Reviewerly/exHarmony huggingface dataset:
Parameters tuning of multi-model database based on deep reinforcement learning

Abstract As we all know, the performance of database management system is directly linked to a vast array of knobs, which control various aspects of system operation, ranging from memory and thread counts settings to I/O optimization. Improper settings of configuration parameters are shown to have detrimental effects on performance, reliability and availability of the overall database management system. This is also true for multi-model databases, which use a single platform to support multiple data models. Existing approaches for automatic DBMS knobs tuning are not directly applicable to multi-model databases due to the diversity of multi-model database instances and workloads. Firstly, in cloud environment, they have difficulty adapting to changing environments and diverse workloads. Secondly, they rely on large-scale high-quality training samples that are difficult to obtain. Finally, they focus primarily on throughput metrics, ignoring tuning requirements for resource utilization. Therefore, in this paper, we propose a multi-model database configuration parameters tuning solution named MMDTune. It selects influential parameters, recommends the optimal configurations in a high-dimensional continuous space. For different workloads, the TD3 algorithm is improved to generate reasonable parameter adjustment plans according to the internal state of the multi-model databases. We conduct extensive experiments under 5 different workloads on real cloud databases to evaluate MMDTune. Experimental results show that MMDTune adapts well to a new hardware environment or workloads, and significantly outperforms the representative tuning tools, such as OtterTune, CDBTune.
Towards semiotically driven empirical studies of ballet as a communicative form

Abstract This paper treats dance as a movement-based semiotic system, focusing on classical ballet as an example in order to show how dance can be made accessible to both detailed description and empirical investigation as a form of communication. The study contributes to a growing tradition of multidisciplinary research that looks at a variety of dance forms from the perspectives of linguistics, communication studies and social semiotics, drawing additionally on recent developments in the formal semantics of non-verbal semiotic systems and on empirical methods emerging within functional accounts of multimodality. The paper consequently develops a particular treatment of ballet that offers a principled means of linking the physical stream of movement, recorded using motion caption technology, and discourse interpretations, such as those that are typically narratively relevant in classical ballet but which may be found in other forms of dance as well. The paper sets out how this may then support further empirical research by importing well-defined methods and even specific questions from linguistics and related fields.
Nowcasting unemployment rate during the COVID-19 pandemic using Twitter data: The case of South Africa

The global economy has been hard hit by the COVID-19 pandemic. Many countries are experiencing a severe and destructive recession. A significant number of firms and businesses have gone bankrupt or been scaled down, and many individuals have lost their jobs. The main goal of this study is to support policy- and decision-makers with additional and real-time information about the labor market flow using Twitter data. We leverage the data to trace and nowcast the unemployment rate of South Africa during the COVID-19 pandemic. First, we create a dataset of unemployment-related tweets using certain keywords. Principal Component Regression (PCR) is then applied to nowcast the unemployment rate using the gathered tweets and their sentiment scores. Numerical results indicate that the volume of the tweets has a positive correlation, and the sentiments of the tweets have a negative correlation with the unemployment rate during and before the COVID-19 pandemic. Moreover, the now-casted unemployment rate using PCR has an outstanding evaluation result with a low Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Symmetric MAPE (SMAPE) of 0.921, 0.018, 0.018, respectively and a high R 2 -score of 0.929.
Stimulated Emission of Superradiant Atoms in Waveguide Quantum Electrodynamics

We investigate the stimulated emission of superradiant atoms coupled to a waveguide induced by a coherent-state photon pulse. We provide an analytical result when a short $\ensuremath{\pi}$ pulse is incident, which shows that the atoms emit photons coherently into the output pulse, which remains a coherent state in the short pulse limit. An incident pulse is amplified in a phase-preserving manner, where noise is added almost entirely in the phase direction in phase space. This property improves the ratio of the intensity signal to noise after the amplification for sufficiently short pulses. This is a unique feature different from general phase-preserving linear amplifiers, where the signal-to-noise ratio deteriorates in the amplification process. We also discuss the dependence of the photon-emission probability on pulse parameters, such as the pulse area and the duration.
Increasing the Frame Rate of Echocardiography Images Based on a Novel Interpolation Technique

High-temporal cardiac imaging allows for more accurate diagnostics for heart diseases. The objective of the present work is to propose a technique to improve the temporal resolution of echocardiography images. More specifically, we use a new non-polynomial interpolation method to increase echocardiographic image temporal resolution without significantly (i.e., less than 5%) reducing the spatial resolution or size of the reconstructed images. The proposed method involves two steps. In the first step, analytically derived interpolation functions are constructed according to the problem conditions. In the second step, the unknown pixel intensity variation time curves (IVTC) values of the unknown frames are approximated by the functions created in the first step, to increase the temporal resolution. The method presented has been tested on a clinical B-mode echocardiographic dataset consisting of 114 frames with a size of 411 by 481 pixels. The quantitative results show that our proposed interpolation approach is effective, robust, and able to reconstruct images at different times of cardiac cycles. For example, the mean squared error is reduced from 0.6 to 0.3 compared to methods based on polynomials (e.g., cubic spline and B-spline). Although our method has a similar temporal complexity to the cubic spline, the time processing analysis for different down-sampling ratios shows that not only our method is more accurate in terms of quantitative and qualitative results, but it also does not increase the computational time.
Multi-circle Detection Using Multimodal Optimization

Object and shape detection in digital image were one of the hot topic over the last two decades. Especially automatic multi circle detection has received more attention over last years. Hough transform (HT) is a well-known and most popular method for lines and circles detection. However, HT has huge computational complexity expense. This paper proposed a new successful heuristic method to reduce computation time and improve the speed of HT for circle detection. In this proposed method the edges information of the image is obtained by means of Robert edge detection. Then, multimodal particle swarm optimization (PSO) and local search is employed to locate all exciting circle in the image. The experiments on benchmark images show that our scheme can perform multi circle detection successfully.
Optimal prediction of cloud spot instance price utilizing deep learning

Cloud platforms often offer a variety of virtual machine (VM) models of various types and capacities, enabling users to choose the instances that best meet their requirements. Cloud providers have devised systems to make the most of their redundant computing resources. The cost fluctuates dynamically based on supply and demand. "Spot price" is a common term for this. To be able to use this instance, the user must create a suitable offer above the spot price. Accurate spot price prediction allows users to pre-prepare bid prices and run time to increase the reliability of the method. For this purpose, we consider Amazon EC2 as a testbed and use its spot price history to predict the future price by constructing a proposed modified gated recurrent unit (MGRU) model and providing a proposed dropout method. Compared with other sophisticated methods, test results show that the proposed method works superior and more accurately.
Methodology for the evaluation of media literacy in children and youth. Recommendations and trends

In the current communicational scenario, transformed by technological and digital advances, media literacy is considered a crucial competence for active and informed participation in society. In this context, the main objective of this study was to assess the media literacy competences of 416 students of the 4th, 6th, and 9th grades in Portugal, identifying training needs and presenting suggestions for intervention. A non-experimental quantitative methodology was used, and the participants completed a self-response questionnaire. The results indicate that the three grades studied have a transversal need regarding the use of media, the production of media content, and participation in society through the media, and also regarding the analysis, understanding, and assessment of media content. We confirm the importance of media education in the development of media literacy and present some intervention suggestions for the promotion of these competences in the school context, namely the reinforcement of some curricular content, the use of guiding documents, and the development of critical thinking. We emphasize the importance of training teachers and parents on the subject, and finally, we also emphasize the importance of deconstructing the belief that digital natives have natural competences in this regard and therefore do not need training in this area.
Heavy metals and micronutrients assessment in soil and groundwater using geospatial analyses under agricultural exploitation in dry areas

Abstract This study used a geospatial model to evaluate the regional distributions and buildup of various Heavy Metals (HM) and micronutrients as a result of groundwater irrigation used for lengthy periods in Egypt’s El-Kharga and El-Farafra oases, which were formed on western desert terrain, were the subject of the study. To fulfill this aim, field sampling of soil and groundwater was integrated with cartographic modeling. The evaluated HM concentrations in the water samples are below the permitted level for irrigation. The surface soil’s element concentrations were within acceptable limits. However, protracted mobility and a potency risk for soil smearing were not spotted. The apportionment of HM concentricity versus soil profundity showed that the assemblage was primarily in the topsoil. Future management plans should consider ongoing HM level monitoring. The lands are divided into three classes: II, III, and IV, according to their capabilities, while the soils are divided into three classes based on their suitability: moderately suitable class S2, marginally acceptable class S3, and permanently unsuitable class N2. Additionally, soil fertility and quality were categorized into low and moderate categories. Land evaluations indicate that they are promising areas for agricultural investment under the conditions of using modern agricultural techniques. The most important results of the study are that the non-technical use of irrigation water in some areas has exacerbated the problems of salinity, especially in those where the drainage characteristics are poor. Concentrations of HM were found only in agricultural areas adjacent to urban areas and poor drainage, especially in agricultural areas reclaimed for more than 50 years. High concentrations of HM were also found in shallow wells close to the same places, and there were no significant concentrations in blind wells. The main recommendation of the study is the necessity of using modern agricultural and irrigation systems in those places.
Votes-as-a-Proof (VaaP): Permissioned Blockchain Consensus Protocol Made Simple

With the development of Blockchain technology, permissioned Blockchains are getting more and more attention from researchers because applications based on permissioned Blockchains are more practical and easier to be carried out. This paper aims to design a dedicated consensus protocol for permissioned Blockchains. The existing consensus protocols applied to permissioned Blockchains are either derived from public Blockchains such as Proof of Work (PoW) or Proof of Stake (PoS), with full decentralization, resulting in low transaction processing efficiency; or derived from traditional Byzantine fault-tolerant (BFT) consensus protocols such as Practical BFT (PBFT) or HoneyBadgerBFT, with high communication complexity of the consensus process, resulting in low scalability. Therefore, we propose a dedicated consensus protocol for permissioned Blockchains called Votes-as-a-Proof (VaaP) with high transaction processing efficiency while ensuring high scalability. Every node in VaaP runs a simple consensus process based on voting in parallel. Faulty nodes will only deprive themselves of using consensus service. We present the comparison of VaaP and Sphinx, one of the state-of-the-art consensus protocols, analytically and experimentally (up to 500 nodes). The results indicate that VaaP outperforms Sphinx in throughput, latency and scalability.
Privacy-Preserving Data Aggregation Scheme for E-Health

E-Health is using digital services and communication technology to support healthcare. E-Health services are becoming increasingly popular. With E-Health, large amounts of data need to be collected, stored, and sent to other places while remaining private. This raises the need for privacy-preserving data aggregation schemes to be implemented. Many other privacy-preserving data aggregation schemes already exist for E-Health services utilizing tools such as homomorphic encryption, which can be slow with large amounts of data. This paper proposes a privacy-preserving scheme to aggregate data in an E-Health setting. Our scheme allows all patients’ data to remain private. Doctors can utilize partial decryption in our scheme to collect specific patient information, such as how many patients have high blood pressure, without seeing all patients’ data.
Economic growth through digitalization in Africa: does financial sector development play a mediating role?

Purpose This paper aims to analyse economic growth in Africa focussing on the role of digitalization and financial sector development. Design/methodology/approach The authors employ country-level data from 36 African countries over the period 2000–2020 and used fixed effect, random effect and the Hausman–Taylor estimation techniques. Findings The study, first finds that, digitalization propels financial sector development in Africa. Building on this, the study further finds that, digitalization conditioned on financial sector development at best does not promote economic growth in Africa. However, results of the net effects suggest that digitalization, overall, improve economic growth in Africa. Social implications In the current environment of a sluggish global economy, digitalization can play an important role in assisting policymakers to spur economic growth. This has attracted the attention of many researchers in the developed world. However, little is done about the subject matter in Africa. Originality/value The findings of this paper are novel in the African sub-region with important policy implications.
Fast stitching method for multi-view images of cupping spots

Aiming at the problems of cupping machine camera with a small field of view, complete and high-definition cupping spot characteristics need to be monitored in real time, so as to effectively ensure that the cupping machine can accurately control the cupping time to avoid harm to the human body and extracting the complete back contour for automatic acupuncture point positioning, a fast stitching method for multi-view image of cupping spots is proposed. The present study uses linear transformation and Gaussian smoothing to preprocess the images to enhance image details, improve contrast, and reduce the influence of tank occlusion; Then, we combine the BRISK algorithm and the match factor to match the image and estimate the overlapping area through establishing a binary tree model so as to improve the efficiency of feature point detection and matching. Experimental results show that compared with the AutoStitch, the image stitched which is high definition has no obvious seams, ghosting, and distortion. What’s more, the algorithm in this paper is more efficient and the back contour is more complete. The algorithm in this paper has good real-time performance and the clarity of the cupping spots is high, which is conducive to the subsequent real-time detection of the cupping spots. At the same time, the back contour of the image stitched by the algorithm in this paper is complete, which is conducive to the subsequent automatic acupuncture point positioning.
The Effects of The Blended Project-Based Literacy that Integrates School Literacy Movement Strengthening Character Education Learning Model on Metacognitive Skills, Critical Thinking, and Opinion Expression

&lt;p style="text-align: justify;"&gt;Metacognitive, critical thinking and opinion expression are in high demand. This study aimed to investigate the effects of the blended project- based literacy that integrates school literacy movement strengthening character education (literasi berbasis proyek terintegrasi GLS dan PPK: Li-Pro-GP) learning model on students' metacognitive skills, critical thinking, and opinion expression. A post-test experimental design was used to answer the research question. The study was conducted from August to October 2021 at Government Junior High School 23 Malang. Seventh-grade students were selected as research participants. The participants included 30 students from class VII-2. The research instrument was five essay questions to measure critical thinking skills. Material and assessment experts validated the essay questions developed by the researcher. The items that were declared valid were tested for validity. The result showed five valid items with high reliability of .670. Metacognitive skills were measured using the Metacognition Awareness Instrument (MAI), which consists of 40 items. The questions declared valid were tested for validity with a very high reliability of .953 for 37 items, and only three items were invalid. The ability to express an opinion was measured with an observation questionnaire validated by experts with a valid instrument score. Data analysis was performed by path analysis using the SmartPLS software. The results showed that the Li-Pro-GP blended learning model significantly strengthened students' metacognitive skills, critical thinking, and opinion formation.&lt;/p&gt;
Two-photon spectral modulation via temporal manipulation: Quantum optical synthesis of spectral modes from temporal square waves

Precise manipulation of the time–frequency modes of entangled photons is crucial for future quantum science and technologies. Recently, the frequency-domain-quantum-optical-synthesis (FD-QOS) method was demonstrated by creating a superposition of different joint temporal amplitudes: those temporal distributions can be controlled by manipulating the joint spectral amplitude in 2D frequency space via a Fourier optical relation. This FD-QOS method provides an efficient, flexible, and easy-to-control way to precisely modulate the temporal distributions of the entangled photon in an ultrafast region. However, manipulation of only the temporal modes is not sufficient for various applications in quantum information, since spectral modulations are also needed on many occasions. Here, we present a proof-of-concept experiment of two-photon spectral modulation via temporal manipulation of a biphoton wave packet. This protocol, called time-domain-quantum-optical-synthesis (TD-QOS), is achieved by adjusting the relative phases between two joint temporal distributions. In addition, the two-photon joint spectral distributions are characterized by measuring the joint spectral intensities and Hong–Ou–Mandel interferences. The combination of FD-QOS and TD-QOS enables complete control over the biphoton states. Our work would further develop quantum technologies that rely on the time–frequency modes of entangled photons.
The relation between players' motivation, virtual communities and stickiness in augmented reality games

Purpose The purpose of this research was to explore the stickiness of players' motivation in a virtual community and to explore the important factors for gamers. Design/methodology/approach In this research, motivation was the independent variable; the virtual community was the mediator; and stickiness was the dependent variable. An online questionnaire survey was conducted, with users of augmented reality (AR) as the research objects. Statistical analysis was carried out using SPSS and AMOS software to verify the research model and research hypotheses, to understand the relation between player motivation and stickiness and to determine whether there were any changes in the virtual community. Findings The authors found that the relation between players' motivation in AR-based games and the virtual community had a significant positive impact. Ingress had a significant positive impact on the virtual community and stickiness, and Pokémon had a significant positive impact too. The virtual community of the Ingress game played a completely mediating role in motivation and stickiness, but the virtual community in Pokémon did not have a mediating effect. Originality/value The novel approach adopted in this study enabled us to determine the causal relation between player motivation, the virtual community and stickiness, on the basis of the theoretical framework formulated, and the latter was used to construct a path analysis model diagram. The correlation between motivation and the virtual community, between the virtual community and stickiness, and the causal relation between all three was verified. The study results and conclusions may help companies understand how to use virtual communities in AR games to improve stickiness and motivate gamers to continue playing.
Real time static and dynamic sign language recognition &lt;/strong&gt;&lt;strong&gt;using deep learning

Sign language recognition systems are used for enabling communication between deaf-mute people and normal user. Spatial localization of the hands could be a challenging task when hands-only occupies 10% of the entire image. This is overcome by designing a real-time efficient system that is capable of performing the task of extraction, recognition, and classification within a single network with the use of a deep convolution network. The recognition is performed for static image dataset with a simple and complex background, dynamic video dataset. Static image dataset is trained and tested using a 2D deep-convolution neural network whereas dynamic video dataset is trained and tested using a 3D deep-convolution neural network. Spatial augmentation is done to increase the number of images of static dataset and key-frame extraction to extract the key-frames from the videos for dynamic dataset. To improve the system performance and accuracy Batch-Normalization layer is added to the convolution network. The accuracy is nearly 99% for dataset with a simple background, 92% for dataset with complex background, and 84% for the video dataset. By obtaining a good accuracy, the system is proved to be real-time efficient in recognizing and interpreting the sign language gestures.