Reviewerly/exHarmony huggingface dataset:
Copyright as an Obstacle or an Enabler? A European Perspective on Text and Data Mining and Its Role in the Development of AI Creativity

Text and data mining (TDM) may be performed in a variety of fields and for different purposes. Among other things, TDM techniques may be used to ‘train’ Artificial Intelligence (AI), also for the purpose of AI-driven creativity. In this context, copyright restrictions might be in place, even if copies made of pre-existing content are only used internally and are instrument to the creation of something ‘new’. Recently, in the context of the Directive 2019/790 on copyright in the Digital Single Market, the EU legislature introduced two new mandatory exceptions for TDM. This contribution discusses the interplay between TDM and AI creativity by focusing, first, on the potential and technicalities of TDM and the interplay with legal restrictions. Second, it reviews the copyright-related issues facing TDM and the debate underlying the adoption of this new piece of EU legislation. Third, it reflects on the future of AI creativity in Europe.This article concludes that, even despite the adoption of two new mandatory TDM exceptions in the DSM Directive which are now to be transposed by individual EU Member States, copyright restrictions might continue affecting and restricting significantly the possibility of undertaking TDM activities in Europe.
Designing a Digital Service for Users with Low Understanding of a Language : A Case Study

During the last decades Europe has seen an increase in the number of refugees coming to the region. When arriving in a new country one faces several challenges to overcome. In Sweden, a lot of the ...
Cluster & Tune: Enhance BERT Performance in Low Resource Text Classification

In data-constrained cases, the common practice of fine-tuning BERT for a target text classification task is prone to producing poor performance. In such low resources scenarios, we suggest performing an unsupervised classification task prior to fine-tuning on the target task. Specifically, as such an intermediate task, we perform unsupervised clustering, training BERT on predicting the cluster labels. We test this hypothesis on various data sets, and show that this additional classification step can reduce the demand for labeled examples. We further discuss under which conditions this task is helpful and why.
자서전과 수업 실천 분석을 통한 교사의 전문적 정체성 이해

This study aims to explore a teacher’s professional identity, which is expressed through a curriculum perspective. Researchers analysed a teacher’s autobiographical journals and interviews to understand her/his identity and re-interpreted this identity based on an analysis of her/his actual teaching behaviour. By analysing her autobiographical journal and her interview to explore the past, present, and future, the teacher understood in depth that she was following a learner-centered perspective. By analysing her teaching practice, she could clarify her viewpoint better, develop a new perspective, and experience a reconstructing of herself while realizing, with a sense of embarrassment, that her behaviour in classroom practice was different from her own perspective. These results suggest the need for a teacher education program using the autobiographical method to reinterpret one’s classroom behaviour to become aware of one’s professional identity more clearly and deeply.
Adaptiveness and Lock-free Synchronization in Parallel Stochastic Gradient Descent

The emergence of big data in recent years due to the vast societal digitalization and large-scale sensor deployment has entailed significant interest in machine learning methods to enable automatic data analytics. In a majority of the learning algorithms used in industrial as well as academic settings, the first-order iterative optimization procedure Stochastic gradient descent (SGD), is the backbone. However, SGD is often time-consuming, as it typically requires several passes through the entire dataset in order to converge to a solution of sufficient quality. In order to cope with increasing data volumes, and to facilitate accelerated processing utilizing contemporary hardware, various parallel SGD variants have been proposed. In addition to traditional synchronous parallelization schemes, asynchronous ones have received particular interest in recent literature due to their improved ability to scale due to less coordination, and subsequently waiting time. However, asynchrony implies inherent challenges in understanding the execution of the algorithm and its convergence properties, due the presence of both stale and inconsistent views of the shared state. In this work, we aim to increase the understanding of the convergence properties of SGD for practical applications under asynchronous parallelism and develop tools and frameworks that facilitate improved convergence properties as well as further research and development. First, we focus on understanding the impact of staleness, and introduce models for capturing the dynamics of parallel execution of SGD. This enables (i) quantifying the statistical penalty on the convergence due to staleness and (ii) deriving an adaptation scheme, introducing a staleness-adaptive SGD variant MindTheStep-AsyncSGD, which provably reduces this penalty. Second, we aim at exploring the impact of synchronization mechanisms, in particular consistency-preserving ones, and the overall effect on the convergence properties. To this end, we propose LeashedSGD, an extensible algorithmic framework supporting various synchronization mechanisms for different degrees of consistency, enabling in particular a lock-free and consistency-preserving implementation. In addition, the algorithmic construction of Leashed-SGD enables dynamic memory allocation, claiming memory only when necessary, which reduces the overall memory footprint. We perform an extensive empirical study, benchmarking the proposed methods, together with established baselines, focusing on the prominent application of Deep Learning for image classification on the benchmark datasets MNIST and CIFAR, showing significant improvements in converge time for Leashed-SGD and MindTheStep-AsyncSGD.
STRATEGI PEMBELAJARAN UNTUK MENGATASI SISWA YANGMENGALAMI KESULITAN BELAJAR DISLEKSIA

Dyslexia learning difficulties are a student problem that must be considered by the teacher, or parents. Dyslexia students must be detected as early as possible in order to be overcome, properly and in finding the right solution. Students who have difficulty learning dyslexia will, experience things that become distractions for themselves during the learning process, and this will be a danger if left unchecked. Both teachers and parents must be able to recognize the characteristics of students who have difficulty learning dyslexia, then find out the factors causing these difficulties. After knowing these two things, there needs to be a strategy used to overcome the learning difficulties of dyslexic students, which of course is not easy to guide it requires patience and is done gradually.The purpose of this study is to: (1) Explain the characteristics of dyslexic student learning difficulties, (2) Explain the factors that cause dyslexic learning difficulties, (3) Explain the strategies or methods used in overcoming dyslexia learning difficulties.The approach used in this research is a qualitative approach, with the research method being Literature Study or Literature Study. Due to the existence of Covid - 19 which makes researchers have difficulty in retrieving data, therefore researchers are looking for sources from literature in the form of Books, Journals, Articles, Previous Thesis, and also a Thesis that is relevant to research.Research results are presented from several journals, articles, theses and also a thesis that has been analyzed by researchers to obtain data on findings, can be seen from several strategies or methods used that are very diverse. This is where the role of the teacher in overcoming learning difficulties dyslexia is needed, a teacher must be able to guide students who experience dyslexia. The method they used on average was successful even though it was done in stages. Whether or not the strategy or method is used successfully is not a problem, the most important thing is students who have difficulty learning dyslexia must be handled specifically, requiring more attention compared to other normal students
Geophysical Research Results of Archaeological Sites on The Site of Barun-Hal II in The Baikal Region

Summary The purpose of this work is to analyze the results and assess the capabilities of geophysical methods in the area of the Chernorud practice base. Recent decades have been marked by the formation of a new direction in applied earth sciences - archaeo-geophysics. The use of geophysical methods has proved particularly effective in the early, reconnaissance stages of archaeological research. Archaeological excavations are very labour-intensive, without the use of geophysics. Therefore, based on the above, archaeologists are very interested in using geophysical methods to replace continuous excavations with selective ones. A task has been set: to analyze the results of the work already carried out with the help of magneto-exploration methods and electrotomography in the area of Barun Hal II.
Measures of Harm from Heavy Metal Pollution in Battery Technician within Ilorin Metropolis, Kwara State, Nigeria

Soil pollution with Heavy Metals (HMs) has been of much interests lately and is one of the major issues to be faced globally and requires attention because heavy metals above their normal ranges are extremely threatened to both biotic and abiotic life. It was therefore of interest to conduct study to assess the extent of heavy-metal contamination of soils within battery technicians’ workshops within Ilorin metropolis, Kwara State, Nigeria. A total of twenty-five composite soil samples were collected from six selected battery charger workshop within Ilorin metropolis and analyzed for the presence of heavy metals using atomic absorption spectrophotometer. Result reveals significant positive relationship between Mn and Fe (r=0.511**, p<0.001), Mn and Cu (r=0.565**, p<0.001), Fe and Cr (r=0.895**, p<0.001), Fe and Cu (r=0.823**, p<0.001) and between Cr and Cu (r=830**, p<0.001). Result also shows significant negative relationship between Mn and Cr (r=-0.679**, p<0.001), Pb and Cu (r=-0.468*, p<0.05) respectively. The pollution status of heavy metals in soils was evaluated using quantitative indices (pollution index–PI). The result shows that Zn was moderately contaminated while other heavy metals (Pb, Cd, Cr and Cu) had very slight contamination (pollution index<0.1). The Ilorin metropolis soils of Kwara State were found to have a moderate to very slight contamination respectively. Large variations in PI values of Zn revealed that soil in those areas of the city, which are influenced by anthropogenic activities, have moderate concentrations of Zn resulting in “considerable risk”. The findings of this study recommend comprehensive continuous annual monitoring and auditing and further studies on the level of these heavy metals in the near future to ascertain long-term effects of anthropogenic impact is forestalled to protect the men and the environment. This should also involve larger coverage with studies on ground water around such locations. Furthermore, continuous metals speciation should be carried out so that the form and extent of metal bioavailability can be evaluated further.
Teaching reading comprehension through SMART strategy to the tenth graders of vocational high school

The objective of this research was to find out whether or not it was significantly effective to teach reading comprehension through self-monitoring approach to reading and thinking (SMART) strategy to the tenth-grade students of SMK Pertanian Negeri 2 Tugumulyo. The method applied in this research was quantitative with the pre-experimental method. The total number of population was 130 students of the tenth-grade students of SMK Pertanian Negeri 2 Tugumulyo. The sample of this research had taken by using cluster random sampling. The number of the sample was 33 students of class X.2. The instrument of the research was in form of multiple-choice tests. The data were analyzed through four techniques: individual score, conversion based on minimum mastery criteria (MMC), normality testing, and paired t-test. Based on the result of data analysis, the writers found that there were: the studentsâ€™ average score in the pre-test was 58.18 and the post-test was 76.36, normality testing in the pre-test was 2.0724 and the post-test was 4.471, and the result of paired t-test was 6.401, which was higher than 1.697 with 95% (0.05) significance level for one tailed-test. It means that the null hypothesis (Ho) was rejected and the alternative hypothesis (Ha) was accepted. In conclusion, it was significantly effective to teach reading comprehension through Self-monitoring approach to reading and thinking (SMART) strategy to tenth-grade students of SMK Pertanian Negeri 2 Tugumulyo.
A Corpus-Based Word Sense Disambiguation For Geez Language

Abstract In natural language processing, languages have a number of ambiguous words and solving such kind of problem for the language can help the development of word sense disambiguation using corpus­based Approach. Due to the absence of automatic word sense disambiguation to the language can be a challenge for the development of natural language processing applications of the language. So, this study was to design a word sense disambiguation prototype model for Geez Language words using Corpus-Based technique to extract training sets that minimize the quantity of the desired human intervention. because of the inaccessibility of the Geez wordNet and annotated datasets, six words were Chosen.These words were ሀለፈ(Halafe), ቆመ(ḱome), ባረከ(bareke), አስተርዓየ(astaraya), ገብረ(gebire), ሰዓለ(Se’ale).Separate information sets victimization six ambiguous words were ready for the event of this Geez WSD model. the ultimate classification task was done on the absolutely labeled coaching set victimization Adaboost, SMO, and AD tree classification algorithms on the WEKA package.We compared the Corpus-­based machine learning approachs which are unsupervised, supervised and semi-supervised and we found that semi­-supervised machine learning approach achieved the best performance. The proposed method achieved an average performance of 92.1%, 91.3%, 91% and 91.1% for Precision, Recall, F1­-score and Accuracy using ADTree algorithm respectively. Window size of 4-­4 has been the optimal window size to identify the meaning of the selected ambiguous words of Geez language using ADTree algorithm.
Principles for the Development of Domain Conceptual Models for Knowledge Organization Systems: An Analysis of Methodologies for Developing Learning Paths in the Field of Corporate Education

This article presents a set of principles for knowledge modeling in knowledge organization systems in specific domains. It discusses the representational problem, comparing the abstraction mechanisms present in the theories related to representation in concept systems, taken from foundational authors of information science, computer science, and terminology approaches. Parallel to this context, several representational possibilities arise to assist the modeler in the activity of elaborating models of representation. It describes the application of theoretical and methodological principles when organizing, representing, and managing navigation on learning paths in the corporate education field. As a concept proof, it exposes a conceptual model of learning paths and discusses a literature review on this subject to verify to what extent these principles are being applied. It concludes that we can consider the principles discussed in this study as relevant, since they expand the modelers’ freedom, not making him hostage to a specific model.
EDUCATION OF THE FUTURE: COLLABORATION OF ONLINE AND OFFLINE EDUCATION. CHALLENGES AND PROSPECTS

The changes taking place in the postindustrial society impose requirements on the introduction of fast and cheap ways of generating and transferring knowledge. E-learning seems to be the tool that can solve this serious problem of modern society in the context of digitalization. The catalyst for the development of open education is the crisis that the system of traditional higher education is currently experiencing. In the study carried out by the authors, the crisis processes in modern higher education have been studied and the existing tendencies to overcome them have been identified. Based on the analysis of the essence of open education, its strengths and the problem field, the authors concluded that the research of the collaboration of online and offline education and management of this system becomes a priority within the framework of the problem under study, which contributes to the innovative nature of the development of higher education. schools. According to the authors, it is the collaboration of online and offline education that will solve a large number of methodological, organizational, and financial problems arising in distance learning. At the same time, in order to use the advantages of online learning, such as mobility, convenience, maximum personalization, openness and accessibility, lower cost, remote work performance, it seems necessary to change the conditions and opportunities for interaction between participants in the educational process.
Text to SQL Query Conversion Using Deep Learning: A Comparative Analysis

In relational databases, a significant amount of world’s knowledge is stored. The ability of users to retrieve facts from a database is limited due to a lack of understanding of query languages. Intersection of Natural Language Processing (NLP) and human-computer intersection is the Natural Language Interface (NLI) which provides humans to interact with computer through the use of natural language. Here, NLI applied to relational databases translating Natural Language Queries to Structured Query Language (SQL). The proposed system is a NLP based system for converting natural language queries to database queries using Deep Neural Network. Spider, a new large-scale, cross-domain semantic parsing and text-to-SQL data set annotated by 11 college students released on November 2018 is used here. The system uses a deep learning framework such as basic sequence to sequence (Encoder-Decoder Model) and sequence to sequence plus attention. The model takes word embeddings of the query as the input and then sequence to sequence modeling is applied. The system is evaluated on the two models based on the exact matching and hardness criteria of questions. The evaluation results on Spider dataset shows that the proposed two deep learning model improves the results of text-to-SQL task. Spider dataset poses a major challenge for future research.
Reducing the psychological burden of isolated oncological patients by means of decision trees

This century has seen several outbreaks of epidemics caused by a common sub-family of coronaviruses such as the responsible for COVID-19 outbreak The most ominous variants have developed a peculiar viral mechanisms that allows the virus to directly attack the pulmonary tissues often causing a set of dangerous symptoms It made quite evident that we need a global response to prepare health systems for future epidemics Unfortunately, during such kind of diseases’ outbreaks a large amount of time is required to the caregivers for sanitization and cleaning operations, therefore tampering with number and duration of visits to patients, especially in oncology wards Such patients are then left alone for a long time, it follows that their perceived quality of service is greatly diminished, often determining ill-fated consequences also on the psychological side, with significant fallbacks on the recovery possibilities and speed In this paper we explore an algorithmic approach to automatic communication interfaces that could enhance and enforce the perceived quality of care by the patients in in order to reduce predisposing factors that could potentially tamper with the patient’s ability to recover, also preventing the occurrence of precipitating factors that could lead a therapy to complete failure The proposed interface could be used to connect the patients with a psychological support when it is most needed, and, moreover, to connect them with their physicians and families, and also to the outside world In particular we aim to provide the psychological support that is actually excluded in pandemics such as the COVID-19 emergency, mainly in order to enforce the healthcare and sanification protocols, due to its potential unsafety related to the introduction of more personnel into the hospital © 2020 Copyright for this paper by its authors
Research guides: Troubleshooting e-resources: Permalinks

Tips for accessing Camosun Library e-resources such as e-journals, e-books, databases, streaming videos on or off campus. Explains how to access and link to Camosun Library licensed eresources such as database articles, streaming videos, or e-books.
AUTONOMOUS REMOTE CONTROLLED CAR USING MACHINE LEARNING TECHNIQUES

As an autonomous car, a car that can move on its own without being dependent on human outputs. In the automotive sector, there has been a huge advancement that brings fresh innovations every day. We addressed the implementation of the autonomous car using machine learning methods in this paper. We used a remote-controlled car with a Raspberry Pi module and a Raspberry Pi camera module installed on top. The camera module would provide pictures required to train the neural network and in the automated mode in the training mode would provide the pictures to the qualified template to estimate the car's motions and orientation.
How to Drive Traffic To Your E-Commerce Website Using SEO?

Are you running an eCommerce website and selling products directly to the customers? Then more traffic is the only goal for you. Because more traffic ...
Uncovering the impact of hyperparameters for global magnitude pruning

A common paradigm in model pruning is to train a model, prune, and then either fine-tune or, in the lottery ticket framework, reinitialize and retrain. Prior work has implicitly assumed that the best training configuration for model evaluation is also the best configuration for mask discovery. However, what if a training configuration which yields worse performance actually yields a mask which trains to higher performance? To test this, we the hyperparameters for mask discovery (H_find) and mask evaluation (H_eval). Using unstructured magnitude pruning on vision classification tasks, we discovered the decoupled find-eval phenomenon, in which certain H_find values lead to models which have lower performance, but generate masks with substantially higher eventual performance compared to using the same hyperparameters for both stages. We show that this phenomenon holds across a number of models, datasets, configurations, and also for one-shot structured pruning. Finally, we demonstrate that different H_find values yield masks with materially different layerwise pruning ratios and that the find-eval phenomenon is causally mediated by these ratios. Our results demonstrate the practical utility of decoupling hyperparameters and provide clear insights into the mechanisms underlying this counterintuitive effect.