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作者： Huang, Ying Zhu, Fujin Porter, Alan L. Zhang, Yi Zhu, Donghua Guo, Ying School of Information Management Wuhan University Wuhan430072 China Ku Leuven Leuven3000 Belgium School of Management and Economics Beijing Institute of Technology Beijing100081 China Centre for Artificial Intelligence Faculty of Engineering and Information Technology University of Technology Sydney UltimoNSW2007 Australia Search Technology Inc. NorcrossGA30092 United States Program in Science Technology and Innovation Policy Georgia Institute of Technology AtlantaGA30332 United States Business School China University of Political Science and Law Beijing100088 China

Technological innovation is a dynamic process that spans the life cycle of an idea, fromscientific research to production. Within this process, there are often a few key innovations that significantly impact a technology's development, and the ability to identify and trace the development of these key innovations comes with a great payoff for researchers and technology managers. In this article, we present a framework for identifying the technology's main evolutionary pathway. What is unique about this framework is that we introduce new indicators that reflect the connectivity and the modularity in the interior citation network to distinguish between the stages of a technology's development. We also show how information about a family of patents can be used to build a comprehensive patent citation network. Finally, we apply integrated approaches of main path analysis (MPA)-namely global MPA and global key-route main analysis-for extracting technological trajectories at different technological stages. We illustrate this approach with dye-sensitized solar cells (DSSCs), a low-cost solar cell belonging to the group of thin-film solar cells, contributing to the remarkable growth in the renewable energy industry. The results show how this approach can trace the main development trajectory of a research field and distinguish key technologies to help decision makers manage the technological stages of their innovation processes more effectively. © 2021 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Decision making

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Privacy-preserving Continual Federated Clustering via Adaptive Resonance Theory

arXiv

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arXiv 2023年

作者： Masuyama, Naoki Nojima, Yusuke Toda, Yuichiro Loo, Chu Kiong Ishibuchi, Hisao Kubota, Naoyuki The Graduate School of Informatics Department of Core Informatics Osaka Metropolitan University 1-1 Gakuen-cho Naka-ku Sakai-Shi Osaka599-8531 Japan The Faculty of Environmental Life Natural Science and Technology Okayama University 3-1-1 Tsushima-Naka Kita-ku Okayama Okayamashi700-8530 Japan The Department of Artificial Intelligence Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur50603 Malaysia The Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Graduate School of Systems Design Department of Mechanical Systems Engineering Tokyo Metropolitan University 6-6 Asahigaoka Hino-Shi Tokyo191-0065 Japan

With the increasing importance of data privacy protection, various privacy-preserving machine learning methods have been proposed. In the clustering domain, various algorithms with a federated learning framework (i.e., federated clustering) have been actively studied and showed high clustering performance while preserving data privacy. However, most of the base clusterers (i.e., clustering algorithms) used in existing federated clustering algorithms need to specify the number of clusters in advance. These algorithms, therefore, are unable to deal with data whose distributions are unknown or continually changing. To tackle this problem, this paper proposes a privacy-preserving continual federated clustering algorithm. In the proposed algorithm, an adaptive resonance theory-based clustering algorithm capable of continual learning is used as a base clusterer. Therefore, the proposed algorithm inherits the ability of continual learning. Experimental results with synthetic and real-world datasets show that the proposed algorithm has superior clustering performance to state-of-the-art federated clustering algorithms while realizing data privacy protection and continual learning ability. The source code is available at https://***/Masuyama-lab/FCAC. © 2023, CC BY-NC-ND.

关键词： Clustering algorithms

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A Comprehensive Survey of Scene Graphs: Generation and Application

arXiv

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arXiv 2021年

作者： Chang, Xiaojun Ren, Pengzhen Xu, Pengfei Li, Zhihui Chen, Xiaojiang Hauptmann, Alex ReLER Lab AAII Faculty of Engineering and Information Technology University of Technology Sydney Australia School of Information Science and Technology Northwest University China Shandong Artificial Intelligence Institute China School of Computer Science Carnegie Mellon University United States

Scene graph is a structured representation of a scene that can clearly express the objects, attributes, and relationships between objects in the scene. As computer vision technology continues to develop, people are no longer satisfied with simply detecting and recognizing objects in images;instead, people look forward to a higher level of understanding and reasoning about visual scenes. For example, given an image, we want to not only detect and recognize objects in the image, but also understand the relationship between objects (visual relationship detection), and generate a text description (image captioning) based on the image content. Alternatively, we might want the machine to tell us what the little girl in the image is doing (Visual Question Answering (VQA)), or even remove the dog from the image and find similar images (image editing and retrieval), etc. These tasks require a higher level of understanding and reasoning for image vision tasks. The scene graph is just such a powerful tool for scene understanding. Therefore, scene graphs have attracted the attention of a large number of researchers, and related research is often cross-modal, complex, and rapidly developing. However, no relatively systematic survey of scene graphs exists at present. To this end, this survey conducts a comprehensive investigation of the current scene graph research. More specifically, we first summarize the general definition of the scene graph, then conducte a comprehensive and systematic discussion on the generation method of the scene graph (SGG) and the SGG with the aid of prior knowledge. We then investigate the main applications of scene graphs and summarize the most commonly used datasets. Finally, we provide some insights into the future development of scene graphs. © 2021, CC BY.

关键词： Surveys

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INSTANCE SEGMENTATION AND TEETH CLASSIFICATION IN PANORAMIC X-RAYS

arXiv

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arXiv 2024年

作者： Budagam, Devichand Ghosh, Sayan Imanbayev, Azamat Zhanatuly Kaplun, Dmitrii Rychenkov, Artem Sinitca, Aleksandr Kumar, Ayush Shrivastav, Anuj Akhmetov, Iskander Rafailovich Antonov, Sergey Cyganov, Gleb Department of Computer Science and Engineering Indian Institute of Technology Kharagpur Kharagpur India Department of Chemical Engineering Indian Institute of Technology Kharagpur Kharagpur India School of IT and Engineering Kazakh-British Technical University Almaty Kazakhstan Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Processes St. Petersburg Electrotechnical University"LETI" St. Petersburg Russia Centre for Digital Telecommunication Technologies St. Petersburg Electrotechnical University"LETI" St. Petersburg Russia Department of Humanities and Social Sciences Indian Institute of Technology Kharagpur Kharagpur India Department of Architecture and Regional Planning Indian Institute of Technology Kharagpur Kharagpur India Faculty of Digital Transformation ITMO University St. Petersburg Russia

Teeth segmentation and recognition are critical in various dental applications and dental diagnosis. Automatic and accurate segmentation approaches have been made possible by integrating deep learning models. Although teeth segmentation has been studied in the past, only some techniques were able to effectively classify and segment teeth simultaneously. This article offers a pipeline of two deep learning models, U-Net and YOLOv8, which results in BB-UNet, a new architecture for the classification and segmentation of teeth on panoramic X-rays that is efficient and reliable. We have improved the quality and reliability of teeth segmentation by utilising the YOLOv8 and U-Net capabilities. The proposed networks have been evaluated using the mean average precision (mAP) and dice coefficient for YOLOv8 and BB-UNet, respectively. We have achieved a 3% increase in mAP score for teeth classification compared to existing methods, and a 10-15% increase in dice coefficient for teeth segmentation compared to U-Net across different categories of teeth. A new Dental dataset was created based on UFBA-UESC dataset with Bounding-Box and Polygon annotations of 425 dental panoramic X-rays. The findings of this research pave the way for a wider adoption of object detection models in the field of dental diagnosis. © 2024, CC BY.

关键词： Object detection

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Utility coupling promotes cooperation in multiplayer snowdrift games on interdependent simplicial networks

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The European Physical Journal Special Topics 2023年第4期233卷 831-842页

作者： Wang, Juan Guo, Shiqiang Xia, Chengyi Perc, Matjaž Tianjin Key Laboratory for Control Theory and Complicated Industry Systems Tianjin University of Technology Tianjin People’s Republic of China School of Electrical Engineering and Automation Tianjin University of Technology Tianjin People’s Republic of China School of Artificial Intelligence Tiangong University Tianjin People’s Republic of China Faculty of Natural Sciences and Mathematics University of Maribor Maribor Slovenia Alma Mater Europaea Maribor Slovenia Complexity Science Hub Vienna Vienna Austria Department of Physics Kyung Hee University Seoul Republic of Korea

Real-world social networks often contain not only one-to-one pairwise interactions, but also multi-person group interactions involving more than two individuals. To reveal the important role of such group interactions (i.e., higher order topology) for the evolution of cooperation, we here use interdependent simplicial network topologies, where the evolutionary interactions, governed by the snowdrift game, are thus explored beyond pairwise links. We also consider utility coupling between two network layers in an uncorrelated interdependence. Systematic Monte Carlo simulations lead us to two main conclusions. First, we find that the level of cooperation can be elevated by increasing the utility coupling value. And second, cooperation tends to decline when we increase the number of 2-simplex interactions. However, despite the later result, the overall cooperation level on both interdependent networks is still higher than that on any isolated network. We hope that these results will inspire future research on cooperation among unrelated individuals in higher order networks.

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Robust Self-Expression Learning with Adaptive Noise Perception

SSRN

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SSRN 2023年

作者： Wang, Yangbo Zhou, Jie Lu, Jianglin Wan, Jun Gao, Can Lin, Qingshui College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China SMILE Lab Department of ECE College of Engineering Northeastern University Boston02115 United States School of Information and Safety Engineering Zhongnan University of Economics and Law Wuhan430073 China Basic Teaching Department Liaoning Technical University Huludao125105 China

Self-expression learning methods often obtain a coefficient matrix to measure the similarity between pairs of samples. However, directly using the raw data to represent each sample under the self-expression framework may not be ideal, as noise points are inevitably involved in the process of representing clean samples. To address this issue, this work proposes a novel self-expression model called robust Self-Expression learning with adaptive Noise Perception (SENP). SENP decomposes each sample into a clean part and a noisy part, and samples with large self-expression losses can be recognized as the noise points. A reliable coefficient matrix can then be learned by using only the clean points to reconstruct the clean part of each sample. By simultaneously detecting the noisy part of each sample and noise points, and adaptively mitigating their negative impacts, the representative ability of the generated coefficient matrix is improved. Moreover, inspired by the solution of non-negative matrix factorization (NMF), an effective algorithm is formed to optimize SENP. Extensive experiments on well-known benchmark datasets demonstrate the superiority of SENP compared to several state-of-the-art methods. © 2023, The Authors. All rights reserved.

关键词： Non-negative matrix factorization

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Edge Computing-Based Short-Term Traffic Flow Forecast for the Smart City Employing 5G Internet Vehicles

Edge Computing-Based Short-Term Traffic Flow Forecast for th...

引用

International Conference on Contemporary Computing and Informatics (IC3I)

作者： S Parveen Banu Yamini Madhav Patil R Somasundaram C. Santhosh Devesh Pratap Singh G. Manikandan Department of Commerce Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Electronics and Telecommunication Sinhgad College of Engineering Vadgaon Pune India Department of Management Studies Kongu Engineering College Erode Tamil Nadu India Department of Artificial Intelligence and Data Science PSG Institute of Technology and Applied Research Coimbatore Tamil Nadu India Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Urban traffic management challenges smart cities due to limited centralized systems and complicated traffic patterns. To address these difficulties, the study proposes a unique technique for short-term traffic flow forecasting that takes use of edge computing and 5G-enabled vehicles. In contrast to traditional techniques, the proposed system decentralized processing tasks to vehicle edge devices, enabling real-time data analysis and prediction. The system uses machine learning (ML) approaches, such as Long ShortTerm Memory (LSTM) networks, to accurately estimate traffic flow for each area. Results show significant improvements in prediction precision, computational effectiveness, and robustness compared to the existing system. The proposed system has a strong correlation coefficient of 0.85, a decreased mean absolute error (MAE) of 5.2, and a root mean square error (RMSE) of 6.8, indicating potential for better urban traffic management and decision-making. It marks a big step forward in the development of more efficient and sustainable smart city transportation systems.

关键词： data privacy Smart cities Decision making Transportation Traffic control Robustness Real-time systems Security Forecasting Edge computing

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How to Teach a Visually Impaired Person to Hear Colours? Multidimensional Training for a Colour-to-Sound Sensory Substitution Device – Design and Evaluation

SSRN

引用

SSRN 2022年

作者： Lukowska, Marta Kalwak, Weronika Osiński, Dominik Janik, Justyna Wierzchoń, Michal Consciousness Lab Institute of Psychology Jagiellonian University Cracow Poland Qualitative Research Lab Institute of Psychology Jagiellonian University Cracow Poland Department of Electronic Systems Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Trondheim Norway Jagiellonian Human-Centered Artificial Intelligence Laboratory Jagiellonian University Cracow Poland

Sensory substitution devices (SSDs) might be perceived as promising non-invasive assistive technology for visually impaired persons. However, SSDs' are still rarely used by the VIPs. One of the reasons seem to be the lack of a structured and supervised training. Thus, we develop and evaluate a training of a colour-to-sound SSD based on the post-cognitivist approaches to perceptual learning and the Personalized Inclusive SSDs Design (PISSD) model. We propose that the training should develop relationships between the Interactive System (IS) components: user, environment, and technology. We adopted the systematic case study approach to evaluate the SSD training efficacy. Five blind users underwent 22-hours-lasting training consisted of four main parts: individual characteristics and adaptations identification, sensorimotor training, semi-structured explorations, and training evaluation. We demonstrated that the training successfully developed the pre-defined skills (sensorimotor contingencies, perceptual skills, and knowledge about colours) and allowed progress along the assumed developmental trajectories (sonification area mode preference, task structurization level, task complexity, environment complexity, object’s complexity, distance to an object, and colours’ complexity). Our results provide a new insight into the complex process of SSD-related perceptual learning, which we understand in the context of the PISSD model as the SSD-related Interactive System restructuration aiming at emergence of some new SSD-supported adaptive behaviours. Additionally, we describe a crucial role of a (non-professional) training assistant in the process since she sustains the initially weak relationships within IS to prevent its decomposition and so reduces the risk of the SSD's abandonment and the user going back to old adaptations. © 2022, The Authors. All rights reserved.

关键词： Wearable technology

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Soft-Voting colorectal cancer risk prediction based on EHLI components

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Informatics in Medicine Unlocked 2022年 33卷

作者： Qarmiche, N. Chrifi Alaoui, M. El Kinany, K. El Rhazi, K. Chaoui, N. Laboratory of Artificial Intelligence Data Science and Emerging Systems National School of Applied Sciences Fez Sidi Mohamed Ben Abdellah University Fez Morocco Laboratory of Modelling and Mathematical Structures Faculty of Science and Technology Fez Morocco Department of Epidemiology Clinical Research and Community Health Faculty of Medicine and Pharmacy of Fez Sidi Mohamed Ben Abdellah University Fez Morocco

Nutrition and lifestyle factors are closely related to the incidence of Colorectal Cancer (CRC). Indeed, the adoption of a healthy lifestyle can potentially minimize the risk of CRC in the long term. Identifying people at risk of developing CRC will guide them to screening and motivate behavior change. We therefore developed and validated a model to predict CRC risk based on age and Extended Healthy Lifestyle Index (EHLI) components in a Moroccan population. The CCR Nutrition database was used to drive and validate the proposed model. It is a database from a multicenter case-control study of the Moroccan population with 1496 pairs. In a comparative study, thirteen machine learning models, ten simple and three combined, were developed to find the best one. For a better applicability in daily medical practice, a web application was developed. For the training and testing, 5-fold cross-validation was used. The Soft-Voting classifier based on CatBoost, LightGBM and Gradient-Boosting models provided an increase performance with an average accuracy equal to 0.6583 ± 0.054. Soft-Voting CRC risk prediction based on age and EHLI components would identify individuals at risk of colorectal cancer in the Moroccan population and could contribute to improve prevention by encouraging the adoption of a healthy lifestyle. © 2022

关键词： Colorectal cancer EHLI Machine learning Risk prediction Soft-voting

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Asymptotic Stability Analysis for Switched Systems under Spatio-temporal Specifications

Asymptotic Stability Analysis for Switched Systems under Spa...

引用

第40届中国控制会议

作者： Qian Liu Lijun Long Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education Beijing Institute of Artificial Intelligence Faculty of Information TechnologyBeijing University of Technology College of Information Science and Engineering Northeastern University China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University

This paper provides some sufficient conditions to achieve asymptotic stability of switched systems under spatiotemporal specifications via a state-dependent switching law. A common finite-time barrier function, multiple barrier functions and multiple Lyapunov functions are merged as a tool for analyzing the asymptotic stability of a switched nonlinear system under spatio-temporal specifications. Finally, a numerical example is presented to demonstrate the effectiveness of our proposed method.

关键词： Switched systems Common finite-time barrier function Multiple barrier functions Multiple Lyapunov functions State-dependent switching law

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