Reviewerly/exHarmony huggingface dataset:
Sistem Pendukung Keputusan Menggunakan Certainty Factor Dalam Mendiagnosa Kategori Tingkat Demam Berdarah

Dengue Fever is still a serious health problem and a major problem in the health sector in various parts of the world and also in Indonesia. This disease is an infectious disease and can infect all ages because there are still many people who still lack awareness in maintaining cleanliness to anticipate transmission. Data from the Ministry of Health of the Republic of Indonesia (Kemenkes RI) Indonesia recorded the number of people infected with Dengue Fever disease as many as 95,893 throughout 2020 and as many as 661 people died from this disease. The method used is using the certainty factor method where if it is interpreted this method will be used to help make a decision to diagnose the category of Dengue Fever level with a level of confidence in the form of a percentage of how much someone is infected with Dengue Fever in the existing category. The results obtained in the calculation of certainty factor get a result in the category of grade 1 of 98%, grade 2 of 98%, grade 3 of 99%, grade 4 of 99%. These results are enough to prove that this certainty factor method has a fairly high percentage level result.
Perancangan Aplikasi Rental Mobil Dengan Framework CodeIgniter

Abstract Car rental is a type of business that is engaged in four-wheeled vehicle rental transportation. With the current technological developments greatly affect the quality in the field of car rental services. Several fields that use information technology are education, agriculture and companies have used information technology to facilitate quick, precise and accurate information to the public. The development of technology is not only used for a field or large-scale company, but can be used by various groups of people to support a business they manage. One of the businesses that utilize this information technology is Sakti car rental, which is engaged in transportation for four-wheeled vehicle rental services. Currently, four-wheeled transportation has become a necessity to support the daily mobility of all people. Data management on Sakti Car Rental still uses manual methods which often cause errors. Technological developments also allow the development of a programming language, one of which is the CodeIgniter Framework. Therefore, the author designed a car rental application with a codeigniter framework that aims to make it easier for owners to manage car data, transaction data and to print transaction reports and avoid errors.
WiC = TSV = WSD: On the Equivalence of Three Semantic Tasks

The Word-in-Context (WiC) task has attracted considerable attention in the NLP community, as demonstrated by the popularity of the recent MCL-WiC SemEval shared task.Systems and lexical resources from word sense disambiguation (WSD) are often used for the WiC task and WiC dataset construction.In this paper, we establish the exact relationship between WiC and WSD, as well as the related task of target sense verification (TSV).Building upon a novel hypothesis on the equivalence of sense and meaning distinctions, we demonstrate through the application of tools from theoretical computer science that these three semantic classification problems can be pairwise reduced to each other, and therefore are equivalent.The results of experiments that involve systems and datasets for both WiC and WSD provide strong empirical evidence that our problem reductions work in practice.
(Not) acquiring meaning in a second language: Are input deficits key?

Word meanings are not always parallel across languages, and second language (L2) learners often use words in non-native ways. Is the learning problem inherent in maintaining conflicting word-to-meaning mappings within an integrated lexical network, or is it due to insufficient attention to and input for acquiring L2 mappings? To help discriminate between these possibilities, we gave English speakers repeated exposures to 40 brief videos of actions, labeled with five novel words that cross-cut English labeling patterns. Half the participants were told only to learn the labels for the actions. The other half were told to figure out their meanings, which might differ from English. The Figure Out Meanings group made test choices faster and were also slightly more likely to produce definitions capturing the intended meanings. However, both groups performed well above chance in generalizing the novel words. High levels of choice performance for both groups point to insufficient input, rather than inherent properties of lexical networks, as the critical limiting factor in more typical L2 learning contexts. Speed and definition performance hint at some advantage to explicit attention in sorting out L1-L2 differences.
Transliteration of Hiragana and Katakana Handwritten Characters Using CNN-SVM

Hiragana and katakana handwritten characters are often used when writing words in Japanese. Japanese itself is often used by native Japanese as well as people learning Japanese around the world. Hiragana and katakana characters themselves are difficult to learn because many characters are similar to one another. In this study, hiragana and basic katakana, dakuten, handakuten, and youon were used, which were taken from the respondents using a questionnaire. This study used the CNN method which will be compared with a combination of the CNN and SVM methods which have been designed to identify each character that has been prepared. Preprocessing of character images uses the methods of image resizing, grayscaling, binarization, dilation, and erosion. The preprocessed results will be input for CNN as a feature extraction tool and SVM as a tool for character recognition. The results of this study obtained accuracy with the following parameters: 69×69 image size, 3 patience values, val_loss monitor callbacks, Nadam optimization function, 0.001 learning rate value, 30 epochs value, and SVM RBF kernel. If using a system that only uses the CNN network, the accuracy is 87.82%. The results obtained when using a combination of CNN and SVM were 88.21%.
Pemanfaatan Teknologi Augmented Reality Dalam Pemasaran Perumahan Mutiara Barangan Palembang

Perumahan Mutiara Barangan is a housing complex that located at tanjung barangan, Palembang, Sumatera Selatan that was developed by PT. Kurnia Hamusri Sukses Mandiri. This This enterprise use pictures, brochures, and miniature for marketing activities. The objective of this research was to implement Augmented Reality Technology in digital marketing system to visualize their house in 3Ds View. The System Development model that used in this study is Multimedia Development Life Cycle (MDLC) which consists of concept, design, material collecting, assembly, testing and distribution. As a result of implementing Augmented Reality in Marketing of housing complex in Perumahan Mutiara Barangan Palembang can offer new experience of user in observing housing complex that was offered.
Improving Stylized Neural Machine Translation with Iterative Dual Knowledge Transfer

Stylized neural machine translation (NMT) aims to translate sentences of one style into sentences of another style, which is essential for the application of machine translation in a real-world scenario. However, a major challenge in this task is the scarcity of high-quality parallel data which is stylized paired. To address this problem, we propose an iterative dual knowledge transfer framework that utilizes informal training data of machine translation and formality style transfer data to create large-scale stylized paired data, for the training of stylized machine translation model. Specifically, we perform bidirectional knowledge transfer between translation model and text style transfer model iteratively through knowledge distillation. Then, we further propose a data-refinement module to process the noisy synthetic parallel data generated during knowledge transfer. Experiment results demonstrate the effectiveness of our method, achieving an improvement over the existing best model by 5 BLEU points on MTFC dataset. Meanwhile, extensive analyses illustrate our method can also improve the accuracy of formality style transfer.
Anomaly Mining - Past, Present and Future

Anomaly mining is an important problem that finds numerous applications in various real world do- mains such as environmental monitoring, cybersecurity, finance, healthcare and medicine, to name a few. In this article, I focus on two areas, (1) point-cloud and (2) graph-based anomaly mining. I aim to present a broad view of each area, and discuss classes of main research problems, recent trends and future directions. I conclude with key take-aways and overarching open problems. Disclaimer. I try to provide an overview of past and recent trends in both areas within 4 pages. Undoubtedly, these are my personal view of the trends, which can be organized differently. For brevity, I omit all technical details and refer to corresponding papers. Again, due to space limit, it is not possible to include all (even most relevant) references, but a few representative examples.
Towards Robust Model Reuse in the Presence of Latent Domains

Model reuse tries to adapt well pre-trained models to a new target task, without access of raw data. It attracts much attention since it reduces the learning resources. Previous model reuse studies typically operate in a single-domain scenario, i.e., the target samples arise from one single domain. However, in practice the target samples often arise from multiple latent or unknown domains, e.g., the images for cars may arise from latent domains such as photo, line drawing, cartoon, etc. The methods based on single-domain may no longer be feasible for multiple latent domains and may sometimes even lead to performance degeneration. To address the above issue, in this paper we propose the MRL (Model Reuse for multiple Latent domains) method. Both domain characteristics and pre-trained models are considered for the exploration of instances in the target task. Theoretically, the overall considerations are packed in a bi-level optimization framework with a reliable generalization. Moreover, through an ensemble of multiple models, the model robustness is improved with a theoretical guarantee. Empirical results on diverse real-world data sets clearly validate the effectiveness of proposed algorithms.
The use of Mobile Augmented Reality in health education review

This paper aims to present the analysis and synthesis of studies in Mobile Augmented Reality in health education, with a critical approach, and to develop suggestions for future researchers. The use of Mobile Augmented Reality technology has shown recently an improvement in teaching and instructing methods, several scientific articles and researches showed how this new technology has a positive impact on education and improving the learning and teaching methods and also has a positive impact on student’s performance. A basic qualitative method was used, Therefore, a total of 52 studies including theses and articles submitted electronically between 2010-2020, were obtained from the National Dissertation database and Dergi Park. The result of those articles has shown different relationships between variable factors selected in health education and Mobile Augmented Reality, and the findings indicated that most of the studies used the academic scale assessment tool to find out how this technology positively affected student’s performance and their academic achievement. In addition, the findings in this study suggest that there should be more experimental studies especially when there is a new development and models are being created every year to make the students experience more related to the real world, however, there were limitations identified, that the researches were limited between 2010-2020, which is carried out only in Turkey.
Geologic and geophysical maps of the Santa Maria and part of the Point Conception 30'×60' quadrangles, California

First posted August 2, 2021 For additional information, contact: Director, Geosciences and Environmental Change Science CenterU.S. Geological SurveyBox 25046, MS-980Denver, CO 80225-0046 This report presents digital geologic, gravity, and aeromagnetic maps for the onshore parts of the Santa Maria and Point Conception 30'x60' quadrangles at a compilation scale of 1:100,000. The map depicts the distribution of bedrock units, surficial deposits, paleontological data, geophysical data and structural features in the Santa Maria basin and the Santa Ynez Mountains to the south, an area corresponding to 26 contiguous 7.5-minute quadrangles. The map also includes offshore faults from the Hosgri fault, a major structural feature, east to the shoreline. This new map revises and supersedes two earlier versions of the 30'x60' Santa Maria quadrangle that were produced as part of the U.S. Geological Survey’s investigations of onshore oil and gas resources of the Santa Maria province (Keller, 1995). The first map was released as a scanned black-and-white image of hand-drawn compilation (Tennyson, 1992); the second map was a digital release that is no longer available (Tennyson and others, 1995). This new map also includes the geology of the onshore part of the adjacent Point Conception 30'x60' quadrangle that encompasses the Santa Ynez Mountains of the western Transverse Ranges. The digital database also contains magnetic and gravity data for the entire region, paleontological data, and interpretation of major offshore structural features that bear on the continuity and connection of the mapped onshore structures.
Connecting Through Zoom in COVID-19 Pandemic

In the Covid-19 pandemic where every working person is working from home to stay safe and to maintain social distancing. In mild of the corona virus crisis, the videoconferencing app has emerged as the usual for connecting with others face-to-face actually in each commercial enterprise and private settings. In Covid19 pandemic the main trouble got here out turned into a way to live related with every other. Connectivity turned into the main trouble for anyone like in schools, colleges, universities, private sector, Government sector, spiritual sector, not unusual place human beings and plenty of more. Every task that become occurring physically among human beings went on-line via video conferencing apps Because within side the pandemic of COVID-19 no individual favored to have an impact on their jobs or employment, studies, business, personal relationship, and lots of various relationships. In mild of the corona virus crisis, the videoconferencing app has emerged as the usual for connecting with others face-to-face actually in each commercial enterprise and private settings. There are many videos conferencing gear to be had like Google meet, skype, slack, join.me, GoToMeeting, Cisco WebEx, etc. Even in case, you do not work from home, you have heard of Zoom. Zoom has emerged as incredibly famous amongst all on this COVID-19 pandemic. Let us have a few mild at the Zoom video conferencing app.
Instance-Aware Coherent Video Style Transfer for Chinese Ink Wash Painting

Recent researches have made remarkable achievements in fast video style transfer based on western paintings. However, due to the inherent different drawing techniques and aesthetic expressions of Chinese ink wash painting, existing methods either achieve poor temporal consistency or fail to transfer the key freehand brushstroke characteristics of Chinese ink wash painting. In this paper, we present a novel video style transfer framework for Chinese ink wash paintings. The two key ideas are a multi-frame fusion for temporal coherence and an instance-aware style transfer. The frame reordering and stylization based on reference frame fusion are proposed to improve temporal consistency. Meanwhile, the proposed method is able to adaptively leave the white spaces in the background and to select proper scales to extract features and depict the foreground subject by leveraging instance segmentation. Experimental results demonstrate the superiority of the proposed method over state-of-the-art style transfer methods in terms of both temporal coherence and visual quality. Our project website is available at https://oblivioussy.github.io/InkVideo/.
Blocking-based Neighbor Sampling for Large-scale Graph Neural Networks

The exponential increase in computation and memory complexity with the depth of network has become the main impediment to the successful application of graph neural networks (GNNs) on large-scale graphs like graphs with hundreds of millions of nodes. In this paper, we propose a novel neighbor sampling strategy, dubbed blocking-based neighbor sampling (BNS), for efficient training of GNNs on large-scale graphs. Specifically, BNS adopts a policy to stochastically block the ongoing expansion of neighboring nodes, which can reduce the rate of the exponential increase in computation and memory complexity of GNNs. Furthermore, a reweighted policy is applied to graph convolution, to adjust the contribution of blocked and non-blocked neighbors to central nodes. We theoretically prove that BNS provides an unbiased estimation for the original graph convolution operation. Extensive experiments on three benchmark datasets show that, on large-scale graphs, BNS is 2X~5X faster than state-of-the-art methods when achieving the same accuracy. Moreover, even on the small-scale graphs, BNS also demonstrates the advantage of low time cost.
Motion Capture and Intelligent Correction Method of Badminton Movement Based on Machine Vision

In recent years, badminton has become more and more popular in national fitness programs. Amateur badminton clubs have been established all over the country, and amateur badminton events at all levels have increased significantly. Due to the lack of correct medical supervision and health guidance, many people have varying degrees of injury during sports. Therefore, it is very important to study the method of badminton movement capture and intelligent correction based on machine vision to provide safe and effective exercise plan for amateur badminton enthusiasts. This article aims to study the methods of motion capture and intelligent correction of badminton. Aiming at the shortcoming of the mean shift algorithm that it is easy to lose the target when the target is occluded or the background is disturbed, this paper combines the mean shift algorithm with the Kalman filter algorithm and proposes an improvement to the combined algorithm. The improved algorithm is added to the calculation of the average speed of the target, which can be used as the target speed when the target is occluded to predict the area where the target may appear at the next moment, and it can also be used as a judgment condition for whether the target is interfered by the background. The improved algorithm combines the macroscopic motion information of the target, can overcome the problem of target loss when the target is occluded and background interference, and improves the robustness of target tracking. Using LabVIEW development environment to write the system software of the Japanese standard tracking robot, the experiment verified the rationality and correctness of the improved target tracking algorithm and motion control method, which can meet the real-time performance of moving target tracking. Experimental results show that 83% of amateur badminton players have problems with asymmetric functions and weak links. Based on machine vision technology, it can provide reliable bottom line reference for making training plans, effectively improve the quality of action, improve the efficiency of action, and promote the development of sports competitive level.
Implementing Little Fox Chinese Video-Tailored Instruction in A Mandarin Listening Class

Listening skills are the first skills that must be mastered by learners before moving on to speaking, reading and writing skills. In the conditions of the Covid-19 pandemic, teaching and learning activities are carried out online, therefore educators must have more effective learning strategies such as the use of learning media. This study discusses the application of video listening to Little Fox Chinese in Mandarin. This research is a qualitative descriptive research type because this study aims to describe the application of the Little Fox Chinese video in Listening 1 subject in the Mandarin Education Study Program, Universitas Negeri Malang. The application of the Little Fox Chinese video was carried out in three stages, namely the initial, main, and final learning activities. Based on the results of observations, it can be concluded that the teaching and learning activities of listening 1 subjects with the application of the Little Fox Chinese video go smoothly and conducive. Based on the results of the interview, students revealed that the Little Fox Chinese video media was very interesting.
Two-Sided Wasserstein Procrustes Analysis

Learning correspondence between sets of objects is a key component in many machine learning tasks.Recently, optimal Transport (OT) has been successfully applied to such correspondence problems and it is appealing as a fully unsupervised approach. However, OT requires pairwise instances be directly comparable in a common metric space. This limits its applicability when feature spaces are of different dimensions or not directly comparable. In addition, OT only focuses on pairwise correspondence without sensing global transformations. To address these challenges, we propose a new method to jointly learn the optimal coupling between twosets, and the optimal transformations (e.g. rotation, projection and scaling) of each set based on a two-sided Wassertein Procrustes analysis (TWP). Since the joint problem is a non-convex optimization problem, we present a reformulation that renders the problem component-wise convex. We then propose a novel algorithm to solve the problem harnessing a Gauss–Seidel method. We further present competitive results of TWP on various applicationscompared with state-of-the-art methods.
Fluctuation and dissipation in memoryless open quantum evolutions

Von Neumann entropy rate for open quantum systems is, in general, written in terms of entropy production and entropy flow rates, encompassing the second law of thermodynamics. When the open-quantum-system evolution corresponds to a quantum dynamical semigroup, we find a decomposition of the infinitesimal generator of the dynamics, that allows to relate the von Neumann entropy rate with the divergence-based quantum Fisher information, at any time. Applied to quantum Gaussian channels that are dynamical semigroups, our decomposition leads to the quantum analog of the generalized classical de Bruijn identity, thus expressing the quantum fluctuation-dissipation relation in that kind of channels. Finally, from this perspective, we analyze how stationarity arises.
Penerapan Metode Forward Chaining dan Naïve Bayes Untuk Mendiagnosa Penyakit Tanaman Kakao

Cocoa is one of the many plantation products in Indonesia which has considerable economic value, and has plantation land and its production each year has increased significantly. The lack of agricultural extension experts in providing direction, guidance, and information dissemination about the development of cocoa plant diseases faced by the cultivators of these plants can have an impact on the cultivators. The method used in this research is the forward chaining and naïve Bayes method. This system is expected to be useful for users in diagnosing cocoa diseases independently, of course easily and efficiently without having to require experts in their fields, with reference to the results and discussions carried out, the accuracy of this system has an accuracy value of about 95% in carrying out the diagnosis