Reviewerly/exHarmony huggingface dataset:
Program source codes conversion system

The growing volume of technologies, the end of actively used development tools support, outdated API etc., entails the need of program codes conversion. In IT companies and not only, often begged the question of deprecated software support, which customers continue to use, or translation of current software to actual technologies. It is more rational for programmers to use the conversion and save most of code base, than rewriting all software by hand, even if manual adjustment is needed. At this moment, there are few high-quality code conversion systems. Largely, conversion systems work well only with similar programming languages. The task of program codes conversion is a deep and complex topic. Programmers are trying to improve translation technologies and facing with many challenges. This article discusses the basic principles of building a system for program codes conversion and some features of its practical implementation.
Deploying Locally and to the Cloud

As you approach the end of the development phase with your Streamlit application, it is time for it to see the light for the first time. In other words, you are ready to deploy the application and unleash your goodness to the world at large. To that end, you will need a machine to serve your application continuously, robustly, and resiliently. And while you can transform your local machine into a server of sorts, you are better off deploying the application to the mighty cloud, with the likes of Amazon Web Services, Microsoft Azure, Google Cloud Platform, and last but not least Streamlit itself.
Images

Images are used extensively nowadays. With the digital revolution, we easily generate, send, and observe images, all at a very low cost. Images are also used extensively in biological research and in the medical clinic. Biological images are studied mainly in basic science research, mostly at the cellular and molecular level. Medical images are used for clinical purposes, and focus on the tissue and organ level. Both kinds of images are often complicated and heterogeneous, and analyzing them requires sophisticated computational techniques. The goal of these techniques is to extract meaningful knowledge about the image content. For example, the automated identification of objects in the image (such as cells, intracellular components, or a cancer tumor), tracking cells, organelles, or cancer cells in consecutive video frames, or phenotype identification by object properties (size, light intensity, shape, etc.). Images also require large volumes of storage, raising the need for efficient compression algorithms, in order to store them.
Makine Öğrenme Yöntemi ile Bitcoin Piyasasının Geleceğinin Araştırılması: Türkiye Örneğinde Uygulama

It is seen that money takes different forms in line with the changing needs and technological developments throughout the historical process. Recently, cryptocurrencies have been included in our lives with Bitcoin. Bitcoin is a digital currency that functions using cryptographic techniques and without the need for the control of a central authority. As a result of technological developments, it is seen that the interest in Bitcoin, which has entered our lives as a new monetary tool and is predicted to be an alternative to currencies, is increasing. In this article, the machine learning method, which is a branch of artificial intelligence, is used as a method. In the example of Turkey, the daily closing data of bitcoin for 2016 were used. The machine learning method is aimed to predict the closing prices of the bitcoin market.According to the analysis findings, it is seen that the closing prices realized in the 4th quarter are higher than the closing prices realized in the 1st quarter. If the volume USD is higher than 5517.34 then it is Q1. Some rules have been produced with the Machine Learning method.It is aimed to contribute to the literature by using themachine learning method for predicting Bitcoin closing prices.
3D matting: A benchmark study on soft segmentation method for pulmonary nodules applied in computed tomography

Usually, lesions are not isolated but are associated with the surrounding tissues. For example, the growth of a tumour can depend on or infiltrate into the surrounding tissues. Due to the pathological nature of the lesions, it is challenging to distinguish their boundaries in medical imaging. However, these uncertain regions may contain diagnostic information. Therefore, the simple binarization of lesions by traditional binary segmentation can result in the loss of diagnostic information. In this work, we introduce the image matting into the 3D scenes and use the alpha matte, i.e., a soft mask, to describe lesions in a 3D medical image. The traditional soft mask acted as a training trick to compensate for the easily mislabelled or under-labelled ambiguous regions. In contrast, 3D matting uses soft segmentation to characterize the uncertain regions more finely, which means that it retains more structural information for subsequent diagnosis and treatment. The current study of image matting methods in 3D is limited. To address this issue, we conduct a comprehensive study of 3D matting, including both traditional and deep-learning-based methods. We adapt four state-of-the-art 2D image matting algorithms to 3D scenes and further customize the methods for CT images to calibrate the alpha matte with the radiodensity. Moreover, we propose the first end-to-end deep 3D matting network and implement a solid 3D medical image matting benchmark. Its efficient counterparts are also proposed to achieve a good performance-computation balance. Furthermore, there is no high-quality annotated dataset related to 3D matting, slowing down the development of data-driven deep-learning-based methods. To address this issue, we construct the first 3D medical matting dataset. The validity of the dataset was verified through clinicians' assessments and downstream experiments. The dataset and codes will be released to encourage further research.1.
Strolling through a NISQ processor

We demonstrate high fidelity multi-particle quantum walks on an 8x8 two-dimensional square superconducting qubit NISQ processor with 62 functional qubits. Further we implement a Mach-Zehnder interferometer where quantum walkers can coherently traverse the circuit.
Performance Evaluation of Block Matching 3D Denoising Technique

The reduction of the noise of the images always prevails as a challenge in the field of image processing. An image obtained after the elimination of noise has a higher clarity in terms of interpretation and study analysis in different fields such as medical , satellite and radar. This research work examines the BM3D Denoising Technique in the presence of different types of noise to achieve a high quality image. performance of the BM3D filter are measured by using parameters such as Mean Square Error (MSE), peak signal to noise ratio (PSNR) .
Research on Image Recognition Method Based on Improved Neural Network

In the 1960s, Hubel et al. proposed the concept of receptive field through the study of cat visual cortex cells[1]. In the 1980s, Fukushima [2] proposed the concept of neurocognitive machine based on the concept of receptive field, which can be regarded as the first implementation of convolutional neural network (CNN). The neurocognitive machine will decompose a visual pattern into many sub-patterns (features), and then enter the hierarchical hierarchical connected feature plane for processing. It tries to model the visual system so that it can complete recognition even when the object has displacement or slight deformation. CNN is a variant of multi-layer perceptron (MLP), developed from biologists Huber and Wessel's [3] early research on the visual cortex of cats. There is a complex structure in the cells of the visual cortex, and these cells are very sensitive to the sub-regions of the visual input space, called the receptive field.[4] CNN was proposed by Yann Lecun of New York University in 1998 [5]. In essence, CNN is widely used neural network architecture. The success lies in the local connection and weight sharing adopted by CNN. On the other hand, the complexity of the model is reduced, that is, the risk of overfitting is reduced. In this paper, using CNN as the research method, image recognition is studied by illustrating a set of comparative experiments. In the comparative experiment, I will optimize the network model of previous experiments, and finally effectively and significantly improve the efficiency of model training.
IMPACT OF N-STAGE LATENT DIRICHLET ALLOCATION ON ANALYSIS OF HEADLINE CLASSIFICATION

Data analysis becomes difficult with the increase of large amounts of data. More specifically, extracting meaningful insights from this vast amount of data and grouping them based on their shared features without human intervention requires advanced methodologies. There are topic modeling methods to overcome this problem in text analysis for downstream tasks, such as sentiment analysis, spam detection, and news classification. In this research, we benchmark several classifiers, namely Random Forest, AdaBoost, Naive Bayes, and Logistic Regression, using the classical LDA and n-stage LDA topic modeling methods for feature extraction in headlines classification. We run our experiments on 3 and 5 classes publicly available Turkish and English datasets. We demonstrate that n-stage LDA as a feature extractor obtains state-of-the-art performance for any downstream classifier. It should also be noted that Random Forest was the most successful algorithm for both datasets.
Hidden Four-Mode Entanglement in a Single-pass Stimulated FWM

We present a hidden entanglement between four-modes generated by stimulated FWM. Theoretically, we show the violation of DGCZ inequality for different sideband bipartitions. Experimentally we present previous results of the auto-homodyne detection and the asymmetry in the sidebands.
Supplemental Material: Stratigraphy of the Eocene–Oligocene Titus Canyon Formation, Death Valley, California, and Eocene extensional tectonism in the Basin and Range Turn on screen reader support

&lt;p&gt;Plate 1. Ten correlated graphical measured sections of the Titus Canyon Formation. Plate should be viewed at 24 inches wide by 36 inches tall.&lt;/p&gt; &lt;p&gt;&lt;br&gt;&lt;/p&gt; &lt;p&gt;Plate 2. Geologic map of the southern Grapevine Mountains and northern Funeral Mountains, with an emphasis on Cenozoic stratigraphy. Plate is intended to be viewed at 50 inches wide and 40 inches tall. Geologic map database can be interactively viewed and downloaded at https://arcg.is/1rriKn. &lt;/p&gt;
Ultrafast free-electron probing of photon statistics inside a laser cavity

We propose using the quantum interaction of free electrons with light to measure the photon statistics inside laser cavities. This approach has unprecedented spatiotemporal resolution, which could resolve fundamental questions associated with many laser systems.
Medical Image Super-Resolution Based on Semantic Perception Transfer Learning

Medical images are an important basis for doctors to diagnose diseases, but some medical images have low resolution due to hardware technology and cost constraints. Super-resolution technology can reconstruct low-resolution medical images into high-resolution images and enhance the quality of low-resolution images, thus assisting doctors in diagnosing diseases. However, traditional super-resolution methods mainly learn the mapping relationships among modal pixels from low resolution to high resolution, lacking the learning of high-level semantic features, resulting in a lack of understanding and utilization of semantic information, such as reconstructed objects, object attributes, and spatial relationships between two objects. In this paper, we propose a medical image super-resolution method based on semantic perception transfer learning. First, we propose a novel semantic perception super-resolution method that empowers super-resolution models to perceive high-level semantics by transferring features of the image description generation network in natural language processing. Second, we construct a semantic feature extraction network and an image description generation network and comprehensively utilized image and text modal data to learn transferable, high-level semantic features. Third, we train an end-to-end, semantic perception super-resolution model by fusing dynamic perceptual convolution, a semantic extraction network, and distillation polarization self-attention. Experiments show that semantic perception transfer learning can effectively improve the quality of super-resolution reconstruction.
Sets, Dictionaries, and Hashing

In many contexts, we are required to handle a large collection of objects in a way that supports inserting a new object, finding if an object is present, and possibly deleting an object. These operations typically appear in a series of arbitrary length. We want all these operations to be done as efficiently as possible. Consider a search engine (and its underlying infrastructure) like Google or Bing. One makes a query (e.g., “who is the King of Asteria”) and gets a response in about 945,000 results (0.44 seconds). One of the basic techniques behind such efficient implementations of search is called hashing.
Cryptocurrency Risk Determinant Impact During Covid-19 Pandemic Moment: Emperical Case From Indonesia

Bitcoin is virtual money or electronic money that does not have a fixed or physical form that can be seen in the real world but is available and used in cyberspace.Bitcoin is one type of cryptocurrency spread in the world.In the current era of the covid-19 pandemic, the use of cryptocurrencies is increasingly in demand, including bitcoin.In this study, the independent variables consisting of Cost Per Transaction, Total Circulating Bitcoin, Total Hast Rate, Transaction Rate Per Secondbitcoin price as dependent variable.The data taken to answer this phenomenon are:secondary data sourced from https://m.id.investing.com/.Researchers used multiple linear regression method with Eviews as a calculation tool to get good results.The number of bitcoins, transaction fees, mining difficulties and bitcoin legalization can affect bitcoin price fluctuations.The results obtained from this study are Variable Cost Per Transaction and the total hash rate has a negative and significant effect, while Total Circulating Bitcoin and the transaction rate per second has a positive and significant effect on bitcoin price fluctuations.
Perceived Ease of Use and Perceived Usefulness of MOOC TITAS Platform in The Era of Revolution Industri 4.0

The Massive Open Online Course (MOOC) platform used in the Islamic and Asian Civilization (TITAS) course is a transformation of new teaching and learning methods that increase students' interest to deepen their knowledge in civilization and current global issues.This study will measure the level of public university student's acceptance of perceived ease of use, perceived usefulness and attitude towards the MOOC TITAS platform.The study employed a quantitative approach and was conducted among the first-year students in 19 Malaysian public universities by distributing online questionnaire instruments in the google form.The study used the purposive sampling technique and a total number of 1373 university students from five different zones (North Zone, Central Zone, South Zone, East Zone, and Sabah/Sarawak Zone) were involved as respondents.The value of Cronbach's Alpha for the pilot test was 0.972 and data were analysed using SPSS 25.0.This study found that public university students have a good level of acceptance of the perceived ease of use, perceived usefulness and attitude towards the MOOC TITAS platform with mean value of 4.0.As a competent and effective platform, the MOOC TITAS platform corresponds to the online learning especially during the Covid-19 pandemic.MOOC platform also provides opportunities for students to have quick access to their lecture notes, activities and assessment regardless of time and location constraints.
Análise Bibliométrica de Cidades Inteligentes e Estudos de Turismo com Técnica de Mapeamento Visual

Smart cities, where technological innovations are used and which provide easy access to information in the city where both local people and tourists are located, may have the potential to become prominent cities in terms of tourism. In the study, the bibliometric method with the Vosviewer package program was used. 1050 studies obtained from the Web of Science data platform were examined, which contain smart city and tourism. The obtained data were analyzed using the visual mapping technique. According to the collocation analysis made on the keywords, twelve different themes were determined and grouped as smart city, tourism, smart tourism destination, tourism. Within the scope of the research, most of the studies are congress papers. In the literature, it has been determined that the subject of the smart city is mainly associated with the concepts of destination, internet, sustainability, accessibility, big data, tourism. According to the findings, the studies started in 2000 and increased in 2020. Engineering, computers and tourism are the disciplines where most studies are done.
Virtual Reality in Contemporary Theatre

This study examines how virtual reality images are used in contemporary theatre. Modern technologies get into various areas of life including the theatrical environment. Therefore, the study of the role of virtual reality in contemporary theatre is crucial for the qualitative understanding of the tendencies in development of the contemporary theatre scene in the context of the development of contemporary art. This determines the relevance of this study and subsequent academic research in this field as well. The purpose of this study is to assess the degree of influence of modern technologies on creating virtual reality in the development of theatrical art. The leading approach for this study is a combination of a systematic study of the main trends in the creation of virtual reality within the framework of the development of modern technologies and the logical construction of conclusions retrieved from the results of this study. The main results obtained in this study are supposed to be the identification of the main areas for creating virtual reality in the activities of contemporary theatres and changes in the work of theatre groups during the COVID-19 pandemic. Prospects for subsequent academic research in this field are determined by the increasing usage of modern technologies for creating virtual reality within the framework of the modern development of theatrical culture and the need for a deep study of this factor as the one that defines the prospects for the technological development of the contemporary theatre scene. The practical significance of this study lies in the possibility of using its results obtained to assess the role and place of modern technologies for creating virtual reality in contemporary theatre.
Job Satisfaction through Performance Appraisal: Moderating Role of Locus of Control

This research paid attention to Performance appraisal as the administration uses these tools. The research is on Performance appraisal and employee job satisfaction and analyzes the moderating role of locus of control (LOC) between performance appraisal and employee job satisfaction. Building on cross-sectional data of white-collar employees (N=214) from several organizations, we apply regression analysis that allows us to more thoroughly evaluate the causality of formal performance appraisals PAs on employee job satisfaction paralleled to earlier “cross-sectional” research. Performance appraisal positively influences job satisfaction, and LOC moderates the link between performance appraisal and employees' overall job satisfaction. Moreover, the study is required to apply internal and external locus of control as well as estimate the causal effects of Performance appraisals related to monetary and non-monetary consequences. Performance appraisals (PA) are considered a helpful human resource management instrument that workers recognize well. PA is a major cause of job satisfaction. This requires that the system be known for the effectiveness of appraisals when preparing performance appraisal structures. The study has been inclined to pay more attention to the association between the formal Performance Appraisal process and employee job satisfaction and the indirect effect of locus of control. Organizations need to acknowledge the importance of Performance Appraisal, which is a robust HR management tool.
Immersive Disaster Training Schema Based on Team Role-Playing

The prevalence of inevitable natural and social disasters is increasing at an alarming rate. With the proliferation of media and Internet technologies, there is a growing awareness of the importance of disaster training. The paper aims to combine the techniques of immersive Virtual Reality (VR) and team-based role-playing to demonstrate a comprehensive disaster training schema to help participants practice safety skills and learn proper decision-making skills in the face of disasters. A VR dome and cylinder combined multidisplay system iwDome was used to immerse participants and provide a view of the overall training; the role of each team was presented through the appropriate Head Mounted Display (HMD) and connected desktop. Rather than just being passive spectators or game players, the participants received puzzles in the virtual scenario that required intra-team and inter-team collaboration to be solved. The scenarios applicable to the system were not restricted to a specific type of disaster, but could be customized, revealing significant application prospects. To test the motivation for improvement, a quality assessment and user experience survey were conducted. The results indicate that participants were more engaged through cooperation in the virtual training environment and could achieve greater learning gains. The correlation of age to user studies was also confirmed. The enhanced enthusiasm of participants for what is relatively standard disaster management demonstrates the promise of using team role-playing-based VR training for vital, but otherwise tedious disaster safety training.
Restoration of User Videos Shared on Social Media

User videos shared on social media platforms usually suffer from degradations caused by unknown proprietary processing procedures, which means that their visual quality is poorer than that of the originals. This paper presents a new general video restoration framework for the restoration of user videos shared on social media platforms. In contrast to most deep learning-based video restoration methods that perform end-to-end mapping, where feature extraction is mostly treated as a black box, in the sense that what role a feature plays is often unknown, our new method, termed Video restOration through adapTive dEgradation Sensing (VOTES), introduces the concept of a degradation feature map (DFM) to explicitly guide the video restoration process. Specifically, for each video frame, we first adaptively estimate its DFM to extract features representing the difficulty of restoring its different regions. We then feed the DFM to a convolutional neural network (CNN) to compute hierarchical degradation features to modulate an end-to-end video restoration backbone network, such that more attention is paid explicitly to potentially more difficult to restore areas, which in turn leads to enhanced restoration performance. We will explain the design rationale of the VOTES framework and present extensive experimental results to show that the new VOTES method outperforms various state-of-the-art techniques both quantitatively and qualitatively. In addition, we contribute a large scale real-world database of user videos shared on different social media platforms. Codes and datasets are available at https://github.com/luohongming/VOTES.git