Reviewerly/exHarmony huggingface dataset:
Intelligent optimization based density matrix reconstruction method with semi-positive constraint

Quantum state tomography (QST) is a technique used to reconstruct the density matrix of unknown quantum states based on experimentally obtained measurements. QST is a fundamental tool in the field of quantum information and quantum technology. It is commonly employed to assess the quality and limitations of experimental platforms. However, the density matrix reconstructed using the standard QST method often fails to guarantee semi-positive definiteness, which is physically unacceptable, due to limitations imposed by the randomness of quantum state measurements, noise in practical applications, and the number of measurements. To address this issue, a method is proposed that combines maximum likelihood (ML) estimation with population intelligence optimization algorithms. First, the issue of guaranteeing the semi-positive definiteness of the density matrix reconstructed using standard QST methods is analyzed. Subsequently, the ML method is introduced, and four commonly used population intelligence optimization algorithms are applied to find the density matrix that maximizes the likelihood of reproducing the experimental measurements. Finally, the superiority of the proposed method is demonstrated using an IBM Quantum (IBMQ) processor in scenarios involving separable and entangled states of multiple qubits, and compared with the standard QST method.
THE NUANCES OF HUMOR TRANSLATION IN TERRY PRATCHETT'S 'GOOD OMENS'

This article explores the complexities of translating humor in Terry Pratchett's renowned novel, 'Good Omens.' The study delves into the challenges faced by translators when attempting to convey the intricacies of humor across different languages and cultures.
Interferograms analysis for measuring the thickness of aluminum thin films

In this work, a specific procedure to measure the thickness of aluminum thin films by interferogram analysis is described. Six interferograms correspond to the image of the aluminum thin film superimposed with interference fringes are obtained from a Michelson-type interferential microscope. A six-frame phase shift algorithm is used to demodulate the optical phase. The measured phase is proportional to the height variation between the thin film wafer and the substrate. Finally, the obtained 3D height map permits us measure the thickness of the sample over an area of 1024 <i>&mu;</i>m &times; 1280 <i>&mu;</i>m.
Automated Sentimental Analysis of Twitter Data

In these days, many intellectuals in the society provide their opinions and perspectives on various topics, products and thoughts through social media. Because of this in the market, the requirement of analysing the sentiment of data became more important and increasing day by day. When people using the media need information about a specific issue in the society and products available, it is very crucial to recognize the statements which make sense. For example, when purchasing a product, the consumer may want to know the details of the product, reviews that are provided about it. If they want to look for the drawbacks of the product, they prefer to get through the negative reviews, whereas when wanting to know the advantages, they prefer to check with positive reviews. This pattern can also be applied to national problems, political areas also. Due to large amount of data, manually analysing the data becomes almost impossible. Therefore, we require a model which has the ability to perform sentiment analysis in an automated manner and without any human intervention. Sentiment analysis can be done with the help of different algorithms. In this model, we use the Natural Language Processing (NLP) for conduction of the analysis and present performance comparisons in terms of accuracy and time taken. The model's graphical user interface (GUI) has been designed to enhance user-friendliness. It offers flexibility by allowing for training with any type of dataset of different size and types of data with the help Graphical user interface (GUI) provided and it also includes a module for testing the sentiment of input statements which are custom. The model shows its essence by providing the sentiment and nature of behind statements, making it to be applicable in various real-time scenarios. For example, it can be employed in to assess the product reviews in shopping platforms, providing an overview for the new consumers to understand the positives and negatives about the products. Additionally, it can be utilized for analysing the sentiments related to political areas. Since, there is lot of growth in number of people expressing their views on political issues through social media beyond print media we can grab lot of opinions on political leaders from the common people regarding their manifestos, schemes implemented, behaviour etc
So2al-wa-Gwab: A New Arabic Question-Answering Dataset Trained on Answer Extraction Models

Question answering (QA) is the task of responding to questions posed by users automatically. A question-answering system is divided into three main components: question analysis, information retrieval, and answer extraction. This paper has focused only on the answer extraction part. In the past couple of years, many QA systems have been developed and become mature and ready for use in different languages. Nevertheless, the advancement of Arabic QA systems still faces different obstacles and a lack of relevant resources and tools for researchers. This paper presents the So2al-wa-Gwab dataset since the publicly available datasets include various faults, such as the use of machine translation to build the data, a short context size, and a small number of question-answer pairings. Thus, this new dataset avoids the aforementioned drawbacks. Furthermore, in this paper, we have trained three deep learning models, namely, Bi-Directional flow network (BiDAF), QA Network (QANet), and BERT model, and tested them on seven different datasets, thus providing a comprehensive comparison between existing Arabic QA datasets. The obtained results emphasize that machine-translated datasets fall back when compared with human-annotated data. Also, the QA task becomes harder as the context, from which to extract the answer, becomes larger.
Comparative performance of high and medium resolution cameras for defect detection in carbon-fiber reinforced composites by digital shearography

The performance of defect detection in composite materials using digital shearography is important for correct decisionmaking in non-destructive testing. In this work, we compared a high-resolution 24-megapixel digital still camera (DSLR) and a conventional medium-resolution 5-megapixel camera to determine the detectability of blind holes in an aerospacegraded carbon-fiber reinforced polymer (CFRP) sample. The hole diameters ranged from 0.2 to 3 mm with a material thickness of 4 mm and the test sample dimensions of 200×200 mm. The sample was heated and observed from the front (defect-free side) by three halogen lamps for 5 minutes in pulsed heating mode. Speckle interferograms were acquired during the heating and cooling phases from both cameras simultaneously using identical shearing interferometers and shearing distances. Phase maps were calculated using the 4+4 temporal phase step algorithm and then unwrapped. Further, defect-induced deformation (DID) phase maps were obtained by polynomial curve fitting. The DID phase maps obtained from the two cameras were compared. Blind holes with diameters up to 1 mm were detected, which are one of the smallest defects detected with shearography and reported in literature. In addition, the DLSR camera was able to detect holes of 0.8 mm in diameter. We observed that nearly comparable detection capabilities were obtained from both cameras, even though the spatial resolution of the second camera (DLSR) was 5 times higher. Possible reasons of this limitation include effects such as fiber-related deformation in CFRP and speckle noise.
Rapid surface metrology of freeform shapes using CGH interferometry

The need for rapid, high resolution, accurate metrology of mass produced aspheric and freeform surfaces continues to grow as the application space evolves. Expanding the use cases of standard interferometers, computer generated holograms enable snapshot, megapixel resolution measurements of freeform surfaces that may have large and non-symmetric departure from spherical. CGH metrology at production scales can be realized without specialized engineers or in-house knowledge of setup. In this paper, we highlight the impact of the CGH vendor to provide not only a hologram but a suite of hardware to streamline high volume aspheric and freeform metrology. Alignment becomes straightforward, data acquisition remains standard to the interferometer and data is processed automatically. Early integration of this metrology solution in product planning helps users scale from prototype to manufacture.
WHICH TEACHERS SHOULD BE REPLACED BY AI

Teaching complex, abstract content is the key foundation of our civilization. Even before Internet there were technologies which thus significantly improved teaching and learning process and accelerated development of human society. With Internet and digital technology a number of new, powerful technologies are available to education. They significantly change the role of the teacher. No teacher can provide data, information, explanation, discussion or advice that cannot be found online nor can do it better than someone already did, or will do online. However, instead trying to do all for all, modern teacher should focus on doing something particular what they excel in for a fraction of students who truly need that teacher. Due to Internet the quantity of that small fraction of students will be larger than ever. Schools also need to transform, both physically and institutionally. Physically they need to provide spaces and resources for collaboration and exploration as well as cohabitation with their teachers. Institutionally they will specialize for some aspect of educational process from creation of new knowledge, its curation to its assessment.
Oriented Crossover in Genetic Algorithms for Computer Networks Optimization

Optimization using genetic algorithms (GA) is a well-known strategy in several scientific disciplines. The crossover is an essential operator of the genetic algorithm. It has been an active area of research to develop sustainable forms for this operand. In this work, a new crossover operand is proposed. This operand depends on giving an elicited description for the chromosome with a new structure for alleles of the parents. It is suggested that each allele has two attitudes, one attitude differs contrastingly with the other, and both of them complement the allele. Thus, in case where one attitude is good, the other should be bad. This is suitable for many systems which contain admired parameters and unadmired parameters. The proposed crossover would improve the desired attitudes and dampen the undesired attitudes. The proposed crossover can be achieved in two stages: The first stage is a mating method for both attitudes in one parent to improving one attitude at the expense of the other. The second stage comes after the first improvement stage for mating between different parents. Hence, two concurrent steps for improvement would be applied. Simulation experiments for the system show improvement in the fitness function. The proposed crossover could be helpful in different fields, especially to optimize routing algorithms and network protocols, an application that has been tested as a case study in this work.
A Double-Layer Blockchain Based Trust Management Model for Secure Internet of Vehicles

The Internet of Vehicles (IoV) enables vehicles to share data that help vehicles perceive the surrounding environment. However, vehicles can spread false information to other IoV nodes; this incorrect information misleads vehicles and causes confusion in traffic, therefore, a vehicular trust model is needed to check the trustworthiness of the message. To eliminate the spread of false information and detect malicious nodes, we propose a double-layer blockchain trust management (DLBTM) mechanism to objectively and accurately evaluate the trustworthiness of vehicle messages. The double-layer blockchain consists of the vehicle blockchain and the RSU blockchain. We also quantify the evaluation behavior of vehicles to show the trust value of the vehicle's historical behavior. Our DLBTM uses logistic regression to accurately compute the trust value of vehicles, and then predict the probability of vehicles providing satisfactory service to other nodes in the next stage. The simulation results show that our DLBTM can effectively identify malicious nodes, and over time, the system can recognize at least 90% of malicious nodes.
Rare words in text summarization

Automatic text summarization is a difficult task, which involves a good understanding of an input text to produce fluent, brief and vast summary. The usage of text summarization models can vary from legal document summarization to news summarization. The model should be able to understand where important information is located to produce a good summary. However, infrequently used or rare words might limit model’s understanding of an input text, as the model might ignore such words or put less attention on them, especially, when the trained model is tested on the dataset, where the frequency distribution of used words is different. Another issue is that the model accepts only a limited amount of tokens (words) of an input text, which might contain redundant information or not including important information as it is located further in the text. To address the problem of rare words, we have proposed a modification to the attention mechanism of the transformer model with pointer-generator layer, where attention mechanism receives frequency information for each word, which helps to boost rare words. Additionally, our proposed supervised learning model uses the hybrid approach incorporating both extractive and abstractive elements, to include more important information for the abstractive model in a news summarization task. We have designed experiments involving a combination of six different hybrid models with varying input text sizes (measured as tokens) to test our proposed model. Four well-known datasets specific to news articles were used in this work: CNN/DM, XSum, Gigaword and DUC 2004 Task 1. Our results were compared using the well-known ROUGE metric. Our best model achieved R-1 score of 38.22, R-2 score of 15.07 and R-L score of 35.79, outperforming three existing models by several ROUGE points.
Optimal Path Control of a Patient for Rapid Infection Testing

In this paper, we propose a path control framework for guiding and simulating the patient’s path of travel to speed up virus testing in pandemic situations, such as COVID-19. We use geographic information and hospital state information to construct graphs to yield optimal travel paths. Pathfinding algorithms A* and Navigation mesh, which have been widely used, are efficient when applied to control agents in a virtual environment. However, they are not suitable for real-time changing cases such as the COVID-19 environment because they guide only predetermined static routes. In order to receive a virus infection test quickly, there are many factors to consider, such as road traffic conditions, hospital size, number of patient movements, and patient processing time, in addition to guiding the shortest distance. In this paper, we propose a framework for digitally twinning various situations by modeling optimization functions considering various environmental factors in real-world urban maps to handle viral infection tests quickly and efficiently.
Real-Life Water-Scrum-Fall: Insights from Large Companies in Czech Republic

Abstract Background. A combination of traditional and agile methodologies into so-called “hybrid approaches” are today’s reality. The intention is to attempt maximization of benefits from both approaches. Water-Scrum-Fall is a methodology for hybrid approaches. However, there are no specific guidelines described for Water-Scrum-Fall. Aim. Explore and describe the reality of Water-Scrum-Fall in selected large companies in the Czech market. Method. Exploratory multiple-case study – four companies, eight participants. Semi-structured interviews and thematic analysis. Results. Insights into real-life Water-Scrum-Fall including involved roles, associated events, and typically created artifacts. Conclusion. In examined companies, modified agile methodologies are used as part of Water-Scrum-Fall, while their benefits are not fully unlocked due to poor implementations. However, project success rate is high. Hybrid approaches still require rigorous analysis before the development is started, which strongly limits the agility in the following development phase. Customer involvement or feedback loop is omitted in the development process. We accompany poor implementations with the unpreparedness of companies to fully adopt Agile, and missing senior roles, like scrum masters, to educate teams and organizations in proper agile practices.
PLACE OF TESTING IN VARIOUS SOFTWARE DEVELOPMENT MODELS

The main models of the software development lifecycle are considered, as well as the place of testing in them
Face Aging with Feature-Guide Conditional Generative Adversarial Network

Face aging is of great importance for the information forensics and security fields, as well as entertainment-related applications. Although significant progress has been made in this field, the authenticity, age specificity, and identity preservation of generated face images still need further discussion. To better address these issues, a Feature-Guide Conditional Generative Adversarial Network (FG-CGAN) is proposed in this paper, which contains extra feature guide module and age classifier module. To preserve the identity of the input facial image during the generating procedure, in the feature guide module, perceptual loss is introduced to minimize the identity difference between the input and output face image of the generator, and L2 loss is introduced to constrain the size of the generated feature map. To make the generated image fall into the target age group, in the age classifier module, an age-estimated loss is constructed, during which L-Softmax loss is combined to make the sample boundaries of different categories more obvious. Abundant experiments are conducted on the widely used face aging dataset CACD and Morph. The results show that target aging face images generated by FG-CGAN have promising validation confidence for identity preservation. Specifically, the validation confidence levels for age groups 20–30, 30–40, and 40–50 are 95.79%, 95.42%, and 90.77% respectively, which verify the effectiveness of our proposed method.
A Study on the Effects of Learning Perception Using Role Play on Interpersonal Competence, Learning Satisfaction, and Learning Outcomes of Aviation Service Major College Students

This study aimed to analyze the relationship between the learning perception of aviation service subjects using role play on the interpersonal competence, learning satisfaction, and learning outcomes of college students majoring in aviation service. A survey was conducted targeting 217 university students majoring in aviation service who are enrolled in universities in the metropolitan area and Chungcheong area and have experience in class using role play. In addition, the hypothesis is verified using the structural equation model. Among the perceptions of role play classes experienced by university students majoring in aviation service, 'interdependency' was found to have a positive (+) effect on all sub-factors of interpersonal competence. And 'usefulness' confirmed the positive (+) influence relationship with 'temperament for others' and 'communication' except for 'relationship promotion' of interpersonal ability. Among sub-factor of interpersonal competence, it is confirmed that only the factors of 'temperament for others' and 'communication' had an effect on learning satisfaction. And it is proved that learning satisfaction is an important determinant of learning outcomes. Therefore, this study is expected to serve as a basis for deriving an efficient and optimal learning method for practical subjects in aviation service majors.
Purifying Adversarial Images Using Adversarial Autoencoder With Conditional Normalizing Flows

We present a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">target-agnostic</i> adversarial autoencoder with conditional normalizing flows specifically designed to, given any <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unlabeled</i> image dataset, purify adversarial samples into clean images, i.e., remove adversarial noise from the images while preserving their visual quality. In our model interpretation, samples are processed by manifold projection in which the encoder brings the sample back into a posterior data distribution in latent space so that the sample is less likely to be irregular to the learned representation of any target classifier. Normalizing flows conditioned on top of our hybrid network structure and walk-back training are used to deal with common drawbacks of generative model and autoencoder-based approaches: not only the trade-off between compression loss and over-fitting on training data but also the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">structural model dependency</i> on dataset classes and labels. Experiments demonstrated that our proposed model is preferable to existing target-agnostic adversarial defense methods particularly for large and unlabeled image datasets.
Electronic Receipt of Document Control System using Technology Acceptance Model

AbstractThe document control system in a company is the company's effort to provide a means to find information and documents related to the company. Implementing a manual system for the documentation system (still in the form of physical paper stored in document shelves/cabinets) takes a long time in the work process, making it difficult for the employee in charge to control the document. The variables used in this study are the ease of use and effectiveness using the Technology Acceptance Model (TAM) method. In collecting the data, the researchers made observations, interviews, literature studies, and questionnaires. Of the 273 population of employees using the document control system, a sample of 73 correspondents was taken based on the formula. Sampling using a simple random sampling technique. The data analysis method used is quantitative analysis with multiple linear regression analysis. In calculating the data, researchers used the SPSS data processing application version 27. Based on the results of the study, it was found that 13.5% of employees accepted the existence of electronic document control technology based on the ease and effectiveness of the electronic document control system. The results of the three hypotheses tested found that two hypotheses were accepted and 1 hypothesis was rejected.