Recent research has shown the possibility of using smartphones' sensors and accessories to extract some behavioral attributes such as touch dynamics, keystroke dynamics and gait recognition. These attributes are known as behavioral biometrics and could be used to verify or identify users implicitly and continuously on smartphones. The authentication systems that have been built based on these behavioral biometric traits are known as active or continuous authentication systems.This paper provides a review of the active authentication systems. We present the components and the operating process of the active authentication systems in general, followed by an overview of the state-of-theart behavioral biometric traits that used to develop an active authentication systems and their evaluation on smartphones. We discuss the issues, strengths and limitations that associated with each behavioral biometric trait. Also, we introduce a comparative summary between them. Finally, challenges and open research problems are presented in this research field.

The proposed method could efficiently detect DIAE signals from SRS data as well as, identifying rare adverse drug reactions (ADRs).

In recent years, the software industry has invested substantial effort to improve software quality in organizations. Applying proactive software defect prediction will help developers and white box testers to find the defects earlier, and this will reduce the time and effort. Traditional software defect prediction models concentrate on traditional features of source code including code complexity, lines of code, etc. However, these features fail to extract the semantics of source code. In this research, we propose a hybrid model that is called CBIL. CBIL can predict the defective areas of source code. It extracts Abstract Syntax Tree (AST) tokens as vectors from source code. Mapping and word embedding turn integer vectors into dense vectors. Then, Convolutional Neural Network (CNN) extracts the semantics of AST tokens. After that, Bidirectional Long Short-Term Memory (Bi-LSTM) keeps key features and ignores other features in order to enhance the accuracy of software defect prediction. The proposed model CBIL is evaluated on a sample of seven open-source Java projects of the PROMISE dataset. CBIL is evaluated by applying the following evaluation metrics: F-measure and area under the curve (AUC). The results display that CBIL model improves the average of F-measure by 25% compared to CNN, as CNN accomplishes the top performance among the selected baseline models. In average of AUC, CBIL model improves AUC by 18% compared to Recurrent Neural Network (RNN), as RNN accomplishes the top performance among the selected baseline models used in the experiments.

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Human motion detection and analysis are important in many medical and dental clinics. Mandibular movements are very complex and difficult to detect by naked eyes. Detecting mandibular movements will aid in proper diagnosis, treatment planning and follow up. Many methods are utilized for measuring mandibular movements. However, most of these methods share the features of being very expensive and difficult to use in the clinic.Using computer vision systems to track such movements may be considered one of the fundamental problems of human motion analysis that may remain unsolved due to its inherent difficulty. However, using markers may greatly simplify the process as long as they are simple, cheap and easy to use. Unlike other tracking systems, this system needs a simple digital video camera, and very simple markers that are created using black-white images that can be stick using any cheap double-sided bonding tape.The proposed system is considered reliable and having a reasonable accuracy. The main advantages in this system are being simple and low cost when compared with any other method having the same accuracy.