Search Results (60)

Accurate estimation of network density is central to egocentric social network analysis, yet existing survey-based methods require researchers to balance accuracy against participant burden and systematic recall bias. Traditional approaches, such as fixed-list name generators, tend to overrepresent salient ties. Although the more recent random sampling method yields better accuracy, it relies on exhaustive free recall, which can be cognitively demanding and impractical for researchers. In this study, we introduce and evaluate an alternative approach—incremental recall—that structures alter nomination across relationship categories to improve coverage of differing tie strengths while reducing respondent burden. Using a large-scale Monte Carlo simulation encompassing over 9 million egocentric networks, we compare incremental recall against traditional fixed-list recall and random sampling across a wide range of network sizes, compositions, and recall bias assumptions. Results show that the incremental recall method consistently outperforms traditional fixed-list recall and performs comparably to or better than random sampling under unbiased and moderately biased recall conditions. Performance advantages persist even when respondents are unable to provide the full number of alters specified by design. We further validate these findings using empirical egocentric network data from 103 participants. Treating observed networks as proxy ground truths, empirical results closely mirror the simulation patterns, confirming the robustness of incremental recall under real-world reporting conditions. These findings demonstrate that incremental recall addresses a central practical challenge in egocentric social network research: balancing feasibility and accuracy in density estimation. The proposed method maintains strong performance while substantially reducing respondent burden and simplifying administration for applied studies. For researchers conducting large scale surveys where network density is one of several measures, incremental recall provides a practical and validated alternative to exhaustive recall that maintains robustness to realistic reporting biases. Full article

The Ego-Centric Sampling Method (ECM) leverages individual-level reports about peers to estimate population proportions within social networks, offering strong privacy protection without requiring full network data. However, the conventional ECM estimator is unbiased only under the restrictive assumption of a homogeneous network, where node degrees are uniform and uncorrelated with attributes. To overcome this limitation, we introduce the Activity Ratio Corrected ECM estimator (${\mathrm{ECM}}_{\mathrm{ac}}$), which exploits network reciprocity to recast the population–proportion problem into an equivalent formulation in edge space. This reformulation relies solely on ego–peer data and explicitly corrects for degree–attribute dependencies, yielding unbiased and stable estimates even in highly heterogeneous networks. Simulations and analyses on real-world networks show that ${\mathrm{ECM}}_{\mathrm{ac}}$ reduces estimation error by up to 70% compared with the conventional ECM. Our results establish a theoretically grounded and practically scalable framework for unbiased inference in network-based sampling designs. Full article

Plastic pollution in aquatic environments is a major ecological problem requiring scalable autonomous solutions for cleanup. This study addresses the coordination of multiple Autonomous Surface Vehicles by formulating the problem as a Partially Observable Markov Game and decoupling the mission into two tasks: exploration to maximize coverage and cleaning to collect trash. These tasks share navigation requirements but present conflicting goals, motivating a multi-objective learning approach. The proposed multi-agent deep reinforcement learning framework involves the utilisation of the same Multitask Deep Q-network shared by all the agents, with a convolutional backbone and two heads, one dedicated to exploration and the other to cleaning. Parameter sharing and egocentric state design leverages agent homogeneity and enable experience aggregation across tasks. An adaptive mechanism governs task switching, combining task-specific rewards with a weighted aggregation and selecting tasks via a reward-greedy strategy. This enables the construction of Pareto fronts capturing non-dominated solutions. The framework demonstrates improvements over fixed-phase approaches, improving hypervolume and uniformity metrics by 14% and 300%, respectively. It also adapts to diverse initial trash distributions, providing decision-makers with a portfolio of effective and adaptive strategies for autonomous plastic cleanup. Full article

The professional development of English as a Foreign Language (EFL) education has been converted from rigid hierarchical models to more flexible and context-sensitive frameworks. This study introduces ego-centred network card analysis as a new methodology to investigate how Indonesian university EFL teachers create and negotiate their professional identities. The data were collected from 11 experienced EFL teachers. The network cards were analysed to find the nodes and sectors of professional identity and development. Drawing on constructivist and sociocultural perspectives, the study findings indicated that the formation is influenced by relational, emotional and institutional influences, and that family support, mentoring, and career goals alleviate pressures such as workload, publication demands, and financial instability. The findings highlight identity as both a product and a driving force for professional development, extending sociocultural theories by visualizing hidden dimensions of teachers’ networks. Methodologically, this study demonstrates the value of visual-relational tools in capturing complexity beyond interviews or surveys. The results suggest that, in practice, teacher education and policy must integrate structured mentorship, peer reflection, and recognition of emotional work in order to maintain professional growth. Full article

Egocentric hand gesture recognition is vital for natural human–computer interaction in augmented and virtual reality (AR/VR) systems. However, most deep learning models struggle to balance accuracy and efficiency, limiting real-time use on wearable devices. This paper introduces a Two-Stream Temporal Shift Module Transformer Fusion Network (TSMTFN) that achieves high recognition accuracy with low computational cost. The model integrates Temporal Shift Modules (TSMs) for efficient motion modeling and a Transformer-based fusion mechanism for long-range temporal understanding, operating on dual RGB-D streams to capture complementary visual and depth cues. Training stability and generalization are enhanced through full-layer training from epoch 1 and MixUp/CutMix augmentations. Evaluated on the EgoGesture dataset, TSMTFN attained 96.18% top-1 accuracy and 99.61% top-5 accuracy on the independent test set with only 16 GFLOPs and 21.3M parameters, offering a 2.4–4.7× reduction in computation compared to recent state-of-the-art methods. The model runs at 15.10 samples/s, achieving real-time performance. The results demonstrate robust recognition across over 95% of gesture classes and minimal inter-class confusion, establishing TSMTFN as an efficient, accurate, and deployable solution for next-generation wearable AR/VR gesture interfaces. Full article

To precisely understand the interaction behaviors of humans, a computer vision system needs to accurately acquire the poses of the hand and its manipulated object. Vision-based hand–object pose estimation has become an important research topic. However, it is still a challenging task due to severe occlusion. In this study, a hand–object pose estimation method based on anchor regression is proposed to address this problem. First, a hand–object 3D center detection method was established to extract hand–object foreground images from the original depth images. Second, a method based on anchor regression is proposed to simultaneously estimate the poses of the hand and object in a single framework. A convolutional neural network with ResNet-50 as the backbone was built to predict the position deviations and weights of the uniformly distributed anchor points in the image to the keypoints of the hand and the manipulated object. According to the experimental results on the FPHA-HO dataset, the mean keypoint errors of the hand and object of the proposed method were 11.85 mm and 18.97 mm, respectively. The proposed hand–object pose estimation method can accurately estimate the poses of the hand and the manipulated object based on a single egocentric depth image. Full article

Accurately predicting future activities in egocentric (first-person) videos is a challenging yet essential task, requiring robust object recognition and reliable forecasting of action patterns. However, the limited number of observable frames in such videos often lacks critical semantic context, making long-term predictions particularly difficult. Traditional approaches, especially those based on recurrent neural networks, tend to suffer from cumulative error propagation over extended time steps, leading to degraded performance. To address these challenges, this paper introduces a novel framework, Virtual Frame-Augmented Guided Forecasting (VFGF), designed specifically for long-term egocentric activity prediction. The VFGF framework enhances semantic continuity by generating and incorporating virtual frames into the observable sequence. These synthetic frames fill the temporal and contextual gaps caused by rapid changes in activity or environmental conditions. In addition, we propose a Feature Guidance Module that integrates anticipated activity-relevant features into the recursive prediction process, guiding the model toward more accurate and contextually coherent inferences. Extensive experiments on the EPIC-Kitchens dataset demonstrate that VFGF, with its interpolation-based temporal smoothing and feature-guided strategies, significantly improves long-term activity prediction accuracy. Specifically, VFGF achieves a state-of-the-art Top-5 accuracy of 44.11% at a 0.25 s prediction horizon. Moreover, it maintains competitive performance across a range of long-term forecasting intervals, highlighting its robustness and establishing a strong foundation for future research in egocentric activity prediction. Full article

We aimed to compare the proportion of tuberculosis infection among social contacts of male tuberculosis Index case with and without alcohol use in the Puducherry district. A cross-sectional study using ego-centric approach was conducted between November 2023 and May 2024. A total of 713 social contacts of 106 male pulmonary tuberculosis index cases were enrolled, stratified by alcohol-use (AUDIT ≥ 8): 358 contacts from 45 alcohol-using cases and 355 from 61 non-alcohol-use cases. Social contacts were defined based on the frequency and duration of shared indoor exposure with index cases within the past three months. Tuberculosis infection was screened with Cy-Tb skin test (≥5 mm induration) at the third month of index case treatment. Univariate and multivariable analysis were conducted to identify factors associated with tuberculosis transmission. Among the 358 social contacts of alcohol-use index cases, 33.8% (n = 121; 95% CI, 29.1–38.8%) tested positive for tuberculosis infection, significantly higher than 21.7% (n = 77; 95% CI, 17.7–26.3%) among 355 contacts of non-alcohol-use cases. Regression analysis revealed that contacts of alcohol-using index cases (aOR = 1.6, p < 0.05), were significantly associated with tuberculosis infection. Alcohol-use among tuberculosis patients significantly increases the risk of tuberculosis infection in their social networks. Full article

In recent social science debates, poverty is seen as a multidimensional phenomenon, not only economic, but also psychological, educational, moral, and relational. The empirical observation and analysis of this latter dimension and its qualities represent a sociological challenge, especially in assessing the integral effectiveness of social projects. As part of this debate, this article proposes an analytical method—based on Social Network Analysis, according to the egocentric or personal approach—and describes its use during an empirical “relational impact assessment” of a specific anti-poverty project in the Northwest region of Argentina. Analysis of the data—collected longitudinally through questionnaires—highlights the changes in the personal “relational configurations” of small entrepreneurs in the tourist area, i.e., the beneficiaries of the project, while also highlighting the emergence of “relational goods”. In this way, this article offers an analytical method to evaluate the “relational impact” of anti-poverty projects in quali–quantitative terms. Full article

The ability to adopt different perspectives, or vantage points, is fundamental to human cognition, affecting reasoning, memory, and imagery. While the first-person perspective allows individuals to experience a scene through their own eyes, the third-person perspective involves an external viewpoint, which is thought to demand greater cognitive effort and different neural processing. Despite the frequent use of perspective switching across various contexts, including modern media and in therapeutic settings, the neural mechanisms differentiating these two perspectives in visual imagery remain largely underexplored. In an exploratory fMRI study, we compared both activation and task-based functional connectivity underlying first-person and third-person perspective taking in the same 26 participants performing two spatial egocentric imagery tasks, namely imaginary tennis and house navigation. No significant differences in activation emerged between the first-person and third-person conditions. The network-based statistics analysis revealed a small subnetwork of the early visual and posterior temporal areas that manifested stronger functional connectivity during the first-person perspective, suggesting a closer sensory recruitment loop, or, in different terms, a loop between long-term memory and the “visual buffer” circuits. The absence of a strong neural distinction between the first-person and third-person perspectives suggests that third-person imagery may not fully decenter individuals from the scene, as is often assumed. Full article

We propose a memory-enhanced multi-stage goal-driven network (ME-MGNet) for egocentric trajectory prediction in dynamic scenes. Our key idea is to build a scene layout memory inspired by human perception in order to transfer knowledge from prior experiences to the current scenario in a top-down manner. Specifically, given a test scene, we first perform scene-level matching based on our scene layout memory to retrieve trajectories from visually similar scenes in the training data. This is followed by trajectory-level matching and memory filtering to obtain a set of goal features. In addition, a multi-stage goal generator takes these goal features and uses a backward decoder to produce several stage goals. Finally, we integrate the above steps into a conditional autoencoder and a forward decoder to produce trajectory prediction results. Experiments on three public datasets, JAAD, PIE, and KITTI, and a new egocentric trajectory prediction dataset, Fuzhou DashCam (FZDC), validate the efficacy of the proposed method. Full article

Scholars have documented positive and negative relationships between adolescents’ critical consciousness and mental health. This study aims to clarify the role of friendship networks contributing to these associations. Using egocentric network data from a nationwide adolescent sample (N = 984, 55.0% female, 23.9% nonbinary, 72.7% non-white), regression analyses examined whether adolescents’ psychological distress and flourishing were predicted by their friend group’s average critical consciousness and the difference between adolescents and their friends on critical consciousness dimensions (sociopolitical action, critical agency, and critical reflection), accounting for network and demographic covariates. Higher friend group critical consciousness positively predicted flourishing, and higher friend group sociopolitical action negatively predicted psychological distress. Adolescents who participated in sociopolitical action more frequently than their friends had higher psychological distress and lower flourishing. Those with higher agency than their friends had lower flourishing. At the individual level, adolescents’ sociopolitical action predicted higher psychological distress and flourishing, critical agency predicted higher flourishing, and critical reflection predicted higher psychological distress and lower flourishing. Adolescent mental health is uniquely related to their friends’ critical consciousness. Findings highlight the utility of social network analyses for understanding social mechanisms that underlie relationships between critical consciousness and mental health. Full article

Egocentric activity recognition is a prominent computer vision task that is based on the use of wearable cameras. Since egocentric videos are captured through the perspective of the person wearing the camera, her/his body motions severely complicate the video content, imposing several challenges. In this work we propose a novel approach for domain-generalized egocentric human activity recognition. Typical approaches use a large amount of training data, aiming to cover all possible variants of each action. Moreover, several recent approaches have attempted to handle discrepancies between domains with a variety of costly and mostly unsupervised domain adaptation methods. In our approach we show that through simple manipulation of available source domain data and with minor involvement from the target domain, we are able to produce robust models, able to adequately predict human activity in egocentric video sequences. To this end, we introduce a novel three-stream deep neural network architecture combining elements of vision transformers and residual neural networks which are trained using multi-modal data. We evaluate the proposed approach using a challenging, egocentric video dataset and demonstrate its superiority over recent, state-of-the-art research works. Full article

The analysis of complex and time-evolving interactions, such as those within social dynamics, represents a current challenge in the science of complex systems. Temporal networks stand as a suitable tool for schematizing such systems, encoding all the interactions appearing between pairs of individuals in discrete time. Over the years, network science has developed many measures to analyze and compare temporal networks. Some of them imply a decomposition of the network into small pieces of interactions; i.e., only involving a few nodes for a short time range. Along this line, a possible way to decompose a network is to assume an egocentric perspective; i.e., to consider for each node the time evolution of its neighborhood. This was proposed by Longa et al. by defining the “egocentric temporal neighborhood”, which has proven to be a useful tool for characterizing temporal networks relative to social interactions. However, this definition neglects group interactions (quite common in social domains), as they are always decomposed into pairwise connections. A more general framework that also allows considering larger interactions is represented by higher-order networks. Here, we generalize the description of social interactions to hypergraphs. Consequently, we generalize their decomposition into “hyper egocentric temporal neighborhoods”. This enables the analysis of social interactions, facilitating comparisons between different datasets or nodes within a dataset, while considering the intrinsic complexity presented by higher-order interactions. Even if we limit the order of interactions to the second order (triplets of nodes), our results reveal the importance of a higher-order representation.In fact, our analyses show that second-order structures are responsible for the majority of the variability at all scales: between datasets, amongst nodes, and over time. Full article

COVID-19, the most recent multi-dimensional global food crisis, challenged leadership and impacted individuals’ personal networks. Two cross-sectional surveys were disseminated to women involved in their state’s women’s leadership committee to understand food waste behaviors. An egocentric network analysis was chosen as the methodology to better understand personal advice network characteristics and examine the impacts of Farm Bureau women’s leadership committee members’ advice networks on their food waste behavior. A multilevel model was conducted to identify factors related to respondents leading their network members toward positive food waste decisions. Independent variables included in the variables at the individual (e.g., each respondent’s race, generation), dyadic (e.g., length respondent has known each member of her network), and network levels (e.g., proportion of the respondent’s network that was female) were included in the model. Women were more likely to report connections with people they led to positive food waste behaviors and food security when: they had higher food waste sum scores, they were part of Generation X, the network member they led to more positive food waste behaviors was a friend, and if there were fewer women in their advice networks. Full article