Reviewerly/exHarmony huggingface dataset:
LibGuides: Keep Learning with Kellar Library: Scan and Deliver

A guide helping students and faculty of BMATS to understand how Kellar Library is responding during the Coronavirus Pandemic and the resources and services that are available to students during this time, You can have portions of print materials delivered to your inbox!
Don't Cry Wolf

Real world anomaly management systems oversee thousands of dynamic data streams and generate an overwhelming number of alerts. As a consequence, important alerts often go unnoticed until there is a crisis. The absence of ground truth, and the fact that the streams are constantly changing (new content, new applications, software and hardware changes) makes assessing the value of alerts difficult. In order to identify groups of important and actionable alerts, we propose: (1) superalerts that reflect characteristics of persistence, pervasiveness and priority, (2) three types of super-alerting based on three types of aggregations and, (3) corresponding metrics for evaluating them. We demonstrate using real-world entertainment data streams.
Effort estimation of Web based projects: A systematic Review

Lately, the importance of effort estimation of web-based projects has increased. Due to the web project's dynamic changes, it becomes more difficult to use a particular estimating method. According to the CHAOS Report 1994, only about 16.2% of the web projects are successfully developed in a given budget and time. In the first half of this study, we have provided a systematic analysis of the effort estimation of web-based projects. This would be useful for users to gain more knowledge on effort estimation in web-based projects since very few research works have been carried out in this field. Nowadays, web projects are more complex and data-centric. So, the inclusion of the database in the estimation process becomes necessary. Secondly, we proposed an estimation model, where the database is taken into consideration for its effort estimation. The result that we found after utilising the model gave more accurate effort estimation.
Active$^2$ Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging

While deep learning is a powerful tool for natural language processing (NLP) problems, successful solutions to these problems rely heavily on large amounts of annotated samples. However, manually annotating data is expensive and time-consuming. Active Learning (AL) strategies reduce the need for huge volumes of labeled data by iteratively selecting a small number of examples for manual annotation based on their estimated utility in training the given model. In this paper, we argue that since AL strategies choose examples independently, they may potentially select similar examples, all of which may not contribute significantly to the learning process. By focusing on sequence tagging NLP tasks, we propose a method, referred to as Active$\mathbf{^2}$ Learning (A$\mathbf{^2}$L), that actively adapts to the deep learning model being trained to further eliminate such redundant examples chosen by an AL strategy. We show that A$\mathbf{^2}$L is widely applicable by using it in conjunction with several different AL strategies and sequence tagging tasks. We empirically demonstrate that the proposed approach is further able to reduce the data requirements of state-of-the-art AL strategies by $\approx\mathbf{2-16\%}$ on multiple sequence tagging tasks while achieving the same performance..
Improving the scalability of cloud monitoring service by low communication overhead mechanisms

With the rapid growth of cloud platforms, an effective monitoring service is playing a more and more important role to provide enhanced quality-of-service (QoS) for a variety of cloud users and providers. Unfortunately, the overhead of large-scale monitoring on a large number of cloud resources tends to be prohibitive and brings a lot of negative effects. In this paper, we present three effective mechanisms, including sliding-window monitoring mechanism, monitoring event mining and filtering mechanism, and self-tuning data transferring mechanism, to handle with different kinds of communication overhead. A set of experiments are conducted to evaluate the proposed mechanisms in a real-world cloud platform, and the results indicate these mechanisms are very helpful to reduce the communication overhead in different monitoring scenarios.
DataNews: contextualisation of quantified values in wires

The Open Data allows the access to plentiful data, with a large coverage, but none of them offers a structured databased around news. Through DataNews, our goal is to seek for data automatically so as to provide means to reuse them. To do so, we first defined an event typology in the specific context of death in AFP wires. Then, by restraining ourselves to the natural disasters, we clustered these wires by events so as to identify them. The goal of the last step is to build extraction patterns so as to collect values corresponding to the death number, as well as the context associated to these values. The results of our evaluations reassured ourselves in the large potential of our method that could lead to several applications.
Multi-Key Homomorphic Encryption based Blockchain Voting System

During the pandemic covid-19. More than 70 national elections scheduled for the rest of the year worldwide, the coronavirus (COVID-19) pandemic is putting into question whether some of these elections will happen on time or at all. We proposed a novel solution based on multi-key homomorphic encryption and blockchain technology, which is unhackable,privacy-preserving and decentralized. We first introduce the importance of a feasible voting system in this special era, then we demonstrated how we construct the system. finally, we made a thorough comparison of the possible solutions.
THE ROLE OF INFORMATION TECHNOLOGY IN CURBING THE CHALLENGES OF INTERNET AND CYBERCRIME IN NIGERIA

Information Technology has become a dynamic and extremely powerful means of collecting, processing, storing, presenting and sharing information in the developing countries. The processed information is usually presented on digital devices, and also to a large extent presented on public infrastructures such as the internet and the World Wide Web. Information Technologies have become widespread and have penetrated deeply into societies in developing countries. The proliferation of Information Technology (IT) has resulted in the change of different aspects of human life, bringing convenience and simplicity to our lives. The contribution of the internet to the development of Nigeria has had a positive impact on various sectors of the country. However, it has come with its own share of problems, which have become one of the major challenges of insecurity in Nigeria. Though it makes life so speedy and fast, but hurled under the eclipse of threat from the deadliest type of criminality termed as 'Cybercrime’. Cyber-crime consists of a variety of criminal acts perpetrated through the Internet, and includes e-mail scams, child pornography, hacking, theft of data, spamming, Automated Teller Machine spoofing, piracy, phishing, identity theft, extortion and a wide array of other nefarious activities. Amidst of these threats of insecurity with cybercrime, Information Technology still has a lot of roles to play to curb these challenges in Nigeria Development. The study sets to find out the roles of information technology in curbing internet and cybercrime.
Towards More Scalable and Robust Machine Learning

Author(s): Yin, Dong | Advisor(s): Ramchandran, Kannan | Abstract: For many data-intensive real-world applications, such as recognizing objects from images, detecting spam emails, and recommending items on retail websites, the most successful current approaches involve learning rich prediction rules from large datasets. There are many challenges in these machine learning tasks. For example, as the size of the datasets and the complexity of these prediction rules increase, there is a significant challenge in designing scalable methods that can effectively exploit the availability of distributed computing units. As another example, in many machine learning applications, there can be data corruptions, communication errors, and even adversarial attacks during training and test. Therefore, to build reliable machine learning models, we also have to tackle the challenge of robustness in machine learning.In this dissertation, we study several topics on the scalability and robustness in large-scale learning, with a focus of establishing solid theoretical foundations for these problems, and demonstrate recent progress towards the ambitious goal of building more scalable and robust machine learning models. We start with the speedup saturation problem in distributed stochastic gradient descent (SGD) algorithms with large mini-batches. We introduce the notion of gradient diversity, a metric of the dissimilarity between concurrent gradient updates, and show its key role in the convergence and generalization performance of mini-batch SGD. We then move forward to Byzantine distributed learning, a topic that involves both scalability and robustness in distributed learning. In the Byzantine setting that we consider, a fraction of distributed worker machines can have arbitrary or even adversarial behavior. We design statistically and computationally efficient algorithms to defend against Byzantine failures in distributed optimization with convex and non-convex objectives. Lastly, we discuss the adversarial example phenomenon. We provide theoretical analysis of the adversarially robust generalization properties of machine learning models through the lens of Radamacher complexity.
Detection of hijab syar’i as smart clothes system for moslem people using high performance of parallel computing

Abstract Moslems are required to remain consistent in wearing syar’i clothing. Sometimes, however, it is very difficult to distinguish between syar’i clothes and the non-syar’i ones, especially in the current millennial era and until the end of time there are many temptations that are not good. Digital image processing in the present day has become one of the areas that can be processed to help various problems associated with images. However, it requires very long processing time and ineffective processing. The CPU alone is not enough. Therefore, it takes processing time allocation that can cut the time to speed up the process. This study utilizes the way to speed up performance analysis of digital images through GPU. The performance analysis of GPU includes CUDA and Yolo because it allows specialists in parallel programming to use GPU resources and can perform the object detection process quickly and accurately by applying an artificial neural network to the image of someone who is wearing syar‘i hijab as smart clothes for Moslem women. Several hardware, software, and file dependency specifications are employed to support the implementation process. The test results achieve the accuracy of 100%, proving that by using the implementation of Yolo under GPU.
Power System Fault Diagnosis Based on Krill Herd Algorithm

When the power system fails, the alarm signal will be uploaded, if a complex fault occurs, the amount of signal will increase greatly and the error alarm signal will be prone to occur. In order to ensure the accuracy of grid fault diagnosis, this paper introduces the actual status and alarm signal of protection and circuit breaker in the fault hypothesis. Aiming at the difference between the expected action state and the actual action state to build the objective function reflects the correctness of the hypothesis, and the error correctness indicator is used to eliminate the false alarm signals, thus an improved method of power grid fault diagnosis is given. The objective function is computed by the Krill Herd Algorithm (KHA) to obtain the actual state of protection and the circuit breaker. The effectiveness of the proposed model is verified by simulation of IEEE39 nodes, which shows that the method can accurately diagnose faults under complex conditions and obtain reasonable fault diagnosis results.
Housing Sale Price Prediction Using Machine Learning Algorithms

Author(s): Zhou, Yichen | Advisor(s): Wu, Yingnian | Abstract: In this thesis, I explore how predictive modeling can be applied in housing sale price prediction by analyzing the housing dataset and use machine learning models. Actually, I try four different models, namely, linear regression, lasso regression, randomforest and xgboost. Additionally, as the data have 79 explanatory variables with many missing values, I spend much time dealing with the data. I do explorary data analysis, feature enginnering before model fitting. And then using rmse and R-squared to measure the model performance. After I try four different models, I get some results. As for the first model - linear regression, it doesn’t meet the assumption of equality of the variances. Therefore we can’t use the linear model as the candidate of our final model. Then I try lasso regression, but the RMSE and R-squared looks not so good. Then I try Random forest. The R squared in this model of training set is very good, but in the test set the R squared is relatively low, which may show the RF model is a little bit overfitting. Finally I try the fourth model - xgboost. All of the results of this xgboost model seem very good. Therefore, I will use this xgboost model as my final model to predict the housing price. The xgboost model also shows which variables have important effects on sale price.
A categorical approach to secure compilation

We introduce a novel approach to secure compilation based on maps of distributive laws. We demonstrate through four examples that the coherence criterion for maps of distributive laws can potentially be a viable alternative for compiler security instead of full abstraction, which is the preservation and reflection of contextual equivalence. To that end, we also make use of the well-behavedness properties of distributive laws to construct a categorical argument for the contextual connotations of bisimilarity.
Research Guides: Getting Started at the Library : Library Tour

Learning the basic skills you need to get the most of everything WKU Libraries has to offer! Contact Information: Chat, Library Phone, Reference Email & Library Tour
LibGuides: Teaching Online: Virtual Library Instruction

This guide provides information, tools, and resources to support online instruction for those teaching online.
KONSEP MANAJEMEN PERPUSTAKAAN DALAM MENINGKATKAN MINAT BACA SISWA DI MTs NEGERI 1 PURWOREJO

ABSTRACT The library is an institution that manages the collection of written works, printed works, and / or recorded works in a professional manner with a standardized system to meet the educational, research, preservation, information, and recreation needs of the visitors. Library management is the implementation of management functions to empower management elements which include all processes from planning, organizing, mobilizing, to supervision. The nature of management is basically basically optimizing human, material, and budget contributions to achieve organizational goals. interest in reading is the tendency of the soul to encourage someone to do something about reading. Interest in reading is indicated by a strong desire to carry out reading activities. This research uses a descriptive qualitative approach. In this study, researchers chose the MTs Negeri 1 Purworejo Library. This research was conducted for one month from January 13, 2020 to February 13, 2020. To obtain and collect accurate and necessary data in this research activity, the researchers determined the respondents (actors) to be examined 1). Head master; 2). Head of Library; 3). Deputy Head of Madrasa Curriculum Section 4) Representatives of grades 7-9.The results of the study are the concept of library management in increasing students' interest in reading at MTs Negeri 1 Purworejo still in the process of development. The management used is still in the management towards a better direction, so it is expected that students can be more comfortable to visit the library so as to increase students' insight and interest in reading at MTs Negeri 1 Purworejo.
Efficient Scale Estimation Methods using Lightweight Deep Convolutional Neural Networks for Visual Tracking

In recent years, visual tracking methods that are based on discriminative correlation filters (DCF) have been very promising. However, most of these methods suffer from a lack of robust scale estimation skills. Although a wide range of recent DCF-based methods exploit the features that are extracted from deep convolutional neural networks (CNNs) in their translation model, the scale of the visual target is still estimated by hand-crafted features. Whereas the exploitation of CNNs imposes a high computational burden, this paper exploits pre-trained lightweight CNNs models to propose two efficient scale estimation methods, which not only improve the visual tracking performance but also provide acceptable tracking speeds. The proposed methods are formulated based on either holistic or region representation of convolutional feature maps to efficiently integrate into DCF formulations to learn a robust scale model in the frequency domain. Moreover, against the conventional scale estimation methods with iterative feature extraction of different target regions, the proposed methods exploit proposed one-pass feature extraction processes that significantly improve the computational efficiency. Comprehensive experimental results on the OTB-50, OTB-100, TC-128 and VOT-2018 visual tracking datasets demonstrate that the proposed visual tracking methods outperform the state-of-the-art methods, effectively.
SCHOOL BULLYING – AN INTEGRATIVE REVIEW

OBJECTIVE: To characterize the scientific production on the scenarios of school bullying through searches in online journals in the areas of Health, Social Sciences and Humanities, from 2013 to 2018. METHOD: This is an integrative literature review. The sample consisted of 43 publications. RESULTS: The survey revealed that the 2014, 2017 and 2018 period presented a number of (21.42%) publications, followed by 2013 to 2016 (14.28%) and finally the 2015 period corresponding to (7.14%). of the total score of publications. As for the predominant country of origin of the main author was Colombia (28.57%). The predominant area of knowledge of the first author was psychology (42.85%). CONCLUSION: The discussion about the practice of school violence, Bullying, has gained notoriety due to the intensification and severity of the phenomenon in the various scenarios, especially in educational institutions, whether public or private. The information gathered through this study stimulates the current, contemporary debate, collaborating for future research on this subject.
LibGuides: Humanities Research Guide : Searching

This guide was created to assist researchers in the Humanities by highlighting available resources at the Alma Jordan Library and pointing to other helpful tools and websites.
Research Foundations of Discourse Studies : Expanding as an Interdisciplinary Research Methodology

본 연구의 목적은 학제간/통합적 ‘연구방법론’으로서 담화/담론연구의 이론적 토대를 탐색하고 구체적인 ‘연구방법’으로 갖는 확장가능성과 한계점을 (비판적) 응용언어학자의 시선과 관련 연구문헌으로 논의해보는 것이다. 지금까지 담화/담론연구는 언어와 사회, 텍스트와 콘텍스트, 이론과 현장, 사회적 현실과 담론의 매개, 미시적 언어 장치와 거시적 권력관계 등의 이항 중 한편에 갇혀 있었기에, 다양한 인문사회 학술 분야에서 서로 다른 이론적 토대를 탐색하면서 보다 다층적이면서도 통합적인 학제간 연구방법론으로 다시 타당화시킬 필요가 있다. 담화/담론연구의 이론적 토대를 점검하기 위해 본 연구는 형태/구조, 기능/사용, 사회적 실천에 관심을 둔 서로 다른 접근방식을 먼저 구분한다. 그런 다음에 담화/담론이 매개하는 네 가지 연구방법(비판적 담론연구, 비평적 텍스트분석, 사회적 구성 텍스트분석, 사회적 구성 담론연구)을 비판적-구성주의적, 콘텍스트-텍스트 지향성의 이항 축으로부터 조합하여, 국내 응용언어학/언어교육 연구사례와 함께 부연하도록 한다. 아울러 담화/담론연구의 확장가능성과 한계점을 간략하게 열거하고 관련 연구자들이 앞으로 감당해야 할 역량과 역할 역시 다뤄보기로 한다.This study is intended to investigate theoretical foundations of discourse analysis as an interdisciplinary research methodology in the eye of a (critical) applied linguistics. It focuses on the status as a research methodology, rather than just one of the methods to analyze qualitative data. The historically categorized foundations will be discussed on different approaches to discourse studies: formal, functional, social. The four major research perspectives, elaborated with empirical studies of mostly applied linguistics and language education in Korea, will then be divided into (1) critical discourse analysis, (2) critical linguistic analysis, (3) social linguistic analysis, (4) interpretive structuralism. The strengths as well as the limitations of discourse analysis (methodology) will also be discussed. This article will conclude with some suggestions for future directions of methodological development. It is expected that this study fills the gap between linguistics and social approaches to discourse analysis, by providing a systematic frame for embracing a strong social constructivist view of social world.
Real-time predictive analytics in postoperative critical care

Predictive analytics in healthcare is not a new endeavor. Since ages physicians are thriving to develop calculated markers that enable them to react promptly and precisely on patient's deterioration before irreversible damage has occurred. The advent of deep learning has opened new possibilities and started excitement in this field. This talk will discuss recent findings, possibilities and challenges of predictive analytics in the data intense environment of critical care.
Use of Smartphones by Research Scholars in Enhancing Digital Information Literacy Skills: A Study

The usage of smartphones has proliferated in recent years. Smartphones have enabled the users in the academic community to access a wealth of information at any time and at any place. With such an accelerated adoption of smartphones at the touch of the fingertips, the authors in this paper sought to investigate the usage of smartphones by research scholars in University of Mysore, Mysuru for their research and academic purpose. The study brings about the purpose of using smartphones, browsers used to search information in smartphones, mobile applications used for research, advantages and limitations of using smartphones. It was evident from the study that all research scholars used smartphones and were connected to the Internet and nearly 77.17% respondents used smartphones to read books and articles related to their research. The most usedbrowser was Google Chrome (89.67%) and all the respondents (100%) used typed keywords to search for information on smartphones. The reliability of information obtained was checked only by 50% of the respondents. It was also observed that the major benefit for using smartphones was their immediate access to information and the barrier was their screen size. The results from this study reflect the research scholars’ exponential adoption of the smartphone for information access.
Hindsight-insight-foresight: a strategic combination for enterprises in a VUCA world

Dotcom-based start-ups have steered their existence, sailing through rough weather, undergoing turbulence, i.e., volatile, uncertain, complex and ambiguous times, termed as VUCA. The objective of this research is to discover the VUCA challenges that the dotcom enterprises have undergone in the last decade, keeping in mind the ontological and epistemological dimensions and the foresighted required for survival in a world of chaos. An influential case selection technique was administered, where one case was selected that influenced the identified findings among a set of similar cases. BookMyShow (BMB), as the incumbent entrepreneurial firm, from the dotcom space was chosen for the study. This research reflects that in the age of turbulence, dotcom-based enterprises (or entrepreneurial firms) should 'think like a futurist', with new leadership literacies engaging 'foresight and innovation', while moving swiftly acquiring a 'strategic foresight', emanating from hindsight-insight-foresight combination.
?멸뎅???좏븰?????援먯뼇 湲?곌린 ?섏뾽??PBL ?곸슜 ?щ? ?곌뎄

The purpose of this paper is to develop PBL teaching model in a college writing classes for foreign students and to introduce examples of applying it...