Reviewerly/exHarmony huggingface dataset:
Assessing the Digital Citizenship Self-efficacy of Pre-service Teachers

In this study, we aim to assess pre-service teachers’ self-efficacy in using computer technology in the digital era as digital citizens. Briefly, students in the Faculty of Education were asked how they perceive computers and technology as a tool in life, education, and future careers. The samples were 615 pre-service teachers selected by Yamane’s method in sample selection from the population of 3093 students in the Faculty of Education, Rajabhat Maha Sarakham University, Thailand. The samples were 1-5th year student teachers from 20 majors in the faculty. The samples were from middle to upper-middle-income families considering the public status of the university with the tuition fee of around 350-400 USD a semester. The sole instrument of the study was a questionnaire with the purpose to investigate computer self-efficacy as digital citizens of the samples. The data were collected in 2020 via an online questionnaire and analyzed by percentage, mean score, standard deviation, t-test, and one-way ANOVA. The results of the study lead to an explanation of the nature of prospective teachers’ beliefs and willingness to develop themselves as a member of the post-digital world, a place where a blurred area of virtual and authentic exist and affect people's lives. It shows that pre-service teachers of a certain socio-economy group had a high level of digital citizenship self-efficacy.
Automated picking of seismic first arrivals using single- to multi-domain self-trained network

Over the last decade, the evolution of survey acquisition design composed of numerous receivers and shot points has exponentially increased the seismic data volume.As a result, the demand for efficient algorithms for detecting seismic first arrivals has been growing.Machine learning methods, especially convolutional neural networks, have shown great potential in previous studies.However, most existing methods lack generalization ability or be sensitive to noise.This paper proposes a new picking strategy based on the single-to multi-domain selftrained network to achieve automated picking for the first arrivals.Our self-trained network learns from a single domain to multiple domains while rejecting the wrong predicted picking and assigning the pseudofirst-arrivals-times to the unlabeled dataset.The model inherits the advantage from both single-and multi-domain picking strategies; it has high generalizability and noise robustness.In addition, the model is designed for a limited amount of manually annotated data to reduce human labor and total seismic processing time.In experiments on an open hard rock seismic multisurvey dataset, our picking method outperforms the benchmark on most evaluation metrics.Remarkably, our approach achieves approximately 10 % accuracy improvement against Lalor site data with a sampling rate that differs from training data.The experimental results demonstrate our method's high generalizability and robustness for unseen datasets.
Sentence Graph Attention for Content-Aware Summarization

Neural network-based encoder–decoder (ED) models are widely used for abstractive text summarization. While the encoder first reads the source document and embeds salient information, the decoder starts from such encoding to generate the summary word-by-word. However, the drawback of the ED model is that it treats words and sentences equally, without discerning the most relevant ones from the others. Many researchers have investigated this problem and provided different solutions. In this paper, we define a sentence-level attention mechanism based on the well-known PageRank algorithm to find the relevant sentences, then propagate the resulting scores into a second word-level attention layer. We tested the proposed model on the well-known CNN/Dailymail dataset, and found that it was able to generate summaries with a much higher abstractive power than state-of-the-art models, in spite of an unavoidable (but slight) decrease in terms of the Rouge scores.
Sequential Transient Detection for RF Fingerprinting

In this paper, a sequential transient detection method for radio frequency (RF) fingerprinting used in the identification of wireless devices is proposed. To the best knowledge of the authors, sequential detection of transient signals for RF fingerprinting has not been considered in the literature. The proposed method is based on an approximate implementation of the generalized likelihood ratio algorithm. The method can be implemented online in a recursive manner with low computational and memory requirements. The transients of wireless transmitters are detected by using the likelihood ratio of the observations without the requirement of any a priori knowledge about the transmitted signals. The performance of the method was evaluated using experimental data collected from 16 Wi-Fi transmitters and compared to those of two existing methods. The experimental test results showed that the proposed method can be used to detect the transient signals with a low detection delay. Our proposed method estimates transient starting points 20-times faster compared to an existing robust method, as well as providing a classification performance of a mean accuracy close to 95%.
Fast 3D Liver Segmentation Using a Trained Deep Chan-Vese Model

This paper introduces an approach for 3D organ segmentation that generalizes in multiple ways the Chan-Vese level set method. Chan-Vese is a segmentation method that simultaneously evolves a level set while fitting locally constant intensity models for the interior and exterior regions. First, its simple length-based regularization is replaced with a learned shape model based on a Fully Convolutional Network (FCN). We show how to train the FCN and introduce data augmentation methods to avoid overfitting. Second, two 3D variants of the method are introduced, one based on a 3D U-Net that makes global shape modifications and one based on a 3D FCN that makes local refinements. These two variants are integrated in a full 3D organ segmentation approach that is capable and efficient in dealing with the large size of the 3D volumes with minimal overfitting. Experiments on liver segmentation on a standard benchmark dataset show that the method obtains 3D segmentation results competitive with the state of the art while being very fast and having a small number of trainable parameters.
Website Vulnerability Testing and Analysis of Website Application Using OWASP

Many businesses, organizations, and social institutions use websites to support their main tasks. The various benefits of the website must be supported by the security aspects of the website in order to avoid hacking. Cyber attacks or hackers can do dangerous things like get more valuable data. So it is necessary to test a good website to find out the level of vulnerability of application features in it. A suitable test for websites where the website is distributed over a network is the grey box penetration test. This study performs a grey box penetration testing technique using the OWASP method and the OWASP ZAP tool. The test steps are collecting test target information, performing automatic scanning with the help of OWASP ZAP, exploiting the scan results, reporting, and providing recommendations. The test results show the target application website has 12 vulnerabilities with 8.3% at the high level vulnerability or 1 alert, 41.7% at the medium level or 5 alerts, 33.3% at the low level or 4 alerts, and 16.7 at the informational level or 2 alerts. These vulnerabilities are related to matters related to A01-Broken Access Control, A03-Injection, A05-Security Misconfiguration, and A08-Software and Data Integrity Failures.
Investigation of Early-Stage Breast Cancer Detection Using Quantum Neural Network

Computer-aided image diagnostics (CAD) have been used in many fields of diagnostic medicine. It relies heavily on classical computer vision and artificial intelligence. Quantum neural network (QNN) has been introduced by many researchers around the world and presented recently by research corporations such as Microsoft, Google, and IBM. In this paper, the investigation of the validity of using the QNN algorithm for machine-based breast cancer detection was performed. To validate the learnability of the QNN, a series of learnability tests were performed alongside with classical convolutional neural network (CCNN). QNN is built using the Cirq library to perform the assimilation of quantum computation on classical computers. Series of investigations were performed to study the learnability characteristics of QNN and CCNN under the same computational conditions. The comparison was performed for real Mammogram data sets. The investigations showed success in terms of recognizing the data and training. Our work shows better performance of QNN in terms of successfully training and producing a valid model for smaller data set compared to CCNN.
An Empirical Study on the Communication and Usage Psychology of Emoji in Wechat

As an indispensable active atmosphere and meaningful emotional expression in social media, emojis describe the emotions of users in specific situations in a funny, humorous, euphemistic, and implicit form and attract users to use them consciously or unconsciously with their quick and convenient features. Currently, the research on the psychology of emoji usage is mostly from the perspective of users, while neglecting the essential attributes of emojis. The purpose of this study was to investigate and summarize the multi-dimensional factors that affect the communication and usage of emojis and to elucidate the influence of the essential attributes of emojis on their use. This study took the users of WeChat (the most widely used instant messaging app in China) as the research object. Data were collected utilizing semi-structured interviews and questionnaires. SPSS 25.0 software was applied to conduct exploratory factor analysis on relevant data to explore and summarize several dimensions of WeChat users’ emoji communication and usage psychology. The results showed that gender, age, education, and occupation were important factors affecting the usage and communication of emojis. The psychology of emoji usage had multi-dimensional characteristics and according to the self-determination theory, it could be divided into internal and external factors. The internal factors included information, efficiency, and entertainment, and the external factors included communicativeness, culture, image, fuzziness, and context. Notably, the influence of fuzziness on the psychology of emoji usage has described the critical role of the essential attributes of emojis, which complemented the neglect of the fuzziness of emoji in previous studies, which might provide a direction for the design of emojis.
Modern Russian University Image in the Mirror of Opinions of Lecturers and Students (on the Example of the Financial University)

The paper reveals the image of a modern Russian university through the prism of the opinions of the two key participants in the educational process: lecturers and students, and highlights the principal components of the corporate culture that form their ideas about it. The demand for higher education among the younger generation is again rapidly declining. There are quite a few reasons for this trend: these are the increasing complexity of final exams, the reduction in the number of state-funded places in more popular fields, and the necessity of quick entering the labor market. Corporate culture plays an important role and forms the basic list of attitudes and, with the accordance, the work of an educational institution should be organized. The educational process allows all participants and ex-participants to form a certain perception of the educational institution among the rest of the public. Existing problems in the external environment, in particular the pre-existing perception of the brand, have a significant impact on the corporate culture of the university.
New Application: A Hand Air Writing System Based on Radar Dual View Sequential Feature Fusion Idea

In recent years, non-contact human–computer interactions have aroused much attention. In this paper, we mainly propose a dual view observation system based on the frontal and side millimeter-wave radars (MWR) to collect echo data of the Air writing digits “0~9”, simultaneously. Additionally, we also propose a novel distance approximation method to make the trajectory reconstruction more efficient. To exploit these characteristics of spatial-temporal adjacency in handwriting digits, we propose a novel clustering algorithm, named the constrained density-based spatial clustering of application with noise (CDBSCAN), to remove background noise or clutter. Moreover, we also design a robust gesture segmentation method by using twice-difference and high–low thresholds. In our trials and comparisons, based on the trajectories formulated by echo data series of time–distance and time–velocity of dual views, we present a lightweight-based convolution neural network (CNN) to realize these digits recognition. Experiment results show that our system has a relatively high recognition accuracy, which would provide a feasible application for future human–computer interaction scenarios.
Quantum-Inspired Classification Based on Voronoi Tessellation and Pretty-Good Measurements

In quantum machine learning, feature vectors are encoded into quantum states. Measurements for the discrimination of states are useful tools for classification problems. Classification algorithms inspired by quantum state discrimination have recently been implemented on classical computers. We present a local approach combining Vonoroi-type tessellation of a training set with pretty-good measurements for quantum state discrimination.
Improved generalization performance of convolutional neural networks with LossDA

Abstract In recent years, convolutional neural networks (CNNs) have been used in many fields. Nowadays, CNNs have a high learning capability, and this learning capability is accompanied by a more complex model architecture. Complex model architectures allow CNNs to learn more data features, but such a learning process tends to reduce the training model’s ability to generalize to unknown data, and may be associated with problems of overfitting. Although many regularization methods have been proposed, such as data augmentation, batch normalization, and Dropout, research on improving generalization performance is still a common concern in the training process of robust CNNs. In this paper, we propose a dynamically controllable adjustment method, which we call LossDA, that embeds a disturbance variable in the fully-connected layer. The trend of this variable is kept consistent with the training loss, while the magnitude of the variable can be preset to adapt to the training process of different models. Through this dynamic adjustment, the training process of CNNs can be adaptively adjusted. The whole regularization process can improve the generalization performance of CNNs while helping to suppress overfitting. To evaluate this method, this paper conducts comparative experiments on MNIST, FashionMNIST, CIFAR-10, Cats_vs_Dogs, and miniImagenet datasets. The experimental results show that the method can improve the model performance of Light CNNs and Transfer CNNs (InceptionResNet, VGG19, ResNet50, and InceptionV3). The average maximum improvement in accuracy of Light CNNs is 4.62%, F1 is 3.99%, and Recall is 4.69%. The average maximum improvement accuracy of Transfer CNNs is 4.17%, F1 is 5.64%, and Recall is 4.05%.
Intelligent Security Model for Password Generation and Estimation Using Hand Gesture Features

Computer security depends mainly on passwords to protect human users from attackers. Therefore, manual and alphanumerical passwords are the most frequent type of computer authentication. However, creating these passwords has significant drawbacks. For example, users often tend to choose passwords based on personal information so that they can be memorable and therefore weak and guessable. In contrast, it is often difficult to remember if the password is difficult to guess. We propose an intelligent security model for password generation and estimation to address these problems using the ensemble learning approach and hand gesture features. This paper proposes two intelligent stages: the first is the password generation stage based on the ensemble learning approach and the proposed S-Box. The second is the password strength estimation stage, also based on the ensemble learning approach. Four well-known classifiers are used: Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Random Forest Tree (RFT), and AdaBoost applied on two datasets: MNIST images dataset and password strength dataset. The experimental results showed that the hand gesture and password strength classification processes accurately performed at 99% in AUC, Accuracy, F1-measures, Precision, and Recall. As a result, the extracted features of hand gestures will directly impact the complexity of generated passwords, which are very strong, hard to guess, and memorable.
Bootstrap-CURE Clustering: An Investigation of Impact of Shrinking on Clustering Performance

Hierarchical clustering is one of the most popular techniques in unsupervised segmentation. However, since it has quadratic complexity as it is based on pairwise distance matrix construction, it tends to be less used with really large data cases. CURE clustering tackles this challenge by accelerating the process through a first hierarchical clustering over a smaller sample from which a set of representative points of resulting clusters is obtained and used to estimate the cluster shape. A KNN process with those representative points allows completing the cluster assignment to the remaining points. This clustering technique scales the hierarchical clustering to large datasets. This work is in continuation of the earlier research, Bootstrap-CURE which uses repeated samples in the first part of the process and gains both robustness and representativeness. Also, the proposed approach uses a criterion for automatic identification of the number of clusters from a dendrogram, so that the bootstrap samples can be automatically processed. In this paper, the concept of shrinkage is proposed as a hyperparameter to the Bootstrap-CURE clustering approach. The inclusion of shrinkage brings the proposed clustering technique closer to the original CURE clustering. The impact of shrinkage on the overall performance of Bootstrap-CURE is further explored. A real-life use case from 3D printers is presented to illustrate the performance of the proposed clustering.
VII Congress of RNTORES n. a. A. S. Popov

Information is provided on the VII Congress of the all-Russian public organization “Russian Scientific and Technical Society of Radio Engineering, Electronics and Communications named after A. S. Popov”, which was held in Moscow on June 7, 2022. The results of the activities of the RNTORES for the reporting period (2016–2022) are considered: publishing magazines and holding conferences. Information is given on the activities of regional branches (Moscow, Nizhny Novgorod, Vladimir, Tula, Krimea), as well as on the organizational work of RNTORES. The composition of the newly elected governing bodies of RNTORES for a 5-year period (president, vice-presidents and the Presidium) is given.
Automatic Weapon and Knife Detection System on Security Cameras: Comparative YOLO Models

Security camera systems are generally used to create safe environment conditions in social and public areas. In today’s security camera systems, artificial intelligence-based computer vision has started to be used instead of human vision. In this study, it is aimed to see and detect images of guns and knives that may be in social and public spaces with computer vision. As a method and model in the study, image processing technology and YOLO algorithms, which are generally known to have very successful results in the literature, were used. Among the YOLO algorithms, YOLOv4, YOLOv5, YOLOR and YOLOX models were used. 5078 images were used as a data set in the study, and 3000 of these images consist of images of weapons and 2078 of knife images. In order to ensure reliability for the experimental study to be obtained from the images, attention has been paid to the fact that the selectivity of the images is difficult. Comparative experimental studies of YOLO algorithms have been carried out and their results have been published. The most successful result in detecting weapons and knives in the images was obtained in the YOLOR model with a map@0,5 value of 97.6%.
PROCEDURAL DIGITAL TWIN GENERATION FOR CO-CREATING IN VR FOCUSING ON VEGETATION

Abstract. An early-stage development of a Digital Twin (DT) in Virtual Reality (VR) is presented, aiming for civic engagement in a new urban development located in an area that is a forest today. The area is presently used for recreation. For the developer, it is important both to communicate how the new development will affect the forest and allow for feedback from the citizen. High quality DT models are time-consuming to generate, especially for VR. Current model generation methods require the model developer to manually design the virtual environment. Furthermore, they are not scalable when multiple scenarios are required as a project progresses. This study aimed to create an automated, procedural workflow to generate DT models and visualize large-scale data in VR with a focus on existing green structures as a basis for participatory approaches. Two versions of the VR prototype were developed in close cooperation with the urban developer and evaluated in two user tests. A procedural workflow was developed for generating DT models and integrated into the VR application. For the green structures, efforts focused on the vegetation, such as realistic representation and placement of different types of trees and bushes. Only navigation functions were enabled in the first user test with practitioners (9 participants). Interactive functions were enabled in the second user test with pupils (age 15, 9 participants). In both tests, the researchers observed the participants and carried out short reflective interviews. The user test evaluation focussed on the perception of the vegetation, general perception of the VR environment, interaction, and navigation. The results show that the workflow is effective, and the users appreciate green structure representations in VR environments in both user tests. Based on the workflow, similar scenes can be created for any location in Sweden. Future development needs to concentrate on the refinement of buildings and information content. A challenge will be balancing the level of detail for communication with residents.