Current Issue : October - December Volume : 2018 Issue Number : 4 Articles : 5 Articles
The paper presents an analysis of computer programming languages based on\nthe semiotics of Charles S. Peirce. The author describes how such languages\ncould evolve in order to achieve some of the expressive characteristics of natural\nlanguages. This description evinces otherwise unnoticed differences between\nnatural languages and programming languages, showing that Peircean\nsemiotics is an efficient analysis tool. This suggests further research on the\ntechnical features needed to implement an easier way to program computers....
This work focuses on a brief discussion of new concepts of using smartphone\nsensors for 3D painting in virtual or augmented reality. Motivation of this research\ncomes from the idea of using different types of sensors which exist in\nour smartphones such as accelerometer, gyroscope, magnetometer etc. to\ntrack the position for painting in virtual reality, like Google Tilt Brush, but\ncost effectively. Research studies till date on estimating position and localization\nand tracking have been thoroughly reviewed to find the appropriate algorithm\nwhich will provide accurate result with minimum drift error. Sensor fusion,\nInertial Measurement Unit (IMU), MEMS inertial sensor, Kalman filter\nbased global translational localization systems are studied. It is observed, prevailing\napproaches consist issues such as stability, random bias drift, noisy acceleration\noutput, position estimation error, robustness or accuracy, cost effectiveness\netc. Moreover, issues with motions that do not follow laws of\nphysics, bandwidth, restrictive nature of assumptions, scale optimization for\nlarge space are noticed as well. Advantages of such smartphone sensor based\nposition estimation approaches include, less memory demand, very fast operation,\nmaking them well suited for real time problems and embedded systems.\nBeing independent of the size of the system, they can work effectively for high\ndimensional systems as well. Through study of these approaches it is observed,\nextended Kalman filter gives the highest accuracy with reduced requirement\nof excess hardware during tracking. It renders better and faster result\nwhen used in accelerometer sensor. With the aid of various software, error\naccuracy can be increased further as well....
Previous researches on Android malware mainly focus on malware detection, and malware�s evolution makes the process face\ncertain hysteresis. The information presented by these detected results (malice judgment, family classification, and behavior\ncharacterization) is limited for analysts. Therefore, a method is needed to restore the intention of malware, which reflects the\nrelation between multiple behaviors of complex malware and its ultimate purpose. This paper proposes a novel description and\nderivation model of Android malware intention based on the theory of intention and malware reverse engineering. This approach\ncreates ontology formalware intention to model the semantic relation between behaviors and its objects and automates the process\nof intention derivation by using SWRL rules transformed fromintentionmodel and Jess inference engine. Experiments on 75 typical\nsamples show that the inference system can perform derivation of malware intention effectively, and 89.3% of the inference results\nare consistent with artificial analysis, which proves the feasibility and effectiveness of our theory and inference system....
The quality of software, in particular developed rapidly, is quite a challenge for businesses and IT-dependent societies.\nTherefore, the H2020 Q-Rapids project consortium develops processes and tools to meet this challenge and improve the\nquality of the software to meet end-users requirements and needs. In this paper, we focus on data analytics that helps software\ndevelopment companies evaluate the quality of the software. In fact, most software development teams use tools such as\nGitLab, SonarQube or JIRA (among others) to assess the basic characteristics and metrics of the developed software. In this\npaper, we propose the framework that gathers basic data from the mentioned tools, and processes the data further (e.g. using\nApache Kafka, Kibana and Spark) to calculate more advanced metrics, product factors, indicators, and to find correlations\nbetween them. In this paper, we present the concept, the technical details, and the initial results of the advanced data analysis\nmethodology. Furthermore, we provide discussion on how to use the system and show the future development directions.\nWe have already implemented the system at software development SME and managed to find interesting characteristics and\ncorrelations about the software quality. The results, based on the real data, were interesting to the company product owners\nand team leaders, and more importantly helped them improve the software quality development process....
Software is an important part of automotive product development, and it is\ncommonly known that software quality assurance consumes considerable effort\nin safety-critical embedded software development. Increasing the effectiveness\nand efficiency of this effort thus becomes more and more important.\nIdentifying problematic code areas which are most likely to fail and therefore\nrequire most of the quality assurance attention is required. This article presents\nan exploratory study investigating whether the faults detected by static analysis\ntools combined with code complexity metrics can be used as software quality\nindicators and to build pre-release fault prediction models. The combination\nof code complexity metrics with static analysis fault density was used to\npredict the pre-release fault density with an accuracy of 78.3%. This combination\nwas also used to separate high and low quality components with a classification\naccuracy of 79%....
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