Sanja Vraneš

Investment Decision Making

Publisher Summary For large, diversified, multiproduct companies, investment decision making is an extremely complicated and multidimensional process. One of the main shortcomings in investment decision making is reliance on a single method and to rectify this, a multi-paradigm approach is recommended. An investment decision making expert system must aid the project analyst and investment decision maker to determine whether a project is acceptable; if it is, whether it is the best alternative; and to calculate the extent of the decision sensitivity to certain critical assumptions. Numerous measures of the financial profitability of each alternative can be calculated using standard financial tables based on an integrated documentation system. A spreadsheet seems to be the most relevant platform for this analysis. A full analysis should be done for major project alternatives, and the summary table should be transferred to the expert system that would perform a heuristic classification of the alternatives, and a multi-criteria analysis of the most promising projects, with the different weights assigned to various key objectives. These weights can be varied or refined according to the decision makers wishes and customer profile. Sensitivity analysis and other risk-bearing methods could be employed so that all impacts of imprecise forecasts on future benefits and costs can be seen in a proper perspective. For the purpose of the more extensive sensitivity analysis with respect to various types of benefits and costs, various degrees of change in key variables, and alternative timings, including the delay of the project completion and the delays in reaching full production, additional data could be included in an alternative projects summary table.

Exploratory spatio-temporal analysis of linked statistical data

Abstract Publishing and sharing open government data in Linked Data format provides many opportunities in terms of data aggregation/integration and creation of information mashups. Statistical data, that contains various performance indicators and their evolution through time, is an example of data that can be used as the foundation for policy prediction, planning and adjustments, and can be re-used in different applications. However, due to Linked Data being relatively a new field, currently there is a lack of tools that enable efficient exploration and analysis of linked geospatial statistical datasets. Therefore, ESTA-LD (Exploratory Spatio-Temporal Analysis) tool was developed to address some of the Linked statistical Data management issues, such as crossing the statistical and the geographical dimensions, producing statistical maps, visualizing different measures, and comparing statistical indicators of different regions through time. This paper discusses the modeling approach that was adopted so that the published data conform to the established standards for representing statistical, spatial and temporal data in Linked Data format. The main contribution is related to the delivery of state-of-the-art open-source tools for retrieving, quality assessment, exploration and analysis of statistical Linked Data that is made available through a SPARQL endpoint.

Application of Complex Event Processing Paradigm in Situation Awareness and Management

This paper describes a novel approach to situation awareness and management in critical infrastructures using an emerging Complex Event Processing paradigm in combination with ECA (Event Condition Action) rules. Every modern infrastructural facility, especially critical ones, would want to catch or predict exceptions and threats at the earliest possible moment. The crucial prerequisite for this is a holistic and accurate situation assessment. To be able to assess the situation, evaluate the risk and provide decision support to the emergency managers, we need to define event chains that identify what is normal, compliant and expected, and what is exceptional and/or dangerous. These problems introduce a novel genre of applications - event-driven applications that make automated decisions based on a complex event or pattern of events, their detection, correlation and aggregation. In cases where an automated reaction is not possible, a recommendation is given to a human operator who remains in the control loop.

NIMFA - Natural language Implicit Meaning Formalization and Abstraction

There are many general purpose Knowledge Representation, Natural Language Processing and Information Extraction techniques that were successfully applied in many applications. However, their more broad use is still limited by the relatively high costs of their application. It seems that these limitations are partly caused by some essential characteristics and some weaknesses of these techniques. In this paper we propose a radically new knowledge representation and interpretation technique, NIMFA, specialized for knowledge expressed in natural languages. To test the basic ideas underlying NIMFA we have implemented a prototype Information Center that provides answers to natural language queries using Web services.

Integrating Serbian public data into the LOD cloud

Linked Open Data (LOD) is a growing movement for organizations to make their existing data available in a machine-readable format. There are two equally important viewpoints to LOD: publishing and consuming. This article analyzes the requirements for both sub-processes and presents an example of publishing statistical data in RDF format and integrating the data into the LOD cloud via the PublicData.eu portal. In particular, it discusses the establishment of the Serbian CKAN metadata repository that serves for publishing open governmental data from Serbia, as well as a source catalogue for the PublicData.eu portal. Furthermore, by using an illustrative case study of the Statistical Office of the Republic of Serbia, it elaborates the adaption of the LOD2 Stack for analysis and dissemination of official statistics information.

Applying neural networks to knowledge representation and determination of its meaning

Knowledge representation is one of the first challenges AI community was confronted with. To be applicable, knowledge representation techniques must be able not only to represent the knowledge, but also to provide means to determine its meaning. The proposed knowledge representation techniques solve the problem of meaning determination by naming, i.e. by describing the meaning of represented knowledge. These descriptions are provided by database, knowledge base, ontology designers, which give names to tables, fields, classes, properties, relationships, etc. An alternative approach to the problem of determining the meaning would be a neural network approach applied to knowledge representation in a natural language that does not use names, but semantic categories. In this paper we propose a Hierarchical Semantic Form (HSF), a modification of localist approach of connectionist model, which, together with Space of Universal Links (SOUL) algorithm, is capable of representing knowledge in a natural language and interpreting its meaning by using the semantic categories.

Proposal for Implementing the EU PSI Directive in Serbia

The Linked Data approach, based on principles defined back in 2006 and best practices for publishing and connecting structured data on the Web elaborated by ICT experts, can play an important role in the domain of semantic interoperability of government services. Therefore, this paper explores the technical aspects and challenges of implementation of the revised European Directive on the Public Sector Information (2013/37/EU) emphasizing the role of Linked Data approach for improved interoperability and re-use. Referring to state-of-the-art approaches in EU member states, the paper proposes a framework for implementing the PSI Directive in Serbia.

Smart-phone application for autonomous indoor positioning

In this paper, we propose an architecture for autonomous indoor positioning system. In particular, we consider a system where motion sensors along with magnetometer and barometer are used to provide accurate positioning information even in environments without GNSS (Global Navigation Satellite System) coverage. We look more into implementation details of one particular block (step detection) on a smart-phone platform. Finally, we evaluate step detection performance in experimental setup.

Students’ Perception of IT Curricula and Career Opportunities in Serbia and Macedonia

The research presented in this paper aims at investigating gender-based preferences for study choices made by male and female students studying information technology (IT) disciplines based on the methods of identifying and analyzing relevant IT study programmes in Serbia and Macedonia, and analyzing the students’ perception of the curricula and career opportunities. The methodology used to perform the research and to analyze the results was developed within the “Higher Education Leading to ENgineering And scientific careers” (HELENA) project.

Building Expert Profiles Models Applying Semantic Web Technologies

Semantic Web (SW) is an emerging research field that has application in different domains such as e-government services, richly interlinked library systems, Web search engines, enterprise knowledge stores, and other. The term “Semantic Web” refers to the World Wide Web Consortium’s (W3C) vision of the Web of linked data (called also the Web of Data) as “...an extension of the current Web in which information is given a well-defined meaning, better enabling computers and people to work in cooperation” (Berners-Lee, Hendler, & Lassila, 2001). Since then, many specifications, guidelines, languages, and tools have been developed that facilitate software development, improve performance and create new business opportunities.