57,143 research outputs found
A Survey of Location Prediction on Twitter
Locations, e.g., countries, states, cities, and point-of-interests, are
central to news, emergency events, and people's daily lives. Automatic
identification of locations associated with or mentioned in documents has been
explored for decades. As one of the most popular online social network
platforms, Twitter has attracted a large number of users who send millions of
tweets on daily basis. Due to the world-wide coverage of its users and
real-time freshness of tweets, location prediction on Twitter has gained
significant attention in recent years. Research efforts are spent on dealing
with new challenges and opportunities brought by the noisy, short, and
context-rich nature of tweets. In this survey, we aim at offering an overall
picture of location prediction on Twitter. Specifically, we concentrate on the
prediction of user home locations, tweet locations, and mentioned locations. We
first define the three tasks and review the evaluation metrics. By summarizing
Twitter network, tweet content, and tweet context as potential inputs, we then
structurally highlight how the problems depend on these inputs. Each dependency
is illustrated by a comprehensive review of the corresponding strategies
adopted in state-of-the-art approaches. In addition, we also briefly review two
related problems, i.e., semantic location prediction and point-of-interest
recommendation. Finally, we list future research directions.Comment: Accepted to TKDE. 30 pages, 1 figur
A fourfold typology of living labs: an empirical investigation amongst the ENoLL community
Living Labs can be seen as a means to structure user involvement in innovation processes. However, in this rather young research domain, there is no consensus yet regarding supporting theories and frameworks. This has resulted in a wide variety of projects and approaches being called ‘Living Labs’, which leaves a clear conceptualization and definition a task in progress. Within this research paper we propose a fourfold categorization of Living Labs based on a literature review and validated by an empirical investigation of the characteristics of 64 ICT Living Labs from the European Network of Living Labs (ENoLL). The four types are Living Labs for collaboration and knowledge support activities, original ‘American’ Living Labs, Living Labs as extension to testbeds and Living Labs that support context research and co-creation with users
Evaluating Content-centric vs User-centric Ad Affect Recognition
Despite the fact that advertisements (ads) often include strongly emotional
content, very little work has been devoted to affect recognition (AR) from ads.
This work explicitly compares content-centric and user-centric ad AR
methodologies, and evaluates the impact of enhanced AR on computational
advertising via a user study. Specifically, we (1) compile an affective ad
dataset capable of evoking coherent emotions across users; (2) explore the
efficacy of content-centric convolutional neural network (CNN) features for
encoding emotions, and show that CNN features outperform low-level emotion
descriptors; (3) examine user-centered ad AR by analyzing Electroencephalogram
(EEG) responses acquired from eleven viewers, and find that EEG signals encode
emotional information better than content descriptors; (4) investigate the
relationship between objective AR and subjective viewer experience while
watching an ad-embedded online video stream based on a study involving 12
users. To our knowledge, this is the first work to (a) expressly compare user
vs content-centered AR for ads, and (b) study the relationship between modeling
of ad emotions and its impact on a real-life advertising application.Comment: Accepted at the ACM International Conference on Multimodal Interation
(ICMI) 201
Quality-aware model-driven service engineering
Service engineering and service-oriented architecture as an integration and platform technology is a recent approach to software systems integration. Quality aspects
ranging from interoperability to maintainability to performance are of central importance for the integration of heterogeneous, distributed service-based systems. Architecture models can substantially influence quality attributes of the implemented software systems. Besides the benefits of explicit architectures on maintainability and reuse, architectural constraints such as styles, reference architectures and architectural patterns can influence observable software properties such as performance. Empirical performance evaluation is a process of measuring and evaluating the performance of implemented software. We present an approach for addressing the quality of services and service-based systems at the model-level in the context of model-driven service engineering. The focus on architecture-level models is a consequence of the black-box
character of services
Exploring the Memory-Bandwidth Tradeoff in an Information-Centric Network
An information-centric network should realize significant economies by
exploiting a favourable memory-bandwidth tradeoff: it is cheaper to store
copies of popular content close to users than to fetch them repeatedly over the
Internet. We evaluate this tradeoff for some simple cache network structures
under realistic assumptions concerning the size of the content catalogue and
its popularity distribution. Derived cost formulas reveal the relative impact
of various cost, traffic and capacity parameters, allowing an appraisal of
possible future network architectures. Our results suggest it probably makes
more sense to envisage the future Internet as a loosely interconnected set of
local data centers than a network like today's with routers augmented by
limited capacity content stores.Comment: Proceedings of ITC 25 (International Teletraffic Congress), Shanghai,
September, 201
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