International Journal of Information and Communication Technology Research
Year:2017 | Volume:9 | Issue:2|Papers Count:7

Complex homogeneous network-on-chip or heterogeneous network-on-chip increases the need of determining and developing simulation tools for designer to evaluate and comparison network performance. Towards this end, ARTEMIS tool, a matlab based simulator environment is developed. This simulator offers some collections of network configuration regarding to the topology graph, routing algorithm and switching strategy, including allocation scheme for a target application. Consequently, designers can choose the number and depth of virtual channels and the capacity of each link by applying an efficient allocation scheme, which is provided by this tool. Average latency and throughput are evaluation performance metrics that are measured with proposed simulator tool.

In this paper, we consider to seek vulnerabilities and we conduct possible attacks on the crucial and essential parts of Android OSs architecture including the framework and the Android kernel layers. As a regard, we explain the Binder component of Android OS from security point of view. Then, we demonstrate how to penetrate into the Binder and control data exchange mechanism in Android OS by proposing a kernel level attack model based on the hooking method. In addition, we provide a method to detect these kinds of attacks on Android frameworks and the kernel layer. As a result, by implementing the attack model, it is illustrated that the Android processes are detectable and the data can be extracted from any process and system calls. On the other hand, by using our detection proposed method the possibility of using this attack approach in the installed applications on the Android smartphones will be sharply decreased.

Probabilistic topic models have been very popular in automatic text analysis since their introduction. These models work based on word co-occurrence, but are not very flexible with respect to the context in which cooccurrence is considered. Many probabilistic topic models do not allow for taking local or spatial data into account. In this paper, we introduce a probabilistic topic model that benefits from an arbitrary-length co-occurrence window and encodes local word dependencies for extracting topics. We assume a multinomial distribution with Dirichlet prior over the window positions to let the words in every position have a chance to influence topic assignments. In the proposed model, topics being shown by word pairs have a more meaningful presentation. The model is applied on a dataset of 2000 documents. The proposed model produces interesting meaningful topics and reduces the problem of sparseness.

Common nearest-neighbor density estimators usually do not work well for high dimensional datasets. Moreover, they have high time complexity of )(2nO and require high memory usage especially when indexing is used. In order to overcome these limitations, we proposed a new method that calculates distances to nearest and farthest neighbor nodes to create dataset subgroups. Therefore computational time complexity becomes of )log(nnO and space complexity becomes constant. After subgroup formation, assembling technique is used to derive correct clusters. In order to overcome high dimensional datasets problem, Principal Component Analysis (PCA) in the clustering method is used, which preprocesses high-dimensional data. Many experiments on synthetic data sets are carried out to demonstrate the feasibility of the proposed method. Furthermore we compared this algorithm to the similar algorithm – DBSCAN-on real-world datasets and the results showed significantly higher accuracy of the proposed method.

This paper presents an automated supervised method for Persian wordnet construction. Using a Persian corpus and a bi-lingual dictionary, the initial links between Persian words and Princeton WordNet synsets have been generated. These links will be discriminated later as correct or incorrect by employing seven features in a trained classification system. The whole method is just a classification system which has been trained on a train set containing a pre-existing Persian wordnet, FarsNet, as a set of correct instances. A set of some sophisticated distributional and semantic features is proposed to be used in the classification system. Furthermore, a set of randomly selected links have been added to training data as incorrect instances. The links classified as correct are collected to be included in the final wordnet. State of the art results on the automatically derived Persian wordnet is achieved. The resulted wordnet with a precision of 91. 18% includes more than 16, 000 words and 22, 000 synsets.

This study clarified the critical success factors (CSFs) that effect on adopting and implementing PACS and its applications in Iranian hospitals. We identified CSFs by literature review and interview by experts. Then examined its importance by T-test with 110 respondents. Kaiser-Meyer test and Varimax rotation are used for validity of data. Factor analysis is used for clustering. And the results are examined in 11hospitals who have implemented PACS. 20 of 23 CSFs, are distinguished important by T-test and clustered in 6 groups by Factor analysis. (1st) Ability to choose and purchase the appropriate PACS; (2nd) Being patient-centered and paying attention to patient satisfaction; were the most important CSFs. 77% questionnaires were completed by less than 2% miss data. The results are approved in 11 hospitals in Iran. This paper fulfils an identified need to study how PACS can be adopted in Iran's hospital by determining 6 CSFs. They can be applicable for policy makers and managers of other hospitals of Iran and some developing countries such as Iran to use of PACS as integrated IT technology.

Internet of things (IoT) is a novel emerging approach in computer networks wherein all heterogeneous objects around us, which usually are resource-constrained objects, can connect to each other and also the Internet by using a broad range of technologies. IoT is a hybrid network which includes the Internet and also wireless sensor networks (WSNs) as the main components of IoT; so, implementing security mechanisms in IoT seems necessary. This paper introduces a novel intrusion detection architecture model for IoT that provides the possibility of distributed detection. The proposed hybrid model uses anomaly and misuse intrusion detection agents based on the supervised and unsupervised optimum-path forest models for providing the ability to detect internal and externals attacks, simultaneously. The number of input features to the proposed classifier is reduced by a hybrid feature selection algorithm, as well. The experimental results of simulated scenarios show the superior performance of proposed security mechanism in multi-faceted detection.