Framework for cloud-based software test data generation service

This paper presents the framework of cloud-based software test data generation service (CSTS) that caters to cost-effective test data generation service in a cloud environment. In contrast to existing conventional or cloud-based testing frameworks, CSTS has a number of unique benefits. First, CSTS is designed to facilitate test data generation in minimum time and cost. Second, unlike existing frameworks which mandates clients to opt for resources to test their jobs, CSTS guides customer for selecting best cluster configuration in order to minimize the cost. While the existing models do not provide any solution for trust establishment in cloud computing services, CSTS delivers it by implementing security mechanism with the provision of role based access control. The security mechanism proposed in this paper ensures the protection of data and code of different users. Third, CSTS provides a mathematical pricing model to fulfill the expectations of customers and also to maximize the net profit of service providers. Cloud service request model has also been designed that postulates service level agreements between customers and service providers. We have evaluated, compared, and analyzed our framework and have found that it outperforms other existing cloud-based frameworks.     

Data deduplication techniques for efficient cloud storage management: a systematic review

The exponential growth of digital data in cloud storage systems is a critical issue presently as a large amount of duplicate data in the storage systems exerts an extra load on it. Deduplication is an efficient technique that has gained attention in large-scale storage systems. Deduplication eliminates redundant data, improves storage utilization and reduces storage cost. This paper presents a broad methodical literature review of existing data deduplication techniques along with various existing taxonomies of deduplication techniques that have been based on cloud data storage. Furthermore, the paper investigates deduplication techniques based on text and multimedia data along with their corresponding taxonomies as these techniques have different challenges for duplicate data detection. This research work is useful to identify deduplication techniques based on text, image and video data. It also discusses existing challenges and significant research directions in deduplication for future researchers, and article concludes with a summary of valuable suggestions for future enhancements in deduplication.     

Stimulus control of copulatory behavior in sexually naive male Japanese quail (Coturnix japonica): Effects of test context and stimulus movement.

Sexually experienced male quail (Coturnix japonica) are more likely to engage in copulatory behavior than sexually naive ones. These experiments suggest that sexual experience in a particular place may facilitate later copulatory responding because of increased familiarity with the contextual cues of the environment. Male quail in Exp 1 did not copulate reliably with taxidermic models of females in a novel context, even though some of the subjects were allowed to copulate with female quail in their home cages. In contrast, sexually naive males in Exps 2 and 3 copulated vigorously with taxidermic models of females in a familiar context. In Exp 4, sexually naive males tested in an unfamiliar context were more likely to copulate with a moving than with a static model. The stimulus control of copulatory behavior in sexually naive male quail was similar to that in sexually experienced ones but only in familiar contexts. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Prediction-based proactive load balancing approach through VM migration

The ever-growing intricacy and dynamicity of Cloud Computing Systems has created a need for Proactive Load Balancing which is an effective approach to improve the scalability of today’s Cloud services. In order to manage the load proactively on the Cloud system during application execution, load should be predicted through machine learning approaches and handled through VM migration approaches. Thus, this paper formulates an effort to focus on the research problem of designing a prediction-based approach for facilitating proactive load balancing through the prediction of multiple resource utilization parameters in Cloud. The involvement of this paper is twofold. Firstly, various machine learning approaches have been tested and compared for predicting host overutilization as well as underutilization. Secondly, the load prediction model having maximum accuracy from the tested models has been utilized for implementing the proactive VM migration using multiple resource utilization parameters. Further, the proposed technique has been validated through performance evaluation parameters using CloudSim and Weka toolkits. The simulation results clearly demonstrate that the proposed approach is effective for handling VM migration, reducing SLA Violations, VM migrations, execution mean and standard deviation time.     

A novel strategy for automatic test data generation using soft computing technique

Software testing is one of the most crucial and analytical aspect to assure that developed software meets prescribed quality standards. Software development process invests at least 50% of the total cost in software testing process. Optimum and efficacious test data design of software is an important and challenging activity due to the nonlinear structure of software. Moreover, test case type and scope determines the quality of test data. To address this issue, software testing tools should employ intelligence based soft computing techniques like particle swarm optimization (PSO) and genetic algorithm (GA) to generate smart and efficient test data automatically. This paper presents a hybrid PSO and GA based heuristic for automatic generation of test suites. In this paper, we described the design and implementation of the proposed strategy and evaluated our model by performing experiments with ten container classes from the Java standard library. We analyzed our algorithm statistically with test adequacy criterion as branch coverage. The performance adequacy criterion is taken as percentage coverage per unit time and percentage of faults detected by the generated test data. We have compared our work with the heuristic based upon GA, PSO, existing hybrid strategies based on GA and PSO and memetic algorithm. The results showed that the test case generation is efficient in our work.     

Interface behavior of grouted connection on monopile wind turbine offshore structure

The interface behavior of a concrete grouted connection is studied in a monopile wind turbine offshore structure. The grouted connection between transition piece and monopile is fixed by filling it with grout materials made of high-strength concrete. We observe a nonlinear equilibrium path from the grouted connection in experiments. For comparison with the experimental results, nonlinear finite element analysis of the grouted connection is performed, taking the effects of both material and geometrical nonlinearities into consideration. A finite element model of the grouted connection between the steel and concrete materials is realistically simulated using an interface element. The finite element results compares well with the experimental results. Thus, the finite element analysis showed that the numerical model based on the interface element is a good method for investigating the behavior of grouted connections of monopile offshore structures.     

Geometric and force errors compensation in a 3-axis CNC milling machine

This paper proposes a new off line error compensation model by taking into accounting of geometric and cutting force induced errors in a 3-axis CNC milling machine. Geometric error of a 3-axis milling machine composes of 21 components, which can be measured by laser interferometer within the working volume. Geometric error estimation determined by back-propagation neural network is proposed and used separately in the geometric error compensation model. Likewise, cutting force induced error estimation by back-propagation neural network determined based on a flat end mill behavior observation is proposed and used separately in the cutting force induced error compensation model. Various experiments over a wide range of cutting conditions are carried out to investigate cutting force and machine error relation. Finally, the combination of geometric and cutting force induced errors is modeled by the combined back-propagation neural network. This unique model is used to compensate both geometric and cutting force induced errors simultaneously by a single model. Experimental tests have been carried out in order to validate the performance of geometric and cutting force induced errors compensation model.     

Load Balancing and Job Migration Techniques in Grid: A Survey of Recent Trends

Grid computing has recently become one of the most important research topics in the field of computing. The Grid computing paradigm has gained popularity due to its capability to offer easier access to geographically distributed resources operating across multiple administrative domains. The grid environment is considered as a combination of dynamic, heterogeneous and shared resources in order to provide faster and reliable access to the Grid resources, the resource overloading must be prevented which can be obtained by proper load balancing and job migration mechanisms. This paper presents an extensive survey of the existing load balancing and job migration techniques proposed so far. A detailed classification has also been included based on different parameters which are depending on the analysis of the existing techniques, a new Load balancing technique, along with Job Migration approach has been proposed and discussed to fulfill the existing research gaps.     

A merged magnetotransistor for 3-axis magnetic field measurement based on carrier recombination–deflection effect

This article presents a novel magnetotransistor based on carrier recombination–deflection effect for detecting magnetic field in three dimensions (B_X, B_Y, and B_Z) by relying on the difference between base and collector currents (?I_CB). This device used low biasing current. It was designed and fabricated using CMOS fabrication technology. The device structure consisted of one emitter, 4 collectors and 4 bases. All four collector terminals were connected with each other. The same was true for the four base terminals. LOCOS oxide was grown to surround the emitter area to limit lateral carrier loss, and therefore reducing the overall biasing current. The experiment showed that, at 0.2 mA of biasing current, the B_X, B_Y and B_−Z direction sensitivity to magnetic field within the range of 0-400 mT are 2, 5 and 14.5%/T, respectively. This research on the merged magnetotransistor produced magnetic sensors with small size, high performance with wide range of applications.     

Enhance Egocentric Grasp Recognition Based Flex Sensor Under Low Illumination

Egocentric recognition is exciting computer vision research by acquiring images and video from the first-person overview. However, an image becomes noisy and dark under low illumination conditions, making subsequent hand detection tasks difficult. Thus, image enhancement is necessary to make buried detail more visible. This article addresses the challenge of egocentric hand grasp recognition in low light conditions by utilizing the flex sensor and image enhancement algorithm based on adaptive gamma correction with weighting distribution. Initially, a flex sensor is installed to the thumb for object manipulation. The thumb placement that holds in a different position on the object of each grasp affects the voltage changing of the flex sensor circuit. The average voltages are used to configure the weighting parameter to improve images in the image enhancement stage. Moreover, the contrast and gamma function are used to adjust varies the low light condition. These grasp images are then separated to be training and testing with pre-trained deep neural networks as the feature extractor in YOLOv2 detection network for the grasp recognition system. The proposed of using a flex sensor significantly improves the grasp recognition rate in low light conditions.