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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Qianliang Wu Yaqing Ding Lei Luo Haobo Jiang Shuo Gu Chuanwei Zhou Jin Xie Jian Yang PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Visual Recognition Group Faculty of Electrical Engineering Czech Technical University in Prague Prague Czech Republic State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Intelligence Science and Technology Nanjing University Suzhou China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Point Cloud Registration (PCR) is a critical and challenging task in computer vision and robotics. One of the primary difficulties in PCR is identifying salient and meaningful points that exhibit consistent semantic and geometric properties across different scans. Previous methods have encountered challenges with ambiguous matching due to the similarity among patch blocks throughout the entire point cloud and the lack of consideration for efficient global geometric consistency. To address these issues, we propose a new framework that includes several novel techniques. Firstly, we introduce a semantic-aware geometric encoder that combines object-level and patch-level semantic information. This encoder significantly improves registration recall by reducing ambiguity in patch-level superpoint matching. Additionally, we incorporate a prior knowledge approach that utilizes an intrinsic shape signature to identify salient points. This enables us to extract the most salient super points and meaningful dense points in the scene. Secondly, we introduce an innovative transformer that encodes High-Order (HO) geometric features. These features are crucial for identifying salient points within initial overlap regions while considering global high-order geometric consistency. We introduce an anchor node selection strategy to optimize this high-order transformer further. By encoding inter-frame triangle or polyhedron consistency features based on these anchor nodes, we can effectively learn high-order geometric features of salient super points. These high-order features are then propagated to dense points and utilized by a Sinkhorn matching module to identify critical correspondences for successful registration. The experiments conducted on the 3DMatch/3DLoMatch and KITTI datasets demonstrate the effectiveness of our method.

关键词： Point cloud compression computer vision Accuracy Shape Semantics Transformers Feature extraction Encoding Intelligent robots

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Experimental Researches of Biometric Authentication Using Convolutional Neural Networks and Histograms of Oriented Graphs 4

Experimental Researches of Biometric Authentication Using Co...

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4th IEEE International Conference on Advanced Information and Communication Technologies, AICT 2021

作者： Kuznetsov, Alexandr Datsenko, Serhii Gorbenko, Yuriy Chupilko, Tetiana Korneyev, Maxim Klym, Victoria V.N. Karazin Kharkiv National University Department of Information Systems and Technologies Security Kharkiv Ukraine University of Customs and Finance Department of Computer Science and Software Engineering Dnipro Ukraine University of Customs and Finance Faculty of Innovative Technologies Dnipro Ukraine University of Customs and Finance Department of Cybersecurity and Information Technologies Dnipro Ukraine

ISBN: (纸本)9781665406185

User authentication technologies are an important component of modern cybersecurity. These technologies are designed to verify the correctness and validity, for example, by verifying the authenticity of entered passwords, identifiers, certain biometric features, and so on. We consider biometric authentication techniques for the human face. Two techniques are used to process biometric features in our work: convolutional neural networks and histograms of directed gradients. We conduct experimental research to study these techniques and compare the results of both in terms of authentication accuracy and the speed of processing biometric images. The obtained results will be useful in the development and implementation of cybersecurity applications that use biometric authentication © 2021 IEEE.

关键词： Authentication

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A graph-based methodology for tracking covid-19 in time series datasets 2

A graph-based methodology for tracking covid-19 in time seri...

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2nd IEEE International Conference on Electronics, Control, Optimization and computer Science, ICECOCS 2020

作者： El Mouden, Zakariyaa Ait Jakimi, Abdeslam Taj, Rachida Moulay Hajar, Moha Moulay Ismail University Software Engineering Information Systems Engineering Faculty of Sciences and Techniques Errachidia Meknes Morocco Moulay Ismail University Operational Research Computer Science Faculty of Sciences and Techniques Errachidia Meknes Morocco

ISBN: (纸本)9781728169217

Since its first appearance in December 2019, Covid-19 has become a wide field of scientific research. Starting from biology to bioinformatic solutions, Artificial Intelligence has contributed in turn as a powerful tool for tracking and predicting the outbreak of Covid-19 using different types of datasets. Chest X-ray images are widely used in computer vision applications and Time series datasets are used for predicting the spread of the novel coronavirus. Graph analytics is a recent field of study that links the mathematical definition and operations of a graph to its application in computer science as a complex data structure, this combination has played a critical role in making graph-based applications present in different fields. One of the most powerful graph analytics is community detection which is an intelligent and unsupervised grouping of a set of graph structured data using the similarity between them. The aim of this work is to highlight the importance of graph-based algorithms in tracking Covid-19 using time series datasets, our work will also focus on Spectral Clustering (SC) as a community detection approach to extract clusters from the input datasets. Further applications are needed in order to validate the proposed theoretical approach. © 2020 IEEE.

关键词： Time series

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Empirical Comparison between Cross-Validation and Mutation-Validation in Model Selection

arXiv

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arXiv 2023年

作者： Yu, Jinyang Hamdan, Sami Sasse, Leonard Morrison, Abigail Patil, Kaustubh R. Research Center Jülich Germany Institute of Systems Neuroscience Medical Faculty Heinrich Heine University Düsseldorf Germany Research Center Jülich Germany Department of Computer Science 3 - Software Engineering RWTH Aachen University Aachen Germany Max Planck School of Cognition Stephanstrasse 1a Leipzig Germany

Mutation validation (MV) is a recently proposed approach for model selection, garnering significant interest due to its unique characteristics and potential benefits compared to the widely used cross-validation (CV) method. In this study, we empirically compared MV and k-fold CV using benchmark and real-world datasets. By employing Bayesian tests, we compared generalization estimates yielding three posterior probabilities: practical equivalence, CV superiority, and MV superiority. We also evaluated the differences in the capacity of the selected models and computational efficiency. We found that both MV and CV select models with practically equivalent generalization performance across various machine learning algorithms and the majority of benchmark datasets. MV exhibited advantages in terms of selecting simpler models and lower computational costs. However, in some cases MV selected overly simplistic models leading to underfitting and showed instability in hyperparameter selection. These limitations of MV became more evident in the evaluation of a real-world neuroscientific task of predicting sex at birth using brain functional connectivity. Copyright © 2023, The Authors. All rights reserved.

关键词： Computational efficiency

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Gender-Wise Perception of Students Towards Blended Learning in Higher Education: Pakistan

arXiv

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arXiv 2022年

作者： Soomro, Saira Soomro, Arjumand Bano Bhatti, Tarique Gulzar, Yonis Department of Distance Continuing and Computer Education Faculty of Education University of Sindh Hyderabad Pakistan Department of Software Engineering Faculty of Engineering and Technology University of Sindh Sindh Jamshoro Pakistan Department of Psychological Testing Guidance & Research Faculty of Education University of Sindh Hyderabad Pakistan Department of Management Information Systems College of Business Administration King Faisal University Al Ahsa Saudi Arabia

Blended learning (BL) is a recent trend among many options that can best fit learners' needs, regardless of time and place. This study aimed to discover students' perceptions of BL and the challenges faced by them while using technology. This quantitative study used data gathered from 300 students enrolled in four public universities in the Sindh province of Pakistan. The findings show that students were comfortable with the use of technology, and it has a positive effect on their academic experience. The study also showed that the use of technology encourages peer collaboration. The challenges found include: neither teacher support nor a training programme was provided to the students for the courses which needed to shift from a traditional face to face paradigm to a blended format, a lack of specialised skills in laboratory assistants for the courses with a blended format and a shortage of hi-tech computer laboratories/computer units to run these courses. Therefore, it is recommended that authorities must develop and incorporate a comprehensive mechanism for the effective implementation of BL in the teaching-learning process. Heads of departments should also provide additional computing infrastructure to their departments. © 2022, CC BY-NC-ND.

关键词： Students

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Artificial Intelligence-based Service Chains Scheduling for Medical Emergency in Healthcare

Artificial Intelligence-based Service Chains Scheduling for ...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Mukesh Soni Maher Ali Rusho Haewon Byeon Azzah AlGhamdi Shaikh Abdul Hannan Parth Rameshchandra Dave Dr. D. Y. Patil Vidyapeeth Dr. D. Y. Patil School of Science & Technology Tathawade Pune Marashtra Department of Lockheed Martin Engineering Management University of Colorado Boulder Colorado Department of AI and Software & Medical Big Data Research Centre Inje University Gimhae Republic of Korea Computer Information Systems Department College of Computer Science and Information Technology Imam Abdalrhman Bin Faisal University Khobar Saudi Arabia Faculty of Computing and Information AlBaha University AlBaha Kingdom of Saudi Arabia Department of Computer Engineering L. D. College of Engineering Ahmedabad Gujarat India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

This study analyses the weekly scheduling challenge of health and medical information network service function chains to meet medical emergency categorization and grading needs. Emergency services are divided into four sorts by urgency and service area, with priorities and weights. The health and medical information network architecture, service model, and operation method are built accordingly. To reduce weighted completion time, a service function chain scheduling model is created. The function chain scheduling scheme is solved by matching game and Q -learning reinforcement learning algorithms for different issue scales. Finally, simulation studies demonstrate that this approach can accomplish graded medical emergency service, build health and medical information networks, and progress smart healthcare.

关键词： Schedules Job shop scheduling Service function chaining Hospitals Processor scheduling Reinforcement learning Games Emergency services Network function virtualization Standards

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Development of a Model for Discriminating Electroencephalograms of Patients with Mild Cognitive Impairment and Healthy Individuals Using a Support Vector Machine

Development of a Model for Discriminating Electroencephalogr...

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International Symposium on Soft Computing and Intelligent systems (SCIS)

作者： Akifumi Tanabe Kyosuke Nakatani Kento Harachi Yusuke Yamamoto Ayumi Muramatsu Hajime Nagahara Noriko Takemura Shinji Shimojo Daisuke Furihata Yuko Mizuno-Matsumoto Kazue Kamae-Shigenobu Kazuhiro Shinosaki Graduate School of Information Science University of Hyogo Kobe Japan School of Life Science and Technology Tokyo Institute of Technology Yokohama Japan Department of Medical Engineering Aino University Ibaraki Japan Institute for Datability Science Osaka University Suita Japan Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Japan Faculty of Software and Information Technology Aomori University Aomori Japan Cyber media center Osaka University Suita Japan Asakayama General Hospital Sakai Japan

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

We performed machine learning using spectrum analysis and power spectra of electroencephalograms (EEGs) of patients with mild cognitive impairment (MCI). Spectrum analysis was performed on the EEGs of MCI patients and healthy individuals, and the power spectrum values of the two groups were calculated and compared. Based on the power spectrum values of the theta and alpha bands, models for discriminating MCI patients were created using a support vector machine (SVM), and their accuracies were compared. In the spectrum analysis, the theta band showed significantly higher power spectrum values in the MCI group. In the comparison of the accuracies of SVM models, the model using the theta band showed higher values in parameters, such as accuracy rate. These results suggested that the power spectrum values of the theta band are useful for discriminating MCI patients.

关键词： Support vector machines Electrodes Analytical models Accuracy Machine learning Brain modeling Electroencephalography Vectors Spectral analysis

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D-distance factor for high voltage transmission line risk and cost analysis 18

D-distance factor for high voltage transmission line risk an...

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18th International Conference on Electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON 2021

作者： Suwanasri, Cattareeya Saribut, Surapol Luejai, Waraporn Suwanasri, Thanapong King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering 1518 Pracharat 1 Rd. Wongsawang Bangsue Bangkok Thailand King Mongkut's University of Technology North Bangkok The Sirindhorn International Thai-German Graduate School of Engineering Department of Electrical and Software Systems Engineering 1518 Pracharat 1 Rd. Wongsawang Bangsue Bangkok Thailand

ISBN: (纸本)9780738111278

Currently, thirty five percent of high voltage transmission lines in Thailand are aged and degraded. These transmission lines need some renovation work with major components;i.e. conductor, insulator, steel structure, foundation, grounding system and right of way according to the remaining performance and importance to the system. With known condition and importance, the renovation task can be properly setup. Therefore, this work focuses on a risk assessment by using d-distance risk factor based condition index and importance index of transmission line. In the condition index calculation, the conditions of transmission line and its components are calculated by taking several testing and visual inspection techniques into consideration. In the importance index calculation, various criteria i.e. line load, voltage rating, contingency, etc. are also considered. The weighting and scoring technique and analytical hierarchy process are applied in the analysis. Finally, the risk is determined. The renovation tasks could be prioritized. The renovation cost is also analysis according to the risk in order to preform effective maintenance and cost management for the transmission line facilities. © 2021 IEEE.

关键词： Risk analysis

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Preface – In service Damage of Materials: Diagnostics and Prediction

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Procedia Structural Integrity 2022年 36卷 1-2页

作者： Petro Yasniy Hryhoriy Nykyforchyn Volodymyr Iasniy Olha Zvirko Ternopil Ivan Puluj National Technical University Faculty Of Computer Information Systems And Software Engineering 56 Ruska St. Ternopil 46011 Ukraine Karpenko Physico-Mechanical Institute of the National Academy of Sciences of Ukraine 5 Naukova St. Lviv 79060 Ukraine

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Efficient Resource Allocation and User Association in NOMA-Enabled Vehicular-Aided HetNets with High Altitude Platforms

arXiv

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arXiv 2024年

作者： Nauman, Ali Maashi, Mashael Alkahtani, Hend K. Al-Wesabi, Fahd N. Aljehane, Nojood O. Assiri, Mohammed Ibrahim, Sara Saadeldeen Khan, Wali Ullah The Department of Information and Communication Engineering Yeungnam University Korea Republic of The Department of Software Engineering College of Computer and Information Sciences King Saud University Po box 103786 Riyadh11543 Saudi Arabia The Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia The Department of Computer Science College of Science & Art at Mahayil King Khalid University Saudi Arabia The Department of Computer Science Faculty of Computers and Information Technology University of Tabuk Tabuk Saudi Arabia The Department of Computer Science College of Sciences and Humanities- Aflaj Prince Sattam bin Abdulaziz University Aflaj 16273 Saudi Arabia The Department of Computer and Self Development Preparatory Year Deanship Prince Sattam bin Abdulaziz University Al-Kharj Saudi Arabia University of Luxembourg Luxembourg

The increasing demand for massive connectivity and high data rates has made the efficient use of existing spectrum resources an increasingly challenging problem. Non-orthogonal multiple access (NOMA) is a potential solution for future heterogeneous networks (HetNets) due to its high capacity and spectrum efficiency. In this study, we analyze an uplink NOMA-enabled vehicular-aided HetNet, where multiple vehicular user equipment (VUEs) share the access link spectrum, and a high-altitude platform (HAP) communicates with roadside units (RSUs) through a backhaul communication link. We propose an improved algorithm for user association that selects VUEs for HAPs based on channel coefficient ratios and terrestrial VUEs based on a caching-state backhaul communication link. The joint optimization problems aim to maximize a utility function that considers VUE transmission rates and cross-tier interference while meeting the constraints of backhaul transmission rates and QoS requirements of each VUE. The joint resource allocation optimization problem consists of three sub-problems: bandwidth allocation, user association, and transmission power allocation. We derive a closed-form solution for bandwidth allocation and solve the transmission power allocation sub-problem iteratively using Taylor expansion to transform a non-convex term into a convex one. Our proposed three-stage iterative algorithm for resource allocation integrates all three sub-problems and is shown to be effective through simulation results. Specifically, the results demonstrate that our solution achieves performance improvements over existing approaches. Copyright © 2024, The Authors. All rights reserved.

关键词： Heterogeneous networks

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