Reviewerly/exHarmony huggingface dataset:
Network Traffic Prediction Based on Boosting Learning

Classification of network traffic is an important topic for network management, traffic routing, safe traffic discrimination, and better service delivery. Traffic examination is the entire process of examining traffic data, from intercepting traffic data to discovering patterns, relationships, misconfigurations, and anomalies in a network. Between them, traffic classification is a sub-domain of this field, the purpose of which is to classify network traffic into predefined classes such as usual or abnormal traffic and application type. Most Internet applications encrypt data during traffic, and classifying encrypted data during traffic is not possible with traditional methods. Statistical and intelligence methods can find and model traffic patterns that can be categorized based on statistical characteristics. These methods help determine the type of traffic and protect user privacy at the same time. To classify encrypted traffic from end to end, this paper proposes using (XGboost) algorithms, finding the highest parameters using Bayesian optimization, and comparing the proposed model with machine learning algorithms (Nearest Neighbor, Logistic Regression, Decision Trees, Naive Bayes, Multilayer Neural Networks) to classify traffic from end to end. Network traffic has two classifications: whether the traffic is encrypted or not, and the target application. The research results showed the possibility of classifying dual and multiple traffic with high accuracy. The proposed model has a higher classification accuracy than the other models, and finding the optimal parameters increases the model accuracy.
Improving Measurement of Effectiveness of Blended Learning in Iraqi Education Using SVM

E-learning has recently become of great importance, especially after the emergence of the Corona pandemic, but e-learning has many disadvantages. In order to preserve education, some universities have resorted to using blended learning. Currently, the Ministry of Higher Education and Scientific Research in Iraq has adopted e-learning in universities and schools, especially in scientific disciplines that need laboratories and a spatial presence. In this work, we collected a dataset based on 27 features and presented a model utilizing a support vector machine with regression that was enhanced with the KNN method, which identifies factors that have a substantial influence on the model for the type of education, whether blended or traditional.Furthermore, the dataset used was primarily focused on three key factors: personal information, the impact of e-Learning platforms, and the influence of the Corona virus. The attributes that were measured revealed that social status, computer skills, and the basic platform gave the user enough tools to continue the learning process. The size of the classrooms and laboratories that meet the health safety conditions is the most significant. The goal of our work is to discover a model that predicts how blended learning will be used during and after the coronavirus pandemic and to produce a model with minimal errors.
New Opportunities for the Publication Presentation of Ceramic Vessels of the Pazyryk Culture

Reviewing the publications, that reflect the finds of the Pazyryk culture ceramic vessels, and work with museum collections along with studying this category of ancient objects revealed the problem of adequate fixation and demonstration of available materials. To solve it, modern digital technologies, adapted to perform the necessary tasks, were involved. The article presents in detail the algorithm of the work performed along with the obtained results and suggests variants of illustrative presentation of an ornamented clay pot. As a sample a vessel from burial mound No. 23 of the Khankarinsky Dol site located in the territory of the North-Western Altai and investigated by the expedition of the Altai State University under the leadership of one of the authors of the article was used. During a comprehensive recording and further modelling, one of the technological methods of Pazyryk pottery manufacturing without a potter’s wheel was revealed. This fact allows paying attention to other similar finds, which amount more than 400 specimens in the database under formation. Th e implemented experience will be used in further work in the study of such massive archaeological sources of the Scythian-Saka time as ceramic vessels.
Detection of Fake Replay Attack Signals on Remote Keyless Controlled Vehicles Using Pre-Trained Deep Neural Network

The keyless systems have replaced the old fashion methods of inserting physical keys in the keyhole to, i.e., unlock the door, because they are inconvenient and easy to be exploited by the threat actors. Keyless systems use the technology of radio frequency (RF) as an interface to transmit signals from the key fob to the vehicle. However, Keyless systems are susceptible to being compromised by a thread actor who intercepts the transmitted signal and performs a reply attack. In this paper, we propose a transfer learning-based model to identify the replay attacks launched against remote keyless controlled vehicles. Specifically, the system makes use of a pre-trained ResNet50 deep neural network to predict the wireless remote signals used to lock or unlock doors of a remote-controlled vehicle system remotely. The signals are finally classified into three classes: real signal, fake signal high gain, and fake signal low gain. We have trained our model with 100 epochs (3800 iterations) on a KeFRA 2022 dataset, a modern dataset. The model has recorded a final validation accuracy of 99.71% and a final validation loss of 0.29% at a low inferencing time of 50 ms for the model-based SGD solver. The experimental evaluation revealed the supremacy of the proposed model.
Characterization and potential production of glass-ceramics biomaterial from basalt rock of local Lampung Province

Glass-ceramics were fine-grained polycrystalline materials produced by controlled crystallization in the glass phase to have outstanding characteristics.The establishment of basalt from Sukadana -Lampung Province into glass-ceramic begins with the process of reduced size and milled under 200 mesh using an ASTM sieve.The initiated glass-ceramics formation was created by melting the basalt powder at a temperature of 1,250 o C for 2hrs to obtain the glass phase.Glass parent crystallization begins with the stage of nucleation growth.The nucleation process occurs at a temperature 600 o C for 1hr.The crystallization temperature at variations of 850, 950, or 1,050 o C for 3 and 8hrs.The mechanical hardness test result was 637.28 HV1N of the sample with 1,050 o C heating for 3hrs.The highest hardness value is at 837.33 HV1N for a sample of 1,050 o C heating for 8hrs.The hardness value was decreased at samples for 3hrs heating, while it increased for 8hrs of a heating process.All of the heating treatments formed olivine and Anorthite phase but a pyroxene phase was initiated from 950 o C heating process for 3hrs.The use of glass ceramic as an engineering material and biomaterials are promising.It is necessary to continue the development studies on the production process of local basalt resources.
EFEKTIVITAS APE MAZE TERHADAP PERKEMBANGAN KOGNITIF ANAK USIA 5-6 TAHUN

This study aims to determine whether the Maze educational game tool is effective in improving the cognitive development of children aged 5-6 years. Cognitive aspect is one aspect of development in children that is important to be developed, which this aspect refers to the stage of the child's ability. The cognitive ability of children aged 5-6 years is where children can know numbers, so that children can determine the picture of numbers, can deal with problems seen in everyday life, children have understood the conditions and consistent results, and children can show exploratory exercises. The method used in this research is systematic literature or literature study, the literature used in the past 6 years. This research consists of 6 stages including: determining the title, exploring information, determining the direction of the research, collecting data, presenting data, and compiling reports. Based on the results of the literature above, it can be concluded that the effectiveness of the Maze Educational Game Tool (APE) is quite effective in helping to improve the Cognitive Development of Adult Children 5-6 years. It can be concluded that APE maze is able to help children's cognitive development well, such as helping children to practice problem solving skills, recognizing and knowing numbers, recognizing and mentioning number symbols and children can know various alphabets.
The Application of PCK Concept and Information Fusion-Oriented Multimedia Technology in Music Education

In order to improve the teaching effect of modern music, this paper combines the concept of PCK with information fusion-oriented multimedia technology to explore methods that can improve the quality of music education, and recharacterizes propositional subject knowledge in a way that students like to hear. Moreover, combined with the teaching practice of music teachers in colleges and universities, this paper sorts out the theoretical framework of this research, in order to establish a paradigm suitable for college music teachers to improve music PCK. In addition, this paper constructs an intelligent music teaching system based on multimedia technology and PCK concept. From the experimental research results, it can be seen that the PCK concept and information fusion-oriented multimedia technology have obvious application effects in music education, and can effectively improve the educational effect of modern music.
A kernel-based quantum random forest for improved classification

The emergence of Quantum Machine Learning (QML) to enhance traditional classical learning methods has seen various limitations to its realisation. There is therefore an imperative to develop quantum models with unique model hypotheses to attain expressional and computational advantage. In this work we extend the linear quantum support vector machine (QSVM) with kernel function computed through quantum kernel estimation (QKE), to form a decision tree classifier constructed from a decision directed acyclic graph of QSVM nodes - the ensemble of which we term the quantum random forest (QRF). To limit overfitting, we further extend the model to employ a low-rank Nystr\"{o}m approximation to the kernel matrix. We provide generalisation error bounds on the model and theoretical guarantees to limit errors due to finite sampling on the Nystr\"{o}m-QKE strategy. In doing so, we show that we can achieve lower sampling complexity when compared to QKE. We numerically illustrate the effect of varying model hyperparameters and finally demonstrate that the QRF is able obtain superior performance over QSVMs, while also requiring fewer kernel estimations.
What Do We Mean When We Say "Insight"? A Formal Synthesis of Existing Theory

Researchers have derived many theoretical models for specifying users' insights as they interact with a visualization system. These representations are essential for understanding the insight discovery process, such as when inferring user interaction patterns that lead to insight or assessing the rigor of reported insights. However, theoretical models can be difficult to apply to existing tools and user studies, often due to discrepancies in how insight and its constituent parts are defined. This paper calls attention to the consistent structures that recur across the visualization literature and describes how they connect multiple theoretical representations of insight. We synthesize a unified formalism for insights using these structures, enabling a wider audience of researchers and developers to adopt the corresponding models. Through a series of theoretical case studies, we use our formalism to compare and contrast existing theories, revealing interesting research challenges in reasoning about a user's domain knowledge and leveraging synergistic approaches in data mining and data management research.
A Novel Affective Analysis System Modeling Method Integrating Affective Cognitive Model and Bi-LSTM Neural Network

The severity of mental health issues among college students has increased over the past few years, having a significant negative impact on not only their academic performance but also on their families and even society as a whole. Therefore, one of the pressing issues facing college administrators right now is finding a method that is both scientific and useful for determining the mental health of college students. In pace with the advancement of Internet technology, the Internet has become an important communication channel for contemporary college students. As one of the main forces in the huge Internet population, college students are at the stage of growing knowledge and being most enthusiastic about new things, and they like to express their opinions and views on study life and social issues and are brave to express their emotions. These subjective text data often contain some affective tendencies and psychological characteristics of college students, and it is beneficial to dig out their affective tendencies to further understand what they think and expect and to grasp their mental health as early as possible. In order to address the issue of assessing the mental health of college students, this study makes an effort to use public opinion data from the university network and suggests a college student sentiment analysis model based on the OCC affective cognitive model and Bi-LSTM neural network. In order to label three different types of positive, negative, and neutral sentiment on the microblog text of college network public opinion, we first design a sentiment rule system based on the OCC affective cognition elicitation mechanism. In order to effectively and automatically identify the sentiment state of college students in the network public opinion, this study uses a Bi-LSTM neural network to classify the preprocessed college network public opinion data. Finally, this study performs comparison experiments to confirm the validity of the Bi-LSTM neural network sentiment recognition algorithm and the accuracy of the OCC sentiment rule labeling system. The findings show that the college student sentiment recognition effect of the model is significantly enhanced when the OCC sentiment rule system is used to label the college network public opinion data set as opposed to the naturally labeled data set. In contrast to SVM and other classification models like CNN and LSTM, the Bi-LSTM neural network-based classification model achieves more satisfactory classification results in the recognition of college opinion sentiment.
Research on Traditional Mongolian-Chinese Neural Machine Translation Based on Dependency Syntactic Information and Transformer Model

Neural machine translation (NMT) is a data-driven machine translation approach that has proven its superiority in large corpora, but it still has much room for improvement when the corpus resources are not abundant. This work aims to improve the translation quality of Traditional Mongolian-Chinese (MN-CH). First, the baseline model is constructed based on the Transformer model, and then two different syntax-assisted learning units are added to the encoder and decoder. Finally, the encoder’s ability to learn Traditional Mongolian syntax is implicitly strengthened, and the knowledge of Chinese-dependent syntax is taken as prior knowledge to explicitly guide the decoder to learn Chinese syntax. The average BLEU values measured under two experimental conditions showed that the proposed improved model improved by 6.706 (45.141–38.435) and 5.409 (41.930–36.521) compared with the baseline model. The analysis of the experimental results also revealed that the proposed improved model was still deficient in learning Chinese syntax, and then the Primer-EZ method was introduced to ameliorate this problem, leading to faster convergence and better translation quality. The final improved model had an average BLEU value increase of 9.113 (45.634–36.521) compared with the baseline model at experimental conditions of N = 5 and epochs = 35. The experiments showed that both the proposed model architecture and prior knowledge could effectively lead to an increase in BLEU value, and the addition of syntactic-assisted learning units not only corrected the initial association but also alleviated the long-term dependence between words.
A Deep Learning Approach for The Detection of Structured Query Language Injection Vulnerability

With the rapid development of Web 2.0 technology, network applications have gradually become an indispensable part of our lives. At the same time, Web applications are confronted with more challenges. As announced by the OWASP (open web application security project) organization, injection attack has been the first of the top 10 security vulnerabilities in 2013 and 2017, and SQL injection attack is one of the most important types among the injection attacks. Due to the rapid growth of SQL injection attacks on web application, this research developed a deep learning model in detecting SQL injection attack. The model was trained on a dataset that contains about 30,635 queries, which includes both injected and non-injected queries. The dataset was gotten from Kaggle database. The dataset was then processed by removing null and duplicate values. Further pre-processing was carried out in terms of tokenization and conversion of text to arrays. CountVectorizer () function was used for data normalization in converting the dataset to arrays in form of 0s and 1s. After the pre-processing stage, Feature selection was done on the dataset using the tfidvectoriser. The selected features were passed to the deep feed forward neural network for training. The model was trained on a step of 20 epochs, the model achieved an accuracy of 97.65%. Confusion matrix depicts the total number of correct prediction and the total number of false classifications. The confusion matrix shows that out of 590 classifications on attacks that are of normal, the model predicted correctly for 572 and predicted falsely for 16 times. Then for attacks that are of SQL injection, the model predicted correctly 251 times and predicted falsely for just 1. This shows the performance of the model is in good shape. The model was saved and deployed to web using python flask for easy testing and usage. The model was compared with other existing models and it outperformed the existing model in terms of accuracy. This research can further be extended by using combinations of deep learning algorithms. It can further be extended by deploying the model to android applications.
Non-Gaussian Pseudolinear Kalman Filtering-Based Target Motion Analysis with State Constraints

For the bearing-only target motion analysis (TMA), the pseudolinear Kalman filter (PLKF) solves the complex nonlinear estimation of the motion model parameters but suffers serious bias problems. The pseudolinear Kalman filter under the minimum mean square error framework (PL-MMSE) has a more accurate tracking ability and higher stability compared to the PLKF. Since the bearing signals are corrupted by non-Gaussian noise in practice, we reconstruct the PL-MMSE under Gaussian mixture noise. If some prior information, such as state constraints, is available, the performance of the PL-MMSE can be further improved by incorporating state constraints in the filtering process. In this paper, the mean square and estimation projection methods are used to incorporate PL-MMSE with linear constraints, respectively. Then, the linear approximation and second-order approximation methods are applied to merge PL-MMSE with nonlinear constraints, respectively. Simulation results demonstrate that the constrained PL-MMSE algorithms result in lower mean square errors and bias norms, which demonstrates the superiority of the constrained algorithms.
FORMATION OF TRANSVERSAL COMPETENCES IN A FOREIGN LANGUAGE PROFESSIONAL TRAINING OF FUTURE TEACHERS: REGULATORY AND LEGISLATIVE ASPECTS

The article is devoted to the problem of researching the phenomenon of transversal competencies in modern global systems of professional training of the future teachers. The views of foreign scientists on the meaning of the concept of "transversal competence", the ways of forming transversal competences in the process of becoming specialists in the pedagogical profile, the role of foreign language training of specialists in the field of education in the formation of their competitiveness in modern conditions are considered. The 21st century professional environment is changing rapidly. Continuous development and modification of the international labor market, reorientation to the requirements of digital industry 4.0. create an urgent need for the introduction of transversal competences in educational programs of professional training of specialists in institutions of higher education. The main goal of innovation processes is the need to achieve integration and fusion of these skills in the development of specialized training programs for training future specialists to master a foreign language. The current socio-cultural and educational situation emphasizes the analysis and construction of opportunities for the formation and development of transversal competences in the process of foreign language training of future teachers. Therefore, it is advisable that in the process of learning from all program disciplines, the design of an appropriate educational foreign language environment, the continuous transformation of the acquired knowledge and skills into the basic foundations of transversal competences, which can be applied regardless of age and activity, should take place. In addition, the attitude and expectations of students in achieving transversal competencies should be taken into account. In the modern educational paradigm, the general level of requirements for foreign language training of teachers of various specialties is increasing, an important indicator in the professional growth of a teacher is his desire and ability for continuous learning throughout his life. Although transversal learning can be difficult and problematic, it has a positive dynamic, contributes to further effectiveness in the professional activity of a teacher.
A No-Reference Quality Assessment Method for Screen Content Images Based on Human Visual Perception Characteristics

The widespread application of screen content images (SCIs) has met the needs of remote display and online working. It is a topic that is challenging and worthwhile discussing in research on quality assessment for SCIs. However, existing methods focus on extracting artificial features to predict image quality, which are subjective and incomplete, or lack good interpretability. To overcome these problems, we propose an effective quality assessment method for SCIs based on human visual perceptual characteristics. The proposed method simulates the multi-channel working mechanism of the human visual system (HVS) through pyramid decomposition and the information extraction process of brains with the help of dictionary learning and sparse coding. The input SCIs are first decomposed at multiple scales, and then dictionary learning and sparse coding are applied to the images at each scale. Furthermore, the sparse representation results are analyzed from multiple perspectives. First, a pooling scheme about generalized Gaussian distribution and log-normal distribution is designed to describe the sparse coefficients with and without zero values, respectively. Then the sparse coefficients are used to characterize the energy characteristics. Additionally, the probability of each atom is calculated to describe the statistical property of SCIs. Since the above process only deals with brightness, color-related features are also added to make the model more general and robust. Experimental results on three public SCI databases show that the proposed method can achieve better performance than existing methods.