Reviewerly/exHarmony huggingface dataset:
내용교과를 위한 문식성에서의 어휘 교육 양상 고찰

This study reviewed the feature of literacy and teaching vocabulary suggested in Korean language education based on the discussion of literacy that can be approached at the content subject, and through this, it was intended to confirm the direction of vocabulary education on content subject.To this end, I first looked at the features of content area literacy and disciplinary literacy, and the literacy of CCSS reflecting this, and confirmed the content and direction of the vocabulary education they emphasized. Also, based on the perspectives of literacy and curriculum literacy in the content area, I reviewed the educational approach of literacy and vocabulary related to content subject reflected in Korean language education.This study suggested the following three approaches to the vocabulary education for content subject. First, both the vocabulary acquisition strategy and the vocabulary itself should be the purpose of teaching vocabulary for content subject. Second, it is necessary to clarify the class of vocabulary to be noticed and emphasized in literacy for content subject and to materialize the content of the education. Third, it is necessary to expand the approach to vocabulary emphasized by the disciplinary literacy in the content subject literacy activity.
Conception of Campus Security Surveillance and Alarm System Based on Moving Object Detection

In the massive amount of surveillance video data, users only need to extract a small amount of useful information of key events, and these events are only small-probability events. How to obtain the information you want from a huge amount of data, or how to achieve intelligent monitoring of the events that have occurred, has become a new research direction for security surveillance systems. This article explores an optimized object search calculation method for a hybrid Gaussian model. This calculation method uses the background difference method to find moving objects, and uses an optimized hybrid Gaussian model to reconstruct the background picture, which significantly improves the recognition accuracy of moving target detection. In the past, monitoring only stored audio and video without alarming itself. The situation has changed, which makes the security monitoring system able to provide real-time alarm messages for the school's security staff on duty, and it is hoped that the campus's emergency response capabilities can be significantly improved.
Hybrid Attention Based Neural Architecture for Text Semantics Similarity Measurement

Text semantics similarity measurement is a crucial problem in many real world applications, such as text mining, information retrieval and natural language processing. It is a complicated task due to the ambiguity and variability of linguistic expression. Previous studies focus on modeling the representation of a sentence in multiple granularities and then measure the similarity based on the representations. However, above methods cannot make full use of the diverse importance of different parts in a sentence. To address this problem, in this paper we propose a neural architecture with hybrid attention mechanism to highlight the important signals in different granularities within a text. We first utilize a Bi-directional Long Short Term Memory (BiLSTM) network to encode each sentence. Then we apply the hybrid attention mechanism on top of BiLSTM network. To detect the important parts of a sentence, we adopt a self-attention component to generate sentence level representations and then measure their relevance with a neural tensor network. To better utilize the interaction information, we devise an inter-attention component to further consider the influence of one sentence on another when modeling finer granularity interactions. We evaluate our proposed method on the task of paraphrase identification using two real world datasets. Experimental results demonstrate the superiority of this framework.
Greedy algorithm for image quality optimisation based on turtle-shell steganography

Information hiding, also known as data hiding, is an emerging field that combines multiple theories and technologies. In recent years, Chang et al. and Liu et al. have proposed new data hiding schemes based on Sudoku, a turtle-shell, etc. These proposed schemes have their own advantages in terms of visual quality and embedded capacity. However, the reference matrices used in these schemes are not optimal. Based on the characteristics of these schemes, Jin et al. employed particle swarm optimisation to select the reference matrix and achieved approximately optimal results in reducing the distortion of the stego-image. However, the complexity is high. In this paper, a turtle-shell matrix optimisation scheme is proposed using a greedy algorithm. The experimental results show that our proposed greedy algorithm is better than the particle swarm optimisation scheme at finding a near-optimal matrix and achieving better stego-image quality, and it outperforms the particle swarm optimisation scheme in terms of computational amount and efficiency.
Development of the Learning Module Book of English for Specific Purposes Course to Realize Relevance and Meaningfulness

This research aims to develop Learning Moduls that support a learning model designed to integrate the theory and the implementation in which relevance and meaningfulness of English for Specific Purposes (ESP) course can be achieved. Therefore, the problem of this research is: What kind of learning module book of English for Specific Purposes course to realize relevance and meaningfulness? This research used Research and Development method that aims to develop and validate products in education. Steps of development method that will be used include (1) collecting pre-development data, (2) designing the product prototype, (3) verifying the product to experts who are competent at the product, (4) revising the product into the product prototype 2, (5) applying the product for obtaining qualitative evaluation, and (6) revising the product into the final product that has been validated after having steps of expert verification and implementation. The research data were qualitative and quantitative data that had been verified by experts and evaluated by students in terms of how the product designed can realize relevance and meaningfulness of the integration between theories and real implementation. The development research result is in accordance with the objective in which the result is an ESP learning module book for students of English Language Education Study Program of Sanata Dharma University.
THE INFLUENCE OF WHIP AROUND TECHNIQUE TOWARD WRITING DESCRIPTIVE PARAGRAPH OF THE TENTH GRADE STUDENTS OF STATE SENIORHIGH SCHOOL 4 OF PALEMBANG

The objective of this study was to find out whether or not there is any significant influenceon studentâ€™s achievement in writing descriptive paragraph tothe tenth grade students of State Senior High School 4 Palembang who are taught through Whip Around Technique and those who are not. Thi study applied quasi-experiment method with nonequivalent control group design. The population of this study was all of the tenth grade students of State Senior High School 4 of Palembang, in the Academic Year 2018/2019 with total number 320 students and 64 students were taken as sample in experimental and control group. In collecting the data, a written test of descriptive paragraph was used, and the data were analyzed by using SPSS 22. The result of this study showed that experimental group studentsâ€™ average score in pretest was 64.37 and in posttest it was 77.93 while in control group, studentsâ€™ average score in pretest was 64.53 and in posttest it was 66.78. The value t-obtained was (11.392) which was higher than the value of t-table (2.000) at the significant level 0.05 in two tailed test with 62 (df). It was assumed that the null hypothesis (H0) was rejected, and the alternative hypothesis (Ha) was accepted. It means that there was significant influenceon studentâ€™s achievement in writing descriptive paragraph tothe tenth grade students of State Senior High School 4of Palembang who were taught through Whip Around Technique.Â Keywords: Teaching, Writing, Descriptive Text, Whip AroundÂ Â Â Â Â Â Â
Composition of channels

As we saw in Chap. 6, our framework for quantitative information flow can provide a robust theory for deriving security properties from a system’s representation as a channel, as long as we can determine what that channel is — but determining an appropriate channel to model a system is itself often a non-trivial task.
Guides: Subject Guide: Economics and Finance: Home

A guide to getting the most out of the Library and Collections resources for Economics and Finance
AI Finance and Blockchain Combine Medical Finance

As we all know, artificial intelligence has long been limited not only to the field of science and technology, but also to various functional departments of many traditional industries and enterprises. In recent years, with the rise of machine learning and deep learning technology, the application of artificial intelligence in various industries has accelerated, and the financial industry is no exception. Coincidentally, the world is also setting off a wave of innovation based on blockchain technology, which can build a scientific trust mechanism and reach consensus among multiple nodes, greatly changing the business model in different fields, and will also trigger a new round of industrial transformation and technological innovation, of course, on the basis of Internet technology, medical finance has more innovative models, and the traditional medical financial service model is seriously affected by these innovative models. This paper analyzes the fusion of artificial intelligence finance and blockchain technology, and also analyzes and predicts the future development of financial health care.
LibGuides: Guitar: Sound Recordings

This is a library guide for students of the guitar. The library has Vinyl records, Compact Discs, and Databases with recordings
Attribute Selection In Naive Bayes Algorithm Using Genetic Algorithms And Bagging For Prediction Of Liver Disease

Liver disease is an inflammatory disease of the liver and can cause the liver to be unable to function as usual and even cause death. According to WHO (World Health Organization) data, almost 1.2 million people per year, especially in Southeast Asia and Africa, have died from liver disease. The problem that usually occurs is the difficulty of recognizing liver disease early on, even when the disease has spread. This study aims to compare and evaluate Naive Bayes algorithm as a selected algorithm and Naive Bayes algorithm based on Genetic Algorithm (GA) and Bagging to find out which algorithm has a higher accuracy in predicting liver disease by processing a dataset taken from the UCI Machine Learning Repository database (GA). University of California Invene). From the results of testing by evaluating both the confusion matrix and the ROC curve, it was proven that the testing carried out by the Naive Bayes Optimization algorithm using Algortima Genetics and Bagging has a higher accuracy value than only using the Naive Bayes algorithm. The accuracy value for the Naive Bayes algorithm model is 66.66% and the accuracy value for the Naive Bayes model with attribute selection using Genetic Algorithms and Bagging is 72.02%. Based on this value, the difference in accuracy is 5.36%. Keywords: Liver Disease, Naive Bayes, Genetic Agorithms, Bagging.
Taming the infinite: framework for resource quantification in infinite-dimensional general probabilistic theories

Resource theories provide a general framework for the characterization of properties of physical systems in quantum mechanics and beyond. Here, we introduce methods for the quantification of resources in general probabilistic theories (GPTs), focusing in particular on the technical issues associated with infinite-dimensional state spaces. We define a universal resource quantifier based on the robustness measure, and show it to admit a direct operational meaning: in any GPT, it quantifies the advantage that a given resource state enables in channel discrimination tasks over all resourceless states. We show that the robustness acts as a faithful and strongly monotonic measure in any resource theory described by a convex and closed set of free states, and can be computed through a convex conic optimization problem. Specializing to continuous-variable quantum mechanics, we obtain additional bounds and relations, allowing an efficient computation of the measure and comparison with other monotones. We demonstrate applications of the robustness to several resources of physical relevance: optical nonclassicality, entanglement, genuine non-Gaussianity, and coherence. In particular, we establish exact expressions for various classes of states, including Fock states and squeezed states in the resource theory of nonclassicality and general pure states in the resource theory of entanglement, as well as tight bounds applicable in general cases.
LibGuides: Library & Technology Services: Recent Grads

A resource guide with information about accessing electronic resources and Law Library and Technology services.
Multiple Dim Moving Targets Detection based on Bayesian Filtering

This paper uses the multi-spectral information and temporal information of target to estimate the state of multiple moving targets, and proposes a method for detecting multiple dim moving targets based on Bayesian filtering. The research idea is as follows: A dynamic model and an image measurement model of multi-target are established. The Markov chain is used to model the target existence variables. The problem can be described as a hybrid filtering problem involving discrete and continuous variables. The solution of hybrid filtering is derived using Bayesian theory. Based on this, multi-spectral infrared image sequence is used to derive a multi-target detection algorithm. The proposed multiple interactive hybrid particle filter is achieved. Simulation scenarios are used to verify the effectiveness of the proposed algorithm.
Establishment of path model of electric power scientific and technological achievements based on information collaborative decision

In view of the development status of the evaluation system of scientific, this paper proposes an algorithm based on the weighted Dijkstra classification path model. It takes the existing scientific and technological achievements as the model input, obtains the classification and division of scientific and technological achievements. According to a variety of criteria, determines the comprehensive index data of the core nodes according to the newly generated data set of scientific and technological achievements, and takes it as the evaluation reference data, performance rating of each scientific and technological achievement. The whole evaluation process is carried out in a standardized, standardized and automated software environment, with high efficiency and accurate data results, which will become a popular method in the evaluation system of scientific and technological achievements <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1].</sup>
LibGuides: LibrarySkills@UCL: Independent research

Our guides, training and support enable you to develop your library and information literacy skills and make the best use of UCL’s libraries and resources.
Modeling Solar Energy Data using Periodic Regression

Solar energy data was collected from two sites in western Michigan, USA that are not homogeneous with respect to location and solar panels used.Â  Data from one site was collected from August 1, 2009 through July 31, 2019 and the second site from July 12, 2017 through July 31, 2019 and summarized statistically.Â  The average monthly solar energy was higher during the summer and lower during the winter. The variation is higher during the winter and lower during the summer.Â  The annual cycle of average monthly solar energy, as well as the variation, was modeled using periodic regression equations comprised of intercept, sine, and cosine terms as well as an additive term for the difference between the two sites.Â  Model parameters were estimated from all collected data as well as from only the 2017 through 2019 data.Â  All estimated values are statistically significant and consistent in magnitude and sign between the two energy equations. The adjusted coefficients of estimates exceeded 80%.Â  It was concluded that the average monthly solar energy pattern at each site was the same but with different magnitudes and not changing in time.Â  Thus, the model could be applied to all sites in the west Michigan area, now and in the future.Â  For the variation in average monthly solar energy models, the adjusted coefficient of estimation was slightly above 75%.Â  While all parameter values were statistically significant, they were different in magnitude and sign indicating the possibility of a change in variation over time.
A Machine Learning Approach to Cloud Masking in Sentinel-3 SLSTR Data

&amp;lt;p&amp;gt;&amp;lt;span&amp;gt;Clouds appear ubiquitously in the Earth's atmosphere, and thus present a persistent problem for the accurate retrieval of remotely sensed information. The task of identifying which pixels are cloud, and which are not, is what we refer as the cloud masking problem. The task of cloud masking essentially boils down to assigning a binary label, representing either &amp;quot;cloud&amp;quot; or &amp;quot;clear&amp;quot;, to each pixel. &amp;lt;/span&amp;gt;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;lt;span&amp;gt;Although this problem appears trivial, it is often complicated by a diverse number of issues that affect the imagery obtained from remote sensing instruments. For instance, snow, sea ice, dust, smoke, and sun glint can easily challenge the robustness and consistency of any cloud masking algorithm. The cloud masking problem is also further complicated by geographic and seasonal variation in acquired scenes. &amp;lt;/span&amp;gt;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;lt;span&amp;gt;In this work, we present a machine learning approach to handle the problem of cloud masking for the Sea and Land Surface Temperature Radiometer (SLSTR) on board the Sentinel-3 satellites. Our model uses Gradient Boosting Decision Trees (GBDTs), to perform pixel-wise segmentation of satellite images. The model is trained using a hand labelled dataset of ~12,000 individual pixels covering both the spatial and temporal domains of the SLSTR instrument and utilises the combined channels of the dual-view swaths. Pixel level annotations, while lacking spatial context, have the advantage of being cheaper to obtain compared to fully labelled images, a major problem in applying machine learning to remote sensing imagrey.&amp;lt;/span&amp;gt;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;lt;span&amp;gt;We validate the performance of our mask using cross validation and compare its performance with two baseline models provided in the SLSTR level 1 product. We show up to 10% improvement in binary classification accuracy compared with the baseline methods. Additionally, we show that our model has the ability to distinguish between different classes of cloud to reasonable accuracy.&amp;lt;/span&amp;gt;&amp;lt;/p&amp;gt;
Digital Libraries and E- literacy: An overview

Change is considered a key factor in the twenty-first century and the result of technology poses many challenges to the lives of highly imposed individuals. Education is one of the areas that has affected form technology. In recent years, with the development of information and communication technologies, new forms of education, the web or online learning, have emerged. Purpose of the study: The purpose of this paper is to review the role of digital libraries in supporting e-learning. Methods: First the definition of a digital library is provided and then the differences between it and the traditional ones are discussed and finally the definition of e-learning and the location of digital libraries are explored in providing new education. Discoveries and results: With the focus on web and online education compared to traditional libraries, space and time are unlimited, there is a lot of opportunity for information representation in the form of multimedia and creating equal educational opportunities for each country. This has led to the precise observation of this type of education. Unlike traditional digital libraries can make services and library resources available over the Internet to support e-learning.
LibGuides: LibrarySkills@UCL: Referencing and managing information: Referencing and managing information

A guide to skills and support for referencing and managing information sources and research data.
Professional-Grade Code

This chapter evangelizes my personal favorite principles of code quality. It's neither an introduction to coding nor a comprehensive guide to writing good code but instead a small set of selected subjects I consider relevant to engineers adapting their academic skills to professional software. I'll note that a couple of these points (like "Don't Check Conditions that Your Codebase Precludes") may be controversial; if your team fervently rejects them, you can do your best to persuade, but remember that "You Can't Change Everything on Your First Day" and you should, when in doubt, "Match The Codebase."
Internet of Medical Things (IoMT) –Architecture, Benefits and Challenges

Internet of Medical Things (IoMT), is a network of medicinal devices and persons where wireless communication is used to exchange the health-related data. IoMT plays a significant role in healthcare industry by providing better services and creating cost effective systems further increasing the accuracy and reliability. The thought at the back of IoMT is to live in connected world. The growth of smart sensors, smart devices and sophisticated communication protocols make it possible to interconnect medical things, monitor and analyze biomedical signals. This paper details the IoMT model and the comparison of architecture with current health care system. The benefits of IoMT through novel services like remote analysis, e-visits that increase convenience at the ends of patients and health care staff are also discussed. Because of the usage of personal devices to access the data IoMT become a popular target for attacks and the need for securing the architecture is also discussed in the article.
Multi-party computation: The Three Judges protocol

Multi-party computation is the solution to the situation in which two (or more) agents say A,B, · · · have private variables a,b,· · · and wish to calculate a public result x = f(a, b, · · · ) without revealing to any one of them more than is strictly necessary — in this case for example A must learn no more than what it can deduce from its variable a (which it knows anyway) and x (which is in the end known by everyone).