Reviewerly/exHarmony huggingface dataset:
AN ANALYSIS OF GRAMMATICAL ERROR IN THESIS ABSTRACT OF FACULTY OF AGRICULTURE STUDENTS OF BORNEO TARAKAN UNIVERSITY

The aims of this research are: (1) to identify and describe the types of grammatical error in thesis abstract of Agriculture Faculty students of Borneo Tarakan University, and (2) to describe the possible causes of the grammatical error. This research is a descriptive study. 56 abstracts from year 2016 were taken as the samples. Taking the samples was done in the university library and those were in the form of softcopy. To collect the data, researcher followed some steps; they were identifying, categorizing, and describing the error. To categorize the errors, researcher used Surface Structure Taxonomy (SST) and also used Comparative Analysis Taxonomy (CAT) to analyze the causes of errors. The results of this research show that 2832 errors found in 56 abstracts and those were categorized based on the four types of SST. The errors percentages are 22.2% for omission error, 27.7% for addition error, 18.7% for misordering error, and 31.1% for misformation error. Besides, the errors based on the CAT, it was found that developmental error were 1331 errors, interlingual error were 830 errors, ambiguous error were 435 errors, and other error were 146 errors. From the results, the errors mostly found are misordering error and developmental error.
Active learning to optimise time-expensive algorithm selection

Hard optimisation problems such as Boolean Satisfiability typically have long solving times and can usually be solved by many algorithms, although the performance can vary widely in practice. Research has shown that no single algorithm outperforms all the others; thus, it is crucial to select the best algorithm for a given problem. Supervised machine learning models can accurately predict which solver is best for a given problem, but they require first to run every solver in the portfolio for all examples available to create labelled data. As this approach cannot scale, we developed an active learning framework that addresses this problem by constructing an optimal training set, so that the learner can achieve higher or equal performances with less training data. Our work proves that active learning is beneficial for algorithm selection techniques and provides practical guidance to incorporate into existing systems.
Mature GAIL: Imitation Learning for Low-level and High-dimensional Input using Global Encoder and Cost Transformation

Recently, GAIL framework and various variants have shown remarkable possibilities for solving practical MDP problems. However, detailed researches of low-level, and high-dimensional state input in this framework, such as image sequences, has not been conducted. Furthermore, the cost function learned in the traditional GAIL frame-work only lies on a negative range, acting as a non-penalized reward and making the agent difficult to learn the optimal policy. In this paper, we propose a new algorithm based on the GAIL framework that includes a global encoder and the reward penalization mechanism. The global encoder solves two issues that arise when applying GAIL framework to high-dimensional image state. Also, it is shown that the penalization mechanism provides more adequate reward to the agent, resulting in stable performance improvement. Our approach's potential can be backed up by the fact that it is generally applicable to variants of GAIL framework. We conducted in-depth experiments by applying our methods to various variants of the GAIL framework. And, the results proved that our method significantly improves the performances when it comes to low-level and high-dimensional tasks.
Nasḫī &amp;ndash; an Efficient Tool for the OCR-Aided Transcription of Printed Texts

This paper presents a simple yet effective solution for the transcrip- tion of printed texts. Our tool consists of a web-based user interface that provides an easy-to-use and ergonomic workflow and a col- laborative environment for the philologists while allowing them to profit from machine learning OCR technology. As the targeted use case is not mass digitisation but the creation of accurate citable digital editions, the user interface for ground truth production and post correction is built to provide the means for rapid proofread- ing while minimising the amount of errors. The productivity of the setup is further improved by enabling progressive OCR train- ing and recognition in the background to constantly increase the accuracy of the predictions. The advantages of the application are showcased in the second part of the paper by documenting our experiences utilising it for di- gitising Arabic and Latin texts. Over the course of several months the tool has been used to create transcriptions of a wide range of sources, among them challenging early modern editions and Ar- abic scripts, producing a large amount of reusable OCR training data as a positive side effect. Finally, there will be a discussion of possible future extensions of the tool and of how it could be adapted to fit the needs of other digitisation projects.
Integrating 3D Content into Online Course Modules Using Leganto

The gap between distance and in-classroom education is getting smaller and smaller. The rapidly expanding options of computer-based delivery methods are increasing the number of students willing to enroll in an online course each semester. 3D models and cell phone aided virtual and augmented reality are at the top of the hot tech that is driving this now mature movement. Using the Ex Libris platform Leganto, Emerging Technologies Librarian Bobby Reed, will demonstrate how the University of Oklahoma is embedding 3D content directly into courses on Canvas. From photogrammetric bone scans to CT scans from partner universities, several methods for showing, hosting, and curating this type of data will be on display. This is not just a lecture. Bring your laptop. Have a test course ready and we can practice building these functionalities together.
Aerial multi-object tracking by detection using deep association networks

A lot a research is focused on object detection and it has achieved significant advances with deep learning techniques in recent years. Inspite of the existing research, these algorithms are not usually optimal for dealing with sequences or images captured by drone-based platforms, due to various challenges such as view point change, scales, density of object distribution and occlusion. In this paper, we develop a model for detection of objects in drone images using the VisDrone2019 DET dataset. Using the RetinaNet model as our base, we modify the anchor scales to better handle the detection of dense distribution and small size of the objects. We explicitly model the channel interdependencies by using Squeeze-and-Excitation (SE) blocks that adaptively recalibrates channel-wise feature responses. This helps to bring significant improvements in performance at a slight additional computational cost. Using this architecture for object detection, we build a custom DeepSORT network for object detection on the VisDrone2019 MOT dataset by training a custom Deep Association network for the algorithm.
Comparative Study of Within-Group and First Difference Fixed Effects Models

This study examines the within-group and first difference fixed effect models using panel data set. Panel data on GDP, inflation, trade, civil-liability and population were collected across six African countries between 1972 and 1991, the data is an inbuilt R data found in amelia package. Performance of these fixed effect models were compared in terms of fitness using R- squared and relative efficiency. Results were generated using R software. Finding shows that in the within-group model, trade was the only independent variable that contributes significantly to GDP but in the first difference model both trade and population contributed significantly to GDP. The finding also reveals that within group model had a better fit with an R2 of 0.77317 as compared to first difference model which reported R2 of 0.75472. The relative efficiency was determined to be 1.3 showing that relative to within-group model the first difference model is preferred.
RSA and SFQ based Secure Heuristic Load Balancing Approach for Cloud Data Centers

A unhackneyed multifunctional paradigm is cloud computing. Suppliers of cloud services i.e., with the assistance of these elastic dynamic features, CSPs can efficiently manage the input loads available in the cloud environment in accordance with the end users' dynamic requirements. The effective secure management of loads in the cloud environment gives a rise to introduce new managerial secure methods, which are responsible for security, distributing the loads among all the available VMs dynamically in cloud data centers within a given fixed period of time. To achieve these goals various methodologies are exists in market but most of them are unable to find the optimality as well as the security of resources means they are actually unable to fulfill the dynamic changeable demands of the end users. In this manuscript, we impose an effective as well as efficient load balancing strategy to address these hurdles effectively: a secure heuristic load balancing approach for cloud data centers based on RSA and SFQ. The effective balance of incoming loads in cloud data centers is our approach. With the help of Start Fair Queuing (SFQ) algorithm, we are able to the fair allocation of VMs in our proposed approach. Hence, it maximizes the resource utilization. With the help of RSA algorithm, we are able to take care of security concerns of our proposed approach.
Web Software Engineering: Principles, Trends, Future Challenges

Inside a brief period, the Internet and World Wide Web have turned out to be universal, outperforming all other mechanical improvements in our history.They've likewise developed quickly in their extension and degree of utilization, altogether influencing all parts of our lives.Businesses, for example, assembling, travel and cordiality, banking, instruction, and government are Web-empowered to improve and upgrade their activities.Web based business has extended rapidly, cutting crosswise over national limits.Indeed, even conventional inheritance data and database frameworks have moved to the Web.Advances in remote innovations and Webempowered machines are setting off another rush of versatile Web applications.Accordingly, we progressively rely upon a scope of Web applications.Since a significant number of us depend on Web based frameworks and applications, they should be solid and perform well.To manufacture these frameworks and applications, Web designers need a sound system, a trained and repeatable procedure, better improvement instruments, and a lot of good rules.The rising field of Web building satisfies these necessities.It utilizes logical, designing, and the board standards and methodical ways to deal with effectively create, send, and keep up excellent Web frameworks and applications.It intends to acquire the present turmoil Web based framework advancement leveled out, limit dangers, and upgrade Web webpage viability and quality.
SISTEM INFORMASI TABUNGAN NASABAH DI KANTOR BANK PERKREDITAN RAKYAT MENGGUNAKAN ENTERPRISE ARCHITECTURE PLANNING (EAP)

The high level of competition requires a company to have the right strategy and quick in reaching the goal. Given the presence of the customer is important paktor to achieve a goal, the company realized how central the role of the customer. for overcoming a permasalahn is penulus create an information system of savings companies must understand the wants and needs of customers in their business so that customers get the satisfaction that optimal in creating an information system saving customers using the pendekantan or methodology Enterprise Architecture Planning (EAP) in the collection of architecture and strategic which includes information, business systems, and architectural engineering. EAP is a modern approach to perancanaan on the quality of data in order to reaching an information system.
ENERGY AWARE CLUSTERING AND AGGREGATE NODE ROTATION WITH SINK RELOCATION IN MANET

As VOIP in MANET is utilized in modern and environmental checking. The most basic issues in VOIP MANET system is to decrease the vitality utilization to broaden the lifetime. The middle of the road jump hubs is working all through the information transmission so those hubs channel out their vitality which naturally diminishes the existence time. To conquer these downsides the EAC-ASR convention (Energy Aware Clustering Aggregate Node Rotation) with sink migration technique. The four significant procedures which are available in this convention was Clustering, information conglomeration, versatile hub pivot by swapping calculation and sink migration. The hypothetical investigation and the recreation examination are done and the outcome demonstrates that the EAC-ASR convention decreases the vitality utilization and increment the vitality proficiency. Better framework organization to the extent surrendered calls along respected QoS objectives.
PENINGKATAN KOMPETENSI TATA BAHASA PRANCIS MAHASISWA JURUSAN PENDIDIKAN BAHASA PRANCIS FBS UNY DENGAN MODEL PEMBELAJARAN INQUIRY

(Title: Improving French Grammar Competence of The Students at French Departement, Faculty of Languages and The Arts, Yogyakarta State Uninersity Through The Inquiry Model). This research aimed to improve the students’ French grammar competence through the Inquiry model with seven steps, ie : developing a question, generating a hypothesis, developing an experimental desain, collecting and recording data, analizing data, reaching conclusions, forming and extending generalizations, communicating results. This research was a class room action reseach study conducted in two cycles from the 2th to 27th May 2016. The research subjects were 21 students atten­ding in Grammaire 4 in classes E and J at the French Departement, Faculty of Languages and the Arts, Yogyakarta State University. The data were collected through observations, field notes, and achievement test. They were analized using qualicative and quantitative descriptive techniques. The results of the study showed that the application of the inquiry model was capable of improving the students’s French grammar learning, both in the process and the product. The success in the process was indicated by student’s activeness in the learning activities for the Grammaire 4 through the inquiry model, and the average attainment in the first cycle was 70.47% and 77.1% in second cycle. The success in product was indicated by improvement of the students’French grammar competence, reflected by the improvement of average score of post-test. The difference between the average score of pre-test and post-test scores was significant because the result of the paired sample t-test both in the first cycle and second one was p (0.001) &lt; 0.05. Keywords: inquiry model, French grammar, action research
The Design Museum of Mount Sinabung with Neo-Vernacular Architecture Approach

Mount Sinabung is a volcano on the plateau of Karo Regency, North Sumatra, Indonesia. Mount Sinabung suddenly erupted in 2010. The last eruption of the volcano occurred since February 19, 2018, and until now become an attraction to find out more about volcanic disasters. Museum Mount Sinabung is present to provide the means for education, dissemination of information in the aspect of the volcano and geological disaster with recreative and educative of nature. The problem raised was "How to design a volcano museum using the neo-vernacular architectural approach?". By applying one flow that develops in this post-modern era to revive memories of a semi-historical culture, representational, plural and eclectic cultural Agenda on to evoke the Design of Museum Mount SinabungIn the application of the neo-vernacular architectural approach to the building of the Museum, the principle of building the Siwaluh Jabu is restored, physically and non-physically.By using the qualitative method of designing the Museum, it was designed because it was rational and planned systematically with the selected location. So, the Museum is conceptualized with a flexible gallery that can be used as a shelter for refugees. Thus, it is expected that the Museum can be a recreational-educational facility and as a means of supporting disaster mitigation.&#x0D; &#x0D;
Communication-Censored Distributed Stochastic Gradient Descent

This paper develops a communication-efficient algorithm to solve the stochastic optimization problem defined over a distributed network, aiming at reducing the burdensome communication in applications such as distributed machine learning.Different from the existing works based on quantization and sparsification, we introduce a communication-censoring technique to reduce the transmissions of variables, which leads to our communication-Censored distributed Stochastic Gradient Descent (CSGD) algorithm. Specifically, in CSGD, the latest mini-batch stochastic gradient at a worker will be transmitted to the server if and only if it is sufficiently informative. When the latest gradient is not available, the stale one will be reused at the server. To implement this communication-censoring strategy, the batch-size is increasing in order to alleviate the effect of stochastic gradient noise. Theoretically, CSGD enjoys the same order of convergence rate as that of SGD, but effectively reduces communication. Numerical experiments demonstrate the sizable communication saving of CSGD.
MapSDI: A Scaled-up Semantic Data Integration Framework for Knowledge Graph Creation

Semantic web technologies have significantly contributed with effective solutions for the problems of data integration and knowledge graph creation. However, with the rapid growth of big data in diverse domains, different interoperability issues still demand to be addressed, being scalability one of the main challenges. In this paper, we address the problem of knowledge graph creation at scale and provide MapSDI, a mapping rule-based framework for optimizing semantic data integration into knowledge graphs. MapSDI allows for the semantic enrichment of large-sized, heterogeneous, and potentially low-quality data efficiently. The input of MapSDI is a set of data sources and mapping rules being generated by a mapping language such as RML. First, MapSDI pre-processes the sources based on semantic information extracted from mapping rules, by performing basic database operators; it projects out required attributes, eliminates duplicates, and selects relevant entries. All these operators are defined based on the knowledge encoded by the mapping rules which will be then used by the semantification engine (or RDFizer) to produce a knowledge graph. We have empirically studied the impact of MapSDI on existing RDFizers, and observed that knowledge graph creation time can be reduced on average in one order of magnitude. It is also shown, theoretically, that the sources and rules transformations provided by MapSDI are data-lossless.
Implementasi Metode Pembelajaran Mind Mapping Untuk Meningkatkan Aktivitas dan Hasil Belajar Peserta Didik pada Pembelajaran Biologi Kelas XI IPA 3 SMAN 1 Pinrang

Abstract. Improving the quality of education through the research of classroom action research which aim to improved learning activities and learning outcomes of learner through applied the learning method of mind mapping in structure and function of animal tissue. The subject of this researched was learners of XI IPA 3 SMAN 1 Pinrang which total 48 learners of school year 2017/2018. Qualitative data which investigated was learning activities observed by observer since learning process, while the quantitative data obtained from the score of making mind mapping, essay test and learning evaluation test. The result of this researched showed that there is enhancement of learning activities on the 1st cycle showed the percentage of learners was 72% for at all indicators which was observed, while on the 2nd cycle enhancement learning activities percentage of learners was 100%. For at all indicators which was observed. The outcomes of learner’s data obtained in the 1st cycle with total the learners not complete was 18 people with percentage 38%, while the outcomes of learners complete was 30 people with percentage 63%. While the outcomes of learners in the 2nd cycle increased with the learners not complete was 6 people with 13%, while the outcomes of learners complete was 42 people with percentage 88%. Keywords: Classroom action research, Mind mapping learning activities, learning outcomes.
Convergence and Expansion of a Study on History of Thought in Silla : (Kim, Bok-Soon, 2016, A Study on History of Thought in Silla, Kyungin Publishing)

A study on history of thought in Silla was published based on the results of six papers presented at the annual conference of the Silla Cultural Research Institute of Dongguk University on the theme of “Samgukyusa” and the achievements of Korea epitaph Collection, Silk Road and Korean Buddhist Culture Project. It is not easy to find a recent book dealing with thought history or Buddhist history from a historical perspective. The research results published by the authors, including Study of Ancient Buddhism in Korea and Silla Buddhism and Kingship as New Thinking, served as a guide for later students. This book is also the result of the author
oHMint: An Online Mathematics Course and Learning Platform for MINT Students

The oHMint project is an initiative with the goal of providing an online course and learning platform for STEM students of higher mathematics.Its flexible design allows it to be used for self-study as well as blended learning scenarios including flipped classrooms.In a pilot project in 2017/18 the chapter Differential Calculus is being produced as a prototype of an oHMint unit.This pilot is organized and funded through the Hamburg Open Online University, with technical support and implementation by integral-learning GmbH.It is intended as a catalyst for the future development of the full oHMint course spanning four semesters.A unique characteristic of oHMint is its broad supporting base among a variety of German institutions in the form of the OMB+ consortium.
Example: SQL Database Development

In the last chapter, we explored the shell using two modes: the traditional SQL command execution and a brief tour of using the X DevAPI to write Python code to interact without SQL databases.
Implementasi Metode Simple Additive Weighting Pada Aplikasi Rekomendasi Tempat Pengungsian Akibat Bencana Banjir

BBWS Cimanuk-Cisanggarung is a technical implementation unit at the Directorate General of Water Resources, Ministry of Pekerjaan Umum and Perumahan Rakyat which has the task of carrying out water resources management in the river area. As for the background of this study is the difficulty of river field officers to notify the announcement to the people who will be affected by the flood due to overflowing rivers, because the announcement is still a simple medium that is using sirens and hoaxes or electric poles. Geotagging technology and Fire Cloud Messaging Technology (FCM) is one of the ways to help field officers make public announcements through smartphone media, namely notifications that include river photos and locations. The difficulty of the people who are close to the Cimanuk-Cisanggarung watershed to get information about evacuation places before the flood occurs, because many people choose a place of refuge with their own choice. This is what makes the government more extra to provide assistance to unknown refugee camps. Google Maps technology can help people get to refugee camps recommended by the government. This research method consists of identifying problems, methods of data collection, system analysis, system design, system implementation, testing, and drawing conclusions. The results of the study show that river field staff and the community can be used as a notification of the river that will overflow and provide information on the place of refuge.