Impact of anonymity on roles of personal and group identities in online communities

Extant research on the control of anonymous human behavior is inconclusive because of its focus on partial aspects of the self-concept. Multiple self-concepts are usually effective in any given situation. Hence, a holistic approach is preferred over a fragmented picture of the self. We investigate anonymous online communities to determine whether multiple self-concepts concurrently control human behavior.Data were collected from 1453 users through a web-based survey. The results showed that multiple self-concepts concurrently influenced argument quality through group norm conformity, whereas private-self directly influenced argument quality. Furthermore, online anonymity decreased the influence of group identity, contrary to existing assertions.     

A Dirichlet process mixture model for the analysis of correlated binary responses

The multivariate probit model is a popular choice for modelling correlated binary responses. It assumes an underlying multivariate normal distribution dichotomized to yield a binary response vector. Other choices for the latent distribution have been suggested, but basically all models assume homogeneity in the correlation structure across the subjects. When interest lies in the association structure, relaxing this homogeneity assumption could be useful. The latent multivariate normal model is replaced by a location and association mixture model defined by a Dirichlet process. Attention is paid to the parameterization of the covariance matrix in order to make the Bayesian computations convenient. The approach is illustrated on a simulated data set and applied to oral health data from the Signal Tandmobiel^® study to examine the hypothesis that caries is mainly a spatially local disease.     

Increasing participation in online communities: A framework for human–computer interaction

Online communities are becoming an accepted part of the lives of Internet users, although participation in these communities is dependent on the types of people that form them. Some of the online community’s members do not participate, people referred to as lurkers, whereas others who have been in the community for a long time, referred to as elders, participate regularly and support others. Understanding what drives these individuals and how they chose whether or not to participate will lead to online communities that thrive. This paper proposes a conceptual framework to describe what drives such individuals to carry out actions such as posting messages and adding content (level 1), the cognitions they use to determine whether or not to take such actions (level 2) and the means by which they go about carrying out the action in the environment (level 3). Finally, the framework is applied to the problem of encouraging members to participate by discussing the methods by which people can be persuaded to participate by changing the way they interpret their desires and their environment.     

A Markov chain model for dynamic binary search tree behaviour

We present a Markov chain model for the analysis of the behaviour of binary search trees (BSTs) under the dynamic conditions of insertions and deletions. The model is based on a data structure called a lineage tree, which provides a compact representation of different BST structures while still retaining enough information to model the effect of insertions and deletions and to compute average path length and tree height. Different lineages in the lineage tree correspond to states in the Markov chain. Transition probabilities are based on the number of BST structures corresponding to each lineage. The model is based on a similar lineage tree model developed for B-trees. The BST model is not intended for practical computations, but rather as a demonstration of the generalizability of the lineage tree approach for modeling data structures such as B-trees, B*-trees, B⁺-trees, BSTs, etc.     

Knowledge sharing in online health communities: A social exchange theory perspective

Online health communities (OHC) are becoming valuable platforms for patients to communicate and find support. These communities are different from general online communities. The knowledge shared in an OHC can be categorized as either general (public) or specific (private), and each category is shared in vastly different ways. Using the social exchange theory, we propose a benefit vs. cost knowledge sharing model for OHCs. The benefits are mainly based on Maslow's hierarchy of needs, and the cost includes cognitive and executional costs. We use this benefit vs. cost model to examine how OHC members share general and specific knowledge. Data were collected from 323 users of two well-known OHCs in China and were analyzed using the structural equation model. The results demonstrate that three factors positively impact the sharing of both general and specific knowledge: a sense of self-worth, members’ perceived social support, and reputation enhancement. Another factor, face concern, has a negative influence on specific knowledge sharing and a positive influence on general knowledge sharing. Executional cost only negatively impacts general knowledge sharing, and cognitive cost only negatively impacts specific knowledge sharing. This study of OHCs reveals that personal benefits promote knowledge sharing and costs prohibit it. These impacts vary between general knowledge and specific knowledge sharing.     

A mathematical model for designing and sizing sow farms

A semi‐Markov model and its application for designing and sizing swine facilities are presented. Swine production is becoming more and more specialised. Hence the sizing of a farm producing piglets is the main strategic decision of farmers who invest in sow production, since a farm comprises different facilities with different possible sizes. The classical approach is simple but the resulting design assumes some security margins without considering variations in sow performance or in the management policy. The model presented here has revealed differences with respect to the classical approach. As a result, the Markov chain approach is useful to determine more accurately the capacity, improve farm design and reduce housing cost.     

A conceptual model for the logical design of temporal databases

Although widely advocated as a tool for the conceptual modelling of data, the Entity-Relationship (E-R) model [4] and its extensions are generally lacking in constructs to model the dynamic nature of the real world, making them inadequate for designing temporal databases. This research first extends the E-R model to a Temporal Event-Entity-Relationship Model (TEERM), by introducing events as an additional construct. Second, a method is proposed for mapping this conceptual model into a temporal relational model for the logical design of temporal relational databases with a corresponding set of integrity constraints. The model is illustrated with an example and evaluated using a set of criteria proposed by ^{Batini et al. [2]}. The model appears to be expressive, simple and easy to use, and should, therefore, aid the temporal database design process significantly.     

慕课教学中基于局部社区发现的主题交互模型

针对慕课教学过程中存在的学生能力差异、缺乏针对性等问题,提出一种基于局部社区发现的主题交互模型,对学生能力评估过程进行主题建模,采用局部社区发现算法对学生各方面能力进行合理的等级分类;同时在原有课程实验平台基础上,改进并搭建能够全方位追踪和收集学生行为信息且具有较强交互性的慕课平台.两者结合应用于信号处理系列课程教学实践中,结果表明,主题交互模型对能力评估的准确率明显高于传统考核方式,并且辅以针对性的课堂交流与培养,学生各方面能力均得到不同程度的提高.     

A model for online learner support based on selecting appropriate peer tutors

Tutors have only limited time to support students. In this paper, we discuss a model that addresses the question of how to help students answer content‐related questions. A small group of students is created, which consists of the student who asked the question and peers who should be able to answer it. Criteria used to compose the group are the content of the question in relation to the knowledge and skills of the peers. The model supports the collaboration with text fragments selected from the study materials. We will introduce the model and briefly discuss the results of the calibration and a simulation of the model. Finally, we will discuss the outcome of an experiment with two groups of approximately 50 students who used the model for a period of 8 weeks. The results indicate that the students positively value the model and that it is possible to resolve a substantial number of their questions.     

A distance-relatedness dynamic model for clustering high dimensional data of arbitrary shapes and densities

It is important to find the natural clusters in high dimensional data where visualization becomes difficult. A natural cluster is a cluster of any shape and density, and it should not be restricted to a globular shape as a wide number of algorithms assume, or to a specific user-defined density as some density-based algorithms require.In this work, it is proposed to solve the problem by maximizing the relatedness of distances between patterns in the same cluster. It is then possible to distinguish clusters based on their distance-based densities. A novel dynamic model is proposed based on new distance-relatedness measures and clustering criteria. The proposed algorithm “Mitosis” is able to discover clusters of arbitrary shapes and arbitrary densities in high dimensional data. It has a good computational complexity compared to related algorithms. It performs very well on high dimensional data, discovering clusters that cannot be found by known algorithms. It also identifies outliers in the data as a by-product of the cluster formation process. A validity measure that depends on the main clustering criterion is also proposed to tune the algorithm's parameters. The theoretical bases of the algorithm and its steps are presented. Its performance is illustrated by comparing it with related algorithms on several data sets.     

一种软件可靠性测试模型研究

在马尔可夫链模型的基础上,将测试问题转化为一个数学问题。通过建立软件的使用链,根据使用链进行序列抽样,产生测试用例,将软件测试结果的分析问题转化为一个经典概率问题。运用实例证明,这种技术具有一定的实用性和有效性。     

Learning switching dynamic models for objects tracking

Many recent tracking algorithms rely on model learning methods. A promising approach consists of modeling the object motion with switching autoregressive models. This article is involved with parametric switching dynamical models governed by an hidden Markov Chain. The maximum likelihood estimation of the parameters of those models is described. The formulas of the EM algorithm are detailed. Moreover, the problem of choosing a good and parsimonious model with BIC criterion is considered. Emphasis is put on choosing a reasonable number of hidden states. Numerical experiments on both simulated and real data sets highlight the ability of this approach to describe properly object motions with sudden changes. The two applications on real data concern object and heart tracking.     

Sequence-based clustering for Web usage mining: A new experimental framework and ANN-enhanced K-means algorithm

We develop a general sequence-based clustering method by proposing new sequence representation schemes in association with Markov models. The resulting sequence representations allow for calculation of vector-based distances (dissimilarities) between Web user sessions and thus can be used as inputs of various clustering algorithms. We develop an evaluation framework in which the performances of the algorithms are compared in terms of whether the clusters (groups of Web users who follow the same Markov process) are correctly identified using a replicated clustering approach. A series of experiments is conducted to investigate whether clustering performance is affected by different sequence representations and different distance measures as well as by other factors such as number of actual Web user clusters, number of Web pages, similarity between clusters, minimum session length, number of user sessions, and number of clusters to form. A new, fuzzy ART-enhanced K-means algorithm is also developed and its superior performance is demonstrated.     

A latent class selection model for nonignorably missing data

When we have data with missing values, the assumption that data are missing at random is very convenient. It is, however, sometimes questionable because some of the missing values could be strongly related to the underlying true values. We introduce methods for nonignorable multivariate missing data, which assume that missingness is related to the variables in question, and to the additional covariates, through a latent variable measured by the missingness indicators. The methodology developed here is useful for investigating the sensitivity of one’s estimates to untestable assumptions about the missing-data mechanism. A simulation study and data analysis are conducted to evaluate the performance of the proposed method and to compare to that of MAR-based alternatives.     

A comparison of empathic communication pattern for teenagers and older people in online support communities

This article reports a study that investigated the occurrences of empathy in online support communities for teenagers. Qualitative content analysis with 400 messages from a discussion board about depression was used to identify how empathy was expressed in the specific online communication. Emphasis was also placed on the comparison of this age group to older people, by comparing the results with those from a previous study on empathy in an online support community about depression for older people. Specifically, the analysis focused on the frequencies of the categories of the code scheme, linguistic characteristics of the communication content, the occurring components of empathy, and the roles as well as activities of the members. From our analysis, we concluded that young people exchanged a substantial amount of empathic emotional communication when participating in an online support community, and they communicated on a more personal level compared to older people, who tended to engage in a more formal communication. In addition, teenagers also showed a high level of understanding but lower level of concern compared to older persons when expressing empathy online.     

A generalized temporal context model for classifying image collections

Semantic scene classification is an open problem in computer vision, especially when information from only a single image is employed. In applications involving image collections, however, images are clustered sequentially, allowing surrounding images to be used as temporal context. We present a general probabilistic temporal context model in which the first-order Markov property is used to integrate content-based and temporal context cues. The model uses elapsed time-dependent transition probabilities between images to enforce the fact that images captured within a shorter period of time are more likely to be related. This model is generalized in that it allows arbitrary elapsed time between images, making it suitable for classifying image collections. In addition, we derived a variant of this model to use in ordered image collections for which no timestamp information is available, such as film scans. We applied the proposed context models to two problems, achieving significant gains in accuracy in both cases. The two algorithms used to implement inference within the context model, Viterbi and belief propagation, yielded similar results with a slight edge to belief propagation. Matthew Boutell received the BS degree in Mathematical Science from Worcester Polytechnic Institute, Massachusetts, in 1993, the MEd degree from University of Massachusetts at Amherst in 1994, and the PhD degree in Computer Science from the University of Rochester, Rochester, NY, in 2005. He served for several years as a mathematics and computer science instructor at Norton High School and Stonehill College and as a research intern/consultant at Eastman Kodak Company. Currently, he is Assistant Professor of Computer Science and Software Engineering at Rose-Hulman Institute of Technology in Terre Haute, Indiana. His research interests include image understanding, machine learning, and probabilistic modeling. Jiebo Luo received his PhD degree in Electrical Engineering from the University of Rochester, Rochester, NY in 1995. He is a Senior Principal Scientist with the Kodak Research Laboratories. He was a member of the Organizing Committee of the 2002 IEEE International Conference on Image Processing and 2006 IEEE International Conference on Multimedia and Expo, a guest editor for the Journal of Wireless Communications and Mobile Computing Special Issue on Multimedia Over Mobile IP and the Pattern Recognition journal Special Issue on Image Understanding for Digital Photos, and a Member of the Kodak Research Scientific Council. He is on the editorial boards of the IEEE Transactions on Multimedia, Pattern Recognition, and Journal of Electronic Imaging. His research interests include image processing, pattern recognition, computer vision, medical imaging, and multimedia communication. He has authored over 100 technical papers and holds over 30 granted US patents. He is a Kodak Distinguished Inventor and a Senior Member of the IEEE. Chris Brown (BA Oberlin 1967, PhD University of Chicago 1972) is Professor of Computer Science at the University of Rochester. He has published in many areas of computer vision and robotics. He wrote COMPUTER VISION with his colleague Dana Ballard, and influential work on the “active vision” paradigm was reported in two special issues of the International Journal of Computer Vision. He edited the first two volumes of ADVANCES IN COMPUTER VISION for Erlbaum and (with D. Terzopoulos) REAL-TIME COMPUTER VISION, from Cambridge University Press. He is the co-editor of VIDERE, the first entirely on-line refereed computer vision journal (MIT Press). His most recent PhD students have done research in infrared tracking and face recognition, features and strategies for image understanding, augmented reality, and three-dimensional reconstruction algorithms. He supervised the undergraduate team that twice won the AAAI Host Robot competition (and came third in the Robot Rescue competition in 2003).     

Parametric and nonparametric Bayesian model specification: A case study involving models for count data

In this paper we present the results of a simulation study to explore the ability of Bayesian parametric and nonparametric models to provide an adequate fit to count data of the type that would routinely be analyzed parametrically either through fixed-effects or random-effects Poisson models. The context of the study is a randomized controlled trial with two groups (treatment and control). Our nonparametric approach uses several modeling formulations based on Dirichlet process priors. We find that the nonparametric models are able to flexibly adapt to the data, to offer rich posterior inference, and to provide, in a variety of settings, more accurate predictive inference than parametric models.     

A Bayesian semi-parametric bivariate failure time model

In this paper we introduce a Bayesian semiparametric model for bivariate and multivariate survival data. The marginal densities are well-known nonparametric survival models and the joint density is constructed via a mixture. Our construction also defines a copula and the properties of this new copula are studied. We also consider the model in the presence of covariates and, in particular, we find a simple generalisation of the widely used frailty model, which is based on a new bivariate gamma distribution.     

A dynamic fuzzy model for a drum-boiler-turbine system

A nonlinear dynamic fuzzy model for natural circulation drum-boiler-turbine is presented. The model is derived from Åström-Bell nonlinear dynamic system and describes the complicated dynamics of the physical plant. It is shown that the dynamic fuzzy model gives in some appropriate sense accurate global nonlinear prediction and at the same time that its local models are close approximations to the local linearizations of the nonlinear dynamic system. This closeness is illustrated by simulation in various conditions.