Reviewerly/exHarmony huggingface dataset:
The Landslide Susceptibility Deliniation Using Analitycal Hierarchy Process in Banjarnegara Regency, Central Java

Understanding the role of controlling factors and their geographical patterns is very important to predict landslide insecurity in the future. Until now, several qualitative, quantitative and semi-quantitative methods have been used to produce landslide-prone maps such as the Analytical Hierarchy Process (AHP). This study aims to: 1) identify landslide-prone factors in Banjarnegara Regency, 2) delineation spatial patterns of landslide-prone areas using the AHP method in Banjarnegara Regency. Factors that cause Banjarnegara Regency to be prone to landslides are slope, rainfall, soil type, lithology, land use, and land shape. Landslide-prone classification using AHP method produces 5 zones, namely very low prone zones (32.97%), low (19.69%), medium (25.31%), high (17.46%) and very high (4.55%). Landslide prone assessment using the AHP method has an accuracy rate of 81%, so the classification of landslide-susceptibility zones is categorized as high.
Semantic Journeys: Quantifying Change in Emoji Meaning from 2012-2018

The semantics of emoji has, to date, been considered from a static perspective. We offer the first longitudinal study of how emoji semantics changes over time, applying techniques from computational linguistics to six years of Twitter data. We identify five patterns in emoji semantic development and find evidence that the less abstract an emoji is, the more likely it is to undergo semantic change. In addition, we analyse select emoji in more detail, examining the effect of seasonality and world events on emoji semantics. To aid future work on emoji and semantics, we make our data publicly available along with a web-based interface that anyone can use to explore semantic change in emoji.
AN AN ANALYSIS OF LITERALNESS ASPECT OF GOOGLE TRANSLATE IN TRANSLATING BUSINESS CORRESPONDENCE

Google Translate is a common platform to help Indonesian employees ensure their English writing and the accuracy effects in a business letter. An unclear and confusing business letter can cause many problems and lead to misunderstandings, delays, lost business, and poor relations. This study aimed to examine the literalness aspect of Google Translate by analysing the produced text in English previously translated from Bahasa. Letters of notification from an insurance company in Indonesia based in Pontianak were chosen as the primary data. This study used a descriptive method and a qualitative approach. The translation indicators from Ismail (2016) was chosen to be used in this research. The analysis findings showed that there were 53% of literalness error, along with other 19% errors of cohesion error, 9% errors of faithfulness error, 11% of grammar error, 2% of omission error, and 6% of usage error. The data findings also showed the inability of Google Translate to understand the texts' contexts as a whole. Another aspect was errors in the source texts that carried into translation errors in the target texts were also the causes of translation errors.
PEMODELAN KONTEN AUGMENTED REALITY DALAM KONSEP INTERACTIVE REALTIME

Augmented Reality (AR) is a new technology in real world modeling in the form of virtual objects. Aggregated reality has been developed in many mobile platforms. The information raised by augmented reality is a representation of the real world through a reference image that is a reference. Modeling that emerges from a scan of static markers in the form, color and information that appears. This obstacle is a problem that is often faced by developers from augmented reality based applications. These constraints make the existing application of augmented reality less attractive and not interactive. While one of the objectives of making augmented reality based applications is the user interaction on the application. To reduce the weaknesses found in augmented reality, one of them is to use the Live texturing concept in modeling augmented reality. This concept is a new breakthrough in the field of augmented reality. the effect of using the Live-texturing concept will give an interactive impression that is more than static augmented reality. By applying live texturing technology to augmented reality, users can provide color and texture scratches that will have a direct / real time impact on the 3D model that is displayed.
A Review on Real Time Object Detection and Tracking on Video Frame

Object Detection in the moving scene is one of the critical researches in the field of computer science due the change in the size of object, speed, occlusions, and appearance variations. Particularly feature selection for the object is essential task during object detection. The process of object reorganization is called object detection. Object detection is useful in vehicle automation, traffic control, parking and many more places where we need object recognition. In this paper we focused on effectively object detection in any moving scene and compare our algorithm with the previous algorithm. On the other hand, few methods use the earlier existing information about object shape, color, texture and so on .this research suggest new goal ,result to tracking object in the video frame.
Frameworks for Implying Copyright Licences

This chapter proposes frameworks under each category of copyright licences which help assess the facts and circumstances and determine whether a licence may be implied and, if so, the scope of the licence. Given that the advantage of implied licences is their flexibility, any rigid set of rules governing the implication could strike at the heart of such flexibility. Therefore, the chapter presents frameworks as opposed to rigid rules, and seeks to balance guidance and flexibility. These frameworks are developed based on the constituents that each category of licence would need to have, had it been an express licence; and when the express licence is absent, these frameworks indicate how the facts and circumstances can be marshalled, so that one can ascertain whether a licence is to be implied. In addition, these frameworks approach bare licences differently from contractual licences.
Who Owns the Blockchain? How Copyright Law Allows Rights Holders to Control Blockchains

The lack of control by a single authority is an oft-described characteristic of blockchain technology. This article shows the extent to which US and EU copyright law protects information stored on a blockchain and the manner by which rights holders are able to control blockchains. While the two legal systems are almost identical in terms of protection of individual information stored on a blockchain, there are significant differences in the protection of the compilation of information. Because of the EU sui generis right for non-original databases, both the operator of a blockchain and participants in the blockchain network can have rights in the compilation of information stored on the blockchain. Therefore, they can both prevent acts necessary for the operation of a blockchain. Since this impedes the development and use, this article argues for an exception for acts that are necessary for the normal use of a blockchain.
Chop Chop BERT: Visual Question Answering by Chopping VisualBERT's Heads

Vision-and-Language (VL) pre-training has shown great potential on many related downstream tasks, such as Visual Question Answering (VQA), one of the most popular problems in the VL field. All of these pre-trained models (such as VisualBERT, ViLBERT, LXMERT and UNITER) are built with Transformer, which extends the classical attention mechanism to multiple layers and heads. To investigate why and how these models work on VQA so well, in this paper we explore the roles of individual heads and layers in Transformer models when handling $12$ different types of questions. Specifically, we manually remove (chop) heads (or layers) from a pre-trained VisualBERT model at a time, and test it on different levels of questions to record its performance. As shown in the interesting echelon shape of the result matrices, experiments reveal different heads and layers are responsible for different question types, with higher-level layers activated by higher-level visual reasoning questions. Based on this observation, we design a dynamic chopping module that can automatically remove heads and layers of the VisualBERT at an instance level when dealing with different questions. Our dynamic chopping module can effectively reduce the parameters of the original model by 50%, while only damaging the accuracy by less than 1% on the VQA task.
Adaptive Combination of Additive and Multiplicative Algorithms for Color Image Enhancement

Recently, Nikolova and Steidl have proposed a hue and range preserving histogram specification method which outputs a weighted average of two images generated by additive (shifting) and multiplicative (scaling) algorithms, where the weights are fixed for all pixels in an image. In this paper, we propose a method for selecting the one of the two algorithms at each pixel adaptively.&nbsp;Therefore, there is no need to prepare the weights for combining additive and multiplicative algorithms in the proposed method.&nbsp;Experimental results show that the proposed method can improve the saturation better than the convex combination method by Nikolova and Steidl.&nbsp;We also verify the property of saturation improvement in the proposed method theoretically. &nbsp
Blueprint: Mini Durian Flesh Separator / Ahmad Azammudin Husaini … [et al.]

The AS2H Enterprise is a new company that will be operated in Malaysia. The name of this company is come out from name of four members on this business. This business consists of four partners that motivate to be a successful entrepreneur. The members are including general manager, Operations manager, marketing manager and financial manager. Our business is located at Batu Caves, Selangor. After doing some research on the business opportunity that available at Batu Caves, we have decided to specify our business in selling our product.Our business will introduce a new product namely Mini Durian Flesh Seperator which is a product that creates for those who love to eat durian cuisine like Tempoyak. We take seriously to make a product that can help people to make durian cuisine quickly and easily without make their hands be smelly. We applied technology knowledge to produce our product.Target market for this product is lover durian in area Batu Caves since population of people is around 7.3 Million in 2017 (Geographical,2018). Medium income users and people in age 30 to 50 will be our target market. We will expand our target market to Klang Valley include Kuala Lumpur, Shah Alam and etc in future
Structured Ensembles: an Approach to Reduce the Memory Footprint of Ensemble Methods

In this paper, we propose a novel ensembling technique for deep neural networks, which is able to drastically reduce the required memory compared to alternative approaches. In particular, we propose to extract multiple sub-networks from a single, untrained neural network by solving an end-to-end optimization task combining differentiable scaling over the original architecture, with multiple regularization terms favouring the diversity of the ensemble. Since our proposal aims to detect and extract sub-structures, we call it Structured Ensemble. On a large experimental evaluation, we show that our method can achieve higher or comparable accuracy to competing methods while requiring significantly less storage. In addition, we evaluate our ensembles in terms of predictive calibration and uncertainty, showing they compare favourably with the state-of-the-art. Finally, we draw a link with the continual learning literature, and we propose a modification of our framework to handle continuous streams of tasks with a sub-linear memory cost. We compare with a number of alternative strategies to mitigate catastrophic forgetting, highlighting advantages in terms of average accuracy and memory.
Save $1.00 – SmartSweets Gummies Printable Coupon

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USING ELECTRONIC CAMBRIDGE DICTIONARY IN TEACHING ENGLISH PRONUNCIATION: PRE- EXPERIMENTAL STUDY ON THE SECOND YEAR OF SEKOLAH MENENGAH ATAS NEGERI 2 SINTANG

This research objective was to identify whether the use of Audio CALD3 is effective inteaching students to pronounce minimal pairs of English word toward second year of SMA Negeri2 Sintang. The sample chosen through cluster random sampling. They were 32 students selectedas participant. The research finding showed the pre-test result was 55.63 and post-test result was84.76 by applying 0.05 level of significance with 31 degree of freedom (df), the t-counted 25.778was higher than t-table 2.042. It indicated that the use of Audio CALD3 is effective in teachingstudents to pronounce minimal pairs of English word.Key words: Pronunciation, the atsecond year of students, CALD3, Pre-experimental research design.
STUDENTS’ ENGLISH SUMMATIVE TEST ANALYSIS: A CASE STUDY AT SMA PERGURUAN RAKYAT 1 JAKARTA

&lt;p&gt;The present research aims at analyzing the English summative test at SMA Perguruan Rakyat 1 in the Second Term of 2018/2019 Academic Year. The analysis concerns to difficulty level and discriminating power to know the teacher-made test English summative test at SMA Perguruan Rakyat 1 whether or not the test has good items, especially of its difficulty level, discriminating power and the effectiveness of the distracters. Such research procedures are employed, such as observation, literature review and statistical analysis. Specifically, the result of the research is expected to be beneficial for educational practitioners to have a more-depth insight of English summative test analysis. Furthermore, the result of the research is also expected to give valuable knowledge for those who are interested in analyzing the English summative test writing, mainly to the indicators of its discriminating power and distracters. Lastly, it is also hoped to be as reference for further researchers to draw related concepts of English summative test analysis that is integrated with various disciplines implemented in the classroom contexts.&lt;/p&gt;
PREDIKSI RISIKO KEMATIAN PASIEN STROKE PERDARAHAN DENGAN MENGGUNAKAN TEKNIK KLASIFIKASI DATA MINING

Stroke is a major health problem in Indonesia and the world, and the cause of disability and death. Hemorrhagic stroke has higher mortality rate compared to ischemic stroke. The objective of this study is to create a mortality risk prediction model by using Logistic Regression, Support Vector Machine, Random Forest, and Decision Tree C4.5 algorithms based on the data demographics and the clinical data in patients with hemorrhagic stroke. Prediction of outcome of patients of stroke help the physician to determine prognosis, targeted treatments and prepare patients and families. 538 subjects obtained from Hospital Stroke Registry in Yogyakarta. This study uses 10 fold cross validation to evaluate a model. The performance of Decision Tree C4.5 is higher than Logistic Regression, Support Vector Machine, and Random Forest. Prediction accuracy of Decision Tree C4.5 is 90.5%. The use of data mining algorithms able to predict the mortality and functional outcome of patients with hemorrhagic stroke. Keywords: mortality risk prediction, data mining, hemorrhagic stroke .
VersaGNN: a Versatile accelerator for Graph neural networks

\textit{Graph Neural Network} (GNN) is a promising approach for analyzing graph-structured data that tactfully captures their dependency information via node-level message passing. It has achieved state-of-the-art performances in many tasks, such as node classification, graph matching, clustering, and graph generation. As GNNs operate on non-Euclidean data, their irregular data access patterns cause considerable computational costs and overhead on conventional architectures, such as GPU and CPU. Our analysis shows that GNN adopts a hybrid computing model. The \textit{Aggregation} (or \textit{Message Passing}) phase performs vector additions where vectors are fetched with irregular strides. The \textit{Transformation} (or \textit{Node Embedding}) phase can be either dense or sparse-dense matrix multiplication. In this work, We propose \textit{VersaGNN}, an ultra-efficient, systolic-array-based versatile hardware accelerator that unifies dense and sparse matrix multiplication. By applying this single optimized systolic array to both aggregation and transformation phases, we have significantly reduced chip sizes and energy consumption. We then divide the computing engine into blocked systolic arrays to support the \textit{Strassen}'s algorithm for dense matrix multiplication, dramatically scaling down the number of multiplications and enabling high-throughput computation of GNNs. To balance the workload of sparse-dense matrix multiplication, we also introduced a greedy algorithm to combine sparse sub-matrices of compressed format into condensed ones to reduce computational cycles. Compared with current state-of-the-art GNN software frameworks, \textit{VersaGNN} achieves on average 3712$\times$ speedup with 1301.25$\times$ energy reduction on CPU, and 35.4$\times$ speedup with 17.66$\times$ energy reduction on GPU.
Empowering Communications in Vehicular Networkswith anIntelligent Blockchain-Based Solution

Blockchains have emerged over time as a reliable and secure wayto record transactions in an immutable manner in a wide rangeof application domains. However, current related solutions arenot yet capable of appropriately checking the authenticity ofdata when their volumes are huge. They are not also capable ofupdating Blockchain data blocks and synchronizing them withinreasonable timeframes. This is the case within the specificcontext of Blockchain vehicular networks, where these solutionsare commonly cumbersome when attempting to add new vehicles tothe network. In order to address these problems, we propose inthis paper a new Blockchain-based solution that intelligentlyimplement selective communication and collaborative endorsementapproaches to reduce communications between vehicles. Oursolution represents the vehicles of the Blockchain asintelligent software agents with a Belief-Desire-Intention(BDI) architecture. Furthermore, we propose an approach basedon multi-endorsement levels to exchange data of varyingsensitive categories. This approach, which is based onendorsing scores, is also used to shorten the admission of newvehicles into the Blockchain. We run simulations using theHyperledger Fabric Blockchain tool. Results show the efficiencyof our solution in reducing the processing times oftransactions within two different scenarios.
PENGGUNAAN PLATFORM LEARNSOCIAL DALAM MENINGKATKAN KEMAMPUAN LISTENING MAHASISWA PRODI PENDIDIKAN BAHASA INGGRIS STKIP KUSUMANEGARA

This research used quantitative research. It was use Classroom Action Research (PTK). In COVID 19 era, research used Platform Learnsocial to improve students’ listening skill. The aims of the research is to knowing that Platform learn social can improve students’ listening skill, to knowing that Platform learn social can be used as one of the media in learning Listening. This research consists of two cycle. Every cycle consists of four steps. The first steps is planning, second is acting, third is observing and the fourth is reflecting. Result of the research in Cycle I, there is 50% students pass in listening skill and 50% students fail in listening skill. Then in Cycle II, there  is 83% students pass in listening skill and 16% students fail in listening skill. It can be shown that Platform Learnsocial can be one of the media that suitable to improve students’listening skill.
A Move Analysis of English SMS Advertisements in Egypt

The current study attempts to conduct a move analysis of English SMS advertisements sent to mobile phones in Egypt. The recent prevalence of SMS advertisements led to the interest in the current study in analysing their linguistic features in order to reach their underlying structure. The researcher adopts Bhatia’s (1993) move analysis framework to examine the schematic structure of the English SMS advertisements collected from the researcher’s family members, friends, acquaintances and students. The findings revealed that some move patterns were recurrent in all SMS advertisements, while others were found in certain themes of these advertisements. For example, the move pattern which occurred the most in all SMS advertisements was “Description of offer/promotion + Call-to-action”, achieving the purpose of advertising for certain products or services by providing information about them and then contact information. Therefore, the move patterns used in the SMS advertisements analysed were found to be suitable for the genre of SMS advertisements.
MLDemon: Deployment Monitoring for Machine Learning Systems

Post-deployment monitoring of ML systems is critical for ensuring reliability, especially as new user inputs can differ from the training distribution. Here we propose a novel approach, MLDemon, for ML DEployment MONitoring. MLDemon integrates both unlabeled data and a small amount of on-demand labels to produce a real-time estimate of the ML model's current performance on a given data stream. Subject to budget constraints, MLDemon decides when to acquire additional, potentially costly, expert supervised labels to verify the model. On temporal datasets with diverse distribution drifts and models, MLDemon outperforms existing approaches. Moreover, we provide theoretical analysis to show that MLDemon is minimax rate optimal for a broad class of distribution drifts.
Respon Siswa Terhadap Gerakan Literasi Sekolah Di SMAN 2 Tualang Kecamatan Tualang Kabupaten Siak

The objective of this research is to analyze student responses to the school literacy movement at SMA Negeri 2 Tualang, Tualang District, Siak Regency.Research conducted in SMA Negeri 2 Tualang, Tualang District, Siak Regency. The population in this research is were all school members, including school principals, teachers, students, parents, and school committees totaling 25 people.To get the data on research about student responses to the school literacy movement at SMA Negeri 2 Tualang, Tualang District, Siak Regency, is by interview, collecting documentation, and observing the findings in the field.The results showed that important role of all school members in the implementation of the school literacy movement. Implementation of the school literacy movementin SMA Negeri 2 Tualang, Tualang District, Siak Regency are going well, althought it still needs improvement. The positive impact of the school literacy movement is increased interest in reading among students at the school.Key words : Student responses, School literacy movement
The concept of online learning in creative universities

One of the pressing problems is the effectiveness of the use of online education in creative universities and how to preserve and not destroy the traditions of Russian education and the accumulated experience of creative workers of culture and arts of various fields. We discuss the need to switch to online education in creative universities. We scientifically substantiate methodic support, which effectively affects the development of the system of creative universities, identify-ing, among other things, ways of using the online learning system in creative universities. A model and stages of planning the pedagogical process in the online learning system in creative universi-ties is designed. The introduction of online education into the educational process of creative uni-versities involves the direct use of information technologies. Interest in the work is also aroused by the considered forms of teaching a subject in the online learning system of creative universities. Ways of solving problems of a technical, pedagogical and methodological nature are proposed and the effectiveness of the implementation of the concept of online education in creative universities is proved.