Traditional heavyweight software development methodologies are rigid, heavily documentation oriented and process oriented. In the present E-Business dominated environment, the above methodologies are hard to follow. In response to this, a new generation of lightweight methodologies such as Extreme Programming (XP) has evolved which has only a few simple rules to adopt, and insist on less documentation. XP proposes four values, a development process and twelve practices. One of the significant benefits among those claimed by the inventors of XP is the reduction of effort in the software development. However, the extent of fulfillment of this claim remains unanswered by empirical and quantitative evidences. Hence, the effects of XP on software development effort are to be investigated. In this study, we developed a process simulation model to analyze the effects of individual XP practices on development effort. System dynamics based simulation, an effective modeling technique for software development process was chosen. This model has accounted for all the twelve practices and processes of XP. We have also introduced a measurement scale for measuring the level of usage of individual XP practices. The factors that affect the cost are collected from literature and a few XP project managers. The process model was simulated for a case study of a typical XP project to investigate the effects of individual XP practices on development effort by varying their usage levels. The decrease in percentage of the development effort for each XP practice when its usage level is varied from minimum to maximum during which all the other practices were maintained at a constant usage level was found. The decrease in percentage of the development effort for each XP practice when its usage level is minimum and maximum was computed and is given below. (i) Planning game -2.67% (ii) Small Release -2.67% (iii) Metaphor -2.01% (iv) Simple design -2.5% (v) Continuous Testing -2.88% (vi) Refactoring -0.677% (vii) On-site Customer -5.48% (viii) Pair programming -4.4% (ix) Collective Code Ownership -4.82% (x) Forty Hours Per Week -2% (xi) Coding Standard -4.82% (xii) Continuous Integration -1.13%. The finding of the present study on the effects of individual XP practices depicts a reduction in software development effort by enhancing their usage levels.

Nowadays, a lot of research involves in the area of e-learning with service-oriented architecture. In e-learning systems, the challenges are increase of the complexity and more interoperability between systems in distributed environment. The lacking is reference architecture in which by reusing web services, reusing learning objects and semantics, ontology, etc. A service-oriented reference architecture describes the essence of a software architecture and the most significant and relevant aspects. Hence, it is proposed to design reference architecture to personalized e-learning systems using Web services and serviceoriented architecture. The objective of this paper to design service-oriented reference architecture for personalized elearning systems (SORAPES) and validate the architecture. Some of the existing e-learning architectures that serve as the domain model for personalized e-learning system were considered, discussed in details and finally proposed a new architecture which is called SORAPES. The SORAPES was designed by re-using web services and learning objects. It is a layered architecture and highly-scalable for personalized elearning system. This architecture was evaluated with a list of quality attributes.

Information and Communication Technology (ICT) is one of the fast growing industries that facilitate many latest services to the users and therefore, the number of users is increasing rapidly. The usage of ICT and its life cycle produce hazardous substances that need to be addressed in efficient and green ways. The adoption of green computing involves many improvements and provide energy-efficiency services for data centers, power management and cloud computing. Cloud computing is a highly scalable and cost-effective infrastructure for running Web applications. However, the growing demand of Cloud infrastructure has drastically increased the energy consumption of data centers, which has become a critical issue. Hence, energy-efficient solutions are required to minimize the impact of Cloud environment. E-learning methodology is an example of Green computing. Thus, it is proposed a Green Cloud Computing Architecture for e-Learning Applications that can lower expenses and reduce energy consumption.

In this Letter, a combined algorithm known as receiver-assisted slow start (RASS) and feedback-assisted recovery (FAR) proposed to address the joint optimisation problems of transmission control protocol (TCP) such as non-optimal initial window (IW) size, slower window growth and spurious window deflation under multi-hop wireless (MHW) networks. The RASS fix the sender's optimum IW size from the receiver's advertised value and replaces the conventional exponential window growth with enhanced window increment mechanism that significantly accelerates the transmission rate. The FAR uses relay router's feedback information to regulate false transmission rate reduction during packet drops. The simulation result reveals that the proposed algorithms had a substantial improvement in throughput and reduction in packet latency over the existing standard New Jersey and non-congestion events algorithms under MHW environment.

Artificial neural network has been extensively consumed training model for solving pattern recognition tasks. However, training a very huge training data set using complex neural network necessitates excessively high training time. In this correspondence, a new fast Linear Adaptive Skipping Training (LAST) algorithm for training artificial neural network (ANN) is instituted. The core essence of this paper is to ameliorate the training speed of ANN by exhibiting only the input samples that do not categorize perfectly in the previous epoch which dynamically reducing the number of input samples exhibited to the network at every single epoch without affecting the network’s accuracy. Thus decreasing the size of the training set can reduce the training time, thereby ameliorating the training speed. This LAST algorithm also determines how many epochs the particular input sample has to skip depending upon the successful classification of that input sample. This LAST algorithm can be incorporated into any supervised training algorithms. Experimental result shows that the training speed attained by LAST algorithm is preferably higher than that of other conventional training algorithms.