Reviewerly/exHarmony huggingface dataset:
Implementation ANFIS Method for Prediction Needs Drug-based Population Diseases and Patient

Prediction needs a drug that not optimal at the hospital have to impact at services medical as well as economically, the cause will overbalance as well as deficiency stock, at this research, Adaptive Neuro-Fuzzy Inference System implemented for prediction needs drug with applies Genetic algorithm for optimizing ANFIS parameters. Training process done at dataset using drug sourced from note medical from year 2015 until year 2017, while testing using dataset year 2015 for prediction year 2016 and dataset year 2016 for prediction 2017, framework analysis training and testing applied at ANFIS method and GA-ANFIS and evaluation with RMSE method of 0.4765 and ANFIS of 0.6598, so that GAANFIS very accurate compared ANFIS.
Zero-shot Semantic Segmentation Using Relation Network

Zero-shot learning (ZSL) is widely studied in recent years to solve the problem of lacking annotations. Currently, most studies on ZSL are for image classification and object detection. But, zero-shot semantic segmentation, pixel level classification, is still at its early stage. Therefore, this work proposes to extend a zero-shot image classification model, Relation Network (RN), to semantic segmentation tasks. We modified the structure of RN based on other state-of-the-arts semantic segmentation models (i.e. U-Net and DeepLab) and utilizes word embeddings from Caltech-UCSD Birds 200-2011 attributes and natural language processing models (i.e. word2vec and fastText). Because meta-learning is limited to binary tasks, this work proposes to join multiple binary semantic segmentation pipelines for multi-class semantic segmentation. It is proved by experiments that RN could improve accuracy of U-Net with the help of semantic side information on binary semantic segmentation and it could also be applied on multi-class semantic segmentation with simpler structure than the baseline model, SPNet, but higher accuracy under ZSL setting. However, the capability of RN under generalized zero-shot learning (GZSL) setting still needs improvement. We also studied on how different word embeddings, network structures and data affect RN and what could be done to improve its results.
On Leveraging Pretrained GANs for Limited-Data Generation

Recent work has shown generative adversarial networks (GANs) can generate highly realistic images, that are often indistinguishable (by humans) from real images. Most images so generated are not contained in the training dataset, suggesting potential for augmenting training sets with GAN-generated data. While this scenario is of particular relevance when there are limited data available, there is still the issue of training the GAN itself based on that limited data. To facilitate this, we leverage existing GAN models pretrained on large-scale datasets (like ImageNet) to introduce additional knowledge (which may not exist within the limited data), following the concept of transfer learning. Demonstrated by natural-image generation, we reveal that low-level filters (those close to observations) of both the generator and discriminator of pretrained GANs can be transferred to facilitate generation in a perceptually-distinct target domain with limited training data. To further adapt the transferred filters to the target domain, we propose adaptive filter modulation (AdaFM). An extensive set of experiments is presented to demonstrate the effectiveness of the proposed techniques on generation with limited data.
Left–right breast asymmetry and risk of screen-detected and interval cancers in a large population-based screening population

To assess the associations between automated volumetric estimates of mammographic asymmetry and breast cancers detected at the same ("contemporaneous") screen, at subsequent screens, or in between (interval cancers).Automated measurements from mammographic images (N = 79,731) were used to estimate absolute asymmetry in breast volume (BV) and dense volume (DV) in a large ethnically diverse population of attendees of a UK breast screening programme. Logistic regression models were fitted to assess asymmetry associations with the odds of a breast cancer detected at contemporaneous screen (767 cases), adjusted for relevant confounders.Nested case-control investigations were designed to examine associations between asymmetry and the odds of: (a) interval cancer (numbers of cases/age-matched controls: 153/646) and (b) subsequent screen-detected cancer (345/1438), via conditional logistic regression.DV, but not BV, asymmetry was positively associated with the odds of contemporaneous breast cancer (P-for-linear-trend (Pt) = 0.018). This association was stronger for first (prevalent) screens (Pt = 0.012). Both DV and BV asymmetry were positively associated with the odds of an interval cancer diagnosis (Pt = 0.060 and 0.030, respectively). Neither BV nor DV asymmetry were associated with the odds of having a subsequent screen-detected cancer.Increased DV asymmetry was associated with the risk of a breast cancer diagnosis at a contemporaneous screen or as an interval cancer. BV asymmetry was positively associated with the risk of an interval cancer diagnosis.The findings suggest that DV and BV asymmetry may provide additional signals for detecting contemporaneous cancers and assessing the likelihood of interval cancers in population-based screening programmes.
Employee Well-being as mediator of correlation between Psychological Capital and Psychological Climate

Individual differences are one of the factors that influence the psychological climate. The existence of bias and the existence of the perception of the influence of other factors on an individual, so that in the same neighborhood but in different individuals our own ingrained perceptions would be different. It affects individual's self-efficacy as one of dimension on psychological capital, psychological capital associated with a person's perception of well-being of employees or referred to as employee well-being, where the employee well-being as well as a factors effect of psychological climate. Therefore, this study aims to determine whether employee well-being as a mediator of the relationship between psychological capital and psychological climate of hospitality employees. Respondents were 378 hospitality employees, the analysis used regression analysis model 4 v3.0 mediation process by Hayes. The results showed that employee well-being acts as a partial mediator in the relationship between psychological capital and psychological climate, so that with the existence of employee well-being the relationship between psychological capital and psychological climate will be greater. Research found that the contribution dimension to psychological climate has the greatest relationship with the dimension of psychological well-being in employee well-being, other than that there were differences in employee well-being in the age range of 18-30 years with ages 31-60 years and also differences in psychological climate in the age range from 18-30 years with ages 31-60 years.
Recognizing Objects From Any View With Object and Viewer-Centered Representations

In this paper, we tackle an important task in computer vision: any view object recognition. In both training and testing, for each object instance, we are only given its 2D image viewed from an unknown angle. We propose a computational framework by designing object and viewer-centered neural networks (OVCNet) to recognize an object instance viewed from an arbitrary unknown angle. OVCNet consists of three branches that respectively implement object-centered, 3D viewer-centered, and in-plane viewer-centered recognition. We evaluate our proposed OVCNet using two metrics with unseen views from both seen and novel object instances. Experimental results demonstrate the advantages of OVCNet over classic 2D-image-based CNN classifiers, 3D-object (inferred from 2D image) classifiers, and competing multi-view based approaches. It gives rise to a viable and practical computing framework that combines both viewpoint-dependent and viewpoint-independent features for object recognition from any view.
Survey on Aerial Manipulator: System, Modeling, and Control – CORRIGENDUM

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Digitalization of the education system in Russia and China: Causes and social consequences

Digitalization is a trend in the 21st century. It covers almost all spheres of our life and to almost all developed countries. Mainly this trend spreads to the economy and then to other spheres of society. The author notes that the economy takes new development paths, digital. This economy requires new professions, for the preparation of which requires modernization of the educational system. There is a need for new professions based on digital technologies. The successful introduction of these professions into the economic system requires the training of highly qualified specialists. This prerogative belongs exclusively to the field of education. Therefore, in the new economic new educational directions are emerging, which contribute to the training of specialists capable of working in the digital economy. The article describes the changes that education has undergone under the influence of the digitalization focusing on two cases: the educational system in Russia and China. The author analyzes the main changes in the educational systems of these two countries, and discusses the main ways of their development. She points out the ways of joint development of the educational system, especially higher education, between Russia and China, provides programs of internships, exchange of students and opportunities for higher education of Chinese students in Russian universities, and Russian students in Chinese universities.
Impact of Altering Data Granularity Levels on Predictive Modelling: A Case Study of Fall Risk Prediction in Older Persons.

Classification systems are widely used in medicine for knowledge representation. The hierarchical relationships between concepts in a classification system can be exploited in prediction models by looking for the optimal predictive granularity level. In this study, we used the Anatomical Therapeutic Chemical (ATC) classification system to cluster medications in the context of predicting medication-related falls in older persons. We compared the performance of fall risk prediction by describing medications at varying granularity levels of the ATC classification system. We found that the level of abstraction significantly affects the predictive performance in terms of both discrimination (measured by the receiver operating characteristic curve AUC-ROC) and calibration. An implication of these findings to the researchers is that data representation at different granularity levels can influence the predictive performance. The optimal granularity level can be determined by experimentation.
The Efficacy of Using Reciprocal Teaching Technique in Teaching Reading to Indonesian English as Foreign Language (EFL) Students

This research aims at finding out the efficacy of using reciprocal teaching technique in teaching reading to Indonesian English as Foreign Language (EFL) students. The main question of this research: Is the use of reciprocal teaching technique effective in teaching reading to Indonesian EFL students?. The research can be an addition in the persisting teaching reading by using reciprocal teaching technique. This research was conducted at the tenth grade students at SMA Kristen Mercusuar Kupang, an Indonesian High School. The total number of samples was 25 students. The research used a pre-experimental method with pre-test and post-test design. The pre-test was given to find out the basic ability of the students in reading and the post-test given to find out the students’ improvement in reading after giving the treatment by using of reciprocal teaching technique. The findings show that using reciprocal teaching technique is effective in teaching reading to Indonesian EFL students. It is supported by the result of significance test through SPSS 20 program that the P was 0.00. Therefore, it is recommended to use reciprocal teaching technique in developing reading skill of Indonesian EFL students.
Privacy and Legal Automation: The DMCA as a Case Study

Advances in artificial intelligence, machine learning, computing capacity, and big data analytics are creating exciting new possibilities for legal automation. At the same time, these changes pose serious risks for civil liberties and other societal interests. Yet, existing scholarship is narrow, leaving uncertainty on a range of issues, including a glaring lack of systematic empirical work as to how legal automation may impact people’s privacy and freedom. This article addresses this gap with an original empirical analysis of the Digital Millennium Copyright Act (DMCA), which today sits at the forefront of algorithmic law due to its automated enforcement of copyright through DMCA notices at mass scale. With literally millions of such notices sent daily, this automation has been criticized for causing large scale chilling effects online, yet few empirical studies have examined this issue in depth. This article does so with a mixed-method empirical study synthesizing survey-based findings with an analysis of 500 Google Blogs and 500 Twitter accounts that have received DMCA notices. The findings offer a number of new insights, including evidence for DMCA notice chilling effects across a range of activities; support for a privacy theory of automated law chilling effects; evidence of differential impacts including that women are disproportionately chilled and that legal information can mitigate chilling effects; and the effectiveness of automated DMCA notices as compared to non-automated ones. This article also explores the implications of these findings for future forms of automated law and lays the foundations for a new theory of governance for personal legal automation.
Privacy-Preserving Genome-Wide Association Study for Rare Mutations - A Secure FrameWork for Externalized Statistical Analysis

This paper proposes a new privacy-preserving framework to perform rare variant case-control association tests with information provided by two parties: a Genomic Research Unit (GRU) with sequencing data from individuals affected by a disease D (cases); a Genomic Research Center (GRC) with sequencing data from healthy individuals (controls). To identify genes with rare variants involved in D, GRU needs to compare cases against controls using association tests (genome-wide association study). The main originality of our proposal is twofold. First, it positions GRC as a proxy between GRU and the server. Doing so makes it possible to use classical cryptographic tools to securely conduct association tests with no computation complexity increase, contrarily to actual state of the art proposals which are of very high complexity being based on homomorphic encryption, for instance. In particular, we show how sensitive data confidentiality can be ensured with secret key based cryptographic hashing with no need to modify statistical algorithms. In our protocol the server simply conducts statistical analyses on partially hashed data. Secondly, we introduce a novel privacy constraint: GRU's identity should remain unknown to the server as this knowledge can give it clues about GRU's data (e.g., diseases and genes of interest). We exhibit how Pretty Good Privacy (PGP) can be used to solve this problem. We illustrate our protocol in the case of one rare variant association test, the Weighted-Sum Statistic (WSS) algorithm, carried out on real genetic data. This secure WSS achieves the same accuracy as its nonsecure version with no increase of complexity. Furthermore, we establish that our protocol can be extended to the different rare variant association tests available in the literature.
GPU-Embedding of kNN-Graph Representing Large and High-Dimensional Data

Interactive visual exploration of large and multidimensional data still needs more efficient $$ND \rightarrow 2D$$ data embedding (DE) algorithms. We claim that the visualization of very high-dimensional data is equivalent to the problem of 2D embedding of undirected kNN-graphs. We demonstrate that high quality embeddings can be produced with minimal time&memory complexity. A very efficient GPU version of IVHD (interactive visualization of high-dimensional data) algorithm is presented, and we compare it to the state-of-the-art GPU-implemented DE methods: BH-SNE-CUDA and AtSNE-CUDA. We show that memory and time requirements for IVHD-CUDA are radically lower than those for the baseline codes. For example, IVHD-CUDA is almost 30 times faster in embedding (without the procedure of kNN graph generation, which is the same for all the methods) of the largest ($$M=1.4\cdot 10^6$$) YAHOO dataset than AtSNE-CUDA. We conclude that in the expense of minor deterioration of embedding quality, compared to the baseline algorithms, IVHD well preserves the main structural properties of ND data in 2D for radically lower computational budget. Thus, our method can be a good candidate for a truly big data ($$M=10^{8+}$$) interactive visualization.
Handwritten Character Recognition using Neural Networks forBanking Applications

Banks often accept handwritten forms for various purposes like application for creating or closure of accounts, loans, net banking, etc.The form takes a lot of user information consisting of sensitive data viz.Aadhar card number, pan card number.This information is usually taken in pen-paper format and needs entry to the bank database to document the particulars in the system or the bank requires to store a physical copy of the form for future reference.Manual entry of these details into the bank database is a tedious process and might be erroneous at times.Also, maintaining the original copy of the form or like document generate stockpiles of paper.In an attempt to overcome these discrepancies, the proposed problem statement provides a solution by making use of Handwritten Character Recognition which will input data in the form of an image to store and maintain it in a digital library
Feature analysis of ontology visualization methods and tools

Visualization is a technique of creating images, graphs or animations to share knowledge. Different kinds of visualization methods and tools are available to envision the data in an efficient way. The visualization tools and techniques enable the user to understand the knowledge in an easy manner. Nowadays most of the information is presented semantically which provides knowledge based retrieval of the information. Knowledge based visualization tools are required to visualize semantic concepts. This article analyses the existing semantic based visualization tools and plug-ins. The features and characteristics of these tools and plug-ins are analyzed and tabulated.
The U Q Platform: A Unifed Approach To Q uantum Annealing

Quantum Annealing is an algorithm for solving instances of quadratic unconstrained binary optimization (QUBO) that is implemented in hardware utilizing quantum effects to quickly find approximate solutions. However, QUBO can obviously also be solved by any classical optimization technique, for which various implementations exist. The UQ platform provides a unified interface to various means of solving QUBO that allows for a seamless switch between classical and quantum methods while implementing features such as load and user management.
Pattern Learning for Detecting Defect Reports and Improvement Requests in App Reviews

Online reviews are an important source of feedback for understanding customers. In this study, we follow novel approaches that target the absence of actionable insights by classifying reviews as defect reports and requests for improvement. Unlike traditional classification methods based on expert rules, we reduce the manual labour by employing a supervised system that is capable of learning lexico-semantic patterns through genetic programming. Additionally, we experiment with a distantly-supervised SVM that makes use of the noisy labels generated by patterns. Using a real-world dataset of app reviews, we show that the automatically learned patterns outperform the manually created ones. Also the distantly-supervised SVM models are not far behind the pattern-based solutions, showing the usefulness of this approach when the amount of annotated data is limited.
Characterizing overstretched NTRU attacks

Abstract Overstretched NTRU is a variant of NTRU with a large modulus. Recent lattice subfield and subring attacks have broken suggested parameters for several schemes. There are a number of conflicting claims in the literature over which attack has the best performance. These claims are typically based on experiments more than analysis. In this paper, we argue that comparisons should focus on the lattice dimension used in the attack. We give evidence, both analytically and experimentally, that the subring attack finds shorter vectors and thus is expected to succeed with a smaller dimension lattice than the subfield attack for the same problem parameters, and also to succeed with a smaller modulus when the lattice dimension is fixed.
Human Activity Recognition Enhanced Robot-Assisted Minimally Invasive Surgery

This paper presents a human activity recognition enhanced robot-assisted minimally invasive surgery (HAR-RAMIS) for the redundant robot, which aims to bridge the human activity recognition techniques and intelligent robot control strategies in the operating room. It allows the robot to follow human activities in the surgical room, and change behavior intelligently and cooperatively. In this study, multiple sensors are combined to enable human activity recognition, and a deep learning technique is applied to understand human behavior and intention. The robot control strategies can be switched automatically according to the different surgical tasks and the different human activities in the operating room. The experimental study was performed in a lab setup environment using the KUKA LWR4+ to demonstrate the efficiency of the developed strategy, indicating that the future surgical robot systems will be able to work with medical staff in a reasonable collaboration manner.
Partial Correlation-Based Attention for Multivariate Time Series Forecasting

A multivariate time-series forecasting has great potentials in various domains. However, it is challenging to find dependency structure among the time-series variables and appropriate time-lags for each variable, which change dynamically over time. In this study, I suggest partial correlation-based attention mechanism which overcomes the shortcomings of existing pair-wise comparisons-based attention mechanisms. Moreover, I propose data-driven series-wise multi-resolution convolutional layers to represent the input time-series data for domain agnostic learning.
Low-Resource G2P and P2G Conversion with Synthetic Training Data

This paper presents the University of Alberta systems and results in the SIGMOR-PHON 2020 Task 1: Multilingual Graphemeto-Phoneme Conversion.Following previous SIGMORPHON shared tasks, we define a lowresource setting with 100 training instances.We experiment with three transduction approaches in both standard and low-resource settings, as well as on the related task of phoneme-to-grapheme conversion.We propose a method for synthesizing training data using a combination of diverse models.
Narrative Mediation as Probabilistic Planning

Narrative mediation is a plan-based process that converts a planning problem into a mediation tree that models an interactive story. Probabilistic planning relaxes the classical planning constraint of deterministic action outcomes by containing domain operators with nondeterministic effects. This paper shows narrative mediation can be viewed as a probabilistic planning problem where the outcomes of user actions are modeled with nondeterministic effects in the domain. The paper gives a process for automatically converting a deterministic mediation planning problem into a probabilistic interactive narrative planning problem.