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Evaluation of Video-Assisted Annotation of Human IMU data Across Expertise, datasets, and Tools

Evaluation of Video-Assisted Annotation of Human IMU Data Ac...

引用

ieee Annual conference on Pervasive computing and Communications Workshops (PerCom)

作者： Alexander Hoelzemann Marius Bock Kristof Van Laerhoven Ubiquitous Computing University of Siegen Siegen Germany Ubiquitous Computing & Computer Vision University of Siegen Siegen Germany

Despite the simplicity of labels and extensive study protocols provided during data collection, the majority of researchers in sensor-based technologies tend to rely on annotations provided by a combination of field experts and researchers themselves. This paper presents a comprehensive study on the quality of annotations provided by expert versus novice annotators for inertial-based activity benchmark datasets. We consider multiple parameters such as the nature of the activities to be labeled, and the annotation tool, to quantify the annotation quality and time needed. 15 participants were tasked to annotate a total of 40 minutes of data from two publicly available benchmark datasets for inertial activity recognition, being simultaneously displayed both video and accelerometer data during annotation. We compare the resulting labels with the ground truth provided by the original dataset authors. Our participants annotated the data using two representative tools. Metrics like F1-Score and Cohen’s Kappa showed experience did not ensure better labels. While experts were more accurate on the complex Wetlab dataset (51% vs 46%), Novices had 96% F1 on the simple WEAR dataset versus 92% for experts. Comparable Kappa scores (0.96 and 0.94 for WEAR, 0.53 and 0.59 for Wetlab) indicated similar quality for both groups, revealing differences in dataset complexity. Furthermore, experts annotated faster regardless of the tool. Given proven success across research, our findings suggest crowdsourcing wearable dataset annotation to non-experts warrants exploration as a valuable yet underinvestigated approach, up to a complexity level beyond which quality may suffer.

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Understanding the Effects of data Encoding on Quantum-Classical Convolutional Neural Networks

Understanding the Effects of Data Encoding on Quantum-Classi...

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Quantum computing and Engineering (QCE), ieee international conference on

作者： Maureen Monnet Nermine Chaabani Theodora-Augustina Drăgan Balthasar Schachtner Jeanette Miriam Lorenz Fraunhofer Institute for Cognitive Systems IKS Germany LMU University Hopsital Germany

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

Quantum machine learning was recently applied to various applications and leads to results that are comparable or, in certain instances, superior to classical methods, in particular when few training data is available. These results warrant a more in-depth examination of when and why improvements can be observed. A key component of quantum-enhanced methods is the data encoding strategy used to embed the classical data into quantum states. However, a clear consensus on the selection of a fitting encoding strategy given a specific use-case has not yet been reached. This work investigates how the data encoding impacts the performance of a quantum-classical convolutional neural network (QCCNN) on two medical imaging datasets. In the pursuit of understanding why one encoding method outperforms another, two directions are explored. Potential correlations between the performance of the quantum-classical architecture and various quantum metrics are first examined. Next, the Fourier series decomposition of the quantum circuits is analyzed, as variational quantum circuits generate Fourier-type sums. We find that while quantum metrics offer limited insights into this problem, the Fourier coefficients analysis appears to provide better clues to understand the effects of data encoding on QCCNNs.

关键词： Measurement Image coding Correlation Ultrasonic imaging Training data Encoding Hardware Convolutional neural networks Quantum circuit Biomedical imaging

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A Prediction Method for the Industry of an Enterprise Based on Knowledge Graph Completion Technology

A Prediction Method for the Industry of an Enterprise Based ...

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ieee international conference on Cloud computing and Big data Analysis (ICCCBDA)

作者： Dezhi Kong Min Tan Yang Cao Dingding Li CETC Big Data Research Institute Co. Ltd. Guiyang city China

ISBN: (数字)9798350373554

ISBN: (纸本)9798350373561

This article proposes a method based on knowledge graph completion technology, aiming to achieve accurate prediction of the industry to which the enterprise belongs. This method first constructs an enterprise knowledge graph, and then completes the knowledge graph through path analysis and relationship description to capture the complex relationship between enterprises and industries. Through experimental verification, this method can effectively improve the accuracy of industry prediction for enterprises. Compared to traditional machine learning methods and statistical models, methods based on knowledge graph completion technology have higher prediction accuracy and wider application prospects.

关键词： Industries Training Accuracy Semantics Redundancy Noise Knowledge graphs

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RIME-SVM for Electromagnetic Radiation Signal Identification in Coal Mining Environment

RIME-SVM for Electromagnetic Radiation Signal Identification...

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ieee international conference on Power, Intelligent computing and Systems (ICPICS)

作者： Pengxue Zhu Shiqi Huang Le Liu Zheng Zhang China West Normal University Nanchong China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

In this study, an innovative signal classification and recognition system is proposed for the early warning needs of disasters such as impact ground pressure in coal mining. The RIME-SVM model is introduced, which combines the Rime Optimization Algorithm and Support Vector Machine to optimize the classifier parameter configuration and significantly improve the classification performance. Experiments have demonstrated that the model has excellent signal classification ability in industrial environments, which is especially suitable for the identification of electromagnetic radiation signals in coal mine environments. Compared with the traditional method, RIME-SVM significantly improves the classification accuracy, demonstrates the efficiency and accuracy of processing complex industrial signal data, and brings important progress to the field of coal mine safety monitoring technology.

关键词： Support vector machines Time-frequency analysis Accuracy Pattern classification Electromagnetic radiation data models Coal mining Classification algorithms Optimization Monitoring

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Development of a Visualization Surrogate Model for Time-Varying Numerical Simulations

Development of a Visualization Surrogate Model for Time-Vary...

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ieee international conference on High Performance computing Workshops (HiPCW)

作者： Tomoya Miyake Naohisa Sakamoto Kobe University Kobe Japan

ISBN: (数字)9798331509118

ISBN: (纸本)9798331509125

Recent advances in HPC-oriented technologies have made large-scale numerical simulations feasible on an extreme scale and enabled their use in solving increasingly complex real-world scientific problems. However, the analysis of such real-world phenomena requires not only highly accurate computations but also realistic turnaround execution time. In this context, research on surrogate models has recently increasingly attracted attention as an effective alternative to traditional numerical simulations. This is mainly because it can significantly reduce computational time by replacing numerical simulations with approximate models, such as machine learning models. However, when applied to large-scale simulations, the output data can proportionately become large, thus leading to potential difficulties in later visualization and analysis tasks. Therefore, a great reduction in the overall processing time can be expected if we can build an image-based, or visualization, surrogate model by also taking visualization processing into account, that is, to directly output rendering images rather than simulation results.

关键词： Analytical models Computational modeling Simulation High performance computing data visualization Machine learning Numerical simulation Rendering (computer graphics) data models Numerical models

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Supporting Opportunistic data Operations for data-Intensive Computational Applications

Supporting Opportunistic Data Operations for Data-Intensive ...

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ieee international conference on Big data

作者： Minh-Tri Nguyen Anh-Dung Nguyen Jarno Rantaharju Touko Puro Matthias Rheinhardt Maarit Korpi-Lagg Hong-Linh Truong Department of Computer Science Aalto University

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

A long running data-intensive computational application acquires costly computing resources. With the emerging new architectures, like computing systems with multiple nodes of many-core CPUs and accelerators, while domain-specific tools and libraries employed in such an application leverage high parallelism on accelerators for intensive computations, the remaining resources can potentially be utilized for other application-related data operations. Such data operations, called opportunistic data operations in this work, must usually be carried out for post-processing or follow-up analytics based on results produced during the runtime of the application. These operations are not easily backfilled or preempted under the guidance of the domain scientist or by common task scheduling systems due to their complex *** this paper, we introduce a framework for domain scientists to identify and execute opportunistic data operation tasks. With a minimal specification or modification of the main application, the scientists can specify, monitor, and execute opportunistic tasks independently from the main application and the framework will detect underutilized resources to execute these tasks, thereby, optimizing utilization efficiency within the allocated resources. We present experiments to demonstrate the applicability of our framework on a magnetic field modeling running on the LUMI computing system.

关键词： Runtime Computational modeling Computer architecture Parallel processing Big data Libraries Magnetic fields Monitoring

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Analysis of Hierarchical data Fusion Target Detection Architecture Based on Autonomous Learning

Analysis of Hierarchical Data Fusion Target Detection Archit...

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ieee international conference on Power, Intelligent computing and Systems (ICPICS)

作者： Jiatao Zhang Runze Yang Chengjun Xu Air Defense Academy of Artillery and Air Defense Forces Hefei China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

This paper proposes an innovative algorithmic framework for drone target detection, aiming to improve image capture performance in complex environments. The algorithm integrates visible light and infrared images, employing a dual-channel architecture to independently extract multi-level characteristics from these two types of images. Through a self-learning hierarchical fusion mechanism, it enhances the complementarity of image information. Based on bidirectional feature pyramid networks, it progressively strengthens the fusion of surface and deep features, thus comprehensively capturing multi-scale information about targets. Independent target prediction on feature mappings at different levels further enhances detector efficiency. Simulation and comparative experiments on multiple public datasets confirm the rational design and outstanding performance of the algorithm, demonstrating robust stability and generalization capabilities.

关键词： Sensitivity Target recognition Robust stability data integration Object detection Computer architecture Detectors Feature extraction Robustness Drones

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Evolving cognitive Models: A Novel Approach to Verbal Learning

Evolving Cognitive Models: A Novel Approach to Verbal Learni...

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ieee international conference on cognitive Machine Intelligence (CogMI)

作者： Noman Javed Dmitry Bennett Laura K. Bartlett Peter C. R. Lane Fernand Gobet Centre for Philosophy of Natural and Social Science London School of Economics London UK School of Physics Engineering and Computer Science University of Hertfordshire Hatfield UK

ISBN: (数字)9798350386721

ISBN: (纸本)9798350386738

A common goal in cognitive science involves explaining/predicting human performance in experimental settings. This study proposes a single GEMS computational scientific discovery framework that automatically generates multiple models for verbal learning simulations. GEMS achieves this by combining simple and complex cognitive mechanisms with genetic programming. This approach evolves populations of interpretable cognitive agents, with each agent learning by chunking and incorporating long-term memory (LTM) and short-term memory (STM) stores, as well as attention and perceptual mechanisms. The models simulate two different verbal learning tasks: the first investigates the effect of prior knowledge on the learning rate of stimulus-response (S-R) pairs and the second examines how backward recall is affected by the similarity of the stimuli. The models produced by GEMS are compared to both human data and EPAM – a different verbal learning model that utilises hand-crafted task-specific strategies. The models automatically evolved by GEMS produced good fit to the human data in both studies, improving on EPAM’s measures of fit by almost a factor of three on some of the pattern recall conditions. These findings offer further support to the mechanisms proposed by chunking theory (Simon, 1974), connect them to the evolutionary approach, and make further inroads towards a Unified Theory of Cognition (Newell, 1990).

关键词： Computational modeling Genetic programming data models Cognition cognitive science Machine intelligence

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Virtual Reality Integration in Teaching Practices for Enhanced Learning Experiences

Virtual Reality Integration in Teaching Practices for Enhanc...

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Electronic Communications, Internet of Things and Big data (ICEIB), international conference on

作者： Wang Xi Guangdong University of Science and Technology Dongguan China

ISBN: (数字)9798350360721

ISBN: (纸本)9798350360738

Virtual reality (VR) is integrated into teaching practices, leveraging its advantages to have students more engaged and understand complex and three-dimensional conceptions. The impact of VR on visualization and understanding was explored. VR allowed learners to interact and gain full knowledge of the subject. It also enhanced learners' involvement, personalized learning, and cognitive understanding. It is recommended to provide a strategic curriculum and professional development for teachers, and the availability of VR resources for the students. VR can change education and meet the learner's need for involvement, interactivity, and inclusion with improved learning outcomes.

关键词： Electric potential Bibliographies Education Virtual reality Big data Internet of Things Best practices

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Exploring Eye Gaze Patterns in Three Dimensions: An Innovative Visualization Dashboard

Exploring Eye Gaze Patterns in Three Dimensions: An Innovati...

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Contemporary computing and Communications (InC4), ieee international conference on

作者： Akshay S Yashwanth M V Manohar N Tian Jipeng Amudha J Department of Computer Science Amrita Vishwa Vidyapeetham School of Computing Mysore Karnataka India Department of Computer Science Computer Science College Zhongyuan University of Technology China Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham School of Computing Bengaluru Karnataka India

ISBN: (数字)9798350383652

ISBN: (纸本)9798350383669

Eye tracking is a method of measuring and analyzing the movements of the eyes as they move and focus on different objects in the visual field. This technique has been used in a wide range of fields, including psychology, neuroscience and human-computer interaction. Eye tracking provides valuable insights into how individuals process visual information, including attentional biases, cognitive processing, and decision-making. Traditionally, eye tracking has relied on licensed software and specialized hardware, making it costly and less accessible for some researchers and applications. However, with recent technological advancements and by leveraging the capabilities of webcams, researchers, and practitioners can now perform eye-tracking experiments without the need for expensive equipment. By utilizing webcams, researchers can conduct eye-tracking experiments in a cost-effective manner, making the method more accessible to a broader range of researchers and practitioners.

关键词： Visualization Three-dimensional displays Neuroscience Webcams data visualization Psychology Gaze tracking

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