Due to the ever-increasing amount of data collected and the requirements for the rapid and reliable exchange of information across many interconnected communication devices, land-based communications networks are experiencing continuous progress and improvement of existing infrastructures. However, maritime communications are still characterized by slow communication speeds and limited communication capacity, despite a similar trend of […]
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Discovery of phosphotyrosine-binding oligopeptides with supramolecular target selectivity
Intra-domain and cross-domain transfer learning for time series data—How transferable are the features?
In practice, it is very challenging and sometimes impossible to collect datasets of labelled data large enough to successfully train a machine learning model, and one possible solution to this problem is using transfer learning. In this study, we investigate how transferable are features between different domains of time series data and under what conditions. […]
Detection of Non-Stationary GW Signals in High Noise from Cohen’s Class of Time-Frequency Representations Using Deep Learning
Analysis of non-stationary signals in a noisy environment is a challenging research topic in many fields often requiring simultaneous signal decomposition in the time and frequency domain. This paper proposes a method for the classification of noisy non-stationary time-series signals based on Cohen’s class of their time-frequency representations (TFRs) and deep learning algorithms. We demonstrated […]
Analysis of Cryptography Algorithms Implemented in Android Mobile Application
This paper evaluates the performances of numerous encryption algorithms on mobile devices running the Android operating system. The primary objective of our research was to measure and compare the relative performances of tested algorithm implementations (Data Encryption Standard (DES), 3DES, Advanced Encryption Standard (AES), ChaCha20, Blowfish, and Rivest Cipher 4 (RC4)) on the Android platform. […]
Modeling Uncertainty in Fracture Age Estimation from Pediatric Wrist Radiographs
In clinical practice, fracture age estimation is commonly required, particularly in children with suspected non-accidental injuries. It is usually done by radiologically examining the injured body part and analyzing several indicators of fracture healing such as osteopenia, periosteal reaction, and fracture gap width. However, age-related changes in healing timeframes, inter-individual variabilities in bone density, and […]
Verifiable Computing Applications in Blockchain
From weak clients outsourcing computational tasks to more powerful machines, to distributed blockchain nodes needing to agree on the state of the ledger in the presence of adversarial nodes, there is a growing need to efficiently verify the results of computations delegated to untrusted third parties. Verifiable computing is a new and interesting research area […]
Transfer learning: Improving neural network based prediction of earthquake ground shaking for an area with insufficient training data
In a recent study we showed that convolutional neural networks (CNNs) applied to network seismic traces can be used for rapid prediction of earthquake peak ground motion intensity measures (IMs) at distant stations using only recordings from stations near the epicenter. The predictions are made without any previous knowledge concerning the earthquake location and magnitude. […]
Presentation at the conference “Digital Innovation and Technology for People”
Assoc. Prof. Dr. Sc. Jonatan Lerga held a talk on “How is AI Changing Financial Services?” at the conference “Digital Innovation and Technology for People” held in Opatija on the 9th of December 2021.
Characterisation of COVID-19-Related Tweets in the Croatian Language: Framework Based on the Cro-CoV-cseBERT Model
This study aims to provide insights into the COVID-19-related communication on Twitter in the Republic of Croatia. For that purpose, we developed an NL-based framework that enables automatic analysis of a large dataset of tweets in the Croatian language. We collected and analysed 206,196 tweets related to COVID-19 and constructed a dataset of 10,000 tweets […]
