Reviewerly/exHarmony huggingface dataset:
Research of Person Re-identification Based on Deep Learning

Person re-identification is widely considered as a sub problem of image retrieval. The main method is giving a query image and retrieve the same identity pedestrian image from the corresponding image library. In this process, there is often a mismatch phenomenon, which leads to the decline of the first matching rate of Person re-identification. In order to alleviate this problem, this paper has made progress in the feature extraction training set of deep convolution neural network One step research, improve the algorithm, improve the matching degree of rank-1 and map. We analyzes the development of deep learning related methods in Person re-identification in recent years firstly, summing up and integrating some excellent algorithms and related network models in this field, and then solves the problem based on this, using feature map for horizontal segmentation, and then calculates the loss method to enhance the learning rate, which improves on the original baseline. Design a strong baseline, add a layer of linear and batchnorm layer, then connect the classifier, remove the last two FC's bias. Then we try to use a global feature which combines the simple training skills to test. When training the input image, we use rectangle to intercept, which can improve the matching degree of the image. Finally, the development direction is prospected and the future development focus is discussed.
Mumps prediction based on improved support vector machine

Abstract The prevention of d isease, has the important guiding significance to safeguard the health of the people. In order to improve the accuracy rate of mumps disease forecast of Yinchuan City, Ning xia, joined the combined kernel function idea based on regression prediction model of the traditional Support Vector Machine, the polynomial and radial basis kernel function combination instead of the traditional single kernel function model, proposed a combination of kernel function of support vector machine algorithm, with mumps Ning xia disease for the practice of Yinchuan City, to conduct research and forecast for the pathogenesis of rules combined with the local weather changes. The meteorological data and the number of diseases using correlation analysis method for processing, select the factor related coefficient ordering large from, then these factors should be selected using a single kernel and mixed kernel function for processing, training and prediction using support vector machine. The experimental results show that the mixed kernel function of Support vector machine prediction model results than the single kernel function regression is better
Study on a New Sparse Rule Algorithm in Liver Disease

Most of the current diagnostic methods of liver diseases are black box model which can't fully display the information hidden in the data.In this paper, we propose a new spare rule extraction algorithm on the basis of random forest, combined with elastic L1 and L2 norm regularization to select high precision and interpretable diagnostic rules. The method is compared with the state of art techniques in hepatitis dataset and the experimental results show that our method can extract more accurate, concise and strong explanatory rules. We believe that the proposed approach achieve excellent performance in tradeoff curve and more effectively predict the risk of liver disease in the health care environment.
Fairness Assessment of Question Paper using Artificial Intelligent Techniques

Examination performance is the only measure of competence in the existing education system of India. Question paper is the primary tool used in an examination, the quality of question paper plays an important role in a student's future. Hence, any individual should take utmost care while framing the question paper. However, setting up a good question paper for assessment is not a straightforward task, particularly when students come from different backgrounds and intellect. So parameters which need to pay attention are fairness, consistency, novelty and elimination of bias while selecting questions in the question paper. This paper explores the possibility of using knowledge graph technology and deep learning. Knowledge graph in particular, addressing syllabus fairness and deep learning for novelty check. The objective is to propose a feasible framework, taking into consideration several aspects. These aspects are: to meet at least three levels of bloom taxonomy, support of course outcomes (CO) and Program Outcomes (PO) attainment. They are achieved by judging syllabus fairness, support for judging more spontaneous answering potential with novelty checks. The prototype implemented using this framework showed promising results motivating for further research.
A Data-Driven Approach to Anomaly Detection and Health Monitoring for Artificial Satellites

In the operation of artificial satellites, overall health monitoring of the system and detection of symptoms of potential troubles are very important. For about twenty years, we have studied the data-driven health monitoring methods for artificial satellites, in which machine learning techniques are applied to the house-keeping data of satellites. In this paper, we review the basic of anomaly detection based on unsupervised learning and some results in early studies and then discuss the lessons learned and challenges to be tackled in the data-driven health monitoring for artificial satellites.
The Framework for Political Communication Text Mining Based on Twitter

In recent years, social media as a medium that is widely used in communication in the community. The phenomenon of communication on social media is increasingly being used in political communication. Social networking site services like Twitter and Facebook are believed to have the potential to increase public participation in politics. Twitter is an ideal platform for voters, politicians, political parties, and political institutions to disseminate not only public information but also political opinions to the public through their networks. This research is related to the effectiveness of social media (Twitter) as a means of political communication used by the public, especially in the election of the Mayor of Makassar and other elections in Indonesia. This paper, using two methods for social media analysis on political communication. Support Vector Machine (SVM) to classify predictable words or sentences, and the K-Means method is used to classify words or sentences related to political communication. The results of this study can be used by voters, candidates, political parties, and political institutions, as information and also as a measurement aid in determining the choice of candidates.
Multi-channel VideoStreaming Technology for Processing Based on Distributed Computing Platform

Abstract With the rapid development of the Internet, sensor network, and mobile Internet technology, a large number of data sets are continuously generated in the form of streaming in various application fields. At the same time, the processing of stream data has drawn more and more attention because of its application in different situations. To meet the urgent need for the processing of streaming data, there are many computing engines on the market, such as Spark and Flink. Traditional data-flow analysis has many problems in the process of streaming data, such as the high time delay, weak extensibility, and bad adaptability. In order to improve them, people use Kafka as the intermediate-cache and the computing engine that uses Spark Structured Streaming as the streaming data to realize the effective process of multichannel flow data. Considering the present demand for low latency and high flux that are analyzed by urban management in traffic video, processing the data of multichannel traffic video on distributed computing platform realized the tracking and search on motor vehicles, non-motor vehicles, and pedestrians of multiple roads and intersections.
Specific Emitter Identification Based on Deep Dense Feature Network

In the specific emitter identification, the current traditional classification and identification methods have difficulty in extracting fingerprint features, and the accuracy of fingerprint identification is low. In order to better extract the fingerprint features, a specific emitter identification algorithm is proposed based on the Convolutional Neural Network Dense Block module, called the Deep Dense Feature Network (DDFN). The network can effectively realize automatic extraction and multiplexing of fingerprint features, strengthen the transfer of features, and reduce the problem of vanishing gradients. The experimental results show that DDFN can achieve a classification accuracy of 93.17%, and can better realize the identification of specific radiation sources in a complex electro-magnetic environment.
A Hopfield Neural Network Based Demodulator For APSK Signals

In order to improve the utilization of frequency band and reduce the effects caused by nonlinear power amplifier, satellite communication has adopted amplitude-phase shift keying (APSK) signals modulation scheme for data transmission. However, APSK is a multilevel modulation and thus complex the demodulation process. This paper provides a Hopfield neural network (HNN) based demodulator to reducing the complexities and enhance the stabilities in demodulating APSK signals. Compared with traditional methods, the HNN based demodulator is faster and more stable for most of the cases in our experiments. Meanwhile, we obtained competing accuracy when using new modulator, and then proves the effectiveness of our work.
Earthquakes Detectability of KUT Infrasound Sensor Network During 2019

Earth and Space Science Open Archive PosterOpen AccessYou are viewing the latest version by default [v1]Earthquakes Detectability of KUT Infrasound Sensor Network During 2019AuthorsIslamHamamaMasa-yukiYamamotoSee all authors Islam HamamaCorresponding Author• Submitting AuthorSchool of Systems EngineeringKochi University of Technology 185MiyanokuchiTosayamadaKamiKochi782-8502JapanNational Research Institute of Astronomy and GeophysicsNRIAGEgyptview email addressThe email was not providedcopy email addressMasa-yuki YamamotoSchool of Systems Engineering, Kochi University of Technology 185, Miyanokuchi, Tosayamada, Kami, Kochi, 782-8502, Japanview email addressThe email was not providedcopy email address
Research on the Security Technology of Federated Learning Privacy Preserving

Abstract With the emergence of data islands and the popular awareness of privacy, federated learning, as an emerging data sharing and exchange model, can realize multi-party collaboration under the premise of protecting data privacy and security because the data distributed in multiple devices cannot be sent locally. To achieve benefits for all parties involved, it has been widely used in many fields such as finance, medical care, and education. However, FL also has various security and privacy issues. Starting from the overview of federated learning, this article describes in detail the threat model and existing security issues, including replay attacks, poisoning attacks, reasoning attacks, etc., and then makes a certain analysis of FL privacy protection security technologies. Compared with SMC and HE, differential privacy is excellent in terms of efficiency. Finally, we discussed the challenges of privacy protection and security issues and future research directions.
Comparative Study on Segmentation Techniques for Biomedical Images

Segmentation is one of the most useful pillars in the medical image processing field, especially for tumors diagnosis and early detection. It is the process of partitioning the image into different regions to extract the object of interest which is the tumor in this work. This paper presents a comparison between different segmentation techniques applied on brain tumor magnetic resonance imaging (MRI) images, as a case study. The techniques under comparison are Region-Growing, Active-Contour, Graph-Cut and Global Thresholding. The performance of these techniques is evaluated based on the Jaccard Index, Dice Index and the F-score, elaborating which one is more accurate than the other.
Algorithm for Fog-degraded Image Enhancement Based on Adaptive Fractional-order PDE

In this study, an image enhancement model based on an inverse diffusion equation with a Riesz fractional derivative is developed. The optimal orders of fractional derivatives for the images are obtained through experiments, and six quantities associated with the brightness and texture of each haze image-mean, variance, skewness, kurtosis, two-norm, and contrast-are calculated. By regression analysis, the linear relationship between the optimal order of the fractional derivative and these statistical quantities is acquired. Finally, the empirical formula for the order of the fractional derivative for the differential equation model is obtained. Experimental results show that the adaptive order of the fractional differential obtained using our approach is close to the optimal order obtained using artificial experiments and that the enhancement effect is optimal. Compared with other image enhancement algorithms, our algorithm can better improve the brightness and contrast of images and provide a better visual effect while defogging. Two objective evaluation indexes- information entropy and average gradient-also indicate the effectiveness of our method.
High-Dynamic-Range Lighting Estimation From Face Portraits

We present a CNN-based method for outdoor highdynamic-range (HDR) environment map prediction from low-dynamic-range (LDR) portrait images. Our method relies on two different CNN architectures, one for light encoding and another for face-to-light prediction. Outdoor lighting is characterised by an extremely high dynamic range, and thus our encoding splits the environment map data between low and high-intensity components, and encodes them using tailored representations. The combination of both network architectures constitutes an end-to-end method for accurate HDR light prediction from faces at real-time rates, inaccessible for previous methods which focused on low dynamic range lighting or relied on non-linear optimisation schemes. We train our networks using both real and synthetic images, we compare our light encoding with other methods for light representation, and we analyse our results for light prediction on real images. We show that our predicted HDR environment maps can be used as accurate illumination sources for scene renderings, with potential applications in 3D object insertion for augmented reality.
Using deep LSD to build operators in GANs latent space with meaning in real space

Generative models rely on the idea that data can be represented in terms of latent variables which are uncorrelated by definition. Lack of correlation among the latent variable support is important because it suggests that the latent-space manifold is simpler to understand and manipulate than the real-space representation. Many types of generative model are used in deep learning, e.g., variational autoencoders (VAEs) and generative adversarial networks (GANs). Based on the idea that the latent space behaves like a vector space Radford et al. (2015), we ask whether we can expand the latent space representation of our data elements in terms of an orthonormal basis set. Here we propose a method to build a set of linearly independent vectors in the latent space of a trained GAN, which we call quasi-eigenvectors. These quasi-eigenvectors have two key properties: i) They span the latent space, ii) A set of these quasi-eigenvectors map to each of the labeled features one-to-one. We show that in the case of the MNIST image data set, while the number of dimensions in latent space is large by design, 98% of the data in real space map to a sub-domain of latent space of dimensionality equal to the number of labels. We then show how the quasi-eigenvectors can be used for Latent Spectral Decomposition (LSD). We apply LSD to denoise MNIST images. Finally, using the quasi-eigenvectors, we construct rotation matrices in latent space which map to feature transformations in real space. Overall, from quasi-eigenvectors we gain insight regarding the latent space topology.
M. P. DRAHOMANOV'S DOCTRINE ABOUT THE IMPROVEMENTOF HIGHER SCHOOL EDUCATION

The article deals with issues of the improvement of higher school education in the scientific heritage of the Ukrainian teacher, politician, researcher of the world history and the national history. M. Drahomanov's pedagogical views and ideas are closely connected with his ideological, scientific views and social activity. It is proved that in the pedagogical heritage of M. Drahomanov, the problems of school, education, teaching in the Ukrainian language, their role in the development of the Ukrainian national culture take the leading place. The scholar devoted considerable attention to the problems of upbringing of the younger generation. The multidimensional and diverse pedagogical Drahomanov's activities contributed to the formation of his pedagogical concept, the holistic vision of formation and development of the personality, the educational process.
Proof-of-Work Difficulty Readjustment with Genetic Algorithm

Blockchain is a decentralized, distributed and public digital ledger technology. It can be visualized as a gradually increasing list of “blocks” which contains data that are linked together using cryptographic hash. Each transaction is verified by several participating nodes to compute a complex mathematical problem. The complexity of this computation, also known as Proof-of-Work (PoW), is governed by the difficulty set on a periodic basis. If the hash rate of the blockchain’s PoW grows or declines exponentially, the blockchain will be unable to maintain the block creation interval. The utilization of genetic algorithm (GA) in addition with the existing difficulty adjustment algorithm is proposed as a response to this by optimizing the blockchain parameters. A simulation of 3 scenarios as well as the default, were performed and the results were recorded. Based on the results, we are able to observe that the blockchain is able to reach the expected block time 74.4% faster than the blockchain without GA. Moreover, the standard deviations of the average block time and difficulty decreased by 99.4% and 99.5% respectively when block and difficulty intervals were considered for optimization, when compared to the default blockchain without GA.
Research on Dynamic Programming of Training Airspace Based on Genetic-Particle Swarm Optimization Algorithm

Aiming at the dynamic programming problem of training airspace, on the basis of analyzing the complexity of the problem, the spatial planning model is constructed, and the genetic-discrete particle swarm optimization algorithm is proposed. By integrating the crossover and mutation ideas in the genetic algorithm, the DPSO algorithm's ability to get rid of the local optimal solution is improved, and the convergence speed and accuracy of the algorithm are improved. In order to ensure the diversity of the population, the adaptive crossover operator and mutation operator are designed. Finally, The improved genetic-particle swarm optimization algorithm is used as an example. Compared with the traditional PSO algorithm, the results show that the algorithm has better results and faster convergence speed.
Distributed Inverse Learning and Deflection-based Particle Swarm Optimization Algorithm for Multimodal Functions

This paper studies the multi-modal function optimization problem. By introducing the idea of inverse learning, a novel distributed inverse learning and deflection-based particle swarm optimization (DILD-PSO) algorithm is proposed, where a deflection operation is involved in the algorithm to obtain a better performance on global exploration. Simulation examples are presented to illustrate the effectiveness of the proposed DILD-PSO algorithm.
New Method of Fuzzy Conditional Inference and Reasoning

We consider fuzzy inference of the form ‘if ··· then ··· ’. ‘if ··· then ··· else ···’ and ‘if ··· and/or ··· and/or ··· then ···’. Muzomoto proposed logical constructs for ‘if. then.’ by applying Godelien and Standard Sequence methods. These method do not satisfy all the intuitions In this paper, We propose method of fuzzy inference and applied on logical constructs. We have shown that our fuzzy inference method satisfy all the intuitions under several criterions.
QuESo-process

To evaluate the quality of open source software ecosystems (OSSECOs) we designed the QuESo-process. This process describes the activities and tasks that support the evaluation of OSSECOs. Our proposal attempts to fill the gap between quality models and their operationalization. In order to do this, we use the QuESo-model, described previously in another paper of one of the authors, as a basis for quality evaluation of OSSECOs.
Impact of AI, BC and IoT for Smart Cities

Blockchain has received greater attention in current years as decentralized, which is the core pillar; and distributed ledger technology offers different properties like security, privacy, scalability, and immutability. This technology has rapid development in the Internet of Things (IoT), which spans in every walk of life and is the way of the future—with internet leading to a strong demand for various technical limitations such as a cyberattack in the form of tampering and modification, a singleton point of failure, and a stalker attack. This chapter aims to identify the need for blockchain technology in an innovative variant for systematic analysis to outline a coherent and ample picture over IoT, which are very luxurious in terms of the utilization of energy and complex data processing. It is much more convenient to blend the blockchain, since modernizing IoT infrastructures is greatly centralized with a singleton point of failure, which primes to complex latency for point-to-point communications. By using smart contracts, which is a safer concept in permissionless blockchain technology, the Ethereum prevents the cybersecurity and selfish mining attack. From an immense perspective, the chapter first employs a massive use of the cryptographic algorithms as improved lightweight hight (ILWH) algorithms and cryptographic hashing to secure all aspects of the code-based smart contract system. The system uses ILWH as it provides a robust and fast mechanism for increasing privacy and security by decreasing reaction time, error deal, and false data. Secondly, it discussed how blockchain security differs from cybersecurity in the permissionless blockchain. Finally, we leverage and outline the operative measures taken by blockchain to exclude the effects of the stalker and a selfish miner variant that choose to hold the blocks it catches as unpublished outcomes in fork creation. Stalker explores to revoke a clear-cut target, and is not pessimistic about relative revenue.
A Case Study in Twitter Bot Identification: Are They Still a Problem?

Social media is used by billions of people and organisations worldwide for entertainment, advertising, news and many other reasons. It is also often used for gauging public opinion and spreading of information. This information can directly influence public opinion. Unfortunately, the potential for automatically influencing public opinion by potentially nefarious organisations and indeed countries through flooding of posts and tweets is increasingly common and of concern. Bots are often used to automate the sending of this information and potential misinformation. In this paper we explore approaches for bot identification in an automated manner using extensive Twitter collections related to the city of Melbourne. Despite the work of social media platforms such as Twitter in cleaning up the use of their platform and minimizing the widespread use/misuse of bots, we identify that a very small percentage of bots may still be active and must be addressed.