Author: Grace Collins

Authors: Valdemar Švábenský、Kristián Tkáčik、Aubrey Birdwell、Richard Weiss、Ryan S. Baker、Pavel Čeleda、Jan Vykopal、Jens Mache、Ankur Chattopadhyay Paper: https://arxiv.org/abs/2408.08531 Introduction As cyber threats become increasingly complex, the demand for cybersecurity experts has surged. Effective teaching methods, such as hands-on exercises, are essential for training these experts. However, the complexity of cybersecurity exercises often leads to student frustration and impedes learning. This paper aims to develop automated tools to predict when a student is struggling, enabling instructors to provide timely assistance. Goals and Scope of This Paper The primary goal is to extract information from student actions in cybersecurity exercises to predict student success or potential…

Authors: Zijian Zhang、Sara Aronowitz、Alán Aspuru-Guzik Paper: https://arxiv.org/abs/2408.08463 A Theory of Understanding for Artificial Intelligence: Composability, Catalysts, and Learning Introduction Understanding is a multifaceted concept that has intrigued philosophers, scientists, and AI researchers alike. This paper proposes a framework for analyzing understanding in artificial intelligence (AI) based on the notion of composability. The authors suggest characterizing understanding by a subject’s ability to process relevant inputs into satisfactory outputs from the perspective of a verifier. This framework is versatile and can be applied to non-human subjects, such as AIs, non-human animals, and institutions. The paper also introduces the concept of catalysts—inputs that…

Authors: Xiaosheng Li、Wenjie Xi、Jessica Lin Paper: https://arxiv.org/abs/2408.07956 Introduction Neural networks are fundamental in machine learning, data mining, and artificial intelligence. Typically, these networks undergo a training phase where their parameters are tuned according to specific learning rules and data. This training often involves backpropagation to optimize an objective function. Once trained, these networks can be deployed for various tasks, including classification, clustering, and regression. Time series clustering, which involves grouping time series instances into homogeneous groups, is a crucial and challenging task in time series data mining. It has applications in finance, biology, climate, medicine, and more. Existing methods for…

Authors: Minje Kim、Jan Skoglund Paper: https://arxiv.org/abs/2408.06954 Introduction Traditional speech and audio coding have long relied on model-based approaches to compress raw audio signals into compact bitstrings and then restore them to their original form. These models aim to maintain the quality of the original signal, such as speech intelligibility or other perceptual sound qualities, which are often subjectively defined. The development of these models typically involves multiple rounds of listening tests to measure the codec’s performance accurately. Figure 1 illustrates the ordinary development process of model-based coding systems. Despite the success of traditional codecs, the manual tuning of model parameters…

Authors: Xiaohan Cheng、Taiyuan Mei、Yun Zi、Qi Wang、Zijun Gao、Haowei Yang Paper: https://arxiv.org/abs/2408.06357 Introduction Medical image recognition technology, leveraging deep learning, has made significant strides. Traditional algorithms, however, require extensive labeled samples and struggle with novel categories. Zero-shot learning (ZSL) addresses this by enabling models to recognize unseen categories. This research explores embedding-based ZSL methods, focusing on aligning features and quasi-semantic information of medical images within a vector space. The challenge lies in avoiding overfitting known categories and ensuring accurate predictions for unknown categories. The study shifts from convolutional neural networks to Transformer-based frameworks, aiming to improve model performance through semantic similarity-based multi-label…