Can You Dance? A Study of Child–Robot Interaction and Emotional Response Using the NAO Robot

This retrospective study presents and summarizes our long-term efforts in the popularization of robotics, engineering, and artificial intelligence (STEM) using the NAO humanoid robot. By a conservative estimate, over a span of 8 years, we engaged at least a couple of thousand participants: approximately 70% were preschool children, 15% were elementary school students, and 15% were teenagers and adults. We describe several robot applications that were developed specifically for this task and assess their qualitative performance outside a controlled research setting, catering to various demographics, including those with special needs (ASD, ADHD). Five groups of applications are presented: (1) motor development activities and games, (2) children’s games, (3) theatrical performances, (4) artificial intelligence applications, and (5) data harvesting applications. Different cases of human–robot interactions are considered and evaluated according to our experience, and we discuss their weak points and potential improvements. We examine the response of the audience when confronted with a humanoid robot featuring intelligent behavior, such as conversational intelligence and emotion recognition. We consider the importance of the robot’s physical appearance, the emotional dynamics of human–robot engagement across age groups, the relevance of non-verbal cues, and analyze drawings crafted by preschool children both before and after their interaction with the NAO robot

A Fast Algorithm for Mining Utility-Frequent

Abstract. Utility-based data mining is a new research area interested in all types of utility factors in data mining processes and targeted at incorporating utility considerations in both predictive and descriptive data mining tasks. High utility itemset mining is a research area of utilitybased descriptive data mining, aimed at finding itemsets that contribute most to the total utility. A specialized form of high utility itemset mining is utility-frequent itemset mining, which – in addition to subjectively defined utility – also takes into account itemset frequencies. This paper presents a novel efficient algorithm FUFM (Fast Utility-Frequent Mining) which finds all utility-frequent itemsets within the given utility and support constraints threshold. It is faster and simpler than the original 2P-UF algorithm (2 Phase Utility-Frequent), as it is based on efficient methods for frequent itemset mining. Experimental evaluation on artificial datasets show that, in contrast with 2P-UF, our algorithm can also be applied to mine large databases.

Improved Joint Probabilistic Data Association (JPDA) Filter Using Motion Feature for Multiple Maneuvering Targets in Uncertain Tracking Situations

To track multiple maneuvering targets in cluttered environments with uncertain measurement noises and uncertain target dynamic models, an improved joint probabilistic data association-fuzzy recursive least squares filter (IJPDA-FRLSF) is proposed. In the proposed filter, two uncertain models of measurements and observed angles are first established. Next, these two models are further employed to construct an additive fusion strategy, which is then utilized to calculate generalized joint association probabilities of measurements belonging to different targets. Moreover, the obtained probabilities are applied to replace the joint association probabilities calculated by the standard joint probabilistic data association (JPDA) method. Considering the advantage of the fuzzy recursive least squares filter (FRLSF) on tracking a single maneuvering target, which can relax the restrictive assumption of measurement noise covariances and target dynamic models, FRLSF is still used to update the state of each target track. Thus, the proposed filter can not only provide the advantage of FRLSF but can also adjust the weights of measurements and observed angles in the generalized joint association probabilities adaptively according to their uncertainty. The performance of the proposed filter is evaluated in two experiments with simulation data and real data. It is found to be better than the performance of other three filters in terms of the tracking accuracy and the average run time

Slovenian Definition Extraction training dataset DF_NDF_wiki_slo 1.0

The Slovenian definition extraction training dataset DF_NDF_wiki_slo contains 38613 sentences extracted from the Slovenian Wikipedia. The first sentence of a term's description on Wikipedia is considered a definition, and all other sentences are considered non-definitions. The corpus consists of the following files each containing one definition / non-definition sentence per line: 1. Definitions: df_ndf_wiki_slo_Y.txt with 3251 definition sentences. 2. Non-definitions: df_ndf_wiki_slo_N.txt with 14678 non-definition sentences which do not contain the term at the beginning of the sentence. 3. Non-definitions: df_ndf_wiki_slo_N1.txt with 20684 non-definition sentences which may also contain the term at the beginning of the sentence. The dataset is described in more detail in Fišer et al. 2010. If you use this resource, please cite: Fišer, D., Pollak, S., Vintar, Š. (2010). Learning to Mine Definitions from Slovene Structured and Unstructured Knowledge-Rich Resources. Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10). https://aclanthology.org/L10-1089/ Reference to training Transformer-based definition extraction models using this dataset: Tran, T.H.H., Podpečan, V., Jemec Tomazin, M., Pollak, Senja (2023). Definition Extraction for Slovene: Patterns, Transformer Classifiers and ChatGPT. Proceedings of the ELEX 2023: Electronic lexicography in the 21st century. Invisible lexicography: everywhere lexical data is used without users realizing they make use of a “dictionary”. Related resources: Jemec Tomazin, M. et al. (2023). Slovenian Definition Extraction evaluation datasets RSDO-def 1.0, Slovenian language resource repository CLARIN.SI, http://hdl.handle.net/11356/184

Stress knowledge map

Stress Knowledge Map (SKMhttps://skm.nib.si) is a publicly available resource containing two complementary knowledge graphs that describe the current knowledge of biochemical, signaling, and regulatory molecular interactions in plants: a highly curated model of plant stress signaling (PSS543 reactions) and a large comprehensive knowledge network (488 390 interactions). Both were constructed by domain experts through systematic curation of diverse literature and database resources. SKM provides a single entry point for investigations of plant stress response and related growth trade-offs, as well as interactive explorations of current knowledge. PSS is also formulated as a qualitative and quantitative model for systems biology and thus represents a starting point for a plant digital twin. Here, we describe the features of SKM and show, through two case studies, how it can be used for complex analyses, including systematic hypothesis generation and design of validation experiments, or to gain new insights into experimental observations in plant biology

Slovenian Definition Extraction evaluation datasets RSDO-def 1.0

The Slovene Definition Extraction evaluation datasets RSDO-def contains sentences extracted from the Corpus of term-annotated texts RSDO5 1.1 (http://hdl.handle.net/11356/1470), which contains texts with annotated terms from four different domains: biomechanics, linguistics, chemistry, and veterinary science. The file and sentence identifiers are the same as in the original RSDO corpus. The labels added to the sentences included in the dataset denote: 0: Non-definition 1: Weak definition 2: Definition The dataset consists of two parts: 1. RSDO-def-random employed a random sampling strategy, with 14 definitions, 98 weak-definitions and 849 non-definitions. 2. RSDO-def-larger added sentences to the random one by the pattern-based definition extraction as presented in Pollak et al. (2014). It contains 169 definitions, 214 weak-definitions and 872 non-definitions. Both parts were manually annotated by five terminographers. In case of discrepancies between annotators, a consensus was reached and the final label was confirmed by all five annotators. Duplicates were removed in both parts. The criteria for annotation are based on the standard ISO 1087-1:2000 (E/F) Terminology Work - Vocabulary, Part 1, Theory and Application, which explains a definition as follows: "Representation of a concept by a descriptive statement which serves to differentiate it from related concepts". Weak definition labels were assigned if the extracted sentences contained a term and at least one delimiting feature without a superordinate concept, or sentences consisting of superordinate concepts without delimiting features but with some typical examples. Instances were labeled as Non-definition if the sentence with the extracted concept did not contain any information about the concept or its delimiting features. The dataset is described in more detail in Tran et al. 2023, where it was used for evaluating definition extraction approaches. If you use this resource, please cite: Tran, T.H.H., Podpečan, V., Jemec Tomazin, M., Pollak, Senja (2023). Definition Extraction for Slovene: Patterns, Transformer Classifiers and ChatGPT. Proceedings of the ELEX 2023: Electronic lexicography in the 21st century. Invisible lexicography: everywhere lexical data is used without users realizing they make use of a “dictionary” (accepted) Reference to the pattern-based definition extraction method used for creating RSDO-def-larger: Pollak, S. (2014). Extracting definition candidates from specialized corpora. Slovenščina 2.0: empirical, applied and interdisciplinary research, 2(1), pp. 1–40. https://doi.org/10.4312/slo2.0.2014.1.1-40 Related resources: - Jemec Tomazin, M. et al. (2021). Corpus of term-annotated texts RSDO5 1.1, Slovenian language resource repository CLARIN.SI, ISSN 2820-4042, http://hdl.handle.net/11356/1470. - Podpečan et al. (2023). DF_NDF_wiki_slo: Definition extraction training sets from Wikipedia, Slovenian language resource repository CLARIN.SI, http://hdl.handle.net/11356/1840