Leveraging linguistic traits and semi-supervised learning to single out informational content across how-to community question-answering archives

Social Q&A communities: A multi-factor study of the influence of users’ knowledge sharing behaviors

Recently, social Q&A communities have grown increasingly popular, serving as a primary platform for people to learn and share information. Nonetheless, fewer knowledge producers in these communities are significant than knowledge consumers. Thus, promoting users’ participation in knowledge sharing is a challenge for managers of social Q&A communities. Even though many scholars have studied factors influencing willingness to share knowledge, they tend to start with one theory and ignore the impact of several factors on behaviors. Thus, this manuscript presents a multi-factor model based on three dimensions of technology, cognition, and security to explore the effects of the six factors of perceived ease of use and perceived usefulness, perceived behavioral control and subjective norms, perceived security, and perceived privacy in terms of the three knowledge sharing methods of browsing including like and favorite, publishing and replying, and to compare users’ willingness to use the three knowledge sharing methods. A total of 482 questionnaires were collected online, and the hypotheses were tested and analyzed using structural equation modeling (SEM). According to the results, the factors affecting different sharing methods are not the same. Perceived behavioral control and perceived security can have a significant influence on their willingness to browse, users’ willingness to publish and reply to posts is significantly influenced by their perception of behavioral control and subjective norms, while perceived usefulness also affects their willingness to respond, it can be seen that cognition is the most important factor affecting users’ knowledge sharing among the three dimensions. In addition, users’ willingness to browse is significantly greater than their willingness to reply, and their willingness to post is the lowest.

A distributed semi-supervised learning algorithm based on manifold regularization using wavelet neural network

This paper aims to propose a distributed semi-supervised learning (D-SSL) algorithm to solve D-SSL problems, where training samples are often extremely large-scale and located on distributed nodes over communication networks. Training data of each node consists of labeled and unlabeled samples whose output values or labels are unknown. These nodes communicate in a distributed way, where each node has only access to its own data and can only exchange local information with its neighboring nodes. In some scenarios, these distributed data cannot be processed centrally. As a result, D-SSL problems cannot be centrally solved by using traditional semi-supervised learning (SSL) algorithms. The state-of-the-art D-SSL algorithm, denoted as Distributed Laplacian Regularization Least Square (D-LapRLS), is a kernel based algorithm. It is essential for the D-LapRLS algorithm to estimate the global Euclidian Distance Matrix (EDM) with respect to total samples, which is time-consuming especially when the scale of training data is large. In order to solve D-SSL problems and overcome the common drawback of kernel based D-SSL algorithms, we propose a novel Manifold Regularization (MR) based D-SSL algorithm using Wavelet Neural Network (WNN) and Zero-Gradient-Sum (ZGS) distributed optimization strategy. Accordingly, each node is assigned an individual WNN with the same basis functions. In order to initialize the proposed D-SSL algorithm, we propose a centralized MR based SSL algorithm using WNN. We denote the proposed SSL and D-SSL algorithms as Laplacian WNN (LapWNN) and distributed LapWNN (D-LapWNN), respectively. The D-LapWNN algorithm works in a fully distributed fashion by using ZGS strategy, whose convergence is guaranteed by the Lyapunov method. During the learning process, each node only exchanges local coefficients with its neighbors rather than raw data. It means that the D-LapWNN algorithm is a privacy preserving method. At last, several illustrative simulations are presented to show the efficiency and advantage of the proposed algorithm.

Distributed semi-supervised learning algorithm based on extreme learning machine over networks using event-triggered communication scheme

In this paper, we propose a distributed semi-supervised learning (DSSL) algorithm based on the extreme learning machine (ELM) algorithm over communication network using the event-triggered (ET) communication scheme. In DSSL problems, training data consisting of labeled and unlabeled samples are distributed over a communication network. Traditional semi-supervised learning (SSL) algorithms cannot be used to solve DSSL problems. The proposed algorithm, denoted as ET-DSS-ELM, is based on the semi-supervised ELM (SS-ELM) algorithm, the zero gradient sum (ZGS) distributed optimization strategy and the ET communication scheme. Correspondingly, the SS-ELM algorithm is used to calculate the local initial value, the ZGS strategy is used to calculate the globally optimal value and the ET scheme is used to reduce communication times during the learning process. According to the ET scheme, each node over the communication network broadcasts its updated information only when the event occurs. Therefore, the proposed ET-DSS-ELM algorithm not only takes the advantages of traditional DSSL algorithms, but also saves network resources by reducing communication times. The convergence of the proposed ET-DSS-ELM algorithm is guaranteed by using the Lyapunov method. Finally, some simulations are given to show the efficiency of the proposed algorithm.