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检索条件"任意字段=5th International Conference on Machine Learning and Human-Computer Interaction, MLHMI 2024"

共 558 条记录，以下是131-140 订阅

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Hybrid Intelligence in Production Systems and Its Effects on human Work: Insights from Four Use-Cases 5

Hybrid Intelligence in Production Systems and Its Effects on...

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5th international conference on Industry 4.0 and Smart Manufacturing (ISM)

作者： Schierhorst, Nikolas J. Johnen, Laura Fimmers, Christian Lohrmann, Vincent Monnet, Josefine Zhang, Hanwen Bergs, thomas Brecher, Christian Mertens, Alexander Nitsch, Verena Rhein Westfal TH Aachen Inst Ind Engn & Ergon IAW Eilfschornsteinstr 18 D-52062 Aachen Germany Rhein Westfal TH Aachen Lab Machine Tools & Prod Engn WZL Campus Blvd 30 D-52074 Aachen Germany Fraunhofer Inst Commun Informat Proc & Ergon FKIE Campus Blvd 55-57 D-52074 Aachen Germany

Industry 4.0 has initiated a data-driven transformation of production systems. With AI applications capitalizing on the surge of data availability, their introduction is reshaping workplaces and altering work tasks and profiles around the world. As AI -driven automation of work proliferates, so does the potential for the substitution of human labor. Rather than replacing human work, the concept of hybrid intelligence seeks to combine human and artificial intelligence with the effect of increasing productivity of the overall work system. As such, the concept may prove useful to support human -friendly automation of work, i.e., automation that supports human well-being and empowerment. this requires a deeper understanding of the projected effects of different automation solutions on human workers. In this context, this paper examines possible effects of AI applications in production systems based on four use -cases of coating and machining processes, thereby focussing on the changes from the perspective of workers and the resulting human -AI interactions. the potential challenges and opportunities of the workplace transformation are discussed, specifically highlighting possible implications for the workforce.

关键词： hybrid intelligence production systems human-AI interaction Operator 4.0

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A machine learning Based Classification of Students' Algebraic Responses Using MathBERT Embeddings 5

A Machine Learning Based Classification of Students' Algebra...

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5th IEEE international conference for Emerging Technology, INCET 2024

作者： Injeti, Anees Sajid Rupsica, G. Neha Reddy, G. Praneetha Balakrishnan, Roshni M. Pati, Peeta Basa Dept of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidhyapeetham Bengaluru India

ISBN: (纸本)9798350361155

Conduction of assessments and analysing the students' knowledge is the most important part of any teaching learning process. Traditional manual grading of written mathematical solutions can be time-consuming and laborious, especially for a larger group of students. It takes lot of time and effort to correct these answer scripts. In order to evaluate students' open responses for algebraic inquiries automatically, this project explores the use of MathBERT embeddings, an advanced natural language processing technique with different machine learning classifiers. the model tries to classify the student's responses to either correct or wrong. XGBoost Classifier on the MathBERT embeddings exhibited the best accuracy for this model with 75.3% mean accuracy with standard deviation of 0.016. © 2024 IEEE.

关键词： Students

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Facial Emotion Recognition for Enhanced human-computer interaction using Deep learning and Temporal Modeling with BiLSTM 5

Facial Emotion Recognition for Enhanced Human-Computer Inter...

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5th international conference on Smart Electronics and Communication, ICOSEC 2024

作者： Karthikeyan, P. Kirutheesvar, S. Sivakumar, S. Srm Institute Of Science And Technology School of computing Tiruchirapalli India

ISBN: (纸本)9798331504403

Facial Expression Recognition is an essential component in the development of human-computer interaction (HCI) because it enables the system to comprehend and appropriately react to human feelings. this study presents deep learning approaches for FER that are based on the combination of a Convolutional Neural Network and a Bidirectional Long Short-Term Memory Network. these approaches are intended to be utilized for the purpose of utilizing spatial and temporal variables in order to develop robust emotion categorization processes. the JAFFE and CK+ datasets are taken into consideration in the suggested technique. Both sets contain images that pertain to seven fundamental emotion classes, which are as follows: happy, sorrow, surprise, anger, disgust, fear, and neutral. the process of preprocessing begins with the detection of faces through the utilization of Multi-task Cascaded Convolutional Networks (MTCNN), followed by the normalization and scaling of the faces in order to maintain uniformity in the input dimensions. On the other hand, the BiLSTM component is responsible for capturing temporal dynamics that describe face expressions over time, while the CNN component is responsible for extracting spatial characteristics from the images. Considering that both sorts of features are incorporated into a single structure, it is evident that such a combination architecture will result in an improvement in recognition accuracy. Following that, it was trained on the preprocessed datasets for a total of 25 epochs, which resulted in a significant improvement in its accuracy. the fact that the model generalizes effectively to data that it has not before encountered is demonstrated by the fact that the accuracy it achieved during training and validation was 95.2% and 93.1%, respectively. the combination of CNN and BiLSTM is able to effectively address the challenges that are associated with FER, such as issues pertaining to illumination, position, and the variable intensity of

关键词： Convolutional neural networks

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Visualization Techniques in VR for Vocational Education: Comparison of Realism and Diegesis on Performance, Memory, Perception and Perceived Usability 1

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5th international conference on Soft Computing and its Engineering Applications (IcSoftComp)

作者： Nava, Eleonora Jalote-Parmar, Ashis Norwegian Univ Sci & Technol Trondheim Norway

ISBN: (数字)9783031537318

ISBN: (纸本)9783031537301;9783031537318

Designing highly realistic and immersive virtual reality (VR) environments has become crucial for enhancing the user experience within human-machine interactions. However, there is limited research regarding the utilization, impact, and influence of different visualization techniques in VR concerning cognition and behavior. this experimental study compares two scenarios: a low-realism (LR) scenario with non-diegetic visual elements and a high-realism (HR) scenario with diegetic visual elements. the experimental context pertains to young jobseekers' training and skills development in the construction industry. the findings indicate that an HR scenario contributes to enhancing memory but may reduce perceived usability if a proper feedback system related to diegetic elements is not implemented. Furthermore, an LR scenario can foster effective immersion if the diegetic narrative remains consistent and a high level of interaction is present, enabling a more inclusive design and making learning more accessible to individuals requiring a lower level of detail. Despite the limitation of a limited number of participants, the results aim to contribute to the establishment of visualization strategies in VR for vocational education and training.

关键词： Virtual Reality human-computer interaction Realism Diegesis Visualization Techniques Cognition Behavior Memory

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Design of Software Engineering Adaptive learning System Based on Artificial Intelligence 5

Design of Software Engineering Adaptive Learning System Base...

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5th international conference on Advances in Electrical Engineering and computer Applications, AEECA 2024

作者： Chen, Jing Wang, Yufan Yunnan Land and Resources Vocational College Kunming China Wucheng Primary School Kunming China

ISBN: (纸本)9798350355253

the complexity of modern software systems necessitates adaptability to better handle complex environments. this paper, from an object-oriented perspective, investigates adaptive learning methods using software agent technology. the study thoroughly examines the software engineering issues of adaptive systems from four perspectives: model, mechanism, language, and platform. the project integrates reinforcement learning and software agent technology, delving into the adaptive mechanisms of reinforcement learning, constructing adaptive intelligent agent software models, and exploring frameworks for adaptive system construction and adaptive learning algorithms. the case analysis of a robotic dog demonstrates that through continuous interaction with the environment, the robotic dog updates its understanding of the environment in real time, allowing it to adjust its strategy to adapt to changes in prey movement patterns and promptly adjust its role to replenish energy as needed to complete its tasks. this research validates the effectiveness of the proposed models, mechanisms, languages, and platforms, highlighting the potential impact of adaptive systems in enhancing system stability. © 2024 IEEE.

关键词： Adversarial machine learning

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Designing an AI driven intelligent Tutorial System 5

Designing an AI driven intelligent Tutorial System

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5th IEEE international conference on Recent Trends in computer Science and Technology, ICRTCST 2024

作者： Alam, Shamsher Kumar, Surjeet Khursheed, Zeeshan Mahato, Hemant Kumar Bashar, Shadan Suman, Anjali R.V.S College of Engineering & Technology Computer Science and Engineering Jamshedpur India Government Engineering College Sheikhpura Computer Science and Engineering Bihar India Jio Platform Limited DGM Mumbai India NSHM Master of Computer Application Durgapur India R.V.S College of Engineering & Technology Master of Computer Application Jamshedpur India R.V.S College of Engineering & Technology Computer Science & Engineering Student Jamshedpur India

ISBN: (纸本)9798350351378

With the fast-evolving field of education, there is an increase in the demand for intelligent tutorial systems that can adapt to the diverse learning needs of individual students. this research paper explores the design and implementation of an AI-driven Intelligent Tutorial System (ITS) aimed at enhancing the personalized learning experience. the system leverages advanced machine learning algorithms to dynamically adjust instructional content, pace, and assessments based on real-time analysis of individual student performance and preferences. the paper delves into the key components of the proposed ITS, including adaptive learning models, natural language processing capabilities for interactive communication, and data-driven decision-making mechanisms. Emphasis is placed on the seamless integration of AI technologies to create an intuitive and responsive educational *** considerations in the development and deployment of the intelligent tutorial system are discussed, addressing issues related to data privacy, algorithmic bias, and the role of human oversight. the paper also highlights the collaborative potential between AI and human educators, emphasizing the system's capacity to support rather than replace teaching professionals. through a combination of theoretical frameworks and practical examples, the research examines the potential impact of the AI-driven ITS on student engagement, learning outcomes, and the overall educational experience. Case studies and pilot implementations are presented to illustrate the system's efficacy in diverse educational *** research contributes to the ongoing discourse on AI in education by providing insights into the design principles, implementation challenges, and ethical considerations associated with developing an intelligent tutorial system. the findings aim to inform educators, policymakers, and technologists about the potential of AI to revolutionize personalized learning and contribute to the ong

关键词： Students

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Workshop on Longitudinal Disease Tracking and Modeling with Medical Images and Data, LDTM 2024, 5th international Workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st Workshop on machine learning for Multimodal/-sensor Healthcare Data, ML4MHD2024 and Workshop on Multimodal learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th international conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2024

Workshop on Longitudinal Disease Tracking and Modeling with ...

引用

Workshop on Longitudinal Disease Tracking and Modeling with Medical Images and Data, LDTM 2024, 5th international Workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st Workshop on machine learning for Multimodal/-sensor Healthcare Data, ML4MHD2024 and Workshop on Multimodal learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th international conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2024

ISBN: (纸本)9783031845246

the proceedings contain 21 papers. the special focus in this conference is on Longitudinal Disease Tracking and Modeling with Medical Images and Data. the topics include: Disease Progression Modelling and Stratification for Detecting Sub-trajectories in the Natural History of Pathologies: Application to Parkinson’s Disease Trajectory Modelling;back to the Future: Challenges of Sparse and Irregular Medical Image Time Series;Individualized Multi-horizon MRI Trajectory Prediction for Alzheimer’s Disease;towards Longitudinal Characterization of Multiple Sclerosis Atrophy Employing SynthSeg Framework and Normative Modeling;segHeD: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints;Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting;registration of Longitudinal Liver Examinations for Tumor Progress Assessment;Tracking Lesion Evolution Using a Boundary Enhanced Approach for MS Change Segmentation (BEAMS);a Radiological-Based Coordinate System for the human Body: A Proof-of-Concept;language Models Meet Anomaly Detection for Better Interpretability and Generalizability;A Diffusion Model Embedded WCSAU-Net for 3D MRI Brain Tumor Segmentation;predicting human Brain States with Transformer;Cross-Modality Image Quality Prediction for Time-Resolved CT from Breathing Signals;RATNUS: Rapid, Automatic thalamic Nuclei Segmentation Using Multimodal MRI Inputs;HyperMM: Robust Multimodal learning with Varying-Sized Inputs;HEMIT: H&E to Multiplex-Immunohistochemistry Image Translation with Dual-Branch Pix2pix Generator;Physics-Informed Latent Diffusion for Multimodal Brain MRI Synthesis;medPromptX: Grounded Multimodal Prompting for Chest X-Ray Diagnosis;predicting Stroke through Retinal Graphs and Multimodal Self-supervised learning;multimodality for Diagnosis of Asian Choroidal Vasculopathy: Results from a Novel Dataset and Deep-learning Experiments;multimodality Frequency Feature Customized learning for Pediatric Ventricu

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Establishing an Effective Adaptive Instructional Systems Community of Practice 5th

Establishing an Effective Adaptive Instructional Systems Com...

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5th international conference of the Adaptive Instructional Systems (AIS) part of the 25th international conference on human-computer interaction (HCI)

作者： Sottilare, Robert A. Soar Technol Inc Orlando FL 32817 USA

ISBN: (纸本)9783031347344;9783031347351

this paper discusses the benefits and challenges associated with a path forward toward the establishment of an adaptive instructional system (AIS) community of practice (COP). At their simplest, AISs may be described as any form of instructional approach or strategy adopted to accommodate individual differences in learners to facilitate the effective and efficient acquisition of knowledge and skill. AISs use artificial intelligence and machine learning approaches to assess learner performance and proficiency, use learner information to select optimal interventions (e.g., feedback or direction) and recommendations for future instruction, and then observe the effect of their learner interactions to enable better decisions in the future. the goal of this paper is to demonstrate the importance of adaptive instruction and make the case for a global community of practice to develop standards, collaboratively develop capabilities, and share innovative solutions. Toward this goal, we identify significant challenges that require a large, multi-disciplinary community of experts to solve. Finally, we examine the potential impact of emerging AIS solutions that address some of the most difficult challenges in tailoring education and training for individuals and teams in the context of an AIS community of practice.

关键词： adaptive instruction adaptive instructional systems (AISs) artificial intelligence community of practice individual differences learner assessments machine learning tailoring instruction

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Data Shed: Interactive Art in the Service of Data Fluency 12th

Data Shed: Interactive Art in the Service of Data Fluency

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12th international conference on Culture and Computing (C and C) Held as Part of the 26th international conference on human-computer interaction (HCII)

作者： Verma, Adit Diamond, Sara Morris, Alexis OCAD Univ 100 McCaul St Toronto ON M5T 1W1 Canada

ISBN: (纸本)9783031611469;9783031611476

As data continues to permeate all aspects of our lives, the need for data fluency becomes paramount. the "Data Shed" project explores the intersection of interactive art, machine learning, and data visualization to facilitate experiential learning about data and its pervasive influence in our digital age. Grounded in the metaphor of shedding, akin to a snake's skin, the installation portrays the implicit generation and shedding of latent data in our digital interactions. the paper delves into related work, emphasizing the accelerating role of algorithms, motivated by the challenges posed by data misuse and potential for bias. It draws on the concepts of data fluency and critical data studies, exploring the paradox of privacy in the face of increasing concerns about data handling by organizations. the study reviews generative artworks and human-AI collaboration before addressing the ability of machine learning to address surveillance and data-shedding concerns. the paper details research creation, the development methodology, and highlights gaps where the "Data Shed" approach can bring focus to these issues. Evaluative insights are provided, emphasizing technical goals, interactivity levels, and assessment of achievement of data fluency objectives. the research underscores the importance of interactive art works, user engagement, and ethical considerations, in fostering a deeper understanding of data in our digital landscape. the project invites users to reflect on the shaping influence of data in our lives and its potential impact on our collective future.

关键词： Data Collection Data Ownership Latent Data Data Shedding machine learning Generative Art Interactive Installation

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Research on the Application of Multimodal Fusion Technology in English Listening and Speaking Teaching 5

Research on the Application of Multimodal Fusion Technology ...

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5th international conference on computers, Information Processing and Advanced Education, CIPAE 2024

作者： Han, Xiuying School of Language and Cultural Shanghai Normal University TIANHUA College Shanghai China

ISBN: (纸本)9798331527662

this study aims to explore the application of multimodal integration technology in English listening and speaking teaching, especially by constructing an innovative English distance teaching system to improve learners' listening and speaking ability. this paper adopts advanced multi-modal feature extraction technologies such as speech recognition, facial expression analysis and eye tracking, combined with deep learning and machine learning algorithms, to achieve accurate capture of learners' verbal output and non-verbal feedback. In the design of the system, this paper pays special attention to the application of hybrid integration strategy, that is, the effective integration of different modal information, in order to provide a richer and more personalized learning experience. through real-time interaction and feedback mechanisms, the system is able to dynamically adjust teaching content and difficulty to suit the needs of different learners. the experimental results show that the system has achieved remarkable results in improving learners' oral fluency, vocabulary mastery and listening comprehension. the results of this study not only provide a new technical path for English distance teaching, but also provide empirical support for the application of multimodal learning theory. © 2024 IEEE.

关键词： Adversarial machine learning

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