Reviewerly/exHarmony huggingface dataset:
항공사의 확장된 서비스스케이프가 브랜드 충성도에 미치는 영향 연구

This study aimed to provide the direction and practical implications of strategic marketing necessary to maximize the competitiveness of airlines by identifying the interrelationships between the physical and social environments, perceived service values, customer satisfaction and brand loyalty of airlines. This study distributed a total of 360 copies through the online research platform Google Docs and offline, and used 322 copies for demonstration analysis. First, the analysis shows that the physical and social environment of the airline service-scape has a unique impact on both perceived service value. Second, the perceived service value has a significant influence on customer satisfaction and brand loyalty. Third, customer satisfaction was shown to have a significant impact on brand loyalty. In order to increase airline sales, the physical and social environment of the airline's service-scape should be well managed to ensure that customers are positively aware of the value of the service, thereby increasing brand loyalty and creating continuous repeat customers. This study is meaningful in that it theoretically suggested the need for service-scape scalability at a time when interest in social service-scape is increasing, and that it provided practical implications for securing a competitive advantage for airlines.
THE EDUCATION OF YOUNG PEOPLE ON THE BASIS OF A SPIRITUAL, MORAL AND TOLERANT CULTURE IN THE EDUCATIONAL PROCESS

This article highlights the philosophical significance of shaping a culture of tolerance in young people. The role of the family and society in the formation of a culture of tolerance among young people in connection with morality, as well as the large-scale work carried out in our country are highlighted. In addition, the importance of moral education in the educational process and the criteria for forming a culture of tolerance, the formation of a tolerant consciousness, tolerant worldview on the basis of spiritual-educational activities in educational institutions are covered.
Glyph: Visualization Tool for Understanding Problem Solving Strategies in Puzzle Games

Understanding player strategies is a key question when analyzing player behavior both for academic researchers and industry practitioners. For game designers and game user researchers, it is important to gauge the distance between intended strategies and emergent strategies; this comparison allows identification of glitches or undesirable behaviors. For academic researchers using games for serious purposes such as education, the strategies adopted by players are indicative of their cognitive progress in relation to serious goals, such as learning process. Current techniques and systems created to address these needs present a few drawbacks. Qualitative methods are difficult to scale upwards to include large number of players and are prone to subjective biases. Other approaches such as visualization and analytical tools are either designed to provide an aggregated overview of the data, losing the nuances of individual player behaviors, or, in the attempt of accounting for individual behavior, are not specifically designed to reduce the visual cognitive load. In this work, we propose a novel visualization technique that specifically addresses the tasks of comparing behavior sequences in order to capture an overview of the strategies enacted by players and at the same time examine individual player behaviors to identify differences and outliers. This approach allows users to form hypotheses about player strategies and verify them. We demonstrate the effectiveness of the technique through a case study: utilizing a prototype system to investigate data collected from a commercial educational puzzle game. While the prototype's usability can be improved, initial testing results show that core features of the system proved useful to our potential users for understanding player strategies.
Multi-objective parameter optimization of the HYPE model using shuffled frog-leaping algorithm

&amp;lt;p&amp;gt;The calibration of hydrological models using bio-inspired meta-heuristic optimization techniques has been extensively tested to find the optimal parameters for hydrological models. Shuffled frog-leaping algorithm (SFLA) is a population-based cooperative search technique containing virtual interactive frogs distributed into multiple memeplexes. The frogs search locally in each memeplex and are periodically shuffled into new memeplexes to ensure global exploration. Though it is developed for discrete optimization, it can be used to solve multi-objective combinatorial optimization problems as well.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In this study, a hydrological catchment model, Hydrological Predictions for the Environment (HYPE) is calibrated for streamflow and nitrate concentration in the catchment using SFLA. HYPE is a semi-distributed watershed model that simulates runoff and other hydrological processes based on physical as well as conceptual laws. SFLA with 200 runtimes and 5 memeplexes containing 10 frogs each is used to calibrate 22 model parameters. It is compared with manual calibration and Differential Evolution Markov Chain (DEMC) method from the HYPE-tool. The preliminary results of the statistical performance measures for streamflow calibration show that SFLA has the fastest convergence speed and higher stability when compared with the DEMC method. NSE of 0.68 and PBIAS of 7.72 are recorded for the best run of SFLA during the calibration of streamflow. In comparison, the HYPE-tool DEMC produced the best NSE of 0.45 and a PBIAS of -3.37 while the manual calibration resulted in NSE of 0.64 and PBIAS of 2.01.&amp;lt;/p&amp;gt;
Preserving High Value Legacy Collections for Future Research &amp;#8211; The McNaughton Collection

&amp;lt;p&amp;gt;During his long career in ionprobe geochemistry, Professor Neal McNaughton built up an impressive collection of samples. Professor McNaughton served as SHRIMP geochronologist for the Centre of Global Metallogeny at the University of Western Australia (1994-2005), the Western Australia Centre for Exploration Targeting (2005-2007), and the John de Laeter Centre (JdLC) at Curtin University (2007-2019), and upon his retirement he donated his collection of epoxy mounted samples to the GSWA. This collection of over 1000 mounts containing over 4000 samples is full of irreplaceable samples, representing over 20 years of geochronological research and development on the SHRIMP II in the JdLC. The collection is a highly valuable resource for future geochemical and geochronological research however, the entire collection lacked a digital footprint. When this project started there was a distinct lack of a unified approach for geoscience metadata or a template for preserving such a collection. In a jointly funded effort by AuScope, GSWA and Curtin University a digital sample catalogue of the collection with digitised materials was successfully created. We operated under the FAIR data principals and utilised International Geo Sample Numbers (IGSNs) as persistent identifiers to create the most impactful, accessible and visible product. The final catalogue, associated metadata and digital materials are now publicly available online on a number of digital platforms such as Research Data Australia and GSWA&amp;amp;#8217;s GeoVIEW.WA and the mounts are able to be borrowed from GSWA for future analysis. These efforts allowed the preservation of physical materials for future loans and analysis as well as visibility in our digital age. We will outline the template and workflow utilised by this project that can be used to preserve similarly high value collections and by current facilities, universities and researchers in their ongoing research, as well as insights for future efforts.&amp;lt;/p&amp;gt;
A Mutual Information Maximization Approach for the Spurious Solution Problem in Weakly Supervised Question Answering

Weakly supervised question answering usually has only the final answers as supervision signals while the correct solutions to derive the answers are not provided. This setting gives rise to the spurious solution problem: there may exist many spurious solutions that coincidentally derive the correct answer, but training on such solutions can hurt model performance (e.g., producing wrong solutions or answers). For example, for discrete reasoning tasks as on DROP, there may exist many equations to derive a numeric answer, and typically only one of them is correct. Previous learning methods mostly filter out spurious solutions with heuristics or using model confidence, but do not explicitly exploit the semantic correlations between a question and its solution. In this paper, to alleviate the spurious solution problem, we propose to explicitly exploit such semantic correlations by maximizing the mutual information between question-answer pairs and predicted solutions. Extensive experiments on four question answering datasets show that our method significantly outperforms previous learning methods in terms of task performance and is more effective in training models to produce correct solutions.
대학 일본어 교양교육 교재에 나타나는 조건표현에 관하여

It points out the lack of education of “to”, “ba”, “tara” and “nara”, which are the forms of conditional expression, and suggests a solution through what this investigates and analyzes how conditional expressions are handled in Japanese language textbooks used in the field of Japanese liberal arts course at universities, and examines whether Korean Japanese learners are properly trained on conditional expressions. Even for liberal arts course, all four forms must be presented at the elementary and intermediate level, but it need a step by step presentation that considers the difficulty level is required rather than a package presentation and the explanation of each form should be done in various ways through example sentences for different meanings and usages. And it is necessary to have an easy-to- understand education on whether to substitute for the 4 forms.
Criteria for the formation of spiritual education of students

Criteria and indicators of the formation of a student's spiritual and moral culture in a multicultural space are considered, a model of the process of forming a student's spiritual and moral culture in a multicultural educational environment is described. The data of a psychological and pedagogical experiment, an assessment of the effectiveness of creating pedagogical conditions after the introduction of the developed model into the educational process of the university are presented. The introduction of a model for the formation of a student's spiritual and moral culture in a multicultural educational environment made it possible to increase the level of the knowledge system, motives, attitudes, value and moral orientations of students, and the ability to interact with partners from other cultures. A positive dynamics is noticed in the student's attitude to the field of culture offered for study, its specific content, in readiness for self-realization and self-development.
Orchestrating a stateful application using Operator

Containerization is a leading technological advancement in cloud-native developments. Virtualization isolates the running processes at the bare metal level but containerization isolates the processes at the operating system level. Virtualization encapsulates all the new virtual instances with a new operating system but containerization encapsulates the software only with its dependencies. Containerization avoids the problem of dependency missing between different operating systems and their distributions. The concept of containerization is old but the development of open-source tools like Docker, Kubernetes, and Openshift accelerated the adaption of this technology. Docker builds container images and Openshift or Kubernetes is an Orchestrating tool. For stateful applications, Kubernetes workload resources are not a better option to orchestrate the application, as each resource has its own identity. In such cases, the operator can be built to manage the entire life cycle of resources in the Kubernetes cluster. Operator combines human operational knowledge into software code in a systematic way. The paper discusses the default control mechanism in Kubernetes and then it explains the procedure to build the operator to orchestrate the stateful application.
Cross-domain error minimization for unsupervised domain adaptation

Unsupervised domain adaptation aims to transfer knowledge from a labeled source domain to an unlabeled target domain. Previous methods focus on learning domain-invariant features to decrease the discrepancy between the feature distributions as well as minimizing the source error and have made remarkable progress. However, a recently proposed theory reveals that such a strategy is not sufficient for a successful domain adaptation. It shows that besides a small source error, both the discrepancy between the feature distributions and the discrepancy between the labeling functions should be small across domains. The discrepancy between the labeling functions is essentially the cross-domain errors which are ignored by existing methods. To overcome this issue, in this paper, a novel method is proposed to integrate all the objectives into a unified optimization framework. Moreover, the incorrect pseudo labels widely used in previous methods can lead to error accumulation during learning. To alleviate this problem, the pseudo labels are obtained by utilizing structural information of the target domain besides source classifier and we propose a curriculum learning based strategy to select the target samples with more accurate pseudo-labels during training. Comprehensive experiments are conducted, and the results validate that our approach outperforms state-of-the-art methods.
High Accuracy in Forex Predictions Using the Neural Network Method Based on Particle Swarm Optimization

In forex trading, trader has to predict the risk in forex transaction and how to gain or increase the profits based on analysis. The purpose of this study is to predict the value of the USD against the IDR by comparing the neural network method with the neural network method based on Particle Swarm Optimization (PSO) to find out which level of accuracy is higher. This method was chosen by the author after reading several previous studies using PSO-based Neural Networks showing a higher level of accuracy compared to using Neural Networks without PSO-based. From the results of the study it was found that predictions using Neural Networks strengthened with PSO resulted in very high accuracy.
유관순 열사와 스승 사애리시 선교사의 리더십 비교연구

Ryu, Gwan-sun was born in Chunan in 1902 and died in 1920 and most Korean people think of her “female fighter against Japan for national independence. Ryu, Gwan-sun, daughter of Ryu, Jung-kwon was admitted to Ewha School as scholarship holder in 1918 and led a protest against Japanese imperialism participating in March First Independence Movement along with her peers in 1919 and was arrested by Japanese army and was sentenced to three years in prison and died for Korea in Seodaemun prison at the age of eighteen. Several people led her to be a fighter against Japan for national independence and one of them is Saerisi (real name: Alice H. Sharp), missionary and her mentor. Saerisi spent thirty nine years in Korea in her life of one hundred and one years and her achievement in Korea was great but her contribution to Korea’s independence was little known and it can be found only in the history of methodist church. Around 1910 when Josun was merged with Japan by force, Saerisi who was female missionary who belonged to methodist church and Ryu Gwan-sun, who was patriotic martyr and a symbol of female fighter for independence were destined to meet. Saerisi had Ryu, Gwan-sun admitted to Yeongmyung Girl School which she established and taught her and had her transferred to Ewha School as scholarship holder in 1916. Saerisi taught Ryu, Gwan-sun text in the Bible and became an example to her so that she practiced the text in the Bible at all times and led her to be a symbol of female fighter for independence. This study aims to examine an influence which Saerisi had on Ryu, Gwan-sun, patriotic martyr in her growing as a fighter for independence. In addition, this study will examine what led her to actively participate in independence movement without any hesitation and her mature piety and character and compare her leadership with her mentor’s leadership. This study expects that Saerisi who sacrificed for independence of Korea without any fame and name and spent her youth on Korean independence will be known and their relationship as mentor and mentee will get spotlight newly and her influence on Ryu, Gwan-sun in her growth as a female fighter for independence.
Underwater high-precision panoramic 3D image generation

As an indispensable part of underwater intelligent operations, underwater unmanned system is an important research direction for consolidating China’s ocean security, national defense security, and helping the country’s great strategy of “ marine power “. Its environmental awareness and cognitive capabilities will become an understanding Fundamental and key issues of the ocean and the economic ocean. Existing systems have great deficiencies in underwater perception and detection capabilities, and they cannot all meet the requirements of operations in complex underwater changing environments. This article conducts technical research on the generation of underwater high-definition panoramic 3D data. Therefore, the distributed sonar and binocular vision hybrid system is used as the perception source, and the efficient data access strategy is adopted to update the weight of the corresponding voxel. The integrated low noise high dynamic range chip is used as the imaging terminal, and the underwater optical communication is used to complete many High-precision stereo vision measurement of scenes with point coordination and the generation of refined panoramic 3D data.
Security Analysis against Differential Cryptanalysis using Active S-Boxes

Cryptography is a technique that uses mathematics for encryption of original data followed by decryption of encrypted data. Cryptography enables the impregnability of information while it passes through the Internet or wireless medium with more concern for security. It is highly challenging to execute the conventional cipher technique algorithms in a resource-constrained environment attributable to their size, speed and throughput. The Lightweight cipher technique is an algorithm that features a low footprint and computational complexity. In cipher methods, the substitution box acts major role in the encryption/decryption algorithm. The procedure of creating powerful S-boxes in cryptographic algorithm never end. Various methods are opened for creating a powerful S-box leading difficulty for an attack. This paper depicts a key-based dynamic Ssubstitution box for every round of encryption in PRESENT algorithm. Differential Cryptanalysis attack is verified with the quantity of active S-boxes in each round.
Fully Abstract and Robust Compilation and How to Reconcile the Two, Abstractly

The most prominent formal criterion for secure compilation is full abstraction, the preservation and reflection of contextual equivalence. Recent work introduced robust compilation, defined as the preservation of robust satisfaction of hyperproperties, i.e., their satisfaction against arbitrary attackers. In this paper, we initially set out to compare these two approaches to secure compilation. To that end, we provide an exact description of the hyperproperties that are robustly satisfied by programs compiled with a fully abstract compiler, and show that they can be meaningless or trivial. We then propose a novel criterion for secure compilation formulated in the framework of Mathematical Operational Semantics (MOS), guaranteeing both full abstraction and the preservation of robust satisfaction of hyperproperties in a more sensible manner.
Scene-Text Analysis

Scene text analysis is the process of analyzing text in scene images for different applications. Scene text detection, recognition, and spotting methods have gained great significance in computer vision and multimedia research groups. Scene text analysis is widely accepted because of different real-life applications, like real-time traffic sign recognition, autonomous vehicles, blind navigation assistance, and multilingual translation. This is however a challenging task since scene text regions have a large variation of scale, aspect ratio, orientation, color, language, font, and script. Such text instances can also be in horizontal, oriented, and curved forms. Therefore, we illustrated various scene text detection, recognition, and spotting mechanism that can handle the traditional shortcomings in the scene text analysis. We have also explained the several deep network architectures that are utilized in the current scenario for scene text analysis. We also compare the detection, recognition, and spotting results for popular approaches on the publicly available scene text datasets.
Contrastive Semantic Similarity Learning for Image Captioning Evaluation with Intrinsic Auto-encoder

Automatically evaluating the quality of image captions can be very challenging since human language is quite flexible that there can be various expressions for the same meaning. Most of the current captioning metrics rely on token level matching between candidate caption and the ground truth label sentences. It usually neglects the sentence-level information. Motivated by the auto-encoder mechanism and contrastive representation learning advances, we propose a learning-based metric for image captioning, which we call Intrinsic Image Captioning Evaluation($I^2CE$). We develop three progressive model structures to learn the sentence level representations--single branch model, dual branches model, and triple branches model. Our empirical tests show that $I^2CE$ trained with dual branches structure achieves better consistency with human judgments to contemporary image captioning evaluation metrics. Furthermore, We select several state-of-the-art image captioning models and test their performances on the MS COCO dataset concerning both contemporary metrics and the proposed $I^2CE$. Experiment results show that our proposed method can align well with the scores generated from other contemporary metrics. On this concern, the proposed metric could serve as a novel indicator of the intrinsic information between captions, which may be complementary to the existing ones.
Domain-Smoothing Network for Zero-Shot Sketch-Based Image Retrieval

Zero-Shot Sketch-Based Image Retrieval (ZS-SBIR) is a novel cross-modal retrieval task, where abstract sketches are used as queries to retrieve natural images under zero-shot scenario. Most existing methods regard ZS-SBIR as a traditional classification problem and employ a cross-entropy or triplet-based loss to achieve retrieval, which neglect the problems of the domain gap between sketches and natural images and the large intra-class diversity in sketches. Toward this end, we propose a novel Domain-Smoothing Network (DSN) for ZS-SBIR. Specifically, a cross-modal contrastive method is proposed to learn generalized representations to smooth the domain gap by mining relations with additional augmented samples. Furthermore, a category-specific memory bank with sketch features is explored to reduce intra-class diversity in the sketch domain. Extensive experiments demonstrate that our approach notably outperforms the state-of-the-art methods in both Sketchy and TU-Berlin datasets. Our source code is publicly available at https://github.com/haowang1992/DSN.