Reviewerly/exHarmony huggingface dataset:
Blockchain Technology in the Energy Sector

Energy networks are developing into more decentralized energy storage systems, adapted to heterogeneous yet competitive energy sources. To facilitate peer-to-to-peer energy transactions, we will introduce microgrids into the mix. Some see blockchains or distributed ledgers as a new and useful technology for energy companies, as well as for startups, government entities, and academia. A number of different industry sectors around the world are very enthusiastic about blockchains, believing they could bring dramatic changes. Blockchains promise to be highly transparent, impossible to falsify, and security-tamperproof, particularly when employed in tandem with smart contracts. For the final step, we extensively study blockchain’s literature and cases in order to educate the new and cutting-edge. Blockchain activities and projects are compiled and disseminated in this work to inform readers of innovative, scholarly, and theoretical research in the energy sector.
Research guides: Troubleshooting e-resources: Library system status

Tips for accessing Camosun Library e-resources such as e-journals, e-books, databases, streaming videos on or off campus. Known problems with access to library systems or eresources such as Single Search (EDS), LibGuides, library catalogue, databases, and more!
Box Embeddings: An open-source library for representation learning using geometric structures

A fundamental component to the success of modern representation learning is the ease of performing various vector operations. Recently, objects with more geometric structure (eg. distributions, complex or hyperbolic vectors, or regions such as cones, disks, or boxes) have been explored for their alternative inductive biases and additional representational capacity. In this work, we introduce Box Embeddings, a Python library that enables researchers to easily apply and extend probabilistic box embeddings. Fundamental geometric operations on boxes are implemented in a numerically stable way, as are modern approaches to training boxes which mitigate gradient sparsity. The library is fully open source, and compatible with both PyTorch and TensorFlow, which allows existing neural network layers to be replaced with or transformed into boxes easily. In this work, we present the implementation details of the fundamental components of the library, and the concepts required to use box representations alongside existing neural network architectures.
Structured Neural Models and Structured Decoding for Natural Language Processing

Author(s): Stern, Mitchell Thomas | Advisor(s): Klein, Dan; Jordan, Michael | Abstract: Neural sequence models have been applied with great success to a variety of tasks in natural language processing in recent years. However, because they generate their outputs one token at a time from left to right, they are not immediately suitable for domains where non-trivial output constraints must be satisfied for well-formedness, or where parallel decoding may be desirable for higher throughput. In this dissertation, we explore how we can overcome these limitations through the use of more highly structured models and more flexibly structured decoding algorithms.On the modeling side, we first introduce a span-based neural model for constituency parsing that permits efficient, globally optimal decoding over the space of parse trees using a chart-based dynamic program. We then present the Abstract Syntax Network, a tree-structured neural model for code generation whose scoring modules are composed together in a way that mirrors the syntactic structure of the program being produced. Next, turning to more flexible decoding algorithms for sequences, we demonstrate how the Transformer sequence model can be extended to accommodate blockwise parallel decoding for significant improvements in decoding speed without compromising accuracy. Finally, we present the Insertion Transformer, an insertion-based sequence model that enables out-of-order generation and logarithmic-time parallel decoding.
LibGuides: Electronic books (e-books): Finding and accessing e-books

Guide to finding, using, and citing e-books at UCL. In this section you'll find information on how to find e-books.
Histogram of Cell Types: Deep Learning for Automated Bone Marrow Cytology

Bone marrow cytology is required to make a hematological diagnosis, influencing critical clinical decision points in hematology. However, bone marrow cytology is tedious, limited to experienced reference centers and associated with high inter-observer variability. This may lead to a delayed or incorrect diagnosis, leaving an unmet need for innovative supporting technologies. We have developed the first ever end-to-end deep learning-based technology for automated bone marrow cytology. Starting with a bone marrow aspirate digital whole slide image, our technology rapidly and automatically detects suitable regions for cytology, and subsequently identifies and classifies all bone marrow cells in each region. This collective cytomorphological information is captured in a novel representation called Histogram of Cell Types (HCT) quantifying bone marrow cell class probability distribution and acting as a cytological patient fingerprint. The approach achieves high accuracy in region detection (0.97 accuracy and 0.99 ROC AUC), and cell detection and cell classification (0.75 mAP, 0.78 F1-score, Log-average miss rate of 0.31). HCT has potential to revolutionize hematopathology diagnostic workflows, leading to more cost-effective, accurate diagnosis and opening the door to precision medicine.
HOTTER: Hierarchical Optimal Topic Transport with Explanatory Context Representations

Natural language processing (NLP) is often the backbone of today's systems for user interactions, information retrieval and others.Many of such NLP applications rely on specialized learned representations (e.g.neural word embeddings, topic models) that improve the ability to reason about the relationships between documents of a corpus.Paired with the progress in learned representations, the similarity metrics used to compare representations of documents are also evolving, with numerous proposals differing in computation time or interpretability.In this paper we propose an extension to a specific emerging hybrid document distance metric which combines topic models and word embeddings: the Hierarchical Optimal Topic Transport (HOTT).In specific, we extend HOTT by using contextenhanced word representations.We provide a validation of our approach on public datasets, using the language model BERT for a document categorization task.Results indicate competitive performance of the extended HOTT metric.We furthermore apply the HOTT metric and its extension to support educational media research, with a retrieval task of matching topics in German curricula to educational textbooks passages, along with offering an auxiliary explanatory document representing the dominant topic of the retrieved document.In a user study, our explanation method is preferred over regular topic keywords.
LibGuides: A Beginner's Guide To Google Docs: Home

A training guide showing users how to upload, edit, save and share documents on Google Docs effectively.
A novel method for discriminating multi-sensor fusion based on coherent feature

Abstract In order to solve the problem of multi-sensor fusion discrimination of the same radar emitter, a multi-sensor fusion discrimination method based on coherent feature is proposed in this paper. First, the simultaneous interpreting of the pulse sequences received by different sensors is carried out. Then the peak value of the autocorrelation function between pulses is used to determine whether the same source is used. The simulation results show the effectiveness of the proposed method.
Database Management System

In 1981 Wayne Erickson founded Microrim, Inc, a company originally focused on marketing a microcomputer version of RIM (Relational Information Manager). Dennis Comfort joined the firm and is now vice president, development. The team developed an advanced spinoff from the NASA system they had originally created, a microcomputer database management system known as R:BASE 4000. Microrim added many enhancements and developed a series of R:BASE products for various environments. R:BASE is now the second largest selling line of microcomputer database management software in the world.
Left Behind Under Covid-19: The Limits and Impact of Online Education at the Basic School Level in Ghana

This study examines the impact of Covid-19 on access to online education or Distance Education at the basic school level. Specifically, it sought to analyse the differential access to online education due to digital divide resulting from the mechanism of income inequalities. The case study strategy within the qualitative approach was deployed for the study. Among other things, the study revealed that differences in income affected children’s access to online education during the school closures. While children from lower income groups hadaccess to online education, access doubled for children from high income groups. Thestudy also found that children accessed online lessons with different technological devicessuch as smart phones, computers, laptops and tablets. It was further found that there arevariations in the multimedia used by schools in the delivery of lessons. The multimediacommonly used by schools include WhatsApp, Zoom and Google classroom, all of whichwere found to have different impact on the quality of online studies
THE USE OF YOUTUBE VIDEO TO IMPROVE THE SPEAKING SKILL OF GRADE XI STUDENTS AT SMK CLARENT KEFAMENANU

This study tends to identify whether the use of YouTube Video can improve students’ speaking skilland to measure to what extent YouTube Video can improve students’ speaking skill. The method used in this study is classroom action reserach. The research was conducted in two cycles. Every cycle consists of four steps; planning, action, observation, and reflection. The data collected were in the form of qualitative and quantitative. The qualitative data were gained through observation checklist, and questionnaire. While the quantitative data were obtained by conducting a test for cycle one and cycle two, and the data were in the forms of the students’ speaking scores.The results of this research showed that using YouTube video in the teaching and learning of speaking was proved to improve the students’ speaking skills. The students were more enthusiastic in learning speaking. They could actively involve in the speaking learning process. Moreover, the students were more confident and fluent in speaking practices. By comparing the total average scores of the students’ cycle one test and cycle two test, the researcher found that there was improvement from 41.5 to 61.4.
Hybrid CNN- BILSTM-Attention Based Identification and Prevention System for Banking Transactions

With the increase in digitalization and more pressure on cashless transactions being put forth by our government there is a major risk of the credit card fraud in budgetary exchanges. Hence, the credit card fraud detection is the utmost responsibility and a crucial challenge up here for researchers to lessen the incurring losses by the banks and customers. The Customary detection method by organizations would mainly rely on rules. But there are some limitations to such rule-based methods as each rule has a corresponding threshold and being absolute in nature, they are inefficient when used alone. Moreover, they cannot treat a large amount of data at the same time. Nowadays, researchers are laboring hard into techniques such as Artificial Intelligence, Machine Learning, Data Mining, etc. In this paper, I have used Deep Learning techniques like CNN, BILSTM with ATTENTION layer. Using these techniques, the maximum efficiency which could be attained is 95%. Analytical outcomes applied to the real-world transaction information given by a business bank shows that our proposed strategy achieve desirable accuracy than any other techniques.
Developing School Information Program: Integrated Management System based on Character Value at SMP Negeri 9 Tapung

This research aims to develop a student data management system at 9 Tapung Junior High School (SMP) Negeri 9 Tapung, which has not documented using a computer or school web using text-based programming languages like HTML, JavaScript, CSS, and PHP. The student information system design at SMP Negeri 9 Tapung in its manufacture uses a literacy methodology where each phase can be carries repeatedly until it gets the desired results. The phase of system manufacture is the investigation, analysis, design, and implementation. To make this application, the author uses PHP and MySQL. This development research results create a student information system application that can display school data, student data, graphics form, and development, including data on honesty and discipline. The system also displays the student interests and talents observed, extracurricular activities, student violations, and achievement. This application still needs development, refinement, and the addition of features. Suggestions and criticism need from all related parties to create a better and structured information system to improve the quality and character of SMP Negeri 9 Tapung.
PERENCANAAN PEMBANGKIT LISTRIK TENAGA SURYA DENGAN SISTEM ON GRID PADA GEDUNG KANTOR KECAMATAN TAPIAN DOLOK SINAKSAK

Electrical energy is a very important need for human beings. But along with the development of renewable energy utilization technology is increasing including solar power generation (PLTS). PLTS relies on solar energy as its source of energy. Where solar energy will not be exhausted and abundant. In conducting the development of solar power plants (PLTS) need a good planning so that the built-in PLTS can operate optimally. In development planning research PLTS will be conducted on the roof of the office building Tapian Dolok Sinaksak Sub-district with the operating system On Grid. PLTS operating system On Grid is chosen because it wants to reduce the electricity consumption of office buildings from PLN because the system is connected to the PLN network. This plan is said to be well-developed and expanded because this planning has been done by getting a performance Ratio (PR) value of 80%, and it is feasible because more than the minimum Performance ratio is 70%. In this planning use 44 solar panels which are 11 in series and 4 are connected in parallel. Based on financial calculations, this planning requires an investment fee of Rp. 161,659,000 which includes the cost for the component. The Return of capital occurred in the 19th year and the 11th month of the operating age of Plts 20 years. With this planning is expected as a reference for society can recognize and utilize plts in daily life.
SeqScore: Addressing Barriers to Reproducible Named Entity Recognition Evaluation

To address a looming crisis of unreproducible evaluation for named entity recognition, we propose guidelines and introduce SeqScore, a software package to improve reproducibility. The guidelines we propose are extremely simple and center around transparency regarding how chunks are encoded and scored. We demonstrate that despite the apparent simplicity of NER evaluation, unreported differences in the scoring procedure can result in changes to scores that are both of noticeable magnitude and statistically significant. We describe SeqScore, which addresses many of the issues that cause replication failures.
Computer-assisted English Teaching Model in Academic Writing: A Case Study of Three-Stage Development

This paper takes a college English Academic teaching mode based on computer network technology as the research object. Firstly, the Challenges of Academic English writing for Chinese College Students are analyzed. Then the advantages of college English autonomous learning dynamic teaching mode under computer network technology are discussed. After that, it demonstrates a case study of a Computer-assisted English teaching model in academic writing.
DMix: Distance Constrained Interpolative Mixup

Interpolation-based regularisation methods have proven to be effective for various tasks and modalities. Mixup is a data augmentation method that generates virtual training samples from convex combinations of individual inputs and labels. We extend Mixup and propose DMix, distance-constrained interpolative Mixup for sentence classification leveraging the hyperbolic space. DMix achieves state-of-the-art results on sentence classification over existing data augmentation methods across datasets in four languages.
Supplementary material for ``Generic identifiability of subnetworks in a linear dynamic network: the full measurement case"

This document contains the supplementary material of Generic identifiability of subnetworks in a linear dynamic network: the full measurement case which was accepted for publication in Automatica, 2021.
Global Explainability of BERT-Based Evaluation Metrics by Disentangling along Linguistic Factors

Evaluation metrics are a key ingredient for progress of text generation systems. In recent years, several BERT-based evaluation metrics have been proposed (including BERTScore, MoverScore, BLEURT, etc.) which correlate much better with human assessment of text generation quality than BLEU or ROUGE, invented two decades ago. However, little is known what these metrics, which are based on black-box language model representations, actually capture (it is typically assumed they model semantic similarity). In this work, we use a simple regression based global explainability technique to disentangle metric scores along linguistic factors, including semantics, syntax, morphology, and lexical overlap. We show that the different metrics capture all aspects to some degree, but that they are all substantially sensitive to lexical overlap, just like BLEU and ROUGE. This exposes limitations of these novelly proposed metrics, which we also highlight in an adversarial test scenario.
LibGuides: Infobase Learning Cloud Research Modules: Hoonuit Information Literacy Modules

Research modules covering: Research Basics, Keyword Generation, Finding Resources, Evaluating Resources, Avoiding Plagiarism and Citing Sources.
IMPLEMENTASI MEKANISME PEMBELAJARAN DARING DI SDN BENTOK KAMPUNG KABUPATEN TANAH LAUT

This study aims to determine the online learning mechanism established at SDN Bentok Kampung, and to determine the supporting and inhibiting factors for the implementation of the online learning mechanism established at SDN Bentok Kampung. The method used is a descriptive method with a qualitative approach. Primary data is in the form of data obtained directly from research subjects, namely in the data collection process at the interview stage, and secondary data in the form of data obtained through intermediary media, namely data on the number, names and addresses of students in written form submitted by the teacher. The data analyzed until it becomes descriptive data. The results of the data summarized and arranged according to the research objectives. The data analysis step used refers to the theory of data analysis techniques proposed by Miles & Huberman in (Sugiyono, 2011: 91), namely data reduction, data display and data verification. Based on the research results, online learning carried out according to the online learning schedule that been made every week by the school. On the schedule of one week, online learning contains only one subject. Online learning carried out on Whatsapp social media by forming groups consisting of one subject teacher and the number of students in one class who have registered. The assignment submitted every Monday. Assignments collected directly from the teacher or by sending files via the WhatsApp group. Supporting factors in online learning at SDN Bentok Kampung are the availability of devices, the availability of good cellular networks, teacher activity, student activity and support and guidance from students’ parents. While inhibiting factors are the availability of devices, unstable cellular networks, and technology-savvy parents.
COSMic: A Coherence-Aware Generation Metric for Image Descriptions

Developers of text generation models rely on automated evaluation metrics as a stand-in for slow and expensive manual evaluations. However, image captioning metrics have struggled to give accurate learned estimates of the semantic and pragmatic success of output text. We address this weakness by introducing the first discourse-aware learned generation metric for evaluating image descriptions. Our approach is inspired by computational theories of discourse for capturing information goals using coherence. We present a dataset of image–description pairs annotated with coherence relations. We then train a coherence-aware metric on a subset of the Conceptual Captions dataset and measure its effectiveness—its ability to predict human ratings of output captions—on a test set composed of out-of-domain images. We demonstrate a higher Kendall Correlation Coefficient for our proposed metric with the human judgments for the results of a number of state-of-the-art coherence-aware caption generation models when compared to several other metrics including recently proposed learned metrics such as BLEURT and BERTScore.
A COMPARATIVE STUDY ON TEACHING LISTENINGCOMPREHENSION USING DRILLING ANDDICTATION WITH AUDIO RECORDING FROMBRITISH COUNCIL IN MTs DARUL HUDAWONODADI BLITAR

ABSTRACTThesis with the title Comparative Study On Teaching Listening CompensationUsing Drilling and Dictation with Audio Recording of British Council in Darul HudaWonodadi Blitar this was written by Irma Novi Rahmania, NIM. 1723143085,superintendent of Dr. Dwi Astuti Wahyu Nurhayati,S.S.M.Pd.Key Words : Teaching, listening, audio recording, drilling, dictation, comparative study,listening comprehensionThe objective of the research is to know whether there is a significant differentability between teaching students using drilling and dictation in listening comprehension forthe third grade of MTs Darul Huda Wonodadi Blitar.Related to the objective of the research, the researcher used the experimental method.This research was conducted in experimental research by using a quantitative approach. Thequantitative approach is every kind of number, not data or the research based on the test.Quantitative research could be defined as the research that uses of collecting data like anumber and the analysis data use of statistics. The purpose of the experimental study is todetermine cause and effect relationship. The research was conducted at MTs Darul HudaWonodadi Blitar, in academic year 2017/2018. The population in this research is the thirdgrade students of MTs Darul Huda Wonodadi Blitar. The sampling of the research is clusterrandom sampling. From the population of MTs Darul Huda Wonodadi Blitar, two classeswere taken randomly as the sample. Both of the samples are the experimental group that isclass XI A and class XI B. Both of them consist of 21 students. This research uses aquantitative approach with factorial design. The technique of collecting data used a test. Thedata were analyzed by using t-test formula and pre-requisite test (normality and homogeneitytest).The finding of this research states that there was any significance different abilitybetween teaching students by using drilling and dictation in listening comprehension. It couldbe seen from the result of the normality of pre-test is in the normal distribution and thehomogeneity test is in the significant of 0,64. And the normality of post-test is in the normaldistribution and the homogeneity test is in the significant of 0,21. The paired samples statisticin pre-test score is in the mean 94,9 and the standard deviation is 3,93, the post-test score isin the mean 85,0 and the standard deviation is 9,00. The paired samples correlations in thepre-test and post-test are 0,50 in the significant of 8.31. Thus, the result of the research studyimplied that there was a different ability between students taught by using drilling and thosetaught by dictation with the audio recording of the British council on teaching listeningcomprehension.