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arXiv 2024年

作者： Awadelkarim, Elsiddig Jasra, Ajay Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia School of Data Science The Chinese University of Hong Kong Shenzhen China

In this paper we consider the filtering problem associated to partially observed McKean-Vlasov stochastic differential equations (SDEs). The model consists of data that are observed at regular and discrete times and the objective is to compute the conditional expectation of (functionals) of the solutions of the SDE at the current time. This problem, even the ordinary SDE case is challenging and requires numerical approximations. Based upon the ideas in [3, 12] we develop a new particle filter (PF) and multilevel particle filter (MLPF) to approximate the afore-mentioned expectations. We prove under assumptions that, for ϵ > 0, to obtain a mean square error of O(ϵ2) the PF has a cost per-observation time of O(ϵ−5) and the MLPF costs O(ϵ−4) (best case) or O(ϵ−4 log(ϵ)2) (worst case). Our theoretical results are supported by numerical experiments. © 2024, CC BY.

关键词： Mean square error

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An Approach for Intelligence (valuation in smart sensors 25

An Approach for Intelligence (valuation in smart sensors

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25th IEEE International Conference on computer Supported Cooperative Work in Design, CSCWD 2022

作者： Da Silva, Ana Clara Correa Luna, Gabriel S. Xavier, Alberto Cecilio Da Costa, Viviane Cunha Farias Oliveira, Luiz De Souza, Jano Federal University of Rio de Janeiro Systems Engineering and Computer Science Program Rio de Janeiro21941-590 Brazil Federal Institute of Rio de Janeiro Niterói Campus Niterói Brazil

ISBN: (纸本)9781665405270

The Internet of Everything (IoE) is the paradigm of intelligent services that supports a ubiquitous and always-connected world of smart sensors and actuators, human and non-human in a cyber-physical lifestyle. Recent years have witnessed the fast proliferation of smart devices. In addition, these functional smart sensors possess powerful computation and communication capabilities and contribute to intelligent services outcomes of IoE-based applications. Then by adopting a knowledge-based approach on sensors smartness, we make a comparative assessment and classify smart sensors (image sensors, temperature sensors, proximity sensors, speed sensors, and sound sensors) regarding intelligence levels. We extracted valuable conclusions and inferences from this classification, providing insights and future directions towards promised benefits offered by distilled intelligence in smart sensors applications. (Abstract) © 2022 IEEE.

关键词： Actuators

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LTE/WI-FI COEXISTENCE ON A FLEXIBLE SOFTWARE DEFINED RADIO TESTBED 57

LTE/WI-FI COEXISTENCE ON A FLEXIBLE SOFTWARE DEFINED RADIO T...

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57th Annual International Telemetering Conference, ITC 2022

作者： Elahi, Mirza M. Valdez, Kevin J. Gonzalez, Virgilio Computational Science Program The University of Texas at El Paso El PasoTX79968 United States Department of Electrical and Computer Engineering The University of Texas at El Paso El PasoTX79968 United States

The rapid rise in usage of mobile devices have not shown any signs of flattening or slowing down. Some efforts in the standardization bodies are underway to define new ways to boost data rate, network capacity and lower the latency by enhancing existing 4G technologies and by incorporating new technologies in 5G. Since lower spectrum has fewer resources, the LTE of unlicensed spectrum LTE-U is being proposed to extend LTE to unlicensed spectrum. Streaming data traffic for licensed spectrum on LTE-U can improve the performance of the system significantly. In this paper LTE-U deployment by the LAA is implemented using the LBT mechanism on a flexible SDR testbed. © 2022 International Foundation for Telemetering. All rights reserved.

关键词： Testbeds

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The Deep Learning Approach For American Sign Language Detection

The Deep Learning Approach For American Sign Language Detect...

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Creative Communication and Innovative Technology (ICCIT), IEEE

作者： Gusti Pangestu Yaya Heryadi Lukas Wayan Suparta Yulyani Arifin Computer Science Department BINUS Graduate Program Computer Science. Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research (CERG) Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

Sign language has importance rule to deal with communication process especially with impairments hearing people. Sign language detection also attract lot of researchers to join the challenge of research to detect and recognize the sign language in the field of computer science. Hence, there is still no any standard approach and method to recognize the meaning in every pose of sign language. This research proposed a mechanism to detect Alphabet American Sign Language by utilizing Convolutional Neural Network (CNN) process. The CNN approach was chosen based on the ability and capability to recognize image. In this research, MNIST dataset is used for traning and testing process. The proposed CNN architecture produced 97% of accuracy that outperform the previous research using the same dataset which made this architecture promising.

关键词： Deep learning computer science Image recognition Gesture recognition computer architecture Auditory system Assistive technologies

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HyperCRX 2.0: A Comprehensive and Automated Tool for Empowering GitHub Insights

HyperCRX 2.0: A Comprehensive and Automated Tool for Empower...

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International Conference on Software engineering - Companion, ICSE, Companion

作者： Yantong Wang Wei Wang Xiaoya Xia Yenan Tang Fenglin Bi Fanyu Han Jiaheng Peng Shengyu Zhao Lan You Data Science and Engineering East China Normal University Shanghai China Ant Group Open Source Program Office Hangzhou China Alibaba Group Hangzhou China School of Electronic and Information Engineering Tongji University Shanghai China School of Computer Science and Information Engineering Hubei University Wuhan China

ISBN: (数字)9798331536831

ISBN: (纸本)9798331536848

HyperCRX is a browser extension designed for the GitHub platform, aimed at enhancing the open-source experience by providing in-depth insights into projects and developers. Unlike traditional tools, HyperCRX seamlessly integrates with GitHub, offering real-time analysis of project ecosystems, devel-oper contributions, and community activities. This helps users better understand and manage open-source projects. In this work, we have extended HyperCRX to deepen the level of insights and integrated LLMs capabilities to enable intelligent open-source operational support and automated project insight analysis. The plugin caters to a wide range of user groups, including developers, project maintainers, and open-source operators, helping them improve efficiency and make data-driven decisions. The source code for HyperCRX is open-sourced on GitHub at https://***/hypertrons/hypertrons-crx. A demonstration of the features is available at https://***/y4mCCfFVux0.

关键词： Source coding Ecosystems Real-time systems Browsers Software measurement Software development management Software engineering

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Seafloor Imaging Maps by Incorporating Side Scan Sonar Technology on Hybrid Autonomous Underwater Glider

Seafloor Imaging Maps by Incorporating Side Scan Sonar Techn...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Ardian Budi Kusuma Atmaja Egi Muhammad Idris Hidayat Bambang Riyanto Trilaksono Simon Siregar Natsir Habibullah School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Department Computer Engineering Faculty of Applied Science Doctoral Program of Electrical Engineering Institut Teknologi Bandung Telkom University

This research aims to construct a two-dimensional image to represent an underwater geometry map with a Side Scan Sonar (SSS) mounted on a Hybrid Autonomous Underwater Glider (HAUG). Building the underwater map has two stages of the process. The first stage is preprocessing which includes Time-varying gains (TVG), slant range correction, and ground range correction. The second is HAUG navigation, the movement of HAUG orientation to the angle and distance of SSS readings and plotting the geometry of the grid map. With these two stages, a two-dimensional grid map will be formed. Meanwhile, the 3D plot utilizes slant range and ground range correction to obtain the surface height of the bottom of the pool. From the experiments conducted to conduct mapping in the pool, two-dimensional and three-dimensional visual forms of the pool floor and pool walls were produced. The results of the estimated measurement of the width of the pool obtained 14.05 meters and the height of the pool 3.199 meters with an SSS beam angle of 58 degrees.

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Exploring Tourist Feedback on Riau Attractions Through Indonesian Language YouTube Opinion Using Naïve Bayes Algorithm

Exploring Tourist Feedback on Riau Attractions Through Indon...

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2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2023

作者： Kurniawan, Rahmad Iskandar, Iwan Lestari, Fitra Harpizon, Habibi Al Rasyid Husti, Ilyas Faculty of Mathematics and Natural Sciences Universitas Riau Department of Computer Science Pekanbaru Indonesia Universitas Islam Negeri Sultan Syarif Kasim Riau Department of Informatics Engineering Pekanbaru Indonesia Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Selangor Bangi Malaysia Graduate Program Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia

ISBN: (纸本)9798350313635

YouTube is a widely-used platform in Indonesia, with 93.8% of its users. As such, it presents a valuable opportunity for marketing tourist destinations, particularly in Riau province, which aims to become Indonesia's top Halal travel destination. Tourism is a vital contributor to the economic growth of regions, and each province in Indonesia competes to promote its tourist attractions to attract more visitors every year. However, the large volume of data can challenge the manual analysis of feedback from YouTube's features, such as likes, dislikes, and comments. A literature review suggests that the Naive Bayes algorithm, which uses machine learning, is helpful for sentiment analysis. Therefore, this study aims to analyze public sentiment toward tourist destinations in Riau province by analyzing YouTube comments using the Naïve Bayes algorithm. The study used 1680 opinions collected from 10 YouTube videos showcasing tourist destinations in Riau. The Naive Bayes algorithm classified 60% of the comments as positive, 32% as neutral, and 8% as negative. The experimental results indicated an accuracy and precision of 73%, a recall of 94%, and an F-1 Score of 82%. The study used the word frequency technique to reveal that Riau could become a popular halal tourist destination based on several frequently occurring words in the comments. © 2023 IEEE.

关键词： Sentiment analysis

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Empowering Urban Accessibility: A Prototype System for People with Disability

Empowering Urban Accessibility: A Prototype System for Peopl...

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International Conference on Advanced Learning Technologies (ICALT)

作者： Arlley Dos Santos Costa Luis Carlos Costa Fonseca Sofiane Labidi Graduate Program in Computer and Systems Engineering State University of Maranhão – UEMA São Luís Brazil Bachelor of Science and Technology Program -BICT Federal University of Maranhão – UFMA São Luis Brazil

ISBN: (数字)9798350362053

ISBN: (纸本)9798350362060

This work demonstrates the development of a prototype system that could improve urban and digital accessibility for People with Disability - PwD. The research involves using an academic questionnaire to enable a wider perspective of the central subject and assessing the effectiveness of the proposed solution. The main goal is to help social integration and make the future solution implementable for smart cities through an experiment in an academic campus. The dissertation is related to technological and social issues that might be used as a resource by researchers, public or private bodies interested in more profound studies. The document recognizes the lack of studies on this subject, especially in Brazil because there are few available articles. The prototype system is designed to tackle the issues faced by persons with disability in urban settings through platforms, applications and mobile phones as it proposes a comprehensive set of solutions when addressing accessibility for PwDs. In turn, this research fosters knowledge advancements in the field of urban accessibility and provides vital recommendations for developing inclusive technologies and policies.

关键词： Smart cities Prototypes People with disabilities Mobile handsets

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Towards Explainability of Dimension Reduction Plots of Unsupervised Learning Model Outcomes

Towards Explainability of Dimension Reduction Plots of Unsup...

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IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology (CIBCB)

作者： Tony E. Astuhuaman Davila Daniel B. Hier Tayo Obafemi-Ajayi Computer Science Dept. Missouri State University Springfield MO USA Kummer Institute Missouri University of Science & Technology Rolla MO USA Engineering Program Missouri State University Springfield MO USA

ISBN: (数字)9798350356632

ISBN: (纸本)9798350356649

Dimension reduction methods are used to visualize the output of unsupervised learning models when applied to complex data. These techniques improve interpretability by transforming a high-dimension space to a lower-dimension space (usually 2D or 3D). The results are typically viewed as 2D scatter plots, and class centroids may be added to increase interpretability. Although useful, the relationship of these class centroids to the underlying feature space remains opaque. The innovative aspect of this work is to create a strong link between the dimension-reduced space and the underlying high-dimension feature space by adding selected feature centroids to the 2D scatter plots. This approach simultaneously visualizes the centers for the classes and the features on the same 2D scatter plot. Since classes are often imbalanced, we provide a method to balance class sizes. We present an automated framework that performs a grid search to find the optimal dimension reduction parameters, balances the class sizes, uses an ensemble approach to find the most important features, and adds class centroids and selected feature centroids to 2D dimension-reduced plots. This is especially useful when applied to complex, feature-rich biomedical data, as addition of feature centroids to 2D scatter plots serve as landmarks for the previously featureless dimension-reduced space. The utility of this approach is demonstrated by its application to seven classes of neurogenetic diseases with 31 defining phenotypic features.

关键词： Dimensionality reduction Three-dimensional displays Phenotypes Data visualization Data models Ensemble learning Bioinformatics Unsupervised learning Computational intelligence Diseases

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White Bread Mold Detection using K-Means Clustering Based on Grey Level Co-Occurrence Matrix and Region of Interest

White Bread Mold Detection using K-Means Clustering Based on...

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International Seminar on Application for Technology of Information and Communication (ISemantic)

作者： Qori Alfatah Putra Christy Atika Sari Eko Hari Rachmawanto Nur Ryan Dwi Cahyo Edy Mulyanto Mohamed Ayad Alkhafaji Study Program in Informatics Engineering University of Dian Nuswantoro Semarang Indonesia Department of Informatics Engineering University of Dian Nuswantoro Semarang Indonesia Department of Computer Science National University of Science and Technology DhiQar Iraq

This research paper presents a bread mold identification system that utilizes digital image processing techniques, specifically K-means clustering and thresholding, to accurately detect and classify mold-infected areas on bread. The system aims to provide valuable information regarding the safety of consuming mold-contaminated bread, enabling consumers to make informed decisions and reduce the risk of health issues associated with consuming mold-infested bread. the K-means clustering algorithm for mold classification, considering factors such as the number of iterations and the distance metric within the feature space to determine its accuracy. The GLCM feature extraction technique is employed to extract texture information from bread images, encompassing features such as standard deviation (110.62), contrast (1.46), energy, and homogeneity (0.96 and 0.77, respectively). Experimental results demonstrate that the Euclidean Distance yielded the highest result of 72.73, the Cosine Distance obtained the highest result of 8.25, and the City Block Distance obtained the highest result of 92.11. These findings showcase the effectiveness of the system in accurately detecting and classifying mold-infected areas on bread, thereby aiding in ensuring food safety and reducing health risks for consumers.

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