Quality-in-Use (QinU) is one of the most important quality aspects of a web application, which represent users' viewpoint. Measuring QinU gives a strong indicator on the success of web applications. In addition, it has been used frequently to evaluate the overall quality of web applications. There are many studies in QinU that enriched the science of web engineering. However, contributions of these studies were dispersed and usually address a certain aspect of QinU. This study attempts to gather and improve the best contributions of the previous studies in a conceptual framework to evaluate QinU based on ISO/IEC 25010 and 25012 standards. The outcome is a proposed framework, which will demonstrate the procedural flow between different stakeholders (Decision-maker, Evaluator, Developer and End-user). This procedural flow affects the evaluation process of web application quality. Furthermore, the framework demonstrates the process of measuring QinU attributes by implementing the proposed Quality-in-Use Evaluation Model (QinUEM). The future works are to evaluate the Quality-in-Use of several web applications using the proposed conceptual framework and test the results using quantitative and qualitative methods. � 2015 IEEE.

Cloud computing technology is a concept of providing dramatically scalable and virtualized resources, bandwidth, software and hardware on demand to users. Users can request cloud services via a web browser or web service. Cloud computing consists of valuable resources, such as, networks, servers, applications, storage and services with a shared network. By using cloud computing, users can save cost of hardware deployment, software licenses and system maintenance. Many security risks such as worm can interrupt cloud computing services; damage the spiteful service, application or virtual in the cloud structure. Nowadays the worm attacks are becoming more sophisticated and intelligent, makes it is harder to be detected than before. Based on the implications posed by this worm, this is the urge where this research comes in. This paper aims to build a new model to detect worm attacks in cloud computing environment based on worm signature extraction and features behavioral using dynamic analysis. Furthermore this paper presents a proof of concept on how the worm works and discusses the future challenges and the ongoing research to detect worm attacks in cloud computing efficiently. � 2014 IEEE.

In this study, the discrete-time performance of BLUE algorithms under bursty and correlated traffics is analyzed using two-state Markov-modulated Bernoulli arrival process (BLUE-MMBP-2). A two-dimensional discrete-time Markov chain is used to model the BLUE algorithm for two traffic classes, in which each dimension corresponds to a traffic class and the parameters of that traffic class. The MMBP is used to replace the conventional and widely-used Bernoulli process (BP) in evaluating and proposing analytical models based on the BLUE algorithm. The BP captures neither the traffic correlation nor the burstiness. The proposed approach is simulated, and the obtained results are compared with that of the BLUE-BP, which can modulate a single traffic class only. The comparison is performed in terms of mean queue length (mql), average queuing delay (D), throughput, packet loss, and dropping probability (DP). The results show that during congestion, particularly heavy congestion under bursty and correlated traffics, the BLUE-MMBP-2 algorithm provides better mql, D, and DP than the BLUE-BP. � 2014 IEEE.

Quality-in-Use (QinU) is considered to be one of the most important quality aspects of a website, which represent users� standpoint of quality. Measuring QinU provides a solid sign of the success of the websites. In addition, QinU has been used frequently to evaluate the overall quality of websites. Also, QinU has been used to evaluate the satisfaction of websites. In a matter of fact, the awareness of the importance of web quality stimulated developers, analysts, researchers, organizations to define and develop many methods and models to ensure the quality of the websites. This study endeavors to improve a new framework to evaluate QinU based on the ISO/IEC 25010 and 25012 standards. The outcome is a new method that will be used to measure QinU using the satisfaction factor and quality aspects, taking into account the roles of different website stakeholders, i.e., Decision-maker, Evaluator, Developer, and End-user. Furthermore, the framework contains two models; the first is the Quality-in-Use Evaluation Model (QinUEM) which identifies the quality aspects and satisfaction attributes, while the second is the Quality-in-Use Evaluation Process (QinUEP) which controls the flow of the evaluation process. Evaluating the Quality-in-Use of several websites using the proposed models will be the future work. � 2017 American Scientific Publishers All rights reserved.

The haze phenomenon exerts a degrading effect that decreases contrast and causes colour shifts in outdoor images. The presence of haze in digital images is bothersome, unpleasant, and, occasionally, even dangerous. The atmospheric light scattering (ALS) model is widely used to restore hazy images. In this model, two unknown parameters should be estimated: airlight and scene transmission. The quality of dehazed images depends considerably on the accuracy of both estimates. Classic methods typically determine airlight based on the brightest pixels in an image. However, in the traffic scene context, this estimate is compromised when other light sources, such as vehicle headlights from the opposite direction, are present. Transmission estimation is usually more complicated. Hence, the complexity of the overall dehazing process is dependent on this estimate. To address this issue, this study proposes a framework for constant-time airlight and linear transmission estimation. This framework consists of two methods: airlight by image integrals (ALII), which is utilized to estimate the airlight value in real time with high accuracy, and bounded transmission (BT), which is proposed for the linear and simplified estimation of transmission maps. To evaluate the proposed framework, three image datasets are used: (1) seven images that are gathered from the works of existing methods (called the global dataset); (2) the synthetic foggy road image database (FRIDA), which is a synthetically generated dataset for simulating different bad weather conditions; and (3) a dataset of images that were extracted from videos in Malaysia (IV-M), which consists of images that were extracted from traffic video sequences, which were captured in various weather conditions from 2014 to 2016 in Malaysia. Experimental results show that the proposed framework is at least seven times faster than existing methods. In addition, the ANOVA test proves that the quality of the dehazed images is statistically similar to or better than the image quality that was achieved using existing methods. � 2018 Elsevier Inc.

Rain, fog and haze are natural phenomena that fade scenes, limit the visibility range, and cause shifts in colors. These phenomena also play a decisive role in determining the degree of reliability of many kinds of outdoor applications, such as aerial and satellite imaging, surveillance, and driver assistance systems. Thus, removing their effects from images/videos is very crucial. Due to its mathematically ill posed nature, enhancement process of rain, fog, and haze plagued images/videos is highly challenging. In this paper, we propose a fast yet robust technique to enhance the visibility of video frames using the dark channel prior combined with fuzzy logic-based technique. The dark channel prior is a statistical regularity of outdoor haze-free images based on the observation that most local patches in the haze-free images contain pixels which are dark in at least one color channel, where the fuzzy logic-based technique is used to map an input space to an output space using a collection of fuzzy membership functions and rules to decide softly in case of uncertainties. The combination of the dark channel and the fuzzy logic-based technique will produce high quality haze-free images in real-time. Furthermore, it will be combined with rules derived from the stable atmospheric scattering model and will yield a fast yet high quality enhancement results. � 2014 IEEE.

Enhancing images that are plagued with weather related conditions; such as haze, fog and rain poses a challenging problem due to its ill-posed nature, which means the unknowns that need to be found are more than the equations that we have. To address such challenges, a fast yet robust method is proposed in this paper where unknowns in the light scattering model are estimated based on physically sound assumptions. Light scattering model describes the formation of those phenomena in an image as a combination of airlight and the original scene, where this combination is controlled by how much transmission value present at the scene's point. The transmission value determines how much of the original scene's intensity were attenuated and how much airlight was added. The attenuation term of the light scattering model causes the reduction in contrast and the airlight term causes the effect of color shift. In this paper, Intensity Deterioration Ratio (IDR) and Saturation Deterioration Ratio (SDR) are proposed, where the former can be used to estimate the reduction of contrast and so gives a clue about the attenuation term, and the latter to estimate the reduction of chromaticity in a scene which gives a clue about the airlight term. IDR and SDR are therefore used to give us a new insight in using the light scattering model when enhancing images. � 2015 IEEE.