Extending parallelization of the self-organizing map by combining data and network partitioned methods

High-dimensional data is pervasive in many fields such as engineering, geospatial, and medical. It is a constant challenge to build tools that help people in these fields understand the underlying complexities of their data. Many techniques perform dimensionality reduction or other “compression” to show views of data in either two or three dimensions, leaving the data analyst to infer relationships with remaining independent and dependent variables. Contextual self-organizing maps offer a way to represent and interact with all dimensions of a data set simultaneously. However, computational times needed to generate these representations limit their feasibility to realistic industry settings. Batch self-organizing maps provide a data-independent method that allows the training process to be parallelized and therefore sped up, saving time and money involved in processing data prior to analysis. This research parallelizes the batch self-organizing map by combining network partitioning and data partitioning methods with CUDA on the graphical processing unit to achieve significant training time reductions. Reductions in training times of up to twenty-five times were found while using map sizes where other implementations have shown weakness. The reduced training times open up the contextual self-organizing map as viable option for engineering data visualization.     

The expression of social presence through the use of figurative language in a web-based learning environment

The present paper’s aim is to investigate how the participants of an online learning environment employed written language in a creative way through the spontaneous use of figurative language. The content analysis showed that figurative language was a means to express the social dimension either to refer to the self, feelings and emotions, or to conceptualize the components of the virtual learning setting. The research context was a 10-week course, delivered at a distance via a computer conferencing system, addressed to 57 student teachers. The analysis was carried out in the social and meta-cognitive reflection areas, those areas which are mainly related to the expression of the social dimension The study had three different purposes: to investigate the distribution of figurative language during the course length; to explore the relation between the participants’ educational background and their use of figurative language, and to examine the relation between figurative language and the structure of the communication threads. The results indicate that participants tended to use figurative language more when meaningful or critical events happened. The higher the emotional involvement was, the more metaphorical language was adopted. Further results suggest that the adoption of figurative language seems to be related more to individual attitude, than to other factors such as educational background. Finally, figurative language occurrences were not concentrated in specific kinds of postings or threads and did not encourage further use of figurative language.     

Self-organizing feature maps for the vehicle routing problem with backhauls

In the Vehicle Routing Problem with Backhauls (VRPB), a central depot, a fleet of homogeneous vehicles, and a set of customers are given. The set of customers is divided into two subsets. The first (second) set of linehauls (backhauls) consists of customers with known quantity of goods to be delivered from (collected to) the depot. The VRPB objective is to design a set of minimum cost routes; originating and terminating at the central depot to service the set of customers. In this paper, we develop a self-organizing feature maps algorithm, which uses unsupervised competitive neural network concepts. The definition of the architecture of the neural network and its learning rule are the main contribution. The architecture consists of two types of chains: linehaul and backhaul chains. Linehaul chains interact exclusively with linehaul customers. Similarly, backhaul chains interact exclusively with backhaul customers. Additonal types of interactions are introduced in order to form feasible VRPB solution when the algorithm converges. The generated routes are then improved using the well-known 2-opt procedure. The implemented algorithm is compared with other approaches in the literature. The computational results are reported for standard benchmark test problems. They show that the proposed approach is competitive with the most efficient metaheuristics.     

Visualizing the runtime behavior of embedded network systems: A toolkit for TinyOS

TinyOS is an effective platform for developing lightweight embedded network applications. But the platform’s lean programming model and power-efficient operation come at a price: TinyOS applications are notoriously difficult to construct, debug, and maintain. The development difficulties stem largely from a programming model founded on events and deferred execution. In short, the model introduces non-determinism in the execution ordering of primitive actions — an issue exacerbated by the fact that embedded network systems are inherently distributed and reactive. The resulting set of possible execution sequences for a given system is typically large and can swamp developers’ unaided ability to reason about program behavior.In this paper, we present a visualization toolkit for TinyOS 2.0 to aid in program comprehension. The goal is to assist developers in reasoning about the computation forest underlying a system under test and the particular branches chosen during each run. The toolkit supports comprehension activities involving both local and distributed runtime behavior. The constituent components include (i) a full-featured static analysis and instrumentation library, (ii) a selection-based probe insertion system, (iii) a lightweight event recording service, (iv) a trace extraction and reconstruction tool, and (v) three visualization front-ends. We demonstrate the utility of the toolkit using both standard and custom system examples and present an analysis of the toolkit’s resource usage and performance characteristics.     

Perspectives on the evolution of online communities

The rapid growth of social networks opens interesting research opportunities to make use of the massive information exchanged in day-to-day communication. One of the active research issues related to this aspect is the study of online community formation and evolution in dynamic social networks. As community structure is usually ambiguous, then defining how it evolves over time becomes a challenge in terms of tracking mechanism and evaluation method. In this study, we review the online communities and their evolution tracking mechanisms and discuss the main categories of approaches for tracking community evolution and how they work. We analyse the different solutions proposed under each community evolution tracking category and provide an assessment of their projected performance. Finally, a discussion of analysis insights concerning community evolution and its influence is introduced.     

B-spline neural network design using improved differential evolution for identification of an experimental nonlinear process

B-Spline Neural Network (BSNN), a type of basis function neural network, is trained by gradient-based methods which may fall into local minima during the learning procedure. To overcome the limitations encountered by gradient-based optimization methods, we propose differential evolution (DE) – an evolutionary computation methodology – which can provide a stochastic search to adjust the control points of a BSNN. In this paper, we propose six DE approaches using chaotic sequences based on logistic mapping to train a BSNN. Chaos describes the complex behavior of a nonlinear deterministic system. The application of chaotic sequences instead of random sequences in DE is a powerful strategy to diversify the DE population and improve the DE's performance in preventing premature convergence to local minima. The numerical results presented here indicate that chaotic DE was effective for building a good BSNN model for the nonlinear identification of an experimental nonlinear yo–yo motion control system.     

A model of a trust-based recommendation system on a social network

In this paper, we present a model of a trust-based recommendation system on a social network. The idea of the model is that agents use their social network to reach information and their trust relationships to filter it. We investigate how the dynamics of trust among agents affect the performance of the system by comparing it to a frequency-based recommendation system. Furthermore, we identify the impact of network density, preference heterogeneity among agents, and knowledge sparseness to be crucial factors for the performance of the system. The system self-organises in a state with performance near to the optimum; the performance on the global level is an emergent property of the system, achieved without explicit coordination from the local interactions of agents.     

Minimizing the expected complete influence time of a social network

Complete influence time specifies how long it takes to influence all individuals in a social network, which plays an important role in many real-life applications. In this paper, we study the problem of minimizing the expected complete influence time of social networks. We propose the incremental chance model to characterize the diffusion of influence, which is progressive and able to achieve complete influence. Theoretical properties of the expected complete influence time under the incremental chance model are presented. In order to trade off between optimality and complexity, we design a framework of greedy algorithms. Finally, we carry out experiments to show the effectiveness of the proposed algorithms.     

The provision of online public goods: Examining social structure in an electronic network of practice

Electronic networks of practice are computer-mediated social spaces where individuals working on similar problems self-organize to help each other and share knowledge, advice, and perspectives about their occupational practice or common interests. These interactions occur through message postings to produce an on-line public good of knowledge, where all participants in the network can then access this knowledge, regardless of their active participation in the network. Using theories and concepts of collective action and public goods, five hypotheses are developed regarding the structural and social characteristics that support the online provision and maintenance of knowledge in an electronic network of practice. Using social network analysis, we examine the structure of message contributions that produce and sustain the public good. We then combine the results from network analysis with survey results to examine the underlying pattern of exchange, the role of the critical mass, the quality of the ties sustaining participation, the heterogeneity of resources and interests of participants, and changes in membership that impact the structural characteristics of the network. Our results suggest that the electronic network of practice chosen for this study is sustained through generalized exchange, is supported by a critical mass of active members, and that members develop strong ties with the community as a whole rather than develop interpersonal relationships. Knowledge contribution is significantly related to an individual's tenure in the occupation, expertise, availability of local resources and a desire to enhance one's reputation, and those in the critical mass are primarily responsible for creating and sustaining the public good of knowledge. Finally, we find that this structure of generalized exchange is stable over time although there is a high proportion of member churn in the network.     

Selecting the right MBA schools – An application of self-organizing map networks

The self-organizing map (SOM) network, an unsupervised neural computing network, is a categorization network developed by Kohonen. The SOM network was designed for solving problems that involve tasks such as clustering, visualization, and abstraction. In this study, we apply the clustering and visualization capabilities of SOM to group and plot the top 79 MBA schools as ranked by US News and World Report (USN&WR) into a two-dimensional map with four segments. The map should assist prospective students in searching for the MBA programs that best meet their personal requirements. Comparative analysis with the outputs from two popular clustering techniques K-means analysis and a two-step Factor analysis/K-means procedure are also included.     

Self-organizing neural networks for the analysis and representation of data: Some financial cases

Many recent papers have dealt with the application of feedforward neural networks in financial data processing. This powerful neural model can implement very complex nonlinear mappings, but when outputs are not available or clustering of patterns is required, the use of unsupervised models such as self-organizing maps is more suitable. The present work shows the capabilities of self-organizing feature maps for the analysis and representation of financial data and for aid in financial decision-making. For this purpose, we analyse the Spanish banking crisis of 1977–1985 and the Spanish economic situation in 1990 and 1991, making use of this unsupervised model. Emphasis is placed on the analysis of the synaptic weights, fundamental for delimiting regions on the map, such as bankrupt or solvent regions, where similar companies are clustered. The time evolution of the companies and other important conclusions can be drawn from the resulting maps.Characters and symbols used and their meaning nx x dimension of the neuron grid, in number of neurons - ny y dimension of the neuron grid, in number of neurons - n dimension of the input vector, number of input variables - (i, j) indices of a neuron on the map - k index of the input variables - w _ijk synaptic weight that connects thek input with the (i, j) neuron on the map - W _ij weight vector of the (i, j) neuron - x _k input vector - X input vector - (t) learning rate - _o starting learning rate - _f final learning rate - R(t) neighbourhood radius - R₀ starting neighbourhood radius - R _f final neighbourhood radius - t iteration counter - t _rf number of iterations until reachingR _f - t _f number of iterations until reaching _f - h(·) lateral interaction function - standard deviation - for every - d (x, y) distance between the vectors x and y     

Comparison of web-based versus paper-and-pencil administration of a humor survey

This study compared the measurement invariance of paper-and-pencil (PP) and web-based (WB) administration formats through a humor survey. Participants were 401 undergraduate students divided into four groups (A, B, C, and D), and each group completed one of the four testing conditions (group A: PP → PP, group B: PP → WB, group C: WB → PP, and group D: WB → WB). The WB and PP versions of the revised Multidimensional Sense of Humor Scale (Wang, Cheng, Liu, & Ho, 2011), which measure humor production, attitudes toward humor, and humor coping, were administered to the participants. The results indicated that both of the PP and WB survey formats were practically invariant. No significant differences across administration situations were found for humor production or humor coping. Interestingly, the mean score of the WB format was significantly higher than that of PP format on attitudes toward humor. The findings suggest that researchers should carefully examine the measurement invariance and the effect of characteristics of the construct on measurement results when using a WB-format instrument.     

Individuals’ personal network characteristics and patterns of Facebook use: A social network approach

Using the theoretical framework of ego-centric networks, this study examines the associations between the characteristics of both Facebook-specific and pre-existing personal networks and patterns of Facebook use. With data from an ego-network survey of college students, the study discovered that various dimensions of Facebook-specific network characteristics, such as multiplexity, proximity, density, and heterogeneity in race, were positively associated with usage patterns, including time spent on Facebook, posting messages, posting photos, and lurking. In contrast, network characteristics of pre-existing relationships, such as density and heterogeneity in race, were negatively associated with Facebook usage patterns. Theoretical implications and limitations were discussed.     

The reciprocal effects of social network site use and the disposition for self-disclosure: A longitudinal study

Since the advent of social network sites (SNSs), scholars have critically discussed the psychological and societal implication of online self-disclosure. Does Facebook change our willingness to disclose personal information? The present study proposes that the use of SNSs and the psychological disposition for self-disclosure interact reciprocally: Individuals with a stronger disposition show a higher tendency to use SNSs (selection effect). At the same time, frequent SNS use increases the wish to self-disclose online, because self-disclosing behaviors are reinforced through social capital within the SNS environment (socialization effect). In a longitudinal panel study, 488 users of SNSs were surveyed twice in a 6 months interval. Data were analyzed using structure equation modeling. The proposed reciprocal effects of SNS activities and self-disclosure were supported by the data: The disposition for online self-disclosure had a positive longitudinal effect on SNS use which in turn positively influenced the disposition for online self-disclosure. Both effects were moderated by the amount of social capital users received as a consequence of their SNS use.     

Classification of binary document images into textual or nontextual data blocks using neural network models

This paper describes a new method for the classification of binary document images as textual or nontextual data blocks using neural network models. Binary document images are first segmented into blocks by the constrained run-length algorithm (CRLA). The component-labeling procedure is used to label the resulting blocks. The features for each block, calculated from the coordinates of its extremities, are then fed into the input layer of a neural network for classification. Four neural networks were considered, and they include back propagation (BP), radial basis functions (RBF), probabilistic neural network (PNN), and Kohonen's self-organizing feature maps (SOFMs). The performance and behavior of these neural network models are analyzed and compared in terms of training times, memory requirements, and classification accuracy. The experiments carried out on a variety of medical journals show the feasibility of using the neural network approach for textual block classification and indicate that in terms of both accuracy and training time RBF should be preferred.     

A modular neural network for direction-of-arrival estimation of two sources

This work addresses the problem of estimating the direction-of-arrival (DOA) of two sources using an array of sensors. This problem is mostly useful in radar applications, where we have few targets at each range bin. Super-resolution algorithms, such as maximum likelihood (ML) estimation and multiple signal classification (MUSIC), have been applied to this problem, but the former involves high computation efforts, while the later has poor estimation performance for coherent sources. In this work, we propose a DOA estimation network, named RBF-AML, which combines the approximated ML (AML) estimator and a radial basis function (RBF) neural network (NN). In the proposed RBF-AML network, the entire two dimensional DOA space is divided into multiple sectors covered by RBF experts. The AML function is then used as a mediator among the experts and selects the most suitable one as the final output of the system. The performance of the RBF-AML network for a two coherent sources case in a Y shape array configuration is evaluated. We show that the performance of the RBF-AML network is similar to the performance of the classical AML DOA estimation for various signal-to-noise ratios (SNRs), phase of the correlation coefficient and signal-to-interference ratios (SIRs). Furthermore, the RBF-AML network requires fewer computational efforts than the classical AML DOA estimation and therefore is an attractive choice for real-time applications.     

Getting acquainted through social network sites: Testing a model of online uncertainty reduction and social attraction

The first aim of this study was to examine which uncertainty reduction strategies members of social network sites used to gain information about a person who they had recently met online. The second aim was to investigate whether and how these uncertainty reduction strategies resulted in social attraction. Drawing on a survey of 704 members of a social network site, we found that respondents had used active, passive, and interactive strategies to reduce uncertainty about their new acquaintance. Interactive strategies were most effective in reducing uncertainty about the target person. Respondents’ level of uncertainty about the acquaintance mediated the relationships between the use of interactive uncertainty strategies and perceived similarity on the one hand and social attraction on the other. Finally, respondents’ perceived valence of the obtained information about the acquaintance moderated the relationship between the level of uncertainty and social attraction.     

Examining online citizenship behaviours in social network sites: a social capital perspective

As users have flocked to social network sites (SNSs), these sites have gained tremendous scale and concomitant social influence. This growth has come at the cost of social disruption caused by the posting of abusive comments and rumours that turn out to be false. To combat these negative phenomena, this study proposes SNS citizenship behaviour and examines it from the perspective of social capital theory. This study further examines how the key characteristics of SNS in terms of the concept of customer value affect social capital development in an SNS context. The test results explain that the structural, relational, and cognitive dimensions of social capital have significant direct and indirect effects on the SNS citizenship behaviour. These findings also explain that four key characteristics (exploration, communication support, playfulness, and responsiveness) of SNS affect the three dimensions of social capital. This study contributes to the literature in its establishment of the concept of SNS citizenship behaviour and examines it from the social capital theory perspective. Its findings have practical implications through its guidance on how to develop SNS features and manage these sites for the citizenship behaviour of their users, which are achievements for the harmonious and effective functioning of SNS.     

The construction of a fuzzy inference network by extension of the rule inference network

Fuzzy logic can bring about inappropriate inferences as a result of ignoring some information in the reasoning process. Neural networks are powerful tools for pattern processing, but are not appropriate for the logical reasoning needed to model human knowledge. The use of a neural logic network derived from a modified neural network, however, makes logical reasoning possible. In this paper, we construct a fuzzy inference network by extending the rule–inference network based on an existing neural logic network. The propagation rule used in the existing rule–inference network is modified and applied. In order to determine the belief value of a proposition pertaining to the execution part of the fuzzy rules in a fuzzy inference network, the nodes connected to the proposition to be inferenced should be searched for. The search costs are compared and evaluated through application of sequential and priority searches for all the connected nodes.