Reviewerly/exHarmony huggingface dataset:
Scalable Variational Bayesian Kernel Selection for Sparse Gaussian Process Regression

This paper presents a variational Bayesian kernel selection (VBKS) algorithm for sparse Gaussian process regression (SGPR) models. In contrast to existing GP kernel selection algorithms that aim to select only one kernel with the highest model evidence, our VBKS algorithm considers the kernel as a random variable and learns its belief from data such that the uncertainty of the kernel can be interpreted and exploited to avoid overconfident GP predictions. To achieve this, we represent the probabilistic kernel as an additional variational variable in a variational inference (VI) framework for SGPR models where its posterior belief is learned together with that of the other variational variables (i.e., inducing variables and kernel hyperparameters). In particular, we transform the discrete kernel belief into a continuous parametric distribution via reparameterization in order to apply VI. Though it is computationally challenging to jointly optimize a large number of hyperparameters due to many kernels being evaluated simultaneously by our VBKS algorithm, we show that the variational lower bound of the log-marginal likelihood can be decomposed into an additive form such that each additive term depends only on a disjoint subset of the variational variables and can thus be optimized independently. Stochastic optimization is then used to maximize the variational lower bound by iteratively improving the variational approximation of the exact posterior belief via stochastic gradient ascent, which incurs constant time per iteration and hence scales to big data. We empirically evaluate the performance of our VBKS algorithm on synthetic and massive real-world datasets.
Automatic Fact-Guided Sentence Modification

Online encyclopediae like Wikipedia contain large amounts of text that need frequent corrections and updates. The new information may contradict existing content in encyclopediae. In this paper, we focus on rewriting such dynamically changing articles. This is a challenging constrained generation task, as the output must be consistent with the new information and fit into the rest of the existing document. To this end, we propose a two-step solution: (1) We identify and remove the contradicting components in a target text for a given claim, using a neutralizing stance model; (2) We expand the remaining text to be consistent with the given claim, using a novel two-encoder sequence-to-sequence model with copy attention. Applied to a Wikipedia fact update dataset, our method successfully generates updated sentences for new claims, achieving the highest SARI score. Furthermore, we demonstrate that generating synthetic data through such rewritten sentences can successfully augment the FEVER fact-checking training dataset, leading to a relative error reduction of 13%.1
Order-Free Learning Alleviating Exposure Bias in Multi-Label Classification

Multi-label classification (MLC) assigns multiple labels to each sample. Prior studies show that MLC can be transformed to a sequence prediction problem with a recurrent neural network (RNN) decoder to model the label dependency. However, training a RNN decoder requires a predefined order of labels, which is not directly available in the MLC specification. Besides, RNN thus trained tends to overfit the label combinations in the training set and have difficulty generating unseen label sequences. In this paper, we propose a new framework for MLC which does not rely on a predefined label order and thus alleviates exposure bias. The experimental results on three multi-label classification benchmark datasets show that our method outperforms competitive baselines by a large margin. We also find the proposed approach has a higher probability of generating label combinations not seen during training than the baseline models. The result shows that the proposed approach has better generalization capability.
Exploratory Data Analysis Based on Remote Health Care Monitoring System by Using IoT

The system proposed in this article is to make Exploratory Data Analysis (EDA) of people's health condition based on remote health care monitoring system in their different activities.Because of several critical physiological parameters of human body, multi body sensor health monitoring approach is vital.A health care monitoring system is displayed for detecting human physiological information.The system implementation consists of heart beat sensor, temperature sensor and pulse oxygen saturation level (SPO2) sensor.In this framework, gathered health information can be observed by utilizing both PC and cell phones.The real time result of the temperature sensor, heart beat sensor, SPO2 sensor to show on the ThingSpeak is tested.EDA analysis is done on measured data statistic to know the human activity for effective health care monitoring using IoT.The analyses are based on both theoretical approaches and experimental approaches according to the confirmation of recent research outcomes and real data from the hospital.The experimental studies have been carried out based on the idea of medical doctors in the hospital.The technology also confirms that the current research trends would be suitable for real world applications.The confirmation of medical doctors and suggestions on this research works were completed according to the standard of commercial products in the market.
Multi-column point-CNN for sketch segmentation

Traditional sketch segmentation methods mainly rely on handcrafted features and complicate models, and their performance is far from satisfactory due to the abstract representation of sketches. Recent success of Deep Neural Networks (DNNs) in related tasks suggests DNNs could be a practical solution for this problem, yet the suitable datasets for learning and evaluating DNNs are limited. To this end, we introduce SketchSeg, a large dataset consisting of 10,000 pixel-wisely labeled sketches. Besides, due to the lack of colors and textures in sketches, conventional DNNs learned on natural images are not optimal for tackling our problem. Therefore, we further propose the Multi-column Point-CNN (MCPNet), which directly takes sampled points as its input to reduce computational costs, and adopts multiple columns with different filter sizes to better capture the structures of sketches. Extensive experiments validate that the MCPNet is superior to conventional DNNs like FCN. The SketchSeg dataset is publicly available on https://drive.google.com/open?id=1OpCBvkInhxvfAHuVs-spDEppb8iXFC3C.
On admissible orders over closed subintervals of [0,1]

In this paper, we make some considerations about admissible orders on the set of closed subintervals of the unit interval I[0,1], i.e. linear orders that refine the product order on intervals. We propose a new way to generate admissible orders on I[0,1] which is more general than those we find in the current literature. Also, we deal with the possibility of an admissible order on I[0,1] to be isomorphic to the usual order on [0,1]. We prove that some orders constructed by our method are not isomorphic to the usual one and we make some considerations about the following question: is there some admissible order on I[0,1] isomorphic to the usual order on [0,1]?
A New Separable Moments Based on Tchebichef-Krawtchouk Polynomials

Orthogonal moments are beneficial tools for analyzing and representing images and objects. Different hybrid forms, which are first and second levels of combination, have been created from the Tchebichef and Krawtchouk polynomials. In this study, all the hybrid forms, including the first and second levels of combination that satisfy the localization and energy compaction (EC) properties, are investigated. A new hybrid polynomial termed as squared Tchebichef–Krawtchouk polynomial (STKP) is also proposed. The mathematical and theoretical expressions of STKP are introduced, and the performance of the STKP is evaluated and compared with other hybrid forms. Results show that the STKP outperforms the existing hybrid polynomials in terms of EC and localization properties. Image reconstruction analysis is performed to demonstrate the ability of STKP in actual images; a comparative evaluation is also applied with Charlier and Meixner polynomials in terms of normalized mean square error. Moreover, an object recognition task is performed to verify the promising abilities of STKP as a feature extraction tool. A correct recognition percentage shows the robustness of the proposed polynomial in object recognition by providing a reliable feature vector for the classification process.
Efficient Fast Semantic Segmentation Using Continuous Shuffle Dilated Convolutions

It is difficult for many semantic segmentation methods to perform useful inferences under extremely resource-constrained devices; therefore, an efficient fast semantic segmentation network (EFSNet) that employs a continuous shuffle dilated convolution (CSDC) and a up-sampling module is proposed in this paper. First, the number of parameters is reduced by group convolutions and the receptive field is enlarged by dilated convolution. Second, a up-sampling module is proposed for reducing noise and increasing inference speed. Finally, with the above CSDC module and up-sampling module, the EFSNet based on a fast semantic segmentation network is obtained. Our experiments on the CamVid and Cityscapes datasets show that the mean intersection over union (mIoU) values obtained by the proposed EFSNet with only 173 k parameters are 61.1% and 61.9%, respectively. The method is able to run in real-time at a speed of 332 frames per second (FPS) and 107 FPS on a single NVIDIA Titan Xp GPU. Compared with several existing methods, our network achieves efficient segmentation with low resource consumption.
Peaceful and Happy Schools: How to Build Positive Learning Environments

It is very important for students and teachers to have a positive learning environment.The school that is an institution for educating children should be a peaceful and happy environment.There are principles such as love, respect, honesty, courage, empathy and kindness in the peaceful and happy schools.This study examined the concept of a "peaceful and happy school" and aimed to describe a peaceful and happy school from the point of view of primary school teachers and primary school students.It was designed with a case-study research model.A peaceful and happy school climate is the "case" of this study.The participants of the study consisted of 126 persons, 103 primary school students, and 23 primary school teachers from public schools in Turkey.Data were collected through face-to-face interviews from primary school students and teachers.Semi-structured interview form was used for both groups.At the end of the study, primary school teachers' and students' definitions of a peaceful and happy school were presented.It was understood from the findings that primary school students and teachers want to improve the same basic issues related to a peaceful and happy school.
The Deep Convolutional Neural Network for NOx Emission Prediction of a Coal-Fired Boiler

This paper presents a methodology for predicting NOx emissions of a coal-fired boiler by using real operation data, coal properties and CNN (Convolutional Neural Network). Two building blocks are carefully designed following the practical guidelines for the light weight CNN architecture design. Furthermore, the building blocks are used to develop the deep CNN-based model for NOx prediction. A comprehensive comparison among different prediction models based on DL (Deep Learning) shows that the proposed deep CNN-based prediction model outperforms other prediction models in terms of RMSE (Root Mean Square Error) criteria. The results indicate that the developed deep CNN-based prediction model has more excellent accuracy and better numerical stability. Besides, the architecture design of the DL-based prediction model has a significant impact on the performance of the prediction model.
Data Augmentation for Spoken Language Understanding via Pretrained Models.

The training of spoken language understanding (SLU) models often faces the problem of data scarcity. In this paper, we put forward a data augmentation method with pretrained language models to boost the variability and accuracy of generated utterances. Furthermore, we investigate and propose solutions to two previously overlooked scenarios of data scarcity in SLU: i) Rich-in-Ontology: ontology information with numerous valid dialogue acts are given; ii) Rich-in-Utterance: a large number of unlabelled utterances are available. Empirical results show that our method can produce synthetic training data that boosts the performance of language understanding models in various scenarios.
False Dreams of Algorithmic Fairness: The Case of Credit Pricing

Algorithmic credit pricing threatens to discriminate against protected groups. Traditionally, fair lending law has addressed such threats by scrutinizing inputs. But input scrutiny has become a fallacy in the world of algorithms.Using a rich dataset of mortgages, I simulate algorithmic credit pricing and demonstrate that input scrutiny fails to address discrimination concerns and threatens to create an algorithmic myth of colorblindness. The ubiquity of correlations in big data combined with the flexibility and complexity of machine learning means that one cannot rule out the consideration of protected characteristics, such as race, even when one formally excludes them. Moreover, using inputs that include protected characteristics can in fact reduce disparate outcomes. Nevertheless, the leading approaches to discrimination law in the algorithmic age continue to commit the input fallacy. These approaches suggest that we exclude protected characteristics and their proxies and limit algorithms to pre-approved inputs. Using my simulation exercise, I refute these approaches with new analysis. I demonstrate that they fail on their own terms, are unfeasible, and overlook the benefits of accurate prediction. These failures are particularly harmful to marginalized groups and individuals because they threaten to perpetuate their historical exclusion from credit and, thus, from a central avenue to greater prosperity and equality.I argue that fair lending law must shift to outcome-focused analysis. When it is no longer possible to scrutinize inputs, outcome analysis provides the only way to evaluate whether a pricing method leads to impermissible disparities. This is true not only under the legal doctrine of disparate impact, which has always cared about outcomes, but also under the doctrine of disparate treatment, which has historically avoided examining disparate outcomes. Now, disparate treatment too can no longer rely on input scrutiny and must be considered through the lens of outcomes. I propose a new framework that regulatory agencies, such as the Consumer Financial Protection Bureau, can adopt to measure disparities and fight discrimination. This proposal charts an empirical course for antidiscrimination law in fair lending and also carries promise for other algorithmic contexts, such as criminal justice and employment.
A unified data security framework for federated prognostics and health management in smart manufacturing

Data security is one of the most important concerns of manufacturing entities. Therefore, enterprises hesitate to share data with third parties for building predictive and prognostic models. In such scenario, building comprehensive models for predicting asset failures is challenging given data from a single enterprise would not include required variety of operation regimes and failure modes. In this article, we present an encrypted and federated framework for training diagnosis and prognosis models that would not require sharing data. The proposed framework guarantees the privacy of the data while generates comprehensive models that leverage the variety of multiple enterprises operations.
Multi-Cleaning Robots Using Cleaning Distribution Method Based on Map Decomposition in Large Environments

Most cleaning robots have a good cleaning performance for small environments such as houses but require a longer cleaning time due to problems such as slow cleaning progress and low battery capacity, making the robots unsuitable for large environments such as libraries and airports.Cleaning large environments with multiple robots is faster than cleaning them with a single robot.Multi-cleaning robots can utilize several robots to simultaneously clean and share the task of cleaning among the robots.However, as the number of robots increases and the effective distribution of cleaning is not efficient during the cleaning process, the cleaning time will consequently be longer due to the frequent collisions between the robots.Therefore, to shorten the cleaning time, multi-cleaning robots require a coverage path planning that uses an effective cleaning distribution method in the cleaning process.In this paper, a coverage path planning using a cleaning distribution method based on map decomposition is proposed to reduce the cleaning time of multi-cleaning robots.The experimental results demonstrated that the proposed multi-cleaning robots' coverage path planning could be used in large environments in the presence of several types of obstacles.Furthermore, the cleaning time was found to be shorter than that of the previous methods in the case of multi-cleaning robots.
Reconstructing Reflection Maps Using a Stacked-CNN for Mixed Reality Rendering

Corresponding lighting and reflectance between real and virtual objects is important for spatial presence in augmented and mixed reality (AR and MR) applications. We present a method to reconstruct real-world environmental lighting, encoded as a reflection map (RM), from a conventional photograph. To achieve this, we propose a stacked convolutional neural network (SCNN) that predicts high dynamic range (HDR) 360° RMs with varying roughness from a limited field of view, low dynamic range photograph. The SCNN is progressively trained from high to low roughness to predict RMs at varying roughness levels, where each roughness level corresponds to a virtual object's roughness (from diffuse to glossy) for rendering. The predicted RM provides high-fidelity rendering of virtual objects to match with the background photograph. We illustrate the use of our method with indoor and outdoor scenes trained on separate indoor/outdoor SCNNs showing plausible rendering and composition of virtual objects in AR/MR. We show that our method has improved quality over previous methods with a comparative user study and error metrics.
Aggressive, Repetitive, Intentional, Visible, and Imbalanced: Refining Representations for Cyberbullying Classification

Cyberbullying is a pervasive problem in online communities. To identify cyberbullying cases in large-scale social networks, content moderators depend on machine learning classifiers for automatic cyberbullying detection. However, existing models remain unfit for real-world applications, largely due to a shortage of publicly available training data and a lack of standard criteria for assigning ground truth labels. In this study, we address the need for reliable data using an original annotation framework. Inspired by social sciences research into bullying behavior, we characterize the nuanced problem of cyberbullying using five explicit factors to represent its social and linguistic aspects. We model this behavior using social network and language-based features, which improve classifier performance. These results demonstrate the importance of representing and modeling cyberbullying as a social phenomenon.
TaskNorm: Rethinking Batch Normalization for Meta-Learning

Modern meta-learning approaches for image classification rely on increasingly deep networks to achieve state-of-the-art performance, making batch normalization an essential component of meta-learning pipelines. However, the hierarchical nature of the meta-learning setting presents several challenges that can render conventional batch normalization ineffective, giving rise to the need to rethink normalization in this setting. We evaluate a range of approaches to batch normalization for meta-learning scenarios, and develop a novel approach that we call TASKNORM. Experiments on fourteen datasets demonstrate that the choice of batch normalization has a dramatic effect on both classification accuracy and training time for both gradient based- and gradient-free meta-learning approaches. Importantly, TASKNORM is found to consistently improve performance. Finally, we provide a set of best practices for normalization that will allow fair comparison of meta-learning algorithms.
ANN-assisted robust GPS/INS information fusion to bridge GPS outage

Abstract Inertial navigation is an edge computing-based method for determining the position and orientation of a moving vehicle that operates according to Newton’s laws of motion on which all the computations are performed at the edge level without need to other far resources. One of the most crucial struggles in Global Positioning System (GPS) and Inertial Navigation System (INS) fusion algorithms is that the accuracy of the algorithm is reduced during GPS interruptions. In this paper, a low-cost method for GPS/INS fusion and error compensation of the GPS/INS fusion algorithm during GPS interruption is proposed. To further enhance the reliability and performance of the GPS/INS fusion algorithm, a Robust Kalman Filter (RKF) is used to compensate the influence of gross error from INS observations. When GPS data is interrupted, Kalman filter observations will not be updated, and the accuracy of the position will continuously decrease over time. To bridge GPS data interruption, an artificial neural network-based fusion method is proposed to provide missing position information. A well-trained neural network is used to predict and compensate the interrupted position signal error. Finally, to evaluate the effectiveness of the proposed method, an outdoor test using a custom-designed hardware, GPS, and INS sensors is employed. The results indicate that the accuracy of the positioning has improved by 67% in each axis during an interruption. The proposed algorithm can enhance the accuracy of the GPS/INS integrated system in the required navigation performance.
An Interledger Blockchain Platform for Cross-Border Management of Cybersecurity Information

Cybersecurity certification is a core notion to support the mitigation of cybersecurity risks of Information and Communication Technologies (ICT). At the European Union (EU) level, the Cybersecurity Act establishes a common cybersecurity certification framework supporting the coexistence of different certification schemes across Member States. However, its realization needs to be sustained by technical approaches to enable ICT stakeholders from different sectors or countries to exchange cybersecurity information and evaluate the up-to-date security level of an ICT system throughout their lifecycle. Toward this end, we propose a blockchain-based platform using a novel interledger design, where ledgers associated with ICT artifacts, cybersecurity certificates, and vulnerabilities are interconnected. The main purpose is to leverage the advantages of blockchain in terms of distributed trust, transparency, and accountability, while at the same time coping with scalability, performance, and interoperability requirements. We analyze the impact of our platform in the current EU legislation and provide insights for its deployment.