Reviewerly/exHarmony huggingface dataset:
English Learning Styles Between Korean and Mongolian Students

Although English has been required to be taught from primary school to university in South Korea and Mongolia, students can be still struggle with English. The purpose of the study was to identify the English learning strategies, styles, and preferences of Korean and Mongolian university students based on their different cultural awareness and linguistic backgrounds. The study was conducted through a research questionnaire based on different questions, designed for the needs of the research. Thus, the paper concerned the importance of learning English and even the problems, regarding students` learning styles, individual learning platforms, and linkages between the two countries.&#x0D; The paper also discussed the present situation in English education by exploring the fields of the study, learning difficulties, and methods. As a result, the study found the differences and similarities between students` usage of online platforms for their own unsupervised or unguided learning. Comparisons were made with regard to the students’ gender, age, learning styles, and other strategies. Our findings also revealed that not only teaching and learning strategies but also face-to-face communication and education systems can be important issues depending on students` linguistic background and learning style preferences.
Students agency in the emergency remote teaching landscape

This chapter explores different difficulties that students have had navigating the online spaces of learning during their pandemic. It explores how different mechanisms such as conflicting emotions, the lack of a sophisticated digital capability, and the institutional culture shapes students' agency in digital spaces, thus their learning experience. The author finds similarities in this regard with her doctoral research study, where similar difficulties were encountered regarding students more reflexive engagement with digital technologies making it evident that the idea of young people being digital natives, brought forward by Prensky in 2001 is still, a decade later, an urban and very convenient myth.
AirChain - Towards Blockchain-based Aircraft Maintenance Record System

Civil aviation (aircraft) is one of the public transportation industries that requires the highest safety standards. However, several accidents on aircraft maintenance system had revealed that there is a need to secure the record integrity and traceability. Motivated by this issue and supported by an airline, in this work, we design and implement AirChain, a blockchain-based aircraft maintenance record system, in which the data can be stored in a way that is both resistant to tampering and easy to access. The experimental results indicate the viability and practicability of our system.
A Novel Unified Self-alignment Method of SINS Based on FGO

The self-alignment process can provide an accurate initial attitude of SINS. The conventional two-procedure method usually includes coarse and fine alignment processes. Coarse alignment converges fast because of its batch estimating characteristics and the initial attitude does not influence the results. But coarse alignment is low accuracy without considering the IMU's bias. The fine alignment is more accurate by applying a recursive Bayesian filter to estimate the IMU's bias, but the attitude converges slowly as the initial value influence the convergence speed of the recursive filter. Researchers have proposed the unified self-alignment to achieve self-alignment in one procedure, existing unified methods make improvements on the basics of recursive Bayesian filter and those methods are still slow to converge. In this paper, a unified method based on batch estimator FGO (factor graph optimization) is raised, which is converge fast like coarse alignment and accurate than the existing method. We redefine the state and rederivation the state dynamic model first. Then, the optimal attitude and the IMU's bias are estimated simultaneously through FGO. The fast convergence and high accuracy of this method are verified by simulation and physical experiments on a rotation SINS.
Synthetic-to-Real Domain Adaptation using Contrastive Unpaired  Translation

The usefulness of deep learning models in robotics is largely dependent on the availability of training data. Manual annotation of training data is often infeasible. Synthetic data is a viable alternative, but suffers from domain gap. We propose a multi-step method to obtain training data without manual annotation effort: From 3D object meshes, we generate images using a modern synthesis pipeline. We utilize a state-of-the-art image-to-image translation method to adapt the synthetic images to the real domain, minimizing the domain gap in a learned manner. The translation network is trained from unpaired images, i.e. just requires an un-annotated collection of real images. The generated and refined images can then be used to train deep learning models for a particular task. We also propose and evaluate extensions to the translation method that further increase performance, such as patch-based training, which shortens training time and increases global consistency. We evaluate our method and demonstrate its effectiveness on two robotic datasets. We finally give insight into the learned refinement operations.
The research on several innovative comparators

This paper introduces six innovative designs of comparators proposed in recent years. The original intention to propose these improvements mainly focuses on the indexes including speed, area, power and offset, which measure the performance of comparators. Several newly proposed techniques are effective in designs. An improved DOC (dynamic-offsetcancellation) could increase speed and save area; Floating inverter pre-amplifier (FIA) can realize maximizing energy efficiency and reducing noise; Importing on-chip inductors in regenerative comparators has been verified reaching attractive goals successfully. Modifying structures and transistor type is also a feasible approach. After optimization, dynamic bias latch-type improves its predecessor’s performance in energy and noise; N-parallel paths could be applied to improve driving capability of latch; A novel voltage comparator termed an edge-race comparator (ERC) outperforms the conventional edition---the edge-pursuit comparator (EPC).
Speaker recognition model with robust front-end processing algorithm

The end-to-end speaker recognition model has made a great breakthrough in the research field and practical application scenarios. However, in practical application, we often suffer the diversity of noise, and the interference of noise will affect the performance of speaker recognition and classification model, and the model usually degrades in unseen scenes with noise. In this paper, a speaker recognition model combined with front-end enhancement is proposed. The front-end enhancement model (DTLN or CRNN) is combined with the back-end speaker recognition model (Res2Net-GhostVLAD) to improve the robustness of the model against noisy scenes, and the generalization capability of the model is increased by the data augmentation method (SpecAugment). Our proposed method was trained on VoxCeleb and AISHELL datasets and tested on VoxCeleb datasets. The test results show that the proposed method significantly improves the performance of the speaker recognition model in noisy scenarios, and the relative improvement of different front-end models is 9% and 13%, respectively.
Cyber-ethics for NGOs during Covid-19: The Eight “Ethical Variables” and a Microsocial Contract

Jürgen Habermas’ theory of communicative action and discourse ethics as a theoretical basis merges several factors that have an impact on the use of digital media by internet users. This article provides a qualitative narrative analysis of a study of five South African-based based non-governmental organisations (NGOs) on their experiences with digital media and cyber-ethics during the Covid-19 pandemic. The author interviewed employers and digital media specialists from the NGOs about their experiences with the “eight ethical variables”, namely, justice, privacy, access, accuracy, truth, human dignity, regulation, and ownership of information, during the global Covid-19 (acronym for the coronavirus disease of 2019) pandemic. The article discusses how the NGOs have been affected by the increasing use of digital media. The article argues that a need exists for a framework of cyber-ethics for self-regulation purposes, to be followed by NGOs to deal with breaches of ethical conduct. Finally, the formulation of a microsocial contract based on the proposed eight ethical variables is offered. The present study contributes to media ethics literature by proposing a framework for ethical conduct for digital media use. This is of importance to internet users and may be achievable if imbedded in employee procedural policies and public policies. It is argued that in formulating appropriate ethical guidelines, Habermas’ discourse ethics should be kept in mind for an optimal microsocial contract to be attainable.
Optimized gradient and Hessian estimators for scalable variational  quantum algorithms

Sampling noisy intermediate-scale quantum devices is a fundamental step that converts coherent quantum-circuit outputs to measurement data for running variational quantum algorithms that utilize gradient and Hessian methods in cost-function optimization tasks. This step, however, introduces sampling errors in the resulting gradient or Hessian computations. To minimize these errors, we introduce tunable finite-difference estimators (including their generalized versions) and provide analytically operational (circuit-averaged) recipes to optimize these estimators. We show that such an optimized estimator additionally offers sampling errors that drop exponentially with the number of circuit qubits for a given sampling-copy number, revealing a direct compatibility with the barren-plateau phenomenon. As a consequence, we prove that there exists a critical sampling-copy number below which an optimized estimator gives a smaller average sampling error in contrast to the corresponding estimator obtained from the so-called parameter-shift rule, which has recently been regarded as a standard for gradient estimation. Furthermore, this critical number grows exponentially with the circuit-qubit number, suggesting the statistical benefits in using these optimally-tuned estimators for scalable quantum computing.
Disentangling Embedding Spaces with Minimal Distributional Assumptions

Interest in understanding and factorizing learned embedding spaces is growing. For instance, recent concept-based explanation techniques analyze a machine learning model in terms of interpretable latent components. Such components have to be discovered in the model's embedding space, e.g., through independent component analysis (ICA) or modern disentanglement learning techniques. While these unsupervised approaches offer a sound formal framework, they either require access to a data generating function or impose rigid assumptions on the data distribution, such as independence of components, that are often violated in practice. In this work, we link conceptual explainability for vision models with disentanglement learning and ICA. This enables us to provide first theoretical results on how components can be identified without requiring any distributional assumptions. From these insights, we derive the disjoint attributions (DA) concept discovery method that is applicable to a broader class of problems than current approaches but yet possesses a formal identifiability guarantee. In an extensive comparison against component analysis and over 300 state-of-the-art disentanglement models, DA stably maintains superior performance, even under varying distributions and correlation strengths.
Fingerprint Biometric for Internet of Things

IoT security is crucial, because the larger number of Internet-of-Things (IoT) devices that require interaction between smart devices and customers. Biometrics presents an intriguing window of opportunity for improving IoT usability and security, and can play a critical role in securing a wide range of developing IoT devices to address security challenges. The goal of this study is to provide a complete overview of current biometrics research in IoT security, with a particular focus on authentication.
Study of Data Protection in IOT based Cyber Security Physical Systems

In this paper it is discussed about how can one protect data by using techniques and algorithm. Cyber Security is to protect computers, servers, mobile/electronic devices from malicious attacks. It is mainly used to maintain the security of data from computers or servers so that data cannot be theft by others or any kind of disruption of the services. Cyber Security risk is traditionally focused on machine-based threat. As IOT contains large amount of data, to protect the confidential as well as sensitive information need to apply security mechanisms or protocols to protect such kind of data from cyber-attackers.
Anti-Scraping Techniques

Web scraping refers to a software program that mimics human web surfing behavior by pointing to a website and collecting large amounts of data that would otherwise be difficult for a human to extract. A typical program will extract both unstructured and semi-structured data, as well as images, and convert the data into a structured format. The activity is deemed illegal, but the change in legality has not stopped people from doing the same. This paper aims to list challenges and proposes techniques to developing anti-scraping application.
Cluster analysis of the national examination: School grouping to maintain the sustainability of high school quality

This study aims to classify high schools in Papua Province Indonesia based on the 2019 Examination scores so that they can be taken into consideration in maintaining the sustainability of school quality in Papua. In this study, all senior high schools in Papua Province were grouped into three clusters, 1 (high), 2 (medium), and 3 (low) using the K-Means Algorithm on the 2019 National Examination Data. Data was obtained through the website official Center of Educational Assessment of the Ministry of Education, Culture, Research, and Technology of the Republic of Indonesia. Clarification is done by grouping data on national exam scores from each school based on the similarity of the data with data from other schools. The results of the high school clustering using the K-Means Algorithm obtained the number of high schools in cluster 1 (high) as many as 18 schools, in cluster 2 (medium) as many as 58 schools, and in cluster 3 (low) as many as 68 schools. The results of the analysis of the K-Means Algorithm show an R2 value of 0.723 and a Silhouette score of 0.42.
An Exploratory Study on Vocabulary Size of Korean High School EFL Learners

The objective of this exploratory study was to investigate vocabulary size of Korean high school learners of English. The relationship between their vocabulary size and performance on English tests was also explored. With 97 Korean high school students of 10th and 11th grades, the Vocabulary Size Test (Nation & Beglar, 2007) was employed to measure their vocabulary size. Their performance on English tests was assessed through a school’s English reading test and the Preliminary College Scholastic Ability Test. Results revealed that 10th and 11th graders knew an average of 3,282 and 3,733 word-families, respectively. These two groups showed a 451 word-family difference in vocabulary size, suggesting that high school students, at least for participants of the current study, gained around 451 word-families over a year. While a large number of students achieved a vocabulary size of 4,000 to 6,000 word-families, a significant number of students seemed to have difficulty in learning the most frequent 2,000 word-families. The study found that the vocabulary size played a crucial role in learners’ English academic performance, most markedly in reading comprehension, in line with previous L2 vocabulary research.
Enhancing infrared color reproducibility through multispectral image processing using RGB and three infrared channels

Various techniques for image color reproducibility under low-light conditions have been proposed, such as high sensitivity, a combination of visible (VIS) and infrared (IR) light, and coloring monochromatic images. However, when the illuminance falls below a certain level, color images cannot be obtained without prior color information. Previously, the exclusive use of IR without VIS illumination was proposed to achieve a pseudocolor image (basic IR color). It improved visibility compared with conventional monochrome images. However, there are cases, depending on the objects, when basic IR light cannot reproduce the correct color. An image processing method for enhancing color reproducibility is proposed, particularly for objects that are not suitable for the basic IR color (enhanced IR color). Moreover, we developed an algorithm to combine the advantages of both VIS and IR colors by utilizing signals of six wavelengths: three wavelengths each of VIS and IR. The proposed method includes an automatic transition between the optimal combination of VIS and IR colors when the illumination level changes, thus providing images with superior color reproducibility under various illumination levels compared with basic IR color.
A Novel Approach to Develop a Movie Piracy Prevention System

Movie piracy has a profound impact on the motion picture industry. These losses in revenue will obviously cause serious financial problems for the studios and even contribute to their current downfall. The aim of the project is to develop an anti-piracy system for movie industries using Steganography technique in MATLAB and to design IR based screen to disable mobile recording and alert system
N-QGN: Navigation Map from a Monocular Camera using Quadtree Generating Networks

Monocular depth estimation has been a popu-lar area of research for several years, especially since self-supervised networks have shown increasingly good results in bridging the gap with supervised and stereo methods. However, these approaches focus their interest on dense 3D reconstruction and sometimes on tiny details that are superfluous for autonomous navigation. In this paper, we propose to address this issue by estimating the navigation map under a quad tree representation. The objective is to create an adaptive depth map prediction that only extract details that are essential for the obstacle avoidance. Other 3D space which leaves large room for navigation will be provided with approximate distance. Experiment on KITTI dataset shows that our method can significantly reduce the number of output information without major loss of accuracy.
Is it possible not to cheat on the Turing Test_Exploring the potential  and challenges for true natural language 'understanding' by computers

The increasing sophistication of NLP models has renewed optimism regarding machines achieving a full human-like command of natural language. Whilst work in NLP/NLU may have made great strides in that direction, the lack of conceptual clarity in how 'understanding' is used in this and other disciplines have made it difficult to discern how close we actually are. A critical, interdisciplinary review of current approaches and remaining challenges is yet to be carried out. Beyond linguistic knowledge, this requires considering our species-specific capabilities to categorize, memorize, label and communicate our (sufficiently similar) embodied and situated experiences. Moreover, gauging the practical constraints requires critically analyzing the technical capabilities of current models, as well as deeper philosophical reflection on theoretical possibilities and limitations. In this paper, I unite all of these perspectives -- the philosophical, cognitive-linguistic, and technical -- to unpack the challenges involved in approaching true (human-like) language understanding. By unpacking the theoretical assumptions inherent in current approaches, I hope to illustrate how far we actually are from achieving this goal, if indeed it is the goal.
Secondary constructions of bm$p$-ary (non-)weakly regular bent functions

Bent functions can be used in many areas, such as symmetric cryptography, sequence design, and association schemes. In this paper, a generic secondary 构造 of bent functions from indirect sum and semi-direct sum methods is presented. Our method can be used to construct high algebraic degree (non-)weakly regular bent functions by employing suitable initial (vectorial) bent functions or their combinations. More precisely, we present some classes of (weakly) regular bent functions by using vectorial $\mathcal{M}$-$\mathcal{M}$ (Maiorana-McFarland) and $\mathcal{PS}$ (partial spread) bent functions, respectively. In particular, the explicit expressions for the dual of these (weakly) regular bent functions are obtained. Based on the vectorial perfect nonlinear ($\mathrm{PN}$) functions, infinite families of non-weakly regular bent functions can also be produced.
Exploring of Online Learning Using Youtube in English Vocabulary During The Covid-19 Pandemic

Since the Covid-19 pandemic, all activities have been hampered, from work to school which hampers the conventional learning process or face-to-face learning. The objectives of this research aims to discover the positive things taken from YouTube for online English learning media daring this covid-19 pandemic. This research is qualitative research in which the author uses the literature study observations taken from previous researchers. Currently, schools have implemented online learning, where teachers are required to use a monotonous lecture method. This is one of the problems that will cause students to be unenthusiastic and unfocused when studying. Therefore, this study shows that utilizing Youtube media can improve vocabulary and student understanding in English lessons, increasing student achievement. However, there must be disadvantages every time there are advantages: the lack of optimal supervision of students, poor signal or internet network, and high quotas are the obstacles to using YouTube as a learning medium
Analysis of E-learning English Teaching Path Based on Reinforcement Learning

With the deepening of China’s opening to the outside world, China’s demand for English talents is gradually increasing. As a compulsory subject for high school students, it is not objective for high school students to take office in the field of English on the premise of large market demand and sufficient talent reserve. Higher vocational colleges have always emphasized the education goal of taking employment as the center. On this basis, this paper hopes to cultivate high-quality talents through the analysis of English education in Higher Vocational Colleges in the cloud computing environment.