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arXiv 2024年

作者： Xu, Yucheng Yang, Jia-Qi Fan, Kebin Wang, Sheng Wu, Jingbo Zhang, Caihong Zhan, De-Chuan Padilla, Willie J. Jin, Biaobing Chen, Jian Wu, Peiheng Key Laboratory of Optoelectronic Devices and Systems with Extreme Performances of MOE School of Electronic Science and Engineering Nanjing University Nanjing210023 China Purple Mountain Laboratories Nanjing211111 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China Department of Electrical and Computer Engineering Duke University Box 90291 DurhamNC27708 United States

Emerging reconfigurable metasurfaces offer various possibilities in programmatically manipulating electromagnetic waves across spatial, spectral, and temporal domains, showcasing great potential for enhancing terahertz applications. However, they are hindered by limited tunability, particularly evident in relatively small phase tuning over 270o, due to the design constraints with time-intensive forward design methodologies. Here, we demonstrate a multi-bit programmable metasurface capable of terahertz beam steering, facilitated by a developed physics-informed inverse design (PIID) approach. Through integrating a modified coupled mode theory (MCMT) into residual neural networks, our PIID algorithm not only significantly increases the design accuracy compared to conventional neural networks but also elucidates the intricate physical relations between the geometry and the modes. Without decreasing the reflection intensity, our method achieves the enhanced phase tuning as large as 300o. Additionally, we experimentally validate the inverse designed programmable beam steering metasurface, which is adaptable across 1-bit, 2-bit, and tri-state coding schemes, yielding a deflection angle up to 68o and broadened steering coverage. Our demonstration provides a promising pathway for rapidly exploring advanced metasurface devices, with potentially great impact on communication and imaging technologies. © 2024, CC0.

关键词： Tuning

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A Data-Driven Approach Towards software Regression Testing Quality Optimization

A Data-Driven Approach Towards Software Regression Testing Q...

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Information Technology (ACIT)

作者： Abdallah Moubayed Nouh Alhindawi Jamal Alsakran MohammadNoor Injadat Mohammad Kanan Computer Engineering Department and Interdisciplinary Research Center for Intelligent Secure Systems King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia School of Computing and Augmented Intelligence Arizona State University Gilbert Arizona USA Department of Software Engineering Jadara University Irbid Jordan Fairleigh Dickinson University Canada Data Science & Artificial Intelligence Department Zarqa University Zarqa Jordan Department of Industrial Engineering University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

software testing is very important in software development to ensure its quality and reliability. As software systems have become more complex, the number of test cases has increased, which presents the challenge of executing all the tests in a limited time frame. Various test case prioritization techniques have been introduced to solve this problem. These methods aim to identify and implement the most critical tests first. In this paper, we propose an implementation of a dynamic test case prioritization strategy to improve software quality by increasing code coverage with special attention to edge case handling. Edge case test prioritization is a technique that improves test efficiency by selecting extreme case scenarios that can reveal critical bugs or unexpected behavior early in development, improving overall software reliability and dependability. In order to prioritize test cases, this paper presents a regression-based method that makes use of machine learning algorithms. The approach leverages previous performance data to optimize regression testing efficiency by examining variables like test time and execution status. Performance evaluations, when compared against industry standards and cutting-edge techniques, show how effective these algorithms are at correctly prioritizing test cases and identifying faults. This study offers simplified yet reliable solutions for regression testing optimization by shedding light on the efficacy of regression algorithms, such as Random Forest and decision trees.

关键词： software testing Machine learning algorithms software algorithms software quality software systems software reliability Regression tree analysis Optimization Standards software development management

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Deep Reinforcement Learning Enabled Joint Deployment and Beamforming in STAR-RIS Assisted Networks

arXiv

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arXiv 2023年

作者： Ma, Zhuoyuan Zhao, Qi Yan, Bai Zhang, Jin The Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China The Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen518055 China Peng Cheng Laboratory Shenzhen518000 China

In the new generation of wireless communication systems, reconfigurable intelligent surfaces (RIS) and simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) have become competitive network components to achieve intelligent and reconfigurable network environments. However, existing work has not fully studied the deployment freedom of STAR-RIS, which limits further improvements in network communication performance. Therefore, this paper proposes a solution based on a deep reinforcement learning algorithm to dynamically deploy STAR-RIS and hybrid beamforming to improve the total communication rate of users in mobile wireless networks. The paper constructs a STAR-RIS assisted multi-user multiple-input single-output (MU-MISO) mobile wireless network and jointly optimizes the dynamic deployment strategy of STAR-RIS and the hybrid beamforming strategy to maximize the long-term total communication rate of users. To solve this problem, the paper uses the Proximal Policy Optimization (PPO) algorithm to optimize the deployment of STAR-RIS and the joint beamforming strategy of STAR-RIS and the base station. The trained policy can maximize the downlink transmission rate of the system and meet the real-time decision-making needs of the system. Numerical simulation results show that compared with the traditional scheme without using STAR-RIS and fixed STAR-RIS deployment, the PPO method proposed in this paper can effectively improve the total communication rate of wireless network users in the service *** Codes G.1.6, I.2.8 Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Decentralized and Lightweight Cross-Chain Transaction Scheme Based on Proxy Re-signature

Decentralized and Lightweight Cross-Chain Transaction Scheme...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Huiying Zou Jia Duan Xi Liu Wei Ren Tao Li Xianghan Zheng Kim-Kwang Raymond Choo School of Computer Science China University of Geosciences Wuhan China The Third Surveying and Mapping Institute of Hunan Province Hunan Engineering Research Center of Geographic Information Security and Application Hunan China Key Laboratory of Data Protection and Intelligent Management Sichuan University Ministry of Education China State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR CASM Beijing China College of Computer and Big Data Fuzhou University School of Information and Intelligent Engineering Sanya College China Department of Information Systems and Cyber Security University of Texas at San Antonio San Antonio USA

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

With the widespread application of digital assets and the rapid development of blockchain technology, achieving secure and efficient transactions between different blockchain networks has become an urgent challenge. Existing cross-chain methods impose limitations, e.g., hash lock technology exhibits low scalability, side-chain technology is overly complex, and notary schemes pose centralization risks. In order to address these issues, we propose a cross-chain transaction solution based on proxy re-signature technology. To tackle the centralization concerns, we employ proxy re-signature technology to decentralize the authority of notaries among transaction participants, minimizing the potential for centralization at a low cost. We further present an extended scheme that can guarantee more requirements such as higher transaction amount and shorter transaction time. Furthermore, we analyze and compare the signature technologies used in the proposed solution and provide a systematic proof of our approach’s security.

关键词： Privacy Systematics Costs Scalability Blockchains Security

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VG4D: Vision-Language Model Goes 4D Video Recognition

arXiv

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arXiv 2024年

作者： Deng, Zhichao Li, Xiangtai Li, Xia Tong, Yunhai Zhao, Shen Liu, Mengyuan School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen China National Key Laboratory of General Artificial Intelligence Peking University China Department of Computer Science ETH Zurich Switzerland National Key Laboratory of General Artificial Intelligence Peking University Shenzhen Graduate School China PKU-Wuhan Institute for Artificial Intelligence China

Understanding the real world through point cloud video is a crucial aspect of robotics and autonomous driving systems. However, prevailing methods for 4D point cloud recognition have limitations due to sensor resolution, which leads to a lack of detailed information. Recent advances have shown that Vision-Language Models (VLM) pre-trained on web-scale text-image datasets can learn fine-grained visual concepts that can be transferred to various downstream tasks. However, effectively integrating VLM into the domain of 4D point clouds remains an unresolved problem. In this work, we propose the Vision-Language Models Goes 4D (VG4D) framework to transfer VLM knowledge from visual-text pretrained models to a 4D point cloud network. Our approach involves aligning the 4D encoder's representation with a VLM to learn a shared visual and text space from training on large-scale image-text pairs. By transferring the knowledge of the VLM to the 4D encoder and combining the VLM, our VG4D achieves improved recognition performance. To enhance the 4D encoder, we modernize the classic dynamic point cloud backbone and propose an improved version of PSTNet, im-PSTNet, which can efficiently model point cloud videos. Experiments demonstrate that our method achieves state-ofthe- art performance for action recognition on both the NTU RGB+D 60 dataset and the NTU RGB+D 120 dataset. Code is available at https://***/Shark0-0/VG4D. Copyright © 2024, The Authors. All rights reserved.

关键词： Visual languages

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A Sociable Robotic Platform to Make Career Advices for Undergraduates 22nd

A Sociable Robotic Platform to Make Career Advices for Under...

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22nd International Conference on Human computer Interaction,HCII 2020

作者： Mithsara, W. K. Malithi Manawadu, Udaka A. De Silva, P. Ravindra S. Robotics and Intelligent Systems Laboratory Department of Computer Science University of Sri Jayewardenepura Nugegoda Sri Lanka Robot Engineering Laboratory Graduate School of Computer Science and Engineering University of Aizu Aizuwakamatsu Japan

ISBN: (纸本)9783030601164

Most of the undergraduates couldn’t figure out a proper direction for their future careers since advising depends on the person and students show aversion in revealing their information to other humans. This research focused on creating a social robotic platform to interact with undergraduates in the field of computer science to realize possible career paths, as recent researches show social companion robots tend to form a stronger bond, which helps the addressee to share their information easily. The robot was created as a tabletop robot employing minimalistic design with a friendly view attached with speech synthesis for communicating purposes. Data was collected from 202 persons, who followed a degree in computer science. Data contains their experience, qualifications, and skills. Thereafter an artificial neural network was created using supervised learning to predict the career path with 95% performance accuracy. The experiment was performed by engaging 15 students from the final year who are a doing degree in computer science and they were asked to provide feedback on the interactions with the robot. The gathered responses highlighted robot animacy, interaction, technology and usefulness. Therefore, from the results it can be concluded that a majority of the students accepted the robot, interacted without hesitation and had friendly conversations with the robot where they valued the generated output from the robot. © 2020, Springer Nature Switzerland AG.

关键词： Neural networks

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Towards Few-shot Inductive Link Prediction on Knowledge Graphs: A Relational Anonymous Walk-guided Neural Process Approach

arXiv

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arXiv 2023年

作者： Zhao, Zicheng Luo, Linhao Pan, Shirui Nguyen, Quoc Viet Hung Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology China Jiangsu Key Laboratory of Image and Video Understanding for Social Security China Department of Data Science and AI Monash University Australia School of Information and Communication Technology Griffith University Australia Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education

Few-shot inductive link prediction on knowledge graphs (KGs) aims to predict missing links for unseen entities with few-shot links observed. Previous methods are limited to transductive scenarios, where entities exist in the knowledge graphs, so they are unable to handle unseen entities. Therefore, recent inductive methods utilize the sub-graphs around unseen entities to obtain the semantics and predict links inductively. However, in the few-shot setting, the sub-graphs are often sparse and cannot provide meaningful inductive patterns. In this paper, we propose a novel relational anonymous walk-guided neural process for few-shot inductive link prediction on knowledge graphs, denoted as RawNP. Specifically, we develop a neural process-based method to model a flexible distribution over link prediction functions. This enables the model to quickly adapt to new entities and estimate the uncertainty when making predictions. To capture general inductive patterns, we present a relational anonymous walk to extract a series of relational motifs from few-shot observations. These motifs reveal the distinctive semantic patterns on KGs that support inductive predictions. Extensive experiments on typical benchmark datasets demonstrate that our model derives new state-of-the-art performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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MUCOR: A Multiparty Conversation Based Robotic Interface to Evaluate Job Applicants 22nd

MUCOR: A Multiparty Conversation Based Robotic Interface to ...

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22nd International Conference on Human computer Interaction,HCII 2020

作者： Senaratna, H.A.S.D. Manawadu, Udaka A. Hansika, W.K.N. Samarasinghe, S.W.A.M.D. De Silva, P. Ravindra S. Robotics and Intelligent Systems Laboratory Department of Computer Science University of Sri Jayewardenepura Nugegoda Sri Lanka Robot Engineering Laboratory Graduate School of Computer Science and Engineering University of Aizu Aizuwakamatsu Japan

ISBN: (纸本)9783030601164

"MUCOR" is a robotic interface made up of three small-sized, table-top robots that go through Linkedin profiles of job applicants and discuss the applicants’ information. Robots categorize applicants into ordered levels based on their competencies mentioned. Robots built following minimalist design concepts consist of a cylindrical head and a body that occupy less space. A score is calculated for each candidate and categorized into levels by a model built including Agglomerative Clustering and Gaussian Mixture Model. The model itself identifies the number of levels into which the applicants can be categorized, through the iterations. MUCOR is built for job recruiters who spend more time to find background information about applicants prior to an interview. Conversation among robots and the user creates a Passive Social Interface in which the user does not involve in the conversation, but gain knowledge by listening. The recruiter only has to input the LinkedIn IDs into a mobile application and listen to the conversation among robots. MUCOR was experimented with professional recruiters and their feedback highlighted that the synchronization of speech with the robot’s body movements has a fair impact on the user experience about the robot behavior. © 2020, Springer Nature Switzerland AG.

关键词： Human robot interaction

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A Novel Long Short-Term Memory (LSTM)-Based Degradation Detection in Photovoltaic systems using Performance Ratio

A Novel Long Short-Term Memory (LSTM)-Based Degradation Dete...

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International Conference on Energy Transition in the Mediterranean Area (SyNERGY MED)

作者： Amirhossein Dadashpour Aref Eskandari Amir Nedaei Sherko Salehpour Parviz Parvin Andreas Livera George Makrides Mohammadreza Aghaei Department of Electrical Engineering Amirkabir University of Technology (AUT) Tehran Iran Department of Electrical Engineering Iran University of Science and Technology (IUST) Tehran Iran Department of Physics and Energy Engineering Amirkabir University of Technology (AUT) Tehran Iran PV Technology Laboratory Department of Electrical and Computer Engineering University of Cyprus (UCY) PHAENTON Centre of Excellence for Intelligent Efficient and Sustainable Energy Solutions Nicosia Cyprus Department of Ocean Operations and Civil Engineering Norwegian University of Science and Technology (NTNU) Alesund Norway Professor Department of Sustainable Systems Engineering (INATECH) University of Freiburg Freiburg Germany

ISBN: (数字)9798350375923

ISBN: (纸本)9798350375930

Photovoltaic (PV) systems deteriorate over time as a result of prolonged exposure to UV light and harsh weather conditions. This degradation can have a major impact on the performance of the PV systems and increase their levelized cost of energy (LCOE). To deal with this problem, in this paper, a method has been proposed based on predicting The PV systems' performance ratio, which is a measure of the actual output compared to the expected output. To estimate the performance ratio, a dataset including a PV array of normal and degraded states under different environmental conditions has been created. The main model uses an architecture with long short-term memory (LSTM). The model results show high accuracy in predicting the performance ratio of systems. The model achieved the outstanding $\mathbf{R}^systems$ scores of $\mathbf{9 9. 4 7 \%}$ and $\mathbf{9 9. 4 6 \%}$ and the root mean squared error (RMSE) values of $\mathbf{0. 0 1 6 3}$ and 0.0169 for the training and testing processes, respectively.

关键词： Degradation Photovoltaic systems Training Accuracy System performance Predictive models Long short term memory Testing Meteorology

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SmartEdge: Smart Healthcare End-to-End Integrated Edge and Cloud Computing System for Diabetes Prediction Enabled by Ensemble Machine Learning

SmartEdge: Smart Healthcare End-to-End Integrated Edge and C...

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IEEE International Conference on Cloud Computing Technology and science (CloudCom)

作者： Alain Hennebelle Qifan Dieng Leila Ismail Rajkumar Buyya Independent Researcher in Information Technology Systems and Artificial Intelligence Applications Abu Dhabi United Arab Emirates Cloud Computing and Distributed Systems (CLOUDS) Lab School of Computing and Information Systems University of Melbourne Melbourne Australia Department of Computer Science and Software Engineering Intelligent Distributed Computing and Systems (INDUCE) Lab College of Information Technology Emirates Center for Mobility Research National Water and Energy Center UAE University Al-Ain Abu Dhabi United Arab Emirates

ISBN: (数字)9798331507589

ISBN: (纸本)9798331507596

The Internet of Things (IoT) revolutionizes smart city domains such as healthcare, transportation, industry, and education. The Internet of Medical Things (IoMT) is gaining prominence, particularly in smart hospitals and Remote Patient Monitoring (RPM). The vast volume of data generated by IoMT devices should be analyzed in real-time for health surveillance, prognosis, and prediction of diseases. Current approaches relying on Cloud computing to provide the necessary computing and storage capabilities do not scale for these latency-sensitive applications. Edge computing emerges as a solution by bringing cloud services closer to IoMT devices. This paper introduces SmartEdge, an AI-powered smart healthcare end-to-end integrated edge and cloud computing system for diabetes prediction. This work addresses latency concerns and demonstrates the efficacy of edge resources in healthcare applications within an end-to-end system. The system leverages various risk factors for diabetes prediction. We propose an Edge and Cloud-enabled framework to deploy the proposed diabetes prediction models on various configurations using edge nodes and main cloud servers. Performance metrics are evaluated using, latency, accuracy, and response time. By using ensemble machine learning voting algorithms we can improve the prediction accuracy by 5% versus a single model prediction.

关键词： Cloud computing Accuracy Smart cities Computational modeling Transportation industry Surveillance Machine learning Predictive models Diabetes Time factors

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