Collaborative learning in the clouds

Cloud computing has emerged as one of the most highly discussed topics both in the academic community and in the computing industry. While most of the work that has been conducted to explore this field focuses either on establishing the basis for cloud computing or almost exclusively on the issues surrounding security and data privacy, this paper takes the first exploratory step into exploring the actual internal working of cloud computing and demonstrates its viability for organizations, more specifically educational establishments . The paper starts by introducing the most important key clouds computing concepts, including virtualization technologies, Web services and Service Oriented Architectures (SOA), and distributed computing. Light will be then shed on the impact and potential benefits of cloud computing on teaching and learning in educational institutions. The paper closes by describing building a private cloud inside educational institution and highlights its offerings for students, staff and lecturers.     

Toward an efficient and scalable feature selection approach for internet traffic classification

There is significant interest in the network management and industrial security community about the need to identify the “best” and most relevant features for network traffic in order to properly characterize user behaviour and predict future traffic. The ability to eliminate redundant features is an important Machine Learning (ML) task because it helps to identify the best features in order to improve the classification accuracy as well as to reduce the computational complexity related to the construction of the classifier. In practice, feature selection (FS) techniques can be used as a preprocessing step to eliminate irrelevant features and as a knowledge discovery tool to reveal the “best” features in many soft computing applications. In this paper, we investigate the advantages and disadvantages of such FS techniques with new proposed metrics (namely goodness, stability and similarity). We continue our efforts toward developing an integrated FS technique that is built on the key strengths of existing FS techniques. A novel way is proposed to identify efficiently and accurately the “best” features by first combining the results of some well-known FS techniques to find consistent features, and then use the proposed concept of support to select a smallest set of features and cover data optimality. The empirical study over ten high-dimensional network traffic data sets demonstrates significant gain in accuracy and improved run-time performance of a classifier compared to individual results produced by some well-known FS techniques.     

Supporting multiple perspectives in feature-based configuration

Feature diagrams have become commonplace in software product line engineering as a means to document variability early in the life cycle. Over the years, their application has also been extended to assist stakeholders in the configuration of software products. However, existing feature-based configuration techniques offer little support for tailoring configuration views to the profiles of the various stakeholders. In this paper, we propose a lightweight, yet formal and flexible, mechanism to leverage multidimensional separation of concerns in feature-based configuration. We propose a technique to specify concerns in feature diagrams and to generate automatically concern-specific configuration views. Three alternative visualisations are proposed. Our contributions are motivated and illustrated through excerpts from a real web-based meeting management application which was also used for a preliminary evaluation. We also report on the progress made in the development of a tool supporting multi-view feature-based configuration.     

Is Deep-Learning and Natural Language Processing Transcending the Financial Forecasting? Investigation Through Lens of News Analytic Process

Computational Economics - This study tries to unravel the stock market prediction puzzle using the textual analytic with the help of natural language processing (NLP) techniques and Deep-learning...     

A Service Level Agreement Aware Online Algorithm for Virtual Machine Migration

The demand for cloud computing has increased manifold in the recent past. More specifically, on-demand computing has seen a rapid rise as organizations rely mostly on cloud service providers for their day-to-day computing needs. The cloud service provider fulfills different user requirements using virtualization - where a single physical machine can host multiple Virtual Machines. Each virtual machine potentially represents a different user environment such as operating system, programming environment, and applications. However, these cloud services use a large amount of electrical energy and produce greenhouse gases. To reduce the electricity cost and greenhouse gases, energy efficient algorithms must be designed. One specific area where energy efficient algorithms are required is virtual machine consolidation. With virtual machine consolidation, the objective is to utilize the minimum possible number of hosts to accommodate the required virtual machines, keeping in mind the service level agreement requirements. This research work formulates the virtual machine migration as an online problem and develops optimal offline and online algorithms for the single host virtual machine migration problem under a service level agreement constraint for an over-utilized host. The online algorithm is analyzed using a competitive analysis approach. In addition, an experimental analysis of the proposed algorithm on real-world data is conducted to showcase the improved performance of the proposed algorithm against the benchmark algorithms. Our proposed online algorithm consumed 25% less energy and performed 43% fewer migrations than the benchmark algorithms.     

A novel composite model approach to improve software quality prediction

Context:How can quality of software systems be predicted before deployment? In attempting to answer this question, prediction models are advocated in several studies. The performance of such models drops dramatically, with very low accuracy, when they are used in new software development environments or in new circumstances.ObjectiveThe main objective of this work is to circumvent the model generalizability problem. We propose a new approach that substitutes traditional ways of building prediction models which use historical data and machine learning techniques.MethodIn this paper, existing models are decision trees built to predict module fault-proneness within the NASA Critical Mission Software. A genetic algorithm is developed to combine and adapt expertise extracted from existing models in order to derive a “composite” model that performs accurately in a given context of software development. Experimental evaluation of the approach is carried out in three different software development circumstances.ResultsThe results show that derived prediction models work more accurately not only for a particular state of a software organization but also for evolving and modified ones.ConclusionOur approach is considered suitable for software data nature and at the same time superior to model selection and data combination approaches. It is then concluded that learning from existing software models (i.e., software expertise) has two immediate advantages; circumventing model generalizability and alleviating the lack of data in software-engineering.     

Design and optimization of LNA topologies with image rejection filters

An important problem in designing RFIC in CMOS technology is the parasitic elements of passive and active devices that complicate design calculations. This article presents three LNA topologies including cascode, folded cascade, and differential cascode and then introduces image rejection filters for low‐side and high‐side injection. Then, a new method for design and optimization of the circuits based on a Pareto‐based multiobjective genetic algorithm is proposed. A set of optimum device values and dimensions that best match design specifications are obtained. The optimization method is layout aware, parasitic aware, and simulation based. Circuit simulations are carried out based on TSMC 0.18 μm CMOS technology by using Hspice. © 2010 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2010.     

Shape optimized design of microwave dielectric resonators by level‐set and topology gradient methods

The finite element method is coupled with the topology gradient (TG) and level‐set (LS) methods for optimizing the shape of microwave components using a computer‐aided design model. On the one hand, the LS approach is based on the classical shape derivative; while on the other hand, the TG method is precisely designed for introducing new perturbations in the optimization domain. These two approaches, which consist in minimizing a cost function related to the component behavior, are first described. Regarding given electrical specifications, these techniques are applied to optimize the distribution of ceramic parts of a dual‐mode resonator in order to improve its behavior. The optimized dielectric resonators result in a wide spurious‐free stop band. A comparison between classical and optimized dual mode resonator is presented. Theoretical results are then validated by careful measurements. © 2009 Wiley Periodicals, Inc. Int J RF and Microwave CAE 2010.     

Implementation of speed controller for rotary hydraulic motor based on LS-SVM

The performance of a model-based control system depends strongly on the accuracy of the process model used. LS-SVM is a powerful method for modeling nonlinear systems. The main objective of this paper is to implement a conventional controller based on LS-SVM model for hydraulic motor. An off-line model is first identified based on LS-SVM, then via simulation tests the parameters of the discrete PI-Controller and its velocity-form are obtained then the controller parameters are applied experimentally for the hydraulic motor as a speed controller. The system performance has been evaluated; results show good performance over a wide range of operating conditions and load disturbances.