Reviewerly/exHarmony huggingface dataset:
Obtaining Data from the Third-Party Systems for Software Development Process Analysis

Organizations nowadays have plenty number of choices to improve their software processes with the help of several third-party software process evaluation applications. Basically, for any kind of evaluation method, the software process metrics should be measured. Mainly it can be organized in different ways, as far as metrics collection from developers' devices is also not an easy task. This paper introduces our proposed framework to track and analyze the software development process, which gives important insights into the process and the product. In addition, integration of the external third-party agents for development management tools like Trello and SonarQube. The detailed architecture of the framework and it's agent relations are given with the precise metrics lists that are retrieved from the mentioned data sources.
National agency for qualifications development: yesterday, today, tomorrow

Reveals the prerequisites, the process of development and structure of the National qualifications system in modern Russia, its significance for managing creation and appliance of qualifications in the labor and education fields and development of their dialogue. The author shows the role, main stages of activity and future tasks of the National Agency for the development of qualifications as an operator and methodological hub of this system, coordinator of interaction between business and education. Presents the key projects implemented by the National Agency: monitoring of the labor market, support for sectoral boards in order to form qualification structures, and build of the independent system of qualifications assessment. Since 2017, in accordance with the decree of the Government of the Russian Federation, the National Agency has been performing the functions of a Basic center for training, retraining and advanced training of workers. This function is particularly in demand today, since the promotion of new professional standards, new qualification requirements, and a new system of recognition of qualifications in the field of vocational training, secondary vocational and higher education is a guarantee of the quality of training.
An ensemble algorithm for breast cancer histopathology image classification

Breast cancer is considered among prime reasons of deaths among women. Early detection can improve the survival rate and can decrease the mortality rate too. Though it’s a difficult and tedious task for pathologists. The motive of this research is to assess the efficiency and accuracy of various deep learning models and ensemble methods. Authors have proposed Stacked Generalized Ensemble algorithm that classifies the images into benign and malignant. The dataset used in experimentation is H&E breast cancer image dataset. Experimental results show that SGE has outperformed on various deep learning single classifiers. The evaluation criteria used for measuring the efficiency of algorithm is accuracy, precision, recall and F1 measure. A comparative analysis has been done with the existing deep learning methods. All the experimental results demonstrate that the Stacked Generalized Ensemble approach performed exponentially good on the histopathological breast cancer image classification and achieved an accuracy of 97.53%.
Aspect Category Sentiment Analysis with Self-Attention Fusion Networks

Aspect category sentiment analysis (ACSA) is a subtask of aspect based sentiment analysis (ABSA). It aims to identify sentiment polarities of predefined aspect categories in a sentence. ACSA has received significant attention in recent years for the vast amount of online reviews toward the target. Existing methods mainly make use of the emerging architecture like LSTM, CNN and the attention mechanism to focus on the informative sentence spans towards the aspect category. However, they do not pay much attention to the fusion of the aspect category and the corresponding sentence, which is important for the ACSA task. In this paper, we focus on the deep fusion of the aspect category and the corresponding sentence to improve the performance of sentiment classification. A novel model, named Self-Attention Fusion Networks (SAFN) is proposed. First, the multi-head self-attention mechanism is utilized to obtain the sentence and the aspect category attention feature representation separately. Then, the multi-head attention mechanism is used again to fuse these two attention feature representations deeply. Finally, a convolutional layer is applied to extract informative features. We conduct experiments on a dataset in Chinese which is collected from an online automotive product forum, and a public dataset in English, Laptop-2015 from SemEval 2015 Task 12. The experimental results demonstrate that our model achieves higher effectiveness and efficiency with substantial improvement.
Intelligent Counting System for Classroom Numbers Based on Video Surveillance

The classroom is the main place for students to attend classes and self-study. Because the school classroom is limited, it often takes more time for students to find a classroom with no classes or few people. Therefore, the real-time statistics of the number of students in the classroom is of great significance for strengthening the school spirit and assisting teachers to understand the classroom situation. And it is meaningful to develop a classroom population statistics system to help students find a suitable self-study room quickly. In order to solve the problem that it is difficult to automatically count the number of students in the classroom and can not be real-time, according to the principle of face detection and referring to the convolution neural network target detection framework, a special single detection architecture and a scale allocation strategy suitable for this framework are proposed. the difficulty of automatic counting of students in the classroom is solved. In this paper, we use a specific data set for verification, achieve advanced detection results, and finally get a statistical accuracy of 97.8%. In classroom surveillance images, individual students are small targets, but the existing R-FCN target detection algorithm based on convolution neural network is difficult to detect small targets. In order to solve this problem, a series of improvements are made on the basis of R-FCN, which greatly improve the ability of R-FCN target detection algorithm to recognize small targets. It is verified on the self-made data set, and the accuracy is up to 89.4%.
A Study of Scrambled Noisy Quantum Image Formation with Geometric Transformation and Its Denoising Using QWT

Quantum image processing reduces the gap between quantum computing and image processing fields. The principles of quantum computing explore the image processing in various ways of handling (capture, manipulate, extract) images of different formats and for different purposes. In this paper, an image denoising scheme based on quantum wavelet transform (QWT) is proposed. Initially, a quantum noisy image is formed with the help of geometric transformation operation and embedded into the wavelet coefficients of the quantum original grayscale image. As a result, it will affect the visual quality of the original quantum image. Then the quantum Daubechies wavelet kernel of fourth order is used to extract wavelet coefficients from the resultant image. Then we consider a Quantum Oracle that implements a suitable thresholding function to decompose the wavelet coefficients into a greater effect applicable for the original image wavelet coefficients and lower effect for the noisy image wavelet coefficients. However, original image wavelet coefficients are greater than the noisy wavelet coefficients. A detail computational time and storage complexity analysis is given and compared with some state-of-the-art denoising techniques at the end of this paper.
Automatic Face Recognition and Finding Occurrence of Actors in Movies

In this paper, artist face recognition, and all movie prediction system is proposed. It comprises of two phases. Initially, the faces in the video are recognized using an l1-minimization CNN + HOG framework, and some keyframes are selected, based on a robust measure of confidence. Then the labels are propagated from the keyframes to the remaining frames, by using transductive learning. The constraints in both feature and temporal spaces are integrated simultaneously. The output of the algorithm is tested on Indian Movie Face—Database and generated all the movies of those actors/actresses of the film.
Research on Virtual Wind Instrument and Performance System Based on Dual Computer Communication

With the development and progress of society, the same new things come into being, but also bring about the emergence of new culture. But it is precisely because of the emergence of new culture that the traditional national culture in China has been impacted, and as the most influential musical instrument culture, it is the first one to bear the brunt. Gradually national music culture began to disappear or become intangible cultural heritage. So, in order to change this situation, we use the most popular dual computer communication and virtual instrument and human-computer interaction technology to protect and inherit the musical instruments carrying the music culture. Through the technology of human-computer interaction and 3D modeling, the virtual musical instrument library is established on the web side, and the transmission equipment integrated with the data transmission module in the dual computer communication is used for data interaction to control the virtual musical instrument for performance. This will take off the hat of national music and culture intangible cultural heritage, and even change the status quo of all traditional cultures under threat of intangible cultural heritage. This paper first describes the research background and the development status of various technologies, and expounds the design ideas, design feasibility and so on.
A Decentralized AI Data Management System In Federated Learning

Federated Learning is a distributed machine learning approach which enables model training without sharing private locally produced data. It has been actively researched for several years as a means to utilize big data while protecting personal information. However, the server must decide which clients to participate in and what results to be used for aggregation each round. Besides, since the server needs to maintain the connection with the client directly, device overload and the processing delay may cause due to changes in the system environment such as network condition. In this paper, we propose a data management system that efficiently addresses the problem of general Federated Learning by improvements of the data management process on the connection between the Federated Learning server and the client. Additionally, it is shown that the proposed system can perform tasks independently and scales for increasing number of devices participating in the Federated Learning tasks.
GECNN-CRF for Prostate Cancer Detection with WSI

Pathological examination is of great significance for the diagnosis and treatment of prostate cancer, but artificial pathological examination is time-consuming, laborious and error prone. Automatic pathological examination can assist doctors in diagnosis and treatment. In this paper, a deep learning convolution network based on group equivariant convolution and conditional random field is proposed, which solves the problem of inconsistent features of data after random rotation in the learning process and enhances the robustness of the network. On the other hand, the conditional random field method is used to produce the same result on the slice image, which makes the segmentation edge of the probability image clearer.
A method for detecting image information leakage risk from electromagnetic emission of computer monitors

The unintentional electromagnetic (EM) emission of computer monitors may cause the leakage of image information displayed on the monitor. Detection of EM information leakage risk is significant for the information security of the monitor. The traditional detection method is to verify EM information leakage by reconstructing an image from EM emission. The detection method based on image reconstruction has limitations: adequate signal sampling rate, accurate synchronization signal, and dependence on operational experience. In this paper, we analyze the principle of image information leakage and propose an innovative detection method based on Convolutional Neural Network (CNN). This method can identify the image information in EM emission to verify the EM information leakage risk of the monitor. It overcomes the limitations of the traditional method with machine learning. This is a new attempt in the field of EM information leakage detection. Experimental results show that it is more adaptable and reliable in complex detection environment.
Effect of Cooling Systems on the Energy Efficiency of Data Centers: Machine Learning Optimisation

The number of data centers is increasing rapidly since more people are now using cloud services for data storage and management. Due to this, the total power consumption of the data centers is also increasing. The energy efficiency of the data centers is not very high due to a variety of reasons like heat loss by equipment and power factor issues. This paper attempts to review the existing work around 2015 to 2019 and understand the issues faced by the data centers. The energy usage by the data centers and the effect of the temperatures are reviewed along with the methods of optimisation through Machine Learning (ML) algorithms. Some of the factors affecting the energy consumption of the data centers are the airflow, heat loss, ambient temperature, among others. The gap in the existing research is obtained by identifying the various factors that affect the cooling of the data centers. The effect of the cooling parameters is optimised at different locations of the data centers as per the requirement. Reinforced learning techniques have been seen to be efficient in terms of optimisation. A combination of Support Vector Machine (SVM) and Ant Colony Optimisation (ACO) is suggested as a future scope of this study.
Non‐stochastic hypothesis testing for privacy

In this study, I consider privacy against hypothesis testing adversaries within a non-stochastic framework. He developed a theory of non-stochastic hypothesis testing by borrowing the notion of uncertain variables from non-stochastic information theory. I define tests as binary-valued mappings on uncertain variables and proved a fundamental bound on the best performance of the tests in non-stochastic hypothesis testing. I provide parallels between stochastic and non-stochastic hypothesis-testing frameworks. I use the performance bound in non-stochastic hypothesis testing to develop a measure of privacy. I then construct the reporting policies with the prescribed privacy and utility guarantees. The utility of a reporting policy is measured by the distance between the reported and original values. Finally, I present the notion of indistinguishability as a measure of privacy by extending the identifiability from the privacy literature to the non-stochastic framework. I prove that the linear quantisers can indeed achieve identifiability for responding to linear queries on private datasets.
Development and Preliminary Evaluation of a Method for Passive, Privacy-Aware Home Care Monitoring Based on 2D LiDAR Data

With an ageing population, the healthcare sector struggles to cope worldwide. Home monitoring using technology is increasingly used to release the pressure on healthcare professionals and keep elderly at home for longer. However, current solutions present technical (i.e. low resolution and convenience) and ethical issues. In this paper, we used 2D LiDAR, as a sensor that can provide significant information on patient’s activities, whilst still ensuring their privacy (i.e. 2D LiDAR only produces anonymous point clouds). Particularly, we developed an algorithm that uses clustering on the raw 2D LiDAR data, object tracking on cluster centroids to identify a user in a room, and semantic enrichment using metadata about the room (i.e. areas of interest and furniture position) to associate stationary and non-stationary points with every day activities (e.g. relaxing on the couch, working at the desk, standing by the window, and walking). We tested our method across different users (N = 3) and two rooms for a total 60 randomly ordered activity sequences, with five activities per sequence and each activity performed for 30 s. We obtained an overall accuracy in identifying the activities of 0.88 (standard deviation [SD], 0.06). Walking was the activity with the highest F1 score, with values of 0.97 (SD, 0.04) and 1.00 (SD, 0.00). As expected, activities where occlusion from pieces of furniture might be in the way had worse performance with an F1 score of 0.81 (SD, 0.24). Although performed on a limited sample, our paper shows potential for 2D LiDAR to be used for remote monitoring of mobility and daily activities of elderly in their home.
Modular‐based secret image sharing in <scp>Internet of Things</scp>: A global <scp>progressive‐enabled</scp> approach

Summary Due to the continuous development and progress of information technology, the Internet has also entered the era of big data based on the Internet of Things (IoT). How to protect the security of data stored and transmitted in the IoT is one of the urgent problems to be solved. This article focuses on the security issues of storage and transmission of image data in the IoT. Secret image sharing (SIS) is a kind of image protection mechanism by dividing an image into n shares, and different shares are given to different participants separately for preservation. Only when the number of shares reaches the threshold can the original image be recovered. From the perspective of image reconstruction mode, there are two types of SIS schemes: one is the traditional ( k , n ) threshold scheme, which provides an all‐or‐nothing reconstruction mode, the other is the progressive scheme, which can gradually restore the original image. In this article, a novel ( k , k 2 ) progressive secret image sharing based on modular operations is proposed, this method can divide the important images stored in the IoT into many parts and then transmit them to people in different places. It takes the whole as a unit in terms of the progressive recovery form. When the share reaches the threshold, certain blocks of the original image can be seen. As the share increases, the image will be clearer. When all shares participate in the reconstruction together, the original image can be restored without loss. Compared with other schemes, our scheme has the same smoothness, shadow size and satisfies the security, and is fine‐grained progressive.
Applicable Scene Text Detection Based on Semantic Segmentation

Abstract In the past few years, scene text detection problem has attracted much attention and widely studied for many applications. Segmentation based methods are quite popular in this filed, it was not only for the simple post-processing procedure, but also for the ability of handle the various shape of text. In this manuscript, a method named CFPM-IDB was proposed which based on a new decoder module Consolidated Feature Pyramid Module (CFPM) and a light weight segmentation head Improved Differentiable Binarization (IDB). The CFPM module originated from Feature Pyramid Networks and achieved an idea trade-off between local details and macro semantic information. IDB module performed binarization process by threshold map and probability map. Combining them results in CFPM-IDB, which improved the performance of scene text detection, both precision and efficiency. Experimental results on two different datasets proved the promising advance in the accuracy and speed of this model.
OCR Assisted Translator

India is a multilingual country where the spoken language changes after every 50 kilometres. Therefore there is no single universal language. Globalization brought the need to have knowledge of English but only 5 percent of the population is familiar with English and the rest of the population uses different languages. Lack of knowledge of English becomes a hindrance in the progress of science and technology in India due to language barrier. The main purpose of OCR(Optical Character Recognition) Assisted Translator using Flutter is to bring down that barrier by enabling people to translate any form of content from English to native language and native language to English, by simply taking a picture of the content they want to translate.
How to reduce the probability of erroneous decisions in the systems of collective intelligence

Abstract This work discusses and solves the methodological problem of reducing the probability of erroneous decisions when solving local problems by groups of intelligent agents. Intellectual agents can be carriers of both natural and artificial intelligence. The basic method of the new information technology is based on the theory of evolutionary decision reconciliation developed by the authors. The method is built on the original use of genetic algorithms in which intelligent agents perform the fitness function based on their knowledge and skills. The technology is based on the knowledge model proposed during the work, the Rasch model, and the mathematical apparatus of the Condorcet theorem. A theorem that allows finding the conditions under which the probability of an erroneous solution tends to zero, is formulated and proved. The results of using the theory for objects recognition from images by a group of neural networks are presented. A significant decrease in the probability of errors of the first kind is obtained in comparison with the classical methods. It is proposed to use the technology of evolutionary decision reconciliation to solve local problems in medical diagnostics, banking, and other fields.
Customizing intelligent recommendation study with multiple advisors based on hierarchy structured fuzzy‐analytic hierarchy process

Summary Evaluation information generated by various users is processed using various requirements and data to make recommendations for solving the problems, and it analyzes satisfaction with the results. Despite people normally utilizes the processed information for decision making, not all information, however, brings positive outcomes to users. There are some users who perceived it negatively. In order to minimize the occurrence of such negative effects, the analysis of various user requirements is essential as well as diversifying user inputs for each requirement. Consequently, the results from individual inputs must be predicted. In the past, since the system relies on a single‐expert system, it is necessary to accept and process various limitations of recommendation and multiple requirements. Therefore, the results of the recommendation also have various problems. In order to solve this problem, this study applied an analytic hierarchy process to multiadvisor configuration. In the proposed system, one or multiple advisors are defined, and after analyzing the predefined requirements, the system accepts only the requirements that can be processed and calculates the individual recommendation results. A recommendation system was going to be studied by learning all situation.