Small Group Of Users Research Articles

“Making meaning”: Communication between sign language users without a shared language

AbstractIn a small group of deaf sign language users from different countries and with no shared language, the signers’ initial conversational interactions are investigated as they meet in pairs for the very first time. This case study allows for a unique insight into the initial stages of pidginisation and the conceptual processes involved. The participants use a wide range of linguistic and communicative resources, and it can be argued that they construct shared multilingual-multimodal cognitive spaces for the purpose of these conversations. This research explores the nature of these shared multilingual-multimodal spaces, how they are shaped by the signers in interaction, and how they can be understood in terms of conceptual blending. The research also focuses on the meta-linguistic skills that signers use in these multilingual-multimodal interactions to “make meaning”.

Utilization of Outpatient Services by Elderly in Dubai: Evidence for Policy Makers and its Implication for Health System

The purpose of the study is to analyze and describe the patterns of outpatient health services by those aged 65 years and older (elderly) in Dubai. We identified patient characteristics and clinical diagnoses associated with outpatient visits on a national level. Dubai database of utilization of outpatient care visits by elderly in 2012 was obtained. The study is based on analysis of this secondary data collected by DHA and published in the Dubai Health Authority Report in 2012. The information recorded on the database includes patient demographics (age, sex, and nationality), health facility type (private, public), and clinical diagnosis (department or disease category). We then analyzed the patterns and frequency of visits by patient age, sex, nationality, and primary diagnosis in both the private and governmental sectors. Dubai’s annual age-adjusted crude visit rate of 4.4 visits per capita is low compared with an average of 6.3 in Organization for Economic Co-operation and Development (OECD) countries. The nationals visit rate was 8 visits per year compared to 3 for expatriates. In nationals, females have higher visit rate, while males have higher visit rate in expatriates. Females have higher visit rate in the governmental sector (DHA), while there was no significant difference in the visit rate between males and females in the private sector. The annual age-specific visit rate of outpatient services for elderly was about 27.5 visits per capita. The annual age-sex specific visit rate of outpatient services for elderly males was about 30 visits per capita (one visit every 15 days). The annual age-sex specific visit rate of outpatient services for elderly females was about 26 visits annually per capita (one visit every 12 days). The three most frequent principal diagnoses of elderly outpatient visits in private sector were diseases of the respiratory system (14 %), and diseases of the musculoskeletal system (12.6 %) and Endocrine, nutritional, metabolic diseases (11.3 %), respectively. The unclassified signs and symptoms constituted about 12 %. The three most frequent principal diagnoses of elderly outpatient visits in DHA were diseases related to family medicine (37 %); accidents and emergency (10.6 %); and endocrine, nutritional, and physical and medical rehabilitation (10 %), respectively. Elderly group was a small group of high users who consume a disproportionate share of outpatient care services. Although they represent about 0.5 % of the Dubai population, elderly account for approximately 5 % of all outpatient visits. Health promotion and preventive policies to elderly patients with these chronic diseases are urgently needed to address these numbers. Preventive services should focus on endocrine, circulatory, respiratory, and musculoskeletal diseases, which accounted for nearly one-half of the primary diagnoses of elderly outpatient visits. There may be a need to substantially increase in health care resources for geriatric care in the near future and shift towards home care.

4-D Statistical Surface Method for Visual Change Detection in Forest Ecosystem Simulation Time Series

Rising uncertainties associated with climate change compel forest management planning to include forest ecosystem simulations. The output of such models is often of high spatio-temporal complexity and difficult to interpret for the user. This contribution describes a novel visualization method called four-dimensional (4-D) statistical surfaces, which aims at improving the visual detection of change in time series. The method visualizes attribute values as surfaces, which are interpolated and animated over time; the interactive attribute surfaces are combined with color-coding and contour lines to support absolute and relative height judgment as well as faster perception and better location of change. A design study and prototypical implementation of the visualization method is described in this contribution. Time-series simulation results of LANDIS-II, a commonly used modeling tool in forest ecology, as well as a temporal vegetation index dataset (NDVI) are visualized using 4-D statistical surfaces. Usability challenges are addressed based on explorative interviews with a small group of users. The method is not limited to ecological model output; it can be used to create three-dimensional (3-D) temporal animations of arbitrary time-series datasets where parameters are supplied in regular raster format.

도시생활권공원의 유형에 따른 이용특성 및 커뮤니티 의식의 관계

This study was constructed to analyze the relationship of the use characteristics and the sense of community by the type of urban neighborhood park, and to provide basic information to make urban neighborhood parks that can contribute to a rise in the social function. The cases for the study were two small parks in Gimcheon and Daegu, two children's parks in Gyungsan, and two community parks in Busan. The data was collected from an investigation into the present condition of the parks and a survey of 520 residents. The survey contained the general characteristics of residents, the use characteristics of the parks, and the community characteristics. The methods of analysis were the analysis of frequency, chi-squared analysis, analysis of variance and Scheffe's test. The results of this study were as follows: First, urban neighborhood parks users visited the parks with different aims, such as to take walks and rest, to workout, to play with kids, to meet friends, and they stayed 54.1 minutes on average. Sometimes they came alone. Second, the user group for the small park came with their children and generally played with them, or walked and rested for about 38.1 minutes. The other group, children's parks users, commonly visited to take a walk and rest alone for an average of 32.8 minutes. The community park users often visited with a friend or neighbor for 1 or 2 hours to walk and relax. Third, the small park user group and community park user group have a stronger sense of community than the children's park user group. In other words, they felt not only a powerful sense of fellowship, belonging, attachment, and closeness but also security, and interest in neighborhood.

Motivating Smartphone Collaboration in Data Acquisition and Distributed Computing

This paper analyzes and compares different incentive mechanisms for a master to motivate the collaboration of smartphone users on both data acquisition and distributed computing applications. To collect massive sensitive data from users, we propose a reward-based collaboration mechanism, where the master announces a total reward to be shared among collaborators, and the collaboration is successful if there are enough users wanting to collaborate. We show that if the master knows the users' collaboration costs, then he can choose to involve only users with the lowest costs. However, without knowing users' private information, then he needs to offer a larger total reward to attract enough collaborators. Users will benefit from knowing their costs before the data acquisition. Perhaps surprisingly, the master may benefit as the variance of users' cost distribution increases. To utilize smartphones' computation resources to solve complex computing problems, we study how the master can design an optimal contract by specifying different task-reward combinations for different user types. Under complete information, we show that the master involves a user type as long as the master's preference characteristic outweighs that type's unit cost. All collaborators achieve a zero payoff in this case. If the master does not know users' private cost information, however, he will conservatively target at a smaller group of users with small costs, and has to give most benefits to the collaborators.

Friends or Foes: Distributed and Randomized Algorithms to Determine Dishonest Recommenders in Online Social Networks

Viral marketing is becoming important due to the popularity of online social networks (OSNs). Companies may provide incentives (e.g., via free samples of a product) to a small group of users in an OSN, and these users provide recommendations to their friends, which eventually increases the overall sales of a given product. Nevertheless, this also opens a door for malicious behaviors: dishonest users may intentionally give misleading recommendations to their friends so as to distort the normal sales distribution. In this paper, we propose a detection framework to identify dishonest users in the OSNs. In particular, we present a set of fully distributed and randomized algorithms, and also quantify the performance of the algorithms by deriving probability of false positive, probability of false negative, and the distribution of number of detection rounds. Extensive simulations are also carried out to illustrate the impact of misleading recommendations and the effectiveness of our detection algorithms. The methodology we present here will enhance the security level of viral marketing in the OSNs.

Emergency department utilization among frequent users with psychiatric visits.

The objective was to assess the incidence of psychiatric visits among frequent emergency department (ED) users and utilization among frequent psychiatric users. This was a multicenter retrospective longitudinal study of 1.76 million adult ED visits from acute care hospitals serving the metropolitan San Diego region (2008 through 2010) using nonpublic data submitted to the Office of Statewide Health Planning and Development. Frequent users were defined as having at least four ED visits within 12 consecutivemonths and were further classified into three groups based on the number of primary psychiatric visits in the same period, as defined by the primary discharge diagnosis (zero, one to three, and four or more visits). Descriptive, univariate, and logistic regression analyses are reported. Patients with at least one primary psychiatric visit were 4.6 (95% confidence interval [CI]=4.5 to 4.7) times more likely to be frequent ED users compared to patients with none. However, the majority of frequent ED users (80.1%) did not have any primary psychiatric visits; 16.6% of frequent ED users were classified as occasional psychiatric users, and only 3.3% were classified as frequent psychiatric users. Compared to frequent users without primary psychiatric visits, frequent psychiatric users were more likely to be male, non-Hispanic white, and without private insurance and visited the ED more frequently. Frequent psychiatric users also suffered from psychiatric, medical, and substance abuse comorbidity. While patients with primary psychiatric visits were more likely to be frequent users of the ED, only a small percentage of frequent ED users were seen primarily and repeatedly for psychiatric care. However, this small group of frequent psychiatric users visited the ED at a much higher rate than other frequent users and was burdened with both chronic medical and psychiatric conditions.

AutoGate: automating analysis of flow cytometry data.

Nowadays, one can hardly imagine biology and medicine without flow cytometry to measure CD4 T cell counts in HIV, follow bone marrow transplant patients, characterize leukemias, etc. Similarly, without flow cytometry, there would be a bleak future for stem cell deployment, HIV drug development and full characterization of the cells and cell interactions in the immune system. But while flow instruments have improved markedly, the development of automated tools for processing and analyzing flow data has lagged sorely behind. To address this deficit, we have developed automated flow analysis software technology, provisionally named AutoComp and AutoGate. AutoComp acquires sample and reagent labels from users or flow data files, and uses this information to complete the flow data compensation task. AutoGate replaces the manual subsetting capabilities provided by current analysis packages with newly defined statistical algorithms that automatically and accurately detect, display and delineate subsets in well-labeled and well-recognized formats (histograms, contour and dot plots). Users guide analyses by successively specifying axes (flow parameters) for data subset displays and selecting statistically defined subsets to be used for the next analysis round. Ultimately, this process generates analysis "trees" that can be applied to automatically guide analyses for similar samples. The first AutoComp/AutoGate version is currently in the hands of a small group of users at Stanford, Emory and NIH. When this "early adopter" phase is complete, the authors expect to distribute the software free of charge to .edu, .org and .gov users.

A novel approach to online social influence maximization

Online social networks are becoming a true growth point of Internet. As individuals constantly desire to interact with each other, the ability for Internet to deliver this networking influence becomes much stronger. In this paper, we study the online social influence maximization problem, which is to find a small group of influential users that maximizes the spread of influence through networks. After a thorough analysis of existing models, especially two classical ones, namely Independent cascade and linear thresholds, we argue that their idea that each user can only be activated by its active neighbors is not applicable to online social networks, since in many applications there is no clear concept for the issue of "activation". In our proposed influence model, if there is a social influence path linking two nonadjacent individuals, the value of influence between these two individuals can be evaluated along the existing social path based on the influence transitivity property under certain constraints. To compute influence probabilities between two neighbors, we also develop a new method which leverages both structure of networks and historical data. With reasonably learned influence probabilities, we recast the problem of online influence maximization to a weighted maximum cut problem which analyzes the influence flow among graph vertices. By running a semidefinite program-based (GW) algorithm on a complete influence graph, an optimal seed set can be identified effectively. We also provide experimental results on real online social networks, showing that our algorithm significantly outperforms greedy methods.

The Micro-Blogging Network Leading Group Recognition Algorithm

Based on the analysis and research of micro-blogging network transmission of information, the transmission of information model is constructed. By studying the network model, a small group of core users of the network information dissemination play a guiding role. To solve the problem that the research of micro-blogging user influence ranking can only ranking order given its influence, but not determine which user play a guiding role in transmission of information, LeadersRank algorithm based on the idea of personalized PageRank algorithm is proposed, and the algorithm is applied to the real micro-blogging data to identify the leading group, the experimental results prove the feasibility and effectiveness of the algorithm.

A Two-Stage Deanonymization Attack against Anonymized Social Networks

Digital traces left by users of online social networking services, even after anonymization, are susceptible to privacy breaches. This is exacerbated by the increasing overlap in user-bases among various services. To alert fellow researchers in both the academia and the industry to the feasibility of such an attack, we propose an algorithm, Seed-and-Grow, to identify users from an anonymized social graph, based solely on graph structure. The algorithm first identifies a seed subgraph, either planted by an attacker or divulged by a collusion of a small group of users, and then grows the seed larger based on the attacker's existing knowledge of the users' social relations. Our work identifies and relaxes implicit assumptions taken by previous works, eliminates arbitrary parameters, and improves identification effectiveness and accuracy. Simulations on real-world collected data sets verify our claim.

How well can early adopters of electric vehicles be identified?

Electric vehicles have recently been introduced to market in Europe. Policy makers as well as car manufacturers have great interest to understand the first group of electric vehicle users, the so-called ‘early adopters’. Several studies have tried to determine the potential early adopters of electric vehicles from different angles. However, the number of available studies is limited and little is known about the actual statistical significance of characteristics for this important user group. Here we characterize the potential first users of electric vehicles from an economic perspective and ask which driving profiles make an electric vehicle cost-effective. To this end, we analyze a large database of German driving profiles and find the share of potential first users from different city sizes and statuses of employment. We first find the potential and in a second step study the statistical significance and robustness of the result by (1) performing Chi-square tests of the differences between potential early adopters and other vehicle owners and (2) varying important input parameters of our estimates. We find our characterization of the early adopters to be robust if battery prices and consumption costs are sufficiently favorable for a not too small group of users.

Understanding Interfirm Relationships in Business Ecosystems with Interactive Visualization

Business ecosystems are characterized by large, complex, and global networks of firms, often from many different market segments, all collaborating, partnering, and competing to create and deliver new products and services. Given the rapidly increasing scale, complexity, and rate of change of business ecosystems, as well as economic and competitive pressures, analysts are faced with the formidable task of quickly understanding the fundamental characteristics of these interfirm networks. Existing tools, however, are predominantly query- or list-centric with limited interactive, exploratory capabilities. Guided by a field study of corporate analysts, we have designed and implemented dotlink360, an interactive visualization system that provides capabilities to gain systemic insight into the compositional, temporal, and connective characteristics of business ecosystems. dotlink360 consists of novel, multiple connected views enabling the analyst to explore, discover, and understand interfirm networks for a focal firm, specific market segments or countries, and the entire business ecosystem. System evaluation by a small group of prototypical users shows supporting evidence of the benefits of our approach. This design study contributes to the relatively unexplored, but promising area of exploratory information visualization in market research and business strategy.

Targeted Intervention Reduces Use in Frequent Users of Emergency Medical Services

Some of the increase in EMS use is driven by a small group of frequent users. Age, housing status, mental illness, and substance abuse have been identified as contributors to frequent use. Creating EMS-based outreach team could be a cost-effective means of reducing frequent use of EMS. We hoped to determine if engagement by the San Francisco Fire Department's (SFFD) Homeless Outreach and Medical Emergency (HOME) Team is associated with decreased EMS use, and to identify predictors of responsiveness to HOME team intervention. Patients who called EMS four or more times per month were identified from SFFD records and through referrals from EMS providers and EDs. Intervention began with a traditional paramedic assessment. Once patients were determined to not have any immediate medical need, the HOME TEAM Interventional Technique was employed. This includes direct transport to medical detoxification services, San Francisco Sobering Center, Psychiatric Emergency Services at San Francisco General Hospital (SFGH), substance abuse treatment clinics, primary care clinics, and access to case management and housing. We retrospectively analyzed data collected by HOME Team regarding EMS utilization in the 7 months before and after their intervention. Change in mean EMS use was the primary outcome measure. Social and demographic characteristics were gathered from the Department of Public Health EMR. Of the 59 subjects included in the final analysis, 14 subjects (nonresponders) had increased EMS use (mean increase 9.71, STD 9.75), and 45 subjects (responders) had decreased use (mean decrease 16.29, STD 18.88). The mean decrease in EMS use for all subjects was 10.1 calls per 7 months period (95% CI 4.4 - 15.9). None of the predictors were statistically associated with nonresponsiveness. HOME team intervention was associated with decrease in overall EMS use. While the response was not uniform, none of the tested factors predicted nonresponse. However, sample size limitations and high prevalence of some factors may have limited this finding. EMS-based outreach programs have the potential to reduce EMS utilization by frequent users.TablePatient Demographic and Social Characteristics.CharacteristicN = 59Sex, Male68%Mean Age in years (IQR)55 (9)Not Housed38.9% (5 missing)Substance Abuse88.9% (5 missing)Psychiatric History83.0% (6 missing)No primary Care Doctor43.4% (6 missing) Open table in a new tab

Fusion Cubes

Self-service business intelligence is about enabling non-expert users to make well-informed decisions by enriching the decision process with situational data, i.e., data that have a narrow focus on a specific business problem and, typically, a short lifespan for a small group of users. Often, these data are not owned and controlled by the decision maker; their search, extraction, integration, and storage for reuse or sharing should be accomplished by decision makers without any intervention by designers or programmers. The goal of this paper is to present the framework we envision to support self-service business intelligence and the related research challenges; the underlying core idea is the notion of fusion cubes, i.e., multidimensional cubes that can be dynamically extended both in their schema and their instances, and in which situational data and metadata are associated with quality and provenance annotations.

Internet Forums and the Rise of the Inventive Energy User

While climate and energy policy voice concerns about citizen’s lack of improving their houses and heating systems, some citizens by far exceed the expectations. Our research on heat pumps revealed over a hundred inventions by citizen users in Finland alone, despite the technology being in many respects uninviting to modify. Users’ capacity to carry out these modifications owes much to their exchanges at userrun Internet forums, a new and proliferating type of setting. These online forums help otherwise dispersed and heterogeneous users to create a specific kind of learning space that helps some users to “grow inventive” even as the majority of users therein remain indifferent towards their specific projects. These findings open a discussion on how the actions of typically a small group of inventive users are embedded in and supported by the activities of a broader user base.

The Glass Infrastructure — Using Common Sense to Create a Dynamic, Place‐Based Social‐Information System

Most organizations have a wealth of knowledge about themselves available online, but little for a visitor to interact with on‐site. At the MIT Media Lab, we have designed and deployed a novel intelligent signage system, the Glass Infrastructure (GI), that enables small groups of users to interact physically through a touch‐screen display with this data and to discover the latent connections between people, projects, and ideas. The displays are built on an adaptive, unsupervised model of the organization and its relationships developed using dimensionality reduction and commonsense knowledge that automatically classifies and organizes the information.The GI is currently in daily use at the lab. We discuss the AI model's development, the integration of AI into a human‐computer interaction (HCI) interface, and the use of the GI during the lab's peak visitor periods. We show that the GI is used repeatedly by lab visitors and provides a window into the workings of the organization.

THE TREND OF CLASS, RACE, AND ETHNICITY IN SOCIAL MEDIA INEQUALITY

Blogs were the original poster child of digital democracy as an egalitarian public forum. Some scholars have challenged this theory of equality based on race and ethnicity, but no empirical analysis of American adults has questioned a class-based divide over time. Blogs, as a form of digital content production, appear to mirror other technological innovations in which a small elite group of users begin to incorporate them in their daily living after which the innovation spreads quickly to the general population, as with basic Internet access. However, the author argues that unlike this consumptive practice, blogging fits into a productive framework that requires more resources. Furthermore, most studies on blogging and inequality, in general, derive from samples of college students, which make it difficult to understand class issues. By drawing on 13 national surveys of American adults from 2002 to 2008, this study incorporates class differences and finds that an educational gap in blogging persists, rather than narrows, even among people who are online. Race and ethnicity do not have a relationship with class in accounting for the inequality.

Discovering target groups in social networking sites: An effective method for maximizing joint influential power

With the tremendous popularity of social networking sites in this era of Web 2.0, increasingly more users are contributing their comments and opinions about products, people, organizations, and many other entities. These online comments often have direct influence on consumers’ buying decisions and the public’s impressions of enterprises. As a result, enterprises have begun to explore the feasibility of using social networking sites as platforms to conduct targeted marking and enterprise reputation management for e-commerce and e-business. As indicated from recent marketing research, the joint influential power of a small group of active users could have considerable impact on a large number of consumers’ buying decisions and the public’s perception of the capabilities of enterprises. This paper illustrates a novel method that can effectively discover the most influential users from social networking sites (SNS). In particular, the general method of mining the influence network from SNS and the computational models of mathematical programming for discovering the user groups with max joint influential power are proposed. The empirical evaluation with real data extracted from social networking sites shows that the proposed method can effectively identify the most influential groups when compared to the benchmark methods. This study opens the door to effectively conducting targeted marketing and enterprise reputation management on social networking sites.

Sizing solar home systems for optimal development impact

The paper compares the development impact of three different sized solar home systems (SHS) (10, 40 and 80Wp) installed in rural East Timor. It describes research aimed to determine whether the higher cost of the larger systems was justified by additional household benefits. To assess the development impact of these different sizes of SHS the research used a combination of participatory and quantitative tools. Participatory exercises were conducted with seventy-seven small groups of SHS users in twenty-four rural communities and supplemented with a household survey of 195 SHS users.The combined results of these evaluation processes enabled the three sizes of SHS to be compared for two types of benefits—those associated with carrying out important household tasks and attributes of SHS which were advantageous compared to the use of non-electric lighting sources. The research findings showed that the small, 10Wp SHS provided much of the development impact of the larger systems. It suggests three significant implications for the design of SHS programs in contexts such as East Timor: provide more small systems rather than fewer large ones; provide lighting in the kitchen wherever possible; and carefully match SHS operating costs to the incomes of rural users.