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***: Creating a Digital Teaching Collection of Common Freshwater Macroinvertebrates of Eastern North America

American Entomologist 2022年第1期68卷 28-31页

作者： Kautz, Andrea R. Louw, Marti R. Bartley, Christopher Morse, John C. Wenzel, John W. Carnegie Museum of Natural History's Powdermill Nature Reserve in Southwestern Pennsylvania United States Human Computer Interaction Institute Carnegie Mellon University Learning Media Design Center United States Principal Research Programmer The Robotics Institute Carnegie Mellon University United States Emeritus of Entomology Clemson University United States Entomology Powdermill Nature Reserve United States

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A Review in E-learning Context: Gamification Elements for Language learning

A Review in E-Learning Context: Gamification Elements for La...

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Technology, Engineering, and Computing Applications (ICTECA), International Conference on

作者： Gamal Kusuma Zamahsari Sahrul Romadhon Mufidah Nur Amalia Lailatul Rifah Arti Prihatini Ardi Wina Saputra Digital Language Learning Center Communication Science Department Faculty of Humanities Bina Nusantara University Jakarta Indonesia Indonesian Language Education Study Program Institute of Islamic Religion Madura Pamekasan Indonesia Digital Language Learning Center Computer Science Department Faculty of Humanities Bina Nusantara University Jakarta Indonesia Digital Language Learning Center Visual Communication Design Faculty of Humanities Bina Nusantara University Jakarta Indonesia Indonesian Language Education Faculty of Teacher Training and Education Unversity of Muhammadiyah Malang Malang Indonesia Indonesian Language Education Study Program Widya Mandala Catholic University Surabaya Indonesia

Recent advancements in e-learning have highlighted the significance of incorporating gamification into language acquisition processes. It is essential to examine the application of learning game features within classroom settings. The foremost goal of this systematic review of literature is to ascertain and thoroughly delineate the elements of gamification considered suitable for incorporation into environments for language learning. This investigation focuses on two main objectives: firstly, to examine the influence of diverse elements of gamification on the enhancement of language abilities, and secondly, to comprehend how these elements shape the educational approaches and experiences of students in a gamified learning environment. The review methodology employed in this study is a systematic literature review of the current literature to comprehensively analyze and map out the effective utilization of gamification in the context of language teaching. The results of our study demonstrate that the deliberate integration of game features can have a substantial impact on student motivation and engagement. Nevertheless, the efficacy of these components is contingent upon the individual attributes of students and the educational goals in question. The paper finishes by providing advice for customizing gamification tactics to accommodate the varying learner profiles in language teaching. Additionally, it suggests potential areas of future research that can contribute to the ongoing development and enhancement of this pedagogical approach.

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Implementation and evaluation of the school's COVID-19 prevention website

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Library Hi Tech 2022年第1期41卷 71-90页

作者： Jui-Hung Chang Chi-Jane Wang Hua-Xu Zhong Pei-Wen Chen Ai-Jou Pan Po-Sheng Chiu Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan Department of Computer and Network Center National Cheng Kung University Tainan Taiwan Department of Nursing National Cheng Kung University Tainan Taiwan National Cheng Kung University Tainan Taiwan Department of E-learning Design and Management National Chiayi University Chiayi Taiwan

Purpose The purpose of this study is to investigate the effects of the Perceptions of coronavirus 2019 (COVID-19) questionnaire and the Information System Success Questionnaire on students when using the school's COVID-19 epidemic prevention website. The study is aimed toward analyzing both questionnaires as well as evaluating an epidemic prevention website. design/methodology/approach The school's COVID-19 prevention website and two questionnaires (Perceptions of COVID-19 and the Information System Success Questionnaire) are examined in order to investigate 73 students' COVID-19 perceptions. An open-ended question was used as the qualitative data to support quantitative data and evaluate a university's COVID-19 epidemic prevention website from a southern university in Taiwan. Findings The findings indicated that most students evaluated the school's COVID-19 website positively and were satisfied. In the open-ended questions, the majority of students rated the quality of the system positively and the need to fix some defects. Students have different COVID-19 perceptions and social distance compliance based on their current situations. Practical implications This study provides researchers and website developers a broader understanding of the construction of the school's COVID-19 prevention website and a better understanding of student's COVID-19 perceptions. Originality/value To the best of the authors' knowledge, this is the first study examining a school's epidemic prevention website, which is measured by the Information Success Questionnaire and the Perceptions of COVID-19 Questionnaire for college students.

关键词： COVID-19 Epidemic prevention website Evaluation Implementation Perception

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SFedXL: Semi-Synchronous Federated learning With Cross-Sharpness and Layer-Freezing

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IEEE Internet of Things Journal 2025年第12期12卷 18692-18707页

作者： Zhao, Mingxiong Zhao, Shihao Feng, Chenyuan Yang, Howard H. Niyato, Dusit Quek, Tony Q. S. Yunnan University National Pilot School of Software Kunming650500 China Ministry of Education Engineering Research Center of Integration and Application of Digital Learning Technology Beijing100039 China Ministry of Education Engineering Research Center of Cyberspace Kunming650504 China Yunnan University of Finance and Economics Yunnan Key Laboratory of Service Computing Kunming650221 China EURECOM Sophia Antipolis06410 France Zhejiang University University of Illinois Urbana-Champaign Institute Zhejiang University Haining314400 China Nanyang Technological University School of Computer Science and Engineering 639798 Singapore Singapore University of Technology and Design Information Systems Technology and Design Pillar 487372 Singapore

Federated learning (FL) emerges as a potential solution for enabling multiple terminal devices to collaboratively accomplish computational tasks within an Unmanned Aerial Vehicle (UAV) swarm. However, traditional FL approaches, predicated on synchronous data aggregation, are not feasible for a UAV swarm owing to the inherently variable and dynamic nature of their communication networks compared with terrestrial systems. Furthermore, the data procured by UAVs is often highly heterogeneous, attributable to disparities in deployment environments and device attributes. Considering the distinct flight paths and unique operational conditions encountered by different UAVs, a considerable amount of data remains unlabeled. To tackle the challenges associated with asynchronous operations and the prevalence of unlabeled data, we introduce a novel framework termed Semi-synchronous FL with Cross-Sharpness and Layer-Freezing (SFedXL), tailored for a UAV swarm. In particular, we devise a cross-sharpness model training strategy aimed at optimizing the utilization of both labeled and unlabeled datasets. Additionally, we propose an innovative semi-synchronous model aggregation protocol, complemented by client-specific layer-freezing and client cluster scheduling, designed to expedite the training process. Our simulation results indicate that the proposed algorithm surpasses current FL methods in terms of object recognition accuracy and communication efficiency, albeit with a trade-off of increased local computation latency. © 2014 IEEE.

关键词： Federated learning

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Agent-based Simulation Evaluation of CBD Tolling: A Case Study from New York City

arXiv

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arXiv 2024年

作者： Liang, Qingnan Yao, Ruili Zhang, Ruixuan Chen, Zhibin Wu, Guoyuan The Department of Civil and Urban Engineering New York University New York United States Key Laboratory of Urban Design and Urban Science NYU Shanghai Shanghai China The Department of Electrical and Computer Engineering University of California Riverside RiversideCA United States Shanghai Frontiers Science Center of Artificial Intelligence and Deep Learning NYU Shanghai Shanghai China The Center of Environmental Research and Technology University of California Riverside RiversideCA United States

Congestion tollings have been widely developed and adopted as an effective tool to mitigate urban traffic congestion and enhance transportation system sustainability. Nevertheless, these tolling schemes are often tailored on a city-by-city or even area-by-area basis, and the cost of conducting field experiments often makes the design and evaluation process challenging. In this work, we leverage MATSim, a simulation platform that provides microscopic behaviors at the agent level, to evaluate performance on tolling schemes. Specifically, we conduct a case study of the Manhattan Central Business District (CBD) in New York City (NYC) using a fine-granularity traffic network model in the large-scale agent behavior setting. The flexibility of MATSim enables the implementation of a customized tolling policy proposed yet not deployed by the NYC agency while providing detailed interpretations. The quantitative and qualitative results indicate that the tested tolling program can regulate the personal vehicle volume in the CBD area and encourage the usage of public transportation, which proves to be a practical move towards sustainable transportation systems. More importantly, our work demonstrates that agent-based simulation helps better understand the travel pattern change subject to tollings in dense and complex urban environments, and it has the potential to facilitate efficient decision-making for the devotion to sustainable traffic management. Copyright © 2024, The Authors. All rights reserved.

关键词： Simulation platform

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Agent-Based Simulation Evaluation of CBD Tolling: A Case Study from New York City

Agent-Based Simulation Evaluation of CBD Tolling: A Case Stu...

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IEEE Forum on Integrated and Sustainable Transportation System (FISTS)

作者： Qingnan Liang Ruili Yao Ruixuan Zhang Zhibin Chen Guoyuan Wu Department of Civil and Urban Engineering New York University New York United States Shanghai Key Laboratory of Urban Design and Urban Science NYU Shanghai Shanghai China Department of Electrical and Computer Engineering University of California Riverside Riverside California United States Shanghai Frontiers Science Center of Artificial Intelligence and Deep Learning NYU Shanghai Shanghai China Center of Environmental Research and Tech-nology University of California Riverside Riverside California United States

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Sports-QA: A Large-Scale Video Question Answering Benchmark for Complex and Professional Sports

arXiv

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arXiv 2024年

作者： Li, Haopeng Deng, Andong Liu, Jun Rahmani, Hossein Guo, Yulan Schiele, Bernt Bennamoun, Mohammed Ke, Qiuhong School of Computing and Information Systems University of Melbourne Australia Center for Research in Computer Vision University of Central Florida United States Pillar Singapore University of Technology and Design Singapore School of Computing and Communications Lancaster University United Kingdom School of Electronics and Communication Engineering Sun Yat-sen University China Department of Computer Vision and Machine Learning Max Planck Institute for Informatics Saarland Informatics Campus Germany School of Physics Maths and Computing University of Western Australia Australia Department of Data Science & AI Monash University Australia

Reasoning over sports videos for question answering is an important task with numerous applications, such as player training and information retrieval. However, this task has not been explored due to the lack of relevant datasets and the challenging nature it presents. Most datasets for video question answering (VideoQA) focus mainly on general and coarse-grained understanding of daily-life videos, which is not applicable to sports scenarios requiring professional action understanding and fine-grained motion analysis. In this paper, we introduce the first dataset, named Sports-QA, specifically designed for the sports VideoQA task. The Sports-QA dataset includes various types of questions, such as descriptions, chronologies, causalities, and counterfactual conditions, covering multiple sports. Furthermore, to address the characteristics of the sports VideoQA task, we propose a new Auto-Focus Transformer (AFT) capable of automatically focusing on particular scales of temporal information for question answering. We conduct extensive experiments on Sports-QA, including baseline studies and the evaluation of different methods. The results demonstrate that our AFT achieves state-of-the-art performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Question answering

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Physics-informed Bayesian optimization of variational quantum circuits 23

Physics-informed Bayesian optimization of variational quantu...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Kim A. Nicoli Christopher J. Anders Lena Funcke Tobias Hartung Karl Jansen Stefan Kühn Klaus-Robert Müller Paolo Stornati Pan Kessel Shinichi Nakajima Transdisciplinary Research Area (TRA) Matter University of Bonn Germany and Helmholtz Institute for Radiation and Nuclear Physics (HISKP) and Berlin Institute for the Foundations of Learning and Data (BIFOLD) Berlin Institute for the Foundations of Learning and Data (BIFOLD) and Technische Universität Berlin Germany Transdisciplinary Research Area (TRA) Matter University of Bonn Germany and Helmholtz Institute for Radiation and Nuclear Physics (HISKP) Northeastern University London UK and Khoury College of Computer Sciences Northeastern University CQTA Deutsches Elektronen-Synchrotron (DESY) Zeuthen Germany Berlin Institute for the Foundations of Learning and Data (BIFOLD) and Technische Universität Berlin Germany and Department of Artificial Intelligence Korea University Korea and Max Planck Institut für Informatik Saarbrücken Germany Institute of Photonic Sciences The Barcelona Institute of Science and Technology (ICFO) Prescient Design gRED Roche Switzerland Berlin Institute for the Foundations of Learning and Data (BIFOLD) and Technische Universität Berlin Germany and RIKEN Center for AIP Japan

In this paper, we propose a novel and powerful method to harness Bayesian optimization for Variational Quantum Eigensolvers (VQEs)—a hybrid quantum-classical protocol used to approximate the ground state of a quantum Hamiltonian. Specifically, we derive a VQE-kernel which incorporates important prior information about quantum circuits: the kernel feature map of the VQE-kernel exactly matches the known functional form of the VQE's objective function and thereby significantly reduces the posterior uncertainty. Moreover, we propose a novel acquisition function for Bayesian optimization called Expected Maximum Improvement over Confident Regions (EMICoRe) which can actively exploit the inductive bias of the VQE-kernel by treating regions with low predictive uncertainty as indirectly "observed". As a result, observations at as few as three points in the search domain are sufficient to determine the complete objective function along an entire one-dimensional subspace of the optimization landscape. Our numerical experiments demonstrate that our approach improves over state-of-the-art baselines.

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QuantYOLO: A High-Throughput and Power-Efficient Object Detection Network for Resource and Power Constrained UAVs

QuantYOLO: A High-Throughput and Power-Efficient Object Dete...

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Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Muhammad Gohar Javed Minahil Raza Muhammad Mohsin Ghaffar Christian Weis Norbert Wehn Muhammad Shahzad Faisal Shafait School of Electrical Engineering and Computer Science National University of Sciences and Technology Pakistan Microelectronic Systems Design Research Group Technische Universität Kaiserslautern Germany Deep Learning Laboratory National Center of Artificial Intelligence (NCAI) Pakistan Data Science in Earth Observation TU Munich Germany

Convolutional Neural Networks (CNNs) are producing state-of-the-art results in the object detection field. However, deep topologies of CNN are computationally intensive and typically require excessive resources (i.e. high-end GPUs), which hinder their deployment on resource and power constrained UAVs. In this work, we present a high-throughput and power efficient quantized object detection network, QuantYOLO, which is based on the Tiny-YOLOv2 topology. We conduct a detailed exploration of precision and filter pruning vs. accuracy, throughput and power consumption trade-off for the object detection task. As a result of these explorations, we select a network with binarized weights and 4-bit activations (except the output layer), which is 21.8× smaller than the Tiny-YOLOv2 achieving a mean Average Precision (mAP) of 51.5% on the PASCAL-VOC dataset. Finally, we present an FPGA based accelerator, which achieves 1.6× higher throughput (FPS) and is 3.1× more power efficient as compared to prior FPGA architectures.

关键词： Quantization (signal) Power demand Network topology Object detection computer architecture Throughput Topology

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Methodology for creation of labeled image datasets of entrained air voids and aggregates in concrete surfaces using confocal laser scanning microscopy

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Advanced Engineering Informatics 2025年 67卷

作者： Viktor Kostic Qadeer Khan Daniel Cremers Jithender Timothy Thomas Kränkel Christoph Gehlen TUM School of Engineering and Design Department of Materials Engineering Chair of Materials Science and Testing Centre for Building Materials (cbm) Technical University of Munich Munich 81245 BY Germany TUM School of Computation Information and Technology Computer Vision Group Technical University of Munich Munich 81245 BY Germany Munich Center for Machine Learning Munich 81245 BY Germany

Several strategies using neural network algorithms have been established to automate the process of evaluating parameters regarding the freeze–thaw resistance of hardened concrete from image data. However, the performance of such models is dependent on the quality of the datasets used for training. Hand-labeling concrete image data is challenging, where especially small micro air voids (<300 μ m) can be overlooked. This potentially leads to algorithms that are unable to accurately detect the micro air void content, despite being required by current standards (e.g. DIN EN 480-11). To account for this, a method was developed using confocal laser scanning microscopy and the “Segment Anything” model. Acquiring surface height-data through confocal laser scanning microscopy expendites the labeling process and creates more reliable training data due to the detectability of air voids through height differences. Furthermore, by combining the height-data with RGB-images it was possible to accurately distinguish between air voids, aggregates and cement paste with high repeatability between repeated labeling iterations. Keeping high magnification levels (120-480x) enabled the detection of air voids down to 5 μ m. The framework and dataset presented in this study are fundamental for further development of new neural network based algorithms, specialized for the characterization and evaluation of the freeze–thaw resistance of concrete.

关键词： Concrete Freeze–thaw resistance Air voids Labeling Confocal laser scanning microscopy Segment anything model

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