Reviewerly/exHarmony huggingface dataset:
Mobile-Assisted Language Learning for EFL: A Conceptual Framework based on the Meta-UTAUT Model

The wide usage of smart mobile phones has drawn policymakers and educators towards the deployment of technology for mass education programs.Particularly in societies where English is not widely spoken, Mobile-Assisted Language Learning (MALL) is seen as an effective tool for educating citizens in learning English as a Foreign Language (EFL).However, despite the availability of broadband Internet and smartphones, the usage of MALL for EFL is not widely used by most non-native speakers.Therefore, it is imperative to determine the factors that drive the technology usage behavior of potential EFL learners. This study proposes a conceptual framework that presents factors that are potential drivers of behavioral intention and actual usage of MALL by EFL learners. The framework is based on the latest version of the Unified Theory of Acceptance and Use of Technology Model (known as meta-UTAUT).The framework theorizes that technology usage behavior is driven by performance expectancy, effort expectancy, social influence, and facilitating factors related to the technology.Furthermore, the relationships between the MALL usage behavior and its' drivers are potentially mediated by the attitude of the learners towards the technology.The outcome of this study is expected to pave the path for future researchers to undertake studies to test the framework to develop an empirically tested model.
A Systematic Literature Review of Meta-Learning Models for Classification Tasks

Meta-learning is a field of learning that aims at addressing the challenges of conventional machine learning approaches such as learning from scratch for every new task. The main aim of this study was to do a systematic literature review of the existing meta-learning models that have been developed, published, and can be used for classification tasks. Systematic literature review method was used, employing a search of journal articles and publications of conference proceedings. The process involved data collection, analysis, and reporting of the results. To achieve the objective, 30 primary papers published since 2016 and relevant to classification tasks in meta-learning were considered. Data was extracted from the papers, then the following was analyzed in each model as presented in the papers; techniques used, the contribution, and the research gap. Although a lot has been done so far in Meta-learning, the existing models are not yet optimal. They still have challenges in few-shot learning, computation time complexity, difficulty in continual learning, and generalizability across multiple related tasks during transfer learning.
Aprendizagem do cuidar a família na comunidade: Usabilidade do Modelo Dinâmico de Avaliação e Intervenção Familiar

Background: The Dynamic Model of Family Assessment and Intervention is used in the teaching-learning process of undergraduate nursing students to guide the process of caring for families in the community.Objective: To analyze the dimensions valued by undergraduate nursing students in the development of family assessment and intervention skills.Methodology: An exploratory-descriptive study was conducted with 350 undergraduate nursing students.A form was used to identify aspects valued in using the theoretical and operational model in clinical teaching.Data were subjected to content analysis, with a priori and a posteriori categorization.All ethical assumptions were met.Results: Five dimensions were identified: Clarity (most valued); Simplicity; Generality; Derivable consequences (intermediate frequency), and Empirical precision (least valued).Interconnection emerges between stages of the care process and the model as an instrument of change and reference in decision-making in clinical settings.Conclusion: The dimensions confirm the importance attributed by students to the structure and meaning in learning about family care and are predictors of integrated learning between nursing theory and practice.
3D Deep Heterogeneous Manifold Network for Behavior Recognition

With the broadening of application scenarios for Internet of Things, intelligent behavior recognition task has attracted more and more attention. Since human behavior is nonrigid motion with strong spatiotemporal topological association, modeling it directly with traditional Euclidean space-based methods may destroy its underlying nonlinearity. Based on the advantages of Riemannian manifold in describing 3D motion, we propose an end-to-end 3D behavior manifold feature learning framework composed of deep heterogeneous networks. This heterogeneous architecture aims to leverage the graph construction to guide manifold backbone network to mine more discriminative nonlinear spatiotemporal features. Therefore, we first model the nonlinear spatiotemporal co-occurrence of 3D behavior in the high-dimensional Riemannian manifold space. Secondly, we implement a non-Euclidean heterogeneous architecture on the Riemannian manifold so that the backbone network can learn deep spatiotemporal features while preserving the manifold topology. Finally, an end-to-end deep graph similarity-guided learning optimization mechanism is introduced to enable the overall model to fully utilize the complex similarity relationship between manifold features. We have verified our 3D deep heterogeneous manifold network on popular skeleton behavior datasets and achieved competitive results.
SkillFence

Voice assistants are deployed widely and provide useful functionality. However, recent work has shown that commercial systems like Amazon Alexa and Google Home are vulnerable to voice-based confusion attacks that exploit design issues. We propose a systems-oriented defense against this class of attacks and demonstrate its functionality for Amazon Alexa. We ensure that only the skills a user intends execute in response to voice commands. Our key insight is that we can interpret a user's intentions by analyzing their activity on counterpart systems of the web and smartphones. For example, the Lyft ride-sharing Alexa skill has an Android app and a website. Our work shows how information from counterpart apps can help reduce dis-ambiguities in the skill invocation process. We build SkilIFence, a browser extension that existing voice assistant users can install to ensure that only legitimate skills run in response to their commands. Using real user data from MTurk (N = 116) and experimental trials involving synthetic and organic speech, we show that SkillFence provides a balance between usability and security by securing 90.83% of skills that a user will need with a False acceptance rate of 19.83%.
BEMD Based Cross Bilateral Filtering Technique for Speckle Reduction in Ultrasound Images

In this paper, Bidimensional Empirical Mode Decomposition (BEMD) based Cross Bilateral Filter (CBF) technique for speckle reduction in ultrasound images has been proposed. The reference image is obtained by denoising the noisy image using pixel-wise Wiener filtering. Then, both the noisy image and the reference image are decomposed into a set of Intrinsic Mode Functions (IMFs) and the residue image using BEMD technique. CBF is applied between noisy image IMFs and the corresponding reference image IMFs. The image is reconstructed back with these modified IMFs and the residue. The proposed method exploits the edge information in the reference image for improving the quality of the denoised image. The performance of the proposed method has been tested for real ultrasound images and simulated images having noise of different variance. The experimental results show that the proposed algorithm performs better than other state-of-art methods in terms of Edge Keeping Index (EKI), Correlation Coefficient (CC), Figure of Merit (FOM), Structural Similarity (SSIM), Peak Signal to Noise Ratio (PSNR) and Signal to Noise Ratio (SNR) for synthetic images. The algorithm gives better performance for real ultrasound images in terms of Mean to Variance Ratio (MVR) and Equivalent Number of Looks (ENL).
Identifying Faked Responses in Questionnaires with Self-Attention-Based Autoencoders

Deception, also known as faking, is a critical issue when collecting data using questionnaires. As shown by previous studies, people have the tendency to fake their answers whenever they gain an advantage from doing so, e.g., when taking a test for a job application. Current methods identify the general attitude of faking but fail to identify faking patterns and the exact responses affected. Moreover, these strategies often require extensive data collection of honest responses and faking patterns related to the specific questionnaire use case, e.g., the position that people are applying to. In this work, we propose a self-attention-based autoencoder (SABA) model that can spot faked responses in a questionnaire solely relying on a set of honest answers that are not necessarily related to its final use case. We collect data relative to a popular personality test (the 10-item Big Five test) in three different use cases, i.e., to obtain: (i) child custody in court, (ii) a position as a salesperson, and (iii) a role in a humanitarian organization. The proposed model outperforms by a sizeable margin in terms of F1 score three competitive baselines, i.e., an autoencoder based only on feedforward layers, a distribution model, and a k-nearest-neighbor-based model.
Methods on Visual Positioning Based on Basketball Shooting Direction Standardisation

The existing basketball training shooting direction correction method has the problems of low correction accuracy and poor self-adaptability, and proposes a basketball training shooting direction correction method based on visual perception. A visual localisation algorithm for tracking feature points of object targets is used as the basis for the process of visual robot localisation and its effects, from camera calibration, template matching, background modelling and foreground target separation to feature point extraction, motion estimation, and Kalman filtering. An in-depth understanding and analysis of the traditional corner point detection algorithm is presented, on the basis of which improvements are proposed. An accurate tracking method based on improved Harris corner point extraction is introduced, which builds on the traditional Harris feature point detection by using the changing relationship between the gradient of the grey value of the pixels near the corner point, using simple operations and analysis to exclude some pseudocorner points and noncorner points, and further processing the retained points to derive the correct feature points. The code of this algorithm is written to finally achieve its detection effect, and compared with the traditional algorithm, it is concluded that this algorithm can then extract more accurate corner points in a shorter time, which lays the foundation for the next step of accurate basketball tracking, reflecting the practicality of this algorithm.
COPING STRATEGIES OF RUSSIAN SCHOOLS TEAMS DURING THE PANDEMIC: GET AHEAD OF THEMSELVES VS SURVIVE AND FORGET

Unlike organizations from other sectors, schools have existed in stable conditions and rarely found themselves in neсcesity of radical changes triggered from outside (for example, market fluctuations). The pandemic situation can be considered as a natural experiment because school teams were forced to cope with the situation of urgent transition by themselves, before the federal and regional educational authorities were ready to provide recommendations on how to arrange remote learning in schools. Thus, it seems to be a unique opportunity to study the variety of school teams’ practices of adaptation to external challenges and integration of the new into school practices. The concept of coping strategies has been used to describe the response to external “shocks” of individuals and school teams. Based on the data of a massive survey in 100 of Russian schools (N = 6931), conducted at the moment of the transition to distance learning (March 2020), 6 profiles of the organizational environment were identified. The responses have made it possible to select schools with different organizational contexts and study how it influences the strategies chosen by the school. With the help of semi-structured in-depth interviews with principals and teachers at Russian schools (N = 43) there were identified and classified the practices that reveal the adaptation to shock innovations of various types at schools. According to the results of the analysis, coping strategies are conceptualized as a system of dilemmas that need to be "resolved" by the leaders of the transformational process and by school teams: (1) authoritative or multi-voice decision-making, (2) uniformity or diversity of educational process practices, (3) quality control of the educational process or "the opportunity to lower the bar," (4) focus on reducing the workload and caring for the well-being of the collective or considering the pandemic as "wartime," (5) openness to new tools or orientation to the use of acquaintances. At the same time, the response to the challenges of the pandemic occurred to be related to the previous experience of introducing innovations at school, the intensity of the exchange of knowledge and practices within the organization and with the external environment as well as the current characteristics of the team. The discussion section considers how the identified coping strategies can be taken into account in the formation of future educational policy.
Instructional Methods and Hybrid Learning in Preservice Teacher Education—Case Studies in Portugal and Spain

The preservice teacher education assumes a predominant role in the quality of the educational process that must be assumed in public education policies, noting the importance of reflection on a variety of aspects, including the curriculum, teaching and learning methods, and pedagogical practices. To this end, this article aims to: i) present and compare the curriculum and study plans of two preservice teacher education programs in Portugal and Spain; ii) study the instructional methods used and hybrid learning format in these programs in pandemic time; and iii) analyze how their pedagogical practices are developed in post-pandemic time, particularly about the use of digital technologies and online education. As result, it is intended to obtain a characterization of the Masters of access to the teaching profession and analyze the way their curricula and instructional methods are developed. We also intend to draw conclusions about the effects of the Covid-19 pandemic on pedagogical practices, namely regarding how technologies can enable motivation, facilitate learning, and have a positive influence on pedagogical practices.
A Comparative Study Of Combining Deep Learning And Homomorphic Encryption Techniques

Deep learning simulation necessitates a considerable amount of internal computational resources and fast training for large amounts of data. The cloud has been delivering software to help with this transition in recent years, posing additional security risks to data breaches. Modern encryption schemes maintain personal secrecy and are the best method for protecting data stored on a server and data sent from an unauthorized third party. However, when data must be stored or analyzed, decryption is needed, and homomorphic encryption was the first symptom of data security issues found with Strong Encryption.It enables an untrustworthy cloud resource to process encrypted data without revealing sensitive information. This paper looks at the fundamental principles of homomorphic encryption, their forms, and how to integrate them with deep learning. Researchers are particularly interested in privacy-preserving Homomorphic encryption schemes for neural networks. Finally, present options, open problems, threats, prospects, and new research paths are identified across networks
Optimizing the Page Hotness Measurement with Re-Fault Latency for Tiered Memory Systems

Constructing tiered memory systems becomes prevalent in cloud and datacenter servers due to its benefits on large capacity and low total cost of ownership. Since such tiered memory systems usually comprise low-latency memory (e.g., DRAM) and high-capacity but high-latency memory (e.g., Nonvolatile memory), accurately measuring the page hotness is one of the most important factors. This paper uncovers the inefficiency of the short-term page hotness measurement proposed by recent work and explores how to detect short-term page hotness more precisely. Our proposed scheme tracks successive page accesses through recursive TLB misses and calculates the latency, called re-fault latency. Page migration decision is performed based on this re-fault latency. We evaluate our scheme on real hardware consisting of DRAM and Optane DC PMM and confirm that the proposed scheme outperforms the recent work by up to 1.51x.
Implementation of Chatbot in Online Classes using Google Classroom

Chatbot can be utilized as an interactive learning media for students. It can be implemented using modular system by dividing the courses into several modular based on the course contents. The proposed program contributes positively to the integration of object-oriented programming into Google classroom by broadening the service without being limited by space, time and number of students, and also helps in increasing students’ interest in learning. The proposed model was meant to establish interactive conversation to understand the concept of Oracle Academy Java Foundation material based on students’ level of understanding when organizing exercise items. The program was applied in three different classes and found to have increased the final exam results with an average difference of 0.60 points from the scale of maximum 10 points from the conventional learning group. The value of post-test was also observed to have increased by 0.39 points from the given pre-test. In general, implementation of chatbot could improve the quality of learning.
Understanding Is a Process

How do we gauge understanding? Tests of understanding, such as Turing's imitation game, are numerous; yet, attempts to achieve a state of understanding are not satisfactory assessments. Intelligent agents designed to pass one test of understanding often fall short of others. Rather than approaching understanding as a system state, in this paper, we argue that understanding is a process that changes over time and experience. The only window into the process is through the lens of natural language. Usefully, failures of understanding reveal breakdowns in the process. We propose a set of natural language-based probes that can be used to map the degree of understanding a human or intelligent system has achieved through combinations of successes and failures.
An Effective High Level Capacity Reversible Data Hiding in Encrypted Images

Data embedding is used by RDH to safely transmit hidden information; it permanently hides data within an unreadable format domain. An input image is converted into unreadable code by using a secret key and while transmitting, includes additional information in the encrypted image with no knowledge of the matter included. Decoding allows the inserted information to be removed, ultimately reinstating the input image. Reversible Data Hiding provides further information that is easy to use but there are currently no methods.The proposed research presents a new method with a very high data inserting capacity. Here, by hiding data behind images and converting them through cryptographic techniques, image steganography provides more security to the sensitive dataset. To find system performance, parameters like Peak Signal to Noise Ratio, Mean Square Error and Cross Correlation will be considered.
Adaptive Visual Tracking via Temporal-Spatial Regularized Multi-Patches

Recently, the research in the field of object tracking has made great progress, and exploiting the combination of correlation filtering method and other ones to improve tracking accuracy effectively has attracted the attention of many researchers. Most methods based on correlation filter introduce redundant background information when searching for the target position in the extracted search box. At the same time, unnecessary boundary effects are introduced due to cyclic shift. Therefore, in this study, initializing multiple particle patches near the real target under the framework of particle filtering is proposed, and a template-based spatio-temporal regularized correlation filter tracking algorithm is used to track reliable patches during the tracking process, simultaneously keeping the number of particle patches at a relatively sufficient level to focus on more real target information without losing important background information. In addition, the scale variation of final target per frame is adaptively estimated by the changes of reliable patches. Extensive comparative experiments conducted on popular general datasets demonstrate a relatively competitive performance on accuracy with some selected state-of-the-art trackers in the past few decades.
SCDetect: Near real-time computationally efficient waveform cross-correlation based earthquake detection during intense earthquake sequences

&amp;lt;p&amp;gt;Aftershock sequences or earthquake swarms generate a high number of seismic events that are not detected by standard regional network routine processes. Undetected earthquakes are mostly due to low signal to noise ratio, overlapping earthquakes, and a network configuration that targets earthquake detection with a homogeneous magnitude of completeness. Furthermore, the analyst&amp;amp;#8217;s workload is increasing dramatically during an intense earthquake sequence, which results in prompt manual review of the largest events, only.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We present a computationally efficient and highly customizable tool (SCDetect) to detect earthquakes in near real-time by applying waveform cross-correlation in the time domain based on a set of template events. SCDetect is a free and open-source SeisComP extension module fully integrated into the SeisComP environment. It may be used to process both archived waveform data, when operated in playback mode, as well as real-time data. In either of the use cases, waveform data is accessed through SeisComP&amp;amp;#8217;s standard RecordStream interface. Multiple template event based detectors may be configured. The individual detector configuration is fully stream based which allows for generic multi-stream event detection. Event parameter products for newly detected events (i.e. origins, picks, amplitudes, station magnitudes) may be sent to SeisComP's messaging system for further processing. In addition to earthquake detection, we implement amplitude calculation by measuring amplitudes on the horizontal components. SCDetect offers multiple magnitude estimation methods based on the amplitudes of the template earthquakes and the new detections (i.e regression, amplitude ratios). Magnitude estimation is configurable using SeisComP&amp;amp;#8217;s bindings configuration.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We applied SCDetect to recent earthquake sequences in Switzerland between 2019 and 2021. The dense seismic network operated by the Swiss Seismological Service offers a unique opportunity to evaluate the performance of the proposed module. Our first results show that these extended earthquake catalogs contain at least ten times more earthquakes than the national earthquake catalogue.&amp;lt;/p&amp;gt;