UNDERSTANDING COLLABORATIVE STICKINESS INTENTION IN SOCIAL NETWORK SITES FROM THE PERSPECTIVE OF KNOWLEDGE SHARING

This study aims to investigate users’ knowledge sharing intention and collaborative stickiness intention towards social network sites (SNS). SNS offer an opportunity for users to interact and form relationships, while knowledge is accrued by integrating user’s information, experience, and practice. However, there have been few systematic studies that ask why people use SNS to share knowledge. We adopt social capital theory, social identity theory, as well as use and gratification theory to explore the determinants of members’ knowledge sharing intention in SNS. The survey was conducted on two education VCs of facebook, while most members were teachers and educators. Data analysis was carried out to validate our research model, and SmartPLS were used to analyze users’ collaborative stickiness intention. The result shows that social capital and social identity have impact on teacher’s knowledge sharing intention, in turn, influence on collaborative stickiness intention toward on SNS. Our findings not only help researchers interpret why members sharing their knowledge in VC, but also assist practitioners in developing better SNS strategy

De-Noising via Wavelet Transforms Using Steerable Filters

Feature extraction remains an important part of low-level vision. Traditional oriented filters have been effective tools to identify features, such as lines and edges. Steerable filters, which can be adjusted at arbitrary orientation, have made decisions of feature orientations more precise. Combined with a pyramid structure of a multiscale representation, these filters can provide a reliable and efficient tool for image analysis. This paper takes advantage of multiscale steerable filters in the context of de-noising. First a set of novel filters are designed, that decompose the frequency plane into distinct directional bands. Next, we identify the dominant direction and strength at each point of an image from quadrature pairs of steerable filters. A nonlinear threshold function is then applied to the filtered coefficients to suppress noise. The denoised image is restored from coefficients modified at each level of transform space. We demonstrate the benefits of multiscale steerable filters for de-noising and show that it can greatly reduce noise while preserving image features. Two examples are presented to verify the efficacy of the technique

Coherence of Multiscale Features for Enhancement of Digital Mammograms

Mammograms depict most of the significant changes in breast disease. The primary radiographic signs of cancer are related to tumor mass, density, size, borders, and shape, and local distribution of calcifications. We show that each of these features can be well described by coherence and orientation measures and provide visual cues for radiologists to identify possible lesions more easily without increasing false positives. In this paper, an artifact-free enhancement algorithm based on overcomplete multiscale representations is presented. First, an image was decomposed using a fast wavelet transform algorithm. At each level of analysis, energy and phase information are computed via a set of separable steerable filters. Then, a measure of coherence within each level was obtained by weighting an energy measure with the ratio of projections of local energy within a specified window. Each projection was computed onto the central point of a window with respect to the total energy within that window. Finally, a nonlinear operation, integrating coherence and orientation information, was applied to modify transform coefficients within distinct levels of analysis. These modified coefficients were then reconstructed, via an inverse fast wavelet transform, resulting in an improved visualization of significant mammographic features. The novelty of this algorithm lies in the detection of directional multiscale features and the removal of aliased perturbations

CORRELATION IN THE BARBELL AND LOWER LIMB KINEMATICS PERFORMANCE PARAMETERS IN THE SNATCH LIFTS: A PILOT STUDY

In our knowledge, there was not a lot of research to understand the relationship of snatch lifting. Therefore, the purpose of this study was to investigate the relationship between the barbell and lower limb kinematics parameters during the second pull of the snatch lifts. There were two digital cameras to record the snatch lifts. The study used the Kwon 3D motion analysis system to obtain the barbell and lower limb kinematics parameters. They study used the Pearson’s product moment correlations to investigate the relationship between the barbell and lower limb kinematics parameters. The results showed the significant relationship between the maximum vertical height of the barbell and the maximum extension angle of the hip joint. It also showed the significant relationship between the maximum extension angle velocity of the knee joint and maximum extension angle of the knee joint. The present study suggests that increasing the muscle quality and power of the lower limbs will increase the maximum vertical velocity of the barbell

Trust-Building Mechanisms and Knowledge Sharing in Virtual Communities

Although trust has received much intention in the virtual communities (VCs) literature, few studies have been conducted to examine how trust develops in VCs. Drawing from prior literature on trust and knowledge sharing, a research model for understanding the antecedents of trust and the role of trust in VCs is presented. Data was collected from 324 members of a technical virtual community to test the model. The results help in identifying how the factors fall into three trust-building mechanisms build trust in the context VCs. The study discusses the theoretical and managerial implications of this study and proposes several future research directions

NP-Free: A Real-Time Normalization-free and Parameter-tuning-free Representation Approach for Open-ended Time Series

As more connected devices are implemented in a cyber-physical world and data is expected to be collected and processed in real time, the ability to handle time series data has become increasingly significant. To help analyze time series in data mining applications, many time series representation approaches have been proposed to convert a raw time series into another series for representing the original time series. However, existing approaches are not designed for open-ended time series (which is a sequence of data points being continuously collected at a fixed interval without any length limit) because these approaches need to know the total length of the target time series in advance and pre-process the entire time series using normalization methods. Furthermore, many representation approaches require users to configure and tune some parameters beforehand in order to achieve satisfactory representation results. In this paper, we propose NP-Free, a real-time Normalization-free and Parameter-tuning-free representation approach for open-ended time series. Without needing to use any normalization method or tune any parameter, NP-Free can generate a representation for a raw time series on the fly by converting each data point of the time series into a root-mean-square error (RMSE) value based on Long Short-Term Memory (LSTM) and a Look-Back and Predict-Forward strategy. To demonstrate the capability of NP-Free in representing time series, we conducted several experiments based on real-world open-source time series datasets. We also evaluated the time consumption of NP-Free in generating representations.Comment: 9 pages, 12 figures, 9 tables, and this paper was accepted by 2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC 2023