Reviewerly/exHarmony huggingface dataset:
A Survey: Sentimental Analysis on Product Reviews Using (MLT) Machine Learning Techniques and Approaches

Social media sites are used today for the development of different types and nature of customers those use such benefits which are often shared by people on social media symbolic or textual opinions, ideas, and feelings. This attitude and orientation draw attention to research and analyze sentiments through online data about customer interest. Therefore, the sentimental analysis idea is proposed. This is among the various uses of Natural Language Processing (NLP) and Machine Learning Analysis (MLA) is very common. The main task of sentimental analysis is the classification of sentiments automatically into three categories that are positive, negative and neutral. Many classification researches are conducted over the years to know the exact feelings and situations of sentimental emotions of people. Classification, fuzzy and clustering, is used. To know the sentiment analysis of the people’s accurate feeling and situation, many times over the years classification research was conducted in past. The accuracy of classification is finding more in Fuzzy based. Fuzzy based classification finds more accurate and for comparative study execution Classical Text Classifications Model is used. In comparative performance, this study shows the possibility of implementing the proposed method able to provide more accurate results when it comes in comparison with conventional classifiers. In this article we have discussed different researchers worked on the method of sentiment analysis and classification. This article also shows the importance of extracting comments and analyze sentiments
The Hierarchical Local Binary Patterns for Pedestrian Detection

Active pedestrian safety protection could avoid collision between vehicles and pedestrians essentially, thus it has become the most promising technique in the field of Advanced Driving Assistant System (ADAS). However, due to the clutter of urban road scenarios, the speed rate of pedestrian detection in the traditional pedestrian detection algorithm is too slow to satisfy the real-time requirements of the vehicle system. Against this background, we proposed a novel feature descriptor which is called Hierarchical LBP for pedestrian detection. In the proposed feature descriptor, we extend the comparison operation of gray value between the central pixel and circular(C-LBP) or rectangle (R-LBP) neighborhood pixels from simple single neighborhood to hierarchical neighborhood (inner-layer and outer-layer neighborhood) in the traditional Local Binary Patterns (LBP) feature extraction process. Besides that, we allocate these central pixels into 32 bins averagely based on their binary values (0~255) instead of assigning them into 59 bins, which would significantly reduce the computation time in the feature extraction process. Finally, we choose a non-linear SVM (LIBSVM) with a radial basis function kernel (RBF) as a classifier for pedestrian detection experiments. The experimental results verify that the proposed feature descriptor can greatly improve the detection speed without sacrificing the detection performance.
The Effect of Test Length on the Accuracy of Estimating Ability Parameter in the Two- and Three-Parameter Logistic Models: Comparison by Using the Bayesian Method of Expected Prior Mode and Maximum Likelihood Estimation

This study aims to compare the effect of test length on the degree of ability parameter estimation in the two-parameter and three-parameter logistic models, using the Bayesian method of expected prior mode and maximum likelihood. The experimental approach is followed, using the Monte Carlo method of simulation. The study population consists of all subjects with the specified ability level. The study includes random samples of subjects and of items. Results reveal that estimation accuracy of the ability parameter in the two-parameter logistic model according to the maximum likelihood method and the Bayesian method increases with the increase in the number of test items. Results also show that with long and average length tests, the effectiveness is related to the maximum likelihood method and to all conditions of the sample size, whereas in short tests, the Bayesian method of prior mode outperformed in all conditions. Results indicate that the increase of the ability parameter in the three-parameter logistic model increases with the increase of test items number. The Bayesian method outperforms with respect to the accuracy of estimation at all conditions of the sample size, whereas in long tests the maximum likelihood method outperforms at all different conditions.&#x0D; &#x0D; Received: 17 September 2021 / Accepted: 24 November 2021 / Published: 3 January 2022
Augmented Reality 3D Heart as Learning Media at Midwifery Lab University of 'Aisyiyah Surakarta

Abstract— Utilization of Information Technology (IT) in the process of delivering information has been developed rapidly. Currently, the latest technology used in the delivery of information is Augmented Reality (AR) technology.. Pada teknologi Augmented Reality (AR), pengguna dapat menvisualisasikan objek atau benda bersejarah dalam bentuk 3 dimensi. Augmented Reality (AR) has the advantage of being interactive and real time so Augmented Reality (AR) is widely implemented in the field of education as a medium to introduce historical objects which are cultural heritage or objects related to a science..Augmented Reality (AR) has the advantage of being interactive and real time, so Augmented Reality (AR) is widely implemented in various fields. Based on the explanation of the study, researchers are interested in conducting research using information technology in the health sector, by making Augmented Reality (AR) modeling to facilitate the community and midwifery students in providing learning about human organs, especially the heart which can be used digitally without having to dissect human organs and bringing him a modeling of the heart with the presence of a 3D heart organ provides convenience in learning to the public about the function and structure of the heart digitally.Keywords :3D, Augmented Reality, Heart 3D Design.
Makerspace as Emerging Technology and Innovation in Academic Libraries: A Call for Adoption in Abubakar Tafawa University Library Bauchi

This study was conducted to examine makerspace as emerging technology and innovation in academic libraries: a call for adoption in ATBU library, Bauchi. Bauchi state, Nigeria. The population of the study comprised of 26 ATBU academic librarians.Survey research method was adopted for the study. Questionnaire was used as a data collection instrument. Frequency counts and simple percentages were used in analysing the data collected. The study revealed that academic librarians of ATBU are unaware of makerspace as a new emerging technology and as such a call for its adoption is paramount. Highlight of some benefits for adoption of makerspace in libraries were made which includes, Increased motivation of academic librarians and students, enhanced learning activity among academic librarians and users, and create interdisciplinary teamwork among the academic librarians and library users. The study recommended among others, Academic librarians should embrace the new emerging technology and participate fully through training on the new technology and bring about the development of Makerspace in their libraries. This will also go a long way in creating and sharing of new ideas among themselves
An Analysis of Students’ Reading Comprehension at an Islamic Institute in Jambi

This study examines each reading comprehension indicator and reading comprehension in general. Forty-five first-semester students of the English Education Department at Public Islamic Institute Jambi, Indonesia, which comprised of twenty-one males and twenty-four females voluntarily participated in this study. They were purposively selected as the sample of the research. This study used six indicators of reading comprehension and reading comprehension in general data. The descriptive quantitative approach was employed in this study as the research methodology. The descriptive statistic of SPSS 23 was applied to analyze the mean and standard deviation of six reading comprehension and reading comprehension in general. The criteria referenced interpretation and norm-referenced interpretation investigated interval of six reading comprehension and reading comprehension in general. The result of descriptive statistics and criteria referenced interpretation, and norm-referenced interpretation analysis showed five reading comprehension indicators in terms of author purpose, topic details, reference, and vocabulary in context in moderate criteria, except the main idea in which in high criteria. The reading comprehension was in moderate criteria similar to five reading comprehension indicators. Therefore, English lecturers hope to select an appropriate strategy and be well prepared to teach reading.
DLMNN: A Deep Learning Modified Neural Network for Balancing the Load of Cloudlets on Cloud

Cloud Computing a revolution in the computing world, has enabled the users to utilize the services on the Cloud platform from anywhere at any time. As there is an increase in the demand for the utilization of a cloud environment, there are several challenges to be addressed by the companies or organizations to provide uninterrupted cloud services. To make the cloud services available without interruption, the challenge of balancing the load on cloud servers is a must. Proper allocation of load on the servers optimize the performance of the cloud and improves the efficiency to offer uninterrupted services. Recent studies have shown, cloud always needs to have a capable algorithm to distribute the load on servers of cloud architecture to be available to process cloudlets submitted by the customers. Our paper looks for a new load balancing algorithm that uses the concepts of neural network and is used to allocate the tasks in the cloud. The proposed algorithm consists of two steps. First, Features of tasks and cloud servers are extracted, and the necessary features are selected. The feature selection can be done by using MPCA. In the second step, the selected features are sent as input to the DLMNN algorithm to schedule the task in the cloud. Finally, the experimental results of the proposed DLMNN are compared with some existing algorithms.
Defining Pedagogical Differentiation in Higher Education

The purpose of this paper is, through content analysis of 19 publications in the Greek and international literature in scientific texts, books, journal articles, and conferences, to investigate the conceptual content of pedagogical differentiation in higher education, as it emerges from the descriptions and discussion of authors, researchers, and experts. From the analysis, twelve dimensions or characteristics of pedagogical differentiation emerged that presented the highest frequency of occurrence in four broad categories. These are a. modification of the supportive learning context, meeting the needs of learners, and continuous improvement of the learning for all the learners who joined the category entitled "processes", b. student-centered teaching and learning, flexible learning context / flexible grouping and the possibility of learning option / multiple options as dimensions of a more general category called "context", c. the success and active participation of the learner in his learning, the development of life skills as well as the development of procedural knowledge skills that were included in the category called "learning outcomes" and d. the modification of "learning" products, the alternative / modern forms of assessment and the continuous assessment that were dimensions of the category "assessment". The results of the research show that the dimension with the highest frequency is a modification of the supportive learning context and follows in order of frequency of occurrence, the modification of learning "products" and meeting the needs of learners. Finally, the dimensions with the lowest frequency of occurrence include the continuous assessment and the development of procedural knowledge skills.
Statistical analysis of the two-decade ASTER archive: Quantitative retrievals of volcanic thermal and gas emissions

Detailed analysis of volcanic thermal and gas emissions over time can constrain subsurface processes throughout the pre-and post-eruption phases.Time series analyses are commonly applied to high temporal datasets like the Moderate Resolution Imaging Spectroradiometer (MODIS); however, this is the first study using the entire Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) twenty-plus year archive.The ASTER archive presents a unique opportunity to quantify volcanic precursors and processes.The spatial, spectral, and radiometric resolution of its thermal infrared (TIR) subsystem allows detection of very low-magnitude surface temperature anomalies and passively emitted small gas plumes.We developed a new statistical algorithm to automatically detect these subtle anomalies and applied it to five recently active volcanoes with well-documented eruptions: Taal (Philippians), Popocatépetl (Mexico), Mt.Etna (Italy), Fuego (Guatemala), and Kluichevskoi (Russia).More than 3,300 ASTER level-1 terrain corrected (L1T), registered, radiance-at-sensor images were downloaded from the NASA EARTHDATA website.These were screened for significant summit cloud coverage, which removed approximately 25% of scenes.The remaining were converted to brightness temperature and a median background temperature per scene was determined from an annulus around the active crater to produce the temperature above background.The algorithm creates a rejection criterion value defined by the median absolute deviation to identify the thermal anomalies.The size and intensity of these anomalies as well as the detection, composition, emission rate of small plumes are retrieved one year prior to the known eruptions for each volcano to identify all precursory signals.The results of this study have the dual purpose of constraining volcanological processes that lead to eruptions as well as providing training data for machine learning modeling.Machine learning is an effective and well-established technique that provides rapid classification of volcanic activity such as thermal anomalies that exceed a certain size and/or intensity.The comparison of these two approaches is documented in a companion abstract in this session.
Deep Learning-Based Object Recognition in Video Sequences

This paper proposes to detect, recognize, and track a specific object in video sequences using Convolutional Neural Networks (CNNs).The CNNs used are the TensorFlow SSD model and Inception model, with a use case of airplane detection as a test subject, although it can be widely extensible to any class of objects as per the application.For the SSD model, images of planes were downloaded and annotated using bounding boxes to identify regions of interest.Training and test sets were split, after which TensorFlow specific records were generated.Whereas, for the Inception model, the last layer of the Neural Network was trained with multiple images of airplanes and random images to obtain a classifier for identify planes vs. no planes.The SSD model was accurate, generating crisp bounding boxes with a relatively high accuracy.The Inception model had a higher accuracy than the SSD model in terms of false positives and false negatives.But it does not display bounding boxes as the model is not meant to find the region of interest.The GPU outperformed the CPU in training and testing by a wide margin.The Inception model is suitable to extract frames in which a specific object is present if the position of the object is not of importance.The SSD model is suitable if the specific object needs to be detected with its position in a video frame.
The Role of Digital Books in Increasing Reading Motivation among Children with Dyslexia

Becoming literate in reading and writing is one of the important basic skills young dyslexics do not easily acquire.Dyslexia is not related to intelligence; instead, dyslexics show disruptions in the rear reading system of the left hemisphere of their brain, which is critical for reading and writing skills.Research has shown that people with dyslexia also usually have lower reading motivation because of repeated experience of failures and social pressure around them.Roughly 10% of the young population in Indonesia have dyslexia, pushing the writers to analyze further about the possibility of developing technology in mobile applications to raise reading motivation among children with dyslexia.One such technology or mobile application is interactive digital picture books.The purpose of this literature review is to determine the kinds of visual elements and interactivity in digital books that appeal to children with dyslexia.The analyses here are done by narrative method: grouping data from past literature and research about digital books, reading motivation, and reading learning methods for dyslexics.Our literature review results show that certain visual elements and interactivity in digital books can increase reading motivation and are effective in teaching reading and writing to young dyslexics.For further research, we expect this literature review to be a foundation for digital book application development to teach, and motivate, reading and writing to young Indonesian dyslexics so they can improve their literacy skills.
SOLAR RADIATION ATLAS IN BANJA LUKA IN THE REPUBLIC OF SRPSKA

The paper presents an atlas of solar radiation for the city of Banja Luka in the Republic of Srpska formed by PVGIS estimation utility. The atlas contains the results of calculating global and direct solar radiation falling on the horizontal surface and global solar radiation falling on the optimally placed surface in Banja Luka in the period from 2007 to 2016. In addition, the intensity of global, direct and diffuse solar radiation falling on the optimally placed surface in Banja Luka is given by months. It was found that 13.89% less solar radiation falls on the horizontal surface and 47.31% less on the vertical surface as compared to the solar radiation that falls on the optimally placed surface. The basic characteristics of fixed, one-axis and dual-axis tracking PV solar power plants power of 1 MWp and the amount of electricity that can be generated by them in Banja Luka, are also given. It was found that with the one-axis tracking solar power plant 30.18% more electricity can be generated, and with the dual-axis tracking solar power plant 33.37% more electricity can be generated as compared to the fixed solar power plant.
Analysis of Intermediary Roles and Technical Components for Smartglass-Assisted Interactive Remote Collaboration

Recent advances in Internet-of-Things and wearable computing have established computationally sufficient infrastructure to remove the barriers of physical locations.The recent COVID-19 pandemic has accelerated the use of remote collaboration, work-from-home, teleconferencing, online education, digital twin, and metaverse enriched with various configurations of augmented reality, virtual reality, mixed reality, and extended reality.Beyond specialized niche wearable applications in the medical and entertainment domains, we aim to identify the underlying characteristics of smartglass-enabled killer applications for everyday use.In this paper, we analyze intermediary roles in remote collaboration consisting of authoritative supervisors, synergetic collaborators, and speculative explorers.We also propose technical components for smartglass-assisted interactive remote collaboration composed of personal tracking, teleconference, and interaction components.More specifically, the application tasks of each intermediary role and objectives of three functional components are defined and elaborated.
Legal Terminology Extraction with the Termolator

Domain-specific terminology is ubiquitous in legal documents.Despite potential utility in populating glossaries and ontologies or as arguments in information extraction and document classification tasks, there has been limited work done for legal terminology extraction.This paper describes some work to remedy this omission.In the described research, we make some modifications to the Termolator, a high-performing, open-source terminology extractor which has been tuned to scientific articles.Our changes are designed to improve the Termolator's results when applied to United States Supreme Court decisions.Unaltered and using the recommended settings, the original Termolator provides a list of terminology with a precision of 23% and 25% for the categories of economic activity (development set) and criminal procedures (test set) respectively.These were the most frequently occurring broad issues in Washington University in St. Louis Database corpus, a database of Supreme Court decisions that have been manually classified by topic.Our contribution includes the introduction of several legal domain-specific filtration steps and changes to the web search relevance score; each incrementally improved precision culminating in a combined precision of 63% and 65%.We also evaluated the baseline version of the Termolator on more specific subcategories and on broad issues with fewer cases.Our results show that a narrowed scope as well as smaller document numbers significantly lower the precision.In both cases, the modifications to the Termolator improve precision.
Hybrid Spam Filtration Method using Machine Learning Techniques

Electronic mail (e-mail) is one of the most prevalent approaches for online communication and transferring data through web because of its quick and easy distribution of data, low distribution cost and permanency. Despite these benefits there are certain weaknesses of e-mail. Among these, spam also known as junk e-mail tops. Spam is set of unwanted or inappropriate messages sent over the internet to a massive amount of users for the purpose of marketing, phishing, disseminating malware, etc.With the internet becoming the dominant platform anti-spam solutions are of great use today. This paper illustrates an efficient hybrid spam filtration method using Naïve Bayes algorithm and Markov Random Field technique, which detects and filters spam messages. The proposed method is evaluated based on its accurateness, meticulousness and time consumption. The results confirm that the proposed hybrid method achieves high percentage of true positive rate in finding e-mail spam messages.
Development and Evaluation of Study-Buddy Pairing Application for Android

This study aimed to develop an Android-Based Study Buddy Pairing Application that would help students find a study partner for a collaborative studying and peer tutoring.The researchers used Agile software development life cycle for the development of the application.It is a method commonly used in a particular approach to project management that is utilized in software development.It is an iterative work sequences wherein the process for calculating a desired result is by means of a repeated cycle of operations.The evaluation instrument used was Android Development Standard that contains 4 major criteria: visual design and user interaction, functionality, compatibility, performance and stability, and security.As a whole, the evaluation result of the Android-based application was found excellent in most of its features.The developed application was a good avenue to studying undertakings between the learner and the tutor.It is recommended that the developed application may be added with features such as file and image attachment and a profanity or obscenity filter on chat activity and an effective verification for learner and tutor.The features and functionality of the application is highly recommended to students who need peer tutoring.