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SSRN 2023年

作者： Kim, Suhyeon Lee, Taek-Ho Lee, Junghye Graduate School of Data Science Kyungpook National University Daegu41566 Korea Republic of Technology Management Economics and Policy Program & Institute of Engineering Research Seoul National University Seoul08826 Korea Republic of Technology Management Economics and Policy Program & Graduate School of Engineering Practice Seoul National University Seoul08826 Korea Republic of

Missing data in multivariate time series (MTS) is a very common issue, often caused by unreliable sensors and data storage or transmission problems. Particularly, such missing data can cause some errors and biases in the MTS forecasting tasks of real-world applications, implying that proper handling of the missing data is essential. Therefore, in this study, we propose a new method for MTS forecasting with missing values, called a temporal matrix factorization-based graph neural network (TMF-GNN), to improve predictive performance outcomes. TMF-GNN basically uses the concept of TMF, which reconstructs partially observed MTS data. We newly present a data-adaptive regularization method for TMF based on graph-based and sequential deep learning algorithms to capture both the variable- and sample-wise information of MTS data affected by the concept known as missingness. We demonstrate the feasibility of the proposed method by conducting various experiments on three MTS datasets and show how it outperforms baseline methods. We believe that our study will have an impact on several MTS-related tasks and that it can be a useful alternative for handling missing values in MTS data. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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USING THE SIMPLIFIED DRUM-BUFFER-ROPE METHOD FOR IDENTIFYING BOTTLENECK PORTS IN THE NORTH AMERICAN LINE

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Journal of technology 2023年第3期38卷 183-189页

作者： Huang, Wen-Tso Dang, Jr-Fong Chang, Tzu-Yun Department of Business Administration Chung Yuan Christian University Taoyuan320314 Taiwan Undergraduate Program of Intellectual Creativity Engineering National Chung Hsing University Taichung402202 Taiwan Program of Industrial and Intelligent Technology National Chung Hsing University Taichung402202 Taiwan Department of Industrial Management Chien Hsin University of Science and Technology Taichung402202 Taiwan

This study utilized the simplified drum-buffer-rope (SDBR) method of the theory of constraint (TOC) to identify the bottleneck ports in the North American line. In particular, it calculated different port utilization rates by vessel operations and obtained the highest port utilization rate from the initiating load port to the terminal unloading port by assuming that the highest port utilization rate was the bottleneck port. The Monte Carlo simulation was used to simulate actual data spanning ten years in the North American line and verify bottleneck ports. © 2023, National Taiwan University of Science and technology. All rights reserved.

关键词： Monte Carlo methods

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Using LSTM and Regression Analysis for Railway Passenger Traffic Volume Prediction 11

Using LSTM and Regression Analysis for Railway Passenger Tra...

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11th IEEE Global Conference on Consumer Electronics, GCCE 2022

作者： Tey, Fu Jie Tuan, Chih-Chi Cai, Cheng-En Wu, Ming-Xuan Wu, Yun-Sheng Wu, Tin-Yu National Taiwan University of Science and Technology Department of Electrical Engineering Taipei Taiwan National Ilan University Master Program of E-Learning for Multimedia and Network Communications Yilan Taiwan National Pingtung University of Science and Technology Department of Management Information Systems Pingtung County Taiwan

ISBN: (纸本)9781665492324

The purpose of this paper is to investigate the factors affecting the passenger traffic volume on the high-capacity MRT, to analyze the traffic volume trends, and to make traffic volume predictions more accurately. This paper uses the LSTM (Long Short-term Memory) model and regression analysis for Taipei MRT passenger traffic volume prediction. The training data, including dates, temperatures and number of passengers, were collected, and a regression analysis was performed to find trends in the data. © 2022 IEEE.

关键词： Long short-term memory

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Assessing the Potential of Biodegradable Plastics in a Circular Economy: A Methodological Outlook

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Circular Economy and Sustainability 2025年第2期5卷 1291-1305页

作者： Scheers, Quinten Nimmegeers, Philippe Galant, Or Spatari, Sabrina Van den Brande, Niko Brancart, Joost Billen, Pieter Faculty of Applied Engineering Intelligence in Processes Advanced Catalysts and Solvents (iPRACS) University of Antwerp Groenenborgerlaan 171 Antwerp 2020 Belgium Faculty of Business and Economics Department of Engineering Management Environmental Economics (EnvEcon) University of Antwerp Prinsstraat 13 Antwerp 2000 Belgium Flanders Make@UAntwerp Antwerp 2000 Belgium Faculty of Civil and Environmental Engineering Technion– Israel Institute of Technology Haifa 3200003 Israel Grand Technion Energy Program Technion– Israel Institute of Technology Haifa 3200003 Israel Physical Chemistry and Polymer Science (FYSC) Vrije Universiteit Brussel Pleinlaan 2 Brussels 1050 Belgium IVL Swedish Environmental Research Institute Valhallavägen 81 Stockholm 11428 Sweden

Increased biodegradability, in other words reduced environmental recalcitrance, has the potential to be an important asset in the battle against plastics pollution. It can partially mitigate global plastic leakages into the environment, which is next to their impact on climate change the biggest concern regarding plastics use. Biodegradation should be regarded as an optimal safety net in many applications and should often not be the targeted waste management option for biodegradable plastics. Biodegradability should not preclude reuse or proper recyclability, which are impactful means to reduce the carbon intensity of plastics lifecycles. Despite the added advantages, the market penetration of biodegradable plastics is only very slowly growing. Moreover, policy recommends its use only in limited applications, driven by sometimes-higher cost and limited recyclability. In this article, we posit that arguments against widespread application of biodegradable plastics are largely driven by misperceptions arising from intrinsic limitations of contemporary sustainability assessments and offer an alternative viewpoint that could compensate for this. In general, sustainability assessments fail to properly assess learning curves, often do not take littering potential into account, and are rather conservative with respect to background systems. Hence, recyclability– or better: circularity– judgements often do not resolve the paradoxical situation in which increased market shares lead to easier and more profitable recycling schemes and vice versa. Judging disruptive innovations by incorporating thermodynamics-inspired, less data-intensive, state-based methods, based on, e.g., statistical entropy analysis, into the decision support framework could allow to forecast packaging markets with performant materials that are intrinsically circular, having reduced environmental recalcitrance. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Biodegradable Plastics Decision Making Statistical Entropy Sustainability Assessments

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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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MMA-Net: Multi-Modal Attention Network for 2-D Object Detection in Autonomous Driving

MMA-Net: Multi-Modal Attention Network for 2-D Object Detect...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Abhilash Gaur Shubh Goel Kanishk Goel Seshan Srirangarajan Po-Hsuan Tseng Kai-Ten Feng IIT Delhi-NYCU Taiwan Joint Doctoral Program Bharti School of Telecommunication Technology and Management Indian Institute of Technology Delhi (IIT Delhi) New Delhi India International College of Semiconductor Technology National Yang Ming Chiao Tung University (NYCU) Hsinchu Taiwan Department of Electrical Engineering IIT Delhi New Delhi India Department of Electrical Engineering Bharti School of Telecommunication Technology and Management IIT Delhi New Delhi India Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan Department of Electronics and Electrical Engineering NYCU Hsinchu Taiwan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Autonomous driving technology relies heavily on sensor data for environment perception. Heterogeneous sensors such as lidar, radar, and camera have their own strengths and limitations. Therefore, relying on any single sensor would restrict the effectiveness of autonomous driving technology. However, integrating data from such heterogeneous sensors poses challenges due to differences in their representations. This article outlines a deep learning network aimed at designing modality-agnostic multi-modal fusion architecture. We study sensor data from different modalities and learn fine-grained representations using modality-specific feature encoders independently. Then, a multimodal attention-based network (MMA-Net) is proposed to fuse the data from heterogeneous modalities. The proposed MMA-Net fuses multi-modal sensor data by jointly exploiting the inter-modality and intra-modality relationships among camera, lidar, and radar sensors. The effectiveness of the proposed multi-modal fusion architecture is demonstrated using 2-D object detection metrics through extensive experiments on a dataset generated using the CARLA simulator.

关键词： Laser radar Fuses Multimodal sensors Radar detection Object detection Signal processing Cameras Speech processing Autonomous vehicles Meteorology

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Pursuing overall welfare in federated learning through sequential decision making 24

Pursuing overall welfare in federated learning through seque...

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Proceedings of the 41st International Conference on Machine Learning

作者： Seok-Ju Hahn Gi-Soo Kim Junghye Lee Department of Industrial Engineering Ulsan National Institute of Science and Technology (UNIST) Ulsan South Korea Department of Industrial Engineering and Artificial Intelligence Graduate School Ulsan National Institute of Science and Technology (UNIST) Ulsan South Korea Technology Management Economics and Policy Program and Graduate School of Engineering Practice and Institute of Engineering Research Seoul National University (SNU) Seoul South Korea

In traditional federated learning, a single global model cannot perform equally well for all clients. Therefore, the need to achieve the client-level fairness in federated system has been emphasized, which can be realized by modifying the static aggregation scheme for updating the global model to an adaptive one, in response to the local signals of the participating clients. Our work reveals that existing fairness-aware aggregation strategies can be unified into an online convex optimization framework, in other words, a central server's sequential decision making process. To enhance the decision making capability, we propose simple and intuitive improvements for suboptimal designs within existing methods, presenting AAggFF. Considering practical requirements, we further subdivide our method tailored for the cross-device and the cross-silo settings, respectively. Theoretical analyses guarantee sublinear regret upper bounds for both settings: O(√T log K) for the cross-device setting, and O(K log T) for the cross-silo setting, with K clients and T federation rounds. Extensive experiments demonstrate that the federated system equipped with AAggFF achieves better degree of client-level fairness than existing methods in both practical settings. Code is available at https://***/vaseline555/AAggFF.

关键词：

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Precision Oral Medicine: A DPR Segmentation and Transfer Learning Approach for Detecting Third Molar Compress Inferior Alveolar Nerve

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IEEE Journal of Translational engineering in Health and Medicine 2025年 13卷 286-298页

作者： Lin, Yuan-Jin Chen, Shih-Lun Mao, Yi-Cheng Chen, Tsung-Yi Peng, Cheng-Hao Tsai, Tzu-Hsiang Li, Kuo-Chen Chen, Chiung-An Tu, Wei-Chen Abu, Patricia Angela R. National Cheng Kung University Department of Program on Semiconductor Manufacturing Technology Academy of Innovative Semiconductor and Sustainable Manufacturing Tainan City701401 Taiwan Chung Yuan Christian University Department of Electronic Engineering Taoyuan City32023 Taiwan Taoyuan Chang Gang Memorial Hospital Department of Operative Dentistry Taoyuan City33305 Taiwan Feng Chia University Department of Electronic Engineering Taichung city40724 Taiwan Chung Yuan Christian University Taoyuan City32023 Taiwan Chung Yuan Christian University Department of Information Management Taoyuan City32023 Taiwan Ming Chi University of Technology Department of Electronic Engineering New Taipei City243303 Taiwan National Cheng Kung University Department of Electrical Engineering Tainan city701401 Taiwan Ateneo de Manila University Department of Information Systems and Computer Science Quezon City Philippines

Objective: Extraction of the third molar of the mandible is one of the most common oral surgical procedures. Preoperative monitoring and assessment are crucial to mitigate neurological risks. Identifying whether the third molar in the mandible compresses the inferior alveolar nerve still relies on dental professionals, a task that is repetitive and time-consuming. Thus, the primary objective is to utilize dental panoramic radiography for image processing and classify whether the third molar compresses the inferior alveolar nerve, aiming to reduce the demand for CT images in symptom diagnosis and mitigate the risks associated with high-dose radiation. technology or Method: This study proposes an innovative dental panoramic radiography segmentation technique to locate the third molar position. Subsequently, an innovative edge masking enhancement method is used to extract features of the inferior alveolar nerve and the third molar. Moreover, a transformer-based image detection model to consider whether the third molar compresses the inferior alveolar nerve. Results: The third molar position localization method achieved an accuracy rate of 97.92%, compared to recent research at least improved by 3.6% accuracy. Subsequently, innovative edge masking and image enhancement methods improve classification accuracy by 4.3%, when supplemented with computed tomography scan images for further evaluation, the maximum accuracy reached 98.45%, representing a 4.5% improvement compared to previous studies. Conclusions: The third molar position detection results will impact the identification of the inferior alveolar nerve compressed by the third molar. Through the innovative edge region segmentation algorithm can effectively distinguish this object, and the overall evaluation accuracy can be improved by approximately 3.8%. © 2013 IEEE.

关键词： Neurophysiology

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AI Recommendations: Friend or Foe? Unraveling the Impact on Consumer Benefits and Privacy

AI Recommendations: Friend or Foe? Unraveling the Impact on ...

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International Conference on ICT For Smart Society (ICISS)

作者： Desyra Sukma Dewanthi Hari Kristopo Nunung Nurul Qomariyah Muhammad Axel Management Department Business Management & Marketing Program BINUS Business School International Undergraduate Program Bina Nusantara University Jakarta Indonesia Food Technology Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computing and Creative Arts Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350389654

ISBN: (纸本)9798350389661

Artificial Intelligence (AI) and marketing have transformed consumer behavior and shopping experiences, especially through Recommender Systems (RSs) in e-commerce. RSs use algorithms to provide personalized recommendations, enhancing shopping convenience. However, this raises data privacy and ethical concerns, as RSs rely on user data. This research explores the quality determinants of RSs and their impact on consumer behavior, focusing on privacy awareness and its effect on repurchase intentions. Using a qualitative approach with semi-structured interviews, the study targets Indonesian respondents aged 18–40 with e-commerce experience. Findings reveal that recommendation quality, especially self-reference and vividness, significantly influences purchasing decisions. Respondents prefer recommendations that match their preferences and are visually appealing. Positive shopping experiences are linked to efficient and accurate RSs, but there's a need for improvements in transparency, novelty, diversity, and advanced technologies like AR. Respondents are aware of digital data storage but have limited understanding of privacy concerns. They trust e-commerce platforms with basic personal details but are hesitant to share sensitive information. Despite privacy issues, e-commerce remains vital, with respondents emphasizing the need for transparency, robust security, and clear explanations for data use to maintain trust. Addressing these issues not only enhances customer experience but also demonstrates that consumer trust directly impacts the sustainability and success of the business.

关键词： Data privacy Privacy Consumer behavior Focusing Electronic commerce Security Artificial intelligence Sustainable development Interviews Recommender systems

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Direct matching between music and image for contextual relationship analysis

Direct matching between music and image for contextual relat...

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Cybernetics and Innovations (ICCI), International Conference on

作者： Smart Wattanapornmongkol Maethaphan Phaisalrattananukul Phatthapol Saksermkiat Parinya Sanguansat Phanomyong Kaewprachum EPLUS+ Program Suankularb Wittayalai School Bangkok Thailand GATE Program Suankularb Wittayalai School Bangkok Thailand DPSTE Bodindecha (Sing Singhaseni) School Bangkok Thailand Faculty of Engineering and Technology Panyapiwat Institute of Management Nonthaburi Thailand The Institute for the Promotion of Teaching Science and Technology Bangkok Thailand

ISBN: (数字)9798350370096

ISBN: (纸本)9798350370102

We wanted to understand the relationship between emotions and objects or events. Thus, we have created a dataset of music-themed images and studied language context models to convert image, text, and audio data into vectors. We then developed two deep neural network models, Multi-Layer Perceptron (MLP) and CLIP-Based, to predict the most suitable song for any given image. It was found that MLP achieved an accuracy of 0.2057 when given both the image and its caption, compared to 0.2710 for CLIP-Based. However, when given only the caption, the accuracy of the CLIP-based neural network decreased to 0.2676, yet the accuracy slightly increase when given only the image to 0.2717. This suggests that direct image data could be enough to relate to songs, but providing descriptions of the images may not help the neural network to identify the relationships more accurately.

关键词： Deep learning Technological innovation Music Artificial neural networks Media Predictive models Data models

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