E. Pejhan; M. Ghasemzadeh
Abstract
This research is related to the development of technology in the field of automatic text to image generation. In this regard, two main goals are pursued; first, the generated image should look as real as possible; and second, the generated image should be a meaningful description of the input text. our ...
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This research is related to the development of technology in the field of automatic text to image generation. In this regard, two main goals are pursued; first, the generated image should look as real as possible; and second, the generated image should be a meaningful description of the input text. our proposed method is a Multi Sentences Hierarchical GAN (MSH-GAN) for text to image generation. In this research project, we have considered two main strategies: 1) produce a higher quality image in the first step, and 2) use two additional descriptions to improve the original image in the next steps. Our goal is to focus on using more information to generate images with higher resolution by using more than one sentence input text. We have proposed different models based on GANs and Memory Networks. We have also used more challenging dataset called ids-ade. This is the first time; this dataset has been used in this area. We have evaluated our models based on IS, FID and, R-precision evaluation metrics. Experimental results demonstrate that our best model performs favorably against the basic state-of-the-art approaches like StackGAN and AttGAN.
D. Data
M. Hajizadeh-Tahan; M. Ghasemzadeh
Abstract
Learning models and related results depend on the quality of the input data. If raw data is not properly cleaned and structured, the results are tending to be incorrect. Therefore, discretization as one of the preprocessing techniques plays an important role in learning processes. The most important ...
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Learning models and related results depend on the quality of the input data. If raw data is not properly cleaned and structured, the results are tending to be incorrect. Therefore, discretization as one of the preprocessing techniques plays an important role in learning processes. The most important challenge in the discretization process is to reduce the number of features’ values. This operation should be applied in a way that relationships between the features are maintained and accuracy of the classification algorithms would increase. In this paper, a new evolutionary multi-objective algorithm is presented. The proposed algorithm uses three objective functions to achieve high-quality discretization. The first and second objectives minimize the number of selected cut points and classification error, respectively. The third objective introduces a new criterion called the normalized cut, which uses the relationships between their features’ values to maintain the nature of the data. The performance of the proposed algorithm was tested using 20 benchmark datasets. According to the comparisons and the results of nonparametric statistical tests, the proposed algorithm has a better performance than other existing major methods.
C.5. Operating Systems
M. Tajamolian; M. Ghasemzadeh
Abstract
In order to achieve the virtual machines live migration, the two "pre-copy" and "post-copy" strategies are presented. Each of these strategies, depending on the operating conditions of the machine, may perform better than the other. In this article, a new algorithm is presented that automatically decides ...
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In order to achieve the virtual machines live migration, the two "pre-copy" and "post-copy" strategies are presented. Each of these strategies, depending on the operating conditions of the machine, may perform better than the other. In this article, a new algorithm is presented that automatically decides how the virtual machine live migration takes place. In this approach, the virtual machine memory is considered as an informational object that has a revision number and it is constantly changing. We have determined precise criteria for evaluating the behavior of a virtual machine and automatically select the appropriate live migration strategy. Also in this article, different aspects of required simulations and implementations are considered. Analytical evaluation shows that using the proposed scheme and the presented algorithm, can significantly improve the virtual machines live migration process.
H.3.8. Natural Language Processing
A. Khazaei; M. Ghasemzadeh
Abstract
This paper compares clusters of aligned Persian and English texts obtained from k-means method. Text clustering has many applications in various fields of natural language processing. So far, much English documents clustering research has been accomplished. Now this question arises, are the results of ...
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This paper compares clusters of aligned Persian and English texts obtained from k-means method. Text clustering has many applications in various fields of natural language processing. So far, much English documents clustering research has been accomplished. Now this question arises, are the results of them extendable to other languages? Since the goal of document clustering is grouping of documents based on their content, it is expected that the answer to this question is yes. On the other hand, many differences between various languages can cause the answer to this question to be no. This research has focused on k-means that is one of the basic and popular document clustering methods. We want to know whether the clusters of aligned Persian and English texts obtained by the k-means are similar. To find an answer to this question, Mizan English-Persian Parallel Corpus was considered as benchmark. After features extraction using text mining techniques and applying the PCA dimension reduction method, the k-means clustering was performed. The morphological difference between English and Persian languages caused the larger feature vector length for Persian. So almost in all experiments, the English results were slightly richer than those in Persian. Aside from these differences, the overall behavior of Persian and English clusters was similar. These similar behaviors showed that results of k-means research on English can be expanded to Persian. Finally, there is hope that despite many differences between various languages, clustering methods may be extendable to other languages.
Mohammad AllamehAmiri; Vali Derhami; Mohammad Ghasemzadeh
Abstract
Quality of service (QoS) is an important issue in the design and management of web service composition. QoS in web services consists of various non-functional factors, such as execution cost, execution time, availability, successful execution rate, and security. In recent years, the number of available ...
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Quality of service (QoS) is an important issue in the design and management of web service composition. QoS in web services consists of various non-functional factors, such as execution cost, execution time, availability, successful execution rate, and security. In recent years, the number of available web services has proliferated, and then offered the same services increasingly. The same web services are distinguished based on their quality parameters. Also, clients usually demand more value added services rather than those offered by single, isolated web services. Therefore, selecting a composition plan of web services among numerous plans satisfies client requirements and has become a challenging and time-consuming problem. This paper has proposed a new composition plan optimizer with constraints based on genetic algorithm. The proposed method can find the composition plan that satisfies user constraints efficiently. The performance of the method is evaluated in a simulated environment.
Mohammad Ghasemzadeh
Abstract
Binary Decision Diagram (BDD) is a data structure proved to be compact in representation and efficient in manipulation of Boolean formulas. Using Binary decision diagram in network reliability analysis has already been investigated by some researchers. In this paper we show how an exact algorithm for ...
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Binary Decision Diagram (BDD) is a data structure proved to be compact in representation and efficient in manipulation of Boolean formulas. Using Binary decision diagram in network reliability analysis has already been investigated by some researchers. In this paper we show how an exact algorithm for network reliability can be improved and implemented efficiently by using CUDD - Colorado University Decision Diagram.
