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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Priyadi, Muhammad Reza Azhar Suharjito Bina Nusantara University BINUS Online Learning Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program-Master of Industrial Engineering Industrial Engineering Department Jakarta11480 Indonesia

ISBN: (纸本)9798350339543

Despite Indonesia's leading position in palm oil processing, the inaccurate assessment of oil palm fruit maturity poses challenges in determining the optimal harvest timing and maintaining product quality. With advancements in information technology, including image processing and object detection techniques like YOLO and EfficientDet, there is potential for automated and precise identification of palm fruit maturity. To explore this, the authors conducted research to identify the most suitable algorithm for detecting the ripeness of palm oil fruit. The study utilized a dataset consisting of 8299 images with six maturity levels, employing augmentation techniques to increase image variability. Results show that YOLO V8s outperformed EfficientDet Lite 4 in terms of object detection precision, achieving higher mean average precision (mAP) scores. YOLO V8s showed a 4% improvement in mAP@50 and a 16% improvement in mAP@50-95 compared to EfficientDet Lite 4. YOLO V8s achieved a mAP of 0.978 for mAP@50 and 0.765 for mAP@50-95, surpassing EfficientDet Lite 4. However, EfficientDet Lite 4 had an advantage in terms of class accuracy and bounding box placement, as indicated by smaller box loss and cIs- loss values. YOLO V8s also demonstrated better frames per second (FPS) performance, ranging from 10 to 15 FPS, compared to EfficientDet Lite 4. © 2023 IEEE.

关键词： Object detection

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A Hybrid Inference Engine to Predict Uncertain Sugar Content for Sugar Industry

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Procedia computer science 2024年 245卷 480-489页

作者： Muhammad Asrol Napthaleni Ditdit Nugeraha Utama Suharjito Christian Harito Muhammad Syafrudin Industrial Engineering Department BINUS Graduate Program – Master of Industrial Engineering Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Department of Artificial Intelligence and Data Science Sejong University Seoul 05006 South Korea

The sugar industry is facing challenges in increasing productivity to meet consumer demand. One opportunity for productivity improvement lies in ensuring sugar content. This study proposes a hybrid model to predict sugar content by considering uncertainty factors. A hybrid model combining fuzzy subtractive clustering, and a fuzzy inference system is proposed to predict sugar content. The clustering results using silhouette and fuzzy subtractive clustering successfully identified 6 cluster centres from 2225 datasets collected in a sugar industry in East Java Province. The hybrid inference engine model is designed with fuzzy rules derived from the clustered data. Two inference models are developed: triangular and Gaussian fuzzy numbers. The testing results indicate that the hybrid model with triangular fuzzy numbers shows the smallest error with an R2 value of 0.95. This model is possible to applied in the sugar industry for decision makers in improving productivity with taking attention into uncertain factors influencing sugar content.

关键词： Clustering Fuzzy Hybrid model Inference System Prediction Sugarcane

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A Scalable Real-time Semantic Segmentation Network for Autonomous Driving 1

A Scalable Real-time Semantic Segmentation Network for Auton...

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1st Workshop on Advanced Multimedia Computing for Smart Manufacturing and engineering, AMC-SME 2023

作者： Liu, Jun Wu, Chao Yuan, Geng Niu, Wei Zhang, Wenbin Song, Houbing Herbert Robotics Institute of School of Computer Science Carnegie Mellon University Pittsburgh United States Department of Electrical & Computer Engineering Northeastern University Boston United States School of Computing University of Georgia Athens United States Department of Computer Science Michigan Technological University Houghton United States Department of Information Systems University of Maryland Baltimore County Baltimore United States

ISBN: (纸本)9798400702730

Computational resources are limited on real-time embedded devices, so the available computing cost of deployment on the target platform must be considered. We develop a feature extraction module based on the MobileNet backbone that can be adjusted in terms of computational complexity and capacity using the depth multiplier parameter, classifier depth, and kernel depth. These three parameters allow us to control the count of channels within the network, effectively managing the model's capacity and computational requirements. To achieve semantic segmentation, we incorporate additional components, such as an extension module. This extension module typically includes 1x1 pointwise convolutional layers for pixel-level classification and a transposed convolutional layer for upsampling the output to the original input image size. By combining the feature extraction module with this extension module, we can create a complete architecture capable of performing semantic segmentation tasks. The feature extraction module provides the initial feature extraction and the extension module adds the necessary components for accurate pixel-wise classification and upsampling. Compared to Hardware-aware Neural Architecture Search (NAS), pruning, runtime pruning, and knowledge distillation methods, our model has several advantages in terms of modular design, structural controllability, ease of implementation, and cost-effectiveness. Our computational efficiency, as measured by FLOPS, is highly competitive. Our method is distinguished by solving the problem of MobileNet's inability to adjust the size and number of convolution kernels. It achieves this through adaptable parameter tuning, including MobileNet's depth multiplier, the kernel size in the FCN head's Separable Convolution layer, and the depth of the first Point-wise Convolution layer. These adjustments are customized to match hardware's max multiply-accumulates (MACs), optimizing network capacity and maximizing resource utilizat

关键词： Feature extraction

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Metaverse with Mobile Twin Edge Computing: Empowering Optimal Efficiency and Accuracy

Metaverse with Mobile Twin Edge Computing: Empowering Optima...

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2024 International Conference on engineering and Computing, ICECT 2024

作者： Bibi, Asma Noor, Umara Rashid, Zahid International Islamic University Faculty of Computing and Information Technology Department of Software Engineering Islamabad Pakistan Kohsar University Department of Computer Science Murree Pakistan Technology Management Economics and Policy Program College of Engineering Seoul National University Seoul08826 Korea Republic of

ISBN: (纸本)9798350349719

The concept of Digital Twin has been widely used by researchers to represent physical entities in computer-generated reality in the metaverse. In this research, a novel concept of 'Mobile Twin' is coined. Mobile Twin serves as a replica of a mobile device in the virtual environment enabling communication. The purpose of introducing Mobile Twin in the metaverse is to bring mobility and benefits similar to tangible mobile devices while eliminating the need for individual energy sources, storage limitations, resource usage, and efficiency. The true benefits of a mobile twin can be achieved with an architecture where computing nodes are decentralized, data processing is near the network's edge with faster response times and reduced latency. For this purpose, the mobile twin-based applications of metaverse are built over edge computing-based network architecture. This idea introduced the aspect of 'Revolutionary Ultra-low latency (RULL)' in the metaverse. © 2024 IEEE.

关键词： Electric power utilization

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On the Impact of Knowledge Distillation for Model Interpretability 40

On the Impact of Knowledge Distillation for Model Interpreta...

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40th International Conference on Machine Learning, ICML 2023

作者： Han, Hyeongrok Siwon, Kim Choi, Hyun-Soo Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Seoul Korea Republic of Department of Computer Science and Engineering Seoul National University of Science and Technology Seoul Korea Republic of ZIOVISION Inc. Chuncheon Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul Korea Republic of

Several recent studies have elucidated why knowledge distillation (KD) improves model performance. However, few have researched the other advantages of KD in addition to its improving model performance. In this study, we have attempted to show that KD enhances the interpretability as well as the accuracy of models. We measured the number of concept detectors identified in network dissection for a quantitative comparison of model interpretability. We attributed the improvement in interpretability to the class-similarity information transferred from the teacher to student models. First, we confirmed the transfer of class-similarity information from the teacher to student model via logit distillation. Then, we analyzed how class-similarity information affects model interpretability in terms of its presence or absence and degree of similarity information. We conducted various quantitative and qualitative experiments and examined the results on different datasets, different KD methods, and according to different measures of interpretability. Our research showed that KD models by large models could be used more reliably in various fields. The code is available at https://***/Rok07/KD_***. © 2023 Proceedings of Machine Learning Research. All rights reserved.

关键词： Distillation

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Multi-robot Map Exploration Based on Multiple Rapidly-Exploring Randomized Trees

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SN computer science 2024年第1期5卷 31页

作者： Mukhopadhyay, Shayok Umari, Hassan Koirala, Koshish Electrical & Computer Engineering and Computer Science Department University of New Haven 300 Boston Post Rd West Haven 06516 CT United States Department of Electrical Engineering American University of Sharjah Sharjah P.O. Box 26666 United Arab Emirates Mechatronics Engineering graduate program American University of Sharjah Sharjah P.O. Box 26666 United Arab Emirates

A map is necessary for tasks such as path planning or localization, which are common to mobile robot navigation. However, a map may be unavailable if the environment in which a robot navigates is unknown. Creating a map requires an exploration algorithm. Such algorithms guide robots to boundaries that separate known portions of a map from the unknown portions. Such boundaries are known as frontiers. There are image processing-based algorithms that detect frontiers. The authors’ previous work uses two, i.e., local and global, rapidly exploring random trees (RRTs) for two-dimensional exploration using a single robot. The present work applies the above to a multiagent system with three robots. A market-based strategy is used to allocate exploration tasks to the robots. Results show that the developed exploration strategy successfully explores a map in a reasonable amount of time compared to image processing-based approaches, and also reduces the map exploration cost. This strategy is readily usable for three-dimensional exploration with drones. It is also shown that the usage of a local frontier detector along with a global frontier detector helps reduce exploration time compared to simply using a global frontier detector alone. As an outline to future work, the paper also shows an extension of the proposed approach towards three-dimensional mapping using a single robot. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Exploration Mapping Multi-agent Navigation ROS RRT

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AI Integrated Dialogic Alphabet Zone Board for Kids in Pre-childhood Education

AI Integrated Dialogic Alphabet Zone Board for Kids in Pre-c...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Asif Mahmud Tahsin Faisal M. Akhtaruzzaman Jamal Uddin Tanvin Sachitra Halder Fahim Shahryer Department of Computer Science and Engineering Military Institute of Science and Technology Dhaka Bangladesh Quantum Robotics and Automation Research Group (QRARG) Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

The smart Dialogic Alphabet Zone Board (DAZ-Board) aims to address the issue of learning process for pre-childhood education among the children. It is importance to facilitate a healthy and effective environment for early childhood education in shaping a child’s cognitive development, language acquisition, and literacy skills. Traditional teaching methods often fail to engage young learners effectively, leading to limited understanding and retention. On the other hand, dependencies on mobile devices raise the physical and mental health issues. This study introduces an interactive, auditory, and multisensory game board providing a wonderful and healthy experiences in tailoring the developmental needs of children. The objective of this research is to design and develop an interactive alphabet board with necessary sensors, electronics, and controller modules. Moreover, designing a Machine Learning (ML) model for voice-based alphabet recognition is one of the important contributions of this research. Among the results of different ML models, Naïve Bayes Classifier outperforms the most with accuracy ≈ . %. The proposed system will facilitate the preschoolers to learn in modern and interactive way, while keeping away from being addicted to the mobile devices.

关键词： Pediatrics Visualization Accuracy Scalability Education Games Speech recognition Sensors Smart phones Systematic literature review

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Predicting Microsatellite Instability from Histology Images with Dilated Neighborhood Attention Transformer in Colorectal Cancer 24

Predicting Microsatellite Instability from Histology Images ...

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24th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2024

作者： Chen, Yun-Chi Chao, Tung Phandita, Wimaya Nitya Sun, Tzu-Yu Wang, Huai-Zhi Hsieh, Yi-Hsien Hsu, Le-Yin Lin, Che Taipei Taiwan College of Bioresources and Agriculture NTU Taipei Taiwan National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei Taiwan Graduate Institute of Communication Engineering NTU Taipei Taiwan NTU Data Science Degree Program Taipei Taiwan NTU Department of Electrical Engineering Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taipei Taiwan NTU Smart Medicine and Health Informatics Program Taipei Taiwan

ISBN: (纸本)9798331518622

Microsatellite instability (MSI) is a pivotal genetic marker influencing the efficacy of immunotherapy in colorectal cancer. Traditional MSI examination often requires additional genetic or immunohistochemical tests, whereas histology images, widely available in colorectal cancer diagnosis, offer a valuable alternative for MSI prediction. Although Transformer-based models have demonstrated promising outcomes in predicting MSI from histology images, they are hampered by traditional local attention mechanisms that struggle to capture long-range interdependencies and establish a comprehensive global receptive field. In this study, we introduce DiNAT-MSI, a novel framework for histology-based MSI prediction that incorporates the Dilated Neighborhood Attention Transformer (DiNAT). This model enhances global context recognition and substantially expands receptive fields, all without additional computational burden. Our results demonstrate that DiNAT-MSI achieves a superior patientwise AUROC compared to ResNet18 and Swin Transformer, along with commendable explainability. Our work not only illustrates a more accessible diagnostic tool for leveraging histological data but also underscores the potential of Transformerbased models with sophisticated attention designs in advancing precision medicine for colorectal cancer patients. © 2024 IEEE.

关键词： Diseases

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Comparative Analysis of Real-Time Multi-Person Pose Detection in Electrical Industrial Safety Scenarios Using YOLOv8-Pose and OpenPose 4

Comparative Analysis of Real-Time Multi-Person Pose Detectio...

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4th IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2024

作者： Tsai, Shang-Te Wu, Zong-Rong Lin, Pin-Hsuan Chen, Chin-Hsiung Chien, Wei Chang, Yu-Cheng School of Art and Design Sanming University Fujian365004 China National Yunlin University of Science and Technology Department of Technical and Vocational Program in Artificial Intelligence Yunlin Taiwan National Central University Department of Business Administration Taoyuan Taiwan Commercial Industrial Vocational Senior High School Department of Electrical Engineering New Taipei Municipal Tamsui New Taipei Taiwan Lunghwa University of Science and Technology Department of Computer Information and Network Engineering Taoyuan Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350360721

We reviewed the application of modern technology for rapid and accurate multi-person real-time pose detection in the hazardous field of electrical engineering. We focused on two leading pose detection technologies: YOLOv8 and OpenPose. To optimize performance, we integrated these two techniques into the LSTM model for training and investigated frame rates and accuracy. © 2024 IEEE.

关键词： Accident prevention

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A Trilogy of AI Safety Frameworks: Paths from Facts and Knowledge Gaps to Reliable Predictions and New Knowledge

arXiv

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arXiv 2024年

作者： Kasif, Simon Department of Biomedical Engineering Program in Bioinformatics Department of Computer Science Boston University United States

AI Safety has become a vital front-line concern of many scientists within and outside the AI community. There are many immediate and long term anticipated risks that range from existential risk to human existence to deep fakes and bias in machine learning systems [1-5]. In this paper, we reduce the full scope and immense complexity of AI safety concerns to a trilogy of three important but tractable opportunities for advances that have the short-term potential to improve AI safety and reliability without reducing AI innovation in critical domains. In this perspective, we discuss this vision based on several case studies that already produced proofs of concept in critical ML applications in biomedical science. © 2024, CC BY-NC-ND.

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