Reviewerly/exHarmony huggingface dataset:
TEACHER STUDENTS TRAINING TO IMPLEMENT AR AND VR TECHNOLOGIES IN FOREIGN LANGUAGE TEACHING

The article is devoted to the problem of students' training of the first educational level of pedagogical specialities (Primary and Preschool education) to the implementation of augmented and virtual reality technologies in foreign languages teaching. The notions of AR and VR technologies are clarified, and the necessity and specifics of the use of AR and VR technologies in foreign language teaching are explained. The article observes the use of AR and VR technologies in foreign language teaching as an essential part of the digital competence of the modern teacher. The choice of teaching methods, technologies, and tools in foreign language teaching is emphasized. Therefore, the professional training of pedagogical speciality students’(the first education level) significance is grounded. The depicted examples of such training are based on the working curriculum of the discipline “Mobile learning technology in foreign language teaching of preschool children”. This discipline is taught at Borys Grinchenko Kyiv University, and it is designed for the students of 012 Preschool education. This article focuses on the module “Mobile learning technology in foreign language teaching of preschool children,” which involves mastering AR technologies in teaching a foreign language at the preschool. The expected learning outcomes for the first education level pedagogical speciality students in terms of their professional training to use AR and VR technologies in foreign language teaching are discussed.
Enhancing Grid-Based 3D Object Detection in Autonomous Driving With Improved Dimensionality Reduction

Point cloud object detection is a pivotal technology in autonomous driving and robotics. Currently, the majority of cutting-edge point cloud detectors utilize Bird’s Eye View (BEV) for detection, as it allows them to take advantage of well-explored 2D detection techniques. Nevertheless, dimensionality reduction of features from 3D space to BEV space unavoidably leads to information loss, and there is a lack of research on this issue. Existing methods typically obtain BEV features by collapsing voxel or point features along the height dimension via a pooling operation or convolution, resulting in a significant decrease in geometric information. To tackle this problem, we present a new point cloud backbone network for grid-based object detection, MDRNet, which is based on adaptive dimensionality reduction and multi-level spatial residual strategies. In MDRNet, the Spatial-aware Dimensionality Reduction (SDR) is designed to dynamically concentrate on the essential components of the object during 3D-to-BEV transformation. Moreover, the Multi-level Spatial Residuals (MSR) strategy is proposed to effectively fuse multi-level spatial information in BEV feature maps. Our MDRNet can be employed on any existing grid-based object detector, resulting in a remarkable improvement in performance. Numerous experiments conducted on nuScenes, KITTI and DAIR-V have shown that MDRNet surpasses existing SOTA approaches. In particular, on the nuScenes dataset, we attained an impressive 7.2% mAP and 5.0% NDS enhancement compared with CenterPoint.
Semantic Classification of Adverbial Nouns Based on Syntactic Treebank and Construction of Collocation Database

Being an adverbial is a grammatical function of a small number of nouns. The nouns act as the adverbial and the subject are located before the predicate, and there is no formal mark, therefore, it is easy to cause syntax parsing mistakes. Meanwhile, there is a lack of semantic resources of adverbial nouns at this stage which makes it more difficult to do related semantic parsing. We extracted the “noun + verb/adjective” collocation corpora from a large-scale structure tree database. By writing code and manually proofreading, nouns that can act as the adverbial to directly modify verbs or adjectives which are also called the adverbial nouns are listed exhaustively. We conducted a comprehensive classification and quantitative analysis according to the semantics expressed by the adverbial nouns, and constructed an “adverbial noun-verb/adjective” collocation database based on this, so as to improve the accuracy of syntactic parsing and provide corresponding semantic information, and also to provide reference for the related research of linguistics.
Hacking stylometry with multiple voices: Imaginary writers can override authorial signal in Delta

Abstract It is a basic assumption of stylometry that texts written by the same person show greater stylometric similarity even if published under multiple pennames. Statistical authorship attribution strongly relies on the ability of Burrows’s Delta and its variants to cluster one author together regardless of pseudonyms. At the same time, the very first computational discoveries by the founder of modern stylometry showed that a single author is capable of producing multiple voices (Burrows, 1987, Computation into Criticism: A Study of Jane Austen’s Novels and an Experiment in Method. Clarendon Press). We investigate two authors whose stylistically autonomous pennames seem to deceive Delta and override authorial signals: a Portuguese poet Fernando Pessoa and a French novelist Romain Gary. Pessoa managed to create at least three pennames (the author himself used the term ‘heteronym’) who exhibit all traits of individual human beings from the stylometric point of view. Gary’s alter ego Emile Ajar, who was an intentional literary mystification, also demonstrates traits of stylometric autonomy. At the same time, other pseudonyms used by Gary lack that autonomy completely. Our investigation shows that there appears to be a continuum between a purely formal use of a penname, which brings almost no distinction from the real name of an author, and a strong literary sub-personality such as those created by Pessoa.
Deep spatial and tonal data optimisation for homogeneous diffusion inpainting

Abstract Diffusion-based inpainting can reconstruct missing image areas with high quality from sparse data, provided that their location and their values are well optimised. This is particularly useful for applications such as image compression, where the original image is known. Selecting the known data constitutes a challenging optimisation problem, that has so far been only investigated with model-based approaches. So far, these methods require a choice between either high quality or high speed since qualitatively convincing algorithms rely on many time-consuming inpaintings. We propose the first neural network architecture that allows fast optimisation of pixel positions and pixel values for homogeneous diffusion inpainting. During training, we combine two optimisation networks with a neural network-based surrogate solver for diffusion inpainting. This novel concept allows us to perform backpropagation based on inpainting results that approximate the solution of the inpainting equation. Without the need for a single inpainting during test time, our deep optimisation accelerates data selection by more than four orders of magnitude compared to common model-based approaches. This provides real-time performance with high quality results.
Database task processing optimization based on performance evaluation and machine learning algorithm

One of the key components of artificial intelligence algorithms is machine learning, involving a variety of fields, and has been applied in many artificial intelligence systems, including computer vision algorithm, radio network algorithm, medical diagnosis algorithm and intelligent robot system algorithm. In the form of machine learning algorithm, the machine learning module of the algorithm is first used to calculate the consumption, the main performance modules are optimized and improved, and the system data under database optimization are obtained, and selected the optimized structure of the database for calculation, data analysis for the calculation results. Finally, in the design of the database optimization system, the separation of the database system storage engine is studied, and the database optimization form under the data processing structure is proposed. In terms of performance and functionality and reliability, it helps to solve the loss problem caused by big data processing in transmission. In the research of data intensive downward moving calculation process, the optimized solution of data processing is estimated. The results show that using computer terminal sampling comparison to select the executable data processing scheme. The result of this paper shows that it can improve the calculation efficiency of data optimization system query.
Enhancing the Energy Efficiency and Robustness of tinyML Computer Vision Using Coarsely-Quantized Log-Gradient Input Images

This paper studies the merits of applying log-gradient input images to convolutional neural networks (CNNs) for tinyML computer vision (CV). We show that log gradients enable: (i) aggressive 1-bit quantization of first-layer inputs, (ii) potential CNN resource reductions, (iii) inherent insensitivity to illumination changes (1.7% accuracy loss across 2 − 5 ⋅⋅⋅2 3 brightness variation vs. up to 10% for JPEG), and (iv) robustness to adversarial attacks (&gt;10% higher accuracy than JPEG-trained models). We establish these results using the PASCAL RAW image data set and through a combination of experiments using quantization threshold search, neural architecture search, and a fixed three-layer network. The latter reveal that training on log-gradient images leads to higher filter similarity, making the CNN more prunable. The combined benefits of aggressive first-layer quantization, CNN resource reductions, and operation without tight exposure control and image signal processing (ISP) are helpful for pushing tinyML CV toward its ultimate efficiency limits.
Numerical analysis of bipartite entanglement evolution in simple cubic 1/2-spin system with additional spin 1 dopant

Abstract The system in consideration is a singular simple cubic cell of spins $$s = \frac{1}{2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>s</mml:mi> <mml:mo>=</mml:mo> <mml:mfrac> <mml:mn>1</mml:mn> <mml:mn>2</mml:mn> </mml:mfrac> </mml:mrow> </mml:math> doped in centre with additional spin $$S = 1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>S</mml:mi> <mml:mo>=</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:math> . The structure is located in external magnetic field and is undergoing dissipative, Markovian evolution. The analysis of system time evolution is focused on the entanglement evaluation and determination of its behaviour over time. We found out the distinct effect of doping the structure with additional spin $$S = 1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>S</mml:mi> <mml:mo>=</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:math> in the time evolution of the bipartite entanglement in $$\vert W \rangle $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>|</mml:mo> <mml:mi>W</mml:mi> <mml:mo>⟩</mml:mo> </mml:mrow> </mml:math> state. We also distinguished the raising and lowering spin interaction with environment and determined its impact on decoherence. Furthermore, we have shown that the entanglement between spins is in the form of a damped superposition of Gaussian functions. Its pulsed nature results from the unitary evolution in spin structure, while the damping is caused by the influence of the environment.
Reviving the information veracity in healthcare supply chain with blockchain: a systematic review

ABSTRACTABSTRACTGlobal drug handling and the ongoing COVID-19 pandemic add to the current complexity in healthcare operation, potentially hindering the visibility and integrity of supply chain information. Blockchain's potential to enhance information veracity has increasingly captured the attention of practitioners and academics. This research aims to understand how blockchain helps the healthcare supply chain maintain trustworthiness of cross-enterprise information amid uncertainty conditions. A systematic literature review was performed on eight prominent databases with blockchain and healthcare supply chain served as central topics. Following the quality assessment, open, axial, and selective coding were conducted to identify blockchain potentials. Next, blockchain capabilities were then mapped to the characteristics efficient supply chain. A total of 43 articles were extracted and identified. Findings indicate 10 characteristics of an efficient supply chain, such as flexibility in managing supply chain, minimum error, and transparency. The coding process also unfolds 14 applicable properties of blockchain in the healthcare supply chain, such as data availability, decentralisation, and data sharing.KEYWORDS: Blockchaininformation veracityhealthcaresupply chaincovid-19 pandemicsystematic review AcknowledgmentsWe thank Ms. Saskia Salmana for providing technical assistance during the submission process.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work has been supported by Universitas Padjadjaran under the 'Hibah Artikel Review Universitas Padjadjaran' program [Grant Agreement No. 1733/UN6.3.1/LT/2020].
Inception Resnet V2-ECANet Based on Gramian Angular Field Image for Specific Emitter Identification

In this paper, we seek to efficiently and accurately identify the specific emitter with the DroneRF dataset. Firstly, we convert one-dimensional data to a Gramian Angular Field (GAF) image showing a spatial correlation, and add three kinds of noise to the original GAF image to prevent overfitting. Secondly, we propose an Inception-Resnet-V2 model based on the attention mechanism ECANet, which can improve the training effect obviously. Finally, we verify the validation and accuracy of the proposed model with GAF in the experimental results. Test accuracy of Inception Resnet V2-ECANet reaches 99.5% by using the same training set of five networks through 10-fold cross-validation.
Group theoretic particle swarm optimization for gray-level medical image enhancement

As a principal category in the promising field of medical image processing, medical image enhancement has a powerful influence on the intermedia features and final results of the computer aided diagnosis (CAD) system by increasing the capacity to transfer the image information in the optimal form. The enhanced region of interest (ROI) would contribute to the early diagnosis and the survival rate of patients. Meanwhile, the enhancement schema can be treated as the optimization approach of image grayscale values, and metaheuristics are adopted popularly as the mainstream technologies for medical image enhancement. In this study, we propose an innovative metaheuristic algorithm named group theoretic particle swarm optimization (GT-PSO) to tackle the optimization problem of image enhancement. Based on the mathematical foundation of symmetric group theory, GT-PSO comprises particle encoding, solution landscape, neighborhood movement and swarm topology. The corresponding search paradigm takes place simultaneously under the guidance of hierarchical operations and random components, and it could optimize the hybrid fitness function of multiple measurements of medical images and improve the contrast of intensity distribution. The numerical results generated from the comparative experiments show that the proposed GT-PSO has outperformed most other methods on the real-world dataset. The implication also indicates that it would balance both global and local intensity transformations during the enhancement process.
Detection of Structural Control on Formation Water Quality, in Hemrin Oilfield, northern Iraq, Using Lineament Analysis and Hydrochemical Data

Formation water analyses of Tertiary reservoir (Euphrates, Jeribe, and Dhiban formations) in Hemrin oilfield reflect high salinity variation between Albufudhul, Nukhaila, and Allas domes. The present study suggests that this variation resulted by two reasons, (1) percolation of surface waters (rain water) which lead to devastation of oil type, (2) interaction between water and host rocks. Satellite image and DEM were used to detect the lineaments and morphometric analyses, which interpret the structural control on salinity variation. The main direction of lineaments are NE-SW, which prevailing with subsurface vertical faults between Albufudhul and Nukhaila domes that playing with SE limb of Albufudhul dome as conduits for percolation of surface water (rainwater), that lead to dilute the salinity and change the formation water properties from Albufudhul to Nukhaila domes, while the present of vertical fault between Nukhaila and Allas dome considered as semi impermeable according to geometry of domes, so Allas dome considered as semi closed system in comparison with Albufudhul and Nukhaila domes, that lead to increase in salinity and change of formation water properties. Formation water is classified as salinity variety water from medium salinity (TDS of 7430 mg/l), to brine, (TDS of 59118 mg/l), of mixed origin from meteoric water and connate water, of variety of pH value (6.75-8.89) from weak acidic to alkaline. Its type is Na-SO4-chloride and anions are ordered as rCl &gt; SO4 &gt; HCO3, whereas cations ordered as rNa &gt; Ca &gt; Mg. This type of water is associated with open system reservoir, influenced by percolation of surface water and consider as bad zone for preservation of hydrocarbon accumulations especially in Albufudhul and north east limb of Nukhaila domes, while Allas dome considered as closed system reservoir, which is a good zone for preservation of hydrocarbon accumulations.
Balance vs. Contingency: Adaption Measures for Organizational Multi-agent Systems

Designing adaptive multi-agent Systems (MAS) is a challenging development effort. A key point of adaptive systems is that they provide alternative options for acting and designers have to weight the number and elaboration of these alternatives. Here, we concentrate on organisation-oriented MAS and show that organisational models provide suitable means for identifying key measures of this adaptivity. We define such measures, namely the balance and contingency of organizations based on our specification formalism for MAS-organisations, called Sonar.
SABRE: Cross-Domain Crowdsourcing Platform for Recommendation Services

Existing recommendation services place emphasis on personalization to achieve promising accuracy of recommendations. This study aims to exploit the user cognition similarity across multiple domains. The purpose is to leverage this information to enhance the user-based collaborative filtering algorithm for cross-domain recommendation services. The main idea of this is i) to collect feedback from users across multiple domains to represent user cognition; ii) to establish a user cognition-based collaborative filtering (UCCF) model for the multi-domain recommendation; iii) generating recommendations in the target domain. The experimental results demonstrate that the prediction performance of the proposed model outperforms in comparison with all baseline methods.
Attribute-Based Encryption for Weighted Threshold Access Structures

Access control is a fundamental security component in a system, especially in rapidly developing domains such as the cloud or the Internet of Things. The nature of these domains renders formerly acclaimed access control techniques inefficient in environments that are distributed and need highly scalable solutions. Attribute-based access control offers a multitude of advantages, especially through its cryptographic implementation, attribute-based encryption. Weighted threshold access structures are structures that closely cover real-life scenarios and have high applicability in practice as access control policies.
Development of a Novel Spherical Light-Based Positioning Sensor in Solar Tracking

Tracking of the sun, which increases the efficiency of solar energy production systems, has shown considerable development in recent years. This development has been achieved by custom-positioned light sensors, image cameras, sensorless chronological systems and intelligent controller supported systems or by synergetic use of these systems. This study contributes to this research area with a novel spherical-based sensor which measures spherical light source emittance and localizes the light source. This sensor was built by using miniature light sensors placed on a spherical shaped three-dimensional printed body with data acquisition electronic circuitry. Besides the developed sensor data acquisition embedded software, preprocessing and filtering processes were conducted on these measured data. In the study, the outputs of Moving Average, Savitzky-Golay, and Median filters were used for the localization of the light source. The center of gravity for each filter used was determined as a point, and the location of the light source was determined. The spherical sensor system obtained by this study is applicable for various solar tracking methods. The approach of the study also shows that this measurement system is applicable for obtaining the position of local light sources such as the ones placed on mobile or cooperative robots.
On the Need for Healthcare Informatics Training among Medical Doctors in Jordan: A Pilot Study

Jordanian healthcare institutes have launched several programs since 2009 to establish health information systems (HISs). Nowadays, the generic expectation is that the use of HIS resources is performed on daily basis among healthcare staff. However, there can be still a noticeable barrier due to a lack of knowledge if medical doctors do not receive proper training on existing HISs. Moreover, the lack of studies on this area hinders the clarity about the received versus the required training skills among medical doctors. To support this research initiative, survey data have been collected from specialized medical doctors who are currently affiliated with five Jordanian universities to assess their need for HIS training. The results also aim to explore the extent of medical doctors’ use of HIS resources in Jordan. Moreover, they examine whether medical doctors require additional training on using HIS resources or not, as well as the main areas of required training programs. Specifically, this paper highlights the main topics that can be suitable subjects for enhanced training programs. The results show that most respondents use HISs in their daily clinical practices. However, most of them have not taken professional training on such systems. Hence, most of the respondents reported the need for additional training programs on several aspects of HIS resources. Moreover, based on the survey results, the most significant areas that require training are biomedical data analysis, artificial intelligence in medicine, health care management, and recent advances in electronic health records, respectively. Therefore, specialized medical doctors in Jordan need training on extracting useful and potential features of HISs. Education and training professionals in healthcare are recommended to establish training programs in Jordanian healthcare centers, which can further improve the quality of healthcare.