Reviewerly/exHarmony huggingface dataset:
A Hidden Markov Model-based Part of Speech Tagger for Shekki’noono Language

Natural language processing plays a great role in providing an interface for human-computer communication. It enables people to talk with the computer in their formal language rather than machine language. This study aims at presenting a Part of speech tagger that can assign word class to words in a given paragraph sentence. Some of the researchers developed parts of speech taggers for different languages such as English Amharic, Afan Oromo, Tigrigna, etc. On the other hand, many other languages do not have POS taggers like Shekki’noono language. POS tagger is incorporated in most natural language processing tools like machine translation, information extraction as a basic component. So, it is compulsory to develop a part of speech tagger for languages then it is possible to work with an advanced natural language application. Because those applications enhance machine to machine, machine to human, and human to human communications. Although, one language POS tagger cannot be directly applied for other languages POS tagger. With the purpose for developing the Shekki’noono POS tagger, we have used the stochastic Hidden Markov Model. For the study, we have used 1500 sentences collected from different sources such as newspapers (which includes social, economic, and political aspects), modules, textbooks, Radio Programs, and bulletins. The collected sentences are labeled by language experts with their appropriate parts of speech for each word. With the experiments carried out, the part of speech tagger is trained on the training sets using Hidden Markov model. As experiments showed, HMM based POS tagging has achieved 92.77 % accuracy for Shekki’noono. And the POS tagger model is compared with the previous experiments in related works using HMM. As a future work, the proposed approaches can be utilized to perform an evaluation on a larger corpus.
Development of Mathematics E-Books in Improving Mathematical Literacy and Entrepreneurial Spirit

This study aimed to create an e-book on mathematics learning for vocational high school students in grade XI, complete with liner program material, using the ADDIE model development methodology (Analysis, Design, Development, Implementation, Evaluation). Based on the findings of the development of e-books on linear programming material for grade XI students at vocational high school in Langsa Aceh majoring in Online Business and Marketing, it can be concluded that the media is valid for use in learning mathematics with linear programming materials, then effective in improving mathematical literacy and entrepreneurial spirit. Finally, practical media is used in mathematics learning, among other conclusions.
Empirical Analysis of Static and Dynamic Stopword Generation Approaches

In natural language processing, stopwords are considered as less meaningful words and contribute to reducing the speed of language processing. Stopwords are removed to ensure fast text processing and to decrease the size of the text. In this paper, experimental analysis of two different stopword list generation approaches—static and dynamic are discussed. In the static approach, we have implemented the classic approach, and in the dynamic approach, we have implemented statistical Zipf’s law for automatic identification and removal of stopword list. We have used a news dataset of 2225 documents for five different categories. We have compressed average document size by 44.53% using the static approach and 52.53% using the dynamic approach. This paper also shows the advantages and limitations of using both approaches.
PENGEMBANGAN MEDIA KARTU DOMINO UNTUK MENINGKATKAN MOTIVASI BELAJAR SISWA KELAS IV SD

The purpose of this study is determine the feasibility of domino card learning media in thematic learning and find out the suitability of domino cards to increase learning motivation of fourth grade elementary school students. This research is development research using the Borg and Gall model. The research was conducted at SDN Permanu 02 with the subject of 33 fourth grade students for the 2019/2020 school year. The results showed the feasibility of domino card learning media based on material validation, media validation, group response trials and large group student response trials getting an average rating of 89% with very valid qualifications. The results of student learning motivation in small groups showed the percentage before 58%with enough categories, after the percentage of 89% with very high category.The results of student learning motivation in large groups showed before the percentage of 39% with the low categoryand after percentage of 76%with high category. Based on the data mentioned above, it can be concluded that the domino card learning media is suitable for use in the learning process and can increase the learning motivation of grade IV SD students
QUALIDADE DO SONO EM UNIVERSITÁRIOS NO CONTEXTO DA PANDEMIA DA COVID-19: um olhar multifatorial

The pandemic of COVID-19 was accompanied by losses in emotional, eating habits, and physical activity. University students were uncertain about their academic life, with the paralyzation of face-to-face activities in universities. The study aimed to compare the profile (emotional states, physical activity and eating habits) of university students with self-perception of sleep quality affected by the pandemic (QSA) with those with self-perception of unaffected sleep quality (n-QSA) in the context of the COVID-19 pandemic. Between 01/2021 and 04/2021, students at the Universidade Federal de Goiás completed, via an online tool, an adaptation of the questionnaire Convid-Pesquisa de Comportamentos. 173 students answered this instrument. Data were tabulated and organized, individuals were divided into two groups: the QSA (103) and the n-QSA (70), and comparison was made between the two using Student's t-test and x2 test. For significance, the criterion adopted was p&lt;0.05. The QSA group showed higher demand for mental health care (0.0431*), higher self-perception of high anxiety/nervousness (0.0095*), sadness (0.0040*), and affective isolation (0.0038*) in the pandemic. The QSA group also had a lower level of physical activity (0.0306*) during the pandemic. Furthermore, the QSA group had a lower frequency of whole food consumption before COVID-19 than the n-QSA group. This indicated greater impairment in the emotional states of the QSA group. Good habits are important for proper health.
The Role of Transparency, Trust, and Social Influence on Uncertainty Reduction in Times of Pandemics: Empirical Study on the Adoption of COVID-19 Tracing Apps (Preprint)

<sec> <title>BACKGROUND</title> Contact tracing apps are an essential component of an effective COVID-19 testing strategy to counteract the spread of the pandemic and thereby avoid overburdening the health care system. As the adoption rates in several regions are undesirable, governments must increase the acceptance of COVID-19 tracing apps in these times of uncertainty. </sec> <sec> <title>OBJECTIVE</title> Building on the Uncertainty Reduction Theory (URT), this study aims to investigate how uncertainty reduction measures foster the adoption of COVID-19 tracing apps and how their use affects the perception of different risks. </sec> <sec> <title>METHODS</title> Representative survey data were gathered at two measurement points (before and after the app’s release) and analyzed by performing covariance-based structural equation modeling (n=1003). </sec> <sec> <title>RESULTS</title> We found that uncertainty reduction measures in the form of the transparency dimensions disclosure and accuracy, as well as social influence and trust in government, foster the adoption process. The use of the COVID-19 tracing app in turn reduced the perceived privacy and performance risks but did not reduce social risks and health-related COVID-19 concerns. </sec> <sec> <title>CONCLUSIONS</title> This study contributes to the mass adoption of health care technology and URT research by integrating interactive communication measures and transparency as a multidimensional concept to reduce different types of uncertainty over time. Furthermore, our results help to derive communication strategies to promote the mass adoption of COVID-19 tracing apps, thus detecting infection chains and allowing intelligent COVID-19 testing. </sec>
Improving multi-goal and target-driven reinforcement learning with supervised auxiliary task

Recent works on Deep Reinforcement Learning (DRL) for episodic multi-goal learning have been using the Universal Value Function Approximation (UVFA) idea to learn a universal policy by taking information from the current and goal states as input. Even though they present good results, there is one aspect that might need more attention: the State Representation Learning (SRL) concept. In machine learning, SRL is not a new subject, and there are many works on it, but it is still not very explored for multi-goal learning. Instead, an end-to-end DRL is commonly adopted to learn state representation implicitly. In simple problems, this approach might be enough, but to others, it can make it harder to learn an optimal policy or lead to overfitting. In light of this, we hypothesize that an auxiliary task closely related to the target policy learning can lead to better results by conditioning the SRL, which is essential to encode the latent state space. Also, motivated by the multi-task learning idea, we propose a framework for simultaneous supervised and reinforcement learning to avoid catastrophic forgetting. Taking the visual-based navigation on a topological urban environment as an instance of the multi-goal learning problem, we use semantic segmentation as the auxiliary task. Based on experimental results, we show that our method accelerates the DRL convergence and allows reaching better policies with higher generalization levels.
"Issues and Challenges of using Blockchain for IoT Data Management in Smart Healthcare"

Gerasimos Vonitsanos1, Theodor Panagiotakopoulos2 and Andreas Kanavos2,3* Author Affiliations 1Computer Engineering and Informatics Department University of Patras, Patras, Greece 2School of Technology and Science Hellenic Open University, Patras, Greece 3Department of Digital Media and Communication Ionian University, Kefalonia, Greece Received: November 19, 2021 | Published: November 29, 2021 Corresponding author: Andreas Kanavos, School of Technology and Science Hellenic Open University, Patras, Department of Digital Media and Communication Ionian University, Kefalonia, Greece DOI: 10.26717/BJSTR.2021.40.006423
An Evidence Combination Rule Based on New Weight Assignment Scheme

Abstract Conflicting evidence and fuzzy evidence have a significant impact on the results of evidence combination in the application of evidence theory. However, the existing weight assignment methods can hardly reflect the significant influence of fuzzy evidence on the combination results. Therefore, a new method for assigning evidence weights and the corresponding combination rule are proposed. The proposed weight assignment method strengthens the consideration of fuzzy evidence and introduces the Wasserstein distance to compute the clarity degree of evidence which is an important reference index for weight assignment in the proposed combination rule and can weaken the effect of ambiguous evidence effectively. In the experiments, it's firstly verified that the impact of fuzzy evidence on the combination results is significant; therefore it should be fully considered in the weight assignment process. Then, the proposed combination rule with new weight assignment method is tested on a set of numerical arithmetic and Iris datasets. Compared with four existing methods, the results show that the proposed method has higher decision accuracy, F1 score, better computational convergence, and more reliable fusion results as well.
KOMBINASI METODE K-MEANS DAN DECISION TREE DENGAN PERBANDINGAN KRITERIA DAN SPLIT DATA

Data mining is a process of looking for patterns or pulling large and selected data information using certain techniques or methods.The K-Means and Decision Tree methods are part of the Data Mining technique.This study will combine the K-Means method to cluster data into three clusters then the results of the clustering will be classified using the Decision Tree Method with a comparison of the Gain Ratio, Information Gain and Gini Index criteria.The data is processed into two, namely training data and testing data with a percentage of 70:30, 80:20 and 90:10.The results of the research are to find out which criteria produce the best decision tree and performance based on the highest accuracy value from each data group.The data is taken from the UCI Repository with a total of 811 records and 52 attributes.From the data processing carried out, it is known that for the 70:30 data split, the accuracy value with the Gain Ratio, Information Gain and Gini Index criteria gets the same value, namely 97.53.The Gain Ratio and Gini Index criteria produce the highest accuracy value, which is 98.15% for 80:20 split data.While Information Gain got the highest accuracy value of 98.77% for 90:10 data split.
Pendampingan Kegiatan Pelatihan Desain Grafis Untuk Warga Desa Ciputri Di Kecamatan Kaduhejo Kabupaten Pandeglang

Abstract&#x0D; Pondok Pesantren, is an educational institution that provides formal education to students aged children. There are many Islamic boarding schools scattered in various villages in Ciputri Village. The low design ability of santri and students is very unfortunate even though they are the successors to the next civilization. Besides that, in Ciputri village there is no course institution for graphic design training, so graphic design training is held within 1 day. The purpose of this activity is to increase the creativity of students and students in the world of design. Make students and students independent so that they can make designs such as pamphlets, banners or banners themselves and do not use the services of a printing company. This training is carried out by delivering material and direct practice for students and students. Participants can get to know graphic design and can practice the applications used to create the graphic design.&#x0D;
Leveraging Scale-Up Machines for Swift DBMS Replication on IaaS Platforms Using BalenaDB

In infrastructure-as-a-service platforms, cloud users can adjust their database (DB) service scale to dynamic workloads by changing the number of virtual machines running a DB management system (DBMS), called DBMS instances. Replicating a DBMS instance is a non-trivial task since DBMS replication is time-consuming due to the trend that cloud vendors offer high-spec DBMS instances. This paper presents BalenaDB, which performs urgent DBMS replication for handling sudden workload increases. Unlike convectional replication schemes that implicitly assume DBMS replicas are generated on remote machines, BalenaDB generates a warmed-up DBMS replica on an instance running on the local machine where the master DBMS instance runs, by leveraging the master DBMS resources. We prototyped BalenaDB on MySQL 5.6.21, Linux 3.17.2, and Xen 4.4.1. The experimental results show that the time for generating the warmed-up DBMS replica instance on BalenaDB is up to 30× shorter than an existing DBMS instance replication scheme, achieving significantly efficient memory utilization.
Multilayer Extreme Learning Convolutional Feature Neural Network Model for the Weak Feature Classification and Status Identification of Planetary Bearing

A convolutional neural network has the characteristics of sharing information between layers, which can realize high-dimensional data processing. In general, the convolutional neural network uses a feedback mechanism to realize parameter self-regulation, which solves the disadvantages of manual parameter adjustment. However, it is unable to determine the iteration number with the best calculation accuracy. Calculation efficiency cannot be guaranteed while achieving the best accuracy. In this paper, a multilayer extreme learning convolutional neural network model is proposed for feature recognition and classification. Firstly, two-dimensional spatial characteristics of planetary bearing status data were enhanced. Then, extreme learning machine is embedded in a convolution layer to solve convex optimization problems. Finally, the parameters obtained from the training model were nested into a network to initialize the model parameters to separate each status feature. Planetary bearing experimental cases show the effectiveness and superiority of the proposed model in the recognition and classification of weak signals.
BME Submission for SIGMORPHON 2021 Shared Task 0. A Three Step Training Approach with Data Augmentation for Morphological Inflection

We present the BME submission for the SIG-MORPHON 2021 Task 0 Part 1, Generalization Across Typologically Diverse Languages shared task.We use an LSTM encoderdecoder model with three step training that is first trained on all languages, then fine-tuned on each language families and finally finetuned on individual languages.We use a different type of data augmentation technique in the first two steps.Our system outperformed the only other submission.Although it remains worse than the Transformer baseline released by the organizers, our model is simpler and our data augmentation techniques are easily applicable to new languages.We perform ablation studies and show that the augmentation techniques and the three training steps often help but sometimes have a negative effect.Our code is publicly available 1 .
Data Model Extension Impact Analysis

Relational database schemas are subject to change. For instance, columns of a table can be modified, deleted or extended. These changes have an impact on the source code that utilizes the corresponding table. They also have an impact on other database elements such as tables, views and stored procedures related to that table. Studies on change impact analysis so far mainly focus on the impact of changes on source code. There are a few tools that can analyze the impact of changes on database elements. However, these tools consider modifications and deletions, while ignoring extensions as a type of change. In this study, we propose an approach and a tool that can analyze the impact of data model extensions. Our approach involves the computation of a similarity score between each pair of tables in the database based on the unique column names defined in these tables. Tables that are similar to the extended tables beyond a threshold level are considered to be under impact. We perform an industrial case study to evaluate the accuracy of our approach and the effects of the threshold level on the accuracy of results.
Perbandingan Naïve Bayes, SVM, dan k-NN untuk Analisis Sentimen Gadget Berbasis Aspek

The Samsung Galaxy Z Flip 3 is one of the gadgets that are currently popular among the public because of its unique shape and features. Youtube is one of the social media that can be accessed and enjoyed by the public, one of which is gadget review content on the GadgetIn channel. Youtube can provide information, whether people accept or are interested in this new gadget or not. This study aims to determine the sentiment of a gadget producer. Based on the results of the analysis and testing that has been carried out on the Youtube comments of the Samsung Galaxy Z Flip 3 gadget with a total of 9,597 comments, more users gave positive opinions in the design aspect and negative opinions on the price, specifications and brand image aspects. By using the CRISP-DM model and comparing the Naïve Bayes (NB), Support Vector Machine (SVM), and k-Nearest Neighbor (k-NN) classification methods, it is proven that the SVM classification model shows the best results. The average accuracy of SVM is 96.43% seen from four aspects, namely the design aspect of 94.40%, the price aspect of 97.44%, the specification aspect of 96.22%, and the brand image aspect of 97.63%.&#x0D;
Feature Extraction for Polish Language Named Entities Recognition in Intelligent Office Assistant

The purpose of this contribution is to present a feature extractor that was designed as a part of a Named Entity Recognition (NER) system, which is to be used in a Robotic Process Automation application with a self-learning ability.The NER system has a screen of the user interface as its input, and tries to recognize and categorize all the named entities that can be located within this screen.The set of features that can be extracted from the input, is discussed in the article.The local context features appear to be very important in the considered problem.Experiments show that the entities are recognized with a rate that is satisfactory from the business perspective.
Design of dual-axis solar tracking system with integrated cleaning system: Case study of Najaf city, Iraq

Najaf city, Iraq currently has no integrated solar power plant to produce electricity, so we seek in this work to design a small, inexpensive solar power plant equipped with cleaning and tracking systems to produce electricity for individual residences. We first review the currently available dual-axis solar tracking systems and simulate the same. A dual-axis solar tracking system increases the output energy of solar panels by tracking the sunlight with the photovoltaic panels. Combining this with a cleaning arm that further increases the output of the solar panels by keeping their surface free of dirt and environmental waste. In the present work, we simulate the proposed system using MATLAB and SolidWorks software and study the weather conditions of Najaf in Iraq, including sunlight intensity, temperature, wind speed, and cloud density. Based on the simulation results and field tests during 2019 and 2020, we conclude that the solar panels tracking is sufficiently stable throughout the year since the cloud density is moderate in Najaf, so the angle of incidence of the sunlight is stable. These results indicate that the use of the Open Loop Sun-tracking system, as opposed to the Closed Loop model, is appropriate for Najaf because the system is less complex and less costly.
Interactive CT/MRI 3D Fusion for cerebral system analysis and as a preoperative surgical strategy and educational tool

Introduction: The development of systems that merge existing technologies with gathered data may bring some spectacular effects that are usable both in preoperative and educational processes. Augmented reality (AR) is one of the key aspects of the new medical approach. Newly fused data sets draw from it and give users a better overall experience. Aim: The main goal of this study was to enable the interactive presentation of patients’ CT and MRI combined data with the incorporation of AR tools considering the accuracy of the data with an emphasis on vascular structures. Material and methods: The registration method, reconstruction of the vascular system using tubular structures, and error analysis using surface distance measurements results were used in the system to provide accurate combined information about bony structures from CT volume and vascular objects and cerebral vessels from MRI. Results and discussion: The strategies concern a series of CTI volumes that could be used to analyze bony surgical procedures. The methods are preferred, especially to the most complicated and individually modified bony structures of the skull. Removing, replacing, or modifying these bony structures or elements of the skull could be used as an analysis of operating procedures at the particular stages of the operation during neurosurgical or otolaryngological techniques. Conclusions: Presented study regarded to an innovative system consisting of a CT and MRI datasets fusion. The distance analysis of the segmented vascular model and proposed method for stabilization of the human head combined with virtual sculpting technique. In conclusion, it was meaningful in many aspects of the scientific-technological merge.