Reviewerly/exHarmony huggingface dataset:
Differential Fault Analysis of Trivium Using Artificial Neural Network on SoC Platform

Stream ciphers are highly frequent in the realm of secret key cryptography because of their low-computational complexity and latency. Though variety of attack models for testing the robustness of various stream ciphers are available, the differential fault analysis (DFA) model is the most popular. DFA is used not only to disclose the identity of any unknown cipher but also to recover the secret KEY and the cipher’s internal state. Deep learning approaches extract patterns from large amounts of cryptographic data, which aids in crypt-analysis. Here, single bit and multi-bit faults are injected into the internal register of trivium cipher, and artificial neural network (ANN) is used to identify the injected fault location without any handcrafted features of keystream unlike other existing state-of-the-art fault analysis techniques. In order to enhance the speed of fault analysis, we have used system on chip (SoC) platform where ANN-based classifier has been placed in the programmable logic area as an accelerator. This enhances the speed of fault analysis by 9% for single bit fault, 12% for two adjacent bit faults, and 13% for three adjacent bit faults compared to their software models. To the best of the author’s knowledge, this is the first of kind research where ANN-based hardware accelerator has been used for fault location identification on SoC platform.
Emotion Recognition Using Convolutional Neural Network

Human emotions are significant to a person’s mental health. In the field of Affective Computing human emotions are one of the main subjects that play an important role. In order to observe emotions of the human a lot of characteristics can be used such as speech, skin responses, heart rate or facial expressions. In the paper the process of emotion recognition using artificial neural network is described. The training set consists of grayscale images which are 48 × 48 pixels and are divided into 7 categories Angry, Disgust, Fear, Happy, Sad, Surprise, Neutral. The Convolutional Neural Network is used for the purpose of emotion recognition along with its pooling and convolutional layers. At the end of the process the trained neural network can successfully recognize the human emotion.
Personalized Recommendation of Literature Resources in University Library Based on Abstract Content Filtering Algorithm

The purpose of this study is to recommend the literature resources of University Libraries Based on content filtering algorithm. This study uses a method that combines the concept of abstract content filtering with personalized recommendation. To achieve this goal, we use a database containing information about books and articles in various fields such as the humanities, social sciences and natural sciences. The main purpose of this study is to understand whether the use of abstract content filters can improve the recommendation quality of different types of users, especially those who are interested in certain topics or have specific needs. In order to achieve these goals, we used two methods: one is based on comparing the results obtained from the two.
Generalized Net Model of the Malignant Melanoma Treatment

In the article a Generalized Net model of the malignant melanoma treatment is proposed. Generalized nets are extension and generalization of the concept of Petri nets, as well as of other Petri nets extensions and modifications. For constructing of the model patients with malignant melanoma registered in Oncology Complex Center in Burgas town were analyzed. The model can be used as a uniform algorithm of behavior with patients with malignant melanoma from the initial examination to the treatment (if it is necessary).
FaceDig: A Deep Neural Network-Based Fake Image Detection Scheme

Social media plays a significant role in contemporary societies in this modern era. The majority of users use social media to exchange text, photographs, and videos on a regular basis. Images are one of the most widely shared forms on social platforms and also the most vulnerable medium for tempering. Small and medium sized businesses may now easily create and distribute these images in a short frame of time, compromising news authenticity and public faith in social networking sites. This work proposes an approach for extracting the content of image, classifying it and verifying the authenticity (i.e., real or fake) of digital images. The algorithm uses convolutional neural network (CNN) to detect real and forged images. In addition, efficiency and refinement of training data are required to support its use in everyday life. The concept of error rate bid data and the deep learning method are used for further solutions. The result shows that the proposed method using convolutional neural network (CNN) technology and error level analysis (ELA), fake image detection can reach more than 96%. The findings of this work will aid in the detection of anomalous material and forged photographs on social media.
Generalized Net Model of Density-Based Spatial Clustering of Applications with Noise (DBSCAN) Algorithm and Its Application on Diabetes Dataset

Density-based spatial clustering of applications with noise (DBSCAN) is a data science algorithm for density-based clustering. A Generalized net (GN) model of the DBSCAN algorithm is constructed. Intuitionistic fuzzy evaluations are defined to estimate the clustering procedure. The GN model of DBSCAN optimizes and estimates the procedure of the standard clustering algorithm. The clustering procedure is implemented using Python programming language. The method is then applied to a diabetes dataset to form clusters.
Generalized Net Model of the Consequences of Earthquake

In order to prevent victims of natural disasters and to be able to help the injured people, local medical authorities must be provided in advance. One of the worst natural disasters is strong earthquake. Therefore, it is good for decision makers to have a preliminary assessment of the possible consequences and injured people of earthquakes of various kinds. In this work we propoze a model of the consequences of eventual earthquake and as well as the number and type of injured people. The model is based on the apparatus of Generalized Nets. After assessing the probability of an earthquake with a given intensity, a decision can be made on the necessary medical supplies to provide adequate assistance to the victims.
A Study on Data Mining for Type of Korean Painting Poetry

This study introduced the work of analyzing the meaning by using the computer information processing method. We extracted the painting poetry of Mukjukdo (Bamboo Paintings) in the early Joseon Dynasty from the Hanguk Munjip Chonggan (Korean Literary Collections in Classical Chinese) of Hanguk Gojeon Jonghap DB (Korea Classics DB). Through the data mining method, we divided the types by extracting and cataloging the painting poetry from the Korean classical literature. Then, the scope was narrowed down to painting poetry of Mukjukdo in the early Joseon Dynasty, and the text was analyzed in units of syllable corpus.
An Intuitionistic Fuzzy Estimation Approach on a Magnetic Resonance Imaging

This work addresses a novel approach of image estimation using fuzzy logic. The research aims to devise a technique for estimating the quality of tomographic images by measuring the level of uncertainty on an image of interest. This approach can be applied in order to improve the quality of a tomographic image of human bones and thus improve the resulted image, especially in case of fracture or anomaly without compromising it. In order to verify the approach, an experiment on real tomographic images has been carried out.
Sports Video Moving Target Detection and Tracking Based on Particle Filter and Related Algorithms

How to improve the real-time, solve target tracking under complex background has always been a hot research topic. The purpose of this paper is to study sports video moving target detection and tracking based on particle filtering and related algorithms. Firstly, several common target detection algorithms are introduced. The principle of particle filter and particle filter tracking algorithm are introduced in detail, and its advantages and disadvantages are analyzed in detail. In order to improve the real-time performance of the algorithm, the average weight of the particles is used as the judgment condition to judge the distribution of the predicted particles. Only when the average weight of the particles is less than a certain threshold, it means that the average probability of the particle state being the true state of the target is small. At this time, the particle swarm optimization algorithm is used to optimize the predicted particles, and then the global extreme value when the algorithm converges is output as Estimation of the target state. The experimental results show that the tracking accuracy of the improved algorithm in this paper is 97%.
InterCriteria Analysis of the Geographic Distribution of the ABO System Blood Groups in the Patients of the University Hospital “Saint Anna”, Sofia, Bulgaria, from 2015 to 2021

The paper presents the results of the application of the method of InterCriteria Analysis on a dataset of the blood group of 47562 Bulgarian individuals, patients of the University Hospital “Saint Anna”, Sofia, Bulgaria collected in the period from 2015 to 2021. Apart of the ABO system blood groups, the recorded data contain information about the patients’ A1 and A2 subgroups and the Rh(D) antigen. In the current leg of research, in addition to the easily extracted information about the patients’ year of birth and sex, here for the first time, the patients’ birthplace regions are algorithmically extracted on the basis of the recorded personal identification numbers. This allows us to have a deeper and more insightful picture of the regional distribution of the frequencies of the ABO system, A1 and A2 subgroups and the Rh(D) antigen in the frames of the Bulgarian population, which is a level of detail that has not been yet reported in the existing literature and is considered an important contribution of the current research work to the state-of-the-art. The conducted InterCriteria Analysis on these data gives additional details that may help decision makers on national and regional level with respect to regional and cross-regional demand and supply of blood and blood products in Bulgaria.
Personalized Course Recommendation Model of University Environmental Design Based on Collaborative Filtering Algorithm

With the development of information technology, the teaching management system of colleges and universities has undergone corresponding changes. In the practice of teaching management in Colleges and universities, a large number of elective courses are provided for students, and there are structural deficiencies and shortcomings in course categories and majors. For example, under the organization and management mode of the course resource center and most of the current course selection methods, it is difficult for students to choose suitable courses that meet personal professional development and personality needs. In view of this, we apply the personalized recommendation technology to the course selection system to provide students with reasonable, scientific and personalized course selection recommendations according to their learning needs and interest preferences. This is a research project aimed at developing a personalized course recommendation model for college environment design. The goal is to provide students with the best learning experience and an environment conducive to their academic success. So as to avoid the blindness of students’ course selection and improve the utilization rate of curriculum resources and the quality of course selection.
Mixed Optimization Strategy of Resource Allocation in Higher Education System

Basic education is the foundation of education and the national quality education. Although China has achieved the grand goal of basically popularizing compulsory education, due to the unbalanced economic and social development, the gap between urban and rural areas, regions and schools in basic education still exists, and there is even an expanding trend in some places and in some aspects. The main reasons for this gap are uneven resource distribution and inefficient allocation. The mixed optimization strategy of resource allocation in higher education system is the combination of static optimization strategy and dynamic optimization strategy. It can be used to determine the best combination of resources (human and financial) in any given situation. The hybrid optimization strategy is used to find the optimal solution to maximize the total value and minimize the cost. The hybrid method consists of two main parts: the static part, which determines the optimal allocation of different types of resources; And a dynamic component that determines how these allocations change over time as new information becomes available.
InterCriteria Analysis of Data Obtained from Patients with Hypercholesterolemia Treated with Linoprixol

Hypercholesterolemia is one of the main factors contributing to number of diseases, leading to permanent disability and/or worldwide. Nowadays the standard treatment of high cholesterol levels is carried out by drugs known as statins. Despite their main effect they also have a number of ADRs. The purpose of the present paper is to analyze the effects of the alternative treatment of patients with hypercholesterolemia with Linoprixol.using the InterCriteria Analysis method. The method of InterCriteria Analysis was applied to study the dependencies between the total cholesterol, LDL and triglycerides levels obtained in a clinical trial conducted in the Vascular Surgery Clinic of University Hospital Burgas.
A Generalized Net Model of Time-Delay Recurrent Neural Networks with the Stochastic Gradient Descent and Dropout Algorithm

Tapped delay lines and recurrent connections are two different components that are used along to design a time-delay recurrent neural network with a stochastic gradient descent algorithm in combination with a dropout method.
Quantum Fault-Tolerant Implementation of a Majority-Based 4-Bit BCD Adder

The greatest irony in the development of viable quantum computers is that decoherence limits scalability and has made quantum information states (qubits) fragile. The recent development of Google’s 54-qubit “SYCAMORE”and IBM’s 127-qubit “EAGLE”fully programmable quantum information processors were significant breakthroughs in the quantum world that encouraged the research community to contribute to the achievement of quantum supremacy. Quantum computers are based on quantum mechanics, and their Hamiltonians are unitary in nature, which distinguishes them from classical logic. As a result, Boolean logic is no longer useful in quantum computing. To limit the effect of decoherence in this Noisy Intermediate Scale Quantum System(NISQ), a quantum error correcting code, such as surface code with transversal operator-based fault-tolerant architecture, has been used. BCD-Adder is a popular adder logic used in digital computers and calculators to perform arithmetic operations directly in the decimal number system, with major applications in the finance sector (e.g. payroll and tax processing). Again, majority logic transforms the ripple carry adder into the carry-look-ahead adder. Using the Clifford+T-group, We present a 4-bit majority-based BCD adder with a fault-tolerant quantum circuit for 1-digit decimal numbers.
Entrepreneurship Platform Under Virtual Reality Technology (VRT)

With the economic development and the enrollment expansion of colleges and universities, the function of universities has changed from “elite education” to “mass education”, and improving the innovation and entrepreneurship service of colleges and universities has become an important task. At present, the entrepreneurship support platform has the following problems: the single support mode is difficult to make up for the short board of College Students’ entrepreneurship; The platform technology lags behind and cannot well adapt to the mobile Internet; The lack of support services can not meet the diversified needs of entrepreneurial college students. Therefore, it is a good choice to apply VRT to the entrepreneurial platform. This paper studies the application of VRT in the entrepreneurial platform, discusses the VRT and its characteristics, the subsystem design of the entrepreneurial platform and the application of VRT in the entrepreneurial platform, tests and analyzes the experience of students and trainers in the application of VRT in entrepreneurship through the index evaluation of the application of virtual technology in the entrepreneurial platform, And test the entrepreneurship under virtual technology and the entrepreneurship effect under the traditional mode. Under different modes, the proportion of entrepreneurship “always adhere to”, “stop halfway”, “entrepreneurial success”, “entrepreneurial failure” and “always develop” is. Teachers and students in the entrepreneurship platform under VRT said that they had a good experience, which significantly improved their interest in learning and success rate. Under the VRT, 86% of them can always insist on entrepreneurship, of which 55% are successful, and 43% of them have been developing and operating; In contrast, only 31% of entrepreneurs under the traditional mode can always adhere to entrepreneurship, only 23% succeed in entrepreneurship, and 21% of companies have been operating and developing. It can be seen that the application of VRT in Entrepreneurship improves students’ learning initiative, enhances real experience, and plays an important role in the success of entrepreneurship and the operation and development of the company.
BWA: Research on Adversarial Disturbance Space Based on Blind Watermarking and Color Space

Effective generation of adversarial examples can help to improve the training of neural models to avoid adversarial example attacks. Watermark-based adversarial example generation methods regard watermark as a meaningful noise to perturb the neural models. Therefore, the resulting adversarial examples are more similar to the original images yet more difficult to defend. Existing Watermark-based adversarial example generation methods adopt the visible watermarking technology. This however may reduce the success rate of the attacks because the adversarial examples with visible watermarks can be easily perceptible by humans. To address this issue, we propose a novel approach to generate adversarial examples based on the combination of frequency domain and color space perturbation. In particular, we use wavelet transform to hide the watermark, making it invisible and introducing noises to the frequency of the images. We then select the Lab color space Similarity as an optimization scheme for perturbations control. Experimental results show that under the same dataset, the maximum attack success rate of the adversarial example generated by our algorithm can reach 98.56%. In addition, the generated adversarial examples are highly portable, the successful attacks on VGG, Resnet101, and Inception-v3 can reach more than 95%, and the color space perturbation optimization achieves an average RGB channel similarity of 97.22%.
Choice Mechanism for Construction of University Enterprise Joint Cryptography Laboratory and Its Application in Hainan University

As the core technology of information security, the teaching of cryptography is paid more attention. Many schools have proposed a ' school - enterprise cooperation to build laboratories ' program. The main function of school-enterprise joint laboratory is to provide students with complete equipment and training environment. But for colleges and universities, how to match the most suitable company cooperation between schools and enterprises become the biggest problem, then establish a school-enterprise cooperation selection mechanism is very necessary. In this paper, the decision tree is used to simply screen all enterprises, and then the analytic hierarchy process is used to solve the weight from the arithmetic average method, the set average method and the eigenvalue method respectively. The final index weight is obtained by averaging the weight obtained by the three methods. Finally, the fuzzy comprehensive evaluation method is used to establish the selection mechanism of school enterprise cooperation. Through the case analysis of six enterprises in Hainan University, it is proved that the evaluation mechanism is effective, so as to provide objective standards for universities to select partners and jointly establish password laboratories. Keywords: First Keyword, Second Keyword, Third Keyword.
Software Fault Prediction Using Deep Neural Networks

Software failure prediction is the process of building models that software interpreters can use to detect faulty constructs early in the software development life cycle. Faults are the main source of time consumption and cost less over the life cycle of applications. Early failure prediction increases device consistency and reliability and decreases the expense of software development. However, machine learning techniques are also valuable in detecting software bugs. There are various machine learning techniques for finding bugs, ambiguities, and faulty software. In this paper, we direct an exploratory review to assess the performance of popular techniques including logistical regression, decision tree, random forest algorithm, SVM algorithms, and DNN. Our experiment is performed on various types of datasets (jedit, Tomcat, Tomcat-1, Xalan, Xerces, and prop-6). The experimental results show that DNN produces a better accuracy among all techniques used above.
Breast Cancer Classification Using ML on WDBC

Breast cancer is one of the most common types of cancer due to which death is happening at higher rate. Development of tumor is one of the symptoms of breast cancer. This paper provides a comprehensive comparative analysis of breast cancer classification concerning six machine learning algorithms: logistic regression, decision tree classifier, random forest classifier, KNN, SVM, and Keras sequential model. Wisconsin’s Breast Cancer Diagnosis dataset for this classification task is partitioned as 70% for training and 30% for testing, respectively. The features are extracted from breast mass images used for FNA tests. Parameters on which the said models are trained are selected manually. All the models mentioned give above 90% testing accuracy for this classification task.
Application Analysis of Blockchain Technology for 6G Network

In the 6G environment, society and information and communication technology are more and more closely combined. With the continuous improvement of communication rate, many data security problems will arise. Risk prevention will also be a focus of 6G technology that cannot be ignored. Blockchain technology takes cryptography as the carrier, and the underlying supporting technology lays the foundation for 6G network. In the 6G era, blockchain will have more room for development. With the rapid development of information technology and the wide application of Internet technology, the accelerated development of global economic integration, and the increasingly frequent communication between countries, human society has entered a new historical period - the information age. In this process, the phenomenon of information leakage is becoming more and more serious. Blockchain has the characteristics of decentralization, transparency and inviolability, and many technical improvements will provide a strong security guarantee for 6G. The latest development of 6G is described in detail, and the key performance of 6G and blockchain is discussed. Deeply analyze the technical basis of 6G blockchain, make a preliminary study on the application of blockchain technology in 6G, introduce blockchain related technologies, introduce the application of blockchain technology in spectrum management, analyze the challenges faced by the application of blockchain technology in 6G, and look forward to the future development prospects.