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共 1136 条记录，以下是541-550 订阅

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Scalable Tree Goal Prediction Based on Decision Interactions

Scalable Tree Goal Prediction Based on Decision Interactions

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conference on Vehicle Control and Intelligence (CVCI)

作者： Linhui Li Jiazheng Su Jing Lian School of Automotive Engineering Dalian University of Technology Dalian China

the road conditions for vehicle movement in urban intersection scenarios are complex. Autonomous vehicles must be able to effectively infer the goals of other vehicles to make reasonable decisions to complete the driving task. Considering the difficulty and specificity of inferring vehicle behavior in different scenarios, it is a challenge to predict the goals of vehicles in an accurate, interpretable and scalable way. Accordingly, this paper proposes a decision interaction based extensible tree (DIET), a scalable goal prediction system. DIET extracts common semantic features from road and vehicle information in the scenario, and extends the feature space and eliminates errors arising from simple behavioral inference by analyzing the decision interactions between surrounding vehicles. Extensive comparative experiments have been conducted on the proposed DIET and several other typical related works under different scenarios. the model reported in this paper outperforms state-of-the-art methods by 10.9% in accuracy of new scene predictions. Clearly, DIET is able to more accurately predict the vehicle's goal in different scenarios, and the process is interpretable.

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Semantic Segmentation Research of Motion Blurred Images by Event Camera

Semantic Segmentation Research of Motion Blurred Images by E...

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conference on Vehicle Control and Intelligence (CVCI)

作者： Xiangyong Liu Fengfeng Yin Wei Jiang Siwen Fan College of Automotive Studies Tongji University Shanghai China State Key Laboratory of Process Automation in Mining & Metallurgy and Beijing Key Laboratory of Process Automation in Mining & Metallurgy Bgrimm Technology Group Shanghai China Department of Electromechanical Engineering University of Macau Shanghai China State Key Laboratory of Process Automation in Mining & Metallurgy and Beijing Key Laboratory of Process Automation in Mining & Metallurgy Bgrimm Technology Group Beijing China Linyi Engineering Branch China Railway Jinan Group Co Ltd Linyi China School of Mechatronical Engineering: Name of Organization Shanghai China

the semantic segmentation of road objects is a prerequisite for autonomous driving. Rapid or bumpy movement of vehicles can lead to the images to be blurred, which reduces the safety of autonomous driving. this study combines the dynamic visual sensor (DVS) and RGB camera to improve the detection accuracy of road semantic segmentation. Firstly, based on DVS and RGB images, we develop two datasets of blurred images, including the stone and run datasets. then, a double channels’ detection network is designed with several Swin-transformer modules, and the two channels are designed to extract features from DVS and RGB images, respectively. In addition, a feature fusion module is designed to extract attention from DVS images. Finally, experiments demonstrate that our proposed double-channels’ fusion method achieves the state-of-the-art segmentation performance for blurred images.

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A Comparative CNN based Analysis of Classification and Detection of Potholes

A Comparative CNN based Analysis of Classification and Detec...

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

作者： Vallamdas Ratheesh S.Govinda Rao V. Keerthi Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad Telangana

Autonomous vehicle (AV) systems can comprehend driving scenes has increased due to improvements in vision-based techniques. AV systems need to identify multiple detections like potholes, pedestrian, curb, road lanes, vehicles, traffic signs, signals etc., accurately and flawlessly for the fast-growing driverless vehicle technology. the prime motive of this research work is to investigate one of the major self-driving problems called pothole detection system. Autonomous vehicles must understand and detect objects based on reliable representations of their features. there are several different reasons why potholes occur on roads, including structural aging, heavy rainfall, dense traffic, thin or weak substructures, etc. It is increasingly difficult to maintain and assess such roads on a regular basis. Current methods have a limited ability to detect potholes precisely. this research work, compares the state-of-the-art deep learning Convolution neural network-based architectures VGG19, ResNet50, and DenseNet121 networks to choose the most suitable benchmark model for classification and detection of potholes using a transfer learning technique. the proposed model architecture, the test accuracy of ResNet50 was 94.6%, the test accuracy of DenseNet121 was 95.71 %, and the test accuracy of VGG19 was 94.29%. In comparison to the other two architectures, DenseNet121 achieved the best results.

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the programming Learning Assessment Model for Measuring Student Performance 1

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7th international conference on Computational science and Technology, ICCST 2020

作者： Chean, Swee-Ling Ho, Sin-Ban Chai, Ian Tan, Chuie-Hong Teh, Sek-Kit Ahmad, Nur Azyyati Lee Kong Chian Faculty Engineering and Science Universiti Tunku Abdul Rahman Kajang Selangor43000 Malaysia Faculty of Computing and Informatics Multimedia University Cyberjaya Selangor63100 Malaysia Faculty of Management Multimedia University Cyberjaya Selangor63100 Malaysia

ISBN: (数字)9789813340695

ISBN: (纸本)9789813340688

With recent pandemic, many students cannot join the class in physical classroom. the needs for e-learning and self-assessment become more salient than before. the teaching mode has been changing from teacher-centered to student-centered method. E-learning environment is practically a highly essential software application in the education field. However, programming-specific functionalities are hardly to be found on most of the general-purpose learning platforms, which may be unwieldy and unnecessarily complex to instructors and students in the programming learning process. this research aims to design a self-assessment model for a better support of programming e-learning, especially with exist of mandatory programming-specific functionalities. It’s believed that student background and effort have close correlation with their programming performance. More data to verify the correlations associated with positive learning outcome. In this research, we highlight the relationship between student background and student performance levels for introducing personalised self-assessment sets for students to learn programming. We propose and discuss Language, Education, Achievement, and programming (LEAP) and programming Learning Assessment (PLA) models to fill in the gap between the background knowledge and student competencies. To measure the correlation between proposed models and student performance, an experiment that involves 65 respondents was conducted. the data was analysed with structured and statistical approaches. Preliminary study shows that there are multivariate effects of the English fluency on PLA model. With the increasing demands of IT and software development skills, this research will help in motivating and encouraging more people to learn programming. © 2021, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： E-learning

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Utilizing Machine Learning Models to Determine the Security Level of Different Cryptosystems

Utilizing Machine Learning Models to Determine the Security ...

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international conference on Trends in Electronics and Informatics (ICEI)

作者： Kantipudi Pranathi Bodepudi Lakshmi Priya A. Yovan Felix Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

Due to recent developments in multimedia technology, digital data security has emerged as an important concern. To improve upon the state of the art in terms of security, researchers often recommend modifications to procedures that have previously been implemented. However, many suggested encryption algorithms have proved unsafe over the previous several decades, putting sensitive data at risk. It is crucial to use the most suitable encryption strategy to defend against such attacks; nevertheless, the type of data being protected might affect the method that is most appropriate for each given situation. However, systematically evaluating various cryptosystems to choose the optimal one may consume significant computational effort. To rapidly and reliably choose the appropriate algorithm, an SVM (support vector machine) is proposed as a security-level identification tool for photo encryption algorithms. In this research, a dataset was compiled with the help of common encryption security criteria, including Security, Peak signal to noise ratio, Homogeneity, Correlation, Contrast, Energy, Entropy, and Mean Square Error. these values are used as extracted characteristics from various cypher pictures. there are three tiers of security for dataset labels: strong, acceptable, and weak. For evaluating the effectiveness of the proposed model, the calculated accuracy and results demonstrate the value of this SVM system.

关键词： Support vector machines Analytical models PSNR Mean square error methods Predictive models Encryption Safety

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A Spatial Resource Allocation Method in Massive MIMO-LEO Satellite Communications 7

A Spatial Resource Allocation Method in Massive MIMO-LEO Sat...

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7th international conference on Computer and Communications, ICCC 2021

作者： Ding, Xiang Xu, Xin Bian, Dongming Zhang, Senbai Army Engineering University of PLA Institute of Communication Engineering Nanjing China

ISBN: (纸本)9781665409506

massive MIMO technology is critical in the 5G mobile communication system, which can greatly improve the frequency efficiency and communication capacity of terrestrial mobile communication systems, and also provides a new thinking for LEO satellite communication systems to fully reuse frequency between beams. In the paper, according to the massive MIMO-LEO satellite communications theory, we focus on the scenario of demands distributing unevenly spatially, and study the resource allocation problem of LEO satellite communications with full frequency reuse from the spatial perspective. Under the LEO satellite coverage-zoning model, we propose a rasterizing-coverage model for LEO satellites based on the characteristics of massive MIMO, establish a spatial resource allocation model with full frequency reuse, which models the spatial resource allocation problem with the objective of maximizing throughput as an integer programming problem, and put forward a greedy-based spatial resource allocation method. the simulation results show that when the service demand is 100% and the number of spatial resource groups is 16, compared with the fixed-beam method and the hopping-beam allocation method, the resource utilization can be improved respectively by 19% and 26%, and the satisfaction can be improved respectively by 16% and 17.5%, whose improvement are enhanced with the size of the on-board MIMO antenna increasing. © 2021 IEEE.

关键词： Resource allocation

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Evaluation of Adversarial Attacks and Detection on Transfer Learning Model

Evaluation of Adversarial Attacks and Detection on Transfer ...

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international conference on Computing Methodologies and Communication (ICCMC)

作者： Dhairya Vyas Viral V. Kapadia Computer Science and Engineering Department The Maharaja Sayajirao University of Baroda Vadodara India

In the field of transfer learning, the ability of models to make accurate predictions about future events can be compromised by targeted or untargeted adversarial attacks. Researchers have found that when images are subjected to such attacks, the feature construction process in every network produces noise, posing both challenges and opportunities for the study of convolutional neural networks (CNNs). To address this issue, the researchers developed a unique feature-map that describes the effects of adversarial attacks on individual object feature-maps. this approach was inspired by the observation of adversarial perturbations on image pixel attacks. By calculating the weight of each object's class activation map, a combined activation map is generated using a novel detection technique. the exploit feature-map developed in this study significantly advances the state-of-the-art in adversarial resistance. It was tested using several transfers learning models, including AlexNet, VGGNet19, and ResNet50, in both white-box and black-box assault scenarios. Moreover, the feature-map was shown to be effective in detecting assaults on ImageNet that use various techniques, such as the Fast Gradient Sign Method (FGSM), DeepFool, and Projected Gradient Descent (PGD).

关键词： Training Resistance Error analysis Computational modeling Perturbation methods Transfer learning Closed box

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Implementing Generative AI and Large Language Models in Education

Implementing Generative AI and Large Language Models in Educ...

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Innovative Approaches in Smart Technologies (ISAS), international Symposium on

作者： Neil Anderson Aidan McGowan Leo Galway Philip Hanna Matthew Collins David Cutting School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast Belfast United Kingdom

the recent advancements in Generative AI have been highlighted by the emergence of Large Language Models (LLMs) like ChatGPT. We track this evolution from the initial recurrent neural networks to the development of architectures like Transformers and Generative Pre-trained Transformers (GPT). ChatGPT, with its impressive ability to comprehend, process, and produce natural language, has piqued the interest of educators, students, and institutions within the education sector through its creation of high-quality textual *** marks the beginning of a new era in educational possibilities: we emphasize the beneficial effects of ChatGPT in learning environments, noting its utility in programming assistance, its clarity in concept explanation, and its role in enhancing automated learning processes. We also recognize potential drawbacks, including the risks of over-reliance, plagiarism, and the inherent constraints of these models in tackling mathematical and linguistic problem-solving *** exploring the Paradox of Automation, we examine the implications of an over-reliance on AI in education. We seek to understand the importance of preserving critical thinking skills and ensuring that technology serves as a tool for augmenting human capabilities rather than supplanting them. Our analysis acknowledges that, while AI, including ChatGPT, can assist in content generation and problem-solving, it is essential for students to cultivate their abilities in analytical thinking, content verification, and error correction.

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Nonlinear Model Predictive Clamping Force Control for Electromechanical Brake System

Nonlinear Model Predictive Clamping Force Control for Electr...

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international conference on Advanced Robotics and Mechatronics (ICARM)

作者： Wenpeng Wei Tianyi He Yifan Men Jinxiang Wang Lu Xiong Guodong Yin Key Laboratory of Measurement and Control of Complex Systems of Engineering School of Mechanical Engineering Southeast University Nanjing China Department of Mechanical and Aerospace Engineering Utah State University Logan UT USA ZDrive.ai 7th Floor Block T2 Ali Center 1398 Shenchang Road Minhang District Shanghai China School of Mechanical Engineering Southeast University Nanjing China School of Automotive Studies Tongji University Shanghai China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

In this paper, the problem of clamping force control for the Electromechanical Brake (EMB) system in autonomous vehicles is addressed. A nonlinear model predictive control framework is proposed to solve for the optimal maneuver at each optimization step to realize accurate clamping force tracking. First, the EMB system is modeled to a state-space representation through the first-principle modeling; Second, a control framework that includes three blocks is proposed to achieve clamping force tracking. the first block designs a clamping force separation strategy and an unknown input observer based on Kalman filter is applied to estimate the clamping force to replace the force sensors; the second block develops an adaptive estimation of polynomial coefficients to deal with the time-varying coefficients to improve model adaptiveness; the last block conceives the nonlinear model predictive control to track the reference force, and the resulted nonlinear programming is solved by the multiple shooting method. Finally, the proposed framework is validated to achieve faster tracking speed and more accurate tracking in multiple simulation scenarios.

关键词： Adaptation models Force Predictive models Observers Prediction algorithms Polynomials Clamps Kalman filters Force control Predictive control

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REEF: A Framework for Collecting Real-World Vulnerabilities and Fixes

REEF: A Framework for Collecting Real-World Vulnerabilities ...

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IEEE international conference on Automated Software engineering (ASE)

作者： Chaozheng Wang Zongjie Li Yun Pena Shuzheng Gao Sirong Chen Shuai Wang Cuiyun Gao Michael R. Lyu School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong China Computer Science and Engineering Department The Chinese University of Hong Kong Hong Kong China

Software plays a crucial role in our daily lives, and therefore the quality and security of software systems have become increasingly important. However, vulnerabilities in software still pose a significant threat, as they can have serious consequences. Recent advances in automated program repair have sought to automatically detect and fix bugs using data-driven techniques. Sophisticated deep learning methods have been applied to this area and have achieved promising results. However, existing benchmarks for training and evaluating these techniques remain limited, as they tend to focus on a single programming language and have relatively small datasets. Moreover, many benchmarks tend to be outdated and lack diversity, focusing on a specific codebase. Worse still, the quality of bug explanations in existing datasets is low, as they typically use imprecise and uninformative commit messages as explanations. To address these issues, we propose an automated collecting framework REEF to collect REal-world vulnErabilities and Fixes from open-source repositories. We focus on vulnerabilities since they are exploitable and have serious consequences. We develop a multi-language crawler to collect vulnerabilities and their fixes, and design metrics to filter for high-quality vulnerability-fix pairs. Furthermore, we propose a neural language model-based approach to generate high-quality vulnerability explanations, which is key to producing informative fix messages. through extensive experiments, we demonstrate that our approach can collect high-quality vulnerability-fix pairs and generate strong explanations. the dataset we collect contains 4,466 CVEs with 30,987 patches (including 236 CWE) across 7 programming languages with detailed related information, which is superior to existing benchmarks in scale, coverage, and quality. Evaluations by human experts further confirm that our framework produces high-quality vulnerability explanations.

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