275,299 research outputs found

    Towards an Active Foveated Approach to Computer Vision

    Get PDF
    In this paper, a series of experimental methods are presented explaining a new approach towards active foveated Computer Vision (CV). This is a collaborative effort between researchers at CONICET Mendoza Technological Scientific Center from Argentina, Argonne National Laboratory (ANL), and Loyola University Chicago from the US. The aim is to advance new CV approaches more in line with those found in biological agents in order to bring novel solutions to the main problems faced by current CV applications. Basically this work enhance Self-supervised (SS) learning, incorporating foveated vision plus saccadic behavior in order to improve training and computational efficiency without reducing performance significantly. This paper includes a compendium of methods’ explanations, and since this is a work that is currently in progress, only preliminary results are provided. We also make our code fully available

    Using a gamified monitoring app to change adolescents' snack intake : the development of the REWARD app and evaluation design

    Get PDF
    Background: As the snacking pattern of European adolescents is of great concern, effective interventions are necessary. Till now health promotion efforts in children and adolescents have had only limited success in changing adolescents' eating patterns and anthropometrics. Therefore, the present study proposes an innovative approach to influence dietary behaviors in youth based on new insights on effective behavior change strategies and attractive intervention channels to engage adolescents. This article describes the rationale, the development, and evaluation design of the 'Snack Track School' app. The aim of the app is to improve the snacking patterns of Flemish 14- to 16-year olds. Methods: The development of the app was informed by the systematic, stepwise, iterative, and collaborative principles of the Intervention Mapping protocol. A four week mHealth intervention was developed based on the dual-system model with behavioral change strategies targeting both the reflective (i.e., active learning, advance organizers, mere exposure, goal-setting, monitoring, and feedback) and automatic processes (i.e., rewards and positive reinforcement). This intervention will be evaluated via a controlled pre-post design in Flemish schools among 1400 adolescents. Discussion: When this intervention including strategies focused on both the reflective and automatic pathway proves to be effective, it will offer a new scientifically-based vision, guidelines and practical tools for public health and health promotion (i.e., incorporation of learning theories in intervention programs)

    Learning Racial Justice: Teachers\u27 Collaborative Learning as Theory and Praxis

    Get PDF
    Activist teachers are increasingly organizing within and beyond their unions to respond to political trends toward austerity and the privatization of public education (Hursh, 2004; Quinn & Carl, 2015; Ravitch, 2010, 2013). Teacher-led grassroots groups often strive to partner in meaningful ways with parents and communities (Weiner, 2012), but simultaneously overlook how deeply embedded community histories shape the community and policy context (Crenshaw, 2011; Delgado & Stefancic, 2012; Gadsden, 1994), and teachers’ organizing and professional practices (Maton, 2016). The enhanced recent visibility of race-inflected social activism (#BlackLivesMatter, 2016) raises significant questions about how politically active teachers understand and engage with issues of racial justice. This dissertation asks: When politically active teachers come together in an inquiry group to discuss structural racism, how do they engage in individual and collective learning processes? And, how do they perceive the shape, form and effect of their learning? Methodologically, the study draws from participatory (Cochran-Smith & Lytle, 2009; McIntyre, 2008) and race feminist (Delgado-Bernal, 1998; Smith, 1987) qualitative research traditions. The study examines the work of an inquiry group composed of nine racially and gender diverse participant who are active members of a change-seeking union caucus. Data sources include inquiry group meetings, interviews, field notes and written texts. The dissertation builds a new theory for understanding the nature, form and function of teachers’ collaborative learning about racial justice. This study defines collaborative learning as the collective and social search for knowledge and transformation, and shows that it is composed of four interconnected and mutually reliant components: learning, pedagogy, relationships, and diffusion. Furthermore, the study finds that inquiry-based collaboration among politically active teachers, on projects where the goal is to build a common mission, vision and project, and where there is diversity in race, gender and a range of experiences with prejudice and discrimination, holds great potential for triggering teacher learning and addressing social justice issues within and beyond activist organizations and schools

    Factors affecting the development of collaborative improvement with strategic suppliers

    Get PDF
    The research presented in this paper was aimed at increasing the current understanding of the process of developing collaborative improvement in Extended Manufacturing Enterprises (EME). Theory suggests a number of factors to affect that process, including shared sense of direction (i.e. vision), trust, power, and commitment. Based on action research of three EMEs involving a total of thirteen companies from five European countries, the present study identifies a number of additional factors. Factors exogenous to, but impacting, the collaboration are joint history and culture. Endogenous factors are approach to establishing the collaboration, project organisation, change and improvement competence, ways and modes of communicating, and political behaviour. Not only do these factors influence each other, they also strongly affect the development of collaborative improvement

    Collaborative Deep Reinforcement Learning for Joint Object Search

    Full text link
    We examine the problem of joint top-down active search of multiple objects under interaction, e.g., person riding a bicycle, cups held by the table, etc.. Such objects under interaction often can provide contextual cues to each other to facilitate more efficient search. By treating each detector as an agent, we present the first collaborative multi-agent deep reinforcement learning algorithm to learn the optimal policy for joint active object localization, which effectively exploits such beneficial contextual information. We learn inter-agent communication through cross connections with gates between the Q-networks, which is facilitated by a novel multi-agent deep Q-learning algorithm with joint exploitation sampling. We verify our proposed method on multiple object detection benchmarks. Not only does our model help to improve the performance of state-of-the-art active localization models, it also reveals interesting co-detection patterns that are intuitively interpretable

    Implementing collaborative improvement, top-down, bottom-up, or both?

    Get PDF
    The research presented in this paper was aimed at increasing the current understanding of the process of developing collaborative improvement in Extended Manufacturing Enterprises (EME). Based on action research and action learning of three EMEs involving a total of thirteen companies from five European countries, the present study identifies three different approaches to collaborative improvement (CoI), that is, inter-organisational continuous improvement. One approach to CoI focuses on learning at a practical level, developing this knowledge into strategic and theoretical knowledge. We call this the bottom-up learning-bydoing approach. Another approach focuses on goal alignment and assessment to provide a foundation for improvement before actually improving. We call this the top-down directive approach. Yet another approach focuses on shared goals/vision and meeting on equal terms, and joint work in a non-directive matter. This is the laissez-faire approach. The different approaches influence the collaborative improvement results achieved, and how and why they do so is the question addressed this article
    • …
    corecore