Identifying Repeated Sections within Documents

Social surveys and observations show a significant lag in the popularity of the sports industry in Russia from European countries and the United States, as well as the insufficient influence of marketing on the promotion of sports services, which increases the urgency of this problem. The purpose of the study is to consider the features of the digital transformation of sports marketing and identify ways to use it to increase the activity of consumers of services of sports events and facilities. The basis of the research methodology is an interdisciplinary approach for complementary analysis of a complex set of economic, socio-demographic, psychological, information technology and other factors. The empirical basis of the study was the materials of selective observations and surveys of fans and spectators of sports events, processed by the methods of grouping, comparative analysis, and expert evaluation. The main result of the study is the substantiation of the need to overcome Russia’s lag behind other countries in the development of the sports industry, visiting sports events and venues, which requires a change in the relationship between the management of sports clubs, associations and audiences, and wider use of the possibilities of digital information and communication technologies. The article, as elements of scientific novelty, shows the dynamics of the ratio of various sources of news information and changes in the consumer behavior of target audiences of viewers, generalizes empirical material on the use of social networks as a communication channel and substantiates the need for their activation as a marketing tool, as well as expanding the use of methodological approaches “analytics of the digital footprint”, “digital portrait” of consumers of sports services to select the most effective management solutions to increase the attendance of sports facilities and events. The practical significance of the study lies in the fact that the digital transformation of the sports industry causes significant changes in the relationship with the target audiences of fans, spectators, fans aimed at enhancing visits to sports events and facilities, as well as increasing their attention to broadcasts on smartphones, tablets, mobile phones, personal computers. A special role is played by social networks integrated with mobile applications as marketing tools that use the discussion of news information, the exchange of opinions and emotions, the demonstration of photo and video materials based on the results of sports events, polls of fans, spectators, fans to promote the services of sports events, evaluate their effectiveness. for sponsors, charity events. The article outlines the directions for the wider use of modern digital technologies, such as social networks integrated with mobile applications and online services, to improve marketing efficiency, stimulate users’ interest in watching sports events.

Contributors

Two models of the maturity exams in information technologies have been developed in Lithuania. The first one is intended to evaluate the students' skills for using information and communication technologies (ICT). Another is focused on programming skills and is intended to promote the professional studies (informatics) in higher education. The first national exam in information technologies (programming) was launched in 2006. The exam consists of a set of tests (questions of IT and questions of programming) and two practical programming tasks. The goal of practical tasks is to create programs for given tasks. Developing programs is one of the most important parts of the exam and also one of the most difficult tasks for students. The paper deals with solutions of practical tasks --- students' developed programs during the national exams in information technologies (programming) in 2006 and 2007.

The present paper is focused on information extraction from key fields of invoices using two different methods based on sequence labeling. Invoices are semi-structured documents in which data can be located based on the context. Common information extraction systems are model-driven, using heuristics and lists of trigger words curated by domain experts. Their performances are generally high on documents they have been trained for but processing new templates often requires new manual annotations, which is tedious and time-consuming to produce. Recent works on deep learning applied to business documents claimed a gain in terms of time and performance. While these systems do not need manual curation, they nevertheless require a large amount of data to achieve good results. In this paper, we present a series of experiments using neural networks approaches to study the trade-off between data requirements and performance in the extraction of information from key fields of invoices (such as dates, document numbers, types, amounts...). The main contribution of this paper is a system that achieves competitive results using a small amount of data compared to the state-of-the-art systems that need to be trained on large datasets, that are costly and impractical to produce in real-world applications.

A massive amount of data/information that is buried in unstructured data sources such as bridge rehabilitation reports offer opportunities to complement other data sources such as structural health monitoring data for improved prediction of future bridge conditions. Extracting these data/information from their sources is, however, challenging for two reasons: (1) utilizing textual data for analytics remains to be a challenge due to the inherently unstructured nature of these data; and (2) existing information extraction methods, which aim to extract structured data/information from text, are limited in their capabilities and generalizability (e.g., a method developed to extract certain information from one type of document may not perform well on another type). To address these gaps, this paper proposes a deep learning-based information extraction (IE) model that automatically recognizes and extracts bridge-related data/information (e.g., bridge deficiencies) from multiple types of unstructured textual sources and represents the extracted data/information in a structured format to support further data analytics. The proposed IE model utilizes deep learning-based BiLSTM; begin, inside, and outside (BIO) encoding for the phrase-level segmentation; and conditional random fields (CRF) for both word-level and phrase-level labeling for automatic sequence labeling. The paper discusses the proposed model and its performance results.

Digital Libraries currently use several advanced information technologies to organize information and make it easy accessible to users. Current digital library trends to be dynamic digital library [1]. It is possible that business rules also can be approached for improving dynamic digital library. Business rules [2] are statements that define or contain some aspects of IT systems by providing a foundation for understanding how an IT system functions. At present, the need for automated business rules is becoming more essential because of the increasing usage of IT systems. However, it is not easy to extract business rules because they are written in a natural language structure and much of it is ignored. Therefore, one important question in this research area is how to automatically extract a business rule from a document? Based on this, information extraction (IE) [3] typically can be applied. Basically, IE is to transform text into information that is more readily analyzed. We believe that if the content of a document is decreased, the accuracy of rules extraction may be increased logically. With this assumption, if irrelevant information is filtered from the document, it is possible to easily extract business rules from the rest. Therefore, this research proposes a method based on probabilistic text classifier to extract synopsis information. It could be said that this work is the pre-processing of a business rules extraction methodology.

TCM (Traditional Chinese Medicine) medical records are the great medical wealth of the Chinese nation. Since 1980s, China has begun to attach importance to the heritage of TCM. That how to effectively and maximize use these valuable resources is a problem for the TCM informationization. However, the dialectical information that includes the core idea of the famous doctors is still stored in the form of natural language. Obtaining structural information must rely on information extraction technology. With the development of science and technology information, the symptoms and pathogenesis entity recognition of TCM medical records is the key to build the TCM information extraction system. Conditional Random Fields (CRF) proposed by Lafferty et al in 2001, which combines the features of the Maximum Entropy Model and Hidden Markov Model. In recent years, it achieved good results in word segmentation, part of speech tagging and named entity recognition sequence labeling tasks [1]. This paper use the latest CRFidea, formulate appropriate feature template, using 500 marked TCM medical records from"11th five-year plan" medical records database to train the CRF model that suit for the symptoms and pathogenesis entity recognition. For verifying the model accuracy, we use this CRF model to label the symptoms and pathogenesis entity. After ten-fold cross validation, its symptoms entity F1 measure reached 81.53%, the pathogenesis entity F1 measure was 83.98%. Experimental results show that its performance is very high, it is suitable for the identification of TCM medical records information extraction.

УДОСКОНАЛЕННЯ НОРМАТИВНО-ПРАВОВОГО ЗАБЕЗПЕЧЕННЯ ПІДГОТОВКИ ФАХІВЦІВ ІТ-ПІДРОЗДІЛІВ ОРГАНІВ ДЕРЖАВНОЇ ВЛАДИ УКРАЇНИ

Due to the increasing of energy consumption in Information and Communication Technology (ICT), green computing has recently drawn a lot of attention. However, the application of green networking to Wireless Mesh Networks (WMN) has seldom been reported in the literature. In this paper, we propose a new framework for energy management in TDMA-based WMNs to support energy efficient communications. Our proposed framework aims at finding an optimal tradeoff between the achieved network throughput and energy consumption. To do so, we use resource planning through green routing and link scheduling. Specifically, we first propose an Optimal approach, called O-GRLS, by formulating the problem as an integer linear program (ILP). As this problem is known to be NP-hard, we then propose a simple yet efficient Ant Colony-based approach, called AC-GRLS to solve the formulated ILP problem. Through extensive simulations, we show that our green framework is able to achieve significant gains in terms of energy consumption as well as achieved network throughput, compared to the Shortest Path (SP) routing. Specifically, we show that the same performance as SP can be attained with minimum energy consumption. On the other hand, with the same energy cost, our proposed framework enhances the achieved throughput by up to 30% compared to SP routing.

Fine-grained spatial information extraction in radiology as two-turn question answering

Effective and efficient selection of projects in strategy-focused environments is extremely vital and consisted of complex processes for Information and communication technology (ICT) sectors to maintain organizational sustainability. In order to decide which of the proposed projects should be selected, various conflicting criteria must be considered. Even though there are many studies available to assist decision makers in doing the process of project selection, there is no agent based system that systematically does the project selection under uncertain status. Therefore, based on the concepts of agent, a framework have been developed that select projects based on fuzzy set theory and its application in integer linear programming. This framework provides managers with a flexible and interactive fuzzy agent to select the optimal projects for increasing the organizations' value.

Constrained routing in multi-partite graph to solve VNF placement and chaining problem

Emerging communication technologies are now leading developers to design IT systems taking into count their energy-related considerations. Much research performed in the area of ad-hoc wireless networks tends to distribute the flows over all nodes of the network, which increases the amount of energy consumed by each node and reduces longevity of the network. To overcome these problems, this paper seeks to aggregate a set of flows within a number of nodes that is as low as possible in order to be capable of routing those flows. This proposal allows to maximize the number of network nodes that may be turned off. The proposed solution was formulated as an integer linear programming (ILP) problem using a set of energy and quality of service (QoS) constraints. This formulation minimizes the total energy consumed by the nodes to construct a topology network that is capable of meeting QoS requirement for a set flows inserted into the network. To evaluate the efficiency of the proposed model, a performance-based comparison was conducted with another routing model. The simulation results show that the proposed model offers better performance in terms of global energy consumption and network load

As a result of the modernization of the information space and various information technologies, we have the opportunity to watch wars, natural disasters, sports events and others on television. Every day we receive international news from all over the world. A characteristic feature of the Internet today is media convergence, the technological fusion of content in various mass media. In recent years, the Internet has truly become a hub of convergence, a place where music, television shows, radio stations, newspapers, magazines, books, and movies are created, distributed, and presented. Today, computers and mobile phones are the most popular digital devices for online convergence, which can themselves create or stream books, movies, music, video games, and any other form of digital media. Today's global media market does not consist of any particular place, space or habit, it no longer has dimensions such as time and borders. It is permanently established in the digital world and cyberspace, on the screens of all computers and interactive televisions connected to the Internet, as well as mobile phones and still unbelievable inventions. In the digital age, where technologies are evolving every day, it is very important to adapt to innovations and news in time. In the modern world, the introduction of technological innovations in the means of mass communication is taking place rapidly, which has completely changed the forms of information dissemination. Today, accuracy, promptness, flexibility and interactivity are the key elements of modern multimedia journalism.

Information and Communication Technologies are increasingly entering in all aspects of our life and in all sectors, opening a world of unprecedented scenarios where people interact with electronic devices embedded in environments that are sensitive and responsive to the presence of users. Indeed, since the first examples of “intelligent” buildings featuring computer aided security and fire safety systems, the request for more sophisticated services, provided according to each user’s specific needs has characterized the new tendencies within domotic research. The result of the evolution of the original concept of home automation is known as Ambient Intelligence (Aarts & Marzano, 2003), referring to an environment viewed as a “community” of smart objects powered by computational capability and high user-friendliness, capable of recognizing and responding to the presence of different individuals in a seamless, not-intrusive and often invisible way. As adaptivity here is the key for providing customized services, the role of person sensing and recognition become of fundamental importance. This scenario offers the opportunity to exploit the potential of face as a not intrusive biometric identifier to not just regulate access to the controlled environment but to adapt the provided services to the preferences of the recognized user. Biometric recognition (Maltoni et al., 2003) refers to the use of distinctive physiological (e.g., fingerprints, face, retina, iris) and behavioural (e.g., gait, signature) characteristics, called biometric identifiers, for automatically recognizing individuals. Because biometric identifiers cannot be easily misplaced, forged, or shared, they are considered more reliable for person recognition than traditional token or knowledge-based methods. Others typical objectives of biometric recognition are user convenience (e.g., service access without a Personal Identification Number), better security (e.g., difficult to forge access). All these reasons make biometrics very suited for Ambient Intelligence applications, and this is specially true for a biometric identifier such as face which is one of the most common methods of recognition that humans use in their visual interactions, and allows to recognize the user in a not intrusive way without any physical contact with the sensor. A generic biometric system could operate either in verification or identification modality, better known as one-to-one and one-to-many recognition (Perronnin & Dugelay, 2003). In the proposed Ambient Intelligence application we are interested in one-to-one recognition,