Competency Questions (CQs) are not merely intended for scoping the prospective content of an ontology and as information-seeking queries posed over an ontology, but serve manifold purposes in the ontology engineering ...
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Much of the information that we use is geospatially referenced. The need for homogeneous representation of global geographic themes is recognised as critical for sustainable development goals. The richness of local ge...
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The hardness of Kolmogorov complexity is intricately connected to the existence of one-way functions and derandomization. An important and elegant notion is Levin’s version of Kolmogorov complexity, Kt, and its decis...
This research examines the employment of attention mechanism driven deep learning models for building subject-independent Brain-computer Interfaces (BCIs). The research evaluated three different attention models using...
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ISBN:
(纸本)9783031538261;9783031538278
This research examines the employment of attention mechanism driven deep learning models for building subject-independent Brain-computer Interfaces (BCIs). The research evaluated three different attention models using the Leave-One-Subject-Out cross-validation method. The results showed that the Hybrid Temporal CNN and ViT model performed well on the BCI competition IV 2a dataset, achieving the highest average accuracy and outperforming other models for 5 out of 9 subjects. However, this model did not perform the best on the BCI competition IV 2b dataset when compared to other methods. One of the challenges faced was the limited size of the data, especially for transformer models that require large amounts of data, which affected the performance variability between datasets. This study highlights a beneficial approach to designing BCIs, combining attention mechanisms with deep learning to extract important inter-subject features from EEG data while filtering out irrelevant signals.
This investigation focuses on a simultaneous wireless information and power transfer (SWIPT) system, significantly enhanced by an active simultaneously transmitting and reflecting reconfigurable intelligent surface (a...
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This paper presents a comprehensive study of learning assessment, delving into the concept of item difficulty and learner perception. It addresses two critical dimensions: the methodologies employed, particularly data...
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ISBN:
(纸本)9783031630279;9783031630286
This paper presents a comprehensive study of learning assessment, delving into the concept of item difficulty and learner perception. It addresses two critical dimensions: the methodologies employed, particularly data-driven approaches, and the necessary data for this analysis. Traditional difficulty estimation methods focus on question content or student performance. Recent studies suggest using machine learning and natural language processing to predict question difficulty. These models are subject-specific and often overlook individual student differences, limiting their wider application. The work aims to examine data of real-world testing scenarii, so that assembling and building a rich and diverse dataset. It offers valuable insights into the factors influencing item difficulty by giving the maximum amount of information considering the test and the student. It presents experiments to build and train predictive machine learning models for difficulty prediction. At the end, thanks to experiments, we can show a nuanced understanding of the assessment challenge and lay the groundwork for incorporating psychological factors into difficulty estimation as a subsequent phase.
Recent advances in AI, machine learning, and NLP have led to the development of a new generation of Large Language Models (LLMs) that are trained on massive amounts of data and often have trillions of parameters. Comm...
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ISBN:
(数字)9783031627002
ISBN:
(纸本)9783031626999;9783031627002
Recent advances in AI, machine learning, and NLP have led to the development of a new generation of Large Language Models (LLMs) that are trained on massive amounts of data and often have trillions of parameters. Commercial applications (e.g., ChatGPT) have made this technology available to the general public, thus making it possible to use LLMs to produce high-quality texts for academic and professional purposes. Schools and universities are aware of the increasing use of AI-generated content by students and they have been researching the impact of this new technology and its potential misuse. Educational programs in computerscience (CS) and related fields are particularly affected because LLMs are also capable of generating programming code in various programming languages. To help understand the potential impact of publicly available LLMs in CS education, we introduce CSEPrompts (https://***/mraihan-gmu/CSEPrompts), a framework with hundreds of programming exercise prompts and multiple-choice questions retrieved from introductory CS and programming courses. We also provide experimental results on CSEPrompts to evaluate the performance of several LLMs with respect to generating Python code and answering basic computerscience and programming questions.
This paper researches strategic decision-making in simulated curling using advanced computational methods. A simulated solution could revolutionize tactical discussions in curling and enhance the strategy of professio...
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Deep representation learning methods for computer-Aided Design (CAD) generative modeling have received increasing attention recently. However, parametric CAD sequences as the key to construct 3D CAD models essentially...
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ISBN:
(纸本)9789819755547;9789819755554
Deep representation learning methods for computer-Aided Design (CAD) generative modeling have received increasing attention recently. However, parametric CAD sequences as the key to construct 3D CAD models essentially contain the implicit geometry, which has not been concerned well. In this paper, we introduce Contrastive Fusion CAD (CF-CAD), a self-supervised multi-modal framework with a Transformer-based architecture. Our model enhances the correlation between the parametric CAD sequence and its geometry via additionally drawing 2D images into parametric CAD sequences with one shared code-book, which can optimize the latent representation of CAD models and empower it to achieve an interesting task of reversing images to parametric CAD sequences simultaneously. To further improve the alignment and uniformity of learned latent space, we also introduce a contrastive strategy to strike the well balance between parametric CAD sequences and 2D images. Extensive experiments on the commonly used benchmark datasets demonstrate the effectiveness of our CF-CAD for 3D CAD generative modeling.
Aiming at the problem of insufficient consideration of skill rarity and worker skill coverage in the task allocation decision of the current software crowdsourcing platform, this paper proposes a task priority-based s...
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