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IEEE Journal on Indoor and Seamless Positioning and Navigation 2024年 2卷 92-129页

作者： Potorti, Francesco Crivello, Antonino Lee, Soyeon Vladimirov, Blagovest Park, Sangjoon Chen, Yushi Wang, Long Chen, Runze Zhao, Fang Zhuge, Yue Luo, Haiyong Perez-Navarro, Antoni Jimenez, Antonio R. Wang, Han Liang, Hengyi De Cock, Cedric Plets, David Cui, Yan Xiong, Zhi Li, Xiaodong Ding, Yiming Franco, Fernando Javier Alvarez Polo, Fernando Jesus Aranda Rodriguez, Felipe Parralejo Moreira, Adriano Pendao, Cristiano Silva, Ivo Ortiz, Miguel Zhu, Ni Li, Ziyou Renaudin, Valerie Wei, Dongyan Ji, Xinchun Zhang, Wenchao Wang, Yan Ding, Longyang Kuang, Jian Zhang, Xiaobing Dou, Zhi Yang, Chaoqun Kram, Sebastian Stahlke, Maximilian Mutschler, Christopher Coene, Sander Li, Chenglong Venus, Alexander Leitinger, Erik Tertinek, Stefan Witrisal, Klaus Wang, Yi Wang, Shaobo Jin, Beihong Zhang, Fusang Su, Chang Wang, Zhi Li, Siheng Li, Xiaodong Li, Shitao Pan, Mengguan Zheng, Wang Luo, Kai Ma, Ziyao Gao, Yanbiao Chang, Jiaxing Ren, Hailong Guo, Wenfang Torres-Sospedra, Joaquin Pisa56124 Italy Electronics and Telecommunications Research Institute Daejeon34129 Korea Republic of Beijing University of Posts and Telecommunications School of Computer Science National Pilot Software Engineering School Beijing100876 China Chinese Academy of Sciences Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Beijing100190 China Universitat Oberta de Catalunya Faculty of Computer Sciences Multimedia and Telecommunication Barcelona08035 Spain Arganda del Rey28500 Spain Huawei Technologies Company Ltd. Shenzhen100015 China IMEC-WAVES/Ghent University Department of Information Technology Gent9052 Belgium Nanjing University of Aeronautics and Astronautics College of Automation Nanjing210016 China University of Extremadura Badajoz06006 Spain University of Trás-os-Montes and Alto Douro Department of Engineering School of Sciences and Technology Vila Real5000-801 Portugal Universidade Do Minho Centro Algoritmi Guimarães4800-058 Portugal Ame Department of the University Gustave Eiffel Geoloc Laboratory Bouguenais44344 France Chinese Academy of Sciences Aerospace Information Research Institute Beijing100094 China Wuhan University Gnss Research Center Wuhan430072 China AutoNavi Software Company Ltd. China Fraunhofer Institute for Integrated Circuits Iis Fraunhofer Iis Nuremberg90411 Germany Erlangen91058 Germany Graz University of Technology Christian Doppler Laboratory for Location-Aware Electronic Systems Graz8010 Austria Institute of Software Chinese Academy of Sciences Beijing100190 China Purple Mountain Laboratories Pervasive Communication Research Center Nanjing210000 China Beijing University of Posts and Telecommunications Beijing100876 China

Indoor positioning is a thriving research area, which is slowly gaining market momentum. Its applications are mostly customized, ad hoc installations;ubiquitous applications analogous to Global Navigation Satellite System for outdoors are not available because of the lack of generic platforms, widely accepted standards and interoperability protocols. In this context, the indoor positioning and indoor navigation (IPIN) competition is the only long-term, technically sound initiative to monitor the state of the art of real systems by measuring their performance in a realistic environment. Most competing systems are pedestrian-oriented and based on the use of smartphones, but several competing tracks were set up, enabling comparison of an array of technologies. The two IPIN competitions described here include only off-site tracks. In contrast with on-site tracks where competitors bring their systems on-site - which were impossible to organize during 2021 and 2022 - in off-site tracks competitors download prerecorded data from multiple sensors and process them using the EvaalAPI, a real-time, web-based emulation interface. As usual with IPIN competitions, tracks were compliant with the EvAAL framework, ensuring consistency of the measurement procedure and reliability of results. The main contribution of this work is to show a compilation of possible indoor positioning scenarios and different indoor positioning solutions to the same problem. © 2023 CCBY.

关键词： Smartphones

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Exploring the integration of multi criteria decision analysis in the clean energy biodiesels applications: A systematic review and gap analysis

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Engineering Applications of Artificial Intelligence 2024年 133卷

作者： Alamoodi, A.H. Garfan, Salem Al-Zuhairi, Omar Zaidan, B.B. Zaidan, A.A. Albahri, O.S. Y.Y. Ahmaro, Ibraheem Albahri, A.S. Yussof, Salman Magableh, Aws A. Institute of Informatics and Computing in Energy Universiti Tenaga Nasional Malaysia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Perak Malaysia Applied Science Research Center Applied Science Private University Amman Jordan Low Dimensional Materials Research Centre Department of Physics Faculty of Science Universiti Malaya Kuala Lumpur50603 Malaysia SP Jain School of Global Management Lidcombe SydneyNSW2141 Australia International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Victorian Institute of Technology Australia Computer Techniques Engineering Department Mazaya University College Nasiriyah Iraq Computer Science Department College of Information Technology Hebron University Hebron Palestine Technical College Imam Ja'afar Al-Sadiq University Baghdad Iraq Department of Information Systems Yarmouk University Irbid Jordan Baghdad Iraq Department of Computing College of Computing and Informatics Universiti Tenaga Nasional Malaysia Departments of Software Engineering Prince Sultan University Riyad Saudi Arabia

Biodiesel has gained tremendous attention from researchers due to its environmentally friendly and renewable fuel manufactured domestically from vegetable oils or animal fats. Multiple factors, including emission and fuel efficiency, measure the performance of biodiesel. These characteristics make the process of evaluation and benchmarking a multi-criteria decision-making (MCDM) problem. This research attempts to survey the literature for multi-criteria decision-making applications on biodiesel. This systematic review was carried out on four scientific databases: PubMed, ScienceDirect, IEEE Xplore, and WoS. The collected articles were summarized and mapped into a coherent taxonomy, while the main challenges, motivations, and recommendations were collected and categorized for better understanding and finding out essential characteristics. The final set of included articles was 33, and they were categorized into six different categories. Most articles (n=15/33) investigated the evaluation and selection of biodiesel alternatives using MCDM. Furthermore, a state-of-art bibliometric analysis is provided. The analysis demonstrates several challenges in this area, such as source selection, optimum fuel blending, and uncertainty. This article is expected to assist future researchers in addressing the current gaps and contribute to advancing this area. © 2024 Elsevier Ltd

关键词： Biodiesel

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A biosensing system using a multiparameter nonlinear dynamic analysis of cardiomyocyte beating for drug-induced arrhythmia recognition

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Microsystems & Nanoengineering 2022年第3期8卷 277-290页

作者： Hao Wang Yue Wu Quchao Zou Wenjian Yang Zhongyuan Xu Hao Dong Zhjing Zhu Depeng Wang Tanxing Wang Ning Hu Diming Zhang State Key Laboratory of Optoelectronic Materials and Technologies Guangdong Province Key Laboratory of Display Material and TechnologySchool of Electronics and Information TechnologySun Yat-sen UniversityGuangzhou 510006China Research Center for Itelligent Sensing Systems Zhejiang LabHangzhou 311121China ZJU-Hangzhou Global Scientific and Technological Innovation Center Department of ChemistryThe Second Affiliated Hospital Zhejiang University School of MedicineDepartment of Clinical Medical EngineeringZhejiang UniversityHangzhou 310058China Key Laboratory of Novel Target and Drug Study for Neural Repair of Zhejang Province School of MedicineSchool of Computer&Computing ScienceZhejiang University City CollegeHangzhou 310015China School of Brain Science and Brain Medicine Zhejiang UniversityHangzhou 310058China College of Energy and Power Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016China E-LinkCare Meditech Co LtdHangzhou 310011China State Key Laboratory of Transducer Technology Chinese Academy of SciencesShanghai 200050China

Cardiovascular disease is the number one cause of death in ***,cardiotoxicity is one of the most important adverse effects assessed by arrhythmia recognition in drug ***,cell-based techniques developed for arhythmia recognition primarily employ linear methods such as time-domain analysis that detect and compare individual waveforms and thus fall short in some applications that require automated and efficient arrhythmia recognition from large *** carried out the frst report to develop a biosensing system that integrated impedance measurement and multiparameter nonlinear dynamic algorithm(MNDA)analysis for druginduced arrhythmia recognition and *** biosensing system cultured cardiomyocytes as physiologically relevant models,used interdigitated electrodes to detect the mechanical beating of the cardiomyocytes,and employed MNDA analysis to recognize drug-induced arrhythmia from the cardiomyocyte beating *** best performing MNDA parameter,approximate entropy,enabled the system to recognize the appearance of sertindoleand norepinephrine-induced arrhythmia in the *** MNDA reconstruction in phase space enabled the system to classify the different arrhythmias and quantify the severity of *** new biosensing system utilizing MNDA provides a promising and alternative method for drug-induced arrhythmia recognition and classification in cardiological and pharmaceutical applications.

关键词： arrhythmia cardio analysis

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Machine Learning-based Energy-Aware Offloading in Edge-Cloud Vehicular Networks 18

Machine Learning-based Energy-Aware Offloading in Edge-Cloud...

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18th International Conference on Mobile systems and Pervasive computing, MobiSPC 2021, The 16th International Conference on Future Networks and Communications, FNC 2021 and the 11th International Conference on Sustainable Energy Information Technology, SEIT 2021

作者： Ismail, Leila Materwala, Huned Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi15551 United Arab Emirates National Water and Energy Center United Arab Emirates University Al Ain Abu Dhabi15551 United Arab Emirates

A vehicular network underpinned by the 3-tier vehicle-edge-cloud infrastructure enables an efficient and safer travel experience. The compute-intensive vehicular applications are often offloaded to the edge and/or cloud servers to enhance the applications' Quality of Services (QoS). The underlying edge-cloud servers consume a high among of energy. Consequently, it becomes crucial to optimizing energy consumption in the offloading process. Current energy-efficient offloading strategies in 2-tier vehicle-edge infrastructure, do not account for cloud computing energy consumption. In this paper, we address this void by proposing a machine learning-based energy-aware offloading algorithm, which optimizes the energy of the edge-cloud computing platform. The offloading strategy is enabled by the Support Vector Machine (SVM) regression model machine learning algorithm used for the edge-cloud power prediction. The experimental results show that the proposed algorithm is a promising approach in energy savings. © 2021 Elsevier B.V.. All rights reserved.

关键词： Energy efficiency

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Alleviating Barren Plateaus in Parameterized Quantum Machine Learning Circuits: Investigating Advanced Parameter Initialization Strategies

arXiv

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arXiv 2023年

作者： Kashif, Muhammad Rashid, Muhammad Al-Kuwari, Saif Shafique, Muhammad eBrain Lab Division of Engineering New York University Abu Dhabi PO Box 129188 Abu Dhabi United Arab Emirates Center for Quantum and Topological Systems NYUAD Research Institute New York University Abu Dhabi United Arab Emirates Department of Computer Engineering Umm Al Qura University Makkah Saudi Arabia Qatar Center for Quantum Computing College of Science and Engineering Hamad bin Khalifa University Qatar

Parameterized quantum circuits (PQCs) have emerged as a foundational element in the development and applications of quantum algorithms. However, when initialized with random parameter values, PQCs often exhibit barren plateaus (BP). These plateaus, characterized by vanishing gradients with an increasing number of qubits, hinder optimization in quantum algorithms. In this paper, we analyze the impact of state-of-the-art parameter initialization strategies from classical machine learning in random PQCs from the aspect of BP phenomenon. Our investigation encompasses a spectrum of initialization techniques, including random, Xavier (both normal and uniform variants), He, LeCun, and Orthogonal methods. Empirical assessment reveals a pronounced reduction in variance decay of gradients across all these methodologies compared to the randomly initialized PQCs. Specifically, the Xavier initialization technique outperforms the rest, showing a 62% improvement in variance decay compared to the random initialization. The He, Lecun, and orthogonal methods also display improvements, with respective enhancements of 32%, 28%, and 26%. This compellingly suggests that the adoption of these existing initialization techniques holds the potential to significantly amplify the training efficacy of Quantum Neural Networks (QNNs), a subclass of PQCs. Demonstrating this effect, we employ the identified techniques to train QNNs for learning the identity function, effectively mitigating the adverse effects of BPs. The training performance, ranked from the best to the worst, aligns with the variance decay enhancement as outlined above. This paper underscores the role of tailored parameter initialization in mitigating the BP problem and eventually enhancing the training dynamics of QNNs. © 2023, CC BY.

关键词： Machine learning

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Fingertip interactive tracking registration method for AR assembly system

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Advances in Computational Intelligence 2022年第2期2卷 1-26页

作者： Jiu, Yong Jianguo, Wei Yangping, Wang Jianwu, Dang Xiaomei, Lei The National Virtual Simulation Experiment Teaching Center of Railway Traffic Information and Control Lanzhou Jiaotong University Lanzhou China College of Intelligence and Computing Tianjin University Tianjin China The National Experimental Teaching Demonstration Center of Computer Science and Technology Lanzhou Jiaotong University Lanzhou China The School of Electronic and Information Engineering Lanzhou Jiaotong University Lanzhou China Gansu Provincial Engineering Research Center for Artificial Intelligence and Graphics and Image Processing Lanzhou China Gansu Meteorological Service Meteorological Information and Technological Supporting Center Lanzhou China

Aiming at the problems of single input mode and lack of naturalness in the assembly process of existing AR systems, a tracking registration method of mobile AR assembly system is proposed based on multi-quantity and multi-degree of freedom natural fingertip interaction. Firstly, the real-time and stable tracking of hand area in complex environment is realized based on the hand region tracking; secondly, the fingertip detection and recognition based on K-COS and parallel vector is used to improve the precision and stability of fingertip recognition; thirdly, the special movement track of fingertip is recognized based on improved DTW algorithm, which has strong compatibility and feature gradient transformation for complex fingertip trajectory recognition; finally, through the real-time transformation of projection relationship between fingertip and virtual object, the interaction between fingertip and virtual object is made more natural and realistic. The experimental results show that in the complex environment of background, illumination, scale and rotation, the precision of fingertip detection and recognition is about 93%, and the precision of fingertip motion template matching is about 91%. The translation error of the registration method based on visual feature recognition is reduced by about 100pix compared with fingertip tracking registration method, and the efficiency of mobile AR-guided assembly method is improved by about 24.77% compared with the traditional manual assisted assembly method. These data verifies the strong interaction and practicability of the fingertips based on the user's multi-quantity and multi-degree of freedom features in the process of mobile AR guided assembly.

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Fundus Image Translation with Scale-Aware Registration and Gradient-Guided GAN

SSRN

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SSRN 2024年

作者： Kim, JuChan Le, Duc-Tai Bum, Junghyun Son, Chang-Hwan Lee, Eun Jung Han, Jong Chul Choo, Hyunseung Department of Artificial Intelligence Systems Engineering Sungkyunkwan University Korea Republic of College of Computing and Informatics Sungkyunkwan University Korea Republic of Sungkyun AI Research Institute Sungkyunkwan University Korea Republic of Software Science and Engineering Major Kunsan National University Korea Republic of Department of Ophthalmology Samsung Medical Center Korea Republic of School of Medicine Sungkyunkwan University Korea Republic of Department of Medical Device Management and Research SAIHST Sungkyunkwan University Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

Two major fundus imaging types, conventional fundus (CF) imaging and ultrawidefield fundus (UF) imaging, are widely used in ophthalmology. In a typical clinical setting, both imaging methods are used simultaneously to diagnose diseases depending on the case. This study introduced an enhanced method of modality transformation to boost the diagnostic support capabilities of deep-learning models in ophthalmology. The method proposed in this study to translate UF images (UFIs) to CF images (CFIs) can replace the common clinical approach of both imaging methods, thereby reducing financial and temporal burdens on patients. This study learns and transforms the relationship between these modalities using UFI–CFI image pairs obtained from the same patient on the same day to minimize distorted information and accurately convert between different modalities. Our proposed model uses scale-aware registration and gradient-guided translation methods to align the UFI–CFI pairs, The generated CFIs not only enhance the quality of images and better represent the retinal area compared to existing methods but also effectively preserve the disease-related details in UFIs, aiding in the diagnosis. In addition, we optimize the image transformation performance, resulting in an 22% improvement in image quality assessment scales compared with previous methods. Furthermore, we acquired the observation that our results are nearly impossible to discern any differences between the real CFIs and the generated CFIs in the view of ophthalmologists. © 2024, The Authors. All rights reserved.

关键词： Ophthalmology

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Reinforcement Learning-assisted Evolutionary Algorithm: A Survey and research Opportunities

arXiv

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arXiv 2023年

作者： Song, Yanjie Wu, Yutong Guo, Yangyang Yan, Ran Suganthan, Ponnuthurai Nagaratnam Zhang, Yue Pedrycz, Witold Das, Swagatam Mallipeddi, Rammohan Ajani, Oladayo Solomon Feng, Qiang National Defense University Haidian District Beijing China Department of Analytics Operations and Systems University of Kent Kent Canterbury United Kingdom School of Systems Science Beijing Jiaotong University Beijing China School of Civil and Environmental Engineering Nanyang Technological University Singapore KINDI Center for Computing Research College of Engineering Qatar University Doha Qatar School of Reliability and Systems Engineering Beihang University China Department of Industrial Systems Engineering and Management National University of Singapore Singapore Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Poland Faculty of Engineering and Natural Sciences Department of Computer Engineering Istanbul Sariyer Turkey Electronics and Communication Sciences Unit Indian Statistical Institute Kolkata India School of Electronics Engineering Kyungpook National University Taegu Korea Republic of

Evolutionary algorithms (EA), a class of stochastic search methods based on the principles of natural evolution, have received widespread acclaim for their exceptional performance in various real-world optimization problems. While researchers worldwide have proposed a wide variety of EAs, certain limitations remain, such as slow convergence speed and poor generalization capabilities. Consequently, numerous scholars actively explore improvements to algorithmic structures, operators, search patterns, etc., to enhance their optimization performance. Reinforcement learning (RL) integrated as a component in the EA framework has demonstrated superior performance in recent years. This paper presents a comprehensive survey on integrating reinforcement learning into the evolutionary algorithm, referred to as reinforcement learning-assisted evolutionary algorithm (RL-EA). We begin with the conceptual outlines of reinforcement learning and the evolutionary algorithm. We then provide a taxonomy of RL-EA. Subsequently, we discuss the RL-EA integration method, the RL-assisted strategy adopted by RL-EA, and its applications according to the existing literature. The RL-assisted procedure is divided according to the implemented functions including solution generation, learnable objective function, algorithm/operator/sub-population selection, parameter adaptation, and other strategies. Additionally, different attribute settings of RL in RL-EA are discussed. In the applications of RL-EA section, we also demonstrate the excellent performance of RL-EA on several benchmarks and a range of public datasets to facilitate a quick comparative study. Finally, we analyze potential directions for future research. This survey serves as a rich resource for researchers interested in RL-EA as it overviews the current state-of-the-art and highlights the associated challenges. By leveraging this survey, readers can swiftly gain insights into RL-EA to develop efficient algorithms, thereby fostering fur

关键词： Reinforcement learning

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Dfier: A Directed Vulnerability Verifier for Ethereum Smart Contracts

SSRN

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SSRN 2023年

作者： Wang, Zeli Dai, Weiqi Li, Ming Choo, Kim-Kwang Raymond Zou, Deqing Chongqing Key Laboratory of Computational Intelligence Key Laboratory of Big Data Intelligent Computing Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing40065 China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Department of Information Systems and Cyber Security University of Texas at San Antonio San Antonio United States

Existing smart contract vulnerability identification approaches mainly focus on complete program detection. Consequently, lots of known potentially vulnerable locations need manual verification, which is energy-exhaustive and error-prone. Fuzzing is an effective way to detect vulnerabilities. However, current fuzzers cannot effectively probe these suspicious targets because they overlook two key factors: exploring all paths toward the targets and providing high-quality directed seed inputs. This paper presents a Directed vulnerability veriFier (DFier), elaborating effective transaction sequences with directed inputs for the fuzzer, to focus on analyzing target paths and automatically detect whether the given locations are vulnerable. Specifically, DFier fuses the effectiveness of static analysis and the efficiency of fuzzing. The static analysis helps locate target paths and generate directed inputs for the fuzzer under three heuristic strategies. The first prefers inputs with the larger possibility of triggering potential vulnerabilities based on target features. The second guides the value mutation to satisfy the path constraints. The third adequately leverages execution information to help find exploits faster. Extensive experiments demonstrate that DFier is effective in verifying contract security, compared with three existing contract fuzzers (i.e., contractFuzzer, sFuzz, and conFuzzius). And the performance losses are in an acceptable range. © 2023, The Authors. All rights reserved.

关键词： Smart contract

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GSLB: The Graph Structure Learning Benchmark

arXiv

引用

arXiv 2023年

作者： Li, Zhixun Wang, Liang Sun, Xin Luo, Yifan Zhu, Yanqiao Chen, Dingshuo Luo, Yingtao Zhou, Xiangxin Liu, Qiang Wu, Shu Yu, Jeffrey Xu Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong Center for Research on Intelligent Perception Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Department of Automation University of Science and Technology of China China School of Cyberspace Security Beijing University of Posts and Telecommunications China Department of Computer Science University of California Los Angeles United States Heinz College of Information Systems and Public Policy Machine Learning Department School of Computer Science Carnegie Mellon University United States

Graph Structure Learning (GSL) has recently garnered considerable attention due to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the computation graph structure simultaneously. Despite the proliferation of GSL methods developed in recent years, there is no standard experimental setting or fair comparison for performance evaluation, which creates a great obstacle to understanding the progress in this field. To fill this gap, we systematically analyze the performance of GSL in different scenarios and develop a comprehensive Graph Structure Learning Benchmark (GSLB) curated from 20 diverse graph datasets and 16 distinct GSL algorithms. Specifically, GSLB systematically investigates the characteristics of GSL in terms of three dimensions: effectiveness, robustness, and complexity. We comprehensively evaluate state-of-the-art GSL algorithms in node- and graph-level tasks, and analyze their performance in robust learning and model complexity. Further, to facilitate reproducible research, we have developed an easy-to-use library for training, evaluating, and visualizing different GSL methods. Empirical results of our extensive experiments demonstrate the ability of GSL and reveal its potential benefits on various downstream tasks, offering insights and opportunities for future research. The code of GSLB is available at: https://***/GSL-Benchmark/GSLB. © 2023, CC BY.

关键词： Benchmarking

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