The synergistic effect between interoceptive accuracy and alcohol use disorder status on pain sensitivity

Andrzej Jakubczyk, Paweł Wiśniewski, Elisa M. Trucco, Paweł Kobyliński, Justyna Zaorska, Jakub Skrzeszewski, Hubert Suszek, Marcin Wojnar, Maciej Kopera

2021 Addictive Behaviors, T. 112, Art. 106607, s. 1-7

Background: Interoceptive accuracy and pain sensitivity are both risk factors in the development of alcohol use disorder (AUD). However, the synergistic association between these two factors has not been investigated in an AUD sample. Therefore, the aim of the current study was to investigate whether the association between interoceptive accuracy and sensitivity to pain differed across AUD status.
Methods: The study group included 165 individuals diagnosed with AUD (88.1% men) and 110 healthy controls (HCs; 74.5% men). Interoceptive accuracy was assessed with the Schandry Task. The Pain Sensitivity Questionnaire was utilized to measure sensitivity to pain. Anxiety, biological sex, and age were included as covariates in a model examining the role of AUD status as a moderator in the association between interoceptive accuracy and pain sensitivity.
Results: A significant interaction was found between interoceptive accuracy and AUD status (b = −4.580, 95% CI = [−8.137, −1.022], p = 0.012, ΔR2 = 0.032). Findings indicate that interoceptive accuracy was negatively associated with pain sensitivity among individuals with AUD, while there was a trend for an opposite association among healthy controls.
Conclusion: We hypothesize that persistent alcohol drinking may contribute to disruption of the normative association between interoception and pain. Future studies should be conducted to develop knowledge on this association and to investigate its possible therapeutic significance and implications.

3D or not 3D? Evaluation of the effectiveness of 3D-enhanced warning messages for communication in crisis situations

Grzegorz Banerski, Katarzyna Abramczuk, Cezary Biele

2020 SAFETY SCIENCE, T. 132, s. 104961

A common problem in crisis situations is the unwillingness of the local communities to undertake individual safety measures. This study examined whether the motivation of the local population can be increased if 3D animation are used for visualization of the potential disaster scenarios. Furthermore, the study aimed at finding out how the use of such material affects the ability of the audience to remember what preventive actions should be taken.
As part of the experiment, three versions of a video message about flood threat were prepared: a video limited to the presenter with no additional elements in the background, a classic TV-like video message format, and a video featuring 3D visualisations of the consequences of flooding. The videos were presented to groups of subjects who live in flood-prone areas. Protection Motivation Theory was used as the experiment’s framework.
We found that the type of warning message did not influence the level of acquired knowledge of protective actions. Important differences were found in the structure of the determinants of self-protective motivation across the groups. The group that watched the 3D enhanced video was the only one in which the more emotional cognitive path of threat appraisal had a significant impact on the level of motivation. As regards the level of motivation to take protective actions, the observed effects were more complex than expected. The paper discusses the potential impact of the results on efficient risk communication design and the usefulness of the use of 3D animation in warning messages.!

A Bidirectional Iterative Algorithm for Nested Named Entity Recognition

Sławomir Dadas, Jarosław Protasiewicz

2020 IEEE Access, T. 8, s. 135091 - 135102

Nested named entity recognition (NER) is a special case of structured prediction in which annotated sequences can be contained inside each other. It is a challenging and significant problem in natural language processing. In this paper, we propose a novel framework for nested named entity recognition tasks. Our approach is based on a deep learning model which can be called in an iterative way, expanding the set of predicted entity mentions with each subsequent iteration. The proposed framework combines two such models trained to identify named entities in different directions: from general to specific ( outside-in ), and from specific to general ( inside-out ). The predictions of both models are then aggregated by a selection policy. We propose and evaluate several selection policies which can be used with our algorithm. Our method does not impose any restrictions on the length of entity mentions, number of entity classes, depth, or structure of the predicted output. The framework has been validated experimentally on four well-known nested named entity recognition datasets: GENIA, NNE, PolEval, and GermEval. The datasets differ in terms of domain (biomedical, news, mixed), language (English, Polish, German), and the structure of nesting (simple, complex). Through extensive tests, we prove that the approach we have proposed outperforms existing methods for nested named entity recognition.

A Neural Network Toolbox for Electricity Consumption Forecasting

Jarosław Protasiewicz

2020 W: Prediction of Customer Status in Corporate Banking Using Neural Networks / Stanisław Osowski, Łukasz Siereński ; Piscataway: IEEE, s. 1-6

Prediction of Customer Status in Corporate Banking Using Neural Networks. Glasgow, 2020-07-19 - 2020-07-24

The aim of this study is to propose a neural toolbox for electricity consumption prediction. The toolkit covers the implementation of three artificial neural networks, namely: (i) a multi-layered perceptron network; (ii) a radial basis function network; and (iii) a self-organising map. Moreover, the toolbox includes tools known as metamodels, which allow easy use of these networks for forecasting. There are two prediction systems, namely: (i) serially connected models; and (ii) a two-levelled structure containing a classifier and a set of parallel models. They have been validated experimentally in the task of electricity consumption prediction. The results and flexibility of use suggest that the neural toolbox should help users to develop prediction systems of electricity consumption more conveniently, as it is designed for that particular purpose. The toolbox has been developed as open source – no commercial software is required to use it.

Academic entrepreneurship and the research productivity in Poland

Marta Bojko, Anna Knapińska, Aldona Tomczyńska

2020 Industry and Innovation, APR 2020, 1-21

Is it possible to reconcile academic entrepreneurship with the internationalisation of scientists’ productivity? We provide an answer to this question through the statistical analysis of a representative survey of 811 scientists in Poland. Based on Robert K. Merton’s distinction between two types of scholars: local (oriented towards the national science system) and cosmopolitan (aiming at international achievements), we found that research productivity positively influences academic entrepreneurship, but that scientists who are productive locally engage in academic entrepreneurship more often than the cosmopolitan ones. This suggests that the internationalisation of one’s scientific activity, at least in countries transitioning from a local to a cosmopolitan mode of research, is an absorbing endeavour that only the most productive researchers can reconcile with academic entrepreneurship. Such countries should balance their policies regarding the career development of their scientists also to include the promotion of science-business cooperation and not just the internationalisation of research.

Annobot: Platform for Annotating and Creating Datasets through Conversation with a Chatbot

Rafał Poświata, Michał Perełkiewicz

2020 W: Proceedings of the 28th International Conference on Computational Linguistics: System Demonstrations / Michal Ptaszynski, Bartosz Ziolko; Barcelona : International Committee on Computational Linguistics (ICCL), 2020, s. 75-79

The 28th International Conference on Computational Linguistics, Barcelona, 2020-12-08 - 2020-12-13

In this paper, we introduce Annobot: a platform for annotating and creating datasets through conversation with a chatbot. This natural form of interaction has allowed us to create a more accessible and flexible interface, especially for mobile devices. Our solution has a wide range of applications such as data labelling for binary, multi-class/label classification tasks, preparing data for regression problems, or creating sets for issues such as machine translation, question answering or text summarization. Additional features include pre-annotation, active sampling, online learning and real-time inter-annotator agreement. The system is integrated with the popular messaging platform: Facebook Messanger. Usability experiment showed the advantages of the proposed platform compared to other labelling tools. The source code of Annobot is available under the GNU LGPL license at

Biologically Plausible Learning of Text Representation with Spiking Neural Networks

Marcin Białas, Marcin Mirończuk, Jacek Mańdziuk

2020 W: Parallel Problem Solving from Nature – PPSN XVI / Thomas Bäck, Mike Preuss, André Deutz, Hao Wang, Carola Doerr, Michael Emmerich, Heike Trautmann; Cham: Springer, s. 433-447

International Conference on Parallel Problem Solving from Nature. Leiden, 2020-09-05 - 2020-09-09

This study proposes a novel biologically plausible mechanism for generating low-dimensional spike-based text representation. First, we demonstrate how to transform documents into series of spikes (spike trains) which are subsequently used as input in the training process of a spiking neural network (SNN). The network is composed of biologically plausible elements, and trained according to the unsupervised Hebbian learning rule, Spike-Timing-Dependent Plasticity (STDP). After training, the SNN can be used to generate low-dimensional spike-based text representation suitable for text/document classification. Empirical results demonstrate that the generated text representation may be effectively used in text classification leading to an accuracy of 80.19% on the bydate version of the 20 newsgroups data set, which is a leading result amongst approaches that rely on low-dimensional text representations.

Childhood trauma, alexithymia, and mental states recognition among individuals with alcohol use disorder and healthy controls

Maciej Kopera, Justyna Zaorska, Elisa M. Trucco, Hubert Suszek, Paweł Kobyliński, Robert A. Zucker, Malwina Nowakowska, Marcin Wojnar, Andrzej Jakubczyk

2020 Drug and Alcohol Dependence, T. 217, Art. 208301, s. 1-8

Background: Although prior work indicates a link between childhood trauma, alexithymia, and mental states recognition, empirical support is limited. Moreover, findings based on adult samples are mixed. Previous studies demonstrate that childhood trauma might either enhance, preserve, or reduce mental states recognition in selected at-risk populations. The current study investigates whether alcohol use disorder (AUD) status moderates the association between childhood trauma, alexithymia, and mental states recognition in a treatment-seeking AUD sample and non-AUD healthy adults.
Methods: Data comes from 255 individuals participating in an ongoing project that compares emotional and behavioral functioning of patients treated in an inpatient setting for AUD and a comparison sample of 172 healthy controls (HCs). Mental states recognition was measured using a computerized version of the Reading the Mind in the Eyes Task (RMET). The presence of childhood trauma was assessed with the Childhood Trauma Questionnaire. Alexithymia was measured with the Toronto Alexithymia Scale (TAS-20). Demographic information, as well as alcohol drinking and psychopathological symptoms were assessed. A moderated mediation model was estimated whereby alexithymia was included as a mediator in the association between childhood trauma and RMET performance, with AUD diagnosis status moderating the link between alexithymia and RMET performance.
Results: Findings provide support for moderated mediation. Childhood emotional trauma impacted negative mental states recognition performance via difficulty describing feelings, but only among HCs (p < 0.01).
Conclusions: Findings highlight the impact that AUD status has on the association between early life emotional trauma and difficulty describing feelings on individual differences in mental states recognition.

Childhood Trauma, Emotion Regulation, and Pain in Individuals With Alcohol Use Disorder

Justyna Zaorska, Maciej Kopera, Elisa M. Trucco, Hubert Suszek, Paweł Kobyliński, Andrzej Jakubczyk

2020 Frontiers in Psychiatry, T. 11, Art. 554150, s. 1-10

Introduction: Several studies have confirmed that the experience of childhood trauma, poor emotion regulation, as well as the experience of physical pain may contribute to the development and poor treatment outcomes of alcohol use disorder (AUD). However, little is known about how the joint impact of these experiences may contribute to AUD.
Objectives: To analyze associations between childhood trauma, emotion regulation, and pain in individuals with AUD.
Methods: The study sample included 165 individuals diagnosed with AUD. The Childhood Trauma Questionnaire (CTQ) was used to investigate different types of trauma during childhood (physical, emotional, and sexual abuse and neglect), the Brief Symptom Inventory was used to assess anxiety symptoms, the Difficulties in Emotion Regulation Scale (DERS) was used to assess emotional dysregulation, and the Pain Resilience Scale and Pain Sensitivity Questionnaire were used to measure self-reported pain tolerance and sensitivity.
Results: Childhood emotional abuse (CTQ subscale score) was positively associated with anxiety, anxiety was positively associated with emotional dysregulation, and emotional dysregulation was negatively associated with pain tolerance. Accordingly, there was support for a significant indirect negative association between childhood emotional abuse and pain tolerance. The serial mediation statistical procedure demonstrated that anxiety and emotional dysregulation mediated the effect of childhood emotional abuse on pain resilience among individuals with AUD.
Conclusions: Targeting emotional dysregulation and physical pain that can result from childhood trauma may have particular therapeutic utility among individuals treated for AUD.

Detection of Strong and Weak Moments in Cinematic Virtual Reality Narration with the Use of 3D Eye Tracking

Paweł Kobyliński, Grzegorz Pochwatko

2020 W: ACHI 2020 : The Thirteenth International Conference on Advances in Computer-Human Interactions / Jaime Lloret Mauri, Diana Saplacan, Klaudia Çarçani, Prima Oky Dicky Ardiansyah, Simona Vasilache; Valencia : IARIA, s. 280-284

International Conference on Advances in Computer-Human Interactions ACHI 2020, Valencia, 2020-11-21 - 2020-11-25

Cinematic Virtual Reality (CVR) is a medium growing in popularity among both filmmakers and researchers. The medium brings challenges for movie and video makers, who need to narrate in a different way than in traditional movies and videos to keep viewers’ attention in the right place of the 360-degree scene. In order to ensure an adequate pace of development, tools are needed to conduct systematic, reliable and objective research on narration in CVR. In the short paper, the authors for the first time fully report results of the initial empirical test of their recently developed Scaled Aggregated Visual Attention Convergence Index (sVRCa). The index utilizes 3D Eye Tracking (3D ET) data recorded during a CVR experience and allows measuring and describing the effectiveness of any system of attentional cues employed by a CVR creator. The results of the initial test are promising. The method seems to substantially augment the process of detection of strong and weak moments in CVR narration.