23,033 research outputs found
Second-Level Digital Divide: Mapping Differences in People's Online Skills
Much of the existing approach to the digital divide suffers from an important
limitation. It is based on a binary classification of Internet use by only
considering whether someone is or is not an Internet user. To remedy this
shortcoming, this project looks at the differences in people's level of skill
with respect to finding information online. Findings suggest that people search
for content in a myriad of ways and there is a large variance in how long
people take to find various types of information online. Data are collected to
see how user demographics, users' social support networks, people's experience
with the medium, and their autonomy of use influence their level of user
sophistication.Comment: 29th TPRC Conference, 200
Multi-Label Zero-Shot Human Action Recognition via Joint Latent Ranking Embedding
Human action recognition refers to automatic recognizing human actions from a
video clip. In reality, there often exist multiple human actions in a video
stream. Such a video stream is often weakly-annotated with a set of relevant
human action labels at a global level rather than assigning each label to a
specific video episode corresponding to a single action, which leads to a
multi-label learning problem. Furthermore, there are many meaningful human
actions in reality but it would be extremely difficult to collect/annotate
video clips regarding all of various human actions, which leads to a zero-shot
learning scenario. To the best of our knowledge, there is no work that has
addressed all the above issues together in human action recognition. In this
paper, we formulate a real-world human action recognition task as a multi-label
zero-shot learning problem and propose a framework to tackle this problem in a
holistic way. Our framework holistically tackles the issue of unknown temporal
boundaries between different actions for multi-label learning and exploits the
side information regarding the semantic relationship between different human
actions for knowledge transfer. Consequently, our framework leads to a joint
latent ranking embedding for multi-label zero-shot human action recognition. A
novel neural architecture of two component models and an alternate learning
algorithm are proposed to carry out the joint latent ranking embedding
learning. Thus, multi-label zero-shot recognition is done by measuring
relatedness scores of action labels to a test video clip in the joint latent
visual and semantic embedding spaces. We evaluate our framework with different
settings, including a novel data split scheme designed especially for
evaluating multi-label zero-shot learning, on two datasets: Breakfast and
Charades. The experimental results demonstrate the effectiveness of our
framework.Comment: 27 pages, 10 figures and 7 tables. Technical report submitted to a
journal. More experimental results/references were added and typos were
correcte
Centering, Anaphora Resolution, and Discourse Structure
Centering was formulated as a model of the relationship between attentional
state, the form of referring expressions, and the coherence of an utterance
within a discourse segment (Grosz, Joshi and Weinstein, 1986; Grosz, Joshi and
Weinstein, 1995). In this chapter, I argue that the restriction of centering to
operating within a discourse segment should be abandoned in order to integrate
centering with a model of global discourse structure. The within-segment
restriction causes three problems. The first problem is that centers are often
continued over discourse segment boundaries with pronominal referring
expressions whose form is identical to those that occur within a discourse
segment. The second problem is that recent work has shown that listeners
perceive segment boundaries at various levels of granularity. If centering
models a universal processing phenomenon, it is implausible that each listener
is using a different centering algorithm.The third issue is that even for
utterances within a discourse segment, there are strong contrasts between
utterances whose adjacent utterance within a segment is hierarchically recent
and those whose adjacent utterance within a segment is linearly recent. This
chapter argues that these problems can be eliminated by replacing Grosz and
Sidner's stack model of attentional state with an alternate model, the cache
model. I show how the cache model is easily integrated with the centering
algorithm, and provide several types of data from naturally occurring
discourses that support the proposed integrated model. Future work should
provide additional support for these claims with an examination of a larger
corpus of naturally occurring discourses.Comment: 35 pages, uses elsart12, lingmacros, named, psfi
A Taxonomy of Workflow Management Systems for Grid Computing
With the advent of Grid and application technologies, scientists and
engineers are building more and more complex applications to manage and process
large data sets, and execute scientific experiments on distributed resources.
Such application scenarios require means for composing and executing complex
workflows. Therefore, many efforts have been made towards the development of
workflow management systems for Grid computing. In this paper, we propose a
taxonomy that characterizes and classifies various approaches for building and
executing workflows on Grids. We also survey several representative Grid
workflow systems developed by various projects world-wide to demonstrate the
comprehensiveness of the taxonomy. The taxonomy not only highlights the design
and engineering similarities and differences of state-of-the-art in Grid
workflow systems, but also identifies the areas that need further research.Comment: 29 pages, 15 figure
A Typographic Dilemma: Reconciling the old with the new using a new cross-disciplinary typographic framework
Current theory and vocabulary used to describe typographic practice and scholarship are based on a historically print-derived framework. As yet, no new paradigm has emerged to address the divergent path that screen-based typography is taking from its traditional print medium. Screen-based typography is becoming as common and widely used as its print counterpart. It is now timely to re-evaluate current typographic references and practices under these environments, which introduces a new visual language and form.
This paper will attempt to present an alternate typographic framework to address these growing changes by appropriating concepts and knowledge from different disciplines. This alternate typographic framework has been informed through a study conducted as part of a research Doctorate in the School of Design at Northumbria University, UK. This paper posits that the current typographic framework derived from the print medium is no longer sufficient to address the growing differences between the print and screen media. In its place, an alternate cross-disciplinary typographic framework should be adopted for the successful integration and application of typography in screen-based interactive media. The development of this framework will focus mainly on three key characteristics of screen-based interactive media ¬¬– hypertext, interactivity and time-based motion – and will draw influences from disciplines such as film, computer gaming, interactive digital arts and hypertext fictions
An Experimental Investigation of Hyperbolic Routing with a Smart Forwarding Plane in NDN
Routing in NDN networks must scale in terms of forwarding table size and
routing protocol overhead. Hyperbolic routing (HR) presents a potential
solution to address the routing scalability problem, because it does not use
traditional forwarding tables or exchange routing updates upon changes in
network topologies. Although HR has the drawbacks of producing sub-optimal
routes or local minima for some destinations, these issues can be mitigated by
NDN's intelligent data forwarding plane. However, HR's viability still depends
on both the quality of the routes HR provides and the overhead incurred at the
forwarding plane due to HR's sub-optimal behavior. We designed a new forwarding
strategy called Adaptive Smoothed RTT-based Forwarding (ASF) to mitigate HR's
sub-optimal path selection. This paper describes our experimental investigation
into the packet delivery delay and overhead under HR as compared with
Named-Data Link State Routing (NLSR), which calculates shortest paths. We run
emulation experiments using various topologies with different failure
scenarios, probing intervals, and maximum number of next hops for a name
prefix. Our results show that HR's delay stretch has a median close to 1 and a
95th-percentile around or below 2, which does not grow with the network size.
HR's message overhead in dynamic topologies is nearly independent of the
network size, while NLSR's overhead grows polynomially at least. These results
suggest that HR offers a more scalable routing solution with little impact on
the optimality of routing paths
When Hashing Met Matching: Efficient Spatio-Temporal Search for Ridesharing
Carpooling, or sharing a ride with other passengers, holds immense potential
for urban transportation. Ridesharing platforms enable such sharing of rides
using real-time data. Finding ride matches in real-time at urban scale is a
difficult combinatorial optimization task and mostly heuristic approaches are
applied. In this work, we mathematically model the problem as that of finding
near-neighbors and devise a novel efficient spatio-temporal search algorithm
based on the theory of locality sensitive hashing for Maximum Inner Product
Search (MIPS). The proposed algorithm can find near-optimal potential
matches for every ride from a pool of rides in time and space for a small . Our
algorithm can be extended in several useful and interesting ways increasing its
practical appeal. Experiments with large NY yellow taxi trip datasets show that
our algorithm consistently outperforms state-of-the-art heuristic methods
thereby proving its practical applicability
Community Perspectives on Access to and Availability of Healthy Food in Rural, Low-Resource, Latino Communities.
IntroductionAttention has focused on the food environment as a result of the growing concern with obesity rates among Latinos in rural areas. Researchers have observed associations between a lack of physical access to affordable produce in areas where supermarkets and grocery stores are limited and poor dietary intake and obesity; these associations are high in rural, low-resource neighborhoods with a high population of Latino residents. We aimed to engage residents of low-resource, Latino-majority neighborhoods in discussions of food access in a rural yet agricultural community setting, which is typically described as a "food desert."MethodsWe used a mixed-methods approach and conducted 3 focus groups (n = 20) and in-depth interviews (n = 59) and surveys (n = 79) with residents of a rural yet agricultural community. We used thematic analysis to explore residents' perceptions of access to healthy foods.ResultsResidents (n = 79; mean age, 41.6 y; 72% female; 79% Latino; 53% Spanish-speaking) reported that dollar and discount stores in this agricultural area provided access to produce; however, produce at retail stores was less affordable than produce at nonretail outlets such as fruit and vegetable stands. Gifts and trades of fruits and vegetables from neighbors and community organizations supplied no-cost or low-cost healthy foods. Residents' suggestions to improve food access centered on lowering the cost of produce in existing retail outlets and seeking out nonretail outlets.ConclusionOur findings contribute to understanding of the food environment in low-resource, rural yet agricultural areas. Although such areas are characterized as "food deserts," residents identified nonretail outlets as a viable source of affordable produce, while indicating that the cost of retail produce was a concern. Innovative policy solutions to increase healthy food consumption must focus on affordability as well as accessibility, and consider alternate, nonretail food outlets in agricultural areas
CMIR-NET : A Deep Learning Based Model For Cross-Modal Retrieval In Remote Sensing
We address the problem of cross-modal information retrieval in the domain of
remote sensing. In particular, we are interested in two application scenarios:
i) cross-modal retrieval between panchromatic (PAN) and multi-spectral imagery,
and ii) multi-label image retrieval between very high resolution (VHR) images
and speech based label annotations. Notice that these multi-modal retrieval
scenarios are more challenging than the traditional uni-modal retrieval
approaches given the inherent differences in distributions between the
modalities. However, with the growing availability of multi-source remote
sensing data and the scarcity of enough semantic annotations, the task of
multi-modal retrieval has recently become extremely important. In this regard,
we propose a novel deep neural network based architecture which is considered
to learn a discriminative shared feature space for all the input modalities,
suitable for semantically coherent information retrieval. Extensive experiments
are carried out on the benchmark large-scale PAN - multi-spectral DSRSID
dataset and the multi-label UC-Merced dataset. Together with the Merced
dataset, we generate a corpus of speech signals corresponding to the labels.
Superior performance with respect to the current state-of-the-art is observed
in all the cases
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