Distributed resource discovery using a context sensitive infrastructure

Distributed Resource Discovery in a World Wide Web environment using full-text indices will never scale. The distinct properties of WWW information (volume, rate of change, topical diversity) limits the scaleability of traditional approaches to distributed Resource Discovery. An approach combining metadata clustering and query routing can, on the other hand, be proven to scale much better. This paper presents the Content-Sensitive Infrastructure, which is a design building on these results. We also present an analytical framework for comparing scaleability of different distribution strategies

Selective self-categorization: Meaningful categorization and the in-group persuasion effect

Research stemming from self-categorization theory (Turner et al., 1987) has demonstrated that individuals are typically more persuaded by messages from their in-group than by messages from the out-group. The present research investigated the role of issue relevance in moderating these effects. In particular, it was predicted that in-groups would only be more persuasive when the dimension on which group membership was defined was meaningful or relevant to the attitude issue. In two studies, participants were presented with persuasive arguments from either an in-group source or an out-group source, where the basis of the in-group/out-group distinction was either relevant or irrelevant to the attitude issue. Participants' attitudes toward the issue were then measured. The results supported the predictions: Participants were more persuaded by in-group sources than out-group sources when the basis for defining the group was relevant to the attitude issue. However, when the defining characteristic of the group was irrelevant to the attitude issue, participants were equally persuaded by in-group and out-group sources. These results support the hypothesis that the fit between group membership and domain is an important moderator of self-categorization effects

Gravity optimised particle filter for hand tracking

This paper presents a gravity optimised particle filter (GOPF) where the magnitude of the gravitational force for every particle is proportional to its weight. GOPF attracts nearby particles and replicates new particles as if moving the particles towards the peak of the likelihood distribution, improving the sampling efficiency. GOPF is incorporated into a technique for hand features tracking. A fast approach to hand features detection and labelling using convexity defects is also presented. Experimental results show that GOPF outperforms the standard particle filter and its variants, as well as state-of-the-art CamShift guided particle filter using a significantly reduced number of particles

Queuing theory-based latency/power tradeoff models for replicated search engines

Large-scale search engines are built upon huge infrastructures involving thousands of computers in order to achieve fast response times. In contrast, the energy consumed (and hence the financial cost) is also high, leading to environmental damage. This paper proposes new approaches to increase energy and financial savings in large-scale search engines, while maintaining good query response times. We aim to improve current state-of-the-art models used for balancing power and latency, by integrating new advanced features. On one hand, we propose to improve the power savings by completely powering down the query servers that are not necessary when the load of the system is low. Besides, we consider energy rates into the model formulation. On the other hand, we focus on how to accurately estimate the latency of the whole system by means of Queueing Theory. Experiments using actual query logs attest the high energy (and financial) savings regarding current baselines. To the best of our knowledge, this is the first paper in successfully applying stationary Queueing Theory models to estimate the latency in a large-scale search engine

Watch Out for the Beast: Fear Information and Attentional Bias in Children

Although valenced information about novel animals changes the implicit and explicit fear beliefs of children (Field & Lawson, 2003), how it might lead to anxiety is unknown. One possibility, based on cognitive models of anxiety, is that fear information creates attentional biases similar to those seen in anxiety disorders. Children between 7 and 9 years old were given positive information about 1 novel animal, negative information about another, and no information about the 3rd. A pictorial dot-probe task was used, immediately or with a 24-hr delay, to test for attentional biases to the different animals. The results replicated the finding that fear information changes children's fear beliefs. Regardless of whether there was a delay, children acquired an attentional bias in the left visual field toward the animal about which they held negative beliefs compared to the control animal. These results imply a possible way in which fear information might contribute to acquired fear

Visuospatial tasks suppress craving for cigarettes.

The Elaborated Intrusion (EI) theory of desire posits that visual imagery plays a key role in craving. We report a series of experiments testing this hypothesis in a drug addiction context. Experiment 1 showed that a mental visual imagery task with neutral content reduced cigarette craving in abstaining smokers, but that an equivalent auditory task did not. The effect of visual imagery was replicated in Experiment 2, which also showed comparable effects of non-imagery visual working memory interference. Experiment 3 showed that the benefit of visual over auditory interference was not dependent upon imagery being used to induce craving. Experiment 4 compared a visuomotor task, making shapes from modeling clay, with a verbal task (counting back from 100), and again showed a benefit of the visual over the non-visual task. We conclude that visual imagery supports craving for cigarettes. Competing imagery or visual working memory tasks may help tackle craving in smokers trying to quit

Content-access QoS in peer-to-peer networks using a fast MDS erasure code

This paper describes an enhancement of content access Quality of Service in peer to peer (P2P) networks. The main idea is to use an erasure code to distribute the information over the peers. This distribution increases the users’ choice on disseminated encoded data and therefore statistically enhances the overall throughput of the transfer. A performance evaluation based on an original model using the results of a measurement campaign of sequential and parallel downloads in a real P2P network over Internet is presented. Based on a bandwidth distribution, statistical content-access QoS are guaranteed in function of both the content replication level in the network and the file dissemination strategies. A simple application in the context of media streaming is proposed. Finally, the constraints on the erasure code related to the proposed system are analysed and a new fast MDS erasure code is proposed, implemented and evaluated

Does Phenomenal Consciousness Overflow Attention? An Argument from Feature-Integration

In the past two decades a number of arguments have been given in favor of the possibility of phenomenal consciousness without attentional access, otherwise known as phenomenal overflow. This paper will show that the empirical data commonly cited in support of this thesis is, at best, ambiguous between two equally plausible interpretations, one of which does not posit phenomenology beyond attention. Next, after citing evidence for the feature-integration theory of attention, this paper will give an account of the relationship between consciousness and attention that accounts for both the empirical data and our phenomenological intuitions without positing phenomenal consciousness beyond attention. Having undercut the motivations for accepting phenomenal overflow along with having given reasons to think that phenomenal overflow does not occur, I end with the tentative conclusion that attention is a necessary condition for phenomenal consciousness