Reviewerly/exHarmony huggingface dataset:
Magnitude determination using cumulative absolute absement for earthquake early warning

Abstract The cumulative absolute absement (CAA) of the 3 s window after P-wave arrival can be used to estimate the magnitude ( $${M}_{CAA}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>M</mml:mi><mml:mrow><mml:mi>CAA</mml:mi></mml:mrow></mml:msub></mml:math> ) of an earthquake. This method can achieve good results even when only the six stations nearest to the epicenter are used. The standard deviation between the estimated CAA magnitude ( $${M}_{CAA}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>M</mml:mi><mml:mrow><mml:mi>CAA</mml:mi></mml:mrow></mml:msub></mml:math> ) and the moment magnitude ( $${M}_{w}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>M</mml:mi><mml:mi>w</mml:mi></mml:msub></mml:math> ) is found to be 0.3 when using either 6 or 20 stations. This means that $${M}_{CAA}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>M</mml:mi><mml:mrow><mml:mi>CAA</mml:mi></mml:mrow></mml:msub></mml:math> can be reliably predicted using the closest 6 stations. On the other hand, the magnitude ( $${M}_{Pd}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>M</mml:mi><mml:mrow><mml:mi>Pd</mml:mi></mml:mrow></mml:msub></mml:math> ) derived from $${P}_{d}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>P</mml:mi><mml:mi>d</mml:mi></mml:msub></mml:math> using the closest 20 stations has a standard deviation of 0.4 between the estimated $${M}_{Pd}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>M</mml:mi><mml:mrow><mml:mi>Pd</mml:mi></mml:mrow></mml:msub></mml:math> and $${M}_{w}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>M</mml:mi><mml:mi>w</mml:mi></mml:msub></mml:math> . This suggests that CAA is a better magnitude determination parameter for the EEW system than $${P}_{d}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>P</mml:mi><mml:mi>d</mml:mi></mml:msub></mml:math> .
Constraction: a tool for the automatic extraction and interactive exploration of linguistic constructions

Abstract A central task in empirical and quantitative language studies is the extraction of linguistic constructions important to linguistic theory and application. The great number and variety of such constructions increasingly necessitates computer-assisted extraction, which often proves challenging as it entails a simultaneous analysis of multiple layers of linguistic information latent in large-scale corpora. To address this, we present Constraction, an open-source tool for the automatic extraction and interactive exploration of linguistic constructions from arbitrary textual corpora. Constraction features a generic algorithm that integrates customizable layers of linguistic annotation (e.g., lexical, syntactic, and semantic) to identify constructional patterns of varying sizes and abstraction levels. Its browser-based interface allows users to configure various extraction parameters and enables visual, interactive exploration of the extracted patterns. We demonstrate the utility of Constraction through case studies and discuss its potential applications in language research and pedagogy.
Explainability as the key ingredient for AI adoption in Industry 5.0 settings

Explainable Artificial Intelligence (XAI) has gained significant attention as a means to address the transparency and interpretability challenges posed by black box AI models. In the context of the manufacturing industry, where complex problems and decision-making processes are widespread, the XMANAI platform emerges as a solution to enable transparent and trustworthy collaboration between humans and machines. By leveraging advancements in XAI and catering the prompt collaboration between data scientists and domain experts, the platform enables the construction of interpretable AI models that offer high transparency without compromising performance. This paper introduces the approach to building the XMANAI platform and highlights its potential to resolve the "transparency paradox" of AI. The platform not only addresses technical challenges related to transparency but also caters to the specific needs of the manufacturing industry, including lifecycle management, security, and trusted sharing of AI assets. The paper provides an overview of the XMANAI platform main functionalities, addressing the challenges faced during the development and presenting the evaluation framework to measure the performance of the delivered XAI solutions. It also demonstrates the benefits of the XMANAI approach in achieving transparency in manufacturing decision-making, fostering trust and collaboration between humans and machines, improving operational efficiency, and optimizing business value.
Comparative analysis of the deep-learning-based super-resolution methods for generating high-resolution texture maps

As display resolution increases, many apps also tend to include high-resolution texture maps. Recent advancements in deep-learning-based image super-resolution techniques make it possible to automate high-resolution texture generation. However, there is still a lack of comprehensive analysis of the application of these techniques to texture maps. In this paper, we selected three recent super-resolution techniques, namely BSRGAN, Real-ESRGAN, and SwinIR (classical and real-world image SR), and applied them to upscale texture maps. We then conducted a quantitative and qualitative analysis of the experimental results. The findings revealed various artifacts after upscaling, which indicates that there are still limitations in directly applying super-resolution techniques to texture-map upscaling.
Department of Applied Physics of the Faculty of Physics

The paper considers the history, stages of formation and development of the Department of Applied Physics of the Faculty of Physics of NSU. The structure of the department, solved and currently solved scientific tasks are described. The results of scientific, pedagogical and innovative activities of the main divisions of the department are considered.
HARNet: automatic recognition of human activity from mobile health data using CNN and transfer learning of LSTM with SVM

Human Activity Recognition (HAR) system is analysing human behaviour using mobile health technology. Mobile Health data (MHEALTH) uses electronic devices to collect data and identify the activity of the patient in real-time. Recordings of 10 patients’ vital signs from various circumstances are included in the dataset. With a sensor attached to their bodies, they were required to carry out a number of physical tasks. Due to the lack of accuracy in the other state-of-the-art algorithms, we proposed Human Activity Recognition Neural Network (HARNet) architecture for automatic recognition of human activity using CNN and LSTM with the transfer learning of SVM. Here, the human health behaviour was analysed and classified using different ML and DL algorithms. The hybrid techniques of CNN and LSTM are selected across the different DL algorithms and it is used to extract independent and discriminating features, which aids the SVM classifier to attain good classification. When compared to other DL methods, HARNet performed better, achieving 99.8% accuracy. Overall, HAR systems have many potential applications in various fields, including healthcare, wellness, sports and surveillance. They have relationships to many different academic disciplines, including sociology, human–computer interaction, medical and may offer individualized help for different domains.
An Improved Fault Detection and Isolation Method for Airborne Inertial Navigation System/Attitude and Heading Reference System Redundant System

The integrity of airborne inertial navigation systems (INSs) is the key to ensuring the safe flight of civil aircraft. The airborne attitude and heading reference system (AHRS) is introduced into the construction of a redundant inertial navigation system. As a backup system for an airborne INS, the AHRS exhibits a different device performance. A sequential weighted generalized likelihood ratio test (SWGLT) method, based on a principal component parity vector (PPV), is proposed. The PPV method improves the adaptability of the detection threshold to the inertial sensors’ noise and improves the probability of correct detection. At the same time, the multiscale problem of a heterogeneous redundant system error is solved by sequential weighting, and the false alarm rate is reduced. Simulation experiments show that the proposed method can improve fault detection sensitivity, reduce false alarm rates, and ensure the integrity of civil aircraft navigation systems.
Bayesian Optimization for Fine-Tuning EKF Parameters in UAV Attitude and Heading Reference System Estimation

In various applications, the extended Kalman filter (EKF) has been vital in estimating a vehicle’s translational and angular motion in 3-dimensional (3D) space. It is also essential for the fusion of data from multiple sensors. However, for the EKF to perform effectively, the optimal process noise covariance matrix (Q) and measurement noise covariance matrix (R) must be chosen correctly. The use of EKF has been challenging due to the need for an easy mechanism to select Q and R values. As a result, this research focused on developing an algorithm that can be easily applied to determine Q and R, allowing us to harness the full potential of EKF. Accordingly, an EKF innovation consistency statistics-driven Bayesian optimization algorithm was employed to achieve this goal. Q and R values were tuned until the expected result met the performance requirement for minimum error through improved measurement innovation consistency. The comprehensive results demonstrate that when the optimum Q and R, as tuned by the suggested technique, were used, the performance of the EKF significantly improved.
Investigating the usability of a new framework for creating, working and teaching artificial neural networks using augmented reality (AR) and virtual reality (VR) tools

Abstract Deep learning is a very popular topic in computer sciences courses despite the fact that it is often challenging for beginners to take their first step due to the complexity of understanding and applying Artificial Neural Networks (ANN). Thus, the need to both understand and use neural networks is appearing at an ever-increasing rate across all computer science courses. Our objectives in this project were to create a framework for creating and training neural networks for solving different problems real-life problems and for research and education, as well as to investigate the usability of our framework. To provide an easy to use framework, this research recruited five instructors who have taught ANNs at two universities. We asked thirty-one students who have previously studied neural networks to fill out an online survey about what were "the major difficulties in learning NNs" and the "key requirements in a Visual Learning Tool including the most desired features of a visualization tool for explaining NNs" they would have used during the course. We also conducted an observational study to investigate how our students would use this system to learn about ANNs. The visual presentation of ANNs created in our framework can be represented in an Augmented Reality (AR) and Virtual Reality (VR) environment thus allowing us to use a virtual space to display and manage networks. An evaluation of the effect of the AR/VR experience through a formative test and survey showed that the majority of students had a positive response to the engaging and interactive features of our framework (RKNet).
Anomaly Detection of Axial Piston Pump Based on the DTW-RCK-IF Composite Method Using Pressure Signals

Axial piston pumps are critical components of hydraulic systems due to their compact design and high volumetric efficiency, making them widely used. However, they are prone to failure in harsh environments characterized by high pressure and heavy loads over extended periods. Therefore, detecting abnormal behavior in axial piston pumps is of significant importance. Traditional detection methods often rely on vibration signals from the pump casings; however, these signals are susceptible to external environmental interference. In contrast, pressure signals exhibit greater stability. In this study, we propose a novel anomaly detection method for axial piston pumps, referred to as DTW-RCK-IF, which combines dynamic time warping (DTW) for data segmentation, a random convolutional kernel (RCK) for feature extraction, and isolation forest (IF) for anomaly detection using pressure signals. The model is trained using normal operating data to enable the effective detection of abnormal states. First, the DTW algorithm is employed to segment the raw data, ensuring a high degree of similarity between the segmented data. Next, the random convolutional kernel approach is used in a convolutional neural network for feature extraction, resulting in features that are representative of normal operating conditions. Finally, the isolation forest algorithm calculates the anomaly scores for anomaly detection. Experimental simulations on axial piston pumps demonstrate that, compared with vibration signals, the DTW-RCK-IF approach using pressure signals yields superior results in detecting abnormal data, with an average F1 score of 98.79% and a good fault warning effect. Validation using the publicly available CWRU-bearing and XJTU-SY-bearing full-life datasets further confirms the effectiveness of this method, with average F1 scores of 99.35% and 99.73%, respectively, highlighting its broad applicability and potential for widespread use.
CNAIS: Performance Analysis of the Clustering of Non-Associated Items Set Techniques

Mining technologies depend upon their outcomes, focusing only on certain data features within the database. They select only certain features related to the process from diverse integrated data resources and transform them into a form suitable for mining tasks. Different implementations of mining techniques run on data sources, which may be of considerable volume, to extract different knowledge outcomes suitable for various analyses and decision-making processes. The proposed study provides the design and development of the Clustering of Non-Associated Items set (CNAIS) within a transactional database. The development of the algorithm and its application to the data set are described and the results are noted. Comparisons with state-of-the-art methods show that CNAIS exhibits better performance.
Integrating PubMed Label Hierarchy Knowledge into a Complex Hierarchical Deep Neural Network

This paper proposes an innovative method that exploits a complex deep learning network architecture, called Hierarchical Deep Neural Network (HDNN), specifically developed for the eXtreme Multilabel Text Classification (XMTC) task, when the label set is hierarchically organized, such as the case of the PubMed article labeling task. In detail, the topology of the proposed HDNN architecture follows the exact hierarchical structure of the label set to integrate this knowledge directly into the DNN. We assumed that if a label set hierarchy is available, as in the case of the PubMed Dataset, forcing this information into the network topology could enhance the classification performances and the interpretability of the results, especially related to the hierarchy. We performed an experimental assessment of the PubMed article classification task, demonstrating that the proposed HDNN provides performance improvement for a baseline based on a classic flat Convolution Neural Network (CNN) deep learning architecture, in particular in terms of hierarchical measures. These results provide useful hints for integrating previous and innate knowledge in a deep neural network. The drawback of the HDNN is the high computational time required to train the neural network, which can be addressed with a parallel implementation planned as a future work.
A Multi-Modal Profiling Fraud-Detection System for Capturing Suspicious Airline Ticket Activities

Although the most widely studied datasets in fraud-detection systems belong to the banking sector, the aviation industry is susceptible to fraud activities that seriously harm airline companies. Therefore, big airline companies have started to purchase or develop their own fraud-detection systems in order to prevent their financial loss and prestige decline. Chronological order and temporal flow are intrinsically of high importance in fraud detection in the banking sector as well as in airline sale channels. Therefore, the transactions in the datasets used in fraud-detection systems should be evaluated not only according to the information they contain but also according to the past transactions they are linked to. One of the best ways to raise awareness about the connected past transactions to the fraud-detection system is to profile the data fields whose historical data is important and dynamically place these profiles on each transaction. In this study, we first draw the baseline, i.e., the first touch in this field, for fraud detection in aviation and then introduce a novel multi-modal profiling mechanism based on deep learning for the detection of fraudulent airline ticket activities. We achieved great success by feeding the new features obtained from those profiles into a deep neural network that is fine-tuned by adjusting the well-known hyperparameters regarding the aviation data. Thanks to the combination of profiling and deep learning, the F1 score of the proposed system reaches up to 89.3% and 93.2% in terms of quantity-based success and cost-based success, respectively.
Synthetic Data for Object Detection with Neural Networks: State of the Art Survey of Domain Randomisation Techniques

Machine learning relies heavily on access to large and well-maintained datasets. In this article, we focus on Computer Vision and object detection applications to survey past research on automatic generation of annotated datasets that does not require costly and time-consuming human labelling. In specific, we analyse research done in the area of Domain Randomisation applied to Neural Networks predominant in object detection since the last decade. We propose a set of criteria for comparison of previously published works, and utilise these criteria to make conclusions about various trends in the area, similarities/differences and key discoveries made since conception. The purpose of this work is to advise practitioner on leading solutions and help researchers gain better understanding of the landscape. The key takeaways from our analysis show the current state of the art solutions within the mid-quartile range allow object detection with typically about 1-25% performance decrease in comparison to manually annotated datasets; while the top performant approaches above the upper quartile gain about 2-32% lead over real data training in their specific application areas. Our survey shows the future outlook is more research into 3D generation techniques, with most innovative yet complex techniques related to end-to-end modifications of entire network architectures to suit synthetic data training.
Personalized Fashion Recommendations for Diverse Body Shapes and Local Preferences with Contrastive Multimodal Cross-Attention Network

Fashion recommendation has become a prominent focus in the realm of online shopping, with various tasks being explored to enhance the customer experience. Recent research has particularly emphasized fashion recommendation based on body shapes, yet a critical aspect of incorporating multimodal data relevance has been overlooked. In this paper, we present the Contrastive Multimodal Cross-Attention Network, a novel approach specifically designed for fashion recommendation catering to diverse body shapes. By incorporating multimodal representation learning and leveraging contrastive learning techniques, our method effectively captures both inter- and intra-sample relationships, resulting in improved accuracy in fashion recommendations tailored to individual body types. Additionally, we propose a locality-aware cross-attention module to align and understand the local preferences between body shapes and clothing items, thus enhancing the matching process. Experimental results conducted on a diverse dataset demonstrate the state-of-the-art performance achieved by our approach, reinforcing its potential to significantly enhance the personalized online shopping experience for consumers with varying body shapes and preferences.
AEKA: FPGA Implementation of Area-Efficient Karatsuba Accelerator for Ring-Binary-LWE-based Lightweight PQC

Lightweight PQC-related research and development have gradually gained attention from the research community recently. Ring-Binary-Learning-with-Errors (RBLWE)-based encryption scheme (RBLWE-ENC), a promising lightweight PQC based on small parameter sets to fit related applications (but not in favor of deploying popular fast algorithms like number theoretic transform). To solve this problem, in this paper, we present a novel implementation of hardware acceleration for RBLWE-ENC based on Karatsuba algorithm, particularly on the field-programmable gate array (FPGA) platform. In detail, we have proposed an area-efficient Karatsuba Accelerator (AEKA) for RBLWE-ENC, based on three layers of innovative efforts. First of all, we reformulate the signal processing sequence within the major arithmetic component of the KA-based polynomial multiplication for RBLWE-ENC to obtain a new algorithm. Then, we have designed the proposed algorithm into a new hardware accelerator with several novel algorithm-to-architecture mapping techniques. Finally, we have conducted thorough complexity analysis and comparison to demonstrate the efficiency of the proposed accelerator, e.g., it involves 62.5% higher throughput and 60.2% less area-delay product (ADP) than the state-of-the-art design for n = 512 (Virtex-7 device, similar setup). The proposed AEKA design strategy is highly efficient on the FPGA devices, i.e., small resource usage with superior timing, which can be integrated with other necessary systems for lightweight-oriented high-performance applications (e.g., servers). The outcome of this work is also expected to generate impacts for lightweight PQC advancement.
Effect of Reaching Movement Modulation on Experience of Control in Virtual Reality

Users’ motion representation in virtual reality (VR) can be modulated visually by introducing a mismatch with their real motion, which can bring benefits to exercise and rehabilitation and has great potential for exergame applications in VR. Users’ experience of control is a critical consideration for user experience in human–computer interaction and should be paid special attention when movement modulation is implemented in VR. However, how movement modulation affects users’ experience of control and motor performance has not been fully investigated in detail. This research included 49 participants and investigated how the experience of control is influenced by reaching movement modulation in two types: the enhancement and reduction modes. Different modulation modes were designed to study their influence on the explicit experience of control in self-ratings and the implicit measured experience of control in intentional binding and electroencephalography. Participants’ movement trajectory, velocity, and completion time were analyzed for motor performance. The results illustrate a significant effect of movement modulation on the users’ motor performance and experience of control in self-ratings and EEG. This study makes a major contribution through a comprehensive analysis of the experience of control with movement modulation and provides important and practical design considerations on movement modulation design in future exercise-based applications with positive controlling experiences in VR.