Reviewerly/exHarmony huggingface dataset:
Measuring English Readability for Vietnamese Speakers

Reading is important for any language learner, but the difficulty level of the text needs to match a reader’s level to enable efficient learning of new vocabulary. Many widely used traditional readability measures are not effective for those who speak English as a second or additional language. This study examines English readability for Vietnamese native speakers (VL1). A collection of text difficulty judgements of nearly 100 English text passages was obtained from 12 VL1 participants, using a 5-point Likert scale. Using the same basic features found in traditional English readability measures we found that SVMs and Dale-Chall features were slightly better than linear models using either Flesch or Dale-Chall. VL1 participants’ text judgements were strongly correlated with their past IELTS test scores. This study introduces a first approximation to readability of English text for VL1, with suggestions for further improvements.
The Effect of the Different Kinds of Capital on Economic Growth of Selected Provinces of Iran

This paper has studied the role of physical, human, and social capital on the economic growth of 20 selected provinces of Iran by using the augmented augmented-Solow model expanded by Ishi and Swada. Mankiw, Romer, and Weil regression equation derived from the research model is specified according to two classes of unit root tests for the panel models in two forms and is estimated by the generalized method of moment for dynamic panel data (GMM/DPD) and orthogonal deviation technique. The results of the estimation of the first form of regression analysis indicate that 1% increase in growth rate of national saving leads to 0.57% increase in growth rate of per capita gross domestic product (GDP), 1% increase in the number of university students (as representative of human capital saving) leads to 0.035% increase in per capita GDP growth rate, and 1% increase in growth rate of closed cases of returned checks (as representative of negative social capital saving) leads to 0.038% decrease in per capita GDP growth rate. The results of the estimation of the second form of the regression analysis confirm the role of three factors of physical, human, and social capital on the economic growth of studied provinces.JEL Classification: O11, H54, R10
BIGDATA AND MACHINE LEARNING MODELS FOR DIMENTIONALITY REDUCTION PLATFORM

Various machine learning, cloud computing and big data models are most utilized control parameters in software applications. Therefore these systems are most demand at current trending decades. But these models are needs very low data access time, speed for process. Day to day life data storage servers and devices are costly and hardware complex, so dimensionality is increases with rapid manner. Any type of optimization techniques access takes more time consumption for high dimensional data. So, more applications related problems are occurs only at high dimensional data space does not at low space dimensional data. In this research proposes a dimensional reduction technique with Logistic regression (LR) model. This LR model is most helpful for dimensional reduction and clustering problems. LRML method has reduced the dimensional data size and achieved thee efficiency by 95.3% and ratio of reduction by 35.76%.
Augmented Reality as a New and Innovative Learning Platform for the Medical Area

This research paper shows an Augmented Reality (AR) project applied to medicine. The project is crystallized through a system, based on this new technology that serves as an innovative and innovative learning platform, which, in turn, helps in both teaching and learning abstract concepts in medicine, which requires of visual and manipulable objects difficult to obtain, due to the large space they occupy in magnetic media or because of how difficult it is to get their models in physical form. The proposed system strengthens the anatomical identification process in the area of medicine, specifically in the physiological activity of the human heart. In addition, this system allows interaction with the students, through which certain body parts of the human heart are identified, and, consequently, facilitates their learning with an iterative operation. Finally, the system is focused, so that the student uses his/her sense of sight, hearing, and kinesthetic, which, together, will allow a better assimilation of knowledge.
Deep Neural Network for Pancreas Segmentation from CT Images

Automatic pancreas segmentation from Computed Tomography (CT) images is a prerequisite of clinical practices such as cancer detection, yet challenging due to the variability in shape. To address this challenge, we propose a Hierarchical Convolutional Neural Network (H-CNN) to fuse multi-scale features, which could remedy the lost image details in progressive convolutional and pooling layers. In our proposed H-CNN, a hierarchical fusion block is designed to fuse low-level and high-level features across different layers. The H-CNN is evaluated on NIH pancreas dataset and outperforms the current state-of-art methods by achieving 86.59% ± 4.33% in terms of DSC. The experimental results confirm the effectiveness of the proposed H-CNN.
Aplikasi Bot Akademik BAAK STMIK Jakarta STI&amp;K Platform Line Messenger Menggunakan Go Languages

BAAK Jakarta STMIK Campus STI &amp; K or the Academic and Student Administration Bureau is a bureau that handles everything related to organizing teaching and learning activities at the STMIK Jakarta STI &amp; K Campus. Problems with the services provided on the site include, among other things, class schedules, exam schedules, academic news, academic calendars, KRS (Study Plan Card) filling schedules and others. The method in this study uses the waterfall in designing BOT applications based on the Line Messenger platform. The results of this study are to facilitate and expand student access to obtain services from BAAK. Not only makes it easy to get information about academics at the STMIK Jakarta STI &amp; K Campus, but also makes it easy to share that information with friends.
Work in Progress: The PCC Task Group on Metadata Application Profiles

The Program for Cooperative Cataloging (PCC) Task Group on Metadata Application Profiles was convened in May 2019 to help the PCC understand issues and practices associated with the management of metadata application profiles. Their work, discussed here, will guide future decisions and implementations of metadata application profiles in cataloging.
Mercurial Cipher: A New Cipher Technique and Comparative Analysis with Classical Cipher Techniques

As the world has made significant progress in the science and technology, it has lead to many changes in the day to day life of an individual, both in his behavior and habits. Earlier men use letters, telegram and radio for communication. Today, these communications have been digitized by use of internet technology, mobile phones, online chatting et al. which has replaced the letters and telegrams. Due to daily advancement in technology many of traditional encryption techniques have become outdated as they suffers from many vulnerabilities. Therefore a new or upgraded version of encryption and ciphering technique is needed. Mercurial Cipher, is a new ciphering technique which is designed by analyzing the flaws, shortcomings and vulnerabilities of existing (XOR cipher etc) and Classical Ciphering techniques (Caesar Cipher, Vigenere Cipher, Rail Fence Cipher et a1). The main idea is to solve the problem of key management as most of the ciphering technique are based on the key and it is the most vulnerable part of any encrypted message and if attacker comes to know about the keys message could easily be retrieved. It totally defeats the aim of achieving security. So, in this Encryption technique, sender’s machine information has been incorporated, so that the original message makes sense to its intended receiver and knows from where the message has come from.
Transport Gaussian Processes for Regression

Gaussian process (GP) priors are non-parametric generative models with appealing modelling properties for Bayesian inference: they can model non-linear relationships through noisy observations, have closed-form expressions for training and inference, and are governed by interpretable hyperparameters. However, GP models rely on Gaussianity, an assumption that does not hold in several real-world scenarios, e.g., when observations are bounded or have extreme-value dependencies, a natural phenomenon in physics, finance and social sciences. Although beyond-Gaussian stochastic processes have caught the attention of the GP community, a principled definition and rigorous treatment is still lacking. In this regard, we propose a methodology to construct stochastic processes, which include GPs, warped GPs, Student-t processes and several others under a single unified approach. We also provide formulas and algorithms for training and inference of the proposed models in the regression problem. Our approach is inspired by layers-based models, where each proposed layer changes a specific property over the generated stochastic process. That, in turn, allows us to push-forward a standard Gaussian white noise prior towards other more expressive stochastic processes, for which marginals and copulas need not be Gaussian, while retaining the appealing properties of GPs. We validate the proposed model through experiments with real-world data.
Towards a computational theory of action, causation and power for normative reasoning

In order to effectively implement guidance structures in a computational social system, directives which are specified in general terms of duties and rights need to be transformed in terms of powers and liabilities attributed to social parties. The present paper is a work in progress report on an axiomatization of power structures in a logic programming setting, covering the intentional level in specifying actions, the connection between productive characterization of actions and causation, the default nature of action specifications, failures and omissions, the relations of causation and power, and the concept of interfering actions.
Website quality of Malaysian Technical University

&lt;span&gt;University website is the platform for university to interact with people. It is also an interphase for people to infer and getting known about the university. In addition, quality of the university website is vital to let people get positive response to the university. Therefore, university website should be evaluated for better performance. This study evaluate website of Malaysian Technical University (MTUN) on year of 2016 based on the criteria selected using online web diagnostics tools. There are nine criteria selected to measure the website; load time, response time, page rank, traffic, design optimization, page size, number of items, markup validation and broken link. The result shows website of UMP performed better in design optimization, page rank and markup validation. Meanwhile, UTHM performed in page rank, load time and page size. UTEM excellences in response time, number of items and broken link. Lastly, UniMAP performed in traffic criteria only. However, the (MTUN) University website is almost not meets with the criteria that selected with the quality standard that have been suggested. &lt;/span&gt;
Off-policy Maximum Entropy Reinforcement Learning : Soft Actor-Critic with Advantage Weighted Mixture Policy(SAC-AWMP)

The optimal policy of a reinforcement learning problem is often discontinuous and non-smooth. I.e., for two states with similar representations, their optimal policies can be significantly different. In this case, representing the entire policy with a function approximator (FA) with shared parameters for all states maybe not desirable, as the generalization ability of parameters sharing makes representing discontinuous, non-smooth policies difficult. A common way to solve this problem, known as Mixture-of-Experts, is to represent the policy as the weighted sum of multiple components, where different components perform well on different parts of the state space. Following this idea and inspired by a recent work called advantage-weighted information maximization, we propose to learn for each state weights of these components, so that they entail the information of the state itself and also the preferred action learned so far for the state. The action preference is characterized via the advantage function. In this case, the weight of each component would only be large for certain groups of states whose representations are similar and preferred action representations are also similar. Therefore each component is easy to be represented. We call a policy parameterized in this way an Advantage Weighted Mixture Policy (AWMP) and apply this idea to improve soft-actor-critic (SAC), one of the most competitive continuous control algorithm. Experimental results demonstrate that SAC with AWMP clearly outperforms SAC in four commonly used continuous control tasks and achieve stable performance across different random seeds.
A Time Sequence Data Processing Method Based on Random Matrix of Smart Electric Meter

With the expansion of the scale of intelligent power distribution network and the complexity of power grid structure, the amount of data in power system increases rapidly, and the power equipment are faced with new challenges. Based on the principle of big data and data mining analysis, this paper proposes a method based on random matrix theory and clustering algorithm to evaluate the running state of electric energy meter. Specifically, time series data of various indicators is characterized by real-time separation window technology. Based on the random matrix theory, the random matrix based analysis model is constructed to calculate and analyze the statistical timing characteristics in real time. The proposed method has good robustness, reliability and timeliness, which provides a new idea for the application research of power grid detection technology.
Local Navigation Using Weight Learning on Image Features

Visual navigation is a challenging subject in robotics, which is involved estimating the target position and direction at an arbitrary location. In this study, we follow the snapshot model, a bio-inspired model to determine the target direction with the snapshots taken at the current location and the target location. From the snapshots, we collect landmarks with three different features, the corner landmarks with SURF (Speeded Up Robust Features), the vertical edge landmarks with HOG (Histogram of Gradient) and the Haar-like feature landmarks. Those methods can play significant roles in finding appropriate visual features depending on the environment. A linear combination of those landmarks, that is, weighted feature landmarks are more suitable to find homing vector than landmarks found with one method alone. We propose that the gradient-descent method should be applied to the weighted feature landmarks to improve the homing performance. The homing results with ALV (Average Landmark Vector) model are demonstrated to show the effectiveness of the method.
Single-Shot Object Detector Based on Attention Mechanism

For object detection, two-stage detectors have been achieving the dominant performance while one-stage detectors have the advantage of high efficiency. In order to better combine the advantages of both, we innovatively introduce the attention mechanism into single-shot object detector. Specifically, we introduce the IoU predictor to forecast the IoU between each predicted bounding box and corresponding ground-truth and propose an IoU-attention module to resample the feature map. Moreover, we introduce SE unit into our feature fusion module to encourage the model to focus on important information. We train the proposed model in an end-to-end way via multi-task loss function. With high efficiency, our model achieves competitive performance among start-of-the-art detectors on the challenging PASCAL VOC 2007, PASCAL VOC 2012 and MS COCO datasets.
Proposal and Trial of Vocational Training for Industrial Robot Technician Compatible with the Smart Factory

In a Factory of the Fourth Industrial Revolution, so-called “Smart Factory” of further autonomy process and labor saving is required. Thus, the highly-skilled technician who can apply an industrial robot to build a smart factory is more necessary. Because of that we develop the teaching aids of an industrial robot, and human resources of college student. This paper reports the industrial robot technician training for student, and the trial education of using the experimental equipment for a miniature model of the autonomy of industrial robot compatible with smart factory.
Blockchain Technology as a Means for Brand Trust Repair – Empirical Evidence from a Digital Transgression

Though much discussion in the realm of blockchain revolves around the concept of trust, research examining blockchain technology as a means for brand trust repair is still at an initial stage.This study conducts an experiment that analyzes blockchain technology as a substantive response to a data breach within a global business-to-consumer information systems application.Thereby, the present study expands trust repair theories to the context of blockchain and branding.Research results indicate that the use of blockchain technology as a reaction to a digital transgression may be able to reinstate brand trust, having a superior impact compared to an approach that uses a centrally managed information systems platform to restore brand trust.Overall, study results suggest that the use of blockchain technology can be an effective component of brand trust repair strategies in the digital space.
Mixed diagnostic tests structure for learning and testing systems

A new method of increasing the efficiency of students' educational activity is proposed.The method is based on the creation of learning trajectories, formed in the process of applying the mixed diagnostic tests.The learning trajectories are constructed by using specific mixed diagnostic tests, which are an integration of known unconditional and conditional tests.The mixed tests are used as in blended education and training, and in massive open online courses.The mixed testing provides the monitoring of the students' knowledge, professional and personal skills and abilities, as well as constructing learning trajectories for each student.The paper presents the innovative interdisciplinary technique for construction of the mixed diagnostic tests.
Object Instance Mining for Weakly Supervised Object Detection

Weakly supervised object detection (WSOD) using only image-level annotations has attracted growing attention over the past few years. Existing approaches using multiple instance learning easily fall into local optima, because such mechanism tends to learn from the most discriminative object in an image for each category. Therefore, these methods suffer from missing object instances which degrade the performance of WSOD. To address this problem, this paper introduces an end-to-end object instance mining (OIM) framework for weakly supervised object detection. OIM attempts to detect all possible object instances existing in each image by introducing information propagation on the spatial and appearance graphs, without any additional annotations. During the iterative learning process, the less discriminative object instances from the same class can be gradually detected and utilized for training. In addition, we design an object instance reweighted loss to learn larger portion of each object instance to further improve the performance. The experimental results on two publicly available databases, VOC 2007 and 2012, demonstrate the efficacy of proposed approach.
Chairil Anwar Service Literature Practices, 1942-1949

Chairil Anwar's literary resistance practices in 1942-1949 were supported by ‘literary habitus’--foreign and local literature, literary totality, and literary rationality-- as a foundation with the support of ‘literary modal’ --literary totality, literary rationality, and ideals of literature-- with consideration of the ‘literary fields’.Although the dominance of a ‘pujangga baru’ generation through literary practices is quite strong, Chairil Anwar's ‘literary practice’ is a successful practice.This success was marked by several things, including (1) Chairil's poetry writing model was considered as a new model that was not available before in Indonesian literature; (2) the influence of Chairil's literary practice which is growing in Indonesian literary society; (3) Chairil's coronation as a pioneer of the 45th generation; and (4) the increasingly massive publishing of Chairil's works which certainly brings 'capital modal' to Chairil's family.&#x0D; Keywords: literary resistance practices, Chairil Anwar, 1942-1949&#x0D; Abstrak&#x0D; Praktik sastra perlawan Chairil Anwar pada tahun 1942-1949 didukung oleh 'habitus sastra' --sastra asing dan lokal, totalitas sastra, dan rasionalitas sastra-- sebagai landasan dengan dukungan 'modal sastra' --totalitas sastra, rasionalitas sastra, rasionalitas sastra , dan cita-cita sastra-- dengan pertimbangan 'ranah sastra'. Meskipun dominasi generasi 'pujangga baru' melalui praktik sastra cukup kuat, 'praktik sastra' Chairil Anwar adalah praktik yang berhasil. Keberhasilan ini ditandai oleh beberapa hal, antara lain(1) Model penulisan sajak Chairil dianggap model baru yang belum ada sebelumnya di kesusasteraan Indonesia; (2) pengaruh praktik sastra Chairil yang semakin besar dalam masyarakat sastra Indonesia; (3) dinobatkannya Chairil sebagai pelopor angkatan 45; dan (4) semakin masifnya penerbitan karya-karya Chairil yang tentunya mendatangkan modal kapital bagi keluarga Chairil.&#x0D; Kata kunci: praktik sastra perlawanan, Chairil Anwar, Tahun 1942-1949
Limits of Detecting Text Generated by Large-Scale Language Models.

Some consider large-scale language models that can generate long and coherent pieces of text as dangerous, since they may be used in misinformation campaigns. Here we formulate large-scale language model output detection as a hypothesis testing problem to classify text as genuine or generated. We show that error exponents for particular language models are bounded in terms of their perplexity, a standard measure of language generation performance. Under the assumption that human language is stationary and ergodic, the formulation is extended from considering specific language models to considering maximum likelihood language models, among the class of k-order Markov approximations; error probabilities are characterized. Some discussion of incorporating semantic side information is also given.
Refining Constructive Hybrid Games

We extend the constructive differential game logic (CdGL) of hybrid games with a refinement connective that relates two hybrid games. We use this connective to prove a folk theorem relating hybrid games to hybrid systems.
Local Nonparametric Meta-Learning

A central goal of meta-learning is to find a learning rule that enables fast adaptation across a set of tasks, by learning the appropriate inductive bias for that set. Most meta-learning algorithms try to find a \textit{global} learning rule that encodes this inductive bias. However, a global learning rule represented by a fixed-size representation is prone to meta-underfitting or -overfitting since the right representational power for a task set is difficult to choose a priori. Even when chosen correctly, we show that global, fixed-size representations often fail when confronted with certain types of out-of-distribution tasks, even when the same inductive bias is appropriate. To address these problems, we propose a novel nonparametric meta-learning algorithm that utilizes a meta-trained local learning rule, building on recent ideas in attention-based and functional gradient-based meta-learning. In several meta-regression problems, we show improved meta-generalization results using our local, nonparametric approach and achieve state-of-the-art results in the robotics benchmark, Omnipush.