Reviewerly/exHarmony huggingface dataset:
Hierarchical infinite factor models for improving the prediction of surgical complications for geriatric patients

Nearly a third of all surgeries performed in the United States occur for patients over the age of 65; these older adults experience a higher rate of postoperative morbidity and mortality. To improve the care for these patients, we aim to identify and characterize high risk geriatric patients to send to a specialized perioperative clinic while leveraging the overall surgical population to improve learning. To this end, we develop a hierarchical infinite latent factor model (HIFM) to appropriately account for the covariance structure across subpopulations in data. We propose a novel Hierarchical Dirichlet Process shrinkage prior on the loadings matrix that flexibly captures the underlying structure of our data while sharing information across subpopulations to improve inference and prediction. The stick-breaking construction of the prior assumes an infinite number of factors and allows for each subpopulation to utilize different subsets of the factor space and select the number of factors needed to best explain the variation. We develop the model into a latent factor regression method that excels at prediction and inference of regression coefficients. Simulations validate this strong performance compared to baseline methods. We apply this work to the problem of predicting surgical complications using electronic health record data for geriatric patients and all surgical patients at Duke University Health System (DUHS). The motivating application demonstrates the improved predictive performance when using HIFM in both area under the ROC curve and area under the PR Curve while providing interpretable coefficients that may lead to actionable interventions.
Bat Algorithm with Different Initialization Approaches for Numerical Optimization

In the field of applied engineering, function optimization has been extensively applied for the purpose of finding an optimal solution for the given problem. BAT Algorithm (BA) is population-based stochastic algorithm utilized to determine the both discrete and continuous kind of problems. The use of robust pattern for population initialization may lead to enhance the performance of optimization algorithms which results in prevention of premature convergence. In this paper, we implemented the BA with the probability distributions based on initialization and introduced the four new methods of BA Initialization such as BA initialized with Beta distribution B-BAT, an Exponential distribution E-BAT, Gamma distribution G-BAT and finally the Weibull distribution W-BAT. The empirical results conclude that our proposed techniques are more robust in term of fast convergence rate.
ACTIVE - Autonomous Car to Infrastructure Communication Mastering Adverse Environments

The following topics are dealt with: target tracking; learning (artificial intelligence); Kalman filters; Monte Carlo methods; Bayes methods; cameras; sensor fusion; object tracking; state estimation and probability.
Adversarial Self-Training with Domain Mask for Semantic Segmentation

Unsupervised Domain Adaptation (UDA) faces challenges in real-world semantic segmentation tasks due to the large difference between labeled training (source) data and unseen test (target) data, especially in driving assistance and autonomous driving scenarios. In particular, this large domain gap may cause a significant performance drop. Various adversarial learning methods were proposed to reduce the domain variance in the feature space and output space (i.e., the output feature space of the segmentation network). However, the domain invariance knowledge is seldom taken into consideration. In this paper, we proposed an Adversarial Self-Training (AST) framework with the observation of the generated pseudo-labels holding the class-balanced intrinsic domain invariance that can be integrated into both output-space and adversarial-space (i.e., the output space of the discriminator network). Furthermore, we separate the modulation of Batch Normalization (BN) layers from training, in order to concentrate the task of adversarial learning and elevate the quality of pseudo-labels. The experiment results show the proposed methods perform favorably against the state-of-the-art methods in terms of accuracy.
Predictive Representation Learning in Motif-Based Graph Networks

Link prediction is an important task for analyzing social networks which also has other applications such as bioinformatics and e-commerce. Network representation learning (NRL), which can significantly enhance the performance for link prediction, has attracted much attention in recent years. However, the existing NRL methods mainly focus on observed network structures without considering hidden prediction knowledge in the representation space. Meanwhile, some random walk based NRL methods are dissatisfactory to learn link knowledge in dense networks with large scales. In this paper, we propose a predictive representation learning (PRL) model, which unifies node representations and motif-based structures, to improve prediction ability of NRL. We firstly enhance node representations based on motif-biased random walks and then employ L2-SVM to learn motif-connected node-pairs. By jointly optimizing two objectives of existent and nonexistent edges representations, we preserve more information of nodes in representation space based on supervised learning. To evaluate the performance of our proposed model, we implement experiments on 5 real data sets. Simulation results illustrate that our proposed model achieves better link prediction performance compared with other state-of-the-arts methods.
Smart Medical Chatbot with Integrated Contactless Vital Sign Monitor

Abstract Artificial intelligence is one of the important fields in modern technologies to help us strive for better life. Healthcare industries nowadays spend a lot of money researching on how artificial intelligence can help improve their services and give the highest satisfaction to their customers. Most healthcare organisations have a passive relationship to their patients when it comes to communication and this situation is often worsened because of a lack of inter-operability between client and provider. Mobile applications on the other hand have become one of the effective strategies in bridging the interaction between provider and end user. In this study, an automated self-learning system is designed to provide conversational healthcare for personalised proactive experience. This system is developed along with the in cooperation of contactless monitoring device using a vision-based real-time monitoring of vital signs which allow patients to monitor their oxygen level, heart rate and respiration rate. This system is also automatically calibrated across patients, allowing precise measurement using highest probability method and natural language processing. Results obtained from the comparative analysis show a promising result with an error of 1.16 for pulse sensor and 2.917 for ECG which are below the threshold error. This allows user to accurately measure vital signs in a non-obtrusive way, and to provide them with the data required to determine to the right timing for any intervention procedure needed. The developed system would also help to bridge the gap of interoperability between client and medical provider.
Hierarchical Convolution Feature for Target Tracking with Kernel-Correlation Filtering

Target tracking is widely used in many fields, but tracking performance still needs to be improved due to factors such as deformation, illumination and occlusion. In this paper, we propose a scale adaptive target tracking solution based on hierarchical convolution features and establish a kernel correlation filtering target tracking framework that combines multi-layer convolution features. The improved convolutional neural network is used to extract multi-layer features, and the correlation filters of each layer are separately trained to perform weighted fusion to obtain the target position. Then, the edge box algorithm is adopted to obtain the size of the actual tracking frame to achieve exact target tracking. An extensive evaluation on OTB-2013 with public test sequences are conducted. Experimental results and analysis indicate that our method is better than other known advanced tracking algorithms even in video sequences with many uncertain factors, while the speed and accuracy of tracking can be effectively improved.
Estimation of Wildfire Size and Location Using a Monocular Camera on a Semi-autonomous Quadcopter

This paper addresses the problem of estimating the location and size of a wildfire, within the frame of a semi-autonomous recon and data analytics quadcopter. We approach this problem by developing three different algorithms, in order to accommodate this problem. Two of these taking into the account that the middle of the camera’s FOV is horizontal with respect to the drone it is mounted. The third algorithm relates to the bottom point of the FOV, directly under the drone in 3D space. The evaluation shows that having the pixels correlate to ratios in percentages rather than predetermined values, with respect to the edges of the fire, will result in better performance and higher accuracy. Placing the monocular camera horizontally in relation to the drone will provide an accuracy of 68.20%, while mounting the camera with an angle, will deliver an accuracy of 60.76%.
Combinatorial Optimization Method Using Expanded Search Mechanism Based on Hierarchical Interpretation in Solution Space

In this paper, we introduce a new expanded search mechanism, which pays attention to updating the group of solutions searched by same search point rather than just a solution. After confirming the affinities to “Hierarchical Interpretation of Solution Space which is also proposed by our research group, we adapt the expanded search mechanism to the search tactics based on the structure above. This assembly allows us to search solutions by expanding the explored area in the combinatorial optimization problem solution space, rather than searching in a route. In addition, remained issues in previous tactics can also be solved. To verify the basic performance of the expanded search mechanism, we propose a new combinatorial optimization method with previous strategies, and compare it with previous method by numerical experiment. Finally, the new mechanism's potentials are discussed.
Conceptualised Framework for Assessing Teachers’ E-learning Readiness in South African Rural Schools

This paper reveals how theoretical triangulation of Technology Readiness Index (TRI) and Technology Acceptance Model (TAM) were adopted to conceptualize a framework for understanding and assessing teachers’ electronic learning (e-learning) readiness within South African schools where access to technology is still a challenge. The paper analysed the TRI’s personality characteristics (optimism, innovativeness, discomforts, and insecurity) and the cognitive dimensions of TAM (perceived ease of use and the perceived usefulness) from both secondary and primary schools in Vhembe district of Venda in Limpopo Province. Even though technology is deemed to be a critical factor affecting teachers’ e-learning readiness by other Information Systems (IS) researcher, in this study, the data which was collected from 120 teachers found technological factor not to be such influential. It is the findings of this study that individual factors, institutional factors, and social factors tend to be critical factors affecting teachers’ e-learning readiness in South African rural schools. The finding of this study can be used by the provincial and national Departments of Basic Education, school governing bodies, and school managers as a reference point to enrich their knowledge when planning for e-learning implementation programmes across schools. This paper contributes practically as the conceptualised framework provides important factors that can be used as a foundation for assessment of teachers’ e-learning readiness particularly in other South African rural schools either in the same province or others.
Optimization of education management system for English teaching based on web

Abstract With the integration of world economy, social reform and the development of English education. The English test has become a significant part of education. Due to the status of English courses and the scale of examinations, computer examination is undoubtedly a convenient and fast way of examination. At the same time, for higher vocational college teaching, some English examination systems based on WCB are not targeted. Therefore, the development of higher vocational English Web examination system. It is an exploration of English teaching reform and English language learning test in higher vocational colleges under the new situation. It has the characteristics of pertinence, science, economy, practicability and operability. It is believed to be of great promotion and application value to higher vocational English teaching, curriculum construction and reform as well as teachers’ own development.
A Fast Autoencoder-based Recommender

High-dimensional and sparse (HiDS) matrices are the basic inputs of recommender systems. Recently, autoencoder-based approaches to analyzing HiDS matrices from recommender systems are becoming increasingly popular, owing to their good representative learning ability and scalability. However, traditional autoencoder-based approaches can usually bring great computational burden on HiDS data due to its frequent manipulations of large latent factor (LF) matrices data. To address this issue, we propose a Fast Autoencoder (FAE)-based recommender. It is capable of manipulating single LFs rather than LF matrices with improved computational efficiency. Moreover, we consider a Hogwild!-based parallelization mechanism for further accelerating its training efficiciency of the proposed recommender. Experimental results show that our proposed method considerably outperform traditional ones, e.g., the classical Autoencoder-based recommenders, in terms of recommendation accuracy and computational efficiency.
Modeling clinical assessor intervariability using deep hypersphere encoder–decoder networks

Abstract In medical imaging, a proper gold-standard ground truth as, e.g., annotated segmentations by assessors or experts is lacking or only scarcely available and suffers from large intervariability in those segmentations. Most state-of-the-art segmentation models do not take inter-observer variability into account and are fully deterministic in nature. In this work, we propose hypersphere encoder–decoder networks in combination with dynamic leaky ReLUs, as a new method to explicitly incorporate inter-observer variability into a segmentation model. With this model, we can then generate multiple proposals based on the inter-observer agreement. As a result, the output segmentations of the proposed model can be tuned to typical margins inherent to the ambiguity in the data. For experimental validation, we provide a proof of concept on a toy data set as well as show improved segmentation results on two medical data sets. The proposed method has several advantages over current state-of-the-art segmentation models such as interpretability in the uncertainty of segmentation borders. Experiments with a medical localization problem show that it offers improved biopsy localizations, which are on average 12% closer to the optimal biopsy location.
Dynamically Sharing Memory between Memcached Tenants using Tingo

Web applications utilize in-memory caching systems to reduce the load on backend databases and improve the performance of the system. These cache environments normally host multiple tenants simultaneously, signifying the need to efficiently manage the underlying physical memory allocation in these environments. Off-the-shelf caches statically divide the memory between tenants, which often leads to poor utilization and low hit rates for some of these tenants. In this work, we present Tingo, a multi-tenant cache environment designed to adequately manage the memory allocation of cache tenants to help them adapt to their workloads and optimize their hit ratios, by dynamically reallocating memory pages among them.
A Bayesian Active Learning Approach to Adaptive Motion Planning

An important requirement for a robot to operate reliably in the real world is a robust motion planning module. Current planning systems do not have consistent performance across all situations a robot encounters. We are interested in planning algorithms that adapt during a planning cycle by actively inferring the structure of the valid configuration space, and focusing on potentially good solutions. Consider the problem of evaluating edges on a graph to discover a good path. Edges are not alike in value—some are important, others are informative. Important edges have a lot of good paths flowing through them. Informative edges, on being evaluated, affect the likelihood of other neighboring edges being valid. Evaluating edges is expensive, both for robots with complex geometries like robot arms, and for robots with limited onboard computation like UAVs. Until now, we have addressed this challenge via laziness, deferring edge evaluation until absolutely necessary, with the hope that edges turn out to be valid. Our key insight is that we can do more than passive laziness—we can actively probe for information. We draw a novel connection between motion planning and Bayesian active learning. By leveraging existing active learning algorithms, we derive efficient edge evaluation policies which we apply on a spectrum of real world problems. We discuss insights from these preliminary results and potential research questions whose study may prove fruitful for both disciplines.
The Broadcast Message Complexity of Secure Multiparty Computation

We study the broadcast message complexity of secure multiparty computation (MPC), namely, the total number of messages that are required for securely computing any functionality in the broadcast model of communication. MPC protocols are traditionally designed in the simultaneous broadcast model, where each round consists of every party broadcasting a message to the other parties. We show that this method of communication is sub-optimal; specifically, by eliminating simultaneity, it is, in fact, possible to reduce the broadcast message complexity of MPC. More specifically, we establish tight lower and upper bounds on the broadcast message complexity of n-party MPC for every $$t<n$$ corruption threshold, both in the plain model as well as common setup models. For example, our results show that the optimal broadcast message complexity of semi-honest MPC can be much lower than 2n, but necessarily requires at least three rounds of communication. We also extend our results to the malicious setting in setup models.
Feature Refine Network for Text-Based CAPTCHA Recognition

Text-based CAPTCHA is a widely used security mechanism. Text-based CAPTCHA recognition aims to automatically detect characters in a text-based CAPTCHA. It reveals the weakness of current CAPTCHA and improves the security ability. In this paper, we propose a novel Feature Refine network (FRN) for text-based CAPTCHA with small-size characters. FRN consists of convolutional layers and deconvolution layers. The convolutional layers enhance the feature extraction capabilities of the network and expand the receptive field. The deconvolution layers increase the resolution of the feature map and restore the details of texts. In addition, our model uses skip ROI pooling to extract multi-scale features with multi levels of abstraction. We test our model on five popular text-based CAPTCHAs, namely eBay, Baidu, Hotmail, Sina and NetEase. The experimental compared with the state-of-the-art methods demonstrate the ability of FRN. The recognition rates are improved above 90%, and these results achieve the new state-of-the-art for real website CAPTCHAs.
The Use of SDAE in Noisy English Mispronunciation Detection and Diagnosis towards Application in Mobile Learning

The problem of noise in the environment is a bottleneck in the application of computer assisted pronunciation training (CAPT) system. In this paper, in order to solve the noise problem in mobile learning, a mispronunciation detection and diagnosis (MD-D) model based on stack denoising autoencoder (SDAE) is proposed for noisy English phonemes. We built a SDAE based recognition network to detect mispronunciation in noisy speech. SDAE is used to obtain robust data features in different noisy environments. The classifier layer at the top of the network is used to generate posterior probability of error types. The parameters of the SDAE network are obtained through unsupervised pretraining and supervised fine-tuning. We used rectified linear unit (ReLU) as the activation function of SDAE to get more advanced features. The verification experiment used SDAE-based MDD model to obtain a better mispronunciation detection performance using the dataset of NOISE-92 and the Speech Accent Archive pronunciation corpus with varied SNR than the performance of SVM. The experimental results showed that our work provided a new example in mispronunciation detection under the situation of noisy mobile learning.
Image Representation Using Variants of Principal Component Analysis: A Comparative Study

Linear and non-linear data reduction techniques proved their effectiveness in the field of image analysis. Principal Component Analysis (PCA) is a powerful data reduction and data representation technique having its linear and non-linear counterparts. It is a statistical technique used to transform high dimensional data into low dimensional representation without losing much of the information. PCA is a widely-used algorithm in the field of pattern recognition, face recognition, image fusion, data compression, and machine vision. Over the period, many PCA based algorithms have been proposed to effectively extract important features from images and to reconstruct images using minimum feature set. In this paper, we compared four widely used PCA based algorithms in terms of their capability of representing images using reduced feature set.
Indonesian community welfare levels clustering using the fuzzy subtractive clustering (FCM) method

Abstract Clustering is a technique used to classify objects or cases into groups based on their similarity, called clusters or groups. Objects in each group tend to resemble each other and differ greatly (not the same) with objects from other clusters. Public welfare is a condition of fulfilling the material, spiritual and social needs of citizens in order to be able to live properly. Fuzzy Subtractive Clustering (FSC) method is a clustering algorithm that can form the number and centroid of clusters in accordance with data conditions. This study aims to determine the FSC results in grouping the level of welfare of the Indonesian people in 2017. The testing results of the cluster validity index show 2 values of Partition Entropy and Classification Entropy forming into 2 clusters that have the best value, indicating that the provincial group has a high welfare level and the provincial group has a low welfare level.
Let's Meet Halfway: Sharing New Responsibilities in a Digital Age

Abstract Which legal instrument can effectively address current challenges in social media governance and how do companies take their share, shifting away from opaque enforcement of terms of services and increasingly copying governmental structures? In a first step, this article describes and analyzes the way that states address hate speech and misinformation in their respective regulatory projects. Secondly, it examines how social media platforms sanction unwanted content and integrate (or plan on integrating) procedural rules such as appeal and due process principles in their moderation policies. Large social media platforms tend to adopt new structures that resemble administrative law—an uncommon development for non-state actors.