Connecting every bit of knowledge: The Structure of Wikipedia’s first link network

Apples, porcupines, and the most obscure Bob Dylan song\u27is every topic a few clicks from Philosophy? Within Wikipedia, the surprising answer is yes: nearly all paths lead to Philosophy. Wikipedia is the largest, most meticulously indexed collection of human knowledge ever amassed. More than information about a topic, Wikipedia is a web of naturally emerging relationships. By following the first link in each article, we algorithmically construct a directed network of all 4.7 million articles: Wikipedia\u27s First Link Network. Here we study the English edition of Wikipedia\u27s First Link Network for insight into how the many inventions, places, people, objects, and events are related and organized. We traverse every path, measuring the accumulation of first links, path lengths, basins, cycles, and the influence each article exerts in shaping the network. We discover scale-free distributions describe path length, accumulation, and influence. Far from dispersed, first links disproportionately accumulate at a few articles\u27flowing from specific to general and culminating around fundamental notions such as Community, State, and Science. Philosophy shapes more paths than any other article by two orders of magnitude. Curiously, we also observe a gravitation towards topical articles such as Health Care and Fossil Fuel. These findings enrich our view of the connections and structure of Wikipedia\u27s ever growing store of knowledge

The effect of time on ear biometrics

We present an experimental study to demonstrate the effect of the time difference in image acquisition for gallery and probe on the performance of ear recognition. This experimental research is the first study on the time effect on ear biometrics. For the purpose of recognition, we convolve banana wavelets with an ear image and then apply local binary pattern on the convolved image. The histograms of the produced image are then used as features to describe an ear. A histogram intersection technique is then applied on the histograms of two ears to measure the ear similarity for the recognition purposes. We also use analysis of variance (ANOVA) to select features to identify the best banana wavelets for the recognition process. The experimental results show that the recognition rate is only slightly reduced by time. The average recognition rate of 98.5% is achieved for an eleven month-difference between gallery and probe on an un-occluded ear dataset of 1491 images of ears selected from Southampton University ear database

Describing and Forecasting Video Access Patterns

Computer systems are increasingly driven by workloads that reflect large-scale social behavior, such as rapid changes in the popularity of media items like videos. Capacity planners and system designers must plan for rapid, massive changes in workloads when such social behavior is a factor. In this paper we make two contributions intended to assist in the design and provisioning of such systems.We analyze an extensive dataset consisting of the daily access counts of hundreds of thousands of YouTube videos. In this dataset, we find that there are two types of videos: those that show rapid changes in popularity, and those that are consistently popular over long time periods. We call these two types rarely-accessed and frequently-accessed videos, respectively. We observe that most of the videos in our data set clearly fall in one of these two types. For each type of video we ask two questions: first, are there relatively simple models that can describe its daily access patterns? And second, can we use these simple models to predict the number of accesses that a video will have in the near future, as a tool for capacity planning? To answer these questions we develop two different frameworks for characterization and forecasting of access patterns. We show that for frequently-accessed videos, daily access patterns can be extracted via principal component analysis, and used efficiently for forecasting. For rarely-accessed videos, we demonstrate a clustering method that allows one to classify bursts of popularity and use those classifications for forecasting

On the Geographic Location of Internet Resources

One relatively unexplored question about the Internet's physical structure concerns the geographical location of its components: routers, links and autonomous systems (ASes). We study this question using two large inventories of Internet routers and links, collected by different methods and about two years apart. We first map each router to its geographical location using two different state-of-the-art tools. We then study the relationship between router location and population density; between geographic distance and link density; and between the size and geographic extent of ASes. Our findings are consistent across the two datasets and both mapping methods. First, as expected, router density per person varies widely over different economic regions; however, in economically homogeneous regions, router density shows a strong superlinear relationship to population density. Second, the probability that two routers are directly connected is strongly dependent on distance; our data is consistent with a model in which a majority (up to 75-95%) of link formation is based on geographical distance (as in the Waxman topology generation method). Finally, we find that ASes show high variability in geographic size, which is correlated with other measures of AS size (degree and number of interfaces). Among small to medium ASes, ASes show wide variability in their geographic dispersal; however, all ASes exceeding a certain threshold in size are maximally dispersed geographically. These findings have many implications for the next generation of topology generators, which we envisage as producing router-level graphs annotated with attributes such as link latencies, AS identifiers and geographical locations.National Science Foundation (CCR-9706685, ANI-9986397, ANI-0095988, CAREER ANI-0093296); DARPA; CAID

Understanding the community based participatory research (CBPR) approach: case study in Ghana

ReportIn 2003, the University of Utah, in cooperation with Kwame Nkrumah University of Science and Technology (KNUST), Komfo Anokye Teaching Hospital (KATH), the Atwima Nwabiagya District Assembly and Barekuma community leaders, formed what is known as the Barekuma Collaborative Community Development Project (BCCDP) in Ghana, West Africa. In 2009, the BCCDP facilitated the establishment of a farmer's cooperative in the village of Barekuma, outside of Kumasi, Ghana, which is called the Amakye‐Bare Youth Co‐operative Farming & Marketing Society Limited. Research performed in the summer of 2011 measured the perceptions of the members of the Agricultural Cooperative toward the BCCDP. Using Community‐Based Participatory Research (CBPR), this paper describes the BCCDP, the Agricultural Cooperative, and Ghanaian culture, while expanding upon the perceptions of Agriculture Cooperative members regarding concepts of loans and ownership, and concludes with recommendations for improvement. CBPR is an approach for community‐engaged research that promotes equitable partnerships between researchers and community members (Wallerstein & Duran, 2003). CBPR seeks to integrate researchers and communities in shared decision‐making and ownership in order to promote social change and eliminate health disparities (de Schweinitz et al., 2009). Equitable partnerships require sharing power, resources, credit, results, and knowledge, as well as a reciprocal appreciation of each partner's knowledge and skills at each stage of the project, including problem definition/issue selection, research design, conducting research, interpreting the results, and determining how the results should be utilized for community development (Amuase, 2011). Because of the potential of CBPR to reduce health disparities, the Institute of Medicine considers CBPR one of eight priority approaches for public health education in the twenty‐first century (Israel, 2005). CBPR is at the heart of all of the projects undertaken by the BCCDP. Every autumn, leaders from Barekuma, University of Utah, KATH, and KNUST meet to discuss potential research projects for the coming summer, along with needed follow up to past projects. Projects undertaken undergo scrutinized research beforehand to determine the best methods for not only conducting the research, but also using validated tools whenever possible. They are only carried out after receiving approval from both the University of Utah Institutional Review Board (IRB) and the IRB at Kwame Nkrumah University of Science and Technology and Komfo Anokye Teaching Hospital

Enhancing Covid-19 Decision-Making by Creating an Assurance Case for Simulation Models

Simulation models have been informing the COVID-19 policy-making process. These models, therefore, have significant influence on risk of societal harms. But how clearly are the underlying modelling assumptions and limitations communicated so that decision-makers can readily understand them? When making claims about risk in safety-critical systems, it is common practice to produce an assurance case, which is a structured argument supported by evidence with the aim to assess how confident we should be in our risk-based decisions. We argue that any COVID-19 simulation model that is used to guide critical policy decisions would benefit from being supported with such a case to explain how, and to what extent, the evidence from the simulation can be relied on to substantiate policy conclusions. This would enable a critical review of the implicit assumptions and inherent uncertainty in modelling, and would give the overall decision-making process greater transparency and accountability.Comment: 6 pages and 2 figure