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No 18
Vol. 5 No. 2
Spring 2017

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As a way of simplifying, size reducing and making sense of the structure of each social network, blockmodeling consists of two major, essential components: partitioning of actors to equivalence classes, called positions, and clarifying relations between and within positions. Partitioning of actors to positions is done variously and the ties between and within positions can be represented by density matrices, image matrices and reduced graphs. While actor partitioning in classic blockmodeling is performed by several equivalence definitions, such as structural and regular equivalence, generalized blockmodeling, using a local optimization procedure, searches the best partition vector that best satisfies a predetermined image matrix. The need for known predefined social structure and using a local search procedure to find the best partition vector fitting into that predefined image matrix, makes generalized blockmodeling be restricted. In this paper, we formulate blockmodel problem and employ a genetic algorithm to search for the best partition vector fitting into original relational data in terms of the known indices. In addition, during multiple samples and various situations such as dichotomous, signed, ordinal or interval valued relations, and multiple relations the quality of results shows better fitness to original relational data than solutions reported by researchers in classic, generalized, and stochastic blockmodeling field.
Saeed NasehiMoghaddam - Mehdi Ghazanfari - Babak Teimourpour
DOI : 10.7508/jist.2017.18.001
Keywords : Social Network Analysis (SNA) ، blockmodeling ، Genetic Algorithm ، likelihood ratio statistics , G2, ، Multi objective optimization
Coreference resolution is the problem of determining which mention in a text refer to the same entities, and is a crucial and difficult step in every natural language processing task. Despite the efforts that have been made in the past to solve this problem, its performance still does not meet today’s applications requirements. Given the importance of the verbs in sentences, in this work we tried to incorporate three types of their information on coreference resolution problem, namely, selectional restriction of verbs on their arguments, semantic relation between verb pairs, and the truth that arguments of a verb cannot be coreferent of each other. As a needed resource for supporting our model, we generate a repository of semantic relations between verb pairs automatically using Distributional Memory (DM), a state-of-the-art framework for distributional semantics. This resource consists of pairs of verbs associated with their probable arguments, their role mapping, and significance scores based on our measures. Our proposed model for coreference resolution encodes verbs’ knowledge with Markov logic network rules on top of deterministic Stanford coreference resolution system. Experiment results show that this semantic layer can improve the recall of the Stanford system while preserves its precision and improves it slightly.
hasan zafari - Maryam Hoorali - Heshaam Faili
DOI : 10.7508/jist.2017.18.002
Keywords : Coreference resolution ، anaphora resolution ، semantically related verbs ، text inference ، NLP
Counting mitotic figures present in tissue samples from a patient with cancer, plays a crucial role in assessing the patient’s survival chances. In clinical practice, mitotic cells are counted manually by pathologists in order to grade the proliferative activity of breast tumors. However, detecting mitoses under a microscope is a labourious, time-consuming task which can benefit from computer aided diagnosis. In this research we aim to detect mitotic cells present in breast cancer tissue, using only texture and pattern features. To classify cells into mitotic and non-mitotic classes, we use an AdaBoost classifier, an ensemble learning method which uses other (weak) classifiers to construct a strong classifier. 11 different classifiers were used separately as base learners, and their classification performance was recorded. The proposed ensemble classifier is tested on the standard MITOS-ATYPIA-14 dataset, where a pixel window around each cells center was extracted to be used as training data. It was observed that an AdaBoost that used Logistic Regression as its base learner achieved a F1 Score of 0.85 using only texture features as input which shows a significant performance improvement over status quo. It also observed that "Decision Trees" provides the best recall among base classifiers and "Random Forest" has the best Precision.
Sooshiant Zakariapour - Hamid Jazayeriy - Mehdi Ezoji
DOI : 10.7508/jist.2017.18.003
Keywords : Mitosis detection ، Breast cancer grading ، Texture Features ، Ensemble learning ، Pathology
Natural disasters are an inevitable part of the world that we inhabit. Human casualties and financial losses are concomitants of these natural disasters. However, by an efficient crisis management program, we can minimize their physical and social damages. The real challenge in crisis management is the inability to timely receive the information from the stricken areas. Technology has come to the aid of crisis management programs to help find an answer to the problem. One of these technologies is wireless sensor network. With recent advances in this field, sensor nodes can independently respond to the queries from the users. This has transformed the processing of the queries into one of the most useful chapters in sensor networks. Without requiring any infrastructure, the sensor network can easily be deployed in the stricken area. And with the help of spatial query processing, it can easily provide managers with the latest information. The main problem, however, is the irregular shape of the area. Since these areas require many points to present them, the transmission of the coordinates by sensor nodes necessitates an increase in the number of data packet transmissions in the sensor network. The high number of packets considerably increases energy consumption. In related previous works, to solve this problem, line simplification algorithm s, such as Ramer-Douglas-Peucker (RDP), were used. These algorithms could lessen energy consumption by reducing the number of points in the shape of the area. In this article, we present a new algorithm to simplify packet shapes which can reduce more points with more accuracy. This results in decreasing the number of transmitted packets in the network, the concomitant reduction of energy consumption, and, finally, increasing network lifetime. Our proposed method was implemented in different scenarios and could on average reduce network’s energy consumption by 72.3%, while it caused only 4.5% carelessness which, when compared to previous methods, showed a far better performance.
Mohammad Shakeri - Seyed Majid Mazinani
DOI : 10.7508/jist.2017.18.004
Keywords : Wireless sensor network ، query processing ، spatial query ، crisis management ، image processing
The aim of this article is improving the accuracy of node classification in social network using Distributed Learning Automata (DLA). In the proposed algorithm using a local similarity measure, new relations between nodes are created, then the supposed graph is partitioned according to the labeled nodes and a network of Distributed Learning Automata is corresponded on each partition. In each partition the maximal spanning tree is determined using DLA. Finally nodes are labeled according to the rewards of DLA. We have tested this algorithm on three real social network datasets, and results show that the expected accuracy of presented algorithm is achieved.
Ahmad Rahnama Zadeh - Mohammad Reza Meybodi - Masoud Taheri Kadkhoda
DOI : 10.7508/jist.2017.18.005
Keywords : Social Network, ، Classification, ، Distributed Learning Automata, ، Node Labeling
The arithmetic units are the most essential in digital circuits’ construct, and the enhancement of their operation would optimize the whole digital system. Among them, multipliers are the most important operational units, used in a wide range of digital systems such as telecommunication signal processing, embedded systems and mobile. The main drawback of a multiplication unit is its high computational load, which leads to considerable power consumption and silicon area. This also reduces the speed that negatively affects the digital host functionality. Estimating arithmetic is a new branch of computer arithmetic implemented by discarding or manipulating a portion of arithmetic circuits and/or intermediate computations. Applying estimated arithmetic in arithmetic units would improve the speed, power consumption and the implementation area by sacrificing a slight amount of result accuracy. An estimated truncated floating-point multiplier for single precision operands which is capable of compensating the errors to a desired level by applying the least significant columns of the partial product matrix is developed and analyzed in this article. These errors are caused by removing a number of carry digits in the partial product matrix that have a direct contribution in rounding the floating-point numbers. The evaluation results indicate that the proposed method improves speed, accuracy and silicon area in comparison to those of the common truncated multiplication methods.
marziye fathi - Hooman Nikmehr
DOI : 10.7508/jist.2017.18.006
Keywords : estimated arithmetic; ، partial product matrix; ، rounding; ، truncated multiplier; ، error correction
Normally, the-state-of-the-art methods in field of object retrieval for large databases are achieved by training process. We propose a novel large-scale generic object retrieval which only uses a single query image and training-free. Current object retrieval methods require a part of image database for training to construct the classifier. This training can be supervised or unsupervised and semi-supervised. In the proposed method, the query image can be a typical real image of the object. The object is constructed based on Speeded Up Robust Features (SURF) points acquired from the image. Information of relative positions, scale and orientation between SURF points are calculated and constructed into the object model. Dynamic programming is used to try all possible combinations of SURF points for query and datasets images. The ability to match partial affine transformed object images comes from the robustness of SURF points and the flexibility of the model. Occlusion is handled by specifying the probability of a missing SURF point in the model. Experimental results show that this matching technique is robust under partial occlusion and rotation. The properties and performance of the proposed method are demonstrated on the large databases. The obtained results illustrate that the proposed method improves the efficiency, speeds up recovery and reduces the storage space.
Hasan Farsi - Reza Nasiripour - Sajjad Mohammadzadeh
DOI : 10.7508/jist.2017.18.007
Keywords : Object retrieval ، Speeded Up Robust Features (SURF) ، Large-scale dataset ، Supervised training ، Training-Free
In cognitive radio networks (CRN), resources available for use are usually very limited. This is generally because of the tight constraints by which the CRN operate. Of all the constraints, the most critical one is the level of permissible interference to the primary users (PUs). Attempts to mitigate the limiting effects of this constraint, thus achieving higher productivity is a current research focus and in this work, cooperative diversity is investigated as a promising solution for this problem. Cooperative diversity has the capability to achieve diversity gain for wireless networks. Thus, in this work, the possibility of and mechanism for achieving greater utility for the CRN when cooperative diversity is incorporated are studied carefully. To accomplish this, a resource allocation (RA) model is developed and analyzed for the heterogeneous, cooperative CRN. In the considered model, during cooperation, a best relay is selected to assist the secondary users (SUs) that have poor channel conditions. Overall, the cooperation makes it feasible for virtually all the SUs to improve their transmission rates while still causing minimal harm to the PUs. The results show a remarkable improvement in the RA performance of the CRN when cooperation is employed in contrast to when the CRN operates only by direct communication.
Mehdi Ghamari Adian
DOI : 10.7508/jist.2017.18.008
Keywords : Cognitive Radio Networks ، Cooperative Diversity ، Heterogeneous Networks ، Resource Allocation

About Journal

Affiliated to :Ict Research Institute at ACECR
Manager in Charge :Habibollah Asghari
Editor in Chief :Masood Shafiei
Editorial Board :
Abdolali Abdipoor
Mahmoud Naghibzadeh
Zabih Ghasemlooy
Mahmoud Moghavemi
Aliakbar Jalali
Ramazan Ali Sadeghzadeh
Hamidreza Sadegh Mohammadi
Saeed Ghazimaghrebi
Ahmad Khademzadeh
Shaban Elahi
Abbasali Lotfi
Alireza Montazemi
Ali Mohammad Djafari
Rahim Saeidi
ISSN :2322-1437
eISSN :2345-2773

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