Reviewerly/exHarmony huggingface dataset:
A Textural Feature Based Cytological Image Classification Using Artificial Neural Network

Cytological image classification is essential for the diagnosis and treatment planning of cancerous diseases. The accuracy of classification is determined from the set of extracted features. Thus, the selection of features is an important part in any supervised learning technique. In this paper, we suggest a texture based cytological image classification scheme utilizing artificial neural network (ANN). The training dataset is prepared from the gray level co-occurrence matrix (GLCM) based textural features obtained using Laws Texture Energy Measure (LTEM) algorithm. The textural features are further classified into desired benign-malignant tissue types. The proposed approach is experimented using cytological images from the NITRC: human imaging database. The result of the classification using the suggested model is compared with that of state-of-the-art schemes. It is observed that encouraging results are obtained with the proposed model.
An Image Classification Approach based on Deep Learning and Transfer Learning

Abstract Target recognition of high-resolution images is an important direction of today’s classification technology, and some classification models have emerged, but there are still many technical problems to be solved. The main source of this paper is the Google Photo Collection, which includes five types of city daisy, rose, tulip, dandelion and sunflower. Using CNN (Convolutional Neural Networks) deep learning model and Google’s Inception transfer learning model to train and classify the sample images, the final accuracy of the overall test set can reach 88.3%; However, the accuracy of training without transfer learning is only 60.2%. Thus, it is more efficient to combine deep learning with transfer learning to image classifications.
Malicious Network Traffic Recognition Method Based on Deep Learning

With the Internet technology developing rapidly, network security has attracted more and more attention. Therefore, in order to protect private information against attack from malware, many people focus on the process of analyzing and recognizing raw traffic data to send an alarm to system in time and prevent damage. In this paper, we propose an improved method to recognize malicious network traffic data based on deep learning neural network, which applied convolutional neural network combined with Squeeze-and-Excitation Networks in order to learn spatial feature of network traffic data effectively and accurately.
Resource Deployment in Data Communication Intensive Cloud Environment

In data communication intensive cloud environment, the number of communication hops is usually few. Hence the congestion cannot be avoided by rerouting. Therefore, when some large data streams occupy communication resources, other applications may have excessive delay, resulting in violation of QoS. In this paper, we propose a data communication intensive cloud environment resource architecture. We both consider the deployment of virtual machines on physical hosts, and the deployment of services on virtual machines. For virtual machine deployment, we formally define the problem and propose a knapsack-based heuristic algorithm. The algorithm makes full use of the physical machine capacity to reduce the communication overhead. Taking the idea of virtual machine deployment and considering the characteristics of service deployment, a service deployment algorithm is proposed. Finally, we verify the effectiveness of our approach through simulation experiments.
A Method of Measuring Baseball Position at the Strike Zone

In this paper, we propose a ball position-measurement method to evaluate the performance of a baseball pitching trajectory analysis system. To evaluate the performance of the trajectory analysis system, a method of accurately measuring the actual ball position is needed. Existing methods cannot perform a highly accurate, continuous performance evaluation of the pitching trajectory analysis system. The proposed method accurately measures ball position using measuring equipment by passing the pitch through a structure in which two cameras are installed. The results demonstrated that the error was smaller than the radius of the ball at 40 points in the structure, and the system can be used for performance evaluation of the pitching trajectory analysis system.
Volatility effect on the adoption and valuation of tokenomics

Motivated by financial analytics for volatile behavior of cryptoassets such as Bitcoin and Ether, we generalize a recent paper of Cong, Li and Wang (2019) to take time-varying volatility process into account and investigate its effect on tokenomics including user adoption and price valuation. Our finding reveals that high adoption of userbase and stability of token's price are associated with volatility reduction of the platform productivity. In addition, this model can be modified for IoT blockchain by considering IoT nodes as the user in a peer-to-peer network.
Trustworthy Self-protection for Data Auditing in Cloud Computing Environment

Cloud computing lineaments enable the users to deal with resources and get benefit from the services in a distinct economic manner. One of the vital cloud’s services is storing the users’ data on a remote location on a cloud’s storage resource, which means that this storage has to be characterized by a high level of security and safety, for instance, to ensure the data integrity and preserve its confidentiality. In the existing cloud environments, these security issues are handled by a third party; neither user nor provider, known as the Cloud Third Party (CTP). Despite the evident important role of CTP, it may adversely affect the security and becomes untrustworthy insider employee (i.e., malicious insider). In this paper, we present an automated framework for enhancing the cloud security “Self-Protecting of Data Auditing (SPDA)” based on the concept of autonomic computing. This framework is divided into three phases in order to handle the two significant security issues: the integrity and the confidentiality. The first stage concerns with encrypting the user’s data automatically to maintain its confidentiality. The second phase is for automated data auditing in order to keep the data integrity. And, the last one is used to automatically decrypt the data to its original correct form when the user requests to retrieve it.
Spatial possibilistic fuzzy C-Mean segmentation method integrated with brain Mid-Sagittal Surface information extracted by an evolutionary algorithm

A normal human brain holds a high level of bilateral reflection symmetry. On the sagittal view, the brain can be separated into the left and the right hemispheres with approximately identical anatomical properties, so that symmetrical mirror pixels are almost similar. As a result, the symmetry info rmation can be used to enhance results of brain segmentation methods. In this paper, I introduced a new version of the Fuzzy C-Mean (FCM) segmentation method which is called Genetic Spatial Possibilistic Fuzzy C-Mean (GSPFCM). GSPFCM integrates symmetry information with SPFCM. It is an extension of Possibilistic Fuzzy C-Mean (PFCM) on 3D Magnetic Resonance (MR) images. GSPFCM uses the spatial information and fuzzy membership values. Spatial and possibilistic information were added in order to solve the noise sensibility defect of FCM. To integrate the symmetry information, I first extracted the Mid-Sagittal Surface using a proposed genetic algorithm. According to this algorithm, inside each axial slice, a Thin-Plate Spline (TPS) surface was constructed and a genetic algorithm was applied to fit this TPS surface to the brain data. Then, the symmetry degree of each symmetry pair voxels was calculated. Finally, the membership values in SPFCM were updated based on the corresponding symmetrical values. The efficiency of GSPFCM, was evaluated using both simulated and real Magnetic Resonance Images (MRI), and was compared to the state-of-the-art methods. My results showed images with different degrees of Intensity Non-Uniformity (INU) and different levels of noise were segmented efficiently by the GSPFCM.
Hardware Implementation Based on Contact IC Card Scalar Multiplication

Based on Montgomery modular multiplication structure, this paper proposed and realized SM2 algorithm core module for IC cards, that is, scalar multiplication hardware structure of elliptic curves in prime field. Modular addition operation was improved so as to gain faster operation and anti-SPA capacity. Point addition and multiple point algorithm of Jacobi projective coordinates and Montgomery modular inversion algorithm were adopted in the structure. Experimental results indicated that, the structure had better performance and met the specific design indicators of the IC cards. Contact IC card communication protocol module (ISO7816 Protocol) was designed, and serving as the interface, it was tested by the contact IC card reader. In previous scalar multiplication designs, designers tended to focus on certain performance improvement of scalar multiplication, rather than a fixed product. This paper designed scalar multiplication encryption module, which was specific to the design of the interface of the contact IC cards widely used in life, so as to make the IC cards work more effectively.
Theoretical Background and State-of-the-Art

This chapter provides an overview of the background knowledge that is relevant to the main areas of application of this book. The areas of software engineering, software reuse, and software quality are discussed in the context of taking advantage of useful data in order to improve the software development process. Upon providing the relevant definitions and outlining the data and metrics provided as part of software development, we discuss how data mining techniques can be applied to software engineering data and illustrate the reuse potential that is provided in an integrated manner.
Data Science is all Set to Revolutionize the Ultrasound Diagnosis in Medical Health Care

Ultrasound (US) with CAD system imaging is a medical diagnostic tool which has become the most commonly performed cross-sectional diagnostic imaging technique in the practice of medicine, and also the one of the most common schemes in the medical diagnostic area to detect diseases in the clinical practice. It is also cheapest, non-ionizing, reliable, portable, and capable of real-time image acquisition, safety, convenience, and low risk, live display makes it acceptance worldwide. US is oldest but with the cult of computer science, data science (machine learning, deep learning) and chip level advancement in the ultrasound devices. This Ultrasound is all set to upgrade and change according to the advancement in evolving technology, information technology with significant challenges and opportunities. High inter- and intra-operator inconsistency and limited image quality management are the few trending issues which are prime focus of the researchers in present days. As mentioned earlier the US devices due to cult introduction of the computer science and data science has become smaller, enhanced computational capability, and become very advanced. Ultrasound also can contribute significantly to decrease changeability with its advanced image processing. However, reading ultrasound imaging is little tricky, in a proposal to make it little supportive to the clinicians and to reduce the load of doctors, many ultrasound computer-aided diagnosis (CAD) systems with different segmentation, classifiers techniques have been launched. In recent years, the success of data science especially deep learning in the image classification and segmentation is very successful. This led to more and more scholars and researchers to think unanimously realizing that improvement in accuracy, Specificity, and sensitivity of the disease finding can be brought by utilizing the deep learning in the ultrasound CAD system. This paper reviews the research which focuses on the ultrasound CAD system utilizing Data science in recent years. Ultrasound CAD system are mainly 1. traditional ultrasound CAD system and deep learning ultrasound CAD system. The different segmentation techniques and the classifier employed in ultrasound CAD system are discussed. Performance of the different CAD systems with different techniques has accounted in the research paper in contrast accuracy, sensitivity, and Specificity in disease finding is noted. This paper will be useful for the researchers who focus on the ultrasound CAD system more over it is the base of the academic research for the fulfillment of the PhD research work.
User Satisfaction Levels Sentiment Analysis Toward Goods Delivery Service On Twitter Using Support Vector Machine Algorithm (SVM)

The data amount is experiencing rapid growth in this era. Data can be in the form of text, images, sound or video. Social media has become one of the factors of data growth. Every person can express their feelings on social media from these opinions can be analyzed. In this study sentiment analysis uses the support vector machine algorithm. The first step is crawling data using the Twitter API with keywords. After collecting data, the preprocessing process is carried out, after the preprocessing process the feature is retrieved for each tweet, the features obtained are then collected into a feature list. The feature list is then transformed into a feature vector in binary form and then transformed using the TF-IDF method. The dataset consists of 2 data, namely training and testing. The training is labelled manually. To test the performance of the algorithm used the K-Fold Cross Validation method. The test results are obtained an average accuracy of 98% with the composition of training data and testing data. From these results, the Support Vector Machine method can be used for sentiment classification of JNE twitter data.
E-textbooks for Ukrainian school: problems of preparing

The article deals with the effectiveness of electronic textbooks in Ukrainian schools, the understanding of the concept of «electronic textbook» in Ukrainian legislation governing publishing. The article analyses basic principles of creating electronic textbooks on three platform: Bristar, Розумники and MozaWeb. The problems of creating electronic textbooks for elementary school have been highlighted. The prospects for further research and the possibility of using electronic textbooks in the educational process have been described. It has been emphasized that the perception of the term «e-book» has changed. Previously, this term was understood as the device which the reading was carried out, now an e-book is a content that requires playback by individual devices. The multimedia educational publications created in the framework of the experiment of introducing the electronic textbook and the electronic platform and their compliance with the Regulations on the electronic textbook are analyzed. Summarizing our analysis, we can highlight that non-compliance with the Regulations has been identified, such as insufficient playback of products on different operating systems, non-unified design between parts of the publication that are interactive, and content of the print edition that created the electronic version. Some editions which are recommended by the Ministry of Education and Science are difficult to buy for later use, and some of them are only available in the demo version. Keywords: e-learning publication, e-textbook, New Ukrainian School, e-textbook, interactive textbook, multimedia content.
Quantum Algorithm for Finding Sets Intersection

We propose a quantum algorithm to solve the intersection problem of two sets by extending Bernstein-Vazirani algorithm. Given set and set . In the classic algorithm, we need to query times to find the solution of set . However, in our quantum algorithm, we need only one quantum query to find the set .
Robust Face detection based on Viola-jones Algorithms

Abstract This paper describes the experimental methods of using face detection based on MATLAB software. The important things are that interesting in the movement, this face detection are accepted to use four general face detection internationally. We also need to use the Viola and Jones principles in MATLAB for face detection because of its ability to process that image quickly and Very accurate. And this paper aims to study principles and algorithms for active development and improvement.
Blind quality assessment for 3D synthesised video with binocular asymmetric distortion

During the process of watching 3D synthesised video (3D-SV) and switching viewpoints, there is a case of asymmetric distortion, the left(right) viewpoint is a synthesised video generated by rendering technique, and the right(left) viewpoint is a real video taken by the camera. How to accurately estimate the quality of 3D-SV with binocular asymmetric distortions is a new and challenging problem. Aiming at this problem, a blind quality assessment method for 3D-SV with binocular asymmetric distortions is proposed. Firstly, the local edge deformations of synthesised videos at different scales are measured by calculating their standard deviations. Secondly, the global naturalness of synthesised videos is computed by analysing their natural statistical characteristics. Thirdly, a strategy for fusing left and right quality scores is proposed, which considers their texture information in different directions. Finally, the random forest is used to obtain an objective quality score. The experimental results show the superiority of the proposed method on asymmetry 3D-SV database.
Scene Text Recognition Based on Deep Learning

Image is used everywhere in our life and can bring us abundant information. Unlike general visual elements, text contains a wealth of semantic information. Obtaining the content of the image can help us better to understand the image. Therefore, it is crucial to recognize and understand text in scene images. In recent years, deep learning technology has developed rapidly and has played a leading role in the field of traditional optical character recognition technology (OCR) and it achieved good results. Based on this, this paper combined with the deep learning method further study the scene text recognition.
A Novel Super-Resolution Reconstruction from Multiple Frames via Sparse Representation

In this paper, we have put forward a novel technique of multiple frame super-resolution (SR) reconstruction based on sparse representation. The available SR reconstruction techniques are used to generate a high-resolution (HR) image either from single low-resolution (LR) frame or multiple LR frames of the same scene. But these techniques require some modification to provide desired SR frame when LR inputs are contaminated by the high amount of noise. The proposed multi-frame based reconstruction technique overrates the SR part as well as noise removal simultaneously to achieve better outputs. Multiple frames of a noisy low-resolution (LR) image of the same scene with a sub-pixel shift or rotation are used as input of the proposed algorithm. The registration technique of these images results in a single HR frame having more information than any one frame from the set of multiple frames. Later noise removal, as well as edge reservation, is done by applying a median filter to the HR frame. Then sparse representation technique is used to reconstruct the super-resolution frame of the filtered HR frame. To ensure the qualitative goodness, some well-known quality metrics like PSNR, SSIM, and BLUR are measured for different image inputs and the results are compared with other techniques to confirm the claims of the authors.
Deviation Power Pricing Mechanism Based on Blockchain Technology for Cross-regional Energy Trading

More trading entities have participated in the electricity market owing to the electricity market reform. The pricing and settlement methods of the deviation power will affect the fairness of the transaction and the enthusiasm of the trading entities. The establishment of a reasonable deviation power settlement method is a key step to ensure the power market transaction. As an emerging distributed data storage technology and distributed accounting technology, blockchain technology has great application prospects in power market transactions and energy Internet. Therefore, the research on the application of blockchain technology to the settlement of deviation power not only contributes to the advancement of power market reform, but also has positive significance for the application of blockchain technology in the power field. In this paper, the concept of the deviation power, the measurement method and the existing settlement methods are briefly introduced. Then, the application of blockchain technology and the cross-regional energy trading based on this are analyzed. Finally, some transaction entities of China Southern Power Grid are taken as an example. The impact of different price and settlement ratios on the economic benefits and behavior of each transaction subject in terms of deviation power indicates how the blockchain technology can be applied to the settlement of deviation power in cross-regional power transactions.
An ITD-Based Method for Individual Recognition of Secondary Radar Radiation Source

In order to study the fine features and individual recognition of radiation signals, a method of individual recognition of secondary radar radiation source based on ITD method is proposed to solve the problem of poor anti-noise performance in current research work. This method USES the inherent time scale decomposition to describe the unintentional modulation characteristics of the radiation source signal and USES the fast entropy algorithm to measure the difference of the subtle characteristics of different radiation source signals. Support vector machine (SVM) was selected as classifier for classification and recognition. Experiments show that the proposed method can significantly improve the recognition effect and speed.
Accurate scale estimation for visual tracking with significant deformation

Scale variation of a target frequently appears in tasks of visual tracking. Accurate scale estimation is challenging due to deformation, occlusion, rotation, change in the view angle and diversity of tracking object categories. Most tracking methods employ an exhaustive search of scales to estimate the target scales. However, only finite and discrete scales are usually searched due to the expensive computation requirement. Here, the authors propose a novel scale estimation method based on bounding box regression (BBR). They first formulate the scale tracking as a regression problem, and search for the entire continuous scale space without being limited by a manually specified number of scales. Then they extend the original single‐channel BBR to multi‐channel situations, to allow for better employment of multi‐channel features. To further take advantage of the time prior information of training samples, they derive a time‐related sample weighted multi‐channel BBR. Besides, they propose a quantitative measurement, scale divergence degree, to reflect the diversity of sampling strategy. Experimental results on OTB‐2015D dataset demonstrate that the proposed approach achieves outstanding scale estimation performance for visual tracking with significant deformation.
An Automatic Software Quality and Function Assurance Case Study for Agile

The integration and automation of development, testing, and verification processes are essential to a large-scale software development project. By linking these processes automatically and seamlessly, we can reduce human errors and minimize communication costs and assure software quality. Manual integration of these process is often error-prone and usually time-consuming. We develop a Software Quality Assurance mechanism which consists of Automated Testing Module, Report and Sampling Module, and Defect Tracking Module. We integrate these modules by CI (Continuous Integration) and seamlessly automate the whole development process from development, testing, verification, to delivery without human-intervention.