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IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)

作者： Matheus Loureiro Fabiana Machado Ricardo Mello Anselmo Frizera Graduate Program in Electrical Engineering Federal University of Espírito Santo (UFES) Vitória ES Brazil Graduate Program in Informatics UFES

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Smart walkers are robotics devices that can be used to help people with mobility impairments due to aging, diseases, or injuries. However, this kind of device can be complex to use correctly due to the presence of numerous sensors. In this context, this paper presents a pilot study of a virtual reality-based visual feedback interface for training users of smart walkers. Ten volunteers were recruited and divided into two groups (Intervention Group and Control Group). The Intervention Group was provided with a visual feedback training interface, whereas the Control Group was provided with verbal feedback. With the training interface, the users were able to reduce their time of execution between 4% to 29% more than users without access to the interface. Our results indicate that the interface helps the user to understand some parameters of the device, reducing their repetition execution time during and after receiving the visual feedback.

关键词： Training Legged locomotion Visualization Biomechatronics Aging Robot sensing systems Intelligent sensors Injuries Diseases

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Automated Test on Multiple Platform Framework Development 22

Automated Test on Multiple Platform Framework Development

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Proceedings of the 7th International Conference on Sustainable Information Engineering and Technology

作者： Muhammad Fadhli Ubaidilah Desy Intan Permatasari Fadilah Fahrul Hardiansyah Informatics Engineering Study Program Politeknik Elektronika Negeri Surabaya Indonesia

ISBN: (纸本)9781450397117

Software testing is a phase in software development to ensure software quality. As more and more software is developed for web and mobile platforms, Software Quality Assurance (SQA) Engineer is responsible to test software on both platforms to ensure functionality on each platform. While automated test can be implemented to reduce the workload of SQA Engineer, it has its setbacks as automated test needs a huge effort to maintain it and available tools to develop automated test currently only support one platform. In this research, an automated test framework is proposed and developed that can test both web and mobile platforms. Compared to an automated test project that uses separate tools, a project that uses the proposed framework has 2.57% higher maintainability index. A user test to 3 SQA Engineers has also been conducted and the proposed framework has generally been given high maintainability rating.

关键词： software testing selenium multi-platform automated test appium

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Fine Tuning Swin Transformer Based Pretrained Model for Microscopic Fungi Images Classification 11

Fine Tuning Swin Transformer Based Pretrained Model for Micr...

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11th International Conference on Computer, Control, informatics and its Applications, IC3INA 2024

作者： Anggara, Muchamad Galih Hindasyah, Ahmad Amda, Julfa Muhammad Waskita, A.A. Graduate Program of Informatics Engineering Universitas Pamulang Pamulang Indonesia Research Center for Computing National Research and Innovation Agency Cibinong Indonesia Research Center for Data and Information Sciences National Research and Innovation Agency Bandung Indonesia

ISBN: (纸本)9798331542313

Accurate image classification of microscopic pathogenic fungi is a challenge in the field of mycology. This study proposes a Swin Transformer-based approach to automate the identification process. The Swin Transformer model, known for its ability to capture complex spatial interactions, is adapted from ImageNet’s pre-trained model. The adaptation process is focused on fine-tuning the final classification layer to match the 5 classes of fungi present in the dataset. This classification layer is replaced by a new fully-connected layer that has the number of output according to the number of fungal classes. The weights on these layers are randomly initialized and trained in conjunction with several final convolutional layers. In addition, regularization techniques such as dropouts are applied to prevent overfitting. Adaptation using 5-fold cross-validation was carried out to find the best fold with hyperparameters including AdamW optimizer with a learning rate of 10−3, StepLR scheduler, batch size of 128 images, and a total of 9 epochs / iterations. After that, fine-tuning is carried out on the best fold with hyperparameters including AdamW optimizer with a learning rate of 10−5, StepLR scheduler, batch size of 32 images, and a total of 100 epochs / iterations. The final result showed excellent performance, with accuracy, precision, recall and an average F1-score of 95%. These results show the potential of the Swin Transformer in becoming an effective tool for mycologists. © 2024 IEEE.

关键词： Deep learning

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Classification Of Ornamental Fish Types Using CNN Method With VGG-16 And U-NET Architecture

Classification Of Ornamental Fish Types Using CNN Method Wit...

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Consumer Technology (ICCT-Pacific), International Conference on

作者： Ni Luh Wiwik Sri Rahayu Ginantra Christina Purnama Yati I Gusti Ayu Agung Diatri Indradewi I Komang Agus Yudiawan Technology and Informatics Informatics Engineering Institut Bisnis dan Teknologi Indonesia (INSTIKI) Denpasar Indonesia Department of Informatics Engineering Information Systems Study Program Faculty of Engineering and Vocational Studies Universitas Pendidikan Ganesha (UNDIKSHA) Denpasar Indonesia

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

Ornamental fish are a type of fish that live in freshwater or seawater habitats and are typically maintained for aesthetic purposes, often in ornamental ponds or as household pets. The diversity of colors, shapes, and patterns observed in ornamental fish presents a challenge in their classification. Therefore, a method capable of accurately recognizing the visual characteristics of these fish is required. The objective of this study is to evaluate the performance of two Convolutional Neural Network (CNN) architectures, specifically VGG-16 and U-Net, using five distinct types of ornamental fish, namely Angel Fish, Betta Fish, Clown Fish, Gold Fish, and Guppy Fish. The dataset comprises 3.240 images that have been processed using TensorFlow and Keras on Google Collaboratory. The results demonstrated that U-Net demonstrated superior performance in segmentation, with an Average Intersection over Union (IoU) of 58.91 % and an Average Dice Coefficient of 73.72%. In contrast, VGG-16 exhibited signs of overfitting, as evidenced by lower evaluation metrics. To enhance the efficacy of the transfer learning model, further optimization is recommended.

关键词： Measurement Image segmentation Visualization Shape Transfer learning Fish Internet Convolutional neural networks Optimization Overfitting

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Exploring the Impact of Word Embedding Dimensions on Depression Data Classification Using BiLSTM Model 8

Exploring the Impact of Word Embedding Dimensions on Depress...

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8th International Conference on Computer Science and Computational Intelligence, ICCSCI 2023

作者： Lestandy, Merinda Abdurrahim Department of Electrical Engineering Faculty of Engineering Universitas Muhammadiyah Malang Malang65144 Indonesia Informatics Master Program Faculty of Industrial Technology Universitas Islam Indonesia Yogyakarta55584 Indonesia

Depression is a mental health issue that impacts over 300 million individuals worldwide. Daily anxiety impacts the relationships of depressed individuals with their family and friends, affects their health, and, in the worst-case scenario, leads to suicide. Due to the an increase of social networks, a vast majority of people now express their thoughts, feelings, and emotions via social media. The researchers mainly focused on features and building predictions, making it difficult for them to detect depression via social media. Word embedding offers a potential solution for processing and transforming unstructured data into meaningful representations. Embedded words represent textual data as numeric numerals with equivalent meaning. Using the Bidirectional Long Short-Term Memory (Bi-LSTM) paradigm, this investigation attempts to experiment with various weighting dimensions using the Word2Vec and GloVe methods. The data used is Depression: Reddit Dataset labelled: depressed and non-depressed. The experimental results of word weight dimensions using Word2Vec with a dimension of 500 are better than Glove. The results of accuracy, precision, recall and f1-score for the Word2Vec method were 96.22%, 97.02%, 95.30% and 96.15%, while the Glove method was 95.91%, 96.40%, 95.30% and 95.85% with a dimension of 200. Overall, Word Embedding Dimensions influences the results of this study. Based on these findings, it is concluded that the proposed Word2Vec method is a significant approach. © 2023 The Authors. Published by Elsevier B.V.

关键词： BiLSTM Depression Social Media Word Embedding

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Privacy-Preserving Training of Monocular Depth Estimators via Self-Supervised Federated Learning 100

Privacy-Preserving Training of Monocular Depth Estimators vi...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： De S. Soares, Elton F. Campos, Carlos Alberto V. Ibm Research Brazil Research Lab RJ Rio de Janeiro20031-170 Brazil Federal University of the State of Rio de Janeiro Graduate Program in Informatics RJ Rio de Janeiro22290-255 Brazil

ISBN: (纸本)9798331517786

Monocular depth estimation is gaining attention in computer vision for autonomous driving due to its cost-effectiveness and versatility. Recent works have used self-supervised learning to tackle this problem. However, they overlook crucial challenges for real-world deployment, such as data privacy, network consumption, computational cost, and unstable connectivity. Recent studies have shown the potential benefits of federated learning in addressing these challenges. Thus, we introduce FedSCDepth, a novel method that combines federated learning and deep self-supervision to efficiently learn monocular depth estimators with performance comparable to the state-of-the-art. Our evaluation using the KITTI dataset's Eigen's Split reveals that FedSCDepth achieves high efficacy, with a test loss below 0.13. It also demonstrates impressive efficiency, requiring only an average of 1.5k training steps and up to 0.415 GB of weight data transfer per autonomous vehicle in each round. © 2024 IEEE.

关键词： Federated learning

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Work system method as an e-learning design framework in private universities 3

Work system method as an e-learning design framework in priv...

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3rd International Conference on Vocational Education and Technology, IConVET 2020

作者： Supriana, C.E. Informatics Engineering Study Program Pasundan University Bandung Indonesia

The development of information and communication technology, especially the internet provides opportunities to improve the ability of lecturers to deliver courses to students without being limited by distance and time. This can be done by utilizing e-learning which will accelerate the transfer of knowledge and skills. E-learning is distance education that utilizes various digital tools and platforms. These tools and platform are used to support interaction, lectures and delivery of instructions between lecturers and students. This study will discuss the use of work system methods and the use of e-learning technology in a business entity. The work system methods are used to design information systems. Meanwhile e-learning technology in a business entity serves lecturers and students, conduct lectures at private universities in increasing knowledge dissemination, educational affordability and the use of technology and information as its support. The result of this research is the works system method model which refers to the analysis of data from the internet accessibility survey of students, analysis of its components, the system development cycle, the revision of the vision, mission and resources in private universities and recommendations of opportunities for the development of higher quality e-learning implementation. © Published under licence by IOP Publishing Ltd.

关键词： Students

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Global cancer stigma research: a U.S. National Cancer Institute workshop report

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Journal of the National Cancer Institute - Monographs 2024年第63期2024卷 4-10页

作者： Heley, Kathryn Vanderpool, Robin C. Vedham, Vidya Health Communication and Informatics Research Branch Behavioral Research Program Division of Cancer Control and Population Sciences National Cancer Institute Rockville MD United States Center for Global Health Office of the Director National Cancer Institute Rockville MD United States

Stigma is a social process characterized by negative beliefs, attitudes, and stereotypes associated with a specific attribute or characteristic that leads to discrimination and social exclusion. Stigma manifests across the cancer control continuum and remains a key challenge for cancer prevention and control worldwide. In this commentary, we provide an overview of the U.S. National Cancer Institute’s (NCI) Global Cancer Stigma Research Workshop, a multi-disciplinary international conference held virtually in September 2022, which focused on the intersection of cancer and stigma. The meeting was unique in its convening of researchers, advocates, clinicians, and non-governmental and governmental organizations, who—as a collective—provided overarching topics, cross-cutting considerations, and future directions for the cancer stigma research community to consider, which we describe herein. In summary, studying cancer stigma comprehensively requires a holistic, adaptive, and multifaceted approach—and should consider interrelated factors and their intersection within diverse cultural and social contexts worldwide. Collectively, there was a call for: an inclusive approach, encouraging researchers and practitioners to identify and measure cancer stigma as a driver for cancer health inequities globally;an expansion of existing research methodology to include diversity of experiences, contexts, and perspectives;and collaborations among diverse stakeholders to develop more effective strategies for reducing stigma and improving cancer outcomes. Such efforts are essential to cultivating effective and equitable approaches to preventing and treating cancer worldwide. © 2024 Oxford University Press. All rights reserved.

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Initial Exploration and Understanding of Parameters for Successful Enterprise Architecture Implementation 11

Initial Exploration and Understanding of Parameters for Succ...

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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Khairina, Dyna Marisa Purwanto, Purwanto Nugraheni, Dinar Mutiara Kusumo School of Postgraduate Studies Universitas Diponegoro Program in Information Systems Semarang Indonesia Universitas Diponegoro Faculty of Engineering Department of Chemical Engineering Semarang Indonesia Universitas Diponegoro Faculty of Science and Mathematics Department of Informatics Semarang Indonesia

ISBN: (纸本)9798350389654

This study provides an initial exploration and further understanding of the factors that are parameters in evaluating the success of Enterprise Architecture (EA) implementation. An evaluation process is needed to ensure that the EA project implementation provides effectiveness, efficiency, and feasibility of the EA information system as well as assessing previous project experience to avoid future EA project risks. The research aims to present a Systematic Literature Review based on the stages of identification, screening, eligibility, and inclusion so that 14 main articles are selected for review with strong actual relevance. The study results provide insights and references that can be used to further explore key factors in evaluating the success of EA implementation in the future. It was found that there were ten factors that most influenced the evaluation of the success of EA implementation and it was concluded that two potential factors were the most influential and required further exploration in evaluating the success of EA implementation which could be applied in various sectors/dimensions. This study contributes as an initial reference for overcoming problems in investigating and exploring the factors that influence the success of EA implementation as an effort to assess the success of achieving future organizational goals. © 2024 IEEE.

关键词： Enterprise Architecture Parameters Success Factors Systematic Literature Review

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Towards Continual Federated Learning of Monocular Depth for Autonomous Vehicles 100

Towards Continual Federated Learning of Monocular Depth for ...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

ISBN: (纸本)9798331517786

Recent investigations in computer vision for autonomous vehicles have focused on depth estimation from images, owing to its cost-efficiency and adaptability. Monocular depth estimation, using a single camera, is notable for its adaptability compared to binocular techniques that require two fixed cameras. Sophisticated methodologies employ self-supervised deep neural networks, while latest research proposes the use of federated learning to tackle crucial challenges for autonomous driving, such as data privacy, network usage, computation distribution, and connectivity robustness. Nevertheless, continual learning presents an additional challenge for both centralized and federated training, as updating models with novel datasets may induce the forgetting of previously acquired samples, thereby diminishing the model's versatility. Meanwhile, recent research indicates that experience replay strategies can be used to alleviate forgetting in autonomous driving use cases. We introduce ERFedSCDepth, an innovative approach amalgamating experience replay, federated learning, and deep self-supervision to allow the training of monocular depth estimators with high effectiveness and efficiency. Assessment using KITTI and DDAD datasets shows the efficacy of our approach, achieving enhanced continual learning performance over state-of-the-art baseline at standard depth metrics. © 2024 IEEE.

关键词： Self-supervised learning

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