Reviewerly/exHarmony huggingface dataset:
Effect of Standardized Yelling on Subjective Perception and Autonomic Nervous System Activity in Motion Sickness

This study investigated the effects of yelling intervention on symptoms and autonomic responses in motion sickness. Forty-two healthy participants were recruited, and they participated in Coriolis stimulation, a technique for inducing motion sickness. The experimental procedure comprised five 1-min rotating stimuli with 1-min rest after each stimulus. Then, the symptom severity was assessed using the Motion Sickness Symptom Rating (MSSR). The d2 Test of Attention scores and cardiovascular responses were recorded before and after Coriolis stimulation. The electrocardiogram results were documented to analyze heart rate variability (HRV). During Coriolis stimulus, the participants were required to yell 5-8 times in the experimental trial, and to keep quiet for each minute of rotation in the control trial. The yelling intervention significantly reduced the MSSR score (p < 0.001). Nevertheless, it did not significantly affect the d2 Test of Attention scores. Yelling while rotating did not significantly affect the heart rate nor blood pressure. However, it decreased the normalized low frequency of HRV (p = 0.036). Moreover, it improved motion sickness, but its effect on attention was not evident. Motion sickness could significantly affect cardiovascular responses and HRV. However, yelling did not affect cardiovascular response, and it reduced sympathetic nervous system activity.
Image Perturbation-Based Deep Learning for Face Recognition Utilizing Discrete Cosine Transform

Face recognition, including emotion classification and face attribute classification, has seen tremendous progress during the last decade owing to the use of deep learning. Large-scale data collected from numerous users have been the driving force in this growth. However, face images containing the identities of the owner can potentially cause severe privacy leakage if linked to other sensitive biometric information. The novel discrete cosine transform (DCT) coefficient cutting method (DCC) proposed in this study combines DCT and pixelization to protect the privacy of the image. However, privacy is subjective, and it is not guaranteed that the transformed image will preserve privacy. To overcome this, a user study was conducted on whether DCC really preserves privacy. To this end, convolutional neural networks were trained for face recognition and face attribute classification tasks. Our survey and experiments demonstrate that a face recognition deep learning model can be trained with images that most people think preserve privacy at a manageable cost in classification accuracy.
Interactive Diagnosis System for Electric Guitar Strumming Using Wrist-worn Real-time Wireless Motion Tracking Device

A wireless wrist-worn motion diagnosis system for electric guitar players has been investigated to realize an interactive guitar training system. A motion recognition algorithm for determining the time period of each down and up strum enabled an intuitive information display on an end device like a portable PC. The small, as well as a long battery life device, can be applicable for a wide range of guitar training situations including online lessons. Combinations of several kinds of diagnosis parameters were tried to find the effective interaction with a professional electric guitar player. It was found that an accented chord strumming technique and other kinds of musical expression can be visualized intuitively without using cameras.
Application of Artificial Intelligence and Wireless Networks to Music Teaching

The study is aimed at improving student’s learning ability and constructing an intelligent, all-around, and three-dimensional innovative classroom by using intelligent technology. Middle school C is taken as the research object. First, the current situation of music teaching in middle school C is analyzed, and an intelligent music teaching mode suitable for middle school C is designed. Second, the intelligent music teaching mode is applied in the actual teaching process, and students’ feelings for intelligent music classrooms are explored by a questionnaire survey. Finally, the teaching effect is analyzed. The results show that 96.7% of the students hold a positive attitude towards the new teaching mode, indicating that contemporary students have relatively high adaptation to and recognition with the development of artificial intelligence (AI). 84% of the students are not particularly skilled in the operation of the intelligent network platform, and 9.3% of the students believe that the operation of the platform is relatively difficult. Therefore, teachers should teach students how to use tablets. 95% of the students hold that teachers can quickly find teaching resources related to the course content on the tablet and introduce them to students in the teaching process, indicating that teachers are familiar with the usage of new teaching equipment. On the whole, students have a good experience of the new teaching mode. This new teaching mode based on AI is a response to the new course concept proposed in China.
Face Recognition Based Driver Detection System

Face Recognition has emerged as a potential field in the Machine Learning domain due to the arrival of big data. Face detection technology has been used in real-life areas. Nowadays, it is essential to detect a driver&#x2019;s face to find out the real driver of the vehicle with a valid vehicle driving license. In the paper, we proposed a system for driver face detection using OpenCV (Open source computer vision) and pyttsx3 library. A driver will be registered to our system with name, vehicle driving license number, and images. We will detect the driver through face and machine voice recognition and open the vehicle license form in the Microsoft Word document. We will save driver info in Microsoft Excel with the exact date and time. We proposed a face detection algorithm, Local Binary Pattern Histogram (LBPH), for face feature facial extraction. A machine learning algorithm Haar Cascade Classifier has been used for face detection. We create a Driver facial expression(DFE) dataset, which contains 1500 images for evaluating the proposed architecture.
Rapid Contrast Enhancement Algorithm for Natural Contrast- Distorted Color Images

Digital images are often obtained with contrast distortions due to different factors that cannot be avoided on many occasions.Various research works have been introduced on this topic, yet no conclusive findings have been made.Therefore, a lowintricacy multi-step algorithm is developed in this study for rapid contrast enhancement of color images.The developed algorithm consists of four steps, in that the first two steps include separate processing of the input image by the probability density function of the standard normal distribution and the softplus function.In the third step, the output of these two approaches is combined using a modified logarithmic image processing approach.In the fourth step, a gamma-controlled normalization function is applied to fully stretch the image intensities to the standard interval and correct its gamma.The results obtained by the developed algorithm have an improved contrast with preserved brightness and natural colors.The developed algorithm is evaluated with a dataset of various natural contrast degraded color images, compared against six different techniques, and assessed using three specialized image evaluation methods, in that the proposed algorithm performed the best among the comparators according to the used image evaluation methods, processing speed and perceived quality.
The 12th International Conference on Instrumental Analysis—Modern Trends and Applications (20–23 September 2021, Virtual Event)

The 12th International Conference on “Instrumental Methods of Analysis” www [...]
Digital Education

The COVID-19 pandemic and the rapid development of technologies have forced restructuring education at all levels. This change from traditional to digital learning demanded that teachers and students possess higher digital literacy. Moreover, it required them to be aware of the importance of security and its countermeasures to protect e-learning systems. The aim of this paper was twofold. First, it focused on security issues when using Moodle, Zoom. Blackboard, and edX. Furthermore, it offers best practices to address security threats and cyber-attacks from the outside network to e-learning systems. From this study, a cryptography mechanism arises as the best technique to protect the confidentiality, integrity, and authentication (CIA) of data on the e-platforms. However, it is not strong enough to mitigate cybersecurity attacks in e-learning systems. For that reason, it is suggested that the cryptography mechanism needs to be combined with other techniques such as biometric authentication, firewalls, IDS, digital watermarking, and security process models.
Static Analysis Tools Against Cross-site Scripting Vulnerabilities in Web Applications : An Analysis

Reports of rampant cross-site scripting (XSS) vulnerabilities raise growing concerns on the effectiveness of current Static Analysis Security Testing (SAST) tools as an internet security device.Attentive to these concerns, this study aims to examine seven open-source SAST tools in order to account for their capabilities in detecting XSS vulnerabilities in PHP applications and to determine their performance in terms of effectiveness and analysis runtime.The representative tools -categorized as either text-based or graph-based analysis tools -were all test-run using real-world PHP applications with known XSS vulnerabilities.The collected vulnerability detection reports of each tool were analyzed with the aid of PhpStorm's data flow analyzer.It is observed that the detection rates of the tools calculated from the total vulnerabilities in the applications can be as high as 0.968 and as low as 0.006.Furthermore, the tools took an average of less than a minute to complete an analysis.Notably, their runtime is independent of their analysis type.
PCGraph: Accelerating GNN Inference on Large Graphs via Partition Caching

Graph neural networks (GNNs) have been emerging as powerful learning tools for unstructured data and successfully applied to many graph-based application domains. Sampling-based GNN inference is commonly adopted in existing graph learning frameworks to handle large-scale graphs. However, this approach is restricted by the problems of redundant vertex embedding computation in GPU and inefficient loading of vertex features from CPU to GPU. In this paper, we propose PCGraph, a system that supports adaptive GNN inference and feature partition caching. PCGraph significantly reduces the vertex embedding computation time by adaptive GNN inference technology, which selects the optimal inference algorithm and minimizes vertex embedding computation. PCGraph also reduces the redundant data transfer between the CPU and GPU by partition the target vertices and caching their corresponding partitions in turn. We evaluate PCGraph against two state-of-the-art industrial GNN frameworks, i.e., PyG and DGL, on a diverse array of benchmarks. Experimental results show that PCGraph reduces up to 99% vertex embedding computation and 98.5% data loading time, and achieves up to 360&#x00D7; performance speedup over the state-of-the-art baselines.
Improving the Manipulability of a Redundant Arm Using Decoupled Hybrid Visual Servoing

This study proposes a hybrid visual servoing technique that is optimised to tackle the shortcomings of classical 2D, 3D and hybrid visual servoing approaches. These shortcomings are mostly the convergence issues, image and robot singularities, and unreachable trajectories for the robot. To address these deficiencies, 3D estimation of the visual features was used to control the translations in Z-axis as well as all rotations. To speed up the visual servoing (VS) operation, adaptive gains were used. Damped Least Square (DLS) approach was used to reduce the robot singularities and smooth out the discontinuities. Finally, manipulability was established as a secondary task, and the redundancy of the robot was resolved using the classical projection operator. The proposed approach is compared with the classical 2D, 3D and hybrid visual servoing methods in both simulation and real-world. The approach offers more efficient trajectories for the robot, with shorter camera paths than 2D image-based and classical hybrid VS methods. In comparison with the traditional position-based approach, the proposed method is less likely to lose the object from the camera scene, and it is more robust to the camera calibrations. Moreover, the proposed approach offers greater robot controllability (higher manipulability) than other approaches.
Design of Asynchronous Polymorphic Logic Gates for Hardware Security

Polymorphic circuits are circuits that perform two or more different functions under varying operating conditions. This paper presents dual-function asynchronous polymorphic logic gates with functionalities selected by a change in supply voltage. Unlike previous polymorphic gates generated using an evolutionary approach for use in synchronous circuits, these asynchronous polymorphic gates are designed with a deliberate methodology that has been applied across multiple process nodes and verified in silicon using the TSMC 65nm process. The asynchronous polymorphic gates were designed to achieve aspects of "hidden" reconfigurability without the typical area and security drawbacks of field-programmable gate array (FPGA) hardware implementations.
PlaneNet: an efficient local feature extraction network

Due to memory and computing resources limitations, deploying convolutional neural networks on embedded and mobile devices is challenging. However, the redundant use of the 1 × 1 convolution in traditional light-weight networks, such as MobileNetV1, has increased the computing time. By utilizing the 1 × 1 convolution that plays a vital role in extracting local features more effectively, a new lightweight network, named PlaneNet, is introduced. PlaneNet can improve the accuracy and reduce the numbers of parameters and multiply-accumulate operations (Madds). Our model is evaluated on classification and semantic segmentation tasks. In the classification tasks, the CIFAR-10, Caltech-101, and ImageNet2012 datasets are used. In the semantic segmentation task, PlaneNet is tested on the VOC2012 datasets. The experimental results demonstrate that PlaneNet (74.48%) can obtain higher accuracy than MobileNetV3-Large (73.99%) and GhostNet (72.87%) and achieves state-of-the-art performance with fewer network parameters in both tasks. In addition, compared with the existing models, it has reached the practical application level on mobile devices. The code of PlaneNet on GitHub: https://github.com/LinB203/planenet.
A Hybrid Online Classifier System for Internet Traffic Based on Statistical Machine Learning Approach and Flow Port Number

Internet traffic classification is a beneficial technique in the direction of intrusion detection and network monitoring. After several years of searching, there are still many open problems in Internet traffic classification. The hybrid classifier combines more than one classification method to identify Internet traffic. Using only one method to classify Internet traffic poses many risks. In addition, an online classifier is very important in order to manage threats on traffic such as denial of service, flooding attack and other similar threats. Therefore, this paper provides some information to differentiate between real and live internet traffic. In addition, this paper proposes a hybrid online classifier (HOC) system. HOC is based on two common classification methods, port-base and ML-base. HOC is able to perform an online classification since it can identify live Internet traffic at the same time as it is generated. HOC was used to classify three common Internet application classes, namely web, WhatsApp and Twitter. HOC produces more than 90% accuracy, which is higher than any individual classifiers.
Effectiveness of Literacy Teaching Design Integrating Local Culture Discourse and Activities to Enhance Reading Skills

While research exists on literacy focused on children in formal school, there is little evidence of how adult literacy is depicted in social practice from the perspective of what adults do in daily life. This study investigates the effectiveness of literacy teaching design integrating local culture discourse and practice to enhance the reading skills of adults in Indonesia. One hundred participants from underdeveloped areas aged 25–50 contributed to the learning process for twelve lesson units. We elicited data by using two instruments, namely multiple-choice questions and interviews. The statistical analysis showed that instructional design profoundly affected the improvement of reading skills. The thematic analysis showed how participants evaluated the literacy teaching design as enriching knowledge, sharing understanding among participants and motivating them to improve their life skills. This study will be useful to teachers who are seeking cognitive and practical instructions to promote reading skills in the classroom.
Sym3DNet: Symmetric 3D Prior Network for Single-View 3D Reconstruction

The three-dimensional (3D) symmetry shape plays a critical role in the reconstruction and recognition of 3D objects under occlusion or partial viewpoint observation. Symmetry structure prior is particularly useful in recovering missing or unseen parts of an object. In this work, we propose Sym3DNet for single-view 3D reconstruction, which employs a three-dimensional reflection symmetry structure prior of an object. More specifically, Sym3DNet includes 2D-to-3D encoder-decoder networks followed by a symmetry fusion step and multi-level perceptual loss. The symmetry fusion step builds flipped and overlapped 3D shapes that are fed to a 3D shape encoder to calculate the multi-level perceptual loss. Perceptual loss calculated in different feature spaces counts on not only voxel-wise shape symmetry but also on the overall global symmetry shape of an object. Experimental evaluations are conducted on both large-scale synthetic 3D data (ShapeNet) and real-world 3D data (Pix3D). The proposed method outperforms state-of-the-art approaches in terms of efficiency and accuracy on both synthetic and real-world datasets. To demonstrate the generalization ability of our approach, we conduct an experiment with unseen category samples of ShapeNet, exhibiting promising reconstruction results as well.
TESOL in the New Era: Introduction to the 2021 Global English Education China Assembly Special Issue

Welcome to the International Journal of TESOL Studies (IJTS) special issue for the 2021 Global English Education China Assembly (henceforth "the Assembly").This is the first of the three special issues of the 2021 Assembly (see also the Call for Papers at the end of this volume).Organised by China Daily in partnership with TESOL International Association, the Assembly is a high-level international English Language Teaching (ELT) event in China.The 2021 Assembly held in July in Hangzhou (Zhejiang Province, China) brought together over 3,000 attendees, both on site and online.They were ELT practitioners, scholars, and researchers in China and from abroad.On the Assembly platform, they exchanged experiences and views and shared research outputs.We are in a new era where one of the world's largest disruptions to daily life is still on-going.Despite the stress everyone is facing during the pandemic, contributors have been productive.As Guest Editors, we feel honoured and pleased to introduce the works of 17 scholars (including two prestigious interviewees) that appear in this volume.Prior to our introduction, we need to congratulate these fine scholars for their work completed successfully in the new era.This special issue features 11 papers from TESOL experts, researchers, and practitioners based not only in China but also in other countries including Canada, the UK, and the USA.These 11 papers are followed by two interviews respectively with two renowned scholars conducted by Ms. Peng Lun, Editor-in-Chief of the Assembly Special Issues.These papers use a wide variety of approaches, ranging from experimental designs, corpus-based methods, survey-based studies, case studies, to mix-methods research.The first paper, entitled "Measuring Chinese EFL Learners' Implicit and Explicit Knowledge of Relative Clauses" by Lei Zhang, adds to the line of research examining the three theoretical hypotheses concerning the acquisition of relative clauses: the Noun Phrase Accessibility Hierarchy Hypothesis, the Perceptual Differential Hypothesis, and the Subject-Object Hierarchy Hypothesis.Zhang utilised an experiment-based design, where the participants (viz.Chinese EFL learners from one university in China) were asked to do the same grammaticality judgment and sentence combination tasks within the allocated time.This paper contributes to furthering our understanding of the hypotheses mentioned above.The second paper, "Use of English Phrasal Verbs of Chinese Students Across Proficiency Levels:
Comparative analysis on path planning of ATR using RRT*, PSO, and modified APF in CG-Space

Path planning of All-Terrain Rover over 3D terrains is one of the most challenging robotics problems. Prior research has been carried out over 2D static and dynamic configuration space. Currently, all available algorithms for 3D path planning of articulated rover require optimization for wheel-terrain interaction. In this comparative analysis, the path planning is carried out over the 10 degrees of freedom Rover CG-Space instead of ground. CG-Space is the collection of all possible center of gravity locations of the Rover while traversing on terrain. The introduction of preobtained CG-Space eliminates the need for optimization during the planning stage. In this work, a comprehensive path planning comparison has been obtained among modified artificial potential field (APF) method, sampling-based technique (RRT*), and particle swarm optimization (PSO) algorithm based on path length, path smoothness, and computational time. All algorithms are applied over CG-Space associated with two categories of terrains: even and uneven. Even terrains have a well-defined surface equation, while uneven terrains are generated using Kinect V2 sensor mounted over the sand arena. A continuous surface from CG-Space is generated using cubic spline interpolation. The simulation results show that all three algorithms generate obstacle-free paths in a different time span. Although sampling-based and APF-based techniques take about five times lesser time, the obtained paths have moderate smoothness. PSO performs better than RRT* and modified APF for the requirements of optimal path length and smoothness. Thus, PSO is the optimal way to plan the path if the computational cost associated is acceptable. On the other hand, if time is the major planning concern, then RRT* is the best because the path smoothening takes very little time when using separate cubic spline techniques after obtaining the path nodes. This comparative analysis is useful in selecting an appropriate algorithm depending on the terrain complexity and obstacle conditions.