Social Set Research Articles

A Multistrain Bacterial Diffusion Model for Link Prediction

Topological link prediction is the task of assessing the likelihood of new future links based on topological properties of entities in a network at a given time. In this paper, we introduce a multistrain bacterial diffusion model for link prediction, where the ranking of candidate links is based on the mutual transfer of bacteria strains via physical social contact. The model incorporates parameters like efficiency of the receiver surface, reproduction rate and number of social contacts. The basic idea is that entities continuously infect their neighborhood with their own bacteria strains, and such infections are iteratively propagated on the social network over time. The probability of transmission can be evaluated in terms of strains, reproduction, previous transfer, surface transfer efficiency, number of direct social contacts i.e. neighbors, multiple paths between entities. The value of the mutual strains of infection between a pair of entities is used to rank the potential arcs joining the entity nodes. The proposed multistrain diffusion model and mutual-strain infection ranking technique have been implemented and tested on widely accepted social network data sets. Experiments show that the MSDM-LP and mutual-strain diffusion ranking technique outperforms state-of-the-art algorithms for neighbor-based ranking.

Charakterystyka postaci w szkole – między stereotypem a kreacją

Self-presentation is one of the basic human communicative behaviours. At school it is connected with the ability to characterize a character, which often results in schematic form and stereotypical content. The routinisation of procedures used by teachers breeds students’ indifference. Contemporary urge for social communication sets new challenges to the ability of characterization, one of which being the ability of effective self-presentation. In this particular context the characteristics loses its status of solely school being and faces the current urge of existence on labour market or in the virtual space. The aim of this performance is to present the results of empirical research carried out in secondary schools (against available research results of chosen “self-presentation genres” found in the Internet, such as blogs or IRC’s) in the form of quantitative-qualitative analysis of students’ abilities of the genre’s realization (complemented with the theoretical works in linguistics and linguistic efficiency development didactics) in a perspective of communicative competence’s development in this particular area.

A Reversible Watermarking Technique for Social Network Data Sets for Enabling Data Trust in Cyber, Physical, and Social Computing

Social network data are being mined for extracting interesting patterns. Such data are collected by different researchers and organizations and are usually also shared via different channels. These data usually have huge volume because there are millions of social network users throughout the world. In this context, ownership protection of such data sets with huge volume becomes relevant. Digital watermarking is a more demanding solution than any other technique for ensuring rights protection and integrity of the original data sets. The objective of this paper is to devise a reversible watermarking technique for the social network data to prove ownership rights and also provide a mechanism for data recovery. Robustness of the proposed technique is evaluated through attack analysis using experimental study. In this paper, Z notation-based formal specification is also provided to show the working of the proposed reversible watermarking technique for social network data sets for enabling data trust in Cyber, Physical, and Social Computing (CPSCom).

Dynamic Weight-Based Individual Similarity Calculation for Information Searching in Social Computing

In the social computing environment, the complete information about an individual is usually distributed in heterogeneous social networks, which are presented as linked data. Synthetically recognizing and integrating these distributed and heterogeneous data for efficiently information searching is an important but challenging work. In this paper, a dynamic weight (DW)-based similarity calculation is proposed to recognize and integrate similar individuals from distributed data environments. First, each link of an individual is weighted by applying DW. Then, a semantic similarity metric is proposed to combine the DW into similarity calculation. Then, a searching system framework for a similarity-based individual is designed and tested in real-world data sets. Finally, massive experiments are conducted both in benchmark and real-world social community data sets. The results show that our approach can produce a good result in similar individual searching in social networks. In addition, it performs significantly better than the existing state-of-the-art approaches in similar individual searching.

A Framework for Minimal Clustering Modification via Constraint Programming

Consider the situation where your favorite clustering algorithm applied to a data set returns a good clustering but there are a few undesirable properties. One adhoc way to fix this is to re-run the clustering algorithm and hope to find a better variation. Instead, we propose to not run the algorithm again but minimally modify the existing clustering to remove the undesirable properties. We formulate the minimal clustering modification problem where we are given an initial clustering produced from any algorithm. The clustering is then modified to: i) remove the undesirable properties and ii) be minimally different to the given clustering. We show the underlying feasibility sub-problem can be intractable and demonstrate the flexibility of our constraint programming formulation. We empirically validate its usefulness through experiments on social network and medical imaging data sets.

МЕТОДОЛОГІЯ ОПЛАТИ ПРАЦІ В ДЕРЖАВНИХ ЗАКЛАДАХ ОХОРОНИ ЗДОРОВ’Я

Досліджено основні аспекти організації обліку видатків на оплату праці в державних закладах охорони здоров’я на підставі діючого нормативно-правового забезпечення. Розглянуто організацію обліку видатків медичного закладу на основну, додаткову зарплату та інші компенсаційні, заохочувальні виплати в системі рахунків бюджетного обліку.

Exploring local knowledge and perceptions on zoonoses among pastoralists in northern and eastern Tanzania.

BackgroundZoonoses account for the most commonly reported emerging and re-emerging infectious diseases in Sub-Saharan Africa. However, there is limited knowledge on how pastoral communities perceive zoonoses in relation to their livelihoods, culture and their wider ecology. This study was carried out to explore local knowledge and perceptions on zoonoses among pastoralists in Tanzania.Methodology and principal findingsThis study involved pastoralists in Ngorongoro district in northern Tanzania and Kibaha and Bagamoyo districts in eastern Tanzania. Qualitative methods of focus group discussions, participatory epidemiology and interviews were used. A total of 223 people were involved in the study. Among the pastoralists, there was no specific term in their local language that describes zoonosis. Pastoralists from northern Tanzania possessed a higher understanding on the existence of a number of zoonoses than their eastern districts' counterparts. Understanding of zoonoses could be categorized into two broad groups: a local syndromic framework, whereby specific symptoms of a particular illness in humans concurred with symptoms in animals, and the biomedical framework, where a case definition is supported by diagnostic tests. Some pastoralists understand the possibility of some infections that could cross over to humans from animals but harm from these are generally tolerated and are not considered as threats. A number of social and cultural practices aimed at maintaining specific cultural functions including social cohesion and rites of passage involve animal products, which present zoonotic risk.ConclusionsThese findings show how zoonoses are locally understood, and how epidemiology and biomedicine are shaping pastoralists perceptions to zoonoses. Evidence is needed to understand better the true burden and impact of zoonoses in these communities. More studies are needed that seek to clarify the common understanding of zoonoses that could be used to guide effective and locally relevant interventions. Such studies should consider in their approaches the pastoralists’ wider social, cultural and economic set up.

Promotion of Research on the HIV Continuum of Care in the United States: The CFAR HIV Continuum of Care/ECHPP Working Group.

Promotion of Research on the HIV Continuum of Care in the United States: The CFAR HIV Continuum of Care/ECHPP Working Group.

Toward Automating HIV Identification: Machine Learning for Rapid Identification of HIV-Related Social Media Data.

"Social big data" from technologies such as social media, wearable devices, and online searches continue to grow and can be used as tools for HIV research. Although researchers can uncover patterns and insights associated with HIV trends and transmission, the review process is time consuming and resource intensive. Machine learning methods derived from computer science might be used to assist HIV domain experts by learning how to rapidly and accurately identify patterns associated with HIV from a large set of social data. Using an existing social media data set that was associated with HIV and coded by an HIV domain expert, we tested whether 4 commonly used machine learning methods could learn the patterns associated with HIV risk behavior. We used the 10-fold cross-validation method to examine the speed and accuracy of these models in applying that knowledge to detect HIV content in social media data. Logistic regression and random forest resulted in the highest accuracy in detecting HIV-related social data (85.3%), whereas the Ridge Regression Classifier resulted in the lowest accuracy. Logistic regression yielded the fastest processing time (16.98 seconds). Machine learning can enable social big data to become a new and important tool in HIV research, helping to create a new field of "digital HIV epidemiology." If a domain expert can identify patterns in social data associated with HIV risk or HIV transmission, machine learning models could quickly and accurately learn those associations and identify potential HIV patterns in large social data sets.

Attribute Couplet Attacks and Privacy Preservation in Social Networks

The emerging of social networks, e.g., Facebook, Twitter, and Instagram, has eventually changed the way in which we live. Social networks are acquiring and storing a significant amount of profile information and daily activities of over billions of active users. The data sets, drawn from the social networks, are becoming great sources for exploring and attracting huge interests from different research communities. However, publishing social network data sets have also raised serious security and privacy risks; it is highly likely that they will be targeted by the hackers due to their substantially commercial values. To deal with the problem of privacy leakage, a number of attack models and corresponding privacy preserving solutions have been proposed recently. A common approach is to anonymize the identities of the users when publishing the data sets while we argue that the remaining relationship is sufficient to identify an anonymized user. In this paper, we define a new type of attack as attribute couplet attack . The attribute couplet attack facilitates the relationship of a couplet of anonymous nodes (i.e., a pair of users) and some limited background information to unveil the protected identities. To achieve privacy-preservation under attribute couplet attacks, we propose a new anonymity concept as k-couplet anonymity. A social network data set satisfies the k-couplet anonymity if, for any pair of nodes, there exist at least the other $k-1$ couplets sharing the same attributes. Then, we design and implement two heuristic algorithms to promote the k-couplet anonymity. Furthermore, we design an approximate algorithm for multiple-attribute social networks to realize the k-couplet anonymity. The evaluation results on multiple data sets demonstrate that the privacy and utility of the social network data sets can be well preserved, when incorporating the proposed k-couplet anonymity and the associate heuristic algorithms.

Contextualizing Discourse; Student Identities and Classroom Teaching

The paper explores the role of classroom interactions in construction of identity of students. The same has been attempted within critical discourse analysis framework provided by Norman Fairclough (1992). The results of discourse analysis of teacher-student interactions in audio-recordings of third-fourth-and fifth grade EVS lessons reveal that the critical discourse analysis provides a viable source of analyzing co-construction of identity and learning within the classroom. Though teachers encourage student participation in educational interactions by asking questions, but the interactions remain limited to what the biased perception of the teacher. The results of the present study show that such interactions impact the construction of students“ identities adversely. The theoretical framework provided by Fairclough emphasize the need to give a close look to specific discursive constructions within interactions between the teacher and students. With its specific discourse strategies of talk and text analysis, one can explore what kind of constructions of self is being carried out in interactions within a particular social set up, who is claiming to have knowledge and the same is hedging and mitigating others“ perceptions. At the end the paper attempts to contextualize the understanding within the Indian scenario of changing curriculum understanding and build a perspective towards its correct and perceived errors that it may produce if not understood conceptually.

A Novel Centrality Cascading Based Edge Parameter Evaluation Method for Robust Influence Maximization

The research of social influence is an important topic in online social network analysis. Influence maximization is the problem of finding $k$ nodes that maximize the influence spread in a specific social network. Robust influence maximization is a novel topic that focuses on the uncertainty factors among the influence propagation models and algorithms. It aims to find a seed set with a definite size that has robust performance with different influence functions under various uncertainty factors. In this paper, we propose a centrality-based edge activation probability evaluation method in the independent cascade model. We consider four different types of centrality measurement methods and add a modification coefficient to evaluate the edge probability. We also propose two algorithms, called NewDiscount and GreedyCIC, by incorporating the edge probability space into previous algorithms. With extensive experiments on various real online social network data sets, we find that our PageRank-based greedy algorithm has the best influence spreads and lowest running times, compared with other algorithms, on some large data sets. The experiment for evaluating the robustness performance shows that all algorithms have optimal robustness performance when the modification coefficient is set to 0.01 under the independent cascade model. This result suggests some further research directions under this model.

Response Analysis Model of Social Networks Using Fuzzy Sets and Feedback-Based System Dynamics

Response Analysis Model of Social Networks Using Fuzzy Sets and Feedback-Based System Dynamics

Incremental Subgraph Feature Selection for Graph Classification

Graph classification is an important tool for analyzing data with structure dependency, where subgraphs are often used as features for learning. In reality, the dimension of the subgraphs crucially depends on the threshold setting of the frequency support parameter, and the number may become extremely large. As a result, subgraphs may be incrementally discovered to form a feature stream and require the underlying graph classifier to effectively discover representative subgraph features from the subgraph feature stream. In this paper, we propose a primal-dual incremental subgraph feature selection algorithm (ISF) based on a max-margin graph classifier. The ISF algorithm constructs a sequence of solutions that are both primal and dual feasible. Each primal-dual pair shrinks the dual gap and renders a better solution for the optimal subgraph feature set. To avoid bias of ISF algorithm on short-pattern subgraph features, we present a new incremental subgraph join feature selection algorithm (ISJF) by forcing graph classifiers to join short-pattern subgraphs and generate long-pattern subgraph features. We evaluate the performance of the proposed models on both synthetic networks and real-world social network data sets. Experimental results demonstrate the effectiveness of the proposed methods.

Irrepressible: The Jazz Age Life of Henrietta Bingham by Emily Bingham

Reviewed by: Irrepressible: The Jazz Age Life of Henrietta Bingham by Emily Bingham Michelle Kuhl Irrepressible: The Jazz Age Life of Henrietta Bingham. By Emily Bingham. (New York: Farrar, Strauss and Giroux, 2015. Pp. xii, 369. $28.00, ISBN 978-0-8090-9464-6.) Emily Bingham is both lucky and skilled. She found a treasure trove of documents from an odd great-aunt in an attic and fleshed them out with extensive archival research to create a dishy romp through time. Irrepressible: The Jazz Age Life of Henrietta Bingham traces the life of a beautiful southern heiress. Henrietta Bingham’s father, Robert Worth Bingham, was an ambitious and high-strung politician whose wealthy wife died in a car accident that scarred the entire family. Henrietta spent the rest of her life struggling with the loss of her mother and the increased expectations of her demanding father. At Smith College, Henrietta fell in love with her English professor, Mina Stein Kirstein. The pair went to London, underwent psychoanalysis with a student of Freud, and wrestled with Freud’s directive to leave behind what was considered the immature love of women for more mature heterosexual relationships. During their stay in England, they fell in with the literary Bloomsbury circle where violet-blue-eyed Henrietta seduced multiple artists, both male and female. Here author Emily Bingham expertly weaves in the love letters from her family’s attic trunk with impressive research from other collections to show the tangled web of passion and heartbreak in this experimental circle. In 1932 Robert Bingham helped carry Kentucky in Franklin Delano Roosevelt’s bid for the White House and, in return, was appointed United States ambassador to the United Kingdom. Henrietta joined her father in [End Page 212] London and hosted events for respectable diplomats. By all accounts she had a magnetism that made her memorable. After meeting Henrietta at a dinner, the Prince of Wales “asked her for an invitation to the embassy” and made multiple appearances there (p. 221). Her father’s wealth and political clout gave her some insulation from conventional morality. For a few years, Henrietta had a fabulous run. She transgressively hosted a party for black jazz singers, inspired sculpture and sketches from British artists, had a scandalous affair with the American actress Hope Williams (whose lesbian chic reportedly inspired Katharine Hepburn), had a long-term romance with John Houseman, took up foxhunting, kept a domestic cottage with the women’s Wimbledon champion, and generally was a star among the artistic and social set of London and New York. In the rebellious decade of the 1920s and even into the 1930s, Henrietta’s life as a wealthy, gay woman seemed a fantastic adventure. But it did not last. Ultimately bowing to family pressure, Henrietta moved to Kentucky where the social climate was less accepting. Forced further into the closet and unable to create a lasting romantic partnership, Henrietta faced increasing struggles with depression and substance abuse. Ultimately, the sexually adventurous flapper aged into a suicidal alcoholic—a sobering reminder that progress is not a steady arc. Emily Bingham reveals the astonishing life of her great-aunt with considerable finesse and gentle warmth. The author weaves together multiple strands from the people in Henrietta’s orbit and deftly places them within the wider context of politics, of intellectual trends, and of sexual mores. Henrietta Bingham’s remarkable story is new, but nothing within it changes the field. However, history is a wide ecosystem; just as there is a place for field-changing declarations, there is certainly a place for engaging narratives that capture the imagination. Like the charming, seductive Henrietta Bingham herself, books like Irrepressible do important work by luring in readers and making them fall in love with history. Michelle Kuhl University of Wisconsin–Oshkosh Copyright © 2017 The Southern Historical Association

'Apostles of Continence': Doctors and the Doctrine of Sexual Necessity in Progressive-Era America.

In the first decades of the twentieth century, a group of doctors under the banner of the social hygiene movement set out on what seemed an improbable mission: to convince American men that they did not need sex. This was in part a response to venereal disease. Persuading young men to adopt the standard of sexual discipline demanded of women was the key to preserving the health of the nation from the ravages of syphilis and gonorrhoea. But their campaign ran up against the doctrine of male sexual necessity, a doctrine well established in medical thought and an article of faith for many patients. Initially, social hygienists succeeded in rallying much of the medical community. But this success was followed by a series of setbacks. Significant dissent remained within the profession. Even more alarmingly, behavioural studies proved that many men simply were not listening. The attempt to repudiate the doctrine of male sexual necessity showed the ambition of Progressive-era doctors, but also their powerlessness in the face of entrenched beliefs about the linkage in men between sex, health and success.

Activating Background Knowledge: An Effective Strategy to Develop Reading Comprehension Skills

<p style="margin: 0cm 0cm 8pt; line-height: 150%;"><span style="line-height: 150%; font-family: 'Times New Roman','serif'; font-size: 12pt;">The present paper is a report on a small classroom based research study. The researcher has tried Activating Background Knowledge as a strategy to improve reading comprehension skills of the students in the First Year B.Com. Classroom in a degree college affiliated to the University of Mumbai. The teacher-researcher assigned two passages dealing with two different social issues and set in two different contexts, i.e., foreign and local, for comprehension to the students in the classroom. The classroom experience indicated that activating and building on the students’ background knowledge did help in facilitating their reading comprehension skills. The paper thus recommends it as an effective strategy to develop the students’ reading comprehension skills. </span></p>

Big Data in a Crisis? Creating Social Media Datasets for Crisis Management Research

Abstract A growing body of research in the area of information systems for crisis management is based on data from social media. After almost every larger disaster studies emerge with the focus on the specific use of social media. Much of this research is based on Twitter data, due to the ease of access of this (mainly public) data, compared to (more closed) data, such as Facebook or Google+. Based on the experience gained from a research project on social media in emergencies and our task to collect social media data sets for other partners, we present the design and evaluation of a graphical user interface that supports those stakeholders (such as emergency services or researchers) that are interested in creating social media datasets for further crisis management research. We do not specifically focus on the analysis of social media data. Rather we aim to support the gathering process and how actors without sophisticated technical skills can be supported to get what they want and especially need: relevant social media data. Within this article, we present a practice-oriented approach and implications for designing tools that support the collection of social media data as well as future work.

Social Networks and Choice Set Formation in Discrete Choice Models

The discrete choice literature has evolved from the analysis of a choice of a single item from a fixed choice set to the incorporation of a vast array of more complex representations of preferences and choice set formation processes into choice models. Modern discrete choice models include rich specifications of heterogeneity, multi-stage processing for choice set determination, dynamics, and other elements. However, discrete choice models still largely represent socially isolated choice processes —individuals are not affected by the preferences of choices of other individuals. There is a developing literature on the impact of social networks on preferences or the utility function in a random utility model but little examination of such processes for choice set formation. There is also emerging evidence in the marketplace of the influence of friends on choice sets and choices. In this paper we develop discrete choice models that incorporate formal social network structures into the choice set formation process in a two-stage random utility framework. We assess models where peers may affect not only the alternatives that individuals consider or include in their choice sets, but also consumption choices. We explore the properties of our models and evaluate the extent of “errors” in assessment of preferences, economic welfare measures and market shares if network effects are present, but are not accounted for in the econometric model. Our results shed light on the importance of the evaluation of peer or network effects on inclusion/exclusion of alternatives in a random utility choice framework.

Social Decision Under Uncertainty and Responsibility for Beliefs

This paper aims to address two issues related to simultaneous aggregation of utilities and beliefs. The first one is related to how to integrate both inequality and uncertainty considerations into social decision-making. The second one is related to how individuals should be responsible for their own beliefs. To accomplish this, whereas individuals are assumed to abide by Savage model's of subjective expected utility, society is assumed to prescribe, either to each individual when the ex ante individual well-being is favored or to itself when the ex post individual well-being is favored, acting in accordance with the maximin expected utility theory of Gilboa and Schmeidler (1989). Furthermore, it adapts an ex ante Pareto-type condition proposed by Gayer et al. (2014), which says that a prospect Pareto dominates another one if the former gives a higher expected utility than the latter one, for each individual, for all individuals' beliefs. In the context where the ex ante individual welfare is favored, our ex ante Pareto-type condition is shown to be equivalent to social utility taking the form of a MaxMinMin social welfare function, as well as to the individual set of priors being contained within the range of individual beliefs. However, when the ex post individual welfare is favored, the same Pareto-type condition is shown to be equivalent to social utility taking the form of a MaxMinMin social welfare function, as well as to the social set of priors containing only weighted averages of individual beliefs.