Volume 3, Issue 2

Hereby in this paper, we are going to refer image classification. The main issue in image classification is features extraction and image vector representation. We expose the Bag of Features method used to find image representation. Class prediction accuracy of varying classifiers algorithms is measured on Caltech 101 images. For feature extraction functions we evaluate the use of the classical Speed Up Robust Features technique against global color feature extraction. The purpose of our work is to guess the best machine learning framework techniques to recognize the stop sign images. The trained model will be integrated into a robotic system in a future work.

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In the present article, we develop multilayer perceptron model for identification of some possible genes mediating different leukemia. The procedure involves grouping of gene based correlation coefficient and finally select of some possible genes. The procedure has been successfully applied three human leukemia gene expression data sets. The superiority of the procedure has been demonstrated seven existing gene selection methods like Support Vector Machine (SVM), Signal-to-Noise Ratio (SNR), Significance Analysis of Microarray (SAM), Bayesian Regularization (BR), Neighborhood Analysis (NA), Gaussian Mixture Model (GMM), Hidden Markov Model (HMM) is demonstrated, in terms of the affluence of each Go attribute of the important genes based on p-value statistics. The result are properly validated by before analysis, t-test, gene expression profile plots. The proposed procedure has been capable to select genes that are more biologically significant for mediating of leukemia then those obtained by existing methods.

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Exploring communities and outliers in Social Network is based on considering of some nodes have overlapped neighbor node within the same group as well as some nodes have no any link to the other node or have no any overlapped value. The existing approaches are based on the overlapping community detection method were only defined the overlap nodes or group of overlap nodes without thinking of which nodes might have individual communities or which nodes are outliers. Detecting communities can be used the similarity measure based on neighborhood overlapping of nodes and identified nodes so called outliers which cannot be grouped into any of the communities. This paper proposed method to detect communities and outliers from Edge Structure with neighborhood overlap by using nodes similarity. The result implies the best quality with modularity measurement which leads to more accurate communities as well as improved their density after removing outliers in the network structure.

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Convolutional Neural Networks (CNNs) have gained attention in recent years for their ability to perform complex machine learning tasks with high accuracy and resilient to noise of inputs. The time-consuming convolution operations of CNNs pose great challenges to both software as well as hardware designers. To achieve superior performance, a design involves careful concerns between exposing the massive computation parallelism and exploiting data reuse in complex data accesses. Existing designs lack comprehensive analysis on design techniques and decisions. The analytical discussion and quantitative proof behind the design criterion, such as choosing proper dimensions to parallelize, are not well studied. This paper performs a series of qualitative and quantitative studies on both the programming techniques and their implications on the GPU architecture. The observations reveal comprehensive understanding on the correlation between the design techniques and the resulting performance. Based on the analyses, we pinpoint the two major performance bottlenecks of CNN on GPGPU: performing computation and loading data from global memory. Software and hardware enhancements are proposed in this paper to alleviate these issues. Experimental results on a cycle-accurate GPGPU simulator have demonstrated up to 4.4x performance enhancement when compared with the reference design.

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Brain tumor is such an abnormality of brain tissue that causes brain hemorrhage. Therefore, apposite detections of brain tumor, its size, and position are the foremost condition for the remedy. To obtain better performance in brain tumor and its stages detection as well as its position in MRI images, this research work proposes an advanced hybrid algorithm combining statistical procedures and machine learning based system Support Vector Machine (SVM) and Artificial Neural Network (ANN). This proposal is initiated with the enhancement of the brain MRI images which are obtained from oncology department of University of Maryland Medical Center. An improved version of conventional K-means with Fuzzy C-means algorithm and temper based K-means & modified Fuzzy C-means (TKFCM) clustering are used to segment the MRI images. The value of K in the proposed method is more than the conventional K-means. Automatically updated membership of FCM eradicates the contouring problem in detection of tumor region. The set of statistical features obtained from the segmented images are used to detect and isolate tumor from normal brain MRI images by SVM. There is a second set of region based features extracted from segmented images those are used to classify the tumors into benign and four stages of the malignant tumor by ANN. Besides, the classified tumor images provide a feature like orientation that ensures exact tumor position in brain lobe. The classifying accuracy of the proposed method is up to 97.37% with Bit Error Rate (BER) of 0.0294 within 2 minutes which proves the proposal better than the others.

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The Internet of Things (IOT) is a revolutionary communication technology which enables numerous heterogeneous objects to be inter-connected. In such a wireless system, interference management between the operating devices is an important challenge. Cognitive Radio (CR) seems to be a promising enabler transmission technology for the 5G-IOT system. The “sense-and-adapt” smart transmission strategy in CR systems can help to overcome the problem of multiple access interference (MAI) in IOT systems. In this paper, a 5G-IOT smart infrastructure system is arranged in the form of CR based virtual antenna array (VAA) system. In VAA based wireless system, knowledge of users’ locations can help the transmitter to achieve interference avoidance by steering the main beam towards the intended recipient. This idea has been applied to the VAA-IOT system, where smart antenna array based location aware beamforming are applied at both transmitter and receiver cluster of smart sensors with the help of spatial modulation principle. The waveform of choice for the CR-IOT clusters is Generalized Frequency Division Multiplexing (GFDM) while corresponding waveform for the primary user (PU) cluster is conventional Orthogonal Frequency Division Multiplexing (OFDM). Computer simulation shows that under multipath fading conditions, the implemented system can reduce the interference to the primary user (PU) system, leading to better coexistence.

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The main target of this article is to study the provision of indoor service (coverage) using outdoor base station at higher frequencies i.e. 10 GHz, 30 GHz and 60 GHz. In an outdoor to indoor propagation, an angular wall loss model is used in the General Building Penetration (GBP) model for estimating the additional loss at the intercept point of the building exterior wall. A novel angular wall loss model based on a separate incidence angle in azimuth and elevation plane is proposed in this paper. In the second part of this study, an Extended Building Penetration (EBP) model is proposed, and the performance of EBP model is compared with the GBP model. In EBP model, the additional fifth path known as the “Direct path” is proposed to be included in the GBP model. Based on the evaluation results, the impact of the direct path is found significant for the indoor users having the same or closed by height as that of the height of the transmitter. For the indoor users located far away from the exterior wall of building, a modified and enhanced approach of ray tracing type is proposed in this article. In the light of acquired simulation results, the impact of a modified ray tracing approach is emphasized.

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Probabilities topic models are active research area in text mining, machine learning, information retrieval, etc. Most of the current statistical topic modeling methods, such as Probabilistic Latent Semantic Analysis (pLSA) and Latent Dirichlet Allocation (LDA). They are used to build models from unstructured text and produce a term-based representation to describe a topic by choosing single words from multinomial word distribution. There are two main weaknesses. First, popular or common words are different topics, often causing ambiguity for understanding the topics; Second, lack of consistent semantics for single words to be represented correctly. To address these problems, this paper proposes a model (A Two-Phase Method for Constructing Topic Model, TPMTM) that combines statistical modeling (LDA) with frequent pattern mining and produces better presentations of rich topics and semantics. Empirical evaluation shows that the results of the proposed model are better than LDA.

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The research reported in this paper proposes an Advanced Process Control system, denoted “i.Process | Steel – RHF”, oriented to energy efficiency improvement in a pusher type billets reheating furnace located in an Italian steel plant. A tailored control method based on a two-layer Model Predictive Control strategy has been created that involves cooperating modules. Different types of linear models have been combined and an overall furnace global linear model has been developed and included in the controller formulation. The developed controller allows handling all furnace conditions, guaranteeing the fulfillment of the defined specifications. The reliability of the proposed approach has been tested through significant simulation scenarios. The controller has been installed on the considered real plant, replacing local standalone controllers manually conducted by plant operators. Very satisfactory field results have been achieved, both on process control and energy efficiency improvement. Optimized trade-offs between energy saving, environmental impact decreasing, product quality improvement and production maximization have been guaranteed. Consequently, Italian energy efficiency certificates have been obtained. The formulated steel industry reheating furnaces control method has been patented.

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On-ramping is being widely used as e method to increase the freeway operational efficiency. The main traffic parameter that must be taken in consideration for the implementation of the feedback control strategies for the on-ramp metering is density on the main section of road. In this paper is given discretized model of traffic which is then improved by a recursive technique called Kalman-Filter with the aid of which is possible to predict the density, by only having the traffic flow measured on the start and end road section. Kalman Filter is based on linear relationship of flow and density. By minimizing the square of error between of the measurements and the estimated values of flows, a gain is derived which then is applied to the densities of the model in order to obtain the greatest accuracy of these values.

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Recently, the number of industrial accidents of chemical plants has been increasing in Japan. The fault detection system is required to keep chemical plant safely. In this study, a fault detection system for a chemical plant using agent framework and negative selection algorithm was proposed. The negative selection algorithm is one of artificial immune systems. The artificial immune system is an imitative mechanism of vital actions to discriminate self/nonself to protect itself. The method was implemented and applied to a complicated chemical plant—which is a boiler plant virtually operated using a dynamic plant simulator. The simulations of fault detection were carried out. And also, the results of simulations are presented in this paper.

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A bistatic radar breadboard for space surveillance has been developed by ONERA for the European Space Agency. This item was operated during the launch of the ExoMars mission on March 14th 2016. The spacecraft, attached to the Proton launcher, was well detected in real-time during its passage over South Europe. This paper presents the setting up of an experiment to detect this particular type of targets with the radar breadboard. The results of its operation as space surveillance system as well as a specific kinematic analysis of the ExoMars spacecraft as viewed from the radar.

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The necessity of navigation in people and mobile robots (MR) through specific environments (indoors or outdoors) has become more and more relevant nowadays. For indoors, generally speaking, the positioning systems can be divided into 2D (two dimensions) or 3D (three dimensions) approaches, where Ultrasonic Local Positioning Systems (ULPS) are often a common solution for MRs in 2D. This work proposes the extension of an already developed 2D ULPS to a 3D ULPS, where the compact design and the suitable performance of the initial 2D ULPS have been maintained. The ultrasonic beacons have been re-arranged to avoid co-planarity, then improving the third coordinate estimation. Furthermore, this work proposes the use of up to four ULPSs together to cover the 3D region of interest. Two configurations have actually been considered, one involving three ULPSs and another based on four. A heuristic Position Dilution of Precision (PDOP) estimation has been carried out, by taking into account two ways of obtaining the 3D-position: a) all beacons from the three different ULPSs are processed simultaneously, so all measurements are considered in the same set of positioning equations; and b) every ULPS is detected and considered separately and, later, the different estimated positions are merged. The second option is more likely to happen in a real scenario and, furthermore, the fusion of the independent positions obtained from each one of the ULPS improves the final position accuracy.

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Nowadays, text annotation plays an important role within real-time social media mining. Social media analysis provides actionable information to its users in times of natural disasters. This paper presents an approach to a real-time two layer text annotation system for social media stream to the domain of natural disasters. The proposed system annotates raw tweets from Twitter into two types such as Informative or Not Informative as first layer. And then it annotates again five information types based on Informative tweets only as second layer. Based on the first and second layer annotation results, this system provides the tweets with user desired informative type in real time. In this system, annotation is done at tweet level by using word and phrase level features with LibLinear classifier. All features are in the form of Ngram nature based on part of speech (POS) tag, Sentiment Lexicon and especially created Disaster Lexicon. The validation of this system is performed based on different disaster related datasets and new Myanmar_Earthquake_2016 dataset derived from Twitter. The annotated datasets generated from this work can also be used by interested research communities to study the social media natural disaster related research.

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Safety-critical systems are well documented and standardized (e.g. IEC 61508, RTCA DO-178B) within system design cycles. However in Defence and Security, systems that are critical to the success of a Mission are not defined within the literature nor are there any guidelines in defining criticality in their design or operational capabilities. When it comes to Vetronics (Vehicle Electronics), a mission-critical system, is a system with much complexity and mixed criticality levels that is a part of the overall platform (military vehicle) offering integrated system capabilities. In this paper, a framework is presented, providing guidelines in designing efficiently and effectively mission-critical systems considering principles of Interoperable Open Architectures (IOA), mission-critical integrity levels and following new standardization activities such as NATO Generic Vehicle Architecture (NGVA). A Defensive Aid Suite (DAS) system is used as a case study to illustrate how this framework can be exploited. The indention of this extension is to provide an approach to precisely estimate threats in order to de-risk missions in the very early stages.

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In this paper an efficient feedback quantization technic for beamforming in MIMO systems is presented. The proposed technic named time domain quantization TD-Q is based on the feedback of time domain parameters necessary for the reproduction of the beamforming matrix at the transmitter. This TD-Q presents the same performance than the conventional Givens rotation quantization GR-Q approach which is adopted in IEEE 802.11ac standardand. The performance and amount of feedback of the proposed TD-Q are studied and compared with the GR-Q in IEEE 802.11ac context.

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The energy marathon is becoming increasingly based on renewable sources, whereas the continuous decrease on the cost of energy production has supported in last decade, for instance, the development of large-scale offshore wind applications. The consistency and availability of the AC-working equipment portfolio is only limited by physical application boundaries, which are quite evident in this sort of accomplishments. The focus of this article is to present a tool developed under the MATLAB environment which allows for a quick and real-time analysis of HVAC links, assessing the impact of voltage, current, power factor or distance conditions. The conclusions are drawn directly by means of a stress-point and operational acceptance range.

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This article examines the hydrogen storage phenomenon in a spherical cavity. The hydrogen gas or liquid is subjected to high pressures, leading to significant loss of mass of hydrogen, and requires materials that can withstand these high pressures also minimize losses. For all these reasons, the problem is considered at the quantum scale. So in quantum mechanics it studies the theory of wave functions corresponding to the hydrogen with the correct expressions development of the radial functions and the spherical harmonics, and also the energy stored, and then the graphic applications that gives a spatial representation of each function with a program informatics.

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Patient generated data, or personal clinical data, is considered an important aspect in improving patient outcomes. However, personal clinical data is difficult to collect and manage due to its distributed nature. For example, they can be located in multiple places such as doctors’ offices, radiology centers, hospitals, or some clinics. Another factor that can make personal clinical data difficult to manage is that it can be heterogeneous data types such as text, images, charts, or paper-based documents. In case of emergencies, this situation makes personal clinical data retrieval very difficult. In addition, since the amount and types of personal clinical data continue to grow, finding relevant clinical data when needed is getting more difficult if no action is taken. In response to such scenarios, we propose an untethered patient health record system that manages personal health data by utilizing meta-data that enables easy retrieval of clinical data. We incorporate cloud-based storage for easy access and sharing with caregivers to implement continuity of care and evidence-based treatment. In emergency cases, we make critical medical information such as current medications and allergies available to relevant caregivers with valid license numbers only. Clinical data needs to be stored or made accessible from one place for easy sharing and retrieval. Well-managed personal cloud space could outlive the lifetime of personal health records system (PHRS) since the discontinuity of the service does not affect the data stored in the cloud space. In our approach, we separate the clinical data from applications in order to make the data independent from the application. Also, the users can have alternative applications for their clinical data. Such independence motivates users to use PHRS with flexibility.

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Tenants associated with a cloud-hosted application seek to reduce running costs and minimize resource consumption by sharing components and resources. However, despite the benefits, sharing resources can affect tenant’s access and overall performance if one tenant abruptly experiences a significant workload, particularly if the application fails to accommodate this sudden increase in workload. In cases where a there is a higher or varying degree of isolation between components, this issue can become severe. This paper aims to present novel solutions for deploying components of a cloud-hosted application with the purpose of guaranteeing the required degree of multitenancy isolation through a mathematical optimization model and metaheuristic algorithm. Research conducted through this paper demonstrates that, when compared, optimal solutions achieved through the model had low variability levels and percent deviation. This paper additionally provides areas of application of our optimization model as well as challenges and recommendations for deploying components associated with varying degrees of isolation.

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Smoking has been proven to negatively affect health in a multitude of ways. As of 2009, smoking has been considered the leading cause of preventable morbidity and mortality in the United States, continuing to plague the country’s overall health. This study aims to investigate the viability and effectiveness of some machine learning algorithms for predicting the smoking status of patients based on their blood tests and vital readings results. The analysis of this study is divided into two parts: In part 1, we use One-way ANOVA analysis with SAS tool to show the statistically significant difference in blood test readings between smokers and non-smokers. The results show that the difference in INR, which measures the effectiveness of anticoagulants, was significant in favor of non-smokers which further confirms the health risks associated with smoking. In part 2, we use five machine learning algorithms: Naïve Bayes, MLP, Logistic regression classifier, J48 and Decision Table to predict the smoking status of patients. To compare the effectiveness of these algorithms we use: Precision, Recall, F-measure and Accuracy measures. The results show that the Logistic algorithm outperformed the four other algorithms with Precision, Recall, F-Measure, and Accuracy of 83%, 83.4%, 83.2%, 83.44%, respectively.

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Smoking has been proven to negatively affect health in a multitude of ways. As of 2009, smoking has been considered the leading cause of preventable morbidity and mortality in the United States, continuing to plague the country’s overall health. This study aims to investigate the viability and effectiveness of some machine learning algorithms for predicting the smoking status of patients based on their blood tests and vital readings results. The analysis of this study is divided into two parts: In part 1, we use One-way ANOVA analysis with SAS tool to show the statistically significant difference in blood test readings between smokers and non-smokers. The results show that the difference in INR, which measures the effectiveness of anticoagulants, was significant in favor of non-smokers which further confirms the health risks associated with smoking. In part 2, we use five machine learning algorithms: Naïve Bayes, MLP, Logistic regression classifier, J48 and Decision Table to predict the smoking status of patients. To compare the effectiveness of these algorithms we use: Precision, Recall, F-measure and Accuracy measures. The results show that the Logistic algorithm outperformed the four other algorithms with Precision, Recall, F-Measure, and Accuracy of 83%, 83.4%, 83.2%, 83.44%, respectively.

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Excellence in hospitals supply management results in better quality, best prices, and good deliveries. One of the questions that come up as healthcare organization to capture the economies of scale in purchasing prices and process costs is whether their purchasing activities should be centralized or decentralized. In most cases, centralization strategy usually gives good supplier’s service with a lower cost, but the consideration of supplier’s cost in the hospital sector are mainly limited to visible ones. The high levels of hidden quality costs generated by suppliers and their unknown presence have serious consequences on the decisions made by the managers. However, the existence of this kind of costs has not been considered yet. Therefore, the main objective of this paper is to propose a decision-making-approach, integrating a new method of measuring supplier’s hidden quality costs, in order to help managers to choose the appropriate purchasing organizational structure in the hospital sector.

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This application developing for the project OCEMA comes as result of architecture building in Android, then the development of a professional modeling in Talend Open Studio for Big Data, which enabled the integration of data from many data sources. One of its uses is the quick identification of people found in areas affected by natural disasters. The application identifies the persons who do not have an ID card, or another identification document, by using identification through fingerprint or personal number. OCEMA Application has access to all the agencies involved in natural disasters managing, such as ISK, HMIK, MIK, FSK and CRA. This application is developed to connect to web applications as well, such as applications, which gather real time information on earthquakes and weather in the world.
The study of literature and the actual work with these systems has shown some important components of success for DWH systems, DataMart and mobile application development.

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A 500 KVA Uninterruptible power supply (UPS) using Fuel Cells (FC) and super-capacitors (SCs) was studied with the worst case of 10 minutes and eight hours of interruption per day. A power management system was established to control the FC and the SCs in order to extract the hybridization benefits with a comparison between a Proton exchange membrane FC (PEMFC) working alone and another combined with SCs. Moreover, possible FC degradations were discussed. The start/stop cycling, the high-power loads and load changes degradations were taken into consideration in order to estimate the FC lifetime span using a prediction formula. Besides, the FC costs were studied to estimate the best average cost. Finally, the SCs filter constant time and their charging currents were revealed.

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Currently the use of distributed energy resources, especially renewable generation, and demand response programs are widely discussed in scientific contexts, since they are a reality in nowadays electricity markets and distribution networks. In order to benefit from these concepts, an efficient energy management system is needed to prevent energy wasting and increase profits. In this paper, an optimization based aggregation model is presented for distributed energy resources and demand response program management. This aggregation model allows different types of customers to participate in electricity market through several tariffs based demand response programs. The optimization algorithm is a mixed-integer linear problem, which focuses on minimizing operational costs of the aggregator. Moreover, the aggregation process has been done via K-Means clustering algorithm, which obtains the aggregated costs and energy of resources for remuneration. By this way, the aggregator is aware of energy available and minimum selling price in order to participate in the market with profit. A realistic low voltage distribution network has been proposed as a case study in order to test and validate the proposed methodology. This distribution network consists of 25 distributed generation units, including photovoltaic, wind and biomass generation, and 20 consumers, including residential, commercial, and industrial buildings.

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Data centers’ mission critical nature, significant power consumption, and increasing reliance on them for digital information, have created an urgent need to monitor and adequately manage these facilities. Metrics are a key part of this effort as their indicators raise flags that lead to optimization of resource utilization. A thorough review of existing data center metrics presented in this paper shows that while existing metrics are valuable, they overlook important aspects. New metrics should enable a holistic understanding of the data center behavior. This paper proposes a novel framework using a multidimensional approach for a new family of data center metrics. Performance is examined across four different sub-dimensions: productivity, efficiency, sustainability, and operations. Risk associated with each of those sub-dimensions is contemplated. External risks are introduced, namely site risk, as another dimension of the metrics, and makes reference to a methodology that explains how it is calculated. Results from metrics across all sub-dimensions can be normalized to the same scale and incorporated in one graph, which simplifies visualization and reporting. The new family of data center metrics can help to standardize a process that evolves into a best practice to help evaluate data centers, to compare them to each other, and to improve the decision-making process.

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This paper surveys on Cloud Based Automated Testing Software that is able to perform Black-box testing, White-box testing, as well as Unit and Integration Testing as a whole. In this paper, we discuss few of the available automated software testing frameworks on the cloud. These frameworks are found to be more efficient and cost effective because they execute test suites over a distributed cloud infrastructure. One of the framework effectiveness was attributed to having a module that accepts manual test cases from users and it prioritize them accordingly. Software testing, in general, accounts for as much as 50% of the total efforts of the software development project. To lessen the efforts, one the frameworks discussed in this paper used swarm intelligence algorithms. It uses the Ant Colony Algorithm for complete path coverage to minimize time and the Bee Colony Optimization (BCO) for regression testing to ensure backward compatibility.

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E-Money mobile payments, also called digital money, are electronic payments, payment transactions using an Internet network integrated with NFC-enabled smartphones and prepaid cards. In Indonesia not only banks that issue e-money products, telecom operators from Telkomsel also issued an e-money product called T-cash. T-cash is a new innovation of electronic money presented by Telkomsel. The purpose of this study was to check the effect of responsiveness, smartness, perceived ease of use, perceived usefulness, social influence, and security against the intention to use T-cash. The data used in this study include primary and secondary data. Respondents in this study are users of T-cash products in Yogyakarta as many as 115 respondents. While the data were collected by using the questionnaire to then be analyzed using the amos analysis technique 22.0. The results of the analysis prove that two characteristics of technology, responsiveness and smartness have a significant effect on perceived usefulness. Ease of use has a significant effect on perceived usefulness. Ease of use, usefulness and security have a significant effect on intention to use. The higher the level of responsiveness, smartness, ease of use, perceived usefulness and security will also increase the use of T-cash social influence factors have no effect on intention to use.

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The aim of this paper is to derive and study a full-bridge three-port converter. Based on the standard design of full-bridge converter, we have modeled and derived a three port converter. The three port converter can be used in renewable energy scenarios, such as solar cells or wind turbines connected to the input port. The input can be taken from two-ports simultaneously or from one port at a time. In order to balance the power mismatch between the input port and load port, the batteries are attached to the third port, to ensure there are no discrepancies in the power generated at the input and power demand at the load. In order to ensure isolation and reduced voltage stress on the switches, a high frequency transformer is also used in the design. The overall design contains four switches, and four diodes. MOSFETs are the strongest candidate for the switches owing to their high switching speed, lower losses and high resistance to higher voltage. Moreover, a buck-boost structure is modeled in order to ensure that it can work for a wide variety of different applications by adjusting the duty cycle of the switches properly. To minimize the switching losses in the converter, Zero-Voltage Switching (ZVS) is also achievable in the modeled system.

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In this paper, we introduce and describe a novel approach to adaptive image steganography which is combined with One-Time Pad encryption and demonstrate the software which implements this methodology. Testing using the state-of-the-art steganalysis software tool StegExpose concludes the image hiding is reliably secure and undetectable using reasonably-sized message payloads (?25% message bits per image pixel; bpp). Payload image file format outputs from the software include PNG, BMP, JP2, JXR, J2K, TIFF, and WEBP. A variety of file output formats is empirically important as most steganalysis programs will only accept PNG, BMP, and possibly JPG, as the file inputs. In this extended reprint, we introduce additional application and discussion regarding cold storage of crypto-currency account and password information, as well as applications for secure transfer in hostile or insecure network circumstances.

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The objective of this paper is to investigate three different approaches of modeling, design and discrete-time implementation of PI closed-loop control strategies in SIMULINK simulation environment, applied to a centrifugal chiller system. Centrifugal chillers are widely used in large building HVAC systems. The system consists of an evaporator, a condenser, a centrifugal compressor and an expansion valve. The overall system is an interconnection of two main control loops, namely the chilled water temperature inside the evaporator, and the refrigerant liquid level control in condenser. The centrifugal chiller dynamics model in a discrete-time state-space representation is of high complexity in terms of dimension and encountered nonlinearities. For simulation purpose the centrifugal chiller model is simplified by using different approaches, especially the development of linear polynomials ARMAX and ARX models. The aim to build linear ARMAX models for centrifugal chiller is to simplify considerable the control design strategies that are investigated in this research paper. The novelty of this research is a new controller design approach, more precisely an improved version of proportional – integral control, the so called Proportional-Integral-Plus control for systems with time delay, based on linear ARMAX models. It is conceived within the context of non-minimum state space control system that “seems to be the natural description of a discrete-time transfer function, since its dimension is dictated by the complete structure of the model”. The effectiveness of this new controller design, its implementation simplicity, convergence speed and robustness are proved in the last section of the paper.

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This study applies systemic functional linguistics (SFL) as complementary framework of analysis of technology acceptance models (TAMs). The purpose is to bridge research methodology language in international business (IB) studies and engineering management science. Currently TAMs and its consolidated version, the Unified Theory of Acceptance and Use of Technology (UTAUT) provides for a typology of one user in one context scenario. The need for the UTAUT model to account for multiple users in multiple work contexts in a single framework of analysis was foregrounded in the study of the workflow processes of a remote services business model of a European founded multinational business enterprise (MBE) with regards to its (i) intra-firm improvements in managing remote services cases, and its (ii) extra-firm selling of life cycle management remote services contracts. The Enterprise has global operations in over 100 countries, of which this study focused on its European operations of improving the quality of remote services for the marine industry. Through an application of SFL unto UTAUT, this study illustrates how multiple users in multiple contexts can be analysed simultaneously, and whose behaviours can be accounted for in a single framework of analysis. The combined SFL UTAUT model addresses the initial statisticity of the UTAUT model, whilst at the same time, expands upon current theoretical perspectives of technology use and acceptance that can be applied in practice.

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Today, computing devices of various types with wireless interconnections are used for diverse tasks and increasingly in ad hoc manners. It is not always obvious which devices are present, reachable, and connected when users and their devices are mobile. In such mobile computing environment, the number of registered lines on the network via network operators cannot qualify a user to carry out any service due to the unpredictable service quality (SQ), dynamic user context and the device in use. To properly manage the SQ, there is need to specify the roles applicable to mobile devices to effectively utilize the constrained and shared resources for the feature-rich applications. The user’s context may change and adaptation to changing behavior, resource usage, and security settings also pose problems. This paper presents the use of semantic web approach in modeling ontology for a richer knowledge representation of users’ activities and social roles on mobile devices. The developed ontology for users’ social roles was implemented in Protégé to determine whether a mobile user with its interaction medium has a functional capability for specific social role or not. The importance on the use of context in interactive applications is shown and a proposed framework for development of context-aware applications is developed. Results revealed that the approach can effectively enhance partnership between mobile operators and content providers of next generation wireless networks for the provision of value-added mobile web services.

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We consider the problem of aligning two very long biological sequences. The score for the best alignment may be found using the Smith-Waterman scoring algorithm while the best alignment itself may be determined using Myers and Miller’s alignment algorithm. Neither of these algorithms takes advantage of computer caches to obtain high efficiency. We propose cache-efficient algorithms to determine the score of the best alignment as well as the best alignment itself. All algorithms were implemented using C and OpenMP, and benchmarked using real data sets from the National Center for Biotechnology Information (NCBI) database. The test computational platforms were Xeon E5 2603, I7-x980 and Xeon E5 2695. Our best single-core cacheefficient scoring algorithm reduces the running time by as much as 19.7% relative to the Smith-Waterman scoring algorithm and our best cache-efficient alignment algorithm reduces the running time by as much as 17.1% relative to the Myers and Miller alignment algorithm. Multicore versions of our cache-efficient algorithms scale quite well up to the 24 cores we tested; achieving a speedup of 22 with 24 cores. Our multi-core scoring and alignment algorithms reduce the running time by as much as 61.4% and 47.3% relative to multi-core versions of the Smith-Waterman scoring algorithm and Myers and Miller’s alignment algorithm, respectively.

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This paper presents a novel direct torque control (DTC) strategy aimed to four-switch three-phase (FSTP) inverter-fed an interior permanent magnet synchronous machine (IPMSM), using a fuzzy logic toolbox in speed control loop. In fact, the introduced DTC approach is based on the emulation of the operation of the standard six-switch three-phase (SSTP) inverter. This fact has been produced thanks to suitable combinations of four unbalanced voltage vectors intrinsically generated by the FSTPI, leading to the synthesis of six balanced voltage vectors yielded by the SSTPI. It has been found from the simulation results that the adaptive fuzzy speed controller implemented for basic and proposed DTC strategies dedicated to FSTPI-fed an IPMSM drives, exhibits interesting performances over different operating conditions, more robustness and less steady-state error especially when there exist motor parameter uncertainties and unexpected load changes occur, compared to the ones yielded by the conventional proportional-integral controller.

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