frameworks over other recent powerful GNN baselines and human experts' manual designs.. Our work paves the way toward a learning-based open-sourced design automation for analog circuits. Our source code is available at https://***/zehao-dong/CktGNN. Copyright © 2023, The Authors. All rights reserved.

关键词： Timing circuits

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A Comprehensive Evaluation of Q-Learning Based Automatic Web GUI Testing

A Comprehensive Evaluation of Q-Learning Based Automatic Web...

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International Conference on Dependable systems and Their Applications (DSA)

作者： Yujia Fan Siyi Wang Sinan Wang Yepang Liu Guoyao Wen Qi Rong Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Huawei Inc China

Recently, reinforcement learning (RL) based automatic Web GUI testing techniques are gaining popularity in both academia and industry as they can enable more intelligent exploration of web applications’ states. However, the existing RL-based techniques often incorporate special features, such as DFA-guided state recovery or contextual input data generation, making the effectiveness of RL itself unclear. Moreover, while these techniques mostly employ Q-learning (QL), a model-free RL method, they were evaluated with different experimental settings, which could lead to unfair comparisons. Motivated by the two observations, we propose a generic QL-based automatic Web GUI testing framework, and conduct the first systematic evaluation, which considers four QL specific configurations, on two open-source benchmark web applications and one industrial portal website. Based on the experimental results, we discuss several findings regarding the effectiveness of QL-based automatic GUI testing. We believe that our findings can provide useful guidance to industrial practitioners and shed light on future research on leveraging RL to improve automatic Web GUI testing.

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Improved Parallel k-means Clustering Algorithm

Improved Parallel k-means Clustering Algorithm

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Computing and Information Technology (ICCIT-1441), 2020 International Conference on

作者： Lama AlGhamdi Mariam Alkharraa Sahar AlZahrani Hajar Bawazir Wadha AlHajri Asma AlHajri Naya Nagy Mohammed Gollapalli Department of Computer Engineering College of Computer Science & Information Technology Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Department of Computer Science Applied College Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Department of Computer Information Systems College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia

The K-means algorithm, one of the most well-known clustering techniques, has been widely employed to solve a variety of problems. In contrast, the k-means clustering algorithm has numerous restrictions. For instance, the difficulty of dealing with voluminous data, the sensitivity of the outlier, and the random selection of the initial centroid. In this paper, a parallel K-means clustering algorithm is proposed that improves the performance of sequential K-means clustering algorithms by removing outliers from the data before clustering, dividing the data into smaller sections among the threads, and selecting the initial centroid with care. Our primary parallelization tool was OpenMP, which was implemented using the C programming language on 234,296 records. This experiment was conducted using sequential and parallel source code, with modifications made to enhance the parallel functionality. The improved parallel execution resulted in a significant reduction in execution time relative to sequential algorithms. The proposed algorithm source code is also available on GitHub for the community.

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