Reviewerly/exHarmony huggingface dataset:
Bahan cetakan disain kelas yang literat di kelas awal sekolah dasar

The Community Partnership Program (PKM) program is the Pare-Pare City Education Office. The main problem of partners is that making literacy media as a display material in early classes that can attract students' attention as a main prerequisite in designing literacy classes is not easy for elementary school teachers. This factual condition is partly due to the limited reading material that can inspire about media and use to create display material in class. The specific problems that cause this are: (1) limited teacher knowledge about various things that can be developed in the classroom so that students have the expected reading skills; (2) limited teacher skills in making various early grade literacy media that can be used as material to design classes that support the development of student literacy skills; and (3) the low ability of teachers to use various types of media in developing students' literacy skills in early grades.  The method used is a participatory training model with the application of ICARE (introduction, connection, application, reflection, and extension). The results obtained are: (1) partners get insights about things that can be developed in the classroom so that students have the expected literacy skills; (2) partners succeeded in creating a variety of early grade literacy media that can be used as  material to design classes that support the development of student literacy skills; and (3) partners can use various media in developing students' literacy skills in the early grades.
Rethinking Noise Synthesis and Modeling in Raw Denoising

The lack of large-scale real raw image denoising dataset gives rise to challenges on synthesizing realistic raw image noise for training denoising models. However, the real raw image noise is contributed by many noise sources and varies greatly among different sensors. Existing methods are unable to model all noise sources accurately, and building a noise model for each sensor is also laborious. In this paper, we introduce a new perspective to synthesize noise by directly sampling from the sensor's real noise. It inherently generates accurate raw image noise for different camera sensors. Two efficient and generic techniques: pattern-aligned patch sampling and high-bit reconstruction help accurate synthesis of spatial-correlated noise and high-bit noise respectively. We conduct systematic experiments on SIDD and ELD datasets. The results show that (1) our method outperforms existing methods and demonstrates wide generalization on different sensors and lighting conditions. (2) Recent conclusions derived from DNN-based noise modeling methods are actually based on inaccurate noise parameters. The DNN-based methods still cannot outperform physics-based statistical methods.
MIST GAN: Modality Imputation Using Style Transfer for MRI

MRI entails a great amount of cost, time and effort for generation of all the modalities that are recommended for efficient diagnosis and treatment planning. Recent advancements in deep learning research show that generative models have achieved substantial improvement in the aspects of style transfer and image synthesis. In this work, we formulate generating the missing MR modality from existing MR modalities as an imputation problem using style transfer. With a multiple-to-one mapping, we model a network that accommodates domain specific styles in generating the target image. We analyse the style diversity both within and across MR modalities. Our model is tested on the BraTS’18 dataset and the results obtained are observed to be on par with the state-of-the-art in terms of visual metrics, SSIM and PSNR. After being evaluated by two expert radiologists, we show that our model is efficient, extendable, and suitable for clinical applications.
Adaptive Focus for Efficient Video Recognition

In this paper, we explore the spatial redundancy in video recognition with the aim to improve the computational efficiency. It is observed that the most informative region in each frame of a video is usually a small image patch, which shifts smoothly across frames. Therefore, we model the patch localization problem as a sequential decision task, and propose a reinforcement learning based approach for efficient spatially adaptive video recognition (AdaFocus). In specific, a light-weighted ConvNet is first adopted to quickly process the full video sequence, whose features are used by a recurrent policy network to localize the most task-relevant regions. Then the selected patches are inferred by a high-capacity network for the final prediction. During offline inference, once the informative patch sequence has been generated, the bulk of computation can be done in parallel, and is efficient on modern GPU devices. In addition, we demonstrate that the proposed method can be easily extended by further considering the temporal redundancy, e.g., dynamically skipping less valuable frames. Extensive experiments on five benchmark datasets, i.e., ActivityNet, FCVID, Mini-Kinetics, Something-Something V1&V2, demonstrate that our method is significantly more efficient than the competitive baselines. Code is available at this https URL.
Efficiency Enhancing Parameters in Speaker Recognition System

In this paper, I present high-level speaker specific feature extraction from speech signals that takes into account intonation, linguistics rhythm, linguistics stress, and prosodic features. I believe the rhythm is linked to linguistic units like syllables and manifests itself as changes in measurable parameters like fundamental frequency, duration, and energy. The syllable type features are used as the basic unit for expressing prosodic features in this research. Automatically locating the vowel starting point approximates the segmentation of continuous speech into syllable units. The knowledge of high-level speaker’s specific speakers is used as a reference for extracting the prosodic features of the speech signal. Highlevel speaker-specific features extracted using this method may be useful in applications such as speaker recognition where explicit phoneme boundaries are not readily available. On TIMIT and HTIMIT corpora that were initially sampled in the TIMIT, the efficiency of the specific characteristics of the specific features used for automatic speaker recognition was evaluated. In summary, the experiment, the basic discriminating system, and the HMM system are formed on TIMIT corpus with a set of phonemes. For TIMIT utterances, the proposed ASR system shows efficiency gains over the traditional ASR system.
An efficient Decision Tree Algorithm for analyzing the Twitter Sentiment Analysis

Opinion mining and sentiment analysis are valuable to extract the useful subjective information out of text documents. The huge amount of information from this medium has become an attractive resource for organizations to monitor the opinions of users, and therefore, it is receiving a lot of attention in the field of sentiment analysis. However, performing sentiment analysis is a challenging task for the researchers in order to find the users sentiments from the large datasets, because of its unstructured nature, slangs, misspells and abbreviations. To address this problem, a new proposed system is developed in this research study. Here, the proposed system comprises of four major phases; data collection, pre-processing, key word extraction, and classification. Initially, the input data were collected from the twitter dataset. After collecting the data, pre-processing was carried-out for enhancing the quality of collected data. The pre-processing phase comprises of two systems; lemmatization, and removal of stop-words and URLs. Then, an effective topic modelling approach Latent Dirichlet Allocation (LDA) was applied to extract the keywords and also helps in identifying the concerned topics. The extracted key-words were classified into three forms (positive, negative and neutral) by applying an effective machine learning classifier: Decision Tree (DT). The experimental outcome showed that the proposed system enhanced the accuracy in sentiment analysis up to 6-20% related to the existing systems.
Risk-sensitive reinforcement learning: a martingale approach to reward uncertainty

We introduce a novel framework to account for sensitivity to rewards uncertainty in sequential decision-making problems. While risk-sensitive formulations for Markov decision processes studied so far focus on the distribution of the cumulative reward as a whole, we aim at learning policies sensitive to the uncertain/stochastic nature of the rewards, which has the advantage of being conceptually more meaningful in some cases. To this end, we present a new decomposition of the randomness contained in the cumulative reward based on the Doob decomposition of a stochastic process, and introduce a new conceptual tool - the chaotic variation - which can rigorously be interpreted as the risk measure of the martingale component associated to the cumulative reward process. We innovate on the reinforcement learning side by incorporating this new risk-sensitive approach into model-free algorithms, both policy gradient and value function based, and illustrate its relevance on grid world and portfolio optimization problems.
Data Correction For Enhancing Classification Accuracy By Unknown Deep Neural Network Classifiers

Deep neural networks provide excellent performance in pattern recognition, audio classification, and image recognition.It is important that they accurately recognize input data, particularly when they are used in autonomous vehicles or for medical services.In this study, we propose a data correction method for increasing the accuracy of an unknown classifier by modifying the input data without changing the classifier.This method modifies the input data slightly so that the unknown classifier will correctly recognize the input data.It is an ensemble method that has the characteristic of transferability to an unknown classifier by generating corrected data that are correctly recognized by several classifiers that are known in advance.We tested our method using MNIST and CIFAR-10 as experimental data.The experimental results exhibit that the accuracy of the unknown classifier is a 100% correct recognition rate owing to the data correction generated by the proposed method, which minimizes data distortion to maintain the data's recognizability by humans.
VHS North and South LibGuides: VHS North Media Center: Procedures, Service Learning Information, Ebooks and Card Catalog: Safety in the Media Center

Here you will find VHS North and South MackinVia and Destiny Catalogs. You will be able to search for books and do research via Webpath Express and Databases. Please wear your mask properly. Please wipe down your area. Please stay socially distanced.
PENGARUH EFEKTIVITAS PENGENDALIAN INTERNAL, ASIMETRI INFORMASI DAN KETAATAN ATURAN AKUNTANSI TERHADAP KECURANGAN AKUNTANSI

The purpose of this research is to examine the influence of effectiveness of internal control, information asymmetry and fidelity to accounting rules on accounting fraud. The population in this study is the regional work unit of Aceh Besar District, which the sampling technique uses non probability sampling with purposive sampling method. Respondents who are the object of this research are those who are most responsible for regional financial management consisting of the Head of Institution, Technical Activity Management Officer (PPTK) or Secretary and Head of Finance at 34 SKPD. Data analysis method used is multiple linear regression with the help of STATA 15. The result of this research shows that (1) effectiveness of internal controls, information asymmetry and fidelity to accounting rules has an effect on accounting fraud (2) ) effectiveness of internal controls has an effect on  accounting fraud (3) information asymmetry has an effect on on accounting fraud and (3) fidelity to accounting rules has an effect on accounting fraud.
Geology, copper mineralization and geochemical behavior of elements and their relationship with Sandstone host rock in Tupchi area, East Azerbaijan province

The study area is located in the NW of Tasuj city, East Azerbaijan province. The mineralization in the Tupchi area is of type copper. The ore minerals include pyrite, chalcocite, bornite, chalcopyrite and other minerals include malachite and azurite that occur within the Miocene sandstone sedimentary rocks. Based on the Index Combined Variety, deposition of mineralized sandstone in Tupchi area, accurred in the first sedimentary cycle. This was confirmed by the presence of unstable minerals such as lithic grains, feldspar and mafic minerals. The study of the source rock and the type of mineralization that occurred in these sandstones, indicate their formation from the erosion of mafic to intermediate igneous rocks in semi-arid climatic conditions. Two and multi variable statistical studies indicate that there is a good correlation between copper –silver and zinc, which is due to the similar behavior of these elements. The Red Bed type mineralization took place in this area according to type of mineralized host rock, ore mineralogy, ore structure, type of alteration, reduce factor and mineralization control (plant remains), and finally, the comparison with known deposits in Iran.
Application of Evolutionary Algorithm in Chinese Word Segmentation

First of all, a brief comparison of the existing Chinese automatic segmentation algorithms is made, and the existing problems are pointed out. Then, aiming at these problems, a Chinese automatic segmentation method based on evolution is designed. Finally, through the analysis of the experimental results, the advantages and disadvantages of this algorithm are compared with the existing algorithms, and the improvements of the algorithm are pointed out.
A New Dataset and Recognition for Egocentric Microgesture Designed by Ergonomists

Virtual and Augmented reality (VR/AR) are widely deployed in industrial, medical, educational, and entertaining fields. The design of interactive interfaces has an impact on usability, comfort, and efficiency. Hand controllers and gestures are popularly used in VR/AR devices. However, users may suffer from overloading on the upper extremities while raising the hand or controller. Therefore, we released a microgesture library with 19 microgestures designed by ergonomists. Users can perform microgestures for an extended duration by resting the forearm on the tables to reduce the load on the upper extremity. Additionally, we collected a microgesture dataset of 2900 samples and utilized the C3D model to recognize the microgesture dataset. Finally, we achieved a recognition accuracy of 93.4% on the microgestures dataset.
Changing Teachers’ Roles in Teaching English through Online Learning

ICT-based digital resources are constantly being used to assist in the teaching of English. English teachers should understand how digital resources of ICT embedded in a framework for teaching English lead to improving the conditions for teaching and the roles of teachers in the modern English teaching-learning environment. Online learning is a method of using interactive ICT tools for distance learning.
Approaches and Applications of Inductive Programming (Dagstuhl Seminar 21192)

In this report the program and the outcomes of Dagstuhl Seminar 21192 Approaches and Applications of Inductive Programming is documented. The goal of inductive programming (IP) is to induce computer programs from data, typically input/output examples of a desired program. IP interests researchers from many areas of computer science, including machine learning, automated reasoning, program verification, and software engineering. Furthermore, IP contributes to research outside computer science, notably in cognitive science, where IP can help build models of human inductive learning and contribute methods for intelligent tutor systems. Building on the success of previous IP Dagstuhl seminars (13502, 15442, 17382, and 19202), the goal of this new edition of the seminar is to focus on IP methods which integrate learning and reasoning, scaling up IP methods to be applicable to more complex real world problems, and to further explore the potential of IP for explainable artificial intelligence (XAI), especially for interactive learning. The extended abstracts included in this report show recent advances in IP research. The included short report of the outcome of the discussion sessions additionally point out interesting interrelation between different aspects and possible new directions for IP.
Proposing a C&RL Registered Reports Option

This was a letter writing was led by Amy Riegelman and included several signatures. In this letter we are proposing a registered reports submission track for College and Research Libraries. This letter was sent to C&RL Wendi Arant Kaspar in June 2020.
IPOC: an efficient approach for dynamic association rule generation using incremental data with updating supports

According to recent statistics, there was drastic growth in online business sector where more number of customers intends to purchase items. Due to these retailers accumulates huge volumes of data from day to day operations and engrossed in analyzing the data to watch the behavior of customers at items which strengthen the business promotions and catalog management. It reveals the customer interestingness and frequent items from large data. To carry out this there was known algorithms present which deals with static and dynamic data. Some of them are lag time and memory consuming and involves unnecessary process. This paper intents to implement an efficient Incremental pre ordered coded tree (IPOC) generation for data updates and applies frequent item set generation algorithm on the tree. While incremental generation of tree, new data items will link to previous nodes in tree by increasing its support count. This removes the lagging issues in existing algorithms and does not need to mine from scratch and also reduces the time, memory consumption by the use of nodeset data structure. The results of proposed method was observed and analyzed with existing methods. The anticipated method shows improved results by means of generated items, time and memory.
Ask&Confirm: Active Detail Enriching for Cross-Modal Retrieval With Partial Query

Text-based image retrieval has seen considerable progress in recent years. However, the performance of existing methods suffers in real life since the user is likely to provide an incomplete description of an image, which often leads to results filled with false positives that fit the incomplete description. In this work, we introduce the partial-query problem and extensively analyze its influence on text-based image retrieval. Previous interactive methods tackle the problem by passively receiving users' feedback to supplement the incomplete query iteratively, which is time-consuming and requires heavy user effort. Instead, we propose a novel retrieval framework that conducts the interactive process in an Ask-and-Confirm fashion, where AI actively searches for discriminative details missing in the current query, and users only need to confirm AI's proposal. Specifically, we propose an object-based interaction to make the interactive retrieval more user-friendly and present a reinforcement-learning-based policy to search for discriminative objects. Furthermore, since fully-supervised training is often infeasible due to the difficulty of obtaining human-machine dialog data, we present a weakly-supervised training strategy that needs no human-annotated dialogs other than a text-image dataset. Experiments show that our framework significantly improves the performance of text-based image retrieval. Code is avaiable at this https URL.
La toma de notas: una mirada desde la ciencia, la tecnología y la sociedad

In this paper it is proposed to explain the note- taking with a view of science, technology and society, to show how it can contribute to the improvement and elevation of the quality of the graduate, both in the Access to Higher Education of the Faculty of Spanish for Non-Spanish Speakers as well as of the careers or masters in Cuba. The objective is to explain note-taking as skill, its importance for the appropriation of content; and its social, economic and political impacts. Theoretical methods were used: histological-logical, analysis and synthesis, induction-deduction and the empirical methods: the documentary study, the observation and consultation of experts. The results show that the note- taking will help students to perform better during their studies in Cuba and will generate impacts that will bring qualitative and quantitative benefits to society and will involve significant changes in the teaching-learning process.
PENERAPAN MODEL PEMBELAJARAN KOOPERATIF TIPE TALKING STICK UNTUK MENINGKATKAN KESIAPAN DAN PRESTASI BELAJAR SISWA (Studi Tindakan Kelas Pada Mata Pelajaran Bahasa Indonesia Kelas V SD Negeri 4 Merapi Selatan Kabupaten Lahat )

Thisresearch aimstodescribethe applicationofcooperativetype of learning modeltalking stickto increasethereadinessandthe students’achievementat IndonesiansubjectofthefifthclassatSD Negeri4MerapiSelatan.Thedesignof researchusedisclassroomactionresearchandthequetiontexperiment.Thesubjects oftheresearcharethestudentsofV.aatthesecondsemesteroftheacademicyear 2019/2020atSDNegeri1MerapiSelatan.Forthesamplesof quetiontexperimentare V.b and V.c nstrumen penelitian ini menggunakanlembar observasi dan tes. The instrumentsusedisthe observationsheet andtest.The datais analizedwithusingthe statisticdescriptive,mean,thepercentageandT-test.The resultshowsthatthe application of cooperative type of learning model talking stick can increase the readinessandthestudents’achievementofthestudentsofthefifthclassatSDNegeri 4Merapi SelatanLahatregency
Dissecting Image Crops

The elementary operation of cropping underpins nearly every computer vision system, ranging from data augmentation and translation invariance to computational photography and representation learning. This paper investigates the subtle traces introduced by this operation. For example, despite refinements to camera optics, lenses will leave behind certain clues, notably chromatic aberration and vignetting. Photographers also leave behind other clues relating to image aesthetics and scene composition. We study how to detect these traces, and investigate the impact that cropping has on the image distribution. While our aim is to dissect the fundamental impact of spatial crops, there are also a number of practical implications to our work, such as revealing faulty photojournalism and equipping neural network researchers with a better understanding of shortcut learning. Code is available at this https URL.
Students' Pronunciation Errors in English Silent Letters

The objective of this research was to find out the types of pronunciation errors are made by the seventh semester students of English education study program at Bengkulu University in the academic year 2020/2021. This research used a descriptive quantitative method. 94 students as the total population 30 students were chosen randomly as the samples. The instrument of the research were pronunciation test and interview. The test comprises 52 sentences which contain 13 silent letters i.e. “k, gh, th, g, p, l, t, s, w, n, b, h, d”. Each letter appears 4 times in the test. The results of the research showed that the students made two types of errors namely pre-systematic errors and systematic errors. The pre-systematic errors occurred more frequently than the systematic errors. The highest errors made by the students were silent letters “g” (e.g. gnaw) and “b” (e.g. doubt) which total numbers of errors of each of them were 93 times (77%). It could be concluded that the students faced high difficulties in pronouncing the silent letters which completely different from Indonesian phonetic system. It was recommended that the English lecturers should pay more attention to students’ pronunciation and find more suitable method or technique of teaching it and students should study harder about the silent letters in English language.