Reviewerly/exHarmony huggingface dataset:
Feelings of the nursing team in the face of COVID-19 in the care of the first patients: descriptive study

Objectives: to analyze the feelings and conflicts experienced by nursing professionals in the care of patients with COVID-19; to analyze the implementation of nursing care for the first patients with COVID-19 in an intensive care unit in the city of Rio de Janeiro. Method: qualitative, descriptive and exploratory research. Participants were 71 professionals from the nursing team who develop work activities in the fight against COVID-19 in an intensive care unit in the city of Rio de Janeiro, through online collection. Descriptive statistics and thematic content analysis were used for the analyses. Results: the content analysis revealed feelings in the face of the unknown virus, fear of getting infected, fear of losing one's life and care aspects in the face of the COVID-19 phenomenon. Conclusion: nursing presented feelings and conflicts, such as insecurity and fear. The group built knowledge and skills to handle the realization of this care in the...
Evaluating Real-Time Search over Tweets

Twitter offers a phenomenal platform for the social sharing of information. We describe new resources that have been created in the context of the Text Retrieval Conference (TREC) to support the academic study of Twitter as a real-time information source. We formalize an information seeking task — real-time search — and offer a methodology for measuring system effectiveness. At the TREC 2011 Microblog Track, 58 research groups participated in the first ever evaluation of this task. We present data from the effort to illustrate and support our methodology.
ET-LDA: Joint Topic Modeling for Aligning Events and their Twitter Feedback

During broadcast events such as the Superbowl, the U.S. Presidential and Primary debates, etc., Twitter has become the de facto platform for crowds to share perspectives and commentaries about them. Given an event and an associated large-scale collection of tweets, there are two fundamental research problems that have been receiving increasing attention in recent years. One is to extract the topics covered by the event and the tweets; the other is to segment the event. So far these problems have been viewed separately and studied in isolation. In this work, we argue that these problems are in fact inter-dependent and should be addressed together. We develop a joint Bayesian model that performs topic modeling and event segmentation in one unified framework. We evaluate the proposed model both quantitatively and qualitatively on two large-scale tweet datasets associated with two events from different domains to show that it improves significantly over baseline models.
An efficient face recognition method using contourlet and curvelet transform

In the paper we propose a novel method for face recognition using contourlet transform (CNT) and curvelet transform (CLT) which improves rate of face recognition under different challenges. We obtain smooth contour information along different directions by applying CNT on the face image while CLT having multiscale, multidirectional and anisotropic properties has been employed to represent the edges more prominently. Pre-processed training images are decomposed up to fourth level using CNT and coefficients of directional subbands are analysed to obtain the features from the images. In another approach CLT has been applied on the pre-processed face images and considering scale of four and angle eight, different statistical features are extracted from the detail subbands. Finally, we integrate the features obtained from two approaches. High dimensionality of feature space has been reduced by selecting important features depending on the entropy of the transform coefficients. Selected features are applied to recognize the face images using support vector machine (SVM) classifier. Experimental results show that the proposed feature extraction method improves recognition accuracy compare to other methods and efficiently handle the effect of Gaussian noise as tested on JAFFE, ORL and FERET database.
Controlled Markov Processes With Safety State Constraints

This paper considers a Markov decision process (MDP) model with safety state constraints, which specify polytopic invariance constraints on the state probability distribution (pd) for all time epochs. Typically, in the MDP framework, safety is addressed indirectly by penalizing failure states through the reward function. However, such an approach does not allow imposing hard constraints on the state pd, which could be an issue for practical applications where the chance of failure must be limited to prescribed bounds. In this paper, we explicitly separate state constraints from the reward function. We provide analysis and synthesis methods to impose generalized safety constraints at all time epochs, unlike current constrained MDP approaches where such constraints can only be imposed on the stationary distributions. We show that, contrary to the unconstrained MDP policies, optimal safe MDP policies depend on the initial state pd. We present novel algorithms for both finite- and infinite-horizon MDPs to synthesize feasible decision-making policies that satisfy safety constraints for all time epochs and ensure that the performance is above a computable lower bound. Linear programming implementations of the proposed algorithms are developed, which are formulated by using the duality theory of convex optimization. A swarm control simulation example is also provided to demonstrate the use of proposed algorithms.
Scalable Large-Margin Distance Metric Learning Using Stochastic Gradient Descent

The key to success of many machine learning and pattern recognition algorithms is the way of computing distances between the input data. In this paper, we propose a large-margin-based approach, called the large-margin distance metric learning (LMDML), for learning a Mahalanobis distance metric. LMDML employs the principle of margin maximization to learn the distance metric with the goal of improving k-nearest-neighbor classification. The main challenge of distance metric learning is the positive semidefiniteness constraint on the Mahalanobis matrix. Semidefinite programming is commonly used to enforce this constraint, but it becomes computationally intractable on large-scale data sets. To overcome this limitation, we develop an efficient algorithm based on a stochastic gradient descent. Our algorithm can avoid the computations of the full gradient and ensure that the learned matrix remains within the positive semidefinite cone after each iteration. Extensive experiments show that the proposed algorithm is scalable to large data sets and outperforms other state-of-the-art distance metric learning approaches regarding classification accuracy and training time.
Visualization Methods for Image Transformation Convolutional Neural Networks

Convolutional neural networks (CNNs) are powerful machine learning models that have become the state of the art in several problems in the areas of computer vision and image processing. Nevertheless, the knowledge of why and how these models present an impressive performance is still limited. There are visualization techniques that can help us to understand the inner working of neural networks. However, they have mostly been applied to classification models. In this paper, we evaluate the application of visualization methods to networks where the input and output are images of proportional dimensions. The results show that visualization brings visual cues associated with how these systems work, helping in their understanding and improvement. We use the knowledge obtained from the visualization of an image restoration CNN to improve the architecture's efficiency with no significant degradation of its performance.
Dynamic Capsule Attention for Visual Question Answering

In visual question answering (VQA), recent advances have well advocated the use of attention mechanism to precisely link the question to the potential answer areas. As the difficulty of the question increases, more VQA models adopt multiple attention layers to capture the deeper visual-linguistic correlation. But a negative consequence is the explosion of parameters, which makes the model vulnerable to over-fitting, especially when limited training examples are given. In this paper, we propose an extremely compact alternative to this static multi-layer architecture towards accurate yet efficient attention modeling, termed as Dynamic Capsule Attention (CapsAtt). Inspired by the recent work of Capsule Network, CapsAtt treats visual features as capsules and obtains the attention output via dynamic routing, which updates the attention weights by calculating coupling coefficients between the underlying and output capsules. Meanwhile, CapsAtt also discards redundant projection matrices to make the model much more compact. We quantify CapsAtt on three benchmark VQA datasets, i.e., COCO-QA, VQA1.0 and VQA2.0. Compared to the traditional multi-layer attention model, CapsAtt achieves significant improvements of up to 4.1%, 5.2% and 2.2% on three datasets, respectively. Moreover, with much fewer parameters, our approach also yields competitive results compared to the latest VQA models. To further verify the generalization ability of CapsAtt, we also deploy it on another challenging multi-modal task of image captioning, where state-of-the-art performance is achieved with a simple network structure.
Association rule mining based parameter adaptive strategy for differential evolution algorithms

It is a very challenging and important task to adaptively adjust the scale factor F and the crossover rate Cr for Differential Evolutionary (DE) algorithms. Most recent adaptive techniques were designed to generate parameters randomly based on successful trial values during the previous evolving process, lacking explicit guidelines to generate appropriate values. This paper proposes a novel parameter adaption strategy, which could incorporate promising F and Cr pairs extracted by using Association Rule Mining (ARM) into DE algorithms. First, all successful F and Cr values generated by their original methods are recorded during the whole evolution, resulting in an increasing dataset. Second, we discretize the dataset and extract the most frequent itemset of parameters by using a modified version of the widely used Apriori algorithm. Third, a greedy operator is developed to generate new parameters in the next generation by comparing the presented ARM-based and original-method-based fitness values. The presented technique provides an additional pair of F and Cr values to be evaluated, without replacing existing strategies for the control parameters. The main contribution of this paper is that we propose a novel way, which utilizes information generated during the evolutionary process, to enhance exploration capabilities by adjusting control parameters. Experimental results demonstrate that the proposed ARM-based parameter adaptive strategy is able to enhance performances of some state-of-the-art DE variants. Further, this methodology might be helpful for other control parameters of Evolutionary Algorithms (EA).
Clustering Functional Data into Groups by Using Projections

Summary We show that, in the functional data context, by appropriately exploiting the functional nature of the data, it is possible to cluster the observations asymptotically perfectly. We demonstrate that this level of performance can sometimes be achieved by the k-means algorithm as long as the data are projected on a carefully chosen finite dimensional space. In general, the notion of an ideal cluster is not clearly defined. We derive our results in the setting where the data come from two populations whose distributions differ at least in terms of means, and where an ideal cluster corresponds to one of these two populations. We propose an iterative algorithm to choose the projection functions in a way that optimizes clustering performance, where, to avoid peculiar solutions, we use a weighted least squares criterion. We apply our iterative clustering procedure on simulated and real data, where we show that it works well.
Data mining approach for predicting the daily Internet data traffic of a smart university

Internet traffic measurement and analysis generate dataset that are indicators of usage trends, and such dataset can be used for traffic prediction via various statistical analyses. In this study, an extensive analysis was carried out on the daily internet traffic data generated from January to December, 2017 in a smart university in Nigeria. The dataset analysed contains seven key features: the month, the week, the day of the week, the daily IP traffic for the previous day, the average daily IP traffic for the two previous days, the traffic status classification (TSC) for the download and the TSC for the upload internet traffic data. The data mining analysis was performed using four learning algorithms: the Decision Tree, the Tree Ensemble, the Random Forest, and the Naïve Bayes Algorithm on KNIME (Konstanz Information Miner) data mining application and kNN, Neural Network, Random Forest, Naïve Bayes and CN2 Rule Inducer algorithms on the Orange platform. A comparative performance analysis for the models is presented using the confusion matrix, Cohen’s Kappa value, the accuracy of each model, Area under ROC Curve, etc. A minimum accuracy of 55.66% was observed for both the upload and the download IP data on the KNIME platform while minimum accuracies of 57.3% and 51.4% respectively were observed on the Orange platform.
IGR Token-Raw Material and Ingredient Certification of Recipe Based Foods Using Smart Contracts

The use of smart contracts and blockchain tokens to implement a consumer trustworthy ingredient certification scheme for commingled foods, i.e., recipe based, food products is described. The proposed framework allows ingredients that carry any desired property (including social or environmental customer perceived value) to be certified by any certification authority, at the moment of harvest or extraction, using the IGR Ethereum token. The mechanism involves the transfer of tokens containing the internet url published at the authority’s web site from the farmer all along the supply chain to the final consumer at each transfer of custody of the ingredient using the Cricital Tracking Event/Key Data Elements (CTE/KDE) philosophy of the Institute of Food Technologists (IFT). This allows the end consumer to easily inspect and be assured of the origin of the ingredient by means of a mobile application. A successful code implementation of the framework was deployed, tested and is running as a beta version on the Ethereum live blockchain as the IGR token. The main contribution of the framework is the possibility to ensure the true origin of any instance or lot of ingredient within a recipe to the customer, without harming the food processor legitimate right to protect its recipes and suppliers.
Not all sensors are created equal: a framework for evaluating human performance measurement technologies

Abstract Recent years have witnessed an explosion in the number of wearable sensing devices and associated apps that target a wide range of biomedical metrics, from actigraphy to glucose monitoring to lung function. This offers big opportunities for achieving scale in the use of such devices in application contexts such as telehealth, human performance and behaviour research and digitally enabled clinical trials. However, this increased availability and choice of sensors also brings with it a great challenge in optimising the match between the sensor and a specific application context. There is a need for a structured approach to first refining the requirements for a specific application, and then evaluating the available devices against those requirements. In this paper we will outline the main features of such an evaluation framework that has been developed with input from stakeholders in academic, clinical and industry settings.
Improving Discrimination in Color Vision Deficiency by Image Re-Coloring

People with color vision deficiency (CVD) cannot observe the colorful world due to the damage of color reception nerves. In this work, we present an image enhancement approach to assist colorblind people to identify the colors they are not able to distinguish naturally. An image re-coloring algorithm based on eigenvector processing is proposed for robust color separation under color deficiency transformation. It is shown that the eigenvector of color vision deficiency is distorted by an angle in the λ , Y-B, R-G color space. The experimental results show that our approach is useful for the recognition and separation of the CVD confusing colors in natural scene images. Compared to the existing techniques, our results of natural images with CVD simulation work very well in terms of RMS, HDR-VDP-2 and an IRB-approved human test. Both the objective comparison with previous works and the subjective evaluation on human tests validate the effectiveness of the proposed method.
Resample-Based Ensemble Framework for Drifting Imbalanced Data Streams

Machine learning in real-world scenarios is often challenged by concept drift and class imbalance.This paper proposes a Resample-based Ensemble Framework for Drifting Imbalanced Stream (RE-DI).The ensemble framework consists of a long-term static classifier to handle gradual and multiple dynamic classifiers to handle sudden concept drift.The weights of the ensemble classifier are adjusted from two aspects.First, a time-decayed strategy decreases the weights of the dynamic classifiers to make the ensemble classifier focus more on the new concept of the data stream.Second, a novel reinforcement mechanism is proposed to increase the weights of the base classifiers that perform better on the minority class and decrease the weights of the classifiers that perform worse.A resampling buffer is used for storing the instances of the minority class to balance the imbalanced distribution over time.In our experiment, we compare the proposed method with other state-of-the-art algorithms on both real-world and synthetic data streams.The results show that the proposed method achieves the best performance in terms of both Prequential AUC and accuracy.
Effective Neural Network Training With a New Weighting Mechanism-Based Optimization Algorithm

First-order gradient-based optimization algorithms have been of core practical importance in the field of deep learning. In this paper, we propose a new weighting mechanism-based first-order gradient descent optimization algorithm, namely NWM-Adam, to resolve the undesirable convergence behavior of some optimization algorithms which employ fixed sized window of past gradients to scale the gradient updates and improve the performance of Adam and AMSGrad. The NWM-Adam is developed on the basis of the idea, i.e., placing more memory of the past gradients than the recent gradients. Furthermore, it can easily adjust the degree to which how much the past gradients weigh in the estimation. In order to empirically test the performance of our proposed NWM-Adam optimization algorithm, we compare it with other popular optimization algorithms in three well-known machine learning models, i.e., logistic regression, multi-layer fully connected neural networks, and deep convolutional neural networks. The experimental results show that the NWM-Adam can outperform other optimization algorithms.
Ordinal and Attribute Aware Response Generation in a Multimodal Dialogue System

Multimodal dialogue systems have opened new frontiers in the traditional goal-oriented dialogue systems.The state-of-the-art dialogue systems are primarily based on unimodal sources, predominantly the text, and hence cannot capture the information present in the other sources such as videos, audios, images etc.With the availability of large scale multimodal dialogue dataset (MMD) (Saha et al., 2018) on the fashion domain, the visual appearance of the products is essential for understanding the intention of the user.Without capturing the information from both the text and image, the system will be incapable of generating correct and desirable responses.In this paper, we propose a novel position and attribute aware attention mechanism to learn enhanced image representation conditioned on the user utterance.Our evaluation shows that the proposed model can generate appropriate responses while preserving the position and attribute information.Experimental results also prove that our proposed approach attains superior performance compared to the baseline models, and outperforms the state-of-the-art approaches on text similarity based evaluation metrics.
Deep Learning-Based Retrieval System for Gigapixel Histopathology Cases and the Open Access Literature

The introduction of digital pathology into clinical practice has led to the development of clinical workflows with digital images, in connection with pathology reports. Still, most of the current work is time-consuming manual analysis of image areas at different scales. Links with data in the biomedical literature are rare, and a need for search based on visual similarity within whole slide images (WSIs) exists.The main objective of the work presented is to integrate content-based visual retrieval with a WSI viewer in a prototype. Another objective is to connect cases analyzed in the viewer with cases or images from the biomedical literature, including the search through visual similarity and text.An innovative retrieval system for digital pathology is integrated with a WSI viewer, allowing to define regions of interest (ROIs) in images as queries for finding visually similar areas in the same or other images and to zoom in/out to find structures at varying magnification levels. The algorithms are based on a multimodal approach, exploiting both text information and content-based image features.The retrieval system allows viewing WSIs and searching for regions that are visually similar to manually defined ROIs in various data sources (proprietary and public datasets, e.g., scientific literature). The system was tested by pathologists, highlighting its capabilities and suggesting ways to improve it and make it more usable in clinical practice.The developed system can enhance the practice of pathologists by enabling them to use their experience and knowledge to control artificial intelligence tools for navigating repositories of images for clinical decision support and teaching, where the comparison with visually similar cases can help to avoid misinterpretations. The system is available as open source, allowing the scientific community to test, ideate and develop similar systems for research and clinical practice.