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Investigating teacher orchestration load in scripted CSCL: A multimodal data analysis perspective

BRITISH JOURNAL OF EDUCATIONAL TECHNOLOGY 2024年第5期55卷 1926-1949页

作者： Hakami, Lubna Hernandez-Leo, Davinia Amarasinghe, Ishari Sayis, Batuhan Univ Pompeu Fabra UPF Dept Informat & Commun Technol Carrer Tanger 122 Barcelona 08018 Spain Radboud Univ Nijmegen Natl Educ Lab & Donders Inst Brain Cognit & Behav Nijmegen Netherlands Univ Cambridge Dept Comp Sci & Technol Cambridge England

Despite the growing interest in using multimodal data to analyse students' actions in Computers-Supported Collaborative Learning (CSCL) settings, studying teacher's orchestration load in such settings remains overlooked. The notion of classroom orchestration, and orchestration load, offer a lens to study the implications of increasingly complex technology-supported learning environments on teacher performance. A combination of multimodal data may aid in understanding teachers' orchestration actions and, as a result, gain insights regarding the orchestration load teachers perceive in scripted CSCL situations. Studying teacher orchestration load in CSCL helps understand the workload teachers experience while facilitating student collaboration and assists in informing design decisions for teacher supporting tools. In this paper, we collect and analyse data from different modalities (i.e. electrodermal activity, observation notes, log data, dashboard screen recordings and responses to self-reported questionnaires) to study teachers' orchestration load in scripted CSCL. A tool called X was used to deploy CSCL activities and a teacher-facing dashboard was used to facilitate teachers in managing collaboration in real time. The findings of the study show the potential of multimodal data analysis in investigating and estimating the orchestration load experienced by teachers in scripted CSCL activities. Study findings further demonstrate factors emerging from multimodal data such as task type, activity duration, and number of students influenced teachers' orchestration load.

关键词： computers-supported collaborative learning (CSCL) multimodal data analysis orchestration load

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Vehicle Intelligent Classification Based on Big multimodal data analysis and Sparrow Search Optimization

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BIG data 2022年第6期10卷 547-558页

作者： Shao, Caixing Cheng, Fengxin Mao, Sun Hu, Jian Southwest Minzu Univ Key Lab Comp Syst State Ethn Affairs Commiss Chengdu Peoples R China Univ Elect Sci & Technol Sch Commun & Informat Engn Chengdu Peoples R China Sichuan Normal Univ Coll Comp Sci Chengdu 610101 Sichuan Peoples R China

Vehicle intelligent classification plays a vital role in the Intelligent Transport Systems. However, due to the dynamic traffic environments, it is difficult to ensure the classification accuracy. Therefore, this article uses a new pulse coherent radar (PCR) to collect road vehicle data, and a vehicle classification method of sparrow search algorithm extreme learning machine (SSA-ELM) based on big multimodal data analysis is proposed. First, the road vehicle data are collected by PCR, where the vehicle length, chassis outline, and height features are extracted as the sample data. Then, the ELM is utilized to learn these three modal features. According to the input feature data, the vehicle type is classified, including cars, sport-utility vehicles, and buses. Finally, the SSA is applied to optimize the initial weights and thresholds of ELM. Experimental results show that SSA-ELM has notable advantages in classification accuracy and convergence speed, compared with existing benchmark methods.

关键词： multimodal data analysis sparrow search optimization and extreme learning machine vehicle classification

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3D Sensing Techniques for multimodal data analysis and Integration in Smart and Autonomous Systems 6th

3D Sensing Techniques for Multimodal Data Analysis and Integ...

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6th International Conference on Communications, Signal Processing, and Systems (ICCSPS)

作者： Fang, Zhenyu Sun, He Ren, Jinchang Zhao, Huimin Zhao, Sophia Marshall, Stephen Durrani, Tariq Univ Strathclyde Dept Elect & Elect Engn Glasgow Lanark Scotland Guangdong Polytech Normal Univ Sch Comp Sci Guangzhou Guangdong Peoples R China Guangzhou Key Lab Digital Content Proc & Secur Te Guangzhou Guangdong Peoples R China

ISBN: (纸本)9789811065712;9789811065705

For smart and autonomous systems, 3D positioning and measurement is essential as the precision can severely affect the applicability of the techniques for a number of applications. In this paper, we summarize and compare different techniques and sensors that can be potentially used in multimodal data analysis and integration. These will provide useful guidance for the design and implementation of relevant systems.

关键词： 3D positioning and measurement multimodal data analysis Depth camera LiDAR SAR Ultrasonic

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Exploratory multimodal data analysis with Standard Multimedia Player Multimedia Containers: A Feasible Solution to Make multimodal Research data Accessible to the Broad Audience 12

Exploratory Multimodal Data Analysis with Standard Multimedi...

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12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP)

作者： Schoening, Julius Gert, Anna L. Acik, Alper Kietzmann, Tim C. Heidemann, Gunther Koenig, Peter Osnabruck Univ Inst Cognit Sci Osnabruck Germany Ozyegin Univ Dept Psychol Istanbul Turkey MRC Cognit & Brain Sci Unit Cambridge CB2 7EF England

ISBN: (纸本)9789897582257

The analysis of multimodal data comprised of images, videos and additional recordings, such as gaze trajectories, EEG, emotional states, and heart rate is presently only feasible with custom applications. Even exploring such data requires compilation of specific applications that suit a specific dataset only. This need for specific applications arises since all corresponding data are stored in separate files in custom-made distinct data formats. Thus accessing such datasets is cumbersome and time-consuming for experts and virtually impossible for non-experts. To make multimodal research data easily shareable and accessible to a broad audience, like researchers from diverse disciplines and all other interested people, we show how multimedia containers can support the visualization and sonification of scientific data. The use of a container format allows explorative multimodal data analyses with any multimedia player as well as streaming the data via the Internet. We proto-typed this approach on two datasets, both with visualization of gaze data and one with additional sonification of EEG data. In a user study, we asked expert and non-expert users about their experience during an explorative investigation of the data. Based on their statements, our prototype implementation, and the datasets, we discuss the benefit of storing multimodal data, including the corresponding videos or images, in a single multimedia container. In conclusion, we summarize what is necessary for having multimedia containers as a standard for storing multimodal data and give an outlook on how artificial networks can be trained on such standardized containers.

关键词： multimodal data analysis Visualization Sonification Gaze data EEG data

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Rough Hypercuboid Based Supervised Regularized Canonical Correlation for multimodal data analysis

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FUNDAMENTA INFORMATICAE 2016年第1-2期148卷 133-155页

作者： Maji, Pradipta Mandal, Ankita Indian Stat Inst Biomed Imaging & Bioinformat Lab Machine Intelligence Unit 203 BT Rd Kolkata 700108 W Bengal India

One of the main problems in real life omics data analysis is how to extract relevant and non-redundant features from high dimensional multimodal data sets. In general, supervised regularized canonical correlation analysis (SRCCA) plays an important role in extracting new features from multimodal omics data sets. However, the existing SRCCA optimizes regularization parameters based on the quality of first pair of canonical variables only using standard feature evaluation indices. In this regard, this paper introduces a new SRCCA algorithm, integrating judiciously the merits of SRCCA and rough hypercuboid approach, to extract relevant and non-redundant features in approximation spaces from multimodal omics data sets. The proposed method optimizes regularization parameters of the SRCCA based on the quality of a set of pairs of canonical variables using rough hypercuboid approach. While the rough hypercuboid approach provides an efficient way to calculate the degree of dependency of class labels on feature set in approximation spaces, the merit of SRCCA helps in extracting non-redundant features from multimodal data sets. The effectiveness of the proposed approach, along with a comparison with related existing approaches, is demonstrated on several real life data sets.

关键词： multimodal data analysis canonical correlation analysis feature extraction rough sets rough hypercuboid approach

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Rough Hypercuboid Based Supervised Regularized Canonical Correlation for multimodal data analysis

Rough Hypercuboid Based Supervised Regularized Canonical Cor...

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6th Workshop on Rough Set Theory (RST)

作者： Maji, Pradipta Mandal, Ankita Indian Stat Inst Biomed Imaging & Bioinformat Lab Machine Intelligence Unit 203 BT Rd Kolkata 700108 W Bengal India

关键词： multimodal data analysis canonical correlation analysis feature extraction rough sets rough hypercuboid approach

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SMSPL: Robust multimodal Approach to Integrative analysis of Multiomics data

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第4期52卷 2082-2095页

作者： Yang, Ziyi Wu, Naiqi Liang, Yong Zhang, Hui Ren, Yanqiong Macau Univ Sci & Technol Collaborat Lab Intelligent Sci & Syst Fac Informat Technol Macau Peoples R China Macau Univ Sci & Technol State Key Lab Qual Res Chinese Med Macau Peoples R China Macau Univ Sci & Technol Inst Syst Engn Macau Peoples R China Macau Univ Sci & Technol Collaborat Lab Intelligent Sci & Syst Macau Peoples R China Guangdong Univ Technol State Key Lab Precis Elect Mfg Technol & Equipmen Guangzhou 510006 Peoples R China Guangdong Univ Technol Guangdong Hong Kong Macao Joint Lab Smart Discret Guangzhou 510006 Peoples R China

With the recent advancement of technologies, it is progressively easier to collect diverse types of genome-wide data. It is commonly expected that by analyzing these data in an integrated way, one can improve the understanding of a complex biological system. Current methods, however, are prone to overfitting heavy noise such that their applications are limited. High noise is one of the major challenges for multiomics data integration. This may be the main cause of overfitting and poor performance in generalization. A sample reweighting strategy is typically used to cope with this problem. In this article, we propose a robust multimodal data integration method, called SMSPL, which can simultaneously predict subtypes of cancers and identify potentially significant multiomics signatures. Especially, the proposed method leverages the linkages between different types of data to interactively recommend high-confidence samples, adopts a new soft weighting scheme to assign weights to the training samples of each type, and then iterates between weights recalculating and classifiers updating. Simulation and five real experiments substantiate the capability of the proposed method for classification and identification of significant multiomics signatures with heavy noise. We expect SMSPL to take a small step in the multiomics data integration and help researchers comprehensively understand the biological process.

关键词： Training Robustness data integration Cancer data models Biological system modeling Classification feature selection multimodal data analysis multiomics data integration self-paced learning (SPL)

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Detecting hate in diversity: a survey of multilingual code-mixed image and video analysis

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JOURNAL OF BIG data 2025年第1期12卷 1-28页

作者： Rehman, Hafiz Muhammad Raza Ur Saleem, Mahpara Jhandir, Muhammad Zeeshan Alvarado, Eduardo Silva Garay, Helena Ashraf, Imran Yeungnam Univ Informat & Commun Engn Gyeongbuk 38541 Gyeongsan South Korea Islamia Univ Bahawalpur Dept Data Sci Bahawalpur Pakistan Univ Europea Atlantico Isabel Torres 21 Santander 39011 Spain Univ Int Iberoamer Campeche 24560 Mexico Fdn Univ Int Colombia Bogota Bogota Colombia Univ Int Cuanza Cuito Bie Angola Univ La Romana La Romana Dominican Rep

The proliferation of damaging content on social media in today's digital environment has increased the need for efficient hate speech identification systems. A thorough examination of hate speech detection methods in a variety of settings, such as code-mixed, multilingual, visual, audio, and textual scenarios, is presented in this paper. Unlike previous research focusing on single modalities, our study thoroughly examines hate speech identification across multiple forms. We classify the numerous types of hate speech, showing how it appears on different platforms and emphasizing the unique difficulties in multi-modal and multilingual settings. We fill research gaps by assessing a variety of methods, including deep learning, machine learning, and natural language processing, especially for complicated data like code-mixed and cross-lingual text. Additionally, we offer key technique comparisons, suggesting future research avenues that prioritize multi-modal analysis and ethical data handling, while acknowledging its benefits and drawbacks. This study attempts to promote scholarly research and real-world applications on social media platforms by acting as an essential resource for improving hate speech identification across various data sources.

关键词： Hate speech detection Social media Feature engineering Deep learning multimodal data analysis

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Egg characteristics assessment as an enabler for in-ovo sexing technology: A review

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BIOSYSTEMS ENGINEERING 2025年 249卷 41-57页

作者： Xu, Shaomin Long, Sifang Su, Zixian Hayat, Khawar Xie, Lijuan Pan, Jinming Zhejiang Univ Coll Biosyst Engn & Food Sci Hangzhou 310058 Peoples R China Key Lab Intelligent Equipment & Robot Agr Zhejiang Hangzhou 310058 Peoples R China

The culling of day-old male chicks remains a significant challenge for the egg-laying industry. While in ovo sexing technology has made strides, it has yet to achieve an optimal balance between early identification of sex at hatching, high sensitivity, and non-invasive operation. This paper aims to provide a comprehensive review of the research progress in in ovo sexing from various perspectives and to explore potential solutions in light of industrial practices and key technological bottlenecks. This study describes an efficient detection method for lowconcentration samples and provides recommendations for the rapid detection of sex markers in chicken embryo allantoic fluid. Given the complexity of optical signals and the significant individual differences among eggs, the paper underscores the importance of evaluating egg characteristics in advancing in ovo sex determination technology. Based on this core concept, the use of multimodal data fusion strategies are advocated for in ovo sexing. This approach aims to deepen researchers' understanding of the complex relationship between sex labels and the high-dimensional data of breeding eggs, thereby injecting new momentum and direction into the development of this field.

关键词： In ovo sex determination Breeding egg Spectroscopy multimodal data analysis Sex difference signals

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multimodal fine-grained reasoning for post quality evaluation

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APPLIED SOFT COMPUTING 2025年 174卷

作者： Guo, Xiaoxu Liang, Siyan Cui, Yachao Zhou, Juxiang Wang, Lei Cao, Han Shaanxi Normal Univ Sch Comp Sci Xian 710119 Shaanxi Provinc Peoples R China Beijing Econ Management Vocat Coll Sch Artificial Intelligence Inst Beijing 100018 Peoples R China Chinese Acad Sci Inst Informat Engn State Key Lab Informat Secur Beijing 100049 Peoples R China Yunnan Normal Univ Key Lab Educ Informatizat Nationalities Kunming 650000 Peoples R China

Accurate assessment of post quality frequently necessitates complex relational reasoning skills that emulate human cognitive processes, thereby requiring the modeling of nuanced relationships. However, existing research on post-quality assessment suffers from the following problems: (1) They are often categorization tasks that rely solely on unimodal data, which inadequately captures information in multimodal contexts and fails to differentiate the quality of students' posts finely. (2) They ignore the noise in the multimodal deep fusion between posts and topics, which may produce misleading information for the model. (3) They do not adequately capture the complex and fine-grained relationships between post and topic, resulting in an inaccurate evaluation, such as relevance and comprehensiveness. Based on the above challenges, the multimodal Fine-grained Topic-post Relational Reasoning(MFTRR) framework is proposed for modeling finegrained cues by simulating the human thinking process. It consists of the local-global semantic correlation reasoning module and the multi-level evidential relational reasoning module. Specifically, MFTRR addresses the challenge of unimodal and categorization task limitations by framing post-quality assessment as a ranking task and integrating multimodal data to more effectively distinguish quality differences. To capture the most relevant semantic relationships, the Local-Global Semantic Correlation Reasoning Module enables deep interactions between posts and topics at both local and global scales. It is complemented by a topic-based maximum information fusion mechanism to filter out noise. Furthermore, to model complex and subtle relational reasoning, the Multi-Level Evidential Relational Reasoning Module analyzes topic-post relationships at both macro and micro levels by identifying critical cues and delving into granular relational cues. MFTRR is evaluated using three newly curated multimodal topic-post datasets, in addition to the pub

关键词： multimodal data analysis Forum posts Post evaluation

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