Reviewerly/exHarmony huggingface dataset:
ANALISIS LITERASI DIGITAL CALON GURU KIMIA DALAM PELAKSANAAN PPL BERBASIS VIRTUAL DI MASA PANDEMI COVID-19

The COVID-19 pandemic has changed the offline learning process to be online, including the implementation of Field Experience Practices (PPL) activities carried out by chemistry education students. The online learning process which is carried out requires students to be proficient in the use of technology. This study aims to analyze the digital literacy skills of chemistry teacher candidate students. This research is a qualitative descriptive which the subjects were 60 students of chemistry education. Data were collected through survey and interview techniques. The instrument used to collect data was a digital literacy questionnaire which was declared valid and reliable based on the instrument test results. The digital literacy aspects tested are information, communication, content creation, safety, and problem solving. Based on the research data analysis, it can be concluded that the digital literacy skills of chemistry teacher candidate students are good. The percentage of the digital literacy level of chemistry education students on each indicator was 87% in the information category, 82% in the communication category, 78% in the content-creator category, 82% in the safety category, and 82% in the problem solving category.
Adaptive Kalman Filter with Linear Equality Road Constraints for Autonomous Vehicle Localization

This paper proposes a positioning method using an adaptive Kalman filter (AKF) with linear equality road constraints. The AKF algorithm used in vehicle localization can adaptively adjust the covariance matrices of both the system noise and the measurement noise based on the innovation sequence. Linear equality road constraints incorporated into AKF architecture can restrict the unconstrained position estimates to the constraint set via projection method and then give the final positioning results. Finally., two simulation cases based on segmented straight roads are provided to show the effectiveness of the proposed method.
Algebraic properties of implication-based intuitionistic fuzzy finite state machine over a finite group

An extensive study is performed on the already constituted algebraic structure called the implication-based intuitionistic fuzzy semiautomaton (IB-IFSA) over a finite group that compromises of implication-based intuitionistic fuzzy kernel and implication-based intuitionistic fuzzy subsemiautomaton. In this paper, the necessary and sufficient condition for an implication-based intuitionistic fuzzy normal subgroup to be an implication-based intuitionistic fuzzy kernel of an implication-based intuitionistic fuzzy semiautomaton is developed. In addition, the fundamental and adequate condition for an implication-based intuitionistic fuzzy subgroup to be an implication-based intuitionistic fuzzy subsemiautomaton of an IB-IFSA is formulated. Moreover, the condition for an implication-based intuitionistic fuzzy kernel of the an IB-IFSA to be implication-based intuitionistic fuzzy subsemiautomaton is also established.
Observed Recent Trends in the Solar Resource across North America: Changing Cloud-cover, AOD and the Implications for PV Yield

This study was prompted by recent low-resource years in the eastern US observed in the SolarAnywhereTM dataset. Herein, we investigate 20-year trends in GHI and clear-sky GHI for every latitude/longitude pair in the continental United States. Metrics we calculate are 20-year rate-of-change, means, and inter-annual variability. We also look at the changes in GHI attributable to changes in AOD over this time-period. Thanks to accurate remote sensing, we can now provide this useful information. Our analysis also provides a solar industry perspective by quantifying the trends in potential kWhAc/kWDc for PV across the same regions; trends influenced largely GHI but also by temperature. These trends are not only important to understand from a bankability and performance assessment perspective for solar energy applications but also for future planning.
Neural Networks Representation For Semantic Networks

Semantic network is one of the very well-known knowledge representation approaches. In various cases there is knowledge inaccuracy or incompleteness, such problems can be overcome through machine learning. In this paper we present an approach to convert semantic networks to neural networks to be ready for machine learning process. The conversion process considers all possible concepts and relations in the semantic network whether they already exist or not. The proposed neural network structure has various and several layers, such as concepts layer, relation layer and concepts and relation layer. For the learning process, it is proposed to have independent training for several neural networks.
Activity-Based Attribute Selection for Effective Advertising Using Pattern Mining

There are many different types of industries in this world, like automobile, food, medicine, education, real estate, etc. Almost all of them have some sort of product they sell, and they keep developing this product on a regular basis. These industries display and market their products mainly through advertisements. These advertisements could be presented to the viewers through television ads, ads in videos, posters stuck on walls and buses, sticky ads in articles and similar Web pages, etc. Business and advertising professionals refer to ads as “sticky ad”, when the ads are displayed on a part or whole of the Web page that is displayed on the screen, and don’t move (stuck on the screen) even after scrolling. The objective of this project is to develop a system which will allow clients and business tycoons to develop an advertisement keeping in mind the most influential attributes that usually determine its success. In this project we will theorize a way to use Data Science which will identify the important parameters for analysis. The process done to remove superficial attributes and keep the most influential attributes is called analysis. In this paper, by applying exploratory, predictive and regression analysis for forecasting behaviour of Sticky Ad data set, we show that our proposed method is efficient for the clients to use to advertise products confidently. We have a dataset of over 500 different advertisements each with over 25 attributes or features. Before analysing the dataset, we’ll be checking whether Linear Regression is suitable for the dataset and how other types of regression won’t work. The analysis part of the project could also be applied to different data sets of various domains by changing the necessary variables, but one has to keep in mind that the dataset available determines the algorithm used, and not the other way around.
Probabilistic Model Using Bayes Theorem Research Paper Recommender System

Uniqueness is inherited in every individual. Each individual links with another individual forming some social organization. Social organization in common places some trends or norms that influence each of its members. Thus, each individual ascribes common liking or behavior. Analysis of this behavioral pattern leads to the development of a recommender system, which helps to predict and determine the preference an individual ought to opt. Recommender system proves significant in e-commerce marketing strategies. Besides, recommender system finds its application in academics, health, entertainment, etc. This paper proposes a probabilistic model research paper recommender system using naive Bayes algorithm. The originality this paper incorporates is to classify the instances of the target paper into attributes such as title, reference, citation, calculated conditional probability and label them into recommendation or not. Deploying naive Bayes classifier, the evaluation of the current research is done generating the confusion matrix and hence calculated accuracy, precision, recall and F-measure. We have achieved an accuracy of 90.47%.
A target-free calibration method for automotive augmented reality head-up displays

In this paper, we introduce a newly developed target-free calibration method for automotive augmented reality head-up displays (AR-HUDs), which can be performed fully automatically using a smartphone camera. Our method requires no calibration target to be set up in front of the vehicle. Instead, it utilizes feature points of the environment, which makes it robust against misplaced targets and allows for an easy deployment, i.e. in garages. Under the pinhole model assumption, we decouple the perspective projection matrix into three parts: intrinsic matrix, relative pose between the vehicle’s 3D sensor and the smartphone camera, and then rotation between the camera space and the HUD field of view (HUD-FOV). Based on the epipolar constraint, we acquire the relative pose. The determination of intrinsic and rotation matrices is also accomplished without any pre-designed calibration target. The calibration itself takes less than 5 minutes for an eye box with 9 different training viewpoints. With our new approach, we achieve a competitive average reprojection error of 6.7 mm at a distance of 7.5 m, which is comparable to the previous work that applied targets.
Application of Image Interpolation Algorithms in Thermal Surveillance Devices

The paper considers several interpolation methods used for digital image enlargement. Each of the presented methods is implemented in FPGA-based video processing unit of thermal vision device. Visual comparison of image enlargement results when observing a small-sized object at different background is given. Mitchell method is recognized as the most suitable way to enlarge the image by interpolation in thermal vision cameras.
Mineralogical and radiometric studies of granitic rocks in Wadi Sabbagh area, south Sinai, Egypt

Wadi Sabbagh area is located at southern part of Sinai Peninsula. It is constrained by Latitudes 28° 05´ and 28° 15´ N and Longitudes 34° 00´ and 34° 15´ E. Gneisses, migmatites, metasediments, syn-and post-orogenic granites, in addition to pegmatites and post-granitic dykes are the main rock units encountered in this area. Geological, petrological and geochemical studies indicate that there are two distinct suites of granitoids: older tonalite to granodiorite assemblage and younger two phases (monzo- to syenogranites and alkaline granites). The field radiometric survey and lab investigations indicate that, the anomalous uranium contents are mainly restricted to the pegmatites and altered monzogranites. The eU contents of pegmatites reach up to 168 ppm with an average 117 ppm, while the eTh reach up to 257 ppm with an average 175 ppm. The eTh/eU ratios range from 1.1 to 2.4 with an average 1.6. In the altered monzogranites, eU reachs up to 305 ppm with an average of 126 ppm. The eTh reaches up to 382 ppm with an average of 166 ppm. The average values of Ra and K are 60 ppm and 2.93 wt percentage, respectively. The average values eTh/eU ratios in the examined anomalies monzgranites (1.56) indicating addition of uranium. The high eU and eTh contents may be attributed to the secondary ascending hydrothermal solutions that lead to the accumulation of radioactive mineralizations mostly along fractures, faults and shear districts. The recognized radioactive minerals in the studied pegmatites are thorite, uranothorite, zircon, fluorite, columbite, samarskite, monazite, xenotime and allanite, whereas thorite, uranothorite, zircon and fluorite are the foremost radioactive minerals in the altered younger granites. Additional accessory minerals such as ilmenite, magnetite, hematite, pyrite and rutile were found in the studied pegmatites. Hematite, magnetite and pyrite are the foremost accessories accompanying the radioactive minerals in the altered younger granites.
Differentiable Trust Region Layers for Deep Reinforcement Learning

Trust region methods are a popular tool in reinforcement learning as they yield robust policy updates in continuous and discrete action spaces. However, enforcing such trust regions in deep reinforcement learning is difficult. Hence, many approaches, such as Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO), are based on approximations. Due to those approximations, they violate the constraints or fail to find the optimal solution within the trust region. Moreover, they are difficult to implement, lack sufficient exploration, and have been shown to depend on seemingly unrelated implementation choices. In this work, we propose differentiable neural network layers to enforce trust regions for deep Gaussian policies via closed-form projections. Unlike existing methods, those layers formalize trust regions for each state individually and can complement existing reinforcement learning algorithms. We derive trust region projections based on the Kullback-Leibler divergence, the Wasserstein L2 distance, and the Frobenius norm for Gaussian distributions. We empirically demonstrate that those projection layers achieve similar or better results than existing methods while being almost agnostic to specific implementation choices.
Public Involvement in the Design of Public Projects

The involvement of the public in the decision-making process is essential, especially in the early stages of a design process. This study aims to achieve the development of an architectural program for a memorial public project, using the outcomes of the Analytical Hierarchy Process (AHP) based on public opinion. It employs a novel approach that sharply focuses on public involvement in the design process, using a quantitative methodology for the development of a suitable building program and selecting a memorial form that meets the public's needs in a practical way. The study drew on data from various memorial projects to identify possible spaces and their selection criteria. A written questionnaire was distributed to a sample of 105 members of the public, to narrow down the number of spaces according to public response. Then, a hearing (spoken) questionnaire was conducted on a sample of 20 to produce the program for development by generating the most strongly preferred form of memorial. The results contradicted the existing norm for a memorial as a sculpture; it was revealed that most of the public preferred memorial landscapes to buildings and great structures. The study concluded that AHP could be used to further involve the relevant stakeholders in the decision-making process of the design of a public project.
Using Teacher’s Feedback to Improve Students’ Grammar Mastery in Writing Cause and Effect Composition

This paper discusses the result of action research which is aimed to improve the students’ grammar mastery in writing cause and effect composition using teacher’s feedback. The objectives of the research are 1) To find out how the teacher’s feedback improves the students’ grammar mastery in writing cause and effect composition; 2) To find out how the teacher’s feedback improves the students’ motivation in writing cause and effect composition. This research was carried out at the eleventh grade of senior high school in Boyolali. The method used in this research was classroom action research (CAR). To collect qualitative data, the researcher used interviews, observation, and questionnaires. While collecting quantitative data, the researcher used writing tests. After the data is collected, the researcher analyzed both quantitative and qualitative data. The result shows that: 1) teacher’s feedback can improve the students’ grammar mastery in writing cause and effect composition; 2) teacher’s feedback can improve the students’ motivation in writing cause and effect composition. It can be concluded that teacher’s feedback can be used as an alternative strategy to improve the students’ motivation and grammar mastery in writing.
Annotation Issues in Universal Dependencies for Korean and Japanese

To investigate issues that arise in the process of developing a Universal Dependency (UD) treebank for Korean and Japanese, we begin by addressing the typological characteristics of Korean and Japanese. Both Korean and Japanese are agglutinative and head-final languages. And the principle of word segmentation for both languages is different from English, which makes it difficult to apply UD guidelines. Following the typological characteristics of the two languages and the issue of UD application, we review the application of UPOS and DEPREL schemes to the two languages. The annotation principles for AUX, ADJ, DET, ADP and PART are discussed for the UPOS scheme, and the annotation principles for case, aux, iobj, and obl are discussed for the DEPREL scheme.
Bayesian Estimation of Geometric Morphometric Landmarks for Simultaneous Localization of Multiple Anatomies in Cardiac CT Images

We propose a robust method to simultaneously localize multiple objects in cardiac computed tomography angiography (CTA) images. The relative prior distributions of the multiple objects in the three-dimensional (3D) space can be obtained through integrating the geometric morphological relationship of each target object to some reference objects. In cardiac CTA images, the cross-sections of ascending and descending aorta can play the role of the reference objects. We employed the maximum a posteriori (MAP) estimator that utilizes anatomic prior knowledge to address this problem of localizing multiple objects. We propose a new feature for each pixel using the relative distances, which can define any objects that have unclear boundaries. Our experimental results targeting four pulmonary veins (PVs) and the left atrial appendage (LAA) in cardiac CTA images demonstrate the robustness of the proposed method. The method could also be extended to localize other multiple objects in different applications.
Machine Learning based Bandwidth Prediction for Dynamic Adaptive Streaming over HTTP

By Digital Transformation, new technologies like ML (Machine Learning), Big Data, Cloud, VR/AR are being used to video streaming technology. We choose ML to provide optimal QoE (Quality of Experience) in various network conditions. In other words, ML helps DASH in providing non-stopping video streaming. In DASH, the source video is segmented into short duration chunks of 2-10 seconds, each of which is encoded at several different bitrate levels and resolutions. We built and compared the performances of five prototypes after applying five different machine learning algorithms to DASH. The prototype consists of a dash.js, a video processing server, web servers, data sets, and five machine learning models.
Database Management System on Raw Material Transaction System Case Study : Sabana Fried Chicken

Franchise business is a business which has high number of raw material transactions with more than 1300 partners number. Currently, the transaction logging is still done manually which relies on the conventional documents, therefore the overall transaction recording of raw materials in the form of database is really necessary to minimize the errors in terms of the data management. The database management system certainly holds an important role in this case. This system is able to provide assistance in solving problems, to speed up the transaction process of incoming and outgoing goods and also to produce report of raw materials in every distributor or every franchise partner
Grouping of Covid-19 Affected Areas in Bogor City Using The K-Means Algorithm

Clustering plays an important role in processing big data, making predictions and overcoming anomalies in data, identical characteristics in data sets are grouped using iterative techniques. Because data is always evolving from day to day, very large data sets with little can be identified into interesting patterns by grouping, special methods are needed to handle it. In December 2019 there was an outbreak of acute respiratory syndrome caused by coronavirus 2 infection that occurred in Wuhan and on February 12, 2020, the World Health Organization officially named the disease Corona Virus 2019 (Covid 19). This research will conduct clustering of areas affected by Covid 19 in the City of Bogor. The clustering was done using the K-Means method and dividing the data into 3 clusters, namely the low-impact cluster, the medium-impact cluster and the high-impact cluster. The results showed that from 68 urban villages in the city of Bogor, 45% of the area was in the low-affected category, 35.29% of the area was in the medium-affected category and 19.12% of the area was in the high-affected category.
Leukocyte (White Blood Cell) Classification with a Multi-stage Support Vector Machine

In this paper we present an automatic method for leukocyte classification using support vector machines in multiple stages that results in higher accuracy and less overfitting than other automatic methods.White blood cells (WBC), also called leukocytes, play an important role in the immune system to help protect the body from virus and bacterial diseases.Leukocyte tests can help diagnose a number of diseases related to blood disorders such as acute leukemia, chronic myeloid leukemia.Manual blood cell classification is commonly used in clinics and hospitals, automated cell classification systems may assist clinicians to increase efficiency and accuracy of diagnosis.In recent years, along with the development of artificial intelligence, there have been many studies using automatic methods to classify and count leukocytes.In this paper, we propose a Multi-stage support vector machine method (Multistage SVM) to classify leukocytes into 4 classes with 93% accuracy and less overfitting than other automatic methods.
Language of gleam: impressionism artwork automatic caption generation for people with visual impairments

User Experience Design (UX Design) comes from focusing on how products, in reality, affect the user's experience. In particular, the design of multi-modal interfaces for blind people facilitates the flexible and natural product or service capacity and improves blind people's interaction by overcoming the various existing constraints associated with any particular interaction. There have been various attempts to help visually impaired people appreciation of visual artwork, including multi-modal associations. However, these methods can only provide general information in terms of edge and pattern recognition by the sense of touch and restrained by the availability and number of specially developed artworks. We propose a novel method explaining visual artworks through image caption generation using artificial intelligence (AI) to improve artwork accessibility. This method can objectively describe any impressionism artwork used as a standalone description of art interpretation for blind people or can aide tactile-based methods. Based on end-to-end learning with a deep neural network, an encoder-decoder architecture model is adopted, and comprehensive experiments perform to confirm the stability of generated image captioning for stylized MS-COCO datasets with impressionism.
Augmented reality-based solar system for e-magazine with 3-D audio effect

Augmented reality (AR) is the newest technology that can be applied to computer vision, audio, video and other sensor-based input projects into 3D vision. It is the backbone for all specialisation of science, medical and engineering concepts. Currently, the reading and learning method through AR-based approaches are quite highly intensive than the existing methods such as papers, books and magazines. This strategy is more expensive but it is more interactive to the user in understanding the root concepts in an effective manner. This paper explores the experiment on solar system revolution pattern along with 3D audio effect in spatial dimension. This novel idea inculcates more vibrancy in the current generation of students to understand the concepts with the clear illustrations and demonstrations.