Reviewerly/exHarmony huggingface dataset:
Basic of Secrete Communication System for Military by Using ML and Steganography

Abstract: Information security is an important factor during transmitting secret information between two objects. Generally, we use cryptography for information hiding and sending secret messages in the form of text. Nowadays, there are several techniques used for hiding information in any medium. One such technique is steganography. In this technique, digital images are used for hiding information and the information is in the form of text, digital image, video or audio file may be used as a secret message. Using LSB Steganography Technique we can implement a high level of information security without any damage to the cover image. In this system we are using the hybrid approach i.e. cryptography andsteganography. So, our system has a higher security level than existing systems. With the development of machine learning, face recognition technology based on CNN (Convolutional Neural Network) has become the main method adopted in the field of face recognition security systems to securely access the confidential system. Keywords: Image data hiding, LSB Steganography, AES cryptography, Face Recognition, Machine Learning CNN technique
Face Mask Detection Analysis for Covid19 Using CNN and Deep Learning

The planet was severely affected by Coronavirus Disease 2019. The wearing of masks in public places is a big way of protecting people. In addition, many providers of public service only require consumers to wear masks correctly. However, only a few studies are focused on image analysis on face mask detection. I propose in this paper, a high-precision and effective mask detector for Mask Detection Method. Recognition from faces is a popular and significant technology in recent years. Face alterations and the presence of different masks make it too much challenging. In the real-world, when a person is uncooperative with the systems such as in video surveillance then masking is further common scenarios. For these masks, current face recognition performance degrades. An abundant number of researches work has been performed for recognizing faces under different conditions like changing pose or illumination, degraded images, etc. Still, difficulties created by masks are usually disregarded. The primary concern to this work is about facial masks, and especially to enhance the recognition accuracy of different masked faces. A feasible approach has been proposed that consists of first detecting the facial regions. So, n orders to detect whether you are wearing a face mask to protect yourself, I decided to construct a simple and basic Convolutional Neural Network model, using TensorFlow and Keras library. With the prevailing pandemic of COVID-19, these systems will benefit many kinds of organizations worldwide. These types of systems are especially important.
A Three-Dimensional Target Recognition Algorithm Utilizing Spatiotemporal Information Perception

Abstract In order to solve the low accuracy of three-dimensional target recognition by traditional algorithms, an improved three-dimensional target recognition model based on spatiotemporal convolutional neural network is established in this paper. The high-level feature presentation ability of deep neural network and the spatiotemporal perception performance of sequential modelling methods are combined perfectly in this model. Triplet loss function is also introduced to improve the representation of three-dimensional moving targets under different attitudes, which effectively reduces the feature distance of the same targets under various attitudes, and increases the feature distance of different targets at the same time. The performance of different feature extractors and aggregation algorithms is compared in this paper. Experimental results demonstrated that the ResNet is more suitable for feature extraction and the local soft attention is more effective in feature aggregation. Compared with the traditional three-dimensional target recognition methods, the spatiotemporal perception model proposed in this paper can achieve higher recognition accuracy for three-dimensional target recognition. At the same time, the calculation speed of the model is fast, which is conducive for its application.
Graph-based Similarity for Document Retrieval in the Biomedical Domain

The growing amount of available data in the biomedical domain turns out to be beneficial for decision-making, but a sufficiently accurate DR system is required. Plenty of NLP techniques and models have been proposed for semantic similarity in DR, but few of them have been able to consider the variations of the language and relationship between distant words in texts. This work is focused on formulating a Graph-based Similarity for DR method (GBS-DR) for the biomedical domain and comparing the obtained results with traditional DR paradigms. The graph-based methods were selected to prove the importance of analyzing the semantic, syntactic, and long-distant word relationships in texts. It will be demonstrated that through the graph's topology the system can extract the structural information of documents, which solves relevant issues that are faced in this research area.
The Human-Air Interface: Responding To Poor Air Quality Through Lived Experience and Digital Information

The Air Quality Index (AQI) is a set of numerical, color-coded metrics used to communicate air pollution levels. AQI data is available through a variety of mediums such as mobile apps, websites and public displays. However, the information communicated through these may not be easily interpretable to everyone. Especially when scientific data and associated representations are used to convey information to communities whose shared knowledge and practices are significantly different from Western scientific contexts. We discuss findings from a qualitative study conducted in Delhi, India to understand how residents across both low- and high-resourced communities, assess their personal risk around air pollution and the safety measures they take to mitigate that risk. We reveal incompatibilities between the air-quality information displayed in digital platforms and whether that information is interpretable for people with different cultural sensitivities. We conclude with design implications for improving the interpretability and relevance of air quality interfaces.
Development of a nuclear fuel rod element positioning measurement equipment based on spacers retrieval network

The fast and precise positioning measurement of nuclear fuel rod element are an important part of the nuclear fuel rod bundle production line. This paper develops a new positioning measurement imaging device and proposes a positioning measurement algorithm based on spacer retrieval network. Firstly, the spacers retrieval network is trained to obtain the spacer’s types and location information, and statistically realize the type determination and initial positioning measurement of the nuclear fuel rod. Then, using the a priori information from the initial positioning, we intercept the regions of interest (ROI) of the spacers and perform contour extraction to achieve the precise positioning measurement of the nuclear fuel rod. The experimental results show that the equipment achieves 0.035° positioning measurement accuracy, and the positioning beat reaches 4.234 s/rod. The efficiency and accuracy of the proposed positioning equipment meet the application requirements of nuclear fuel rod bundle production line.
Towards the Efficiency of the Fusion Step in Language-Based Fashion Image Editing

Text-to-image synthesis is a new research field of image generation. The generated images must be based on textual descriptions and of acceptable quality. Among the generative models, Generative Adversarial Networks (GANs) can generate higher quality images compared to other models. Most GAN-based text-to-image generation methods use simple datasets, including flower and bird images. For more complex datasets (such as the Fashion Synthesis dataset used in this paper), the issue of generating an image from a text becomes more challenging, as the images in this dataset are much richer in content than just the flower images or birds. Most of the methods proposed so far are only able to recognize the color of an object based on the text description and have difficulty in accurately identifying the location in the image where these changes are to be made. One way to solve this problem is to generate an image from the text based on editing this image. In this paper, the methods of combining text and image features in the generation of images are discussed and the effect of different fusing models on the process in terms of quality and accuracy of the description is investigated.
Real-Virtual Platforms for Organizing Research Activities of Students of Technical Universities in the Process of Teaching Technical Translation

The problem of increasing the efficiency of the process of teaching a foreign language in technical universities through the use of modern information technologies is relevant enough. The current article discusses the methods of teaching technical translation using authentic materials from the Internet, provides a theoretical justification for the need to improve the effectiveness of methods of teaching a foreign language in nonlinguistic universities, identifies the specifics of the organization of students' educational activities using virtual-real platforms. The paper presents the results of an empirical study of students' participation in an experiment, during which they conducted a study of the grammatical features of authentic scientific articles. The results of the study prove the high efficiency of using realvirtual platforms in the organization of research activities of students in the process of their training in technical translation.
Spatial Maturity Regression for the Classification of Hematopoietic Cells

In contrast to peripheral blood, cells in bone marrow microscopy images are not only characterized by the cell lineage but also a maturity stage within the lineage. As maturation is a continuous process, the differentiation between various stages falls into the category of (ordinal) regression. In this work, we propose Spatial Maturity Regression - a technique that regularizes the learning process to enforce a sensible positioning of maturity stages in the embedding space. To this end, we propose and evaluate several curve models, target definitions and loss function that incorporate this domain knowledge. We show that the classification F-scores improve up to 2.4 percentage points when enforcing regression targets along learnable curves in the embedding space. This technique further allows visualization of individual predictions by providing the projected position along the learnt curve.
Memory-efficient sensor data compression

We treat scalar data compression in sensor network nodes in streaming mode (compressing data points as they arrive, no pre-compression buffering). Several experimental algorithms based on linear predictive coding (LPC) combined with run length encoding (RLE) are considered. In entropy coding stage we evaluated ( a ) variable-length coding with dynamic prefixes generated with MTF-transform, ( b ) adaptive width binary coding, and ( c ) adaptive Golomb-Rice coding. We provide a comparison of known and experimental compression algorithms on 75 sensor data sources. Compression ratios achieved in the tests are about 1.5/4/1000000 (min/med/max), with compression context size about 10 bytes.
DSIT: A Dynamic Lightweight Cryptography Algorithm for Securing Image in IoT Communication

One of the most significant challenges appears to be securing the Internet of Things (IoT) communication network. As a corollary, information security has become the basis for establishing trustworthiness in IoT network communication. Cryptography is one of the ways for securing information in this case. However, the majority of current approaches are static, making them subject to security threats. As a consequence, a new concept, dynamic encryption, is growing rapidly in IoT communication. In this paper, a dynamic encryption algorithm (DSIT) has been proposed to secure IoT communication. This algorithm is based on Feistel and Substitution–Permutation Network. DSIT is a block cipher that takes the 64-bit block of plaintext, 64-bit secret key, and a secret dynamic box (D-box) as input. It produces a 64-bit ciphertext by performing eight rounds of the DSIT algorithm. For each round, the key and D-box are updated. This dynamic effect provides high security to a dynamic IoT network. The proposed algorithm has been executed in IoT environment using Raspberry Pi 3 Model B[Formula: see text] and 50% average Avalanche effect has been achieved. The proposed algorithm efficiently encrypts the image data to secure the communication and high resistant to cryptanalysis attacks.
Improper integral with exponential function

In this paper, we explore the improper integral with exponential function f = xx is approached to infinite series, and also prove the convergence of these series. An improper integral converges if the limit defining it exists. We use Maple code to calculate the infinite series. The application of improper integral appear in several domain in science. As an application in this paper, three examples are given to illustrate the effectiveness of our main result.
Studying while Muslim: anti-discrimination law, countering violent extremism, and suspect youth

As part of the global War on Terror, governments, including in Australia, have increasingly encouraged teachers to spot students ‘at risk’ of radicalisation, thereby turning schools into sites of surveillance. Despite their important intended goal, these programs have been criticised for leading to over-reporting, misinterpretation, and the stigmatisation of Muslim students who have been disproportionately affected by such surveillance practices. In a climate of Islamophobic suspicion that constructs Muslims as ‘potential terrorists’, such outcomes are not surprising, leaving young Muslims vulnerable to discrimination and stereotyping. This article provides a critical reading of Complainant 201822 v Australian Capital Territory (Represented by the Education Directorate) concerning a young Muslim student who was wrongly reported by his school to public authorities due to his supposedly suspicious behaviour. Using the decision as a case study, the article discusses how young Australian Muslims have been caught in a vicious cycle in which anti-terror laws, policing, and surveillance foster their stereotyping as ‘potential terrorists’. At the same time, the decision illustrates how the law offers little protection against the significant harms caused by this manifestation of systemic Islamophobia.
Tracking a multitude of abilities as they develop

Recently, the Urnings algorithm (Bolsinova et al., 2022, J. R. Stat. Soc. Ser. C Appl. Statistics, 71, 91) has been proposed that allows for tracking the development of abilities of the learners and the difficulties of the items in adaptive learning systems. It is a simple and scalable algorithm which is suited for large-scale applications in which large streams of data are coming into the system and on-the-fly updating is needed. Compared to alternatives like the Elo rating system and its extensions, the Urnings rating system allows the uncertainty of the ratings to be evaluated and accounts for adaptive item selection which, if not corrected for, may distort the ratings. In this paper we extend the Urnings algorithm to allow for both between-item and within-item multidimensionality. This allows for tracking the development of interrelated abilities both at the individual and the population level. We present formal derivations of the multidimensional Urnings algorithm, illustrate its properties in simulations, and present an application to data from an adaptive learning system for primary school mathematics called Math Garden.
Research on Discourse Transfer Analysis Based on Deep Learning of Cross-language Transfer

With the current exchange and communication between different countries becoming more and more frequent, the language conversion of different countries has become a difficult problem. The analysis of a series of problems in cross-language discourse conversion, the study of the discourse conversion path, and innovation motivation based on the deep learning theory of cross-language transfer, it has theoretical and practical significance. This paper aims at the technical difficulties in speech conversion methods to effectively utilize the local mode information of signal time spectrum and the long-term correlation of speech signal. A discourse conversion method based on convolutional recurrent neural network model is proposed. In the model, the extended convolutional neural network is used to model the long-term correlation of speech signals. In the part of speech fundamental frequency estimation, the prosodic information generated by the decomposition of the fundamental frequency by continuous wavelet transform is used as the training target of the fundamental frequency estimation model. The experimental results show that the speech transformation method based on the convolutional cyclic network model proposed in this paper has better quality and intelligibility than the speech transformed by the contrast method.
FILA: Online Auditing of Machine Learning Model Accuracy under Finite Labelling Budget

Machine learning (ML) is increasingly adopted in industrial applications. Typically, a ML pipeline is instantiated to automate the process of collecting training data, training a model, auditing the model accuracy and generating predictions.
Kinship Verification System based on the Color Spaces Analysis

Metric learning has attracted wide attention in face and kinship verification and a number of such algorithms have been presented over the past few years, this system has a number of applications such as organizing collections of images and recognizing resemblances among humans and finding of missing children. In this work , we propose a novel approach based on the Weber Local Descriptor (WLD) with color spaces, and the Multi-Lavel (ML) representation, Moreover, the use of Rank features (TTest) to reduce the number of features and the support vector machine (SVM) for the kinship classification. Our approach consists of six stages which are : (1) Face preprocessing (2) applied the color spaces (3) features extraction using WLD , with face representation , (4) pair features representation, (5) features selection and (6) classification using SVM. The proposed approach istested and analyzed on five publicly available databases (Cornell , UB KinFace, Familly 101, KinFac W-I and W-II).
Evaluation Model of College Physical Education Based on Random Matrix Simulation Algorithm

In this study, improved algorithms based on kernel norm minimization are proposed, namely, standard CUR decomposition and fast CUR decomposition. The CUR decomposition algorithm is to decompose the matrix into three parts, namely, C, U, and R. The matrices C and R are sampled by the column selection algorithm, and then, matrix inversion and matrix multiplication are performed to obtain the cross-matrix U. The matrix C ∗ U ∗ R is an approximation of the original matrix. For the improvement of the singular value algorithm, two random algorithms are proposed, namely, the standard random k-SVD algorithm and the fast random k-SVD algorithm. The main idea is to perform dimensionality reduction and random sampling on the original large-scale data matrix, use the random projection algorithm to obtain an approximation of the original data matrix, then, perform corresponding matrix operations on this approximate matrix, and finally obtain a result similar to the original matrix calculation. Based on the empirical investigation of the current situation of physical education teaching evaluation in a university, this research conducts a questionnaire survey on the student group. Combined with the classification of various majors, the relevant factors of the physical education teaching evaluation system are analyzed and studied, and a more reasonable and unique evaluation model is established. We analyze the dimensions and content of the evaluation index system of physical education, and determine the indicators and weights. From the perspective of the combination of physical education curriculum reform and professional characteristics in a university, this study analyzes the current situation of physical education teaching evaluation and builds a sports teaching evaluation system, so as to deepen the reform of physical education teaching in colleges and universities and create a sports education system with higher vocational characteristics. The teaching evaluation model has certain practical significance.
Robust Deep Learning against Corrupted Data in Cognitive Autonomous Networks

Neural-net-based deep learning algorithms are starting to be utilized in many network functions. Deep neural nets are traditionally not resistant against missing or corrupted inputs, a scenario which is likely to happen in mobile networks. If the data corruption does not stem from malicious intent, the task of reconstructing missing inputs is called imputation. In this paper, we discuss how such imputation methods could be utilized in network functions, to make the network robust against non-adversarial data corruption. We propose an integrated approach, where the imputation is undertaken by the same model which implements the machine learning task in the network function. We evaluate state-of-the-art imputation methods and our integrated imputation thoroughly, using data generated in a mobile network simulator. Our results show excellent performance with the integrated imputation, but also raises some questions with regards to how deep-learning-based network functions should be used in such scenarios.
Implementing a Modular System to Teach Students the Basics of Case-Based Reasoning Data Mining

This paper is devoted to the implementation of a modular case-based reasoning system for data mining based on previous experience in the form of cases. The case-based reasoning system allows you to work with cases presented in a parametric and structured (based on ontologies) form. This software focuses on the study by students of the basic principles of data mining and uses in the educational process in the laboratory workshops of the MPEI Department of Applied Mathematics and Artificial Intelligence.
China’s higher education development evaluation based on GA-BP neural network

The development of higher education supplies a large number of high-level talents to the society, which is the key to building a harmonious society. At present, the development of regional higher education is extremely uneven, and it is the top priority of education development that it is urgent to clarify the situation of regional higher education. This article constructs a comprehensive evaluation index system of higher education development from a total of 19 indicators from five dimensions of talent training, teacher strength, scientific research output, infrastructure and social services, and then uses entropy and genetic algorithm-projection pursuit model to calculate the weight. GA-BP and BP neural network models are used for comprehensive evaluation. It is found that: (1) The most important factors affecting the development of higher education are technology transfer income and the application of R&amp;D achievements in colleges and universities; (2) Compared with BP neural network, GA optimizes BP neural network in terms of effectiveness, convergence speed, and accuracy. (3) Generally speaking, during the research period, the development of China’s higher education has gradually improved, with an average annual growth rate of 3.5%. In terms of sub-regions, the provinces with excellent higher education development levels have increased from 0 in 2008. The number has increased to 5 in 2019, and the development of higher education among provinces is extremely uneven, and the differences between provinces are gradually increasing.