Wearable technology impacts the daily life of its users. Wearable devices are defined as devices embedded within clothes, watches, or accessories. Wrist-worn devices, as a type of wearable devices, have gained popularity among other wearable devices. They allow quick access to vital information, and they are suitable for many applications. This paper presents a comprehensive survey of wearable computing as a research field and provides a systematic review of recent work specifically on wrist-worn wearables. The focus of this research is on wrist-worn wearable studies because there is a lack of systematic literature reviews related to this area. This study reviewed journal and conference articles from 2015 and 2017 with some studies from 2014 and 2018, resulting in a selection of 54 studies that met the selection criteria. The literature showed that research in wrist-worn wearables spans three domains, namely, user interface and interaction studies, user studies, and activity/affect recognition studies. Our study then concludes with challenges and open research directions.

Purpose -Owing to the large amount of information available on Twitter (a micro-blogging service) that is not necessarily true or believable, credibility of news published in such an electronic channel has become an important area for investigation in the field of web credibility. This paper aims to address this issue. Design/methodology/approach -A system was developed to measure the credibility of news content published in Twitter. The system uses two approaches to assign credibility levels (low, high and average) to each tweet. The first approach is based on the similarity between Twitter posts (tweets) and authentic (i.e. verified) news sources. The second approach is based on the similarity with verified news sources in addition to a set of proposed features. Findings -The evaluations of the two approaches showed that assigning credibility levels to Twitter tweets for the first approach has a higher precision and recall. Additional experiments showed that the linking feature has its impact on the second approach results. Research limitations/implications -The proposed system is experimental; thus further experiments are needed to prove these findings. Originality/value -This paper contributes to the research on web credibility. It is believed to be the first which provides a proposed system to evaluate the credibility of Twitter news content automatically.

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Traditional standards employed for pain assessment have many limitations. One such limitation is reliability linked to inter-observer variability. Therefore, there have been many approaches to automate the task of pain recognition. Recently, deep-learning methods have appeared to solve many challenges such as feature selection and cases with a small number of data sets. This study provides a systematic review of pain-recognition systems that are based on deep-learning models for the last two years. Furthermore, it presents the major deep-learning methods used in the review papers. Finally, it provides a discussion of the challenges and open issues.

Due to the large amount of information available on the web that is not necessarily true or believable, credibility of web information is becoming an increasingly important area to understand. Recently, most research has been available to develop automatic measures for web information credibility in languages such as Japanese, Germen and English websites. Unfortunately, there is no research for the credibility of Arabic web content.In this paper we will propose a system to measure information credibility of Arabic web content automatically. The focus will be on Weblogs; since they are considered a significant part of the rapid growth of Arabic content on the web.

The traditional standards employed for pain assessment have many limitations. One such limitation is reliability because of inter-observer variability. Therefore, there have been many approaches to automate the task of pain recognition. Recently, deep-learning methods have appeared to solve many challenges, such as feature selection and cases with a small number of data sets. This study provides a systematic review of pain-recognition systems that are based on deep-learning models for the last two years only. Furthermore, it presents the major deep-learning methods that were used in review papers. Finally, it provides a discussion of the challenges and open issues.