Reviewerly/exHarmony huggingface dataset:
한국어 학술 정형 표현 연구: 빈도와 핵심도를 중심으로

This study creates a list of significant Korean academic formulaic expressions extracted with a morpheme-based N-gram from a corpus of research articles in Korean with about three million words. The corpus consists of five academic disciplines disciplines: humanities, social sciences, art, natural sciences and engineering. The expressions contained in the list (i) are of 3 〜 9-gram length, (ii) are of frequency of more than 20 times, (iii) occur more than 10% of the textand (iv) satisfy the keyness (p<0.0001) between academic and nonacademic corpora. Items whose frequency and keyness are in the top ten are classified as core academic formulaic expressions, and these core expressions are investigated by the five academic disciplines. It is found that some of the core academic formulaic expressions such as -l swu iss-ta, (-um/-ess-um/-ko iss-um)-ul al swu iss-ta and -nun taumkwa kath-ta appear in all five disciplines.
LibGuides: Covid-19 Research Guide: Fall 2020 Higher Education News

Resources available from Zayed University Library, scientific research articles and print books.
Cross-Border Spillover Effects of Macroprudential Policies: A Conceptual Framework

Due to the international dimension of the financial sector within the EU and beyond, domestically oriented macroprudential policies have the potential to create material cross-border spillover effects. This occasional paper provides a detailed overview of the academic and empirical literature on cross-border effects of macroprudential policies. It also summarises a stocktaking exercise, conducted by a task force of the ESCBâ€™s Financial Stability Committee (FSC), on existing national approaches within the EU for assessing and monitoring such cross-border spillover effects. The paper accompanies an FSC report presenting a framework to be used by macroprudential authorities when assessing cross-border spillover effects induced by enacted or planned policy measures.
The Analysis on the Mode of Talent Training of the Profound School-Enterprise Cooperation in Higher Vocational Colleges

In the current higher vocational education, the cooperative school-running mode has become an effective talent training mode. In the cooperative school-running, school-enterprise cooperation is the most common cooperative education mode. Higher vocational colleges can fully promote the integration of production and education and improve the quality and efficiency of talent training through the talent cooperation relationship with the relevant enterprises. This paper mainly introduces the mode of talent training of school-enterprise cooperation in higher vocational colleges, analyzes the importance of school-enterprise cooperation in higher vocational colleges and explores the effective path of school-enterprise cooperation in higher vocational colleges.
Pengembangan Multimedia pada Mata Pelajaran TIK Kelas VII SMP

This research uses Research & Development (R&D), which can be defined as a simplified Brog & Gall model according to the researcher's needs. The steps in this research are: (1) planning, (2) initial product development, (3) product validation, (4) trials, (5) product eligibility. The product validation test was carried out by 3 validators namely 1 material validator and 2 media validators. Data collection uses documentation, format and assessment of questionnaires.So that the results of research that have been carried out by multimedia products on ICT material for grade VII junior high school show that the results of the material validation include the criteria of feasible as well as the results of the media validation test with a validator I value of 5.00 (100) while the validator II validation test results are 4,95 (99) are written worth trying out. So it can be concluded that multimedia learning in ICT subjects in VII grade of SMP is very appropriate to be used according to the eligibility criteria of the media to support the learning process of learning and teaching activities and make learning innovations interesting, interactive and fun.
Implementation of E-Archives at the Faculty of Communication and Information Technology Using the Web-Based Waterfall Method

Information technology is currently developing rapidly so that every aspect of activity cannot be separated from the name technology, of course in the administrative aspect it also really requires fast and accurate information technology. At the Faculty of Communication and Information Technology, National University, currently still using an archive system that has not been developed again, which often causes data synchronization. With the E-Archives application, it is hoped that archiving activities can assist administrative activities accurately so that it can become a reference in making the right decisions
Validating Conditional Density Models and Bayesian Inference Algorithms

Conditional density models f(y|x), where x represents a potentially high-dimensional feature vector, are an integral part of uncertainty quantification in prediction and Bayesian inference. However, such models can be difficult to calibrate. While existing validation techniques can determine whether an approximated conditional density is compatible overall with a data sample, they lack practical procedures for identifying, localizing, and interpreting the nature of (statistically significant) discrepancies over the entire feature space. In this paper, we present more discerning diagnostics such as (i) the Coverage Test (LCT), which is able to distinguish an arbitrarily misspecified model from the true conditional density of the sample, and (ii) Amortized Local P-P plots (ALP), which can quickly provide interpretable graphical summaries of distributional differences at any location x in the feature space. Our validation procedures scale to high dimensions, and can potentially adapt to any type of data at hand. We demonstrate the effectiveness of LCT and ALP through a simulated experiment and a realistic application to parameter inference for galaxy images.
IOT-BASED MUSHROOM CULTIVATION MONITORING INFORMATION SYSTEM

At this time, the need for food is a basic human need. With the increase in population, food is a necessity that must be supplied in large quantities and good quality. One sector that needs to be improved for food supply in agriculture especially oyster mushroom cultivation. Farmers in oyster mushroom cultivation often experience failures due to lack of temperature and humidity which results in less than optimal mushroom production. The temperature required in the cultivation of oyster mushrooms is at 22 - 28 C and humidity of 80 - 90% in order to produce superior oyster mushrooms so as not to cause crop failure. The failure of oyster mushroom cultivation due to incorrect temperature and humidity causes the resulting fungus to dry out and inhibit its growth period or even not grow. The failure of oyster mushroom cultivation as one of the pillars of food needs will result in the supply of food needs of the community to be hampered. Therefore, we need a tool for oyster mushroom cultivation farmers that can detect room temperature and humidity conditions to produce superior oyster mushrooms. This problem able to solve with a smart farm system. The smart farm system is a system that can make it easier for farmers to monitor and maintain conditions of temperature and humidity in oyster mushroom cultivation through the website. This system uses Arduino Uno as a microcontroller, soil moisture sensor to detect the humidity of oyster mushroom growing media, dht22 sensor to detect humidity and air temperature, ESP8266 module to send data to cloud computing which will be displayed via the website and LCD. By using the smart farm system, the growth of oyster mushrooms is faster and physically less brittle.
The ICO Is Dead, Long Live the ICO

Within an unclear legal framework, the challenging Initial Coin Offering turned into an increasingly prominent fundraising mechanism for new companies and a great investment opportunity for retail investors, reaching overwhelming numbers all over the world during last years. However, while the global ICO market became ‘too big to ignore’, many cases of undesired behavior and scams occurred, generating some alarm in the participants and attracting the attention of regulators. Along with it, at the end of 2017, the ‘crypto winter’ started. In this context, we have been witnessing an abrupt downtrend of the ICO market from the second half of 2018, where there was no lack of experts who declared the ICO’s death. This paper goes beyond all the frenzy period and analyses the performance of the ICO as a financial vehicle from its origins, as well as the benefits and characteristics of the ICO, which includes addressing the scope of its regulation.
Style encoding for class-specific image generation

A fundamental problem in employing deep learning algorithms in the medical field is the lack of labeled data and severe class imbalance. In this work, we present novel ways to enlarge small scale datasets. We introduce an autoencoder framework comprised of an encoder and a StyleGAN generator to embed images into the latent space of StyleGAN. The autoencoder learns the disentangled latent representation of the data allowing for encoding real images to the latent space and manipulating the latent vector in a meaningful manner. We suggest ways to use the encoder along with the unique architecture of the StyleGAN generator to control the synthesized images and thus, create class-specific images that can be used to train and improve existing deep learning algorithms.
A Deep Learning Model for an Intelligent Chat Bot System: An Application to E-Learning Domain

Nowadays the use of Chatbots is very popular in a large scale of applications especially in systems that provide an intelligence support to the user. In fact, to speed up the assistance, in many cases, these systems are equipped with Chatbots that can interpret the user questions and provide the right response, in a fast and correct way. This paper proposes an intelligent chat bot system able to give a response in natural language or audio about an image or natural language question in different domain of education. This system will support multiple language (English, French and Arabic). In this System, we used different Deep learning architecture (CNN, LSTM, Transformers), Transfer learning to extract image features vector, Computer Vision and Natural Language Processing techniques. Finally, after the implementation of the proposed model, a comparative study was conducted in order to prove the performance of this system using Image-response model and question-response model using accuracy and BLEU score metrics.
Utilization of Big Data Analytics on StateOwned Enterprises in Indonesia (Case Study at PT Pos Indonesia (Persero))

Industrial Revolution 4.0, forced all agents to change their business processes. Theexplosion of data or Big Data now encourages all institutions to move effectively, efficientlyand productively. Data has an important role and able to utilize large volumes of data thatare fast changing, diverse, and complex, will benefit greatly. Referring to the results ofprevious research in government institutions on the application of Big Data technology, theauthor is interested in examining the extent to which Big Data technology is used by StateOwned Enterprises (SOE’s), given its strategic role, namely carrying out public servicesand business missions. The research method used was descriptive qualitative. Datacollection was carried out through in-depth interview techniques to officials in the Big DataAnalytic Division of PT Pos Indonesia (Persero) Head Office in Bandung. The dataobtained were then analyzed by The Data Warehousing Institute Big Data Maturity Model.The results showed dimensions that had been achieved in managing Big Data and in generalit could be concluded that PT Pos Indonesia (Persero) was categorized at the stage ofcompany adoption.
Robust Anomaly Detection via Sequential Ensemble Learning

In this paper, we propose a robust anomaly detection method to detect anomalies in the observed data via a sequential ensemble based approach in an unsupervised framework. The proposed model, first Isolation Forest algorithm distills anomalies from normal instances. Later, the k-Nearest Neighbor (KNN) algorithm estimates the density of the data and scores the anomaliness of the future samples. Although KNN is not robust to the data containing anomalies, we show that the KNN algorithm achieves robustness via the proposed unsupervised distilling method via the IForest algorithm. Our method is quickly applicable to the real world anomaly detection problems since it does not require supervised distilling or data labeling. Through comprehensive set of experiments, we show that the proposed model significantly outperforms the commonly used anomaly detection algorithms in the literature.
Once Quantized for All: Progressively Searching for Quantized Compact Models

Automatic search of Quantized Neural Networks (QNN) has attracted a lot of attention. However, the existing quantization-aware Neural Architecture Search (NAS) approaches inherit a two-stage search-retrain schema, which is not only time-consuming but also adversely affected by the unreliable ranking of architectures during the search. To avoid the undesirable effect of the search-retrain schema, we present Once Quantized for All (OQA), a novel framework that searches for quantized compact models and deploys their quantized weights at the same time without additional post-process. While supporting a huge architecture search space, our OQA can produce a series of quantized compact models under ultra-low bit-widths(e.g. 4/3/2 bit). A progressive bit inheritance procedure is introduced to support ultra-low bit-width. Our searched model family, OQANets, achieves a new state-of-the-art (SOTA) on quantized compact models compared with various quantization methods and bit-widths. In particular, OQA2bit-L achieves 64.0\% ImageNet Top-1 accuracy, outperforming its 2 bit counterpart EfficientNet-B0@QKD by a large margin of 14\% using 30\% less computation cost.
Data Ownership in Cloud: Legal Issues

Data in cloud inherently possess the issues of copyright, confidentiality, and contract in a business sense as cloud consumers may store their data on their third party contractor's virtual servers, but may lose their rights to the data. The ownership issue of data in cloud has been one of the most complicated ones among the issues (Al-Issa et al., 2019). Hence, it is worth examining the data ownership issue in cloud. To conduct the examination, this research study applies a risk management concept to develop a structured approach and use it to study the issue. In addition, this research study develops the risk mitigation strategy framework to organise and better deal with legal matters surrounding data ownership in cloud. This study also reviews the US case of data breaches, which involves cloud services from a perspective of law enforcement as part of risk mitigation strategy.
Software quality testing process analysis

Introduction: This article is the result of reading, review, analysis of books, magazines and articles well known for their scientific and research quality, which have addressed the software quality testing process. The author, based on his work experience in software development companies, teaching and other areas, has compiled and selected information to argue and substantiate the importance of the software quality testing process. Methodology: the existing literature on the software quality testing process was analyzed at a local, national and global context. The ScienceDirect, Elsevier, Springer, Wiley Online Library, Proquest, Engineering Village, Scopus, Dialnet databases were thoroughly reviewed. Results: important concepts are described in the testing process, characteristics, methodologies and frameworks focused on the proper implementation of a testing process. Conclusions: The Article gives rise to results and conclusions in order for software product development companies to improve performance, effectiveness and optimization of software quality testing processes, and it is also a fundamental base to undertake software quality research processes.
Surficial Iron Mineral Potential Mapping from Aster Data in Malmberget and Adjoining Area in Norrbotten County Sweden

The aim of this work is to develop a simple and effective approach for surficial iron mineral potential zone identification and mapping using optical remote sensing data. Thus, an iron mineral potential model (IMPM) was developed by combining multiple band ratios for iron mineral detection. The developed method was applied on the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data from Malmberget and nearby area from Gällivare district, Norrbotten county, Sweden. The results from the study area revealed that the developed approach is effective in iron mineral potential zone mapping using ASTER data with more than 90% accuracy.
On the Consistency Loss for Leveraging Augmented Data to Learn Robust and Invariant Representations

Data augmentation is one of the most popular techniques for improving the robustness of neural networks. In addition to directly training the model with original samples and augmented samples, a torrent of methods regularizing the distance between embeddings/representations of the original samples and their augmented counterparts have been introduced. In this paper, we explore these various regularization choices, seeking to provide a general understanding of how we should regularize the embeddings. Our analysis suggests how the ideal choices of regularization correspond to various assumptions. With an invariance test, we show that regularization is important if the model is to be used in a broader context than the in-lab setting because non-regularized approaches are limited in learning the concept of invariance, despite equally high accuracy. Finally, we also show that the generic approach we identified (squared l2 norm regularized augmentation) performs better than several recent methods, which are each specially designed for one task and significantly more complicated than ours, over three different tasks.
FrugalMCT: Efficient Online ML API Selection for Multi-Label Classification Tasks.

Multi-label classification tasks such as OCR and multi-object recognition are a major focus of the growing machine learning as a service industry. While many multi-label prediction APIs are available, it is challenging for users to decide which API to use for their own data and budget, due to the heterogeneity in those APIs' price and performance. Recent work shows how to select from single-label prediction APIs. However the computation complexity of the previous approach is exponential in the number of labels and hence is not suitable for settings like OCR. In this work, we propose FrugalMCT, a principled framework that adaptively selects the APIs to use for different data in an online fashion while respecting user's budget. The API selection problem is cast as an integer linear program, which we show has a special structure that we leverage to develop an efficient online API selector with strong performance guarantees. We conduct systematic experiments using ML APIs from Google, Microsoft, Amazon, IBM, Tencent and other providers for tasks including multi-label image classification, scene text recognition and named entity recognition. Across diverse tasks, FrugalMCT can achieve over 90% cost reduction while matching the accuracy of the best single API, or up to 8% better accuracy while matching the best API's cost.
A Parallel Adaptive Filtering Algorithm Based on the Mean-Square Deviation Analysis for Large-Scale Data

This paper proposes a parallel adaptive filtering algorithm via analysis of the mean-square deviation for largescale data. In this algorithm, large-scale data is divided into several sub-blocks to reduce the computational cost. Based on each data sub-block, a normalized least-mean-square algorithm estimates the parameters of interest at each sub-filter. Furthermore, the mean-square deviation analysis of the estimation result at each sub-filter leads to a variable step-size method and an intermittent-update method. These methods provide not only fast convergence rate and small steady-state error but also high computational efficiency. Finally, the estimation results of each sub-filter are combined through a combination method by determining the weights for each estimation result based on their error variance. The proposed combination method provides robustness to abnormalities of the data. Simulation results show that the proposed algorithm performs well for estimation with large-scale data.
Combinatorial enumerations and Graycodeness on restricted growth functions avoiding vincular patterns

The manuscript presents enumerations for restricted growth functions (RGFs) avoiding (i) single vincular pattern sets of length at most 3 and (ii) two-pattern sets of a vincular pattern and a classic pattern of length at most 4. The presented enumerations are counted by known classic sequences like Bell, Fibonacci, binary strings, etc. Moreover, we show a sufficient condition for the 3-Graycodeness by the reflected Gray code order of RGFs avoiding a pattern set. Consequently, it allows us to prove the 3-Graycodeness for some classes of RGFs avoiding particular pattern sets in (i) and (ii) by checking the condition satisfaction. This, in many cases, helps to reduce remarkably routine work for proving the 3-Graycodeness on RGFs.
A Critical Pragmatic Analysis of Women Oppression

Oxford English Dictionary defines insult as To assail with offensively dishonoring or contemptuous speech or action; to treat with scornful abuse or offensive disrespect; to offer indignity to; to affront, outrage -perhaps by manifest arrogance, scorn, contempt, or insolence. Meriam Websters Dictionary defines oppression as unjust or cruel exercise of authority or power. The present study investigates women insulting jokes from a critical pragmatic perspective. Females have been oppressed and underrepresented by jokes and many scholars have stated that joke saying is mainly a male activity (Bing, 2007 :1). The present study investigates women insulting jokes from a critical pragmatic perceptive. It aims at investigating how women are oppressed and insulted through a specific kind of jokes. Al-Hindawi and Mohamad (2018:1) state that using an impolite speech act rather than a polite one falls within the realm of critical Pragmatics. Since insulting jokes as speech acts are used to insult women, the present study falls under the realm of critical pragmatics. The current study is a qualitative onebased on analytical model that is developed for the present study. This study is limited to the analysis of nine women insulting jokes. The results of the present study are significant in supporting the notion that critical pragmatic field can investigate the use of insulting jokes for gaining dominance over women.
Design to Break Barriers

In this experience, learners will develop a critical consciousness of barriers to participation rooted in ableism that are “baked into” instruments. This critical consciousness will assist learners in creating controllers with lower/no barriers for disabled persons/persons with disabilities. Learners will encounter concepts such as the “social model of disability” that come from Disability Studies scholarship in order to find and address disabling features of commercially-available instruments and to design new instruments, controllers, and practices that are anti-ableist. Through these experiences, learners will develop skills and knowledge related to using microcontrollers such as the Makey Makey and coding platforms such as Scratch through an iterative design process.