Semantic Textual Similarity (STS) evaluationassesses the degree to which two partsof texts are similar, based on their semanticevaluation. In this paper, we describe threemodels submitted to STS SemEval 2017.Given two English parts of a text, each ofproposed methods outputs the assessmentof their semantic similarity.We propose an approach for computingmonolingual semantic textual similaritybased on an ensemble of three distinctmethods. Our model consists of recursiveneural network (RNN) text auto-encodersensemble with supervised a model of vectorizedsentences using reduced part ofspeech (PoS) weighted word embeddingsas well as unsupervised a method basedon word coverage (TakeLab). Additionally,we enrich our model with additionalfeatures that allow disambiguation of ensemblemethods based on their efficiency.We have used Multi-Layer Perceptron as anensemble classifier basing on estimationsof trained Gradient Boosting Regressors.Results of our research proves that usingsuch ensemble leads to a higher accuracydue to a fact that each memberalgorithmtends to specialize in particulartype of sentences. Simple model basedon PoS weighted Word2Vec word embeddingsseem to improve performance ofmore complex RNN based auto-encoders inthe ensemble. In the monolingual EnglishEnglishSTS subtask our Ensemble basedmodel achieved mean Pearson correlationof .785 compared with human annotators.
The purpose of this paper is to present the wavelet tools that enable the detection of temporal interactions of concurrent processes. In particular, the determination of interaction coherence of time-varying signals is achieved using a complex continuous wavelet transform. This paper has used electrocardiogram (ECG) and seismocardiogram (SCG) data set to show multiple continuous wavelet analysis techniques based on Morlet wavelet transform. MATLAB Graphical User Interface (GUI), developed in the reported research to assist in quick and simple data analysis, is presented. These software tools can discover the interaction dynamics of time-varying signals, hence they can reveal their correlation in phase and amplitude, as well as their non-linear interconnections. The user-friendly MATLAB GUI enables effective use of the developed software what enables to load two processes under investigation, make choice of the required processing parameters, and then perform the analysis. The software developed is a useful tool for researchers who have a need for investigation of interaction dynamics of concurrent processes.
#Prostate cancer (PCa) is the most common diagnosed cancer and cause of cancer-related death among men. This paper describes novel, deep learning based PCa CAD system that uses statistical central moments and Haralick features extracted from MR images, integrated with anamnestic data. Developed system has been trained on the dataset consisting of 330 lesions and evaluated on the challenge dataset using area under curve (AUC) related to estimated receiver operating characteristic (ROC). Two configurations of our method, based on statistical and Haralick features, scored 0.63 and 0.73 of AUC values. We draw conclusions from the challenge participation and discussed further improvements that could be made to the model to improve prostate classification.
We have used genetic algorithm with a fitness function based on signals convolution to find time delay between investigated signals. Two methods of cross-correlation are proposed: one that finds single delay for analyzed signals, and one returns a vector of delay values for each of wavelet transform sub-band center frequencies. Algorithms were implemented using MATLAB.
I’ve changed my surname from Sukiennik to Sobecki in September, 2015.