The Experts below are selected from a list of 75 Experts worldwide ranked by ideXlab platform
Mohammad Zulkernine - One of the best experts on this subject based on the ideXlab platform.
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droid Mood Swing dms automatic security modes based on contexts
International Conference on Information Security, 2017Co-Authors: Shahrear Iqbal, Mohammad ZulkernineAbstract:Smartphones are becoming ubiquitous and we use them for different types of tasks. One problem of using the same device for multiple tasks is that each task requires a different security model. To address this problem, we introduce Droid Mood Swing (DMS), an operating system component that applies different security policies to detected security modes automatically. DMS uses a context manager that tracks the context of the phone from the available sensors. DMS then determines the security mode from the contexts and can impose a number of security measures, namely fine-grained permissions, an intent firewall, a context-aware SD card filesystem, and a permission verification system. The permission verification system uses machine learning techniques to detect suspicious apps and anomalous permission requests. DMS also provides an API that enables third-party developers to make their apps behave differently in different modes. DMS is designed especially for end users and does not compromise the usability of the phone. Device vendors will be able to control configurations (a switching logic and security policies) of the modes through DMS. We implement DMS using the Android Open Source Project (AOSP) and evaluate it in terms of portability, functionality, security, and operational overheads. The evaluation results show that DMS offers a more secure smartphone operating system without incurring any noticeable overhead.
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ISC - Droid Mood Swing (DMS): Automatic Security Modes Based on Contexts
Lecture Notes in Computer Science, 2017Co-Authors: Shahrear Iqbal, Mohammad ZulkernineAbstract:Smartphones are becoming ubiquitous and we use them for different types of tasks. One problem of using the same device for multiple tasks is that each task requires a different security model. To address this problem, we introduce Droid Mood Swing (DMS), an operating system component that applies different security policies to detected security modes automatically. DMS uses a context manager that tracks the context of the phone from the available sensors. DMS then determines the security mode from the contexts and can impose a number of security measures, namely fine-grained permissions, an intent firewall, a context-aware SD card filesystem, and a permission verification system. The permission verification system uses machine learning techniques to detect suspicious apps and anomalous permission requests. DMS also provides an API that enables third-party developers to make their apps behave differently in different modes. DMS is designed especially for end users and does not compromise the usability of the phone. Device vendors will be able to control configurations (a switching logic and security policies) of the modes through DMS. We implement DMS using the Android Open Source Project (AOSP) and evaluate it in terms of portability, functionality, security, and operational overheads. The evaluation results show that DMS offers a more secure smartphone operating system without incurring any noticeable overhead.
Shahrear Iqbal - One of the best experts on this subject based on the ideXlab platform.
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droid Mood Swing dms automatic security modes based on contexts
International Conference on Information Security, 2017Co-Authors: Shahrear Iqbal, Mohammad ZulkernineAbstract:Smartphones are becoming ubiquitous and we use them for different types of tasks. One problem of using the same device for multiple tasks is that each task requires a different security model. To address this problem, we introduce Droid Mood Swing (DMS), an operating system component that applies different security policies to detected security modes automatically. DMS uses a context manager that tracks the context of the phone from the available sensors. DMS then determines the security mode from the contexts and can impose a number of security measures, namely fine-grained permissions, an intent firewall, a context-aware SD card filesystem, and a permission verification system. The permission verification system uses machine learning techniques to detect suspicious apps and anomalous permission requests. DMS also provides an API that enables third-party developers to make their apps behave differently in different modes. DMS is designed especially for end users and does not compromise the usability of the phone. Device vendors will be able to control configurations (a switching logic and security policies) of the modes through DMS. We implement DMS using the Android Open Source Project (AOSP) and evaluate it in terms of portability, functionality, security, and operational overheads. The evaluation results show that DMS offers a more secure smartphone operating system without incurring any noticeable overhead.
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ISC - Droid Mood Swing (DMS): Automatic Security Modes Based on Contexts
Lecture Notes in Computer Science, 2017Co-Authors: Shahrear Iqbal, Mohammad ZulkernineAbstract:Smartphones are becoming ubiquitous and we use them for different types of tasks. One problem of using the same device for multiple tasks is that each task requires a different security model. To address this problem, we introduce Droid Mood Swing (DMS), an operating system component that applies different security policies to detected security modes automatically. DMS uses a context manager that tracks the context of the phone from the available sensors. DMS then determines the security mode from the contexts and can impose a number of security measures, namely fine-grained permissions, an intent firewall, a context-aware SD card filesystem, and a permission verification system. The permission verification system uses machine learning techniques to detect suspicious apps and anomalous permission requests. DMS also provides an API that enables third-party developers to make their apps behave differently in different modes. DMS is designed especially for end users and does not compromise the usability of the phone. Device vendors will be able to control configurations (a switching logic and security policies) of the modes through DMS. We implement DMS using the Android Open Source Project (AOSP) and evaluate it in terms of portability, functionality, security, and operational overheads. The evaluation results show that DMS offers a more secure smartphone operating system without incurring any noticeable overhead.
Sarah Healy - One of the best experts on this subject based on the ideXlab platform.
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decreased hedonic responsiveness during a brief depressive Mood Swing
Journal of Affective Disorders, 1994Co-Authors: Paul Willner, Sarah HealyAbstract:Abstract Female volunteers completed the Fawcett-Clark Pleasure Capacity Scale (FCPCS) and rated the pleasantness and desirability of a taste stimulus (cheese), before and during a depressive Mood Swing. Mood change was induced by reading negative self-referent statements, with additional ‘booster’ periods of Mood induction to prolong the duration of the Mood Swing. The Mood induction procedure (MIP) caused a decrease in contentment and alertness, as derived from a set of visual analogue Mood scales, and also decreased hedonic capacity, as measured by responses to the taste stimulus and by the FCPCS. No changes on any measure were shown by a control group who read an equivalent set of positive self-referent statements. Prior to the MIP, there were no significant correlations between Mood measures and hedonic measures, or between taste responses and the FCPCS. However, there were significant correlations between the size of the changes in these various measures following the depressive MIP. The results suggest that hedonic capacity is responsive to acute depressive Mood Swings.
Rongrong Fu - One of the best experts on this subject based on the ideXlab platform.
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Automated Detection of Driver Fatigue Based on Entropy and Complexity Measures
IEEE Transactions on Intelligent Transportation Systems, 2014Co-Authors: Chi Zhang, Hong Wang, Rongrong FuAbstract:This paper presents a real-time method based on various entropy and complexity measures for detection and identification of driving fatigue from recorded electroencephalogram (EEG), electromyogram, and electrooculogram signals. The complexity features were used to distinguish whether the subjects are experienced drivers by calculating the Lempel-Ziv complexity of EEG approximate entropy (ApEn). Different threshold values can be set for the two kinds of drivers individually. The entropy-based features, namely, the wavelet entropy (WE), the peak-to-peak value of ApEn (PP-ApEn), and the peak-to-peak value of sample entropy (PP-SampEn), were extracted from the collected signals to estimate the driving fatigue stages. We proposed WE in a sliding window (WES), PP-ApEn in a sliding window (PP-ApEnS), and PP-SampEn in a sliding window (PP-SampEnS) for real-time analysis of driver fatigue. The real-time features obtained by WE, PP-ApEn, and PP-SampEn with sliding window were applied to artificial neural network for training and testing the system, which gives four situations for the fatigue level of the subjects, namely, normal state, mild fatigue, Mood Swing, and excessive fatigue. Then, the driver fatigue level can be determined in real time. The accuracy of estimation is about 96.5%-99.5%. Receiver operating characteristic (ROC) curve was used to present the performance of the neural network classifier. The area under the ROC curve is 0.9931. The results show that the developed method is valuable for the application of avoiding some traffic accidents caused by driver's fatigue.
Daniel J Safer - One of the best experts on this subject based on the ideXlab platform.
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Mood Swing and Mood stabilizer how specific are these terms
Bipolar Disorders, 2010Co-Authors: Daniel J SaferAbstract:Safer DJ. Mood Swing and Mood stabilizer: how specific are these terms? Bipolar Disord 2010: 12: 685–690. © 2010 The Author. Journal compilation © 2010 John Wiley & Sons A/S. Background: In the DSM-IIIR in 1987, the category title for depressive and bipolar disorders was changed from affective disorders to Mood disorders. Within a short period of time thereafter, Mood Swing and Mood stabilizer became very commonly used terms in psychiatry with bipolar implications. Methods: Terms and definitions in recent texts, articles, and dictionaries pertaining to Mood fluctuations have been reviewed. Results: The term Mood was seldom part of psychiatric terminology until the late 1970s. Mood Swing and Mood stabilizer as used in the psychiatric literature are primarily nonspecific and often misleading concepts—particularly as a basis for treatment decisions. Affective fluctuations and shifts to irritability and/or anger in persons with personality and depressive disorders are being viewed by many in the mental health field as cyclically biphasic—between depressed to elated—which is clearly at variance with research findings. Conclusions: More data-based research on Mood variations is needed to authoritatively remedy this situation.