We propose a sign language translation system based on human keypoint estimation. It is well-known that many problems in the field of computer vision require a massive dataset to train deep neural network models. The situation is even worse when it comes to the sign language translation problem as it is far more difficult to collect high-quality training data. In this paper, we introduce the KETI (Korea Electronics Technology Institute) sign language dataset, which consists of 14,672 videos of high resolution and quality. Considering the fact that each country has a different and unique sign language, the KETI sign language dataset can be the starting point for further research on the Korean sign language translation. Using the KETI sign language dataset, we develop a neural network model for translating sign videos into natural language sentences by utilizing the human keypoints extracted from the face, hands, and body parts. The obtained human keypoint vector is normalized by the mean and standard deviation of the keypoints and used as input to our translation model based on the sequence-to-sequence architecture. As a result, we show that our approach is robust even when the size of the training data is not sufficient. Our translation model achieved 93.28% (55.28%, respectively) translation accuracy on the validation set (test set, respectively) for 105 sentences that can be used in emergency situations. We compared several types of our neural sign translation models based on different attention mechanisms in terms of classical metrics for measuring the translation performance.

Monitoring players’ fatigue is essential to maintaining the best performance of players during sports games. The level of fatigue can be measured by the external workload, the aggregated amount of physical activity or internal workload, which is an individual’s psycho-physiological response to that activity. There have been a growing number of studies focusing on the relationship between external and internal workloads for efficient fatigue monitoring. However, they utilize aggregated features to represent the external workload, losing raw data details such as sequential information. This study proposes a deep learning algorithm to predict Rate of Perceived Exertion (RPE) from players’ movement data instead of aggregated features. Electronic Performance and Tracking Systems (EPTS) powered by GPS sensors collected players’ movement data and the RPE from training and match sessions during a Korean professional soccer team season. We preprocessed the raw GPS data to obtain linear and angular components of velocity, acceleration, and jerk. Our proposed model, named FatigueNet, effectively predicted the RPE with mean absolute error (MAE) = 0.8494 ± 0.0557 and root mean square error (RMSE) = 1.2166 ± 0.0737 using the preprocessed movement features. To interpret the predictions of the FatigueNet, we also performed regression activation mapping to localize the discriminative time intervals that contributed more to the prediction results. Our experimental results imply the possibility of automated and objective fatigue monitoring systems based on deep learning instead of arduous manual data collection from players.

The advancement of neural-based models has driven significant progress in modern code intelligence, accelerating the development of intelligent programming tools such as code assistants and automated software engineering systems. This study presents a comprehensive and systematic survey of neural methods for programming tasks within the broader context of software development. Guided by six research questions, this study synthesizes insights from more than 250 scientific papers, the majority of which were published between 2015 and 2025, with earlier foundational works (dating back to the late 1990s) included for historical context. The analysis spans 18 major programming tasks, including code generation, code translation, code clone detection, code classification, and vulnerability detection. The survey methodologically examines the development and evolution of neural approaches, the datasets employed, and the performance evaluation metrics adopted in this field. It traces the progress in neural techniques from early code modeling approaches to advanced Code-specific Large Language Models (Code LLMs), emphasizing their advantages over traditional rule-based and statistical methods. A taxonomy of evaluation metrics and a categorized summary of datasets and benchmarks reveal both progress and persistent limitations in data coverage and evaluation practices. The review further distinguishes neural models designed for natural language processing and programming languages, highlighting the structural and functional characteristics that influence model performance. Finally, the study discusses emerging trends, unresolved challenges, and potential research directions, underscoring the transformative role of neural-based architectures, particularly Code LLMs, in enhancing programming and software design activities and shaping the future of AI-driven software development.