Reviewerly/exHarmony huggingface dataset:
Building a measure of competitive driving behavior of handball coaches from the point of view of players of first-class clubs in Iraq

The current research aims to build a measure of competitive driving behavior of a handball coach from the point of view of the players of first-class clubs in Iraq to be able to know the behavior of each coach during the events of the matches and to know the strengths and weaknesses of that behavior, After the two researchers reviewed the metrics and literature in the subject of sports management and organization, which presented the topic of leadership behavior, it was noticed that there is an urgent need to study this behavior during sports competition, and everything related to the events of the match and more accurately, away from variables outside the sports competition because of its significant immediate impact on the performance and results of the team, And here the problem of research lies in knowing the behavior that coaches follow with the players during the competition in order to reach the goals, and the purposes that he seeks to succeed, and the descriptive approach was used in a survey way to suit the nature of the problem, as the research sample consisted of players of first-class clubs for a period from 1-1-2023 until 1-4-2023. The researchers concluded that handball coaches in the first division possess a good level of leadership during sports competition due to the accumulated experience, as the researchers recommended conducting a similar study of the subject with different samples.
Lexical and Grammatical Features of Russian-Language Tweets in Comparison with Everyday Spoken Russian

This study examines the features of computer-mediated discourse, often perceived as neither purely written nor spoken. Twitter discourse serves as a case in point, reflecting attributes of both spoken and written language. The aim of the study is to discern how closely Russian-language Twitter discourse mirrors everyday spoken Russian. A dataset of 152,223 Russian-language tweets (over 2 million tokens) was examined and juxtaposed against transcripts from the ORD speech corpus, which captures 508 macro episodes of daily conversation, totaling just under 900,000 tokens. Both lexical and grammatical aspects of the tweets and the spoken episodes are analyzed. A detailed comparison of unigrams, discourse words, and pragmatic markers is undertaken, supplemented by a multidimensional analysis spanning 22 grammatical features. The findings indicate that while the lexical attributes of Russian Twitter discourse closely align with spoken Russian, its grammatical features differ. Notably, both the tweets and the speech episodes share a significant overlap in lemmas, discourse words and pragmatic markers. However, when viewed grammatically, the Twitter discourse diverges from spontaneous spoken language. These insights hold potential for refining computer-mediated discourse generation systems for the Russian language.
Analytical Tool for the University Data Management

Every university needs some kind of information system that will allow the university to record students, teachers, student results and other elements related to the student's studies. Such software is called an academic information system. This paper describes the analysis, design, implementation and testing of similar system. However, unlike a typical academic information system, this system, which was created as part of a bachelor's thesis on the topic Analytical Tool for the University Data Management, also contains advanced analytical tools that will allow both students and teachers to analyse the course of study of individual students and find the weak points of teaching and educational process. In order to create the analytical tool, it was first necessary to analyse the transition of students through their studies. The Faculty of Management and Informatics of the University of Žilina was used as a source of this analysis. Based on this analysis, we designed a database that uses Oracle SQL language running on the Oracle Cloud technology, and then created a tool in the Oracle Application Express environment. The tool allows registering of students, their exams, teachers, and subjects. At the same time, it also contains functions for searching, sorting, and filtering according to various criteria. Another functionality is the ability of user to display summary and individual statistics for teachers, students, and study groups. Each student and teacher have their own account to access global statistics. The tool offers possibilities for future expansion, such as comparing students and advanced analysis using the functionalities of the Oracle Machine Learning service.
On Audit and Certification of Machine Learning Systems

Obviously, machine learning applications are being used more and more in a wide variety of fields. The general rule today is that in the absence of analytical models, one always turns to machine learning. In itself, machine learning has become synonymous with artificial intelligence. The reverse is also true - artificial intelligence today is machine learning. Sometimes this definition is somewhat limited, and they only talk about artificial neural networks and deep learning in the context of artificial intelligence, but this does not change the essence of the matter. At the same time, it is also obvious that the spread of machine learning technologies leads to the need for their application in the so-called critical areas, where there are special requirements for confirming the operability and quality of software. These areas include, for example, avionics, nuclear power, autonomous vehicles, etc. Audit and, of course, certification are the procedures for evaluating machine learning models.
Handwritten Paragraph Recognition Using Spatial Information on Russian Notebooks Dataset

Handwritten paragraph recognition is a vital aspect of handwritten document analysis, enhancing accuracy and usability across various applications. However, recognizing paragraphs in handwritten documents is challenging due to layout variations and irregularities. Spatial information, encompassing spatial relationships between text elements, is essential for accurate paragraph segmentation and document comprehension. Recent works in handwritten Russian recognition have primarily focused on character and line-level recognition. This study is the first attempt on paragraph-level recognition for Russian handwriting, utilizing the Vertical Attention Network (VAN) with a hybrid attention method. Key contributions include the preparation of a unique Russian dataset at the paragraph level, containing around 2600 images with PAGE XML-encoded ground truth. The VAN model was fine-tuned for whole paragraph recognition, and comprehensive experiments were conducted, comparing its performance against alternative non-layout-aware approaches. This work advances layout-aware recognition in handwritten Russian documents, addressing an unexplored area in the field.
Parameter Tuning for Maritime Track-Before-Detect

Track-before-detect (TBD) is a joint detection and tracking approach that takes advantage of a targets motion over time. To work effectively, it requires latent parameters to describe the underlying dynamics of the targets relative to the sensor system. Determining the latent parameters is critical for success, with errors potentially resulting in failure of the tracking algorithm. In this paper, we propose a parameter tuning method for the Bayesian TBD algorithm known as the Bernoulli particle filter. The parameter estimation formulation uses a Bayesian framework with the particle Markov-chain Monte Carlo algorithm. Experimental results for a maritime radar scenario are used to validate the new algorithm.
Sequential Multi-model Unscented Kalman Filter for Shipborne High Frequency Surface Wave Radar

Shipborne high frequency surface wave radar (HFSWR) is a development of HFSWR, which install the transmitting and receiving equipment on the ship. In tracking problem, existing shipborne HFSWR are typically centered on the shipborne platform for detection and transferred to a coordinate system with a fixed origin. Therefore, the accuracy of the measurement on the shipborne platform is critical. However, in practice, it is a challenge to determine the accuracy of shipborne platform information measurements, which can lead to a reduction in the accuracy oftarget condition estimates. In this paper, we propose a sequential multi-model unscented Kalman filter (SMUKF) to address the uncertainty in the measurement of the shipborne platform. The SMUKF can get thejoint state estimation of shipborne platforms and targets. By applying multi-model method and using a more accurate filter value in the traceless Kalman filter, the SMUKF fuses the multi-model output to mitigate the impact of measurement errors on the target state estimation accuracy. The simulation results demonstrate that the proposed SMUKF outperforms the unscented Kalman filter (UKF) in terms of state estimation accuracy, and is more suitable for shipborne HFSWR.
EM-Based Radar Signal Processing and Tracking of Maneuvering Targets

The accuracy of radar tracks depends strongly on the variances of the measurements, and those variances are inversely proportional to the signal-to-noise (SNR) produced the hardware and signal processor. The signal processor uses matched filter processing, and the efficiency of that depends on knowledge of the kinematics of the target. In particular, the matched filter performance depends heavily on range rate and range acceleration. Traditionally, the predicted state of the target from the track filter is used for matched filter processing, but the predicted kinematic state can have rather large errors, and those errors result in match filter loss. This loss can be very large for maneuvering (i.e., accelerating) targets. In this paper, an expected-maximization (EM) approach is taken to jointly address signal processing and tracking. The signal processor maximizes the SNR using the predicted state and produces measurements. The state estimator (e.g., Kalman filter) uses those measurements to produce expected values of the kinematic state (i.e. the nuisance parameters). The signal processor then maximizes the SNR using the new state estimates. This process continues until the maximum likelihood values of the measurements are achieved. In this paper, the Interacting Multiple Model (IMM) estimator is introduced for the tracking function better address sudden maneuvers. The EM-Based approach to join signal processing and tracking are presented along with a discussion of the real-time computing. Monte Carlo simulation results are given to illustrate a 6 dB improvement in SNR and enhanced tracks for a maneuvering target.
Bayesian Filtering and Smoothing with Unknown Measurement Noise Covariance

Bayesian filtering and smoothing problems with unknown measurement noise covariance are investigated for linear Gaussian systems. The measurement noise covariance is assumed to be inverse Wishart distributed. A Bayesian filter and smoother calculating the joint posteriors for the state and the measurement noise covariance are derived by using a scale Gaussian approximation of t-distribution and moment matching. The proposed filter and smoother are non-iterative unlike the existing Bayesian solutions in the literature. The performance of the proposed algorithms is illustrated on a two-dimensional target tracking scenario. The simulation results show that the proposed filter and smoother have similar performance as the state of the art solutions with lower computational load.
SETRN: A Transform Structure with Adaptive 3D Attention Mechanism and Global Semantic Supervision for Mobile-Captured Retail Receipts Recognition

Retail receipts serve as important vouchers issued by shopping malls. They accurately record key transaction information, enabling effective analysis of user behavior patterns and merchant sales. Data mining and analysis of receipts offer an effective approach to optimize shopping mall operation strategies and enhance consumers’ shopping experience. While the current mainstream text recognition methods for scanned documents perform well, they face significant challenges when applied to receipt images captured by mobile devices, such as cell phones. To achieve greater accuracy, this research proposes a novel method for global semantic feature-based supervised text recognition. It incorporates a 3D attention module to extract high-dimensional visual features and a pure text image supervised encoder to extract global semantic features. The experiment results on both public benchmarks and a large real-world receipt text recognition dataset show that this method is robust and achieves high accuracy.
Evaluation of usability, navigability, and accessibility regarding the interface design for the digital repository for educational podcasts - a user view

To evaluate the interface of the Digital Repository for Educational Podcasts. The objective is to verify the main usability, navigability, and accessibility requirements used during the implementation, as well as contemplate the interaction level on the part of the users when using the system and verify its influence in accessing information. This article is supported by an exploratory qualitative approach with the purpose of detecting the level of usability, navigability, and accessibility measured during the use of the system. Data collection was carried out with the aid of a survey-type form available at address &lt; https://forms.gle/xuDEq6RR46R4npVP8 &gt; and based on some parameters contained in the Reeves Method used for educational software assessment, with the aim of identifying the acceptance of the interface by its audience. The research was applied to a class of the Special Undergraduate Program for Training Professors for Professional Education (PEG). The results obtained helped identify the requirements not adopted, as well as suggestions of possible future corrections; upon completing the analysis, it was possible to identify the difficulty on the part of the respondents in finding problems and, consequently, suggesting corrections related to the criteria analyzed. Through the evaluation of the Repository interface, it was possible to contemplate that it meets the main usability, navigability, and accessibility requirements, presenting a low error rate and few problems.
An FPGA-Based Low-Power Mobile-NetV2 Accelerator

Convolutional neural networks (CNNs) have been widely recognized and applied in the field of robotics. However, the huge amount of computation and parameters of CNN bring great challenges to its deployment on mobile terminals. MobileNet series networks have achieved excellent performance in edge scenarios. In this paper, we propose a low power FPGA-based accelerator using MobileNetV2 as the target network. Finally, the accelerator is implemented on Xilinx MPSOC-XCZU9EG FPGA, and achieves 70.8% Top-1 accuracy under 8-bit quantization, using 74% BRAM, 68% DSP, and 85% LUT. The final power consumption is 5.014W at 150M clock frequency.
Peningkatan Hasil Belajar Kognitif: Implementasi Model Pembelajaran NHT Materi Atmosfer Berbantuan Bahan Ajar di SMA Maarif Lawang

Learning outcomes influenced several components, namely learning methods, learning environment, and social interaction. Problems encountered when observations were students less active during the lesson, less enthusiastic when following lesson, and didn’t pay attention teacher when explaining. This is caused teacher's learning methods being monotonous and lacking in variety. Only few students are active in their learning so can have an impact on cognitive learning outcomes. Learning model can increase student activity and learning outcomes through NHT learning model. Research aim is determine whether NHT learning model can improve learning outcomes of class X MIPA students on atmospheric material assisted by teaching materials. Research type is classroom action research with participant types. Classroom action research stages carried out included planning, implementation, observation, and reflection carried out through two cycles. Data collection techniques use tests, observation, and documentation. Instrument research used observation sheets and test question sheets. Data analysis technique uses descriptive. Research results showed that individual learning outcomes in pre-cycle were 66.45 with a fairly good classification. Average value for cycle 1 was 71.50 with a fairly good classification. Average value of cycle 2 was 79.79 with good classification. NHT learning model has a positive impact in improving individual students' cognitive learning outcomes. Then, learning outcomes with group activities using the NHT learning model also increased by 3.33 points with percentage increase of 3.70%-16.66% in cycle 2. Thus, NHT learning model was successful in improving cognitive learning outcomes of class X students MIPA at Maarif Lawang High School.
A Lightweight Stereo Matching Neural Network Based on Depthwise Separable Convolution

Deep learning-based stereo matching methods have significantly improved the accuracy of disparity maps. However, they also pose challenges such as increased computational complexity and redundant model parameters, which hinder deployment on resource-constrained devices. To overcome this challenge, this paper introduces depthwise separable convolution to decompose the 3D convolution and analyzes the computational overhead corresponding to different decomposition orders of 3D convolutions. By strategically selecting the decomposition block with the lower computational cost during network construction, we effectively reduce the computational requirements of the feature extraction and cost aggregation modules. Experimental results show that our lightweight network model achieves a 41% reduction in computational requirements and a 36% reduction in parameters compared to the baseline model, while maintaining comparable accuracy. This demonstrates its competitiveness against other state-of-the-art models.
Explainable Machine Learning Method for Open Fault Detection of NPC Inverter Using SHAP and LIME

Machine learning methods (ML) have been widely used for fault detection of NPC inverters. However, ML methods such as deep neural networks or ensemble models often act as black boxes, making it challenging to identify critical factors that significantly affect the performance of these models. The lack of interpretability in these models can be a drawback, especially when there is a need to understand the underlying factors driving their predictions for fault detection of NPC inverter. To address this challenge, there is a need for more reliable and trustworthy ML model. Therefore, this study focuses on analyzing the importance of features for open fault detection using Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive explanations (SHAP). SHAP and LIME are popular techniques used to provide interpretability and explainability in machine learning models. The simulation results show that SHAP and LIME methods can identify and analyze the most important features of the open fault of the NPC inverter.
Exploring the Intersections between the Metaverse and Web3 Emerging Technologies

The advancements of the metaverse has validated a new world of possibilities with Web2 and Web3 technologies that are increasingly immersive, interconnected, spatial and decentralized. One of the key Web3 technologies that is intersecting with the metaverse is blockchain technology, that has now imbued metaverse worlds and platform as what are now identified as Layer-1 and Layer-2 metaverse, and in tandem the intersecting of decentralized Web3 technologies including but not limited to metaverse currencies and cryptocurrencies, non-fungible tokens and tokenization, marketplaces, digital wallets, and blockchain main and side-chain networks. In combination with interoperable and interconnected avatars, the metaverse is evolving beyond phygital experiences and asset architecting into financial experiences and asset trading. This research paper investigates the intersections between the metaverse and Web3 emerging technologies by firstly conducting a systematic literature review of the metaverse and its intersection with emerging technologies which currently has a dearth in academic and research literature despite the developments of the Web3 metaverse and its Layer-1 Layer-2 protocol. The review is followed by pilot-testing the metaverse platforms intersecting with Web3 technologies in real-world, particularly on the use of buy-and-sell, trading and item ownership under decentralized finance and financial technology. Finally, the research assesses the resulting metaverse based on user reviews and experiences on the feasibility of Web3 metaverse. The intersection of Web3 and the metaverse is still an early adoption of IR4.0 emerging technologies, yet the convergence of the metaverse, blockchain technologies and advancements in artificial intelligence is ultimately the precursor to the future Web4.
Interactive Dance with Visual Background Recomposition using BLE Beacons

This paper introduces an interactive dance enabled through the innovative concept of a body area network. By strategically attaching Bluetooth Low Energy (BLE) beacons to various parts of the dancer’s body, this novel study firstly leverages signal strength measurements from lightweight, small-scale, and cost-effective beacon nodes for proximity monitoring between the dancer’s body parts and the identification of choreographic units. Secondly, his enables a holistic dancer-centric interactive dance system to empower performers to actively participate in designing visual effects, cultivating a more profound artistic engagement. Lastly, the practicality and viability of the proposed system are demonstrated through a live performance, offering invaluable insights from both the dancers’ and the audience’s perspectives. This methodology employs a mobile device as a gateway, capturing signal strength from beacon nodes and analysing dynamic received signal strength values. Algorithms are designed to use these values to monitor the proximity between body parts, and subsequently, identify a curated collection of choreographic units. The processing outcomes contribute to the adaptive recomposition of visual backgrounds that dynamically respond to the dancers’ movements.
Case Study: Jakarta AQI Classification Using Extreme Learning Machine Method with Oversampling Technique

Good air quality is essential in maintaining the continuity of life. Air quality is very influential on the quality of oxygen needed by the human body. Air pollution is one of the components that greatly affects the quality of oxygen. The capital city of Indonesia, Jakarta, is ranked 9th for air quality and urban pollution. Air quality information is certainly needed by humans. People need information about air quality to be more concerned about the effects of air pollution on their health. The air quality information needed is the air quality index. Therefore, in this research, the classification of the air quality index is carried out. The classification process is carried out using a machine learning algorithm with the Extreme Learning Machine (ELM) method. The Extreme Learning Machine (ELM) algorithm was chosen because it has the advantages of faster learning, easy to use for complex problems, and relevant to the real world. The dataset used for this research comes from Jakarta Open Data and Jakarta Low Emission. This research proves that the use of machine learning with the Extreme Learning Machine (ELM) method is effective in performing the classification process. In the classification process, the Extreme Learning Machine (ELM) produces good performance using balanced data and imbalance data. Experiments with imbalance data with a high accuracy of 94% and balanced data with an accuracy of 96%.
Modeling the adaptive system of professional training of future IT specialists as a means of forming professional competence

Modeling the adaptive system of professional training of future IT specialists as a means of forming professional competence