Content Reuse and Interest Sharing in Tagging Communities

Tagging communities represent a subclass of a broader class of user-generated content-sharing online communities. In such communities users introduce and tag content for later use. Although recent studies advocate and attempt to harness social knowledge in this context by exploiting collaboration among users, little research has been done to quantify the current level of user collaboration in these communities. This paper introduces two metrics to quantify the level of collaboration: content reuse and shared interest. Using these two metrics, this paper shows that the current level of collaboration in CiteULike and Connotea is consistently low, which significantly limits the potential of harnessing the social knowledge in communities. This study also discusses implications of these findings in the context of recommendation and reputation systems.Comment: 6 pages, 6 figures, AAAI Spring Symposium on Social Information Processin

Case report: Flow changes in routes of collateral circulation in patients with LVO and low NIHSS: a point favor to treat

The effectiveness of endovascular thrombectomy in patients presenting low National Institutes of Health Stroke Scale (NIHSS) scores remains controversial, and the acquisition of additional evidence is required to refine the selection of candidates who may benefit the most from this therapeutic modality. In this study, we present the case of a 62-year-old individual, with left internal carotid occlusion stroke and low NIHSS, who had compensatory collateral flow from Willis polygon via the anterior communicating artery. The patient subsequently exhibited neurological deterioration and collateral flow failure from Willis polygon, indicating the need for urgent intervention. The study of collaterals in patients with large vessel occlusion stroke has garnered considerable attention, with research suggesting that individuals with low NIHSS scores and poor collateral profiles may be at a heightened risk of early neurological deterioration. We postulate that such patients may derive significant benefits from endovascular thrombectomy, and may posit that an intensive transcranial Doppler monitoring protocol could facilitate the identification of suitable candidates for such intervention

A global analysis of Y-chromosomal haplotype diversity for 23 STR loci

In a worldwide collaborative effort, 19,630 Y-chromosomes were sampled from 129 different populations in 51 countries. These chromosomes were typed for 23 short-tandem repeat (STR) loci (DYS19, DYS389I, DYS389II, DYS390, DYS391, DYS392, DYS393, DYS385ab, DYS437, DYS438, DYS439, DYS448, DYS456, DYS458, DYS635, GATAH4, DYS481, DYS533, DYS549, DYS570, DYS576, and DYS643) and using the PowerPlex Y23 System (PPY23, Promega Corporation, Madison, WI). Locus-specific allelic spectra of these markers were determined and a consistently high level of allelic diversity was observed. A considerable number of null, duplicate and off-ladder alleles were revealed. Standard single-locus and haplotype-based parameters were calculated and compared between subsets of Y-STR markers established for forensic casework. The PPY23 marker set provides substantially stronger discriminatory power than other available kits but at the same time reveals the same general patterns of population structure as other marker sets. A strong correlation was observed between the number of Y-STRs included in a marker set and some of the forensic parameters under study. Interestingly a weak but consistent trend toward smaller genetic distances resulting from larger numbers of markers became apparent.Peer reviewe

Quantifying the value of peer-produced Information in social tagging systems

Commons-based peer production systems are marked by three main characteristics, they are: radically decentralized, non-proprietary, and collaborative. Peer production is in stark contrast to market-based production and/or on a centralized organization (e.g., carpooling vs. car rental; couch surfing vs. hotels; Wikipedia vs. Encyclopedia Britannica). Social tagging systems represent a class of web systems, where peer production is central in their design. In these systems, decentralized users collect, share, and annotate (or tag) content collaboratively to produce a public pool of annotated content. This uncoordinated effort helps filling the demand for labeling an ever increasing amount of user-generated content on the web with textual information. Moreover, these labels (or simply tags) can be valuable as input to mechanisms such as personalized search or content promotion. Assessing the value of individuals contributions to peer production systems is key to design user incentives to bring high quality contributions. However, quantifying the value of peer-produced information such as tags is intrinsically challenging, as the value of information is inherently contextual and multidimensional. This research aims to address these two issues in the context of social tagging systems. To this end, this study sets forth the following hypothesis: assessing the value of peer-produced information in social tagging systems can be achieved by harnessing context and user behavior characteristics. The following questions guide the investigations. Characterization: (Q1). What are the characteristics of individual user activity? (Q2). What are the characteristics of social user activity? (Q3). What are the aspects that influence users perception of tag value? Design: (Q4). How to assess the value of tags for exploratory search? (Q5). What is the value of peer-produced information for content promotion? This study applies a mixed methods approach. The findings show that patterns of user activity can inform the design of supporting mechanisms for tagging systems. Moreover, the results suggest that the proposed method to assess value of tags is able to differentiate between valuable tags from less valuable tags, as perceived by users. Moreover, the analysis of the value of peer-produced information for content promotion shows that peer-produced sources can oftentimes outperform expert-produced sources.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat

Scaling of applications that process large amounts of data in computational grids

Aplicações que processam grandes quantidades de dados demandam grandes transferências de dados quando executadas em grids computacionais. Estas transferências têm um alto custo associado. Portanto, considerar as transferências de dados é fundamental para se obter escalonamentos eficientes para tais aplicações. Além disso, em ambientes heterogêneos como os grids, as heurísticas que produzem escalonamentos eficientes tipicamente usam informação dinâmica sobre o grid e as aplicações (disponibilidade de rede e CPU, tempo de execução das tarefas, etc). Porém, estas informações são, em geral, difíceis de se obter com precisão. Embora existam escalonadores que alcançam bom desempenho sem usar informações dinâmicas, eles não são desenvolvidos para considerar o impacto das transferências de dados. Neste trabalho apresentamos Storage Affinity, uma nova heurística de escalonamento para aplicações do tipo Bag-of-Tasks que processam grandes quantidades de dados sem depender de informação de difícil obtenção. Além disso, o ambiente de execução considerado é um grid computacional. Storage Affinity explora os padrões de reutilização de dados, comuns em muitas aplicações, pois isto permite considerar as transferências de dados sem usar informações dinâmicas sobre os recursos, reduzindo o tempo total de execução da aplicação. Através do uso de uma estratégia de replicação de tarefas, Storage Affinity efetua escalonamentos eficientes sem depender de informação dinâmica. Os resultados mostram que Storage Affinity pode alcançar uma performance, em média, melhor do que os escalonadores estado-da-arte que dependem de informação, mesmo em situações onde tais escalonadores usam informação perfeita. Em contrapartida, há um acréscimo no consumo de ciclos de CPU (em média, ) para alcançar este desempenho devido a replicação de tarefas.Data-intensive applications executing over a computational grid demand large data transfers. These are costly operations. Therefore, taking them into account is mandatory to achieve efficient scheduling of data-intensive applications on grids. Further, within an heterogeneous environment such as a grid, good schedules are typically attained by heuristics that use dynamic information about the grid and the applications (network and CPU loads, completion time of tasks, etc). However, these information are often difficult to be obtained accurately. Although there are schedulers that attain good performance without requiring that kind of information, they were not designed to take data transfer delays into account. This work presents Storage Affinity, a novel scheduling heuristic for Bag-of-Tasks and data-intensive applications running on grid environments. Storage Affinity exploits a data reuse pattern, common on many data-intensive applications, allowing it to take data transfer delays into account and reduce the makespan of the application. Further, it uses a replication strategy that yields efficient schedules without relying upon dynamic information that is difficult to obtain. Our results show that Storage Affinity may attain performance that is in average better than that of state-of-the-art knowledge-dependent schedulers, even in the unlikely c a s e when the latter are fed with perfect information. This is achieved at the expense of consuming more CPU cycles (in average, more than not using replication)