Reviewerly/exHarmony huggingface dataset:
Substance, name and thing in aristotle’s «computer» ontology

In this article, we will discuss the elements of classical and nonclassical ontological systems in Aristotle’s doctrine of the substance, categories and language. It is amazing that the classic heritage of ancient philosophical thought include ontological models similar to the contemporary analytic philosophy. Aristotle was the first to speculate on the substance in terms of language categories. It is the transition from the subject individual to a logical entity and then to a part of speech. The nature of knowledge is based on a single representation of the universal. According to Aristotle, only a plurality of randomly designated unique individual things exists. However, the species and genera build some logical relations between them. Therefore, the language updating of the knowledge of the world is possible only with respect to species and genera, i.e., a logical structure as thing in itself (not described) does not have any features but exists independently in the reality. On the one hand, Aristotle supports the classical nominalistic ontology (the material world is a complexity of things existing in the reality of single non-attributive objects). On the other hand, Aristotle’s ontology is a complex of objects believed existing by a statement. That is, the objects can be from imaginary or impossible worlds, but the language descriptions credit them with the function of propositional value. In both cases, language is just a method of consistent description.
A Qualitative Case Study on Reading Practices and Habits of High School Students

Reading has been identified as an indicator of successful academic achievement. The present qualitative study explored reading literacy practices of Omani adolescent students. Three high school students participated in semi-structured interviews. The findings revealed four main factors that determined their reading practices: motivation to read, home literacy practices, students' reading rituals, and digital literacy practices. These four factors were discussed in light of other related topics which included reading interest and school reading activities as they influenced students' motivation to read, and parental involvement and availability of home library as examples of home literacy practices. Furthermore, students' reading rituals were discussed considering their quest for meaning, pre-reading preparation tasks, and preferred time of day to read, whereas the digital literacy factor encompassed the sub-topics of internet and mobile applications and the role of other devices provoking reading interest. Understanding the reading literacy practices of adolescent learners offers insights into how to develop effective strategies to improve those practices. These insights can subsequently aid educators and parents in their reading instruction and ability to be productively involved in their children’s reading literacy development, respectively. Moreover, other students may learn from their peers’ successful reading practices.
A Methodological Study of Human-Computer Interaction: A Review

Abstract: The concept of human-computer interaction has emerged as a result of advancements in computer technology. As emerging technologies in the fields of mobile and cloud computing, as well as the internet of things (IoT), become more and more ingrained in our daily lives, they have started to pose a serious challenge for professionals in human-computer interaction (HCI). These technologies call for greater dedication from HCI experts in terms of systems interface design. A comprehensive study of human-computer interaction (HCI) has been presented in the current study. The design process of human-computer interaction and the current state-of-art of the same is explained in brief along with the applications of HCI. Based on the results of the review, the study recommended combining human-centred design with agile interface design methodologies. It also encouraged future research using a qualitative or quantitative approach to further explore HCI interface design methodologies, with a focus on cloud-based, AR, VR technologies and other organizational information systems.
Learning Global Average Attention Pooling (GAAP) on Resnet50 Backbone for Person Re-identification Problem

Person re-identification has been an extremely challenging task in computer vision which has been seen as a success with deep learning approaches.Despite successful models, there are gaps in the form of unbalanced labels, poor resolution, uncertain bounding box annotations, occlusions, and unlabelled datasets.Previous methods applied deep learning approaches based on feature representation, metric learning, and ranking optimization.In this work, we propose Global Average Attention Pooling (GAAP) on Resnet50 applied on four benchmark Re-ID datasets for classification tasks.We also perform an extensive evaluation on the proposed Attention module with different deep learning pipelines as backbone architecture.The four benchmark person Re-ID datasets used is Market-1501, RAiD, Partial-iLIDS, and RPIfield.We computed cumulative matching characteristics (CMC) and mean Average Precision (mAP) as the performance evaluation parameters of the proposed against the state of the art.The results obtained have shown that the added attention layer has improved the overall recognition precision over the baselines.
Superpixel Sizes using Topology Preserved Regular Superpixel Algorithm and their Impacts on Interactive Segmentation

Interactive Image Segmentation is a type of semiautomated segmentation that uses user input to extract the object of interest.It is possible to speed up and improve the end result of segmentation by using pre-processing steps.The use of superpixels is an example of a pre-processing step.A superpixel is a collection of pixels with similar properties such as texture and colour.Previous research was conducted to assess the impact of the number of superpixels (based on SEEDS superpixel aglorithms) required to achieve the best segmentation results.The study, however, only examined one type of input (strokes) and a small number of images.As a result, the goal of this study is to extend previous work by performing interactive segmentation with input strokes and a combination of bounding box and strokes on images from Grabcut image data sets generated by Topology preserved regular superpixel (TPRS).Based on our findings, an image with 1000 to 2500 superpixels and a combination of bounding box and strokes will help the interactive segmentation algorithm produce a good segmentation result.Finally, the size of the superpixels would influence the final segmentation results as well as the input type.
Annotated Corpus with Negation and Speculation in Arabic Review Domain: NSAR

Negation and speculation detection are critical for Natural Language Processing (NLP) tasks, such as sentiment analysis, information retrieval, and machine translation.This paper presents the first Arabic corpus in the review domain annotated with negation and speculation.The Negation and Speculation Arabic Review (NSAR) corpus consists of 3K randomly selected review sentences from three well-known and benchmarked Arabic corpora.It contains reviews from different categories, including books, hotels, restaurants, and other products written in various Arabic dialects.The negation and speculation keywords have been annotated along with their linguistic scope based on the annotation guidelines reviewed by an expert linguist.The inter-annotator agreement between two independent annotators, Arabic native speakers, is measured using the Cohen's Kappa coefficients with values of 95 and 80 for negation and speculation, respectively.Furthermore, 29% of this corpus includes at least one negation instance, while only 4% of this corpus contains speculative content.Therefore, the Arabic reviews focus more on negation structures rather than speculation.This corpus will be available for the Arabic research community to handle these critical phenomena 1 .
Effectiveness of Human-in-the-Loop Sentiment Polarization with Few Corrected Labels

In this work, we investigated the effectiveness of adopting Human-in-the-Loop (HITL) aimed to correct automatically generated labels from existing scoring models, e.g.SentiWordNet and Vader to enhance prediction accuracy.Recently, many proposals showed a trend in utilizing these models to label data by assuming that the labels produced are near to ground truth.However, none investigated the correctness of this notion.Therefore, this paper fills this gap.Bad labels result in bad predictions, hence hypothetically, by positioning a human in the computing loop to correct inaccurate labels accuracy performance can be improved.As it is infeasible to expect a human to correct a multitude of labels, we set out to answer the questions of "What is the smallest percentage of corrected labels needed to improve prediction quality against a baseline?"and "Would randomly selecting automatic labels for correction produce better prediction than specifically choosing labels with distinct data points?".Naïve Bayes (NB) and Decision Tree (DT) were employed on AirBnB and Vaccines public datasets.We could conclude from our results that not all ML algorithms are suited to be used in a HITL environment.NB fared better than DT at producing improved accuracy with small percentages of corrected labels, as low as 1%, exceeding the baseline.When selected for human correction, labels with distinct data points assisted in enhancing the accuracy better than random selection for NB across both datasets, yet partially for DT.
Data Fusion Model of Road Sensors based IoT Feature Clustering

The collection of traffic data can play a role in analyzing and predicting highway design, planning, and realtime traffic management.The accuracy requirements for road dynamic data collection are low, and the accuracy is usually 3%-5%.However, it is required that vehicles can pass at high speed and obtain traffic information such as vehicle classification and vehicle speed.The prerequisite for the application of Internet of Things (IoT) technology to road information monitoring lies in the research and development of sensor technology in the perception layer and communication technology in the network layer, so that can obtain a large amount of perception data to serve the development and application of algorithms.To achieve the goal of low-cost and long-term monitoring of comprehensive traffic information and road service status information, this paper constructs a road vibration monitoring system, carries out road vibration monitoring under complex road environments, and proposes a traffic information monitoring method driven by road vibration data.By deploying the pavement vibration monitoring system in the actual road, the original signal of pavement vibration under the action of vehicle moving load is obtained.Through smooth processing and eigenvalue extraction, the monitoring of vehicle speed, wheelbase driving direction, vehicle load position and traffic flow is realized.The experimental results prove that the analysis of the road dynamic response under working conditions, as well as smoothing processing and eigenvalue extraction, the numerical modeling method in this paper realizes the monitoring of the position of the vehicle load and the traffic flow.The calculation error of vehicle speed and wheelbase is within ±4%, which is helpful to find the characteristic index of road vibration signal for evaluating road service status, and provides a reference for the application of road vibration response in road damage early warning and scientific maintenance.
An Extractive Text Summarization based on Candidate Summary Sentences using Fuzzy-Decision Tree

This study aims to predict candidate summary sentences in extractive summary using the Fuzzy-Decision Tree method.The fuzzy method is quite superior and the most widely used in extractive summaries, because Fuzzy has advantages in calculations that are not cryptic, so it is able to calculate uncertain possibilities.However, in its implementation, the fuzzy rule generation process is often carried out randomly or based on expert understanding so that it does not represent the distribution of the data.Therefore, in this study, a Decision Tree (DT) technique was added to generate fuzzy rules.From the fuzzy final result, important sentences are obtained that are candidates for summary sentences.The performance of our proposed method was tested on the 2002 DUC dataset in the ROUGE-1 evaluation.The results showed that our method outperformed other methods (baseline and sentence ranking) with an average precision of 0.882498, Recall 0.820443 and F Measure 0.882498 with CI for F1 0.821-0.879at the 95% confidence level.
Autonomous Movement Control of Coaxial Mobile Robot based on Aspect Ratio of Human Face for Public Relation Activity Using Stereo Thermal Camera

In recent years, robots that recognize people around them and provide guidance, information, and monitoring have been attracting attention. The mainstream of conventional human recognition technology is the method using a camera or laser range finder. However, it is difficult to recognize with a camera due to fluctuations in lighting 1), and it is often affected by the recognition environment such as misrecognition 2) with a person's leg and a chair's leg with a laser range finder. Therefore, we propose a human recognition method using a thermal camera that can visualize human heat. This study aims to realize human-following autonomous movement based on human recognition. In addition, the distance from the robot to the person is measured with a stereo thermal camera that uses two thermal cameras. A coaxial two-wheeled robot that is compact and capable of super-credit turning is used as a mobile robot. Finally, we conduct an autonomous movement experiment of a coaxial mobile robot based on human recognition by combining these. We performed human-following experiments on a coaxial two-wheeled robot based on human recognition using a stereo thermal camera and confirmed that it moves appropriately to the location where the recognized person is in multiple use cases (scenarios). However, the accuracy of distance measurement by stereo vision is inferior to that of laser measurement. It is necessary to improve it in the case of movement that requires more accuracy.
Pelatihan tes Uji Kemahiran Berbahasa Indonesia (UKBI) di SMK Al–Husna Bojong Gede

Language skills are needed at all times in every segment of social life. The level of sound and correct Indonesian language skills differs for each individual. Testing a person's ability to speak Indonesian both verbally and in writing can be done by undergoing the "Indonesian Language Competency Test." With this, one can know the level of Indonesian language proficiency and can adjust to the expected language needs. Indonesian Language Proficiency Test is a means to measure a person's proficiency in spoken and written Indonesian. UKBI consists of five sections, namely Section I (Listening), Section II (Responding to Rules), Section III (Reading) in the form of multiple choice, Section IV (Writing) in the form of written presentations, and Section V (Speaking) in the form of presentations. Oral. During the current Covid-19 pandemic, UKBI cannot be held offline and in bulk in one place. UKBI Adaptive is a test to measure Indonesian speakers' language proficiency, which is a new thing for students at SMK AL-Husna Bojong Gede, so it needs to be socialized and tested. This study aimed to determine the Indonesian language's mastery for teachers at SMK Al-Husna Bojong Gede. Descriptive and quantitative methods were used in the study. The results showed that most of the participants scored between 300-400, which means that their abilities were in the pretty good category. Thus, it can be said that the UKBI is a test tool that can be used to measure the mastery of the Indonesian language and the teacher's good and correct use of Indonesian
GAMBARAN KEPUASAN DOKTER GIGI DALAM PEMESANAN GIGI TIRUAN DI LABORATORIUM ATHALLAH DENTAL KOTA PEKANBARU

Statisfacation is the perception of something that has met expectations. Based on the initial survey conducted at the Dentist's Practice, which ordered dentures at the Dental Athallah Laboratory, out of 7 dentist respondents who ordered dentures, there were 6 respondents who were dissatisfied with the quality of the product produced, such as the base thickness, and the dentures that were made. loose, 5 respondents felt the service provided was not optimal, 5 respondents felt that the price of denture made from thermos was more expensive than other laboratories, 4 respondents complained about the inappropriate timing. The purpose of this study was to find out a description of the satisfaction of dentists in ordering dentures at Athallah Dental Laboratory Pekanbaru City in 2020. This type of research uses quantitative research which is descriptive in the data collection method using a questionnaire with 29 dentists as respondents. The results of this study indicate that the satisfaction of ordering dentures at the Athallah Dental Laboratory is 15 respondents (51.7%) who are dissatisfied, and 14 respondents who are satisfied (48.3%). The service for ordering dentures at the Dental Athallah Laboratory was not good, there were 10 respondents (34.5%) and 19 respondents (65.5%) felt that the service provided was good. There were 13 respondents (44.8%) who felt that the price for ordering dentures at the Athallah Dental Laboratory was expensive and 16 respondents (55.2%) felt that the price given was cheap. There were 15 respondents (51.7%) who were not on time to order dentures at the Dental Athallah Laboratory and 14 respondents (48.3%) who were not on time. The suggestion from the researchers is to improve the quality of service and the timeliness of dentures..&#x0D; Bibliography : 34 (2010-2019)&#x0D; Keywords : Satisfaction, Denture, Laboratory
Content-Based Image Retrieval for Painting Style with Convolutional Neural Network

With the advancement of digital paintings in online collection platform, new image processing algorithms are required to manage digital paintings saved on database. Image retrieval has been one of the most difficult disciplines in digital image processing because it requires scanning a large database for images that are comparable to the query image. It is commonly known that retrieval performance is largely influenced by feature representations and similarity measures. Deep Learning has recently advanced significantly, and deep features based on deep learning have been widely used because it has been demonstrated that the features have great generalisation. In this paper, a convolutional neural network (CNN) is utilised to extract deep and high-level features from the paintings. Next, the features were used for similarity measure between the query image and database images; subsequently, similar images are ranked by the distance between both pair features. Our experiments show that this strategy significantly improves the performance of content-based image retrieval for the style retrieval task of painting. Keywords: Content-based Image Retrieval, Deep Learning, Convolutional Neural Network
Sentiment Analysis on Game Reviews: A Comparative Study of Machine Learning Approaches

Sentiment analysis is one of the major topics of natural language processing which is used to determine whether data is positive, negative or neutral. It is often performed on textual data to help businesses monitor brand and product sentiment in customer feedback to understand their customers’ needs. This paper explores various machine learning algorithms including Logistic Regression (LR), Multinomial Naïve Bayes (MNB), Support Vector Classifier (SVC), Multi-layer Perceptron Classifier (MLP) and Extreme Gradient Boosting Classifier (XGB) to build sentiment analysis models tailored for the gaming domain to classify reviews into positive, negative and neutral. The models were trained on game reviews obtained from Metacritic and Steam. Various data preprocessing and model optimization techniques have been employed and the performance of the models were evaluated and compared. SVC has been determined as the best-performing model among all the models. Keywords: Sentiment Analysis, Natural Language Processing, Machine Learning, Support Vector Machine, Game Reviews
Analysis the Effect of Learning Organization, Communication Skill, and Compensation on Funding Officer Employee Performance with Motivation as an Intervening Variable (Study on E-Channel Marketing at PT. Bank Rakyat Indonesia Agroniaga, Tbk)

The purpose of this study is to see whether there is a significant influence between the variables of learning organization, communication skills, and compensation on performance through motivational variable as intervening variable which conducted at PT Bank Rakyat Indonesia Agroniaga, Tbk. This study uses quantitative research methods with data collection through the distribution of questionnaires and interviews (discussions) to employee as a respondent at PT Bank Rakyat Indonesia Agroniaga, Tbk. The results showed that learning organization, communication skills, and compensation had a significant positive effect on employees' work motivation. Furthermore, learning organization had a significant positive effect on employee performance, while communication skills and compensation had no significant effect on employee performance. The results of the Sobel Test showed that the motivation variable did not succeed in mediating the learning organization, communication skill, and compensation variables. Keywords: learning organization, communication skill, compensation, motivation, performance.
Real Time Social Distance Detection : A Covid 19 Contamination Prevention Measure

Deep learning models have been used to support a method for determining social distance. A red frame and line will appear between two people to indicate that they have violated the policy if they are within a certain distance of one another and the computer vision system is unable to tell them apart. With the use of a film depicting people strolling down a street, the applicability of the suggested technique was examined. The results visualization revealed that the technique suggested is an alternative that can be used in a variety of settings, including commercial organizations and educational institutions, among others.The results of this study can be improved in a number of ways, including adjustments to the algorithm used for pedestrian recognition, the addition of extra detection techniques like the detection of build temperature, the improvement of the processing power of the hardware, and calibration of the camera's field of view. These are merely a few ideas on how to complete the assignment more successfully