With the significant growth of social media, individuals and organizations are increasingly using public opinion in these media to make their own decisions. The purpose of Sentiment Analysis is to Automatically extract peoplechr('39')s emotions from these social networks. Social networks related to financial markets, including stock markets, have recently attracted the attention of many individuals and organizations. People on these social networks share their opinions and ideas about each share in the form of a post or tweet. In fact, sentiment analysis in this area is measuring peoplechr('39')s attitudes toward each share. One of the basic approaches in automatic analysis of emotions is lexicon-based methods. Most conventional lexicon is manually extracted, which is a very difficult and costly process. In this article, a new method for extracting a lexicon Automatically in the field of stock social networks is proposed. A special feature of these networks is the availability of price information per share. Taking into account the price information of the share on the day of tweeting for that share, we extracted lexicon to improve the quality of opinion mining in these social networks. To evaluate the lexicon produced using the proposed method, we compared it with the Persian version of the SentiStrength lexicon, which is designed for general purpose. Experimental results show a 20% improvement in accuracy compared to the use of general lexicon.

Test data generation is one of the costly parts of the software testing, which is performed according to the designed test cases. The problem of designing test cases and then generating Optimized test data is one of the challenges of the software testing, including the mutation testing technique. mutation testing has the ability to measure the test cases quality and determine the adequate test cases. However, to perform mutation testing, you need a test set that provides the maximize Coverage of source code and thus have the ability to identify the program errors. In this work, we use code coverage techniques to design test cases and Automatically Generate Optimized test data using the meta-heuristic FA-MABC algorithm. The results are a test suite that cover and test the maximum number of source code lines. Such test suite is more likely to identify errors and get a higher score in the mutation testing. In the proposed method to obtain effective test cases, first Generated test cases are applied to mutation testing and then effective test cases are extracted using the Extinguished mutation table. The results of the evaluation show that the FA-MABC algorithm reduces the time of the test data generation, and “modified condition / decision coverage”, increases the mutation score.

Protective steel doors are widely used in buildings due to their high resistance against the impact loads. However, its heavy weight has been always considered as a major drawback for these doors. In this paper, a new Optimized stiffened impact-protective steel door incorporating sandwich panel with aluminum foam core (OSSA) is examined. This door consists of two face sheets, main and secondary stiffeners, and aluminum foam as the inner core. In order to optimize the door, at first the rigidity and weight functions of the stiffened steel door were extracted. Then an optimal door weighing 42% less than the primary door was obtained. Due to the high energy absorption capacity of the combined foam core and stiffened steel door structure, the use of aluminum foam core in the Optimized steel door was proposed. By doing numerical analysis, and depending on the thickness of the face sheet of OSSA, 20 to 32% reduction in the maximum displacement was observed. The results also showed that, with 67% increase in the peak overpressure, OSSA has kept almost the same maximum displacement as that of the steel door without an aluminum foam. In other words, by using aluminum foam core in the Optimized stiffened door, the door will resist 67% more impact load.

We present FarsWikiKG, a Persian knowledge graph extracted from Wikipedia. Wikipedia infoboxes have been used as a valuable resource for building knowledge graphs in recent years. FarsWikiKG consists of more than 2 million entities, as well as 5. 7 million facts about the entities. Using Wikidata, we constructed an ontology with more than 6000 classes representing entity types. As the second Persian knowledge graph, which has the ability of self-update, FarsWikiKG shows improvement on NLP tasks, especially question answering systems. Although FarsWikiKG is a dynamic knowledge graph, our evaluation shows a coverage of 90% on Persian Wikipedia pages. As Wikipedia information is constantly changing, a fixed knowledge graph can provide unstable data to the user. The proposed system, in addition to solving the problem of unstable data, reduces the need for experts to extract and construct knowledge graphs manually. Storing information in RDF as a standard method of storing knowledge graph information, FarsWikiKG allows NLP systems to run SPARQL queries on it.

A word is the smallest unit in a language that has ‘ form’ and ‘ meaning’ . The word might have more than one meaning in which its exact meaning is determined according to the context it is appeared. Collecting all words’ senses manually is a tedious and time consuming task. Moreover, it is possible that the words’ meanings change over time such that the meaning of an existing word will become unusable or a new meaning will be added to the word. Computational methods is one of the approaches used for identifying words’ senses with respect to the linguistic contexts. In this paper, we put an effort to propose an algorithm to identify senses of Persian words Automatically without a human supervision. To reach this goal, we utilize the word embedding method in a vector space model. To build words’ vectors, we use an algorithm based on the neural network approach to gather the context information of the words in the vectors. In the proposed model of this research, the divisive clustering algorithm as one of hierarchical clustering algorithms fits with the requirements of our research question. In the proposed model, two modes, namely the Sentence-based and the Context-based, are introduced to identify words’ senses. In the Sentence-based mode, all of the words in a sentence that contain the target word are involved to build the sentence vector; while in the Context-based mode, only a limited number of surrounding words of the target word is involved to build the sentence vector. Two evaluation metrics, namely internal and external, are required to evaluate the performance of the clustering algorithm. The silhouette score for each cluster is computed as the internal evaluation metric for both modes of the proposed model. The external evaluation requires a gold standard data for which a data set containing 20 ambiguous words and 100 sentences for each target word is developed. According to the obtained results of the internal evaluation, the Sentence-based mode has higher density of clusters than the Contextbased mode, and the difference between them is statistically significant. According to the V-and F-measure evaluation metrics in the external evaluation, the Contextbased mode has obtained higher performance against the baselines with statistically significant difference.