Reviewerly/exHarmony huggingface dataset:
Unidirectional Gaussian One‐Way Steering

Abstract Steering is a type of quantum nonlocality that exhibits an inherent asymmetry between two observers. In a nondegenerate three‐level laser coupled to a two‐mode squeezed vacuum reservoir, under realistic experimental conditions, the Gaussian steering of two laser modes, and , generated within the cascade transitions, respectively are examined. It is found that the steerability is always higher than that from ; in addition, the steering asymmetry cannot exceed ln 2, which implies that the state never diverges to an extremal asymmetry state. It is shown how squeezed noise can play a constructive role in realizing one‐way steering. As the main result, it is demonstrated that the state can exhibit one‐way steering solely from , which is shown to emerge as a consequence of the fact that the intensity difference of the modes and is verified to remain always positive, irrespective of the physical and environmental parameters of . The generated unidirectional one‐way steering may provide a useful resource for the distribution of the trust in future asymmetric quantum information tasks.
Application of Pentagonal Neutrosophic Number in Shortest Path Problem

Real-human kind issues have distinct sort of ambiguity and among them; one of the critical troubles is solving the shortest path problem. In this contribution, we applied the developed score function and accuracy function of pentagonal neutrosophic number (PNN) into a shortage path selection problem. Further, a time dependent and heuristic cost function related shortest path algorithm is considered here in PNN area and solved it utilizing an influx of dissimilar rational pioneer thinking. Lastly, estimation of total ideal time of the graph reflects the importance of this noble work.
Metadata

Meta­data in various forms pervades our institutions, technologies, and daily lives. Meta­data is a distinct focus of academic research and professional practice for many people within the library and information sciences (LIS). This article is an exploration of the concept of “meta­data.” It presents a high-level introduction to the topic with analysis of key research problems and practical challenges. The paper discusses varying understandings of what “meta­data” means, the origin and evolution of meta­data as an important topic within information and data fields, and the central characteristics of that which gets called “meta­data.” Meta­data can be understood as both process and product and can result from both human effort and computational techniques. Given the central role meta­data have in the establishment of know­ledge, evidence, and truth, it is necessary for researchers and professionals within LIS to think critically about our meta­data practices and systems.
New trends in higher education: massive open online courses as an innovative tool for increasing university performance

The name massive open online courses (MOOC) was offered in 2008, thus, this is relatively new phenomenon for higher education and especially for Russian universities. In 2017, the national project was launched by the Russian Government, which is called 'Development of the export potential of the Russian education system'. According to the projects' objectives, Russian institutions of higher education should implement efficient measures aimed to increase the attractiveness and competitiveness of Russian education on the international market of educational services in order to increase the share of non-primary exports of the Russian Federation (Passport of the National Project 'Development of the Export Potential of the Russian Education System', 2017). In the framework of this project, the authors have analysed data received by the Russian universities on their foreign language MOOCs and developed recommendations for their promotion on international market.
Predicting Risk in Sentiment Analysis using Machine Learning

The purpose of this research is to do risk modeling after a sentiment analysis of Twitter posts based on a particular or certain sentiment with the help of the PRISM model .The model is named PRISM as the results obtained are an amalgamation of seven different attributes used in the research for comparison and tabulation of quantitative scores. These attributes are Accuracy, Precision, Recall, F1-Score, Support, Confusion Matrix, and Tweets. PRISM model can serve the law enforcement agencies in many ways and help them maintain peace, law and order in society as it is a proactive model. The sub-modules which are part of the PRISM model help to give quantitative values to predict the risk level on the sentiment of interest. After analysis of obtained testing results, it is observed that Support Vector Machine gives better results in accuracy, precision, F1-Score, Support and Recall as compared to the other three classifier models i.e. Naive Bayes, Decision Tree, and K nearest neighbor. It is also observed that with an increase or decrease in data, regarding the number of tweets, the fluctuation in performance of SVM is most stable i.e. it shows the least deviation and variation. The other algorithms show a considerable deviation in their performance.
Bolstering IoT security

The UK Government is pushing ahead with legislation to make Internet of Things (IoT) devices more secure. Meanwhile, Microsoft has warned of more than 25 flaws it has discovered in such devices.
Sentiment Evaluation of Public Transport in Social Media using Naïve Bayes Method

Nowadays, there is a trend in business organization to use social media as a medium to get feedback from customers. This gives advantage in improving the business values such as increasing customers’ satisfactions and building better company reputation. However, the response and feedback from the customers are varies and hold different perspectives. It might be led to ambiguous answer.In this work, we utilized Naïve Bayes machine learning approach for analyzing sentiment at social media on transportation services. We collected all feedback from Facebook and Twitter about transportation services. From the unstructured comments and feedback, we classified accordingly to determine the related scope of the sentiment. By using the Naïve Bayes method those massivecomments and feedback are presented in appropriate way and easier to understand.
Optimizing the Cost of Executing Mixed Interactive and Batch Workloads on Transient VMs

Container Orchestration Platforms (COPs), such as Kubernetes, are increasingly used to manage large-scale clusters by automating resource allocation between applications encapsulated in containers. Increasingly, the resources underlying COPs are virtual machines (VMs) dynamically acquired from cloud platforms. COPs may choose from many different types of VMs offered by cloud platforms, which differ in their cost, performance, and availability. In particular, while transient VMs cost significantly less than on-demand VMs, platforms may revoke them at any time, causing them to become unavailable. While transient VMs' price is attractive, their unreliability is a problem for COPs designed to support mixed workloads composed of, not only delay-tolerant batch jobs, but also long-lived interactive services with high availability requirements. To address the problem, we design TR-Kubernetes, a COP that optimizes the cost of executing mixed interactive and batch workloads on cloud platforms using transient VMs. To do so, TR-Kubernetes enforces arbitrary availability requirements specified by interactive services despite transient VM unavailability by acquiring many more transient VMs than necessary most of the time, which it then leverages to opportunistically execute batch jobs when excess resources are available. When cloud platforms revoke transient VMs, TR-Kubernetes relies on existing Kubernetes functions to internally revoke resources from batch jobs to maintain interactive services' availability requirements. We show that TR-Kubernetes requires minimal extensions to Kubernetes, and is capable of lowering the cost (by 53%) and improving the availability (99.999%) of a representative interactive/batch workload on Amazon EC2 when using transient compared to on-demand VMs.
Detection of Dyslexia using Eye Tracking Measures

Dyslexia is one of the most common and hidden learning disabilities found in people, especially in the young age. It particularly affects reading, where the impaired reader takes a longer time to read and grasp the concept than the non-impaired reader. This further leads to academic failures. So studies to detect such issues have been conducted considering various factors like the reading times, fixation times, number of saccades(sudden movements in the eye), of both the impaired and non-impaired subjects, and give the best possible results. Thus, we plan to use the same eye tracking technique supported with machine learning models to detect and classify the individuals with and without dyslexia. The factors considered during the study are font-size, typeface, frequency of words(fixation times of non-impaired readers are more if frequency of encountered words is less) and age(people with learning disorders tend to enhance their reading skills with age), etc.
Total quality management practices and their impact on performance: case study of Royal Jordanian Airlines

This study aims to examine the relationship between TQM practices and the organisational and quality performance of the Royal Jordanian Airlines and to specify which of the studied practices control the performance. The methodology used in this research is based on a questionnaire survey. The independent variables included the TQM practices (top management commitment, training and education, teamwork and continuous improvement) and the dependent variables are the organisational performance and quality performance. The data were obtained from 179 members in the top managerial positions of the RJ out of 247. A statistical analysis (SPSS) was used to analyse and examine the study hypotheses; correlation analysis was employed to test the validity of the procedure. Reliability and validity were examined along with measuring central tendency. Findings reveal a strong relationship between TQM practices and organisational and quality performance of the Royal Jordanian Airlines. The study recommends that managers should consider TQM practices to enhance performance, allocate resources and develop an environment that supports continuous improvement ideas.
Development of Digital Competence of Technical Specialists in the Electronic Information and Education Environment

Internationalization and digitalization of economy have caused future specialists to master new job functions, with digital competence becoming a critical one. The paper describes the impact of online education environment on the development of students' digital competence. Analysis of an education process in online education environment is given; features of online education environment as an educational phenomenon are identified; the principles of creating an online education environment and its characteristics are established. Professional tasks of engineering specialists and their classification into categories are presented based on professional standards analysis and expert evaluation method. Possibilities of an online education environment concerning the preparation of specialists to solve professional tasks are determined. The results of the experimental approbation of the impact of online education environment on the development of digital competence of future engineering specialists are presented.
Data breach at major healthcare firms

Quest Diagnostics, a major medical testing firm in the US, and blood-testing firm LabCorp have both suffered data breaches affecting more than 19 million people via a service supplier they share.
Sensor-based detection of abnormal events for elderly people using deep belief networks

Various technological developments in home-care systems have allowed elderly people to live independently without compromising their safety. A pilot study employing deep learning algorithm was conducted to study the daily routines of elderly people. We monitored unsupervised, diverse daily activities of elderly people such as household chores, sleeping, cooking, cleaning, using the bathroom, watching television, and meditating. The activities were monitored to track human-environment interactions by using motion sensors, actuators, and surveillance systems that were mounted inside living rooms, bedrooms, and kitchens and on bathroom doorways to detect safety hazards in the environment for elderly people. Such collected data were used in deep belief networks to ascertain and identify activities that are related to various health and self-care problems. Simulation results show that the proposed system outperforms the support vector machines in terms of F1 score and accuracy in identifying daily activities.
Traffic Camera Calibration via Vehicle Vanishing Point Detection

In this paper we propose a traffic surveillance camera calibration method based on detection of pairs of vanishing points associated with vehicles in the traffic surveillance footage. To detect the vanishing points we propose a CNN which outputs heatmaps in which the positions of vanishing points are represented using the diamond space parametrization which enables us to detect vanishing points from the whole infinite projective space. From the detected pairs of vanishing points for multiple vehicles in a scene we establish the scene geometry by estimating the focal length of the camera and the orientation of the road plane. We show that our method achieves competitive results on the BrnoCarPark dataset while having fewer requirements than the current state of the art approach.
AI and the Board Within Italian Corporate Law: Preliminary Notes

Artificial Intelligence (hereafter: AI) is transforming our everyday life in many important respects. The corporate realm is no exception. Many corporations cannot avoid facing the variety of issues raised by the increasing importance that AI plays within firms. Can an AIbased system be appointed as a board member? Can boards delegate specific tasks to AI-based systems? What are the implications and consequences of AI-involvement when designing the corporate structure? How does the choice to resort to, or to depart from the suggestions of an AI-based system expose, eliminate, or alleviate directorial liability? After a general discussion of AI and its projected use in the corporate context, this article aims to address, in a preliminary fashion, these basic questions within the framework of Italian corporate law. Artificial Intelligence, AI, Corporate Governance, Board of Directors, Blockchain, FinTech, RegTech, Business Judgment Rule, Director´s Liability
Phase-based frame interpolation method for videos with high accuracy using odd frames

In this venture, an imaginative unpredictability movement that has low vector preparing calculation toward the end side is proposed for movement repaid video vector frame interpolation. By handling this calculation, we typically demonstrates the issues of broken edges and disfigured structures in an inserted frame by progressively refining movement vectors on various square sizes. In the proposed strategy, the info has been taken as video rather than pictures in the current framework and the recuperation yield is taken as pictures and further process has been experienced to get the yield as video. There are some extraordinary systems in this strategy, for example, stage-based interpolation method, multistage movement repaid interpolation, and so forth is usually used to get high reason picture with diminished haze in the pictures to get the unmistakable picture of the information videos. Exploratory outcomes will demonstrate the visual quality, toughness in the video groupings containing quick moving video.
Adapting Best Path for Mobile Robot By Predicting Obstacle Size

In this paper we are proposing an approach to find obstacle size to reduce the searching area for path planning for mobile robot. With this approach, the vehicle is able to reduce searching area compare to other algorithms. This is main problem normally all the algorithms were concentrate how to optimal path but without reducing searching area it’s not possible .The approach we use in our model consists of various kinds of obstacles such as motor cycles, pedestrians, animals, etc. The presence of and the appropriate path is planned according to the dimensions of the obstacle as different obstacles have different dimensions.
Environmental uncertainty, leadership and organisation culture as predictors of strategic knowledge management and flexibility

Knowledge management and flexibility have been considered two important concerns in the current dynamic environment. Rapid technological changes, globalisation, increasing number of competitors have increased the threat for long-term performance and survival. Moreover, more knowledgeable customers and their diverse and changing demands act as catalysts in this threat. Knowledge management and flexibility strengthen organisation to become aware about the uncertain environment and act accordingly. Environmental uncertainty, leadership and organisation culture are the three important macro variables that effect strategic knowledge management and flexibility processes in a significant manner. The purpose of this study is to find the interrelationships of environmental uncertainty, organisation culture, and leadership with strategic knowledge management and flexibility. This paper is based on the empirical survey conducted on Indian IT and automobile industry. Stepwise regression and linear regression analyses have been used to develop a framework which explains the interrelationship of environmental uncertainty, leadership, and organisation culture with strategic knowledge management and flexibility.
Balanced clustering based on collaborative neurodynamic optimization

Balanced clustering is a semi-supervised learning approach to data preprocessing. This paper presents a collaborative neurodynamic algorithm for balanced clustering. The balanced clustering problem is formulated as a combinatorial optimization problem and reformulated as an Ising model. A collaborative neurodynamic algorithm is developed to solve the formulated balanced clustering problem based on a population of discrete Hopfield networks or Boltzmann machines reinitialized upon their local convergence by using a particle swarm optimization rule. The algorithm inherits the desirable property of almost-sure convergence of collaborative neurodynamic optimization. Experimental results on six benchmark datasets are elaborated to demonstrate the superior convergence and performance of the proposed algorithm against four existing balanced clustering algorithms in terms of balanced clustering quality.
Yolo Target Detection Algorithm in Road Scene Based on Computer Vision

Using artificial intelligence, computer vision and other technologies, cars can monitor driving conditions to ensure safety or assist driving. As a driver&#x2019;s primary task, finding a parking space is equivalent to &#x201C;eyes&#x201D;, which need to identify cars and pedestrians in front of the road. The purpose of this paper is to study the Yolo object detection algorithm for road scenes based on computer vision. This paper expounds the research basis and importance of road object detection, as well as the status quo of traditional road object detection, and deeply studies the progress of road object detection optimization. For the analysis of related technologies, firstly, the grayscale of the image is introduced, then the binarization of the image is introduced, and finally the YOLOv4 is analyzed in detail. The software and hardware environment used in the experiment is introduced, the image collection and label labeling process in the data set used in the experiment is described, and the evaluation indicators commonly used to evaluate target detection algorithms are introduced. Through the comparison of FasterR-CNN, SSD and YOLOv4, The average accuracy rate (mAP) of YOLOv4 reaches 87.2%, which meets the requirements of autonomous vehicles.
Intersection over Union based analysis of Image detection/segmentation using CNN model

Neural networks are capable of learning high-dimensional hierarchical structures of objects from huge quantities Deep-learning systems can learn to recognize photographs based on a large amount of training data. Artificial intelligence has this as one of its features. Deep-learning algorithms for picture interpretation may be divided into two groups. SegNet, U-Net, and SharpMask are examples of fully convolutional methods that use an encoder-decoder architecture. Region-based methods, on the other hand, use a convolutional neural network (CNNs) stack to extract features, such as Mask-RCNN, PSP Net and DeepLab. When the networks are trained on a large enough number of annotated datasets, region-based methods beat for most image segmentation tasks, fully convolutional techniques are used. We designed and incorporated deep-learning techniques based on Mask-RCNN to detect 2D images while creating a segmentation for each mask item in this paper.
An Enhanced Posture Prediction-Bayesian Network Algorithm for Sleep Posture Recognition in Wireless Body Area Networks

Wireless body area networks have taken their unique recognition in providing consistent facilities in health monitoring. Several studies influence physiological signal monitoring through a centralized approach using star topology in regular activities like standing, walking, sitting, and running which are considered active postures. Unlike regular activities like walking, standing, sitting, and running, the in-bed sleep posture monitoring of a subject is highly necessary for those who have undergone surgery, victims of breathing problems, and victims of COVID-19 for whom oxygen imbalance is a major issue as the mortality rate in sleep is high due to unattended patients. Suggestions from the medical field state that the patients with the above-mentioned issues are highly suggested to follow the prone sleep posture that enables them to maintain the oxygen level in the human body. A distributed model of communication is used where mesh topology is used for the data packets to be carried in a relay fashion to the sink. Heartbeat rate (HBR) and image monitoring of the subject during sleep are closely monitored and taken as input to the proposed posture prediction-Bayesian network (PP-BN) to predict the consecutive postures to increase the accuracy rate of posture recognition. The accuracy rate of the model outperforms the existing classification and prediction algorithms which take the cleaned dataset as input for better prediction results.
Detection and Classification of Breast Cancer from Mammogram Images Using Adaptive Deep Learning Technique

Breast cancer is one of the deadliest diseases that affect women over the age of 40. The death rate from cancer is reduced when it is detected and diagnosed early. The removal of cancer cells from the infected region before they move to other organs via lymph nodes remains a serious challenge. The proposed technique aims to create a reliable method for detecting breast cancer cells in their early stages utilizing mammogram scans. A mammogram is a breast x-ray image. Mammograms are used as the first stage in the detection of breast cancer by medical professionals. To detect breast cancer indications from Mammogram images, the proposed method employs an Adaptive Deep Convolution Neural Network (ADCNN). The algorithm automatically identifies breast cancer as Normal, Benign, or Malignant based on the images. The study experiment is carried out to estimate the region of infection in which the cancer cells have converged using a convolution neural network method with a 99&#x0025; accuracy.