Reviewerly/exHarmony huggingface dataset:
Effect of stark shift on three-level atom interacting with a correlated two-mode of non-linear coherent state

In this manuscript, we study a new non-linear system which represents a ?-type five level atom interacting with a quantized four-mode electromagnetic field. A non-linear Stark shift is introduced, through the elimination of intermediate levels (two and four) using the adiabatic elimination technique. By using the Schrodinger equation we obtain the analytic solution this model. Some statistical aspects through the effective Hamiltonian are presented such as the collapses-revivals phenomenon, degree of purity, concurrence, and the squeezing phenomenon with respect to study the effect of Stark shift parameters on these statistical aspects. For small values of the Stark shift parameter, the collapse times increase and the atomic inversion symmetry axis shifts upward, while the entanglement decreases significantly. It has been noted that the system is affected by the Stark shift parameter.
Analytical formula of the wehrl entropy and wehrl phase distribution of the field in generalized coherent squeezed states

In this framework, the effect of a Kerr-like medium and the coupling function dependent on the number of photons operator on the interaction between a two-level atom and a non-linear field is studied. A relation between the Kerr-like medium parameter and the field-atom coupling parameter is used to obtain a simplified formula for Rabi frequency. The wave function of the proposed model is obtained, followed by the derivation of the phase distribution and from which the wehrl entropy formula is calculated. The effect of the initial state and the non-linear func?tion dependent on the number of photons operator and the Kerr-like medium on entanglement is calculated through the Wehrl entropy formula, wehrl distribution and the behaviour of photons by studying the correlation function. The entangle?ment decreases when the function dependent on the number of photons operator is taken into account, while the entanglement gradually improves when the squeezed state is considered, and the entanglement decreases significantly when considering the Kerr medium. An oscillatory distribution is formed between the classical and non-classical in the coherent state. The non-classical distribution disappears when considering the squeezed state and the Kerr-like medium.
IMPACT OF ARTISANAL PERMA GOLD MINING ON GROUNDWATER QUALITY

The artisanal exploitation of gold in Perma is not without impact on the environment and even the quality of groundwater. For this, three gold panning sites were selected. Samples of water (surface and underground) and sediments were taken during the rainy season between the months of August and September 2021. The results from the physico-chemical analyzes show that the waters are slightly acidic (6 &lt; pH &lt; 7.42), weakly mineralized and conductive (21 &lt; EC &lt; 448 ÂµS/cm) the water temperature varies between 27.7 Â°C and 30.8 Â°C. The concentrations of cyanides and zinc vary respectively from 0 Âµg/L and 30 Âµg/L and from 0.01 and 0.5 mg/L. With regard to the sediments, the physico-chemical analyzes show the following results: the water pH is close to neutral [6.79 6.98] and the KCl pH is slightly acidic [6.42 6.53]. Sediments from the Kouaterna site record high concentrations of total phosphorus 7934.00 meq/kg, total carbon 1.1094%, NTK 1200 mg/kg and organic matter 1.91%. As for the exchangeable K and the cation exchange capacity, they evolve respectively between 0.0859 meq/kg and 0.1791 meq/kg and 4.360 meq/kg &lt; CEC &lt; 6.160 meq/kg. Groundwater in the area is weakly mineralized, of good quality and has no impact on consumption, unlike unsuitable surface water.
Trusted Distributed Computing

Data is the key to any business and Intertrust's suite of computing products looks to better manage and understand it, as CEO Talal Shamoon explains to New Electronics
Empirical Study on Dimensionality Reduction Approach for F-Commerce Dataset by Principal Component Analysis

Data have been grown exponentially since the last decade. For further use and study, proper maintenances of these data are essential. Principal Component Analysis (PCA) is a dimensionality reduction technique that helps to reduce vast amounts of data without losing minor information. In this research, we applied PCA to existing survey data from consumers of different e-commerce groups' Facebook (F-Commerce)-pages. We have shown that Artificial Neural Network (ANN) and Random Forest (RF) perform nearly the same with the dimension reduced dataset compared to the previous experiment with the dataset that holds all the features. Our relevant contribution is to use PCA to reduce the components and determine the models' performances for different variances.
New Year's Prospects for the Korean Economy (2021)

The year 2020 was one of considerable hardship. Korea witnessed its first instance of negative growth since the 1997 Asian financial crisis, with sharp decreases in consumption and exports due to the COVID-19 pandemic. However, after the bottoming-out of major economic indices, the Korean government’s unprecedented and timely policy response and widespread public adherence to quarantine rules made it possible for the economy to rebound more quickly than expected. As a result, expectations are currently very high for Korea’s early recovery from the economic side effects of COVID-19.The year 2021 will be the year in which Korea climbs out of the valley of recession and returns to the path of recovery. Uncertainty surrounding COVID-19 is still the most concerning variable. Despite this, gradual improvements in both domestic and international consumption and the base effect of 2020 (due to negative growth) are expected to result in a 2021 growth rate of 3.2 percent. Exports are expected to return to an upward trend in the automobile, shipbuilding, and general machinery industries, while industries that maintained strong performances throughout the COVID-19 pandemic, including ICT devices, semiconductors, and rechargeable batteries, are expected to continue to do well. This analytical brief summarizes the outlook for the Korean and global economies in 2021.
IMQA: A Knowledge Graph Driven Question Answering System for Internal Medicine

The prevention and treatment of internal medicine diseases are a focus of public attention. The emergence of various medical websites makes online consultation a popular trend. Knowledge graph can construct the correlation between different knowledge and provide the data support for question answering systems. Therefore, to assist patients with self-diagnosis, a knowledge graph driven question answering system for internal medicine is constructed in this paper. In the implementation, the ontology schema of internal medicine diseases, which is the basis of knowledge graph construction, is designed. Multiple entities and relationships, extracted from the data on professional healthcare websites, are stored in the Neo4j. After the segmentation of the question, the Aho-Corasick automaton algorithm and semantic similarity are applied to entity recognition. The rule-based approach, an accurate way, is used to identify intentions. This intelligent system, which has high value for the treatment of internal medicine diseases, can effectively solve health issues and reduce the cost of the consultation.
Role of chiral symmetry in a kicked Jaynes-Cummings model

We have studied the role of chiral symmetry in a periodically kicked Jaynes--Cummings (KJC) model by freezing an initial phase. We show that commensurate kicks ($2\ensuremath{\pi}k$ periodicity with integer $k$) conserve the chiral symmetry in the KJC model under the resonant condition, while incommensurate kicks break the symmetry. The chiral symmetry preserves the phase of an initial state against phase fluctuations during the dynamical evolution, but broken chiral symmetry erases the initial phase. The frozen phase is preserved within a finite evolution time for slight deviations of the kick period from an integer multiple of $2\ensuremath{\pi}$ and small variations of detuning from the resonant condition. The chiral symmetry-protected phase information is noteworthy as it provides various uses in quantum computation and information.
Introduction

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from experiments, field measurements, and large-scale simulations at multiple spatiotemporal scales. Machine learning (ML) offers a wealth of techniques to extract ...Read More
Analysis and Implications of Trends in Lifelong Education Policies at Home and Abroad after COVID-19

The purpose of this study is to examine the overall trend of lifelong education after COVID-19 and major policies for lifelong education at home and abroad. we analyzed the cases of core policies and changes in lifelong education in Germany, the United States, the United Kingdom, and Japan. And the purpose of lifelong education in Korea and changes in the COVID situation were analyzed. Based on the research results, some suggestions for revitalizing lifelong education are as follows. First, it is inevitable to establish an online education system and infrastructure to support quality lifelong learning in response to the rapidly changing educational environment. Second, local fence activities for community lifelong education innovation should be activated and a lifelong learning ecosystem based on the network should be established. Third, customized programs for each life cycle emphasized in the lifelong education policy and support for free online education and training programs for the unemployed in preparation for an increase in the unemployment rate are also needed. Fourth, it is necessary to expand and operate public online lifelong learning to bridge the digital informatization gap and participate in lifelong learning for the socially vulnerable.
The Importance of Eye Movement Research for Gaining Insight into Morphological Processing

During the last 50 years, eye-tracking has become a highly popular method to study natural language processing. Especially recently, the number of eye-tracking studies has increased exponentially, as eye-trackers have become less expensive and more user-friendly. In the realm of morphology, eye-tracking is used to answer a number of important questions. In this chapter, we discuss how eye movement studies inform us about the temporal unfolding of morphological information during the processing of complex words. Moreover, we discuss studies demonstrating the role sentence context may have on morphological processing. Finally, we discuss eye-tracking studies on the role of morphology in more applied domains such as second language acquisition and language development, and among special populations. Here, the main focus is on how individual differences in morphological skills show up in the eye movement record. The final section demonstrates how broadly eye-tracking is used in studying morphology, and includes suggestions for further research.
EYE MOVEMENT TRACKING

Eye tracking is a sensor technology that can detect a person's presence and follow what they are looking at and blinks in real-time .The technology converts eye movements into a data stream that contains information such as pupil position, the number of blinks, and gaze point.Essentially, the technology decodes eye movements and translates them into insights that can be used in a wide range of applications or as an additional input modalityalso focus upon the effect of panel shade on the ecosystem.Eye tracking also would be a great biometric tool for analysis in various applications.In this paper we discuss eye tracking technology and its various applications.Now days, ET is being employed in almost all field including psychology, human computer interaction, marketers, designers, academics, medical, research and many more.
Building an annotated corpus for automatic metadata extraction from multilingual journal article references

Bibliographic references containing citation information of academic literature play an important role as a medium connecting earlier and recent studies. As references contain machine-readable metadata such as author name, title, or publication year, they have been widely used in the field of citation information services including search services for scholarly information and research trend analysis. Many institutions around the world manually extract and continuously accumulate reference metadata to provide various scholarly services. However, manually collection of reference metadata every year continues to be a burden because of the associated cost and time consumption. With the accumulation of a large volume of academic literature, several tools, including GROBID and CERMINE, that automatically extract reference metadata have been released. However, these tools have some limitations. For example, they are only applicable to references written in English, the types of extractable metadata are limited for each tool, and the performance of the tools is insufficient to replace the manual extraction of reference metadata. Therefore, in this study, we focused on constructing a high-quality corpus to automatically extract metadata from multilingual journal article references. Using our constructed corpus, we trained and evaluated a BERT-based transfer-learning model. Furthermore, we compared the performance of the BERT-based model with that of the existing model, GROBID. Currently, our corpus contains 3,815,987 multilingual references, mainly in English and Korean, with labels for 13 different metadata types. According to our experiment, the BERT-based model trained using our corpus showed excellent performance in extracting metadata not only from journal references written in English but also in other languages, particularly Korean. This corpus is available at http://doi.org/10.23057/47 .
Waterfall Model for Design and Development Coffee Shop Website at Malang

Along with the development of technology and information, various product transaction activities can be easily carried out by utilizing the internet network. Prospective buyers can make transactions faster with potential sellers via the internet without having to visit first. A company can easily sell a product through the website. Dua Ruang is a cafe selling drinks from coffee to mocktails in Malang City, Indonesia. The implementation of transactions at the Dua Ruang cafe still uses manual transactions where prospective buyers have to come to the cafe to buy their products, with this method less than optimal in business development. Based on these problems, we designed a website that will make transactions easier and digital. We use the Waterfall method for the design and design of the Dua Ruang website. This method starts with Requirements, Design, Development, Testing, and Evaluation. The test results show the website can function properly and can be used for further implementation. The existence of a website-based sales application will be the best solution for solving problems at the Dua Ruang Cafe so that it can increase sales turnover.
Nonlinear System Identification Based on an Online SCFNN With Applications in IoTs

In this paper, an online self-constructing fuzzy neural network (SCFNN) is proposed to solve four kinds of nonlinear dynamic system identification (NDSI) problems in the internet of things (IoTs). The SCFNN is capable of constructing a simple network without the need for knowledge of the NDSI. Thus, carefully setting conditions for the increased demands for fuzzy rules will make the architecture of the constructed SCFNN fairly simple. The applications of neural networks in IoTs are discussed. The authors also propose a new identification model for NDSI. Through an experimental example, it is proved that online learning can arrange membership functions in a more appropriate vector space. The performance of the online SCFNN is compared with both MLP and RBF through four extensive simulations. The comparison terms are convergence rate, training root mean square error (RMSE), test RMSE, and prediction accuracy (PA). The simulation results show that SCFNN is superior to MLP and RBF in NDSI problems.
Real-Time Sign Language Detection Leveraging Real-Time Translation

The outlook for the disabled people has been instantiated through the phenomena of analysing the gestures through signs which is converted in real-time into text. The aid to the people possessing an anomaly of being unable to hear or to speak through automation has been excruciatingly scrutinized over the past years. Diverse modus operandi has been induced for collation of Sign Language Detection with Text-to-Speech, escalating its utilization among common people having any disability. With the actuation of this prospect, these people can convey any message to the ones unknown to sign language. In the proposed articulation, we’ve tried to consummate the prowess of Convolutional Neural Networks for Sign Language Detection and thereby contemplating the efficiency through increasing the depth of the network. The output we got was phenomenal giving us a potent outlook for amalgamating it into the real-time.
A pupil detection method based on Unet with attention module and shape-prior loss

In recent years, pupil detection in human eye images or videos has played a key role in many fields. In the field of eye tracking, the position of the center of the pupil is a basic problem, and the error of pupil detection will be magnified in subsequent calculations, which will seriously affect the performance of eye tracking. In this paper, we propose to use the currently popular semantic segmentation network for pupil detection task. We first train the Unet architecture as a benchmark, then introduce two different attention modules into Unet, and compare with the benchmark network. The results show that our method has a higher detection rate within 1-15 pixel errors. We also added an ellipse fitting error term to the loss function of the network to further improve the network performance. The training of the model is done on the LPW dataset. Finally, we also investigate the effect of data augmentation on generalization performance, with the model trained on the LPW dataset and tested on the I-SOCIAL_DB dataset. Although data enhancement slightly reduces the detection rate of the model in the original data set, it can improve the generalization performance of the model.
Retracted: Anomaly detection with ensemble empirical mode decomposition and approximate entropy for quick user datagram protocol internet connection‐based distributed Blockchain systems

Abstract Retraction: [Yuanlong Cao, Keyang Gu, Junyi Wu, Xiang Zou, Lei Tao, Xin Huang, Changgen Jiang, Anomaly detection with ensemble empirical mode decomposition and approximate entropy for quick user datagram protocol internet connection‐based distributed Blockchain systems, IET Software 2023 ( https://doi.org/10.1049/sfw2.12096 )]. The above article from IET Software , published online on 20 January 2023 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Editor‐in‐Chief, Hana Chockler, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal’s peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract.
Studying the Parameters of Genetic Algorithms and Their Impact on Problems of Finding the Optimal Solution

Interest in artificial intelligence has recently increased, because of its proven competence and effectiveness in addressing many outstanding issues and problems, as it is a modern science that derives its concepts from simulating the style of thinking and analysis in humans. Genetic algorithms are a branch of this science, which requires that their determinants be selected according to the problem at hand. In this paper, we examined the effect of changing some of the determinants of genetic algorithms, namely mutation probability and population size, on the accuracy of results for three problems of different frequency spectrum. The effect of the election algorithm in obtaining accurate results was also studied, by comparing the roulette wheel algorithm and Elitism algorithm.
Digitalization of Education: Students’ Perception of Distance Learning and Risks

An important component of the interdisciplinary discussion of the technological transformation of society is the digitalization of university students learning, which has accelerated during the pandemic and caused a variety of assessments due to the numerous problems of its implementation. So far, there has not been a consensus on the relationship between distance learning opportunities and the associated socio-humanitarian risks that affect the identity and world outlook of modern students – generation of digital natives. The article deals with the stu­dents perception of the pros and cons of digital technologies, awareness of the impact of new technical means on an individual student and society as a whole. The analysis is carried out through methodological reflection of the so­ciological data of student surveys conducted in Belarus in 2021–2022. This anal­ysis revealed the different cognitive potential of using the online survey and free interview methods: in the second case, students could comment on the ques­tions themselves, while when using the online survey, in case of difficulties, stu­dents simply preferred not to answer. The identified trends raise the question of finding new methods of working with students that will allow them to be aware of the problems of digitalization.
A Qualitative Case Study on the Experiences of Youth in Out-of-Home Care After Court Notification of Juvenile Act

The purpose of this study is to deeply understand notification experiences of youths in out-of-home care through their voices and to suggest implications from the perspective of social welfare. This study investigated the specific aspects of youth''s experiences in notification and post-notification. Furthermore, this study tried to understand the experiences of youths in out-of-home care at a crossroads between the out-of-home care system and the juvenile justice system, searching for support strategies in both systems from the perspective of social welfare. To fulfill the research question, this study collected qualitative data through in-depth interviews with youths in out-of-home care who had notification experience and conducted a qualitative case study. The results of the study were as follows. Through cross-case analysis, the study tried to understand the notification experience and discover its meaning in the context of living conditions and situations around each point of notification experience of the participants. As a result, 12 meaning units, 4 subcategories, and 1 main topic were derived. The main topic covering the notification experiences of the participants was ‘drift and discriminated life by others’. This study suggested policy implications based on the research results and discussions.