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arXiv 2022年

作者： Vukovic, Manojlo Jakovetic, Dusan Bajovic, Dragana Kar, Soummya University of Novi Sad Faculty of Technical Sciences Department of Fundamental Sciences Serbia University of Novi Sad Faculty of Sciences Department of Mathematics and Informatics Serbia University of Novi Sad Faculty of Technical Sciences Department of Power Electronic and Communication Engineering Serbia Department of Electrical and Computer Engineering Carnegie Mellon University United States

We consider distributed recursive estimation of consensus+innovations type in the presence of heavy-tailed sensing and communication noises. We allow that the sensing and communication noises are mutually correlated while independent identically distributed (i.i.d.) in time, and that they may both have infinite moments of order higher than one (hence having infinite variances). Such heavy-tailed, infinite-variance noises are highly relevant in practice and are shown to occur, e.g., in dense internet of things (IoT) deployments. We develop a consensus+innovations distributed estimator that employs a general nonlinearity in both consensus and innovations steps to combat the noise. We establish the estimator’s almost sure convergence, asymptotic normality, and mean squared error (MSE) convergence. Moreover, we establish and explicitly quantify for the estimator a sublinear MSE convergence rate. We then quantify through analytical examples the effects of the nonlinearity choices and the noises correlation on the system performance. Finally, numerical examples corroborate our findings and verify that the proposed method works in the simultaneous heavy-tail communication-sensing noise setting, while existing methods fail under the same noise *** Codes 93E10, 93E35, 60G35, 94A13, 62M05 Copyright © 2022, The Authors. All rights reserved.

关键词： Internet of things

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Effective Image Representation using Double Colour Histogram for Content-Based Image Retrieval

Informatica (Slovenia)

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Informatica (Slovenia) 2021年第7期45卷 97-105页

作者： Martey, Ezekiel Mensah Lei, Hang Li, Xiaoyu Appiah, Obed School of Information & Software Engineering University of Electronic Science and Technology of China China Department of Computer Science and Informatics University of Energy and Natural Resource Ghana

Image representation is critical to the successful realisation of Content-Based Image Retrieval (CBIR) systems. The choice of features to represent the image affects retrieval performance. Nowadays, image databases are heterogeneous and different feature types can be used for appropriate descriptions. This paper proposes an image representation for CBIR that combines Stacked Colour Histogram (SCH) and Conventional Colour Histogram (CCH) to improve image retrieval precision. This presented technique is designed to capture the colour and texture information of the image. The colour properties of an image are represented by CCH and that of texture by SCH. The weighted similarity measure is used to estimate the proportion of similarity values in the retrieval task. The novel descriptor has been widely tested on four standard image datasets, namely Batik, Coil100, Corel10K and Outext. Batik, Coil100 and Outext are used to assess texture discrimination. Corel10K is used to assess the discrimination of heterogeneous images. Experimental results and comparisons with SCH, CMTH, MTH, TCM, CTM and NRFUCTM demonstrate that the proposed descriptor has superior retrieval performance. © 2021 Slovene Society Informatika. All rights reserved.

关键词： Image representation

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Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge

arXiv

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arXiv 2025年

作者： Luo, Gongning Xu, Mingwang Chen, Hongyu Liang, Xinjie Tao, Xing Ni, Dong Jeong, Hyunsu Kim, Chulhong Stock, Raphael Baumgartner, Michael Kirchhoff, Yannick Rokuss, Maximilian Maier-Hein, Klaus Yang, Zhikai Fan, Tianyu Boutry, Nicolas Tereshchenko, Dmitry Moine, Arthur Charmetant, Maximilien Sauer, Jan Du, Hao Bai, Xiang-Hui Raikar, Vipul Pai Montoya-Del-Angel, Ricardo Martí, Robert Luna, Miguel Lee, Dongmin Qayyum, Abdul Mazher, Moona Guo, Qihui Wang, Changyan Awasthi, Navchetan Zhao, Qiaochu Wang, Wei Wang, Kuanquan Wang, Qiucheng Dong, Suyu School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Department of Mathematics Faculty of Science National University of Singapore Singapore National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Laboratory Shenzhen University Shenzhen China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China Pohang Korea Republic of Heidelberg Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Germany Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany Department of Biomedical Engineering and Health KTH Royal Institute of Technology Stockholm Sweden France FathomX Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore Philips Research University of Girona Spain Department of Robotics and Mechatronics Engineering DGIST Korea Republic of Department of Interdisciplinary Studies of Artificial Intelligence DGIST Korea Republic of National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Lab School of Communication and Information Engineering Shanghai University Shanghai China Faculty of Science Mathematics and Computer Science Informatics Institute University of Amsterdam Amsterdam1090 GH Netherlands Department of Biomedical Engineering and Physics Amsterdam UMC Amsterdam1081 HV Netherlands Xi’an Jiaotong-Liverpool University China Department of Ultrasound Harbin Medical University Cancer Hospital No. 150 Haping Road Nangang

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a newer approach for breast screening, which has many advantages over handheld mammography such as safety, speed, and higher detection rate of breast cancer. Tumor detection, segmentation, and classification are key components in the analysis of medical images, especially challenging in the context of 3D ABUS due to the significant variability in tumor size and shape, unclear tumor boundaries, and a low signal-to-noise ratio. The lack of publicly accessible, well-labeled ABUS datasets further hinders the advancement of systems for breast tumor analysis. Addressing this gap, we have organized the inaugural Tumor Detection, Segmentation, and Classification Challenge on Automated 3D Breast Ultrasound 2023 (TDSC-ABUS2023). This initiative aims to spearhead research in this field and create a definitive benchmark for tasks associated with 3D ABUS image analysis. In this paper, we summarize the top-performing algorithms from the challenge and provide critical analysis for ABUS image examination. We offer the TDSC-ABUS challenge as an open-access platform at https://***/ to benchmark and inspire future developments in1algorithmic research. © 2025, CC BY.

关键词： Image segmentation

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An Empirical Study of Pre-trained Language Models in Simple Knowledge Graph Question Answering

arXiv

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arXiv 2023年

作者： Hu, Nan Wu, Yike Qi, Guilin Min, Dehai Chen, Jiaoyan Pan, Jeff Z. Ali, Zafar School of Computer Science and Engineering Southeast University 2 Dongda Rd Jiangsu Nanjing211189 China Department of Computer Science The University of Manchester Oxford Rd ManchesterM13 9PL United Kingdom School of Informatics The University of Edinburgh 10 Crichton St EdinburghEH8 9AB United Kingdom

Large-scale pre-trained language models (PLMs) such as BERT have recently achieved great success and become a milestone in natural language processing (NLP). It is now the consensus of the NLP community to adopt PLMs as the backbone for downstream tasks. In recent works on knowledge graph question answering (KGQA), BERT or its variants have become necessary in their KGQA models. However, there is still a lack of comprehensive research and comparison of the performance of different PLMs in KGQA. To this end, we summarize two basic KGQA frameworks based on PLMs without additional neural network modules to compare the performance of nine PLMs in terms of accuracy and efficiency. In addition, we present three benchmarks for larger-scale KGs based on the popular SimpleQuestions benchmark to investigate the scalability of PLMs. We carefully analyze the results of all PLMs-based KGQA basic frameworks on these benchmarks and two other popular datasets, WebQuestionSP and FreebaseQA, and find that knowledge distillation techniques and knowledge enhancement methods in PLMs are promising for KGQA. Furthermore, we test ChatGPT, which has drawn a great deal of attention in the NLP community, demonstrating its impressive capabilities and limitations in zero-shot KGQA. We have released the code and benchmarks to promote the use of PLMs on KGQA. Copyright © 2023, The Authors. All rights reserved.

关键词： Efficiency

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Framework for standardizing digital health in resource-constrained settings: a case study of Uganda's digital health communication infrastructure

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Oxford open digital health 2024年第2期2卷 oqae018页

作者： Andrew Alunyu Egwar Mercy Rebekah Amiyo Josephine Nabukenya Department of Computer Engineering and Informatics Busitema University Tororo Uganda. Department of Information Systems Makerere University Kampala Uganda.

Uganda has several digital health interventions, but most are not interoperable, failing the goal of digitizing the healthcare environment. One major reason for this failure is that the implementations are not based on a common set of standards formally agreed upon by the country's health ministry. Therefore, Uganda and other countries that have fallen behind in standardizing their health systems need to implement and fast-track the formulation of contextual digital health standards. This study followed a design science approach to develop a framework for standardizing the digital health communication infrastructure. The design went through three cycles of design and improvement. Results show that 17 digital health stakeholders in Uganda who evaluated the framework agreed that they are suitable to guide standardization activities in Uganda. This study argues that other countries with digital health challenges similar to those in Uganda can adopt the framework.

关键词： ICT network communication infrastructure digital health eHealth interoperability standardization framework standards

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Joint Optimization of Power Allocation and Beamforming for UAV-RIS Communication System

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IEEE Wireless Communications Letters 2025年

作者： Guo, Xinying Liu, Longfei Zhang, Jiankang Chen, Sheng Ministry of Education Key Laboratory of Grain Information Processing and Control Henan University of Technology China Henan University of Technology College of Information Science and Engineering Zhengzhou450001 China Bournemouth University Department of Computing and Informatics BH12 5BB United Kingdom University of Southampton School of Electronics and Computer Science SouthamptonSO17 1BJ United Kingdom

This paper considers the communication system assisted by a fixed-trajectory unmanned aerial vehicle (UAV), which is equipped with a reconfigurable intelligent surface (RIS). For this UAV-RIS assisted system, we jointly optimize the power allocation and active beamforming at the base station (BS) and the passive beamforming at the RIS, by a novel phase block coordinate descent algorithm framework aimed at maximizing the system sum-rate. Specifically, the joint optimization problem is decomposed into two phases, and we propose two optimization algorithms: one for BS power allocation using fractional programming (FP) and the other for jointly optimizing active and passive beamforming using FP-manifold, which alternately optimize two phases. Simulation results not only highlight the rapid convergence and evident superiority of our proposed framework but also reveal that the optimal UAV-RIS placement is related to the flight height. © 2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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WKLM-FA: A Web Application for Automated Human-computer Interaction Modeling of Web Form Filling Tasks 24

WKLM-FA: A Web Application for Automated Human-Computer Inte...

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24th Pan-Hellenic Conference on informatics, PCI 2020

作者： Karousos, Nikos Tatsis, Fotios Karousos, Dionisis Katsanos, Christos Xenos, Michalis Tselios, Nikos Hellenic Open University Patras Greece Imis Lab Department of Mechanical Engineering and Aeronautics University of Patras Greece Department of Informatics Aristotle University of Thessaloniki Thessaloniki Greece Educational Sciences and Early Childhood Education Department University of Patras Greece Computer Engineering and Informatics Department University of Patras Patras Greece

ISBN: (纸本)9781450388979

This paper presents the web-based KLM-Form Analyzer (WKLM-FA), a web application that allows the automated estimation of time that a user needs to fill out a web form. Based on feedback from previous studies, this implementation comes to fulfill the need for a cross-platform application of human-computer interaction models, namely the Keystroke Level Model (KLM) and Fitts's Law. WKLM-FA is a new web-based implementation of the KLM-Form Analyzer (KLM-FA), a desktop application that we have developed in our previous work. WKLM-FA also provides additional functionality compared to KLM-FA, such as a user account mechanism, an evaluation tracking feature, and an admin panel to support multiuser functionality. WKLM-FA is the first online KLM-based time estimation tool for web forms and it can be easily used not only by HCI evaluators but also for educational purposes teaching the KLM and the Fitts's law. © 2020 ACM.

关键词： Websites

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AI in Industry: Real-World Applications and Case Studies

TechRxiv

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TechRxiv 2023年

作者： Rahmaniar, Wahyu Maarif, Alfian ul Haq, Qazi Mazhar Iskandar, Muchammad Edo Institute of Innovative Research Tokyo Institute of Technology Kanagawa226-8503 Japan Department of Electrical Engineering Universitas Ahmad Dahlan Yogyakarta55166 Indonesia International Bachelor Program in Informatics and Computer Science Yuan Ze University Taoyuan320315 Taiwan Ezy Industries Jakarta Indonesia

Artificial intelligence (AI) has advanced rapidly and is becoming a cornerstone technology that drives innovation and efficiency in various industries. This paper examines the real-world application of AI in multiple sectors, including healthcare, finance, agriculture, retail, energy, and automotive. Several case studies are described to understand better the practical applications, results, and challenges of implementing AI. While many industries have reaped enormous benefits from AI, inherent challenges include data privacy, the potential for bias, and the continuing demand for skilled labor. This comprehensive review aims to provide AI application insights to professionals and researchers. Thus, as AI grows, there may be challenges and avenues for future research. © 2023, CC BY.

关键词： Agriculture

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Mapping and Visualization of Artificial Intelligence for Digital Economy Research: A Bibliometric Approach

Mapping and Visualization of Artificial Intelligence for Dig...

引用

International Conference of computer Science and Information Technology

作者： Fairuz Iqbal Maulana Agung Purnomo Andi Pramono Mohammad Nazir Arifin Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Indonesia Interior Design Department School of Design Bina Nusantara University Jakarta Indonesia Informatics Engineering Study Program University of Madura Pamekasan Indonesia

ISBN: (纸本)9798350399080

Artificial Intelligence (AI) seems to be a disruptive technology that defines and reshapes the economy, more efficient industrial processes, new business models, and the service sector, becoming the development of different practices than before. The literature shows that it will be included in all fields and people's lives, as is the case in the digital economy. Artificial Intelligence for Digital Economy has not been studied significantly. This study uses bibliometric analysis to graphically map scientific publications and research trends in the Artificial Intelligence for Digital Economy sector around the world in the last ten years. The Scopus database was used to collect metadata information for this study, and VOSViewer was used to demonstrate bibliometric network mapping. We use an article selection procedure starting with the searched keywords and year constraints and then exporting the database to a RIS file format. Over the last ten years, we retrieved 540 scientific publications published between 2012 and 2021 from the Scopus database. From the data obtained, researchers at the Russian Federation have the most published papers indexed by Scopus among the most productive countries (N=131), the most productive authors are Petrenko, S.A. (N=4), and the most subject area is computer Science (N=246). We also use VOSViewer to map the Network Theme. This study recommends incorporating research subjects Artificial Intelligence for Digital Economy: Thing, Education, Platform, Global Economy, abbreviated as TEPGE research theme.

关键词： computer science Databases Bibliometrics Taxonomy Education Metadata Market research

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Estimation of Weibull Distribution Parameters by Analytical Methods for the Wind Speed of Jhimpir, Pakistan - A Comparative Assessment

Estimation of Weibull Distribution Parameters by Analytical ...

引用

Emerging Trends in Electrical, Control, and Telecommunication engineering (ETECTE), International Conference on

作者： Ghulam Abbas Jason Gu Muhammad Usman Asad Valentina E. Balas Umar Farooq Irfan A. Khan Department of Electrical Engineering The University of Lahore Lahore Pakistan Department of Electrical and Computer Engineering Dalhousie University Halifax Canada Department of Automation and Applied Informatics Aurel Vlacieu University of Arad Arad Romania Clean and Resilient Energy Systems (CARES) Lab Texas A&M University Galveston TX USA

ISBN: (纸本)9781665492430

Assessing the potential of a wind farm requires looking into how the wind behaves throughout a certain time frame. One of the most popular ways to statistically model wind data is with the Weibull distribution. Estimating two parameters of the Weibull PDF is crucial for a better fit between the PDF and wind speed data. In this study, Weibull distribution parameters for 2019 wind speed data in the Jhimpir region of Pakistan are determined using four analytical techniques: the empirical method (EM), the maximum likelihood method (MLM), the method of moments (MoM), and the energy pattern factor (EPF) approach. Each technique is evaluated using several different metrics, including the root mean squared error (RMSE), mean absolute error (MAE), mean absolute relative error (MARE), and the coefficient of correlation (R). Statistical analyses show that the shape (k) and scale (c) parameters of the Weibull distribution estimated by the EM, MLM, and MoM are quite close to one another compared to the ones obtained by EPF for the available data. The MATLAB environment-based numerical results expressed that the EPF method performed the best in terms of R and RMSE and worst in terms of MAE and MARE.

关键词： Statistical analysis Wind energy Shape Wind speed Wind farms Probability density function Probability

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