Reviewerly/exHarmony huggingface dataset:
Efektivitas Pembelajaran Daring Berbasis WhatsApp Group Berbantuan LKPD Ditinjau dari Motivasi Siswa Kelas VII di SMP Negeri 2 Jetis

ABSTRAK  Penelitian ini merupakan penelitian Eksperimen kuasi yang bertujuan untuk mengetahui apakah pembelajaran daring berbasis WhatsApp Group berbantuan LKPD lebih efektif dibandingkan pembelajaran daring berbasis WhatsApp Group tanpa berbantuan LKPD ditinjau dari motivasi belajar siswa terhadap hasil belajar matematika siswa kelas VII SMP Negeri 2 Jetis. Subjek penelitian ini adalah siswa kelas VII SMP Negeri 2 Jetis. Teknik pengumpulan data menggunakan teknik dokumentasi, teknik tes, dan angket motivasi belajar matematika. Hasil penelitian menunjukkan bahwa pembelajaran daring berbasis WhatsApp Group berbantuan LKPD sama efektifnya dengan pembelajaran daring berbasis WhatsApp Group tanpa berbantuan LKPD dan hasil belajar siswa dengan motivasi tinggi dan sedang lebih baik dibandingkan dengan hasil belajar siswa dengan motivasi rendah. ABSTRACT This research is a quasi experimental research which aims to determine whether online learning based on WhatsApp Group with LKPD assistance is more effective than online learning based on WhatsApp Group without assistance from LKPD in terms of student learning motivation towards mathematics learning outcomes of seventh grade students of SMP Negeri 2 Jetis. The subjects of this study were students of class VII SMP Negeri 2 Jetis. Data collection techniques used documentation techniques, test techniques, and mathematics learning motivation questionnaire. The results showed that online learning based on WhatsApp Group with the assistance of LKPD was as effective as online learning based on WhatsApp Group without the assistance of LKPD and the learning outcomes of students with high and moderate motivation were better than the learning outcomes of students with low motivation. Keyword : Effectiveness, Learning, Online, WhatsApp, LKPD, and Motivation.
TEXT STRUCTURE IN CULINARY GUIDES WINE AND DINE: SINGAPORE’S TOP RESTAURANTS

Abstract. This study aims to find the stages and language features of the descriptive text structure in Wine &amp; Dine: Singapore's Top Restaurants. The contribution of this research is that it can be used as a reference in the preparation of descriptive texts that will have social functions as information reports on the topic of restaurants. The theory used in this study is the theory of Descriptive Text by Knapp and Watkins (2005). This research method uses qualitative methods. This research data is the descriptive text in the culinary guidebook. The research found that the topic of data is a restaurant and there are 12 (twelve) stages in the restaurant descriptive text that are Food, Identity, Decoration, Services, Location, Facilities, Comment, Beverages, Branches, Abstract, Price, Employees. The language features used in restaurant descriptive text are the use of simple present tense, relational verbs, action verbs, adjectives, and adverbs. This research concludes that although 12 (twelve) stages have been found in the restaurant descriptive text, the stages used are 3-6 stages in the composition of the descriptive text. Relational verbs and action verbs are not applied to all sentences in one text. Adjectives are used more often than Adverb or Adverbial phrases in restaurant descriptive text.
Addressing Domain Shift between Real and Synthetic Data for Semantic Segmentation using GANs

Autonomous driving is without a shadow of doubt the future of automotive and it currently needs efficient solutions. Deep learning methods have shown excellent results for object classification and detection, but the specific task needed for driving is the more challenging semantic segmentation: each pixel of a depicted scene should be recognized as belonging to a specific object. For this setting, it is crucial to collect a large amount of per-pixel labeled data, which need an expensive manual classification process. Synthetic datasets from simulators can be used in order to reduce the amount of data required and they come with free annotation by design. However, the big style difference between synthetic and real images does not allow a direct knowledge transfer across the two domains and it asks for specific domain adaptation solutions. This work starts from a review of the most recent literature on deep learning for semantic segmentation and it proposes to boost domain adaptation techniques based on Generative Adversarial Networks (GANs) by combining them with features-based adaptive strategies that take into consideration category-level adaptation and self-supervision. The experimental analysis considers a model learned on synthetic labeled images from the GTA V videogame and applied on the real Cityscapes dataset collected in different cities in Germany. The obtained results show how the proposed combination can be useful in case of limited availability of real world annotated images.
SISTEM PEMINJAMAN DAN PENGEMBALIAN BUKU PERPUSTAKAAN KELILING BERBASIS WEB MENGGUNAKAN ALGORITMA STRING MATCHING PADA SUKU DINAS PERPUSTAKAAN DAN KEARSIPAN KOTA ADMINISTRASI JAKARTA UTARA

The Mobile Library at the North Jakarta Administration City Library and Archives Office is a mobile library engaged in learning to increase readers' interest, especially in terms of learning. Where in the process of borrowing and returning books in the mobile library is still not well computerized and some processes are still done by hand and stored in catalog books. So that unorganized data is difficult to find, is often lost and makes it difficult for administrators to prepare reports. From the existing problems the writer designed and built a mobile library information system which is expected to be able to handle these problems. In looking for needs, system development is carried out through data collection methods by observation. In the system development method, the authors use object-oriented development methods with tools using the Unified Modeling Language (UML) in system analysis and design. Researchers use the PHP and MYSQL programming languages ​​as the database. It is hoped that with this mobile library information system, the processes involved in borrowing and returning books will be easier, because this mobile library information system can be accessed multi-user.
Pengaruh Penggunaan Model Picture And Picture Terhadap Hasil Belajar Bahasa Indonesia Siswa Kelas IV

The problem in this research is the lack of understanding of students in reading texts in Indonesian subjects so that it has an impact on student learning outcomes. This research is a descriptive quantitative research with an experimental model with the aim of knowing the effect of the model using the picture and picture model on the learning outcomes of fourth grade Indonesian students. The results showed that there was a significant effect of using the picture and picture model on the learning outcomes of fourth grade students with a t-count value (5.931) greater than t-table 2.02439, which means H0 is rejected and H1 is accepted. Based on the results of the analysis above, it can be concluded that the picture and picture model has an impact on students in understanding Indonesian reading texts so that they can improve their learning outcomes. From these conclusions, the impact resulting from this study is known that the use of the picture and picture model can improve the learning outcomes of fourth grade Indonesian students.
Implementation of behavior tree for creating an in-game cut-scene

Currently the development of multimedia in this case 3d animation is very rapid, especially in the game industry. One of the things that make the game preferred besides the graphic display is the story that is presented through animation, and one important thing that can make the animation in the game conveys a story that can be understood and enjoyed by players is the placement of a virtual camera in the game. This prompted a lot of research on virtual cameras in games. In this research, one method will be used, namely Behavior Tree to create a cut scene in an unreal game engine. The contribution of this research is to explore the ability of the Behavior Tree in making in-game cut scenes using the unreal engine. The use of Behavior Tree in this study is proven to be able to create in-game cut scenes that are in accordance with the designed scenario.
A Discourse Markers In A Vlog Video By BNay Channel

English learning process of EFL learners is often evaluated through their performance in using the language, especially their speaking performance. This research concern about the use of discourse markers in informal occasion which may be a great help to improve speaking fluency of EFL. This research revealed the types and functions of discourse markers used in vlog video by BNay Channel. To conduct this research, the researcher relied on Biber et al (1999) theory in classifying the types of discourse markers and Castro (2009) theory for defining the function of the discourse markers. To get the data, the researcher applied qualitative approach. From this research, the writer found ten types of discourse markers applied by the speakers in their speech. After further analyzing the data, the writer found that the discourse markers used by the speaker served both textual and interpersonal functions divided into several sub-functions. This research showed that the dominant function of discourse markers shown in the data is response or reaction markers and back-channel signals which helps the speaker express a response or reaction to the previous discourse including back-channel signals of understanding and continued attention while other speaker is having his/her turn.
Poster: Differentially Private Release of Machine Learning Models

The parameters of the learned model can disclose sensitive users information in the learning phase. Differential Privacy is one of the frontier methods of privacy preserving which was first defined by Dwork et al. In this paper, we propose a a new deep learning based model which we have trained on the rain in Australia dataset. We compare our model with differentially private SVM, DNN, and Naive Bayes. Our experiment result shows that our algorithm has faster running time compared to other learning algorithms. The goal of this paper is to compare the accuracy of differentially private algorithms.
A Probabilistic Interpretation of Self-Paced Learning with Applications to Reinforcement Learning

Funding Information: This project has received funding from the DFG project PA3179/1-1 (ROBOLEAP) and from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 640554 (SKILLS4ROBOTS). Calculations for this research were conducted on the Lichtenberg high performance computer of the TU Darmstadt. Publisher Copyright: © 2021 Pascal Klink, Hany Abdulsamad, Boris Belousov, Carlo D'Eramo, Jan Peters, Joni Pajarinen.
SteelCore: An Extensible Concurrent Separation Logic for Effectful Dependently Typed Programs

Much recent research has been devoted to modeling effects within type theory. Building on this work, we observe that effectful type theories can provide a foundation on which to build semantics for more complex programming constructs and program logics, extending the reasoning principles that apply within the host effectful type theory itself. Concretely, our main contribution is a semantics for concurrent separation logic (CSL) within the F⋆ proof assistant in a manner that enables dependently typed, effectful F⋆ programs to make use of concurrency and to be specified and verified using a full-featured, extensible CSL. In contrast to prior approaches, we directly derive the partial-correctness Hoare rules for CSL from the denotation of computations in the effectful semantics of non-deterministically interleaved atomic actions. Demonstrating the flexibility of our semantics, we build generic, verified libraries that support various concurrency constructs, ranging from dynamically allocated, storable spin locks, to protocol-indexed channels. We conclude that our effectful semantics provides a simple yet expressive basis on which to layer domain-specific languages and logics for verified, concurrent programming.
Challenges and Opportunities of Building Fast GBDT Systems

In the last few years, Gradient Boosting Decision Trees (GBDTs) have been widely used in various applications such as online advertising and spam filtering. However, GBDT training is often a key performance bottleneck for such data science pipelines, especially for training a large number of deep trees on large data sets. Thus, many parallel and distributed GBDT systems have been researched and developed to accelerate the training process. In this survey paper, we review the recent GBDT systems with respect to accelerations with emerging hardware as well as cluster computing, and compare the advantages and disadvantages of the existing implementations. Finally, we present the research opportunities and challenges in designing fast next generation GBDT systems.
Blueprint : Green Hiking Nature Jacket / Hadi Zami Hasnor

Our business will introduce a new product namely GHN Jacket which is a product that is creates for hikers that always go hiking. This product highlights the importance of creativity and innovation to the entrepreneurs in industry. If we survey at the hiking shop or accessories shop, sometimes they do not have innovation for the existing jacket. The latest for the innovation just the material of the jacket which is waterproof and windproof. Our company found something that to be made. For instance, the company take this problem seriously to solve problem of hikers with technology. This is also because of the trend that spread nowadays which technology plays the important roles in this era. Our target markets for this product are hikers, traveller, or sport team community the accessories shops
On the Status of Geodetic Education in Germany during the COVID-19 Pandemic

The professional association ‘German Association of Surveying – Society for Geodesy, Geoinformation and Land Management (DVW)’ has the main objective to promote and to elevate the diverse and complex field of surveying as well as related sciences in general. Its working group 1 (WG1) is focusing on profession and education. In order to gather representative and meaningful insights into the effects of the first wave (March–July 2020) of the COVID-19 pandemic, WG1 carried out an extensive online survey (approx. 1.500 participants, 25 questions) on the surveying-related education system in Germany. This education system is divided into (i) dual education and (ii) higher education. The dual education system combines apprenticeships in a company and vocational education at a vocational school, while higher education is carried out at universities. Both career pathways as well as the individual professional development ‘legal traineeship for geospatial authoritative positions’ were taken into account in the survey. As the education of young surveyors is carried out in classical classroom settings and intensive practical training, significant impact due to the COVID-19 pandemic was expected, due to government regulations and further guidelines of the educational institutions especially. Within the paper, the database of the online survey is described in detail. The design of the survey enables to distinguish between the perspectives of young surveyors and teachers. To assess the effect of the COVID-19 pandemic, we compare their perceptions regarding, for example, working from home, communication processes, teaching and learning settings, completeness of education programs, exams, and level of satisfaction. In contrast to well-planned online teaching, the current strategy of education systems with respect to COVID-19 is described as emergency remote teaching. Besides important conclusions regarding recent experiences of emergency remote teaching, COVID-19 significantly supported the further development of e-learning for instance through the digitization of teaching as well as learning processes. Hence the online survey focused on the future of teaching and learning, aiming to present the individual views of the participants on this future-oriented change process.
LibGuides: TechCentral MakerSpace: Laser Engraving and Cutting

This guide serves as an introduction to the TechCentral MakerSpace, at Cleveland Public Library's Main Library.
SOCIAL CONFLICTS IN WILLIAM FAULKNER’S NOVEL LIGHT IN AUGUST

This study aims to analyze the social conflict in William Faulkner’s novel. It is done to reveal the types of conflicts found in the novel and causes of conflict in this novel. In conducting this research, the research used qualitative methods. This research is classified as library research because the data collection is done by reading novels and reading several books and other references related to the topic. The data were analyzed based on the type of conflict and its causes and then presented descriptively. In other words, the researcher analyzed the data according to the topic and then present the result in a narrative. In analyzing the data, the researcher used a mimetic approach. The results of the research show that there are two categories of social conflict in the novel, namely: conflict between individuals and individuals and conflict between individuals and group.
Survey of Deduplication Techniques and application of their Methodologies in Cloud

Deduplication of data is helpful for the using disk space efficiently and proves as a solution for preventing data redundancy. Deduplication process allows only unique set of data to be stored in the storage and all other copies are pointed towards connecting pointer that points towards unique data. In case of cloud computing, privacy of data is the major issue to be concerned. This paper studies about different deduplication techniques and their methods. The survey results are arranged in a table format for the easy understanding of development in deduplication techniques. During thesurvey, it is known that confidentiality and security of user data is compromised at different stages. Thus, it is essential that extra secure method is needed in data deduplication.
Analysis Of Phishing Websites Using An Competent Feature-Based Framework

Phishing attacks take place through various forms such as email, websites and malware. To perform email phishing, attackers design fake emails which claim to be arriving from a trusted company. They send fake emails to millions of online users assuming that at least thousands of legitimate users would fall for it. Phishing attacks are one of the most common and least defended security threats today. Objective of study to identify phishing attacks using five machine learning algorithms. The proposed system handles feature selection through learning algorithm, after feature selection, training and prediction is done. The objective of our study to find an efficient algorithm, which achieves highest accuracy.
Challenges and solutions for processing big data stream processing and planning technology in real time

Cloud and Big Data Analytics have been two of the most critical developments in recent years in the linear IT industry. In human society, data form and volume are rising in unprecedented pace, as modern services, such as cloud computers, the internet of stuff, and location-based services, have created an age of Big Data. Since data was an important resource, how big data was best handled and used has attracted considerable attention. This article proposes a multi-level storage and application process for LMA to solve the RTDP system’s mobility in real-time. In addition to the data processing terminal for the data collection system, the DSMS, CEP, batch-based map collected and other processing methods and the FPGA, GPU, CP user, and ASIC technologies are used. This multiple datum processing system is necessary in real time as it is fast and can process the data in a minimum amount of time, with a clear comparison between the different data stream processing systems and a model based on the previous comparison.
Enhanced force-field calibration via machine learning

We introduce a toolbox to calibrate microscopic force fields by analyzing the trajectories of a Brownian particle using machine learning, namely, recurrent neural networks. We demonstrate that this machine-learning approach outperforms standard methods when characterizing the force fields generated by harmonic potentials if the available data are limited. More importantly, it provides a tool to calibrate force fields in situations for which there are no standard methods, such as non-conservative and time-varying force fields. In order to make this method readily available for other users, we provide a Python software package named DeepCalib, which can be easily personalized and optimized for specific force fields and applications.
Multi-Dimensional Role of Transformational Leadership on the Staff Motivation of Higher Education Institutions in Oman

The current research aims to assess the multi-dimensional role of transformational leadership on the staff motivation in the higher education institutions in Oman. Four dimensions of transformational leadership such as individualized characteristics, Intellectual Stimulation, idealized influencing and inspiring motivations were conceptualised. Data was collected from 391 participants from the higher education institutions in Muscat. Using SmartPLS 3.3.2, data was analysed by employing structural equational modelling. The findings revealed that all four dimensions of transformational leadership, individualized characteristics, Intellectual Stimulation, idealized influencing and inspiring motivations have significant impact on the staff motivation. The study contributed to the body of knowledge by examining multidimensional role of transformational leadership on staff motivation. To the best of our knowledge, no such study has been conducted and the current study is one of its own kind.
Effects of Virtual Reality in the Area of Responsible Decision-Making Training on Adolescents

The number of emotional illnesses rises every year . The efforts of using HCI and VR technology to create prevention tools require to extend the design learning knowledge to provide appropriate emotional cognitive experiences. Research has shown that VR provides better engagement and improvement on basic emotional and social dimensions of learning, however, the literature shows that it is necessary for the continuous study to develop effective emotional learning experience addressed to the next generations on immersive virtual learning environment. This study aims to identify how VR experiences impact the learning of a specific emotional dimension on adolescents (responsible decision-making RDM) and identify which cognitive and experience elements have incidence in the design of the learning experience. Based on literature and theories of VR and learning sciences, we experimented using VR on learning sessions with a control group based in ethical and emotional situations using the SODAS method to learn responsible decision making. Results show that the VR group gets a higher score after the sessions, and qualitative and quantitative data reveals that learning timing, cognitive articulation, learning attribution, cognitive load, and specific emotional dimensions might be impacted by the emotional learning experience. The analyses provide helpful information for the future design of cognitive experiences on VR technology.