Maciej Wielgosz

Muhammad Naveed Riaz, Maciej Wielgosz, Antonio M. López Peña

We present ARCANE-PedSynth, an open-source CARLA-based software framework for generating synthetic multi-pedestrian datasets with dense behavioural annotations for pedestrian crossing prediction in autonomous driving. The framework overcomes CARLA's native 9% crossing rate through a hybrid AI-manual pedestrian control architecture, enabling configurable target rates up to 75%. A 12-state behavioural finite state machine with five character archetypes produces diverse crossing behaviours. The framework generates synchronised RGB, LiDAR, and DVS data with per-frame crossing labels, behavioural states, and estimated 2D pose keypoints. We demonstrate ARCANE-PedSynth through PedSynth++, an example dataset generated with the framework, comprising 533 multi-pedestrian clips across 12 weather conditions with RGB, LiDAR, and DVS streams. ARCANE-PedSynth is fully reproducible via CLI parameterisation and Docker containerisation.

Szymon Mazurek, Maciej Wielgosz

In this paper, we delve into the critical aspect of dataset quality assessment in machine learning classification tasks. Leveraging a variety of nine distinct datasets, each crafted for classification tasks with varying complexity levels, we illustrate the profound impact of dataset quality on model training and performance. We further introduce two additional datasets designed to represent specific data conditions - one maximizing entropy and the other demonstrating high redundancy. Our findings underscore the importance of appropriate feature selection, adequate data volume, and data quality in achieving high-performing machine learning models. To aid researchers and practitioners, we propose a comprehensive framework for dataset quality assessment, which can help evaluate if the dataset at hand is sufficient and of the required quality for specific tasks. This research offers valuable insights into data assessment practices, contributing to the development of more accurate and robust machine learning models.

Maciej Wielgosz, Antonio M. López, Muhammad Naveed Riaz

We present a sample dataset featuring pedestrians generated using the ARCANE framework, a new framework for generating datasets in CARLA (0.9.13). We provide use cases for pedestrian detection, autoencoding, pose estimation, and pose lifting. We also showcase baseline results. For more information, visit https://project-arcane.eu/.

Jan Krupiński, Maciej Wielgosz, Szymon Mazurek et al.

This paper presents a computer-aided cytology diagnosis system designed for animals, focusing on image quality assessment (IQA) using Convolutional Neural Networks (CNNs). The system's building blocks are tailored to seamlessly integrate IQA, ensuring reliable performance in disease classification. We extensively investigate the CNN's ability to handle various image variations and scenarios, analyzing the impact on detecting low-quality input data. Additionally, the network's capacity to differentiate valid cellular samples from those with artifacts is evaluated. Our study employs a ResNet18 network architecture and explores the effects of input sizes and cropping strategies on model performance. The research sheds light on the significance of CNN-based IQA in computer-aided cytology diagnosis for animals, enhancing the accuracy of disease classification.

Krystian Strzałka, Szymon Mazurek, Maciej Wielgosz et al.

This paper explores the innovative use of simulation environments to enhance data acquisition and diagnostics in veterinary medicine, focusing specifically on gait analysis in dogs. The study harnesses the power of Blender and the Blenderproc library to generate synthetic datasets that reflect diverse anatomical, environmental, and behavioral conditions. The generated data, represented in graph form and standardized for optimal analysis, is utilized to train machine learning algorithms for identifying normal and abnormal gaits. Two distinct datasets with varying degrees of camera angle granularity are created to further investigate the influence of camera perspective on model accuracy. Preliminary results suggest that this simulation-based approach holds promise for advancing veterinary diagnostics by enabling more precise data acquisition and more effective machine learning models. By integrating synthetic and real-world patient data, the study lays a robust foundation for improving overall effectiveness and efficiency in veterinary medicine.

Jakub Caputa, Maciej Wielgosz, Daria Łukasik et al.

The primary objective of this research was to enhance the quality of semantic segmentation in cytology images by incorporating super-resolution (SR) architectures. An additional contribution was the development of a novel dataset aimed at improving imaging quality in the presence of inaccurate focus. Our experimental results demonstrate that the integration of SR techniques into the segmentation pipeline can lead to a significant improvement of up to 25% in the mean average precision (mAP) segmentation metric. These findings suggest that leveraging SR architectures holds great promise for advancing the state of the art in cytology image analysis.

Karol C. Jurzec, Tomasz Szydlo, Maciej Wielgosz

Spiking neural networks (SNNs) communicate via discrete spikes in time rather than continuous activations. Their event-driven nature offers advantages for temporal processing and energy efficiency on resource-constrained hardware, but training and deployment remain challenging. We present a lightweight C-based runtime for SNN inference on edge devices and optimizations that reduce latency and memory without sacrificing accuracy. Trained models exported from SNNTorch are translated to a compact C representation; static, cache-friendly data layouts and preallocation avoid interpreter and allocation overheads. We further exploit sparse spiking activity to prune inactive neurons and synapses, shrinking computation in upstream convolutional layers. Experiments on N-MNIST and ST-MNIST show functional parity with the Python baseline while achieving ~10 speedups on desktop CPU and additional gains with pruning, together with large memory reductions that enable microcontroller deployment (Arduino Portenta H7). Results indicate that SNNs can be executed efficiently on conventional embedded platforms when paired with an optimized runtime and spike-driven model compression. Code: https://github.com/karol-jurzec/snn-generator/

Binbin Xiang, Maciej Wielgosz, Theodora Kontogianni et al.

Detailed forest inventories are critical for sustainable and flexible management of forest resources, to conserve various ecosystem services. Modern airborne laser scanners deliver high-density point clouds with great potential for fine-scale forest inventory and analysis, but automatically partitioning those point clouds into meaningful entities like individual trees or tree components remains a challenge. The present study aims to fill this gap and introduces a deep learning framework, termed ForAINet, that is able to perform such a segmentation across diverse forest types and geographic regions. From the segmented data, we then derive relevant biophysical parameters of individual trees as well as stands. The system has been tested on FOR-Instance, a dataset of point clouds that have been acquired in five different countries using surveying drones. The segmentation back-end achieves over 85% F-score for individual trees, respectively over 73% mean IoU across five semantic categories: ground, low vegetation, stems, live branches and dead branches. Building on the segmentation results our pipeline then densely calculates biophysical features of each individual tree (height, crown diameter, crown volume, DBH, and location) and properties per stand (digital terrain model and stand density). Especially crown-related features are in most cases retrieved with high accuracy, whereas the estimates for DBH and location are less reliable, due to the airborne scanning setup.

Maciej Wielgosz, Stefano Puliti, Binbin Xiang et al.

This research advances individual tree crown (ITC) segmentation in lidar data, using a deep learning model applicable to various laser scanning types: airborne (ULS), terrestrial (TLS), and mobile (MLS). It addresses the challenge of transferability across different data characteristics in 3D forest scene analysis. The study evaluates the model's performance based on platform (ULS, MLS) and data density, testing five scenarios with varying input data, including sparse versions, to gauge adaptability and canopy layer efficacy. The model, based on PointGroup architecture, is a 3D CNN with separate heads for semantic and instance segmentation, validated on diverse point cloud datasets. Results show point cloud sparsification enhances performance, aiding sparse data handling and improving detection in dense forests. The model performs well with >50 points per sq. m densities but less so at 10 points per sq. m due to higher omission rates. It outperforms existing methods (e.g., Point2Tree, TLS2trees) in detection, omission, commission rates, and F1 score, setting new benchmarks on LAUTx, Wytham Woods, and TreeLearn datasets. In conclusion, this study shows the feasibility of a sensor-agnostic model for diverse lidar data, surpassing sensor-specific approaches and setting new standards in tree segmentation, particularly in complex forests. This contributes to future ecological modeling and forest management advancements.

Muhammad Naveed Riaz, Maciej Wielgosz, Abel Garcia Romera et al.

Pedestrian intention prediction is crucial for autonomous driving. In particular, knowing if pedestrians are going to cross in front of the ego-vehicle is core to performing safe and comfortable maneuvers. Creating accurate and fast models that predict such intentions from sequential images is challenging. A factor contributing to this is the lack of datasets with diverse crossing and non-crossing (C/NC) scenarios. We address this scarceness by introducing a framework, named ARCANE, which allows programmatically generating synthetic datasets consisting of C/NC video clip samples. As an example, we use ARCANE to generate a large and diverse dataset named PedSynth. We will show how PedSynth complements widely used real-world datasets such as JAAD and PIE, so enabling more accurate models for C/NC prediction. Considering the onboard deployment of C/NC prediction models, we also propose a deep model named PedGNN, which is fast and has a very low memory footprint. PedGNN is based on a GNN-GRU architecture that takes a sequence of pedestrian skeletons as input to predict crossing intentions.

Binbin Xiang, Maciej Wielgosz, Stefano Puliti et al.

The segmentation of forest LiDAR 3D point clouds, including both individual tree and semantic segmentation, is fundamental for advancing forest management and ecological research. However, current approaches often struggle with the complexity and variability of natural forest environments. We present ForestFormer3D, a new unified and end-to-end framework designed for precise individual tree and semantic segmentation. ForestFormer3D incorporates ISA-guided query point selection, a score-based block merging strategy during inference, and a one-to-many association mechanism for effective training. By combining these new components, our model achieves state-of-the-art performance for individual tree segmentation on the newly introduced FOR-instanceV2 dataset, which spans diverse forest types and regions. Additionally, ForestFormer3D generalizes well to unseen test sets (Wytham woods and LAUTx), showcasing its robustness across different forest conditions and sensor modalities. The FOR-instanceV2 dataset and the ForestFormer3D code are publicly available at https://bxiang233.github.io/FF3D/.

Szymon Mazurek, Jakub Caputa, Jan K. Argasiński et al.

Three-factor learning rules in Spiking Neural Networks (SNNs) have emerged as a crucial extension to traditional Hebbian learning and Spike-Timing-Dependent Plasticity (STDP), incorporating neuromodulatory signals to improve adaptation and learning efficiency. These mechanisms enhance biological plausibility and facilitate improved credit assignment in artificial neural systems. This paper takes a view on this topic from a machine learning perspective, providing an overview of recent advances in three-factor learning, discusses theoretical foundations, algorithmic implementations, and their relevance to reinforcement learning and neuromorphic computing. In addition, we explore interdisciplinary approaches, scalability challenges, and potential applications in robotics, cognitive modeling, and AI systems. Finally, we highlight key research gaps and propose future directions for bridging the gap between neuroscience and artificial intelligence.

Mustafa Sakhai, Kaung Sithu, Min Khant Soe Oke et al.

Event cameras like Dynamic Vision Sensors (DVS) report micro-timed brightness changes instead of full frames, offering low latency, high dynamic range, and motion robustness. DVS-PedX (Dynamic Vision Sensor Pedestrian eXploration) is a neuromorphic dataset designed for pedestrian detection and crossing-intention analysis in normal and adverse weather conditions across two complementary sources: (1) synthetic event streams generated in the CARLA simulator for controlled "approach-cross" scenes under varied weather and lighting; and (2) real-world JAAD dash-cam videos converted to event streams using the v2e tool, preserving natural behaviors and backgrounds. Each sequence includes paired RGB frames, per-frame DVS "event frames" (33 ms accumulations), and frame-level labels (crossing vs. not crossing). We also provide raw AEDAT 2.0/AEDAT 4.0 event files and AVI DVS video files and metadata for flexible re-processing. Baseline spiking neural networks (SNNs) using SpikingJelly illustrate dataset usability and reveal a sim-to-real gap, motivating domain adaptation and multimodal fusion. DVS-PedX aims to accelerate research in event-based pedestrian safety, intention prediction, and neuromorphic perception.

Szymon Mazurek, Jakub Caputa, Maciej Wielgosz

Spiking Neural Networks (SNNs) offer energy-efficient and biologically plausible alternatives to traditional artificial neural networks, but their performance depends critically on the tuning of neuron model parameters. In this work, we identify and characterize an operational space - a constrained region in the neuron hyperparameter domain (specifically membrane time constant tau and voltage threshold vth) - within which the network exhibits meaningful activity and functional behavior. Operating inside this manifold yields optimal trade-offs between classification accuracy and spiking activity, while stepping outside leads to degeneration: either excessive energy use or complete network silence. Through systematic exploration across datasets and architectures, we visualize and quantify this manifold and identify efficient operating points. We further assess robustness to adversarial noise, showing that SNNs exhibit increased spike correlation and internal synchrony when operating outside their optimal region. These findings highlight the importance of principled hyperparameter tuning to ensure both task performance and energy efficiency. Our results offer practical guidelines for deploying robust and efficient SNNs, particularly in neuromorphic computing scenarios.

Maciej Wielgosz, Simon Berg, Heikki Korpunen et al.

This paper presents a deep learning-based framework for classifying forestry operations from dashcam video footage. Focusing on four key work elements - crane-out, cutting-and-to-processing, driving, and processing - the approach employs a 3D ResNet-50 architecture implemented with PyTorchVideo. Trained on a manually annotated dataset of field recordings, the model achieves strong performance, with a validation F1 score of 0.88 and precision of 0.90. These results underscore the effectiveness of spatiotemporal convolutional networks for capturing both motion patterns and appearance in real-world forestry environments. The system integrates standard preprocessing and augmentation techniques to improve generalization, but overfitting is evident, highlighting the need for more training data and better class balance. Despite these challenges, the method demonstrates clear potential for reducing the manual workload associated with traditional time studies, offering a scalable solution for operational monitoring and efficiency analysis in forestry. This work contributes to the growing application of AI in natural resource management and sets the foundation for future systems capable of real-time activity recognition in forest machinery. Planned improvements include dataset expansion, enhanced regularization, and deployment trials on embedded systems for in-field use.

Mustafa Sakhai, Szymon Mazurek, Jakub Caputa et al.

This study examines the effectiveness of Spiking Neural Networks (SNNs) paired with Dynamic Vision Sensors (DVS) to improve pedestrian detection in adverse weather, a significant challenge for autonomous vehicles. Utilizing the high temporal resolution and low latency of DVS, which excels in dynamic, low-light, and high-contrast environments, we assess the efficiency of SNNs compared to traditional Convolutional Neural Networks (CNNs). Our experiments involved testing across diverse weather scenarios using a custom dataset from the CARLA simulator, mirroring real-world variability. SNN models, enhanced with Temporally Effective Batch Normalization, were trained and benchmarked against state-of-the-art CNNs to demonstrate superior accuracy and computational efficiency in complex conditions such as rain and fog. The results indicate that SNNs, integrated with DVS, significantly reduce computational overhead and improve detection accuracy in challenging conditions compared to CNNs. This highlights the potential of DVS combined with bio-inspired SNN processing to enhance autonomous vehicle perception and decision-making systems, advancing intelligent transportation systems' safety features in varying operational environments. Additionally, our research indicates that SNNs perform more efficiently in handling long perception windows and prediction tasks, rather than simple pedestrian detection.

Stefano Puliti, Grant Pearse, Peter Surový et al.

The FOR-instance dataset (available at https://doi.org/10.5281/zenodo.8287792) addresses the challenge of accurate individual tree segmentation from laser scanning data, crucial for understanding forest ecosystems and sustainable management. Despite the growing need for detailed tree data, automating segmentation and tracking scientific progress remains difficult. Existing methodologies often overfit small datasets and lack comparability, limiting their applicability. Amid the progress triggered by the emergence of deep learning methodologies, standardized benchmarking assumes paramount importance in these research domains. This data paper introduces a benchmarking dataset for dense airborne laser scanning data, aimed at advancing instance and semantic segmentation techniques and promoting progress in 3D forest scene segmentation. The FOR-instance dataset comprises five curated and ML-ready UAV-based laser scanning data collections from diverse global locations, representing various forest types. The laser scanning data were manually annotated into individual trees (instances) and different semantic classes (e.g. stem, woody branches, live branches, terrain, low vegetation). The dataset is divided into development and test subsets, enabling method advancement and evaluation, with specific guidelines for utilization. It supports instance and semantic segmentation, offering adaptability to deep learning frameworks and diverse segmentation strategies, while the inclusion of diameter at breast height data expands its utility to the measurement of a classic tree variable. In conclusion, the FOR-instance dataset contributes to filling a gap in the 3D forest research, enhancing the development and benchmarking of segmentation algorithms for dense airborne laser scanning data.

Jakub Grzeszczyk, Michał Karwatowski, Daria Łukasik et al.

This paper shows the machine learning system which performs instance segmentation of cytological images in veterinary medicine. Eleven cell types were used directly and indirectly in the experiments, including damaged and unrecognized categories. The deep learning models employed in the system achieve a high score of average precision and recall metrics, i.e. 0.94 and 0.8 respectively, for the selected three types of tumors. This variety of label types allowed us to draw a meaningful conclusion that there are relatively few mistakes for tumor cell types. Additionally, the model learned tumor cell features well enough to avoid misclassification mistakes of one tumor type into another. The experiments also revealed that the quality of the results improves with the dataset size (excluding the damaged cells). It is worth noting that all the experiments were done using a custom dedicated dataset provided by the cooperating vet doctors.

Maciej Wielgosz, Stefano Puliti, Phil Wilkes et al.

This article introduces Point2Tree, a novel framework that incorporates a three-stage process involving semantic segmentation, instance segmentation, optimization analysis of hyperparemeters importance. It introduces a comprehensive and modular approach to processing laser points clouds in Forestry. We tested it on two independent datasets. The first area was located in an actively managed boreal coniferous dominated forest in Våler, Norway, 16 circular plots of 400 square meters were selected to cover a range of forest conditions in terms of species composition and stand density. We trained a model based on Pointnet++ architecture which achieves 0.92 F1-score in semantic segmentation. As a second step in our pipeline we used graph-based approach for instance segmentation which reached F1-score approx. 0.6. The optimization allowed to further boost the performance of the pipeline by approx. 4 \% points.

Mustafa Sakhai, Maciej Wielgosz

Cybersecurity is essential, and attacks are rapidly growing and getting more challenging to detect. The traditional Firewall and Intrusion Detection system, even though it is widely used and recommended but it fails to detect new attacks, zero-day attacks, and traffic patterns that do not match with any configured rules. Therefore, Machine Learning (ML) can be an efficient and cost-reduced solution in cybersecurity. We used Netflow datasets to extract features after applying data analysis. Then, a selection process has been applied to compare these features with one another. Our experiments focus on how efficient machine learning algorithms can detect Bot traffic, Malware traffic, and background traffic. We managed to get 0.903 precision value from a dataset that has 6.5% Bot flows, 1.57% Normal flows, 0.18% Command&Control (C&C) flows, and 91.7% background flows, from 2,753,884 total flows. The results show low false-negative with few false-positive detections.

Marcin Pietron, Maciej Wielgosz

Convolutional neural networks (CNN) play a major role in image processing tasks like image classification, object detection, semantic segmentation. Very often CNN networks have from several to hundred stacked layers with several megabytes of weights. One of the possible methods to reduce complexity and memory footprint is pruning. Pruning is a process of removing weights which connect neurons from two adjacent layers in the network. The process of finding near optimal solution with specified drop in accuracy can be more sophisticated when DL model has higher number of convolutional layers. In the paper few approaches based on retraining and no retraining are described and compared together.

Marcin Radzio, Maciej Wielgosz, Matej Mertik

Falls prevention, especially in older people, becomes an increasingly important topic in the times of aging societies. In this work, we present Gated Recurrent Unit-based neural networks models designed for predicting falls (syncope). The cardiovascular systems signals used in the study come from Gravitational Physiology, Aging and Medicine Research Unit, Institute of Physiology, Medical University of Graz. We used two of the collected signals, heart rate, and mean blood pressure. By using bidirectional GRU model, it was possible to predict the syncope occurrence approximately ten minutes before the manual marker.

Krzysztof Wróbel, Marcin Pietroń, Maciej Wielgosz et al.

Convolutional neural networks are modern models that are very efficient in many classification tasks. They were originally created for image processing purposes. Then some trials were performed to use them in different domains like natural language processing. The artificial intelligence systems (like humanoid robots) are very often based on embedded systems with constraints on memory, power consumption etc. Therefore convolutional neural network because of its memory capacity should be reduced to be mapped to given hardware. In this paper, results are presented of compressing the efficient convolutional neural networks for sentiment analysis. The main steps are quantization and pruning processes. The method responsible for mapping compressed network to FPGA and results of this implementation are presented. The described simulations showed that 5-bit width is enough to have no drop in accuracy from floating point version of the network. Additionally, significant memory footprint reduction was achieved (from 85% up to 93%).

Maciej Wielgosz, Matej Mertik, Andrzej Skoczeń et al.

This paper focuses on an examination of an applicability of Recurrent Neural Network models for detecting anomalous behavior of the CERN superconducting magnets. In order to conduct the experiments, the authors designed and implemented an adaptive signal quantization algorithm and a custom GRU-based detector and developed a method for the detector parameters selection. Three different datasets were used for testing the detector. Two artificially generated datasets were used to assess the raw performance of the system whereas the 231 MB dataset composed of the signals acquired from HiLumi magnets was intended for real-life experiments and model training. Several different setups of the developed anomaly detection system were evaluated and compared with state-of-the-art OC-SVM reference model operating on the same data. The OC-SVM model was equipped with a rich set of feature extractors accounting for a range of the input signal properties. It was determined in the course of the experiments that the detector, along with its supporting design methodology, reaches F1 equal or very close to 1 for almost all test sets. Due to the profile of the data, the best_length setup of the detector turned out to perform the best among all five tested configuration schemes of the detection system. The quantization parameters have the biggest impact on the overall performance of the detector with the best values of input/output grid equal to 16 and 8, respectively. The proposed solution of the detection significantly outperformed OC-SVM-based detector in most of the cases, with much more stable performance across all the datasets.

Krzysztof Wróbel, Maciej Wielgosz, Marcin Pietroń et al.

This paper presents the analysis of the impact of a floating-point number precision reduction on the quality of text classification. The precision reduction of the vectors representing the data (e.g. TF-IDF representation in our case) allows for a decrease of computing time and memory footprint on dedicated hardware platforms. The impact of precision reduction on the classification quality was performed on 5 corpora, using 4 different classifiers. Also, dimensionality reduction was taken into account. Results indicate that the precision reduction improves classification accuracy for most cases (up to 25% of error reduction). In general, the reduction from 64 to 4 bits gives the best scores and ensures that the results will not be worse than with the full floating-point representation.

Matej Mertik, Maciej Wielgosz

The Large Hadron Collider (LHC) is one of the most complex machines ever build. It is composed of many components which constitute a large system. The tunnel and the accelerator is just one of a very critical fraction of the whole LHC infrastructure. Hardware comissioning as one of the critical processes before running the LHC is implemented during the Long Shutdown (LS) states of the macine, where Electrical Quality Assurance (ELQA) is one of its key components. Here a huge data is collected when implementing various ELQA electrical tests. In this paper we present a conceptual framework for supporting a rapid design of web applications for ELQA data analysis. We show a framework's main components, their possible integration with other systems and machine learning algorithms and a simple use case of prototyping an application for Electrical Quality Assurance of the LHC.

Maciej Wielgosz, Andrzej Skoczeń, Matej Mertik

This paper presents a model based on Deep Learning algorithms of LSTM and GRU for facilitating an anomaly detection in Large Hadron Collider superconducting magnets. We used high resolution data available in Post Mortem database to train a set of models and chose the best possible set of their hyper-parameters. Using Deep Learning approach allowed to examine a vast body of data and extract the fragments which require further experts examination and are regarded as anomalies. The presented method does not require tedious manual threshold setting and operator attention at the stage of the system setup. Instead, the automatic approach is proposed, which achieves according to our experiments accuracy of 99%. This is reached for the largest dataset of 302 MB and the following architecture of the network: single layer LSTM, 128 cells, 20 epochs of training, look_back=16, look_ahead=128, grid=100 and optimizer Adam. All the experiments were run on GPU Nvidia Tesla K80

Maciej Wielgosz

This paper presents a concept of a novel method for adjusting hyper-parameters in Deep Learning (DL) algorithms. An external agent-observer monitors a performance of a selected Deep Learning algorithm. The observer learns to model the DL algorithm using a series of random experiments. Consequently, it may be used for predicting a response of the DL algorithm in terms of a selected quality measurement to a set of hyper-parameters. This allows to construct an ensemble composed of a series of evaluators which constitute an observer-assisted architecture. The architecture may be used to gradually iterate towards to the best achievable quality score in tiny steps governed by a unit of progress. The algorithm is stopped when the maximum number of steps is reached or no further progress is made.

Maciej Wielgosz, Andrzej Skoczeń, Matej Mertik

The superconducting LHC magnets are coupled with an electronic monitoring system which records and analyses voltage time series reflecting their performance. A currently used system is based on a range of preprogrammed triggers which launches protection procedures when a misbehavior of the magnets is detected. All the procedures used in the protection equipment were designed and implemented according to known working scenarios of the system and are updated and monitored by human operators. This paper proposes a novel approach to monitoring and fault protection of the Large Hadron Collider (LHC) superconducting magnets which employs state-of-the-art Deep Learning algorithms. Consequently, the authors of the paper decided to examine the performance of LSTM recurrent neural networks for modeling of voltage time series of the magnets. In order to address this challenging task different network architectures and hyper-parameters were used to achieve the best possible performance of the solution. The regression results were measured in terms of RMSE for different number of future steps and history length taken into account for the prediction. The best result of RMSE=0.00104 was obtained for a network of 128 LSTM cells within the internal layer and 16 steps history buffer.

Matej Mertik, Maciej Wielgosz, Andrzej Skoczeń

This article presents a development of web application for quench prediction in \gls{te-mpe-ee} at CERN. The authors describe an ELectrical Quality Assurance (ELQA) framework, a platform which was designed for rapid development of web integrated data analysis applications for different analysis needed during the hardware commissioning of the Large Hadron Collider (LHC). In second part the article describes a research carried out with the data collected from Quench Detection System by means of using an LSTM recurrent neural network. The article discusses and presents a conceptual work of implementing quench prediction application for \gls{te-mpe-ee} based on the ELQA and quench prediction algorithm.

Maciej Wielgosz, Michał Karwatowski, Marcin Pietroń et al.

Video resolutions used in variety of media are constantly rising. While manufacturers struggle to perfect their screens it is also important to ensure high quality of displayed image. Overall quality can be measured using Mean Opinion Score (MOS). Video quality can be affected by miscellaneous artifacts, appearing at every stage of video creation and transmission. In this paper, we present a solution to calculate four distinct video quality metrics that can be applied to a real time video quality assessment system. Our assessment module is capable of processing 8K resolution in real time set at the level of 30 frames per second. Throughput of 2.19 GB/s surpasses performance of pure software solutions. To concentrate on architectural optimization, the module was created using high level language.

Maciej Wielgosz, Marcin Pietroń

This paper examines the performance of a Spatial Pooler (SP) of a Hierarchical Temporal Memory (HTM) in the task of noisy object recognition. To address this challenge, a dedicated custom-designed system based on the SP, histogram calculation module and SVM classifier was implemented. In addition to implementing their own version of HTM, the authors also designed a profiler which is capable of tracing all of the key parameters of the system. This was necessary, since an analysis and monitoring of the system performance turned out to be extremely difficult using conventional testing and debugging tools. The system was initially trained on artificially prepared videos without noise and then tested with a set of noisy video streams. This approach was intended to mimic a real life scenario where an agent or a system trained to deal with ideal objects faces a task of classifying distorted and noisy ones in its regular working conditions. The authors conducted a series of experiments for various macro parameters of HTM SP, as well as for different levels of video reduction ratios. The experiments allowed them to evaluate the performance of two different system setups (i.e. 'Multiple HTMs' and 'Single HTM') under various noise conditions with 32--frame video files. Results of all the tests were compared to SVM baseline setup. It was determined that the system featuring SP is capable of achieving approximately 12 times the noise reduction for a video signal with with distorted bits accounting for 13\% of the total. Furthermore, the system featuring SP performed better also in the experiments without a noise component and achieved a max F1 score of 0.96. The experiments also revealed that a rise of column and synapse number of SP has a substantial impact on the performance of the system. Consequently, the highest F1 score values were obtained for 256 and 4096 synapses and columns respectively.

Maciej Wielgosz, Marcin Pietroń

We present a method to classify objects in video streams using a brain-inspired Hierarchical Temporal Memory (HTM) algorithm. Object classification is a challenging task where humans still significantly outperform machine learning algorithms due to their unique capabilities. We have implemented a system which achieves very promising performance in terms of recognition accuracy. Unfortunately, conducting more advanced experiments is very computationally demanding; some of the trials run on a standard CPU may take as long as several days for 960x540 video streams frames. Therefore we have decided to accelerate selected parts of the system using OpenCL. In particular, we seek to determine to what extent porting selected and computationally demanding parts of a core may speed up calculations. The classification accuracy of the system was examined through a series of experiments and the performance was given in terms of F1 score as a function of the number of columns, synapses, $min\_overlap$ and $winners\_set\_size$. The system achieves the highest F1 score of 0.95 and 0.91 for $min\_overlap=4$ and 256 synapses, respectively. We have also conduced a series of experiments with different hardware setups and measured CPU/GPU acceleration. The best kernel speed-up of 632x and 207x was reached for 256 synapses and 1024 columns. However, overall acceleration including transfer time was significantly lower and amounted to 6.5x and 3.2x for the same setup.