Reviewerly/exHarmony huggingface dataset:
An Intelligent Test Method of Distributed Network Inbreak Based on Cluster Analysis

Due to the lack of large-scale processing of network data in the process of distributed network inbreak intelligent detection, the detection accuracy is low. Therefore, a distributed network inbreak intelligent test method based on cluster analysis is proposed. The distributed network data is preprocessed through data attribute feature transformation, data normalization, data standardization and data dimensionality reduction. According to the preprocessing results, the distributed network data collection is divided into multiple clusters using the fuzzy K-means clustering algorithm., compute the remove from each cluster centre to other data targets, use Euclidean remove to construct the goal function of partition quality, extract distributed network inbreak data, determine the cost function of the convolutional neural networks-gate recurrent unit network model, and use the stochastic gradient descent algorithm to The convolutional neural networks-gate recurrent unit network model is trained, and the extracted distributed network inbreak data is input as an initial sample into the trained convolutional neural networks-gate recurrent unit network model, the model is solved, and the distributed network inbreak intelligent detection results are output. The analysis of the experimental results shows that the proposed method has higher precision and better detection effect in the intelligent detection of distributed network inbreak.
Traffic Statistics Based on Lightweight Multi-objective Tracking Algorithm

In the field of intelligent transportation, traffic flow statistics has been an important research branch. With the continuous development of deep learning technology, there have been many algorithms applied in the field of intelligent transportation, but the existing traffic flow statistics methods have poor accuracy, are greatly affected by environmental lighting, etc., and the large amount of computing leads to high requirements for hardware equipment and other disadvantages. In this paper, we propose a lightweight multi-target tracking algorithm based on the improved YOLOv5 and DeepSORT. A new structure of self-attentive mechanism and convolutional network integration is added to the backbone network of YOLOv5, which effectively improves the accuracy of the algorithm without enhancing the original computation. In DeepSORT tracking, a light-weight network ShufflenetV2 is used instead of the original heavy identification network to reduce the amount of network computation and make the algorithm less configurable for mobile devices. The experimental results show that the proposed algorithm is highly accurate, feasible and can calculate the traffic flow in real time.
New Algorithm for Chunks Based Transfer and Image Rotation CAPTCHA Validation

In the case of the Wireless Sensor Networks, the two main issues which are required to be highlighted, first is the authentication of the nodes, that is nodes which are participating in the communication process, need to verify their identity. Secondly, the data communications how we can save the nodes from get overloaded in the data communication process, when the communication channel is of low quality. The authentication password which we have generated with the combination of the photo based SHA-512 extract and the pattern generated using image CAPTCHA , are then tested with the various online tools for the examination of the strength of the pattern and simultaneously we test the patterns which are obtained from the base papers. The results are compared on the basis of the years required to crack the pattern as well as on the basis of entropy, and results shown that the proposed work perform better in all respects.
AFFIRMATION IN EDUCATION INNOVATIVE SOLUTIONS IN PROVIDING CLASSES FOR CHILDERN AT EARLY SCHOOL EDUCATION STATEMENT OF EXPERIMENTAL LONGITUDINAL RESEARCH

This article discusses the qualitative research supplementing a three-year (2015-2018) pedagogical experiment carried out in schools in southern Poland. The experiment was based on implementing an innovative visual art education program into the primary school curriculum in grades 1-3. This program the Science-Art-Education Model was created on the basis of international experience from countries such as Slovakia, Slovenia, and Ukraine. The innovative outline of art workshops for children used the assumptions of positive pedagogy and the psychology of happiness affirmation and visualization. This paper presents the results of psychometric and qualitative research that accompanied the quantitative research (children essays and self-portraits) as well as opinions of teachers who carried out the experiment regarding two variables: self-awareness and positive hope.
Cross Synergetic Mobilenet-VGG16 for UML Multiclass Diagrams Classification

Unified Modeling Language (UML) diagrams are a standard modeling language to represent design of software systems. Specifically, UML includes several types of diagrams permitting to assist developing and designing efficiently any software. Additionally, class diagrams (i.e., class diagrams, activity diagrams, sequence diagrams, and use case diagrams) are the most widely used UML diagrams as a standard modeling language for object-oriented software. However, manually classifying UML diagrams is time-consuming and requires an important effort. In addition, there is a need to automate UML class diagrams classification in order to assist researchers, software developers, and academicians to efficiently study and analysis software. One solution can be considered is to use DL-based classification methods as these methods have gained special popularity in different computer vision tools. However, while the majority of innovatory deep-learning efforts using Convolutional Neural Networks (CNNs) focus on improving more refined and strong architectures (e.g., Mobilenet, VGG16, ResNet, U-Net, GANs), there is very restricted work on how to concatenate dissimilar CNN architectures to enhance their relational learning of CNN-to-CNN interactions. In this paper, our purpose is to address this limitation in order to automate the UML diagrams classification. For this reason, we propose a Cross Synergetic Mobilenet-VGG16 (CS-Mobilenet-VGG16), which aims to tackle two crucial problems in computer vision classification and ensemble CNN learning: (1) a bi-directional flow of information between two CNNs (Mobilenet-VGG16) where information proceeds in a directional method from Mobilenet to the VGG16, and (2) synergetic variability across UML diagrams. Our Cross Synergetic Mobilenet-VGG16 significantly (p<0.05) outperformed several conventional CNN architectures (Mobilenet and VGG16).
Complementary Double Pulse-Width-Modulation for 3D Shape Measurement of Complex Surfaces

Pulse-width modulation has the advantages of fast projection speed and high precision. However, when it is used to measure the three-dimensional (3D) shape of complex surfaces, it suffers from harmonic interference caused by slight defocusing and is sensitive to pixel oversaturation. To achieve high-speed and accurate 3D measurement of complex surfaces using this method, in this paper, a complementary double pulse-width-modulation (CDPWM) is proposed and used to generate binary phase-shifted fringe patterns. In our method, a triangular carrier signal with a zero phase is first used to generate a set of double pulse-width-modulation (DPWM) patterns, and then a triangular carrier signal with a phase of π/2 is used to generate another set of DPWM patterns. Then, the CDPWM is obtained by fusing these two sets of DPWM patterns. We demonstrate that CDPWM can tremendously suppress harmonic interference so that in practical applications, a smaller defocus can be used to robustly reconstruct complex surfaces. Moreover, it is proved that the patterns corresponding to the π/2 phase are the compensation patterns of the patterns corresponding to the zero phase, which enables CDPWM to eliminate or reduce the pixel oversaturation effect caused by a surface with a high dynamic range. Through experiments on simulated and real data, the proposed method is shown to substantially improve the phase quality and measurement accuracy over a wide range of fringe periods (e.g., from 18 to 912 pixels), especially when the defocus level is low. Different camera exposure times are used to demonstrate the robustness of the proposed method to oversaturated pixels. Furthermore, we measured a set of ice surfaces containing both low- and high-reflection areas. The projector was set to a slight defocus, which better preserved the binary characteristics of the patterns it projected. The measurement results proved our method can significantly improve the measurement accuracy of surfaces with complex reflection characteristics.
Personalized recommendation algorithm for enterprise science and technology talents based on labeling

In view of the current enterprise science and technology talent personalized recommendation algorithm in practical application, the number of correctly recommended talents is small. The algorithm recall rate is low. This paper proposes research on enterprise science and technology talent personalized recommendation algorithms based on labeling. In this paper, the four-tuple is used to label the scientific and technological posts and talents in enterprises, and the labels of scientific and technological posts and talents are established. The matrix is used to establish the relationship between scientific and technological talents and posts in enterprises. According to the recruitment behavior of enterprise users and the recruitment behavior of scientific and technological talents, the co-occurrence matrix is used to calculate the correlation between scientific and technological post labels and scientific and technological talents labels. The similarity between scientific and technological talent labels and post labels is calculated from the semantic aspect of labels. By combining the two indicators, the matching degree between the label of scientific and technological talents and the label of scientific and technological posts is determined. The recommendation list is generated, which realizes the personalized recommendation of enterprise scientific and technological talents. Experiments show that the recall rate of the design algorithm is higher than that of the traditional algorithm, which provides effective support for the personalized recommendations of scientific and technological talents in enterprises.
GVC: efficient random access compression for gene sequence variations

Abstract Background In recent years, advances in high-throughput sequencing technologies have enabled the use of genomic information in many fields, such as precision medicine, oncology, and food quality control. The amount of genomic data being generated is growing rapidly and is expected to soon surpass the amount of video data. The majority of sequencing experiments, such as genome-wide association studies, have the goal of identifying variations in the gene sequence to better understand phenotypic variations. We present a novel approach for compressing gene sequence variations with random access capability: the Genomic Variant Codec (GVC). We use techniques such as binarization, joint row- and column-wise sorting of blocks of variations, as well as the image compression standard JBIG for efficient entropy coding. Results Our results show that GVC provides the best trade-off between compression and random access compared to the state of the art: it reduces the genotype information size from 758 GiB down to 890 MiB on the publicly available 1000 Genomes Project (phase 3) data, which is 21% less than the state of the art in random-access capable methods. Conclusions By providing the best results in terms of combined random access and compression, GVC facilitates the efficient storage of large collections of gene sequence variations. In particular, the random access capability of GVC enables seamless remote data access and application integration. The software is open source and available at https://github.com/sXperfect/gvc/ .
Fokker-Planck treatment of nonlinearities in the dispersive coupling of an ion and an optical cavity

We complement previous studies of an ion coupled with an optical cavity in the dispersive regime, for a model which exhibits bistability of different configurations in the semiclassical description. Our approach is based on a truncated evolution in phase space and is intended to explore an especially interesting parameter region where the fully quantum-mechanical solution becomes hard but the crudest semiclassical approach fails to capture essential phenomena. We compare the results of our techniques with the ones from numerical diagonalization of the quantum evolution and find that although the treatment leads to a smoothening and a slight shift of the transitions in the system, it still provides a clear improvement over localized semiclassical approximations.
Correction

This article refers to:Trends in Library Collections: Interview with Judith Nixon, Editor of Collection Management Journal, by Alison Lehner-Quam
CNN-Based Breast Cancer Detection

One in eight females globally suffers from breast cancer. It is identified by recognizing the malevolence of breast tissue cells. Current medical image processing techniques use histopathological pictures recorded by a microscope to examine them employing various procedures and methodologies. Nowadays machine learning algorithms are widely used for the interpretation of medical-related images and tools related to pathology. As identifying cancer cells manually is time consuming and may be prone to errors in operation, computer-aided processes are used to get better outcomes than manual pathological detection methods. This is often accomplished in deep learning by the process of feature extraction fully aided by a convolutional neural network (CNN) followed by the classification process via a fully connected network. Deep learning is widely used in medical imaging since it does not need prior knowledge in a related discipline. The process involved in the current study is training a CNN and accomplished prediction accuracy of up to 86.9%.
Construction of Chinese Knowledge Graph Based on Multiple Data Mining Algorithms

The importance of language research is self-evident. In modern society, people have higher requirements for language learning. In order to enable information and data to serve humanity and national construction faster and better. How to dig out the knowledge graph from the text is one of the topics worthy of consideration and in-depth discussion. This article will study this through a variety of data mining algorithms. This article mainly uses experimental methods and examples to conduct sampling experiments on the established language knowledge graph. Through data analysis, data mining is carried out on the text, and the accuracy rate and recall rate are obtained. The experimental results show that the accuracy of map sampling is above 90%, and the design can meet the basic requirements.
Towards Constructing Consistent Pattern Strength Meters with User’s Visual Perception

Pattern lock strength meters designed for securing Android devices are inconsistent in their metering, e.g., assigning higher scores to weaker patterns. In this paper, we raise this inconsistency problem by analyzing five existing pattern strength meters. We reveal that they commonly miss some important visual features and even assign erroneous weights to features. As a preliminary study toward a consistent pattern strength meter in the future, we design a rigorous user study to identify the visual features of a pattern that correspond to real-world users’ criteria to score the strength of the pattern. We conducted an online survey for 3,851 users to collect reliable labels for 625 patterns. The statistical result of the user study sheds light on a pattern strength meter that reflects the user’s visual perception with various visual features.
Public Key Encryption with Hierarchical Authorized Keyword Search

Public key encryption with keyword search (PEKS), which was introduced by Boneh et al. at EUROCRYPT’ 04, is a breakthrough approach to searching encrypted data under a public key setting. In this cryptographic primitive, senders can generate searchable ciphertexts for specific keywords to be retrieved from a given document; receivers can generate corresponding trapdoors for search by using their private keys. Recently, Jiang et al. (ACISP’ 16) proposed an improved PEKS scheme called public key encryption with authorized keyword search (PEAKS); this scheme enables authorized users to generate trapdoors for specific sets of keywords even if these users do not have access to the private key. Unfortunately, authorized users cannot delegate this power to other unauthorized users because the authorization in PEAKS is insufficiently flexible; therefore, this scheme is not suitable for enterprise scenarios in general. In this work, we introduce a novel cryptographic primitive called public key encryption with hierarchical authorized keyword search (PEHAKS) to solve this problem. In contrast to PEAKS, the proposed primitive enables authorized users to further hierarchically delegate their power of generating trapdoors to unauthorized users. We formally define the system model of PEHAKS under a multikeyword setting, and the security requirements are designed to withstand attacks in a real scenario. Furthermore, we propose a provably secure scheme using the technique of dual pairing vector spaces and demonstrate that the scheme is secure under the hardness of the n-extended decisional Diffie–Hellman assumption. Therefore, the proposed scheme is secure and can be applied in scenarios that require hierarchical authorization. To the best of the authors’ knowledge, no PEKS variant schemes with this property have been previously designed.
Development of control systems that operate independently without human intervention

The development of control systems that operate independently without human intervention is a rapidly growing field in instrumentation and control engineering. These systems are designed to function autonomously, using Artificial Intelligence (AI) and Machine Learning (ML) techniques to make decisions and perform tasks. Applications of these systems can be found in a variety of fields, including autonomous vehicles, robotics, and industrial automation. The key benefits of these systems are their ability to perform tasks without human intervention, which can lead to increased efficiency and productivity. They also have the ability to operate in environments that may be too dangerous for humans, such as deep-sea exploration or space. Additionally, they are able to collect and process large amounts of data in realtime, which can be used to improve decision-making and control. This includes the use of machine learning and evolutionary algorithms to improve the ability of systems to adapt to changing conditions. This research is also focused on the integration of these systems with communication networks, such as the internet, to enable remote monitoring and control. With advancements in technology and increasing demand for automation, it is expected that autonomous systems will play an increasingly important role in various industries in the future.
Entropy of Video Lecture

In this paper, the relation between video lectures Shannon and perception quality defined by number of known criteria (guidelines) of an optimal video lecture is studied.The entropy both for sound and video sub-channels are calculated using Matlab.The obtained results show that perception quality increases when the video entropy is decreased.It is also found that there is correlation between the type of the video lecture and video entropy.
A New Class of Trapdoor Verifiable Delay Functions

A verifiable delay function (VDF) is a function whose evaluation involves lengthy sequential operations, yet its outcome is publicly verifiable. As an extension, a trapdoor-VDF is a VDF with a shortcut that speeds up the evaluation process. This paper presents a new class of trapdoor-VDFs featuring a large ensemble of trapdoors for each instantiation of the function. This way, a client can randomly choose a private trapdoor from the ensemble, thereby using it to encapsulate a secret to the future as a unique puzzle. To solve the puzzle, the server, which does not know the trapdoor, requires a prescribed number of sequential steps to evaluate the function. Any client can efficiently verify the correctness of the server’s evaluation with zero knowledge of the trapdoor being used. We present an approach for constructing the proposed class of trapdoor-VDFs based on bilinear pairings and a long walk on supersingular isogeny graphs. Finally, we examine the security of our construction under trapdoor-VDF security notions.
ACCURATE MEASUREMENT OF HAEMOGLOBIN AND HAEMOCRIT BY USING 3 - PART HEMATOLOGY ANALYZER -SYSMEX XP-300

One of the most fundamental laboratory tests in haematology is the evaluation of blood smear because variations in blood cell counts from reference values might signal a number of illness problems. Based on CBC values and blood cell shape, R studio can aid technicians in the analysis and classification of blood cells as well as the identification of certain medical diseases. The objective of this project is to create software that can recognise and categorise haemoglobin and haematocrit in different blood cells from peripheral blood smear samples. Manually locating, recognising, and classifying the Hg and Hct can be tiresome, time-consuming, and erroneous. However, automatic haematology that solely perform quantitative analysis and rely on the flow cytometry categorization concept are expensive. This suggested study seeks to produce a preliminary framework of Automatic analysers Analysis of Human Blood Smear for identification, classification of Hg and HCT of blood cells by employing various data taking into consideration the broad range of data in the field of medicine. Key Words: Sysmex XP-300 Hematology analyser, blood samples, blood analyser, haematocrit.