Realizing the Activation Potential of Online Communities

Online communities suffer from the 1-9-90 principle, which states that 1% of the community\u27s user base generates original content, an additional 9% is limited to interacting with existing content, while the remaining 90% of the participants is passively lurking. In this work we present a data-driven stochastic framework that estimates (1) the activation potential (i.e., the users that are currently lurkers but present a high likelihood of becoming heavy contributors) of an online community and (2) when and which users are more likely to become heavy contributors. Our proposed framework captures the transitional evolution of a user by a Hidden Markov Model, and estimates each user\u27s propensity to become a heavy contributor by employing parametric survival models. We build and evaluate our models on a unique large dataset of a specialized online community about diabetes

Community Detection and Growth Potential Prediction from Patent Citation Networks

The scoring of patents is useful for technology management analysis. Therefore, a necessity of developing citation network clustering and prediction of future citations for practical patent scoring arises. In this paper, we propose a community detection method using the Node2vec. And in order to analyze growth potential we compare three ''time series analysis methods'', the Long Short-Term Memory (LSTM), ARIMA model, and Hawkes Process. The results of our experiments, we could find common technical points from those clusters by Node2vec. Furthermore, we found that the prediction accuracy of the ARIMA model was higher than that of other models.Comment: arXiv admin note: text overlap with arXiv:1607.00653 by other author

Can enlightenment be traced to specific neural correlates, cognition, or behavior? No, and (a qualified) Yes

The field of contemplative science is rapidly growing and integrating into the basic neurosciences, psychology, clinical sciences, and society-at-large. Yet the majority of current research in the contemplative sciences has been divorced from the soteriological context from which these meditative practices originate and has focused instead on clinical applications with goals of stress reduction and psychotherapeutic health. In the existing research on health outcomes of mindfulness-based clinical interventions, for example, there have been almost no attempts to scientifically investigate the goal of enlightenment. This is a serious oversight, given that such profound transformation across ethical, perceptual, emotional, and cognitive domains are taken to be the natural outcome and principle aim of mindfulness practice in the traditional Buddhist contexts from which these practices are derived. If short-term interventions as short as a few sessions are now beginning to produce neuroplastic changes, it may be that even in secular contexts, practitioners are already developing states and traits that are associated with progress toward enlightenment. In order to carefully assess the potential effects of meditative interventions it is of singular importance to ask whether enlightenment can be traced to specific neural correlates, cognition, or behavior

The Impact of User Interface Design on Idea Integration in Electronic Brainstorming: An Attention-Based View

This paper introduces an attention-based view of idea integration that underscores the importance of IS user interface design. The assumption is that presenting ideas via user interface plays a key role in enabling and motivating idea integration in electronic brainstorming (EBS), and thus advances productivity. Building upon Cognitive Network Model of Creativity and ability-motivation framework, our attention-based theory focuses on two major attributes of user interface: visibility and prioritization. While visibility enables idea integration via directing attention to a limited set of ideas, prioritization enhances the motivation for idea integration by providing individuals with a relevant and legitimate proxy for value of the shared ideas. The theory developed in this paper is distinct from previous research on EBS in at least two ways: (1) this theory exclusively focuses on idea integration as the desired outcome and studies it in the context of IS user interface; and (2) rather than debating whether or not EBS universally outperforms verbal brainstorming, the proposed theory revisits the links between user interface and idea integration as an attention-intensive process that contributes to EBS productivity. Idea integration by individuals within a group is an essential process for organizational creativity and thus for establishing knowledge-based capabilities. Lack of such integration significantly reduces the value of idea sharing, which has been a predominant focus of the EBS literature in the past. The current theory posits that the ability of electronic brain-storming to outperform nominal or verbal brainstorming depends on its ability to leverage information system (IS) artifact capabilities for enhancing idea integration to create a key pattern of productivity. The developed theory provides a foundation for new approaches to EBS research and design, which use visibility and prioritization, and also identify new user interface features for fostering idea integration. By emphasizing idea integration, designers and managers are provided with practical, cognition-based criteria for choosing interface features, which can improve EBS productivity. This theory also has implications for both the practice and research of knowledge management, especially for the attention-based view of the organization.

The Mechanics of Embodiment: A Dialogue on Embodiment and Computational Modeling

Embodied theories are increasingly challenging traditional views of cognition by arguing that conceptual representations that constitute our knowledge are grounded in sensory and motor experiences, and processed at this sensorimotor level, rather than being represented and processed abstractly in an amodal conceptual system. Given the established empirical foundation, and the relatively underspecified theories to date, many researchers are extremely interested in embodied cognition but are clamouring for more mechanistic implementations. What is needed at this stage is a push toward explicit computational models that implement sensory-motor grounding as intrinsic to cognitive processes. In this article, six authors from varying backgrounds and approaches address issues concerning the construction of embodied computational models, and illustrate what they view as the critical current and next steps toward mechanistic theories of embodiment. The first part has the form of a dialogue between two fictional characters: Ernest, the �experimenter�, and Mary, the �computational modeller�. The dialogue consists of an interactive sequence of questions, requests for clarification, challenges, and (tentative) answers, and touches the most important aspects of grounded theories that should inform computational modeling and, conversely, the impact that computational modeling could have on embodied theories. The second part of the article discusses the most important open challenges for embodied computational modelling

Traffic Prediction Based on Random Connectivity in Deep Learning with Long Short-Term Memory

Traffic prediction plays an important role in evaluating the performance of telecommunication networks and attracts intense research interests. A significant number of algorithms and models have been put forward to analyse traffic data and make prediction. In the recent big data era, deep learning has been exploited to mine the profound information hidden in the data. In particular, Long Short-Term Memory (LSTM), one kind of Recurrent Neural Network (RNN) schemes, has attracted a lot of attentions due to its capability of processing the long-range dependency embedded in the sequential traffic data. However, LSTM has considerable computational cost, which can not be tolerated in tasks with stringent latency requirement. In this paper, we propose a deep learning model based on LSTM, called Random Connectivity LSTM (RCLSTM). Compared to the conventional LSTM, RCLSTM makes a notable breakthrough in the formation of neural network, which is that the neurons are connected in a stochastic manner rather than full connected. So, the RCLSTM, with certain intrinsic sparsity, have many neural connections absent (distinguished from the full connectivity) and which leads to the reduction of the parameters to be trained and the computational cost. We apply the RCLSTM to predict traffic and validate that the RCLSTM with even 35% neural connectivity still shows a satisfactory performance. When we gradually add training samples, the performance of RCLSTM becomes increasingly closer to the baseline LSTM. Moreover, for the input traffic sequences of enough length, the RCLSTM exhibits even superior prediction accuracy than the baseline LSTM.Comment: 6 pages, 9 figure