Developing Adaptive and Personalized Mobile Applications: A Framework and Design Issues

The rapid growth of mobile technology has expedited ubiquitous information access via handheld devices. However, the fundamental natures of mobile information systems are different from those of desktop applications in terms of purpose of use, device features, communication networks, and working environments. This poses various challenges to mobile information systems on how to deliver and present multimedia content in an effective and adaptive manner. One of the major challenges is to deliver personalized information to the right person in a preferred format based on the changing environment. This paper proposes an innovative framework for developing mobile applications that deliver personalized, context-aware, and adaptive content to mobile users. The framework consists of four major components: information selection, content analysis, media transcoding, and customized presentation. It can be applied to a variety of mobile applications such as mobile web, news alert services, and mobile commerce

Пошук і реферування в системі електронного документообігу

Робота присвячена проблемі пошуку документів у масиві за атрибутами та на основі повнотекстового пошуку. Представлено модифікований метод рубрикації та метод реферування на основі рубрикації. Показано переваги використання цього підходу на прикладі системи електронного документообігу SmartBase.SEDO.This work deals with the problem of document search in arrays by attributes and uses full-text search technology. Modification of rubrication method is presented and abstracting rubrication-based method is developed. The advantages of this conception usage is demonstrated on the electronic documents circulation system SmartBase.SEDO

THE METHOD FOR DETECTING PLAGIARISM IN A COLLECTION OF DOCUMENTS

The development of the intelligent system for searching for plagiarism by combining two algorithms of searching fuzzy duplicate is considered in this article. This combining contributed to the high computational efficiency. Another advantage of the algorithm is its high efficiency when small-sized documents are compared. The practical use of the algorithm makes it possible to improve the quality of the detection of plagiarism. Also, this algorithm can be used in different systems text search

SciTech News Volume 70, No. 1 (2016)

Columns and Reports From the Editor 3 SciTech News Call for Articles 3 Assistant Editor wanted 4 Division News Science-Technology Division 5 Chemistry Division 7 Engineering Division 12 Aerospace Section of the Engineering Division 13 Call for Nominations & Applications Sparks Award for Professional Development11 Reviews Sci-Tech Book News Reviews 1

Towards Personalized and Human-in-the-Loop Document Summarization

The ubiquitous availability of computing devices and the widespread use of the internet have generated a large amount of data continuously. Therefore, the amount of available information on any given topic is far beyond humans' processing capacity to properly process, causing what is known as information overload. To efficiently cope with large amounts of information and generate content with significant value to users, we require identifying, merging and summarising information. Data summaries can help gather related information and collect it into a shorter format that enables answering complicated questions, gaining new insight and discovering conceptual boundaries. This thesis focuses on three main challenges to alleviate information overload using novel summarisation techniques. It further intends to facilitate the analysis of documents to support personalised information extraction. This thesis separates the research issues into four areas, covering (i) feature engineering in document summarisation, (ii) traditional static and inflexible summaries, (iii) traditional generic summarisation approaches, and (iv) the need for reference summaries. We propose novel approaches to tackle these challenges, by: i)enabling automatic intelligent feature engineering, ii) enabling flexible and interactive summarisation, iii) utilising intelligent and personalised summarisation approaches. The experimental results prove the efficiency of the proposed approaches compared to other state-of-the-art models. We further propose solutions to the information overload problem in different domains through summarisation, covering network traffic data, health data and business process data.Comment: PhD thesi

Data Science and Knowledge Discovery

Data Science (DS) is gaining significant importance in the decision process due to a mix of various areas, including Computer Science, Machine Learning, Math and Statistics, domain/business knowledge, software development, and traditional research. In the business field, DS's application allows using scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data to support the decision process. After collecting the data, it is crucial to discover the knowledge. In this step, Knowledge Discovery (KD) tasks are used to create knowledge from structured and unstructured sources (e.g., text, data, and images). The output needs to be in a readable and interpretable format. It must represent knowledge in a manner that facilitates inferencing. KD is applied in several areas, such as education, health, accounting, energy, and public administration. This book includes fourteen excellent articles which discuss this trending topic and present innovative solutions to show the importance of Data Science and Knowledge Discovery to researchers, managers, industry, society, and other communities. The chapters address several topics like Data mining, Deep Learning, Data Visualization and Analytics, Semantic data, Geospatial and Spatio-Temporal Data, Data Augmentation and Text Mining