Reviewerly/exHarmony huggingface dataset:
Fighting COVID-19 with Fever Screening, Face Recognition and Tracing

Abstract Since the outbreak of COVID-19 corona virus in late 2019, it has become a tremendous threat to the whole world. Driven by the mission to save lives, we develop a fever screening and tracing system which can detect patients with fever symptom, and identify the patients using face recognition. In addition, our big data AI platform enables the tracing of the patients possible. A real-time alert sent to the personnel on duty on a web or mobile app activates the action to trace the patient and the close contacts providing an effective means to control the spread of the virus.
An Ensemble Deep Neural Network Model for Onion-Routed Traffic Detection to Boost Cloud Security

Anonymous network communication using onion routing networks such as Tor are used to guard the privacy of sender by encrypting all messages in the overlapped network. These days most of the onion routed communications are not only used for decent cause but also cyber offenders are ill-using onion routings for scanning the ports, hacking, exfiltration of theft data, and other types of online frauds. These cyber-crime attempts are very vulnerable for cloud security. Deep learning is highly effective machine learning method for prediction and classification. Ensembling multiple models is an influential approach to increase the efficiency of learning models. In this work, an ensemble deep learning-based classification model is proposed to detect communication through Tor and non-Tor network. Three different deep learning models are combined to achieve the ensemble model. The proposed model is also compared with other machine learning models. Classification results shows the superiority of the proposed model than other models.
Early Detection of Readmission Risk for Decision Support Based on Clinical Notes

Hospital readmission shortly after discharge is contributing to rising medical care costs. Attempts have been exerted to reduce readmission rates by predicting patients at high risk of this episode on the basis of unstructured clinical notes. Discharge summary as part of the clinical prose is effective at modeling readmission risk. However, the predictive value of notes written upon discharge offers few opportunities to reduce the chance of readmission because the target patient might have already been discharged. This paper presents the use of early clinical notes in building a machine learning model to predict readmission at 48 h immediately after a patient's admission. Extensive feature engineering, testing multiple algorithms, and algorithm tuning were performed to enhance model performance. A risk scoring framework that combines the data- and knowledge-driven feature scores in risk computation was developed. The proposed predictive model showed better prognostic capability than the machine learning model alone in terms of the ability to detect readmission. In specific, the proposed algorithm showed improvements of 11%–28% in sensitivity and 1%–3% in the area-under-the-receiver operating characteristic curve.
EFFICIENT QUATERNION MOMENTS FOR REPRESENTATION AND RETRIEVAL OF BIOMEDICAL COLOR IMAGES

Biomedical color (BMC) images are being used on a wide scale by physicians, where their diagnosis would be more accurate. Hence, it is recommended to develop new approaches that are able to represent and retrieve the BMC images efficiently. This work proposes two methods to represent BMC images: Quaternion Associated Laguerre. Moments (Q_ALMs), and Quaternion Chebyshev Moments (Q_CMs). Q_ALMs and Q_CMs are derived by extending the ALMs and CMs to the quaternion field. ALMs and CMs represent discrete orthogonal moments, and they are defined using the Associated Laguerre Polynomials (ALPs) and Chebychev Polynomials, respectively. Hospitals and medical institutes everywhere in the world create and store a large variety of datasets of BMC images during the routine clinical practices; hence, the mastery to retrieve the BMC images correctly is crucial for precise diagnoses and also for the researchers in medical sciences. So that in this study, we also introduced two image retrieval systems for BMC images based on the Q_CMs and Q_ALMs approaches. Our approaches extensively assessed with two standard benchmark datasets: LGG Segmentation dataset for brain magnetic resonance MR images and NEMA-CT for the computed tomography (CT) images. The performance of the proposed retrieval systems is assessed through three performance metrics: Average retrieval precision (ARP), average retrieval rate (ARR), and F_score. Results have shown the outperformance of Q_CMs over Q_ALMs in both the cases of representing and retrieval of BMC images.
Advancing Multiple Instance Learning with Attention Modeling for Categorical Speech Emotion Recognition

Categorical speech emotion recognition is typically performed as a sequence-to-label problem, i.e., to determine the discrete emotion label of the input utterance as a whole. One of the main challenges in practice is that most of the existing emotion corpora do not give ground truth labels for each segment; instead, we only have labels for whole utterances. To extract segment-level emotional information from such weakly labeled emotion corpora, we propose using multiple instance learning (MIL) to learn segment embeddings in a weakly supervised manner. Also, for a sufficiently long utterance, not all of the segments contain relevant emotional information. In this regard, three attention-based neural network models are then applied to the learned segment embeddings to attend the most salient part of a speech utterance. Experiments on the CASIA corpus and the IEMOCAP database show better or highly competitive results than other state-of-the-art approaches.
Design and Analysis of News Category Predictor

Recent technological advancements have changed significantly the way news is produced, consumed, and disseminated. Frequent and on-spot news reporting has been enabled, which smartphones can access anywhere and anytime. News categorization or classification can significantly help in its proper and timely dissemination. This study evaluates and compares news category predictors' performance based on four supervised machine learning models. We choose a standard dataset of British Broadcasting Corporation (BBC) news consisting of five categories: business, sports, technology, politics, and entertainment. Four multi-class news category predictors have been developed and trained on the same dataset: Naïve Bayes, Random Forest, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). Each category predictor's performance was evaluated by analyzing the confusion matrix and quantifying the test dataset's precision, recall, and overall accuracy. In the end, the performance of all category predictors was studied and compared. The results show that all category predictors have achieved satisfactory accuracy grades. However, the SVM model performed better than the four supervised learning models, categorizing news articles with 98.3% accuracy. In contrast, the lowest accuracy was obtained by the KNN model. However, the KNN model's performance can be enhanced by investigating the optimal number of neighbors (K) value.
A Scalable MIP-based Method for Learning Optimal Multivariate Decision Trees

Several recent publications report advances in training optimal decision trees (ODT) using mixed-integer programs (MIP), due to algorithmic advances in integer programming and a growing interest in addressing the inherent suboptimality of heuristic approaches such as CART. In this paper, we propose a novel MIP formulation, based on a 1-norm support vector machine model, to train a multivariate ODT for classification problems. We provide cutting plane techniques that tighten the linear relaxation of the MIP formulation, in order to improve run times to reach optimality. Using 36 data-sets from the University of California Irvine Machine Learning Repository, we demonstrate that our formulation outperforms its counterparts in the literature by an average of about 10% in terms of mean out-of-sample testing accuracy across the data-sets. We provide a scalable framework to train multivariate ODT on large data-sets by introducing a novel linear programming (LP) based data selection method to choose a subset of the data for training. Our method is able to routinely handle large data-sets with more than 7,000 sample points and outperform heuristics methods and other MIP based techniques. We present results on data-sets containing up to 245,000 samples. Existing MIP-based methods do not scale well on training data-sets beyond 5,500 samples.
QuEST: Quantized Embedding Space for Transferring Knowledge

Knowledge distillation refers to the process of training a student network to achieve better accuracy by learning from a pre-trained teacher network. Most of the existing knowledge distillation methods direct the student to follow the teacher by matching the teacher’s output, feature maps or their distribution. In this work, we propose a novel way to achieve this goal: by distilling the knowledge through a quantized visual words space. According to our method, the teacher’s feature maps are first quantized to represent the main visual concepts (i.e., visual words) encompassed in these maps and then the student is asked to predict those visual word representations. Despite its simplicity, we show that our approach is able to yield results that improve the state of the art on knowledge distillation for model compression and transfer learning scenarios. To that end, we provide an extensive evaluation across several network architectures and most commonly used benchmark datasets.
Comprehensive Attention Self-Distillation for Weakly-Supervised Object Detection

Weakly Supervised Object Detection (WSOD) has emerged as an effective tool to train object detectors using only the image-level category labels. However, without object-level labels, WSOD detectors are prone to detect bounding boxes on salient objects, clustered objects and discriminative object parts. Moreover, the image-level category labels do not enforce consistent object detection across different transformations of the same images. To address the above issues, we propose a Comprehensive Attention Self-Distillation (CASD) training approach for WSOD. To balance feature learning among all object instances, CASD computes the comprehensive attention aggregated from multiple transformations and feature layers of the same images. To enforce consistent spatial supervision on objects, CASD conducts self-distillation on the WSOD networks, such that the comprehensive attention is approximated simultaneously by multiple transformations and feature layers of the same images. CASD produces new state-of-the-art WSOD results on standard benchmarks such as PASCAL VOC 2007/2012 and MS-COCO.
Improving Auto-Augment via Augmentation-Wise Weight Sharing

The recent progress on automatically searching augmentation policies has boosted the performance substantially for various tasks. A key component of automatic augmentation search is the evaluation process for a particular augmentation policy, which is utilized to return reward and usually runs thousands of times. A plain evaluation process, which includes full model training and validation, would be time-consuming. To achieve efficiency, many choose to sacrifice evaluation reliability for speed. In this paper, we dive into the dynamics of augmented training of the model. This inspires us to design a powerful and efficient proxy task based on the Augmentation-Wise Weight Sharing (AWS) to form a fast yet accurate evaluation process in an elegant way. Comprehensive analysis verifies the superiority of this approach in terms of effectiveness and efficiency. The augmentation policies found by our method achieve superior accuracies compared with existing auto-augmentation search methods. On CIFAR-10, we achieve a top-1 error rate of 1.24%, which is currently the best performing single model without extra training data. On ImageNet, we get a top-1 error rate of 20.36% for ResNet-50, which leads to 3.34% absolute error rate reduction over the baseline augmentation.
Enhancing Question Answering by Injecting Ontological Knowledge through Regularization

Deep neural networks have demonstrated high performance on many natural language processing (NLP) tasks that can be answered directly from text, and have struggled to solve NLP tasks requiring external (e.g., world) knowledge.In this paper, we present OSCR (Ontology-based Semantic Composition Regularization), a method for injecting task-agnostic knowledge from an Ontology or knowledge graph into a neural network during pre-training.We evaluated the performance of BERT pretrained on Wikipedia with and without OSCR by measuring the performance when finetuning on two question answering tasks involving world knowledge and causal reasoning and one requiring domain (healthcare) knowledge and obtained 33.3 %, 18.6 %, and 4 % improved accuracy compared to pre-training BERT without OSCR.
Entropic Uncertainty Relations via Direct-Sum Majorization Relation for Generalized Measurements

We derive an entropic uncertainty relation for generalized positive-operator-valued measure (POVM) measurements via a direct-sum majorization relation using Schur concavity of entropic quantities in a finite-dimensional Hilbert space. Our approach provides a significant improvement of the uncertainty bound compared with previous majorization-based approaches (Friendland, S.; Gheorghiu, V.; Gour, G. Phys. Rev. Lett.2013, 111, 230401; Rastegin, A.E.; Życzkowski, K. J. Phys. A, 2016, 49, 355301), particularly by extending the direct-sum majorization relation first introduced in (Rudnicki, Ł.; Puchała, Z.; Życzkowski, K. Phys. Rev. A2014, 89, 052115). We illustrate the usefulness of our uncertainty relations by considering a pair of qubit observables in a two-dimensional system and randomly chosen unsharp observables in a three-dimensional system. We also demonstrate that our bound tends to be stronger than the generalized Maassen-Uffink bound with an increase in the unsharpness effect. Furthermore, we extend our approach to the case of multiple POVM measurements, thus making it possible to establish entropic uncertainty relations involving more than two observables.
Detecting Independent Pronoun Bias with Partially-Synthetic Data Generation

We report that state-of-the-art parsers consistently failed to identify "hers" and "theirs" as pronouns but identified the masculine equivalent "his".We find that the same biases exist in recent language models like BERT.While some of the bias comes from known sources, like training data with gender imbalances, we find that the bias is amplified in the language models and that linguistic differences between English pronouns that are not inherently biased can become biases in some machine learning models.We introduce a new technique for measuring bias in models, using Bayesian approximations to generate partially-synthetic data from the model itself.
FU-Net: Multi-class Image Segmentation Using Feedback Weighted U-Net

In this paper, we present a generic deep convolutional neural network (DCNN) for multi-class image segmentation. It is based on a well-established supervised end-to-end DCNN model, known as U-net. U-net is firstly modified by adding widely used batch normalization and residual block (named as BRU-net) to improve the efficiency of model training. Based on BRU-net, we further introduce a dynamically weighted cross-entropy loss function. The weighting scheme is calculated based on the pixel-wise prediction accuracy during the training process. Assigning higher weights to pixels with lower segmentation accuracies enables the network to learn more from poorly predicted image regions. Our method is named as feedback weighted U-net (FU-net). We have evaluated our method based on T1- weighted brain MRI for the segmentation of midbrain and substantia nigra, where the number of pixels in each class is extremely unbalanced to each other. Based on the dice coefficient measurement, our proposed FU-net has outperformed BRU-net and U-net with statistical significance, especially when only a small number of training examples are available. The code is publicly available in GitHub (GitHub link: https://github.com/MinaJf/FU-net).
Predicting breast cancer recurrence using principal component analysis as feature extraction: an unbiased comparative analysis

Breast cancer recurrence is among the most noteworthy fears faced by women. Nevertheless, with modern innovations in data mining technology, early recurrence prediction can help relieve these fears. Although medical information is typically complicated, and simplifying searches to the most relevant input is challenging, new sophisticated data mining techniques promise accurate predictions from high-dimensional data. In this study, the performances of three established data mining algorithms: Naïve Bayes (NB), k-nearest neighbor (KNN), and fast decision tree (REPTree), adopting the feature extraction algorithm, principal component analysis (PCA), for predicting breast cancer recurrence were contrasted. The comparison was conducted between models built in the absence and presence of PCA. The results showed that KNN produced better prediction without PCA (F-measure = 72.1%), whereas the other two techniques: NB and REPTree, improved when used with PCA (F-measure = 76.1% and 72.8%, respectively). This study can benefit the healthcare industry in assisting physicians in predicting breast cancer recurrence precisely.
Recent Advances in Medical Image Processing

&lt;b&gt;&lt;i&gt;Background:&lt;/i&gt;&lt;/b&gt; Application and development of the artificial intelligence technology have generated a profound impact in the field of medical imaging. It helps medical personnel to make an early and more accurate diagnosis. Recently, the deep convolution neural network is emerging as a principal machine learning method in computer vision and has received significant attention in medical imaging. &lt;b&gt;&lt;i&gt;Key Message:&lt;/i&gt;&lt;/b&gt; In this paper, we will review recent advances in artificial intelligence, machine learning, and deep convolution neural network, focusing on their applications in medical image processing. To illustrate with a concrete example, we discuss in detail the architecture of a convolution neural network through visualization to help understand its internal working mechanism. &lt;b&gt;&lt;i&gt;Summary:&lt;/i&gt;&lt;/b&gt; This review discusses several open questions, current trends, and critical challenges faced by medical image processing and artificial intelligence technology.
Acknowledgement Entity Recognition in CORD-19 Papers

Acknowledgements are ubiquitous in scholarly papers.Existing acknowledgement entity recognition methods assume all named entities are acknowledged.Here, we examine the nuances between acknowledged and named entities by analyzing sentence structure.We develop an acknowledgement extraction system, ACKEXTRACT based on open-source text mining software and evaluate our method using manually labeled data.ACKEXTRACT uses the PDF of a scholarly paper as input and outputs acknowledgement entities.Results show an overall performance of F 1 = 0.92.We built a supplementary database by linking CORD-19 papers with acknowledgement entities extracted by ACKEXTRACT including persons and organizations and find that only up to 50-60% of named entities are actually acknowledged.We further analyze chronological trends of acknowledgement entities in CORD-19 papers.
An HCI Approach to Extractive Text Summarization: Selecting Key Sentences Based on User Copy Operations

Automatic text summarization is a very complex problem. Despite being intensively researched, automatic summaries are still considered to be of lower quality than manual summaries. This paper introduces a novel HCI approach to web page summarization. The proposed Crowd-Copy Summarizer follows the extractive text summarization approach of summarizing by selecting sentences within the text. The selection is performed by examining how frequently users copy certain sentences to their clipboards, for their own purposes. The most frequently copied sentences are included in the summary. Results from an early experiment are promising, as key sentences, such as introductory sentences, definitions, and important highlights, are copied frequently. Consequently, the generated summaries can provide good coverage of the main topics. This novel text summarization approach combines the best of both worlds: summarization based on collective human wisdom, without the expensive burden of manual summarization work.
Neural Anisotropy Directions

In this work, we analyze the role of the network architecture in shaping the inductive bias of deep classifiers. To that end, we start by focusing on a very simple problem, i.e., classifying a class of linearly separable distributions, and show that, depending on the direction of the discriminative feature of the distribution, many state-of-the-art deep convolutional neural networks (CNNs) have a surprisingly hard time solving this simple task. We then define as neural anisotropy directions (NADs) the vectors that encapsulate the directional inductive bias of an architecture. These vectors, which are specific for each architecture and hence act as a signature, encode the preference of a network to separate the input data based on some particular features. We provide an efficient method to identify NADs for several CNN architectures and thus reveal their directional inductive biases. Furthermore, we show that, for the CIFAR-10 dataset, NADs characterize the features used by CNNs to discriminate between different classes.
Set2Graph: Learning Graphs From Sets

Many problems in machine learning (ML) can be cast as learning functions from sets to graphs, or more generally to hypergraphs; in short, Set2Graph functions. Examples include clustering, learning vertex and edge features on graphs, and learning triplet data in a collection. Current neural network models that approximate Set2Graph functions come from two main ML sub-fields: equivariant learning, and similarity learning. Equivariant models would be in general computationally challenging or even infeasible, while similarity learning models can be shown to have limited expressive power. In this paper we suggest a neural network model family for learning Set2Graph functions that is both practical and of maximal expressive power (universal), that is, can approximate arbitrary continuous Set2Graph functions over compact sets. Testing our models on different machine learning tasks, including an application to particle physics, we find them favorable to existing baselines.
Sequence generative adversarial nets with a conditional discriminator

The success of Generative Adversarial Networks (GANs) in image generation attracts researchers to design sequence GANs in text generation. However, the discriminators of those sequence GANs usually provide only one signal per sequence, which can not reflect detailed information, e.g. whether a token is appropriate in a sequence. In addition, maximum likelihood pre-training is typically used in those models, which is time-consuming and obscures the effects of adversarial training. To cope with these problems, we propose a new sequence GAN that consists of a conditional discriminator and a discriminator-augmented generator. The conditional discriminator provides a sequence with token-level signals. The generator is designed to approximate a discriminator-augmented distribution, which avoids pre-training. Experiments show that the conditional discriminator provides more informative guidance, and our model outperforms existing models according to metrics involving both sampling quality and sampling diversity.