Reviewerly/exHarmony huggingface dataset:
Students Comprehension To The New Terminology of Covid-19

Purpose of Study: The pandemic that hit the world at this time has led to the existence of new terms COVID-19 in society. This study aims to find out the use of new terms in the COVID-19 press conference. Methodology: This study is a quantitative study that uses a survey method which is a questionnaire used to identify new terms in the COVID-19 press conference and examine the level of student knowledge of terms in the COVID-19 press conference. Main Finding: A total of 200 students from four public universities were selected using stratified random sampling. The findings of the study found that UIAM students make a higher comprehensive, followed by UPSI, UKM, and UM students. The total is 3952, UIAM students take the first as 1001 comprehensive of COVID-19 terminology followed by UPSI (998), UKM (995), and UM (958). The terms of R-Naught, Surveying Activities, and Triaging is the newest terminology not understood by all students from all chosen universities. The final finding shows that students comprehensive are really good toward the new terminology of the COVID-19 pandemic. Research Implication: The implication from this research would like to give some more information to the community about terminology. This research also enhanced the lexicon of the Malay language, thus helping the Malay language to subscribe and describe the new terminology to the other fields. The novelty of This Study: Based on the research, this paper is the new novelty would like to seek comprehensive students toward the COVID-19 terminology.
Fast motion estimation of one-dimensional laser speckle image and its application on real-time audio signal acquisition

Vibration measurement using laser speckle image, which can precisely detect object vibration without contacting target object, is an appealing topic. However, due to the limitation of image processing speed, previous researches on this topic have not achieved high-speed real-time detection and regeneration of audio signal. In this manuscript, we detect object vibration using one-dimensional laser speckle image. A fast and sub-pixel accuracy algorithm has been proposed to estimate the displacement of captured one-dimensional laser speckle images. Experiment results show that the proposed system can achieve 20kHz rea-time sampling rate, and the high frequency audio signal can be restored with high quality in real time.
A Game-Incorporated Alternative Least Squares-Based Approach to Latent Factor Analysis

A latent factor analysis (LFA) model is highly efficient in a high-dimensional and sparse (HiDS) matrix from a big data-related application like a recommender system (RS). Alternating least squares (ALS) is a frequently-adopted learning algorithm when building an LFA model. However, the computational cost of an ALS model is cubic with the dimension of the latent factor (LF) space, which yields the dilemma that large LF leads to high computational cost while small LF dimension results in accuracy loss. From this point of view, this paper carefully investigates the connections between a dynamic game method and an ALS algorithm, and find that the ALS-based training process is actually playing game among LFs related to the same entity. From this point of view, this paper proposes a game-incorporated alternating least squares (GALS) algorithm for building an LFA model efficiently. Its main idea is two-fold, a) considering LFs corresponding to the same entity as players and their learning objectives as strategies, and b) making these players choose strategies to achieve highest gains based on a single LF-dependent ALS algorithm following the principle of Backward Induction widely accepted in dynamic game methods. Experimental results indicate that with a GALS algorithm, an LFA model is able to decrease its computational cost as well as maintain its prediction accuracy for missing data of an HiDS matrix.
Research and design of an attention monitoring system based on head posture estimation

Attention is one of the essential factors affecting learning effectiveness. The duration students focusing on the screen is a fundamental measure of concentration in the relaxed setting of using computers for experiments in universities. To effectively monitor students’ attention in experimental teaching, we propose a method for measuring students’ attention based on head posture estimation and provide a scheme design for a Raspberry Pi-based attention monitoring system by integrating the idea of edge computing. Firstly, the head angles of yaw and pitch are estimated using ERT and EPnP algorithms to determine whether the students’ eyesight is within the screen range in the Raspberry Pi. Secondly, the results are transmitted wirelessly to the central server. To this end, a visual web page of the students’ attention is provided based on these data to show and manage those devices. Experiments have shown that the system is feasible in detecting student attention and has broad application potential.
DeepIris: An ensemble approach to defending Iris recognition classifiers against Adversarial Attacks

Despite being known for their robust performance in the biometrics domain, Deep Convolutional Neural Networks always face a high risk of being fooled by precisely engineered input samples. These samples are called adversarial examples and such attacks are called adversarial attacks. These attacks pose great threat to any biometric security system. In this paper, to guard against adversarial iris images, we propose defensive schemes. The first strategy we propose relies on our adversarial denoising encoder architecture. The second strategy relies on wavelet transformation to divide them into wavelet sub-bands following an U-net architecture wavelet domain denoising on processing each sub-band to remove the adversarial noise. We measure the efficiency against numerous attack scenarios of the suggested adversarial defence mechanism and equate the findings with state-of-the-art defence strategies.
Multi-Channel Feature Dimension Adaption for Correlation Tracking

Recent discriminative trackers especially based on Correlation Filters (CFs) have shown dominant performance for visual tracking. This kind of trackers benefit from multi-resolution deep features a lot, taking the expressive power of deep Convolutional Neural Networks (CNN). However, distractors in complex scenarios, such as similar targets, occlusion, and deformation, lead to model drift. Meanwhile, learning deep features results in feature redundancy that the increasing number of learning parameters introduces the risk of over-fitting. In this paper, we propose a discriminative CFs based visual tracking method, called dimension adaption correlation filters (DACF). First, the framework adopts the multi-channel deep CNN features to obtain a discriminative sample appearance model, resisting the background clutters. Moreover, a dimension adaption operation is introduced to reduce relatively irrelevant parameters as possible, which tackles the issue of over-fitting and promotes the model effectively adapting to different tracking scenes. Furthermore, the DACF formulation optimization can be efficiently performed on the basis of implementing the alternating direction method of multipliers (ADMM). Extensive evaluations are conducted on benchmarks, including OTB2013, OTB2015, VOT2016, and UAV123. The experiments results show that our tracker gains remarkable performance. Especially, DACF obtains an AUC score of 0.698 on OTB2015.
Interactive gesture feature recognition method in 3D virtual laboratory based on mobile terminal

In order to further improve the accuracy of laboratory interactive gesture recognition. Therefore, this paper proposes an interactive gesture feature recognition method for 3D virtual laboratory under mobile terminal, which extracts gradient direction histogram and local binary pattern features respectively, and carries out feature fusion. The fusion features include not only the gradient direction information of the local region of the image, but also the texture information, which can more comprehensively describe the gesture features. The fusion feature vector is input into SVM classifier to complete gesture recognition. Experiments show that the method of gesture recognition in 3D virtual laboratory under mobile terminal has higher accuracy than traditional methods. In this experiment, a variety of gestures are recognized, and the maximum recognition rate is significantly improved.
Secure Centralized Kalman Filter for Network Environments by Homomorphic Encryption

This paper presents a secure and private estimator in Cyber-Physical Systems (CPSs) where the estimator generates the state estimation without knowing the real inputs and outputs of the system. In particular, a secure and centralized Kalman filter is proposed by employing the Paillier encryption. Our proposed encrypted Kalman filter has less computational complexity in comparison to other investigated encrypted Kalman filters. In our structure, the encryption will improve the confidentiality of data transmitted through the communication network. Besides, the algebraic computations needed for the estimation are performed over the encrypted data. Therefore, calculating on encrypted signals improves the security and privacy of the estimation process. Accordingly, even if an adversary has obtained unauthorized entree to the estimator and associated transmission networks, he/she will not be able to gain enough information about the systems state to manage the attack. A numerical example is utilized to demonstrate the theoretical results.
The Science of Guessing in Collision-Optimized Divide-and-Conquer Attacks

Recovering keys ranked in very deep candidate space efficiently is a very important but challenging issue in side-channel attacks (SCAs). State-of-the-art collision-optimized divide-and-conquer attacks (CODCAs) extract collision information from a collision attack to optimize the key recovery of a divide-and-conquer attack, and transform the very huge guessing space to a much smaller collision space. However, the inefficient collision detection makes them time consuming. The very limited collisions exploited and large performance difference between the collision attack and the divide-and-conquer attack in CODCAs also prevent their application in much larger spaces. In this article, we propose a Minkowski distance enhanced collision attack (MDCA) with performance closer to template attack (TA) compared to traditional correlation-enhanced collision attack (CECA), thus making the optimization more practical and meaningful. Next, we build a more advanced CODCA named full-collision chain (FCC) from TA and MDCA to exploit all collisions. Moreover, to minimize the thresholds while guaranteeing a high success probability of key recovery, we propose a fault-tolerant scheme to optimize FCC. The full key is divided into several big “blocks,” on which a fault-tolerant vector (FTV) is exploited to flexibly adjust its chain space. Finally, guessing theory is exploited to optimize thresholds determination and search order of subkeys. Experimental results show that FCC notably outperforms the existing CODCAs.
Research on Intelligent Flight Test Monitoring Technology Based on Expert System

Abstract The current manual-based traditional flight test monitoring technology can no longer fulfill the requirements of high integration, system complexity and technological advancement of new aircraft. In addition, the existing flight monitoring systems are mostly single-point, single-system applications, including ground-assisted systems such as meteorology, radar, and response system. It is easy to cause problems such as data islands, which is not conducive to data sharing and comprehensive evaluation and analysis. This paper proposes a new method for analyzing and mining existing test data based on expert system technology to build a flight test expert system library. A new real-time monitoring system is designed that can meet the real-time processing and integration of multi-source data, comprehensive evaluation and analysis, intelligent warning and assistant decision-making. This system expands the function and intelligence of the existing system, and further improves the quality, safety and efficiency of the flight test monitoring system.
Graph-based variational auto-encoder for generalized zero-shot learning

Zero-shot learning has been a highlighted research topic in both vision and language areas. Recently, generative methods have emerged as a new trend of zero-shot learning, which synthesizes unseen categories samples via generative models. However, the lack of fine-grained information in the synthesized samples makes it difficult to improve classification accuracy. It is also time-consuming and inefficient to synthesize samples and using them to train classifiers. To address such issues, we propose a novel Graph-based Variational Auto-Encoder for zero-shot learning. Specifically, we adopt knowledge graph to model the explicit inter-class relationships, and design a full graph convolution auto-encoder framework to generate the classifier from the distribution of the class-level semantic features on individual nodes. The encoder learns the latent representations of individual nodes, and the decoder generates the classifiers from latent representations of individual nodes. In contrast to synthesize samples, our proposed method directly generates classifiers from the distribution of the class-level semantic features for both seen and unseen categories, which is more straightforward, accurate and computationally efficient. We conduct extensive experiments and evaluate our method on the widely used large-scale ImageNet-21K dataset. Experimental results validate the efficacy of the proposed approach.
Simulation Application of VR Technology in Communication Network

Abstract With the further improvement of the level of informatization, the three-dimensional visualization technology of coal mine safety production has always been a hot issue of current research, among which virtual reality technology is the key technology to achieve three-dimensional visualization. This research mainly discusses the simulation application of VR technology in communication network. First of all, the exchange of data in communication is done in the form of data packets. The data packet transmitted between processes is in binary format, and the content of the data packet is composed of a data header and data content. Then determine the location of the roadway, equipment, and cable to be constructed, and design the object model in 3DS Max. Finally, in order to realize the docking and integration of the digital platform of the whole mine, VIR tools are used to develop the virtual scene platform. In the open-phase fault simulation experiment, the voltage and current parameters of the fault phase are all zero, and the diagnosis result is greater than 0.8. This research improves the management efficiency of power supply and distribution network through real-time communication of mine grid data and integrated display and control of virtual scenes.
An Adaptive Video-to-Video Face Identification System Based on Self-Training

Video-to-video face recognition in unconstrained conditions is still a very challenging problem, as the combination of several factors leads to an in general low-quality of facial frames. Besides, in some real contexts, the availability of labelled samples is limited, or data is streaming or it is only available temporarily due to storage constraints or privacy issues. In these cases, dealing with learning as an unsupervised incremental process is a feasible option. This work proposes a system based on dynamic ensembles of SVM's, which uses the ideas of self-training to perform adaptive Video-to-video face identification. The only label requirements of the system are a few frames (5 in our experiments) directly taken from the video-surveillance stream. The system will autonomously use additional video-frames to update and improve the initial model in an unsupervised way. Results show a significant improvement in comparison to other state-of-the-art static models.
Single Color Image Dehazing Based on Variational Model

Combining variational approach with the atmospheric attenuation model, two single color image dehazing algorithms on the basis of dark channel prior and MTV model are proposed which are called H-MTV model and NL-H-MTV model. Then, we use Dual Bregman algorithm to calculate the H-MTV model and Split Bregman algorithm to calculate the NL-H-MTV model. We conducted three experiments to adjust the parameters. Finally, comparing the proposed models with He algorithm and Kimmel Retinex algorithm. Experimental results show that the proposed methods can remove fog while keeping texture features effectively.
A Feasibility Study on Local Hand-crafted Feature Descriptors for Sketch-based Image Retrieval

Image retrieval plays a major role in medical diagnosis, forensic labs, military, crime prevention, and web searching. Most of the image retrieval systems are text-based, but images normally have little or not carry any textual information. The sketch-based Image Retrieval (SBIR) method allows the user to search natural images using freehand sketches instead of text. From the previous investigation, it is found that SBIR may cause the unmatching of the sketches with the database set due to the user's sketches or the algorithm itself. This work is to study the effectiveness of three local handcrafted descriptors which are Scale-Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF), and Oriented FAST and Brief Descriptor (ORB) for SBIR. It is done by comparing the similarity score between the sketch image and the real image using three different local descriptors. The results demonstrate that each used descriptor produces different matched keypoints and the feature vectors for the similarity measure. To calculate the similarity percentage, Euclidean distance was chosen among the other distance measurement methods. From the results obtained, SIFT has the highest percentage followed by SURF and ORB.
Reducing the Variance of Variational Estimates of Mutual Information by Limiting the Critic's Hypothesis Space to RKHS

Mutual information (MI) is an information-theoretic measure of dependency between two random variables. Several methods to estimate MI from samples of two random variables with unknown underlying probability distributions have been proposed in the literature. Recent methods realize parametric probability distributions or critic as a neural network to approximate unknown density ratios. These approximated density ratios are used to estimate different variational lower bounds of MI. While, these estimation methods are reliable when the true MI is low, they tend to produce high variance estimates when the true MI is high. We argue that the high variance characteristics is due to the uncontrolled complexity of the critic's hypothesis space. In support of this argument, we use the data-driven Rademacher complexity of the hypothesis space associated with the critic's architecture to analyse generalization error bound of variational lower bound estimates of MI. In the proposed work, we show that it is possible to negate the high variance characteristics of these estimators by constraining the critic's hypothesis space to Reproducing Hilbert Kernel Space (RKHS), which corresponds to a kernel learned using Automated Spectral Kernel Learning (ASKL). By analysing the generalization error bounds, we augment the overall optimisation objective with effective regularisation term. We empirically demonstrate the efficacy of this regularization in enforcing proper bias variance tradeoff on four different variational lower bounds of MI, namely NWJ, MINE, JS and SMILE.
A hybrid deep generative neural model for financial report generation

Generating long macro reports from a piece of breaking news is quite a challenging task. Essentially, this task is a long text generation problem from short text. Apparently, the difficulty of this task lies in the logic inference of human beings. To address this issue, this paper proposes a novel hybrid deep generative neural model which first learns the outline of the input news and then generates macro financial reports from the learnt outline. In the outline generation component, we generate the outline text using the framework of Pointer-Generator network with attention mechanism. In the target report generation component, we generate the macro financial reports by the revised VAE model. To train our end-to-end model, we have collected the experimental dataset containing over one hundred thousand pairs of news-report data. Extensive experiments are then evaluated on this dataset. The proposed model achieves the SOTA performance against both the baseline models and the state-of-the-art models with respect to evaluation criteria BLEU, ROUGE and human scores. Although the readability of the generated reports by our approach is better than that of the rest models, it remains an open problem which needs further efforts in the future.
Research on character correction method based on machine learning

Traditional industrial production is dominated by labor force, and the quality inspection personnel in production line have a large workload and long working time, which is easy to reduce the detection efficiency and increase the error rate due to visual fatigue. Therefore, it is urgent to use machine vision technology to identify characters instead of manual. However, when the image is obtained from the camera, the character may be inverted, which will lead to the phenomenon that "W" is recognized as "m" in the process of character recognition, which seriously affects the recognition accuracy. In order to solve this problem, this paper proposes a character correction method based on deep learning. Firstly, Otsu method and global threshold algorithm are used to segment the image. Then, the method of introducing directional gradient histogram (HOG) and support vector machine (SVM) is proposed to classify and correct the character images extracted before. The experimental results show that the algorithm proposed in this paper has sufficient practicability and robustness, realizes the automatic correction of inverted characters, and can effectively improve the accuracy of character recognition.
Real-time arbitrary video style transfer

Video style transfer aims to synthesize a stylized video that has similar content structure with a content video and is rendered in the style of a style image. The existing video style transfer methods cannot simultaneously realize high efficiency, arbitrary style and temporal consistency. In this paper, we propose the first real-time arbitrary video style transfer method with only one model. Specifically, we utilize a three-network architecture consisting of a prediction network, a stylization network and a loss network. Prediction network is used for extracting style parameters from a given style image; Stylization network is for generating the corresponding stylized video; Loss network is for training prediction network and stylization network, whose loss function includes content loss, style loss and temporal consistency loss. We conduct three experiments and a user study to test the effectiveness of our method. The experimental results show that our method outperforms the state-of-the-arts.