Obesity and overweight are considered a health threat globally. Saudi Arabia is a country that has a high percentage of people suffering from obesity. These people can be helped to lose weight through the usage of mobile apps as these apps can collect users' personal information. These collected data is used to provide precise and personalized weight loss advices. However, weight loss apps must be user friendly, provide data security and user privacy protection. In this paper, we analyze the usability, security, and privacy of a weight loss app. Our main aim to clarify the data privacy and security procedure and test the usability level of the new Arabic weight loss app 'Akser Waznk' that is developed considering the social and cultural norms of Saudi users.
Prediction of traffic crowd movement is one of the most important component in many applications' domains ranging from urban management to transportation schedule. The key challenge of citywide crowd flows prediction is how to model spatial and dynamic temporal correlation. However, in recent years several studies have been done, but they lack the ability to effectively and simultaneously model spatial and temporal dependencies among traffic crowd flows. To address this issue, in this paper a novel spatio-temporal deep hybrid neural network proposed termed STD-Net to forecast citywide crowd traffic flows. More specifically, STD-Net contains four major branches, i.e., closeness, period volume, weekly volume, and external branches, respectively. We design a residual neural network unit for each property to depict the spatio-temporal features of traffic flows. For various branches, STD-Net provides distinct weights and then combines the outputs of four branches together. Extensive experiments on two large-scale datasets from New York bike and Beijing taxi have demonstrated that STD-Net achieves competitive performances the existing state-of-the-art prediction baselines. INDEX TERMSDeep learning, Urban crowd flows, Neural networks, Long short-term memory, Convolutional neural network
s-Obesity is considered as the main health issue worldwide. The obesity rate within Saudi's citizens is rising alarmingly. The Internet of Things (IoT)-enabled mobile apps can assist obese Saudi users in losing weight via collecting sensitive personal information and then providing accurate and personalized weight loss advice. These data can be collected using embedded IoT devices in a smartphone. However, these IoTenabled apps should be usable and able to provide data security and user privacy protection. This paper aims to continue our usability study for two Arabic weight loss IoT-enabled apps by performing a qualitative analysis for them. It discusses users' and health professionals' feedbacks, concerns and suggestions. Based on the analysis, a comprehensive usability guideline for developing a new Arabic weight loss IoT-enabled app for obese Saudi users is provided.
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