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Dynamic Terrain Data Exchange in a Collaborative Terrain Editor

Informatica (Slovenia) 2022年第4期46卷 575-582页

作者： Tarigan, Jos Timanta Sitompul, Opim Salim Zarlis, Muhammad Nababan, Erna Budhiarti Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Information Systems Management Department BINUS Graduate Program Bina Nusantara University Jakarta Indonesia

In a computer supported cooperative work (CSCW), data consistency between collaborating users is a crucial issue. Based on the type of the application, ensuring data consistency can be a lengthy process that takes time and affects the system’s performance. In most 3D application, terrain data are massive due to its size. Exchanging this data may be expensive and may cause significant delay. In a real-time collaborative terrain editor, this issue becomes more significant due to terrain data exchange is consistently occurred between collaborating users. We present a solution to perform a conflict-free dynamic terrain data exchange in a real-time collaborative terrain editor. Our objective is to develop a method that able to ensure data consistency amongst collaborating peers in real-time manner. The main idea of our method is to split the terrain into smaller patches and synchronize the changes efficiently by only exchanging the modified patches. We applied our solution to a collaborative terrain editor application to test its performance in a real-time collaborative editing session. The tests were done in multiple scenarios, using different patch model, brush size (in the terrain editor), and connection setup between server and collaborating clients. The result shows that our protocol is capable to maintain data consistency between collaborating clients in a real-time terrain edition session. The delay is varied and highly depends on the data size and client-server environment setup. The overall test shows that it is possible to perform a collaborative terrain editing with an acceptable response time delay. In this paper, we present our proposed method, the implementation, and the result data from the test. © 2022 Slovene Society Informatika. All rights reserved.

关键词： Electronic data interchange

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3D Image Generation from X-ray Projections Using Generative Adversarial Networks 23

3D Image Generation from X-ray Projections Using Generative ...

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23rd IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2023

作者： AlShurbaji, Mohammad Dhou, Salam Biomedical Engineering Graduate Program American University of Sharjah Sharjah United Arab Emirates Department of Computer Science and Engineering American University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9798350393118

The human brain can effortlessly imagine a 3D image from only 2D images with a little expertise and imagination, but for machines, this is not a trivial task. Because of this, reconstructing 3D images from 2D ones is a hot topic and has many applications. In this paper, we propose a Generative Adversarial Network (GAN)-based approach that generates CT-like images using pairs of orthogonal X-ray projections taken from different angles. In this work, a variety of orthogonal pairs from different angles, ranging from 0 &90 to 60 &150, were considered as input to the 3D image generation model. The effectiveness of the proposed method was assessed by measuring the Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR), which resulted in values of 0.641 and 29.21, respectively. Furthermore, the model's ability to capture the respiratory motion in the input projections and reflect it in the generated images was also assessed. This work demonstrated the feasibility of generating CT-like images from X-ray projections captured from different orthogonal angles taking into consideration the respiratory motion exhibited in these projections. © 2023 IEEE.

关键词： Generative adversarial networks

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Optimizing Government Funding Allocation with Ensemble Machine Learning Models

Optimizing Government Funding Allocation with Ensemble Machi...

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International Conference on Computing, Engineering, and Design (ICCED)

作者： Budi Prasetyo Suharjito Computer Science Department Binus Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Barat Indonesia

ISBN: (数字)9798331529376

ISBN: (纸本)9798331529383

Given the limited availability of state funds, managing state finances in an effective, efficient, and prudent manner is essential. High demand for additional state funds without sound justification can lead to inefficiencies and poor budget execution. Therefore, decisions regarding the allocation of additional funds should be made selectively and based on historical data reflecting the quality of the work unit in budget management. This research aims to develop a machine learning application to assist decision-makers in recommending additional funding percentages. Utilizing the Budget Implementation Quality Indicator (IKPA) data, we performed feature selection using Principal Component Analysis (PCA), resulting in three selected features. These features were then used to build models with base models (Decision Tree Regression, K-Nearest Neighbor Regression) and ensemble learning methods (Stacking, Bagging, Random Forest, Boosting: AdaBoost, XGBoost, LightGBM). After to-Fold Cross-Validation and hyperparameter tuning, LightGBM demonstrated the lowest error rate with a Root Mean Square Error (RMSE) of 0.0646 and a Mean Absolute Error (MAE) of 0.0520, outperforming XGBoost in predicting additional fund allocation proportions. The application supports informed and accountable financial decision-making, promoting efficient and prudent national financial management. By comparing base models and ensemble techniques, this research provides critical insights into machine learning applications in financial management, driving methodological innovation and advancing the field.

关键词： Biological system modeling Government Machine learning Financial management Data models Resource management Ensemble learning Root mean square Regression tree analysis Principal component analysis

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Active Learning and Case-Based Reasoning for the Deceptive Play in the Card Game of Truco 10th

Active Learning and Case-Based Reasoning for the Deceptive P...

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10th Brazilian Conference on Intelligent Systems, BRACIS 2021

作者： Vargas, Daniel P. Paulus, Gustavo B. Silva, Luis A. L. Graduate Program in Computer Science Federal University of Santa Maria – UFSM RS Santa Maria Brazil

ISBN: (纸本)9783030917012

Deception is an essential behavior in many card games. Despite this fact, it is not trivial to capture the intent of a human strategist when making deceptive decisions. That is even harder when dealing with deception in card games, where components of uncertainty, hidden information, luck and randomness introduce the need of case-based decision making. Approaching this problem along with the investigation of the game of Truco, a quite popular game in Southern regions of South America, this work presents an approach that combines active learning and Case-Based Reasoning (CBR) in which agents request a human specialist to review a reused game action retrieved from a case base containing played Truco hands. That happens when the agents are confronted with game situations that are identified as opportunities for deception. The goal is to actively capture problem-solving experiences in which deception can be used, and later employ such case knowledge in the enhancement of the deceptive capabilities of the Truco agents. Experimental results show that the use of the learned cases enabled different kinds of Truco agents to play more aggressively, being more deceptive and performing a larger number of successful bluffs. © 2021, Springer Nature Switzerland AG.

关键词： Case based reasoning

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Pantanal wetland wildfires are associated with drought in interconnected biomes

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Natural Hazards 2025年 1-27页

作者： de Sousa, Bruna Rodrigues Fonseca, Humberto Paiva Arévalo, Santiago Maria Márquez Lorenzon, Alexandre Simões Torres, Fillipe Tamiozzo Pereira do Amaral, Cibele Hummel Applied Meteorology Graduate Program Federal University of Vicosa Viçosa Brasil Forest Science Graduate Program Federal University of Vicosa Viçosa Brasil Cooperative Institute for Research in Environmental Sciences University of Colorado Boulder USA Environmental Data Science Innovation and Inclusion Lab University of Colorado Boulder USA Earth Lab University of Colorado Boulder USA

Pantanal is the largest tropical wetland in the world and has been extremely affected by these wildfires recently, such as in 2020 and 2024. The largest rivers that supply the Pantanal originate in the Brazilian highlands (“plateau”), so the availability of surface water in the biome (“plain”) is strongly associated with the hydrological dynamics and land use and cover of surrounding biomes such as the Amazon, Cerrado, and Atlantic Forest. Although the hydrological connections between these biomes and the local factors contributing to wildfires have been well explored, there appears to be a lack of comprehensive studies at a macro-scale that demonstrate the associations between historical above-average fires in the Pantanal and the climatic and biospheric conditions of its hydrologically interconnected biomes. Therefore, this study aimed to analyze a 32-year historical series (1989–2020) of burned areas in the Pantanal and identify the drivers of above-average wildfires. We explored local environmental variables as well as those of surrounding biomes. We found that 13 out of 32 analyzed years stand out for a total burned area above normal, and only 1999 showed a larger burned area than 2020. The results of this study demonstrate that water dynamics from soil to atmosphere, such as decreased soil moisture, vegetation moisture and evapotranspiration, and increased drought, including during previous years, in the Pantanal and the surrounding forested biomes (Amazon and Atlantic Forest) are associated with the occurrence of above-average wildfires in the Pantanal wetland. In addition, the 32-year trend analysis revealed that widespread drought has increased in the Pantanal and all analyzed interconnected biomes, indicating that the world’s largest tropical wetland will likely see more above-average wildfires. Our results have implications for macro-scale adaptive management, integrated watershed management, fire risk early detection, and the preparedness of teams respon

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Optimizing Government Funding Allocation with Ensemble Machine Learning Models 10

Optimizing Government Funding Allocation with Ensemble Machi...

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10th International Conference on Computing, Engineering and Design, ICCED 2024

作者： Prasetyo, Budi Suharjito Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta Barat Indonesia

ISBN: (纸本)9798331529376

关键词： Budget control

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Mapping the Landscape of Social Learning: Integrating Key Success Factors, Challenges, and Digital Platforms 9

Mapping the Landscape of Social Learning: Integrating Key Su...

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9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Marcel Meyliana Warnars, Harco Leslie Hendric Spits Mursitama, Tirta Nugraha BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia BINUS Business School Doctor of Research in Management Bina Nusantara University Management Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

This research develops a conceptual model to understand the influence of digitalization on social learning, especially in the context of community empowerment. This model maps the relationship between factors that influence success, challenges faced, and variations in the use of digital platforms in social learning. By using a literature study approach, the research results show that effective communication, collaboration and empowerment are the main keys to the success of social learning in society. Key challenges identified include limited access to information, resource constraints, and digital literacy barriers, all of which require specific strategies to overcome. By examining the relationship between success factors, challenges, and use of digital platforms, this research provides new insights that support the development of more effective strategies for using digital technology to support social learning that leads to community empowerment. This research has great potential to increase people's access to learning opportunities and information, allowing them to be more active in the social learning process. © 2024 IEEE.

关键词： Federated learning

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Investigating the Impact on Data Recovery in computer Forensics

Investigating the Impact on Data Recovery in Computer Forens...

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Cryptography, Informatics, and Cybersecurity (ICoCICs), IEEE International Conference on

作者： Semi Yulianto Benfano Soewito Computer Science Department Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department Graduate Program. Master of Computer Science Bina Nusantara University Jakarta Indonesia

Defragmentation can potentially be employed as a tactic by perpetrators to conceal, misrepresent, or eliminate digital evidence. This study explores the effects of minor defragmentation, a potential method to conceal digital evidence, on recovering file system data in digital forensics. Our investigation sought to determine the influence of minor defragmentation on the effectiveness of data recovery and to identify methods that can augment the success rate post-defragmentation. We limited the scope of this study to defragmentation in Hard Disk Drives (HDDs), solid-state drives (SSDs), and USB drives. A mixed-method approach employs a literature review, case studies, and controlled experiments. Comparative analysis was used as the main data analysis technique to investigate its impact. Preliminary findings suggest that minor defragmentation hampers data recovery; however, certain strategies can augment success rates. These results can significantly influence the development of data recovery policies, particularly those of digital forensic analysts and law enforcement. The primary objective of this study is to bolster the efficiency and dependability of file system data recovery post-defragmentation while upholding ethical and legal standards.

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Antibiotic resistant bacteria and genes (ARB and ARG) in water and sewage treatment units: A review

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Environmental Nanotechnology, Monitoring and Management 2024年 21卷

作者： Flach, Kauane Andressa Bones, Ubiratan Alegransi Wolff, Delmira Beatriz de Oliveira Silveira, Andressa da Rosa, Genesio Mario Carissimi, Elvis Silvestri, Siara Graduate Program in Environmental Engineering Federal University of Santa Maria Brazil Graduate Program in Environmental Science and Technology Federal University of Santa Maria Brazil

Microorganisms have historically coexisted with humans, animals, and the environment in a natural way. Like any other living being, microorganisms are subjected to environmental pressures that can eventually extinguish or strengthen their existence. In recent years, the emergence of antibiotic resistant bacteria (ARB) and antibiotic resistance genes (ARG) has raised an alert about their influence on human and animal health. In addition to ARBs found in nosocomial environments, recent studies point to the environment as an auxiliary hotspot for the spread of antimicrobial resistance. In this sense, this review covers research that investigated ARB and ARG in E. coli and Salmonella, occurrence and potential environmental reservoirs for the spread of antimicrobial resistance, as well as a discussion on how bacterial resistance affects public health, the detection and treatment methods currently employed to reduce or remove this pollutant from water treatment plants (WTP) and sewage treatment plants (WWTP). The results indicated that the great increase in bacterial resistance may be linked, among other reasons, to repetitive contact with residual concentrations of antibiotics present in the environment, causing the bacteria to suffer from selective pressure and if resistant to one or more antibiotics. Thus, public health is compromised, since commonly used antibiotics are often ineffective in treating disease-resistant pathogenic bacteria. From this perspective, studies have shown the importance of consistent detection methods, which allow the tracking and analysis of sources of antimicrobial resistance on a global scale, in addition to the need to improve advanced treatment to reduce the ARG and ARB of WTPs and WWTPs. The first two sections of this review article present an overview of the problems related to the occurrence and dissemination of antimicrobial resistance (AMR) in bacteria, with a focus on E. coli and Salmonella, identified by the WHO as bacteria of globa

关键词： Advanced treatments Analytical determination Antimicrobial resistance Environmental compartments Public health

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Perfomance Improvement of mpdCNN as Ensemble Learning Model on Low Resolution Image-Face Recognition System

Perfomance Improvement of mpdCNN as Ensemble Learning Model ...

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International Conference on Electronics, Materials Engineering and Nano-Technology (IEMENTech)

作者： Daniel Anando Wangean Derwin Suhartono Master of Computer Science Binus Graduate Program Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331520762

ISBN: (纸本)9798331520779

Low resolution image-face recognition system is one of the challenging aspects of face recognition models' development. From machine learning, deep learning, and into ensemble learning are implemented to develop face recognition models. Including mpdCNN that published by Mishra in a journal article. This mpdCNN already achieved 88.6% accuracy score on SCFace dataset while published. Based on literature review, ensemble learning model can be improved by modifying parallel layers. The purpose of this research is to improve mpdCNN's performance by implementing some modifications include adding parallel layers, alternating the fusion layers, expanding the layers, and adding residual connections. Some modifications were successfully improved mpdCNN's accuracy score. By adding parallel layers and residual connections into mpdCNN's architecture, the modified mpdCNN that proposed in this research achieved 92.33% accuracy score, measured by Rank-k evaluation metric.

关键词： Measurement Deep learning Accuracy Image resolution Image recognition Face recognition Instruments Robustness Ensemble learning Systematic literature review

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