Automated user modeling for personalized digital libraries

Digital libraries (DL) have become one of the most typical ways of accessing any kind of digitalized information. Due to this key role, users welcome any improvements on the services they receive from digital libraries. One trend used to improve digital services is through personalization. Up to now, the most common approach for personalization in digital libraries has been user-driven. Nevertheless, the design of efficient personalized services has to be done, at least in part, in an automatic way. In this context, machine learning techniques automate the process of constructing user models. This paper proposes a new approach to construct digital libraries that satisfy user’s necessity for information: Adaptive Digital Libraries, libraries that automatically learn user preferences and goals and personalize their interaction using this information

Аналіз та управління безпекою телекомунікаційних систем на основі інтелектуальних технологій

This paper presents the peculiarities of providing information, taking into account the subjective aspect of this process. The main purpose of the study is to develop an algorithm for analyzing and managing integrated security, which will unify the approaches to information security management. Security does not exist by itself, in isolation from a person. It is provided for a person and it is appreciated. Therefore, the notion of security has not only an objective but also a subjective aspect, since the assessment of its level is ultimately man. Using cognitive modeling methods can greatly improve the analysis and management of the security of the telecommunication system. The advantages of the cognitive approach are the ability to simulate poorly structured (poorly formalized) systems that are characterized by incomplete or uncertain knowledge of them. The application of the developed algorithm will allow the specialists to begin to develop appropriate computational procedures and modules, which can be further used in telecommunication system security. The results of the research will be useful for information security specialists.В данной статье анализируются особенности обеспечения защиты информации, принимая во внимание субъективную сторону данного процесса. Основной целью исследования является разработка алгоритма анализа и управления комплексной безопасностью, который позволит унифицировать подходы к управлению информационной безопасностью. Безопасность не существует сама по себе, в отрыве от человека. Она обеспечивается для человека и им же оценивается. Поэтому, понятие безопасности имеет не только объективную, но и субъективную сторону, поскольку оценка ее уровня проводится в конечном итоге человеком. Использование методов когнитивного моделирования позволяет значительно улучшить процессы анализа и управления безопасностью телекоммуникационной системы. Преимущества когнитивного подхода заключаются в возможности моделирования слабоструктурированных (тех, что плохо формализуются) систем, которые характеризуются неполнотой или неопределенностью знаний о них. Применение разработанного алгоритма позволит специалистам приступить к разработке соответствующих вычислительных процедур и модулей, которые могут быть в дальнейшем использоваться при обеспечении защиты телекоммуникационной системы. Результаты исследований будут также полезны службам, которые занимаются обеспечением информационной безопасности.  У даній статті аналізуються особливості забезпечення захисту інформації, приймаючи до уваги суб'єктивну сторону даного процесу. Основною метою дослідження є розробка алгоритму аналізу та управління комплексною безпекою, котрий дозволить уніфікувати підходи до управління інформаційною безпекою. Безпека не існує сама по собі, у відриві від людини. Вона забезпечується для людини і нею ж оцінюється. Тому, поняття безпеки має не тільки об'єктивну, але й суб'єктивну сторону, оскільки оцінка її рівня проводиться в кінцевому підсумку людиною. Використання методів когнітивного моделювання дозволяє значно покращити процеси аналізу та управління безпекою телекомунікаційної системи. Переваги когнітивного підходу полягають у можливості моделювання слабоструктурованих (тих, що погано формалізуються) систем, які характеризуються неповнотою або невизначеністю знань про них. Застосування розробленого алгоритму дозволить фахівцям приступити до розробки відповідних обчислювальних процедур і модулів, які можуть бути в подальшому використовуватися при забезпеченні захисту телекомунікаційної системи. Результати досліджень будуть також корисні службам, які займаються забезпеченням інформаційної безпеки

SciTech News Volume 71, No. 1 (2017)

Columns and Reports From the Editor 3 Division News Science-Technology Division 5 Chemistry Division 8 Engineering Division Aerospace Section of the Engineering Division 9 Architecture, Building Engineering, Construction and Design Section of the Engineering Division 11 Reviews Sci-Tech Book News Reviews 12 Advertisements IEEE

Proceedings of the 15th Conference on Knowledge Organization WissOrg'17 of theGerman Chapter of the International Society for Knowledge Organization (ISKO),30th November - 1st December 2017, Freie Universität Berlin

Wissensorganisation is the name of a series of biennial conferences / workshops with a long tradition, organized by the German chapter of the International Society of Knowledge Organization (ISKO). The 15th conference in this series, held at Freie Universität Berlin, focused on knowledge organization for the digital humanities. Structuring, and interacting with, large data collections has become a major issue in the digital humanities. In these proceedings, various aspects of knowledge organization in the digital humanities are discussed, and the authors of the papers show how projects in the digital humanities deal with knowledge organization.Wissensorganisation ist der Name einer Konferenzreihe mit einer langjährigen Tradition, die von der Deutschen Sektion der International Society of Knowledge Organization (ISKO) organisiert wird. Die 15. Konferenz dieser Reihe, die an der Freien Universität Berlin stattfand, hatte ihren Schwerpunkt im Bereich Wissensorganisation und Digital Humanities. Die Strukturierung von und die Interaktion mit großen Datenmengen ist ein zentrales Thema in den Digital Humanities. In diesem Konferenzband werden verschiedene Aspekte der Wissensorganisation in den Digital Humanities diskutiert, und die Autoren der einzelnen Beiträge zeigen, wie die Digital Humanities mit Wissensorganisation umgehen

The development of a fuzzy expert system to help top decision makers in political and investment domains

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel UniversityThe world’s increasing interconnectedness and the recent increase in the number of notable regional and international events pose greater and greater challenges for political decision-making, especially the decision to strengthen bilateral economic relationships between friendly nations. Typically, such critical decisions are influenced by certain factors and variables that are based on heterogeneous and vague information that exists in different domains. A serious problem that the decision-maker faces is the difficulty in building efficient political decision support systems (DSS) with heterogeneous factors. One must take many factors into account, for example, language (natural or human language), the availability, or lack thereof, of precise data (vague information), and possible consequences (rule conclusions). The basic concept is a linguistic variable whose values are words rather than numbers and are therefore closer to human intuition. A common language is thus needed to describe such information which requires human knowledge for interpretation. To achieve robustness and efficiency of interpretation, we need to apply a method that can be used to generate high-level knowledge and information integration. Fuzzy logic is based on natural language and is tolerant of imprecise data. Fuzzy logic’s greatest strength lies in its ability to handle imprecise data, and it is perfectly suited for this situation. In this thesis, we propose to use ontology to integrate the scattered information resources from the political and investment domains. The process started with understanding each concept and extracting key ideas and relationships between sets of information by constructing object paradigm ontology. Re-engineering according to the object-paradigm (OP) provided quality for the developed ontology where conceptualization can provide more expressive, reusable object and temporal ontology. Then fuzzy logic has been integrated with ontology. And a fuzzy ontology membership value that reflects the strength of an inter-concept relationship to represent pairs of concepts across ontology has been consistently used. Each concept is assigned a fixed numerical value representing the concept consistency. Concept consistency is computed as a function of strength of all the relationships associated with the concept. Fuzzy expert systems enable one to weigh the consequences (rule conclusions) of certain choices based on vague information. Rule conclusions follow from rules composed of two parts, the if antecedent (input) and the then consequent (output). With fuzzy expert systems, one uses fuzzy logic toolbox graphical user interface (GUI) tools to build up a fuzzy inference system (FIS) to aid in decision-making. This research includes four main phases to develop a prototype architecture for an intelligent DSS that can help top political decision makers

Integrated intelligent systems for industrial automation: the challenges of Industry 4.0, information granulation and understanding agents .

The objective of the paper consists in considering the challenges of new automation paradigm Industry 4.0 and reviewing the-state-of-the-art in the field of its enabling information and communication technologies, including Cyberphysical Systems, Cloud Computing, Internet of Things and Big Data. Some ways of multi-dimensional, multi-faceted industrial Big Data representation and analysis are suggested. The fundamentals of Big Data processing with using Granular Computing techniques have been developed. The problem of constructing special cognitive tools to build artificial understanding agents for Integrated Intelligent Enterprises has been faced

A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

A new data-driven neural fuzzy system with collaborative fuzzy clustering mechanism

© 2015 Elsevier B.V. In this paper, a novel fuzzy rule transfer mechanism for self-constructing neural fuzzy inference networks is being proposed. The features of the proposed method, termed data-driven neural fuzzy system with collaborative fuzzy clustering mechanism (DDNFS-CFCM) are; (1) Fuzzy rules are generated facilely by fuzzy c-means (FCM) and then adapted by the preprocessed collaborative fuzzy clustering (PCFC) technique, and (2) Structure and parameter learning are performed simultaneously without selecting the initial parameters. The DDNFS-CFCM can be applied to deal with big data problems by the virtue of the PCFC technique, which is capable of dealing with immense datasets while preserving the privacy and security of datasets. Initially, the entire dataset is organized into two individual datasets for the PCFC procedure, where each of the dataset is clustered separately. The knowledge of prototype variables (cluster centers) and the matrix of just one halve of the dataset through collaborative technique are deployed. The DDNFS-CFCM is able to achieve consistency in the presence of collective knowledge of the PCFC and boost the system modeling process by parameter learning ability of the self-constructing neural fuzzy inference networks (SONFIN). The proposed method outperforms other existing methods for time series prediction problems