1,721,076 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Resource Efficiency and Circular Economy in European SMEs: Investigating the Role of Green Jobs and Skills
Full text linkThis study explored the size and potential of green employment for circular economy (CE) in small and medium enterprises (SMEs) in the European Union, and investigated the role of green jobs and skills for the implementation of CE practices. The data were collected in a Eurobarometer survey, and refer to resource efficiency, green markets, and CE procedures. Lack of environmental expertise is one of the factors that might be perceived as an obstacle when trying to implement resource-efficiency actions. Previous research has shown that, although resource-efficiency practices are adopted by firms in all European countries, there are differences both within and between countries. The analysis of the determinants of green behavior by European SMEs was completed by a study of heterogeneity across firms and within countries with a multilevel latent class model, a hierarchical clustering method. A general important observation is that having no workers dedicated to green jobs is strongly correlated to the probability of adopting resource-efficiency practices, while perceiving the need of extra environmental skills has a positive effect on the intention to implement actions in the future. Other characteristics of the firms play a significant impact on resource efficiency: in general, older and bigger firms, with larger yearly turnover, are more prone to implement actions
Variations on the Author
Full text link“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
Trust the Robot! Enabling Flexible Collaboration With Humans via Multi-Sensor Data Integration
No full textCollaborative robots in manufacturing offer significant potential for developing synergies between the skills of the workforce and the capabilities of the robots, increasing efficiency and reducing the psycho-physical effort required from workers. One of the main barriers to the adoption of these systems is the workers' lack of trust in robots, which can negatively affect both performance and well-being. To address this issue, a precise monitoring phase must be conducted with sensors and algorithms that enable data fusion from different sources. Nevertheless, in an operative context, the presence of cumbersome setups to monitor both workers and cobots can slow down performance and create bias and unsatisfactory working conditions. For this reason, this work proposes a framework that describes the transition from an integrated Human Digital Twin toward a lighter monitoring setup, exploiting the potential of Machine Learning algorithms during the operative phase to reduce the number of required sensors. In fact, while extensive data is valuable during the design phase of collaborative workstations, the operational phase should minimize sensor use, leveraging pre-gathered data to train Machine Learning networks for estimating missing quantities. To achieve such a level of data quality in the pre-deployment and design phases, we introduce an algorithm for real-time alignment of body poses estimated by different Motion Capture technologies. This method provides accurate, occlusion-robust body pose estimation while also solving the drifting phenomena that affect inertial measurement units. Consequently, the proposed approach establishes a robust foundation for enhancing Human-Robot Collaboration by ensuring precise and reliable real-time body pose estimation, a crucial step for advancing safety and efficiency in the manufacturing field
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe 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
Enhancing Robot Collaboration by Improving Human Motion Prediction Through Fine-Tuning
No full textIn Collaborative Robotics, 3D Human Motion Prediction (HMP) is of paramount importance to enable proactive robot assistance. It exploits past knowledge to provide insight into future body trajectories to integrate automation and humans. Unfortunately, data collection for robotics is often expensive and time-consuming, and only limited information is available. In this work, we propose a fine-tuning approach to improve the prediction accuracy for HMP in context-specific datasets. A state-of-the-art Deep Learning model, namely Position-Velocity Recurrent Encoder-Decoder (PVRED), is first pre-trained on the Human 3.6M dataset for HMP, and then tuned to suit specific motions. The experiments involved three smaller target datasets, considered in portions of increasing size, and two different levels of the PVRED architecture complexity. Compared to a scratch approach, the results showed that fine-tuning (i) reduced the number of training epochs, (ii) lowered the prediction error, and (iii) required a smaller dataset size. Moreover, the fine-tuned model showed even more advantages than increasing the PVRED complexity for scratch training. The proposed approach successfully transferred knowledge from the source domain to the fine-tuned model to predict human motion from a smaller target dataset. This demonstrates the significant potential of the proposed solution in practical applications with minimal training data for Collaborative Robotics
A Framework for a Closed Loop Control System of a Human Operator in a Manual Workstation
No full textVision systems are being increasingly used in the digitalization process of industrial and manufacturing plants. Among them, marker-less Motion Capture (MOCAP) technology represent a valuable tool since it frees human operators from uncomfortable equipment on their body, allowing them to perform their activities as normal. We are currently implementing this technology at the Industrial Plants and Logistics Laboratory of the University of Padua, by applying a set of Intel Realsense depth cameras to a manual workstation and by linking them with a skeleton tracking software. The aim is to create a closed control loop that can monitor the activities performed by a human operator, offering real-time feedback. In this work we present a framework that describes the functioning of the closed loop and we present an example of its laboratory implmentation
Enhancing Real-Time Body Pose Estimation in Occluded Environments Through Multimodal Musculoskeletal Modeling
Full text linkIn recent years, there has been a growing interest in Human-Robot Collaboration (HRC). One of the main challenges in developing effective tools for HRC is accurately estimating human pose in real-time, ensuring both human safety and efficient collaboration. To address this, we propose a novel approach enabling accurate and robust full-body pose estimation in real-time, even in the presence of occlusions. Our system combines information from RGB-D cameras and inertial measurement units, leveraging it to control a musculoskeletal model of the human through a multimodal inverse kinematics optimization. This approach ensures improvements in the anatomical realism and accuracy of the tracked movement while allowing flexibility in accommodating various sensor configurations. The consideration of the underlying anatomical structure also enhances the ability to estimate body poses in occluded environments. We conducted several HRC experiments where the operator's view was obstructed by various types of occlusions. The outcomes demonstrate how our methodology significantly improves pose estimation accuracy, even with a limited set of sensors and in the presence of occlusions in the scene. Our work aims to facilitate advanced HRC applications that require a precise understanding of human movement
Hi-ROS: Open-source multi-camera sensor fusion for real-time people tracking
No full textThis paper presents Hi-ROS (Human Interaction in ROS), an open source framework focused on real-time accurate assessment of human motion. The system offers a series of tools to track multiple people in real-time by exploiting a calibrated camera network. No assumptions are made about the typology or number of cameras, nor about the body pose estimation algorithm used to extract the 3D poses of the people in the scene. The tools provided by Hi-ROS include a Skeleton Tracker to ensure temporal consistency of the detected poses, a Skeleton Merger to fuse the tracks from multiple cameras, thus limiting flickering phenomena, a Skeleton Optimizer to ensure limb length consistency, and a Skeleton Filter to perform real-time smoothing of the detected joint trajectories. Accuracy, tracking robustness, and real-time performance of the proposed system were evaluated on a public dataset, containing both single-person and multi-person sequences with up to 4 people interacting. The results obtained using different subsets of the proposed tools show how the complete Hi-ROS pipeline provides accurate and reliable estimates also in challenging scenarios, with a reduction of the RMSE of up to 27% with respect to a pure tracking approach. This work aims to push forward the development of unobtrusive human–robot interaction applications, multi-person automated posture analyses, rehabilitation performance assessments, and any possible application enabled by real-time accurate assessment of human motion via markerless motion capture
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