Reviewerly/exHarmony huggingface dataset:
Iot Based Dual Axis Solar Tracker Implementation For Polycrystalline Photovoltaic With Energy Storage

This project has been identified as IoT Based Dual Axis Solar Tracker Implementation for Polycrystalline Photovoltaic with Energy Storage that has two degrees of freedom that act as axes of rotation, both horizontal and vertical. Polycrystalline panel is great in both diffuse light and specular light and yet known for its minimal cost. This motorized servo-motor system based final year project is a self-orient the solar panel towards the maximum intensity of sunlight using Light Dependent Resistor (LDR) and hence, it will generate supply to the load. The main system that use to control the movement of motor and sense the LDR is Arduino Uno. This portable 10W solar project can be used anywhere at any time as its main concern to educate people of basic solar tracker concept aside to help supplying 5V Direct Current (DC) load. An Internet of Things (IoT) is therefore added as a feature for this project to monitor the performance of photovoltaic voltage through Blynk application in cell phone
Improving Causal Inference by Increasing Model Expressiveness

The ability to learn and reason with causal knowledge is a key aspect of intelligent behavior. In contrast to mere statistical association, knowledge of causation enables reasoning about the effects of actions. Causal reasoning is vital for autonomous agents and for a range of applications in science, medicine, business, and government. However, current methods for causal inference are hobbled because they use relatively inexpressive models. Surprisingly, current causal models eschew nearly every major representational innovation common in a range of other fields both inside and outside of computer science, including representation of objects, relationships, time, space, and hierarchy. Even more surprisingly, a range of recent research provides strong evidence that more expressive representations make possible causal inferences that are otherwise impossible and remove key biases that would otherwise afflict more naive inferences. New research on causal inference should target increases in expressiveness to improve accuracy and effectiveness.
Implementing a Real Time Virtual Mouse System and Fingertip Detection based on Artificial Intelligence

Artificial intelligence refers to the simulation of human intelligence in computers that have been trained to think and act like humans. It is a broad branch of computer science devoted to the creation of intelligent machines capable of doing activities that would normally need human intelligence. Despite the fact that Artificial intelligence is a heterogeneous science with several techniques, developments in machine learning and deep learning are driving a paradigm shift in practically every business. Human-computer interaction requires the identification of hand gestures utilizing vision-based technology. The keyboard and mouse have grown more significant in human-computer interaction in recent decades. This involves the progression of touch technology over buttons, as well as a variety of other gesture control modalities. A normal camera may be used to construct a hand tracking-based virtual mouse application. We combine camera and computer vision technologies, such as finger- tip identification and gesture recognition, into the proposed system to handle mouse operations (volume control, right click, left click), and show how it can execute all that existing mouse devices can.
A Graphical Interface for Curating Schemas

Much past work has focused on extracting information like events, entities, and relations from documents.Very little work has focused on analyzing these results for better model understanding.In this paper, we introduce a curation interface that takes an Information Extraction (IE) system's output in a pre-defined format and generates a graphical representation of its elements.The interface supports editing while curating schemas for complex events like Improvised Explosive Device (IED) based scenarios.We identify various schemas that either have linear event chains or contain parallel events with complicated temporal ordering.We iteratively update an induced schema to uniquely identify events specific to it, add optional events around them, and prune unnecessary events.The resulting schemas are improved and enriched versions of the machineinduced versions.
Support Vector Machine Technique to Prognosis Breast Cancer

Breast cancer is one of the most inescapable infections among ladies in the India and around the world. Early determination is a critical advance in recovery and treatment. Nonetheless, it is anything but a simple one because of a few vulnerabilities in discovery utilizing mammograms. AI (ML) strategies can be utilized to plan and create instruments for doctors that can be utilized as a powerful system for early discovery and analysis of bosom malignancy which will significantly improve the endurance pace of patients. This paper utilizes support vector machine calculation to anticipation the bosom malignancy. The Wisconsin Diagnosis Breast Cancer informational index was utilized as to gauge exactness. The outcomes acquired are serious and can be utilized for identification and treatment.Kushwah, Virendra Singh Saxena, Aradhana Pahariya, Janki Sharan Goyal, Sandip Kumar
DYNAMIC MIN-MAX ALGORITHM FOR RESOURCE PROVISIONING IN CLOUD ENVIRONMENT

Cloud computing is a service-oriented architecture.Cloud offers different service models such as Software as a Service(SaaS), Infrastructure as a Service(IaaS) and Platform as a service(PaaS).The core component of every cloud is resource provisioning.Resources such as CPU, memory, and storage are provisioned dynamically to support cloud applications.This paper addresses scheduling of the tasks dynamically by taking into account the total execution or completion time of the tasks and resource utilization.The Cloud scheduler queries the Cloud Information System to check for the availability of resources, knowing their properties, and then scheduling the resources as per task requirements.The results are sent back to users once the task completes.The challenge lies in scheduling the tasks in a heterogeneous environment.
Conditioning and Coordination a Necessity in Appropriation of Learning Program Artistic Gymnastics with Students

The purpose of this study is to recognize, evaluate, compare and develop the level of conditioning-coordinating preparation of students, in the context of realizing the learning process of program elements in practical lessons.This study was attended by 27 students.Participants were selected randomly (of 10 groups having the 2nd Bachelor course two were separated) and were included in this study at their desire.The student group taken in this study would be trained for 8 weeks, according to a defined physical fitness "supplement" program during this period, with the twice-weekly focus on developing plyometric jump and strength exercises for the major muscle groups., as well as other speed-force coordinate exercises with varying tempo and direction, accompanied by flexion, muscle extension and articulation mobility exercises (stretching).All students included in this study were measured before and after the 8-week training period on the tests: vertical jump, long jump, 20m running, 5x10m running, flexibility, and anthropometric parameters related to these tests.At the end of the training period, the students included in this study had a significant improvement (p <0.05), in the vertical jump with the help of the arms with the hands at the waist by (3.6 ± 0.05cm or 7.4%) and in the vertical jump with the help of the arms (4.8 ± 0.07cm or 8.89%), in the long jump (9.3 ± 1.66 cm or 3.72%), in 20m running (-0.32 ± 0.15sec or 9.6%), in 5x10m running with a change of direction (-0.28 ± 0.35sec or 2.13%), in flexion of front trunk (2.22 ± 2.39cm or 18.7%) as well as back-to-back rotation of the shoulder (6.63 ± 3.39cm or 7.75%).These tests were associated with anthropometric parameters such as, age (22.7 years), weight (75.2 ± 7.5 kg), height (1.82 ± 0.07cm), BMI (22.4 ± 1.39), fitness index (Esslinger) at first assisted measurement of the arms with a second measurement difference of 1.33 ± 0.78 or 1.29% and without the help of arms with a second measurement difference of (1.38 ± 1.02 or 1.56%), which had less significant improvements.The results of this study showed that the effects of the plyometric and strength-velocity training methods, coupled with the methods of developing passive-active flexibility, increased with significant indicators especially on the conditioning-coordination performance skills of the students taken in this study.
Empirical Best Practices On Using Product-Specific Schema.org

Schema.org has experienced high growth in recent years. Structured descriptions of products embedded in HTML pages are now not uncommon, especially on e-commerce websites. The Web Data Commons (WDC) project has extracted schema.org data at scale from webpages in the Common Crawl and made it available as an RDF `knowledge graph' at scale. The portion of this data that specifically describes products offers a golden opportunity for researchers and small companies to leverage it for analytics and downstream applications. Yet, because of the broad and expansive scope of this data, it is not evident whether the data is usable in its raw form. In this paper, we do a detailed empirical study on the product-specific schema.org data made available by WDC. Rather than simple analysis, the goal of our study is to devise an empirically grounded set of best practices for using and consuming WDC product-specific schema.org data. Our studies reveal five best practices, each of which is justified by experimental data and analysis.
Kesalahan Berbahasa dalam Modul I Bahasa Indonesia PPG Dalam Jabatan Tahun 2020

This research is here to explain several forms of errors in the material module of the Pendidikan Profesi Guru Dalam Jabatan Tahun 2020. Research on the analysis of language errors in the PPG module has never been carried out. In data collection, used the Listening method with the Note Technique. The data are recorded in such a way in tabulations. In analyzing the data, the Intralingual Matching method was used with HBS and HBB techniques. HBS and HBB techniques are realized by comparing between language data and applicable rules. Furthermore, deviant linguistic data are classified based on the types of violations against linguistic rules and theories. The presentation of the results of data analysis in this study is based on the taxonomy of linguistic categories in language error analysis. The results showed that in the module I PPG Dalam Jabatan Tahun 2020 there were spelling errors in the form of punctuation errors, capital letters errors, italicization errors, and word writing errors; morphological errors in the form of word formation errors and word non-conformity; syntactic errors in the form of misuse of conjunctor and ineffective sentences.
Forms and Functions of Graphicons in Facebook Private Conversations Among Young Filipino Users

Understanding the functions of graphicons such as emojis, images, memes, videos, GIFs, emoticons, and stickers has become increasingly relevant as they have become extensively integrated into textual messages on Facebook, especially in group chats. This study aimed to investigate the forms and functions of graphicons used by young Filipino users (ages 18-31) on Facebook group chats. The datasets were extracted from the corpora, ten Facebook group chats, each lasting for three months, and analyzed using or computer-mediated discourse analysis or language-focused content analysis. According to the findings of this study, emoji was the most widely used graphicon by young Filipino users on Facebook, while sticker was the least. Adopting Herring and Dainas’ six functions of graphicons (2017), the researcher discovered additional five functions on Facebook group chats. These functions are identified as mention, reaction, riff, tone modification, action, narrative sequence, response, sharing, replacement, complement, and attention. It was also discovered that a graphicon could serve more than one function in a conversation. Tone modification was the most commonly used function, while the narrative sequence was the least. It was found out that in both emojis and emoticons, ‘tone modification’ was the most used function while ‘sharing’ in both images and videos. Meanwhile, ‘action’ was the most used function in GIFs, ‘attention’ in memes, and ‘mention’ in stickers. Because of the significantly increased use of online communication, this study may provide insight on how people may use these graphicons in their everyday conversations.
Learning of Structurally Unambiguous Probabilistic Grammars

The problem of identifying a probabilistic context free grammar has two aspects: the first is determining the grammar's topology (the rules of the grammar) and the second is estimating probabilistic weights for each rule. Given the hardness results for learning context-free grammars in general, and probabilistic grammars in particular, most of the literature has concentrated on the second problem. In this work we address the first problem. We restrict attention to structurally unambiguous weighted context-free grammars (SUWCFG) and provide a query learning algorithm for strucuturally unambiguous probabilistic context-free grammars (SUPCFG). We show that SUWCFG can be represented using co-linear multiplicity tree automata (CMTA), and provide a polynomial learning algorithm that learns CMTAs. We show that the learned CMTA can be converted into a probabilistic grammar, thus providing a complete algorithm for learning a strucutrally unambiguous probabilistic context free grammar (both the grammar topology and the probabilistic weights) using structured membership queries and structured equivalence queries. We demonstrate the usefulness of our algorithm in learning PCFGs over genomic data.
DESAIN APLIKASI LAPORAN KEUANGAN USAHA KOS-KOSAN BERDASARKAN SAK EMKM MENGGUNAKAN MICROSOFT ACCESS

Abstract&#x0D; The purpose of this study was to design existing financial reports using applications that are easier, faster, more complete, accurate, and timely in presenting financial statement information. This research was conducted at the Kost Katto 2 Kec. Mapanget, Manado. The data obtained from the author's interviews with business owners and employees. The research method used was a qualitative method with a descriptive approach. Data collection was carried out by means of observation, interviews, and literature study.The results showed that in preparing financial reports based on SAK EMKM using Microsoft Access, all difficulties and problems faced in preparing financial reports could be resolved. This arrangement was based on the accounting cycle by preparing the required forecast tables, after which it can input transactions and can see the results of the financial statement design in the form of an income statement, a statement of financial position, and notes to financial statements. The recommendation from the results of this study is that Kost Katto 2 should make bookkeeping reports in accordance with existing standards and make financial reports using the Microsoft Access application program designed based on SAK EMKM&#x0D; Keywords: Application design, financial report, SAK EMKM
RANCANG BANGUN MEDIA PEMBELAJARAN INTERAKTIF KOMPUTER DAN JARINGAN DASAR SMK

The purpose of this research is to design and build a valid, practical and effective interactive learning media using Macromedia Flash 8 software. This type of research uses the IDI (Instructional Development Institute)model. The research and development step consists of three stages, namely define, develop, and evaluate. Three are 3 research instruments to collect data, namely the validation questionnaire, practicality questionnaire, and effectiveness test. The data analysis technique was performed using the Aiken’s V formula. The results of the design carried out produced an application that could be run on all computers/laptops of each student. Based on the results of research in Computer and Basic Network subjects class X TKJ Vocational High School 5 Padang declared valid with Aiken’s V coefficient value obtained an average value of 0,86 from media experts and 0,82 from material experts. The practicality of the media obtained a value of 85,67% from the teacher and 88% from students in the very practical category. The effectiveness test obtained students’ classical completeness of 86,2% and the average value of the gain score of 0,56 in the medium category. Based on the percentage of students’ classical completeness and the gain score, it can be concluded that this interactive learning media was declared effective. Keywords: Interactive Learning Media, Instructional Development Institute, Aiken’s V.
Proxy Graph Matching with Proximal Matching Networks

Estimating feature point correspondence is a common technique in computer vision. A line of recent data-driven approaches utilizing the graph neural networks improved the matching accuracy by a large margin. However, these learning-based methods require a lot of labeled training data, which are expensive to collect. Moreover, we find most methods are sensitive to global transforms, for example, a random rotation. On the contrary, classical geometric approaches are immune to rotational transformation though their performance is generally inferior. To tackle these issues, we propose a new learning-based matching framework, which is designed to be rotationally invariant. The model only takes geometric information as input. It consists of three parts: a graph neural network to generate a high-level local feature, an attention-based module to normalize the rotational transform, and a global feature matching module based on proximal optimization. To justify our approach, we provide a convergence guarantee for the proximal method for graph matching. The overall performance is validated by numerical experiments. In particular, our approach is trained on the synthetic random graphs and then applied to several real-world datasets. The experimental results demonstrate that our method is robust to rotational transform and highlights its strong performance of matching accuracy.
A Study on Deep Learning Model-based Object Classification for Big Data Environment

2021년 6월 한국소프트웨어감정평가학회 논문지 제17권 제1호-59 -segmentation techniques provide sufficient accuracy to handle foreign objects for pick and place operations.These image segmentation techniques could be identify and distinguish geometrical shapes and edges that are sufficient for robust handling of foreign objects.However, these features are insufficient for the identification of foreign objects [6-8], and this limitation in technology necessitates the presence of a manual worker that provides the identification of foreign objects in an order fulfillment process [9].In order to full automation to be realized, their system should be able to autonomously recognize the object, so that the object can be transported to its corresponding destination.Accordingly,
Preventing Overfitting via Sample Reweighting for Recommender System Incremental Update (Student Abstract)

Incremental update of recommender system models using only newly arrived data may easily cause the model to overfit to the current data. To address this issue without relying on historical data, we propose a sample reweighting method based on prediction performance of previous model on current data. The proposed method effectively alleviates the problem of overfitting and improves the performance of incremental update.
Weakly-Supervised Hierarchical Models for Predicting Persuasive Strategies in Good-faith Textual Requests

Modeling persuasive language has the potential to better facilitate our decision-making processes. Despite its importance, computational modeling of persuasion is still in its infancy, largely due to the lack of benchmark datasets that can provide quantitative labels of persuasive strategies to expedite this line of research. To this end, we introduce a large-scale multi-domain text corpus for modeling persuasive strategies in good-faith text requests. Moreover, we design a hierarchical weakly-supervised latent variable model that can leverage partially labeled data to predict such associated persuasive strategies for each sentence, where the supervision comes from both the overall document-level labels and very limited sentence-level labels. Experimental results showed that our proposed method outperformed existing semi-supervised baselines significantly. We have publicly released our code at https://github.com/GT-SALT/Persuasion_Strategy_WVAE.
Imam Safii Heart Disease Classification using Gain Ratio Feature Selection with Hidden Layer Modification in Extreme Learning Machine

Heart disease is a non-communicable disease that causes a high mortality rate and is still a problem both in developed and developing countries. This disease often occurs because of the narrowing of blood vessels which causes the functioning of the heart is disturbed. The number of cases of heart disease in Indonesia is still quite high, making medical staff require a fairly in diagnosing the patient's conditional. The research proposed to implement Gain Ratio in selecting the most important feature that influences heart disease and building the classification models based on the modification of hidden layer weight on Extreme Learning Machine. The research collected the heart disease dataset which was obtained from Kaggle UCI Machine Learning consist of 1.025 samples, 14 attributes, and 2 labels. The data preprocessing include using data cleaning and normalization to find out dirty data or missing values. The experiment reported that Gain Ratio succeeds to generate the attribute ranking of heart disease dataset, then Gain Ratio score was added to the weighting of the hidden layer input on learning methods. The research used various validation sampling using the splitting test between training data and testing such as 70:30, 80:20, 90:10%, and set up 1500 hidden layers. The accuracy average performance of Extreme Learning Machine with modification using Gain Ratio reached 100% for the training phase and 97.67% for the testing phase.&#x0D; &#x0D; Keyword: Heart Disease, Gain Ratio, Modification, Classification, Extreme Learning Machine
Prototipe Media Pembelajaran Rambu Lalu Lintas Berbasis Android

This study is motivated by motorists' lack of knowledge and discipline when it comes to traffic signs, which leads to frequent traffic accidents, congestion, and other issues on the road. The problem formulation is how to design and develop an Android-based traffic learning media application. The goal of this research is to provide motorists with information about road signs. The purpose of this research is to examine the literature on the theory of instructional media and the various types of android-based application development. The black box method was used to test this application. This study yielded an android-based traffic learning media application. Based on the test results, it is possible to conclude that this application was created to assist motorists in locating traffic signs on the highway