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Abstract

Rapidly developing artificial intelligence technologies are expected to help us in various sectors of life, but their applications also entail certain risks.
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Authors and Affiliations

Piotr Kaczmarek-Kurczak
1

  1. Centre for Space Studies, Kozminski University– Kozminski ESA Lab in Warsaw
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Abstract

Artificial intelligence technologies are moving forward by leaps and bounds, right before our very eyes. How well prepared are we to treat them not as tools or rivals, but as autonomous partners?
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Authors and Affiliations

Artur Modliński
1
Aleksandra Przegalińska
2

  1. University of Łódź
  2. Kozminski University in Warsaw
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Abstract

When we look at works of art, our brain reacts to what we see in subconscious ways. Certain aspects of our perceptions can be captured using algebraic methods.
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Authors and Affiliations

Marek Kuś
1
Jacek Rogala
2
Joanna Dreszer
3
Beata Bajno
4

  1. PAS Center for Theoretical Physics in Warsaw
  2. Center for Research on Culture, Languageand Mind, University of Warsaw
  3. Institute of Psychology Nicolaus CopernicusUniversity in Toruń
  4. Association of Polish Artists and Designers,Warsaw Section
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Abstract

Modern technologies are now allowing education to seamlessly transfer into the virtual realm, creating a user-friendly environment where students can acquire new skills.
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Authors and Affiliations

Aureliusz Górski
1

  1. Founder & CEO of CampusAI in Warsaw
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Abstract

This is a modest endeavour written from an engineering perspective by a nonphilosopher to set things straight if somewhat roughly: What does artificial intelligence boil down to? What are its merits and why some dangers may stem from its development in this time of confusion when, to quote Rémi Brague: “From the point of view of technology, man appears as outdated, or at least superfluous”?

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Authors and Affiliations

Jacek Koronacki
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Abstract

The evolution of the economy and the formation of Industry 4.0 lead to an increase in the importance of intangible assets and the digitization of all processes at energy enterprises. This involves the use of technologies such as the Internet of Things, Big Data, predictive analytics, cloud computing, machine learning, artificial intelligence, robotics, 3D printing, augmented reality etc. Of particular interest is the use of artificial intelligence in the energy sector, which opens up such prospects as increased safety in energy generation, increased energy efficiency, and balanced energy-generation processes. The peculiarity of this particular instrument of Industry 4.0 is that it combines the processes of digitalization and intellectualization in the enterprise and forms a new part of the intellectual capital of the enterprise. The implementation of artificial intelligence in the activities of energy companies requires consideration of the features and stages of implementation. For this purpose, a conceptual model of artificial intelligence implementation at energy enterprises has been formed, which contains: the formation of the implementation strategy; the design process; operation and assessment of artificial intelligence. The introduction of artificial intelligence is a large-scale and rather costly project; therefore, it is of interest to assess the effectiveness of using artificial intelligence in the activities of energy companies. Efficiency measurement is proposed in the following areas: assessment of economic, scientific and technical, social, marketing, resource, financial, environmental, regional, ethical and cultural effects as well as assessment of the types of risks associated with the introduction of artificial intelligence.
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Bibliography

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Authors and Affiliations

Hanna Doroshuk
1
ORCID: ORCID

  1. Department of Menegement, Odessa Polytechnic State University, Ukraine
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Abstract

Machine learning methods, such as the random forests algorithm, have revolutionized how we analyze growing volumes of data. The algorithm can be usefully applied in studying… real forests.
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Authors and Affiliations

Łukasz Pawlik
1
Marcin K. Dyderski
2

  1. Institute of Earth Sciences,Faculty of Natural Sciences,University of Silesia in Katowice
  2. Institute of Dendrology,Polish Academy of Sciences in Kórnik
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Abstract

We all face a wide array of different choices every day of our lives. Asst. Prof. Miłosz Kadziński explains how artificial intelligence could be used to help us make decisions.

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Authors and Affiliations

Miłosz Kadziński
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Abstract

Dr. Aleksandra Przegalinska explains why we find humanoid robots so creepy and considers whether watching machines play football is actually fun.

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Authors and Affiliations

Aleksandra Przegalińska
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Abstract

Turmeric is affected by various diseases during its growth process. Not finding its diseases at early stages may lead to a loss in production and even crop failure. The most important thing is to accurately identify diseases of the turmeric plant. Instead of using multiple steps such as image pre-processing, feature extraction, and feature classification in the conventional method, the single-phase detection model is adopted to simplify recognizing turmeric plant leaf diseases. To enhance the detection accuracy of turmeric diseases, a deep learning-based technique called the Improved YOLOV3-Tiny model is proposed. To improve detection accuracy than YOLOV3-tiny, this method uses residual network structure based on the convolutional neural network in particular layers. The results show that the detection accuracy is improved in the proposed model compared to the YOLOV3-Tiny model. It enables anyone to perform fast and accurate turmeric leaf diseases detection. In this paper, major turmeric diseases like leaf spot, leaf blotch, and rhizome rot are identified using the Improved YOLOV3-Tiny algorithm. Training and testing images are captured during both day and night and compared with various YOLO methods and Faster R-CNN with the VGG16 model. Moreover, the experimental results show that the Cycle-GAN augmentation process on turmeric leaf dataset supports much for improving detection accuracy for smaller datasets and the proposed model has an advantage of high detection accuracy and fast recognition speed compared with existing traditional models.
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Authors and Affiliations

V. Devisurya
1
R. Devi Priya
1
N. Anitha
1

  1. Department of Information Technology, Kongu Engineering College, Perundurai, India
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Abstract

This paper presents a deep learning-based image texture recognition system. The methodology taken in this solution is formed in a bottom-up manner. It means we swipe a moving window through the image in order to categorize if a given region belongs to one of the classes seen in the training process. This categorization is done based on the Deep Neural Network (DNN) of fixed architecture. The training process is fully automated regarding the training data preparation, investigation of the best training algorithm, and its hyper-parameters. The only human input to the system is the definition of the categories for further recognition and generation of the samples (region markings) in the external application chosen by the user. The system is tested on road surface images where its task is to categorize image regions to a different road category (e.g. curb, road surface damage, etc.) and is featured with 90% and above accuracy.

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Authors and Affiliations

R. Kapela
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Abstract

Artificial Intelligence begins to play an increasingly important role in medicine, in particular in diagnostics, therapy selection and drug design. This article shows how the latest machine learning algorithms support the work of physicians and pharmacists. However, the effective implementation of Artificial Intelligence methods in everyday medical practice requires overcoming a number of barriers. These challenges are discussed in the article. The objectives and functioning of the Artificial Intelligence Center in Medicine of the Medical University of Bialystok were also discussed, as an example of Polish contribution to the development of the latest computer algorithms supporting diagnostics and therapy.
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Authors and Affiliations

Konrad Wojdan
1 2
Marcin Moniuszko
3

  1. Politechnika Warszawska, Instytut Techniki Cieplnej
  2. Transition Technologies Science sp. z o.o.
  3. Uniwersytet Medyczny w Białymstoku
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Abstract

Science and technology frequently contribute to one another: scientific advances lead to the development of new technologies, and new technologies broaden the experimental potential of science, enabling advancement of research. This is a motivation behind introduction of the concept of technoscience addressing the integration of science and technology – the process progressing from the beginning of the twentieth century, which has been the source of extraordinary achievements of our civilisation, but – at the same time – has engendered global socioeconomic transformations whose negative side effects may endanger humanity. This paper is devoted to an outline of ethical challenges implied by the development of technoscience, with special emphasis of those which are rooted in the development of information technologies. It is suggested that those challenges should be met by people of technoscience in a concerted effort undertaken with philosophers and educators.
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Authors and Affiliations

Roman Z. Morawski
1

  1. Politechnika Warszawska, Wydział Elektroniki Technik Informacyjnych
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Abstract

This paper proposes a new approach to the processing and analysis of medical images. We introduce the term and methodology of medical data understanding, as a new step in the way of starting from image processing, and followed by analysis and classification (recognition). The general view of the situation of the new technology of machine perception and image understanding in the context of the more well known and classic techniques of image processing, analysis, segmentation and classification is shown below

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Authors and Affiliations

R. Tadeusiewicz
M.R. Ogiela
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Abstract

In the paper an application of evolutionary algorithm to design and optimization of combinational digital circuits with respect to transistor count is presented. Multiple layer chromosomes increasing the algorithm efficiency are introduced. Four combinational circuits with truth tables chosen from literature are designed using proposed method. Obtained results are in many cases better than those obtained using other methods.

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Authors and Affiliations

A. Słowik
M. Białko
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Abstract

Background: a humidity sensor is used to sense and measure the relative humidity of air. A new composite system has been fabricated using environmental pollutants such as carbon black and low-cost zinc oxide, and it acts as a humidity sensor. Residual life of the sensor is calculated and an expert system is modelled. For properties and nature confirmation, characterization is performed, and a sensing material is fabricated. Methodology: characterization is performed on the fabricated material. Complex impedance spectroscopy (CIS), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD) and scanning electron microscopy (SEM) are all used to confirm the surface roughness, its composite nature as well as the morphology of the composite. The residual lifetime of the fabricated humidity sensor is calculated by means of accelerated life testing. An intelligent model is designed using artificial intelligence techniques, including the artificial neural network (ANN), fuzzy inference system (FIS) and adaptive neuro-fuzzy inference system (ANFIS). Results: maximum conductivity obtained is 6.4×10⁻³ S/cm when zinc oxide is doped with 80% of carbon black. Conclusion: the solid composite obtained possesses good humidity-sensing capability in the range of 30–95%. ANFIS exhibits the maximum prediction accuracy, with an error rate of just 1.1%.

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Authors and Affiliations

C. Bhargava
J. Aggarwal
P.K. Sharma
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Abstract

Due to fast-paced technical development, companies are forced to modernise and update

their equipment, as well as production planning methods. In the ordering process, the customer

is interested not only in product specifications, but also in the manufacturing lead

time by which the product will be completed. Therefore, companies strive towards setting

an appealing but attainable manufacturing lead date.

Manufacturing lead time depends on many different factors; therefore, it is difficult to predict.

Estimation of manufacturing lead time is usually based on previous experience. In the

following research, manufacturing lead time for tools for aluminium extrusion was estimated

with Artificial Intelligence, more precisely, with Neural Networks.

The research is based on the following input data; number of cavities, tool type, tool category,

order type, number of orders in the last 3 days and tool diameter; while the only output

data are the number of working days that are needed to manufacture the tool. An Artificial

Neural Network (feed-forward neural network) was noted as a sufficiently accurate method

and, therefore, appropriate for implementation in the company.

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Authors and Affiliations

Nika Sajko
Simon Kovacic
Mirko Ficko
Iztok Palcic
Simon Klancnik
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Abstract

In recent years, due to the growing importance of eco-design and tightening EU regulations entrepreneurs are required to implement activities related to environmental protection. It influences the development of methods and tools enabling the implementation of eco-design into practice, which are increasingly used by modern information technologies. They are based on intelligent solutions that allows them to better match the requirements of designers and allows for the automation of processes, and in some cases they are able to do the work themselves, replacing designers. Details are useful in areas that require calculations, comparisons and making choices, which is the process of eco-design. The paper describes methodology of pro-ecological product design oriented towards recycling, based on agent technology, enables the design of environmentally friendly products including recycling. The description of the methodology was preceded by a literature analysis on the characteristics of tools supporting eco-design and the process of its development was presented. The proposed methodology can be used at the design stage of devices to select the best product in terms of ecology. It is based on the original set of recycling indicators, used to evaluate the recycling of the product, ensure the ability to operate in a distributed design environment, and the use of data from various CAD systems, allows full automation of calculations and updates (without user participation).
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Authors and Affiliations

Ewa Dostatni
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Abstract

This article presents Nick Bostrom’s considerations of the future included in his book Superintelligence: Paths, Dangers, Strategies. Bostrom studies such issues as determining the hypothetic ways of attaining superintelligence, its nature and different aspects of this technology. He shows threats regarding such powerful systems, as well as constructing strategies of preventing undesirable activities of superintelligent beings. Bostrom’s input is an important part of present discussion concerning the development of artificial intelligence and its ethical problems.
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Authors and Affiliations

Sebastian Kozera
1

  1. Wydział Filozofii i Socjologii, UMCS, Lublin, Pl. M.Curie-Skłodowskiej 4, Lublin
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Abstract

Artificial Intelligence, both as a hope of making a substantial progress, and a fear of the unknown and unimaginable, has its roots in human dreams. These dreams were materialized by means of rational intellectual efforts. We see beginnings of such a process in Lullus’s fancies. Many scholars and enthusiasts participated in the development of Lullus’s art, ars combinatoria. Amongst them, Athanasius Kircher was distinguished. Gottfried Leibniz ended the period in which the idea of artificial intelligence had been shaped, and started a new one when artificial intelligence could be considered a part of science, according to today’s standards.

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Authors and Affiliations

Kazimierz Trzęsicki
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Abstract

Our aim was to test existing sex and age stereotypes related to emotional expressivity, gender and age. This was a complex analysis of facial expressions of all basic emotions (anger, disgust, fear, happiness, sadness, and surprise) to everyday life stimuli observing a large sample (2,969 unique participants creating 39,694 recordings) using an Emotion Artificial Intelligence. Our data partially support emotion-specific stereotype that women express more affiliate emotions and men express more dominant emotions except for sadness. There were found correlations of emotion expression with age, however intensity and frequency of emotion expression did not follow the same pattern. Not eliminating the differences between men and women in the baseline facial appearance resulted in men expressing dominant emotions (anger and disgust) more intensively, and women expressing more affiliative emotions (happiness, fear, and surprise). To sum up, facial appearance can be one of the origins of the existing gender stereotypic socialisation stereotype.
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Authors and Affiliations

Mária Gablíková
1
Júlia Halamová
1

  1. Institute of Applied Psychology, Faculty of Social and Economic Sciences, Comenius University in Bratislava
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Abstract

From a management perspective, water quality is determined by the desired end use. Water intended for leisure, drinking water, and the habitat of aquatic organisms requires higher levels of purity. In contrast, the quality standards of water used for hydraulic energy production are much less important.
The main objective of this work is focused on the development of an evaluation system dealing with supervised classification of the physicochemical quality of the water surface in the Moulouya River through the use of artificial intelligence. A graphical interface under Matlab 2015 is presented. The latter makes it possible to create a classification model based on artificial neural networks of the multilayer perceptron type (ANN-MLP).
Several configurations were tested during this study. The configuration [9 8 3] retained gives a coefficient of determination close to the unit with a minimum error value during the test phase.
This study highlights the capacity of the classification model based on artificial neural networks of the multilayer perceptron type (ANN-MLP) proposed for the supervised classification of the different water quality classes, determined by the calculation of the system for assessing the quality of surface water (SEQ-water) at the level of the Moulouya River catchment area, with an overall classification rate equal to 98.5% and a classification rate during the test phase equal to 100%.
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Authors and Affiliations

Imad Manssouri
1
ORCID: ORCID
Abdelghani Talhaoui
2
Abdellah El Hmaidi
2
ORCID: ORCID
Brahim Boudad
3
Bouchra Boudebbouz
1
Hassane Sahbi
4

  1. Moulay Ismail University, National School of Arts and Crafts, Laboratory of Mechanics, Mechatronics, and Command, Team of Electrical Energy, Maintenance and Innovation, Meknes, Marjane 2, BP: 298 Meknes 50050, Morocco
  2. Moulay Ismail University, Faculty of Sciences, Water Sciences and Environmental Engineering team, Meknes, Morocco
  3. Moulay Ismail University, Faculty of Sciences, Department of Geology, Laboratory of Geo-Engineering and Environment, Meknes, Morocco
  4. Moulay Ismail University, Meknes, Morocco
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Abstract

The article presents the developed IT solutions supporting the material and technological conversion process in terms of the possibility of using the casting technology of selected alloys to produce products previously manufactured with the use of other methods and materials. The solutions are based on artificial intelligence, machine learning and statistical methods. The prototype module of the information and decision-making system allows for a preliminary assessment of the feasibility of this type of procedure. Currently, the selection of the method of manufacturing a product is based on the knowledge and experience of the technologist and constructor. In the described approach, this process is supported by the proprietary module of the information and decision-making system, which, based on the accumulated knowledge, allows for an initial assessment of the feasibility of a selected element in a given technology. It allows taking into account a large number of intuitive factors, as well as recording expert knowledge with the use of formal languages. Additionally, the possibility of searching for and collecting data on innovative solutions, supplying the knowledge base, should be taken into account. The developed and applied models should allow for the effective use and representation of knowledge expressed in linguistic form. In this solution, it is important to use methods that support the selection of parameters for the production of casting. The type, number and characteristics of data have an impact on the effectiveness of solutions in terms of classification and prediction of data and the relationships detected.
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Authors and Affiliations

D. Wilk-Kołodziejczyk
1 2
ORCID: ORCID
K. Jaśkowiec
2
ORCID: ORCID
A. Bitka
2
ORCID: ORCID
Z. Pirowski
2
ORCID: ORCID
M. Grudzień-Rakoczy
2
ORCID: ORCID
K. Chrzan
2
ORCID: ORCID
M. Małysza
2
ORCID: ORCID
M. Doroszewski
1
ORCID: ORCID

  1. AGH University of Science and Technology, Faculty of Metals Engineering and Industrial Computer Science, Al. Mickiewicza 30, 30-059 Kraków, Poland
  2. Centre of Casting Technology, The Łukasiewicz Research Network – Cracow Technology Institute, Poland

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