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International Conference on Applied System Innovation (ICASI)

作者： Taweelap Suwattanapunkul Lung-Jen Wang International Master Program in Information Technology and Applications National Pingtung University Taiwan Department of Computer Science and Information Engineering National Pingtung University Taiwan

According to the trend of worldwide car sales have grown up, this cause may increase accidents on the road due to human error. The self-driverless car has been developed to solve this problem. One task of the self-driverless car is traffic sign detection and recognition (TSDR), which will help drivers notify the traffic sign installed on the road in advance. Taiwan roads have specific traffic signs, and no Taiwan traffic sign public dataset is available. In this paper, our proposed object detection method was experimentally performed using YOLOv5s6 and YOLOv8s models on three different datasets, as Tsinghua-Tencent 100K (TT100k), the self-created Taiwan traffic sign (TWTS), and the hybrid dataset, which combine the traffic scenes between TT100k and TWTS dataset. The output results from each dataset and each model, which is trained on the same parameter, will be compared to validate the proposed method. The experiment results’ comparison of the hybrid dataset between YOLOv5s6 and YOLOv8s models display the results of the mAP@.5 is about 65% and 76.2%, respectively, which means the performance of the YOLOv8s is higher than the YOLOv5s6 when using hybrid dataset.

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Body Politics: Unpacking Tensions and Future Perspectives for Body-Centric Design Research in HCI

Body Politics: Unpacking Tensions and Future Perspectives fo...

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2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025

作者： Tsaknaki, Vasiliki Alaoui, Sarah Fdili Homewood, Sarah Fritsch, Jonas Brynskov, Anna Núñez-Pacheco, Claudia Carlson, Kristin Spiel, Katta Gillies, Marco Fyfe Pietro Harrington, Christina IT University of Copenhagen Copenhagen Denmark Creative Computing Institute University of the Arts London London United Kingdom Department of Computer Science University of Copenhagen Copenhagen Denmark Department of Computer Science and Media Technology Malmö University Malmö Sweden Creative Technologies Program Illinois State University Normal IL United States Crip Collective Human-Computer Interaction Group TU Wien Vienna Austria Computing Goldsmiths University of London London United Kingdom HCI Institute Carnegie Mellon University Pittsburgh PA United States

ISBN: (纸本)9798400713958

Human bodies are deeply political as they carry historical and social meanings, including race, gender, sexuality, ethnicity, class, and abilities. The expanding body-centric research in HCI can be traced in the plurality of methods, theories and domains that take bodies as a central point of departure, when designing or studying interaction with technologies. This one-day workshop will bring together researchers and practitioners within the CHI community to discuss, map, and unpack emerging tensions and challenges on the topic of body politics for HCI. Interested participants are invited to submit examples from their own research, which, in the workshop, will be used as a point of departure to critically reflect on and expand body-centric methods, theories and domains through the lens of body politics. Workshop outcomes will include charting future directions for body-centric research to address challenges and opportunities of acknowledging that bodies are always political in design research. © 2025 Copyright held by the owner/author(s).

关键词： Bodies design research politics

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NNSplit-SØREN: Supporting the model implementation of large neural networks in a programmable data plane

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computer Networks 2023年 222卷

作者： Li, David Chunhu Maulana, Muhamad Rizka Chou, Li-Der Information Technology and Management Program Ming Chuan University Taoyuan City333321 Taiwan Department of Computer Science and Information Engineering National Central University Taoyuan City320317 Taiwan

Embedding neural network (NN) models in the data plane is one of the very promising and attractive ways to leverage the computational power of computer network switches. This method became possible with the advent of the P4 language controlling the programmable data plane. However, most data planes today have some constraints, such as a limited set of operations and limited memory size. The computational cost of training large-scale NNs is high. In addition, while complex large-scale NN architectures are often used to improve prediction accuracy, they affect the functional performance of the data plane because of factors such as numerous input parameters and complex model design. Therefore, determining how to reduce the performance cost incurred by implementing large NN models in the data plane is a critical issue that needs to be addressed. This research proposes a technique called Neural Network Split (NNSplit) to solve the performance problems of embedding a large NN in a data plane by splitting the NN layers across multiple data planes. To support layer splitting, a new protocol called SuppORting ComplEx Computation in the Network (SØREN) is also proposed. The SØREN protocol header carries the activation value and bridges the NN layers in all switches. A multi-class classification use case of network traffic is used as the context for the experimental analysis. Experimental results show that compared to non-splitting NN architectures, NNSplit can reduce memory usage by nearly 50% and increase network traffic throughput with the cost of a 14% increase in round-trip time. In addition, when the SØREN protocol is encapsulated into data packets, the average processing time of the switch is 773 µs, which has very little impact on the processing time of the packets. Experimental results also show that the proposed NNSplit–SØREN can support large NN models on the data plane with a small performance cost. © 2022 Elsevier B.V.

关键词： Cost reduction

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Cuisine Image Recognition and Recommender System using Convolutional Neural Network

Cuisine Image Recognition and Recommender System using Convo...

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IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE)

作者： Chin-Chih Chang Chi-Hung Wei Kuan-Yun Wu Hao-Xiang Kao Sean Hsiao Department of Computer Science and Information Engineering Chung Hua University Hsinchu Taiwan Ph.D. Program in Engineering Science Chung Hua University Hsinchu Taiwan Department of Computer Science and Information Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan Bachelor Program of Applied Foreign Language and Smart Commerce Chung Hua University Hsinchu Taiwan

The fusion of technology and culinary exploration has allowed for the emergence of advanced online customer service systems. We developed a novel approach to enhance the dining experience. We used a Cuisine Image Recognition and Recommender System (CIRRS) powered by Convolutional Neural Network (CNN) to identify and suggest diverse cuisines based on visual inputs. CIRRS swiftly identified diverse cuisines from user-captured images, offering information on origin, ingredients, and variations by crawling websites. The system enhanced dining experiences by suggesting personalized menus based on individual preferences and past selections. Extensively tested across various culinary genres, CIRRS consistently demonstrated accuracy and adaptability. User feedback validated its potential to simplify dining choices and elevate satisfaction. This innovative system enriched dining experiences as a valuable tool for culinary enthusiasts, chefs, and restaurateurs, bridging the gap between technology and gastronomy, It also offers a unique way to explore Taiwanese cuisines.

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Customer Segment Classification Prediction in the Australian Retail Based on Machine Learning Algorithms 23

Customer Segment Classification Prediction in the Australian...

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Proceedings of the 2023 4th International Conference on Machine Learning and computer Application

作者： Yiming Luo Rui Zhang Fei Wang Tao Wei Business Schoo The University of Sydney Austialia Computer and information sciences program Harrisburg University of Science and Technology United States Faculty of Science and Technology Beijing Normal University-Hong Kong Baptist University United International College China School of Chemical and Environmental Engineering Sichuan University of Science & Engineering China

ISBN: (纸本)9798400709449

Customer segmentation is an essential area of business analytics today. Accurate customer segmentation is access to improves the efficiency of marketing campaigns and customer satisfaction. This study employs multiple machine learning methods to classify Australian Retail company BIGW's customer segments and first to apply multiple model explanation methods to find insights related to customer segmentation identification. After rigorous comparison and hyperparameter fine-tuning, XGBoost is the most adept for this dataset. We derive three key insights through the model results and model interpretive methods. First, BIGW's primary clientele comprises young families in urban areas who prefer cost-effective products, establishing the foundation of their consumer base. Second, the model result indicates a notable gap in BIGW's understanding of its high-end customers, suggesting an area for immediate attention. Third, a specific customer segment emerges from the data: individuals favoring online shopping, demonstrating high total expenditure but low interest in promotions, representing a high-value segment.

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Modelling e-waste management data in smart cities 2

Modelling e-waste management data in smart cities

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2nd Sustainable Cities Latin America Conference, SCLA 2021

作者： De Novais, Marcos Pereira De Almeida Xavier, Victor Xavier, Lucia Helena Hwang, Junseok Seoul National University Technology Management Economics and Policy Program Siheung-si Korea Republic of University of Rio de Janeiro Program in Computer Science and Systems Engineering Rio de Janeiro Brazil Centre for Mineral Technology Ministry of Science Technology and Inovation Rio de Janeiro Brazil

ISBN: (纸本)9781665437684

The smart cities concept is strongly related to energy efficiency and low carbon patterns, what that refers to meeting the sustainability criteria established by regulatory mechanisms. The Smart City Index (SCI) data can reach a higher quality of living by observing aspects as mobility, health, safety, transparency, smart management, and many others, like the waste management. Thus, this set of indicators was used to evaluate the e-waste management as an emergent challenge in urban cities, providing both, the generation of waste from the intense consumption of electronic devices, as well meeting the questions arising from urbanization from the integration of digital solutions. This study proposes a model of analysis using simple linear regression and k-mean clustering as correlation methods. The findings suggested correlation between the SCI ranking, the E-waste generated, and the economic situation of the entire set of smart cities. Nevertheless, it was found weak correlation for the best rated cities. The results provided the perception that it is possible to identify correlation from the Smart City Index and aspects from the e-waste management system, enabling the discussion over the importance of next smart city indexes and given a new perspective to the e-waste management. © 2021 IEEE.

关键词： Environmental management

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Alert system with IoT Techniques for Stray Children

Alert system with IoT Techniques for Stray Children

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Cybernetics and Innovations (ICCI), International Conference on

作者： Patiyuth Pramkeaw Thittaporn Ganokratanaa Thidarat Pinthong Napathsorn Kitprasert Media Technology Program King Mongkut's University of Technology Thonburi Bangkok Thailand Applied Computer Science Program King Mongkut's University of Technology Thonburi Bangkok Thailand Dep. of Business Information Management Phetchaburi Rajabhat University Phetchaburi Thailand

The purpose of this research was to design, produce and evaluate a stray child warning system. The device has been designed by using 2 ESP8266 boards to receive signals as specified. The system will notify the information via Line Notify to let users know how far the board that acts as a Client is from the board that acts as an Access Point. Expressed in meters (m) in the notification line notify process. The test results of the system in terms of signal transmission between the Access Point and the Client can work well. Buzzer speaker and LED notification light according to the specified distance every time. Notifications to LINE Notify to mobile phones can be done every time.

关键词： Meters Pediatrics Technological innovation LED lamps Alarm systems Turning Mobile handsets

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An Empirical Generation Technique on Background Music Using Gated Recurrent Neural Networks

An Empirical Generation Technique on Background Music Using ...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Varinya Phanichraksaphong Wei-Ho Tsai International Graduate Program of Electrical Engineering and Computer Science (IEECS) National Taipei University of Technology Taipei Taiwan Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan

The implementation of Gated Recurrent Neural Networks (GRU) to generate background music (BGM) combines deep learning technology with music that is used for the visual content of a commercial or educational. Indeed, this BGM is necessary to enhance the intended message expressed to the other audience. This work aimed to provide the model network of GRU which is based on RNN to generate multi-label genres of music by using the open source of GTZAN to evaluate the new BGM. Our GRU networks can solve the vanishing gradient problem by utilizing both the reset gate and the update gate on the network. In the results, we achieved a new BGM that synchronized with the human mood which made more variety of sounds.

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E-Government Interoperability: Provincial-Level Architecture Model to Enable Fast Healthcare Interoperability Resources (FHIR)

E-Government Interoperability: Provincial-Level Architecture...

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International Conference on Information Management and technology (ICIMTech)

作者： Diana Wijayanti Satriawansyah Urbaya Taufiq Sitompul Verry Adrian Department of Information Systems BINUS Graduate Program Bina Nusantara University Jakarta Indonesia Country Health Information Systems and Data Use (CHISU) Program USAID Indonesia Jakarta Indonesia Department of Informatics University of Oslo Oslo Norway Computer Science Department Binus Graduate Program – Doctor Computer Science Bina Nusantara University Jakarta Indonesia Center of Data and Information Technology Jakarta Provincial Health Office Jakarta Indonesia

ISBN: (数字)9798350390025

ISBN: (纸本)9798350390032

The process of using ICT to provide services to the public is known as the Indonesian e-Government system, or Sistem Pemerintahan Berbasis Elektronik (SPBE). The e-Government initiative in Jakarta Provincial Health Office involves enhancing collaboration among public health entities for efficient data exchange and streamlined processes, especially between the Provincial and District Health Offices, public hospitals, government clinics, and primary health care centers (Puskesmas). Achieving interoperability requires standardized protocols and a well-defined architectural model to integrate data seamlessly. This study presents a provincial-level architectural model focused on improving electronic health records interoperability, aiming to promote the adoption of the national Fast Healthcare Interoperability Resources (FHIR) health information exchange platform and enhance the integrity of health data in Jakarta. The study methodology involves conducting literature reviews, observations, and discussions with representatives from healthcare facilities to develop the e-Government architecture model and prototype of the infrastructure layer aiming to facilitate the interoperability of Electronic Health Records (EHRs) across 93 healthcare facilities, all of which are part of the SPBE users.

关键词： Prototypes Medical services Data models Security Public healthcare Electronic medical records Interoperability Information exchange Standards Electronic government

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Minimization of High Computational Cost in Data Preprocessing and Modeling Using MPI4Py

SSRN

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SSRN 2023年

作者： Ebenezer, Oluwasakin Thomas, Torku Tingting, Sun Yinusa, Ahmeed Hamden, Sammi Poudel, Samir Vargas, Jorge Poudel, Khem Narayan Computation and Data Science Program Middle Tennessee State University TN United States Engineering Technology Middle Tennessee State University TN United States Computer Science Program Middle Tennessee State University TN United States

Data preprocessing is a fundamental stage in deep learning modeling and serves as the cornerstone of reliable data analytics. These deep learning models require significant amounts of training data to be effective, with small datasets often resulting in overfitting and poor performance on large datasets. One solution to this problem is parallelization in data modeling, which allows the model to fit the training data more effectively, leading to higher accuracy on large data sets and higher performance overall. In this research, we developed a novel approach that effectively deployed tools such as MPI and MPI4Py from parallel computing to handle data preprocessing and deep learning modeling processes. As a case study, the technique is applied to COVID-19 data from state of Tennessee, USA. Finally, the effectiveness of our approach is demonstrated by comparing it with existing methods without parallel computing concepts like MPI4Py. Our results demonstrate promising outcome for the deployment of parallel computing in modeling to minimize high computational cost. © 2023, The Authors. All rights reserved.

关键词： Large dataset

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