Reviewerly/exHarmony huggingface dataset:
Effects of hybrid education in pre-service EFL and bilingual primary teachers’ digital literacy

Foreign language proficiency and digital literacy are key aspects of today’s education. Consequently, if teachers are responsible for providing students with opportunities for the best learning experiences so that they can discover their potential and put their skills into practice, it seems evident that the competences of these professionals cannot be overlooked. Research is profuse when analyzing the teachers’ digital literacy in different contexts, including in-service and pre-service ones in areas such as English as a Foreign Language and bilingual education. However, it is necessary to examine how future teachers view their digital competence in hybrid education settings derived from the COVID-19 pandemic. This paper examines the self-perceived digital competence of a group of pre-service primary teachers of English and bilingual education studying at the University of Córdoba (Spain) that have received hybrid education during the 2020/21 academic year. A quasi-experimental design and a quantitative approach based on the use of a questionnaire are followed. Findings show that surveyed teachers show general positive attitudes in relation to their digital literacy, and gender differences are only statistically significant when considering self-perceived competence in terms of searching, accessing, and selecting online content for teaching purposes in favor of men. It seems clear that specific training is still needed among future teachers of English and bilingual education in this respect.
An information security diagnostic of Electronic Data Capture Systems for the Personal Health Train

A Case Report Form (CRF) is a questionnaire mostly used in clinical trial research to collect patients’ information. Electronic Case Report Form (eCRF) is taking the place of the normal paper CRF due to its digital convenience. An eCRF is a feature of an Electronic Data Capture (EDC) system and it collects users’ information, which might be sensitive. This research aims to find appropriate EDC systems for the Personal Health Train (PHT). This platform enables organizations and researchers to work with health data from diverse sources without sharing these data. Therefore, it is necessary to ensure that the system is prepared for privacy and security regulations. Due to the diversity of privacy regulations worldwide, this research investigates whether the EDC systems comply with the European Union General Data Protection Regulation (GDPR). This paper presents a short review of the PHT concept, the role of the Findable, Accessible, Interoperable and Reusable (FAIR) data principles, potential risks, protection measures for hospital information systems. In our evaluation, we provided an overview of five EDC systems: Castor, OpenClinica, REDCap, NowEDC, and ShareCRF. We evaluated their security and privacy features and techniques to store data. A comparison of their features is given, and a preliminary usability test is presented.
Smart-Fragile Authentication Scheme for Robust Detecting of Tampering Attacks on English Text

Content authentication, integrity verification, and tampering detection of digital content exchanged via the internet have been used to address a major concern in information and communication technology. In this paper, a text zero-watermarking approach known as Smart-Fragile Approach based on Soft Computing and Digital Watermarking (SFASCDW) is proposed for content authentication and tampering detection of English text. A first-level order of alphanumeric mechanism, based on hidden Markov model, is integrated with digital zero-watermarking techniques to improve the watermark robustness of the proposed approach. The researcher uses the first-level order and alphanumeric mechanism of Markov model as a soft computing technique to analyze English text. Moreover, he extracts the features of the interrelationship among the contexts of the text, utilizes the extracted features as watermark information, and validates it later with the studied English text to detect any tampering. SFASCDW has been implemented using PHP with VS code IDE. The robustness, effectiveness, and applicability of SFASCDW are proved with experiments involving four datasets of various lengths in random locations using the three common attacks, namely insertion, reorder, and deletion. The SFASCDW was found to be effective and could be applicable in detecting any possible tampering.
Application of Method for Constructing a Complex Hierarchical Logic in Intelligent Agriculture Context

The paper considers the possibility of using complex logic to create shorter and clearer models for the purposes of intelligent agriculture. Some aspects for developing a suitable environment for program modeling of scenarios and processes of intelligent agriculture have been explored. Creating an interpreter of hierarchical complex logic will greatly facilitate modeling and decision making. Here is presented an opportunity to interpret complex logic using the logic programming language Prolog and its inference machine. The results are illustrated by an example of creating an irrigation plan.
Multi-task learning based approach for surgical video desmoking

Over the past few decades, minimally invasive surgical techniques have gained wide acceptance due to multiple benefits it offers. In these surgeries, a camera with a light source is inserted via a small incision. The video feed from the camera is the only source for visualization of internal organs. Certain procedures produce fumes that severely degrade the video feed. Various image processing based de-smoking systems are proposed to provide a continuous, good quality video feed. However, most of the existing approaches perform de-smoking at the frame level and fail to exploit the dynamic properties of the smoke. We propose a novel de-smoking model that harnesses both spatial and temporal properties of smoke. We evaluate the performance of the proposed model on the Cholec-80 dataset and observe a superior performance in terms of MS-SSIM and PSNR metrics compared to existing works.
Implementation System of Network User Abnormal Behavior Detection Algorithm Based on Data Layering

With the rapid de velopment and popularization of mobile Internet in recent years, security issues have become increasingly prominent. Based on the mobile Internet, this paper studies the security issues caused by abnormal users for users, mobile devices, and social network service platforms. Aiming at the needs of users for detecting abnormal network behaviors in a real-time network environment, this paper designs and implements a data-based Layered network abnormal behavior detection system. The system is divided into a data acquisition module, a data conversion module, a rule editing module, an anomaly detection module, and a detection result visualization module.
Write-Optimized and Consistent RDMA-based Non-Volatile Main Memory Systems

To deliver high performance in cloud computing, many efforts leverage RDMA (Remote Direct Memory Access) in networking and NVMM (Non-Volatile Main Memory) in end systems. Due to no CPU involvement, one-sided RDMA becomes efficient to access the remote memory, and NVMM technologies have the strengths of non-volatility, byte-addressability and DRAM-like latency. However, due to the need to guarantee Remote Data Atomicity (RDA), the synergized scheme has to consume extra network round-trips, remote CPU participation and double NVMM writes. In order to address these problems, we propose a write-optimized log-structured NVMM design for Efficient Remote Data Atomicity, called Erda. In Erda, clients directly transfer data to the destination memory addresses in the logs on servers via one-sided RDMA writes without redundant copies and remote CPU consumption. To detect the atomicity of the fetched data, we verify a checksum without client-server coordination. We further ensure metadata consistency by leveraging an 8-byte atomic update in a hash table, which also contains the addresses of previous versions of data in the log for consistency. When a failure occurs, the server properly and efficiently restores to become consistent. Experimental results show that compared with state-of-the-art schemes, Erda reduces NVMM writes approximately by 50%, significantly improves throughput and decreases latency.
Singular value decomposition for skew-Takagi factorization with quantum applications

We obtain an algorithm for the skew-Takagi factorization based on the singular value decomposition. As an example application of such an algorithm, we consider the computation of Heisenberg evolution of the fermionic creation and annihilation operators in the case of the quadratic Hamiltonian of a special form
Tiny neural network search and implementation for embedded FPGA: a software-hardware co-design approach

Embedded FPGA has been widely adopted as a hardware platform for implementing artificial intelligence (AI) systems on the edge due to its flexibility, versatility and efficiency. In general, there are three steps to deploy edge AI algorithms on embedded FPGAs (Fig. 1a): (a) manually train a neural network (NN) model for a specific dataset; (b) prune and quantize the NN model; (c) design a hardware accelerator architecture to implement the NN model. Such an approach, however, usually results in inferior local-optimal solutions because of the mutual influence between the algorithm and hardware. Recently, neural architecture search (NAS) has been intensively exploited to automatically identify an optimal NN architecture on given constraints [1–6]. In particular, hardware-aware NAS (HW-NAS), a NAS strategy that takes hardware constraints (e.g., on-chip memory and DSP) and performance requirements (e.g., FPS and energy efficiency etc.) into consideration (Fig. 1b), has drawn great interest in finding an optimal NN architecture to maximize the algorithm accuracy and hardware efficiency. Nevertheless, current HW-NAS designs, mainly for general AI scenarios, generally have complicated search space (e.g., oversized NN model) and are unaffordable for edge AI devices (limited resources and energy).
A Feasibility Study of An Intelligent Environmental Monitoring System Based On The Ethereum Blockchains

In today's era, the security and authenticity of information have always received extensive attention, especially when people have doubts about the authenticity of data. Hence, a traceable data storage platform with the feature that theoretically cannot be tampered with becomes extremely important. Under such demands, the deployment of smart contracts can effectively trace the source and discover problems. This article introduces an environmental data detection platform based on blockchain technology and smart contracts. It aims to record the data continuously and verify the authority of each transaction.
A new IMM interacting approach for unequal dimension states for multitarget tracking in cluttered environments

Since it is unknown in advance whether the target is maneuvering in practical target tracking environments, multiple model tracking techniques are introduced by applying various target dynamic models. Among the multiple model tracking techniques, interacting multiple model (IMM) method has shown excellent performance with low complexity due to the interaction process of each state of mode. When various dynamic models are designed, the dimension of each state may be unequal, which may cause biased estimate. To deal with this problem, mode interacting approaches to two dynamic models of unequal dimensions have been studied in other literatures. Here, a new interacting approach for the case of three dynamic models with unequal dimensions is proposed to reduce the bias in the extra state estimates of the higher dimensional modes, and it is shown that the tracking performance is better than the existing IMM algorithm with the conventional interaction step through Monte Carlo simulation for multi-target tracking in cluttered environments.
Prediction of Path Loss Propagation in Case of Light Rail Transit (LRT) using Machine Learning Approaches

Path loss propagation is a very important role in the development of wireless communication networks. Path loss prediction is used for planning and optimizing the communication network. The accuracy of the path loss prediction determines the quality of the signal received by the customer. Path loss has different characteristics in different areas and different conditions. The different conditions of the transceiver, receiver, and object on the surrounding condition cause different path loss characteristics. This research aims to propose a path loss prediction model using machine learning, namely Deep Neural Network (DNN) and Support Vector Regression (SVR) in the Light Rail Transit (LRT) transportation scenario. The model is compared with the empirical path loss model, namely Okumura-Hatta and Ericsson. The dataset used is a measurement dataset in the field using the Drive Test method. This research shows that modeling using machine learning, namely DNN, has the best level of accuracy with an RMSE value of 4.155, followed by SVR, Ericsson, and Okumura-Hatta models with a value of 7.695, 40.971, and 92.079. Path loss prediction using the DNN algorithm can be used as a reference in wireless network planning.
DACA: Distributed adaptive grid decision graph based clustering algorithm

Abstract Clustering algorithms play a very important role in machine learning. With the development of big‐data artificial intelligence, distributed parallel algorithms have become an important research field. To reduce the computational complexity and running time of large‐scale datasets in the clustering process, this study proposes a distributed clustering algorithm DACA (distributed adaptive grid decision graph based clustering algorithm). In a distributed environment, DACA uses relative entropy to adaptively mesh the data to form an obvious sparse grid and dense grid. Then, the decision graph is used to determine the cluster center mesh object. Finally, the KD‐tree is used to accelerate the determination of the cluster center of sparse points to complete clustering. The algorithm is implemented using the popular Apache Spark computing framework, compared with other distributed clustering algorithms, DACA can adaptively divide the grid according to the data distribution to obtain better clustering effect. At the same time, KD tree algorithm is used to speed up the decision‐making of clustering center. Numerous experiments show that the DACA algorithm has excellent performance and accuracy on six standard datasets and real GPS trajectory datasets.
Comparison of CNN Architecture for Thermal Face Emotion Classification

After the corona pandemic, people's feelings of depression are increasing in most countries. Emotion recognition is an essential technology for the increasing demand for non-face-to-face medical services to reduce the risk of infection. A skin temperature pattern can be used as one of the indicators indicating the emotional state. In this paper, the self-constructed thermal image DB was applied to the existing CNN architecture for facial expression classification and performance evaluation was performed. As a result, it was confirmed that the network in which several kernels are applied in parallel in order to extract various features has good performance.
Acceleration and higher precision by discrete wavelet transform for single image super-resolution using convolutional neural networks

Recently, convolutional neural networks (CNNs) are good at providing hierarchical features and have succeeded for single image super-resolution (SISR). Most of them focus on designing a deeper and wider network to learn more discriminative high-level features; thereby, they take much time to process. There has been attention on using discrete wavelet transform (DWT) in SISR to solve this problem. For higher speed and higher performance, this paper proposes a new DWTL (DWT Layer) and IDWTL (Inverse DWTL), which generalized the combining DWT with existing CNNs of SISR. By putting existing CNNs between DWTL and IDWTL, new methods with new CNNs will be generated. This paper applied the proposed methods to four well-known CNNs of SISR, which are SRCNN, VDSR, EDSR, and RDN, and generated new SRWCNN, VDWSR, EDWSR, and RDWNSR. Evaluation results of these four new CNNs in generated image execution time and peak signal-to-noise ratio for the performance show that SRWCNN, VDWSR achieved about three times faster and higher performance. EDWSR, RDWNSR increased its speed about 3.6 times with equivalent performance.
Detection of Motion on a Trampoline with a Smartwatch

In this study, we develop a method for detecting the motions performed on a trampoline using an accelerometer mounted on a smartwatch. This method will lead to a system that can be used to promote trampoline exercise using a home trampoline by detecting motions on the trampoline using a smartwatch. We proposed a method based on the convolutional neural network to detect the motions on a trampoline. As a result of the performance evaluation by leave-one-subject-out cross-validation on eight subjects, our method achieves 78.8% estimation accuracy, which is the best estimation accuracy compared to the baseline methods. We also evaluate the inference time and the battery consumption when the model is actually running on a smartwatch. Our method is effective for on-device prediction.
Joint Extraction of Entities and Relations from Ancient Chinese Medical Literature

Extracting important knowledge from literature is a challenging Natural Language Processing (NLP) task involving two subtasks, Named Entity Recognition (NER) and Relation Extraction (RE). Traditionally these two sub-tasks are served as a pipeline model and the error propagation is inevitable. In this paper, considering of the characteristics of ancient Chinese medical books, we propose a new joint model that performs NER and RE simultaneously. This model also involves a Multi-Head Attention layer to extract long distance related features and adversarial training method to improve robustness and stability. Experiments show that our model performs well on multi-context joint entity and relation extraction in TCM (Traditional Chinese Medicine) dataset.
Design and Implementation of Elliptic Curve Digital Signature Using Bit Coin Curves Secp256K1 and Secp384R1 for Base10 and Base16 Using Java

Public key ciphers like Rivest, Shamir, and Adleman algorithm (RSA) have been implemented extensively in computer network security. Earlier researchers found RSA is not computationally feasible due to exponential growth of key size and is not suitable for resource constrained devices where speed, storage, and bandwidth play a prime importance. Due to which RSA cannot be used with Internet of things [IoT]—end nodes like Raspberry Pi and Arduino. Elliptic curve cryptography is a special type of cryptographic technique which actually is based upon geometrical interpretations and is a classic combination of algebraic properties and geometric techniques. In this paper, elliptic curves used by bit coins like secp256K1 and secp384R1 for base10 and base16 are implemented using Java programming language on Raspberry Pi. This paper focuses on implementation of elliptic curve digital signature (ECDH) on Raspberry Pi using bit coin curves secp256K1 and secp384R1.
Noise Compensation in Super-Resolution Problem Using the Huber Loss Function

In this paper, we analyze an algorithm for super-resolution reconstruction from a set of images using the gradient descent method with an additional constraint on the smoothness of the resulting image. The final minimized function has two addends: the quadratic loss function characterizing the discrepancy between the reconstructed super-resolution image and the observed low-resolution images, and the Huber loss function characterizing the smoothness of neighboring pixels of the reconstructed image. The paper investigates how the noise present in low-resolution images and the parameters of the function to be minimized affect the resulting reconstructed images.
Theoretical Comparative Energy Efficiency Analysis of Dual Axis Solar Tracking Systems

This paper developed a theoretical model substantially based on the principle that only the normal component of solar radiation is actually converted into electrical energy.This theoretical model helped to predict minimum and maximum daily energy gain (compared to static PV system tilted with certain angle) when using dual axis PV solar tracking systems, at any given location on earth without prior experimental data.Based on equations derived from model, minimum and maximum energy gain is computed and summarized in tables of minimum and maximum.Furthermore, the model equations could be used to set up future experimental studies related to the matter.
A Comparative Study on Language Models for Task-Oriented Dialogue Systems

The recent development of language models has shown promising results by achieving state-of-the-art performance on various natural language tasks by fine-tuning pre-trained models. In task-oriented dialogue (ToD) systems, language models can be used for end-to-end training without relying on dialogue state tracking to track the dialogue history but allowing the language models to generate responses according to the context given as input. This paper conducts a comparative study to show the effectiveness and strength of using recent pre-trained models for fine-tuning, such as BART and T5, on end-to-end ToD systems. The experimental results show substantial performance improvements after language model fine-tuning. The models produce more fluent responses after adding knowledge to the context that guides the model to avoid hallucination and generate accurate entities in the generated responses. Furthermore, we found that BART and T5 outperform GPT-based models in BLEU and F1 scores and achieve state-of-the-art performance in a ToD system.
Robotic Jigsaw: A Non-Holonomic Cutting Robot and Path Planning Algorithm

Bladed tools such as jigsaws are common tools for wood workers on job-sites and in workshops, but do not currently have sufficient autonomous hardware or path planning algorithms to enable automation. Here we present a system of an autonomous robot and a path planning algorithm for automating jigsaw operations. The robot can drill holes, insert the jigsaw, and cut plywood. Our algorithm converts complex shapes into paths for the jigsaw, drill holes, and traversal movements for the robot. The algorithm decomposes input shapes into cuttable sections and determines possible locations for drilling entry holes for inserting the blade. We cast the drill hole problem as a set coverage problem with a trade-off between number of holes and cutting distance. We characterize the algorithm on a series of shapes and determined the algorithm found valid solutions. We executed an example on the robot to demonstrate the end-to-end system.
Modern McEliece Cryptosystem Using Generalized ($L, G$)-Codes

In this paper, we consider the modern McEliece cryptosystem using generalized ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L, G$</tex> )-codes. We discussed the modification of the Patterson algorithm and consider square root algorithm for its implementation and comprise the parameters of the cryptosystem built on classical Goppa codes and on the generalized ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L, G$</tex> )-codes.