Dept. of Mathematics and Computer Science, mail to: amarinescu[at]cs.ubbcluj.ro

A. Marinescu, Bach 2.7 – A musical similarity metric based on Symbolic Aggregate Approximation, ESANN 2020, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pending review

“We have continued our work in the field of AI-driven music synthesis and have improved upon our previous 3-layer gated recurrent unit neural network, with results confirming higher accuracy and much smaller validation loss. In order to achieve this, we have designed a recurrent neural architecture that is more suited to learning the musical style of J. S. Bach from an enhanced database of partitas and sonatas. This architecture is based on four independent channels, each having two gated-recurrent units, with a bi-directional long short-term memory unit in between. However, the main incentive of this paper is finding a metric which is able to measure on a 0 to 1 scale the similarity between an artificial musical composition and the style of the famous composer. This measure is heavily based on a signal processing technique known as symbolic aggregate approximation. As a final note, we perform a statistical analysis of the recurring motifs in the works of J. S. Bach, and hint on the possibility of exploiting them to further increase the quality of the output.”

A. Marinescu, T. Ileni, A. Darabant, A Versatile 3D Face Reconstruction from Multiple Images for Face Shape Classification, SOFTCOM 2019, 2019 International Conference on Software, Telecommunications and Computer Networks

“In this paper we present a 3D facial reconstruction algorithm for facial shape classification. We propose a reconstruction method that preserves facial ratios independent on the captured poses allowing thus a precise facial shape classification/determination. We use an Active Appearance Model on a set of facial image captures with different poses on which we auto-calibrate the camera parameters (intrinsic and extrinsic). We then fit/morph the statistical model to a set of facial landmarks in order to obtain an accurate 3D face reconstruction. We show the obtained results and compare them with other approaches in the literature.”

A. Marinescu, Bach 2.0 – Generating Classical Music using Recurrent Neural Networks, KES 2019, 23rd International Conference on Knowledge-Based and Intelligent Information & Engineering Systems

“The main incentive of this paper is to approach the sensitive subject of classical music synthesis in the form of musical scores by providing an analysis of different Recurrent Neural Network architectures. We will be discussing in a side-by-side comparison two of the most common neural network layers, namely Long-Short Term Memory and Gated Recurrent Unit, respectively, and study the effect of altering the global architecture meta-parameters, such as number of hidden neurons, layer count and number of epochs on the categorical accuracy and loss. A case study is performed on musical pieces composed by Johann Sebastian Bach and a method for estimating the repetition stride in a given musical piece is introduced. This is identified as the primary factor in optimizing the input length that must be fed during the training process.”

A. Marinescu, A. Andreica, Evolving Mathematical Formulas using LINQ Expression Trees and Direct Applications to Credit Scoring, SYNASC 2018, 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing

“Credit scoring is a well established and scrutinized domain within the artificial intelligence field of research and has direct implications in the functioning of financial institutions, by evaluating the risk of approving loans for different clients, which may or may not reimburse them in due time. It is the clients who fail to repay their debt that we are interested in predicting, which makes it a much more difficult task, since they form only a small minority of the total client count. From an input-output perspective, the problem can be stated as: given a set of client properties, such as age, marital status, loan duration, one must yield a 0-1 response variable, with 0 meaning “good” and 1, “bad” clients. Many techniques with high accuracy exist, such as artificial neural networks, but they behave as black box units. We add to this whole context the constraint that the output must be a concrete, tractable mathematical formula, which provides significant added value for a financial analyst. To this end, we present a means for evolving mathematical formulas using genetic programming coupled with Language Integrated Query expression trees, a feature present in the C# programming language.”

A. Marinescu, Z. Bálint, L. Dioșan, A. Andreica, Unsupervised and Fully Autonomous 3D Medical Image Segmentation based on Grow Cut, SYNASC 2018, 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing

“Extending and optimizing cellular automata to handle 3D volume segmentation is a non-trivial task. First, it does not suffice to simply alter the cell neighborhood (be it von Neumann or Moore), and second, going from 2D to 3D means that the number of operations increases by an order of magnitude, thus GPU acceleration becomes a necessity, advantage inherent to cellular automata approaches. When discussing 3D medical imagistics, we mean that the entire stack of slices from a certain sequence within an acquisition is stored as a single entity. This, in turn, enables us to accurately segment whole volumes in a single run, which would otherwise need per-slice segmentation followed by a stitching post-process. This paper focuses mainly on a thorough benchmark analysis of the 3D Unsupervised Grow Cut technique. We discuss algorithm speed of convergence, stability and behavior with respect to global meta-parameters such as segmentation threshold, keeping track of output quality metrics as the algorithm unfolds. Our end goal is to segment the heart cavities from cardiac MRI and to yield an interactive 3D reconstruction which can be easily handled and analyzed by the radiologist.”

A. Marinescu, Z. Bálint, L. Dioșan, A. Andreica, Dynamic autonomous image segmentation based on Grow Cut, ESANN 2018, 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

“The main incentive of this paper is to provide an enhanced approach for 2D medical image segmentation based on the Unsupervised Grow Cut algorithm, a method that requires no prior training. This paper assumes that the reader is, to some extent, familiar with cellular automata and their function as they make up the core of this technique. The benchmarks were performed on 2D MRI images of the heart and chest cavity. We obtained a significant increase in the output quality as compared to classical Unsupervised Grow Cut by using standard measures, based on the existence of accurate ground truth. This increase was obtained by dynamically altering the local threshold parameter. In conclusion, our approach provides the opportunity to become a building block of a computer aided diagnostic system.”

A. Marinescu, A Genetic Algorithm Approach for Evolving Neural Networks, Studia Universitatis Babeș-Bolyai Informatica

“We present an alternative approach for training feed-forward neural networks (abbrev. NN) by means of a genetic algorithm (abbrev. GA) that alters the network’s hidden weights and biases. We, by no means, out-rule the back-propagation training algorithm, but instead use it to train the evolved NNs for a much smaller number of generations and focus more on the mutation and crossover operators and how they can be applied. The basic principle involved is that each and every NN can be treated as a chromosome for a GA and, as a consequence, is subject to the mutation and crossover operators. A notable advantage of our approach is that we not only avoid over-fitting the NN, but are also able to alter the number of hidden neurons that make up the hidden layer of the NN, effectively removing the need for the user to specify them explicitly. We manage to outperform plain-vanilla NNs by a factor of 1 to 10 percent on well known data sets. At first this may not seem significant, but it becomes crucial when dealing with applications where accuracy is critical and training time is not an issue (such as disease diagnosis).”

A. Marinescu, C. Mihălceanu, D. Lapsanschi, M. Albert, MoodFlux – a NUI-based music player, Intel RealSense App Challenge 2014 Pioneer Track Winners

“Intel RealSense App Challenge is a worldwide developer challenge which aims to change how users interact with their devices, and the world around them. With 3D capabilities, hand and finger tracking, facial analysis and voice command, new collaboration and sharing technologies, and more, the computing experience will be taken to a whole new level. Developers in over 30 different countries competed for a bucket of $1,000,000 (USD) prizes in cash. A great team of developers from Evozon competed in the Intel® RealSense™ App Challenge in 2014 and won 2nd place worldwide with an innovative app, a music player with futuristic controls and an impressive colorful UI called MoodFlux. Its sensor is capable of face and hand tracking, being able to follow dozens of face landmarks and recognizing basic emotions (happy, scared, neutral, surprised, nervous, angry, etc.).”

A. Marinescu, Optimizations in Perlin Noise-Generated Procedural Terrain, Studia Universitatis Babeș-Bolyai Informatica

“The following article wishes to be the first of a series focused on different aspects involving procedural generation, with its ultimate goal being that of building an entire realistic 3D world from a single number (known in literature as the “seed”). It should allow for free exploration and render at interactive frame rates on mid to high-end graphics hardware. To begin with, we will discuss the manner in which we have generated procedural landscapes and some techniques we have borrowed from rendering algorithms in order to optimize the terrain generation and drawing process. Regarding the rendering framework, we have gone for Microsoft XNA Game Studio, the key factors of our choice being its simplicity plus the fact that we can focus more on the structure and realization of the algorithms and less on implementation and API details. As in our previous work, we have considered that this outweighs its limitations and that the concepts presented here should very well fit any programming language/drawing API.”

A. Marinescu, Achieving Real-Time Soft Shadows Using Layered Variance Shadow Maps (LVSM) in a Real-Time Strategy (RTS) Game, Studia Universitatis Babeș-Bolyai Informatica

“While building a game engine in Microsoft XNA 4 that powered a RTS (real-time strategy) tower defense type game, we were faced with the issue of increasing the amount of visual feedback received by the player and adding value to the gameplay by creating a more immersive atmosphere. This is a common goal shared by all games, and with the recent advancements in graphics hardware (namely OpenGL, DirectX and the advent of programmable shaders) it has become a necessity. In this paper we will build upon the shadowing techniques known as VSM (variance shadow map) and LVSM (layered variance shadow map) and discuss some of the issues and optimizations we employed in order to add real-time soft shadowing capabilities to our game engine.”