A phonetic grammar of the Polish language

The aim of the present paper is to give an overview of the investigation of the phonic articulatory systems of Polish on the basis of the detailed articulatory descriptions existent in phonetic literature and with the use computational tools. First the theoretical foundations of the phonetic grammar are briefly introduced, then the main problem of the choice of an appropriate repertory of phones for Polish is also discussed. The last section is devoted to the presentation of the computional amalysis of the collected data. The created application enables us to collect a given phonetic inventory, taking into consideration the division into particular languages and the database generated here makes further computer analyses possible. Owing to the introduction of numeric interpretation of the articulatory features and dimensions, the phones can be treated as vectors in n-dimensional metric space. Then the measures of distances can be employed as measures of similarity between respective phones. By means of applying the Data Mining algorithms the interdependencies in the set of phones can be automatically shown. The present paper is a first attempt to apply the axiomatic theory of language at the phonetic level to the analysis and synthesis of the phonetic system of Polish

Status menopauzalny – główny czynnik determinujący dokładność prognostyczną modeli diagnostyki różnicowej guzów przydatków

Objectives: The aim of this study was to externally validate the diagnostic performance of the International Ovarian Tumor Analysis logistic regression models (LR1 and LR2, 2005) and other popular prognostic models including the Timmerman logistic regression model (1999), the Alcazar model (2003), the risk of malignancy index (RMI, 1990), and the risk of malignancy algorithm (ROMA, 2009). We compared these models to subjective ultrasonographic assessment performed by an experienced ultrasonography specialist, and with our previously developed scales: the sonomorphologic index and the vascularization index. Furthermore, we evaluated diagnostic tests with regard to the menopausal status of patients. Materials and methods: This study included 268 patients with adnexal masses; 167 patients with benign ovarian tumors and 101 patients with malignant ovarian tumors were enrolled. All tumors were evaluated by using transvaginal ultrasonography according to the diagnostic criteria of the nalyzed models. Materials and methods: This study included 268 patients with adnexal asses; 167 patients with benign ovarian tumors and 101 patients with malignant ovarian tumors were enrolled. All tumors were evaluated by using transvaginal ultrasonography according to the diagnostic criteria of the analyzed models. Results: The subjective ultrasonographic sessment and all of the studied predictive models achieved similar diagnostic performance in the whole study population. However, significant differences were observed when preand postmenopausal patients were analyzed separately. In the subgroup of premenopausal atients, the highest area under the curve (AUC) was achieved by subjective ultrasonographic assessment (0.931), the Alcazar model (0.912), and LR1 (0.909). Alternatively, in the group of postmenopausal patients, the highest AUC was noted for the Timmerman model (0.973), ROMA (0.951), and RMI (0.938). Conclusions: Menopausal status is a key factor that affects the utility of prognostic models for differential diagnosis of ovarian tumors. Diagnostic models of ovarian tumors are reasonable tools for predicting tumor malignancy.Cel: Celem pracy była zewnętrzna walidacja wybranych modeli prognostycznych: autorstwa grupy International Ovarian Tumor Analysis opartych na regresji logistycznej (LR1 i LR2, 2005) oraz innych popularnych modeli przeznaczonych do diagnostyki różnicowej guzów jajnika takich jak: model zaproponowany przez Timmerman’a i wsp. (1999), Alcazar’a i wsp., (2003), indeks ryzyka nowotworu (RMI – risk of malignancy index, 1990) oraz testu ROMA (risk of malignancy algorithm, 2009). Modele zostały porównane z subiektywną oceną ultrasonograficzną rzeprowadzoną przez doświadczonego specjalistę oraz skalami diagnostycznymi utworzonymi w naszym ośrodku: indeksem sonomorfologicznym (SM, 2004) i indeksem waskularyzacji (SD, 2004). Użyteczność analizowanych modeli została oceniona w zależności od różnych cech kliniczno-patologicznych, między innymi w zależności od statusu menopauzalnego pacjentki. Metodyka: W badaniu poddano analizie 268 guzów przydatków, w tym 167 guzów niezłośliwych i 101 nowotworów złośliwych jajnika. Każdy z guzów został oceniony w odniesieniu do kryteriów diagnostycznych analizowanych testów. Przed operacją oznaczono również poziom markerów CA125 i HE4. Wyniki: W całej badanej populacji wszystkie modele predykcyjne wykazały podobną wartość diagnostyczną. Natomiast, stwierdzono istotne różnice pomiędzy testami w sytuacji gdy analizowano osobno pacjentki przed i po menopauzie. Największe pole pod krzywą ROC (AU-ROC - area under the ROC curve) w grupie pacjentek przed menopauzą uzyskały: subiektywna ocena ultrasonograficzna (0,931), model Alcazar’a (0,912) oraz LR1 (0,909). Natomiast w grupie kobiet po menopauzie największy AU-ROC uzyskały: model Timmerman’a (0,973), ROMA (0,951) i RMI (0,938). Wnioski : Status menopauzalny jest podstawowym czynnikiem determinującym użyteczność modelu predykcyjnego w diagnostyce różnicowej guzów przydatków. Wszystkie z badanych modeli uzyskały wartość diagnostyczną umożliwiającą stosunkowo dokładną diagnostykę przedoperacyjną guzów przydatków

Federated Similarity-Based Learning with Incomplete Data

In the analysis of social, medical, and business issues, the problem of incomplete data often arises. In addition, in situations where privacy policy makes it difficult to share data with organizations conducting related activities, it is necessary to exchange knowledge instead of data, that is, to use federated learning. In this scenario there are several private data clients, whose models are improved through the aggregation of model components. Here, we propose a methodology for training local models to deal well with missing data, with an algorithm using similarity measures that take into account the uncertainty present in many types of data, such as medical data. Therefore, this paper describes a federated learning model capable of processing imprecise and missing data. Federated learning is a technique to overcome limitations resulting from data governance and privacy by training algorithms without exchanging the data itself. The performance of the proposed method is demonstrated using medical data on breast cancer cases. Results for different data loss scenarios and corresponding measures of classification quality are presented and discussed

The Sugeno Integral Used for Federated Learning with Uncertainty for Unbalanced Data

Data is crucial in the digital economy. Many businesses collect and use their data to enhance their performance. However, limited data or low data quality can hinder model development, particularly in dynamic environments. To overcome this, companies collecting similar data may opt to exchange knowledge without sharing their data, due to privacy or legal issues. This is where federated learning comes in. In horizontal federated learning, each client (organization) iteratively improves its model, so that it can be regularly aggregated and shared with all clients participating in the federation for further improvements. In federated averaging, the aggregation mechanism is based on the weighted average and the weights depend on the amount of data available to each client. In this paper, we propose to use a more advanced aggregation mechanism, namely the Sugeno integral. The initial results are promising

Predicting Injuries in Football Based on Data Collected from GPS-Based Wearable Sensors

The growing intensity and frequency of matches in professional football leagues are related to the increasing physical player load. An incorrect training model results in over- or undertraining, which is related to a raised probability of an injury. This research focuses on predicting non-contact lower body injuries coming from over- or undertraining. The purpose of this analysis was to create decision-making models based on data collected during both training and match, which will enable the preparation of a tool to model the load and report the increased risk of injury for a given player in the upcoming microcycle. For this purpose, three decision-making methods were implemented. Rule-based and fuzzy rule-based methods were prepared based on expert understanding. As a machine learning baseline XGBoost algorithm was considered. Taking into account the dataset used containing parameters related to the external load of the player, it is possible to predict the risk of injury with a certain precision, depending on the method used. The most promising results were achieved by the machine learning method XGBoost algorithm (Precision 92.4%, Recall 96.5%, and F1-score 94.4%)

Gaze and Event Tracking for Evaluation of Recommendation-Driven Purchase

Recommendation systems play an important role in e-commerce turnover by presenting personalized recommendations. Due to the vast amount of marketing content online, users are less susceptible to these suggestions. In addition to the accuracy of a recommendation, its presentation, layout, and other visual aspects can improve its effectiveness. This study evaluates the visual aspects of recommender interfaces. Vertical and horizontal recommendation layouts are tested, along with different visual intensity levels of item presentation, and conclusions obtained with a number of popular machine learning methods are discussed. Results from the implicit feedback study of the effectiveness of recommending interfaces for four major e-commerce websites are presented. Two different methods of observing user behavior were used, i.e., eye-tracking and document object model (DOM) implicit event tracking in the browser, which allowed collecting a large amount of data related to user activity and physical parameters of recommending interfaces. Results have been analyzed in order to compare the reliability and applicability of both methods. Observations made with eye tracking and event tracking led to similar results regarding recommendation interface evaluation. In general, vertical interfaces showed higher effectiveness compared to horizontal ones, with the first and second positions working best, and the worse performance of horizontal interfaces probably being connected with banner blindness. Neural networks provided the best modeling results of the recommendation-driven purchase (RDP) phenomenon

Predicting Injuries in Football Based on Data Collected from GPS-Based Wearable Sensors

The growing intensity and frequency of matches in professional football leagues are related to the increasing physical player load. An incorrect training model results in over- or undertraining, which is related to a raised probability of an injury. This research focuses on predicting non-contact lower body injuries coming from over- or undertraining. The purpose of this analysis was to create decision-making models based on data collected during both training and match, which will enable the preparation of a tool to model the load and report the increased risk of injury for a given player in the upcoming microcycle. For this purpose, three decision-making methods were implemented. Rule-based and fuzzy rule-based methods were prepared based on expert understanding. As a machine learning baseline XGBoost algorithm was considered. Taking into account the dataset used containing parameters related to the external load of the player, it is possible to predict the risk of injury with a certain precision, depending on the method used. The most promising results were achieved by the machine learning method XGBoost algorithm (Precision 92.4%, Recall 96.5%, and F1-score 94.4%)