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92
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Introduction
I am a member of the GEIST research team at the AGH University of Science and Technology.
I participated in several international research projects where I was involved in tasks concerning knowledge engineering and rule based inference.
My current reaserch interests are: machine learning, ambient intelligence, context awareness and knowledge engineering.
Additional affiliations
Education
October 2013 - July 2016
Publications
Publications (92)
Uncertainty handling is one of the most important aspects of modelling of context-aware systems. It has direct impact on the adaptability, understood as an ability of the system to adjust to changing environmental conditions or hardware configuration (missing data), changing user habits (ambiguous concepts), or imperfect information (low quality se...
Building systems that acquire, process and reason with context data is a major challenge. Model updates and modifications are required for the mobile context-aware systems. Additionally, the nature of the sensor-based systems implies that the data required for the reasoning is not always available nor it is certain. Finally, the amount of context d...
In this paper we introduce a new method to incorporate the user as an additional information source for the purpose of indoor localization. Therefore, the user is interrogated about certain characteristics in his/her environment. The questions are generated by a knowledge-based system built on ontologies. We provide a new statistical model to evalu...
With the rapid evolution of mobile devices, the concept of context aware applications has gained a remarkable popularity in recent years. Smartphones and tablets are equipped with a variety of sensors including accelerometers, gyroscopes, and GPS, pressure gauges, light and GPS sensors. Additionally, the devices become computationally powerful whic...
Context-aware systems gained huge popularity in recent years due to rapid evolution of personal mobile devices. Equipped with variety of sensors, such devices are sources of a lot of valuable information that allows the system to act in an intelligent way. However, the certainty and presence of this information may depend on many factors like measu...
This study is located in the Human-Centered Artificial Intelligence (HCAI) and focuses on the results of a user-centered assessment of commonly used eXplainable Artificial Intelligence (XAI) algorithms, specifically investigating how humans understand and interact with the explanations provided by these algorithms. To achieve this, we employed a mu...
This paper introduces a dataset that is the result of a user study on the comprehensibility of explainable artificial intelligence (XAI) algorithms. The study participants were recruited from 149 candidates to form three groups representing experts in the domain of mycology (DE), students with a data science and visualization background (IT) and st...
To optimise the operation of his wind farm, the farm manager needs to make precise diagnostic decisions for scheduling efficiently the maintenance actions. To assist him in this task, this paper proposes an approach aimed at designing a hybrid diagnoser model for two main goals. The first one is to detect anomalies at an early stage, and the second...
The development of black-box survival models has created a need for methods that explain their outputs, just as in the case of traditional machine learning methods. Survival models usually predict functions rather than point estimates. This special nature of their output makes it more difficult to explain their operation. We propose a method to gen...
Predictive Maintenance (PdM) emerged as one of the pillars of Industry 4.0, and became crucial for enhancing operational efficiency, allowing to minimize downtime, extend lifespan of equipment, and prevent failures. A wide range of PdM tasks can be performed using Artificial Intelligence (AI) methods, which often use data generated from industrial...
The aim of this paper is to describe the tool for combining the information of control charts, widely used for statistical quality control with the information obtained from the process of explanation the classification decision made using the machine learning model. Control charts are used to monitor production and show deviations from normal beha...
Data analysis is one of the most important parts of data mining and machine learning tasks. In recent years, explainable artificial intelligence methods have been used very often to support this phase. However, the explanations themselves are very often difficult to understand by domain experts, who play one of the most important roles in the phase...
In many practical applications data used for training a machine learning model and the deployment data does not always preserve the same distribution. Transfer learning and, in particular, domain adaptation allows to overcome this issue, by adapting the source model to a new target data distribution and therefore generalizing the knowledge from sou...
Anomaly detection in industrial environment is a complex task, which requires to consider multiple characteristics of the data from industrial sensors and anomalies itself. Such data is often highly imbalanced and the availability of labels is limited. The data is generated in streaming fashion, which means that it is unbounded and potentially infi...
In an industrial setting, predicting the remaining useful life-time of equipment and systems is crucial for ensuring efficient operation, reducing downtime, and prolonging the life of costly assets. There are state-of-the-art machine learning methods supporting this task. However, in this paper, we argue, that both efficiency and understandability...
Data generated by manufacturing processes can often be represented as a data stream. The main characteristics of these data are that it is not possible to store all the data in memory, the data are generated continuously at high speeds, and it may evolve over time. These characteristics of the data make it impossible to use ordinary machine learnin...
This paper discusses the use of optimal control for improving the performance of industrial processes. Industry 4.0 technologies play a crucial role in this approach by providing real-time data from physical devices. Additionally, simulations and virtual sensors allow for proactive control of the process by predicting potential issues and taking me...
Explainable Artificial Intelligence (XAI) fills the role of a critical interface fostering interactions between sophisticated intelligent systems and diverse individuals, including data scientists, domain experts, end-users, and more. It aids in deciphering the intricate internal mechanisms of ``black box'' Machine Learning (ML), rendering the reas...
This study evaluates quality management practices in Industry 4.0 in a specific case of steel manufacturing. We formulate a novel proposal based on Semantic Data Mining techniques a step towards knowledge‐driven decision support based on the industrial Six Sigma approach. In our research we combine machine learning classifiers, and explanation gene...
In an industrial setting, predicting the remaining useful life-time of equipment and systems is crucial for ensuring efficient operation, reducing downtime, and prolonging the life of costly assets. There are state-of-the-art machine learning methods supporting this task. However, in this paper, we argue, that both
efficiency and understandability...
Pattern discovery in multidimensional data sets has been the subject of research for decades. There exists a wide spectrum of clustering algorithms that can be used for this purpose. However, their practical applications share a common post-clustering phase, which concerns expert-based interpretation and analysis of the obtained results. We argue t...
Explainable Artificial Intelligence (XAI) aims at introducing transparency and intelligibility into the decision-making process of AI systems. In recent years, most efforts were made to build XAI algorithms that are able to explain black-box models. However, in many cases, including medical and industrial applications, the explanation of a decision...
Generic emotion prediction models based on physiological data developed in the field of affective computing apparently are not robust enough. To improve their effectiveness, one needs to personalize them to specific individuals and incorporate broader contextual information. To address the lack of relevant datasets, we propose the 2nd Study in Bio-...
Extensive research on machine learning models, which in the majority are black-boxes, created a great need for the development of Explainable Artificial Intelligence (XAI) methods. Complex machine learning (ML) models usually require an external explanation method to understand their decisions. The interpretation of the model predictions are crucia...
Explainable Artificial Intelligence (XAI) aims to introduce transparency and intelligibility into the decision-making process of AI systems. Most often, its application concentrates on supervised machine learning problems such as classification and regression. Nevertheless, in the case of unsupervised algorithms like clustering, XAI can also bring...
Development of predictive maintenance (PdM) solutions is one of the key aspects of Industry 4.0. In recent years, more attention has been paid to data-driven techniques, which use machine learning to monitor the health of an industrial asset. The major issue in the implementation of PdM models is a lack of good quality labelled data. In the paper w...
We present a refinement of the Immersive Parallel Coordinates Plots (IPCP) system for Virtual Reality (VR). The evolved system provides data-science analytics built around a well-known method for visualization of multidimensional datasets in VR. The data-science analytics enhancements consist of importance analysis and a number of clustering algori...
Pattern discovery in multidimensional data sets has been a subject of research since decades. There exists a wide spectrum of clustering algorithms that can be used for that purpose. However, their practical applications share in common the post-clustering phase, which concerns expert-based interpretation and analysis of the obtained results. We ar...
Explainable Artificial Intelligence (XAI) aims at introducing transparency and intelligibility into decision-making process of AI systems. Most of the work in this area is focused on supervised machine learning tasks such as classification and regression. Unsupervised algorithms such as clustering can also be explained with existing approaches. Thi...
The paper provides insights into two main threads of analysis of the BIRAFFE2 dataset concerning the associations between personality and physiological signals and concerning the game logs' generation and processing. Alongside the presentation of results, we propose the generation of event-marked maps as an important step in the exploratory analysi...
Mining ubiquitous sensing data is important but also challenging, due to many factors, such as heterogeneous large-scale data that is often at various levels of abstraction. This also relates particularly to the important aspects of the explainability and interpretability of the applied models and their results, and thus ultimately to the outcome o...
With advances of artificial intelligence (AI), there is a growing need for provisioning of transparency and accountability to AI systems. These properties can be achieved with eXplainable AI (XAI) methods, extensively developed over the last few years with relation for machine learning (ML) models. However, the practical usage of XAI is limited now...
Learning from uncertain or incomplete data is one of the major challenges in building artificial intelligence systems. However, the research in this area is more focused on the impact of uncertainty on the algorithms performance or robustness, rather than on human understanding of the model and the explainability of the system. In this paper we pre...
Explainable Artificial Intelligence (XAI) methods form a large portfolio of different frameworks and algorithms. Although the main goal of all of explanation methods is to provide an insight into the decision process of AI system, their underlying mechanisms may differ. This may result in very different explanations for the same tasks. In this work...
Cluster discovery from highly-dimensional data is a challenging task, that has been studied for years in the fields of data mining and machine learning. Most of them focus on automation of the process, resulting in the clusters that once discovered have to be carefully analyzed to assign semantics for numerical labels. However, it is often the case...
In this article, we propose using personality assessment as a way to adapt affective intelligent systems. This psychologically-grounded mechanism will divide users into groups that differ in their reactions to affective stimuli for which the behaviour of the system can be adjusted. In order to verify the hypotheses, we conducted an experiment on 20...
We propose an experimental framework for Affective Computing based of video games. We developed a set of specially designed mini-games, based of carefully selected game mechanics, to evoke emotions of participants of a larger experiment. We believe, that games provide a controllable yet overall ecological environment for studying emotions. We discu...
In the paper we describe the industrial process of hot rolling of steel. In cooperation with ArcelorMittal Poland we consider a specific fully automated production line. While it is equipped with a number of industrial sensors, the acquired data has only been analyzed on a basic statistical level, mainly for reporting. In the paper we outline oppor...
Development of models for emotion detection is often based on the use of machine learning. However, it poses practical challenges, due to the limited understanding of modeling of emotions, as well as the problems regarding measurements of bodily signals. In this paper we report on our recent work on improving such models, by the use of explainable...
The paper describes BIRAFFE2 data set, which is a result of an affective computing experiment conducted between 2019 and 2020, that aimed to develop computer models for classification and recognition of emotion. Such work is important to develop new methods of natural Human-AI interaction. As we believe that models of emotion should be personalized...
This is our 2nd Study in Bio-Reactions and Faces for Emotion-based Personalization for AI Systems (BIRAFFE2). It is a dataset consisting of electrocardiogram (ECG), galvanic skin response (GSR), changes in facial expression signals and hand movements (represented by gamepad's accelerometer and gyroscope) recorded during affect elicitation by means...
In this paper we introduce the BIRAFFE data set which is the result of the experiment in affective computing we conducted in early 2019. The experiment is part of the work aimed at the development of computer models for emotion classification and recognition. We strongly believe that such models should be personalized by design as emotional respons...
We present BIRAFFE, a dataset consisting of electrocardiogram (ECG), galvanic skin reaction (GSR) and changes in facial expression signals recorded during affect elicitation by means of audio-visual stimuli (from IADS and IAPS databases) and our two proof-of-concept affective games ("Affective SpaceShooter 2" and "Fred Me Out 2"). All the signals w...
In this paper, we consider the use of wearable sensors for providing affect-based adaptation in Ambient Intelligence (AmI) systems. We begin with discussion of selected issues regarding the applications of affective computing techniques. We describe our experiments for affect change detection with a range of wearable devices, such as wristbands and...
Affective computing gained a lot of attention from researchers and business over the last decade. However, most of the attempts for building systems that try to predict, or provoke affective state of users were done for specific and narrow domains. This complicates reusing such systems in other, even similar domains. In this paper we present such a...
In our work, we focus on detection of affective states, their proper identification and interpretation with use of wearable and mobile devices. We propose a data acquisition layer based on wearable devices able to gather physiological data, and we integrate it with mobile context-aware framework. Furthermore, we formulate a method for personalizati...
Knowledge discovery from data streams in recent years become one of the most important research area in a domain of data science. This is mainly due to the rapid development of mobile devices, and Internet of things solutions which allow for obtaining petabytes of data within minutes. All of the modern approaches either use representation that is f...
Over the last decades, number of embedded and portable computer systems for monitoring of activities of miners and underground environmental conditions that have been developed has increased. However, their potential in terms of computing power and analytic capabilities is still underestimated. In this paper we elaborate on the recent examples of t...
Building mobile context‐aware systems is inherently complex and non‐trivial task. It consists of several phases starting from acquisition of context, through modeling to execution of contextual models. Today, such systems are mostly implemented on mobile platforms, that introduce specific requirements, such as intelligibility, robustness, privacy,...
The paper outlines a mobile sensor platform aimed at processing physiological data from wearable sensors.
We discuss the requirements related to the use of low-cost portable devices in this scenario.
Experimental analysis of four such devices, namely Microsoft Band 2, Empatica E4, eHealth Sensor Platform and BITalino (r)evolution is provided.
Criti...
In this overview paper we focus on our recent progress in the work on the mobile platform for AfC. We provide the main assumptions about the platform, as well as describe affective data acquisition and interpretation. We discuss our most recent experiments and provide an outlook of our future works.
Design of Business Intelligence systems capable of effectively handling a domain knowledge is a well known, but currently not solved challenge for both Software and Knowledge Engineers. There exist several approaches to extract and model the Business Knowledge, most notably Business Processes and Business Rules. However, each of them has its own we...
Rule-based systems constitute the state of the art solutions in the area of artificial intelligence. They provide fast, human readable and self explanatory mechanism for encoding knowledge. Due to large popularity of rules, dozens of inference engines were developed over last few decades. They differ in the reasoning efficiency depending on many fa...
Knowledge engineering aims at providing methods for efficient knowledge encoding to allow for automatic reasoning. Most of the research in this field is devoted to the design of expressive modeling languages or effective reasoning mechanisms. We argue that powerful knowledge representation and inference mechanism is not enough to assure high qualit...
Mobile context-aware systems are becoming more and more popular due to the rapid evolution of personal mobile devices. The variety of sensors that are available on such devices allow building intelligent applications that adapt automatically to user preferences and needs. Together with a growth of such self-adaptable systems, number of tools for co...
Abstract. Despite a large amount of research devoted to improving meta-learning
techniques, providing and using background knowledge for this task remains a challenge. In
this paper we propose a mechanism for automatic recommendation of suitable machine
learning algorithms and their parameters. We used OpenML database and use rule-based
configu...
Location is one of the most valuable and extensively used information in mobile context-aware systems. Its understanding may vary from geolocation that uses GPS infrastructure to locate objects on Earth, up to microlocation, which aims at locating users and objects inside closed areas. Although geolocation can be considered as a mature field, there...
Mobile context-aware systems gained huge popularity in recent years due to the rapid evolution of personal mobile devices. Nowadays smartphones are equipped with a variety of sensors that allow for on-line monitoring of user context and reasoning upon it. Contextual information in such systems is very dynamic. It changes rapidly and these changes m...
Location is one of the most commonly used contextual information in mobile context-aware systems. It can be considered on many different levels of granularity, varying from geolocation that is based on GPS systems, up to microlocation that uses Bluetooth Low Energy devices and WiFi access points for locating users inside buildings. Most common use...
Context-aware systems make use of contextual information to adapt their functionality to current environment state, or user needs and habits. One of the major problems concerning them is the fact, that there is no war-ranty that the contextual information will be available, nor certain at the time when the reasoning should be performed. This may be...
Research in the area of context-awareness has recently been revolutionized by the rapid development of mobile devices like smart phones and tablets, which became omnipresent in daily human life. Such devices are valuable sources of information about their user location, physical and social activity, profiles and habits, etc. However, the informatio...
Engaging users in threat reporting is important in order to improve threat monitoring in urban environments. Today, mobile applications are mostly used to provide basic reporting interfaces. With a rapid evolution of mobile devices, the idea of context awareness has gained a remarkable popularity in recent years. Modern smartphones and tablets are...
Formalized process models help to handle, design and store processes in a form understandable for the designers and users. As model repositories often contain similar or related models, they should be used when modelling new processes in a form of automated recommendations. It is important, as designers prefer to receive and use suggestions during...
Mobile devices are valuable sources of information about their user location, physical and social activity, profiles and habits. Such an information can be used to build context-aware applications, that are able to adapt their functionality to user needs and preferences. A lot of research have been done in this field of science, providing multiple...
With the rapid development of mobile technologies like, e.g., RFID tags, smartphones, and tablets, ambient intelligence applications have gained a huge popularity in recent years. However, most of the existing approaches aim at developing ambient environments that are rather static, and do not take the aspect of social interaction between the inhab...
With a rapid evolution of mobile devices, the idea of context awareness has gained a remarkable popularity in recent years. Modern smartphones and tablets are equipped with a variety of sensors including accelerometers, gyroscopes, pressure gauges, light and GPS sensors. Additionally, the devices become computationally powerful which allows real-ti...
Context-aware mobile systems have gained a remarkable popularity in recent years. Mobile devices are equipped with a variety of sensors and become computationally powerful, which allows for real-time fusion and processing of data gathered by them. However, most of existing frameworks for context-aware systems, are usually dedicated to static, centr...
Formalized process models help to handle, design and store processes in a form understandable for the designers and users. Modeling of business processes is a complex task, which can be supported by recommendations. It is important, as designers prefer to receive and use suggestions during the modeling process. Recommendations make modeling faster...