Reviewerly/exHarmony huggingface dataset:
Gender Bias in Neural Natural Language Processing

We examine whether neural natural language processing (NLP) systems reflect historical biases in training data. We define a general benchmark to quantify gender bias in a variety of neural NLP tasks. Our empirical evaluation with state-of-the-art neural coreference resolution and textbook RNN-based language models trained on benchmark data sets finds significant gender bias in how models view occupations. We then mitigate bias with counterfactual data augmentation (CDA): a generic methodology for corpus augmentation via causal interventions that breaks associations between gendered and gender-neutral words. We empirically show that CDA effectively decreases gender bias while preserving accuracy. We also explore the space of mitigation strategies with CDA, a prior approach to word embedding debiasing (WED), and their compositions. We show that CDA outperforms WED, drastically so when word embeddings are trained. For pre-trained embeddings, the two methods can be effectively composed. We also find that as training proceeds on the original data set with gradient descent the gender bias grows as the loss reduces, indicating that the optimization encourages bias; CDA mitigates this behavior.
Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals

Being able to learn dense semantic representations of images without supervision is an important problem in computer vision. However, despite its significance, this problem remains rather unexplored, with a few exceptions that considered unsupervised semantic segmentation on small-scale datasets with a narrow visual domain. In this paper, we make a first attempt to tackle the problem on datasets that have been traditionally utilized for the supervised case. To achieve this, we introduce a two-step framework that adopts a predetermined mid-level prior in a contrastive optimization objective to learn pixel embeddings. This marks a large deviation from existing works that relied on proxy tasks or end-to-end clustering. Additionally, we argue about the importance of having a prior that contains information about objects, or their parts, and discuss several possibilities to obtain such a prior in an unsupervised manner.Experimental evaluation shows that our method comes with key advantages over existing works. First, the learned pixel embeddings can be directly clustered in semantic groups using K-Means on PASCAL. Under the fully unsupervised setting, there is no precedent in solving the semantic segmentation task on such a challenging benchmark. Second, our representations can improve over strong baselines when transferred to new datasets, e.g. COCO and DAVIS. The code is available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
A Proposed Sentiment Analysis Deep Learning Algorithm for Analyzing COVID-19 Tweets

With the rise in cases of COVID-19, a bizarre situation of pressure was mounted on each country to make arrangements to control the population and utilize the available resources appropriately. The swiftly rising of positive cases globally created panic, anxiety and depression among people. The effect of this deadly disease was found to be directly proportional to the physical and mental health of the population. As of 28 October 2020, more than 40 million people are tested positive and more than 1 million deaths have been recorded. The most dominant tool that disturbed human life during this time is social media. The tweets regarding COVID-19, whether it was a number of positive cases or deaths, induced a wave of fear and anxiety among people living in different parts of the world. Nobody can deny the truth that social media is everywhere and everybody is connected with it directly or indirectly. This offers an opportunity for researchers and data scientists to access the data for academic and research use. The social media data contains many data that relate to real-life events like COVID-19. In this paper, an analysis of Twitter data has been done through the R programming language. We have collected the Twitter data based on hashtag keywords, including COVID-19, coronavirus, deaths, new case, recovered. In this study, we have designed an algorithm called Hybrid Heterogeneous Support Vector Machine (H-SVM) and performed the sentiment classification and classified them positive, negative and neutral sentiment scores. We have also compared the performance of the proposed algorithm on certain parameters like precision, recall, F1 score and accuracy with Recurrent Neural Network (RNN) and Support Vector Machine (SVM).
GAN Prior Embedded Network for Blind Face Restoration in the Wild

Blind face restoration (BFR) from severely degraded face images in the wild is a very challenging problem. Due to the high illness of the problem and the complex unknown degradation, directly training a deep neural network (DNN) usually cannot lead to acceptable results. Existing generative adversarial network (GAN) based methods can produce better results but tend to generate over-smoothed restorations. In this work, we propose a new method by first learning a GAN for high-quality face image generation and embedding it into a U-shaped DNN as a prior decoder, then fine-tuning the GAN prior embedded DNN with a set of synthesized low-quality face images. The GAN blocks are designed to ensure that the latent code and noise input to the GAN can be respectively generated from the deep and shallow features of the DNN, controlling the global face structure, local face details and background of the reconstructed image. The proposed GAN prior embedded network (GPEN) is easy-to-implement, and it can generate visually photo-realistic results. Our experiments demonstrated that the proposed GPEN achieves significantly superior results to state-of-the-art BFR methods both quantitatively and qualitatively, especially for the restoration of severely degraded face images in the wild. The source code and models can be found at https://github.com/yangxy/GPEN.
Improving Calibration for Long-Tailed Recognition

Deep neural networks may perform poorly when training datasets are heavily class-imbalanced. Recently, two-stage methods decouple representation learning and classifier learning to improve performance. But there is still the vital issue of miscalibration. To address it, we design two methods to improve calibration and performance in such scenarios. Motivated by the fact that predicted probability distributions of classes are highly related to the numbers of class instances, we propose label-aware smoothing to deal with different degrees of over-confidence for classes and improve classifier learning. For dataset bias between these two stages due to different samplers, we further propose shifted batch normalization in the decoupling framework. Our proposed methods set new records on multiple popular long-tailed recognition benchmark datasets, including CIFAR-10-LT, CIFAR-100-LT, ImageNet-LT, Places-LT, and iNaturalist 2018.
Convolutional Neural Network Pruning with Structural Redundancy Reduction

Convolutional neural network (CNN) pruning has become one of the most successful network compression approaches in recent years. Existing works on network pruning usually focus on removing the least important filters in the network to achieve compact architectures. In this study, we claim that identifying structural redundancy plays a more essential role than finding unimportant filters, theoretically and empirically. We first statistically model the network pruning problem in a redundancy reduction perspective and find that pruning in the layer(s) with the most structural redundancy outperforms pruning the least important filters across all layers. Based on this finding, we then propose a network pruning approach that identifies structural redundancy of a CNN and prunes filters in the selected layer(s) with the most redundancy. Experiments on various benchmark network architectures and datasets show that our proposed approach significantly outperforms the previous state-of-the-art.
Virtual and augmented reality for biomedical applications

3D visualization technologies such as virtual reality (VR), augmented reality (AR), and mixed reality (MR) have gained popularity in the recent decade. Digital extended reality (XR) technologies have been adopted in various domains ranging from entertainment to education because of their accessibility and affordability. XR modalities create an immersive experience, enabling 3D visualization of the content without a conventional 2D display constraint. Here, we provide a perspective on XR in current biomedical applications and demonstrate case studies using cell biology concepts, multiplexed proteomics images, surgical data for heart operations, and cardiac 3D models. Emerging challenges associated with XR technologies in the context of adverse health effects and a cost comparison of distinct platforms are discussed. The presented XR platforms will be useful for biomedical education, medical training, surgical guidance, and molecular data visualization to enhance trainees' and students' learning, medical operation accuracy, and the comprehensibility of complex biological systems.
Explaining anomalies detected by autoencoders using Shapley Additive Explanations

Deep learning algorithms for anomaly detection, such as autoencoders, point out the outliers, saving experts the time-consuming task of examining normal cases in order to find anomalies. Most outlier detection algorithms output a score for each instance in the database. The top-k most intense outliers are returned to the user for further inspection; however, the manual validation of results becomes challenging without justification or additional clues. An explanation of why an instance is anomalous enables the experts to focus their investigation on the most important anomalies and may increase their trust in the algorithm. Recently, a game theory-based framework known as SHapley Additive exPlanations (SHAP) was shown to be effective in explaining various supervised learning models. In this paper, we propose a method that uses Kernel SHAP to explain anomalies detected by an autoencoder, which is an unsupervised model. The proposed explanation method aims to provide a comprehensive explanation to the experts by focusing on the connection between the features with high reconstruction error and the features that are most important in terms of their affect on the reconstruction error. We propose a black-box explanation method, because it has the advantage of being able to explain any autoencoder without being aware of the exact architecture of the autoencoder model. The proposed explanation method extracts and visually depicts both features that contribute the most to the anomaly and those that offset it. An expert evaluation using real-world data demonstrates the usefulness of the proposed method in helping domain experts better understand the anomalies. Our evaluation of the explanation method, in which a “perfect” autoencoder is used as the ground truth, shows that the proposed method explains anomalies correctly, using the exact features, and evaluation on real-data demonstrates that (1) our explanation model, which uses SHAP, is more robust than the Local Interpretable Model-agnostic Explanations (LIME) method, and (2) the explanations our method provides are more effective at reducing the anomaly score than other methods.
Mesh Graphormer

We present a graph-convolution-reinforced transformer, named Mesh Graphormer, for 3D human pose and mesh reconstruction from a single image. Recently both transformers and graph convolutional neural networks (GC-NNs) have shown promising progress in human mesh re-construction. Transformer-based approaches are effective in modeling non-local interactions among 3D mesh vertices and body joints, whereas GCNNs are good at exploiting neighborhood vertex interactions based on a pre-specified mesh topology. In this paper, we study how to combine graph convolutions and self-attentions in a transformer to model both local and global interactions. Experimental results show that our proposed method, Mesh Graphormer, significantly outperforms the previous state-of-the-art methods on multiple benchmarks, including Human3.6M, 3DPW, and FreiHAND datasets. Code and pre-trained models are available at https://github.com/microsoft/MeshGraphormer.
Texture feature based classification on microscopic blood smear for acute lymphoblastic leukemia detection

This paper presents an effective scheme for classification of the normal white blood cells from the affected cells in a microscopic image. The proposed method initially pre-processes the input images using Y component of the CMYK image and a triangle method of thresholding. Subsequently, it utilizes discrete orthonormal S-transform (DOST) to extract the texture features, and its dimensionality is reduced using linear discriminant analysis. The reduced features are then supplied to the proposed Adaboost algorithm with RF (ADBRF) classifier where the random forest is used as the base classifier. A publicly available dataset, ALL-IDB1 is used to validate the proposed scheme. The simulation results based on the five runs of k-fold stratified cross-validation indicate that the proposed method yields superior accuracy (99.66%) as compared to existing schemes.
TL-GDBN: Growing Deep Belief Network With Transfer Learning

A deep belief network (DBN) is effective to create a powerful generative model by using training data. However, it is difficult to fast determine its optimal structure given specific applications. In this paper, a growing DBN with transfer learning (TL-GDBN) is proposed to automatically decide its structure size, which can accelerate its learning process and improve model accuracy. First, a basic DBN structure with single hidden layer is initialized and then pretrained, and the learned weight parameters are frozen. Second, TL-GDBN uses TL to transfer the knowledge from the learned weight parameters to newly added neurons and hidden layers, which can achieve a growing structure until the stopping criterion for pretraining is satisfied. Third, the weight parameters derived from pretraining of TL-GDBN are further fine-tuned by using layer-by-layer partial least square regression from top to bottom, which can avoid many problems of traditional backpropagation algorithm-based fine-tuning. Moreover, the convergence analysis of the TL-GDBN is presented. Finally, TL-GDBN is tested on two benchmark data sets and a practical wastewater treatment system. The simulation results show that it has better modeling performance, faster learning speed, and more robust structure than existing models. Note to Practitioners-Transfer learning (TL) aims to improve training effectiveness by transferring knowledge from a source domain to target domain. This paper presents a growing deep belief network (DBN) with TL to improve the training effectiveness and determine the optimal model size. Facing a complex process and real-world workflow, DBN tends to require long time for its successful training. The proposed growing DBN with TL (TL-GDBN) accelerates the learning process by instantaneously transferring the knowledge from a source domain to each new deeper or wider substructure. The experimental results show that the proposed TL-GDBN model has a great potential to deal with complex system, especially the systems with high nonlinearity. As a result, it can be readily applicable to some industrial nonlinear systems.
DEEPSEC: A Uniform Platform for Security Analysis of Deep Learning Model

Deep learning (DL) models are inherently vulnerable to adversarial examples - maliciously crafted inputs to trigger target DL models to misbehave - which significantly hinders the application of DL in security-sensitive domains. Intensive research on adversarial learning has led to an arms race between adversaries and defenders. Such plethora of emerging attacks and defenses raise many questions: Which attacks are more evasive, preprocessing-proof, or transferable? Which defenses are more effective, utility-preserving, or general? Are ensembles of multiple defenses more robust than individuals? Yet, due to the lack of platforms for comprehensive evaluation on adversarial attacks and defenses, these critical questions remain largely unsolved. In this paper, we present the design, implementation, and evaluation of DEEPSEC, a uniform platform that aims to bridge this gap. In its current implementation, DEEPSEC incorporates 16 state-of-the-art attacks with 10 attack utility metrics, and 13 state-of-the-art defenses with 5 defensive utility metrics. To our best knowledge, DEEPSEC is the first platform that enables researchers and practitioners to (i) measure the vulnerability of DL models, (ii) evaluate the effectiveness of various attacks/defenses, and (iii) conduct comparative studies on attacks/defenses in a comprehensive and informative manner. Leveraging DEEPSEC, we systematically evaluate the existing adversarial attack and defense methods, and draw a set of key findings, which demonstrate DEEPSEC's rich functionality, such as (1) the trade-off between misclassification and imperceptibility is empirically confirmed; (2) most defenses that claim to be universally applicable can only defend against limited types of attacks under restricted settings; (3) it is not necessary that adversarial examples with higher perturbation magnitude are easier to be detected; (4) the ensemble of multiple defenses cannot improve the overall defense capability, but can improve the lower bound of the defense effectiveness of individuals. Extensive analysis on DEEPSEC demonstrates its capabilities and advantages as a benchmark platform which can benefit future adversarial learning research.
FineGAN: Unsupervised Hierarchical Disentanglement for Fine-Grained Object Generation and Discovery

We propose FineGAN, a novel unsupervised GAN framework, which disentangles the background, object shape, and object appearance to hierarchically generate images of fine-grained object categories. To disentangle the factors without supervision, our key idea is to use information theory to associate each factor to a latent code, and to condition the relationships between the codes in a specific way to induce the desired hierarchy. Through extensive experiments, we show that FineGAN achieves the desired disentanglement to generate realistic and diverse images belonging to fine-grained classes of birds, dogs, and cars. Using FineGAN's automatically learned features, we also cluster real images as a first attempt at solving the novel problem of unsupervised fine-grained object category discovery. Our code/models/demo can be found at https://github.com/kkanshul/finegan.
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>X</mml:mi><mml:mi>Y</mml:mi></mml:mrow></mml:math> mixers: Analytical and numerical results for the quantum alternating operator ansatz

The quantum alternating operator ansatz (QAOA) is a promising gate-model metaheuristic for combinatorial optimization. Applying the algorithm to problems with constraints presents an implementation challenge for near-term quantum resources. This paper explores strategies for enforcing hard constraints by using $XY$ Hamiltonians as mixing operators (mixers). Despite the complexity of simulating the $XY$ model, we demonstrate that, for an integer variable admitting $\ensuremath{\kappa}$ discrete values represented through one-hot encoding, certain classes of the mixer Hamiltonian can be implemented without Trotter error in depth $O(\ensuremath{\kappa})$. We also specify general strategies for implementing QAOA circuits on all-to-all connected hardware graphs and linearly connected hardware graphs inspired by fermionic simulation techniques. Performance is validated on graph-coloring problems that are known to be challenging for a given classical algorithm. The general strategy of using $XY$ mixers is borne out numerically, demonstrating a significant improvement over the general $X$ mixer, and moreover the generalized $W$ state yields better performance than easier-to-generate classical initial states when $XY$ mixers are used.
A sequential deep learning application for recognising human activities in smart homes

The recent advancement and development of computer electronic devices has led to the adoption of smart home sensing systems, stimulating the demand for associated products and services. Accordingly, the increasingly large amount of data calls the machine learning (ML) field for automatic recognition of human behaviour. In this work, different deep learning (DL) models that learn to classify human activities were proposed. In particular, the long short-term memory (LSTM) was applied for modelling spatio-temporal sequences acquired by smart home sensors. Experimental results performed on the Center for Advanced Studies in Adaptive Systems datasets show that the proposed LSTM-based approaches outperform existing DL and ML methods, giving superior results compared to the existing literature.
Integrating virtual reality devices into the body: effects of technological embodiment on customer engagement and behavioral intentions toward the destination

Virtual reality devices create a high integration of technologies with human senses. However, few studies analyze how embodied technologies affect customer pre-experiences with a destination. Results from a lab experiment show that compared to desktop PC and mobile phones, VR head-mounted displays generate more immersive experiences, higher sensory stimulation, more engagement, and higher behavioral intentions toward the destination. Immersion and sensory stimulation mediate the effects of technological embodiment on engagement and behavioral intentions. Furthermore, active (versus passive) tourism content strengthens these effects. Our results stress the role of technological embodiment to generate effective pre-experiences with potential tourists’ destinations.
FDML

Most current distributed machine learning systems try to scale up model training by using a data-parallel architecture that divides the computation for different samples among workers. We study distributed machine learning from a different motivation, where the information about the same samples, e.g., users and objects, are owned by several parities that wish to collaborate but do not want to share raw data with each other.
One-Bit OFDM Receivers via Deep Learning

This paper develops novel deep learning-based architectures and design methodologies for an orthogonal frequency division multiplexing (OFDM) receiver under the constraint of one-bit complex quantization. Single bit quantization reduces greatly the complexity and power consumption but makes accurate channel estimation and data detection difficult. This is particularly true for multicarrier waveforms that have high peak-to-average power ratio in the time domain and fragile subcarrier orthogonality in the frequency domain. The severe distortion for one-bit quantization typically results in an error floor even at moderately low signal-to-noise-ratio (SNR) such as 5 dB. For channel estimation (using pilots), we design a novel generative supervised deep neural network that can be trained with a reasonable number of pilots. After channel estimation, a neural network-based receiver-specifically, an autoencoder-jointly learns a precoder and decoder for data symbol detection. Since quantization prevents end-to-end training, we propose a two-step sequential training policy for this model. With synthetic data, our deep learning-based channel estimation can outperform least squares channel estimation for unquantized (full-resolution) OFDM at average SNRs up to 14 dB. For data detection, our proposed design achieves lower bit error rate (BER) in fading than unquantized OFDM at average SNRs up to 10 dB.