Reviewerly/exHarmony huggingface dataset:
Research on Target Tracking Simulation System Framework for Multi-Static Sonar Buoys

Abstract In the modern war, with the improvement of the target mobility, the increase of the diversity of the target platform, the decrease in the radiation noise of underwater targets, active sonar technology is becoming increasingly important in the underwater target detection. The data processing has also been continuously improved. Especially with the development of information technology in the modern warfare, the target tracking technology is highly valued by various countries around the world, and it has now become a very important field of research. This article proposes a general tracking framework based on the Multi-Static Sonar Buoys System and a variety of tracking algorithms. It lays the foundation for further completing the multi-target tracking simulation system.
A court line extraction algorithm for badminton tournament videos with horizontal line projection learning

Abstract Court line extraction is one of the important steps in the analysis of sport videos. The court extraction is the foundation of the analysis of badminton video, and an efficient method with horizontal line projection K‐means machine learning algorithm to extract court lines from different broadcast badminton tournament videos is proposed in this paper. The horizontal lines are projected into 1‐D histogram signal; then the signal is trained to learn the intensity of the histogram signal for locating the positions of the horizontal court lines. After the equations of the horizontal court lines and the court lines in the vertical direction have been formulized, the intersection points of the court lines can be calculated and the court line can be extracted. The experimental results show that the proposed method can extract the court lines more efficiently than that done by the Hough transform‐related algorithms, which are widely applied in computer vision and self‐driving car applications.
Transforming Data with Ontology and Word Embedding for an Efficient Classification Framework

Transforming data into appropriate formats is crucial because it can speed up the training process and enhance the performance of classification algorithms. It is, however, challenging due to the complicated process, resource-intensive and preserved meaning of the data. This study proposes new approaches to building knowledge representation models using word-embedding and ontology techniques, which can transform text data into digital data and still keep semantic/context information of themselves in order to enhance modeling data later. To evaluate the effectiveness of the built models, a classification framework is proposed and performed on a public real dataset. Experimental results show that the constructed knowledge representation models contribute significantly to the performance of classification methods.
The rhythm-adaptive Fourier series decompositions of cyclic numerical functions and one-dimensional probabilistic characteristics of cyclic random processes

The paper presents rhythm adaptive Fourier series decompositions of cyclic numerical functions and one-dimensional probabilistic characteristics of cyclic random processes, which are mathematical models of many deterministic and stochastic cyclic signals with a variable (irregular) rhythm. The adaptability of such decompositions to the change of rhythm of the investigated cyclic signals, on the one hand, ensures the elimination of the approximation error characteristic for classical Fourier series for periodic functions, and on the other hand, does not lead to an increase in the number of spectral components in the corresponding Fourier images, which occurs when Fourier transform is applied to cyclic deterministic and stochastic signals with variable rhythm. The work also presents the interconnected characteristics of the rhythm of cyclic deterministic and stochastic signals, and establishes their main properties for the base components of rhythm-adaptive Fourier series. The efficiency of rhythm adaptive Fourier series decompositions of cyclic numerical functions and one-dimensional probabilistic characteristics of cyclic random processes is confirmed by the results of a computer simulation.
SCALib: A Side-Channel Analysis Library

Side-channel attacks exploit unintended leakage from an electronic device in order to retrieve secret data.In particular, attacks exploiting physical side-channels such as power consumption or electromagnetic radiations to recover cryptographic keys are an important threat to embedded devices.Countermeasures against these attacks have been extensively researched for more than two decades and are often deployed in security-critical devices.
Jump Classification with Age and Gender Detection

The major issue is being able to identify human behaviour. The main issue for video categorization systems is common human actions in videos. For instance, a running motion will be included in a long jump or running sports film. Due to its multiple applications in areas like person monitoring, human-to-object interaction, and more, human action recognition is a crucial study subject in the science of computer vision. The computer vision community finds the video classification problem to be very difficult. The main reason that the video categorization problem is so challenging is the shared activities that are seen in the video. A high jump sports film, for instance, combines two distinct actions—running and high jumping—that are also shown in other videos, like running or hurdling sports videos. With just one frame that captures the specific action of the event, the human brain can quickly identify the correct occurrence in a film. By removing a few significant frames from the video and using those frames to conduct the classification procedure, the same premise may also be used in video classification systems.
Project work in a foreign language at the university: Challenges and benefits. Students’ opinion

The authors of the article present and analyze surveys of first, second and third year students of the State University of Management in Moscow on issues and problems related to project activities in a foreign language within the framework of curricula in the disciplines “Foreign Language” and “Foreign language of professional business communication”. As a result of the study, the authors conclude that students mostly come to the university with project work skills and improve them over several semesters at the university. The study revealed the most popular programs and platforms that students use to complete project tasks, and also the examples of managerial capacity and volitional powers that, in the opinion of students, a student should acquire in the process of project implementation. The article describes the main factors that can encourage them to do project work, and some problems associated with the implementation of project activities at the State University of Management. The results obtained reflect the university realities of students’ project work in a foreign language and can be useful to methodologists and developers of university curricula.
Sentimental Analysis of Movie Reviews Using Machine Learning

Sentiment analysis is a rapidly growing field in natural language processing that aims to extract subjective information from text data. One of the most common applications of sentiment analysis is in the movie industry, where it is used to gauge public opinion on films. In this research paper, a sentimental analysis of movie reviews has been presented using a dataset of over 25,000 reviews collected from various sources. A machine learning model with different classifiers was built using Naïve Bayes, Logistic Regression and Support Vector Machines for classifying movie reviews as positive, negative or neutral. A comparison of three popular machine learning algorithms was made. After pre-processing the dataset by removing stop words, a stemming technique was applied to reduce the dimensionality of the dataset. The recognition algorithms were evaluated in terms of performance matrices such as accuracy, precision, recall and F1-score. Compared to others, it was observed that the SVM algorithm performed the best among all three algorithms, achieving an accuracy of 73%. The results of this analysis demonstrated the eﬀectiveness of the model in accurately classifying movie reviews and provided valuable insights into the current state of public opinion on films. The comparison of the three algorithms provided insight into the best algorithm to be used for a specific dataset and scenario.
Colorslope: a balanced visualization of overview and details on ranks over time

Abstract Users are often interested in exploring ranks over time data to compare the performance or ranking of multiple observations with respect to each other. However, predominant visualization techniques suffer from a high cognitive load due to visual clutter. We propose Colorslope, a hybrid of Tufte’s slope graph and temporal heatmap, to depict ranks over time in one graph while maintaining an overview and details with scalability. Colorslope combines both canonical visualization methods’ complementary benefits: depicting overall trends and enabling the estimation of detailed values. We evaluated the efficacy and effectiveness of Colorslope by comparing it with a standard bump chart and temporal heatmap on various data sizes. We conclude that Colorslope contributes by (1) allowing users to identify extremes of the data and rate of change effectively in a relatively large number of series; (2) allowing the visualization to have better scalability in a larger amount of data (e.g., 30 ∼ 50) than a bump chart; and (3) allowing users to gain a better estimate of data values than a heatmap. For a certain size of ranks over time data, Colorslope provides an alternative solution to visualize multiple time series simultaneously that provides both an overview and a certain level of detail.
Research on named entity recognition of adverse drug reactions based on NLP and deep learning

Introduction: Adverse drug reactions (ADR) are directly related to public health and become the focus of public and media attention. At present, a large number of ADR events have been reported on the Internet, but the mining and utilization of such information resources is insufficient. Named entity recognition (NER) is the basic work of many natural language processing (NLP) tasks, which aims to identify entities with specific meanings from natural language texts. Methods: In order to identify entities from ADR event data resources more effectively, so as to provide valuable health knowledge for people, this paper introduces ALBERT in the input presentation layer on the basis of the classic BiLSTM-CRF model, and proposes a method of ADR named entity recognition based on the ALBERT-BiLSTM-CRF model. The textual information about ADR on the website "Chinese medical information query platform" (https://www.dayi.org.cn) was collected by the crawler and used as research data, and the BIO method was used to label three types of entities: drug name (DRN), drug component (COM), and adverse drug reactions (ADR) to build a corpus. Then, the words were mapped to the word vector by using the ALBERT module to obtain the character level semantic information, the context coding was performed by the BiLSTM module, and the label decoding was using the CRF module to predict the real label. Results: Based on the constructed corpus, experimental comparisons were made with two classical models, namely, BiLSTM-CRF and BERT-BiLSTM-CRF. The experimental results show that the F1 of our method is 91.19% on the whole, which is 1.5% and 1.37% higher than the other two models respectively, and the performance of recognition of three types of entities is significantly improved, which proves the superiority of this method. Discussion: The method proposed can be used effectively in NER from ADR information on the Internet, which provides a basis for the extraction of drug-related entity relationships and the construction of knowledge graph, thus playing a role in practical health systems such as intelligent diagnosis, risk reasoning and automatic question answering.
Enhancing genomic mutation data storage optimization based on the compression of asymmetry of sparsity

Background: With the rapid development of high-throughput sequencing technology and the explosive growth of genomic data, storing, transmitting and processing massive amounts of data has become a new challenge. How to achieve fast lossless compression and decompression according to the characteristics of the data to speed up data transmission and processing requires research on relevant compression algorithms. Methods: In this paper, a compression algorithm for sparse asymmetric gene mutations (CA_SAGM) based on the characteristics of sparse genomic mutation data was proposed. The data was first sorted on a row-first basis so that neighboring non-zero elements were as close as possible to each other. The data were then renumbered using the reverse Cuthill-Mckee sorting technique. Finally the data were compressed into sparse row format (CSR) and stored. We had analyzed and compared the results of the CA_SAGM, coordinate format (COO) and compressed sparse column format (CSC) algorithms for sparse asymmetric genomic data. Nine types of single-nucleotide variation (SNV) data and six types of copy number variation (CNV) data from the TCGA database were used as the subjects of this study. Compression and decompression time, compression and decompression rate, compression memory and compression ratio were used as evaluation metrics. The correlation between each metric and the basic characteristics of the original data was further investigated. Results: The experimental results showed that the COO method had the shortest compression time, the fastest compression rate and the largest compression ratio, and had the best compression performance. CSC compression performance was the worst, and CA_SAGM compression performance was between the two. When decompressing the data, CA_SAGM performed the best, with the shortest decompression time and the fastest decompression rate. COO decompression performance was the worst. With increasing sparsity, the COO, CSC and CA_SAGM algorithms all exhibited longer compression and decompression times, lower compression and decompression rates, larger compression memory and lower compression ratios. When the sparsity was large, the compression memory and compression ratio of the three algorithms showed no difference characteristics, but the rest of the indexes were still different. Conclusion: CA_SAGM was an efficient compression algorithm that combines compression and decompression performance for sparse genomic mutation data.
Unraveling Soft Squeezing Transformations in Time-Variant Elastic Fields

Quantum squeezing, an intriguing phenomenon that amplifies the uncertainty of one variable while diminishing that of its conjugate, may be studied as a time-dependent process, with exact solutions frequently derived from frameworks grounded in adiabatic invariants. Remarkably, we reveal that exact solutions can be ascertained in the presence of time-variant elastic forces, eschewing dependence on invariants or frozen eigenstate formalism. Delving into these solutions as an inverse problem unveils their direct connection to the design of elastic fields, responsible for inducing squeezing transformations onto canonical variables. Of particular note is that the dynamic transformations under investigation belong to a class of gentle quantum operations, distinguished by their delicate manipulation of particles, thereby circumventing the abrupt energy surges commonplace in conventional control protocols.
Emoji Prediction Using Bi-Directional LSTM

Messengers and social media dominate today’s internet usage across the globe. For the large population, a typical day starts with messages flooding on mobiles, from simple good morning wishes, business meeting invites, reminders, and schedules for the day and the list is endless. A striking feature of today’s digital communication is the variety of emojis used, without which text communication almost look incomplete. Emojis are graphic symbols/logograms used with text communication to enhance the effectiveness of emotions and set an undertone that makes texting a more fun experience for the users. Emojis are the visual language of the new generation. They give consumers a means to communicate their feelings while reducing the quantity of text that needs to be typed by the sender. Every social media and messenger platform like Facebook, Instagram, Twitter, WhatsApp, and many more have its own emoji set. To lure more and more users, many new emojis are added day by day. Predicting and suggesting emojis based on the text, emotion and user patterns to the user is an important feature of today’s messengers and social media applications. If you start typing a message, relevant emojis will be displayed from which users can choose an emoji, further enhancing the user texting experience. This process is done using natural language processing and machine learning techniques. In this paper, we study emoji prediction techniques and propose an emoji prediction model using bi-directional LSTMs. We compare emoji prediction NLP techniques, including RNN, LSTM, LSTM networks, and Bi-LSTM. Based on our implementation, we suggest that the bi-directional LSTM model is the most effective technique. Our model outperforms many baseline approaches with an accuracy of 94% when tested on a CodaLab Twitter data set with 60000 rows and two columns. Our study shows the effectiveness and efficiency of bi-directional LSTMs for text-based systems for communication.
Development of E-Modules Based on Read-Answer-Discuss-Explain and Create (RADEC) Assisted by 3D Pageflip Professional on the Theme 6 Subtheme 1 "Temperature and Heat"

teaching materials with the help of 3D Pageflip Professional on the theme 6 subtheme 1 “temperature and heat” in grade 5 of elementary school. Based on preliminary study data through interviews that SDN 17 Kampung Baru, SDN 07 Kampung Jawa II, SDN 04 Rawang Pariaman city settlements that the use of less attractive printed teaching materials and materials that do not match the characteristics and environment of students. This development research uses the ADDIE model and uses instruments questionnaires, tests and observation sheets. Where after conducting research involving five validators for the validation aspect, three teachers and students in three schools for the practicality aspect, and fifth graders at three schools to test effectiveness. Obtained results of teaching materials are in the category of very validity, teaching material is in the class of very practicality, and the effectiveness of teaching materials is very effective.
EFFICIENCY OF USING CONTACTLESS READING ID CARD TECHNOLOGY IN THE BANKING SECTOR

This article is devoted to the use of contactless ID card reading technology in the banking sector, it is shown that, from the point of view of security, the use of this technology in banks ensures the protection of banks and customer data and minimizes the likelihood of loss of this information.
Estimation of habit-related information from male voice data using machine learning-based methods

Abstract According to a survey on the cause of death among Japanese people, lifestyle-related diseases (such as malignant neoplasms, cardiovascular diseases, and pneumonia) account for 55.8% of all deaths. Three habits, namely, drinking, smoking, and sleeping, are considered the most important factors associated with lifestyle-related diseases, but it is difficult to measure these habits autonomously and regularly. Here, we propose a machine learning-based approach for detecting these lifestyle habits using voice data. We used classifiers and probabilistic linear discriminant analysis based on acoustic features, such as mel-frequency cepstrum coefficients (MFCCs) and jitter, extracted from a speech dataset we developed, and an X-vector from a pre-trained ECAPA-TDNN model. For training models, we used several classifiers implemented in MATLAB 2021b, such as support vector machines, K-nearest neighbors (KNN), and ensemble methods with some feature-projection options. Our results show that a cubic KNN method using acoustic features performs well on the sleep habit classification, while X-vector-based models perform well on smoking and drinking habit classifications. These results suggest that X-vectors may help estimate factors directly affecting the vocal cords and vocal tracts of the users (e.g., due to smoking and drinking), while acoustic features may help classify chronotypes, which might be informative with respect to the individuals’ vocal cord and vocal tract ultrastructure.
Optimised Image Storage and Retrieval on Hadoop

With the exponential growth of data, it is difficult to efficiently store and retrieve data using traditional methods. There is a need to optimize the storage and to efficiently retrieve relevant data matching the user query. Traditional methods lack optimized storage and to effectively retrieve data. To overcome these limitations, in this project, we propose a distributed architecture framework to optimize memory usage and to effectively retrieve relevant data using Content-Based Image Retrieval (CBIR). The experimental results show that the proposed model enhances storage performance and retrieval time by 20%.
Sign Language Interpretation using Ensembled Deep Learning Models

Communication is an integral part of our day-to-day lives. People experiencing difficulty in speaking or hearing often feel neglected in our society. While Automatic Speech Recognition Systems have now progressed to the purpose of being commercially viable, Signed Language Recognition Systems are still in the early stages. Currently, all such interpretations are administered by humans. Here, we present an approach using ensembled architecture for the classification of Sign Language characters. The novel ensemble of InceptionV3 and ResNet101 achieved an accuracy of 97.24% on the ASL dataset.
Maintaining Secure Level on Symmetric Encryption under Quantum Attack

Quantum computing is currently being researched in many countries, and if implemented in the near future, it may pose a threat to existing encryption standards. In the quantum computer environment, asymmetric encryption can be solved by Shor’s Algorithm in polynomial time, and the difficulty of breaking symmetric encryption using brute force is reduced from N times to square root N times by Grover’s Algorithm. We take the Advanced Encryption Standard as the theme and increase the key length from the original standard 192 bits and 256 bits to 384 bits and 512 bits, respectively, in order to maintain the security level of AES 192/256 under the environment of quantum computing, so we propose the key schedule of AES 384/512, and write the software in C++ on FPGA. The experimental results show that our scheme can achieve Level III and Level V security levels in a quantum computer attack environment. In addition to increasing the length of the key, we use the LUT method in the process of writing SubBytes to replace the array and speed up the computation to optimize the execution speed. In addition, the proposed scheme is still based on 128-bit computing blocks, rather than computing blocks in larger blocks.