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.

Quality-in-Use (QinU) is one of the critical quality factors in measuring website quality. Most existing studies on measuring website quality only focuses on evaluating quality from the user point of view but not on the similarities and the differences between the users and decision-makers perspective. Different stakeholders have a different preference in term of quality aspects that are important. The objective of this study is to analyze the quality aspects of the websites from different stakeholders' perspectives and rank the websites based on the results. In this study, we develop a Quality-in-Use Evaluation Model (QinUEM) to identify the quality aspects' priorities. Two quantitative approaches were used for this purpose. The first was a Multi-Criteria Decision Making (MCDM) approach using the Fuzzy Analytic Hierarchy Process (FAHP) method to determine the priority and the weight of each quality aspect from the users' viewpoint. Then the statistical analysis was used to determine the priority of the same quality aspect from the developers' perspective. To evaluate the model, we conducted a survey. The respondents of the survey were the students (users) and developers (decision-makers) from six Malaysian universities with 486 numbers of questionnaires been distributed. Based on the results, it shows users (students) prefer Functional Quality rather than Content and Appearance Qualities while the decision makers (developers) favor on Content rather than Appearance and Functional Qualities. These results show different viewpoint and priority in quality aspects needed for users and decision-makers. Based on the results we then used the QinUEM to rank the universities websites according to the defined QinU. � 2018, Insight Society.

In a learning environment, a low student-lecturer ratio is considered a practical solution by many educational institutions. However, the number of students in information technology is increasing every year. This could lead to a significant increase in the workload of lecturers, who need to evaluate assignments, quizzes and projects. Hence, it is desirable that an automated assessment tool is used to lessen their workload. In the era where mobile devices are getting popularity, the high demand to execute suitable quality code is there and the cost is on the processing power of the CPU which has a direct implication on the power source or the battery used. With various implementations of cryptography algorithms available, many of them could satisfy different level of needs. In this research, we introduce the architecture for a multi-layered security of automated assessment of programming code. First, we review the existing research studies in the area. We describe the features of the tool, as part of a complex elearning environment. We also discuss the implementation of security, to protect data transmission and storage used by the tool. Challenges the system might face and the potential solutions, are also described. 2015 The Mohd Fadzli Marhusin, Muhammad Firdaus Zul Kafli, Rossilawati Sulaiman, Shaharudin Ismail and Zul Hilmi Abdullah.

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.

The focus of our study is on one set of malware family known as Brontok worms. These worms have long been a huge burden to most Windows-based user platforms. A prototype of the antivirus was able to scan files and accurately detect any traces of the Brontok malware signatures in the scanned files. In this study, we developed a detection model by extracting the signatures of the Brontok worms and used an n-gram technique to break down the signatures. This process makes the task to remove redundancies between the signatures of the different types of Brontok malware easier. Hence, it was used in this study to accurately differentiate between the signatures of both malicious and normal files. During the experiment, we have successfully detected the presence of Brontok worms while correctly identifying the benign ones. The techniques employed in the experiment provided some insight on creating a good signature-based detector, which could be used to create a more credible solution that eliminates any threats of old malware that may resurface in the future.

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.

An innovative breakthrough in computer science is cloud computing and involves several computers which are connected via the Internet or it is dispersed over a network. A large database, services, applications, software and resources are an integral part of this technology. It has the capability to operate a program or applications on numerous connected computers simultaneously and permits the users to enter applications and resources through a web browser or web service via the Internet anytime and anywhere. Current susceptibility in elementary technologies gravitates to expose doors for intrusions. Cloud computing offers enormous advantages such as cost reduction, dynamic virtualized resources, significant data storage and enhanced productivity. At the same time, numerous risks occur regarding security and intrusions, for example, worm can intercept cloud computing services, impair service, application or virtual in the cloud formation. Worm attacks are now more complex and resourceful making intruders more difficult to detect than previously. The motivation of this research is founded on ramifications presented by the worms. This paper presents different intrusion detection systems affecting cloud resources and service. Moreover, this paper illustrates how genetic algorithm can be integrated in detecting worm attacks in cloud computing more efficiently. � 2006-2015 Asian Research Publishing Network (ARPN).