Book
Intelligent Computing: SAI 2020: Volume 3
This book focuses on the core areas of computing and their applications in the real world. Presenting papers from the Computing Conference 2020 covers a diverse range of research areas, describing various detailed techniques that have been developed and implemented.
The Computing Conference 2020, which provided a venue for academic and industry practitioners to share new ideas and development experiences, attracted a total of 514 submissions from pioneering academic researchers, scientists, industrial engineers and students from around the globe. Following a double-blind, peer-review process, 160 papers (including 15 poster papers) were selected to be included in these proceedings.
Featuring state-of-the-art intelligent methods and techniques for solving real-world problems, the book is a valuable resource and will inspire further research and technological improvements in this important area.
Clustering large and heterogeneous data of user-profiles from social media is problematic as the problem of finding the optimal number of clusters becomes more critical than for clustering smaller and homo- geneous data. We propose a new approach based on the deformed R ́enyi entropy for determining the optimal number of clusters in hierarchical clustering of user-profile data. Our results show that this approach allows us to estimate R ́enyi entropy for each level of a hierarchical model and find the entropy minimum (information maximum). Our approach also shows that solutions with the lowest and the highest number of clusters correspond to the entropy maxima (minima of information).
In this paper, we consider opinion word extraction, one of the key problems in sentiment analysis. Sentiment analysis (or opinion mining) is an important research area within computational linguistics. Opinion words, which form an opinion lexicon, describe the attitude of the author towards certain opinion targets, i.e., entities and their attributes on which opinions have been expressed. Hence, the availability of a representative opinion lexicon can facilitate the extraction of opinions from texts. For this reason, opinion word mining is one of the key issues in sentiment analysis. We designed and implemented several methods for extracting opinion words. We evaluated these approaches by testing how well the resulting opinion lexicons help improve the accuracy of methods for determining the polarity of the reviews if the extracted opinion words are used as features. We used several machine learning methods: SVM, Logistic Regression, Naive Bayes, and KNN. By using the extracted opinion words as features we were able to improve over the baselines in some cases. Our experiments showed that, although opinion words are useful for polarity detection, they are not su fficient on their own and should be used only in combination with other features.
Proceeding of the 15th International Conference on Artificial Intelligence: Methodology, Systems, Applications , AIMSA 2012, Varna, Bulgaria, September 12-15, 2012.
Almost all of the technologies that are now part of the cloud paradigm existed before, but so far the market has not been proposals that bring together emerging technologies in a single commercially attractive solution. However, in the last decade, there were public cloud services, through which these technologies, on the one hand, available to the developer, and on the other - it is clear to the business community. But many of the features that make cloud computing attractive, may be in conflict with traditional models of information security.
Due to the fact that cloud computing bring with them new challenges in the field of information security, it is imperative for organizations to control the process of information risk management in the cloud. In this article on the basis of Common Vulnerability Scoring System, allowing to determine the qualitative indicator of exposure to vulnerabilities of information systems, taking into account environmental factors, we propose a method of risk assessment for different types of cloud deployment environments.
Information Risk Management, determine the applicability of cloud services for the organization is impossible without understanding the context in which the organization operates and the consequences of the possible types of threats that it may face as a result of their activities. This paper proposes a risk assessment approach used in the selection of the most appropriate configuration options cloud computing environment from the point of view of safety requirements. Application of risk assessment for different types of deployment of cloud environments will reveal the ratio counter possible attacks and to correlate the amount of damage to the total cost of ownership of the entire IT infrastructure of the organization.
This paper is an overview of the current issues and tendencies in Computational linguistics. The overview is based on the materials of the conference on computational linguistics COLING’2012. The modern approaches to the traditional NLP domains such as pos-tagging, syntactic parsing, machine translation are discussed. The highlights of automated information extraction, such as fact extraction, opinion mining are also in focus. The main tendency of modern technologies in Computational linguistics is to accumulate the higher level of linguistic analysis (discourse analysis, cognitive modeling) in the models and to combine machine learning technologies with the algorithmic methods on the basis of deep expert linguistic knowledge.
This book constitutes the refereed proceedings of the 12th Industrial Conference on Data Mining, ICDM 2012, held in Berlin, Germany in July 2012. The 22 revised full papers presented were carefully reviewed and selected from 97 submissions. The papers are organized in topical sections on data mining in medicine and biology; data mining for energy industry; data mining in traffic and logistic; data mining in telecommunication; data mining in engineering; theory in data mining; theory in data mining: clustering; theory in data mining: association rule mining and decision rule mining.
We consider certain spaces of functions on the circle, which naturally appear in harmonic analysis, and superposition operators on these spaces. We study the following question: which functions have the property that each their superposition with a homeomorphism of the circle belongs to a given space? We also study the multidimensional case.
We consider the spaces of functions on the m-dimensional torus, whose Fourier transform is p -summable. We obtain estimates for the norms of the exponential functions deformed by a C1 -smooth phase. The results generalize to the multidimensional case the one-dimensional results obtained by the author earlier in “Quantitative estimates in the Beurling—Helson theorem”, Sbornik: Mathematics, 201:12 (2010), 1811 – 1836.
We consider the spaces of function on the circle whose Fourier transform is p-summable. We obtain estimates for the norms of exponential functions deformed by a C1 -smooth phase.