Reviewerly/exHarmony huggingface dataset:
Deep Preset: Blending and Retouching Photos with Color Style Transfer

End-users, without knowledge in photography, desire to beautify their photos to have a similar color style as a well-retouched reference. However, the definition of style in recent image style transfer works is inappropriate. They usually synthesize undesirable results due to transferring exact colors to the wrong destination. It becomes even worse in sensitive cases such as portraits. In this work, we concentrate on learning low-level image transformation, especially color-shifting methods, rather than mixing contextual features, then present a novel scheme to train color style transfer with ground-truth. Furthermore, we propose a color style transfer named Deep Preset. It is designed to 1) generalize the features representing the color transformation from content with natural colors to retouched reference, then blend it into the contextual features of content, 2) predict hyper-parameters (settings or preset) of the applied low-level color transformation methods, 3) stylize content to have a similar color style as reference. We script Lightroom, a powerful tool in editing photos, to generate 600,000 training samples using 1,200 images from the Flick2K dataset and 500 user-generated presets with 69 settings. Experimental results show that our Deep Preset outperforms the previous works in color style transfer quantitatively and qualitatively.
The High Performance Image Encryption Method Based on HAD-L

With the vast development of the communication medium, the use of digital data is increased rapidly. The lots of development increased and also side by side illegal disruptions are also be developed for misuse of digital data. For that case specifically for digital images, developed the HAD-L (Harmonic Level, systematic Block Level, and Additional Data Level) of encryption system. The first level with customized encryption with specified harmonic encryption properties. The second level of the encryption process with the higher block level vector scheme. Then that additional data also combined with the encryption scheme to deliver the third level of the encryption process. The overall effect of the scheme will deliver a robust encryption scheme among the communication channels. The experimental results show that the good randomness effect of the scheme and also deliver the pseudorandom properties for the key evaluations. Furtherly results from the security analysis and capacity analysis show much more performance among transmission channel.
2015 개정 영어과 교육과정에 따른 중2 영어 교과서 어휘 분포 분석

This study investigates how the vocabulary in Middle School English 2 textbooks is distributed in order to see the effect of vocabulary control of 2015 Revised English National Curriculum on vocabulary size in different textbooks. Listening and reading texts of seven textbooks were analyzed in terms of lexical sophistication, lexical density, and the frequency of new words. The results show quite a big difference in vocabulary size across textbooks that have been under the 90% vocabulary control of 2015 Revised English National Curriculum; both the frequency of graded word tokens and the number and presentation of advanced word types are significantly different. The amount of content words in reading texts also varies significantly. A few suggestions are made for further research along with limitations of the study.
Continuous Improvement Practices in Higher Education

The following sections are included:IntroductionKaizen — Successes and Shortcomings in Higher Education SectorTQM — Successes and Shortcomings in Higher Education SectorSix Sigma — Successes and Shortcomings in Higher Education SectorLean Methodology — Successes and Shortcomings in Higher Education SectorLSS in Higher Education SectorSummary, Discussion and Directions for ResearchReferences
Long-tail Session-based Recommendation

Session-based recommendation focuses on the prediction of user actions based on anonymous sessions and is a necessary method in the lack of user historical data. However, none of the existing session-based recommendation methods explicitly takes the long-tail recommendation into consideration, which plays an important role in improving the diversity of recommendation and producing the serendipity. As the distribution of items with long-tail is prevalent in session-based recommendation scenarios (e.g., e-commerce, music, and TV program recommendations), more attention should be put on the long-tail session-based recommendation. In this paper, we propose a novel network architecture, namely TailNet, to improve long-tail recommendation performance, while maintaining competitive accuracy performance compared with other methods. We start by classifying items into short-head (popular) and long-tail (niche) items based on click frequency. Then a novel is proposed and applied in TailNet to determine user preference between two types of items, so as to softly adjust and personalize recommendations. Extensive experiments on two real-world datasets verify the superiority of our method compared with state-of-the-art works.
Security Enhancement and Deduplication Using Zeus Algorithm Cloud

Data deduplication method allows the cloud users to manipulate their cloud storage efficiently by avoiding the storage of certain repeated data having the same bandwidth. To ensure that the data is secure and confidential, and the data is stored by encrypting it. We can use various encryption types like the RSA, DES, and AES. Convergent encryption algorithm is commonly used to retrieve storage capacity and lower data upload throughput and yet poses two challenges. The first problem is that they might be unsafe for statistical attack. The other thing is that they have convergent key management problems. The current paper assists in the usage of key-sharing approach which is verified by the proof of ownership to rectify these problems. As in only the initial data uploader, the holder encrypts the data with a randomly selected convergent key and then disperses the key in the cloud, and the key can only be accessed by users who have the reported data. In the existing framework, we found out that in the convergent encryption, the key of any given data can be generated by anyone in a deterministic way. An attacker can generate a key from plaintext encrypt the plain text and check if the resulting cipher text is already existing or not. Thus to overcome the issues, we propose the much more secure Advanced Encryption Standard (AES) without the third-party administrator (TPA) and to reduce the burden of computational overhead on the admin. We have used the Zeus algorithm to stop the duplicate data string in the database. Zeus algorithm offers better deduplication process and work to reduce the computational overhead of the admin. Analysis shows that this design is more effective, and the proposed security model remains stable.
ZiMM: a deep learning model for long term and blurry relapses with non-clinical claims data

This paper considers the problems of modeling and predicting a long-term and ``blurry'' relapse that occurs after a medical act, such as a surgery. The relapse is observed only indirectly, in a ``blurry'' fashion, through longitudinal prescriptions of drugs over a long period of time after the medical act. We introduce a new model, called ZiMM (Zero-inflated Mixture of Multinomial distributions) in order to capture long-term and blurry relapses. On top of it, we build an end-to-end deep-learning architecture called ZiMM Encoder-Decoder (ZiMM ED) that can learn from the complex, irregular, highly heterogeneous and sparse patterns of health events that are observed through a claims-only database. ZiMM ED is applied on a ``non-clinical'' claims database, that contains only timestamped reimbursement codes for drug purchases, medical procedures and hospital diagnoses, the only available clinical feature being the age of the patient. This setting is more challenging than a setting where bedside clinical signals are available. Our motivation for using such a non-clinical claims database is its exhaustivity population-wise, compared to clinical electronic health records coming from a single or a small set of hospitals. Indeed, we consider a dataset containing the claims of almost \emph{all French citizens} who had surgery for prostatic problems, with a history between 1.5 and 5 years. We consider a long-term (18 months) relapse (urination problems still occur despite surgery), which is blurry since it is observed only through the reimbursement of a specific set of drugs for urination problems. Our experiments show that ZiMM ED improves several baselines, including non-deep learning and deep-learning approaches, and that it allows working on such a dataset with minimal preprocessing work.
Library: Finding Science and Engineering Data: Home

A guide to sources for Science and Engineering data, with a particular emphasis on the Knovel Library database
Model-based Clustering using Automatic Differentiation: Confronting Misspecification and High-Dimensional Data.

We study two practically important cases of model based clustering using Gaussian Mixture Models: (1) when there is misspecification and (2) on high dimensional data, in the light of recent advances in Gradient Descent (GD) based optimization using Automatic Differentiation (AD). Our simulation studies show that EM has better clustering performance, measured by Adjusted Rand Index, compared to GD in cases of misspecification, whereas on high dimensional data GD outperforms EM. We observe that both with EM and GD there are many solutions with high likelihood but poor cluster interpretation. To address this problem we design a new penalty term for the likelihood based on the Kullback Leibler divergence between pairs of fitted components. Closed form expressions for the gradients of this penalized likelihood are difficult to derive but AD can be done effortlessly, illustrating the advantage of AD-based optimization. Extensions of this penalty for high dimensional data and for model selection are discussed. Numerical experiments on synthetic and real datasets demonstrate the efficacy of clustering using the proposed penalized likelihood approach.
Manage your Data

Digital humanities (DH) research benefits from the integration of social and scientific research methods and tools. Multidisciplinary methodologies regularly facilitate the production and use of increasingly large datasets and complex technical infrastructures. The growing potential for multidisciplinary research and innovation has brought more attention to a broader interest in traditional humanities and humanities data. (ECAR;2017). The problem: DH research relies heavily on data collected and analysed for increasingly diverse reasons. While multidisciplinary methods and practices offer immense opportunities for innovation, there is, simultaneously, a higher likelihood that researchers will struggle to communicate strategies for curating and preserving their data. What can be done? Research funding agencies are acknowledging the value and importance of research data management strategies, but have done little to reconcile the multi or interdisciplinary nature of DH research. Acknowledging and tackling the challenge of data management and digital curation is not, thankfully, an unknown issue. For at least as long as DH has existed as an area of research and work, information professionals (e.g. librarians, archivists and research data managers) have investigated and developed strategies for data management. The Academic Libraries and Research Data Services outlines that information professionals can aid DH researchers in the areas of data discovery, mining, analysis, metadata creation, selection, curation, and research partnerships. These partnerships can result in the creation of research data services departments and DH labs in academic libraries and facilitate data management services to researchers who need support with the creation and curation of their research data. However, despite emerging best practices in the information professions, researchers are not always aware of them. To address this potential roadblock, our chapter presents methods for negotiating interests, needs and understandings of research data management.
The Investigation of ICT Needs in the Learning Process of Digital Natives

The study aims to investigate and analyze the needs of information and communications technology (ICT) in the learning process of digital-native students of the Faculty of Economics, Universitas Negeri Padang in Indonesia. The data were collected by using a structured questionnaire, and it was analyzed undergoing the descriptive method. The results of this study indicated that the majority of students use technology in the learning process, such as smartphone and laptop all the time. In general, students prefer using WhatsApp and e-learning to support their learning activity. The adoption of technology is affected by easiness to use and provide a piece of fast and comprehensive information. Therefore, they believe that the use of technology is relatively high within the learning process of the digital natives that technology integration in education is highly demanded.
LibGuides: Universal Citation: Caselaw Access Project

A South Carolina focused guide to discussions of citation reform, sometimes referred to as vendor neutral citation, medium neutral citation, uniform citation, or public domain citation.
ERROR ANALYSIS IN USING SIMPLE PRESENT TENSE IN DESCRIPTIVE PARAGRAPHS MADE BY THE SEVENTH GRADE STUDENTS OF SMP KATOLIK TRISAKTI 1 MEDAN

This research method used a case study method. The subject of this included the Seventh Grade Students of SMP Katolik Trisakti 1 Medan. Which is comprised of 30 stusents. In analysis the datan the writer collected the data from the students identifiedthe grammatical errors, classified the errors of using simple present tense in the studentsâ€™ descriptive paragraph to determine the frequency of errors, and calculated the data into percentages. The data by using formula:P= fnx100%Â P= Percentage, F= Frequency of error made, N= Total of studentsâ€™ errors.the data was taken from the test: it was written test. The result demonstrate that the studentsâ€™ errors can be categorized into four kinds of errors. Which include 14,7% errors of omission, 6,7% errors of additon, 73,3% errors in misformation and 5% error in misordering. The writer observed 218 total errors. The witer concluded that misformation was the dominant kind of errors Made by the Seventh Grade of SMP Katolik Trisakti 1 Medan. The teacher recommended practicing grammatical rules in Simple Present Tense in order to minimize the errors. Morever, other researchers can provide techniques in increase studentsâ€™ mastery of simple present tense.
The books with ex-libris in the rare book fund in the research library of Ulyanovsk State Pedagogical University named after I. N. Ulyanov

An ex-libris (a bookplate) is a valuable historical and cultural information resource. It helps to determine the ownership of a book in a particular collection, allows the understanding the history of existence of a copy that had several owners, and clarification the degree of its uniqueness. The purpose of the article is to analyze the bookplates available on the book rarities of the first quarter of the XIX century from the collection of the research library of Ulyanovsk State Pedagogical University named after I. N. Ulyanov. By means of the identification of book signs, the belonging of rare publications to private collections and the peculiarities of existence of each particular copy in the history of national culture are established. In particular, one of the books belonging to the lost Usolskaya library of count V. P. Orlov-Davydov, dispersed among the libraries of the country and reconstructed by specialists, was identified. Two other books were the part of private libraries of historical figures who were in fairly close relations with prominent figures of Russian literature (the poet M. Yu. Lermontov) and science (the inventor of radio A. S. Popov). The results of the research can expand the understanding of the uniqueness of rare copies of Russian and foreign publications from the Fund of the research library of the Ulyanovsk State Pedagogical University, such as” Selected spiritual works of Fenelon, Archbishop of Cambrai” (1820–1821), “Nouveau dictionnaire géographique universel” (New dictionary of geographical names) by Jacques McCarthy (1824) and «Histoire de France» (History of France) by Louis-Philippe Segur.
Learning discrete distributions: user vs item-level privacy

Much of the literature on differential privacy focuses on item-level privacy, where loosely speaking, the goal is to provide privacy per item or training example. However, recently many practical applications such as federated learning require preserving privacy for all items of a single user, which is much harder to achieve. Therefore understanding the theoretical limit of user-level privacy becomes crucial. We study the fundamental problem of learning discrete distributions over $k$ symbols with user-level differential privacy. If each user has $m$ samples, we show that straightforward applications of Laplace or Gaussian mechanisms require the number of users to be $\mathcal{O}(k/(mα^2) + k/εα)$ to achieve an $\ell_1$ distance of $α$ between the true and estimated distributions, with the privacy-induced penalty $k/εα$ independent of the number of samples per user $m$. Moreover, we show that any mechanism that only operates on the final aggregate counts should require a user complexity of the same order. We then propose a mechanism such that the number of users scales as $\tilde{\mathcal{O}}(k/(mα^2) + k/\sqrt{m}εα)$ and hence the privacy penalty is $\tildeΘ(\sqrt{m})$ times smaller compared to the standard mechanisms in certain settings of interest. We further show that the proposed mechanism is nearly-optimal under certain regimes. We also propose general techniques for obtaining lower bounds on restricted differentially private estimators and a lower bound on the total variation between binomial distributions, both of which might be of independent interest.
Vocational Education at the University: Renewal Priorities

The priorities of updating vocational education at a university are disclosed. The characteristic of the university as a historically proven system of higher education is given. In the historical and pedagogical analysis revealed a variety of types of higher schools in different eras. The saturation line of the higher education level of the professional component in domestic and foreign experience is traced. The experience of continuing the traditions of vocational education at Gzhel State University is given.
Enhance Advertisement for Offline Shopping by Using BLED One Touch Information

We develop a system and a phone application using BLE Device technology to facilitates the analysis of result by stakeholder in order to highlight shortcoming in the service. We use a Bluetooth low energy (BLE) based BLE Device. This system consists of a smartphone application that senses the user location through the BLE Device technology to deliver relevant content based on the location. It also includes web-based platform with two interfaces. The system announces the next direction to go to the destination at the right place and timing. We think that this type of particular place guidance can be realized when the Device module place two types of role: Quite and Notified Device modules. We introduce an historical guidance system by placing these modules along passage ways in a historical places appropriately to change the contents of advertised information displayed on the smartphone depending on the history of that place. This method for changing the contents of advertised information enables to navigating pedestrians to their destination.
Introducing Adaptive Analysis Methods: The Present Analysis &amp;amp; Future Analysis

Mastery of theory, analysis, approach models and decision making, is increasingly important, so finding analytical methods to achieve something better is a logical consequence.For this reason, adaptive analysis method is introduced, which is a method of dynamizing SWOT analysis using commutative properties with the operation of the cross-axis coordinate (X; Y), based on the theory of lines and rules of a sword, phenomenal, natural and relevant, to obtain a grand strategy chosen as a unified strategy like two sides of a coin.The method used is the theoretical and empirical study of adaptive analysis on one of the global issues, namely phenomenal terrorism until now.It is hoped that this method can be used in a variety of problems and is highly determined how a talent plays a role and is not intended to criticize existing analytical methods that have been used.
Distortion of Nepalese Culture and Social Norms in Upadhyay’s Buddha’s Orphans

This research article is mainly focused on how Samrat Upadhyay has distorted Nepalese Culture and social norms in is novel Buddha’s Orphans. Nepalese culture and society are represented in a biased way from the western perspective. By using oriental stereotypes, the author has represented Nepal and the religious belief of Nepalese people in the wrong way. The characters of this novel like Bokey Ba, Kaki, Raja, Jamuna, and Ganga Da are presented as irrational and badly conservative people who believe in witch doctors, not in the medical doctors. They are presented as impulsive and eccentric. The setting of this novel is represented as exotic, inscrutable, and mysterious. Kathmandu valley and Nepalese people didn’t well describe as the real Kathmandu. As a qualitative research, the researcher has taken the novel as a primary text and the concepts from orientalism, and the Foucauldian notion of representation is used as a tool to interpret the text. The researcher concluded that the novel is the misrepresentation of Nepalese culture and society. &#x0D;
Parameter estimation by type-2 fuzzy logic in case that data set has outlier

One of the problems encountered in estimating the unknown parameters of the regression models is the presence of outliers in the data set.This situation may cause problems in providing some assumptions such as the normal distribution for the parameter estimation process and the homogeneity of the variances.The case of the presence of outlier observations in the data set, estimation methods based on fuzzy logic that can be minimized the level of impact of this data are emerged as available methods.If fuzzy logic is used in regression analysis, there are two main steps for parameter estimation.The …rst of these is to de…ne the clusters that compose the data set, and the other is calculate the degree of membership to determining the contributions of the data to each model for the clusters.In this study, type-2 fuzzy clustering algorithm de…ned as an expansion of fuzzy c-means algorithm in the determination of membership degrees of data sets was bene…ted.The presence of outliers in the data set is addressed.An algorithm has been proposed to estimate the unknown belonging to parameters of the regression model using the membership degrees obtained relating to the cluster elements.The parameters were estimated using regression methods to examine the e¤ectiveness of the algorithm that called robust methods, and the results obtained were compared.