Reviewerly/exHarmony huggingface dataset:
Preliminary tests of an anticipatory classifier system with experience replay

The paper describes the first attempts toward designing and evaluating Anticipatory Classifier System ACS2 in conjunction with Experience Replay (ER). Promising results verified by statistical tests are obtained both on single- and multi-step problems, albeit limited to deterministic and discrete tasks. The analysis indicates that ACS2 using memorized experiences has the potential for significant improvements in learning efficiency and knowledge generality.
Fitness diversification in the service of fitness optimization

Blindly chasing after fitness is not the best strategy for optimization of hard problems, as it usually leads to premature convergence and getting stuck in low-quality local optima. Several techniques such as niching or quality-diversity algorithms have been established that aim to alleviate the selective pressure present in evolutionary algorithms and to allow for greater exploration. Yet another group of methods which can be used for that purpose are fitness diversity methods. In this work we compare the standard single-population evolution against three fitness diversity methods: fitness uniform selection scheme (FUSS), fitness uniform deletion scheme (FUDS), and convection selection (ConvSel). We compare these methods on both mathematical and evolutionary design benchmarks over multiple parametrizations. We find that given the same computation time, fitness diversity methods regularly surpass the performance of the standard single-population evolutionary algorithm.
Compositional measurement of gas emissions in the Eastern Carpathians (Romania) using the Multi-GAS instrument: Approach for in situ data gathering at non-volcanic areas

The Multi-GAS, a robust and low-cost instrument for real-time in-situ gas measurements, has previously been used mainly for compositional measurements of active volcanic plumes. Here we demonstrate novel use of a specially designed Multi-GAS instrument adapted to low temperature degassing areas. We performed compositional measurements in the Eastern Carpathians on dry and bubbling gas emissions using a sensor kit that allows measurement of CO2, CH4 and H2S (three major components of low-temperature hydrothermal/volcanic manifestations). Our results demonstrate good agreement between Multi-GAS measurements and independently obtained CO2 concentrations from gas chromatography. We also provide some novel H2S information for some anomalous sites, which we relate to possible alteration processes of sulphide minerals. The use of Multi-GAS in such environments could open new possibilities for data collection at non-volcanic areas and exploration of mineral resources. Moreover, it can also be a useful tool in exploration surveys to select the best sampling sites for more detailed laboratory measurements.
Geoecological assessment and environmental monitoring within the boundaries of the impact of the development of gold deposits using GIS technologies

Abstract An interdisciplinary approach is used in the study of this problem. Well-known methods are used in the work. The ground-based phase of the research consisted of sampling. The results indicate intense pollution. High concentrations of compounds of mercury, arsenic, copper, lead, zinc were recorded. Deciphering the Landsat 8 (GloVis) images made it possible to supplement the mining and environmental monitoring by studying the dynamics of changes in the areas of placer gold mining. Data processing was carried out on the basis of the open geoinformation system Qgis. Image processing methods: False Color Composite (FCC) images in artificial colors; Band Ratio (BR) creates complex band ratios. Digitization of the images showed increasing areas of disturbed land. For the first time, a geo-ecological assessment using GIS technologies was carried out. Over a long period of time, ground monitoring data are entered into the Qgis database. The practical significance of the work is confirmed by the Patent of the Russian Federation and substantiated by the creation of a method to ensure the environmental safety of technogenically polluted areas using bioremediation.
IMPLEMENTATION OF STEM APPROACH AND ITS VARIETIES IN BULGARIA - HISTORICAL OVERVIEW AND CURRENT SITUATION

IMPLEMENTATION OF STEM APPROACH AND ITS VARIETIES IN BULGARIA - HISTORICAL OVERVIEW AND CURRENT SITUATION
Subspace clustering by directly solving Discriminative K-means

Applications in many domains such as text mining and natural language processing need to deal with high-dimensional data. High-dimensional data may present better clustering characteristics on a selected low-dimensional subspace. Subspace clustering is to project the data onto a low-dimensional subspace before clustering. Traditional subspace clustering methods employ eigenvalue decomposition to find the projection of the input data and perform K-means or kernel K-means to obtain the clustering matrix. This kind of methods is not only inefficient, but also adopts a two-step method to generate an approximate solution. Although Discriminative K-means (DisKmeans) integrates dimensionality reduction and clustering into a joint framework and solves the optimization problem by kernel K-means, such method needs to find the centroids in the kernel space and class labels iteratively and has a square time complexity. Accordingly, in this paper, we propose an algorithm, namely Fast DisKmeans (FDKM), to obtain the cluster indicator matrix in a direct way. Moreover, our proposed method has a linear time complexity, which is a significant reduction compared with the squared time complexity of DisKmeans. We also demonstrate that solving the object function of DisKmeans is equivalent to representing the cluster assignment matrix by a low-dimensional linear mapping of the data. Based on this observation, we propose the second algorithm, namely Iterative Fast DisKmeans (IFDKM), which also has a linear time complexity. A series of experiments were conducted on several datasets, and the experimental results showed the superior performance of FDKM and IFDKM.
DESIGN OF A ROBOTIC BOARD FOR TEACHING THE WATER CYCLE

Appears in: EDULEARN22 Proceedings Publication year: 2022Pages: 2990-2993ISBN: 978-84-09-42484-9ISSN: 2340-1117doi: 10.21125/edulearn.2022.0751Conference name: 14th International Conference on Education and New Learning TechnologiesDates: 4-6 July, 2022Location: Palma, Spain
Assessment of the effect of constraints in a new multivariate mixed method for statistical matching

A Multivariate Mixed method for Statistical Matching (MMSM) is proposed. The MMSM is a predictive mean matching method to impute values when integrating two datasets from the same population without overlapping units measuring several common and non-common variables. It considers the multivariate structure of the data by using multivariate Bayesian regression. The MMSM can also include auxiliary information from an additional dataset to improve the computation of intermediate values, and constraints to improve the selection of the donors. The results from a simulation study show that including information from an auxiliary dataset leads to far better results, especially in terms of bias and percentage of correct imputations. The inclusion of constraints also increases the quality of the imputations, and hence of the statistical matching.
Max-Cut Problem Implementation and Analysis on a Quantum Computer

Advances in integrated circuit complexity are enabled by electronic design automation (EDA) tool developments. The field of EDA has many combinatorial optimisation problems that are NP-hard in nature. The complexity of these problems means that they are almost always computationally intractable and expensive to solve on classical computers for large problem instances. Heuristics are often used, and due to computation timing constraints, the results do not guarantee the optimal solution. This paper implements and analyses an instance of the max-cut problem, which is evaluated on IBM quantum computers as well as quantum simulators. As quantum computers are highly prone to noise, this paper also analyses quantum circuit noise and connectivity, proposing some ways to obtain reliable results on real quantum computers through experiments carried out in Qiskit.
AFOM: Advanced Flow of Motion Detection Algorithm for Dynamic Camera Videos

The surveillance videos taken from dynamic cam-eras are susceptible to multiple security threats like replay attacks, man-in-the-middle attacks, pixel correlation attacks etc. Using unsupervised learning, it is a challenge to detect objects in such surveillance videos, as fixed objects may appear to be in motion alongside the actual moving objects. But despite this challenge, the unsupervised learning techniques are efficient as they save object labelling and model training time, which is usually a case with supervised learning models. This paper proposes an effective computer vision-based object identification algorithm that can detect and separate stationary objects from moving objects in such videos. The proposed Advanced Flow Of Motion (AFOM) algorithm takes advantage of motion estimation between two consecutive frames and induces the estimated motion back to the frame to provide an improved detection on the dynamic camera videos. The comparative analysis demonstrates that the proposed AFOM outperforms a traditional dense optical flow (DOF) algorithm with an average increased difference of 56 % in accuracy, 61 % in precision, and 73 % in pixel space ratio (PSR), and with minimal higher object detection timing.
Efficient Active Learning Strategies for Monocular 3D Object Detection

Processing camera information to perceive their 3D surrounding is essential for building scalable autonomous driving vehicles. For this task, deep learning networks provide effective real-time solutions. However, to compensate for missing depth information in cameras compared to LiDARs, a large amount of labeled data is required for training. Active learning is a training framework where the network actively participates in the data selection process to improve data efficiency and performance. In this work, we propose an active learning pipeline for 3D object detection from monocular images. The main components of our approach are (1) two training-efficient uncertainty estimation strategies, (2) a diversity-based selection strategy to select images that contain the most diverse set of objects, (3) a novel active learning strategy more suitable for training autonomous driving perception networks. Experiments show that combining our proposed uncertainty estimation methods provides a better data saving rate and reaches a higher final performance than baselines. Furthermore, we empirically show performance gains of the presented diversity-based selection strategy and the efficiency of the proposed active learning strategy.
Residual MBConv Submanifold Module for 3D LiDAR-based Object Detection

In LiDAR-based point cloud, objects are always represented as 3D bounding boxes with direction. LiDAR-based object detection task is similar to image-based task but comes with additional challenges. In LiDAR-based detection for autonomous vehicles, the size of 3D object is significant smaller compared with size of input scene represented by point cloud, thus conventional 3D backbones cannot effectively preserve detail geometric information of object with only a few points. To resolve this problem, this paper presents a MBConv Submanifold module, which is simple and effective for voxel-based detector from point cloud. The novel convolution architecture introduces inverted bottleneck and residual connection into 3D sparse backbone, which enable detector to learn high dimension feature from point cloud. Experiments shows that MBConv Submanifold module bring consistent improvement over the baseline method: MBConv Submanifold achieves the AP of 68.03% and 54.74% in the moderate cyclist and pedestrian category on the KITTI validation benchmark, surpass the baseline method significantly. Our code and pretrained models are available at: https://github.com/s1mpleee/ResMBSubmanifold.
A Comparative Analysis of Decision-Level Fusion for Multimodal Driver Behaviour Understanding

Visual recognition inside the vehicle cabin leads to safer driving and more intuitive human-vehicle interaction but such systems face substantial obstacles as they need to capture different granularities of driver behaviour while dealing with highly limited body visibility and changing illumination. Multimodal recognition mitigates a number of such issues: prediction outcomes of different sensors complement each other due to different modality-specific strengths and weaknesses. While several late fusion methods have been considered in previously published frameworks, they constantly feature different architecture backbones and building blocks making it very hard to isolate the role of the chosen late fusion strategy itself.This paper presents an empirical evaluation of different paradigms for decision-level late fusion in video-based driver observation. We compare seven different mechanisms for joining the results of single-modal classifiers which have been both popular, (e.g. score averaging) and not yet considered (e.g. rank-level fusion) in the context of driver observation evaluating them based on different criteria and benchmark settings. This is the first systematic study of strategies for fusing outcomes of multimodal predictors inside the vehicles, conducted with the goal to provide guidance for fusion scheme selection.
Mono-DCNet: Monocular 3D Object Detection via Depth-based Centroid Refinement and Pose Estimation

3D object detection is a well-known problem for autonomous systems. Most of the existing methods use sensor fusion techniques with Radar, LiDAR, and Cameras. However, one of the challenges is to estimate the 3D shape and location of the adjoining vehicles from a single monocular image without other 3D sensors; such as Radar or LiDAR. To solve the lack of the depth information, a novel method for 3D vehicle detection is presented. In this work, instead of using the whole depth map and the viewing angle (allocentric angle), only the depth mask of each object is used to refine the projected centroid and estimate its egocentric angle directly. The performance of the proposed method is tested and validated using the KITTI dataset, obtaining similar results to other state-of-the-art methods for Monocular 3D Object Detection.
Research on Performance Limitations of Visual-based Perception System for Autonomous Vehicle under Severe Weather Conditions

Visual-based perception systems are widely used in autonomous vehicles (AVs). In severe weather conditions, hazardous events of AVs may be induced by the performance limitations of perception system. We propose a staged analyzing method to quantitatively evaluate the performance limitations of visual-based perception system under severe weather conditions and explore the influence mechanism. In our method, the working process of visual-based perception systems is divided into two stages of image obtaining by camera and target recognition by recognition algorithm. Firstly, in image obtaining stage, the quality of images obtained in scenarios with different weather types and intensity is evaluated using monofactor analysis method. The relationship between different weather and metrics of image quality is analyzed. Secondly, in target recognition stage, metrics values of image quality and recognition results are fitted with (weighted) multiple linear regression model, and a regression model representing the influence relationship is acquired. Finally, the importance of indicators in image quality metrics is verified with BP neural network, and the performance of the regression model is analyzed with the results acquired in two example scenarios. With the obtained monofactor analysis results and the regression model, the influence mechanisms of high luminance and fog conditions are analyzed and compared, which shows the effectiveness of the method in performance limitation and its influence mechanism analysis.
Traffic Sign Classifiers Under Physical World Realistic Sticker Occlusions: A Cross Analysis Study

Recent adversarial attacks with real world applications are capable of deceiving deep neural networks (DNN), which often appear as printed stickers applied to objects in physical world. Though achieving high success rate in lab tests and limited field tests, such attacks have not been tested on multiple DNN architectures with a standard setup to unveil the common robustness and weakness points of both the DNNs and the attacks. Furthermore, realistic looking stickers applied by normal people as acts of vandalism are not studied to discover their potential risks as well the risk of optimizing the location of such realistic stickers to achieve the maximum performance drop. In this paper, (a) we study the case of realistic looking sticker application effects on traffic sign detectors performance; (b) we use traffic sign image classification as our use case and train and attack 11 of the modern architectures for our analysis; (c) by considering different factors like brightness, blurriness and contrast of the train images in our sticker application procedure, we show that simple image processing techniques can help realistic looking stickers fit into their background to mimic real world tests; (d) by performing structured synthetic and real-world evaluations, we study the difference of various traffic sign classes in terms of their crucial distinctive features among the tested DNNs.
Efficient Prediction of Blood Alcohol Level Using ML and Accelerometer Data

IoT sensors are extensively used in a variety of medical applications such as patient monitoring. In recent years, multiple papers have been published on the measurement of blood alcohol level (BAL) using more specialized biosensors. Transdermal alcohol content (TAC), a practical and non-invasive tool for measuring BAL, was employed in several recent research works. As BAL affects the person's way of walking, accelerometers (ACC) combined with TAC data fed to machine learning algorithms (MLA) were applied to predict the state, drunk or sober, of a person. However, the accuracy of prediction was not high enough to be considered reliable. In this paper, using an archived “BAR CRAWL” dataset, we implemented five MLA: Linear Discriminant Analysis (LDA), Decision Tree (DT), Random Forest (RF), Extra Trees (ET), and Ada Boost (AB), with a variety of features to accurately predict the state of a person based on his alcohol levels. Furthermore, we defined various alcohol thresholds to predict the level of intoxication. Also, we were able to identify the intoxicated person. Our experimental results showed that we can achieve up to 28.9 % higher prediction accuracy with less processing time (PT) when compared to previous published works using the same dataset.
Robust Decentralized Federated Learning Using Collaborative Decisions

Federated Learning (FL) has attracted a lot of attention in numerous applications due to recent data privacy regulations and increased awareness about data handling issues, combined with the ever-increasing big-data sizes. This paper proposes a server-less, robust FL training mechanism that allows any set of participating data-owners to train a neural network (NN) model collaboratively without the assistance of any central node and while being resilient to Byzantine attacks. The proposed approach makes use of a dual-way update mechanism to allow each node to take a model forwarding decision towards a global collaborative decision of isolating any malicious updates. The efficiency of the proposed approach in detecting cardiac irregularities is verified using simulation results conducted based on the Physikalisch-Technische Bundesanstalt Database electro-cardiogram (PTBDB ECG) dataset.
Analysis and Evaluation on Conversions Between SDR and HDR

As one of the most fascinating features of ultra-high definition television (UHDTV), high dynamic range (HDR) can significantly improve visual experience compared with standard dynamic range (SDR). During the transition from SDR to HDR, it is possible to mix SDR and HDR cameras or contents in the process of production. Consequently, various conversions, including SDR-HDR conversion, HDR-SDR conversion, SDR-HDR-SDR round-trip conversion and etc., are necessary to combine the contents of different formats in the same workflow, in which it is challenging to keep consistent video quality after complex conversions. This paper analyzes scene-referred and display-referred methods of conversions between SDR and HDR in the practice of UHDTV production and conducts experiments to evaluate the conversion performance. Based on the experimental results, suggestions on conversions between SDR and HDR are provided for practical application scenarios.
Blockchain for 5G Advanced Wireless Networks

Fifth generation (5G) wireless network can provide wide network coverage, high transmission rate, low communication latency, and support massive connections. However, it is not able to support user privacy information security and virtual intellectual property protection. Furthermore, the 5G system is centralized which is easy to be attacked and cause data monopoly. Thus, 5G advanced is expected to solve the above issues. Applying blockchain in 5G advanced will provide decentralized service and ensure the trustworthiness. In this paper, applications for blockchain enabled 5G advanced are presented. In addition, this paper also investigates the solutions to support bolckchain based on 5G RAN (radio access network) architecture.
Decentralized Data Allocation via Local Benchmarking for Parallelized Mobile Edge Learning

Multi-Access Edge Computing (MEC) has emerged as a computing paradigm that can facilitate the use of Mobile Edge Learning (MEL), where Machine Learning (ML) models are processed at the edge. In MEL, it is important to address system heterogeneity in a way that minimizes staleness to improve learning accuracy. To do so, a centralized data allocation approach is typically used. However, this approach tends to overlook the privacy of learners, since learners' capabilities are assumed to be known beforehand by the orchestrator. In this context, we propose the Data Allocation via Benchmarking (DAB) scheme. DAB is a decentralized data allocation scheme that eliminates staleness and achieves a certain QoS while preserving the privacy of learners. DAB does not allow any information about the learners to be known to the orchestrator. Instead, each learner estimates the upper bound on the amount of data that it can train such that a certain training deadline is not exceeded. In addition, DAB proposes a novel method to enable each learner to accurately estimate its own hardware characteristics via benchmarking. Extensive performance evaluations on a real testing environment have shown that DAB can outperform the centralized data allocation scheme by up to 12% and 26% in terms of loss and prediction accuracy, respectively. Performance evaluations also show that the proposed benchmarking scheme yields an 83% reduction in benchmarking error compared to a prominent baseline scheme.
Convolutional Neural Network for Vehicle Detection in A Captured Image

Currently, vehicle detection is an important task in intelligent transportation systems. Indeed, it is very responsive in various intelligent applications such as traffic management and advanced driver assistance systems services. Computer vision can be influenced by several factors such as weather conditions, time of day, etc. Therefore, it is necessary to develop effective detection systems. In this work, we used YOLOv3 object detect algorithm with transfer learning to develop a vehicle detection model in a captured image. Firstly, we reset the size of the original anchors using k-means clustering. Then, we added preprocessing and data augmentation techniques to improve the accuracy and robustness of the algorithm. Finally, we added a script, for the total vehicle count. This model has been tested on two databases using the Google Colab Pro online tool. The proposed model has achieved good results performances.