Reviewerly/exHarmony huggingface dataset:
Sistem Pakar untuk Diagnosa Penyakit Ayam Pedaging

Tanah Laut Regency is the largest producer of broilers in South Kalimantan. However, public knowledge about symptoms and diseases in chickens is still lacking, so this is a problem that must be resolved because it can cause losses. Therefore, this research was conducted with the aim of overcoming this problem, namely by building an expert system application that can be used by the public and extension workers to diagnose chicken disease. The method used to build this system is a waterfall with the forward chaining technique as a forward-looking solution, meaning that inference is done by looking for input data in the form of symptom data in the form of chicken disease. The system built is equipped with features that display disease treatment and prevention solutions based on the symptoms that have been entered, so that disease-stricken chickens can be handled appropriately and quickly according to their disease. Based on blackbox testing carried out by involving potential users, the results obtained that all functionality can work as expected. Accuracy testing is also done by comparing manual calculations with an accuracy value of 95%. This is influenced by the weighting of the symptom value which affects the decrease in the value of the symptom result when compared to ideal conditions. In conclusion, the implications of using this system can make it easier for the community and instructor to diagnose chicken disease.
Implementasi Convolutional Neural Network untuk Klasifikasi Variasi Intensitas Emosi pada Dynamic Image Sequence

Facial emotion recognition (FER) is a research topic that focuses on the analysis of human facial expressions. There are many FER research has been conducted on single images or photo. Emotion analysis on single images has many disadvantages compared to dynamic image sequences or videos. This is due to human emotions or expressions within a certain time. The classification of emotions becomes complicated when considering different emotions. There are some people who are very expressive, there are some people who have low or moderate expressions. Predictions of emotion with variety intensities has decresed error due to data sets that provide only a few emotions intensities. Data annotation is a major problem in recognition fields that require a lot of time and effort to annotate new data. This study aims to find information about facial emotions with emotional intensity from subtle to sharp in a sequence images or videos. The dataset will be trained using Convolutional neural network by augmentation to add data annotations. The proposed method was evaluated using the public BP4D-Spontaneous dataset. The evaluation results show that the average emotion recognition in video sequences using the holdout method is 18%. Evaluation of the loss function parameter shows overfitting where the curve generalization gap is too high. The last evaluation is the evaluation of the emotion class between the real class and the prediction class in 14.28%. This shows that the classification of emotion recognition in dynamic image sequences is quite low.
MINERAL CHEMISTRY AND GEOCHEMISTRY OF HYPERSTHENE-BEARING DIORITE FROM ERUSU AKOKO, SOUTHWESTERN NIGERIA

Samples of mafic intrusive rock were analyzed for their mineralogical and chemical properties. The textural relationship was studied using the petrographic microscope, elemental composition of minerals was determined using the Electron Microprobe and the whole rock chemical analysis was done using the XRF and ICP-MS. The following minerals were observed in order of abundance; pyroxene, amphibole, plagioclase, biotite, opaque minerals, quartz and chlorite, with apatite and zircon occurring as accessory mineral. Two types of pyroxenes were observed; orthopyroxene (hypersthene) and clinopyroxene. Texturally, amphiboles have inclusions of plagioclase and pyroxene. The plagioclase has undergone sericitization. The chemical composition of the pyroxene is En51.95Fs44.53Wo3.52, biotite has Fe/(Fe+Mg):0.42, Mg/(Fe+Mg):0.59, and plagioclase is Ab63.5An34.55Or1.95. Whole rock chemistry shows a chemical composition; SiO2: 45.15 %, Al2O3: 14.04 %, Fe2O3: 16.01 %, MgO: 5.65 %, CaO: 7.58 % and TiO2: 3.59 %. There is an enrichment of LREE and a depletion of HREE. Based on the minerals, mineral chemistry and the geochemistry of the studied rock, the rock is mafic and hydrous minerals formed by hydration recrystallization of pyroxene. The rock has extensively retrogressed but has not been affected by any form of deformation.
Multilayer GMDH-neuro-fuzzy network based on extended neo-fuzzy neurons and its application in online facial expression recognition

Real-time image recognition is required in many important practical problems. Interaction with users in online mode requires flexibility and adaptability from applications. The Group Method of Data Handling (GMDH) allows changing the model structure and adjusting the system architecture to the characteristics of each task under consideration. Moreover, the approximating properties of neo-fuzzy neurons used as elements of the system provide the high recognition accuracy under conditions of short data samples. This paper proposes a multilayer GMDH-neuro-fuzzy network based on extended neo-fuzzy neurons. The learning algorithm has filtering and tracking properties, guarantees the required speed important for real-time applications. The effectiveness of the proposed system is confirmed for the human emotions recognition.
TëXtmarkers at SemEval-2020 Task 10: Emphasis Selection with Agreement Dependent Crowd Layers

In visual communication, the ability of a short piece of text to catch someone's eye in a single glance or from a distance is of paramount importance.In our approach to the SemEval-2020 task "Emphasis Selection For Written Text in Visual Media", we use contextualized word representations from a pretrained model of the state-of-the-art BERT architecture together with a stacked bidirectional GRU network to predict token-level emphasis probabilities.For tackling low inter-annotator agreement in the dataset, we attempt to model multiple annotators jointly by introducing initialization with agreement dependent noise to a crowd layer architecture.We found our approach to both perform substantially better than initialization with identities for this purpose and to outperform a baseline trained with token level majority voting.Our submission system reaches substantially higher Match m on the development set than the task baseline (0.779), but only slightly outperforms the test set baseline (0.754) using a three model ensemble.
UoB at SemEval-2020 Task 12: Boosting BERT with Corpus Level Information

Pre-trained language model word representation, such as BERT, have been extremely successful in several Natural Language Processing tasks significantly improving on the state-of-the-art.This can largely be attributed to their ability to better capture semantic information contained within a sentence.Several tasks, however, can benefit from information available at a corpus level, such as Term Frequency-Inverse Document Frequency (TF-IDF).In this work we test the effectiveness of integrating this information with BERT on the task of identifying abuse on social media and show that integrating this information with BERT does indeed significantly improve performance.We participate in Sub-Task A (abuse detection) wherein we achieve a score within two points of the top performing team and in Sub-Task B (target detection) wherein we are ranked 4 of the 44 participating teams.
PROFESSIONAL PEDAGOGICAL MOBILITY AND SPECIFIC FEATURES OF ITS FORMATION

Researching the content of the issue of professional and pedagogical mobility, we note the following. At the present stage of development of modern society, the problem of professional and pedagogical mobility is especially relevant. It is characterized by the growth of intensive and progressive changes in society, covering all spheres of human life, including the professional sphere. For a modern specialist to be successful and necessary, he must be mobile, ready for changes, able to quickly adapt to changing conditions, that is, he must be professionally mobile. Great importance is attached to the consideration of professional and pedagogical mobility as a pedagogical category. Concepts such as "mobility", "professional mobility", "pedagogical" are analyzed. On the basis of research and supplementation of these concepts there is a problem of independent understanding of professional and pedagogical mobility as a characteristic of the individual, which is reflected in the process of professional activity, readiness to develop, self-structuring and adapt to changes in the changing professional environment. The article clarifies the need to create a personal professional image, to organize and implement practical activities to get closer to this person, to detail the main steps for reorganization in connection with the changing conditions of the professional and pedagogical environment.
Hitachi at SemEval-2020 Task 3: Exploring the Representation Spaces of Transformers for Human Sense Word Similarity

In this paper, we present our system for SemEval-2020 task 3, Predicting the (Graded) Effect of Context in Word Similarity.Due to the unsupervised nature of the task, we concentrated on inquiring about the similarity measures induced by different layers of different pre-trained Transformer-based language models, which can be good approximations of the human sense of word similarity.Interestingly, our experiments reveal a language-independent characteristic: the middle to upper layers of Transformer-based language models can induce good approximate similarity measures.Finally, our system was ranked 1st on the Slovenian part of Subtask1 and 2nd on the Croatian part of both Subtask1 and Subtask2.
Hate Speech Detection with Machine-Translated Data: The Role of Annotation Scheme, Class Imbalance and Undersampling

While using machine-translated data for supervised training can alleviate data sparseness problems when dealing with less-resourced languages, it is important that the source data are not only correctly translated, but also follow the same annotation scheme and possibly class balance as the smaller dataset in the target language. We therefore present an evaluation of hate speech detection in Italian using machine-translated data from English and comparing three settings, in order to understand the impact of training size, class distribution and annotation scheme.1
THE USABILITY OF WHATSAPP MESSENGER AS ONLINE TEACHING-LEARNING MEDIA

In order to prevent the spread of Covid-19, whether ready or not, education policy makers in Indonesia have established a policy of online learning from home. Some schools use a special Learning Management System (LMS) application, while others simply use existing common communication applications, including WhatsApp Messenger. This study aims to measure its usability in its use as an online learning medium. This research uses a descriptive quantitative approach. The data were obtained from an online survey of students and teachers who were determined by convenience sampling technique. Measurements were carried out with ISO for HCI and Usability 2001. The results showed that the Usability of WhatsApp was rated in the sufficient category, close to the good category.
PSINES

Autonomy and adaptability are essential components of ambient intelligence. For example, in smart homes, proactive acting and occupants advising, adapted to current and future contexts of living, are essential to go beyond limitations of previous domotic services. To reach such autonomy and adaptability, ambient systems need to automatically grasp their users’ ambient context. In particular, users’ activities and availabilities for communication are valuable pieces of contextual information that can help such systems to adapt to user needs and behaviours. While significant research work exists on activity recognition in homes, less attention has been given to prediction of future activities, as well as to availability recognition and prediction in general. In this article, we investigate several Dynamic Bayesian Network (DBN) architectures for activity and availability prediction of occupants in homes, including our novel model, called Past SItuations to predict the NExt Situation (PSINES). This predictive architecture utilizes context information, sensor event aggregations, and latent user cognitive states to accurately predict future home situations based on previous situations. We experimentally evaluate PSINES, as well as intermediate DBN architectures, on multiple state-of-the-art datasets, with prediction accuracies of up to 89.52% for activity and 82.08% for availability on the Orange4Home dataset.
Culturally Sensitive Website Elements and Features: A Cross-National Comparison of Websites from Selected Countries

The goal of this case study is to compare websites from 9 different countries&#8213;Austria, Chile, China, Japan, Latvia, Nigeria, Saudi Arabia, the US and the Czech Republic&#8213;and, based on this comparison, to provide the missing linking of website elements with cultural dimensions for better cultural adaptation of web content. Hofstede&#8217;s cultural dimensions were used for the selection of countries for this study. To examine the influence of culture on websites, countries with extreme values of cultural dimensions were selected. An important benefit is that this study takes into account all of Hofstede's cultural dimensions, including the latest one (indulgence vs restraint). For each country 50 websites were selected from areas that most closely reflect the culture of the country. The main focus was on the selection of an appropriate representative sample of websites for each state. A total of 450 pages was analyzed. For each website, 42 web elements determined as the most important were monitored. Moreover, the presence of various types of social networks and five general characteristics were monitored. The findings show that culture influences website design. The results of this study reveal a connection between website elements and Hofstede&#8217;s cultural dimensions. For example, headlines are important for countries with a high value of individualism, uncertainty avoidance, and a low value of power distance and indulgence. Newsletters are associated with a high value of indulgence and a low value of long-term orientation and search option with a high value of power distance. Overall, about 20 culturally sensitive website elements were identified. The study also provides a comprehensive overview of website characteristics for each of the selected countries. For UX designers, web localization specialists, academicians, and web developers, this study provides an original view into culturally sensitive website elements and features.
The Impact of FDI Inflows on GDP Growth in Iraq for the period (2004-2017): An applied study

This paper aims to examine the relationship between the changing foreign direct investment inflows and gross domestic product growth using yearly time series observations for the period (2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017).The current study proposed the hypothesis which investigated utilizing the novel econometrics methods such as unit root test, cointegration test and Granger causality test, the results revealed that existence of long run equilibrium relationship between the foreign direct investment inflows and gross domestic product growth during the period of the study, probably due to the little inflow to Iraq in the form to participate in pushing Iraqi economic growth due to the nonoil exports structure where average of total values of petroleum exports to total Iraqi value of exports reach to (99%) during the study period.
Celebrating 25 years of seismology at schools in France

Abstract. An educational program focusing on seismological activities for school and university students that trains in observational sciences and raises citizen awareness of natural hazards has been active in France since 1995. Over this quarter century, different generations of students have learned various lessons concerning instrument installation, data recording, and analysis. These actions have led them into the field of scientific interrogation and interpretation, making them better prepared for our modern technological societies. We describe these student commitments that have been motivated by the installation of the first educational broadband seismometer in southern France. Analysis of regional earthquakes has generated a greater awareness of the seismic hazards where students live, while records of strong earthquakes all around the world have led to interaction between students, especially after the deployment of additional seismometers in schools. The natural extension of such an educational seismic network, first at the national level in France in 2006 and later in many countries through various collaborations, has enriched the pedagogical practices of teachers, increasing their skills in seismology and natural sciences among various other disciplines and complementing standard educational resources. We describe the necessary and sustainable relations between teachers and researchers over time. Combining students' motivation, teachers' experience and researchers' expertise has led to different hosting structures over the years. We conclude by presenting the feedback from a survey carried out in 2019 among all the teachers involved, highlighting the strong and weak points of such a long-term adventure. Recent integration into the official syllabus of the new Geosciences high school curricula in France illustrates the impact of such an exceptional experience.
ETHAN at SemEval-2020 Task 5: Modelling Causal Reasoning in Language Using Neuro-symbolic Cloud Computing

I present ETHAN: Experimental Testing of Hybrid AI Node implemented entirely on free cloud computing infrastructure.The ultimate goal of this research is to create modular reusable hybrid neuro-symbolic architecture for Artificial Intelligence.As a test case I model natural language comprehension of causal relations from open domain text corpus that combines semi-supervised language model (Huggingface Transformers) with constituency and dependency parsers (Allen Institute for Artificial Intelligence).The experimental results presented in this paper show potential for SOTA-level performance and demonstrate hybrid Artificial Intelligence in action.On SemEval-2020 Task 5 Subtask 1 ETHAN achieves F1 0.878 which is the second best result at the time of my writing.On Subtask 2 ETHAN currently scores 0.673 which is within the top ten results.All code used for experiments is available as open source and can be executed directly from the web browser without any installation or configuration.I briefly discuss some theoretical approaches that led to the proposed solutions and future directions of this research.
Multi-criteria Optimization Using l-diversity and t-closeness for k-anonymization

k-anonymity is a commonly used anonymization principle. It provides an anonymous table by grouping the individuals of the table in sets of at least k elements. This principle guarantees a good privacy while limiting the data alteration. Within the k-anonymization process, only quasi-identifier attributes are considered. Sensitive attributes are not. As a consequence, in k-anonymous tables, sensitive values might be disclosed. Thus, the concepts of l-diversity and t-closeness have been defined. Considering anonymization principles that take into account the distribution of the sensitive attributes values in the anonymous table, this paper tackles the link between k-anonymity, l-diversity and t-closeness. It then proposes to generate k-anonymous tables which simultaneously optimize data alteration, l-diversity and t-closeness. To do so, this paper describes seven optimization strategies, usable in an anonymization algorithm, that are combinations of minimization of data alteration, maximization of l-diversity and minimization of t-closeness. At the end, this study provides comparative experimental results of these strategies on the Adult Data Set, a commonly used data set within the anonymization research field that we extended with randomly generated data following several distributions.
E-Government During the Pandemic: Maturity Assessment of the Provincial And Regency/City Website in West Sumatra

E-government uses information technology to change government relations with citizens, the private sector, and other institutions. The importance of identifying e-government performance cannot be separated from the use of the website. Thus, assessing the website quality is very important as it helps determine the quality of e-government services and improve service processes. In almost two years of implementing e-government in West Sumatra, there has been no evaluation of the provincial and district/city government websites, which are the primary tools for implementing e-government today. In the Covid-19 pandemic period, the website's role has become more critical as a means for the public to get the latest information regarding developments in handling the pandemic. This study focuses on assessing and evaluating the level of maturity (maturity assessment) of the provincial and district/city government websites in implementing e-government and its relationship in response to the Covid-19 pandemic. Forty websites were assessed for their e-government maturity in this study. The website consists of 19 district/city websites, one provincial webpage, and 20 websites for handling Covid-19 for each district/city and province. The maturity assessment was measured using the United Nations (UN) e-government maturity model. This study found that government websites are still at stage 1 and stage 2 maturity models. Meanwhile, the Covid-19 handling website is on stage1. It is hoped that the government in West Sumatra will pay more attention to improving e-government through websites in the current pandemic era.
Usability of Electrical E-Wiring Module Using Mobile Apps

In education, the use of smart-phone apps is very helpful in the process of teaching and learning. Therefore, this situation inspires and opportunities for the development of a smart-phone app namely the Electrical e-Wiring Module (called, MePE). The MePE app is a smart-phone app or mobile apps developed to help students who are taking electrical wiring courses to refer how to learn electrical wiring via smart-phone applications. In addition, this application can also be used as a teaching tool in the process of learning electrical wiring. The study was conducted to identify the requirements for MePE app, develops and tests the functionality of MePE app. There are four main sections in this application. This application is developed in accordance with the evolutionary prototype methodology. As a result of the functionality testing performed on the nine functions available on the MePE app all the functions passed the tests that have been performed. In addition, 24 of 31 respondents agreed that the MePE application is helpful and effective in learning electrical wiring. It is hoped that with this application it will help students that study electrical wiring courses to make reference to electrical wiring through this application.