Objective: The present research aims to examine the behavior of a widely used preposition in Modern Persian (Farsi), namely ‹az› within the framework of cognitive semantics. The analysis is geared towards determining the prototypical and various distinct senses of this preposition and proposing a semantic network for it.Method: The nature of the research is descriptive- analytical and the type is corpus-based. The selected corpus is Farhang-e Sokhan (Sokhan Dictionary) and the tools applied are those of cognitive semantics e.g. categorization tools, prototype, cognitive schemas, semantic predominance, semantic network and protoscene.Conclusion: ‹Az› shows that it fits the criteria for the determination of the prototypical sense of a linguistic item e.g. highest frequency of occurrence and composite formation. The prototypical sense of ‹Az› constitutes the core sense within a semantic network, which is presented. This network makes it possible to establish and explain the differences between the concrete and the abstract (metaphorical) functions of the preposition under study.

The root of 'SJD' and it's branches have used in 81 verses of the Holy Quran. Although we can find the usage of 'Mosqe' in other versas of Holy Quran, but in present reaserch only 81verses of 'sjd' have been studied. Knowing the Semantic network of 'sjd' root in Holy Quran to understand the status of Mosques, is very important. Mosque has an important role in Islamic society and the main factor of Islamic belief and Muslim sympathy. The sjd' verses have been mentioned in 32 chapters of the Holy Quran and 92 times have been repeated in the Holy Quran. These verses in 12 categories have been studied. Public prostration, not obeying of Iblis (Satan), Miraj (spiritual journey) from mosque to the mosque, spiritual centers protection, the zerar mosque, fulfilment of divine promise and memorial mosque of the Cave companion.There are relations between mosque and 'sjd' verses. Some of these relations are negative and others are positive.

The present research with aim to analyze the concept of beauty in the holy Quran with applying the semantic network has been scheduled that is from perceiving the meaning of a concept through studying the related concepts as the important and specific aspects of that concept and their relations become visible. For achieving this general mentioned goal, the stages of the conceptology of the beauty lexicon and beautifying (being adorned), the recognition of the lexicon position, the recognition of the conceptual relation between lexicons, inferring the key concepts proportion to beauties and beautifying and at the end, the analysis of the concept of real beauty which is leaded to good and the real beauty which is leaded to evil and adornment has been formed. The method of research is the analysis based on the semantic network and for this aim, the verses of the holy Quran, the Quran exegeses and the authentic lexical books have been used. The results of this research are: it should be differentiated between the concepts of beauty which results good and the concepts of real beauty and adornment which results evil. The concept of beauty and adornment (beautifying) intensively are intertwined. In reality, the instance of beauty and adornment (being adorned) can be itself cause, effect or concurrent with the other beauties (and adornment). In other word, the beauty and adornment constantly develops and grows in the successive rings. These productive concepts (beauty or adornment) have been selected as the key concepts. In the analysis of the concept of the real beauty leading to good and the real beauty leading to evil with studying the common concepts of the ijabi (affirmative or positive) and salbi (privative or negative) relation and the illuminative relationship which are among these concepts, it should be mentioned that the concept of faith (iman) in God has a main position in the beauty leading to good. In the real beauty leading to evil and adornment although at the beginning they are completely different but at the end of the path they leaded to one point. in both, kufr (unbelief) has a crucial role.

The Argot language is one of the standard varieties of language that is formed among young people or a group of delinquents. Each social group has its own terms and expressions that must be learned in order to enter that group. Argot language is not separate from the language. Rather, it is one of its various varieties. This language represents a heterogeneous society, with each group having an impact on language. One can argue beyond this, claiming that the difference between each Argot language and the language commonly depends on the group attribute that uses this Argot language. The more different these groups are, the more they use different language forms to establish and maintain a relationship with the linguistic community. ...

The current interdisciplinary, field study tries to determine and analyze the semantic network of some basic Persian verbs, by applying graph theory, in the general framework of cognitive linguistics, (cognitive) lexical semantics, and computational linguistics. Carroll (2008: 110) believes that the structure of the lexicon is composed of semantic networks with interconnected components. These components are concepts, here the verbs, that are connected to each other through various relationships. Saeed (2003: 63), while discussing the semantic relations hold between lexical items, maintains that “, there are a number of different types of lexical relations”,and “, A particular lexeme may be simultaneously in a number of these relations. ”, So, the research objective is to identify the strongest and most frequent lexical (sense) and intralingual relations between 50 basic Persian verbs in the mental lexicon of Persian speakers whose mother tongue, as well as whose mothers’,mother tongue, is Persian by availing from graphs and computer science. To meet this end, while having in mind the importance of frequency mentioned by different researchers, such as Schur (2007) and Bijankhan et al. (2014), 50 basic verbs of high-frequency were randomly selected, by a consolidated and comparative method, from the verified lists of Ebadi et al. (2014), Bijankhan et al. (2014) and Sahraee et al. (2017) and a new list was formed. Then the verbs were included in Schur’, s (2007) questionnaire after it was nativized. Since the current research is mainly based on the cognitive approach, the clear role of examinees in semantic encoding and decoding was of particular importance, therefore, the data were collected from their questionnaires. Persian-speaking students studying in a Scientific-Applied Center as well as those of the Sharif University of Applied Science and Technology in Tehran made the research population. The research sample consisted of 137 students and according to the table of Morgan and Karjesi (1985), the number of approved questionnaires for these examinees was 101. Therefore, 101 questionnaires were distributed among the examinees. The questionnaire consisted of two parts. The first part included the personal information of the examinees (consisting of their age, education, gender, and mother tongue) and the second part included a two-column table. In the first column, a verb from the new list was presented, and the examinees were asked to write in the second column all the verbs in the list that had a semantic relation, collocation, or similarity with this verb. It was also explained to them that they could use each verb several times. Besides, an audio file was presented to them which explained how to respond correctly. From among the questionnaires, 16 were excluded because the examinees had used verbs outside the list for semantic relations. From the remaining 84 questionnaires, regarding the viewpoints of Guida and Lancey (2007), those questionnaires were selected that identified more than 75% of the verbs as related to each other. That is, the questionnaires of the examinees who had only twelve isolated vertices (or more) were deleted. Finally, 79 standard questionnaires remained which were reviewed and analyzed. To indicate the pattern of semantic relations between the verbs, the authors used graphs. A graph is a set including two components: vertices and edges. Vertices are members of the set that are connected by defined relations or edges. By applying Java and Python soft wares, a social graph (a graph in which each semantic relation/connection is drawn by at least one examinee) and shared graphs (graphs that show which percent of the examinees have a semantic relation/connection between two or more verbs) were drawn and the features of the social graph, highfrequency sense and intralingual relations, the number of drawn edges and semantic clusters were determined. The findings reveal that the social graph pattern represents sense relations of synonymy, reverse antonymy, polysemy, entailment (including troponymy (hyponymy) and meronymy) as well as causative relation and collocation. From among these, collocation (29. 61%), entailment (23. 85%), and reverses (16. 71%) are the most frequent semantic relations. In this graph, the verbs to do, to understand, to go and to move have the highest degree of vertices, and the verbs to reach, to deliver, to return, and to come have the highest average size of the semantic cluster component. The finding of this research is the identification of the semantic network of the basic verbs in the mental lexicon of the Persian-speakers’,examinees. This not only can be used in preparing and compiling proper materials for basic lessons of non-Persian speakers who are learning Persian, but also in applying new educational methods such as teaching verb clusters. Moreover, identifying similarities and differences between the graphs of sense and intralingual relations of bilingual and foreign Persian learners and comparing them with the above-mentioned social graph could be a great subject for future studies.

Background and Objectives: Embedding social networks has attracted researchers’ attention so far. The aim of network embedding is to learn a low-dimensional representation of each network vertex while maintaining the structure and characteristics of the network. Most of these existing network embedding methods focus on only preserving the structure of networks, but they mostly ignore the semantic and centrality-based information. Moreover, the vertices selection has been done blindly (greedy) in the existing methods.Methods: In this paper, a comprehensive algorithm entitled CSRW stands for centrality, and a semantic-based random walk is proposed for the network embedding process based on the main criteria of the centrality concept as well as the semantic impact of the textual information of each vertex and considering the impact of neighboring nodes. in CSRW, textual analysis based on the BTM topic modelling approach is investigated and the final display is performed using the Skip-Gram model in the network.Results: The conducted experiments have shown the robustness of the proposed method of this paper in comparison to other existing classical approaches such as DeepWalk, CARE, CONE, COANE, and DCB in terms of vertex classification, and link prediction. And in the criterion of link prediction in a Subgraph with 5000 members, an accuracy of 0.91 has been reached for the criterion of closeness centrality and is better than other methods.Conclusion: The CSRW algorithm is scalable and has achieved higher accuracy on larger datasets.

The importance and demand of visual scene understanding have been increasing because of autonomous systems development. Optical flow is known as an important tool for scene understanding. Current optical flow methods present general assumptions and spatial homogeneous for spatial structure of flow. In fact, the optical flow in an image depends on object class and the type of object movement. The first assumption in many methods in this field is the brightness constancy during movements of pixels between frames. This assumption is proven to be inaccurate in general. In this paper, we use recent development of deep convolutional networks in semantic segmentation of static scenes to divide an image in to different objects and also depends on type of the object different movement patterns are defined. Next, estimation of the optical flow is performed by using deep neural network for initial image which has been semantically segmented. The proposed method provides minimum error in optical flow measures for KITTI-2015 database and results in more accurate segmentation compared to state-of-the-art methods for several natural videos.