Reviewerly/exHarmony huggingface dataset:
Cognitive Predictors of Coherence in Adult ESL Learners’ Writing

Background. Coherence is considered one of the most important qualities of written discourse. Despite its fundamental importance, it is still considered a fuzzy and abstract concept in most English Second Language (ESL) contexts. Consequently, many ESL learners struggle to produce a coherent text. Morphological, phonological, orthographic awareness, vocabulary knowledge, and grammatical competence have been identified as predictors of writing quality in novice writers. There is, however, a lack of data to assess whether such linguistic skills also predict coherence in adult ESL learners’ writing.&#x0D; Purpose. The purpose of the study was to find out the relationships among a set of linguistic skills measures which included morphological, phonological, orthographic awareness, vocabulary knowledge and grammatical competence and coherence in adult ESL learners’ writing. &#x0D; Methods. To testify to the potential predictors of coherence in ESL writing, adult university students (126) were assessed by the measures of the linguistic skills mentioned above in addition to four measures of coherence: two relatively reader-based measures (ILETS and the Holistic Coherence Scale) and two relatively text-based measures (Topical Structure Analysis and Topic Based Analysis). All measures of the study were proved valid and reliable.&#x0D; Results. The findings revealed that vocabulary knowledge, morphological awareness, and grammatical competence were related to the coherence measures, particularly the reader-based measures. In contrast, measures of phonological and orthographic awareness generally did not correlate with the coherence measures.&#x0D; Implication. Reasons for the associations among the variables of the study were discussed and areas for future research were offered.
A Fast Adaptive Multi-Scale Kernel Correlation Filter Tracker for Rigid Object

The efficient and accurate tracking of a target in complex scenes has always been one of the challenges to tackle. At present, the most effective tracking algorithms are basically neural network models based on deep learning. Although such algorithms have high tracking accuracy, the huge number of parameters and computations in the network models makes it difficult for such algorithms to meet the real-time requirements under limited hardware conditions, such as embedded platforms with small size, low power consumption and limited computing power. Tracking algorithms based on a kernel correlation filter are well-known and widely applied because of their high performance and speed, but when the target is in a complex background, it still can not adapt to the target scale change and occlusion, which will lead to template drift. In this paper, a fast multi-scale kernel correlation filter tracker based on adaptive template updating is proposed for common rigid targets. We introduce a simple scale pyramid on the basis of Kernel Correlation Filtering (KCF), which can adapt to the change in target size while ensuring the speed of operation. We propose an adaptive template updater based on the Mean of Cumulative Maximum Response Values (MCMRV) to alleviate the problem of template drift effectively when occlusion occurs. Extensive experiments have demonstrated the effectiveness of our method on various datasets and significantly outperformed other state-of-the-art methods based on a kernel correlation filter.
Effect of micromotion and local stress in quantum simulations with trapped ions in optical tweezers

The ability to program and control interactions provides the key to implementing large-scale quantum simulation and computation in trapped-ion systems. Adding optical tweezers, which can tune the phonon spectrum and thus modify the phonon-mediated spin-spin interaction, was recently proposed as a way of programing quantum simulators for a broader range of spin models [Arias Espinoza et al., Phys. Rev. A 104, 013302 (2021)]. In this work we study the robustness of our findings in the presence of experimental imperfections: micromotion, local stress, and intensity noise. We show that the effects of micromotion can be easily circumvented when designing and optimizing tweezer patterns to generate a target interaction. Furthermore, while local stress, whereby the tweezers apply small forces on individual ions, may appear to enable further tuning of the spin-spin interactions, any additional flexibility is negligible. We conclude that optical tweezers are a useful method for controlling interactions in trapped-ion quantum simulators in the presence of micromotion and imperfections in the tweezer alignment, but require intensity stabilization on the subpercent level.
READINESS FOR MOBILE LEARNING AMONG CHINESE AS A SECOND LANGUAGE LEARNERS IN A PRIVATE UNIVERSITY COLLEGE IN MALAYSIA

Although learning Chinese as a second language (CSL) is becoming increasingly important due to the rapid development of China's comprehensive national power, CSL learning abroad faces great challenges. This study proposes to use mobile learning (m-learning), especially mobile assisted language learning (MALL), to facilitate CSL learning. This research aimed to investigate learners' readiness to engage in m-learning, their levels of mobile perception, mobile motivation, and mobile self-efficacy, as well as the correlations between these constructs. Respondents were selected using the purposive sampling method from a private university college in Melaka, Malaysia. The total number of valid cases was 95. The three instruments which were adopted to collect data in this study were examined the reliability and validity with pilot study. Descriptive analysis revealed that respondents had moderate levels of mobile perception, mobile motivation, and mobile self-efficacy. Bivariate correlation analysis revealed that all three constructs were highly correlated. Perceived usefulness (PU) ranked comparatively first, followed by perceived ease to use (PEOU) and perceived difficulty (PD). Respondents who perceived difficulty found mobile apps less useful. Respondents who perceived ease to use and usefulness were more motivated than respondents who perceived difficulty. Respondents who perceived difficulty preferred more outside help rather than relying on themselves. Respondents with moderate self-efficacy (MSE) were more motivated than respondents with high self-efficacy (HSE). In general, respondents in the higher education system have their own mobile device and are ready for m-learning. Therefore, policy makers and teachers should consider integrating m-learning and MALL into the classroom to improve formal teaching.
Understanding and Creating Spatial Interactions with Distant Displays Enabled by Unmodified Off-The-Shelf Smartphones

Over decades, many researchers developed complex in-lab systems with the overall goal to track multiple body parts of the user for a richer and more powerful 2D/3D interaction with a distant display. In this work, we introduce a novel smartphone-based tracking approach that eliminates the need for complex tracking systems. Relying on simultaneous usage of the front and rear smartphone cameras, our solution enables rich spatial interactions with distant displays by combining touch input with hand-gesture input, body and head motion, as well as eye-gaze input. In this paper, we firstly present a taxonomy for classifying distant display interactions, providing an overview of enabling technologies, input modalities, and interaction techniques, spanning from 2D to 3D interactions. Further, we provide more details about our implementation—using off-the-shelf smartphones. Finally, we validate our system in a user study by a variety of 2D and 3D multimodal interaction techniques, including input refinement.
Joint learning for disaster event extraction

Natural disasters such as forest fires, earthquakes, and droughts will have a huge impact on people's lives and the social economy, which makes people and governments pay great attention to these disasters. Therefore, the automatic extraction of disaster events and relevant information about events has high research value. The event extraction task based on Pipeline will have cascading errors and the use of natural language processing tools in the event extraction task will cause the problem of error accumulation. This paper proposes a joint event extraction method(BERT-LSTM-GRAPH, BLG) for disasters. This method converts the task of event extraction into a sequence labeling task and uses a graph convolutional neural network to fuse character information and word information to complete the joint extraction of triggers and arguments. Experiments show that the BLG model can improve the extraction performance of triggers and arguments in the field of disasters.
Pre-service Mathematics Teachers’ lesson plans as a source of Information about their readiness to teach online

This paper presents a study of lesson plans created by pre-service mathematics teachers (PMTs) from two universities (Faculty of Education, Charles University in Prague, and Faculty of Mathematics, Physics and Informatics, Comenius University in Bratislava) in the course Didactics of Mathematics. The collaboration of the two research teams brought new perspectives on several questions that were come across in one or the other group of PMTs. The focus of the study was on lesson plans since the lesson plan is one of the significant parts of mathematics teacher education. The studied issues were the PMTs’ willingness to implement digital technologies (DT) in their lesson plans and the changes in their lesson plans that could be linked to the COVID-19 pandemic. For data processing, a mixed research design was used. In the qualitative part of the research, the thematic analysis resulted in identification of several codes related to the hybrid mode of teaching. In the quantitative part, Statistical Implicative Analysis (C.H.I.C) was applied. Relations inside the groups of PMTs and among them are presented and discussed in the paper by interpreting the results from the implicative trees and graphs. We concluded that (i) PMTs from our sample are, to a limited extent, prepared for using DT in their teaching, (ii) PMTs used DT in their teaching mainly for testing and feedback collection, (iii) PMTs in our sample focused on the content and the choice of software (or application, applet, etc.) when preparing a lesson plan. Besides that, we found (by using C.H.I.C.) that not all codes identified in thematic analysis were connected to the others. The findings are of interest to teacher educators in general, researchers interested in teachers’ lesson planning, and in-service teachers in general. In this paper we focus on PMTs’ lesson plans. We plan to do a similar analysis of in-service teachers’ lesson plans in order to gain a deeper insight into the role of experience in the studied domain.
The Impact on Learners’ Identities of Aligning Pedagogy, Design and Technologies With Theory in Online Courses

In the past two decades, much of e-learning scholarship has reflected the tensions between what we as educators aspire to do, the theory we have to support what we do, and what we actually accomplish when it comes to technology supported learning. Some scholars are recognizing that inroads to resolving these tensions can be gained through a deeper understanding of the kinds of identities we promote in learners through our pedagogies, course designs and technologies. If our goals are to promote empowered, self-directed and life-long learners in our programs, then the practices, designs and technologies we employ must all be framed by theories that support such goals. Too often this is not the case. This study forms part of a larger longitudinal inquiry aimed at aligning these three components with 21st century goals and contemporary e-learning theory at a fully online higher education (HE) technical institution in Chile. An earlier pilot focussed on the reaction to this new alignment from the institution’s broad student body of working adults (n=2,300), the majority from socially and academically disadvantaged backgrounds. Narrowing the lens on a small cohort of students (n=14) entering the institution into a cross section of career disciplines, we adopted a qualitative case study approach to uncover a deeper understanding of the identities of these students on first arrival. Our objective was to determine how highly interactive group work-based teaching practices, learner-centred instructionally designed programs and social media-based technologies such as forums and live videoconferencing sessions, impacted their identities over the course of the first 5-month period. Data collection involved in-depth individual and focus group bi-weekly interviews, reflective feedback from routine student questionnaires, field notes, instructor observations and digital activity online. Findings indicated the crucial nature of this initial period for influencing student’s learning trajectories in terms of retention and for promoting the kinds of 21st century learner identities to which the institutional programs aspired. As a surge of institutions worldwide are more motivated than ever to finding effective e-learning solutions after experiencing challenges in their online programs during the pandemic, these results could provide empirical evidence of a viable pathway forward.
Designing a curriculum for digital competencies towards teaching and learning

The COVID-19 pandemic initiated a fundamental change in learning and teaching in (higher-) education [HE]. On short notice, traditional teaching in HE suddenly had to be transformed into online teaching. This shift into the digital world posed a great challenge to in-service teachers at schools and universities, and pre-service teachers, as the acquisition of digital competences was no longer an option but a real necessity. The previously rather hidden or even neglected importance of teachers’ digital competences for successful teaching and learning became manifest and clearly visible. &#x0D; In this work, we investigate necessary digital competences to ensure high quality teaching and learning in and beyond the current COVID-19 pandemic. Based upon the European DigComp 2.1 (Carretero et al., 2017), DigCompEdu (Redecker, 2017) frameworks, the Austrian Digi.kompP framework (Virtuelle PH, 2021), and the recommendations given by German Education authorities (KMK 2017; KMK 2021; HRK 2022), we developed a curriculum consisting of 5 modules: 2 for individual digital media competence, and 3 for media didactic competence. For each module, competence-oriented learning goals and corresponding micro-learning contents were defined to meet the needs of teachers while considering their time constraints.&#x0D; Based on three online workshops, the curriculum and the corresponding learning goals were discussed with university teachers, pre-service teachers, and policymakers. The content of the curriculum was perceived as highly relevant for these target groups; however, some adaptations were required. From the university teachers’ perspective, we got feedback that they were overwhelmed with the situation and urgently needed digital competences. Policymakers suggested that further education regarding digital competences needs to offer a systematic exchange of experiences with peers. From the perspective of in-service teachers, it was stated that teacher education should focus more on digital competences and tools.&#x0D; In this paper, we will present the result of the workshop series that informed the design process of the DIGIVID curriculum for teaching professionals.
A new approach to data differential privacy based on regression models under heteroscedasticity with applications to machine learning repository data

Generation of massive data in the digital age leads to possible violations of individual privacy. The search for personal data becomes an increasingly recurrent exposure today. The present work corresponds to the area of differential privacy, which guarantees data confidentiality and robustness against invasive identification attacks. This area stands out in the literature for its rigorous mathematical basis capable of quantifying the loss of privacy. A differentially private method based on regression models was developed to prevent inversion attacks while retaining model efficacy. In this paper, we propose a novel approach to improve the data privacy based on regression models under heteroscedasticity, a common aspect, but not studied, in practical situations of differential privacy. The influence of privacy restriction on the statistical performance of the estimators of model parameters is evaluated using Monte Carlo simulations, including a study of performance associated with test rejection rates for the proposed approach. The results of the numerical evaluation show high inferential distortion for stricter privacy restrictions. Empirical illustrations with real-world data are presented to show potential applications.
A STUDY ON PRIVACY PRESERVING BIG DATA MINING: TECHNIQUES AND CHALLENGES

The basic goal of data mining algorithms is to extract previously undiscovered patterns from the data. When mining the data, sensitive and confidential information should be secured simultaneously to protect privacy. Due to the widespread use of information technology, enormous amounts of data are being produced at an exponential rate by several organisations, including hospitals, insurance providers, banks,e-commerce, and stock exchanges, making privacy a crucial concern in data mining. Anonymization, Perturbation, Generalization, and Cryptography are some of the privacy-preserving data mining techniques that have been proposed in the literature. In this study, we have reviewed all of these state of art techniques and presented a tabular comparison of work done by different authors as well as discussed the challenges of privacy preserving data mining.
Efficient Content Based Video Retrieval System by Applying AlexNet on Key Frames

The video retrieval system refers to the task of retrieving the most relevant video collection, given a user query. By applying some feature extraction models the contents of the video can be extracted. With the exponential increase in video data in online and offline databases as well as a huge implementation of multiple applications in health, military, social media, and art, the Content-Based Video Retrieval (CBVR) system has emerged. The CBVR system takes the inner contents of the video frame and analyses features of each frame, through which similar videos are retrieved from the database. However, searching and retrieving the same clips from huge video collection is a hard job because of the presence of complex properties of visual data. Video clips have many frames and every frame has multiple properties that have many visual properties like color, shape, and texture. In this research, an efficient content-based video retrieval system using the AlexNet model of Convolutional Neural Network (CNN) on the keyframes system has been proposed. Firstly, select the keyframes from the video. Secondly, the color histogram is then calculated. Then the features of the color histogram are compared and analyzed for CBVR. The proposed system is based on the AlexNet model of CNN and color histogram, and extracted features from the frames are together to store in the feature vector. From MATLAB simulation results, the proposed method has been evaluated on benchmark dataset UCF101 which has 13320 videos from 101 action categories. The experiments of our system give a better performance as compared to the other state-of-the-art techniques. In contrast to the existing work, the proposed video retrieval system has shown a dramatic and outstanding performance by using accuracy and loss as performance evaluation parameters.
Students, Participatory Design, and Serious Games in a Response to: ‘No Algorithmization without Representation: Pilot Study on Regulatory Experiments in an Exploratory Sandbox’

Abstract No Algorithmization without Representation tracked a cohort of ‘Lithopy’ crypto-government sandbox participants in a longitudinal study looking at COVID-19 contact tracing app acceptance. These survey responses extended experiences with theoretical blockchain town governance by also tracking reasons for and against compliance with contact tracing apps. They found that the expressed opinions of students were incoherent and demanded technical or policy responses outside of the students’ direct experiences. In this response to that paper, I leverage the paper’s (commendable) open data to suggest that the sandbox’s claims of ‘No Algorithmization without Representation’ is a rediscovery of participatory design within the context of the serious games movement. While Role-Playing Games and War Games are excellent pedagogic tools xor planning tools—using undergraduate students’ participation in them as the basis of a claim for increased representation in technology policy is a bold claim. This claim is not fully substantiated by the paper’s data. Nevertheless, there is a need for better decision-making and public representation within technology design and policy-making spaces—making the claim for serious games as a meaningful public policy contribution not without merit.
Why does negation of the predicate shorten a clause?

According to the Menzerath-Altmann law, the mean word length is greater in shorter clauses than in longer ones. In Czech, negation is mostly realized by adding the prefix ne- to the beginning of the word, which makes the word longer (and, consequently, it also increases the mean word length in the clause). Therefore, we predict that clauses in which the predicate is in the affirmative form are longer than ones with the negative predicate. We test the hypothesis on a sample of 59 pairs of affirmative and negative forms of the same verb from the Prague Dependency Treebank 3.0.
Defogging Algorithm Based on Polarization Characteristics and Atmospheric Transmission Model

We propose a polarized image defogging algorithm according to the sky segmentation results and transmission map optimization. Firstly, we propose a joint sky segmentation method based on scene polarization information, gradient information and light intensity information. This method can effectively segment the sky region and accurately estimate the global parameters such as atmospheric polarization degree and atmospheric light intensity at infinite distance. Then, the Gaussian filter is used to solve the light intensity map of the target, and the information of the polarization degree of the target is solved. Finally, based on the segmented sky region, a three-step transmission optimization method is proposed, which can effectively suppress the halo effect in the reconstructed image of large area sky region. Experimental results shows that defogging has a big improvement in the average gradient of the image and the grayscale standard deviation. Therefore, the proposed algorithm provides strong defogging and can improve the optical imaging quality in foggy scenes by restoring fog-free images.