Authors:István Péntek, Ábel Garai, and Attila Adamkó
In this paper, a unique healthcare solution is described that supports the even more effective operation of the hospital information systems. The main question is whether the emerging opportunities of the Internet of things devices can also be exploited in the industrial hospital information system landscape. This demonstrated research describes the most feasible way to integrate the Internet of things capability into hospital information production systems. The initial goal was the design and implementation of a single, unified telemedicine hub offering community-based solution for integrated medical systems. This solution allows the intercepted information to be collected and interpreted at community level. The designed and implemented system acts as a transmitter between the physician and patient. The software solution operates with sensor-based information collected from the individual. Emerging Internet of things devices and solutions open new horizons for today’s health care systems. The presented and detailed system provides the ability to real-time health-monitoring and in-depth health analyzing through open application programming interfaces. The telemedicine hub system makes it easier to integrate the Internet of things capability into the operating health care systems.
gambler is losing during the session, the frequency of reinforcement, betting options chosen, and the volatility of the game). Such analyses may be made possible using modern technological advances in the analysis of bigdata, but would require greater
Összefoglalás. Az elektronikusan tárolt információ biztonsága,
általánosabban véve a kiberbiztonság, az egyik legnagyobb kihívás a 21. században.
Folyamatosan jelennek meg újabb és újabb fenyegetések, melyekre innovatív és újszerű
megoldásokat kell adni. Ezek az innovatív megoldások mindenképpen magukkal hozzák az
olyan új típusú technológiák használatát az információbiztonságban, mint például a
Nagy Adatokból (Big Data) való építkezés és az erre épülő mesterséges intelligencia.
Ennek támogatása érdekében az Európai Unió a 2021 és 2027 közötti időszakban kiemelt
fontosságúnak tartja a kiberbiztonsági innovációkat. A tanulmány bemutatja a
kiberbiztonsági kompetenciahálózatok tervezetét, illetve ismerteti, hogy milyen
kutatás-fejlesztés-innovációs lehetőségek lesznek a következő évtizedben
Summary. Security of stored digital information and more generally,
cybersecurity is one of the biggest challenges of the 21st century. Besides the
negative effects of cybercrime, cyberespionage, or other state sponsored activities,
like cyberwarfare, our society and economy should face the exposure of
infocommunication systems all around us. At the dawn of 4th industrial revolution
when the whole world is going to be digitalized and will be surrounded by networked
digital devices in homes, cities and industry, new threats are constantly emerging
that need to be responded with new innovative solutions. These innovative solutions
should include the usage of big data and artificial intelligence built onto it. They
should also give a response for the inherited risks of legacy systems that can be
found in many critical information infrastructures. Meanwhile, they should protect
the digital privacy of citizens by not giving out unnecessary user data which is
contradictory with the need of big data and AI mentioned before.
Due to the emerging cybersecurity threats and the virtually non-existence of European
cybersecurity market, European Union gives high importance for cybersecurity
innovation and will support it between 2021 and 2027. In the proposed budget for this
period, approximately 3 billion of euros is expected to be spent to cybersecurity
related research. On the one hand, that fund may help European research institutes,
enterprises, and startups to appear on the global market, on the other hand this is
the only possible way to regain Europe’s digital independence from the United States
and China. In alignment with the European security policy, these innovative solutions
may also lead to reducing the amount of cybercrime, ensure the resilience of
continental critical information infrastructure and can help to establish strong
European cyberwarfare capabilities. As Ursula von der Leyden, president of the
European Commission said in her op-ed in February 2020, “The point is that Europe’s
digital transition must protect and empower citizens, businesses and society as a
whole. It has to deliver for people so that they feel the benefits of technology in
their lives. To make this happen, Europe needs to have its own digital capacities –
be it quantum computing, 5G, cybersecurity or artificial intelligence (AI). These are
some of the technologies we have identified as areas for strategic investment, for
which EU funding can draw in national and private sector funds.” The study presents
the draft of cybersecurity competence networks and describes what R&D&I
possibilities will be in Europe in the next decade.
Authors:S. Feranchuk, N. Belkova, U. Potapova, I. Ochirov, D. Kuzmin, and S. Belikov
The methods for data presentation are important in bioinformatics as data processing algorithms. The article describes the software package for the extensive analysis of tables with estimates of bacterial abundance levels in environmental samples. The package was designed to be executed in a distributed hardware environment, with powerful packages in Python in the backend and interactive front-end forms. Most of microbial ecology-specific functionality is implemented by the scikit-bio Python package, together with the other Python packages intended for big data analysis. Interactive visualisation tools are implemented by the D3.js software library, therefore, the software project is named D3b. The package is a suite of tools for the analysis of microbial ecology data implemented as a web-service and as a desktop application. It supports a substantial part of the graphical and analytical descriptions of microbial communities used in scientific publications. Source codes are available at github (sferanchuk/d3b_charts) and the on-line version of the system is accessible at d3b-charts.bri-shur.com.
Összefoglalás. A mesterséges intelligencia az elmúlt években hatalmas
fejlődésen ment keresztül, melynek köszönhetően ma már rengeteg különböző
szakterületen megtalálható valamilyen formában, rengeteg kutatás szerves részévé
vált. Ez leginkább az egyre inkább fejlődő tanulóalgoritmusoknak, illetve a Big Data
környezetnek köszönhető, mely óriási mennyiségű tanítóadatot képes szolgáltatni.
A cikk célja, hogy összefoglalja a technológia jelenlegi állapotát. Ismertetésre
kerül a mesterséges intelligencia történelme, az alkalmazási területek egy nagyobb
része, melyek központi eleme a mesterséges intelligencia. Ezek mellett rámutat a
mesterséges intelligencia különböző biztonsági réseire, illetve a kiberbiztonság
területén való felhasználhatóságra. A cikk a jelenlegi mesterséges intelligencia
alkalmazások egy szeletét mutatja be, melyek jól illusztrálják a széles felhasználási
Summary. In the past years artificial intelligence has seen several
improvements, which drove its usage to grow in various different areas and became the
focus of many researches. This can be attributed to improvements made in the learning
algorithms and Big Data techniques, which can provide tremendous amount of
The goal of this paper is to summarize the current state of artificial intelligence.
We present its history, introduce the terminology used, and show technological areas
using artificial intelligence as a core part of their applications. The paper also
introduces the security concerns related to artificial intelligence solutions but
also highlights how the technology can be used to enhance security in different
applications. Finally, we present future opportunities and possible improvements. The
paper shows some general artificial intelligence applications that demonstrate the
wide range usage of the technology.
Many applications are built around artificial intelligence technologies and there are
many services that a developer can use to achieve intelligent behavior. The
foundation of different approaches is a well-designed learning algorithm, while the
key to every learning algorithm is the quality of the data set that is used during
the learning phase. There are applications that focus on image processing like face
detection or other gesture detection to identify a person. Other solutions compare
signatures while others are for object or plate number detection (for example the
automatic parking system of an office building). Artificial intelligence and accurate
data handling can be also used for anomaly detection in a real time system. For
example, there are ongoing researches for anomaly detection at the ZalaZone
autonomous car test field based on the collected sensor data. There are also more
general applications like user profiling and automatic content recommendation by
using behavior analysis techniques.
However, the artificial intelligence technology also has security risks needed to be
eliminated before applying an application publicly. One concern is the generation of
fake contents. These must be detected with other algorithms that focus on small but
noticeable differences. It is also essential to protect the data which is used by the
learning algorithm and protect the logic flow of the solution. Network security can
help to protect these applications.
Artificial intelligence can also help strengthen the security of a solution as it is
able to detect network anomalies and signs of a security issue. Therefore, the
technology is widely used in IT security to prevent different type of attacks.
As different BigData technologies, computational power, and storage capacity increase
over time, there is space for improved artificial intelligence solution that can
learn from large and real time data sets. The advancements in sensors can also help
to give more precise data for different solutions. Finally, advanced natural language
processing can help with communication between humans and computer based
Parameters governing the retention and movement of water and chemicals in soils are notorious for the difficulties and high labor costs involved in measuring them. Often, there is a need to resort to estimating these parameters from other, more readily available data, using pedotransfer relationships.
This work is a mini-review that focuses on trends in pedotransfer development across the World, and considers trends regarding data that are in demand, data we have, and methods to build pedotransfer relationships. Recent hot topics are addressed, including estimating the spatial variability of water contents and soil hydraulic properties, which is needed in sensitivity analysis, evaluation of the model performance, multimodel simulations, data assimilation from soil sensor networks and upscaling using Monte Carlo simulations. Ensembles of pedotransfer functions and temporal stability derived from “big data” as a source of soil parameter variability are also described.
Estimating parameter correlation is advocated as the pathway to the improvement of synthetic datasets. Upscaling of pedotransfer relationships is demonstrated for saturated hydraulic conductivity. Pedotransfer at coarse scales requires a different type of input variables as compared with fine scales. Accuracy, reliability, and utility have to be estimated independently. Persistent knowledge gaps in pedotransfer development are outlined, which are related to regional soil degradation, seasonal changes in pedotransfer inputs and outputs, spatial correlations in soil hydraulic properties, and overland flow parameter estimation.
Pedotransfer research is an integral part of addressing grand challenges of the twenty-first century, including carbon stock assessments and forecasts, climate change and related hydrological weather extreme event predictions, and deciphering and managing ecosystem services.
Overall, pedotransfer functions currently serve as an essential instrument in the science-based toolbox for diagnostics, monitoring, predictions, and management of the changing Earth and soil as a life-supporting Earth system.