Reviewerly/exHarmony huggingface dataset:
Speaker Diarization in Vietnamese Voice

Speaker diarization is the process of partitioning an input audio stream into homogeneous segments according to different speakers. It is an important process to support speaker recognition systems and identify a speaker in broadcasts, meeting recordings, and voice mail. Especially it is a fundamental step of automatic checklist reading evaluation system in operating rooms. In this study, we introduce an approach for speaker diarization in Vietnamese voices. The proposed method consists of vectorizing voice based on x-vector and then clustering by mean-shift, k-means, and agglomerative hierarchical techniques to identify speakers in audio. This method attained the accuracy of 89.29% on the 2-speaker mock dialogue generated from the testing set of the VIVOS Corpus dataset.
The organization of students’ project practice based on cloud technologies

Abstract The organization of project activities of students is discussed. Main stages of the project creation are highlighted and the activities of students at stage are presented. Main advantages of using cloud-based software in the educational environment over traditional educational software are considered. The selection of cloud services for their use at various stages of the project activity in accordance with the requirements of the educational process is presented. The proposed algorithm for organizing the project activities of students is illustrated by the description of the work on the project on the topic ”Parabola in Physics and Practical Human Activity. The presented materials were tested by the authors at the Faculty of Physics and Mathematics of Moscow Region State University during training sessions.
Visual Tracking via Robust and Efficient Temporal Regularized Correlation Filters

We present a robust and efficient temporal regularized correlation filters (RETCF) model to achieve outstanding performance with hand-crafted features in visual tracking. The model of online learning is overly dependent on the fixed learning rate; When the condition of the object makes a difference, the model cannot learn useful information adaptively. Therefore, we take advantage of temporal regularization to obtain a robust discriminative correlation filters model instead of updating the model of the object. Besides, this correlation filter model can be efficiently optimal via the alternating direction method of multipliers (ADMM). Comparing with other top-ranked of CF trackers, the proposed tracker (RETCF) achieves real-time and outperformance for the challenging benchmark sequences (OTB2013, OTB2015, and TC128).
Validity and reliability of Cybersecurity Judgment Questionnaire for middle and high school students: A validation study with Rasch analysis

In cybersecurity practice, two methodological challenges exist, that is, how to improve validity and reliability of cybersecurity assessment as a weak hypothesis and how to assess and mitigate cybersecurity risks among the K-12 school population as the weakest link. To address these two challenges, the present study aimed to verify the validity and reliability of one newly developed questionnaire, Cybersecurity Judgment Questionnaire for Middle and High School Students, using the Rasch modeling. A total of 398 middle and high school students from the GenCyber Camps participated in this study. The results of the Rasch model analysis show that (1) the Cybersecurity Judgment Questionnaire was unidimensional, measuring one single construct of cybersecurity judgment; (2) the questionnaire had acceptable item construct validity and person construct validity; (3) the item reliability and person reliability of the questionnaire were also acceptable, and (4) a few items might need revisions to increase item difficulty and reduce redundancy, if this would be confirmed by more replication studies with more representative samples. Suggestions for improvement and administration of the questions are discussed.
Data update for Vietnam economics research institutions as of October 2021

Research data in Vietnam have been fragmented, and this is a long-standing fact. Although several universities repeatedly claim that they have been the leading players in economics research, actual data may not support their claims.In a recent attempt, Dr. Le Van Ut of Ton Duc Thang University in Ho Chi Minh City compiled a data set to figure out Vietnamese scholars’ internal capacity. Nonetheless, this report covers too large a spectrum that economics research has little weight. In fact, only one person represents the humanities and social sciences in Dr. Ut’s 22-person lifetime achievement list, and only a handful in his 2021 most influential list.
Use of interactive methods of collaboration between educational process participants in the conditions of distance learning

The purpose of the article was to study three groups of factors of perception of the introduction of interactive methods of distance education in the educational environment of Higher Education Institutions. The research used general scientific research methods, questionnaire methods, methods for integrated assessment, mathematical methods of data processing: calculation of consolidated characteristics of motivation, sign test. The study revealed a lo w level of student knowledge about distance education technologies and, by contrast, a high level of student interest in mastering interactive distance education methods. The article detects the influence of the educational environment of the HEIs in the formation of the disposition of the students to use interactive technologies of distance education. The article considers properties of interactive methods such as mobility, openness, accessibility and the use of interactive distance learning technologies. The article aims to study the peculiarities of the students' attitude towards the introduction of interactive distance learning technologies in the educational process. Other research perspectives are in the creation of a system for monitoring the preparation of students for the implementation of innovative technologies in relation to the constant development of scientific and technological progress.
Cost Optimization for Dynamic Data Migration and Re-placement with Load-Balancing in Geo-Distributed Systems

With wide usage of smart digital devices and social media, enormous data is being continuously produced. Big data techniques are evolving to fulfill the ever-growing data processing demands, for which heterogeneous techniques are being used. Heterogeneous data processing consists of a collection of powerful, scalable and distributed nodes with different storage and processing capacity. Data replication is a strategy used to improve such system’s availability and reliability. The data migration is a relocation scheme, in which data is moved from one node to another, either in the same or different data centers. To minimize the cost of access latency, migration cost and maximize the availability of the data, load balancing among the data nodes is done. Towards this goal, in this paper we formulate an optimization problem for data replica placement and migration. We have proposed a dynamic data migration scheme which moves the data nearer to the users accessing the data. If a node is overloaded or removed, we relocate the data partitions to the lightly loaded or new node. Our simulation results show that, the proposed dynamic data migration strategy balances the load across the nodes and improves the system performance as compared to the existing methods.
Construction of Hybrid Model for English News Headline Sarcasm Detection by Word Embedding Technique

People often use sarcasm to taunt, anger, or amuse one another. Scathing undertones can't be missed, even when using a simple sentiment analysis tool. Sarcasm may be detected using a variety of machine learning techniques, including rule-based approaches, statistical approaches, and classifiers. Since English is a widely used language on the internet, most of these terms were created to help people recognize sarcasm in written material. Convolutional Neural Networks (CNNs) are used to extract features, and Naive Bayes (NBs) are trained and evaluated on those features using a probability function. This suggested approach gives a more accurate forecast of sarcasm detection based on probability prediction. This hybrid machine learning technique is evaluated according to the stretching component in frequency inverse domain, the cluster of the words and word vectors with embedding. Based on the findings, the proposed model surpasses many advanced algorithms for sarcasm detection, including accuracy, recall, and F1 scores. It is possible to identify sarcasm in a multi-domain dataset using the suggested model, which is accurate and resilient.
Selectively Invariant Control Systems Design of the Unmanned Mobile Robots' Movement

The paper proposes the analytical design method of the control system for movement of a mobile robot with external, not measured disturbances. To exclude the influence of these disturbances on error of the control system the digital selectively-invariant control system is offered to use. The control on output and impacts and the Kulebakin's images of the disturbances are used for solution of the design problem of the selectively-invariant control system. The offered approach can be used for the rejection of the influence of different external disturbances on control systems.
Brain MR Image Segmentation using a Fast Fuzzy Clustering Approach

Segmentation of brain magnetic resonance (MR) images is a vital requisite in health care applications. Spatial Fuzzy C-means (SFCM) clustering method segments the brain MR images degraded with noise and artifacts. Due to the iterative computation of the clustering parameters, the run time is high. To address this issue, we suggest a fast spatial FCM (FSFCM) clustering technique for brain MR image segmentation. The basic idea is that, the input image is processed through morphological reconstruction and local spatial similarity factor calculation prior to the clustering process. The morphological reconstruction operation helps in eliminating the noise, while inclusion of the local spatial similarity factor preserves the structural details. The proposed technique is relatively faster than SFCM because the local spatial similarity factor is to be computed only once prior to computation of the clustering parameters. The proposed method is investigated with various volumes of synthetic and clinical brain MR images. The outcomes of the experiment demonstrate better results in comparison to the state-of-the-art methods.
Geosystems model of uranium deposits

The formation and origin of uranium deposits is of interest for uranium exploration necessary for the sustainability of nuclear energy production. The proper organization of this knowledge is the most important. Prior models or classifications of uranium deposit types do not emphasize the geological relationships between deposit types illustrated by the uranium geochemical cycle. The proposed Geosystems Model/Classification of uranium deposit types starts at rift shoulders where thermal upwelling promotes anatexis, mafic intrusions, A-type granitic batholiths, uraniferous calderas, and voluminous ash-flow tuffs. Numerous deposit types are in this igneous portion of the uranium geochemical cycle. Intra-cratonic sedimentary basins over geological time have provided multiple depositional opportunities. Several deposit types are outliers. The proposed Geosystems Model accommodates IAEA classes into the tectonic-uranium geochemical cycle–based interrelationships. New perspectives are the initiation of magmatism at rift shoulders, diversity of caldera environments, intra-cratonic basin template applicable over time, and surface uranium deposit types with accessibility to voluminous uraniferous air fall tuffs. The uranium to thorium ratio is a useful property for uniquely identifying different uranium sources, processes, and deposit types.
Real Time Exposure Fusion Based on the Polarization Camera

The new sensors with the pixel-level polarizer structure make us able to shoot once and get four images with different light characters at the same time. It is similar to change the exposure four times and creates opportunities to obtain images with higher dynamic range. Based on the character of the novel polarization camera and the image acquisition pipeline of the regular digital camera, we propose a real-time exposure fusion method to reconstruct the high dynamic images. Polarization information is used to construct the base layer as a reference for the calculation of the blending weights in the method. The result will benefit visual navigation as a result of the better input image source. We also compare our method with three exposure fusion methods. Experimental results indicate our comparable performance and lower complexity.
Teaching an Algorithm How to Catalog a Book

This paper presents a study of a strategy for automated cataloging within an OPAC or for online bibliographic catalogs generally. The aim of the analysis is to offer a set of results, while searching in library catalogs, that goes further than the expected one-to-one term correspondence. The goal is to understand how ontological structures can affect query search results. This analysis can also be applied to search functions other than in the library context, but in this case, cataloging relies on predefined rules and noncontrolled dictionary terms, which means that the results are meaningful in terms of knowledge organization. The approach was tested on an Edisco database, and we measured the system’s ability to detect whether a new incoming record belonged to a specific set of textbooks.
A Similarity-based Framework of Participant Selections for Federated Learning in Edge Computing (Extended Abstract)

In federated learning, each edge device executes the training locally and uploads local parameters onto the server for the further model aggregation. The major difference between federated learning and distributed learning is that client devices generate and process their data locally without exposing their original data. But, it increases the communication cost between the server and clients because of the iterative training. In this work, we formulate the problem of participant selections and propose a framework for reducing the communication cost of federated learning by considering internal and external similarities. We also introduce some potential methods for computing these similarities.
High Camouflage Intrusion Detection Method for Structured Database Based on Multi Pattern Matching

Abstract with the rise and rapid development of mobile communication, intelligent terminal and data system, we are entering the era of mobile Internet. In recent years, more and more data need to be processed and transmitted in daily life, and structured data is becoming more and more important. Among them, multi-mode matching technology can search data in a wider range. Matching for multiple patterns at a time avoids unnecessary matching, accelerates the matching process, and helps to find longer matching information and obtain higher accuracy. This paper mainly introduces the high camouflage intrusion detection method of structured database based on multi-mode matching. This paper uses the high disguised intrusion detection method of structured database based on multi-mode matching, collects sensitive information of wireless access points and stations through the communication of WLAN in multimodal matching, then intercepts and forges data packets to initiate replay attack. Replay attack is characterized by abnormal traffic in the network, which can be detected by statistical analysis. The experimental results show that the high camouflage intrusion detection method based on multi-mode matching makes the camouflage intrusion detection rate increase by 23%. The limitations of the design and research of camouflage intrusion detection are analyzed, discussed and summarized, so as to enrich the academic research results.
SIN: Superpixel Interpolation Network

Superpixels have been widely used in computer vision tasks due to their representational and computational efficiency. Meanwhile, deep learning and end-to-end framework have made great progress in various fields including computer vision. However, existing superpixel algorithms cannot be integrated into subsequent tasks in an end-to-end way. Traditional algorithms and deep learning-based algorithms are two main streams in superpixel segmentation. The former is non-differentiable and the latter needs a non-differentiable post-processing step to enforce connectivity, which constraints the integration of superpixels and downstream tasks. In this paper, we propose a deep learning-based superpixel segmentation algorithm SIN which can be integrated with downstream tasks in an end-to-end way. Owing to some downstream tasks such as visual tracking require real-time speed, the speed of generating superpixels is also important. To remove the post-processing step, our algorithm enforces spatial connectivity from the start. Superpixels are initialized by sampled pixels and other pixels are assigned to superpixels through multiple updating steps. Each step consists of a horizontal and a vertical interpolation, which is the key to enforcing spatial connectivity. Multi-layer outputs of a fully convolutional network are utilized to predict association scores for interpolations. Experimental results show that our approach runs at about 80 fps and performs favorably against state-of-the-art methods. Furthermore, we design a simple but effective loss function which reduces much training time. The improvements of superpixel-based tasks demonstrate the effectiveness of our algorithm. We hope SIN will be integrated into downstream tasks in an end-to-end way and benefit the superpixel-based community. Code is available at: https://github.com/yuanqqq/SIN.
Ensemble Learning Based Decision-Making Models on the Aero-Engine Bearing Fault Diagnosis

The aero-engine is an extremely complex and precise mechanical system. Due to the changing working conditions, there are many factors that cause structural vibrations. The normal operation of each component is related to the stability and safety of the entire system. Failure of components can cause huge disasters. This paper analyzes the aero-engine bearing vibration failure caused by the low-pressure rotor imbalance, and finds that the main difficulty in constructing a fault diagnosis model of aero-engine lies in the processing of imbalance data and the extraction of multidimensional feature data. To solve these problems, a fault diagnosis method is proposed. This method first uses the border-line smote method to balance the data, and then proposes a weight-based Gini coefficient feature extraction method for feature extraction. Finally, the extracted features are used to construct an aero-engine fault diagnosis model. The principle of the fault diagnosis model is to build classifiers to distinguish between normal classes and fault classes. This paper mainly studies the classification effect of the three algorithms xgboost, GDBT and adaboost, and discusses the superiority of different algorithms. And using the bench data of a research institute for testing, it is found that the fault diagnosis model constructed by using the xgboost algorithm has a better fault diagnosis accuracy. It shows that this method has a good application in aero-engine fault diagnosis.
Optimal no‐hit‐zone frequency hopping sequence sets with wide gap

Using no-hit-zone frequency-hopping (FH) sequences with wide gap (WG NHZ FH sequences), the quasi-synchronous (QS) FH communication systems not only have good capability of preventing muti-path interference but also have strong capabilities of resisting the enemy's intentional interferences in the complicated electromagnetic interference conditions. This paper designs a new class of optimal WG NHZ FH sequence set which was not presented previously. It significantly meet the needs of high reliable QS FH communication under complicated electromagnetic interference conditions.
Faithful Post-hoc Explanation of Recommendation Using Optimally Selected Features

Recommendation systems have improved the accuracy of recommendations through the use of complex algorithms; however, users struggle to understand why the items are recommended and hence become anxious. Therefore, it is crucial to explain the reason for the recommended items to provide transparency and improve user satisfaction. Recent studies have adopted local interpretable model-agnostic explanations (LIME) as an interpretation model by treating the recommendation model as a black box; this approach is called a post-hoc approach. In this chapter, we propose a new method based on LIME to improve the model fidelity, i.e., the recall of the interpretation model to the recommendation model. Our idea is to select an optimal number of explainable features in the interpretation model instead of using complete features because the interpretation model becomes difficult to learn when the number of features increases. In addition, we propose a method to generate user-friendly explanations for users based on the features extracted by LIME. To the best of our knowledge, this study is the first one to provide a post-hoc explanation with subjective experiments involving users to confirm the effectiveness of the method. The experimental evaluation shows that our method outperforms the state-of-the-art method, named LIME-RS, with a 2.5%–2.7% higher model fidelity of top 50 recommended items. Furthermore, subjective evaluations conducted on 50 users for the generated explanations demonstrate that the proposed method is statistically superior to the baselines in terms of transparency, trust, and satisfaction.
Dimensionality Reduction Using Variational Autoencoders

In the past few years, the problem of processing of big data in less time and with great accuracy has been a major concern in the world of machine learning. Problems like overfitting and underfitting have also been on the troubling side of projects in the years leading to the current stages of artificial intelligence. This model works on the principle of dimensionality reduction, a potential solution to the above problems, with the growth of artificial intelligence and machine learning, data has become one of the most important assets and yet the biggest challenge. Over the years fast and accurate processing of data has been a constant pursuit, and this is where dimensionality reduction comes into play. In dimensionality reduction, authors reduce the dimension of the data keeping the essence and all the important points of the data intact, this leads to faster and equally accurate result. In my project, I have shown the concept of dimensionality reduction on images using the fonts dataset. Variational autoencoders are used to reduce dimension of the images from larger dimensions to smaller dimensions. The model was then checked for the training loss and the validation loss. Also, GPU based approach is used for the upscaling of the model and obtaining competent and satisfying results.