Generation of Asynchronous Component Adapters

Utilizing services of legacy and third party COTS components has become vital for the timely development and evolution of software systems. Component interfaces typically have to be adapted before they can be re-used in a system context. This paper presents an approach to generating component adapters. The approach focuses on mapping multiple events, and their associated parameters, generated from a source component to multiple calls in the target component. The novelty of our mechanism is that it allows developers to generate asynchronous as well as synchronous adapters. As a result we can generate adapters for components with incompatible event structure. A prototype called Asynchronous Component Adapter (ACA) was developed which demonstrates the idea with Java Beans, but the methodology can be applied to any component-based system that supports introspection like Microsoft .NET assemblies.     

Synergistic verification and validation of systems and software engineering models

In this paper, we present a unified approach for the verification and validation of software and systems engineering design models expressed in UML 2.0 and SysML 1.0. The approach is based on three well-established techniques, namely formal analysis, programme analysis and software engineering (SwE) techniques. More precisely, our contribution consists of the synergistic combination of model checking, static analysis and SwE metrics that enables the automatic and efficient assessment of design models from static and dynamic perspectives. Additionally, we present the design and implementation of an automated computer-aided assessing framework integrating the proposed approach. Moreover, we discuss the related technical details and the underlying synergism. Finally, we illustrate the proposed approach by assessing a design case study that is composed of state machine and sequence diagrams.     

MASS BALANCE APPROACH TO FORMULATE A380.0 ELECTRICAL CONDUIT FITTINGS FROM RECYCLED ALUMINUM ALLOY SCRAPS

This paper describes calculations using the mass balance method on aluminum alloy scraps, including soft drink cans, aluminum engine components, low alloy aluminum materials, and aluminum computer disks, to formulate A380.0 casting alloy. All recycled aluminum alloys from the same source were considered to be identical. Six approaches of mixing by weight percent, with the aid of an X-ray fluorescence technique, were successfully completed. A380.0 electrical conduit fittings were produced using the die casting technique. Mechanical tests including compression, impact, tensile, and suspended load indicated that die cast recycled aluminum alloy conduit fittings under classification 4 (heavy load condition) satisfied the requirements as stated in BSEN 50086-1: 1993.     

Improving the performance of the needleman-wunsch algorithm using parallelization and vectorization techniques

The Needleman-Wunsch (NW) is a dynamic programming algorithm used in the pairwise global alignment of two biological sequences. In this paper, three sets of parallel implementations of the NW algorithm are presented using a mixture of specialized software and hardware solutions: POSIX Threads-based, SIMD Extensions-based and a GPU-based implementations. The three implementations aim at improving the performance of the NW algorithm on large scale input without affecting its accuracy. Our experiments show that the GPU-based implementation is the best implementation as it achieves performance 72.5X faster than the sequential implementation, whereas the best performance achieved by the POSIX threads and the SIMD techniques are 2X and 18.2X faster than the sequential implementation, respectively.

     

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM FOR AUTOMATIC SLEEP MULTISTAGE LEVEL SCORING EMPLOYING EEG,EOG, AND EMG EXTRACTED FEATURES

A new system for sleep multistage level scoring by employing extracted features from twenty five polysomnographic recording is presented. For the new system, an adaptive neuro-fuzzy inference system (ANFIS) is developed for each sleep stage. Initially, three types of electrophysiological signals including electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG) were collected from twenty five healthy subjects. The input pattern used for training the ANFIS subsystem is a set of extracted features based on the entropy measure which characterize the recorded signals. Finally an output selection subsystem is utilized to provide the appropriate sleep stage according to the ANFIS stage subsystems outputs. The developed system was able to provide an acceptable estimation for six sleep stages with an average accuracy of about 76.43% which confirmed its ability for multistage sleep level scoring based on the extracted features from the EEG, EOG and EMG signals compared to other approaches.     

The Compatibility of Binary Characters on Phylogenetic Networks: Complexity and Parameterized Algorithms

In a recent article, Nakhleh, Ringe and Warnow introduced perfect phylogenetic networks—a model of language evolution where languages do not evolve via clean speciation—and formulated a set of problems for their accurate reconstruction. Their new methodology assumes networks, rather than trees, as the correct model to capture the evolutionary history of natural languages. They proved the NP-hardness of the problem of testing whether a network is a perfect phylogenetic one for characters exhibiting at least three states, leaving open the case of binary characters, and gave a straightforward brute-force parameterized algorithm for the problem of running time O(3 ^k n), where k is the number of bidirectional edges in the network and n is its size. In this paper, we first establish the NP-hardness of the binary case of the problem. Then we provide a more efficient parameterized algorithm for this case running in time O(2 ^k n ²). The presented algorithm is very simple, and utilizes some structural results and elegant operations developed in this paper that can be useful on their own in the design of heuristic algorithms for the problem. The analysis phase of the algorithm is very elegant using amortized techniques to show that the upper bound on the running time of the algorithm is much tighter than the upper bound obtained under a conservative worst-case scenario assumption. Our results bear significant impact on reconstructing evolutionary histories of languages—particularly from phonological and morphological character data, most of which exhibit at most two states (i.e., are binary), as well as on the design and analysis of parameterized algorithms. Research of I.A. Kanj was supported in part by DePaul University Competitive Research Grant.     

Groundwater Modelling in Aquifers with highly Karstic and Heterogeneous Characteristics (KHC) in Palestine

Groundwater modelling is hindered by the lack of adequateinformation about the groundwater system and hence theneed for an interactive and efficient system for datapreparation and results analysis. Such a lack ofinformation usually necessitates the use of tediousiterative methodology within a sensitivity analysisscheme. The heterogeneous aquifer systems complicate theissue since more data is required to simulate the system.This study demonstrates the integrated approach to bridgethe gap between data handling and modelling. The karstcretaceous aquifer system (complex aquifer system) of theEastern Basin in the West Bank is used to illustrate thisapproach. The groundwater modelling approach integrates the outputsfrom different programs for data preparation andanalysis. These include (1) Groundwater Database (GWW)(2) Geographic Information System (GIS) (3) GroundwaterModelling System (GMS). In addition, the paper will summarize the data collectionefforts, problems faced and experience gained workingwith heterogeneous media. This involves linking theresults from various field investigations for groundwaterdevelopment programs in the West Bank.     

Automated sleep stage identification system based on time-frequency analysis of a single EEG channel and random forest classifier

In this work, an efficient automated new approach for sleep stage identification based on the new standard of the American academy of sleep medicine (AASM) is presented. The propose approach employs time-frequency analysis and entropy measures for feature extraction from a single electroencephalograph (EEG) channel. Three time-frequency techniques were deployed for the analysis of the EEG signal: Choi-Williams distribution (CWD), continuous wavelet transform (CWT), and Hilbert-Huang Transform (HHT). Polysomnographic recordings from sixteen subjects were used in this study and features were extracted from the time-frequency representation of the EEG signal using Renyi's entropy. The classification of the extracted features was done using random forest classifier. The performance of the new approach was tested by evaluating the accuracy and the kappa coefficient for the three time-frequency distributions: CWD, CWT, and HHT. The CWT time-frequency distribution outperformed the other two distributions and showed excellent performance with an accuracy of 0.83 and a kappa coefficient of 0.76.     

Decision-Support System for Domestic Water Demand Forecasting and Management

A decision support system (DFMS) for forecastingdomestic water demand including demand management was developedas part of a highly integrated decision-support system forriver-basin management. The system provides water resourcesplanners with the facilities for estimating future water demandfor domestic use for any demand region and time period, havingregard, to the possibility of introducing demand managementmeasures. The system has the capability of predicting domestic-water demand by various methods according to data availability.It can compute conservation effectiveness due to theimplementation of various demand-management measures,forecasting the number of customers for different consumptionunits (person, household and water connection) and facilitatingthe development of demand-scenarios for evaluating variousoptions. In general, the system comprises of the followingcomponents: (1) geographic information system, (2) databasemanagement system, (3) expert system, (4) multi-decisioncomponent, (5) prediction models, (6) user-interface, (7) hypertext files. The system has been tested and demonstratedusing the data of Swindon demand zone of Thames Water Utility.     

User identification using deep learning and human activity mobile sensor data

International Journal of Information Security - The ownership of user actions in computer and mobile applications is an important concern, especially when using shared devices. User identification...