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Fine-grained distributed averaging for large-scale radio interferometric measurement sets

Research in Astronomy and Astrophysics 2021年第4期21卷 93-100页

作者： Shou-Lin Wei Kai-Da Luo Feng Wang Hui Deng Ying Mei Faculty of Information Engineering and Automation Computer Technology Application Key Lab of Yunnan ProvinceKunming University of Science and TechnologyKunming 650500China Center for Astrophysics Guangzhou UniversityGuangzhou 510006China

The Square Kilometre Array(SKA)would be the world’s largest radio telescope with eventually over a square kilometre of collecting ***,there are enormous challenges in its data *** use of modern distributed computing techniques to solve the problem of massive data processing in the SKA is one of the most important *** this study,basing on the Dask distribution computational framework,and taking the visibility function integral processing as an example,we adopt a multi-level parallelism method to implement distributed averaging over time and *** Array was used to implement super large matrix or arrays with supported *** maximize the usage of memory,we further exploit the data parallelism provided by Dask that intelligently distributes the computational load across a network of computer agents and has a built-in fault tolerance *** validity of the proposed pattern was also verified by using the Common Astronomy Software Application(CASA),wherein we analyze the smearing effects on images reconstructed from different resolution visibilities.

关键词： techniques:interferometric methods:data analysis methods:numerical instrumentation:interferometers

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Logical Rules Enhanced Multimodal Reasoning Based on Biomedical Knowledge Graph

Logical Rules Enhanced Multimodal Reasoning Based on Biomedi...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Peifu Han Tao Song Jianmin Wang College of Computer Science and Technology China University of Petroleum Qingdao China The Interdisciplinary Graduate Program in Integrative Yonsei University Incheon Republic of Korea

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Biomedical knowledge graph reasoning is capable of discovering hidden new knowledge based on existing biomedical data, providing ideas and references for new drug discovery, disease research, and so on. The entire graph topology structure formed by triplets and the attribute descriptions for each entity are crucial information for discovering new knowledge. Some add a variety of additional information to aid reasoning, namely multimodal reasoning. However, current multimodal reasoning techniques often rely solely on vector space distance inferences based on triplets themselves, making it difficult to capture more complex relationships and dependencies between facts. This work integrated triplet entity relations, graph topology structures, and attribute descriptions for each entity to incorporate richer information, and utilized logical rules as external knowledge for relational reasoning in biomedical knowledge graphs. We have evaluated our approach on PharmKG.

关键词： Accuracy Biological system modeling Semantics Knowledge based systems Knowledge graphs Cognition Topology Drug discovery Bioinformatics Diseases

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Integration of the Use of Conventional technology Towards Smart Home technology for People in the City of Jakarta

Integration of the Use of Conventional Technology Towards Sm...

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International Conference on ICT For Smart Society (ICISS)

作者： Tanty Oktavia Febian Wijaya Lintang Gerald Christian Ongko Griselda Valerie Michelle Benedicta Vieri Aska Juneio Sembiring Zaldy Raditya Ananta Takaaki Hosoda Ford Lumban Gaol Information Systems Management Department BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Jakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Decision Science Department Advanced Institute of Industrial Technology Tokyo Japan Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia

Smart Home technology offers technological concepts that can be controlled through smart devices to make it easier for the community. Therefore, smart home technology is starting to seize the home electronics market in Indonesia, especially in the city of Jakarta. This study aims to determine the integration of conventional technology into smart home technology for people in Jakarta. The research model in this study uses UTAUT2 which analyzes nine variables, namely Perceived Usefulness, Perceived Cost, Perceived Ease of Use, Social Influence, Perceived Compatibility, Trust, Perceived Pleasure, and Effort Expectations, Intention to Use Smart Home technology. After the responses were received, data analysis was carried out using the PLS-SEM approach with the SmartPLS application. The results of this study show that there are many people who live in Jakarta who are helped by the presence of smart home technology. However, there are some people who don't really know how to use a smart home because of a lack of knowledge about this technology.

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Machine Learning Implementations in Childhood Stunting Research: A Systematic Literature Review

Machine Learning Implementations in Childhood Stunting Resea...

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International Conference on information Management and technology (ICIMTech)

作者： Reza Rahutomo Gregorius Natanael Elwirehardja Mahmud Isnan Faisal Asadi Bens Pardamean Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

Childhood stunting is a condition anticipated to affect the growth potential of children under the age of five. With numerous stunting researches that have been conducted, stunting datasets are now widely available to facilitate stunting research. This provides an opportunity to implement machine learning (ML) principles to produce a broader insight or a novel technique in stunting prediction. A systematic literature review is necessary to discover the landscape of machine learning implementation in the application domain as a preliminary study for creating an effective research roadmap. This paper presents a systematic literature review (SLR) of 22 curated manuscripts that focuses on identifying the ML models applied in stunting research, as well as the datasets used in such studies that were published during 2017–2022. The SLR process found that ML principles have been applied in stunting research since 2017, and the diversity of ML implementation has become more varied in 2021–2022. In terms of ML models, XGBoost and Random Forest are recognized as the two most utilized models, and stunting prediction is the most common ML implementation. The majority of stunting research utilizing ML has been conducted in Indonesia. Although national survey data has been the most commonly utilized dataset in stunting research, researchers in Indonesia have shown a preference for utilizing data from regional or independent surveys. This study will be followed by developing a classifier model for stunted children using XGBoost and Random Forest algorithms. The model will be trained on a dataset generated from StuntingDB.

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Synergistic Swarm Optimization Algorithm

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computer Modeling in Engineering & sciences 2024年第6期139卷 2557-2604页

作者： Sharaf Alzoubi Laith Abualigah Mohamed Sharaf Mohammad Sh.Daoud Nima Khodadadi Heming Jia Faculty of Computer Sciences and Informatics Amman Arab UniversityAmman11953Jordan Computer Science Department Al Al-Bayt UniversityMafraq25113Jordan Department of Electrical and Computer Engineering Lebanese American UniversityByblos13-5053Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan MEU Research Unit Middle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains MalaysiaPulau Pinang11800Malaysia School of Engineering and Technology Sunway University MalaysiaPetaling Jaya27500Malaysia Department of Industrial Engineering College of EngineeringKing Saud UniversityP.O.Box 800Riyadh11421Saudi Arabia College of Engineering Al Ain UniversityAbu Dhabi112612United Arab Emirates Department of Civil and Architectural Engineering University of MiamiCoral Gables1251USA School of Information Engineering Sanming UniversitySanming365004China

This research paper presents a novel optimization method called the Synergistic Swarm Optimization Algorithm(SSOA).The SSOA combines the principles of swarmintelligence and synergistic cooperation to search for optimal solutions efficiently.A synergistic cooperation mechanism is employed,where particles exchange information and learn from each other to improve their search *** cooperation enhances the exploitation of promising regions in the search space while maintaining exploration ***,adaptive mechanisms,such as dynamic parameter adjustment and diversification strategies,are incorporated to balance exploration and *** leveraging the collaborative nature of swarm intelligence and integrating synergistic cooperation,the SSOAmethod aims to achieve superior convergence speed and solution quality performance compared to other optimization *** effectiveness of the proposed SSOA is investigated in solving the 23 benchmark functions and various engineering design *** experimental results highlight the effectiveness and potential of the SSOA method in addressing challenging optimization problems,making it a promising tool for a wide range of applications in engineering and *** codes of SSOA are available at:https://***/matlabcentral/fileexchange/153466-synergistic-swarm-optimization-algorithm.

关键词： Synergistic swarm optimization algorithm optimization algorithm metaheuristic engineering problems benchmark functions

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DiCTI: Diffusion-based Clothing Designer via Text-guided Input

DiCTI: Diffusion-based Clothing Designer via Text-guided Inp...

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International Conference on Automatic Face and Gesture Recognition

作者： Ajda Lampe Julija Stopar Deepak K. Jain Shinichiro Omachi Peter Peer Vitomir Štruc Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Faculty of Electrical Engineering University of Ljubljana Ljubljana Slovenia Dalian University of Technology China Tohoku University Graduate School of Engineering Sendai Japan

ISBN: (数字)9798350394948

ISBN: (纸本)9798350394955

Recent developments in deep generative models have opened up a wide range of opportunities for image synthesis, leading to significant changes in various creative fields, including the fashion industry. While numerous methods have been proposed to benefit buyers, particularly in virtual try-on applications, there has been relatively less focus on facilitating fast prototyping for designers and customers seeking to order new designs. To address this gap, we introduce DiCTI (Diffusion-based Clothing Designer via Text-guided Input), a straightforward yet highly effective approach that allows designers to quickly visualize fashion-related ideas using text inputs only. Given an image of a person and a description of the desired garments as input, DiCTI automatically generates multiple high-resolution, photorealistic images that capture the expressed semantics. By leveraging a powerful diffusion-based inpainting model conditioned on text inputs, DiCTI is able to synthesize convincing, high-quality images with varied clothing designs that viably follow the provided text descriptions, while being able to process very diverse and challenging inputs, captured in completely unconstrained settings. We evaluate DiCTI in comprehensive experiments on two different datasets (VITON-HD and Fashionpedia) and in comparison to the state-of-the-art (SoTa). The results of our experiments show that DiCTI convincingly outperforms the SoTA competitor in generating higher quality images with more elaborate garments and superior text prompt adherence, both according to standard quantitative evaluation measures and human ratings, generated as part of a user study. The source code of DiCTI will be made publicly available.

关键词： Industries Visualization Shape Image synthesis Source coding Clothing Semantics

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Comparative Analysis of Stock Price Prediction Using Machine Learning

Comparative Analysis of Stock Price Prediction Using Machine...

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Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

作者： Abdelrahman Osama Hager Saeid Seham Mohsen Sarah Saad Eldin Department of Computer Science Faculty of Graduate Studies for Statistical Researches Cairo University Cairo Egypt Department of Information Systems Advanced Academy Cairo Egypt

ISBN: (数字)9798350367775

ISBN: (纸本)9798350367782

The exchange of stocks, bonds, and other assets on the stock market is a complicated and dynamic process that is impacted by several variables. Machine learning methods have drawn more and more interest lately in financial forecasting. With the unavoidable uncertainty and volatility in the prices of stocks, investors keep seeking techniques to foresee future trends to avert losses and maximize probable profits. The purpose of this comparison study is to assess how well four machine learning models—Long Short-Term Memory (LSTM), Random Forest, Support Vector Regression (SVR), and Linear Regression—predict Oracle stock prices. We evaluated the model using metrics like RMSE, MSE, and MAE using a dataset with 9,648 samples collected between December 3, 1986, and June 21, 2024. According to the findings, SVR is the best-performing model with the lowest error rates, making it the preferred choice for stock price prediction. LSTM also performed well, closely matching SVR. While Linear Regression produced reasonably accurate results, the Random Forest model did not perform as effectively and showed notably poor performance, suggesting that it may not be suitable for this particular dataset or problem.

关键词： Accuracy Uncertainty Linear regression Predictive models Data models Vectors Convolutional neural networks Stock markets Long short term memory Random forests

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Mining fuzzy local periodic activity pattern for Smart home applications

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Knowledge-Based Systems 2024年 293卷

作者： Iqbal, Mohammad Prayitno, Adila Sekarrati Dwi Pao, Hsing-Kuo Mukhlash, Imam Department of Mathematics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Sukolilo East Java Surabaya60111 Indonesia Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei106 Taiwan

Perceiving resident movements from ubiquitous sensor devices can aid health and safety management in smart home environment. One can find resident movement patterns to get notification about what should be aware of to prepare for possible accidents. The goal of this work is to propose a method to search for periodic residents’ activity patterns given smart home applications. Specifically, the proposed method is aimed to capture fuzzy local periodic activity patterns (FLPAP) from the smart home data. A FLPAP is an activity pattern that periodically occurs for a particular duration in fuzzy time intervals. We construct an algorithm to help to find the FLPAP called Fuzzy Local Periodic Activity Pattern Miner (FLoPMiner). On the side, a parameter called k-scale period is well-chosen to reorganize the data to make it suitable for the need of periodic activity pattern mining. The proposed method was demonstrated for its effectiveness on a public smart home dataset. After all, we successfully retrieved periodic resident activity patterns, which can be applied to self-health management systems, especially when focusing on a precise scale period. Overall, the proposed method shows descent pattern representation against the state-of-the-art methods of the same category. © 2024 Elsevier B.V.

关键词： Automation

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Mobile Application Implementation as Agriculture 4.0 Strategy for Sugarcane Yield Prediction

Mobile Application Implementation as Agriculture 4.0 Strateg...

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HCI and UX Conference in Indonesia (CHIuXiD), International

作者： Hangger Gahara Mawandha Reza Rahutomo Teddy Suparyanto Bens Pardamean Faculty of Agricultural Technology Institut Pertanian Stiper Yogyakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350304084

ISBN: (纸本)9798350304091

information engineering strategies play a significant and transformative role in shaping the landscape of digital agriculture. It drives innovation and efficiency in modern farming practices, with a specific focus on enhancing the value of agricultural commodities, such as sugarcane. The application of mobile technology plays a crucial part in achieving increased sugarcane productivity. Mobile applications, equipped with advanced developer features, offer rapid and user-friendly access to vital information. Traditional methods of estimating sugarcane production relied on manual data recording on paper, followed by data transfer to computer systems, typically managed by sugar factory junior plant officers. This conventional approach presents several inherent weaknesses, including time and effort intensiveness during data recording and entry. Additionally, the potential for errors in calculation and data input is a concern. The storage capacity for paper-based documents is finite, and farmers are often unable to autonomously assess their production potential. The objective of this research is to confront these obstacles by creating a sugarcane production application for Android. This application functions as an information engineering approach focused on forecasting sugarcane yields for plantation owners and their assistants. The development procedure adhered to the systematic waterfall method, following the principles of the Software Development Life Cycle (SDLC) model. Data collection was carried out through observations, while interviews with junior plant officers provided valuable insights into sugarcane estimation techniques. Analysis involved the synthesis of observational and interview data to inform the design of the application’s interface and algorithmic system. The resulting application significantly simplifies the process of estimating production potential for farmers, enabling them to access sugarcane productivity data during harvest. Consequently, sugar

关键词： Productivity Operating systems Estimation Production facilities Raw materials Mobile applications Recording

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DiCTI: Diffusion-based Clothing Designer via Text-guided Input

arXiv

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arXiv 2024年

作者： Lampe, Ajda Stopar, Julija Jain, Deepak K. Omachi, Shinichiro Peer, Peter Štruc, Vitomir University of Ljubljana Faculty of Electrical Engineering Ljubljana Slovenia University of Ljubljana Faculty of Computer and Information Science Ljubljana Slovenia Dalian University of Technology China Tohoku University Graduate School of Engineering Sendai Japan

关键词： Semantics

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