Reviewerly/exHarmony huggingface dataset:
Generalized Matrix Means for Semi-Supervised Learning with Multilayer Graphs

We study the task of semi-supervised learning on multilayer graphs by taking into account both labeled and unlabeled observations together with the information encoded by each individual graph layer. We propose a regularizer based on the generalized matrix mean, which is a one-parameter family of matrix means that includes the arithmetic, geometric and harmonic means as particular cases. We analyze it in expectation under a Multilayer Stochastic Block Model and verify numerically that it outperforms state of the art methods. Moreover, we introduce a matrix-free numerical scheme based on contour integral quadratures and Krylov subspace solvers that scales to large sparse multilayer graphs.
A Numerical Investigation of the Minimum Width of a Neural Network

Neural network width and depth are fundamental aspects of network topology. Universal approximation theorems provide that with increasing width or depth, there exists a neural network that approximates a function arbitrarily well. These theorems assume requirements, such as infinite data, that must be discretized in practice. Through numerical experiments, we seek to test the lower bounds established by Hanin in 2017.
Sekolah Alam Mekar Bakti Dalam Pembuatan Video Promosi Penunjang Informasi

Information technology holds an important role in the world of education. Like Natural Blooming Tangerang Schools Act currently does not yet have the promotion. So the need for a promotional video, school of natural-shaped blooms in other schools of different service because of the way the lessons are in the wild. The results of this promotional video in order to be known by the public at large, so that the results of this promotional video can be developed in the community. The promotional video is very important if in create and disseminate in through social media.
Neural View-Interpolation for Sparse Light Field Video

We suggest representing light field (LF) videos as "one-off" neural networks (NN), i.e., a learned mapping from view-plus-time coordinates to high-resolution color values, trained on sparse views. Initially, this sounds like a bad idea for three main reasons: First, a NN LF will likely have less quality than a same-sized pixel basis representation. Second, only few training data, e.g., 9 exemplars per frame are available for sparse LF videos. Third, there is no generalization across LFs, but across view and time instead. Consequently, a network needs to be trained for each LF video. Surprisingly, these problems can turn into substantial advantages: Other than the linear pixel basis, a NN has to come up with a compact, non-linear i.e., more intelligent, explanation of color, conditioned on the sparse view and time coordinates. As observed for many NN however, this representation now is interpolatable: if the image output for sparse view coordinates is plausible, it is for all intermediate, continuous coordinates as well. Our specific network architecture involves a differentiable occlusion-aware warping step, which leads to a compact set of trainable parameters and consequently fast learning and fast execution.
ChainSplitter: Towards Blockchain-based Industrial IoT Architecture for Supporting Hierarchical Storage.

The fast developing Industrial Internet of Things (IIoT) technologies provide a promising opportunity to build large-scale systems to connect numerous heterogeneous devices into the Internet. Most existing IIoT infrastructures are based on a centralized architecture, which is easier for management but cannot effectively support immutable and verifiable services among multiple parties. Blockchain technology provides many desired features for large-scale IIoT infrastructures, such as decentralization, trustworthiness, trackability, and immutability. This paper presents a blockchain-based IIoT architecture to support immutable and verifiable services. However, when applying blockchain technology to the IIoT infrastructure, the required storage space posts a grant challenge to resource-constrained IIoT infrastructures. To address the storage issue, this paper proposes a hierarchical blockchain storage structure, \textit{ChainSplitter}. Specially, the proposed architecture features a hierarchical storage structure where the majority of the blockchain is stored in the clouds, while the most recent blocks are stored in the overlay network of the individual IIoT networks. The proposed architecture seamlessly binds local IIoT networks, the blockchain overlay network, and the cloud infrastructure together through two connectors, the \textit{blockchain connector} and the \textit{cloud connector}, to construct the hierarchical blockchain storage. The blockchain connector in the overlay network builds blocks in blockchain from data generated in IIoT networks, and the cloud connector resolves the blockchain synchronization issues between the overlay network and the clouds. We also provide a case study to show the efficiency of the proposed hierarchical blockchain storage in a practical Industrial IoT case.
A Summary of the Research on the Errors in the Acquisition of Adverbs by Thai Students

Chinese adverbs belong to a teaching difficulty in teaching Chinese as a foreign language.The number of adverbs is large, and there are great differences in the use of each adverb, which is also a learning difficulty for foreign students.In this paper, a general description of the errors of the adverbs of foreign students in Thailand is made by searching the study of the bias of the adverbs of the foreign students in Thailand, and the wrong type of the adverb acquisition, the cause of the bias and the countermeasures of the deviation are summarized.
Aplicação dos recursos de climatização em um data center

According to Manoel Veras (2012) teacher, researcher and writer, the efficiency of a Data Center, not so long ago, was measured solely in terms of indicators linked to availability and performance. With aspects issues increasingly being considered, increased energy costs and power supply of some providers, it is natural for IT infrastructure managers to rethink the Data Center strategy and consider the green aspect in the many choices they have to make, including equipment and the operation itself. Studies at Stanford University indicate that Data Center power consumption represents 1,2% of all US electricity consumption. The Uptime Institute (2005) raised that energy costs today account for up to 44% of all data center operating expenses, and that proper project design can help with resource efficiency.
Minimal Variance Sampling in Stochastic Gradient Boosting

Stochastic Gradient Boosting (SGB) is a widely used approach to regularization of boosting models based on decision trees. It was shown that, in many cases, random sampling at each iteration can lead to better generalization performance of the model and can also decrease the learning time. Different sampling approaches were proposed, where probabilities are not uniform, and it is not currently clear which approach is the most effective. In this paper, we formulate the problem of randomization in SGB in terms of optimization of sampling probabilities to maximize the estimation accuracy of split scoring used to train decision trees. This optimization problem has a closed-form nearly optimal solution, and it leads to a new sampling technique, which we call Minimal Variance Sampling (MVS). The method both decreases the number of examples needed for each iteration of boosting and increases the quality of the model significantly as compared to the state-of-the art sampling methods. The superiority of the algorithm was confirmed by introducing MVS as a new default option for subsampling in CatBoost, a gradient boosting library achieving state-of-the-art quality on various machine learning tasks.
PENERAPAN MEDIA ROSETTA STONE DALAM MENGAJAR KOSAKATA BAHASA INGGRIS MELALUI METODE DYNAMIC IMMERSION TERHADAP SISWA SMP 3 NEGERI 3 PADANGSIDIMPUAN

This research aims to improve the vocabulary of students' English vocabulary and student learning outcomes using Rosetta Stone through Dynamic Immersion media. The Dynamic Immersion is an audiovisual method without translation, so it is hoped that the brains of students will immediately become accustomed to associating English words with visual images.After the instrument is declared valid and has a high level of reliability, the next step is to provide pre-test and post-test to both groups. The test given is a learning achievement test in the form of an objective test in the form of multiple-choice related to the vocabulary theme of singular and plural vocabulary which is done by students of the control class and the experimental class.Hypothesis testing is done to find out whether there is a significant influence on the Rosetta Stone media through the dynamic immersion method to the learning outcomes of Grade VII students of SMP Negeri 3 Padangsidimpuan. Hypothesis testing in this study was conducted using a test analysis with analysis using the SPSS statistical program. The results of calculations from the t-test were 4.93 with Df = na + nb - 2, Df = 68, a significance level of 5%, then t-table = 1.99. So the result of the t-test is 4.93> 1.99. So, the t-test is bigger than the t-table. Based on the results, it means that there are significant differences in the post-test scores between the experimental group and the control group.Based on the decision-making basis of the independent sample t-test in this study, it can be concluded that the implementation of Rosetta Stone useful as a medium in teaching vocabulary mastery of seventh-grade students of SMPN 3 Padangsidimpuan. Keywords : vocabulary, rosetta stone, dynamic immersion
Semi-Supervised Natural Language Approach for Fine-Grained Classification of Medical Reports

Although machine learning has become a powerful tool to augment doctors in clinical analysis, the immense amount of labeled data that is necessary to train supervised learning approaches burdens each development task as time and resource intensive. The vast majority of dense clinical information is stored in written reports, detailing pertinent patient information. The challenge with utilizing natural language data for standard model development is due to the complex nature of the modality. In this research, a model pipeline was developed to utilize an unsupervised approach to train an encoder-language model, a recurrent network, to generate document encodings; which then can be used as features passed into a decoder-classifier model that requires magnitudes less labeled data than previous approaches to differentiate between fine-grained disease classes accurately. The language model was trained on unlabeled radiology reports from the Massachusetts General Hospital Radiology Department (n=218,159) and terminated with a loss of 1.62. The classification models were trained on three labeled datasets of head CT studies of reported patients, presenting large vessel occlusion (n=1403), acute ischemic strokes (n=331), and intracranial hemorrhage (n=4350), to identify a variety of different findings directly from the radiology report data; resulting in AUCs of 0.98, 0.95, and 0.99, respectively, for the large vessel occlusion, acute ischemic stroke, and intracranial hemorrhage datasets. The output encodings are able to be used in conjunction with imaging data, to create models that can process a multitude of different modalities. The ability to automatically extract relevant features from textual data allows for faster model development and integration of textual modality, overall, allowing clinical reports to become a more viable input for more encompassing and accurate deep learning models.
PRIMARY SCHOOL CHILDREN`S ATTITUDE TOWARD RECREATIONAL AND ACADEMIC READING: 10.24250/JPE/2/2019/RH

This article aims to investigate primary school children`s attitude toward reading using The Elementary Reading Attitude Survey (McKenna & Kear, 1990). A number of 106 Romanian students from 4th grade participated in this study. The results showed a rather positive attitude towards reading, both in recreational and academic reading. The data did not indicate differences in attitude toward reading between boys and girls. 12.26% of the students showed high negative attitudes on both subscales. Only a few students with atypical profiles (very high scores in one type of reading, and very low scores in the other) were identified.
THE RANGE OF ACTIVITIES FOR DEVELOPMENT OF PRE-SERVICE ENGLISH TEACHERS’ LINGUOSOCIOCULTURAL COMPETENCE BY MEANS OF DRAMATISATION

The problem of forming pre-service English teachers' sociocultural competence is of paramount importance.The paper presents the range of activities and tasks for forming linguosociocultural competence of pre-service English teachers in the process of teaching speaking by means of dramatisation.The structure of linguosociocultural competence, which includes sociocultural, sociolinguistic, social competences, is described.Modern requirements to the level of linguosociocultural competence of future English teachers as outlined in a university curriculum and contemporary research are considered.The necessity of purposeful formation of the stated competence of students at the initial stage of study at a foreign languages faculty is substantiated.The contents of linguosociocultural competence comprises sociocultural, sociolinguistic, social knowledge, subskills, skills, intercultural awareness, attitude and determine the structure of the set of activities and tasks.Dramatisation has a big educational potential in teaching foreign languages skills such as speaking.Drama games, role plays, improvisations, simulations, theatre projects are considered to be effective means of forming linguosociocultural competence of pre-service English teachers.The forms of dramatisation that can be used to develop the linguosociocultural competence of future English teachers are defined, the structure of the range, types and kinds of activities and tasks are determined.Examples of activities and tasks are given.
Dual Illumination Estimation for Robust Exposure Correction

Exposure correction is one of the fundamental tasks in image processing and computational photography. While various methods have been proposed, they either fail to produce visually pleasing results, or only work well for limited types of image (e.g., underexposed images). In this paper, we present a novel automatic exposure correction method, which is able to robustly produce high-quality results for images of various exposure conditions (e.g., underexposed, overexposed, and partially under- and over-exposed). At the core of our approach is the proposed dual illumination estimation, where we separately cast the under- and over-exposure correction as trivial illumination estimation of the input image and the inverted input image. By performing dual illumination estimation, we obtain two intermediate exposure correction results for the input image, with one fixes the underexposed regions and the other one restores the overexposed regions. A multi-exposure image fusion technique is then employed to adaptively blend the visually best exposed parts in the two intermediate exposure correction images and the input image into a globally well-exposed image. Experiments on a number of challenging images demonstrate the effectiveness of the proposed approach and its superiority over the state-of-the-art methods and popular automatic exposure correction tools.
Model enhancement and personalization using weakly supervised learning for multi-modal mobile sensing

Always-on sensing of mobile device user's contextual information is critical to many intelligent use cases nowadays such as healthcare, drive assistance, voice UI. State-of-the-art approaches for predicting user context have proved the value to leverage multiple sensing modalities for better accuracy. However, those context inference algorithms that run on application processor nowadays tend to drain heavy amount of power, making them not suitable for an always-on implementation. We claim that not every sensing modality is suitable to be activated all the time and it remains challenging to build an inference engine using power friendly sensing modalities. Meanwhile, due to the diverse population, we find it challenging to learn a context inference model that generalizes well, with limited training data, especially when only using always-on low power sensors. In this work, we propose an approach to leverage the opportunistically-on counterparts in device to improve the always-on prediction model, leading to a personalized solution. We model this problem using a weakly supervised learning framework and provide both theoretical and experimental results to validate our design. The proposed framework achieves satisfying result in the IMU based activity recognition application we considered.
Aspek Sosial Budaya dalam Novel Arah Langkah Karya Fiersa Besar: Tinjaun Sosiologi Sastra

This study aims to (1) Describe the constructing structure in Fiersa Besari's novel Arah Langkah in terms of the sociology of literature. (2) Describing the social aspects contained in Fiersa Besari's novel Arah Langkah with a review sociology of literature. (3) Describes the implementation of social aspects in the novel Step Direction as a literary teaching material in high school. The method used is descriptive qualitative with the object of research in the form of social aspects in the novel Arah Langkah by Fiersa Besari with the object of novel text research by Arah Langkah by Fiersa Besari published by Mediakita, 300 pages. Data collection techniques use library techniques, see and note. The data validation technique used is the theory triangulation technique. The data analysis technique uses the dielectic method. Based on the results of the analysis carried out, it can be concluded as follows. (1) Structurally, the flow in the novel Arah Langkah, namely the progressive. The characters in the novel consist of the main characters, namely Fiersa and additional figures, namely Father, Mother, Sister, Baduy, and Prem. The timeframe takes place on April 2018 until the end of April 2018. The social background in this novel tells of idealism, honesty, mutual care, and mutual respect for each other which is maintained despite being in a community full of differences from one another. (2) The social aspects in the novel Arah Langkah there are three social aspects, namely cultural aspects, social environmental aspects, and economic aspects. (3) The results of this study can also be used as teaching msterials in high school especially class XI in the level of 1 KD 3.7 Analyzing values (culture, social, moral, religious, and education) in novel verbally or in writing because it can be used as a benchmark or guideline for student in learning Indonesia.
Metaliterate Learning for a Post-Truth World edited by T.P. Mackey and T.E. Jacobson. Chicago, IL: American Library Association, 2019. (256p. $64.99). ISBN-13: 978-0-8389-1776-3

This study examined the Twitter streams and websites of 36 university innovation centers and identified 14 service categories the centers offered. Exploring the present Twitter use practices of innovation centers and the services the centers provide can inform the design and planning of service offerings at new innovation centers and support training for center staff in the use of this social media platform. In addition, existing innovation centers can benchmark their service offerings against those services. Furthermore, mapping the services the innovation centers offer to the activities in an innovation workflow model can help center managers optimize the information architecture of their websites and resource guides. In this way, students can easily be informed about the help and resources available for each activity or phase of the innovation process. A comparison of the tweet categories identified in the present study with those of academic libraries assembled in a previous study revealed significant overlap, but some differences as well. In contrast to the Twitter accounts of academic libraries, the Twitter accounts of innovation centers did not tweet about their information services even if they offered them. Innovation centers also did not use Twitter to provide Q&A services to their users. Furthermore, innovation centers tweeted not only about the technological resources they provided, but also about the human resources they recruited to serve as student mentors and advisors. Finally, technology use was more mediated in innovation centers than in libraries, and some centers offered their users fee-based assistance from professionals with their 3D design and printing tasks.
Probabilistic Inference for Camera Calibration in Light Microscopy under Circular Motion

Robust and accurate camera calibration is essential for 3D reconstruction in light microscopy under circular motion. Conventional methods require either accurate key point matching or precise segmentation of the axial-view images. Both remain challenging because specimens often exhibit transparency/translucency in a light microscope. To address those issues, we propose a probabilistic inference based method for the camera calibration that does not require sophisticated image pre-processing. Based on 3D projective geometry, our method assigns a probability on each of a range of voxels that cover the whole object. The probability indicates the likelihood of a voxel belonging to the object to be reconstructed. Our method maximizes a joint probability that distinguishes the object from the background. Experimental results show that the proposed method can accurately recover camera configurations in both light microscopy and natural scene imaging. Furthermore, the method can be used to produce high-fidelity 3D reconstructions and accurate 3D measurements.
Asymptotically Unbiased Generative Neural Sampling.

We propose a general framework for the estimation of observables with generative neural samplers focusing on modern deep generative neural networks that provide an exact sampling probability. In this framework, we present asymptotically unbiased estimators for generic observables, including those that explicitly depend on the partition function such as free energy or entropy, and derive corresponding variance estimators. We demonstrate their practical applicability by numerical experiments for the 2d Ising model which highlight the superiority over existing methods. Our approach greatly enhances the applicability of generative neural samplers to real-world physical systems.
PENINGKATAN KETERAMPILAN MENULIS KARYA ILMIAH MELALUI MODEL PEMBELAJARAN WORKSHOP DAN KOLABORASI (PENELITIAN TINDAKAN DI PROGRAM STUDI PBSI STKIP MUHAMMADIYAH BOGOR)

Effective writing not only requires linguistic knowledge but also requires an appropriate learning model because the writer is like an athlete who requires practice. The purpose of this study is to analyze the application of a workshop and collaborative learning model to improve students' scientific writing skills. This type of research is classroom action research. This research emerged from the real problems faced by lecturers in the learning process and then reflected alternative solutions to the problem to be followed up with concrete actions that are planned and measured. The subjects of this study were S1 students majoring in Indonesian Language Education, STKIP Muhammadiyah Bogor semester 5 of 2018/2019 school year in the city of Bogor. Data were collected by observing student learning activities, while tests were conducted to measure students' abilities in writing scientific papers. Descriptive analysis was conducted to analyze student learning activities and the progress of their scientific paper writing abilities. The results of this study indicate that the application of workshop learning and collaboration models can improve students' writing scientific papers. Emphasis on the aspects of independence and cooperation in this learning model is something that is rarely done, and in fact, can improve student competency and their comfort in learning academic writing.