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2024 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2024

作者： Mendonca, Vanessa Santos, Mikael Munoz, Daniel M. Humberto Llanos, Carlos Federal Institute of Brasilia Department of Computer Science Brazil Mechatronics Graduate Program Department of Mechanical Engineering Brazil Federal Institute of Brasilia Department of Agronomy Brazil University of Brasilia Faculty of Gama Electronics Engineering Undergraduate Program Brasilia Brazil

ISBN: (纸本)9798350374575

Technological advancements are increasingly evident across various sectors, including automobiles, industry, and healthcare. In precision agriculture, significant progress has been made, with AgroTICs and Smart Agriculture gaining substantial traction in the market. However, a gap remains between cutting-edge technology and family farming, presenting a challenge from both social and applied research perspectives. However, there is still a gap between cutting-edge technology and family farming, which creates a challenge from a social and applied research point of view. In this context, this paper proposes a monitoring model based on Fuzzy Logic and sensor automation applied to estimate the health of a corn crop. The proposed Fuzzy inference system involves calculating an indicator of nutrients as well as the average color and area of corn plants. The nutrient indicator is automatically computed by an ESP32 microcontroller using sensor readings, while the average color and area inputs are manually entered via a mobile application. Additionally, the Fuzzy inference is integrated into the ESP32. The model underwent experimental validation on the health of the plantation, and the results were evaluated in four areas: one was designated for testing, and three were for validation. The model achieved an accuracy of 97.5% in Scenario 3, categorized as 'Very Favorable', and an accuracy of 65% in Scenarios 2 and 4, categorized as 'Unfavorable'. The implications of this research contribute to the advancement of AgroTICs among small producers, with the potential to enhance and automate the monitoring of their harvest production. © 2024 IEEE.

关键词： Agribusiness

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Investigating Cases and Clusters-Based Reuse Policies for Card-Playing Agents 5

Investigating Cases and Clusters-Based Reuse Policies for Ca...

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5th Annual IEEE Conference on Games, CoG 2023

作者： Paulus, Gustavo B. Vargas, Daniel P. Assuncao, Joaquim V. C. Silva, Luis A. L. Federal Institute of Education Science and Technology of Rio Grande do Sul Ibirubá Brazil Federal University of Santa Maria Graduate Program in Computer Science Santa Maria Brazil Federal University of Santa Maria Applied Computing Department Santa Maria Brazil

ISBN: (纸本)9798350322774

computer games continue to present challenging experimental fields for developing Artificial Intelligence (AI) models. With Case-Based Reasoning and Clustering, this work proposes novel cases and clusters-based reuse criteria for implementing card-playing agents with diversified playing skills. Using the game of Truco, a common game in South America, we detail how game actions are reused from past cases selected as query answers for given game problems. In doing so, the majority rule, the probability-based lottery, the probability of victory, and the number of points won reuse policies are used to select a cluster of cases. Then these policies are also used to select game actions from the cases within the selected cluster. Investigating the combined exploration of reuse policies, experiments of different natures evaluate the performance of implemented Truco bots disputing matches against each other. Players in these tournaments are equipped with varied policies and use case bases constructed differently. © 2023 IEEE.

关键词： Case based reasoning

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Cloud-Based Mixed Reality System in Machinery Vocational School: ICT Blueprint

Cloud-Based Mixed Reality System in Machinery Vocational Sch...

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2022 International Conference on science and Technology, ICOSTECH 2022

作者： Suryodiningrat, Satrio Pradono Gaol, Ford L. Supangkat, Suhono Harso Ranti, Benny Bina Nusantara University Binus Graduate Program Doctor of Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University Faculty of Computing and Media Computer Science Department Jakarta Indonesia Bina Nusantara University Jakarta Doctor of Computer Science Graduate Program Jakarta Indonesia Smart City and Community Innovation Center Bandung Institute of Technology Bandung Indonesia

ISBN: (纸本)9781665479851

Education is always the most important part of our lives. To make education make a significant impact on today's learners, teaching and learning must evolve to match with today's learner's profile. Any education including vocational education has an important role in economic development and one of the ways to maximize the contribution of vocational educations is strengthening the link and match with the world of work [1]. As the technology also keeps evolving and it is getting more and more advanced, one technology that has significant traction lately is the mixed reality system and is often used for education purposes. Most big ICT companies like Microsoft, and HTC are racing to develop mixed reality systems to help the teaching and learning process to be more effective and efficient. Where everything is cloud-based, having a cloud-based mixed reality system is just makes sense but will have a great challenge and considered as a big ICT project. One of the ways to make the ICT project successful is to make a blueprint before it is started. The objective of this paper is how to develop an ICT Blueprint for implementing a cloud-based mixed reality system for machinery vocational schools to make the project have a higher success rate. © 2022 IEEE.

关键词： Blueprints

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Predicting Abundance of Fish Species Populations in Manila Bay, Philippines Based on Ensemble Learning Approach 20

Predicting Abundance of Fish Species Populations in Manila B...

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20th International Joint Conference on computer science and Software Engineering, JCSSE 2023

作者： Rasdas, Sherrlyn M. Fajardo, Arnel C. Limbago, Jomel S. Graduate Program Technological Institute of the Philippines Manila Philippines College of Computing Science Information and Communication Technology -CCSICT Isabela State University-Cauayan Philippines Cavite Philippines

ISBN: (纸本)9798350300505

Manila Bay is a significant contributor to the Philippines' fish production, but its resources have been depleted due to overfishing, pollution, and damage, leading to a decline in fish catch and a shift towards less valuable species. Conventional approaches to fisheries stock assessment impose constraints on our comprehension of fish population dynamics. These limitations can be overcome through the utilization of machine learning techniques, which enable the forecasting and modeling of fisheries populations with improved accuracy and understanding. In this study, the commercial fisheries populations data collected from 2018 to 2021 in Manila Bay were used to predict the abundance of species fisheries production data using the K-NN - MLP - Logistic Regression (KNMLPR) model based on the majority voting ensemble approach. Analysis revealed that it is possible to combine the strengths of multiple models and improve overall predictive performance. The results also suggest that the k-nearest neighbors and logistic regression models have the best performance in predicting fish species population dynamics, while the neural network model shows slightly lower accuracy. This study provides valuable insights for fishery management and policymaking to support sustainable fishing practices in the region. Further research could focus on exploring additional machine learning algorithms and incorporating environmental factors to improve the prediction accuracy of the model. © 2023 IEEE.

关键词： Fisheries

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Ensuring Success in Quick Commerce by Evaluating Its Application Development Capability Maturity Using COBIT 2019 8

Ensuring Success in Quick Commerce by Evaluating Its Applica...

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8th International Conference on Information Management and Technology, ICIMTech 2023

作者： Heruatmadja, Chandra Hermawan Gaol, Ford Lumban Supangkat, Suhono Harso Ranti, Benny Bina Nusantara University Binus Graduate Program - Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia Institute of Technology Bandung Faculty of Digital Communication and Hotel & Tourism Bandung Indonesia Universitas Indonesia Faculty of Computer Science Computer Science Department Depok Indonesia

ISBN: (纸本)9798350326093

In the digital era, the demand for quick access to goods (q-Commerce) has driven retail companies to develop online shopping applications, aiming to attract more consumers. To cater to the demands of q-Commerce, specialized mobile applications that prioritize fast delivery and user-friendly experiences are crucial. However, developing and maintaining these applications present challenges in terms of speed of development and security. This research focuses on a retail company, PT X, with a q-Commerce application serving 3.5 million active users monthly. The research aims to assess PT X's capability maturity level in application development governance using the COBIT 2019 Capability Maturity Model. Additionally, the study investigates the differences in assessment methods between COBIT 2019 and previous versions. The findings contribute to understanding in which level of capability maturity that companies can achieve in developing quick commerce applications with a large user base. Findings of this research provides an overview for PT X to initiate the necessary initial steps to comply with the good application development according to COBIT 2019 standard. This research also highlights to future researchers that COBIT 2019 is primarily a framework for enterprise-level assessment and need to be adjusted to be suitable for measurements below the enterprise level. © 2023 IEEE.

关键词： Mobile commerce

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Utilization of Decentralized Autonomous Organization (DAO) for Virtual Property Transaction Governance in Metaverse: A Conceptual Framework 3

Utilization of Decentralized Autonomous Organization (DAO) f...

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3rd International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2023

作者： Martha, Gede Indra Raditya Gaol, Ford Lumban Supangkat, Suhono Harso Ranti, Benny Bina Nusantara University Graduate Program - Computer Science Jakarta Indonesia Bina Nusantara University Computer Science Departement Computer Science Jakarta Indonesia Institute Technology Bandung Faculty of Digital Communication and Hotel & Tourism Bandung Indonesia University of Indonesia Faculty of Computer Science Jakarta Indonesia

ISBN: (纸本)9798350394559

Research related to digital assets & ownership is currently rising along with the escalation utilization of Blockchain through various use cases and its derevatives, for example in facilitating Digital Assets in the form of virtual property (virtual houses / buildings and virtual land) in the Metaverse. These digital assets often have utilities, features, and even limited copies of quantities so that these digital objects have more value for specific users on a platform and even general people who are interested in and value them. To be able to preserve the value of digital assets in the Metaverse such as virtual property previously mentioned especially for transactions, a set of regulations/governance, as well as sustainable development in the future in the Metaverse is really needed so that the use of Decentralized Autonomous Organization (DAO) in the metaverse is one way to maintain the sustainability of the ecosystem & market and its potential of virtual property. After conducting research for DAO development for virtual property transactions, starting with determining goals & purposes, breaking down the DAO focus object (virtual property), clustering relevant attributes, matching socio-economic impacts, and finally designing smart contracts & governance consensus, we obtained 4 main layers namely Consensus Governance, Smart Contract, Virtual Property & Socio-Economic. © 2023 IEEE.

关键词： Smart contract

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Real-Time 3D Object Detection, Recognition and Presentation Using a Mobile Device for Assistive Navigation

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SN computer science 2023年第5期4卷 1-18页

作者： Chen, Jin Zhu, Zhigang Visual Computing Laboratory Computer Science Department The City College-CUNY New York 10031 NY United States Nearabl Inc. New York 10023 NY United States PhD Program in Computer Science The Graduate Center-CUNY New York 10016 NY United States

This paper presents an integrated solution for 3D object detection, recognition, and presentation to increase accessibility for various user groups in indoor areas through a mobile application. The system has three major components: a 3D object detection module, an object tracking and update module, and a voice and AR-enhanced interface. The 3D object detection module consists of pre-trained 2D object detectors and 3D bounding box estimation methods to detect the 3D poses and sizes of the objects in each camera frame. This module can easily adapt to various 2D object detectors (e.g., YOLO, SSD, mask RCNN) based on the requested task and requirements of the run time and details for the 3D detection result. It can run on a cloud server or mobile application. The object tracking and update module minimizes the computational power for long-term environment scanning by converting 2D tracking results into 3D results. The voice and AR-enhanced interface integrates ARKit and SiriKit to provide voice interaction and AR visualization to improve information delivery for different user groups. The system can be integrated with existing applications, especially assistive navigation, to increase travel safety for people who are blind or have low vision and improve social interaction for individuals with autism spectrum disorder. In addition, it can potentially be used for 3D reconstruction of the environment for other applications. Our preliminary test results for the object detection evaluation and real-time system performance are provided to validate the proposed system. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： 3D object detection ARKit Assistive technology Blind or low vision Voice assistance

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Defect Recognition on Single Layer Bare Printed Circuit Boards for Quality Control and Visual Inspection: A Low-Sample-Size Deep Transfer Learning Approach 9

Defect Recognition on Single Layer Bare Printed Circuit Boar...

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9th International Conference on Signal Processing and Intelligent Systems, ICSPIS 2023

作者： Austria, Yolanda D. Fajardo, Arnel C. Technological Institute of the Philippines Graduate School Program Quezon City Philippines Isabela State University College of Computing Science Information and Communication Technology Isabela Cauayan City Philippines

ISBN: (纸本)9798350369922

Defect detection is a critical component in ensuring the highest quality of printed circuit board manufacture. The work investigates the application of transfer learning to identify YOLOv8 design flaws. The YOLOv8 model performs exceptionally well, with mean Average Precision (mAP) of 98.3%, Precision of 98.6%, and Recall of 96.0%. Numerous datasets provide high detection rates across a range of fault types following thorough testing and inference. This extensive evaluation demonstrates the robustness of the model by reliably identifying Fissure, Mousebite, EC-Short, MC-Spur, Breakout, Pinhole, and Copper defects. The study emphasizes how the YOLOv8 design can identify flaws, providing a dependable and effective way to guarantee the dependability and caliber of printed circuit boards throughout the production process. The foundation for utilizing YOLOv8 in industrial quality control is established by this study, which holds significant potential for improving defect detection and quality control in the manufacturing industry. © 2023 IEEE.

关键词： Printed circuit boards

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Structured Multi-Track Accompaniment Arrangement via Style Prior Modelling 38

Structured Multi-Track Accompaniment Arrangement via Style P...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhao, Jingwei Xia, Gus Wang, Ziyu Wang, Ye Institute of Data Science NUS Singapore School of Computing NUS Singapore Integrative Sciences and Engineering Programme NUS Graduate School Singapore Machine Learning Department MBZUAI United Arab Emirates Computer Science Department NYU Shanghai China

In the realm of music AI, arranging rich and structured multi-track accompaniments from a simple lead sheet presents significant challenges. Such challenges include maintaining track cohesion, ensuring long-term coherence, and optimizing computational efficiency. In this paper, we introduce a novel system that leverages prior modelling over disentangled style factors to address these challenges. Our method presents a two-stage process: initially, a piano arrangement is derived from the lead sheet by retrieving piano texture styles;subsequently, a multi-track orchestration is generated by infusing orchestral function styles into the piano arrangement. Our key design is the use of vector quantization and a unique multi-stream Transformer to model the long-term flow of the orchestration style, which enables flexible, controllable, and structured music generation. Experiments show that by factorizing the arrangement task into interpretable sub-stages, our approach enhances generative capacity while improving efficiency. Additionally, our system supports a variety of music genres and provides style control at different composition hierarchies. We further show that our system achieves superior coherence, structure, and overall arrangement quality compared to existing baselines. © 2024 Neural information processing systems foundation. All rights reserved.

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Defect Detection and Classification in Printed Circuit Boards using Convolutional Neural Networks 2

Defect Detection and Classification in Printed Circuit Board...

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2nd International Conference on Edge computing and Applications, ICECAA 2023

作者： Austria, Yolanda D. Fajardo, Arnel C. Graduate School Program Technological Institute of the Philippines Quezon City Philippines College of Computing Science Information and Communication Technology Isabela State University Isabela Cauayan City Philippines

ISBN: (纸本)9798350347579

PCB defect detection and classification is vital in electronics manufacturing to ensure high-quality and safe products. Manual inspection is time-consuming, costly, and prone to errors. To address this, automated methods using deep learning models have gained preference. These models learn from diverse datasets of defect images or videos. In this study, a deep learning model was developed and trained using a dataset containing various defect classes, including breakout, copper, crack, cut, excessive conductor, missing conductor, mouse bite, pinhole, spur, and scratch. The model showed promising results in detecting defects across all classes. © 2023 IEEE.

关键词： Image segmentation

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