Defense Information - Semantic Aspects

The study focuses on the semantic aspects of information, the role of cognitive technologies in the time of information defense, the emergence of new concepts related to information security and protection, cybersecurity, and resilience. It can be argued that information is a complex quantity; measuring information means measuring complexity. Artificial intelligence and problem areas related to this area are also the subject of analysis. We are also looking for an answer to the link between cybernetics and cyber technology and to the popular term “cyber” that has recently become buzzword. Another aspect of the study is various models of data structuring and information protection from the point of view of modern strategies.

Applications of Blockchain in Taxation: New Administrative Opportunities

The purpose of this article is to consider the prospects of using blockchain technology in taxation. The article discusses the essence of blockchain and its possible implementation in the tax system. The study focuses on the benefits of blockchain as one of the most promising methods of improving and simultaneously simplifying the tax system for both the state and taxpayers. The main focus of the study was on the specifics of the implementation of blockchain in tax administration, for example, data structuring, cost-effectiveness, security (fraud detection), decentralized accounting technology (transparency), verification of transfer pricing, and the use of smart contracts. Blockchain can reconstruct accounting and automate the method of payments, transfers, and asset accounting. In the conclusions, the authors identify such potential advantages of implementing blockchain in tax administration as reducing transaction costs, faster, more transparent, and efficient taxation function.

HBIM MODELLING FOR AN HISTORICAL URBAN CENTRE

The research in the geospatial data structuring and formats interoperability direction is the crucial task for creating a 3D Geodatabase at the urban scale. Both geometric and semantic data structuring should be considered, mainly regarding the interoperability of objects and formats generated outside the geographical space. Current reflections on 3D database generation, based on geospatial data, are mostly related to visualisation issues and context-related application. The purposes and scale of representation according to LoDs require some reflections, particularly for the transmission of semantic information.This contribution adopts and develops the integration of some tools to derive object-oriented modelling in the HBIM environment, both at the urban and architectural scale, from point clouds obtained by UAV (Unmanned Aerial Vehicle) photogrammetry.One of the paper’s objectives is retracing the analysis phases of the point clouds acquired by UAV photogrammetry technique and their suitability for multiscale modelling. Starting from UAV clouds, through the optimisation and segmentation, the proposed workflow tries to trigger the modelling of the objects according to the LODs, comparing the one coming from CityGML and the one in use in the BIM community. The experimentation proposed is focused on the case study of the city of Norcia, which like many other historic centres spread over the territory of central Italy, was deeply damaged by the 2016-17 earthquake.

FORECASTING CROWDFUNDING PLATFORM REVENUES USING ARIMA MODEL

Purpose – In recent years, crowdfunding platforms have become very popular as intermediaries between fundraisers and funders. However, various campaigns published on the platform might be of bad quality or fraudulent, so the crowdfunding platform must be very careful when choosing the right ones. Also, the proper selection depends on the profits of a crowdfunding platform. In most cases, crowdfunding platforms mainly earn money from transaction and administration fees from successful campaigns on their platforms. While it is very hard to select successful cam-paigns, it is possible to analyse already published campaigns and forecast future revenues of crowdfunding platforms. And based on this, to select new projects which might be successful too. The aim of this work is to develop a model to forecast the revenues of crowdfunding platforms. Research methodology – In this research, comparative and statistical analysis will be used, data structuring, modelling and forecasting, performed with the ARIMA model. Findings – Main findings of this research present the three most successful campaign categories from the Kickstarter platform that receives the highest funding. Fees were calculated from those three campaign categories, and revenues for the platform were forecasted using the ARIMA model. Research limitations – Main limitations are that there were used data from a very short period of time. For better results accuracy, a longer period is needed. Practical implications – this research might be of practical use since the forecasts show that the revenues will continue to grow from the successful campaign categories. Consequently, investors should continue to support technology, games and design categories the most. At the same time, crowdfunding platforms should give more attention to these categories when choosing new projects to launch on the platform.

New Sensor Data Structuring for Deeper Feature Extraction in Human Activity Recognition

For the effective application of thriving human-assistive technologies in healthcare services and human–robot collaborative tasks, computing devices must be aware of human movements. Developing a reliable real-time activity recognition method for the continuous and smooth operation of such smart devices is imperative. To achieve this, light and intelligent methods that use ubiquitous sensors are pivotal. In this study, with the correlation of time series data in mind, a new method of data structuring for deeper feature extraction is introduced herein. The activity data were collected using a smartphone with the help of an exclusively developed iOS application. Data from eight activities were shaped into single and double-channels to extract deep temporal and spatial features of the signals. In addition to the time domain, raw data were represented via the Fourier and wavelet domains. Among the several neural network models used to fit the deep-learning classification of the activities, a convolutional neural network with a double-channeled time-domain input performed well. This method was further evaluated using other public datasets, and better performance was obtained. The practicability of the trained model was finally tested on a computer and a smartphone in real-time, where it demonstrated promising results.