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No 29
Vol. 8 No. 1
Winter 2020

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Lately, one of the biggest challenges in enterprise is the strategic alignment of information technology with business. Enterprises use various methods to achieve this strategic alignment. Enterprise architecture is an effective approach that allows optimal management of Enterprise’s information technology and strategic alignment of IT functionalities and business requirements. These days, considering dynamic environments, there is the possibility of any kind of change in enterprise’s conditions, especially after implementation of Enterprise architecture. This change of conditions demonstrates itself in a variety of forms. In these cases, decision making should be done as an appropriate response to these changes which should be predictable. Otherwise it would lead to lack of proper response and consequently, readjustment of Enterprise architecture document, which imposes many human and financial costs on the enterprise. In addition, the enterprise’s progress toward competitive advantages will be stopped. The appropriate response and adaptability to these changes is a concept represented under the title of adaptive maintenance of Enterprise architecture. In the following article with the help of a software maintenance method and examining the possibility of extending into the Enterprise architecture maintenance, through a case study on Power Distribution Company of Golestan province, it is shown how the improvement of adaptive maintenance of Enterprise architecture can be assisted and the possibility of implementation of various types of changes in business and information technology without disturbing the Enterprise is provided.
Fereidoon shams Aliee - safura oviesi
DOI : 0
Keywords : Enterprise architecture; ، strategic alignment; ، Enterprise architecture maintenance; ، Adaptability;
Word Sense Induction (WSI) aims at inducing word senses from data without using a prior knowledge. Utilizing no labeled data motivated researchers to use clustering techniques for this task. There exist two types of clustering algorithm: parametric or non-parametric. Although non-parametric clustering algorithms are more suitable for inducing word senses, their shortcomings make them useless. Meanwhile, parametric clustering algorithms show competitive results, but they suffer from a major problem that is requiring to set a predefined fixed number of clusters in advance. The main contribution of this paper is to show that utilizing the silhouette score normally used as an internal evaluation metric to measure the clusters’ density in a parametric clustering algorithm, such as K-means, in the WSI task captures words’ senses better than the state-of-the-art models. To this end, word embedding approach is utilized to represent words’ contextual information as vectors. To capture the context in the vectors, we propose two modes of experiments: either using the whole sentence, or limited number of surrounding words in the local context of the target word to build the vectors. The experimental results based on V-measure evaluation metric show that the two modes of our proposed model beat the state-of-the-art models by 4.48% and 5.39% improvement. Moreover, the average number of clusters and the maximum number of clusters in the outputs of our proposed models are relatively equal to the gold data
Masood Ghayoomi
DOI : 0
Keywords : Word Sense Induction; ، Word Embedding; ، Clustering; Silhouette Score; ، Unsupervised Machine Learning; ، Distributional Semantic; ، Density;
In massive Multiple Input Multiple Output (MIMO) or large scale MIMO systems, uplink detection at the Base Station (BS) is a challenging problem due to significant increase of the dimensions in comparison to ordinary MIMO systems. In this letter, a novel iterative method is proposed for detection of the transmitted symbols in uplink multiuser massive MIMO systems. Linear detection algorithms such as minimum-mean-square-error (MMSE) and zero-forcing (ZF), are able to achieve the performance of the near optimal detector, when the number of base station (BS) antennas is enough high. But the complexity of linear detectors in Massive MIMO systems is high due to the necessity of the calculation of the inverse of a large dimension matrix. In this paper, we address the problem of reducing the complexity of the MMSE detector for massive MIMO systems. The proposed method is based on Gram Schmidt algorithm, which improves the convergence speed and also provides better error rate than the alternative methods. It will be shown that the complexity order is reduced from O(〖n_t〗^3) to O(〖n_t〗^2), where n_t is the number of users. The proposed method avoids the direct computation of matrix inversion. Simulation results show that the proposed method improves the convergence speed and also it achieves the performance of MMSE detector with considerable lower computational complexity.
Mojtaba Amiri - Mahmoud Ferdosizade Naeiny
DOI : 0
Keywords : Massive MIMO; ، Iterative Method; ، Matrix Inversion; ، Maximum Likelihood; ، MMSE Detection;
Query recommendation is now an inseparable part of web search engines. The goal of query recommendation is to help users find their intended information by suggesting similar queries that better reflect their information needs. The existing approaches often consider the similarity between queries from one aspect (e.g., similarity with respect to query text or search result) and do not take into account different lexical, syntactic and semantic templates exist in relevant queries. In this paper, we propose a novel query recommendation method that uses a comprehensive set of features to find similar queries. We combine query text and search result features with bipartite graph modeling of user clicks to measure the similarity between queries. Our method is composed of two separate offline (training) and online (test) phases. In the offline phase, it employs an efficient k-medoids algorithm to cluster queries with a tolerable processing and memory overhead. In the online phase, we devise a randomized nearest neighbor algorithm for identifying most similar queries with a low response-time. Our evaluation results on two separate datasets from AOL and Parsijoo search engines show the superiority of the proposed method in improving the precision of query recommendation, e.g., by more than 20% in terms of p@10, compared with some well-known algorithms.
Elham Esmaeeli-Gohari - Sajjad Zarifzadeh
DOI : 0
Keywords : Recommendation Systems; ، Search Engine; ، Clustering; ، Query; ، Click;
Sentiment analysis in social networks has been an active research field since 2000 and it is highly useful in the decision-making process of various domains and applications. In sentiment analysis, the goal is to analyze the opinion texts posted in social networks and other web-based resources to extract the necessary information from them. The data collected from various social networks and web sites do not possess a structured format, and this unstructured format is the main challenge for facing such data. It is necessary to represent the texts in the form of a text representation model to be able to analyze the content to overcome this challenge. Afterward, the required analysis can be done. The research on text modeling started a few decades ago, and so far, various models have been proposed for performing this modeling process. The main purpose of this paper is to evaluate the efficiency and effectiveness of a number of commons and famous text representation models for sentiment analysis. This evaluation is carried out by using these models for sentiment classification by ensemble methods. An ensemble classifier is used for sentiment classification and after preprocessing, the texts is represented by selected models. The selected models for this study are TF-IDF, LSA, Word2Vec, and Doc2Vec and the used evaluation measures are Accuracy, Precision, Recall, and F-Measure. The results of the study show that in general, the Doc2Vec model provides better performance compared to other models in sentiment analysis and at best, accuracy is 0.72.
Sajjad Jahanbakhsh Gudakahriz - Amir Masoud Eftekhari Moghadam - Fariborz Mahmoudi
DOI : 0
Keywords : Text Representation Models; ، Sentiment Analysis; ، Sentiment Classification; ، Ensemble Classifiers;
Combinatorial designs are powerful structures for key management in wireless sensor networks to address good connectivity and also security against external attacks in large scale networks. Many researchers have used key pre-distribution schemes using combinatorial structures in which key-rings, are pre-distributed to each sensor node before deployment in a real environment. Regarding the restricted resources, key distribution is a great engagement and challenging issue in providing sufficient security in wireless sensor networks. To provide secure communication, a unique key should be found from their stored key-rings. Most of the key pre-distribution protocols based on public-key mechanisms could not support highly scalable networks due to their key storage overhead and communication cost that linearly increasing. In this paper, we introduce a new key distribution approach for hierarchical clustered wireless sensor networks. Each cluster has a construction that contains new points or that reinforces and builds upon similar ideas of their head clusters. Based on Residual Design as a powerful algebraic combinatorial architecture and hierarchical network model, our approach guarantees good connectivity between sensor nodes and also cluster heads. Compared with similar existing schemes, our approach can provide sufficient security no matter if the cluster head or normal sensor node is compromised
Vahid Modiri - Hamid Haj Seyyed Javadi - Amir Masoud Rahmani - Mohaddese Anzani
DOI : 0
Keywords : Wireless sensor networks; ، Key pre-distribution; ، Residual Design; ، Hierarchical network model;
Network connectivity is one of the major design issues in the context of Wireless Sensor Networks (WSNs). Due to diverse communication patterns, some nodes lying in high-traffic zones may consume more energy and eventually die out resulting in network partitioning. This gives rise to a situation when some alive nodes are trapped in a disconnected cluster and do not have enough radio range required to communicate their data to destination, i.e. to a sink or a relay node connected to the main part of the network. This phenomenon may deprive a large number of alive nodes of sending their important time-critical data to the sink. In this paper, we propose a virtual antenna based cooperative beamforming approach in order to retrieve valuable data from these disconnected nodes. In the proposed scheme, the sensor nodes in isolated partitions work together to form a directional beam which significantly increases their overall communication range to reach out a distant relay node connected to the main part of the network. The proposed methodology of cooperative beamforming based partition connectivity works efficiently if an isolated cluster gets partitioned with a favorably large number of nodes. Furthermore, we propose a cooperative beamforming based scanning mechanism to search for the nearest alive node connected to the main part of the network. The proposed mechanism is then elaborated through simulation results. The simulation result shows that our proposed mechanism achieves upto 70% partition reduction through beamforming as partition healing.
Abbas Mirzaei - Shahram Zandian
DOI : 0
Keywords : Wireless Sensor Networks (WSNs); ، Connectivity Restoration; ، Network Partitioning; ، Cooperative Beamforming; Fault Recovery;

About Journal

Affiliated to :ICT Research Institute of ACECR
Manager in Charge :Habibollah Asghari
Editor in Chief :Masood Shafiei
Editorial Board :
Abdolali Abdipour
Mahmoud Naghibzadeh
Zabih Ghasemlooy
Mahmoud Moghavemi
Aliakbar Jalali
Ramazan Ali Sadeghzadeh
Hamidreza Sadegh Mohammadi
Saeed Ghazimaghrebi
Shaban Elahi
Alireza Montazemi
Ali Mohammad Djafari
Rahim Saeidi
Shohreh Kasaei
Mehrnoush Shamsfard
ISSN :2322-1437
eISSN :2345-2773

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