Wyniki wyszukiwania - BazTech

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Industry 4.0: selected aspects of algorithmization of work environment

Osika Grażyna

Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska

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2022

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z. 155

431--450

EN

Purpose: The aim of the article is to describe and forecast possible difficulties related to the development of cognitive technologies and the progressing of algorithmization of HRM processes as a part of Industry 4.0. Design/methodology/approach: While most of the studies to date related to the phenomenon of Industry 4.0 and Big Data are concerned with the level of efficiency of cyber-physical systems and the improvement of algorithmic tools, this study proposes a different perspective. It is an attempt to foresee the possible difficulties connected with algorithmization HRM processes, which understanding could help to "prepare" or even eliminate the harmful effects we may face which will affect decisions made in the field of the managing organizations, especially regarding human resources management, in era of Industry 4.0. Findings: The research of cognitive technologies in the broadest sense is primarily associated with a focus of thinking on their effectiveness, which can result in a one-sided view and ultimately a lack of objective assessment of that effectiveness. Therefore, conducting a parallel critical reflection seems even necessary. This reflection has the potential to lead to a more balanced assessment of what is undoubtedly "for", but also of what may be "against". The proposed point of view may contribute to a more informed use of algorithm-based cognitive technologies in the human resource management process, and thus to improve their real-world effectiveness. Social implications: The article can have an educational function, helps to develop critical thinking about cognitive technologies, and directs attention to areas of knowledge by which future skills should be extended. Originality/value: This article is addressed to all those who use algorithms and data-driven decision-making processes in HRM. Crucial in these considerations is the to draw attention to the dangers of unreflective use of technical solutions supporting HRM processes. The novelty of the proposed approach is the identification of three potential risk areas that may result in faulty HR decisions. These include the risk of "technological proof of equity", overconfidence in the objective character of algorithms and the existence of a real danger resulting from the so-called algorithm overfitting. Recognition of these difficulties ultimately contributed to real improvements in productivity by combining human performance with technology effectiveness.

2

Improving the university recruitment process with web analytics

Wiechetek Łukasz, Mędrek Marek

Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska

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2022

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z. 158

679--695

EN

Purpose: The main aim of the article is to know the information needs of candidates for university courses and indicate the importance of web analytics tools in the university recruitment process. The authors present the recruitment process for data science high study programme that was conducted in the middle of 2021 at one of the biggest universities in eastern Poland. Theoretical background: Digital transformation is an irreversible process today. Data produced by people, things, administration units and business organizations can be the source of valuable information. That transformation causes new possibilities for fast development, but also creates challenges for education processes and professional work. Furthermore, the digital transformation resulted in creating new professions like data science (DS). Because of data volume and its importance DS professionals became one of the most wanted specialists in the 21st century, and therefore many universities try to launch new study programs related to automated data processing and try to get the attention of potential students. Design/methodology/approach: The process was supported with analytics tools Hotjar and Google Analytics. The results presented in the paper base on the analysis of 974 pageviews recorded by Hotjar and activity of 824 page users reported by Google Analytics. Findings: The analysis showed that web analytics tools are very easy to use in the recruitment process, and that gathered data allows for better understanding of candidates' needs and improving the future requirement processes and tools. Results indicated that the most important topics for candidates were study programme and payment. Form the technical point of view the responsiveness of applications used for the recruitment process is crucial because a lot of traffic was generated by both users of desktop computers and mobile devices. The greatest interest in the program was recorded before the holiday months. Originality/value: The research contributes to academia in the field of recruitment. Paper presents the data science high study programme and indicates the importance of web analytics tools in the university recruitment process.

3

A Python library for the Jupyteo IDE Earth observation processing tool enabling interoperability with the QGIS System for use in data science

Bednarczyk Michał

Geomatics and Environmental Engineering

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2022

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Vol. 16, no. 1

117--144

EN

This paper describes JupyQgis – a new Python library for Jupyteo IDE enabling interoperability with the QGIS system. Jupyteo is an online integrated development environment for earth observation data processing and is available on a cloud platform. It is targeted at remote sensing experts, scientists and users who can develop the Jupyter notebook by reusing embedded open-source tools, WPS interfaces and existing notebooks. In recent years, there has been an increasing popularity of data science methods that have become the focus of many organizations. Many scientific disciplines are facing a significant transformation due to data-driven solutions. This is especially true of geodesy, environmental sciences, and Earth sciences, where large data sets, such as Earth observation satellite data (EO data) and GIS data are used. The previous experience in using Jupyteo, both among the users of this platform and its creators, indicates the need to supplement its functionality with GIS analytical tools. This study analyzed the most efficient way to combine the functionality of the QGIS system with the functionality of the Jupyteo platform in one tool. It was found that the most suitable solution is to create a custom library providing an API for collaboration between both environments. The resulting library makes the work much easier and simplifies the source code of the created Python scripts. The functionality of the developed solution was illustrated with a test use case.

4

Sparse data classifier based on first-past-the-post voting system

Cudak Magdalena, Piech Mateusz, Marcjan Robert

Computer Science

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2022

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T. 23 (2)

277--296

EN

A point of interest (POI) is a general term for objects that describe places from the real world. The concept of POI matching (i.e., determining whether two sets of attributes represent the same location) is not a trivial challenge due to the large variety of data sources. The representations of POIs may vary depending on the basis of how they are stored. A manual comparison of objects is not achievable in real time; therefore, there are multiple solutions for automatic merging. However, there is no yet the efficient solution solves the missing of the attributes. In this paper, we propose a multi-layered hybrid classifier that is composed of machine-learning and deep-learning techniques and supported by a first-past-the-post voting system. We examined different weights for the constituencies that were taken into consideration during a majority (or supermajority) decision. As a result, we achieved slightly higher accuracy than the best current model (random forest), which also is based on voting.

5

Dilemmas of social life algorithmization - technological proof of equity

Osika Grażyna

Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska

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2021

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z. 151

525--538

EN

Purpose: The aim of the article is to describe and forecast possible dilemmas related to the development of cognitive technologies and the progressing process of algorithmization of social life. Design/methodology/approach: Most of the current studies related to the Big Data phenomenon concern the level of efficiency improvement the algorithmic tools or protection against autonomization of machines, in this analysis a different perspective is proposed, namely - thoughtless way of using data-driven instruments, termed technological proof of equity. This study is to try to anticipate possible difficulties connected with algorithmization, which understanding could help to "prepare" or even eliminate the harmful effects we may face which will affect decisions made in the field of the social organization and managing organizations or cities etc. Findings: The proposed point of view may contribute to a more informed use of cognitive technologies, machine learning, artificial intelligence and an understanding of their impact on social life, especially unintended consequences. Social implications: The article can have an educational function, helps to develop critical thinking about cognitive technologies and directs attention to areas of knowledge by which future skills should be extended. Originality/value: The article is addressed to data scientist and all those who use algorithms and data-driven decision-making processes in their actions. Crucial in this considerations is the introduction the concept of technological proof of equity, which helps to "call" the real threat of the appearance of technologically grounded heuristic thinking and it’s social consequences.

6

OECD Recommendation’s draft concerning access to research data from public funding: a review

Madeyski Lech, Lewowski Tomasz, Kitchenham Barbara

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2021

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Vol. 69, nr 1

art. no. e135401

EN

Sharing research data from public funding is an important topic, especially now, during times of global emergencies like the COVID-19 pandemic, when we need policies that enable rapid sharing of research data. Our aim is to discuss and review the revised Draft of the OECD Recommendation Concerning Access to Research Data from Public Funding. The Recommendation is based on ethical scientific practice, but in order to be able to apply it in real settings, we suggest several enhancements to make it more actionable. In particular, constant maintenance of provided software stipulated by the Recommendation is virtually impossible even for commercial software. Other major concerns are insufficient clarity regarding how to finance data repositories in joint private-public investments, inconsistencies between data security and user-friendliness of access, little focus on the reproducibility of submitted data, risks related to the mining of large data sets, and sensitive (particularly personal) data protection. In addition, we identify several risks and threats that need to be considered when designing and developing data platforms to implement the Recommendation (e.g., not only the descriptions of the data formats but also the data collection methods should be available). Furthermore, the non-even level of readiness of some countries for the practical implementation of the proposed Recommendation poses a risk of its delayed or incomplete implementation.

7

Big problems with Big Data

Goczyła Krzysztof

TASK Quarterly : scientific bulletin of Academic Computer Centre in Gdansk

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2020

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Vol. 24, No 1

73--81

EN

The article presents an overview of the most important issues related to the phenomenon called big data. The characteristics of big data concerning the data itself and the data sources are presented. Then, the big data life cycle concept is formulated. The next sections focus on two big data technologies: MapReduce for big data processing and NoSQL databases for big data storage.

8

Network Device Workload Prediction: A Data Mining Challenge at Knowledge Pit

Janusz Andrzej, Przyborowski Mateusz, Biczyk Piotr, Ślęzak Dominik

Annals of Computer Science and Information Systems

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2020

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Vol. 21

77--80

EN

FedCSIS 2020 Data Mining Challenge: Network Device Workload Prediction was the seventh edition of the international data mining competition organized at Knowledge Pit, in association with the Conference on Computer Science and Information Systems. The main goal was to answer the question of whether it is possible to reliably predict workload-related characteristics of monitored network devices based on historical readings. We describe the scope and explain the motivation for this challenge. We also analyze solutions uploaded by the most successful participants and investigate prediction errors which had the greatest influence on the results. Finally, we describe our baseline solution to the considered problem, which turned out to be the most reliable in the final evaluation.

9

Hybrydowy system rekomendacji planów treningowych

Kaczanowski Maciej

Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki

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2019

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nr 20

29--40

PL

Hybrydowe systemy rekomendacji łączą zalety metod stosowanych powszechnie w rekomendacji. Głównym celem tego artykułu jest przedstawienie zastosowania uczenia maszynowego do budowy hybrydowego silnika rekomendacji. Uczenie maszynowe jest poddziedziną sztucznej inteligencji, która wykazuję obiecujące rezultaty w klasyfikacji, predykcji, wykrywaniu anomalii i rekomendacji. W tym artykule zaproponowano koncepcję spersonalizowanego modelu systemu rekomendacji opartego na parametrach i planach treningowych sportowców. Badania przeprowadzono w środowisku chmurowym Microsoft Azure Machine Learning Studio na zbiorze danych wygenerowanym na podstawie danych referencyjnych.

EN

Hybrid recommendation systems combine the advantages of commonly used methods in recommendations. This main objective of this article is to present application of machine learning to build a hybrid recommendation engine. Machine learning is subdomain of artificial intelligence that show promising results in classification, prediction, anomaly detection and recommendations. This paper proposed a personalized recommendation system model based on athletes parameters and training plans. The researches were carried out in the cloud environment Microsoft Azure Machine Learning Studio on football data set.

10

Measuring the impact of academic policy innovation

Akynova A., Jumasheva S., Rybiński K.

Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska

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2018

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z. 119

7--29

EN

This paper presents application of Data science models to the quality assurance processes at the university. In 2015 Narxoz University in Almaty, Kazakhstan introduced a major change to its academic policy. Final year bachelor students were required to complete team-based diploma projects, focused on solving real business problems, instead of writing standard, often purely descriptive and theoretical diploma theses. This article uses data science models (linear regression, logit and CART) to analyze the effects of this policy change on recent graduates’ employability and wage levels using results of telephone survey conducted among 1956 graduates in 2015 and 2016. Estimated models show that students who have firm plans to find jobs, acquire valuable competences when preparing bachelor diploma and actively participate in student life, have higher probability of employment and earn higher wages. It means that the process of writing bachelor diploma should be carefully designed and monitored and that student learning outcomes also depend on their extra-curricular activity. Such analysis provides deep and interesting insights into the learning and assessment processes and should be a part of annual quality assurance review in every university. For example, results indicate that grading practices at Narxoz require a major review, as graduates with higher GPA did not exhibit higher employability nor higher wages. Similarly, curricula of some specializations (majors) should be reviewed due to low employment rate of graduates. Appropriate changes have already been implemented at Narxoz in 2017. Finally, we found that economic shocks have much stronger short-term impact on recent graduates’ employability and wages than reforms of academic curricula.

11

Data science approaches to diagnostics of metal stress-strain state using semiconductor sensor suitable for system design

Balitskii O., Kolesnikow W., Owsyannikow A., Lizunow S., Eliasz J.

Badania Nieniszczące i Diagnostyka

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2018

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nr 1

28--32

EN

Article describes the data science approaches to diagnostics of metal stress-strain state using semiconductor sensor suitable for system design. It has been described the elongation curves (on permanent loading 370-450 MPa) in time of St3 (kp, sp) specimens in initial state, after treatment in He and H2 with pressure 35 MPa and temperature 623 К during 10 hours as well as a curves of the average signal of semiconductor sensors that controls this process and spectral sensitivity of the semiconductor sensors of the visible range depending on the time of the exposure of the samples.

PL

W artykule pokazano możliwość diagnozowania stanu naprężenia-odkształcenia w metalach za pomocą sensorów półprzewodnikowych, przydatne dla zbioru danych naukowych i projektowania systemowego. Przedstawiono krzywe wydłużenia (przy stałym naprężeniu 370-450 MPa) w czasie próbek ze stali St3 (kp, sp) w stanie wejściowym, po obrobce w He i w H2 z ciśnieniem 35 MPa przy temperaturze 623 К w ciągu 10 godz., a także krzywe średniego sygnału sensora półprzewodnikowego, który obserwuje ten proces i czujność spektralna sensorów półprzewodnikowych diapazonu widocznego w zależności od czasu ekspozycji próbek.

12

Application of Sensor Fusion and Data Mining for Prediction of Methane Concentration in Coal Mines

Zagorecki A.

Mining – Informatics, Automation and Electrical Engineering

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2015

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R. 53, nr 4

33--38 [tekst ang.], 67--72 [tekst pol.]

EN

In recent years we have experienced unprecedented increase of use of sensors in many industrial applications. Modern sensors are capable of not only generating large volumes of data but as well transmit ting that data through network and storing it for further analysis. These enable to create systems capable of real-time data fusion in order to predict events of interest. The goal of this work is to predict methane concentration levels in coal mines using data fusion and data mining techniques. The paper describes an application of a generic method that can be applied to arbitrary set of multivariate time series data in order to perform classification or regression tasks. The solution presented here was developed within the framework of IJCRS‘15 data mining competition and resulted in the winning model outperforming other solutions.

PL

W ostatnich latach można było zaobserwować niespotykany wzrost użycia czujników w wielu zastosowaniach przemysłowych. Nowoczesne czujniki są w stanie nie tylko generować duże ilości danych, lecz równie ż przysyłać te dane za pomocą sieci i przechowywać je do późniejszej analizy. Umożliwia to opracowanie systemów do łączenia danych w czasie rzeczywistym w celu prognozowania określonych zdarzeń. Celem niniejszej pracy jest prognozowanie poziomów stężenia m etanu w kopalniach węgla za pomoc ą technik fuzji danych i eksploracji danych. Artykuł przedstawia zastosowanie generycznej metody, która może być użyta do dowolnego zbioru danych wielowymiarowych szeregów czasowych w celu przeprowadzenia zadań klasyfikacji lub regresji. Zaprezentowane rozwiązanie zostało opracowane w ramach konkursu eksploracji danych IJCRS’15 i – pokonując inne rozwiązania – zostało jego zwycięzcą.