Reviewerly/exHarmony huggingface dataset:
Demotivation Level and Demotivators Among EFL Students In Home Online English Learning During The Pandemic

This research is aimed at investigating the level of demotivation and the demotivating factors experienced by Indonesian EFL learners during home online English learning as response to social distancing order amid the COVID-19 pandemic. This research employed quantitative and qualitative data. Questionnaire with 27 items was the main instrument used to obtain data about learners’ level of demotivation and demotivators as well as to identify the frequency of each demotivator. An open-ended question was attached at the end of the questionnaire to dig other additional demotivators. An interview was also conducted to gain supporting data for in-depth analysis. The results showed that the level of the 198 students’ demotivation was 2.9, categorized as lowly demotivated. The most-frequently rated demotivators in home online English learning were lack of interaction with 78.9% responses followed by 66.2% for increasing assignments and 62.1% for slow Internet connection. In terms of content and material, expectation to use grammatically correct English was the most demotivating factor with 59.6% responses. The study also revealed additional demotivators namely unsupportive parents, doing house chores, and Wi-fi absence.
Towards Pointsets Representation Learning via Self-Supervised Learning and Set Augmentation

Deep metric learning is a supervised learning paradigm to construct a meaningful vector space to represent complex objects. A successful application of deep metric learning to pointsets means that we can avoid expensive retrieval operations on objects such as documents and can significantly facilitate many machine learning and data mining tasks involving pointsets. We propose a self-supervised deep metric learning solution for pointsets. The novelty of our proposed solution lies in a self-supervision mechanism that makes use of a distribution distance for set ranking called the Earth’s Mover Distance (EMD) to generate pseudo labels and a pointset augmentation method for supporting the learning solution. Our experimental studies on documents, graphs, and point clouds datasets show that our proposed solutions outperform baselines and state-of-the-art approaches under the unsupervised settings. The learned self-supervised representation can also be used as a pre-trained model, which can boost downstream tasks with a fine-tuning step and outperform state-of-the-art language models.
MSDNet

This paper presents a novel algorithm to dehaze a given hazy input image using a Multi-Stage progressive image Dehazing Network (MSDNet) architecture. The proposed multi-stage strategy framework splits the challenge of image recovery into sub-tasks for a degraded image to be gradually restored. The network is trained with images from two benchmark datasets - RESIDE and NTIRE 2021. Experimental evaluations have been conducted with several hazy images from different datasets. The performance evaluation of MSDNet is done using various metrics such as Peak-Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measurement (SSIM), Feature Similarity Metric (FSIM), Visibility Index (VI), Realness Index (RI), Fog Aware Density Evaluator (FADE), Naturalness Image Quality Evaluator (NIQE) and Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE). An average PSNR of 27.687, 36.25, 34.98, 20.52, and 20.97 have been obtained with images from ITS, SOTS indoor, SOTS outdoor, Dense-Haze, and NH-Haze datasets, respectively. The experimental results reveals the enhanced performance of MSDNet compared to other state-of-the-art techniques.
3D Animation and Virtual Reality Integrated Cognitive Computing for Teaching and Learning in Higher Education

This paper proposes the and justify how we can enhance the quality of medical education through immersive learning and AI (Artificial Intelligence) use in education. A Multimodal Approach for Immersive Teaching and learning through Animation, AR (Augmented Reality) &amp; VR (Virtual Reality) is aimed at providing specifically medical students with knowledge, skills, and understanding. It is important to understand the current challenge involved in medical education. This paper reports the findings of a novel study on the technology enable teaching with Animation, AR and VR by and MR impact. A case study was conducted involving 521 participants from different states of India. The data was analyzed by their feedback after using this Virtual reality-based teaching procedure in classroom. Recommendations from this paper that are expected to effectively improving the quality of medical education in faster way.
GIF IMAGE HARDWARE COMPRESSORS

Increasing requirements for data transfer and storage is one of the crucial questions now. There are several ways of high-speed data transmission, but they meet limited requirements applied to their narrowly focused specific target. The data compression approach gives the solution to the problems of high-speed transfer and low-volume data storage. This paper is devoted to the compression of GIF images, using a modified LZW algorithm with a tree-based dictionary. It has led to a decrease in lookup time and an increase in the speed of data compression, and in turn, allows developing the method of constructing a hardware compression accelerator during the future research.
Resilience against Adversarial Examples: Data-Augmentation Exploiting Generative Adversarial Networks

Recently, malware classification based on Deep Neural Networks (DNN) has gained significant attention due to the rise in popularity of artificial intelligence (AI).DNN-based malware classifiers are a novel solution to combat never-before-seen malware families because this approach is able to classify malwares based on structural characteristics rather than requiring particular signatures like traditional malware classifiers.However, these DNNbased classifiers have been found to lack robustness against malwares that are carefully crafted to evade detection.These specially crafted pieces of malware are referred to as adversarial examples.We consider a clever adversary who has a thorough knowledge of DNN-based malware classifiers and will exploit it to generate a crafty malware to fool DNN-based classifiers.In this paper, we propose a DNN-based malware classifier that becomes resilient to these kinds of attacks by exploiting Generative Adversarial Network (GAN) based data augmentation.The experimental results show that the proposed scheme classifies malware, including AEs, with a false positive rate (FPR) of 3.0% and a balanced accuracy of 70.16%.These are respective 26.1% and 18.5% enhancements when compared to a traditional DNNbased classifier that does not exploit GAN.
Training Support Vector Machines for Dealing with the ImageNet Challenging Problem

We propose the parallel multi-class support vector machines (Para-SVM) algorithm to efficiently perform the classification task of the ImageNet challenging problem with very large number of images and a thousand classes. Our Para-SVM learns in the parallel way to create ensemble binary SVM classifiers used in the One-Versus-All multi-class strategy. The stochastic gradient descent (SGD) algorithm rapidly trains the binary SVM classifier from mini-batches being created by under-sampling training dataset. The numerical test results on ImageNet challenging dataset show that the Para-SVM algorithm is faster and more accurate than the state-of-the-art SVM algorithms. Our Para-SVM achieves an accuracy of 74.89% obtained in the classification of ImageNet-1000 dataset having 1,261,405 images in 2048 deep features into 1,000 classes in 53.29 min using a PC Intel(R) Core i7-4790 CPU, 3.6 GHz, 4 cores.
Confidence-based Fall Detection Using Multiple Surveillance Cameras

The major cause of serious or even fatal injury for the elderly is a fall. Among various technologies developed for detecting falls, the camera-based approach provides a non-invasive and reliable solution for fall detection. This paper introduces a confidence-based fall detection system using multiple surveillance cameras. First, a model for predicting the confidence of fall detection on a single camera is constructed using a set of simple yet useful features. Then, the detection results from multiple cameras are fused based on their confidence levels. The proposed confidence prediction model can be easily implemented and integrated with single-camera fall detectors, and the proposed system improves the accuracy of fall detection through effective data fusion.
QuizHuntAR: A location-based Augmented Reality game for education

Augmented Reality (AR) is a technology whose interest and number of applications has grown constantly in several areas, having great popularity in entertainment, whose advantages benefit areas such as education, namely in the gamification component. Although several attempts have been made in applying Game-based Learning (GBL) in the classroom context to keep students motivated, there are some essential advantages that can be obtained from AR and a location-based component, such as social relationships, cooperation, and physical activity. This paper proposes a system composed of a location-based AR game for mobile devices named “QuizHuntAR”, heavily inspired by the game Pokémon GO, with education as its theme. In this game, players must physically move to hotspots indicated on the map and perform tasks (solving exercises) in a treasure hunting logic.
Academic writing anxiety among Chinese international postgraduates

Language learning is a common but complicated activity that involves the development and application of different language skills, including listening, speaking, reading and writing. This study adopted a mixed method to investigate the problem of academic writing anxiety of Chinese international graduate students. The sampling method used in the research is purposive sampling. A total of 30 Chinese postgraduate students (15 males and 15 females), who enrolled in the faculty of modern language and communication. The purpose of study is to investigate the causes of second language learning writing anxiety. It is hope that this study can provide insightful implications for the acquisition and instruction of L2 academic writing. In addition, unlike the massive studies of anxiety on General English (GE), anxiety studies on English for Academic Purposes (EAP) has received insufficient attention, therefore, this study can enrich the research field of EAP.
Prioritizing COVID-19 Contact Tracing During a Surge Using Chatbot Technology

When COVID-19 cases surge, identifying ways to improve the efficiency of contact tracing and prioritize vulnerable communities for isolation and quarantine support services is critical. During a fall 2020 COVID-19 resurgence in San Francisco, California, prioritization of telephone-based case investigation by zip code and using a chatbot to screen for case participants who needed isolation support reduced the number of case participants who would have been assigned for a telephone interview by 31.5% and likely contributed to 87.5% of Latinx case participants being successfully interviewed. (Am J Public Health. 2022;112(1):43–47. https://doi.org/10.2105/AJPH.2021.306563 )
Image Processing

Computerised image processing techniques are playing a growing role in manipulating these images in order to aid in the clinical decision-making process. This chapter focuses on two fields: image filtering and image segmentation. Image filtering techniques can be broken down into spatial domain operations and frequency domain operations. Spatial domain operations can be further broken down into point-processing and mask-processing operations. Both produce a new version of the original image; the difference lies in which pixel/voxel intensities are used to determine the new intensity at any given point. As well as performing image filtering operations in the spatial domain, similar operations can be performed in the frequency domain. Frequency domain representations of one-dimensional signals are based on the fact that any signal can be expressed as a weighted sum of sine waves of different frequencies and phases. In reality, many segmentation problems are challenging than this and a more complex series of operations may be required to produce satisfactory segmentations.
Quingo: A Programming Framework for Heterogeneous Quantum-Classical Computing with NISQ Features

The increasing control complexity of Noisy Intermediate-Scale Quantum (NISQ) systems underlines the necessity of integrating quantum hardware with quantum software. While mapping heterogeneous quantum-classical computing (HQCC) algorithms to NISQ hardware for execution, we observed a few dissatisfactions in quantum programming languages (QPLs), including difficult mapping to hardware, limited expressiveness, and counter-intuitive code. In addition, noisy qubits require repeatedly performed quantum experiments, which explicitly operate low-level configurations, such as pulses and timing of operations. This requirement is beyond the scope or capability of most existing QPLs. We summarize three execution models to depict the quantum-classical interaction of existing QPLs. Based on the refined HQCC model, we propose the Quingo framework to integrate and manage quantum-classical software and hardware to provide the programmability over HQCC applications and map them to NISQ hardware. We propose a six-phase quantum program life-cycle model matching the refined HQCC model, which is implemented by a runtime system. We also propose the Quingo programming language, an external domain-specific language highlighting timer-based timing control and opaque operation definition, which can be used to describe quantum experiments. We believe the Quingo framework could contribute to the clarification of key techniques in the design of future HQCC systems.
A Unified Anomaly Detection Methodology for Lane-Following of Autonomous Driving Systems

Autonomous Vehicles (AVs) are equipped with various sensors and controlled by Autonomous Driving Systems (ADSs) to provide high-level autonomy. When interacting with the environment, AVs suffer from a broad attack surface, and the sensory data are susceptible to anomalies caused by faults, sensor malfunctions, or attacks, which may jeopardize traffic safety and result in serious accidents. Most of the current works focus on anomaly detection of specific attacks, such as GPS spoofing or traffic sign attacks. There are no works on scenario-aware anomaly detection for ADSs. In this paper, focusing on the lane-following scenario, we introduce a novel transformer-based one-class classification model to identify time series anomalies and adversarial image examples. It can detect GPS spoofing, traffic sign recognition and lane detection attacks with high efficiency and accuracy. We further design a Swin-transformer model to enhance the detection performance. Experiments on Baidu Apollo and two public data sets (GTSRB and Tusimple) show that compared with the state-of-the-art methods, our method, on average, improves the detection performance by 9.7%, 14.7% and 15.7% for GPS spoofing, traffic sign recognition and lane detection attacks, respectively.
Development of the dual-channel frequency meter for measurements with hydrostatic pressure sensor

Abstract The paper describes the process and the results of development of the dual-channel frequency meter, which function is to measure the output frequency generated by bottom-mounted pressure sensors. The sensors are actively used to monitor the marine environment. AVR family microcontrollers were used as the computing core of the presented device. This solution allows to obtain far lower power consumption, which is especially important when operating with no industrial power supply system in the coastal zone. As a result, we can deploy a reliable monitoring equipment capable of long-term saving data and if necessary transmit it for further processing. The developed frequency-meter is able to continually record the ambient temperature, atmospheric pressure and dynamically varying output frequency, which depends on hydrostatic pressure (sea level). To obtain more accurate data, we implemented a frequency measure method called reciprocal counter with lower relative error not affected by value of the output frequency. A laboratory experiment has been conducted, which confirms the suitability of the developed frequency meter for field-oriented conditions.
An Introduction to Handwritten Character and Word Recognition

There is a worldwide interest in the development of handwritten character and word recognition systems. These systems are used in many situations such as recognition of postcodes, interpretation of amount and verification of signature on bank checks, and law enforcement on public roads. This chapter introduces principles of handwriting recognition for on-line and off-line systems. It discusses some considerations about performance evaluation of a handwriting recognition system. Handwriting recognition systems acquire data by means of either offline devices or on-line devices. Most current approaches to off-line handwriting recognition consist of three main phases, namely, preprocessing, segmentation, and recognition. Recognition of handwritten characters is undoubtedly the basic task of most automatic reading systems. Most approaches to on-line handwriting recognition fall into two main categories: analysis by synthesis, which is based on the modeling of handwriting generation, and application of off-line recognition methods to on-line recognition.
$$360^{\circ }$$ real-time and power-efficient 3D DAMOT for autonomous driving applications

Abstract Autonomous Driving (AD) promises an efficient, comfortable and safe driving experience. Nevertheless, fatalities involving vehicles equipped with Automated Driving Systems (ADSs) are on the rise, especially those related to the perception module of the vehicle. This paper presents a real-time and power-efficient 3D Multi-Object Detection and Tracking (DAMOT) method proposed for Intelligent Vehicles (IV) applications, allowing the vehicle to track $$360^{\circ }$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mn>360</mml:mn> <mml:mo>∘</mml:mo> </mml:msup> </mml:math> surrounding objects as a preliminary stage to perform trajectory forecasting to prevent collisions and anticipate the ego-vehicle to future traffic scenarios. First, we present our DAMOT pipeline based on Fast Encoders for object detection and a combination of a 3D Kalman Filter and Hungarian Algorithm, used for state estimation and data association respectively. We extend our previous work ellaborating a preliminary version of sensor fusion based DAMOT, merging the extracted features by a Convolutional Neural Network (CNN) using camera information for long-term re-identification and obstacles retrieved by the 3D object detector. Both pipelines exploit the concepts of lightweight Linux containers using the Docker approach to provide the system with isolation, flexibility and portability, and standard communication in robotics using the Robot Operating System (ROS). Second, both pipelines are validated using the recently proposed KITTI-3DMOT evaluation tool that demonstrates the full strength of 3D localization and tracking of a MOT system. Finally, the most efficient architecture is validated in some interesting traffic scenarios implemented in the CARLA (Car Learning to Act) open-source driving simulator and in our real-world autonomous electric car using the NVIDIA AGX Xavier, an AI embedded system for autonomous machines, studying its performance in a controlled but realistic urban environment with real-time execution (results).
Comparison of the earthquake detection effects of phaseNet and EQTransformer considering the Yangbi and Maduo Earthquakes

PhaseNet and EQTransformer are two state-of-the-art earthquake detection methods that have been increasingly applied worldwide. To evaluate the generalization ability of the two models and provide insights for the development of new models, this study took the sequences of the Yunnan Yangbi M 6.4 earthquake and Qinghai Maduo M 7.4 earthquake as examples to compare the earthquake detection effects of the two abovementioned models as well as their abilities to process dense seismic sequences. It has been demonstrated from the corresponding research that due to the differences in seismic waveforms found in different geographical regions, the picking performance is reduced when the two models are applied directly to the detection of the Yangbi and Maduo earthquakes. PhaseNet has a higher recall than EQTransformer, but the recall of both models is reduced by 13–56% when compared with the results reported in the original papers. The analysis results indicate that neural networks with deeper layers and complex structures may not necessarily enhance earthquake detection performance. In designing earthquake detection models, attention should be paid to not only the balance of depth, width, and architecture but also to the quality and quantity of the training datasets. In addition, noise datasets should be incorporated during training. According to the continuous waveforms detected 21 days before the Yangbi and Maduo earthquakes, the Yangbi earthquake exhibited foreshock, while the Maduo earthquake showed no foreshock activity, indicating that the two earthquakes’ nucleation processes were different.
The Parametric Optimization of Voltage Multiplier

The evolutionary model of voltage multiplier parametric optimization which includes 5 diodes and 5 capacitors is reviewed. It executes the transformation of alternating into constant voltage using a five times larger amplitude. The valve work is modelled according to the scheme of an ideal key. The original mathematical model of voltage multiplier which includes additional logical variables is deducted. It aссepts binary meanings 0 and 1, where 0 corresponds to closed valve status and 1 corresponds to open. In order to receive such a model, it is necessary to indicate the amount of open and closed valve combinations. Then for each of them, it is necessary to write the system of differential equations. Comparing them with each other the single differential equation system with additional logical variables is written as a generalization. The evolutional model is used in order to select the capacitor volume meaning. The goal function forecasts two conditions: maximum meaning of output voltage 1 kV and its minimal fluctuations in the stable regime.
RepoMedUNM: A New Dataset for Feature Extraction and Training of Deep Learning Network for Classification of Pap Smear Images

Morphological changes in the cell structure in Pap Smear images are the basis for classification in pathology. Identification of this classification is a challenge because of the complexity of Pap Smear images caused by changes in cell morphology. This procedure is very important because it provides basic information for detecting cancerous or precancerous lesions. To help advance research in this area, we present the RepoMedUNM Pap smear image database consisting of non-ThinPrep (nTP) Pap test images and ThinPrep (TP) Pap test images. It is common for research groups to have their image datasets. This need is driven by the fact that established datasets are not publicly accessible. The purpose of this study is to present the RepoMedUNM dataset analysis performed for texture feature cells on new images consisting of four classes, normal, L-Sil, H-Sil, and Koilocyt with K-means segmentation. Evaluation of model classification using reuse pretrained network method. Convolutional Neural Network (CNN) implements the pre-trained CNN VGG16, VGG19, and ResNet50 models for the classification of three groups namely TP, nTP and all datasets. The results of feature cells and classification can be used as a reference for the evaluation of future classification techniques.