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6th Workshop on Leveraging Multimodal Data for Generating Meaningful Feedback, CROSSMMLA 2023

作者： Li, Huiyong Majumdar, Rwitajit Yang, Yuanyuan Ogata, Hiroaki Academic Center for Computing and Media Studies Kyoto University Japan Graduate School of Informatics Kyoto University Japan

This work aims to propose a learner model-based feedback model for self-direction skills (SDS) acquisition to address the challenge of providing learning services with multimodal data in K-12 settings. The feedback model leverages students' daily life activity data from learning systems and wearable devices, creates a learner model of SDS, and provides multi-dimensional feedback to K-12 students: (1) feedback on contextual activity;(2) feedback on self-direction management and (3) feedback on skill assessment. We also implement the feedback model with two learning dashboards in English learning and physical activity contexts for illustration and show the potential effects of the feedback model on student engagement and skill improvement with two case studies in K-12 settings. The results of the case studies indicate that K-12 students can continuously engage in both learning and physical activities, and take the feedback regularly with the learning dashboard support. The implications and challenges of SDS development with multimodal data in K-12 settings are also discussed. © 2020 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Students

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Hybrid Deep Learning Architectures for Histological Image Segmentation 6

Hybrid Deep Learning Architectures for Histological Image Se...

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6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Hasan, Md Jahid Ahmad, Wan Siti Halimatul Munirah Wan Ahmad Fauzi, Mohammad Faizal Abas, Fazly Salleh Multimedia University Faculty of Engineering Cyberjaya Malaysia Multimedia University Center for Visual Computing Faculty of Computing and InformaticsFaculty of Engineering Cyberjaya Malaysia Multimedia University Faculty of Engineering and Technology Cyberjaya Malaysia

ISBN: (纸本)9798350344349

In histopathology image analysis, accurate segmentation of nuclei holds immense significance, particularly in the early detection and treatment of diseases like breast cancer. Nuclei segmentation is a fundamental but challenging task because of the intricate variations in nuclear shapes, sizes, densities, and overlapping instances. In this paper, we evaluate eight convolutional neural network (CNN) models, two of them existing models namely U-Net, SegNet, and six hybrid models by combining U-Net and SegNet modify decoder with ResNet, VGG and DenseNet (ResNet-UNet, ResNet-SegNet, VGG-UNet, VGG-SegNet, DenseNet-UNet, and DenseNet-SegNet. This experiment aims to identify the best deep-learning model for segmenting hematoxylin and eosin (HandE) stain images using a publicly available dataset called MoNuSeg. From the experimented work, we found that VGG-UNet outperforms other models with an F1 score of 0.8452 and IoU of 0.6929 respectively. This research will serve as a foundation for the future construction of more complex deep learning models with cascade or any combination of the models studied. © 2024 IEEE.

关键词： Image segmentation

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Impact of Self-analysis Behaviors in GOAL for Japanese High School EFL Learners 31

Impact of Self-analysis Behaviors in GOAL for Japanese High ...

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31st International Conference on Computers in Education, ICCE 2023

作者： Wang, Zixu Majumdar, Rwitajit Ogata, Hiroaki Graduate School of Informatics Kyoto University Japan Academic Center for Computing and Media Studies Kyoto University Japan

ISBN: (纸本)9786269689019

In the 21st century, self-directed learning (SDL) has become an essential skill for personal growth and success. This applies to the context of language learning as well. To enhance students' SDL skills, we previously proposed the DAPER (Data, Analyze, Plan, Execution, Monitoring, Reflect) model and developed the GOAL (Goal-oriented Active Learner) system to support DAPER. In this study, we focused on the Analysis phase of GOAL. 113 students tracked their scores in 39 weekly tests (19 for English vocabulary and 20 for English grammar) over one year in GOAL. Their self-analysis behaviors after studying and taking tests were also prompted in GOAL. We investigated the relationship between analysis behavior and test scores, finding a significant difference in learning outcomes between students who conducted self-analysis and those who did not. While the current result cannot definitively conclude that self-analysis improves study performance in language learning, it does show that it is meaningful to promote students' SDL behavior tends to perform better. © 2023 Asia-Pacific Society for Computers in Education.

关键词： Students

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A survey on federated learning:a perspective from multi-party computation

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Frontiers of Computer Science 2024年第1期18卷 93-103页

作者： Fengxia LIU Zhiming ZHENG Yexuan SHI Yongxin TONG Yi ZHANG Institute of Artificial Intelligence and Key Laboratory of Mathematics Informatics Behavioral Semantics Beihang UniversityBeijing 100191China State Key Laboratory of Software Development Environment and Advanced Innovation Center for Future Blockchain and Privacy Computing Beihang UniversityBeijing 100191China Pengcheng Laboratory Shenzhen 518055China Zhongguancun Laboratory Beijing 100190China Institute for Mathematical Sciences and Engineering Research Center of Financial Computing and Digital Engineering Renmin University of ChinaBeijing 100872China

Federated learning is a promising learning paradigm that allows collaborative training of models across multiple data owners without sharing their raw *** enhance privacy in federated learning,multi-party computation can be leveraged for secure communication and computation during model *** survey provides a comprehensive review on how to integrate mainstream multi-party computation techniques into diverse federated learning setups for guaranteed privacy,as well as the corresponding optimization techniques to improve model accuracy and training *** also pinpoint future directions to deploy federated learning to a wider range of applications.

关键词： sfederated learning multi-party ycomputation privacy-preserving data mining distributed learning

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Towards Flow Graph Prediction of Open-Domain Procedural Texts 8

Towards Flow Graph Prediction of Open-Domain Procedural Text...

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8th Workshop on Representation Learning for NLP, RepL4NLP 2023, co-located with ACL 2023

作者： Shirai, Keisuke Kameko, Hirotaka Mori, Shinsuke Graduate School of Informatics Kyoto University Japan Academic Center for Computing and Media Studies Kyoto University Japan

ISBN: (纸本)9781959429777

Machine comprehension of procedural texts is essential for reasoning about the steps and automating the procedures. However, this requires identifying entities within a text and resolving the relationships between the entities. Previous work focused on the cooking domain and proposed a framework to convert a recipe text into a flow graph (FG) representation. In this work, we propose a framework based on the recipe FG for flow graph prediction of open-domain procedural texts. To investigate flow graph prediction performance in non-cooking domains, we introduce the wikiHow-FG corpus from articles on wikiHow, a website of how-to instruction articles. In experiments, we consider using the existing recipe corpus and performing domain adaptation from the cooking to the target domain. Experimental results show that the domain adaptation models achieve higher performance than those trained only on the cooking or target domain data. © 2023 Association for Computational Linguistics.

关键词： Graphic methods

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Multi-Level Cross-Modal Alignment for Speech Relation Extraction

Multi-Level Cross-Modal Alignment for Speech Relation Extrac...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Zhang, Liang Yang, Zhen Fu, Biao Lu, Ziyao Shao, Liangying Liu, Shiyu Meng, Fandong Zhou, Jie Wang, Xiaoli Su, Jinsong School of Informatics Xiamen University China Xiamen Key Laboratory of Intelligent Storage and Computing School of Informatics Xiamen University China Pattern Recognition Center WeChat AI Tencent Inc. China

ISBN: (纸本)9798891761643

Speech Relation Extraction (SpeechRE) aims to extract relation triplets from speech data. However, existing studies usually use synthetic speech to train and evaluate SpeechRE models, hindering the further development of SpeechRE due to the disparity between synthetic and real speech. Meanwhile, the modality gap issue, unexplored in SpeechRE, limits the performance of existing models. In this paper, we construct two real SpeechRE datasets to facilitate subsequent researches and propose a Multi-level Cross-modal Alignment Model (MCAM) for SpeechRE. Our model consists of three components: 1) a speech encoder, extracting speech features from the input speech;2) an alignment adapter, mapping these speech features into a suitable semantic space for the text decoder;and 3) a text decoder, autoregressively generating relation triplets based on the speech features. During training, we first additionally introduce a text encoder to serve as a semantic bridge between the speech encoder and the text decoder, and then train the alignment adapter to align the output features of speech and text encoders at multiple levels. In this way, we can effectively train the alignment adapter to bridge the modality gap between the speech encoder and the text decoder. Experimental results and in-depth analysis on our datasets strongly demonstrate the efficacy of our method. Our source code is available at https://***/DeepLearnXMU/SpeechRE-MCAM. © 2024 Association for Computational Linguistics.

关键词： Semantics

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Digital Travel Directions

Digital Travel Directions

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International Conference on Management, Tourism and Technologies, ICMTT 2024

作者： Waterworth, John Tjostheim, Ingvar Department of Informatics Umeå University Umeå901 87 Sweden Hauge School of Management Oslo Norway Norwegian Computing Center Oslo Norway

ISBN: (纸本)9783031748240

In this article we look at two of the principle ways in which digital technology may plausibly change the nature of, and to some extent replace, physical travel. The most obvious is to simulate another place as a virtual reality, one that can convey a convincing sense of being there. This can be seen as making the digital world more physical, as far as the user’s experience is concerned, and is a strong form of digital travel. Video gaming and some work with therapeutic applications for relaxation, regeneration and exercise point to the potential of this kind of digital travel to have a wider and more engaging role in the future. In contrast, widely used and accepted systems for on-line meetings, and various phone apps providing location-sensitive information to their users, and also augmented reality approaches (none of which convey a strong sense of being in another location), suggest another approach - that of making the physical world more digital. We characterise this as a weaker form of digital travel. To examine people’s attitudes to these two approaches, we created two fictional scenarios, each exemplifying key characteristics of these forms of digital travel. We report opinion ratings about the two scenarios, focusing on participants opinions of their realism, their likelihood to become common practice, and their suitability for different purposes. Finally, we speculate about the possible future for designed digital travel experiences. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Virtual environments

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SmartDeco: AR for Soft Furnishing Design 1st

SmartDeco: AR for Soft Furnishing Design

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1st International Conference on Extended Reality, ICXR 2024

作者： Wang, Jingyuan Zhang, Yichun Liu, Ruilong Chen, Shiyi Hu, Yang Yao, Junfeng Beijing University of Posts and Telecommunications Haidian District Beijing China Center for Digital Media Computing School of Film School of Informatics Xiamen University Xiamen361005 China

ISBN: (纸本)9789819636785

Augmented reality (AR) is increasingly being integrated into the home renovation sector, yet current AR solutions often fail to address the challenges of soft decoration design. This study introduces SmartDeco, an AR-based application for the Chinese market, enhanced by large language models (LLMs) to improve user experiences in home decoration. Serving merchants, interior designers, and users, SmartDeco enables AR simulations of soft furnishing combinations, supports collaborative design, and utilizes LLMs to offer real-time design recommendations, addressing potential issues and improving decision-making. User testing demonstrates SmartDeco’s effectiveness in enhancing usability, simplifying design processes, reducing errors, and boosting satisfaction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decision making

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Copy-Move Forgery Detection Optimization Using Harmony Search with Clonal Selection Algorithm 6

Copy-Move Forgery Detection Optimization Using Harmony Searc...

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6th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2024

作者： Hijazi, Mohd Hanafi Ahmad Josmin, Ezlyn Sherlyn Data Technology and Applications Research Group Creative Advanced Machine Intelligence Research Center Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Malaysia

ISBN: (纸本)9798350389692

In today's world, where technology is advancing rapidly, the extensive use of digital images on social media brings up concerns about their authenticity. One common form of deception is image forgery, especially copy-move forgery, where parts of an image are duplicated and inserted elsewhere within the same image. Traditional detection methods, like randomized search algorithms, often struggle with adaptability. This study presents a new approach that uses the Harmony Search with Clonal Selection Algorithm (HSCSA) to improve the detection of copy-move forgeries by fine -tuning the hyperparameters of Convolutional Neural Network (CNN) models. Our method involves preprocessing the CoMoFoD dataset, optimizing essential hyperparameters using HSCSA, and comparing the performance of the optimized model with standard CNN models. The results showed a notable improvement, with accuracy rising from 67.72% to 78.20%. © 2024 IEEE.

关键词： Convolutional neural networks

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Towards Predictable Process and Consequence Attributes of Data-Driven Group Work: Primary Analysis for Assisting Teachers with Automatic Group Formation

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Educational Technology and Society 2023年第4期26卷 90-103页

作者： Liang, Changhao Horikoshi, Izumi Majumdar, Rwitajit Flanagan, Brendan Ogata, Hiroaki Graduate School of Informatics Kyoto University Japan Academic Center for Computing and Media Studies Kyoto University Japan Center for Innovative Research and Education in Data Science Kyoto University Japan

Data-driven platforms with rich data and learning analytics applications provide immense opportunities to support collaborative learning such as algorithmic group formation systems based on learning logs. However, teachers can still get overwhelmed since they have to manually set the parameters to create groups and it takes time to understand the meaning of each indicator. Therefore, it is imperative to explore predictive indicators for algorithmic group formation to release teachers from the dilemma with explainable group formation indicators and recommended settings based on group work purposes. Employing learning logs of group work from a reading-based university course, this study examines how learner indicators from different dimensions before the group work connect to the subsequent group work processes and consequences attributes through correlation analysis. Results find that the reading engagement and previous peer ratings can reveal individual achievement of the group work, and a homogeneous grouping strategy based on reading annotations and previous group work experience can predict desirable group performance for this learning context. In addition, it also proposes the potential of automatic group formation with recommended parameter settings that leverage the results of predictive indicators. © (2023). All Rights Reserved.

关键词： Correlation analysis CSCL GLOBE Group formation Group work indicator Group work prediction Teacher assistance

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