Reviewerly/exHarmony huggingface dataset:
A Study on the Trends of Lexical Errors for Chinese Korean Learners

The purpose of this study is to analyze the trends of lexical error research for Chinese Korean learners, to find out the results of the lexical error analysis research and research directions of the future. The subjects of this study were a total of 186 papers which were classified by study period, study target(learner’s level, learning environment), lexical category, and research data type for the review of overall trends. As a result firstly, research on lexical errors of beginner learners is still insufficient. Secondly, error studies should be conducted on migrants such as marriage immigrants and migrant workers. Thirdly, it is necessary to examine in detail the patterns and causes of lexical errors of Chinese learners through a large number of corpus. Fourthly, the research of synonyms, suffixes, loanword, address terms and idioms is insufficient. Finally, there is a lack of research that focuses on vocabulary for academic purposes. (Kyung Hee University)
Analysis Of Variation In The Number Of MFCC Features In Contrast To LSTM In The Classification Of English Accent Sounds

Various studies have been carried out to classify English accents using traditional classifiers and modern classifiers. In general, research on voice classification and voice recognition that has been done previously uses the MFCC method as voice feature extraction. The stages in this study began with importing datasets, data preprocessing of datasets, then performing MFCC feature extraction, conducting model training, testing model accuracy and displaying a confusion matrix on model accuracy. After that, an analysis of the classification has been carried out. The overall results of the 10 tests on the test set show the highest accuracy value for feature 17 value of 64.96% in the test results obtained some important information, including; The test results on the MFCC coefficient values of twelve to twenty show overfitting. This is shown in the model training process which repeatedly produces high accuracy but produces low accuracy in the classification testing process. The feature assignment on MFCC shows that the higher the feature value assignment on MFCC causes a very large sound feature dimension. With the large number of features obtained, the MFCC method has a weakness in determining the number of features.
Web-Based Online Queuing Information System at the Lendang Nangka Health Center

The online queuing system is a system that can make it easier for users through the administration process at the Lendang Nangka health center. However, the system implemented at the health center is still done manually, such as patients coming directly to the health center to register, and officers taking notes, and taking queues. So, this takes a long time and makes the dating patient feel bored and bored. The purpose of this study is to create a web-based online queuing system, system feasibility tester, user response and performance of this system. The model used for this study is ADDIE with five stages, namely analysis, design, development, implementation, and evaluation. Data collection techniques use questionnaires given to system experts and users. Meanwhile, the data analysis is used descriptively and uses the GT Metrix tool to see the efficiency of the system that has been created. Our findings are in the form of a web-based online queue information system that can facilitate the administrative process at this health center. In addition, this system is also categorized as very feasible and has a very high response. This system also includes grade A, where the performance result is 90%, and the structure are 90%. So that the system can help make it easier for patients, staff, and doctors in the administrative process at this health center.
A Survey of Novel Chaotic Image Encryption Techniques

Images are fundamentally different that text and thus need different encryption techniques to keep data secure. Images exhibit a high correlation with adjacent pixels. Images are also much bulkier than text and thus need a different approach to encrypt them in an acceptable time. Chaotic cryptosystem and chaotic theory offer us a feasible solution. Since chaotic maps are very sensitive to initial conditions and are capable of producing seemingly random values it is suitable for image encryption. Many types of chaotic maps and algorithms for confusion and diffusion have been proposed. This paper will be a brief survey of modern chaotic techniques.
Explainable Deep-Learning Model Reveals Past Cardiovascular Disease in Patients with Diabetes Using Free-Form Visit Reports

Writing notes remains the most widespread method by which medical doctors record clinical events. As a result, a relevant portion of the information concerning a patient’s health status is recorded in the form of text. This is particularly relevant in the case of people affected by chronic diseases, such as diabetes, who need frequent medical attention, and, thus, produce substantial amounts of textual data. Natural language processing methods are valuable in this context, as they can enable the automatic conversion of free-form text into structured data. However, in everyday clinical practice, these approaches, which often rely on artificial intelligence, are not widely used as they are perceived as black boxes. In this study, we used a typical electronic health record dataset, paired with a subset of a hospital discharge registry, to develop a tool to automatically identify past cardiovascular disease (CVD) hospitalisations using the free-form text of routine visits of diabetic patients. Thanks to its high performance (F1-score = 0.927), the proposed tool may be reliably used to help clinicians to fill medical records in a structured fashion by raising an alert when the model identifies a CVD hospitalisation, but no history of CVD is annotated within the patient’s electronic health record. We also provided a robust interpretability framework following the trustworthy artificial intelligence principles of i) human agency, ii) human oversight, and iii) technical robustness and safety. Leveraging the provided interpretability framework, it is also possible to understand the most relevant factors that led to the alert, empowering the user to decide whether to follow up on the algorithm’s suggestion or ignore it.
ADVFilter: Adversarial Example Generated by Perturbing Optical Path

Deep Neural Networks (DNNs) have achieved great success in many applications, and they are taking over more and more systems in the real world. As a result, the security of DNN system has attracted great attention from the community. In typical scenes, the input images of DNN are collected through the camera. In this paper, we propose a new type of security threat, which attacks a DNN classifier by perturbing the optical path of the camera input through a specially designed filter. It involves many challenges to generate such a filter. First, the filter should be input-free. Second, the filter should be simple enough for manufacturing. We propose a framework to generate such filters, called ADVFilter. ADVFilter models the optical path perturbation by thin plate spline, and optimizes for the minimal distortion of the input images. ADVFilter can generate adversarial pattern for a specific class. This adversarial pattern is universal for the class, which means that it can mislead the DNN model on all input images of the class with high probability. We demonstrate our idea on MNIST dataset, and the results show that ADVFilter can achieve up to 90 $$\%$$ success rate with only 16 corresponding points. To the best of our knowledge, this is the first work to propose such security threat for DNN models.
Ensuring the security of using virtualization to protect data on the example of a peer-to-peer decentralized network TheOoL.net

Virtual workspaces are often used in modern information systems. Due to its technical features, such as no direct access to hardware, access balancing, instant migration, and much more, this technology has proven itself well in the field of information security. However, the possibility of obtaining secret data from the virtual workspace by introducing malware (for example, spyware) on the user's host, as well as the vulnerabilities of the virtual spaces themselves, remain quite serious problems. Various internal regulations or technologies used to ensure an adequate level of security may not be effective enough. Using the approach to ensuring the security of virtual spaces in the TheOoL.net system as an example, the author examines the use of a number of technological features to close the vulnerabilities of such systems.
교육감과 관련한 일본의 지방교육자치제도에 대한 연구

The purpose of this study is to identify the problems with the education autonomy system and conflicts of the Japan, which is implementing the local education autonomy system to find a way to resolve conflicts and disputes that occur between the Minister of Education and the educational superintendent., and to suggest reasonable improvements. In Japan, as in Korea, there are conflicts and disputes between the central government and local governments. However, what is different from us is that the autonomy of local governments has been strengthened following the decentralization reform promoted by Japan in the 1990s. The Local Autonomy Act restricts the central government's involvement with local governments, and requires its intervention to meet the specified requirements and to follow due process, detailed in the relevant laws. In other words, the case of Japan, where there is a legal regulation making the central government not restricting or interfering autonomy of local governments, suggests that more detailed regulations are required within the scope that the government’s involvement and intervention doesn’t undermine the autonomy of local governments to us, where the requirements and process of the central governments’ intervention in local governments are simplified. In Korea, similar to the National and Local Dispute Resolution Committee in Japan, an administrative consultation and mediation committee exists under Article 168, but it is ineffective because there are insufficient regulations on the authority of the committee and the effect of the decision of the committee on the central and local governments. It is necessary to prepare a new type of dispute mediation body and related regulations that can minimize the loss that may occur in case of a dispute or conflict through comparative review of dispute mediation bodies in Korea and Japan.
The face lattice of the set of reduced density matrices and its coatoms

The lattice of faces of the convex set of reduced density matrices is essential for the construction of the information projection to a hierarchical model. The lattice of faces is also important in quantum state tomography. Yet, the description and computation of these faces is elusive in the simplest examples. Here, we study the face lattice of the set of two-body reduced density matrices: We show that the three-qubit lattice has no elements of rank seven and that it has a family of coatoms of rank five. This contrasts with the three-bit lattice, where every coatom has rank six. We discovered the coatoms of rank five using a novel experimental method, which employs convex duality, semidefinite programming, and algebra. We also discuss nonexposed points for three and six qubits. Using frustration-free Hamiltonians, we provide a new characterization of probability distributions that factor.
Dynamic Target Tracking and Localization for Small UAV in Unstructured Outdoor Environment

This paper presents a visual perception system of small UAVs for tracking and localization of dynamic targets to tackle the issues caused by occlusions and similar target interference. A DTC-Tracker module comprising detector, tracker and checker is designed to improve the tracking performance in cluttered outdoor environments. A dynamic tracking result checking and target recapture mechanism are proposed to adaptively invoke detector and checker for better tracking robustness and real time performance on onboard embedded computer. An onboard depth camera and visual odometry are incorporated to localize the dynamic target in UAV’s local coordinates. Experiments on a multirotor UAV platform were conducted to verify the effectiveness and investigate the performance proposed approach in a grove.
Autonomous Immune Reconfiguration Mechanism Based on Capability Entropy and Trust Entropy Flow

Aiming at the problems of navigation and communication deception and interference faced by the collaborative location of the distributed multi-UAV system, a trust mechanism is introduced. The location accuracy of UAV is described as the location ability, quantified as the ability entropy, and the ability to provide location services to the formation is described as the trust entropy. Both of them are interrelated and promoted. Based on the above, an observation structure of the collaborative location system is constructed, and the demonstration and verification environment is built at the same time. In the last algorithm, experiments are carried out. When the number of deceptive individuals in the system is less than 1/3, the deception recognition rate can be attained to more than 96%, which meets the requirements of the expected design indexes.
Hierarchical Learning of Outliers

Outlier analysis and data clustering both are important tasks in the data analysis domain. In this paper, we decompose data points at multiple levels of hierarchy to identify the presence of outliers at multiple levels. This approach creates a tree-like structure of outlier’s hierarchically. Experimenting on a real dataset gives us vital information about the characteristics of outliers and numerical results establish strong evidence for validating the proposed approach.
Hand Gesture Recognition Using CNN

Computer is useful and important in our life and used in different fields. Interaction between computer and human is accomplished with computer input devices like mouse, printer, keyboard, etc. Using hand gestures as useful medium between human and computer can make the connection easier for disable persons. Disable persons do not have an interaction with normal people because, the normal persons do not know the sign language. We use the CNN for the recognition of hand gestures. As CNN can intake image processing, we use this technique. Recent research has proved the supremacy of convolutional neural network. Whatever, the images that can be captured are compared with datasets and compared the accuracy and give the message of respected dataset. With this software, the disable people can communicate with others. The model of augmented data received accuracy 97.12% which is nearly 4% higher than the model without augmentation (92.87%). So, nonlinearity exists in this type of problem. Also, we added some applications such as mouse movement and volume changes.
A Design of Automatic Food Delivery Robot System Based on Machine Vision Technologies

Machine vision technologies have been widely used by automation nowadays. In order to improve the efficiency of food delivery in restaurants and strengthen the prevention and control of the virus epidemic, this paper has designed an automatic food delivery robot with the STM32F103ZET6 system as the micro-processor, which can realize the function of automatic food delivery in the restaurant. The robot uses OpenMV as the visual sensor module. OpenMV has two main roles in this design, one is to identify the color and the other takes the role of patrolling. The patrol algorithm is the Telson linear regression algorithm while table number instruction being sent to the micro-processor via Bluetooth. Afterwards, a optimal route is defined according to the position based on the table number received. Then, the robot follows the route to the target location. After delivering the food to the destination, the car will return automatically so as to completes the delivery task.
DLCNN Model with Multi-exposure Fusion for Underwater Image Enhancement

Oceans comprise a huge portion of our planet’s surface, and these water resources determine our planet’s health. Underwater flora and fauna research is an essential component of oceanographic research. In order to create a more aesthetically attractive image, underwater image improvement is a crucial element. There are a variety of image processing techniques available, most of which are extremely easy and quick to use. However, traditional methods failed to provide the highest level of precision. Due to haze caused by light reflected from the surface and separated by the water particles, the underwater picture captured by the camera has low prominence, and colour variation is caused by attenuation of different wavelengths. To address these shortcomings, the proposed study employs the advanced deep learning convolution neural network (DLCNN) model. The network is trained and evaluated using features based on the Laplacian and Gaussian pyramids. These characteristics are used to change the parameters of the multiple exposure fusion (MEF), allowing for precise improvement. The proposed DLCNN-MEF technique gives better results when compared with state-of-the-art approaches technique with respect to the various parameters including such as PSNR, BRISQUE and MSE values for comparing the results.
Entrepreneurial marketing strategy and customer loyalty: An empirical evidence from coffee shops

For many decades, the main focus of successful businesses has been customer orientation. However, the role of the entrepreneur in shaping the landscape of marketing and business performance was recognized recently. Entrepreneurial marketing (EM) is described as the marketing done by entrepreneurs or business owners of entrepreneurial businesses by employing different strategies that are distinct from the typical textbook marketing to attract new businesses. In EM, the entrepreneur is a crucial actor in the marketing process (Adel et al., 2020). Grounded on the concept of entrepreneurial marketing and business performance, the aim of this study revolves around analysing the relationship among entrepreneurial marketing, business performance, antecedents of entrepreneur, and customer loyalty. Six hundred customers who regularly visit the coffee shops in Muwaileh, Sharjah, were a part of this study. The study used a descriptive research method. Entrepreneurial marketing was measured using five sub-variables namely, Product strategy, Pricing strategy, Promotion strategy, Behavioural strategy, and Locality/Area characteristics (Al-Shaikh, 2022). Statistical techniques like descriptive analysis, reliability analysis, structural equation modelling (SEM), and mean comparison tests were performed in this study. The study found that all the factors of entrepreneurial marketing have a significant impact on business performance. It was also evident from the study that the antecedent of entrepreneur, as well as customer loyalty, have a positive influence on business performance.
Smart Pruning of Deep Neural Networks Using Curve Fitting and Evolution of Weights

Compression of the deep neural networks is a critical problem area when it comes to enhancing the capability of embedded devices. As deep neural networks are space and compute-intensive, they are generally unsuitable for use in edge devices and thereby lose their ubiquity. This paper discusses novel methods of neural network pruning, making them lighter, faster, and immune to noise and over-fitting without compromising the accuracy of the models. It poses two questions about the accepted methods of pruning and proffers two new strategies - evolution of weights and smart pruning to compress the deep neural networks better. These methods are then compared with the standard pruning mechanism on benchmark data sets to establish their efficiency. The code is made available online for public use.
An Improved Deep Q-Network with Convolution Block Attention

Deep Reinforcement Learning technology combines Reinforcement Learning and Deep Learning, and uses the strong representation ability of Deep Learning to deal with high-dimensional input problems. However, as the first classical algorithm of Deep Reinforcement Learning, Deep Q-Network (DQN) algorithm does not make full use of some high value information in the input state, and the computational burden of network model needs to be improved. In order to solve these problems, this paper proposes an improved DQN algorithm based on Convolution Block Attention (CBA-DQN). This algorithm introduces Convolution Block Attention (CBA) on the basis of traditional DQN. By calculating the spatial and channel attention of the input state, the agent can focus on the parts with great value, so as to improve the computational burden of the algorithm. Experimental results show that CBA-DQN can improve the performance of traditional DQN algorithm in some Atari game tasks.
TAD: Tracking-Aided Detection Siamese Network for Visual Drone Surveillance

There is an urgent demand for detecting and tracking intruding drones, where visual-based methods are commonly used. Nevertheless, prior work on visual-based drone surveillance is not capable of achieving high precision and high frame rate at the same time. In this paper, we propose a tracking-aided detection Siamese network for drone surveillance in urban environments, which can maintain a high precision and high speed simultaneously. The proposed framework consists of a detection module and a tracking module, where the tracking module reuses part of the backbone of the detection module to pursue lower computational costs. To ensure high precision, the detection module is used to provide the drone’s initial location and the corrected results for the tracking module if needed. The tracking module fully exploits the temporal information between the frames and aids the detection module in drone localizing, thus achieving high speed. Furthermore, we propose a rule-based heuristic scheduling algorithm to schedule alternate runs of the tracking and detection branches to achieve a good balance between precision and speed. Experiments on the Drone-vs-Bird dataset demonstrated that the speed of the proposed framework is faster than detection-only algorithms, and the accuracy is better than tracking-only algorithms in the meantime.
Locality in Exceptional Tagalog Ā-Extraction

This article investigates Tagalog Ā-extraction, considering cases conforming with and cases violating the well-known Tagalog extraction restriction. A unified analysis is proposed using properties of the lower phase and ways this boundary can be circumvented. Two mechanisms are available for this purpose. First, arguments may escape the lower phase through independently attested operations. Second, the phase is transparent to clause-peripheral Ā-probes when material from the inflectional domain is absent. This proposal accounts for the expanded range of phenomena considered, which poses problems for the predominant approach to Tagalog Ā-extraction, where Ā-probes must target the highest c-commanded DP.