Reviewerly/exHarmony huggingface dataset:
Quantum discord dynamics of the GHZ state in Markovian environments

In quantum mechanics, quantum discord is a valuable quantum resource. As a generalization of the initial quantum discord, the multipartite quantum discord (MQD) exhibits excellent properties in the complete monogamy relation. In this paper, the dynamics of MQD are investigated for the Greenberger–Horne–Zeilinger (GHZ) state in Markovian environments. More specifically, it indicates that MQD of the GHZ state drops significantly over time in the depolarizing noise channel. Interestingly, quantum decoherence has no influence on MQD of the GHZ state in the Pauli channel [Formula: see text]. Subsequently, the analytical formulas of MQD are derived with six parameters in the Pauli channels [Formula: see text] and [Formula: see text], respectively. This is conducive to more efficient use of the GHZ state for various quantum tasks.
Nandai Pada Etnik Serawai di Kabupaten Seluma Sebagai Sumber Pembelajaran Apresiasi Sastra Lama

This study aims (1) to inventory or collect marks on the Serawai ethnicity in Seluma Regency so that it can be a source of learning for the appreciation of old literature. (2) Finding and explaining the moral values ​​contained in the mark of the Serawai ethnicity in Seluma Regency. The method used in this research is a descriptive qualitative method. The data collection technique was carried out by (1) Observation (2) Defect technique (3) Recording (4) Documentation. Data Analysis Techniques (1) perform translation (2) find and explain moral values ​​(3) conclusions. Research Results (1). Until now, markers are still found in 5 sub-districts in Seluma district, although they are no longer told productively because it is difficult to find sources who can still mark, plus many have had health problems and died. The Nandai that was found was the Nandai ghenai which is a type of legend and fairy tale. (2) There are moral messages and values ​​that can be taken in the markings on the Serawai ethnicity such as individual moral values ​​(honest, patient, disciplined, and responsible) and social moral values ​​(respect for every human being, respect for women, respect for the opinions of others , loyal, polite, and true to promises). (3) Nandai can be used as a source of learning appreciation for old literature in Seluma district at the Junior High School, Senior High School, and General levels because it has two languages ​​(Serawai and Indonesian) and Glossary.
AUTOMATIC SOLAR TRACKING SYSTEM WITH OVERCHARGING PROTECTION

Solar energy as a renewable energy source has been widely used in the past year, and residents who use solar energy as an alternative energy source will benefit.The main objective of this project is to develop a microcontroller-based solar panel tracking system that will align the solar panel with the sun for maximum solar energy harvesting.When the light intensity decreases, the system automatically changes direction for maximum light intensity.LDRs are used as sensors in solar tracking systems.In order to rotate the panel to the correct position, a gear motor is used and a battery overcharge protection circuit is used, which automatically cuts off the circuit when the voltage reaches the limit.to use.Finally, the solar panel is able to sense and track the intensity of the sun to maximize motor speed output.
Design and Implementation of Private Cloud Infrastructure based on OpenStack for Strategic Organizations

Cloud technologies have become indispensable part of organizations today.As an evolving paradigm for anytime, anywhere, cost effective computing, cloud computing has attracted huge attention among academics and industry.Many commercial companies are providing public cloud solutions through internet.In public clouds, control over the entire cloud infrastructure and the data hosted on it remains with the company providing cloud solution.However, government and defense organizations require that control over the cloud infrastructure and the hosted data is retained with them.Hence private clouds are more suitable for strategic organizations.This paper discusses design choices and challenges involved in implementing an Open Stack based private cloud system for an organization and customizing it for developing strategic applications while saving recurrent costs and also providing data security.It also focuses on automating the entire deployment with minimal manual work.As a case study for PaaS, deep learning frameworks have been provided on cloud for training deep learning models.
Impact of Deblurring on Image Matching Algorithms

Motion blur can have a significant impact on the performance of image matching tasks, particularly when using feature-based matching algorithms like SIFT (Scale-Invariant Feature Transform) or SURF (Speeded Up Robust Features).In this work we explore the impact of image deblurring on image matching algorithms.For our work we chose one classical algorithm SIFT and one Neural network model D2NET.For deblurring the images we used the state of the art deblurGAN model.It is observed that Image matching has significantly improved on the deblurred images compared to the blurred images as observed from the precision recall curves.
Prediction of Twitter Sentiment Analysis by Using Naive Bayes Algorithm

The purpose of our study is to do a sentiment analysis of tweets to determine whether they are positive, negative, or neutral. Many businesses use sentiment analysis to determine how well their products and companies are performing in the marketplace. In a burgeoning discipline, sentiment analysis is now being used to predict the outcome of political elections and polls. Twitter is an internet platform that allows users to send tweets with a maximum of 140 characters. We intend to get a representation of public opinion by analyzing the sentiments conveyed in the tweets due to the enormous amount of data we are using. This research investigates sentiment analysis, which involves extracting many tweets and analyzing them using a machine-learning technique like the naive Bayes classifier. Results are classified as several positive, negative or neutral and it is also expressed in the pie chart and bar chart. With the speedup development of websites, informal organizations, websites, online entries, surveys, feelings, proposals, evaluations, and criticism are created by scholars. This author created opinion content that can be about books, individuals, lodgings, items, research, occasions, and so on. These sentiments become extremely useful for organizations, state-run administrations, and people. While this content is intended to be helpful, a greater part of this essayist's produced content requires utilizing the message mining procedures and feel analysis. In any case, there are a few difficulties confronting the opinion examination and assessment process. These difficulties become snags in dissecting the exact significance of feelings and distinguishing the appropriate opinion extremity. Opinion analysis is the act of applying regular language handling and text examination strategies to recognize and remove abstract data from text. This paper presents a review of the feeling examination challenges pertinent to their methodologies and strategies
IEEE/ACM ASONAM 2022: Message from the General Chairs

The initial announcement for the fourteenth ASONAM Conference invited the submission of research papers and special sessions proposals to the Social Networks Analysis and Mining (ASONAM 2022), Hague, Netherlands / 3-6 August 2022. However, the coronavirus epidemic is still affecting every human activity that included gatherings of people and travel is still limited for international conferences for many researchers. It was therefore decided to move the conference to a blended conference later in the 2022 and change the conference to a hybrid in-person conference and on-line conference at a different location. The final announcement was therefore ASONAM 2022-The 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 10-13 November 2022, Istanbul and Virtual on Zoom.
Gaussian Process Regression with GPyTorch

So far, we have grasped the main components of a Bayesian optimization procedure: a surrogate model that provides posterior estimates on the mean and uncertainty of the underlying objective function and an acquisition function that guides the search for the next sampling location based on its expected gain in the marginal utility. Efficiently calculating the posterior distributions becomes essential in the case of parallel Bayesian optimization and Monte Carlo acquisition functions. This branch evaluates multiple points simultaneously discussed in a later chapter.
Bayesian Decision Theory and Expected Improvement

The previous chapter used Gaussian processes (GP) as the surrogate model to approximate the underlying objective function. GP is a flexible framework that provides uncertainty estimates in the form of probability distributions over plausible functions across the entire domain. We could then resort to the closed-form posterior predictive distributions at proposed locations to obtain an educated guess on the potential observations.
Case Study: Tuning CNN Learning Rate with BoTorch

By now, we have established a good foundation regarding the theoretical inner workings of a typical Bayesian optimization process: a surrogate function that approximates the underlying true function and gets updated as new data arrives and an acquisition function that guides the sequential search under uncertainty. We have covered popular choices of acquisition function, including expected improvement (EI, with its closed-form expression derived in Chapter 3), the more general Monte Carlo acquisition functions such as knowledge gradient (KG, computed via multi-start stochastic ascent in Chapter 6), and their parallel versions (qEI and qKG) and multi-step lookahead extensions (both accelerated via one-shot optimization, also in Chapter 6). This foundation carries us a long way, as we will demonstrate via two case studies: a simulated case that seeks the global maximum of the six-dimensional Hartmann function and an empirical case that finds the optimal learning rate of a convolutional neural network (CNN) used to classify the widely used MNIST dataset.
Knowledge Gradient: Nested Optimization vs. One-Shot Learning

In the previous chapter, we learned the inner workings of the optimization procedure using BoTorch, highlighting the auto-differentiation mechanism and modular design of the framework. This paves the way for many new acquisition functions we can plug in and test. In this chapter, we will extend our toolkit of acquisition functions to the knowledge gradient (KG), a nonmyopic acquisition function that performs better than expected improvement (EI) in many cases.
Deep Edge Detection Methods for the Automatic Calculation of the Breast Contour

Breast cancer conservative treatment (BCCT) is a form of treatment commonly used for patients with early breast cancer. This procedure consists of removing the cancer and a small margin of surrounding tissue, while leaving the healthy tissue intact. In recent years, this procedure has become increasingly common due to identical survival rates and better cosmetic outcomes than other alternatives. Although significant research has been conducted on BCCT, there is no gold-standard for evaluating the aesthetic results of the treatment. Recent works have proposed the automatic classification of cosmetic results based on breast features extracted from digital photographs. The computation of most of these features requires the representation of the breast contour, which becomes key to the aesthetic evaluation of BCCT. State-of-the-art methods use conventional image processing tools that automatically detect breast contours based on the shortest path applied to the Sobel filter result in a 2D digital photograph of the patient. However, because the Sobel filter is a general edge detector, it treats edges indistinguishably, i.e., it detects too many edges that are not relevant to breast contour detection and too few weak breast contours. In this paper, we propose an improvement to this method that replaces the Sobel filter with a novel neural network solution to improve breast contour detection based on the shortest path. The proposed solution learns effective representations for the edges between the breasts and the torso wall. We obtain state of the art results on a dataset that was used for developing previous models. Furthermore, we tested these models on a new dataset that contains more variable photographs and show that this new approach shows better generalization capabilities as the previously developed deep models do not perform so well when faced with a different dataset for testing. The main contribution of this paper is to further improve the capabilities of models that perform the objective classification of BCCT aesthetic results automatically by improving upon the current standard technique for detecting breast contours in digital photographs. To that end, the models introduced are simple to train and test on new datasets which makes this approach easily reproducible.
Adapting the Multilingual Assessment Instrument for Narratives (MAIN) to Tibetan

This paper introduces the Tibetan version of the Multilingual Assessment Instrument for Narratives (MAIN). We describe the main typological properties of the Tibetan language, including word order, case markers, and evidentiality. Finally, the motivation for adaptation, the process of adaptation, and the challenges encountered are discussed.
Research on Optimization of Accounting Information System under the Background of Big Data

Big data technology affects all walks of life, posing new challenges to the accounting industry and accounting information systems. Cloud computing provides convenient and effective network terminal access ports through its strong service model, and can also establish a resource sharing platform to improve the efficiency of enterprise accounting information management. This article explores the optimization of accounting information systems from the perspective of big data. With the help of big data technology, accounting information systems can achieve data optimization, storage function optimization, report optimization, analysis function optimization, decision-making function optimization, and coordination optimization. In addition, this article conducts research on the construction of enterprise accounting information systems under cloud accounting systems, and proposes optimization strategies aimed at optimizing accounting information systems and improving the economic benefits of enterprises. While using big data technology to optimize accounting information systems, enterprises should always pay attention to the effective application of big data on the premise of good security precautions.
Crossover Service Requirements: Analysis and Design

Crossover services often span multiple industry boundaries, and user requirements are typically diverse and personalized, leading to many challenges in requirements analysis and system design. In essence, crossover services are developed to meet user requirements across multiple domains. An approach of requirements convergence-analysis, design and evolution is proposed to support the convergence process in service requirements analysis and design.
Movie Recommendation System Using Machine Learning and MERN Stack

The entertainment industry is booming, and machine learning is playing a vital role in the technical world. Content consumption habits are growing more complicated and evolving at a faster rate than ever before. Machine learning-based recommendation systems forms self-sufficient system which learn from their experiences and improve without having to be explicitly coded. It is a mechanism that allows a user to find information that is relevant to him or her from huge amounts of data. Every entertainment company uses a complex recommendation algorithm to display meaningful content to a user based on his preferences. It helps them to increase their sales and retain their user base. Movie recommendations systems have various approaches such as collaborative filtering (CF) which compares users for similarity of content consumption or content-based filtering which uses the movie’s features such as year of release, genre, and actors. A hybrid approach incorporates two or more different approaches of movie recommendation. We present a solution in this paper of movie recommendation system architecture that uses MERN stack and ML and handles the cold-start problem.
HMM-Assisted Proactive Vulnerability Mitigation in Virtualization Datacenter Though Controlled VM Placement

Virtualization is the tool to offer data center resources to remote users. Virtualization brings higher resource utilization by sharing large physical resource to multiple users in form of virtual machines. The advantages of virtualization are overshadowed by various attacks like hyper jacking, intrusion, data thefts, etc. Co-location is the security loop hole most adopted by attackers to launch such attacks. This work proposes a hidden Markov model (HMM)-assisted proactive vulnerability mitigation mechanism by effective control of VM placements to defend against co-location attacks. The mechanism monitors VM/user behavior continuously and classifies the behavior of VM into security risk labels. Based on the risk label, VM placement is adapted to reduce the probability of vulnerability.
The model of implementation of distance learning in out-of-school education institutions

АBSTRACTThe educational community boldly began to master and use digital technologies as a means of interaction with students, when organizing distance learning.Teachers began to look for new forms and methods of working with children.The aim of the study is to determine, substantiate and experimentally verify the effectiveness of the model of distance learning implementation in out-ofschool education institutions.The experiment was carried out in the period from 2021-2022 academic year on the basis of the Poltava Regional Center for Scientific and Technical Creativity of Pupil Youth of the Poltava Regional Council.118 teachers of the Poltava Regional Center of Scientific and Technical Creativity of Pupil Youth of the Poltava Regional Council took part in the survey and ranking.Research methods: analysis of scientific and methodical literature; pedagogical observation; pedagogical experiment; pedagogical testing; methods of mathematical statistics.Results of the pedagogical experiment proved the effectiveness of the distance learning implementation model in out-of-school education institutions, which consists of theoretical, technological and reflective blocks, which is confirmed by qualitative modeling.
Conveying narrative items through music: An analysis of the adaptation of Hansel and Gretel's Fable to opera

A prominent artist well-known for his remarkable productivity and provision of informative content, the composer Engelbert Humperdick has pioneered in a special music style that might inspire new forms of music even after his death.Humperdinck also stands out among other artists since he has attached great importance to the role of music education during the chaotic times of the romantic era.This paper focuses on how music and literature are integrated by intellectuals.It also makes some comments and evaluations regarding the adaptation of Hansel and Gretel to opera.The presence of aesthetic elements in Hansel and Gretel opera significantly contributes to its artistic value.Thus, the steps taken by Humperdink to contribute to the attempts aiming to integrate literature and music are unique and noteworthy.Based on the principles of qualitative research methodology, the present study employs document analysis technique while accessing the related data.The findings revealed that Humperdink was inspired by his music style while adapting this popular fairy tale to opera.The study also showed an apparent artistic accord between the musical approach adopted in Hansel and Gretel opera and the fairy tale narration.The findings regarding this accord were reported, evaluated and interpreted within the scope of the study.
Scientist and Teacher – two Facets of Being a Lecturer of High School in a Postmodern World

The article is devoted to the problem of the dualistic unity of the values of being a teacher who is both a research scientist and an educator. The specifics of the professional combination of scientific research and practice of live teaching are considered. The paper comprehends the extraordinary phenomenon of education in the context of the scientific and ideological paradigm of understanding, which is a professional basis for both teaching and research activities. Education is represented as a person’s spiritual face, which is formed under the influence of moral and spiritual values, which is the property of its cultural circle. The examples of stereotyped education are proposed, which leads to the idea that an indicator of human educatedness is undoubtedly the worldview and methodological layer of acquired philosophical knowledge, which embodies the spiritual world. The reality and illusory nature of the fact of the “research point” in the activity of the scientist are reflected and the reflection of the determinants of the problem of “different personalities of scientists” is offered. The issue of teaching authors is singled out as a discourse of style, manners, creativity, content, interpretations, etc.
A study of face authentication methods using thermal images

Biometrics has been introduced in many facilities, and face authentication is one of the authentication methods that has attracted attention. However, face authentication is influenced easily by changes in lighting conditions. This study proposes a face authentication method using only a thermal camera, which is robust to lighting changes. The proposed method applies FaceNet[1], an authentication method for visible light images, to each of the four parts of the face (face, eyes, nose, and mouth) and tries to improve the accuracy by using a majority voting method.
A Dataset of Noisy Typing on QWERTY Keyboards

Text entry is a common and important part of many intelligent user interfaces. However, inferring a user’s intended text from their input can be challenging: motor actions can be imprecise, input sensors can be noisy, and situations or disabilities can hamper a user’s perception of interface feedback. Numerous prior studies have explored input on touchscreen phones, smartwatches, in mid-air, and on desktop keyboards. Based on these prior studies, we are releasing a large and diverse data set of noisy typing input consisting of thousands of sentences written by hundreds of users on QWERTY-layout keyboards. This paper describes the various subsets contained in this new research dataset as well as the data format.
Predicting Personalized Textual Reviews via Collaborative Filtering using Document Embedding

As the number of online product reviews is exploding in an e-commerce website, it is increasingly difficult for users to view all reviews and find their most helpful reviews for a product that they are interested in. Providing personalized helpful reviews on new products to individual users is valuable for users to decide on purchasing new products. Predicting the expected review on a new product by a user even before the user purchases will provide useful insight to the user on how the user would like the product after purchase. We propose a novel personalized textual product review prediction method via collaborative filtering. The method is based on user similarity using product ratings and semantic textual similarity via document embedding of textual product reviews. Initial experiments of the proposed prediction method on a textual product review dataset show promising results. The textual reviews predicted based on semantic textual similarity via document embedding are semantically comparable with the actual textual review.