13,672 research outputs found
Stay Awhile and Listen: User Interactions in a Crowdsourced Platform Offering Emotional Support
Internet and online-based social systems are rising as the dominant mode of
communication in society. However, the public or semi-private environment under
which most online communications operate under do not make them suitable
channels for speaking with others about personal or emotional problems. This
has led to the emergence of online platforms for emotional support offering
free, anonymous, and confidential conversations with live listeners. Yet very
little is known about the way these platforms are utilized, and if their
features and design foster strong user engagement. This paper explores the
utilization and the interaction features of hundreds of thousands of users on 7
Cups of Tea, a leading online platform offering online emotional support. It
dissects the level of activity of hundreds of thousands of users, the patterns
by which they engage in conversation with each other, and uses machine learning
methods to find factors promoting engagement. The study may be the first to
measure activities and interactions in a large-scale online social system that
fosters peer-to-peer emotional support
Aesthetic-Driven Image Enhancement by Adversarial Learning
We introduce EnhanceGAN, an adversarial learning based model that performs
automatic image enhancement. Traditional image enhancement frameworks typically
involve training models in a fully-supervised manner, which require expensive
annotations in the form of aligned image pairs. In contrast to these
approaches, our proposed EnhanceGAN only requires weak supervision (binary
labels on image aesthetic quality) and is able to learn enhancement operators
for the task of aesthetic-based image enhancement. In particular, we show the
effectiveness of a piecewise color enhancement module trained with weak
supervision, and extend the proposed EnhanceGAN framework to learning a deep
filtering-based aesthetic enhancer. The full differentiability of our image
enhancement operators enables the training of EnhanceGAN in an end-to-end
manner. We further demonstrate the capability of EnhanceGAN in learning
aesthetic-based image cropping without any groundtruth cropping pairs. Our
weakly-supervised EnhanceGAN reports competitive quantitative results on
aesthetic-based color enhancement as well as automatic image cropping, and a
user study confirms that our image enhancement results are on par with or even
preferred over professional enhancement
VELOS : a VR platform for ship-evacuation analysis
Virtual Environment for Life On Ships (VELOS) is a multi-user Virtual Reality (VR) system that aims to support designers to assess (early in the design process) passenger and crew activities on a ship for both normal and hectic conditions of operations and to improve ship design accordingly. This article focuses on presenting the novel features of VELOS related to both its VR and evacuation-specific functionalities. These features include: (i) capability of multiple users’ immersion and active participation in the evacuation process, (ii) real-time interactivity and capability for making on-the-fly alterations of environment events and crowd-behavior parameters, (iii) capability of agents and avatars to move continuously on decks, (iv) integrated framework for both the simplified and advanced method of analysis according to the IMO/MSC 1033 Circular, (v) enrichment of the ship geometrical model with a topological model suitable for evacuation analysis, (vi) efficient interfaces for the dynamic specification and handling of the required heterogeneous input data, and (vii) post-processing of the calculated agent trajectories for extracting useful information for the evacuation process. VELOS evacuation functionality is illustrated using three evacuation test cases for a ro–ro passenger ship
Top-down inputs enhance orientation selectivity in neurons of the primary visual cortex during perceptual learning.
Perceptual learning has been used to probe the mechanisms of cortical plasticity in the adult brain. Feedback projections are ubiquitous in the cortex, but little is known about their role in cortical plasticity. Here we explore the hypothesis that learning visual orientation discrimination involves learning-dependent plasticity of top-down feedback inputs from higher cortical areas, serving a different function from plasticity due to changes in recurrent connections within a cortical area. In a Hodgkin-Huxley-based spiking neural network model of visual cortex, we show that modulation of feedback inputs to V1 from higher cortical areas results in shunting inhibition in V1 neurons, which changes the response properties of V1 neurons. The orientation selectivity of V1 neurons is enhanced without changing orientation preference, preserving the topographic organizations in V1. These results provide new insights to the mechanisms of plasticity in the adult brain, reconciling apparently inconsistent experiments and providing a new hypothesis for a functional role of the feedback connections
Sticks, balls or a ribbon? Results of a formative user study with bioinformaticians
User interfaces in modern bioinformatics tools are designed for experts. They are too complicated for\ud
novice users such as bench biologists. This report presents the full results of a formative user study as part of a\ud
domain and requirements analysis to enhance user interfaces and collaborative environments for\ud
multidisciplinary teamwork. Contextual field observations, questionnaires and interviews with bioinformatics\ud
researchers of different levels of expertise and various backgrounds were performed in order to gain insight into\ud
their needs and working practices. The analysed results are presented as a user profile description and user\ud
requirements for designing user interfaces that support the collaboration of multidisciplinary research teams in\ud
scientific collaborative environments. Although the number of participants limits the generalisability of the\ud
findings, the combination of recurrent observations with other user analysis techniques in real-life settings\ud
makes the contribution of this user study novel
Resource Allocation for Network-Integrated Device-to-Device Communications Using Smart Relays
With increasing number of autonomous heterogeneous devices in future mobile
networks, an efficient resource allocation scheme is required to maximize
network throughput and achieve higher spectral efficiency. In this paper,
performance of network-integrated device-to-device (D2D) communication is
investigated where D2D traffic is carried through relay nodes. An optimization
problem is formulated for allocating radio resources to maximize end-to-end
rate as well as conversing QoS requirements for cellular and D2D user equipment
under total power constraint. Numerical results show that there is a distance
threshold beyond which relay-assisted D2D communication significantly improves
network performance when compared to direct communication between D2D peers
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