Reviewerly/exHarmony huggingface dataset:
Pre-service Elementary School Teachers’ Perception of Online Micro-teaching of English and Evaluations

초록·키워드 목차 오류제보하기 This study investigated pre-service elementary school teachers" perceptions of the micro-teaching of English in an evaluation conducted online using ZOOM during the COVID-19 period. Eleven pre-service teachers enrolled in the third grade of A University of Education participated in this study. They judged their English proficiency as intermediate or upper-intermediate and said that their speaking and writing skills were lower than their reading and listening skills. Research data were obtained from the responses of the participants to the basic questionnaires and written interview questions. According to the results, pre-service teachers responded positively to the process of conducting and evaluating online English micro-teaching using ZOOM. In addition, by participating in this evaluation process, they were able to improve their micro-teaching competence and had a meaningful and growing experience from the online micro-teaching. However, it was also found that there is room for reconsideration in some issues, such as the form of self-reflection tasks, the amount of peer evaluation, and misconduct that may occur during online micro-teaching. Based on the results, several suggestions were made for a more appropriate evaluation of online micro-teaching in preparation for the post-COVID-19 era. #COVID-19 #elementary English #evaluation #online micro-teaching #pre-service teachers #코로나19 #초등영어 #평가 #온라인 수업실연 #예비교사 I. 서론II. 선행 연구III. 연구 방법IV. 결과 및 논의V. 결론참고문헌
The phantom steering effect in Q&A websites

Abstract Virtual rewards, such as badges, are commonly used in online platforms as incentives for promoting contributions from a userbase. It is widely accepted that such rewards “steer” people’s behaviour towards increasing their rate of contributions before obtaining the reward. This paper provides a new probabilistic model of user behaviour in the presence of threshold rewards, such a badges. We find, surprisingly, that while steering does affect a minority of the population, the majority of users do not change their behaviour around the achievement of these virtual rewards. In particular, we find that only approximately 5–30% of Stack Overflow users who achieve the rewards appear to respond to the incentives. This result is based on the analysis of thousands of users’ activity patterns before and after they achieve the reward. Our conclusion is that the phenomenon of steering is less common than has previously been claimed. We identify a statistical phenomenon, termed “Phantom Steering”, that can account for the interaction data of the users who do not respond to the reward. The presence of phantom steering may have contributed to some previous conclusions about the ubiquity of steering. We conduct a qualitative survey of the users on Stack Overflow which supports our results, suggesting that the motivating factors behind user behaviour are complex, and that some of the online incentives used in Stack Overflow may not be solely responsible for changes in users’ contribution rates.
A Robust Feature-Based Approach for Recognition of Line Coding Schemes

Decoding communication signals in a non-cooperative environment has always been a challenging task. Even after the estimation of various transmission-related parameters, the unknown received signal still cannot be decoded without the correct classification of the incorporated line coding scheme. In this paper, a robust short-sample feature-based approach is presented which recognizes line coding schemes in a sequential manner by an in-depth examination of linked characteristic features. The proposed approach provides an overall correct classification accuracy higher than 90 percent with an input of just 13 bit-waveforms whereas perfect classification accuracy (100 percent) is achieved with just 30 bit-waveforms of the unknown received signal. A detailed comparison considering noiseless as well as noisy channel environment is also carried out vis-&#x00E0;-vis existing approach based on extensive simulation results. Additionally, the paper bridges the gap between theory and simulations to justify the obtained accuracy results for conventional line codes under consideration. The substantial increase in classification accuracy for a smaller number of input bit-waveforms shall aid effective decoding of the unknown received signal even at the initial stages of reception. In general, it can benefit many practical spectrum surveillance applications, where proactiveness is paramount.
Very short-term parametric ambient temperature confidence interval forecasting to compute key control parameters for photovoltaic generators

In recent years, various forecasters have been developed to decrease the uncertainty related to the intermittent nature of photovoltaic generation. While the vast majority of these forecasters are usually just focused on deterministic or probabilistic prediction points, few studies have been carried out in relation to prediction intervals. In increasing the reliability of photovoltaic generators, being able to set a confidence level is as important as the forecaster's accuracy. For instance, changes in ambient temperature or solar irradiation produce variations in photovoltaic generators’ output power as well as in control parameters such as cell temperature and open voltage circuit. Therefore, the aim of this paper is to develop a new mathematical model to quantify the confidence interval of ambient temperature in the next 10 min. Several error metrics, such as the prediction interval coverage percentage, the Winkler score and the Skill score, are calculated for 95%, 90% and 85% confidence levels to analyse the reliability of the developed model. In all cases, the prediction interval coverage percentage is higher than the selected confidence interval, which means that the estimation model is valid for practical photovoltaic applications.
Underwater Image Enhancement with Multi-Scale Residual Attention Network

Underwater images suffer from low contrast, color distortion and visibility degradation due to the light scattering and attenuation. Over the past few years, the importance of underwater image enhancement has increased because of ocean engineering and underwater robotics. Existing underwater image enhancement methods are based on various assumptions. However, it is almost impossible to define appropriate assumptions for underwater images due to the diversity of underwater images. Therefore, they are only effective for specific types of underwater images. Recently, underwater image enhancement algorisms using CNNs and GANS have been proposed, but they are not as advanced as other image processing methods due to the lack of suitable training data sets and the complexity of the issues. To solve the problems, we propose a novel underwater image enhancement method which combines the residual feature attention block and novel combination of multi-scale and multi-patch structure. Multi-patch network extracts local features to adjust to various underwater images which are often Non-homogeneous. In addition, our network includes multi-scale network which is often effective for image restoration. Experimental results show that our proposed method outperforms the conventional method for various types of images.
Quantum computing formulation of some classical Hadamard matrix searching methods and its implementation on a quantum computer

Finding a Hadamard matrix (H-matrix) among all possible binary matrices of corresponding order is a hard problem that can be solved by a quantum computer. Due to the limitation on the number of qubits and connections in current quantum processors, only low order H-matrix search of orders 2 and 4 were implementable by previous method. In this paper, we show that by adopting classical searching techniques of the H-matrices, we can formulate new quantum computing methods for finding higher order ones. We present some results of finding H-matrices of order up to more than one hundred and a prototypical experiment of the classical-quantum resource balancing method that yields a 92-order H-matrix previously found by Jet Propulsion Laboratory researchers in 1961 using a mainframe computer. Since the exactness of the solutions can be verified by an orthogonality test performed in polynomial time; which is untypical for optimization of hard problems, the proposed method can potentially be used for demonstrating practical quantum supremacy in the near future.
Online Trolling: The Impact of Antisocial Online Content, Social Media Use, and Gender

The present study aimed to observe the relationships between online trolling, exposure to antisocial online content, frequency of social media use, and gender, using the GAM as a theoretical framework. Four hundred twenty-nine Brazilian internet users (mean = 25.07 years; SD = 7.59; EP = 0.36), most of whom were women (71.8%), participated in the survey. Bivariate correlations indicated a positive relationship between online trolling, exposure to antisocial online content (r = 0.12; p < 0.01), Facebook use (r = 0.21; p < 0.01), Twitter Use (r = 0.12; p < 0.01), and gender (r = 0.15; p < 0.01). An explanatory model including these variables was tested, and obtained a significant model fit (GFI = 0.99; Comparative Fit-Index = 0.99; Tucker Lewis Index = 0.97; Root Mean Square Residual = 0.02; RMSEA = 0.02 | CI = 0 .01-0.07 |). Were also observed indirect effects for exposure to antisocial online content through Twitter use and Facebook use on trolling (λ = 0.03; CI = 0.01-0.05; p < 0.05). It is possible to conclude that the research objectives were fulfilled, emphasizing the role of situational variables in the understanding of online trolling.
Design and Development of Efficient Information Hiding using SFVMI 16*16 Algorithm

Image scrambling make unchangeable of computerized image between, original image and modification of image, which is mentioned precisely data scrim. Modernity of image scrambling is depend upon data protection, if data is non-protected means no authentication data protection computation are required. Data stowage not focus on revolt inside the warfare of unified data, through it attract the attention an organizing the unusable warfare computations. Manage data warfare is an mandatory part of data warfare, and its cretic way has use open manage for own data transposition. In case of image scrambling while encryption performed the computations data is scattered,so assaialiant unable to unwrap data. A couple of made improvement progressed tide marking advancement can enforce scrambling technique to modify the scattering of the mix-up piece in the image to push ahead the strength of mechanized tide marking development. Arnold scrambling computation process has provided the higher security from mapping the one image pixel to a different pixel, that computation process is very used into marking technique.
A Logic of Interactive Proofs

We introduce the probabilistic two-agent justification logic $$\mathsf {IPJ}$$ , a logic in which we can reason about agents that perform interactive proofs. In order to study the growth rate of the probabilities in $$\mathsf {IPJ}$$ , we present a new method of parametrizing $$\mathsf {IPJ}$$ over certain negligible functions. Further, our approach leads to a new notion of zero-knowledge proofs.
Single-Image Super-Resolution Using Rational Fractal Interpolation and Adaptive Wiener Filtering

In this paper, we have propounded a neoteric procedure for the super-resolution of an image using a single image. An image of low resolution is given as input which is upscaled to an image while preserving the information that is stored in textural and structural details of an image. The image provided as input which is of low resolution is segregated into two sections, namely textured and non-textured according to the features of the image. Rational fractal interpolation is employed in the section of the image considered as textured and rational interpolation is employed in the remaining image which is considered to be non-textured. Thereafter, pixel mapping is performed. The result obtained from interpolation is found to contain Gaussian noise. To subdue the effect of this noise, an adaptive Wiener filter is applied. Finally, an image of high resolution is obtained. Profound simulations and assessments demonstrate that competitive performance is achieved by our algorithm. The mean square error reduces approximately up to $$5\%$$ , whereas the structural similarity index improves marginally.
Introduction

Abstract Financial opinion mining is a branch of traditional opinion mining and sentiment analysis which shares the basic notions of traditional approaches and adds its own domain-specific characteristics. In Sect. 1.1, we start with a common definition of general opinion mining after which we briefly overview traditional research directions.
Hardware Optimization and FPGA Implementation of Pipelined ZUC Architecture

The ZUC stream cipher belongs to synchronous stream cipher category. The cipher is word oriented and produces two words (32-bits) cipher text per cycle. The ZUC uses 128 bits key and 128-bits Initialization Vector (IV) for its operation. The research work considers FPGA implementation of pipelined ZUC architecture. The presented work uses compact architecture for modulo adders (over 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">31</sup> -1) used in feedback path and modulo adders (Over 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">32</sup> ) used in Finite State Machine (FSM). It also uses novel S-box architectures to minimize hardware resources. The presented architecture coded using VHDL language using Xilinx ISE 14.2 software for synthesis and simulation. The hardware implementation of the design uses Xilinx FPGA Virtex-5 XC5VFX130T having package FF1738. The presented hardware attained throughput of 5411.8 Mbps at clock frequency of 169.119MHz. The result shows the considerable hardware optimization.
Adaptive Runtime Monitoring of Service Level Agreement Violations in Cloud Computing

The cloud service level agreement (SLA) manage the relationship between service providers and consumers in cloud computing. SLA is an integral and critical part of modern era IT vendors and communication contracts. Due to low cost and flexibility more and more consumers delegate their tasks to cloud providers, the SLA emerges as a key aspect between the consumers and providers. Continuous monitoring of Quality of Service (QoS) attributes is required to implement SLAs because of the complex nature of cloud communication. Many other factors, such as user reliability, satisfaction, and penalty on violations are also taken into account. Currently, there is no such policy of cloud SLA monitoring to minimize SLA violations. In this work, we have proposed a cloud SLA monitoring policy by dividing a monitoring session into two parts, for critical and non-critical parameters. The critical and non-critical parameters will be decided on the interest of the consumer during SLA negotiation. This will help to shape a new comprehensive SLA based Proactive Resource Allocation Approach (RPAA) which will monitor SLA at runtime, analyze the SLA parameters and try to find the possibility of SLA violations. We also have implemented an adaptive system for allocating cloud IT resources based on SLA violations and detection. We have defined two main components of SLA-PRAA i.e., (a) Handler and (b) Accounting and Billing Manager. We have also described the function of both components through algorithms. The experimental results validate the performance of our proposed method in comparison with state-of-the-art cloud SLA policies.
A Case Study on Digital Literacy with Respect to India

Digital Literacy means having the required knowledge and skills what human beings of modern world needs to learn and work in a society where communication and access to information is done through digital technologies like internet, social media etc, with the help of digital devices like computer, laptops, desktops, tablets, or mobile and refers to an individual’s ability to find, evaluate and compose clear information through writing and other media on various digital platforms. In this paper we will study the meaning of digital literacy along with the present status of digital literacy in the current world with a special reference to India. The current study has been casual, exploratory and empirical in nature and the data needed for research work has been collected by using both direct and indirect method of data collection.
Use computer technology to integrate environmental protection online monitoring data

At present, China's online environmental protection testing work has a low popularization of computer technology, and the application of data information is insufficient. The use of computer technology does not help the online environmental monitoring data in China to greatly improve the quality of environmental monitoring. However, there are still some problems in the integration of environmental protection online monitoring data in China, the accuracy of relevant instruments is relatively low, and the installation and management of online monitoring system is extremely complex, with a high professional degree. Therefore, this paper mainly introduces the advantages of computer technology and the characteristics of system management, and further analyzes the effectiveness of integrating environmental protection online monitoring data.
The Problem of Measuring Human Capacity Using Ahangyol (Way to Harmony) Methods and the Need for Fuzzy Evaluation

A range of methods are exploited to study human capacity (capacity of social groups, social systems). This article proposes a completely new approach. Already widely known to the scientific community, the Science of Ahangyol proposed by the author helps to define such an approach. According to Ahangyol, there are ways of collecting information when determining the capacity of a person, which is regarded as a “cosmo-bio-psycho-socio” being. At the same time, scientific analysis is carried out to determine each factor more accurately. According to specific indicators, it is possible to determine ways of selecting the more important influential factors from the collected information. These selected indicators can play an important role in developing recommendations for the future success of new hires or existing staff. It can be useful in studying, forecasting and managing social processes.
Power Failure Prediction Method Based on Data Analysis

Normally, the fault diagnosis of the power system is to use the information generated after the fault to infer the fault after the fault occurs. After the power system fails, when the information obtained by SCADA is incomplete and inaccurate, the accuracy of fault diagnosis will be greatly reduced. If the fault information is made complete, the correct rate of fault diagnosis can be improved. Furthermore, if a short-term prediction of power system failures is carried out based on the operation of the power system before the power system fails, to ensure the safe and stable operation of the power system and reduce the loss caused by the failure. This research combines the matter-element extension method to evaluate the situation of the power system and realize the risk prediction.
Utilizing Hidden Markov Models to Classify Maneuvers and Improve Estimates of an Unmanned Aerial Vehicle

Estimating the states of a Unmanned Aerial Vehicle (UAV) without the use of onboard sensors can be difficult, particularly if the UAV is performing high dynamic maneuvers. This paper examines if data driven modelling can assist in estimating UAV states, as well as classification of UAV maneuvers. A standard Extended Kalman Filter (EKF) that uses radar measurements and a constant acceleration dynamic model is used as the baseline estimation technique for dynamic UAV maneuvers. The UAV maneuvers are then modelled as Hidden Markov Models (HMM), which classify maneuvers and generate additional state information in the form of acceleration and jerk estimates. These HMM estimates are incorporated into an EKF to create a fusion EKF+HMM. This paper evaluates the robustness of the HMM classification accuracy and compares the EKF+HMM to a standard EKF using both simulated and experimental data.
Design and Social Behavior

In 2004, my son Noah was turning one year old and I had a problem: How do I make him a birthday cake? He was seriously allergic to dairy, soy, and egg. A mis-read food label or a bite snuck from another child’s plate at daycare could send us to the emergency room. But he was turning one – I wanted him to have birthday cake. I found a website called kidswithfoodallergies.org, and asked on the forum there: Did anyone have a dairy-, soy- and egg-free cake recipe? In response to my query, I got a flurry of warm welcomes from parents on the site. They shared an excellent safe cake recipe, and provided a host of other support. Parents on the site helped me figure out how to make a clear and effective allergy-awareness sheet for his daycare teachers. They shared tips for how to safely order food in a restaurant. Their experience was invaluable, and they also were emotionally supportive in a way no one else could be. Parenting an allergic toddler is stressful, and they understood completely.
Revisiting group oriented secret sharing schemes

In a (t,n) threshold scheme any t or more shares can reconstruct the secret s, but less than t shares reveal no information about s. However, an unauthenticated adversary can pretend to be the shareholder at the reconstruction stage. If there were more than t honest shareholders, the unauthenticated adversary without valid share can obtain the secret. To deal with this type of attacks, a model of (t,m,n) group oriented secret sharing (GOSS) scheme was proposed by Miao et al. in 2015. Here the group oriented property means that if m>t parties try to reconstruct the secret, they should all have the authentic shares in advance. It was claimed by Miao et al. that the group oriented property in their GOSS schemes holds in the information-theoretic sense. In this paper, we revisit two instantiations of (t,m,n) group oriented secret sharing schemes and show that these constructions cannot provide the so-called “group oriented property”. Specifically, we develop concrete attacks which allow an unauthenticated adversary with no valid share to participate in the reconstruction phase and obtain the secret provided that there are at least t honest shares presented at the reconstruction phase.
Reducing Time-To-Fix For Fuzzer Bugs

At Google, fuzzing C/C++ libraries has discovered tens of thousands of security and robustness bugs. However, these bugs are often reported much after they were introduced. Developers are provided only with fault-inducing test inputs and replication instructions that highlight a crash, but additional debugging information may be needed to localize the cause of the bug. Hence, developers need to spend substantial time debugging the code and identifying commits that introduced the bug. In this paper, we discuss our experience with automating a fuzzing-enabled bisection that pinpoints the commit in which the crash first manifests itself. This ultimately reduces the time critical bugs stay open in our code base. We report on our experience over the past year, which shows that developers fix bugs on average 2.23 times faster when aided by this automated analysis.
Processing Large Satellite Imagery to Estimate Solar Irradiance

We use satellite imagery to identify cloud types and correlate with irradiance data from a solar platform. Processing the large images takes several minutes. Integrating into a web service would enable multiple users to estimate solar irradiance using just satellite imagery. It would also allow for the processing of many images at once as more remote sensing satellites are launched and data is easily accessible. The Sentinel 2 Cirrus Band shows an 81 % correlation with the diffuse solar irradiance values measured at the platform.