Maria Bielikova

Maria Bielikova
Kempelen Institute of Intelligent Technologies

Prof.

About

360
Publications
59,944
Reads
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2,803
Citations
Additional affiliations
September 2020 - present
Kempelen Institute of Intelligent Technologies
Position
  • Professor
Description
  • KInIT is an independent institute, dedicated to research of intelligent technologies - artificial intelligence and several areas of computing and information technologies (data science, machine learning, natural language processing, information security, software engineering). The Institute also deals with questions of ethics in information technologies and specifically, in artificial intelligence. http://kinit.sk
August 1989 - January 2020
Slovak University of Technology in Bratislava - Slovenska technicka univerzita v Bratislave
Position
  • Professor
Description
  • PeWe (Personalized Web, http://pewe.fiit.stuba.sk/) is an informal research group concerned to the new trends in design, development and use of adaptive social web-based systems with semantics, which allow personalized presentation in various domains.
Education
September 1989 - September 1995
Slovak University of Technology in Bratislava
Field of study
  • Computer Science
September 1984 - June 1989
Slovak University of Technology in Bratislava
Field of study
  • Computer Science

Publications

Publications (360)
Article
Full-text available
This paper contributes to the discussion on effective regulation of facial recognition technologies (FRT) in public spaces. In response to the growing universalization of FRT in the United States and Europe as merely intrusive technology, we propose to distinguish scenarios in which the ethical and social risks of using FRT are unattainable from ot...
Article
Full-text available
The widespread use of temporal aspects in user modeling indicates their importance, and their consideration showed to be highly effective in various domains related to user modeling, especially in recommender systems. Still, past and ongoing research, spread over several decades, provided multiple ad-hoc solutions, but no common understanding of th...
Conference Paper
In this paper, we describe a black-box sockpuppeting audit which we carried out to investigate the creation and bursting dynamics of misinformation filter bubbles on YouTube. Pre-programmed agents acting as YouTube users stimulated YouTube's recommender systems: they first watched a series of misinformation promoting videos (bubble creation) and th...
Preprint
False information has a significant negative influence on individuals as well as on the whole society. Especially in the current COVID-19 era, we witness an unprecedented growth of medical misinformation. To help tackle this problem with machine learning approaches, we are publishing a feature-rich dataset of approx. 317k medical news articles/blog...
Preprint
The negative effects of misinformation filter bubbles in adaptive systems have been known to researchers for some time. Several studies investigated, most prominently on YouTube, how fast a user can get into a misinformation filter bubble simply by selecting wrong choices from the items offered. Yet, no studies so far have investigated what it take...
Preprint
Full-text available
Most of the research in the recommender systems domain is focused on the optimization of the metrics based on historical data such as Mean Average Precision (MAP) or Recall. However, there is a gap between the research and industry since the leading Key Performance Indicators (KPIs) for businesses are revenue and profit. In this paper, we explore t...
Preprint
We appreciate the value of the public debate on societal impact of artificial intel-ligence in general. Our aim is to contribute to the public debate and present ourideas on the proposed regulation of artificial intelligence, which was introducedby the European Commission on April 21, 2021. In this paper we present ourstance on specific areas of th...
Article
Full-text available
There is no previous research exploring the relationship between self-criticism and pupillary reactivity. Biomarkers such as pupillary reactivity inform about the quantity of the information being processed. Potentially, they can improve predictions of self-criticism levels and identification of pathological levels of self-criticism. The goal of ou...
Article
Full-text available
Whether the Anger Superiority Effect prevails over the Happiness Superiority Effect has been the subject of much discussion. Problems with the research design and methodology used in this type of research could account for differences in the results. This face-in-the-crowd study attempted a more ecologically valid design using nine multiplied ident...
Article
Purpose Partisan news media, which often publish extremely biased, one-sided or even false news, are gaining popularity world-wide and represent a major societal issue. Due to a growing number of such media, a need for automatic detection approaches is of high demand. Automatic detection relies on various indicators (e.g. content characteristics) t...
Preprint
Full-text available
The online spreading of fake news is a major issue threatening entire societies. Much of this spreading is enabled by new media formats, namely social networks and online media sites. Researchers and practitioners have been trying to answer this by characterizing the fake news and devising automated methods for detecting them. The detection methods...
Article
Most of the research in the recommender systems domain is focused on the optimization of the metrics based on historical data such as Mean Average Precision (MAP) or Recall. However, there is a gap between the research and industry since the leading Key Performance Indicators (KPIs) for businesses are revenue and profit. In this paper, we explore t...
Preprint
Full-text available
Cold-start problem, which arises upon the new users arrival, is one of the fundamental problems in today's recommender approaches. Moreover, in some domains as TV or multime-dia-items take long time to experience by users, thus users usually do not provide rich preference information. In this paper we analyze the minimal amount of ratings needs to...
Article
Full-text available
The online spreading of fake news is a major issue threatening entire societies. Much of this spreading is enabled by new media formats, namely social networks and online media sites. Researchers and practitioners have been trying to answer this by characterising the fake news and devising automated methods for detecting them. The detection methods...
Chapter
Massive spreading of medical misinformation on the Web has a significant impact on individuals and on society as a whole. The majority of existing tools and approaches for detection of false information rely on features describing content characteristics without verifying its truthfulness against knowledge bases. In addition, such approaches lack e...
Chapter
While digital space is a place where users communicate increasingly, the recent threat of COVID-19 infection even more emphasised the necessity of effective and well-organised online environment. Therefore, it is nowadays, more whenever in the past, important to deal with various unhealthy phenomena, that prohibit effective communication and knowle...
Preprint
Full-text available
One of the most critical problems in e-commerce domain is the information overload problem. Usually, an enormous number of products is offered to a user. The characteristics of this domain force researchers to opt for session-based recommendation methods, from which nearest-neighbors-based (SkNN) approaches have been shown to be competitive with an...
Conference Paper
Full-text available
Research on database and information technologies has been rapidly evolving over the last couple of years. This evolution was lead by three major forces: Big Data, AI and Connected World that open the door to innovative research directions and challenges, yet exploiting four main areas: (i) computational and storage resource modeling and organizati...
Article
Many intelligent systems in business, government or academy process natural language as an input for their inference or they might even communicate with users in natural language. The natural language processing within them is currently often done utilizing machine learning models. However, machine learning needs training data and such data are oft...
Article
A large dataset that contains the eye movements of N=216 programmers of different experience levels captured during two code comprehension tasks is presented. Data are grouped in terms of programming expertise (from none to high) and other demographic descriptors. Data were collected through an international collaborative effort that involved eleve...
Book
This book constitutes thoroughly reviewed and selected papers presented at Workshops and Doctoral Consortium of the 24th East-European Conference on Advances in Databases and Information Systems, ADBIS 2020, the 24th International Conference on Theory and Practice of Digital Libraries, TPDL 2020, and the 16th Workshop on Business Intelligence and B...
Data
This paper is an extended version of the conference paper presented at DIONE 2020, held online on 17, April 2020.
Article
Full-text available
Exploratory search (in contrary to the traditional lookup search) is characterized by the search tasks that have exploration, learning, and investigation as their goals. An example of this task in the domain of digital libraries is exploration of a new domain, a task that is typically performed by a researcher novice, such as a master’s or a doctor...
Chapter
The presence of external links or sources in the articles are considered as one of the indicators for assessing their quality by a librarian and information community. In this article, we explore linking patterns of the most popular traditional and “alternative” (partisan) digital news media in two V4 countries of Central Europe: Czech and Slovak R...
Chapter
Onboarding of new employees is a common process in all companies. Many hours of qualified employees’ time need to be invested to teach new employees how to use the company’s internal systems. This process can be significantly eased by onboarding solutions leveraging application guides. However, if not personalized, the guides can quickly become ann...
Article
The popularity of e-commerce is increasing day-by-day. In order to provide a seamless experience and tailored offer for the customers, the knowledge of their preferences and behavior is required. The demography of customers is one of the important information used for, e.g., segmentation. To provide optimal service, machine learning is often used f...
Book
This book constitutes the proceedings of the 20th International Conference on Web Engineering, ICWE 2020, which was planned to take place in Helsinki, Finland, during June 9-12, 2020. Due to the corona pandemic the conference changed to a virtual format. The total of 24 full and 10 short contributions presented in this volume were carefully reviewe...
Conference Paper
Full-text available
The online spreading of fake news (and misinformation in general) has been recently identified as a major issue threatening entire societies. Much of this spreading was enabled by new media formats, namely social networks and online media sites. Researchers and practitioners have been trying to answer this by characterizing the fake news and devisi...
Chapter
Eye-tracking data provide many new options in domain of user modeling. In our work we focus on the automatic detection of web-navigation skill from eye-tracking data. We strive to gain a comprehensive view on the impact of navigation skills on addressing specific user studies and overall interaction on the Web. We proposed an approach for estimatin...
Conference Paper
Onboarding users to a complex application or a new functionality can be a serious issue, especially for organizations that need to train their new employees. Using a complex application without proper training or guidance can lead to users' confusion and frustration. In this paper, we introduce the onboarding platform YesElf intended for web applic...
Conference Paper
It is our great pleasure to welcome you to the UMAP 2019 LBR and Demo Track, in conjunction with the 27th Conference on User Modelling, Adaptation and Personalization, held in Larnaca, Cyprus on June 9-12th, 2019. This track encompasses two categories: (i) Demos, which showcase research prototypes and commercially available products of UMAP-based s...
Conference Paper
Users preferences evolve over the time. This socalled dynamics is a serious challenge which is widely researched in several domains. In these domains, users are usually active for a long period of time and they tend to interact with a wide range of items. To make it more complicated, users preferences are likely to evolve only in some aspects while...
Preprint
In this paper, we present neural model architecture submitted to the SemEval-2019 Task 9 competition: "Suggestion Mining from Online Reviews and Forums". We participated in both subtasks for domain specific and also cross-domain suggestion mining. We proposed a recurrent neural network architecture that employs Bi-LSTM layers and also self-attentio...
Article
Full-text available
One of the important purposes of data mining on the web is to reveal hidden characteristics of users including their behavior. These characteristics are often used to analyze previous user actions, his/her preferences, and also to predict the future behavior. An average user session consists of only few actions, which brings several complications f...
Article
In university courses as well as in MOOCs, Community Question Answering (CQA) systems have been recently recognized as a promising alternative to standard discussion forums for mediating online discussions. Despite emerging research on educational CQA systems, a study investigating when and how to use these systems to support university education i...
Article
Full-text available
Support for adaptive learning with respect to increased interaction and collaboration over the educational content in state-of-the-art models of web-based educational systems is limited. Explicit formalization of such models is necessary to facilitate extendibility, reusability and interoperability. Domain models are the most fundamental parts of a...
Chapter
Machine learning is an increasingly important approach to Natural Language Processing. Most languages however do not possess enough data to fully utilize it. When dealing with such languages it is important to use as much auxiliary data as possible. In this work we propose a combination of multitask and multilingual learning. When learning a new ta...
Article
Full-text available
Nowadays, personalized recommendations are widely used and popular. There are a lot of systems in various fields, which use recommendations for different purposes. One of the basic problems is the distrust of users of recommended systems. Users often consider the recommendations as an intrusion of their privacy. Therefore, it is important to make r...
Poster
Recommender systems generate items that should beinteresting for the customers. However, recommenders usually fail in the cold-start scenario - when a new item or a newcustomer appears. In our work, we study the cold-start problemfor a new customer. For a cold-start customer we find the most similar customers and use a “their” pre-trained collabora...
Conference Paper
Full-text available
When analyzing user implicit feedback in recommender systems, several biases need to be taken into account. A user is influenced by the position (i.e., position bias) or by the appeal of the items (i.e., visual bias). Since images have become an essential part of the Web, the study of their impact on user behavior during the decision-making tasks i...
Conference Paper
In this paper, we present neural models submitted to Shared Task on Implicit Emotion Recognition, organized as part of WASSA 2018. We propose a Bi-LSTM architecture with regularization through dropout and Gaussian noise. Our models use three different embedding layers: GloVe word embeddings trained on Twitter dataset, ELMo embeddings and also sente...
Preprint
Growing amount of comments make online discussions difficult to moderate by human moderators only. Antisocial behavior is a common occurrence that often discourages other users from participating in discussion. We propose a neural network based method that partially automates the moderation process. It consists of two steps. First, we detect inappr...
Chapter
User Experience is one of the most important criteria when designing and testing user interfaces with emotions as its essential element. To assess, how emotions could be used for automatic detection of usability issues, we carried out a user study with a website which included intentionally inserted usability issues. We classified valence of emotio...
Article
Full-text available
The costs of eye-tracking technologies steadily decrease. This allows research institutions to obtain multiple eye-tracking devices. Already, several multiple eye-tracker laboratories have been established. Researchers begin to recognize the subfield of group eye-tracking. In comparison to the single-participant eye-tracking, group eye-tracking bri...
Article
Identification of typical user behaviour within a web application is a crucial assumption for revealing user characteristics, preferences and habits. Typical and repeating features of user behaviour during his/her interaction with web application can be generalized through behavioural patterns. In this paper, we propose HyBPMine—a novel method, for...
Article
User behaviour in data intensive applications such as the Web-based applications represents a complex set of actions influenced by plenty of factors. Thanks to this complexity, it is extremely hard for human to be able to understand all its aspects. Despite of this, by observing user actions from multiple views, we are able to extract and to model...
Article
Word feature vectors have been proven to improve many natural language processing tasks. With recent advances in unsupervised learning of these feature vectors, it became possible to train it with much more data, which also resulted in better quality of learned features. Since it learns joint probability of latent features of words, it has the adva...
Conference Paper
Students' performance in Massive Open Online Courses (MOOCs) is enhanced by high quality discussion forums or recently emerging educational Community Question Answering (CQA) systems. Nevertheless, only a small number of students answer questions asked by their peers. This results in instructor overload, and many unanswered questions. To increase s...
Conference Paper
The workshop focus is on considering temporal aspects for recommender systems in general, regardless of the specific domain and application, trying to develop a holistic approach for dealing with temporal aspects in recommender systems, like personal assistants, news, tourism, health care, TV, e-commerce, social networks and so on.
Conference Paper
Full-text available
The context of a user is a notoriously researched topic in the recom-mender systems community. It greatly influences user preferences and respectively his/her behaviour. The research focuses on the actual influence affecting user and temporal preferences of users. These tell us what the user likes, but fail at describing his/her behavior. We believ...
Conference Paper
Full-text available
Behavioral patterns represent repeating sequences or sets of actions, which website users often perform together. Such patterns can be used to identify user preferences, recommend interesting content to him, etc. For dynamic sites with fast changing content (e.g., news, social networks) we need to recognize such patterns in an online time. In this...
Conference Paper
Full-text available
Modern constructivist approaches to education dictate active experimentation with the study material and have been linked with improved learning outcomes in STEM fields. During classroom time we believe it is important for students to experiment with the lecture material since active recall helps them to start the memory encoding process as well as...