مجله رایانش نرم و فناوری اطلاعات
سال هفتم شماره 1 (بهار و تابستان 1397)

The processing systems utilized in the satellites or nuclear reactors are highly susceptible to produce wrong results because of the existence of different radiations. In this paper, a new method is proposed for enhancing the program executions on the fault-prone processors in extreme environments. The main goal is the detection of control flow errors caused by soft errors that maybe produced by transient faults occurred in the underlying hardware. This method detects the errors by recognizing the deviations from the proper program flow execution. The proposed method operates by allocating two specific signatures and at most three control instructions to each basic block inside a program. This way, many control flow errors are detected. The experimental results based on the simulation of the proposed method together with the previous methods show that the proposed method detects the control flow errors better than the previous methods with respect to three main parameters including fault coverage, performance overhead and memory overhead.

Recently, human action recognition in videos has become an interesting area of research due to its variety of important applications such as intelligent security supervisions, smart environments, education, health-care monitoring systems, data mining, etc. There are, however, number of challenges that makes the development of these systems a bit harder than the common machine vision systems, both in accuracy and efficiency: changes in illumination, moving background, cluttered backgrounds, camera motions, complexity of the actions, to name a few. One of the commonly used methods for automatic human action recognition is to, firstly, extract some feature points within the video frames, then describe those points locally, and finally, code (cluster) them to feed a learning algorithm to build the action recognition model. In this paper, we aim to increase the accuracy of these methods by introducing the use of texture information extracted using a human retina-inspired algorithm (FREAK) together with the appearance-based information of the moving objects. In order to increase the efficacy and eliminate the overhead of furthered texture information in model building phase and, of course, in hope of increasing the accuracy as well, we propose to use a cascade approach to build the desired model. Experiments on a publicly available large dataset namely UCF101, confirm that the proposed method achieves a very comparable results with the state-of-the-art methods.

Nowadays, trust is a major topic under discussion in the cloud computing environments. The existence of a mechanism for building and preserving trust among service consumers and providers is a critical component in the success of providing cloud services. Therefore, computational trust in cloud computing environments has turned into a considerable concern. The lack of trust among service providers and consumers may result in the reduction of the general appeal for the acceptance of cloud services among users. In other words, even though the services provided by each vendor maybe similar in terms of functionality, they may vastly differ in terms of quality of service. For this reason, a false choice in selecting a service may have devastating impacts on the requirements and goals of the end-user. To this aim, in this paper, a computational trust model based on the credibility of users’ feedbacks for the calculation of services’ trustworthiness is introduced. The proposed model, not only takes into account the existing experiences that other users have with a service provider, but also considers the credibility of such feedbacks as well as the persistency of correct and constructive feedbacks through time. In the end, the efficiency of the proposed model is investigated through a series of evaluation scenarios and it is shown that the proposed model is capable of accurately calculating the trustworthiness of the system. Such accuracy is maintained even in the presence of malicious users.

Today's data centers consist of thousands of servers hosting a variety of cloud-based services. In this paper, a scalable and new technique is presented for traffic engineering in software-defined data center networks helping to design an optimal demand-path mapping with a tolerable computational complexity. The proposed method is based on linear programming model and attempts to minimize the maximum link utilization that causes minimum link congestion. The method focuses on an optimal solution to load balancing in networks. It proposes a new decomposition technique that can limit the search space of the original Linear Programming problem, such that the time required to solve the problem can be reduced substantially. To reduce time complexity, a decomposition technique is used to divide the problem model into smaller sub-problems, which are then solved simultaneously by applying parallelizing techniques (multiple-core computing and OpenMP).Simulation results show that solving time and load balancing are considerably improved.

Native Quality Assessment Model of news sites (NEWSQUAL), Native Quality Assessment Model of news sites (NEWSQUAL): Effective measurements and accurate analysis of the quality of news sites are the first steps necessary to improve their performance and improve the reporting methods in these centers. Therefore, this research is looking for quality assessment of news sites. The present study consists of using the Delphi method as a qualitative method for evaluating the indicators by the experts in the field of news has led to the provision of a native model for assessing the quality of news sites. The native model (NEWSQUAL) consists of five dimensions and 72 indicators, from five perspectives of design and graphic design, content, technical system, services, and executive management of news sites, each of which consists of several sub-criteria. Subsequently, by selecting 61 news sites, their qualitative evaluation was calculated using a questionnaire and the quality score of each site was obtained.

In this paper, based on Liu chaotic system, a novel hyperchaotic system possessing an origin equilibrium point is introduced. To demonstrate the existence of hyperchaos phenomenon in this system, several mathematical criteria are used and discussed. These criteria comprise dissipativity checking, instability proof of the equilibrium point, draw of phase portraits of strange attractor, time responses of state variables, calculation of Lyapunov exponents, extract of fractional dimension and high sensitivity analysis of system’s time responses to initial conditions. It is shown that positive Lyapunov exponents of the introduced system are larger than ones of other hyperchaotic systems. By altering each of the parameters of the system, different types of dynamical behaviors including chaos, limit cycle, quasi periodic and hyperchaos are observed. An analog electronic circuit is designed to realize the hyperchaotic system composed of linear resistors, linear capacitors, operational amplifiers, and analog multipliers. Moreover, the designed nonlinear circuit is simulated by using ORCAD16.6 software. Next, it is physically implemented and tested in our laboratory. Both simulation results and experimental observations depict the occurrence of hyperchaos phenomenon in the designed circuit.

A major challenge in the development of wireless sensor networks is Increase network lifetime. Cluster-based routing protocols are proposed as a solution to improve energy consumption in wireless sensor networks. Clustering in each round and the single-hop transmission to base station is base of many algorithms that have been presented so far. Clustering in each round increases the number of control messages, collision and reduces energy of the network. Multi-hop routing increases the life time of cluster nodes and improves network performance. Proposed algorithm use the benefits of clustering and multi-hop routing, we consider a new fuzzy clustering based routing protocol with fixed threshold. The innovations of proposed paper include clustering nodes in different rounds, consideration of a fixed threshold, using different clustering algorithms, and multi-hop routing by considering the appropriate intermediate node In order to send data from each cluster head to the base station. Fuzzy inference using parameters such as "remaining energy", "neighbors' number", and "distance" of each node. The proposed algorithm has been compared with other algorithms on the field of network lifetime parameters, number of dead nodes per round, first dead node, half dead, and last dead node. The simulation results show that the proposed algorithm increases the network life expectancy of 44.5 percent in compared with other methods.