Polish-Japanese Academy of Information Technology

W artykule opisano wybrane elementy z biografii i spuścizny pisarskiej Marcina Giżyckiego jako krytyka sztuki i filmu. Podstawą są zachowane materiały prasowe, prywatne archiwum krytyka oraz odnaleziona w 2023 roku, a nieopublikowany za życia zbiór Sztuka na metry, będący ciekawym zapisem czasu nie tylko w biografii intelektualnej trzydziestoletniego wtedy Giżyckiego, lecz także dla badań nad sztuką i krytyką końca lat 70. XX wieku w PRL. Gdy przegląda się teksty zamieszczone w Sztuce na metry, można dostrzec wyróżniki późniejszego pisarstwa Giżyckiego o sztuce. Ponadto autor artykułu podjął próbę odpowiedzi na pytanie, dlaczego Giżycki nie wydał tej książki.

Autorka analizuje różne formy przemocy w japońskiej kulturze i kinematografii, zestawiając subtelną przemoc ukazaną w filmach Yasujirō Ozu, takich jak Tokijska opowieść (Tōkyō Monogatari, 1953), z bardziej otwartymi przedstawieniami we współczesnym kinie, np. w Monster (Kaibutsu, 2023) i Złodziejaszkach (Manbiki Kazoku, 2018) Hirokazu Koreedy. W dziełach Ozu przemoc wyraża się w milczeniu i presji wynikającej m.in. z konfucjańskich norm, co obrazuje napięcie między tradycyjnymi wartościami a procesem modernizacji powojennej Japonii. Filmy Koreedy skupiają się na jawnych formach przemocy, kryzysie instytucji oraz wykluczeniu społecznym, ukazując, jak zniszczone struktury symboliczne prowadzą do alienacji i chaosu. Autorka porusza również temat wyobraźni posttraumatycznej, analizując, w jaki sposób współcześni japońscy twórcy filmowi wykorzystują język kina do przetwarzania traumy zbiorowej i indywidualnej.

Abstract Over the course of his twenty-year artistic career, Wojciech Smarzowski has demonstrated his ability to employ fast-paced, emotionally charged cinematic language, all while maintaining a keen observational eye on Poland. While academic discussions frequently focus on the stylistic and thematic aspects of his cinema, the aim of this article is to clarify the reasons for his surprisingly limited exposure to international audiences, suggesting three major causes. First, Smarzowski straddles the intersection of ambitious auteurist cinema akin to that of his contemporaries Jan Jakub Kolski and Dorota Kędzierzawska, on the one hand, and, on the other hand, the entertainment industry, represented above all by Władysław Pasikowski and Patryk Vega. That synthesis makes it difficult to market his works with specific target groups in mind. Second, his filmmaking practice is persistently compartmentalized as “exclusively Polish” and framed as continuing Andrzej Wajda's engaged national legacy, thus supposedly incomprehensible to a foreign viewer. Third, labeling Smarzowski cinema as “too Polish” becomes an obstacle in international promotion as Polish jury and committee members seem to opt for safer and more “appropriate” films. An examination of the international sales of Polish language films and the processes involved in Polish film submissions to the Academy Awards reveals that domestic politics and the confounding categorization of Smarzowski's art exert a decisive influence on its exposure to international audiences.

Spectral clustering methods are known for their ability to represent clusters of diverse shapes, densities etc. However, the results of such algorithms, when applied e.g. to text documents, are hard to explain to the user, especially due to embedding in the spectral space which has no obvious relation to document contents. Therefore, there is an urgent need to elaborate methods for explaining the outcome of the clustering. We have constructed in this paper a theoretical bridge linking the clusters resulting from Graph Spectral Clustering and the actual document content, given that similarities between documents are computed as cosine measures in tf or tfidf representation. This link enables to provide with explanation of cluster membership in clusters produced by GSA. We present a proposal of explanation of the results of combinatorial and normalized Laplacian based graph spectral clustering. For this purpose, we show (approximate) equivalence of combinatorial Laplacian embedding and of K-embedding (proposed in this paper) and term vector space embedding. We performed an experimental study showing that K-embedding approximates well Laplacian embedding under favourable block matrix conditions and show that approximation is good enough under other conditions. We show also perfect equivalence of normalized Laplacian embedding and the [Formula: see text]-embedding (proposed in this paper) and (weighted) term vector space embedding. Hence a bridge is constructed between the textual contents and the clustering results using both combinatorial and normalized Laplacian based Graph Spectral Clustering methods. We provide a theoretical background for our approach. An initial version of this paper is available at arXiv, (Starosta B 2023). The Reader may refer to that text to get acquainted with formal aspects of our method and find a detailed overview of motivation.

Abstract The paper presents the results of the ParlaMint II project, which comprise comparable corpora of parliamentary debates of 29 European countries and autonomous regions, covering at least the period from 2015 to 2022, and containing over 1 billion words. The corpora are uniformly encoded, contain rich metadata about their 24 thousand speakers, and are linguistically annotated up to the level of Universal Dependencies syntax and named entities. The paper focuses on the enhancement made since the ParlaMint I project and presents the compilation of the corpora, including the encoding infrastructure, use of GitHub, the production of individual corpora, the common pipeline for producing their distribution, and use of CLARIN services for dissemination. It then gives a quantitative overview of the produced corpora, followed by the qualitative additions made within the ParlaMint II project, namely metadata localisation, the addition of new metadata, such as the political orientation of political parties, the machine translation of the corpora to English and its tagging with semantic classes, and the production of pilot speech corpora. Finally, outreach activities and further work are discussed.

Neurodegenerative diseases (NDs), such as Alzheimer's disease (AD) and Parkinson's disease (PD), are debilitating conditions that affect millions worldwide, and the number of cases is expected to rise significantly in the coming years. Because early detection is crucial for effective intervention strategies, this study investigates whether the structural analysis of selected brain regions, including volumes and their spatial relationships obtained from regular T1-weighted MRI scans (N = 168, PPMI database), can model stages of PD using standard machine learning (ML) techniques. Thus, diverse ML models, including Logistic Regression, Random Forest, Support Vector Classifier, and Rough Sets, were trained and evaluated. Models used volumes, Euclidean, and Cosine distances of subcortical brain structures relative to the thalamus to differentiate among control (HC), prodromal (PR), and PD groups. Based on three separate experiments, the Logistic Regression approach was optimal, providing low feature complexity and strong predictive performance (accuracy: 85%, precision: 88%, recall: 85%) in PD-stage recognition. Using interpretable metrics, such as the volume- and centroid-based spatial distances, models achieved high diagnostic accuracy, presenting a promising framework for early-stage PD identification based on MRI scans.

We present a platform for automatic assessment of technical data science skills (hard skills) and competencies that help to apply those technical skills in practice (soft skills). The platform serves so-called assessment platform tasks that resemble data-focused tasks typical for online data science competitions. Actually, these tasks are designed based on international conference competitions that we have been organizing on our competition platform knowledgepit.ai. The main idea relies on our observation that the given person’s behavior during a competition (dynamics of submitting solutions, activity on competition forum, etc.) can be correlated with his/her soft competencies. Accordingly, our goal is to translate the specifics of international conference competitions into a framework that investigates the behaviors of data science job candidates, employees who want to improve their work in data science projects, students who wish to build their future professional careers on AI solutions, etc. We claim that such behaviors – if properly measured based on solving our assessment platform tasks – can effectively indicate both hard and soft types of competencies.