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International Conference on Transportation Information and Safety (ICTIS)

作者： Xinxin Zou Xianqiao Chen Song Gao ZhongLiang Zhou Chaozhong Wu School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China Intelligent Transport System Research Center Wuhan University of Technology National Engineering Research Center for Water Transport Safety Wuhan China

The digital software of transportation infrastructure is the foundation of digital transportation. With the development of information technology, its software technology architecture is also evolving iteratively. Based on the status quo in the field of transportation infrastructure engineering, the core basic capabilities of the software are determined, including data aggregation and storage, data analysis and processing, engineering data delivery, engineering visualization, system integration expansion, application customization development, etc. The characteristics of the existing software architecture are studied, and the logical architecture of the transport infrastructure data software system is designed, including operation support layer, convergence storage layer, data service layer, and application service layer. In addition, the technical reference architecture and operation architecture of digital software for transport infrastructure are emphatically introduced, and the development trend of digital software architecture for transport infrastructure has been prospected.

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Hybrid Relay and Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems

Hybrid Relay and Reconfigurable Intelligent Surface Assisted...

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作者： Kan, Te-Yi Chang, Ronald Y. Chien, Feng-Tsun Chen, Bing-Jia Poor, H. Vincent Academia Sinica Research Center for Information Technology Innovation Taipei11529 Taiwan Princeton University Department of Electrical and Computer Engineering PrincetonNJ08544 United States National Yang Ming Chiao Tung University Institute of Electronics Hsinchu30010 Taiwan

Reconfigurable intelligent surfaces (RISs) are viewed as key enablers for next-generation wireless communications. This paper investigates a multiuser downlink multiple-input single-output (MISO) system in which a multiantenna base station (BS) transmits information to multiple single-antenna users with the aid of both a half-duplex decode-and-forward (DF) relay and a full-duplex RIS. Active beamforming at the BS and the DF relay, as well as passive beamforming at the RIS, are jointly designed for system sum-rate maximization. The design problem is challenging to solve due to coupled beamforming variables. An alternating optimization (AO) based algorithm is proposed to tackle this complex co-design problem. Numerical results demonstrate the superior performance of the proposed hybrid relay-RIS system with a judicious joint beamforming design. Convergence and complexity analysis shows that the convergence rate of the proposed algorithm is dominated by the numbers of users and RIS elements, and the proposed scheme can converge in a few iterations even in the configuration of large numbers of users and RIS elements. Interesting tradeoffs posed in the joint design are discussed. An extension of the proposed design method to a related energy efficiency (EE) optimization problem is also outlined and implemented. © 1967-2012 IEEE.

关键词： Beamforming

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Enhancing ESG Reporting Analysis: Leveraging GPT and Resampling Methods for Improved Multi-Label Classification

Enhancing ESG Reporting Analysis: Leveraging GPT and Resampl...

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Electronic Communications, Internet of Things and Big Data (ICEIB), International Conference on

作者： Jun-Wei Wu Lydia Hsiao-Mei Lin Richard Tzong-Han Tsai Department of Computer Science and Information Engineering National Central University Taoyuan Taiwan Department of Interaction Design National Taipei University of Technology Taipei Taiwan Center for GIS Research Center for Humanities and Social Sciences Academia Sinica Taipei Taiwan

ISBN: (数字)9798350360721

ISBN: (纸本)9798350360738

ESG reporting, covering environmental, social, and governance aspects, is a crucial resource for investors, companies, and governments to understand a company's value. However, the sheer volume of data and information in the reports makes it difficult to retrieve specific information on ESG. ESG-BERT models are developed to categorize the content of ESG reports based on the relevant field. However, the accurate prediction of multiple classifications is difficult due to unevenly distributed labels. To address this problem, we used a self-made assistant based on GPT to label the data and then resampling technology to adjust the balance of the data set. After the data set was balanced, we adjusted to ESG-BERT to improve its multi-label classification accuracy. We evaluated multiple resampling methods and determined the most suitable strategy for classifying ESG report content. Thus, the accuracy of ESG content analysis was improved through more refined model adjustment and preprocessing methods. The results indicated that balancing the data for improved classification ensures fairness and objectivity in distributing content for ESG reports with multiple labels.

关键词： Analytical models Accuracy Government Training data Companies Big Data Data models

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Comparison of Biocode Based Machine Learning and Segmentation Model for Automated Prawn Size Prediction for Real Prawn Farm 5

Comparison of Biocode Based Machine Learning and Segmentatio...

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5th Asia-Pacific Conference on Image Processing, Electronics and computers, IPEC 2024

作者： Zariful, Muhammad Wafiq Abd Tashim, Najeebah Az-Zahra Lim, Tiong Hoo Fakhrurroja, Hanif Chuprat, Suriayati Putra, Seno Adi Liu, Pengcheng Basri, Aida Maryam Universiti Technologi Brunei Electrical and Electronic Engineering Bandar Seri Begawan Brunei National Research and Innovation Agency Research Center for Smart Mechatronics Bandung Indonesia Universiti Teknologi Malaysia Razak Faculty of Technology and Informatics Kuala Lumpur Malaysia Telkom Universiti School of Industrial and System Engineering Bandung Indonesia University of York Department of Computer Science York United Kingdom Universiti Technologi Brunei School of Applied Science and Matematics Bandar Seri Begawan Brunei

ISBN: (纸本)9798350374407

The aquaculture industry has witnessed a significant surge in technological integration over the past decade, leveraging advancements in the Internet of Things (IoT) and Artificial Intelligence (AI) to enhance production efficiency. Monitoring the growth of aquatic cultures, such as fishes and prawns, plays a pivotal role in optimizing production. However, traditional methods, notably in prawn aquaculture in Asia, still heavily rely on stress-inducing techniques like netting and manual measurements. This paper proposes a novel approach using YOLO-based detection algorithm with biocode reference point based on the ArUco design to predict prawn size based on the length and are dimension to analysed the improvement in predicting the size. The propose solution is compared extensively using statistical tests against segmentation model with and without fine-tuning. Initial results show that both YOLOv8 segmentation and YOLOv8 with Segment Anything Model (SAM) give similar results with the actual mean length of 15.7 cm and predicted mean lengths are 20.5 cm and 20.8 cm respectively. However, after fine-tuning the parameter, the accuracy of predicted results improved significantly with 15.8 cm for YOLOv8 segmentation and 16 cm for YOLOv8 with SAM. The mean absolute error of 0.93 cm for YOLOv8 segmentation and 1.10 cm for YOLOv8 with SAM, and the width of 0.48 cm and 0.78 cm respectively. © 2024 IEEE.

关键词： Deep learning

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IRL-PM: An Inverse Reinforcement Learning-based Power Minimization in Multi-User MISO Networks 8

IRL-PM: An Inverse Reinforcement Learning-based Power Minimi...

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8th International Conference on computer and Communication Systems, ICCCS 2023

作者： Tian, Xingcong Gao, Bo Liu, Ming Xiong, Ke Fan, Pingyi Letaief, Khaled Ben Eng. Res. Center of Network Management Technology for High Speed Railway of Ministry of Education School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Beijing Jiaotong University Collaborative Innovation Center of Railway Traffic Safety Beijing100044 China National Engineering Research Center of Advanced Network Technologies Beijing Jiaotong University Beijing100044 China Tsinghua University Department of Electronic Engineering Beijing100084 China The Hong Kong University of Science and Technology Hong Kong Hong Kong

ISBN: (纸本)9781665456128

This paper aims to efficiently save energy consumption of multiple-input-single-output (MISO) networks. To this end, an optimization problem is formulated to minimize the transmit power of the base station (BS) while ensuring the minimal required information rate of each user. Reinforcement learning (RL) is a significant solution to tackle this non-convex problems. Nevertheless, designing efficient reward functions that accurately describe the involved optimization problems for RL is a challenging problem, which may result in low learning efficiency and even inaccurate learning. To cope with such an issue, this paper proposes an inverse reinforcement learning (IRL)-based approach to minimize the transmit power of MISO networks, in which the generative adversarial imitation learning (GAIL) is employed as the IRL framework and the reward function is represented by an artificial neural network (ANN) model, whose parameters are learned automatically in terms of an expert policy based on the semidefinite relaxation (SDR)-based approach. Since SDR is able to achieve the near optimal solution, the learned reward function is more accurate than traditional artificially designed one. Simulations show that, the proposed IRL-based approach reaches similar energy consumption to the SDR-based approach, and reduces about 50% transmit power compared to traditional RL-based one. © 2023 IEEE.

关键词： Reinforcement learning

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SfPUEL: shape from polarization under unknown environment light 24

SfPUEL: shape from polarization under unknown environment li...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Youwei Lyu Heng Guo Kailong Zhang Si Li Boxin Shi School of Artificial Intelligence Beijing University of Posts and Telecommunications State Key Laboratory for Multimedia Information Processing and National Engineering Research Center of Visual Technology School of Computer Science Peking University

ISBN: (纸本)9798331314385

Shape from polarization (SfP) benefits from advancements like polarization cam eras for single-shot normal estimation, but its performance heavily relies on light conditions. This paper proposes SfPUEL, an end-to-end SfP method to jointly estimate surface normal and material under unknown environment light. To handle this challenging light condition, we design a transformer-based framework for en hancing the perception of global context features. We further propose to integrate photometric stereo (PS) priors from pretrained models to enrich extracted features for high-quality normal predictions. As metallic and dielectric materials exhibit different BRDFs, SfPUEL additionally predicts dielectric and metallic material segmentation to further boost performance. Experimental results on synthetic and our collected real-world dataset demonstrate that SfPUEL significantly outperforms existing SfP and single-shot normal estimation methods. The code and dataset is available at https://***/YouweiLyu/SfPUEL.

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Integrate an AI Chatbot-Based Learning Butler Digital system to enhance Students’ Grit and Growth Mindset for Improving Learning Outcomes

Integrate an AI Chatbot-Based Learning Butler Digital system...

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International Conference on Advanced Learning Technologies (ICALT)

作者： Chih-Yen Chen Yu-Sheng Juan Jen-Hang Wang Su-Hang Yang Gwo-Dong Chen Department of Computer Science and Information Engineering National Central University Taoyuan Taiwan Research Center for Science and Technology for Learning National Central University Taoyuan Taiwan Department of Hospitality Management Chien Hsin University of Science and Technology Taoyuan Taiwan

ISBN: (数字)9798350362053

ISBN: (纸本)9798350362060

In the digital age, chatbots are widely applied in education. chatbots have become prevalent in education, proving to enhance learning outcomes. However, Students often face challenges in time and task management, which simultaneously test their ability to continue learning and cope with setbacks. To address this, we developed a "Learning Butler" chatbot system. incorporating the GPT language model and prompt engineering to support students' task and schedule management. This system fosters grit and a growth mindset through interactive dialogues, boosting their perseverance and adaptability in the face of challenges. thereby enhancing learning outcomes. Experiment results show significant improvements in students' grit, growth mindset and overall learning outcomes. Furthermore, surveys and web usage records also indicate the chatbot's beneficial impact on education.

关键词： Surveys Schedules Education Virtual reality Chatbots Market research Prompt engineering

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Metacognitive-Based Collaborative Programming: A Novel Approach to Enhance Learning Performance in Programming Courses 6th

Metacognitive-Based Collaborative Programming: A Novel Appro...

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Proceedings of the 6th International Conference on Innovative Technologies and Learning, ICITL 2023

作者： Li, Wei Tseng, Judy C. R. Cheng, Li-Chen STEM Education Research Center Wenzhou University Zhejiang Province Wenzhou China Ph.D. Program in Engineering Science Chung Hua University Hsinchu Taiwan Department of Computer Science and Information Engineering Chung Hua University Hsinchu Taiwan Department of Information and Finance Management National Taipei University of Technology Taipei City Taiwan

ISBN: (纸本)9783031401121

Students’ computational thinking and programming skills may grow due to collaborative programming. But as the researchers have noted, students frequently do not use metacognition to manage their cognitive activities while collaborating, which negatively affects learning. This study created a metacognition-based collaborative programming (MCP) system to improve students’ performance in collaborative programming. A seven-week study examined how the approach affected students’ performance in programming courses. The 88 middle school students were split into two groups: the experimental group received the metacognition-based collaborative programming approach, and the control group received the conventional computer-supported collaborative programming approach. The results indicated that the metacognitive-based collaborative programming approach enhanced students’ academic scores in programming courses and their computational thinking tendencies. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Learning systems

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Enhanced exciton diffusion from interlayer charge-transfer transitions in PtSe2/MoSe_(2) van der Waals heterojunction

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Nano research 2023年第11期16卷 12809-12816页

作者： Jiarong Wang Dawei He Zhiying Bai Guili Li Jinxuan Bai Keqin Liu Fangying Ren Xiaojing Liu Jiaqi He Weiya Zhou Jianlin Sun Yongsheng Wang Xiaoxian Zhang Yuchao Yang Key Laboratory of Luminescence and Optical Information Ministry of EducationInstitute of Optoelectronic TechnologyBeijing Jiaotong UniversityBeijing 100044China Beijing Advanced Innovation Center for Integrated Circuits School of Integrated CircuitsPeking UniversityBeijing 100871China School of Electronic and Computer Engineering Peking UniversityShenzhen 518055China Center for Brain Inspired Chips Institute for Artificial IntelligenceFrontiers Science Center for Nano-optoelectronicsPeking UniversityBeijing 100871China Center for Brain Inspired Intelligence Chinese Institute for Brain Research(CIBR)Beijing 102206China College of Mathematics and Physics Beijing University of Chemical TechnologyBeijing 100029China Beijing National Laboratory for Condensed Matter Physics Institute of Physics Chinese Academy of SciencesBeijing 100190China School of Physical Sciences University of Chinese Academy of SciencesBeijing 100049China

Artificial van der Waals(vdWs)heterostructures offer unprecedented opportunities to explore and reveal novel synergistic electronic and optical phenomena,which are beneficial for the development of novel optoelectronic devices at atomic ***,due to the damage caused by the device fabrication process,their inherent properties such as carrier mobility are obscured,which hinders the improvement of device performance and the incorporation of vdWs materials into next-generation integrated ***,combining pump-probe spectroscopic and scanning probe microscopic techniques,the intrinsic optoelectronic properties of PtSe_(2)/MoSe_(2)heterojunction were nondestructively and systematically *** heterojunction exhibits a broad-spectrum optical response and maintains ultrafast carrier dynamics(interfacial charge transfer~0.8 ps and carrier lifetime~38.2 ps)*** in-plane exciton diffusion coefficient of the heterojunction was extracted(19.4±7.6 cm^(2)∙s^(−1)),and its exciton mobility as high as 756.8 cm^(2)∙V−1∙s^(−1)was deduced,exceeding the value of its *** enhancement was attributed to the formation of an n-type Schottky junction between PtSe_(2)and MoSe_(2),and its built-in electric field assisted the ultrafast transfer of photogenerated carriers from MoSe_(2)to PtSe_(2),enhancing the in-plane exciton diffusion of the *** results demonstrate that PtSe_(2)/MoSe_(2)is suitable for the development of broadspectrum and sensitive optoelectronic ***,the results contribute to a fundamental understanding of the performance of various optoelectronic devices based on such PtSe_(2)two-dimensional(2D)heterostructures.

关键词： van der Waals heterostructures ultrafast carrier dynamics exciton diffusion pump-probe spectroscopy scanning probe microscopy

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CMD: A Cache-assisted GPU Memory Deduplication Architecture

arXiv

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arXiv 2024年

作者： Zhao, Wei Feng, Dan Tong, Wei Wei, Xueliang Wu, Bing The Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China School of Computer Science and Technology Huazhong University of Science and Technology China

Massive off-chip accesses in GPUs are the main performance bottleneck, and we divided these accesses into three types: (1) Write, (2) Data-Read, and (3) Read-Only. Besides, We find that many writes are duplicate, and the duplication can be inter-dup and intra-dup. While inter-dup means different memory blocks are identical, and intra-dup means all the 4B elements in a line are the same. In this work, we propose a cache-assisted GPU memory deduplication architecture named CMD to reduce the off-chip accesses via utilizing the data duplication in GPU applications. CMD includes three key design contributions which aim to reduce the three kinds of accesses: (1) A novel GPU memory deduplication architecture that removes the inter-dup and inter-dup lines. As for the inter-dup detection, we reduce the extra read requests caused by the traditional read-verify hash process. Besides, we design several techniques to manage duplicate blocks. (2) We propose a cache-assisted read scheme to reduce the reads to duplicate data. When an L2 cache miss wants to read the duplicate block, if the reference block has been fetched to L2 and it is clean, we can copy it to the L2 missed block without accessing off-chip DRAM. As for the reads to intra-dup data, CMD uses the on-chip metadata cache to get the data. (3) When a cache line is evicted, the clean sectors in the line are invalidated while the dirty sectors are written back. However, most read-only victims are re-referenced from DRAM more than twice. Therefore, we add a full-associate FIFO to accommodate the read-only (it is also clean) victims to reduce the re-reference counts. Experiments show that CMD can decrease the off-chip accesses by 31.01%, reduce the energy by 32.78% and improve performance by 37.79%. Besides, CMD can improve the performance of memory-intensive workloads by 50.18%. © 2024, CC BY-NC-ND.

关键词： Cache memory

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