3,378 research outputs found
TRULLO - local trust bootstrapping for ubiquitous devices
Handheld devices have become sufficiently powerful
that it is easy to create, disseminate, and access digital content
(e.g., photos, videos) using them. The volume of such content is
growing rapidly and, from the perspective of each user, selecting
relevant content is key. To this end, each user may run a trust
model - a software agent that keeps track of who disseminates
content that its user finds relevant. This agent does so by
assigning an initial trust value to each producer for a specific
category (context); then, whenever it receives new content, the
agent rates the content and accordingly updates its trust value for
the producer in the content category. However, a problem with
such an approach is that, as the number of content categories
increases, so does the number of trust values to be initially set.
This paper focuses on how to effectively set initial trust values.
The most sophisticated of the current solutions employ predefined
context ontologies, using which initial trust in a given
context is set based on that already held in similar contexts.
However, universally accepted (and time invariant) ontologies
are rarely found in practice. For this reason, we propose a
mechanism called TRULLO (TRUst bootstrapping by Latently
Lifting cOntext) that assigns initial trust values based only on
local information (on the ratings of its user’s past experiences)
and that, as such, does not rely on third-party recommendations.
We evaluate the effectiveness of TRULLO by simulating its use
in an informal antique market setting. We also evaluate the
computational cost of a J2ME implementation of TRULLO on
a mobile phone
SIRT1 and SIRT3 deacetylate homologous substrates: AceCS1,2 and HMGCS1,2.
SIRT1 and SIRT3 are NAD+-dependent protein deacetylases that are evolutionarily conserved across mammals. These proteins are located in the cytoplasm/nucleus and mitochondria, respectively. Previous reports demonstrated that human SIRT1 deacetylates Acetyl-CoA Synthase 1 (AceCS1) in the cytoplasm, whereas SIRT3 deacetylates the homologous Acetyl-CoA Synthase 2 (AceCS2) in the mitochondria. We recently showed that 3-hydroxy-3-methylglutaryl CoA synthase 2 (HMGCS2) is deacetylated by SIRT3 in mitochondria, and we demonstrate here that SIRT1 deacetylates the homologous 3-hydroxy-3-methylglutaryl CoA synthase 1 (HMGCS1) in the cytoplasm. This novel pattern of substrate homology between cytoplasmic SIRT1 and mitochondrial SIRT3 suggests that considering evolutionary relationships between the sirtuins and their substrates may help to identify and understand the functions and interactions of this gene family. In this perspective, we take a first step by characterizing the evolutionary history of the sirtuins and these substrate families
Workshop on evaluating personal search
The first ECIR workshop on Evaluating Personal Search was
held on 18th April 2011 in Dublin, Ireland. The workshop
consisted of 6 oral paper presentations and several discussion sessions. This report presents an overview of the scope and contents of the workshop and outlines the major outcomes
Social interactions or business transactions? What customer reviews disclose about Airbnb marketplace
Airbnb is one of the most successful examples of sharing economy marketplaces. With rapid and global market penetration, understanding its attractiveness and evolving growth opportunities is key to plan business decision making. There is an ongoing debate, for example, about whether Airbnb is a hospitality service that fosters social exchanges between hosts and guests, as the sharing economy manifesto originally stated, or whether it is (or is evolving into being) a purely business transaction platform, the way hotels have traditionally operated. To answer these questions, we propose a novel market analysis approach that exploits customers’ reviews. Key to the approach is a method that combines thematic analysis and machine learning to inductively develop a custom dictionary for guests’ reviews. Based on this dictionary, we then use quantitative linguistic analysis on a corpus of 3.2 million reviews collected in 6 different cities, and illustrate how to answer a variety of market research questions, at fine levels of temporal, thematic, user and spatial granularity, such as (i) how the business vs social dichotomy is evolving over the years, (ii) what exact words within such top-level categories are evolving, (iii) whether such trends vary across different user segments and (iv) in different neighbourhoods
Science Education for Citizenship and a Sustainable Future
In this article Jerry Wellington argues very strongly in favour of the role of science in citizenship education. He emphasizes the need for knowledge, skills and action and suggests areas and ways in which pupils can be engaged in the struggle for a sustainable future where interdependence and interconnectedness mesh well with notions of equity and justice
Who benefits from the "sharing" economy of Airbnb?
Sharing economy platforms have become extremely popular in the last few years, and they have changed the way in which we commute, travel, and borrow among many other activities. Despite their popularity among consumers, such companies are poorly regulated. For example, Airbnb, one of the most successful examples of sharing economy platform, is often criticized by regulators and policy makers. While, in theory, municipalities should regulate the emergence of Airbnb through evidence-based policy making, in practice, they engage in a false dichotomy: some municipalities allow the business without imposing any regulation, while others ban it altogether. That is because there is no evidence upon which to draft policies. Here we propose to gather evidence from the Web. After crawling Airbnb data for the entire city of London, we find out where and when Airbnb listings are offered and, by matching such listing information with census and hotel data, we determine the socio-economic conditions of the areas that actually benefit from the hospitality platform. The reality is more nuanced than one would expect, and it has changed over the years. Airbnb demand and offering have changed over time, and traditional regulations have not been able to respond to those changes. That is why, finally, we rely on our data analysis to envision regulations that are responsive to real-time demands, contributing to the emerging idea of “algorithmic regulation”
Targeting Mucosal Sites by Polymeric Immunoglobulin Receptor-directed Peptides
Polymeric immunoglobulins provide first line humoral defense at mucosal surfaces to which they are specifically transported by the polymeric immunoglobulin receptor (pIgR) on mucosal and glandular epithelial cells. Previous studies from our laboratory suggested that amino acids 402–410 of the Cα3 domain of dimeric IgA (dIgA) represented a potential binding site for the pIgR. Here by binding human secretory component to overlapping decapeptides of Cα3, we confirm these residues and also uncover an additional site. Furthermore, we show that the Cα3 motif appears to be sufficient to direct transport of green fluorescent protein through the pIgR-specific cellular transcytosis system. An alternative approach identified phage peptides, selected from a library by the in vitro Madin Darby Canine Kidney transcytosis assay, for pIgR-mediated transport through epithelial cells. Some transcytosis-selected peptides map to the same 402–410 pIgR-binding Cα3 site. Further in vivo studies document that at least one of these peptides is transported in a rat model measuring hepatic bile transport. In addition to identifying small peptides that are both bound and transported by the pIgR, this study provides evidence that the pIgR-mediated mucosal secretion system may represent a means of targeting small molecule therapeutics and genes to mucosal epithelial cells
Analyzing and predicting the spatial penetration of Airbnb in U.S. cities
In the hospitality industry, the room and apartment sharing platform of Airbnb has been accused of unfair competition. Detractors have pointed out the chronic lack of proper legislation. Unfortunately, there is little quantitative evidence about Airbnb's spatial penetration upon which to base such a legislation. In this study, we analyze Airbnb's spatial distribution in eight U.S. urban areas, in relation to both geographic, socio-demographic, and economic information. We find that, despite being very different in terms of population composition, size, and wealth, all eight cities exhibit the same pattern: that is, areas of high Airbnb presence are those occupied by the \newpart{``talented and creative''} classes, and those that are close to city centers. This result is consistent so much so that the accuracy of predicting Airbnb's spatial penetration is as high as 0.725
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