Wyniki wyszukiwania - BazTech

1

When to Trust AI: Advances and Challenges for Certification of Neural Networks

Kwiatkowska Marta, Zhang Xiyue

Annals of Computer Science and Information Systems

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2023

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Vol. 35

25--37

EN

Artificial intelligence (AI) has been advancing at a fast pace and it is now poised for deployment in a wide range of applications, such as autonomous systems, medical diagnosis and natural language processing. Early adoption of AI technology for real-world applications has not been without problems, particularly for neural networks, which may be unstable and susceptible to adversarial examples. In the longer term, appropriate safety assurance techniques need to be developed to reduce potential harm due to avoidable system failures and ensure trustworthiness. Focusing on certification and explainability, this paper provides an overview of techniques that have been developed to ensure safety of AI decisions and discusses future challenges.

2

Analyzing and improving tools for supporting fighting against COVID-19 based on prediction models and contact tracing

Gruda Martyna, Kedziora Michal

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2021

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Vol. 69, nr 4

art. no. e137414

EN

In the paper, we are analyzing and proposing an improvement to current tools and solutions for supporting fighting with COVID-19. We analyzed the most popular anti-covid tools and COVID prediction models. We addressed issues of secure data collection, prediction accuracy based on COVID models. What is most important, we proposed a solution for improving the prediction and contract tracing element in these applications. The proof of concept solution to support the fight against a global pandemic is presented, and the future possibilities for its development are discussed.

3

Superiority of Simplicity: A Lightweight Model for Network Device Workload Prediction

Acker Alexander, Wittkopp Thorsten, Nedelkoski Sasho, Bogatinovski Jasmin, Kao Odej

Annals of Computer Science and Information Systems

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2020

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Vol. 21

7--10

EN

The rapid growth and distribution of IT systems increases their complexity and aggravates operation and maintenance. To sustain control over large sets of hosts and the connecting networks, monitoring solutions are employed and constantly enhanced. They collect diverse key performance indicators (KPIs) (e.g. CPU utilization, allocated memory, etc.) and provide detailed information about the system state. Storing such metrics over a period of time naturally raises the motivation of predicting future KPI progress based on past observations. This allows different ahead of time optimizations like anomaly detection or predictive maintenance. Predicting the future progress of KPIs can be defined as a time series forecasting problem. Although, a variety of time series forecasting methods exist, forecasting the progress of IT system KPIs is very hard. First, KPI types like CPU utilization or allocated memory are very different and hard to be modelled by the same model. Second, system components are interconnected and constantly changing due to soft- or firmware updates and hardware modernization. Thus a frequent model retraining or fine-tuning must be expected. Therefore, we propose a lightweight solution for KPI series prediction based on historic observations. It consists of a weighted heterogeneous ensemble method composed of two models - a neural network and a mean predictor. As ensemble method a weighted summation is used, whereby a heuristic is employed to set the weights. The lightweight nature allows to train models individually on each KPI series and makes model retraining feasible when system changes occur. The modelling approach is evaluated on the available FedCSIS 2020 challenge dataset and achieves an overall R^2 score of 0.10 on the preliminary 10\% test data and 0.15 on the complete test data. We publish our code on the following github repository: https://github.com/citlab/fed\_challenge.

4

Short-term air pollution forecasting based on environmental factors and deep learning models

Arsov Mirche, Zdravevski Eftim, Lameski Petre, Corizzo Roberto, Koteli Nikola, Mitreski Kosta, Trajkovik Vladimir

Annals of Computer Science and Information Systems

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2020

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Vol. 21

15--22

EN

The effects of air pollution on people, the environment, and the global economy are profound - and often under-recognized. Air pollution is becoming a global problem. Urban areas have dense populations and a high concentration of emission sources: vehicles, buildings, industrial activity, waste, and wastewater. Tackling air pollution is an immediate problem in developing countries, such as North Macedonia, especially in larger urban areas. This paper exploits Recurrent Neural Network (RNN) models with Long Short-Term Memory units to predict the level of PM10 particles in the near future (+3 hours), measured with sensors deployed in different locations in the city of Skopje. Historical air quality measurements data were used to train the models. In order to capture the relation of air pollution and seasonal changes in meteorological conditions, we introduced temperature and humidity data to improve the performance. The accuracy of the models is compared to PM10 concentration forecast using an Autoregressive Integrated Moving Average (ARIMA) model. The obtained results show that specific deep learning models consistently outperform the ARIMA model, particularly when combining meteorological and air pollution historical data. The benefit of the proposed models for reliable predictions of only 0.01 MSE could facilitate preemptive actions to reduce air pollution, such as temporarily shutting main polluters, or issuing warnings so the citizens can go to a safer environment and minimize exposure.

5

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.

6

Data Mining-Based Phishing Detection

Bohacik Jan, Skula Ivan, Zabovsky Michal

Annals of Computer Science and Information Systems

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2020

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Vol. 21

27--30

EN

Webpages can be faked easily nowadays and as there are many internet users, it is not hard to find some becoming victims of them. Simultaneously, it is not uncommon these days that more and more activities such as banking and shopping are being moved to the internet, which may lead to huge financial losses. In this paper, a developed Chrome plugin for data mining-based detection of phishing webpages is described. The plugin is written in JavaScript and it uses a C4.5 decision tree model created on the basis of collected data with eight describing attributes. The usability of the model is validated with 10-fold cross-validation and the computation of sensitivity, specificity and overall accuracy. The achieved results of experiments are promising.

7

A HPC (High Performance Computing) based model for optimisation of the waste management systems in metropolitan regions

Gaska K., Generowicz A., Ciuła J.

Technical Transactions

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2018

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Vol. 115, iss. 3

153--163

EN

The article presents the conception of an intelligent system for monitoring and managing the municipal waste disposal in metropolises. Applying advanced IT solutions using intelligent computational techniques enables the passage from the passive position of selfgovernment units (JST) in managing the waste disposal to the active position, especially in decision making during the problem solving of planning systems associated with the organisation management of the complex infrastructure of the waste disposal. The aim of using ICT systems is an increase in the reliability of the economy of systemic waste, monitoring in real time, the stabilization of the work of the system and the optimization of logistic and technological processes in the context of the raw material, energy application and simultaneously limiting the influence on all components of the environment.

PL

W artykule przedstawiono koncepcję inteligentnego systemu monitorowania i zarządzania gospodarką odpadami komunalnymi w metropoliach. Zastosowanie zaawansowanych rozwiązań informatycznych wykorzystujących inteligentne techniki obliczeniowe umożliwia na przejście z biernej pozycji jednostek samorządowych (JST) w zarządzaniu gospodarką odpadami do aktywnego działania, w tym szczególnie, podejmowanie decyzji podczas rozwiązywania problemów planistycznych związanych z organizacją systemu zbiórki i systemu transportu odpadów i kompleksowym zarządzaniem złożoną infrastrukturą systemów gospodarki odpadami. Celem wykorzystania systemów ICT jest zwiększenie niezawodności systemów gospodarki odpadami, monitoring w czasie rzeczywistym, stabilizacja pracy systemu oraz optymalizacja procesów logistycznych i technologicznych w kontekście wykorzystania surowcowego, energetycznego przy jednoczesnym ograniczeniu wpływu na wszystkie komponenty środowiska (woda, powietrze, gleba).

8

Effect of particle shape and imperfect filler-matrix interface on effective thermal conductivity of epoxy-aluminum composite

Pietrak K., Kubiś M., Langowski M., Kropielnicki M., Wultański P.

Composites Theory and Practice

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2017

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R. 17, nr 4

183--188

EN

The predictions of major effective medium models and 2-dimensional numerical models implemented in Ansys Fluent were tested against the results of experimental measurements of macroscopic thermal conductivity for a polymer filled with aluminum powder. The examined composite may be regarded as a representative of materials used for heat management purposes, for example for the manufacture of electronic device housings. The study demonstrates the effect of particle shape and imperfect filler-matrix interface on the theoretical value of thermal conductivity of the considered material. It also creates the opportunity to discuss the versatility and accuracy of various methods devised to predict the effective thermal conductivity of heterogeneous materials. It was found that the effective medium approximation proposed by Duan et al., which considers the effect of the particle aspect ratio, outrivaled other predictive schemes in accuracy and cost-effectiveness. Effective medium approximations that assume spherically-shaped reinforcement as well as finite volume models implemented in Ansys Fluent, greatly underestimated the parameter in question.

PL

Przewidywania popularnych, analitycznych modeli predykcyjnych efektywnej przewodności cieplnej kompozytów cząsteczkowych zostały porównane z danymi eksperymentalnymi uzyskanymi dla kompozytów polimerowych napełnionych proszkiem aluminiowym oraz z wynikami obliczeń numerycznych wykonanych metodą objętości skończonych w programie Ansys Fluent. Testowany materiał reprezentuje grupę materiałów stosowanych w technice cieplnej, np. do wytwarzania obudów urządzeń elektronicznych. Wyniki badania pokazują efekt kształtu wtrąceń oraz niedoskonałego kontaktu termicznego na granicy zbrojenie-osnowa na teoretyczną wartość efektywnej przewodności cieplnej rozważanego materiału. Są też podstawą do dyskusji na temat wad i zalet stosowania analitycznych metod przewidywania przewodności cieplnej materiałów kompozytowych (tzw. effective medium models). Najlepszą zgodność z eksperymentem otrzymano za pomocą jednego z modeli analitycznych (Duan i in.), który uwzględnia wydłużony kształt cząsteczek napełniacza. Przewidywania modeli analitycznych zakładających sferyczny kształt cząsteczek okazały się silnie zaniżone, podobnie jak przewidywania dwuwymiarowych modeli numerycznych zaimplementowanych w środowisku Ansys Fluent.

9

Model adaptacyjnego algorytmu wspomagania decyzji w systemie utrzymania ruchu

Kosicka E., Mazurkiewicz D., Gola A.

Zarządzanie Przedsiębiorstwem

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2016

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Vol. 19, nr 4

29--35

EN

Currently used predictive maintenance systems predict future events by monitoring residual processes using the enforced predictive model. Despite the benefits resulting from their implementation in companies (e.g. savings resulting from preventing failure), it is necessary to draw attention to the fact that such models lack flexibility in adapting to the dynamically changing values of observation vectors due to real-time readout which can provide more accurate predictions. The paper proposes a model of adaptive algorithm for maintenance decision support system which - depending on the changing parameters of residual processes - selects an adequate mathematical model based on predictive and in-formative criteria. Moreover, to produce more accurate predictions this model uses additional input data for prediction including values of residual processes as well as technical or quality-related aspects due to the extended range of observed factors that affect failure occurrence. The proposed model additionally contains a maintenance decision-related part which - based on the information about actions taken by maintenance services - generates a constrained optimal time interval for performing the necessary maintenance work.

10

Modelling of chlorophyll-a content in running waters

Neverova-Dziopak E., Kowalewski Z.

Proceedings of ECOpole

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2016

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Vol. 10, No. 2

455--462

EN

Chlorophyll-a is one of the key parameters for assessment of trophic status of surface waters. However, Polish standard environmental monitoring procedures assume a low frequency of chlorophyll measurements in running waters, which does not provide the possibility of permanent control of eutrophication process and taking the appropriate preventive and protective measures sufficiently in advance. The article is focusing on constructing of predicting model of chlorophyll-a content based on data obtained within monitoring realized by Regional Inspectorates for Environmental Protection. Multivariate linear regression (MLR) model for chlorophyll-a content prediction was formulated on the base of chosen parameters like: pH, oxygen saturation, different forms of nitrogen and phosphorus. Formulation of the model was followed by a test of the applicability of each of the individual components of the regression equation. The main purpose was to develop an algorithm allowing for quick adaptation of model to local conditions in the rivers in order to make a reliable prediction of chlorophyll content.

PL

Chlorofil-a jest jednym z kluczowych parametrów służących do oceny stanu troficznego wód. W Polsce w ramach standardowego monitoringu rzek jest jednak badany rzadko. Artykuł skupia się na skonstruowaniu modelu predykcji zawartości chlorofilu-a w oparciu o dane pochodzące z rutynowego monitoringu realizowanego przez Wojewódzkie Inspektoraty Ochrony Środowiska. W tym celu na podstawie parametrów jakości wód, takich jak pH, nasycenie tlenem oraz różne formy azotu i fosforu, został sformułowany model regresyjny, a następnie przeprowadzono test zasadności zastosowania w nim poszczególnych składników równania regresji. Ostatnim etapem było opracowanie algorytmu pozwalającego na szybkie dostosowywanie modelu do lokalnych warunków w rzekach w celu dokonania wiarygodnej prognozy zawartości chlorofilu.

11

Wpływ wstępnego przetwarzania danych wejściowych na jakość modeli predykcyjnych budowanych technikami Data Mining

Nęcka K.

Śląskie Wiadomości Elektryczne

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2014

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Nr 2 (113)

11--17

PL

Artykuł porusza problematykę wstępnego przetwarzania danych wejściowych wykorzystywanych do prognozowania godzinowego zapotrzebowania na energię elektryczną. Analizy zostały przeprowadzone na danych uzyskanych w wyniku badań własnych wykonanych w ubojni drobiu. Zaprezentowane w pracy wyniki dotyczą przykładowych prognoz godzinowego zapotrzebowania na energię elektryczną wykonanych technikami data mining z wykorzystaniem zmiennych wejściowych poddanych różnym przekształceniom..

EN

This article contains issue of data pre-processing used in prediction of hourly energy consumption. All analyses and studies were done based on own researching made in poultry abattoir and data achieved this way. Results presented in this article applies to hourly prediction for energy consumption achieved with Data Mining techniques with utilization of input variables subjected to various transformations..

12

Prognozowanie obciążeń w systemie elektroenergetycznym przy użyciu sieci neuronowych SVM i MLP

Ciechulski T.

Spektrum

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2014

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nr 9-10

I--IV

PL

W artykule dokonano analizy metod predykcji obciążeń małego systemu elektroenergetycznego w centralnej Polsce. Bazują one na wykorzystaniu modeli predykcyjnych w postaci dwóch rodzajów sieci neuronowych: SVM i MLP. Symulacje sieci neuronowych zostały przeprowadzone w środowisku MATLAB. Uwzględniono wyprzedzenie dobowe (24 - godzinne).

EN

An analysis is made of load prediction methods applied in a small power system in central Poland. The methods are based on the use of prediction models in the form of two types of neural networks: SVM and MLP. Neural networks’ simulations were conducted in the MATLAB environment. An advance period of 24 hours was taken into account.

13

Predictive regression models of monthly seismic energy emissions induced by longwall mining

Jakubowski J., Tajduś A.

Archives of Mining Sciences

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2014

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Vol. 59, no. 3

705--720

EN

This article presents the development and validation of predictive regression models of longwall mining-induced seismicity, based on observations in 63 longwalls, in 12 seams, in the Bielszowice colliery in the Upper Silesian Coal Basin, which took place between 1992 and 2012. A predicted variable is the logarithm of the monthly sum of seismic energy induced in a longwall area. The set of predictors include seven quantitative and qualitative variables describing some mining and geological conditions and earlier seismicity in longwalls. Two machine learning methods have been used to develop the models: boosted regression trees and neural networks. Two types of model validation have been applied: on a random validation sample and on a time-based validation sample. The set of a few selected variables enabled nonlinear regression models to be built which gave relatively small prediction errors, taking the complex and strongly stochastic nature of the phenomenon into account. The article presents both the models of periodic forecasting for the following month as well as long-term forecasting.

PL

W artykule przedstawiono budowę i walidację predykcyjnych modeli regresyjnych sejsmiczności indukowanej eksploatacją w ścianie, opartych na obserwacjach w 63 ścianach kopalni Bielszowice prowadzonych w 12 pokładach w latach 1992-2012. Zmienna prognozowaną jest logarytm miesięcznej sumy energii sejsmicznej wstrząsów w ścianie. Zestaw predyktorów składa się z siedmiu zmiennych ilościowych i jakościowych opisujących wybrane czynniki górnicze i geologiczne w ścianach. Do budowy modeli zastosowano dwie metody uczenia się maszyn: drzewa wzmacniane oraz sieci neuronowe. Zastosowano dwa rodzaje walidacji modeli: na losowej próbie walidacyjnej oraz na czasowej próbie walidacyjnej. Zestaw kilku wybranych zmiennych pozwolił na zbudowanie nieliniowych modeli regresyjnych, które, biorąc pod uwagę złożoną i silnie stochastyczną naturę zjawiska, dają względnie małe błędy pro gnozy. W artykule przedstawiono zarówno modele do prognozy okresowej na kolejny miesiąc jak i do prognozy długoterminowej.