نشریه فناوری اطلاعات و ارتباطات ایران
سال دهم شماره 37 (پاییز و زمستان 1397)

The need for financing in the early stages of the new IT knowledge-based companies and advances in information technology led to quickly spread crowdfunding in the world. In Iran, multiple platforms established in this regard, some successful and some unsuccessful. So, there is a need to investigate the factors affecting this method to develop it in Iran. Because crowdfunding is a novel phenomenon. So, is needed to identify factors affecting on this method, the right manner in order to support the emerging knowledge-based businesses, to inform and raise awareness in the society. Crowd model building method like crowdfunding is based on social networking and web 2.0 with the aim of understanding new phenomena is through collective intelligence. So in this article uses Crowd model building with create a group in telegram and people participation were discussed the effective factors on crowdfunding in Iran in support of the new IT knowledge-based companies.

The strategic approach to project management is a new attitude that has attracted the attention of business academics in the last decade. Having a strategic approach in the field of IT projects forces managers in the first step of project management to choose the appropriate strategy by considering the various factors that influence the strategy selection. Rational and adaptive approaches are two predominant strategies in project management that require different tools, resources, and procedures. Despite the increasing growth of investment in IT projects and the dependence of organizations on such technologies, there are still few studies that assist IT managers to select the appropriate strategy for such projects. Therefore, the main purpose of this study is to develop an appropriate approach for selecting a strategy in the management of IT projects. To achieve this goal, in the first step, after reviewing the comprehensive literature in the related field and in order to identify the factors influencing the strategy selection of the IT project management, the focus group has been used. In the second step, for validating the extracted factors, and in the third step, in order to weigh each of the factors in selecting the two dominant rational and adaptive approaches, the survey method was used for reviewing experts’ viewpoints. In the proposed methodology, factors that influence the strategy selection in IT projects are process factors, human factors, system-related factors, and context factors. The final strategy for managing IT projects is a spectrum of rational 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 the Web search engines. However, there are major drawbacks with the present learning to rank techniques. Current L2R algorithms do not pay attention to the records of users’ interactions with Web search engines during their search sessions. Also, the learning process needs a large volume of features required from the users’ queries as well as the Web documents. Such situation has made the application of L2R techniques questionable in the real-world applications. Recently, based on the click-through data model and by generating click-through features, a novel approach is proposed, named as MGP-Rank. By the use of the layered genetic programming model, the MGP-Rank algorithm has achieved noticeable performance on the ranking of the English Web data. 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 the major ranking methods. This performance improvement is particularly more noticeable in the top part of the search results list, which is 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.