The association between passive social network usage and depression/negative emotions with envy as a mediator.

This study aimed to investigate the relationships between passive social network usage (PSNU) and depression/negative emotions over time with the mediating role of envy among Vietnamese adolescents. First, it revealed that PSNU had a simultaneous effect on depression/negative emotions as well as at different time points, indicating that social network site behaviors can predict psychological states over time (explained by the social comparison theory). Second, the autoregressive effect also confirmed a potential reciprocal relationship between PSNU and depression, whereas PSNU appeared to have an impact on negative emotions but not the other way around. Specifically, depression at Time 1 was positively associated with PSNU at Time 2, whereas negative emotions did not exhibit a similar pattern (explained by the cognitive dissonance theory). The different associations were interpreted as depression having cognitive elements, while negative emotions were thought to be purely emotional states. The results demonstrated that behavior may potentially have a long-lasting effect on mental health (both negative emotions and depression), while it was depression, rather than negative emotions, that had a long-term effect on behaviors. Third, envy played a mediating role that connected the changes of PSNU and depression/negative emotions. The implications and limitations of these findings are discussed.

Vehicle Mode and Driving Activity Detection Based on Analyzing Sensor Data of Smartphones.

In this paper, we present a flexible combined system, namely the Vehicle mode-driving Activity Detection System (VADS), that is capable of detecting either the current vehicle mode or the current driving activity of travelers. Our proposed system is designed to be lightweight in computation and very fast in response to the changes of travelers' vehicle modes or driving events. The vehicle mode detection module is responsible for recognizing both motorized vehicles, such as cars, buses, and motorbikes, and non-motorized ones, for instance, walking, and bikes. It relies only on accelerometer data in order to minimize the energy consumption of smartphones. By contrast, the driving activity detection module uses the data collected from the accelerometer, gyroscope, and magnetometer of a smartphone to detect various driving activities, i.e., stopping, going straight, turning left, and turning right. Furthermore, we propose a method to compute the optimized data window size and the optimized overlapping ratio for each vehicle mode and each driving event from the training datasets. The experimental results show that this strategy significantly increases the overall prediction accuracy. Additionally, numerous experiments are carried out to compare the impact of different feature sets (time domain features, frequency domain features, Hjorth features) as well as the impact of various classification algorithms (Random Forest, Naïve Bayes, Decision tree J48, K Nearest Neighbor, Support Vector Machine) contributing to the prediction accuracy. Our system achieves an average accuracy of 98.33% in detecting the vehicle modes and an average accuracy of 98.95% in recognizing the driving events of motorcyclists when using the Random Forest classifier and a feature set containing time domain features, frequency domain features, and Hjorth features. Moreover, on a public dataset of HTC company in New Taipei, Taiwan, our framework obtains the overall accuracy of 97.33% that is considerably higher than that of the state-of the art.