نشریه فناوری اطلاعات و ارتباطات ایران
سال دوازدهم شماره 43 (بهار و تابستان 1399)

Given the fact that universities are known as the pioneer of progress and innovation in society, the expectation of e-government maturity in universities is higher than in other government organizations. Given the high capacity of human capital, universities have the ability to adapt to e-government sooner than other organizations. To meet these expectations, the e-government of universities and higher education institutions must be continuously evaluated. In this paper, in order to design a model for assessing the readiness of e-government at the level of government organizations, a model has been extracted that includes "organizational-Affiliated" and "independent of the organization" indicators. The order of importance is: "Technological Infrastructure", "Monitoring, Support and Coordination", "Trust", "Executive Policy and Law", "Human Resources", "Digital Culture", "Integrity" and "Budget", as well as the " National Infrastructure" index is considered as an independent indicator of the organization. In the field study, the model was used to assess the readiness of e-government at Tarbiat Modares University.

Phishing is regarded as a kind of internet attack on the web which aimed to steal the users’ personal information for online stealing. Phishing plays a negative role in reducing the trust among the users in the business network based on the E-commerce framework. therefore, in this research, we tried to detect phishing websites using data mining. The detection of the outstanding features of phishing is regarded as one of the important prerequisites in designing an accurate detection system. Therefore, in order to detect phishing features, a list of 30 features suggested by phishing websites was first prepared. A new idea based on two steps: feature selection and feature extraction, has been proposed. To evaluate the proposed method, the performance of decision tree J48, random forest, naïve Bayes methods were evaluated on the reduced features. The results indicated that accuracy of the model created to determine the phishing websites by using the two-stage feature reduction-based Wrapper and Principal Component Analysis (PCA) algorithm in the random forest method of 96.58%, which is a desirable outcome compared to other methods.

This paper presents an optimization model to increase the amount of data collected and balance the bandwidth consumption of the edges and uses network coding. To solve this model, the dual method is used and to calculate a lower bound and find the optimal answer and point in the optimization model, the Karush-KunTucker condition is used, which requires calculating the derivative of the Lagrangian function with respect to its variables. Solving this problem and equations in polynomial time is very difficult, time consuming and almost impractical due to the involvement of different parameters and limited resources of wireless sensor networks with a large number of nodes, so to solve this problem, a distributed and iterative algorithm is proposed. It uses the sub gradient method and the network flow separation method. The effectiveness of the proposed model and algorithm has been investigated by simulation in terms of number of source sensor nodes, Lagrangian coefficient and step size. Up to 23% of bandwidth consumed, average network life and energy consumed.

Community detection in dynamic social networks is one of the most important research topics that has been considered in recent years. There are various approaches to detecting communities in dynamic social networks, among which the label propagation approach has chosen due to simplicity and efficiency. This approach involves many methods that are often random. Among these methods, LabelRankT(Time) is a deterministic method. Of course, this method also has problems, one of the problems is that when a node wants to join a community, the internal structure of that community is not considered. So, for solving this problem, a greedy approach has been used to improve the label publishing approach. The proposed approach and other evaluated methods are tested in real and Synthetic datasets. The results show that the proposed method has performed relatively better than the other methods in terms of accuracy and modularity.

Due to the increasing demand for business in the web environment, the variety and number of services have increased. As a result, large-scale computing data centers have been created that increase costs and electrical power. Inadequate and inefficient cooling systems not only overheat the resources and reduce the devices' service life but also produce carbon, which plays an important role in weather conditions. Therefore, it is necessary to reduce the total energy consumption of these systems with appropriate methods. In this research, an effective method of energy resource management in virtualized cloud data centers is presented. In addition to reducing energy consumption and operating costs, it has also increased the quality of services. This study presents a resource allocation strategy in cloud systems to reduce energy and implementation costs and examine its application in the cloud computing environment. The simulation results showed that the proposed method could reduce the average energy consumption up to 0.626 kWh compared to NPA, DVFS, ST, and MM methods. Also, the need for migration and SLA violations are decreased.

Despite the tremendous advances in the design of human computation systems, most of them suffer from low or low-quality contributions, and a high percentage of them fail. The success of these systems mostly depends on the behavior of people who participated in the system. Because human computation systems involve small work units, and each work brings little benefit to the participants, humans exhibit desirable behavior if they are well motivated. In this paper, we investigated this issue in the human computation intrusion detection (HCID) system. Our goal is to design a mechanism to get tasks done by experts with the utmost effort and accuracy for the lowest possible cost with a high percentage of participation. After choosing the appropriate motivation, we design the reward incentive mechanism for this system. The idea behind this mechanism was to use worker's skills in determining their rewards, and we used the Kappa coefficient to evaluate worker's agreement. After designing this mechanism, we use game theory to analyze the mechanism and determine the minimum possible reward for each task category. We prevent system failure by encouraging the workers to be high and quality participation. Also, we manage the system's financial resources by allocating the least necessary financial resources to the workers. This mechanism's design leads to an increase in the participants' accuracy and, consequently, to an increase in the human computation intrusion detection system's accuracy in identifying new attacks and reducing their false alert rate.

In organizations and large companies that are using business process management systems (BPMSs), process model can change due to upstream laws, market conditions. BPMSs have flexible to these changes. Effect of these change are saved in storage devises and event logs; these changes are sometimes applied suddenly or gradually on the event logs. Changing the season or starting a new financial term can be a factor to make these changes. This change is called concept drift in business process model. On time detection and recognition of process concept drift can affect the decision making of managers and administrations of systems. An analysis of the event logs in BPMS allows the automatic detection of the concept drift. This paper presents an innovative method by introducing a modified distance function to identify the concept drift. Experimental results were performed on 72 datasets in the research history, which included 648 concept drifts in 12 different types. It shows that the proposed method detects 98.18% of the drifts, while the proposed method is much faster than other state of the art methods.

Financial markets have always been under influence of media news; therefore, text analysis of news is considered as an effective method of stock exchange forecasting. Research in this context has been conducted with the help of information retrieval techniques, in which high frequency words in a document that appeared sporadically in the whole corpus received higher weight than others. In contrast, the words which appeared in many news of a corpus, during a certain time, indicate the importance of an event. In our research, to address this contradiction, a new technique of assigning weight to influential words of news is presented. Financial news of Iran Central Bank (CBI) and actual data of Tehran Stock Exchange Index (TSEI) in the duration of 2005 to 2020 AD were utilized to evaluate the proposed method. The empirical results show 64% and 41% accuracy of trend prediction when TSEI moves upward and downward respectively and about 10% decreasing in Mean Absolute Error (MAE) to compare with prevalent techniques. While, the changes of the ratio between the number of positive and negative words in news does not offer predictive or analytical evidences, our results show that, there still exists a meaningful relationship between CBI news and TSEI fluctuations.

MIMO technology offers a wide range of transmission rates for modern wireless LANs. In order to improve the performance of the rate control module, statistical information on the history of state and usage of each transmission rate is maintained at the MAC layer to help determine the rate at which future packets are sent. However, the great diversity of transmission rates in the 802.11n and 802.11ac standards imposes an overhead for updating this information. In this article, to reduce the state space of transmission rates while keeping statistics approximately up to date for each rate, a method for clustering rates is presented so that when sending a packet over a transmission rate, statistical information relating to all the rates belonging to the same cluster is updated. As a result, statistics for a greater number of rates can be updated even when sending a fewer number of packets. We implemented our proposed mechanism in the Linux kernel environment and evaluated its performance under different conditions on an experimental testbed deployed in our research laboratory. The results show that the proposed method outperforms the de-facto Minstrel-HT rate control mechanism in terms of throughput and number of successful transmissions.

Genetic algorithm is one of the most famous methods for solving Combinatorial Optimization Problems. It had various applications in different field of studies such as Electronics, Computer Science and Mathematics and still has. In this algorithm, the population members which contribute for producing the next generation are selected according to their fitness values. The combination of the members is through Crossover Operator; And in some versions a few of the best members migrate to the next generation directly. Normally, the weak members of population may participate to the next generation. In this study, the combination operators are aware of the best member of generation; Only those child which are as good as the best member, are allowed to form the next generation. The proposed method is applied on graph coloring and finding metric-dimension of graph problems. The results are compared with the common genetic algorithm. Experimental results shows the superior performance of the proposed method in comparison to common genetic algorithm.

Since methods for sequencing machine learning sequences were not successful in classifying healthy and cancerous proteins, it is imperative to find a way to represent these sequences to classify healthy and ill individuals with deep learning approaches. In this study different methods of protein sequence representation for classification of protein sequence of healthy individuals and leukemia have been studied. Results showed that conversion of amino acid letters to one-dimensional feature vectors in classification of 2 classes was not successful and only one disease class was detected. By changing the feature vector to colored numbers, the accuracy of the healthy class recognition was slightly improved. The binary protein sequence representation method was more efficient than the previous methods with the initiative of sequencing the sequences in both one-dimensional and two-dimensional (image by Gabor filtering). Protein sequence representation as binary image was classified by applying Gabor filter with 100% accuracy of the protein sequence of healthy individuals and 98.6% protein sequence of those with leukemia. The findings of this study showed that the representation of protein sequence as binary image by applying Gabor filter can be used as a new effective method for representation of protein sequences for classification.

With the advancement of science and technology and the importance of using robots, the need to use automated systems seems essential. Since most applications of snake robots move in unfamiliar and sometimes complex environments, there is a need to develop different control methods for them. The product of the integration of the two sciences of neuroscience and robotics, are motor neuron producers known as model central generators, which is the problem of producing motion in the robot. In this paper, we control the movement of a snake-like robot with a central pattern generator (CPG) that is able to produce coordinated patterns of output signals with different frequencies. For this purpose, it is necessary to model the snake robot first and then apply control Be. In this paper, the control of robot motion control in two modes of open loop and closed loop for CPG network is presented. At the same time, this study with simulations shows that the lower the level of stimulation and the lower its level, the lower the frequency of motion production and vice versa. Then, the effect of CPG models, which are used as neural networks, is simulated in motion control. In this paper, the remarkable point in comparison with other controllers is that in the central generating neural networks, the pattern of simple signals is sufficient to stimulate and induce the movement of robots, which is shown in the simulation.