IRANIAN COMMUNICATION AND INFORMATION TECHNOLOGY
Year:2019 | Volume:10 | Issue:37-38|Papers Count:6

The method of crowdfunding in the world has expanded rapidly due to the need for financing in the early stages of start-up businesses as well as advances in information technology. In Iran, several financing platforms have been established so far, some of which have been successful and some of which have been unsuccessful. Therefore, it is necessary to help the development of this method by examining the factors affecting it. Since mass financing is a new and new phenomenon, it is necessary to increase its awareness in the society in an appropriate way while determining the factors affecting this method. The collective modeling method is based on social networks and the Web 2 with the aim of recognizing new phenomena. Therefore, in this article, using collective modeling, the factors affecting crowdfunding in Iran in order to support start-up companies in the field of IT are discussed.

The strategic approach to project management is a new approach that has received more and more attention from business thinkers in the last decade. Having a strategic approach in the field of information technology projects requires managers to choose the right strategy in the first step of project management, taking into account several factors influencing the choice of strategy. Logical and adaptive approaches are the two dominant strategies in project management, each of which requires different tools, resources, and procedures. Despite the increase in investment in IT projects and the dependence of organizations on such technologies, still No study has been conducted to assist IT managers in choosing the appropriate strategy for such projects. Therefore, the main purpose of this study is to identify the approach of selecting the appropriate strategy in the management of information technology projects. In order to achieve this goal, in the first stage, in order to identify the factors influencing the choice of IT project management strategy, after a comprehensive review of the literature in the relevant field, the opinions of experts in the focus group have been used. In the second stage, in order to validate the extracted factors, and in the third stage, in order to weight each of the factors in selecting the two dominant logical and comparative approaches, experts are surveyed using the survey method. In the developed methodology, the factors that influence the choice of strategy are: process factors, human factors, system related factors and contextual factors. The final strategy of IT project management is obtained as a range of logical strategy and adaptive strategy.

Secure Hash Algorithms (SHA) are essential parts of cryptographic algorithms that with advent of applications such as using PDAs in our society, their importance has increased dramatically in order to preserve confidentiality. Besides that, with technology development, the necessity of implementation of such algorithms on flexible platforms can be challenging. Therefore, using fewer resources and increasing the speed of operations are the main challenges in designing and implementing such algorithms. In this paper, a new architecture is proposed for FPGA-based processor for cryptographic algorithms SHA-2. In proposed processor, using memory units and multiport datapath and followed by parallel performance of processor reduces use of resources and increases data process speed. Processor architecture is modeled by VHDL language for SHA-2 and its implementation has been done on FPGA platforms for Virtex series by ISE software. The results of implementation indicate that the proposed processor compared with earlier works with similar objectives, was able to preserve desired level of throughput and efficiency by increasing 25% frequency for SHA-256 and occupying 55% less area for SHA-512. Proposed processor is appropriate for applications such as Trusted Mobile Platforms (TMP), Digital Currencies (Bitcoin) and secure routing of Networkon Chip (NoC).

Learning to rank (L2R) has emerged as a promising approach in handling the existing challenges of Web search engines. However, there are major drawbacks with the present learning to rank techniques. Current L2R algorithms do not take into account to the search behavior of the users embedded in their search sessions’ logs. On the other hand, machine-learning as a data-intensive process requires a large volume of data about users’ queries as well as Web documents. This situation has made the usage of L2R techniques questionable in the real-world applications. Recently, by the use of the click-through data model and based on the generation of click-through features, a novel approach is proposed, named as MGP-Rank. Using the layered genetic-programming model, MGP-Rank has achieved noticeable performance on the ranking of the English Web content. In this study, with respect to the specific characteristics of the Persian language, some suitable scenarios are presented for the generation of the click-through features. In this way, a customized version of the MGP-Rank is proposed of the Persian Web retrieval. The evaluation results of this algorithm on the dotIR dataset, indicate its considerable improvement in comparison with major ranking methods. The improvement of the performance is particularly more noticeable in the top part of the search results lists, which are most frequently visited by the Web users.

One of the important aspect of cloud computing is processing of amount of big data. MapReduce has been widely used as a powerful data processing model. It has efficiently solved a wide range of large-scale computing problems. MapReduce is a vital programming model for large-scale data processing in the cloud computing, which simplifies the development of traditional distributed program and provides a simple parallel programming method. On the other hand, Task scheduling is critical, which is an NP-completeness problem, plays a critical key role in cloud computing systems. In this paper, we propose a parallel genetic based algorithm to schedule the task on heterogeneous cloud environments. We prompt the algorithm on heterogeneous systems, where resources are of computational and communication heterogeneity. During the implementation of our method, we use Hadoop platform as the backend MapReduce engine. At the last part, through a series of simulation experiments, we prove that our approach has a much better runtime performance than other approach. The main goal of the proposed method is to use MapReduce framework to reduce the overall execution time of the program. The results of tests on a series of directional dag with random input indicate that the proposed method compare with three other existing method in this proposed method the speed of convergence is improved.

Influence maximization is the problem of finding a small subset of nodes (seed nodes) in a social networks that maximize the influential spreading. Recently, there is a quick growing in studying on and diffusion. In recent years, several algorithms have been proposed for influence maximization problem. These studies include viral marketing, spreading of rumors, innovation, spread of diseases, etc. In this paper we present a new method for solving the for influence maximization problem that is called ICIM-GREEDY algorithm. In the proposed algorithm, we consider two important criteria that are not considered in previous work, one is strength of influence and another is sensitivity to influence. The proposed method is evaluated on standard datasets. The results show that the proposed method compared to other comparable algorithms has a better quality in finding the 50 influential nodes (seeds). Furthermore, ICIM-GREEDY often has better time complexity than other compared algorithms due to relatively fast convergence.