1,720,963 research outputs found
A Comparative Study of Machine Learning Approaches for Autism Detection in Children from Imaging Data
Get PDFAutism Spectrum Disorder (ASD) is a neurodevelopmental condition that affects language, communication, cognitive, and social skills. Early detection of ASD in children is crucial for effective intervention, and machine learning techniques have emerged as promising tools to improve the accuracy and efficiency of detection. This paper presents a range of Machine Learning approaches that have been applied to identify individuals with ASD, with a particular focus on children, using images as input data. The results of these studies demonstrate the potential for Machine Learning to aid in the early detection and diagnosis of ASD in children, which can lead to better outcomes for individuals with this condition
Distance Estimation of Fixed Objects in Driving Environments
Get PDFAutonomous driving is a highly relevant topic today, particularly among major car manufacturers attempting to lead in technological innovation and enhance driving safety. An autonomous vehicle must possess the capability to sense its environment and navigate without human intervention. Thus, it serves as both a driver support system and, in some cases, a substitute. A crucial aspect involves identifying the positions of pedestrians, traffic signs, traffic lights, and other vehicles while computing distances from them. This enables the vehicle to emit alerts to the driver in potentially dangerous situations, such as impending obstacles due to external factors or driver distraction. In this paper, we introduce an approach for identifying traffic signs and determining the distance from them. Our method utilizes the YOLOv4 network for identification and a customized network for distance computation. This integration of AI technologies facilitates the timely detection of hazards and enables proactive alert mechanisms, thereby advancing the capabilities of autonomous vehicles and enhancing driving safety
Eye-Tracking System with Low-End Hardware: Development and Evaluation
Get PDFEye-tracking systems have emerged as valuable tools in various research fields, including psychology, medicine, marketing, car safety, and advertising. However, the high costs of the necessary specialized hardware prevent the widespread adoption of these systems. Appearance-based gaze estimation techniques offer a cost-effective alternative that can rely solely on RGB cameras, albeit with reduced accuracy. Therefore, the aim of our work was to present a real-time eye-tracking system with low-end hardware that leverages appearance-based techniques while overcoming their drawbacks to make reliable gaze data accessible to more users. Our system employs fast and light machine learning algorithms from an external library called MediaPipe to identify 3D facial landmarks. Additionally, it uses a series of widely recognized computer vision techniques, like morphological transformations, to effectively track eye movements. The precision and accuracy of the developed system in recognizing saccades and fixations when the eye movements are mainly horizontal were tested through a quantitative comparison with the EyeLink 1000 Plus, a professional eye tracker. Based on the encouraging registered results, we think that it is possible to adopt the presented system as a tool to quickly retrieve reliable gaze information
A Fast and Accessible Neural Network Based Eye-Tracking System for Real-Time Psychometric and HCI Applications
Get PDFEye-tracking technology has long been a valuable tool across various domains, and recent advancements in neural networks have significantly expanded its versatility and potential. However, real-world applications continue to face challenges such as accommodating users’ natural movements, variations in lighting, occlusions of the eyes, and the limited availability of large, open-source datasets for training models. To address these issues, we developed a comprehensive pipeline that produces a lightweight and efficient model, requiring only an RGB camera as external hardware, making it easily deployable on standard PCs. Key input features include facial images, eye regions, head pose angles, the Eye Aspect Ratio (EAR), and a face grid that determines the face’s location within the camera’s frame. The model was trained using a custom dataset, in which participants were instructed to fixate on both randomly positioned points and the standard 9-point grid commonly employed in eye-tracking calibration. The resulting system was integrated into a real-time application, offering fast and accessible gaze tracking, making it well-suited for studies requiring rapid gaze assessments across broad regions of the screen, such as psychometric research and Human-Computer Interaction (HCI) tasks. Its design is particularly advantageous for gaze laterality studies, which explore hemispheric dominance and attentional bias in cognitive and emotional processing, key concepts relevant to ADHD and dyslexia. Moreover, the system’s capabilities naturally extend to emotional and decision-making tasks, where broad-area gaze tracking can support the analysis of preference formation and attentional patterns without the need for specialized hardware
A Real-time Hand Gesture Recognition System for Human-Computer and Human-Robot Interaction
Get PDFThe proposed hand gesture recognition (HGR) system is designed to enhance human-computer interaction (HCI) and human-robot interaction (HRI), which are crucial areas of research aimed at improving the way humans interact with computer or robot systems. With the growing need for intelligent computers and robots in a range of applications, including healthcare, manufacturing, and education, both HCI and HRI have gained significant importance. In this context, the HGR system plays a vital role by enabling natural and intuitive communication between humans and technology through hand gestures. The presented system uses a single camera and efficient image processing techniques that enable real-time gesture detection. Unlike other methods, our approach employs a basic video camera, which is widely available on most computers, eliminating the need for expensive and specialized hardware
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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