BioGames: a new paradigm and a behavioral biometrics collection tool for research purposes

Purpose The purpose of this paper is to present a new paradigm, named BioGames, for the extraction of behavioral biometrics (BB) conveniently and entertainingly. To apply the BioGames paradigm, the authors developed a BB collection tool for mobile devices named BioGames App. The BioGames App collects keystroke dynamics, touch gestures, and motion modalities and is available on GitHub. Interested researchers and practitioners may use it to create their datasets for research purposes. Design/methodology/approach One major challenge for BB and continuous authentication (CA) research is the lack of actual BB datasets for research purposes. The compilation and refinement of an appropriate set of BB data constitute a challenge and an open problem. The issue is aggravated by the fact that most users are reluctant to participate in long demanding procedures entailed in the collection of research biometric data. As a result, they do not complete the data collection procedure, or they do not complete it correctly. Therefore, the authors propose a new paradigm and introduce a BB collection tool, which they call BioGames, for the extraction of biometric features in a convenient way. The BioGames paradigm proposes a methodology where users play games without participating in an experimental painstaking process. The BioGames App collects keystroke dynamics, touch gestures, and motion modalities. Findings The authors proposed a new paradigm for the collection of BB on mobile devices and created the BioGames application. The BioGames App is an Android application that collects BB data on mobile devices and sends them to a database. The database design allows multiple users to store their sensor data at any time. Thus, there is no concern about data separation and synchronization. BioGames App is General Data Protection Regulation (GDPR) compliant as it collects and processes only anonymous data. Originality/value The BioGames App is a publicly available tool that combines the keystroke dynamics, touch gestures, and motion modalities. In addition, it uses a methodology where users play games without participating in an experimental painstaking process.

Real time Drowsy Driver Detection using Polynomial Kernel based Support Vector Machine

Casualty increases from road accidents day by day. There are so many reasons that accident causes and mostly due to human errors. Driver drowsiness is one of them. A small drowsiness may turn it into a big accident that resulted heavy casualties. If any of the system automatically detects the driver’s drowsiness and alert at real time may secure many lives. Drowsiness can be recognized by different situations such as by opening full mouth, by closing both the eyes and a combination of both. This may advised not to drive at drowsy state. There are various techniques through which drowsiness can be detected at real time but accuracy matters. OpenCV is a highly utilized open source computer vision library through which facial features can be recognized effectively. Polynomial kernel based support vector machine (SVM) is an advanced classification technique through which drowsiness can be classified from face. SVM is advanced machine learning approach through which linear and non-linear data can be classified with higher level of accuracy. System pertained 96.17 % of accuracy. Polynomial kernel is useful for non-linear data separation. Here system classifies the expressional features of face and result accordingly for drowsiness detection. Keywords: Support Vector Machine (SVM), OpenCV, Machine Learning, Non-Linear SVM Model, Drowsiness Detection, Face Detection, Computer Vision.

Text Classification on the Instagram Caption Using Support Vector Machine

Instagram is one of the world’s top ten most popular social networks. Instagram is the most popular social networking platform in the United States, India, and Brazil, with over 1 billion monthly active users. Each of these countries has more than 91 million Instagram users. The number of Instagram users shows the various reasons and goals for them to play this social media. Social Media Marketing does not escape being one of the purposes of using Instagram, with benefits to place a market for their products. Using text classification to categorize Instagram captions into organized groups, namely fashion, food & beverage, technology, health & beauty, lifestyle & travel, this paper is expected to help people know the current trends on Instagram. The Support Vector Machine algorithm in this research is used in 66171 post captions to classify trending on Instagram. The TF-IDF (Term Frequency times Inverse Document Frequency) method and percentage variations were used for data separation in this study. This study result indicates that the use of SVM with a percentage ratio 70% of dataset for training and 30% of dataset for testing produces a higher level of accuracy compared to the others.

Overcoming challenges in spatio-temporal modelling of large-scale (global) data

<p>Modelling spatio-temporal data on a large scale presents a number of obstacles for statisticians and environmental scientists. Issues such as computational complexity, combining point and areal data, separation of sources into their component processes, and the handling of both large volumes of data in some areas and sparse data in others must be considered. We discuss methods to overcome such challenges within a Bayesian hierarchical modelling framework using INLA.</p><p>In particular, we illustrate the approach using the example of source-separation of geophysical signals both on a continental and global scale. In such a setting, data tends to be available both at a local and areal level. We propose a novel approach for integrating such sources together using the INLA-SPDE method, which is normally reserved for point-level data. Additionally, the geophysical processes involved are both spatial (time-invariant) and spatio-temporal in nature. Separation of such processes into physically sensible components requires careful modelling and consideration of priors (such as physical model outputs where data is sparse), which will be discussed. We also consider methods to overcome the computational costs of modelling on such a large scale, from efficient mesh design, to thinning/aggregating of data, to considering alternative approaches for inference. This holistic approach to modelling of large-scale data ensures that spatial and spatio-temporal processes can be sensibly separated into their component parts, without being prohibitively expensive to model.</p>

Garbage Collection Algorithms in Flash-Based Solid State Drives

Solid state drives (SSDs)have emerged as faster and more reliable data storages over the last few years. Their intrinsic characteristics prove them to be more efficient as compared to other traditional storage media such as the Hard Disk Drives (HDDs). Issues such as write amplification, however, degrade the performance and lifespan of an SSD. This issue is in turn handled by the Garbage Collection (GC) algorithms that are put in place to supply free blocks for serving the writes being made to the flash-based SSDs and thus reduce the need of extra unnecessary writes. The LRU/FIFO, Greedy, Windowed Greedy and D choices algorithms have been described to lower write amplification for incoming writes which are different in nature. The performance of the GC algorithms varies based on factors such as pre-defined hot/cold data separation, hotness of data, uniform/non-uniform nature of incoming writes, the GC window size and the number of pages in each block of the flash memory package. Finally, it can be seen that the number of write frontiers so used, can dictate the separation of hot/cold data and increase the performance of a GC algorithm.

Formal Specification and Verification of Data Separation for Muen Separation Kernel

Introduction: Development of integrated mixed-criticality systems is becoming increasingly popular for application-specific systems, which needs separation mechanism for available onboard resources and the processors equipped with hardware virtualization. Hardware virtualization allow the partitions to physical resources, which include processor cores, memory, and I/O devices, among guest virtual machines (VMs). For building mixed criticality computing environment, traditional virtual machine systems are inappropriate because they use hypervisors to schedule separate VMs on physical processor cores. In this article, we discuss the design of an environment for mixed-criticality systems: The Muen an x86/64 separation kernel for high assurance. The Muen Separation Kernel is an Open Source microkernel which has no runtime errors at the source code level. The Muen separation kernel has been designed precisely to encounter the challenging requirements of high-assurance systems built on the Intel x86/64 platform. Muen is under active development, and none of the kernel properties of it has been verified yet. In this paper, we present a novel work of verifying one of the kernel properties formally. Method: The CTL used in NuSMV is a first-order modal along with data-depended processes and regular formulas. CTL is a branching-time logic, meaning that its model of time is a tree-like structure in which the future is not determined; there are different paths in the future, any one of which might be an actual path that is realized . This section shows the verification of all the requirements mentioned in section 3. In NuSMV tool the command used for verification of the formulas written in CTL is checkctlspec -p ”CTL-expression”. The nearest quantifier binds each occurrence of a variable in the scope of the bound variable, which has the same name and the same number of arguments. Result: Formal methods have been applied to various projects for specification and verification purpose. Some of them are the SCOMP , SeaView , LOCK,and Multinet Gateway projects. The TLS was written formally. Several mappings were done between the TLS and the SCOMP code: Informal English language to TLS, TLS to actual code , and TLS to pseudo-code. The authors present an ACL2 model for a generic separation kernel also known as GWV approach. Conclusion: We consider the formal verification of data separation property which is one of the crucial modules to achieve the separation functionality. The verification of the data separation manager is carried out on the design level using the NuSMV tool. Furthermore, we present the complete model of the data separation unit along with its code written in the NuSMV modelling language. Finally, we have converted the non-functional requirements into the formal logic, which then has verified the model formally.