Reviewerly/exHarmony huggingface dataset:
Virtual Technology Based Learning

The goal of the paper is to present the results of the primary research which has been carried out at University of Dubrovnik, Department of Economics and Business and refers to the students' perceptions towards virtual technology (VR) application in the learning process. The experiences vary based on the customer‘s active or passive participation and on absorption or immersion in the experience. According to the results of the primary research which has been carried out in 2020., using the method of personal interview and including the sample of 150 students it is visible that students have positive attitudes towards virtual (VR) and augmented (AR) reality and their implementation in education.
Άρση της αμφισημίας με τον Mnemosyne Tagger

This paper presents the Mnemosyne tagger, a tool for dealing with ambiguity in computing environments. When the POS of a word is ambiguous, the computer must consider all its possible syntactic roles and, if more than one rule is activated, to produce all the structures they dictate. The Mnemosyne tagger is aimed at removing the lexical ambiguity in Modern Greek. It is based not on statistics but on the corresponding linguistic environment of the words. It supports the complete removal of lexical ambiguity not only to remove ambiguity in the POS but also in the gender and in the case of the ambiguous word. The tagger characterizes words that do not exist in the computational lexicon.
Improving Learning Results with Make A Match Method

This research is a literature study that aims to determine the improvement of learning outcomes after applying the make a match learning model, the object of this research is Classroom Action Research and related books, the data collection technique in this study is descriptive qualitative analysis. The results of the analysis in this study indicate that there is an increase in using the make a match learning model, the average percentage of learning outcomes through the make a match model, researchers get an increase from 44% after using the make a match learning model to 89%, so it can be concluded that the make a match learning model can improve student learning outcomes.
SINTESIS Fe3O4/TiO2-Ag DAN UJI AKTIVITASNYA SEBAGAI FOTOKATALIS

Sintesis fotokatalis Fe3O4/TiO2-Ag telah berhasil dilakukan. Sintesis dilakukan secara bertahap yaitu sintesis Fe3O4 menggunakan metode sono-kopresipitasi, kemudian pelapisan TiO2 melalui proses sol-gel, dan dilanjutkan modifikasi permukaan TiO2 dengan nanopartikel perak (Ag). Hasil sintesis material dikarakterisasi dengan spektrofotometri Fourier Transform Infrared (FT-IR), X-Ray Diffraction (X-RD), Scanning Electron Microscopy-Energy Dispersive X-ray (SEM-EDX), Transmission Electron Microscopy (TEM), Specular Reflectance Ultra Violet-Visible (SR UV-Vis) spectrophotometry. Pengujian aktivitas fotokatalis Fe3O4/TiO2-Ag untuk degradasi metilen biru dalam air dilakukan melalui sistem batch dalam reaktor tertutup pada paparan sinar UV dan tampak. Hasil penelitian menunjukkan bahwa material fotokatalis Fe3O4/TiO2-Ag berbentuk partikel bulat dengan ukuran partikel sebesar 12,3 nm dan responsif pada daerah serapan sinar tampak. Nilai Energi celah pita fotokatalis Fe3O4/TiO2-Ag dengan rasio berat Ag/TiO2 0, 1, 3, 5, dan 8 % secara berturut-turut sebesar 3,11; 2,93; 2,88; 2,82; 3,02 eV. Degradasi metilen biru dalam air 5 ppm terkatalisis 5 mg Fe3O4/TiO2-Ag 5 % pada pH 10 dan waktu reaksi 120 menit sebesar 73,3 dan 34,2% berturut-turut pada paparan sinar tampak dan UVSynthesis of Fe3O4/TiO2-Ag photocatalyst has been performed. The synthesis was carried out in several stages; synthesis of Fe3O4 by sono-coprecipitation method, next coating TiO2 by sol-gel proccess, then modification of the surface TiO2 by nanoparticle silver (Ag). The characterization was conducted by infrared spectrophotometry, X-Ray Diffraction (X-RD), Scanning Electron Microscopy-Energy Dispersive X-ray (SEM-EDX), Transmission Electron Microscopy (TEM), Specular Reflectance Ultra Violet-Visible (SR UV-Vis) spectrophotometry. The activity test of Fe3O4/TiO2-Ag photocatalyst was evaluated by batch system for degradation methylene blue in water on using closed reactor with UV and visible irradiation. The result showed that Fe3O4/TiO2-Ag photocatalyst shaped sphere particle with a size of 12.3 nm and enhanced under visible light absorption. The band gap energy of Fe3O4/TiO2-Ag photocatalyst with Ag/TiO2 weight ratio of 0, 1, 3, 5, and 8% were: 3.11; 2.93; 2.88; 2.82; 3.02 eV, respectively. Degradation of methylene blue solution 5 ppm catalyzed by 5 mg Fe3O4/TiO2-Ag 5% at a pH of 10 and with the reaction time of 120 minutes gave the percentage of degradation were 73.3 and 34.2 % under visible and ultraviolet irradiation, respectively.
SAR Target Configuration Recognition Based on Integrated Graph Model

Focusing on the problem of target configuration recognition in synthetic aperture radar (SAR) images, a recognition method using an integrated graph model (IGM) is proposed in this paper. The information of the samples is preserved in different views. To achieve stronger discriminative power, a modified sparse graph employing sign correction of the sparse coefficients is proposed to describe the relationships of the samples. Furthermore, to realize more complete information capturing, the classical Euclidean distance-based affinity matrix together with the proposed modified sparse graph are fused to achieve an integrated graph. Experimental results on the moving and stationary target acquisition and recognition (MSTAR) datasets verify the effectiveness of the proposed method. Satisfying configuration recognition results can be obtained by using the proposed methods.
Επεξεργασία και ανάλυση ηλεκτροεγκεφαλογραφικών σημάτων για την αναγνώριση συγκέντρωσης του υποκειμένου

In the last years, the use of Electroencephalogram (EEG) signals in the field of MachineLearning has obtained a lot of interest, since it would be very useful to deeply understandthese signals. In this context, the aim of the present thesis is to create an algorithm, which willuse humans’ EEG signals in order to recognize whether or not these humans are concentratedduring the measurement (Attentiveness Recognition System).To develop this system, an open access dataset of EEG signals was used. These signals wereprocessed digitally so they are suitable to be fed in the Pattern Recognition System that wasbuilt for the purpose of this thesis. This System analyzes the EEG signals, employing FeatureExtraction and Feature Selection techniques, in order to classify them depending on theconcentration of the volunteers during the EEG measurement. Thus, the data were classifiedinto two classes; the signals which correspond to volunteers who were concentrated belong tothe first class, and the signals which correspond to volunteers who were in relaxed-notconcentrated state, belong to the second class.The results of the System show that it is possible to successfully discriminate and classifyEEG signals according to the concentration of the subject.
DATA MINING FOR DETERMINING BOOK LOAN PATTERNS IN-LIBRARY USING APRIORI ALGORITHM

Data mining is the process of exploring the added value of knowledge so far not manually known from a data set using these techniques or methods. Data mining is a new science that has its roots in various fields of science such as artificial intelligence, machine learning, statistics, and databases. In the process of borrowing books, of course raw data will be processed by dividing it into different pieces of data. Among the lending data tables processed are general lending tables, 2-itemset candidate tables, lending tabular tables, support value tables, confidence value tables.
Applying and Verifying an Explainability Method Based on Policy Graphs in the Context of Reinforcement Learning

The advancement on explainability techniques is quite relevant in the field of Reinforcement Learning (RL) and its applications can be beneficial for the development of intelligent agents that are understandable by humans and are able cooperate with them. When dealing with Deep RL some approaches already exist in the literature, but a common problem is that it can be tricky to define whether the explanations generated for an agent really reflect the behaviour of the trained agent. In this work we will apply an approach for explainability based on the creation of a Policy Graph (PG) that represents the agent’s behaviour. Our main contribution is a way to measure the similarity between the explanations and the agent’s behaviour, by building another agent that follows a policy based on the explainability method and comparing the behaviour of both agents.
Russian SuperGLUE 1.1: Revising the Lessons not Learned by Russian NLP-models

In the last year, new neural architectures and multilingual pre-trained models have been released for Russian, which led to performance evaluation problems across a range of language understanding tasks.This paper presents Russian SuperGLUE 1.1, an updated benchmark styled after GLUE for Russian NLP models.The new version includes a number of technical, user experience and methodological improvements, including fixes of the benchmark vulnerabilities unresolved in the previous version: novel and improved tests for understanding the meaning of a word in context (RUSSE) along with reading comprehension and common sense reasoning (DaNetQA, RuCoS, MuSeRC).Together with the release of the updated datasets, we improve the benchmark toolkit based on jiant framework for consistent training and evaluation of NLP-models of various architectures which now supports the most recent models for Russian.Finally, we provide the integration of Russian SuperGLUE with a framework for industrial evaluation of the open-source models, MOROCCO (MOdel ResOurCe COmparison), in which the models are evaluated according to the weighted average metric over all tasks, the inference speed, and the occupied amount of RAM.Russian SuperGLUE is publicly available at https: //russiansuperglue.com/.
Relaxed Wasserstein, Generative Adversarial Networks, Variational Autoencoders and their applications

Author(s): Yang, Nan | Advisor(s): Guo, Xin | Abstract: Statistical divergences play an important role in many data-driven applications. Two notable examples are Distributionally Robust Optimization (DRO) problems and Generative Adversarial Networks (GANs).In the first section of my dissertation, we propose a novel class of statistical divergence called Relaxed Wasserstein (RW) divergence, which combines Wasserstein distance and Bregman divergence. We begin with its strong probabilistic properties, and then to illustrate its uses, we introduce Relaxed Wasserstein GANs (RWGANs) and compare it empirically with several state-of-the-art GANs in image generation. We show that it strikes a balance between training speed and image quality. We also discuss the potential use of Relaxed Wasserstein to construct ambiguity sets in DRO problems.In the second section of my dissertation, we show the application of another type of generative neural network, the Variational AutoEncoder (VAE), to metagenomic binning problems in bioinformatics. Shotgun sequencing is used to produce short reads from DNA sequences in a sample from a microbial community, which could contain thousands species of discovered or unknown microbes. The short reads are then assembled by connecting overlapping subsequences and thus forming longer sequences called contigs. Metagenomic binning is the process of grouping contigs from multiple organisms based on their genomes of origin. We propose a new network structure called MetaAE, which combines compositional and reference-based information in a nonlinear way. We show that this binning algorithm improves the performance of state-of-the-art binners by 20\% on two independent synthetic datasets.
Development of Bluetooth Enabled (BLE) IOT Digital Surveillance Contact Tracer and Smart Contact Tracing Index

The world has ingresses into Smart Healthcare era in which, it is a construct under the context of Smart City, in making solutions in healthcare to be smart. Even with extensive advancements in smart healthcare solutions, the world especially Malaysia were caught unprepared for smart contact tracing solutions in facing Covid-19 pandemic. Although four main contact tracing solutions were developed by Malaysia and implemented nationwide, there is still no solution yet to made available for the front liners in preventing asymptomatic human-to-human Covid-19 infection. Thus, this study will serve as an applied research in closing the real-life problem stated, by developing Bluetooth Enabled (BLE) IOT digital surveillance contact tracer up to a stage that it is deployment-ready. Not just that, a secondary exploratory research in developing Smart Contact Tracing Index which is non-existent at the moment, also will be conducted.Purpose – The purpose of this study is to develop Bluetooth Enabled (BLE) IOT digital surveillance contact and to develop Smart Contact Tracing Index.
Influence of Knowledge Management Capabilities on the Performance of Mobile Telephone Firms in Kenya

With intensifying rivalry in the mobile telephone industry, mobile telephone firms are finding knowledge management capabilities as vital resource for ensuring their performance. However, there is scanty empirical literature to help these firm associate knowledge management capabilities to their performance, hence this study which sought to ascertain the inspiration of knowledge management capabilities on the performance of mobile telephone firms in Kenya. it objectives were to; establish the influence of knowledge acquisition, find out the influence of knowledge application, determine the role of knowledge transfer, ascertain how knowledge protection influence, and stablish the joint effect of knowledge management capabilities (knowledge acquisition, knowledge application, knowledge transfer and knowledge protection) on the performance of the mobile telephone firms in Kenya. This study adopted descriptive survey design where it used a target population of the 63 heads of human resources, information marketing and finance from the 21 telephone firms operating in Kenya. Since the target population was easily manageable and readily accessible, census was used. The data which was collected data from primary sources using questionnaires was analysed using quantitative approach to produce descriptive and inferential statistics. In addition, the study tested for its hypothesis testing using; t-test,. F test and Analysis of Variance. The study found that each of; knowledge acquisition, knowledge application, knowledge transfer and knowledge protection have a positive significant effect of performance. The study concluded that on embracing knowledge management capabilities; knowledge acquisition; knowledge application; knowledge transfer; and knowledge protection, mobile telephone firms in Kenya experienced significant improved in performance and as they also gained competitive advantage over their competitors.
Analysis And Design Of Accounting Information Systems Using Single Step Method Case Study Of Enslave Convection

Along with its development, the company must experience the development of business activities. The development of business activities results in an increase in the amount of data and will require greater storage capacity, activities will become more complex and control will be more difficult. Therefore, it is necessary to create an automated procedure that can process data regarding the company's business activities, store it, and distribute it. Sales activity in a trading company can be said as the main activity. Because without sales, a trading company can be said to be dead. Convection Enslave was established in 2013 where 4 years earlier to be precise in 2009. The problem that often occurs is that in keeping the books of transactions that have occurred, companies are often faced with various obstacles such as unrecorded data, or miscalculation of income, so that it affects the profit calculation. make a loss. In the single step method, all revenues (Operational Income, Other Income) are grouped into one, namely in the income group and all expenses (Operating Expenses, Other Expenses) are also grouped into one, namely in the expense group, the income group is placed at the beginning of the report and added up, then the expenses are added up, to get the company's net profit/loss, the income is reduced by the expenses.
Object Annotation Using Cost-Effective Active Learning

Deep learning models require large amount of training data to reach high accuracies. However, labeling large volumes of training data is a labor-intensive and time-consuming process. Active learning is an approach that seeks to maximize the performance of a model with the least possible amount of labeled data. It is of great practical importance to develop a framework by combining deep learning and active learning methods that transfer features from a small number of unlabeled training data to classifiers. With this study, we combine active learning and deep learning models, which allows for fine-tuning deep learning models with a small number of training data. We use images of shelf products belonging to the same product group with 13 classes and examine them using different deep learning classifier models. Experimental results show that we are able to achieve higher performance by annotating and using a part of the data for training compared to annotating and using the entire dataset. This way, we save from the annotations costs, and at the same time reach an improved object classification system.
基於Meta Pseudo Labeling之半監督聯邦式學習

Due to the rising importance of data privacy, Federated Learning (FL) solved this issue very well by transmitting model parameters instead of raw data. Even under the EU General Data Protection Regulations (GPDR) restrictions, FL can still collaborate with various companies without disclosing private data. However, compared to centralized learning, in addition to the impact of not identically and independently distributed (Non-IID), the limited labeled data issue is more critical under the setting of FL. Unlike centralized learning, which can seek the help of human resources and experts, the data of Federated Learning can only be labeled by the users who own it. Hence, users are more likely to face the problems of Time-Consuming and Domain Know-How. Although current research attempts to enhance model capabilities with Semi-Supervised Learning (SSL), incorrect predictions on unlabeled data will cause confirmation bias, making the model learn from wrong pseudo labels and unable to learn from unlabeled data successfully. We introduce a novel SSL called Meta Pseudo Labels (MPL), which uses the Teacher-Student architecture and the feedback mechanism between models to reduce confirmation bias. Under the FL setting, MPL can be divided into three architectures: FedAvgTc, FedAvgSd, or FedAvgBoth, as the architecture choices for the Federated Average (FedAvg). Through experiments, we can get the best choice between the quality of the network and the model's performance. Moreover, the Non-IID issue will cause weight divergence after FedAvg iteration. The Teacher will produce pseudo labels of different qualities after FedAvg. And MPL through the feedback mechanism causes the training process to be a loop; the communication between the Teacher and Student model is not direct enough. We use the concept of Pseudo-Ensemble for model space noise and add a Shortcut between the Teacher and Student model, bringing the model space into consistency regularization. In the experimental results, the model can still maintain a confident performance after adding Shortcut even when the data is Non-IID.
한국인 초급 일본어 학습자의 작문교육에 관하여 －오용 실태조사를 중심으로－

Among the conventional studies on composition, the analysis of misuse produced by Japanese learners has been actively conducted, but very few studies have reviewed effective guidance methods based on the results of the study. Accordingly, this study was to extract linguistic items through the investigation and analysis on the composition data prepared by Korean beginner Japanese learners, and to grope for the teaching method of effective composition by reviewing the characteristics and causes of misuse by each item. As a result of the investigation, for the linguistic items of high misuse frequency, this study has shown that it took (1) particles, (2) misuse in expression, (3) verbs, (4) いadjectives, (5) transcription of foreign words, (6) Chinese characters, (7) な adjectives (8) demonstratives, (9) giving and receiving expressions, (10) conjunctions in order. It is true that there are many elements common to Korean and Japanese, so it is advantageous to Korean learners, but by recklessly translating directly, we can see that there is a lot of misuse. Therefore, in the case of composition education, it would be necessary to present the items of composition error as language items that should be particularly noted to learners and to provide intensive guidance. In addition, it needs to exclude the influence by the learner’s mother tongue as well as it will have to guide natural Japanese expressions at the same time.
Information System Risk Management Analysis Using Octave-S Method

Currently the development of information technology is growing rapidly, therefore every cooperative must use information technology for cooperative members themselves easily and get facilities in the form of Savings and Loans. Even if the employee does not have the status of a civil servant, he can still be part of a special member for all health workers throughout the Sumedang area. To analyze the application of risk management to Savings and Loans by Analyzing the application of risk management to Savings and Loans Using the Octave-s Method then tested for validity and reliability. The stages of the OCTAVE-S method that are applied in the form of risk mitigation recommendations include, management must accept and act on routine reports that summarize security-related information, the organization makes documentation, reviews and tests the plans that have been set, makes security procedures to maintain important facilities. . This research was conducted to determine the risks that may occur in the Savings and Loans Information System. Technique of measuring data using the help of Microsoft Excel application program. Based on the results of the data measurement, it can be seen that from the 15 statements in the questionnaire 30 items. So the conclusion is that in order to be able to reduce the situation that will occur assisted by risk management that is applied periodically to suppress any risks that arise, they can be solved together
NP2021_Cite Data. Link Data

Slides from “Linked data and the future of information sharingDescription: Research data are valuable contributions to the scientific record along with papers, software, methods, and more. Understanding their connections to the progress of science is important as we continue to address the complexities of our world. Journals that cite data, include ORCIDs for authors, and connect to funding identifiers, help to build the links we need to discover how our work is connected with others in new ways. Our two expert speakers will share their views on the current state of data as a linked research output, how it continues to be recognized for its value in the scientific community, what the future brings, and what is still needed to get us there.