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.     

Understanding lurkers in online communities: A literature review

In internet culture, lurkers are a special group of website users who regularly login to online communities but seldom post. This study aims to provide an overall understanding of lurkers by explaining the definition of lurkers, discussing the reasons for lurking and providing suggestions on de-lurking. To understand the reason for lurking, this study first explains why people participate in online communities by building an integrated model of motivational factors of online behaviors. This model classifies motivational factors into four categories: the nature of the online community, individual characteristics, the degree of commitment and quality requirement. Based on this model, four types of lurking reasons are identified: environmental influence, personal preference, individual-group relationship and security consideration. Finally, several strategies for motivating participation in online communities are provided, including external stimuli, improved user-friendliness, encouragement of participation and guidance for newcomers.     

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 functional and structural diagnosis of online health communities sustainability: A focus on resource richness and site design features

The reality of online communities’ under-contribution issues has often been clouded with theoretical rather than real-world insight. The present study aims to neutralize this disparity, through content analysis on 196 health websites and online communities to systematically evaluate their functional and structural interfaces–the ingredients for a thriving online environment. Particular attention is paid to what variables equate to successful site traffic and impressions, ultimately providing suggestions to facilitate and optimize user contribution. While the majority of health websites and online health communities offered users fairly rich information about general health concerns, user environments in online health communities significantly lacked both structural and functional cues to encourage user contribution. External sponsorship could mitigate the discrepancy between the real world situations and academic suggestions.     

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 macro model of online information quality perceptions: A review and synthesis of the literature

Online information quality (IQ) plays a critical role in influencing the quality of consumers' experience and decisions in the online environment. Much research has been conducted in the literature to understand the impact of consumers’ perceptions of online IQ on various factors that define the online user experience. This article reviews this existing research and provides a holistic view of this literature culminating in a macro model of online IQ that outlines the current status of research in this area and highlights under-researched areas worthy of future research. Through this comprehensive analysis of 452 articles, we draw several overarching contributions. First, we find that most online IQ research employs an explaining & predicting approach (i.e., explain what is occurring and provide testable predictions with causal relationships) as opposed to a design & action approach (i.e., design tools or frameworks for online IQ assessment). Second, our review reveals that a large proportion of papers have focused on the U.S. and student segments. Third, this review reveals that online IQ research focuses largely on online IQ consequences, with few studies focusing on perceived online IQ antecedents. Based on these findings, we suggest that future studies in this area: (i) Focus more on design &action research; (ii) Use a diversity of sampling populations; and (iii) Focus on the antecedents of the IQ construct as well as moderators for the relations between perceived online IQ and relevant factors.     

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.     

Latent variable iterative learning model predictive control for multivariable control of batch processes

A latent variable iterative learning model predictive control (LV-ILMPC) method is presented for trajectory tracking in batch processes. Different from the iterative learning model predictive control (ILMPC) model built from the original variable space, LV-ILMPC develops a latent variable model based on dynamic partial least squares (DyPLS) to capture the dominant features of each batch. In each latent variable space, we use a state–space model to describe the dynamic characteristics of the internal model, and an LV-ILMPC controller is designed. Each LV-ILMPC controller tracks the set points of the current batch projection in the corresponding latent variable space, and the optimal control law is determined and the persistent process disturbances is rejected along both time and batch horizons. The proposed LV-ILMPC formulation is based on general LV-MPC and incorporates an iterative learning function into LV-MPC. In addition, the real physical input that drives the process can be reconstructed from the latent variable space. Therefore, this algorithm is particularly suitable for multiple-input, multiple-output (MIMO) systems with strong coupling and serious collinearity. Three studies are used to illustrate the effectiveness of the proposed LV-ILMPC .     

A data propagation model for wireless gossiping

Wireless sensor networks require communication protocols for efficiently propagating data in a distributed fashion. The Trickle algorithm is a popular protocol serving as the basis for many of the current standard communication protocols. In this paper we develop a mathematical model describing how Trickle propagates new data across a network consisting of nodes placed on a line. The model is analyzed and asymptotic results on the hop count and end-to-end delay distributions in terms of the Trickle parameters and network density are given. Additionally, we show that by only a small extension of the Trickle algorithm the expected end-to-end delay can be greatly decreased. Lastly, we demonstrate how one can derive the exact hop count and end-to-end delay distributions for small network sizes.     

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。     

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.     

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.     

A review of the Expectation Maximization algorithm in data-driven process identification

The Expectation Maximization (EM) algorithm has been widely used for parameter estimation in data-driven process identification. EM is an algorithm for maximum likelihood estimation of parameters and ensures convergence of the likelihood function. In presence of missing variables and in ill conditioned problems, EM algorithm greatly assists the design of more robust identification algorithms. Such situations frequently occur in industrial environments. Missing observations due to sensor malfunctions, multiple process operating conditions and unknown time delay information are some of the examples that can resort to the EM algorithm. In this article, a review on applications of the EM algorithm to address such issues is provided. Future applications of EM algorithm as well as some open problems are also provided.