Reviewerly/exHarmony huggingface dataset:
Linguistic and statistical analysis of the lexical ‘Langue-Parole’ dichotomy in a restricted domain

Development of new digital methods for analyzing the ‘Langue-Parole’ dichotomy is one of the most sought-after, but least researched problems of modern theoretical and applied linguistics. This determines the relevance of this study, the purpose of which is to develop a methodology for the automated linguastatistical analysis of a domain-related lexical layer in the context of the ‘Langue-Parole’ dichotomy and to apply the methodology to the Russian-language domain “Research on athlete integrative physiology” (RAIP). The study was conducted on the material of the Russian-language corpus including 56 RAIP domain texts of 300,000 wordforms in total published over the 2013-2020 period in the scientific journals “People. Sport. Medicine” (formerly “SUSU Bulletin. Series “Education, Healthcare, Physical Culture”), “Theory and Practice of Physical Culture”, etc. The key methodological approach is the ontological analysis of corpus data using statistical and linguistic modeling methods. The domain-specific language and speech are modeled by the corresponding lexicon and corpus, while the ‘Langue-Parole’ lexical dichotomy is represented by the values of the linguistic-statistical concept verbalization parameters of the domain concepts in the lexicon and corpus. The computational parameters include the indices of lexical diversity, structural complexity, conceptual syncretism, lexical structural complexity vs. conceptual syncretism correlation, and syncretical concept junction when verbalized in the corpus. The main results of the study are: 1) а methodology for analyzing domain-specific lexical dichotomy ‘Langue-Parole’, which can be ported to other domains and national languages; 2) the RAIP domain-related resources, including language-independent ontology, conceptually annotated Russian corpus, onto-lexicon, linguistic-statistical parameter values of the lexical ‘Langue-Parole’ dichotomy; and 3) tools that automate certain stages of the study.
One Training for Multiple Deployments: Polar-based Adaptive BEV Perception for Autonomous Driving

Current on-board chips usually have different computing power, which means multiple training processes are needed for adapting the same learning-based algorithm to different chips, costing huge computing resources. The situation becomes even worse for 3D perception methods with large models. Previous vision-centric 3D perception approaches are trained with regular grid-represented feature maps of fixed resolutions, which is not applicable to adapt to other grid scales, limiting wider deployment. In this paper, we leverage the Polar representation when constructing the BEV feature map from images in order to achieve the goal of training once for multiple deployments. Specifically, the feature along rays in Polar space can be easily adaptively sampled and projected to the feature in Cartesian space with arbitrary resolutions. To further improve the adaptation capability, we make multi-scale contextual information interact with each other to enhance the feature representation. Experiments on a large-scale autonomous driving dataset show that our method outperforms others as for the good property of one training for multiple deployments.
Effects on Saudi Female Student Learning Experiences in a Programming Subject Using Mobile Devices: An Empirical Study

There is a dearth of research on how mobile learning might help Saudi female students in particular perform better in computer programming courses. This paper aims to measure educational experiences ofSaudi female students using an empirical study, based on aframework related to mobile learning in a computer programming subject.&#x0D; The empirical study sample involved 21 students who used the ViLLE software tool in their quizzes in programming class. This study includes a survey with a mixed-method approach. The study results showed that students were satisfied with their mobile learning experience. The degree of satisfaction demonstrates how mobile learning can enhance learning in a computer programming subject.
Protecting health data in the cloud through steganography: A table-driven, blind method using neural networks and bit-shuffling algorithm

Securing health data stored in the cloud, including DNA sequences, patient information, and treatment histories, has become increasingly difficult due to modern cyber threats. To address this challenge, one approach is to employ steganography, a method of concealing sensitive information by merging it with other data. Recent studies have explored hiding secret information within DNA sequences, but these approaches have drawbacks. They either fail to generate a suitable cover from the modified sequence, known as the stego DNA sequence, or result in a significant increase in data that needs to be added to the original DNA sequence. Some methods even inadvertently include the original secret information or cover within the stego DNA sequence, creating security risks. To overcome these limitations, this article presents a table-driven, blind steganographic technique specifically designed for DNA sequences stored in the cloud. Our proposed technique offers superior embedding capabilities, reduces DNA expansion, and minimizes security risks. The proposed method introduces two unique proposals: Employing machine learning techniques for secret encryption and utilizing bit-shuffling to enhance message protection, thereby increasing resistance against cyber-attacks. Furthermore, the method supports hiding various types of messages. Compared to existing methods, the suggested scheme achieves a lower stego expansion rate. Additionally, a newly introduced parity check method strengthens security by preventing modifications to the stego DNA, such as man-in-the-middle attacks, chosen stego attacks, and modification attacks. These innovations enhance the overall security capabilities of the system. Experimental results validate the effectiveness of the proposed method, demonstrating accurate and error-free reconstruction of the cover, healthcare data, and DNA sequence. The proposed technique outperforms competing methods across all performance metrics, establishing its superiority in securing health data in the cloud.
A local path planning algorithm based on improved dynamic window approach

In order to solve the problem that the traditional DWA algorithm cannot have both safety and speed because of the fixed parameters in the complex environment with many obstacles, a parameter adaptive DWA algorithm (PA-DWA) is proposed to improve the robot running speed on the premise of ensuring safety. Firstly, the velocity sampling space is optimized by the current pose of the mobile robot, and a criterion of environment complexity is proposed. Secondly, a parameter-adaptive method is presented to optimize the trajectory evaluation function. When the environment complexity is greater than a certain threshold, the minimum distance between the mobile robot and the obstacle is taken as the input, and the weight of the velocity parameter is adjusted according to the real-time obstacle information dynamically. The current velocity of the mobile robot is used as input to dynamically adjust the weight of the direction angle parameter. In the Matlab simulation, the total time consumption of PA-DWA is reduced by 47.08% in the static obstacle environment and 39.09% in the dynamic obstacle environment. In Gazebo physical simulation experiment, the total time of PA-DWA was reduced by 26.63% in the case of dynamic obstacles. The experimental results show that PA-DWA can significantly reduce the total time of the robot under the premise of ensuring safety.
Task-aware network: Mitigation of task-aware and task-free performance gap in online continual learning

Online continual learning (OCL) is a challenging task that accesses training data only once and trains a deep model to cover a new task while preserving the capability of previous tasks. Due to the poor performance, early works tend to measure the performance in a task-aware (TA) manner, which assumes task labels in the testing time. However, requiring such an assumption is far from the real world. Although recent works also measure the performance without the assumption (i.e., a task-free (TF) manner), there is still a large performance gap between TA and TF in the online environment. In this paper, we observe that the severe performance gap is due to the overlapped information between different tasks. Inspired by this observation, we propose the Task-Aware Network (TANet), which learns both class-specific and task-specific information using a dual-encoder framework. However, it is difficult to learn the global distribution of previous tasks since only a few samples (i.e., exemplars) of the tasks can be accessed. To solve the problem, we utilize the prototype feature that reflects the global distribution of each task. Using the prototype features, TANet learns the task-specific information with Prototype-based Task Contrastive Learning (PTCL) and generates pseudo-task labels. In testing time, we introduce the hybrid task-free (TF) classifier, which only activates the task that the pseudo label indicates. The proposed method achieves state-of-the-art performances in various datasets for OCL.
Virtual reality content evaluation visualization tool focused on comfort, cybersickness, and perceived excitement

ABSTRACTABSTRACTWith the advancement of technology, the Virtual Reality (VR) industry is expanding rapidly. However, cybersickness continues to be a major concern. Therefore, the aim of this study is to develop a visualisation tool that comprehensively considers hardware, software, and user factors that can affect the VR experience, taking into account both objective and subjective measurements. To accomplish this, we primarily refer to the technical guidelines established by the IEEE std 3079™−2020, which focus on resolving VR sickness due to Head Mounted Display (HMD). Hardware and user factors are measured using tools developed in previous studies, while content factors are developed and integrated into the present study. Content evaluation tools are developed using the Delphi and Analytic Hierarchy Process (AHP) methods, which involve the opinions of 20 experts and weigh content cybersickness-causing factors based on four steps. Along with content evaluation tools, a visualisation tool is developed by combining hardware and user factor evaluation tools. The visualisation tool evaluates VR content based on the perceived excitement level (x-axis) and the sickness level (y-axis) in 16 categories.KEYWORDS: Cybersicknessvirtual realityperceived excitementvisualisation toolfatigueVR contents Disclosure statementNo potential conflict of interest was reported by the author(s).Informed consentEach participant was given detail information about the project which they read and freely accepted to participate. For those who accepted, they signed informed consent form and returned it to the surveyor before receiving the questionnaire to be self-administered.Additional informationFundingThis work was supported by Education and Research promotion programme of KOREATECH in 2022.
Review of Multiple Human Action Recognition Techniques

During the last decades topics such as video analysis and image understanding have acquired a big importance due to its inclusion in applications such as security, intelligent spaces, assistive living and focused marketing. Human action detection is investigated in utilization of artificial intelligence and computer vision. Numerous effective action recognition strategies have demonstrated and the action information are successfully gained from motion videos and still pictures. In order to get equivalent actions, the proper activity information gained from various kind of media like video or picture might be connected. The majority of existing video activity action identification strategies experience the ill effects of inadequate recordings. In this review article we are going to discussed about some earlier human action recognition techniques. In past numbers of researchers, developers dose a nicest work in same specific domain, in this review article/Paper we are going to review their work. During this we are going to find out and conclude the result and working of different Human action recognition techniques. In this process we are going to serve our focus on some specific action resignation techniques like: Action Recognition System using 3D Convolutional Neural Networks, Using Deep Learning, Deep Convolutional Neural Networks, Image Processing.
Using expression parsing and algebraic operations to generate test sequences.

It has become a popular trend to build software's regular expression or extended regular expression models in order to generate test sequences from these models. Such test sequences tend to have promising test coverage and fault detection capability. During this process, one critical step is expression parsing based on algebraic operations. However, the parsing can be very challenging as different algebraic systems have different algebraic operators and algebraic operations. Besides, the parsing difficulty continues to grow as software complexity increases. To address the above challenges, this paper proposes a general expression parsing framework for test sequence generation. The proposed framework consists of three stages, expression decomposition, algebraic operations, and subexpression combination. To implement the framework, an expression parsing algorithm based on abstract syntax tree is developed. Case studies based on 117 expressions collected from the literature over the past 30 years as well as 13 software systems are conducted to evaluate the effectiveness of the proposed algorithm. The results indicate that our algorithm is superior to three existing and commonly used algorithms with respect to expression parsing and software fault detection.
An unsupervised embedding harmonization system for privacy-preserving data mining in healthcare

Sharing data across hospitals for disease modeling is challenging due to concerns over patient privacy and the lack of an efficient privacy-preserving data mining framework. Contextual embedding models, which encode medical events into vector representations while preserving the contextual dependencies between events, have shown promise in privacy-preserving data mining without requiring original data disclosure. However, the medical event representations learned from multiple data sources lie in different embedding spaces and cannot be directly integrated. Existing embedding harmonization algorithms require a list of common medical events between different data sources and use them as corresponding pairs for transformation, known as the supervised harmonization method. However, common medical events can be difficult to collect in clinical practice. To promote data mining across hospitals, we developed a novel unsupervised embedding harmonization system that introduces an unsupervised harmonization algorithm to align contextual embeddings without the need for corresponding pairs. The proposed framework also considered different contextual embedding techniques, including Word2Vec and Med2Vec, to explore the robustness of the proposed unsupervised harmonization algorithm. The proposed framework was evaluated using medical events extracted from the Medical Information Mart for Intensive Care III database. By integrating the embeddings from multiple sources, the proposed framework can achieve better disease prediction accuracy and medical event clustering compared to models built on a single data source. The proposed unsupervised harmonization method, which achieves similar performance to the supervised harmonization model under different contextual embedding techniques, holds great promise for predictive modeling and event clustering in healthcare.
The isotropy of cryptocurrency volatility

Abstract We examine the fractal volatility and long‐range dependence of Bitcoin, Ethereum, Tether and USD Coin by employing the continuous wavelet transform, maximal overlap discrete wavelet transform and rescaled range. Our dataset consists of daily prices spanning from January 2017 through to October 2022, encapsulating pre‐ and post‐epidemic eras. Generally, our findings suggest that Tether presents the least overall volatility throughout the time‐frequency spectrum. USD Coin demonstrates ephemeral turbulence, contrary to Tether's maturity in influencing market equilibrium through token issuance and trade responses. In the post‐epidemic sample, both stablecoins indicate mean reversion, with USD Coin showing marginally better efficiency. Conversely, investment tokens display persistent clusters due to retail traders and long‐term fundamental institutions. Although both tokens illustrate multifractal volatility, Ethereum unveils more essence of self‐similarity than Bitcoin. Hence, there is no evidence that Ethereum truly duplicates Bitcoin since policy‐related events differ between them, as both return series move incongruously. Conditional dynamics signify that all cryptocurrencies, except Tether, were affected by the pandemic transition of COVID‐19 and subsequent macroeconomic news. The unconditional volatility of stablecoins evinces zero‐mean errors, antithetical to investment tokens exhibiting annual cycles. The fractal geometry suggests that investment tokens simulate one‐dimensional lines, whereas stablecoins mimic two‐dimensional planes.
Active power filter with enhanced echo state network in radar power supply system

Abstract There have been concerns raised on load non‐linearity as more and more load types are being introduced into the radar power supply system. The non‐linear load produces harmonic current while it seriously threatens the normal and safe operation environment of other system equipment. It can be difficult to design shunt active power filter (APF) to eliminate the harmonic effect while achieving stability and reliability. This paper designs a hybrid optimization shunt active power filter, which adopts enhanced echo state network (ESN) to predict harmonics and reduce the delay problem in traditional control. The prediction performance of enhanced ESN is verified by simulation. The modelling and simulation of the hybrid optimization shunt APF are completed in Simulink, and the parameters of APF are reset according to the radar power supply system. Furthermore, a prototype based on DSP TMS320F28335 was developed to test whether the theory is feasible and accurate. The experimental results demonstrate that the APF with harmonic prediction has improved steady‐state response performance and rapid dynamic response performance.
Multi-patch multi-scale model for motion deblurring with high-frequency information

Nowadays deep learning-based models have made encouraging accomplishments in the field of image motion deblurring, but this type of algorithms does not effectively use the known prior knowledge and the physical model of motion blur. So as to settle the above disputes, this paper suggests a multi-patch multi-scale input model that fuses high-frequency information, structural self-similarity (SSS), multi-scale channel spatial attention (MSCSA), and superpixel gradient loss for motion deblurring. The multi-patch multi-scale input model can fully extract the salient information, scale information, and high-frequency information of the image to restore the sharp image. The SSS module can take full advantage of the prior information of the structure self-similarity of the image. And the MSCSA module dynamically and selectively enhances relevant features and suppresses irrelevant features by fusing spatial, scale, and channel characteristics. Moreover, this paper advises a superpixel gradient loss function, which can remove motion blur and restore sharp images more effectively. And to increase the diversity of scenes, for the first time, we propose a motion blur dataset (Underwater-Blur) specifically for underwater scenes. Massive comparative experiments conducted on three public datasets of GoPro, RealBlur, RWBI, and the Underwater-Blur dataset prove that our model outperforms the state-of-the-art in terms of comprehensive performance.
Multi-View Projection Learning via Adaptive Graph Embedding for Dimensionality Reduction

In order to explore complex structures and relationships hidden in data, plenty of graph-based dimensionality reduction methods have been widely investigated and extended to the multi-view learning field. For multi-view dimensionality reduction, the key point is extracting the complementary and compatible multi-view information to analyze the complex underlying structure of the samples, which is still a challenging task. We propose a novel multi-view dimensionality reduction algorithm that integrates underlying structure learning and dimensionality reduction for each view into one framework. Because the prespecified graph derived from original noisy high-dimensional data is usually low-quality, the subspace constructed based on such a graph is also low-quality. To obtain the optimal graph for dimensionality reduction, we propose a framework that learns the affinity based on the low-dimensional representation of all views and performs the dimensionality reduction based on it jointly. Although original data is noisy, the local structure information of them is also valuable. Therefore, in the graph learning process, we also introduce the information of predefined graphs based on each view feature into the optimal graph. Moreover, assigning the weight to each view based on its importance is essential in multi-view learning, the proposed GoMPL automatically allocates an appropriate weight to each view in the graph learning process. The obtained optimal graph is then adopted to learn the projection matrix for each individual view by graph embedding. We provide an effective alternate update method for learning the optimal graph and optimal subspace jointly for each view. We conduct many experiments on various benchmark datasets to evaluate the effectiveness of the proposed method.
Image Structure-Induced Semantic Pyramid Network for Inpainting

The existing deep-learning-based image inpainting algorithms often suffer from local structure disconnections and blurring when dealing with large irregular defective images. To solve these problems, an image structure-induced semantic pyramid network for inpainting is proposed. The model consists of two parts: the edge inpainting network and the content-filling network. U-Net-based edge inpainting network restores the edge of the image defect with residual blocks. The edge inpainting map is input into the pyramid content-filling network together with the image in the prior condition. In the content-filling network, the attention transfer module (ATM) is designed to reconfigure the encoding features of each scale step by step, and the recovered feature map is linked to the decoding layer and the corresponding potential feature fusion decoding to improve the global consistency of the image and finally obtain the restored image. The quantitative analysis shows that the average L1 loss is reduced by about 1.14%, the peak signal-to-noise ratio (PSNR) is improved by about 3.51, and the structural similarity (SSIM) is improved by about 0.163 on the CelebA-HQ and Places2 datasets compared with the current mainstream algorithms. The qualitative analysis shows that this model not only generates semantically sound content as a whole but also better matches the human visual perception in terms of local structural connectivity and texture synthesis.