Forensic taxonomy of android productivity apps

Android productivity apps have provided the facility of having a constantly accessible and productive workforce to the information and work capabilities needed by the users. With hundreds of productivity apps available in the Android app market, it is necessary to develop a taxonomy for the forensic investigators and the end users to allow them to know what personal data remnants are available from the productivity apps. In this paper, 30 popular Android productivity apps were examined. A logical extraction of the Android phone was collected by using a well-known mobile forensic tool- XRY to extract various information of forensic interest such as user email ID and list of tasks. Based on the findings, a two-dimensional taxonomy of the forensic artefacts of the productivity apps is proposed with the app categories in one dimension and the classes of artefacts in the other dimension. The artefacts identified in the study of the apps are summarised using the taxonomy. In addition, a comparison with the existing forensic taxonomies of different categories of Android apps is provided to facilitate timely collection and analysis of evidentiary materials from mobile devices.     

Efficient schemes for similarity-aware refinement of aggregation queries

Interactive data exploration platforms in Web, business and scientific domains are becoming increasingly popular. Typically, users without prior knowledge of data interact with these platforms in an exploratory manner hoping they might retrieve the results they are looking for. One way to explore large-volume data is by posing aggregate queries which group values of multiple rows by an aggregate operator to form a single value: an aggregated value. Though, when a query fails, i.e., returns undesired aggregated value, users will have to undertake a frustrating trial-and-error process to refine their queries, until a desired result is attained. This data exploration process, however, is growing rather difficult as the underlying data is typically of large-volume and high-dimensionality. While heuristic-based techniques are fairly successful in generating refined queries that meet specified requirements on the aggregated values, they are rather oblivious to the (dis)similarity between the input query and its corresponding refined version. Meanwhile, enforcing a similarity-aware query refinement is rather a non-trivial challenge, as it requires a careful examination of the query space while maintaining a low processing cost. To address this challenge, we propose an innovative scheme for efficient Similarity-Aware Refinement of Aggregation Queries called (EAGER) which aims to balance the tradeoff between satisfying the aggregate and similarity constraints imposed on the refined query to maximize its overall benefit to the user. To achieve that goal, EAGER implements efficient strategies to minimize the costs incurred in exploring the available search space by utilizing similarity-based and monotonic-based pruning techniques to bound the search space and quickly find a refined query that meets users’ expectations. Our extensive experiments show the scalability exhibited by EAGER under various workload settings, and the significant benefits it provides.     

Bifurcation control in a delayed two-neuron fractional network

The issue of bifurcation control for a delayed fractional network involving two neurons is concerned. Delay-dependent stability conditions and the bifurcation point are established by discussing the associated characteristic equation of the proposed network. Then, a delayed feedback controller is firstly designed to stabilize the Hopf bifurcation, and desirable dynamics is achieved. It is indicated that the designed controller is extremely effective which can postpone the onset of bifurcation by carefully selecting the feedback gain. Finally, simulation results are given to verify the efficiency of the theoretical results.     

An adaptive local binarization method for document images based on a novel thresholding method and dynamic windows

Binary image representation is essential format for document analysis. In general, different available binarization techniques are implemented for different types of binarization problems. The majority of binarization techniques are complex and are compounded from filters and existing operations. However, the few simple thresholding methods available cannot be applied to many binarization problems. In this paper, we propose a local binarization method based on a simple, novel thresholding method with dynamic and flexible windows. The proposed method is tested on selected samples called the DIBCO 2009 benchmark dataset using specialized evaluation techniques for binarization processes. To evaluate the performance of our proposed method, we compared it with the Niblack, Sauvola and NICK methods. The results of the experiments show that the proposed method adapts well to all types of binarization challenges, can deal with higher numbers of binarization problems and boosts the overall performance of the binarization.     

Flexible and Autonomous Integrated System for Characterizing Metal Oxide Gas Sensor Response in Dynamic Environment

Characterization and calibration of gas sensor is a complex problem due to the dynamic behavior of gases and the limitations of current technology. This article reports a flexible, robust, and autonomous integrated system that is able to perform characterization on metal oxide-based gas sensors in dynamic environments. The system controls the concentration and flow of the relevant gases into the gas chamber and simultaneously measuring the sensor response. This feature allows the characterization of the sensor under continuous dynamic flow of gases similar to conditions on a robot or flow pipes. Several experiments have been performed on the system using hydrogen sulfide. The results provide information on the general characteristics of the sensor as well as its sensitivity. The noise levels were studied with different reference voltages. Overall, the results verify that the system is reliable and able to produce repeatable measurements.     

MODIFICATION OF GEOMETRY AND TECHNOLOGY OF CYLINDRICAL WORMS

The objective of this research is to develop a new method of grinding process that can efficiently make concave profile of the worm thread smooth. The grinding is carried out using a wheel with curvature profile in the axial plane in the form of an arc of logarithmic spiral. The proposed method will provide maximum line of contact between the tool and the worm with minimum stress concentration at any point. The generating equation of the grinding tool profile and its arrangement with respect to the worm during thread grinding were determined.     

Adsorptive performance of penta-bismuth hepta-oxide nitrate, Bi?O?NO?, for removal of methyl orange dye

The adsorption of methyl orange dye from aqueous solution onto penta-bismuth hepta-oxide nitrate, Bi(5)O(7)NO(3), synthesized by precipitation method, was studied in a batch adsorption system. The effects of operation parameters such as adsorbent dose, initial dye concentration, pH and temperature were investigated. The adsorption equilibrium and mechanism of adsorption was evaluated by Langmuir and Freundlich isotherm and different kinetic models, respectively. The results indicate that adsorption is highly dependent on all operation parameters. At optimum conditions, the adsorption capacity was found to be 18.9 mg/g. The adsorption data fits well with the Langmuir isotherm model indicating monolayer coverage of adsorbate molecules on the surface of Bi(5)O(7)NO(3). The kinetic studies show that the adsorption process is a second-order kinetic reaction. Although intra-particle diffusion limits the rate of adsorption, the multi-linearity plot of intra-particle model shows the importance of both film and intra-particle diffusion as the rate-limiting steps of the dye removal. Thermodynamic parameters show that the adsorption process is endothermic, spontaneous and favourable at high temperature.     

Learning mixtures of point distribution models with the EM algorithm

This paper demonstrates how the EM algorithm can be used for learning and matching mixtures of point distribution models. We make two contributions. First, we show how shape-classes can be learned in an unsupervised manner. We present a fast procedure for training point distribution models using the EM algorithm. Rather than estimating the class means and covariance matrices needed to construct the PDM, the method iteratively refines the eigenvectors of the covariance matrix using a gradient ascent technique. Second, we show how recognition by alignment can be realised by fitting a mixture of linear shape deformations. We evaluate the method on the problem of learning the class-structure and recognising Arabic characters.     

Preparation of an exponentially rising optical pulse for efficient excitation of single atoms in free space

We report on a simple method to prepare optical pulses with exponentially rising envelope on the time scale of a few ns. The scheme is based on the exponential transfer function of a fast transistor, which generates an exponentially rising envelope that is transferred first on a radio frequency carrier, and then on a coherent cw laser beam with an electro-optical phase modulator. The temporally shaped sideband is then extracted with an optical resonator and can be used to efficiently excite a single (87)Rb atom.     

Discretized Markov chain in damage assessment using Rainflow cycle with effects of mean stress on an automobile crankshaft

We studied the effect of mean stress correction factor using the Rainflow counting technique to assess the fatigue damage of an automobile crankshaft under service loading by considering the stochastic process of the Markov chain. The failure of the crankshaft will cause serious damage to the engine and also to other connecting subcomponents. The service loading is computationally generated from the Discrete Markov chain model and the fatigue cycle is counted using the Rainflow counting technique with the consideration of the local minima and maxima load. To quantify the fatigue damage, the strain-life curve using the fatigue mean stresses was used to model the fatigue failure of the material used in for the crankshaft at N _f = 10 ⁶ . The fatigue mean stresses were used to estimate the effects of the mean stress on the fatigue strength of the component under service loading condition. Statistical verification with the boundary condition of the 90% confidence level was performed to observe the difference between the stochastic algorithms when compared towards the fatigue life behavior of the ductile cast iron material. We concluded that for the practical application, the proposed stochastic model provides a highly accurate assessment of fatigue damage prediction for improving the safety and controlling the risk factors in terms of structural health monitoring.