Reviewerly/exHarmony huggingface dataset:
Synthetic Corpus Generation for Deep Learning-Based Translation of Spanish Sign Language

Sign language serves as the primary mode of communication for the deaf community. With technological advancements, it is crucial to develop systems capable of enhancing communication between deaf and hearing individuals. This paper reviews recent state-of-the-art methods in sign language recognition, translation, and production. Additionally, we introduce a rule-based system, called ruLSE, for generating synthetic datasets in Spanish Sign Language. To check the usefulness of these datasets, we conduct experiments with two state-of-the-art models based on Transformers, MarianMT and Transformer-STMC. In general, we observe that the former achieves better results (+3.7 points in the BLEU-4 metric) although the latter is up to four times faster. Furthermore, the use of pre-trained word embeddings in Spanish enhances results. The rule-based system demonstrates superior performance and efficiency compared to Transformer models in Sign Language Production tasks. Lastly, we contribute to the state of the art by releasing the generated synthetic dataset in Spanish named synLSE.
Beyond hard workout: A multimodal framework for personalised running training with immersive technologies

Abstract Training to run is not straightforward since without proper personalised supervision and planning, people will not improve their performance and will increase the risk of injuries. This study aims to identify the different factors that influence running training programmes, examining the benefits, challenges or limitations of personalised plans. Moreover, this study explores how multimodal, immersive and artificial intelligence technologies can support personalised training. We conducted an exploratory sequential mixed research consisting of interviews with 11 running coaches from different countries and a survey of 12 running coaches. Based on the interviews and survey analysis, we identified and extracted relevant factors of the training process. We identified four relevant aspects for running training: physical, technical, mental and body awareness. Using these aspects as a reference, we derived a framework using a bottom‐up approach. This framework proposes multimodal, immersive and artificial intelligence technologies to facilitate personalised running training. It also allows coaches to personally guide their athletes on each aspect. Practitioner notes What is already known about this topic Running is a popular sport that provides health benefits and is practised by many people around the world. Training is a process that enables athletes to improve their development in various aspects of their sport; in the case of running, it helps them to increase their speed and endurance. Personalised training supports the needs and abilities of athletes, by helping them to achieve their potential through individualised activities or programmes. Sports science research indicates that personalised training can be improved by applying technology to tackle its challenges and limitations. What this paper adds We show that personalising the training requires not only focusing on the runners' physical condition but also on their mental, technical and body awareness aspects, where each of them has a different adaptation to training. We show that multimodal and immersive technologies offer suitable and portable ways to measure and target the mental and body awareness aspects during running training. Implications for practice and/or policy This paper presents a list of factors, measures and devices that coaches can use to plan and design their training sessions in a more personalised manner. This study can serve as a foundation for future research that aims to identify and target the various factors that influence the learning and training of sports.
Concept of «Mystery» and its Realization in the Works of J. Lippard and N. Buntline

Introduction. The article is devoted to the analysis of the linguistic means expressing the concept of «mystery» in the fictional works of J. Lippard and N. Buntline, the allocation of its socio-cultural component and the field model construction of lexical units representing this concept. The relevance of the research is due to the interest of modern linguistics in the study of the conceptual sphere of artistic works of the «urban mysteries» genre by American authors. Methodology and sources. The study was conducted within the framework of a linguistic and cultural approach using methods of description, comparison, interpretative analysis, conceptual analysis, the method of dictionary definitions analyzing, structural and semantic analysis, and the method of quantitative calculations. The illustrative material of the study consisted of more than 74 lexical units representing the «mystery» concept in the language, 45 of which were found in the analyzed works. The material was obtained by the method of directed sampling from the fictional works of American authors of the 19th century. Results and discussion. This article discusses and analyzes various interpretations of the concept of «mystery» by domestic and foreign scientists; defines the role of the concept in the work of fiction as a whole and in the analyzed novels of two authors J. lippard and N. Buntline; identifies the features of the linguistic implementation of the «mystery» concept; classifies the linguistic units of the concept according to their parts of speech, semantics and stylistic coloring; analyzes the writers’ linguistic pictures of the world and the conceptual sphere of their works, the illustrative zone of which reveals the reasons for the similarity not only of the storylines of the works, but also of their lexical content. Conclusion. The study made it possible to describe and illustrate the individual style of American authors of the 19th century through a system of expressive means that correlates the inner world of the writer with the artistic reality, the artistic world of the text created by the «poetic» language. The mental world can be interpreted in cognitive terminology as an individual poetic conceptual sphere, or the conceptual system of the author, consisting of a conceptual, objective, associative, figurative and symbolic layer of the concept. The study showed that the learning of the writer's linguistic picture of the world and the conceptual sphere of the work can be carried out using a field model, analyzing the LSF forming the «mystery» concept. The analyzed LSF is distinguished by a more specific vocabulary composition, the complication of the seme structure of words, the seme expansion and the restructuring of their hierarchy, which together form the artistic worldview of the authors.
Dehazing, enhancing the boundaries and corners in hazed images using optimal adaptive technique

Objects or images captured from the remote area will not have good quality and will not be visible because of mist, smoke, moisture and fog. Here, we rub/remove the unwanted hazes from the image using the optimal adaptive technique. An examination of the built-in boundary constraint of the transmission function is extremely valuable to our method. This constraint is handled as an optimisation problem to predict the unknown scene transmission. In addition, transmission refining techniques are used to remedy the issue based on variable splitting methodology. With only a few broad assumptions, the proposed method may recover haze-free, high-quality images having original colours and better image information. Experiment findings are done with plenty of foggy images indicating the efficiency and effectiveness of the proposed technique by using image quality parameters.
Finite impulse response design based on two‐level transpose Vedic multiplier for medical image noise reduction

Abstract Medical signal processing requires noise and interference‐free inputs for precise segregation and classification operations. However, sensing and transmitting wireless media/devices generate noise that results in signal tampering in feature extractions. To address these issues, this article introduces a finite impulse response design based on a two‐level transpose Vedic multiplier. The proposed architecture identifies the zero‐noise impulse across the varying sensing intervals. In this process, the first level is the process of transpose array operations with equalization implemented to achieve zero noise at any sensed interval. This transpose occurs between successive array representations of the input with continuity. If the continuity is unavailable, then the noise interruption is considerable and results in signal tampering. The second level of the Vedic multiplier is to optimize the transpose speed for zero‐noise segregation. This is performed independently for the zero‐ and nonzero‐noise intervals. Finally, the finite impulse response is estimated as the sum of zero‐ and nonzero‐noise inputs at any finite classification.
Optimized systems of multi-layer perceptron predictive model for estimating pile-bearing capacity

Abstract The primary goal of this research is to leverage the advancements in machine learning techniques to forecast the bearing capacity of piles effectively. Accurately predicting load-bearing capability is an indispensable aspect in the field of substructure engineering. It is worth noting that determining load-bearing capability via in-place burden tests is a resource-intensive and labor-intensive process. This study presents a pragmatic soft computing methodology to tackle the aforementioned challenge, employing a multi-layer perceptron (MLP) for the estimation of load-bearing capacity. The dataset employed in this research encompasses a multitude of field-based pile load tests, with a meticulous selection of the most impactful factors influencing pile-bearing capacity as input variables. For a comprehensive comparative analysis, genetic algorithm-based optimizers (Crystal Structure Algorithm (CSA) and Fox Optimization (FOX)) were incorporated with MLP, leading to the development of hybrid models referred to as MLFO and MLSC, both structured with three layers. The performance of these models was rigorously evaluated using five key performance indices. The findings indicated a consistent superiority of MLFO over MLSC across all three layers. Remarkably, MLFO exhibited exceptional performance in the second layer (MLFO (2)), boasting an impressive R 2 value of 0.992, an RMSE of 33.470, and a minimal SI value of 0.031. On the other hand, MLCS (1) registered the lowest accuracy in predicting the process with the least R 2 value related to the validation phase of the model with 0.953. Taken together, these results affirm that the optimized MLP model stands as a valuable and practical tool for accurately estimating pile-bearing capacity in civil engineering applications.
Prediction and explainability in AI: Striking a new balance?

The debate regarding prediction and explainability in artificial intelligence (AI) centers around the trade-off between achieving high-performance accurate models and the ability to understand and interpret the decisionmaking process of those models. In recent years, this debate has gained significant attention due to the increasing adoption of AI systems in various domains, including healthcare, finance, and criminal justice. While prediction and explainability are desirable goals in principle, the recent spread of high accuracy yet opaque machine learning (ML) algorithms has highlighted the trade-off between the two, marking this debate as an inter-disciplinary, inter-professional arena for negotiating expertise. There is no longer an agreement about what should be the “default” balance of prediction and explainability, with various positions reflecting claims for professional jurisdiction. Overall, there appears to be a growing schism between the regulatory and ethics-based call for explainability as a condition for trustworthy AI, and how it is being designed, assimilated, and negotiated. The impetus for writing this commentary comes from recent suggestions that explainability is overrated, including the argument that explainability is not guaranteed in human healthcare experts either. To shed light on this debate, its premises, and its recent twists, we provide an overview of key arguments representing different frames, focusing on AI in healthcare.
Knowledge-based recommender systems: overview and research directions

Recommender systems are decision support systems that help users to identify items of relevance from a potentially large set of alternatives. In contrast to the mainstream recommendation approaches of collaborative filtering and content-based filtering, knowledge-based recommenders exploit semantic user preference knowledge, item knowledge, and recommendation knowledge, to identify user-relevant items which is of specific relevance when dealing with complex and high-involvement items. Such recommenders are primarily applied in scenarios where users specify (and revise) their preferences, and related recommendations are determined on the basis of constraints or attribute-level similarity metrics. In this article, we provide an overview of the existing state-of-the-art in knowledge-based recommender systems. Different related recommendation techniques are explained on the basis of a working example from the domain of survey software services. On the basis of our analysis, we outline different directions for future research.
Time-dependant Bayesian knowledge tracing—Robots that model user skills over time

Creating an accurate model of a user's skills is an essential task for Intelligent Tutoring Systems (ITS) and robotic tutoring systems. This allows the system to provide personalized help based on the user's knowledge state. Most user skill modeling systems have focused on simpler tasks such as arithmetic or multiple-choice questions, where the user's model is only updated upon task completion. These tasks have a single correct answer and they generate an unambiguous observation of the user's answer. This is not the case for more complex tasks such as programming or engineering tasks, where the user completing the task creates a succession of noisy user observations as they work on different parts of the task. We create an algorithm called Time-Dependant Bayesian Knowledge Tracing (TD-BKT) that tracks users' skills throughout these more complex tasks. We show in simulation that it has a more accurate model of the user's skills and, therefore, can select better teaching actions than previous algorithms. Lastly, we show that a robot can use TD-BKT to model a user and teach electronic circuit tasks to participants during a user study. Our results show that participants significantly improved their skills when modeled using TD-BKT.
LiDAR point clouds analysis computer tools for teaching autonomous vehicles perception algorithms

Abstract The technological developments behind autonomous vehicles cover several areas and engineers training in this field represents a challenge. The main layers include perception, decision making, and acting. In the first one, different technologies can be used. The processing of the information provided by the sensors must allow successive modules to understand the environment and Laser imaging Detection and Ranging (LiDAR) technology is one of the most promising ones nowadays for this task. It offers great robustness in detection, but the extraction of information from the point cloud involves the development of complex algorithms that could be very time‐consuming if an experimental teaching is intended. This article presents two educational solutions for deepening in perception algorithms using LiDAR for autonomous driving: a closed ad‐hoc computer application for two‐dimensional (2D) LiDAR point cloud processing and an oriented set of commands for three‐dimensional (3D) LiDARs in Matlab. Their use allows main concept exploration in practical sessions with little time consumption and provides students a general overview of the tasks that must be performed by the perception layer in the autonomous vehicles. Furthermore, these tools provide the possibility of organizing different activities in the classroom related to theoretical and experimental issues, and understanding of results because the most tedious tasks are eased.
Benchmarking the influence of pre-training on explanation performance in MR image classification

Convolutional Neural Networks (CNNs) are frequently and successfully used in medical prediction tasks. They are often used in combination with transfer learning, leading to improved performance when training data for the task are scarce. The resulting models are highly complex and typically do not provide any insight into their predictive mechanisms, motivating the field of "explainable" artificial intelligence (XAI). However, previous studies have rarely quantitatively evaluated the "explanation performance" of XAI methods against ground-truth data, and transfer learning and its influence on objective measures of explanation performance has not been investigated. Here, we propose a benchmark dataset that allows for quantifying explanation performance in a realistic magnetic resonance imaging (MRI) classification task. We employ this benchmark to understand the influence of transfer learning on the quality of explanations. Experimental results show that popular XAI methods applied to the same underlying model differ vastly in performance, even when considering only correctly classified examples. We further observe that explanation performance strongly depends on the task used for pre-training and the number of CNN layers pre-trained. These results hold after correcting for a substantial correlation between explanation and classification performance.
Interpreting, translating or investigating? Framing the competences of a hybrid translational field: intercept interpreting

When monitoring suspected criminals’ communications by bugging telephones, apartments or cars, the police collect information for ongoing investigations and the later proceedings in court. If foreign-language-speaking suspects are involved, intercept interpreters are indispensable for police work. Intercept interpreting is a hybrid translational activity that encompasses elements of interpreting and translation. This article explores the competences and skills required for this activity. It is based on 24 semi-structured interviews with intercept interpreters and police officers and on an observational study conducted in a Swiss police department. Core elements of translation and interpreting competence models are combined to reflect the hybrid nature of the activity. In a qualitative data analysis, we deductively identified typical translation and interpreting sub-competences, while a concurrent inductive analysis allowed us to extend the competence framework by new, specific elements of intercept interpreting. The results show that intercept interpreters not only exhibit special investigative and forensic skills, but that they also have many mental and emotional skills that enable them to carry out their demanding activity.
Efficient Information Retrieval and Response Generation with Retrieval-Augmented Generation (RAG)

&lt;strong&gt; How to efficiently retrieve information for different applications &lt;/strong&gt; &lt;strong&gt; Author: Wenyi Pi &lt;/strong&gt; &lt;strong&gt; https://orcid.org/0009-0002-2884-2771 &lt;/strong&gt; This article aims to explore various ways in which Retrieval-Augmented Generation (RAG) can be utilised to retrieve information and generate responses effectively within the dialogue system.
Real-Time Pose Recognition for Billiard Players Using Deep Learning

In this book chapter, the authors propose a method for player pose recognition in billiards matches by combining keypoint extraction and an optimized transformer. Given that those human pose analysis methods usually require high labour costs, the authors explore deep learning methods to achieve real-time, high-precision pose recognition. Firstly, they utilize human key point detection technology to extract the key points of players from real-time videos and generate key points. Then, the key point data is input into the transformer model for pose analysis and recognition. In addition, the authors design a human skeletal alignment method for comparison with standard poses. The experimental results show that the method performs well in recognizing players' poses in billiards matches and provides real-time and timely feedback on players' pose information. This research project provides a new and efficient tool for training billiard players and opens up new possibilities for applying deep learning in sports analytics. In addition, one of these contributions is the creation of a dataset for pose recognition.
Risky Dynamic Typing Related Practices in Python: An Empirical Study

Python’s dynamic typing nature provides developers with powerful programming abstractions. However, many type related bugs are accumulated in code bases of Python due to the misuse of dynamic typing. The goal of this paper is to aid in the understanding of developers’ high-risk practices towards dynamic typing and the early detection of type related bugs. We first formulate the rules of six types of risky dynamic typing related practices (type smells for short) in Python. We then develop a rule-based tool named RUPOR which builds an accurate type base to detect type smells. Our evaluation shows that RUPOR outperforms the existing type smell detection techniques (including the LLM-based approaches, Mypy, and PYDYPE) on a benchmark of 900 Python methods. Based on RUPOR, we conduct an empirical study on 25 real-world projects. We find that type smells are significantly related to the occurrence of post-release faults. The fault-proneness prediction model built with type smell features slightly outperforms the model built without them. We also summarize the common patterns including inserting type check to fix type smell bugs. These findings provide valuable insights for preventing and fixing type related bugs in the programs written in dynamic-typed languages.
Smart Attendance System using Face Recognition

&#x0D; Face recognition offers a wide range of valuable applications in social media, security, and surveillance contexts. The software used for building facial recognition algorithms is Python and OpenCV. "Attendance using Face Recognition" is a method for tracking and managing attendance that makes use of facial recognition technology. By seamlessly integrating the 'Face Recognition' module, a native Python feature, and the OpenCV library, our system excels in accuracy and dependability. The system then stores attendance records in a database and provides real-time reports. In this article, we demonstrate how to create a face recognition system in Python utilizing the built-in "Face Recognition" module and the OpenCV library. Our results show that our system achieves high accuracy and robustness while being efficient and scalable, catering to a wide spectrum of educational institutions, organizations, and enterprises.