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Analyzing the Effectiveness of the Brain–Computer Interface for Task Discerning Based on Machine Learning
PublicationThe aim of the study is to compare electroencephalographic (EEG) signal feature extraction methods in the context of the effectiveness of the classification of brain activities. For classification, electroencephalographic signals were obtained using an EEG device from 17 subjects in three mental states (relaxation, excitation, and solving logical task). Blind source separation employing independent component analysis (ICA) was...
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Detecting Apples in the Wild: Potential for Harvest Quantity Estimation
PublicationKnowing the exact number of fruits and trees helps farmers to make better decisions in their orchard production management. The current practice of crop estimation practice often involves manual counting of fruits (before harvesting), which is an extremely time-consuming and costly process. Additionally, this is not practicable for large orchards. Thanks to the changes that have taken place in recent years in the field of image...
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Generowanie tekstu z użyciem sieci typu Transformer
PublicationOpisano działanie wybranych modeli uczenia maszynowego znajdujących zastosowanie w przetwarzaniu języka naturalnego w szczególności wy- korzystywanych do generowania tekstu. Przedstawiono również model BERT i jego różne wersje, a także praktyczne wykorzystanie modeli typu Transformer. Przedstawiono ich działanie w aplikacji zmieniającej nastrój tekstu w sposób sekwencyjny.
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A Novel IoT-Perceptive Human Activity Recognition (HAR) Approach Using Multi-Head Convolutional Attention
PublicationTogether with fast advancement of the Internet of Things (IoT), smart healthcare applications and systems are equipped with increasingly more wearable sensors and mobile devices. These sensors are used not only to collect data, but also, and more importantly, to assist in daily activity tracking and analyzing of their users. Various human activity recognition (HAR) approaches are used to enhance such tracking. Most of the existing...
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Neural network training with limited precision and asymmetric exponent
PublicationAlong with an extremely increasing number of mobile devices, sensors and other smart utilities, an unprecedented growth of data can be observed in today’s world. In order to address multiple challenges facing the big data domain, machine learning techniques are often leveraged for data analysis, filtering and classification. Wide usage of artificial intelligence with large amounts of data creates growing demand not only for storage...
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Wykorzystanie sieci neuronowych do syntezy mowy wyrażającej emocje
PublicationW niniejszym artykule przedstawiono analizę rozwiązań do rozpoznawania emocji opratych na mowie i możliwości ich wykprzystania w syntezie mowy z emocjami stosując do tego celu sieci neuronowe. Wskazano również przydatnośc parametrów typowo stosowanych do rozpoznawania mowy w detekcji emocji w śpiewie i rozróżnianiu tych emocji w obu przypadkach. Przedstawiono aktualne rozwiązania dotyczące rozpoznawania emocji w mowie i metod syntezy...
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The Neural Knowledge DNA Based Smart Internet of Things
PublicationABSTRACT The Internet of Things (IoT) has gained significant attention from industry as well as academia during the past decade. Smartness, however, remains a substantial challenge for IoT applications. Recent advances in networked sensor technologies, computing, and machine learning have made it possible for building new smart IoT applications. In this paper, we propose a novel approach: the Neural Knowledge DNA based Smart Internet...
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Evaluation of aspiration problems in L2 English pronunciation employing machine learning
PublicationThe approach proposed in this study includes methods specifically dedicated to the detection of allophonic variation in English. This study aims to find an efficient method for automatic evaluation of aspiration in the case of Polish second-language (L2) English speakers’ pronunciation when whole words are analyzed instead of particular allophones extracted from words. Sample words including aspirated and unaspirated allophones...
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Structure and Randomness in Planning and Reinforcement Learning
PublicationPlanning in large state spaces inevitably needs to balance the depth and breadth of the search. It has a crucial impact on the performance of a planner and most manage this interplay implicitly. We present a novel method \textit{Shoot Tree Search (STS)}, which makes it possible to control this trade-off more explicitly. Our algorithm can be understood as an interpolation between two celebrated search mechanisms: MCTS and random...
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CNN Architectures for Human Pose Estimation from a Very Low Resolution Depth Image
PublicationThe paper is dedicated to proposing and evaluating a number of convolutional neural network architectures for calculating a multiple regression on 3D coordinates of human body joints tracked in a single low resolution depth image. The main challenge was to obtain a high precision in case of a noisy and coarse scan of the body, as observed by a depth sensor from a large distance. The regression network was expected to reason about...
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Automatic Clustering of EEG-Based Data Associated with Brain Activity
PublicationThe aim of this paper is to present a system for automatic assigning electroencephalographic (EEG) signals to appropriate classes associated with brain activity. The EEG signals are acquired from a headset consisting of 14 electrodes placed on skull. Data gathered are first processed by the Independent Component Analysis algorithm to obtain estimates of signals generated by primary sources reflecting the activity of the brain....
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Zastosowanie sieci neuronowych w cyfrowej syntezie dźwięku
PublicationRozwój technik związanych z uczeniem maszynowym umożliwia nowe podejście i nowe definiowanie wielu dotychczasowych problemów. Heurystyczne algorytmy stosowane do problemów takich jak klasyfikacja danych w postaci wektorów cech, czy wyróżnianie grup obiektów o podobnych własnościach mogą znaleźć także zastosowanie w takich dziedzinach jak analiza i synteza dźwięków muzycznych. W referacie przybliżone zostały podstawowe zasady projektowania...
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Adding Intelligence to Cars Using the Neural Knowledge DNA
PublicationIn this paper we propose a Neural Knowledge DNA based framework that is capable of learning from the car’s daily operation. The Neural Knowledge DNA is a novel knowledge representation and reasoning approach designed to support discovering, storing, reusing, improving, and sharing knowledge among machines and computing devices. We examine our framework for drivers' classification based on their driving behaviour. The experimental...
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Toward Intelligent Vehicle Intrusion Detection Using the Neural Knowledge DNA
PublicationIn this paper, we propose a novel intrusion detection approach using past driving experience and the neural knowledge DNA for in-vehicle information system security. The neural knowledge DNA is a novel knowledge representation method designed to support discovering, storing, reusing, improving, and sharing knowledge among machines and computing systems. We examine our approach for classifying malicious vehicle control commands...
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Limitation of Floating-Point Precision for Resource Constrained Neural Network Training
PublicationInsufficient availability of computational power and runtime memory is a major concern when it comes to experiments in the field of artificial intelligence. One of the promising solutions for this problem is an optimization of internal neural network’s calculations and its parameters’ representation. This work focuses on the mentioned issue by the application of neural network training with limited precision. Based on this research,...
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Artificial Intelligence in the Diagnosis of Onychomycosis—Literature Review
PublicationOnychomycosis is a common fungal nail infection that is difficult to diagnose due to its similarity to other nail conditions. Accurate identification is essential for effective treatment. The current gold standard methods include microscopic examination with potassium hydroxide, fungal cultures, and Periodic acid-Schiff biopsy staining. These conventional techniques, however, suffer from high turnover times, variable sensitivity,...
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Respiratory Rate Estimation Based on Detected Mask Area in Thermal Images
PublicationThe popularity of non-contact methods of measuring vital signs, particularly respiratory rate, has increased during the SARS-COV-2 pandemic. Breathing parameters can be estimated by analysis of temperature changes observed in thermal images of nostrils or mouth regions. However, wearing virus-protection face masks prevents direct detection of such face regions. In this work, we propose to use an automatic mask detection approach...
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Systematic Literature Review on Click Through Rate Prediction
PublicationThe ability to anticipate whether a user will click on an item is one of the most crucial aspects of operating an e-commerce business, and clickthrough rate prediction is an attempt to provide an answer to this question. Beginning with the simplest multilayer perceptrons and progressing to the most sophisticated attention networks, researchers employ a variety of methods to solve this issue. In this paper, we present the findings...
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From Scores to Predictions in Multi-Label Classification: Neural Thresholding Strategies
PublicationIn this paper, we propose a novel approach for obtaining predictions from per-class scores to improve the accuracy of multi-label classification systems. In a multi-label classification task, the expected output is a set of predicted labels per each testing sample. Typically, these predictions are calculated by implicit or explicit thresholding of per-class real-valued scores: classes with scores exceeding a given threshold value...
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Machine-aided detection of SARS-CoV-2 from complete blood count
PublicationThe current gold standard for SARS-CoV-2 detection methods lacks the functionality to perform population screening. Complete blood count (CBC) tests are a cost-effective way to reach a wide range of people – e.g. according to the data of the Central Statistical Office of Poland from 2016, there are 3,000 blood diagnostic laboratories in Poland, and 46% of Polish people have at least one CBC test per year. In our work, we show...
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COVID-19 severity forecast based on machine learning and complete blood count data
PublicationProper triage of COVID-19 patients is a key factor in eective case management, especially with limited and insucient resources. In this paper, we propose a machine-aided diagnostic system to predict how badly a patient with COVID-19 will develop disease. The prognosis of this type is based on the parameters of commonly used complete blood count tests, which makes it possible to obtain data from a wide range of patients.We chose...
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COVID-19 severity forecast based on machine learning and complete blood count data
PublicationProper triage of COVID-19 patients is a key factor in eective case management, especially with limited and insucient resources. In this paper, we propose a machine-aided diagnostic system to predict how badly a patient with COVID-19 will develop disease. The prognosis of this type is based on the parameters of commonly used complete blood count tests, which makes it possible to obtain data from a wide range of patients.We chose...
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Computer-Aided Diagnosis of COVID-19 from Chest X-ray Images Using Hybrid-Features and Random Forest Classifier
PublicationIn recent years, a lot of attention has been paid to using radiology imaging to automatically find COVID-19. (1) Background: There are now a number of computer-aided diagnostic schemes that help radiologists and doctors perform diagnostic COVID-19 tests quickly, accurately, and consistently. (2) Methods: Using chest X-ray images, this study proposed a cutting-edge scheme for the automatic recognition of COVID-19 and pneumonia....
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Bimodal Emotion Recognition Based on Vocal and Facial Features
PublicationEmotion recognition is a crucial aspect of human communication, with applications in fields such as psychology, education, and healthcare. Identifying emotions accurately is challenging, as people use a variety of signals to express and perceive emotions. In this study, we address the problem of multimodal emotion recognition using both audio and video signals, to develop a robust and reliable system that can recognize emotions...
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Tagged images with bees
Open Research DataImages taken from bee hive with tagged bees. The images are prepared for training yolo5 deep neural network (supplied with the data).
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Very low resolution depth images of 200,000 poses
Open Research DataA dataset represents simulated images of depth sensor seeing a single human pose, performing 200,000 random gestures. The depth images as vectors of pixels are stored with ground truth positions of every relevant joint.
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Tagged images with bees 2
Open Research DataImages taken from bee hive with tagged bees.
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Dawid Wieczerzak mgr inż.
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Muhammad Jamshed Abbass Phd in Electrical Engineering
PeopleMuhammad Jamshed Abbass received the M.S. degree in electrical engineering from Riphah International University, Islamabad. He is currently pursuing the Ph.D. degree with the Wrocław University of Science and Technology, Wroclaw, Poland. His research interests include machine learning, voltage stability within power systems, control design, analysis, the modeling of electrical power systems, the integration of numerous decentralized...
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Jan Tobolewski
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Paulina Alicja Leszczełowska
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Kashif Shaheed
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Faramarz Bagherzadeh MSc.
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Tomasz Menet mgr inż.
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Emilia Lewandowska
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Piotr Kopa Ostrowski
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Rafał Buler mgr inż.
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Remigiusz Martyniak mgr inż.
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Agata Polejowska
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Dharm Jain
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Architektura a dekonstrukcja. Przypadek Petera Eisenmana i Bernarda Tschumiego
PublicationArchitecture and Deconstruction Case of Peter Eisenman and Bernard Tschumi Introduction Towards deconstruction in architecture Intensive relations between philosophical deconstruction and architecture, which were present in the late 1980s and early 1990s, belong to the past and therefore may be described from a greater than...