THE CSI JOURNAL ON COMPUTER SCIENCE AND ENGINEERING
Year:2018 | Volume:16 | Issue:1|Papers Count:6

With the advent of Cloud Computing and its ever growing role in today's IT marketplace, Cloud Federation has recently emerged as a new frontier to be tackled by researchers so as to enable opaque resource sharing and cooperation between separate (public or private) clouds. Current federation models, which are based on either centralized or P2P methods, can be violated by the natural selfish behavior of cloud service providers, and no clear consensus exists as to which models perform more efficiently under different situations. In this paper, alongside introducing a new weighted P2P method, we use mathematical models to capture the behavior of different federation mechanisms based on each method, taking into consideration how the selfish behavior of cloud service providers might violate the federation mechanism. Our goal is to show how each model behaves under these violations and how they compare based on obtained individual profits and overall federation efficiency. Our results show that these three cloud federation methods, whilst providing the same amount of social welfare, counter the aforementioned violations differently and that individual cloud profits, whilst comparable, are in correlation with different federation scenarios.

Trust-based recommender systems use trust relationships between users to improve the quality of recommendations. One of the most important features of trust is context-dependency. Despite the importance of context-dependency, this feature has been largely neglected in the current literature. In this paper, we propose a new approach that considers the semantic context of items to infer trust relationships between users. In this approach, the level of trust between two users varies depending on different contexts. Therefore, the trustworthy neighbors of an active user will be different for different target items, and these neighbors are determined according to the target context. The focus on context-specific ratings instead of all ratings results in fewer online computations, thus increasing the efficiency of the system as well as the accuracy of recommendations. Experimental results on a real-world data set show the higher accuracy and efficiency of the proposed approach compared to its counterparts.

This paper presents a stochastic model for a multi-operational-mode TCP variant optimised to effectively utilise the bandwidth in high-speedlong-delay networks. Particularly, a model for a TCP variant that increases its rate as power function of the current congestion window, and uses multiplicative and subtractive decrease to reduce the congestion window upon packet loss. The power function is adapted dynamically using powers of {0. 5, 1, 2} according to the level of congestion in the network. The proposed model can be generalised to other powers; it captures the dynamics of the congestion window size evolution and sending rate under the influence of random packet loss by providing a closed-form expression for both the congestion window size and the normalised sending rate. We show that depending on the increase/decrease rules adopted for the congestion window size evolution, the congestion window size can be modelled either as function of exponential random variable or a Markov chain. Simulation results validate the analytical model and show that the multiplicative decrease factor has more impact on the rate especially when a quadratic – instead of linear – increase rule is adopted under heavy packet loss rate. In addition to that, results show that the initial value of additive increase when a square-root rule is adopted has minimal effect on the average congestion window size.

This paper proposes a new automatic image enhancement method via improving the image dynamic range. The improvement is performed via modifying the Gamma value of pixels in the image. Gamma distortion in an image is due to the technical limitations in the imaging device, and imposes a nonlinear effect. The severity of distortion in an image varies depending on the texture and depth of the objects. The proposed method locally estimates the Gamma values in an image. In this method, the image is initially segmented using a pixon-based approach. All of the pixels in each segment have similar characteristics in terms of the need for Gamma correction. Then, the Gamma value for each segment is estimated by minimizing the homogeneity of co-occurrence matrix. This feature can represent image details. The minimum value of this feature in a segment shows maximum details of the segment. The quality of an image is improved once more details are presented in the image via Gamma correction. In this study, it is shown that the proposed method performs well in improving the quality of images. Subjective and objective image quality assessments performed in this study attest the superiority of the proposed method compared to the existing methods in image quality enhancement.

The lack of frequency, low utilization and static allocation of spectrum have been important problems in wireless network in prior methods. To solve this problem, a concept called Cognitive Radio Network was introduced to allow the use of empty spaces of licensed spectrum. The purpose of this paper was to provide an intelligent method for detecting and allocating spectrum in cognitive radio network. In this method, Hidden Marcov model is used to predict the status of free or occupied channels, then some types of learning automata are used to allocate channel to secondary users. Also, it is a way to reduce the waiting time of users who were simultaneously requesting a channel to use a mechanism for fairness in this algorithm. The simulation results indicated that the proposed method is more effective in channel allocation to secondary users thanks to using the proposed mechanisms whose results have a greater convergence speed.

Given the growth of textual documents, the classification of documents is crucial for reducing the complexity of information and easy and quick access to them. Classification is usually carried out through extraction of keywords, sentences, and matching the paragraphs. The major method for finding similarities in the texts is using keywords based on word frequency. The word count is done through various methods such as TF, and then a specific weight is attributed to each word. The main challenge in Text Document Classification (TDC) is to choose the feature. That is the case because Feature Selection (FS) is an effective factor in enhancing the classification accuracy and reduction of calculation time. Hence, in this paper, Shuffled Frog-Leaping Algorithm (SFLA) for FS and ID3 tree for document classification has been used. A problem with SFLA is that it sticks in local optimums; and in the proposed model, a hybrid of the best and the worst situations of the frog is used for enhancement in order to avoid local optimums. The general method in this paper is to enhance SFLA by means of ID3 tree for classification accuracy. The obtained results on Reuters-21578, WebKb, Cade 12, and 20 Newsgroup datasets indicate that the improved proposed model with ID3 tree has a higher accuracy. The results confirm the efficiency of the proposed FS method in improving TDC accuracy.