Reviewerly/exHarmony huggingface dataset:
Producing Radiologist-Quality Reports for Interpretable Deep Learning

Current approaches to explaining the decisions of deep learning systems for medical tasks have focused on visualising the elements that have contributed to each decision. We argue that such approaches are not enough to “open the black box” of medical decision making systems because they are missing a key component that has been used as a standard communication tool between doctors for centuries: language. We propose a model-agnostic interpretability method that involves training a simple recurrent neural network model to produce descriptive sentences to clarify the decision of deep learning classifiers. We test our method on the task of detecting hip fractures from frontal pelvic x-rays. This process requires minimal additional labelling despite producing text containing elements that the original deep learning classification model was not specifically trained to detect. The experimental results show that: 1) the sentences produced by our method consistently contain the desired information, 2) the generated sentences are preferred by the cohort of doctors tested compared to current tools that create saliency maps, and 3) the combination of visualisations and generated text is better than either alone.
Rectified Exponential Units for Convolutional Neural Networks

Rectified linear unit (ReLU) plays an important role in today’s convolutional neural networks (CNNs). In this paper, we propose a novel activation function called Rectified Exponential Unit (REU). Inspired by two recently proposed activation functions: Exponential Linear Unit (ELU) and Swish, the REU is designed by introducing the advantage of flexible exponent and multiplication function form. Moreover, we propose the Parametric REU (PREU) to increase the expressive power of the REU. The experiments with three classical CNN architectures, LeNet-5, Network in Network, and Residual Network (ResNet) on scale-various benchmarks including Fashion-MNIST, CIFAR10, CIFAR100, and Tiny ImageNet demonstrate that REU and PREU achieve improvement compared with other activation functions. Our results show that our REU has relative error improvements over ReLU of 7.74% and 6.08% on CIFAR-10 and 100 with the ResNet, the improvements of PREU is 9.24% and 9.32%. Finally, we use the different PREU variants in the Residual unit to achieve more stable results.
Improving collaborative filtering recommendations by estimating user preferences from clickstream data

For practical applications of collaborative filtering, we need a user-item rating matrix that encodes user preferences for items. However, estimation of user preferences is inevitably affected by some degree of noise, which can markedly degrade the recommender performance. The primary aim of this research is to obtain a high-quality rating matrix by the effective use of clickstream data, which are a record of a user's page view (PV) history on an e-commerce site. To this end, we use the shape-restricted optimization model for estimating item-choice probabilities from the recency and frequency of each user's previous PVs. Experimental results based on real-world clickstream data demonstrate that higher recommender performance is achieved with our method than with baseline methods for constructing a rating matrix. Moreover, high recommender performance is maintained by our shape-restricted estimation even when only a limited number of training samples are available.
Unique, Persistent, Resolvable: Identifiers as the Foundation of FAIR

The FAIR principles describe characteristics intended to support access to and reuse of digital artifacts in the scientific research ecosystem. Persistent, globally unique identifiers, resolvable on the Web, and associated with a set of additional descriptive metadata, are foundational to FAIR data. Here we describe some basic principles and exemplars for their design, use and orchestration with other system elements to achieve FAIRness for digital research objects.
A deep learning classifier for sentence classification in biomedical and computer science abstracts

The automatic classification of abstract sentences into its main elements (background, objectives, methods, results, conclusions) is a key tool to support scientific database querying, to summarize relevant literature works and to assist in the writing of new abstracts. In this paper, we propose a novel deep learning approach based on a convolutional layer and a bidirectional gated recurrent unit to classify sentences of abstracts. First, the proposed neural network was tested on a publicly available repository containing 20 thousand abstracts from the biomedical domain. Competitive results were achieved, with weight-averaged Precision, Recall and F1-score values around 91%, and an area under the ROC curve (AUC) of 99%, which are higher when compared to a state-of-the-art neural network. Then, a crowdsourcing approach using gamification was adopted to create a new comprehensive set of 4111 classified sentences from the computer science domain, focused on social media abstracts. The results of applying the same deep learning modeling technique trained with 3287 (80%) of the available sentences were below the ones obtained for the larger biomedical dataset, with weight-averaged Precision, Recall and F1-score values between 73 and 76%, and an AUC of 91%. Considering the dataset dimension as a likely important factor for such performance decrease, a data augmentation approach was further applied. This involved the use of text mining to translate sentences of the computer science abstract corpus while retaining the same meaning. Such approach resulted in slight improvements (around 2 percentage points) for the weight-averaged Recall and F1-score values.
Attribute hierarchy based multi-task learning for fine-grained image classification

Fine-grained image classification aims to distinguish subcategories belonging to the same basic-level category, such as 200 subcategories belonging to bird. It is a challenging problem in computer vision and multimedia field due to: attribute similarity (e.g. color and texture) among different subcategories and attribute variance (e.g. pose and viewpoint) in the same subcategory. Attribute similarity causes the difficulty to classify different subcategories even for human, while attribute variance causes the learned feature representations chaotic and confused. Naturally, classification can benefit from a hierarchy of subcategories: since going to a coarser granularity leverages high-level semantic features, while going to a finer granularity leverages discriminative and subtle features. Therefore, we propose an attribute hierarchy based multi-task learning (AHMTL) approach, and its main novelties are: (1) Attribute hierarchy: We reassign all images to multi-granularity subcategories automatically, namely coarse-grained, fine-grained and ultra-fine-grained subcategories. Similar fine-grained subcategories are reassigned to the same coarse-grained subcategory according to their attribute similarity, which pays more attention to the high-level semantic feature representations. Simultaneously, the same fine-grained subcategory but with different attributes are divided into different ultra-fine-grained subcategories, which can obtain more discriminative and subtle feature representations for attribute variance. (2) Multi-task learning: A multi-task learning framework is designed to effectively learn robust feature representations by jointly optimizing coarse-grained, fine-grained and ultra-fine-grained image classification tasks. These three level tasks learn coarse to fine feature representations, meaning high-level semantic to subtle features, which can regularize and boost each other to prevent overfitting, and the mutual promotion of them ensures feature representations more discriminative. Compared with more than 10 state-of-the-art methods on two widely-used CUB-200-2011 and Cars-196 datasets, our AHMTL approach achieves the best classification performance.
Automatic Modulation Classification Under Non-Gaussian Noise: A Deep Residual Learning Approach

During the last few years, automatic modulation classification (AMC) has attracted widespread attention in both civilian and military applications. Conventional AMC schemes are primarily developed under Gaussian noise assumptions. However, recent empirical studies show that non-Gaussian noise has emerged in a variety of wireless networked systems. The bursty nature of non-Gaussian noise fundamentally challenges the applicability of the conventional AMC schemes. In order to improve the classification performance under non-Gaussian noise, in this paper, a novel modulation classification method is proposed by using cyclic correntropy spectrum (CCES) and deep residual neural network (ResNet). First, CCES is introduced to effectively suppress non-Gaussian noise through the designated Gaussian kernel. CCES also provides significantly different CCES graphs with respect to different modulation schemes, enabling AMC to directly operate with the graphs without further feature extraction. Next, based on the CCES graphs, an end-to-end deep ResNet-based AMC is developed to recognize the correct modulation by iteratively evaluating the residual information in a cascade of multiple learning layers. Experimental results confirm that the proposed algorithm outperforms existing designs with much higher classification accuracy, i.e., 3 dB less in the required generalized signal to noise ratio for 100&#x0025; accuracy, in non-Gaussian noise environments.
A new method for automatic counting of ovarian follicles on whole slide histological images based on convolutional neural network

The ovary is a complex endocrine organ that shows significant structural and functional changes in the female reproductive system over recurrent cycles. There are different types of follicles in the ovarian tissue. The reproductive potential of each individual depends on the numbers of these follicles. However, genetic mutations, toxins, and some specific drugs have an effect on follicles. To determine these effects, it is of great importance to count the follicles. The number of follicles in the ovary is usually counted manually by experts, which is a tedious, time-consuming and intense process. In some cases, the experts count the follicles in a subjective way due to their knowledge. In this study, for the first time, a method has been proposed for automatically counting the follicles of ovarian tissue. Our method primarily involves filter-based segmentation applied to whole slide histological images, based on a convolutional neural network (CNN). A new method is also proposed to eliminate the noise that occurs after the segmentation process and to determine the boundaries of the follicles. Finally, the follicles whose boundaries are determined are classified. To evaluate its performance, the results of the proposed method were compared with those obtained by two different experts and the results of the Faster R–CNN model. The number of follicles obtained by the proposed method was very close to the number of follicles counted by the experts. It was also found that the proposed method was much more successful than the Faster R–CNN model.
Deep convolutional reconstruction for gradient-domain rendering

It has been shown that rendering in the gradient domain, i.e., estimating finite difference gradients of image intensity using correlated samples, and combining them with direct estimates of pixel intensities by solving a screened Poisson problem, often offers fundamental benefits over merely sampling pixel intensities. The reasons can be traced to the frequency content of the light transport integrand and its interplay with the gradient operator. However, while they often yield state of the art performance among algorithms that are based on Monte Carlo sampling alone, gradient-domain rendering algorithms have, until now, not generally been competitive with techniques that combine Monte Carlo sampling with post-hoc noise removal using sophisticated non-linear filtering. Drawing on the power of modern convolutional neural networks, we propose a novel reconstruction method for gradient-domain rendering. Our technique replaces the screened Poisson solver of previous gradient-domain techniques with a novel dense variant of the U-Net autoencoder, additionally taking auxiliary feature buffers as inputs. We optimize our network to minimize a perceptual image distance metric calibrated to the human visual system. Our results significantly improve the quality obtained from gradient-domain path tracing, allowing it to overtake state-of-the-art comparison techniques that denoise traditional Monte Carlo samplings. In particular, we observe that the correlated gradient samples --- that offer information about the smoothness of the integrand unavailable in standard Monte Carlo sampling --- notably improve image quality compared to an equally powerful neural model that does not make use of gradient samples.
View Invariant 3D Human Pose Estimation

The recent success of neural networks has significantly advanced the performance of 3D human pose estimation from 2D input images. However, the diversity of capturing viewpoints and the flexibility of the human poses remain some significant challenges. In this paper, we propose a view-invariant 3D human pose estimation module to alleviate the effects of viewpoint diversity. The proposed framework consists of a base network, which provides an initial estimation of a 3D pose, a view-invariant hierarchical correction network (VI-HC) on top of that to learn the 3D pose refinement under consistent views, and a view-invariant discriminative network (VID) to enforce high-level constraints over body configurations. In VI-HC, the initial 3D pose inputs are automatically transformed to consistent views for further refinements at the global body and local body parts level, respectively. For the VID, under consistent viewpoints, we use adversarial learning to differentiate between estimated 3D poses and real 3D poses to avoid implausible results. The experimental results demonstrate that the constraint on viewpoint consistency can dramatically enhance the performance of 3D human pose estimation. Our module shows robustness for different 3D pose base networks and achieves a significant improvement (about 9%) over a powerful baseline on the public 3D pose estimation benchmark Human3.6M.
A Unified Optimization Approach for CNN Model Inference on Integrated GPUs

Modern deep learning applications urge to push the model inference taking place at the edge devices for multiple reasons such as achieving shorter latency, relieving the burden of the network connecting to the cloud, and protecting user privacy. The Convolutional Neural Network (CNN) is one of the most widely used model family in the applications. Given the high computational complexity of the CNN models, it is favorable to execute them on the integrated GPUs at the edge devices, which are ubiquitous and have more power and better energy efficiency than the accompanying CPUs. However, programming on integrated GPUs efficiently is challenging due to the variety of their architectures and programming interfaces. This paper proposes an end-to-end solution to execute CNN model inference on the integrated GPUs at the edge, which uses a unified IR to represent and optimize vision-specific operators on integrated GPUs from multiple vendors, as well as leverages machine learning-based scheduling search schemes to optimize computationally-intensive operators like convolution. Our solution even provides a fallback mechanism for operators not suitable or convenient to run on GPUs. The evaluation results suggest that compared to state-of-the-art solutions backed up by the vendor-provided high-performance libraries on Intel Graphics, ARM Mali GPU, and Nvidia integrated Maxwell GPU, our solution achieves similar, or even better (up to 1.62×), performance on a number of popular image classification and object detection models. In addition, our solution has a wider model coverage and is more flexible to embrace new models. Our solution has been adopted in production services in AWS and is open-sourced.
Distance Metrics for Measuring Joint Dependence with Application to Causal Inference

Many statistical applications require the quantification of joint dependence among more than two random vectors. In this work, we generalize the notion of distance covariance to quantify joint dependence among d≥2 random vectors. We introduce the high-order distance covariance to measure the so-called Lancaster interaction dependence. The joint distance covariance is then defined as a linear combination of pairwise distance covariances and their higher-order counterparts which together completely characterize mutual independence. We further introduce some related concepts including the distance cumulant, distance characteristic function, and rank-based distance covariance. Empirical estimators are constructed based on certain Euclidean distances between sample elements. We study the large-sample properties of the estimators and propose a bootstrap procedure to approximate their sampling distributions. The asymptotic validity of the bootstrap procedure is justified under both the null and alternative hypotheses. The new metrics are employed to perform model selection in causal inference, which is based on the joint independence testing of the residuals from the fitted structural equation models. The effectiveness of the method is illustrated via both simulated and real datasets. Supplementary materials for this article are available online.
Application of Internet of Things and Blockchain Technologies to Improve Accounting Information Quality

Following the rapid development and the maturity of blockchain (BC) and the Internet of Things (IoT) technologies, this paper proposes two applications of a BC-IoT transaction model in the accounting field. The BC-IoT transaction model is capable of automatically collecting, uploading, and recording all relevant data in the enterprise transaction process under the premise of satisfying certain assumptions. This model does not require manual intervention at any point in the process; no data recorded on the distributed ledgers can be tampered with or revoked. The data collected using this model can be used as information on events, providing data support for the events approach to basic accounting theory. This practice responds to the important insight of sorter, who proposes the provision of accounting information in real time and on demand to substantially improve the usefulness of accounting information. This paper also identifies that BC technology can significantly improve the relevance, faithful representation, timeliness, comparability, and other aspects of accounting information quality.
Data-Parallel Hashing Techniques for GPU Architectures

Hash tables are a fundamental data structure for effectively storing and accessing sparse data, with widespread usage in domains ranging from computer graphics to machine learning. This study surveys the state-of-the-art research on data-parallel hashing techniques for emerging massively-parallel, many-core GPU architectures. This survey identifies key factors affecting the performance of different techniques and suggests directions for further research.
Multi-modal active perception for information gathering in science missions

Robotic science missions in remote environments, such as deep ocean and outer space, can involve studying phenomena that cannot directly be observed using on-board sensors but must be deduced by combining measurements of correlated variables with domain knowledge. Traditionally, in such missions, robots passively gather data along prescribed paths, while inference, path planning, and other high level decision making is largely performed by a supervisory science team located at a different location, often at a great distance. However, communication constraints hinder these processes, and hence the rate of scientific progress. This paper presents an active perception approach that aims to reduce robots' reliance on human supervision and improve science productivity by encoding scientists' domain knowledge and decision making process on-board. We present a Bayesian network architecture to compactly model critical aspects of scientific knowledge while remaining robust to observation and modeling uncertainty. We then formulate path planning and sensor scheduling as an information gain maximization problem, and propose a sampling-based solution based on Monte Carlo tree search to plan informative sensing actions which exploit the knowledge encoded in the network. The computational complexity of our framework does not grow with the number of observations taken and allows long horizon planning in an anytime manner, making it highly applicable to field robotics with constrained computing. Simulation results show statistically significant performance improvements over baseline methods, and we validate the practicality of our approach through both hardware experiments and simulated experiments with field data gathered during the NASA Mojave Volatiles Prospector science expedition.
Import2vec: Learning Embeddings for Software Libraries

We consider the problem of developing suitable learning representations (embeddings) for library packages that capture semantic similarity among libraries. Such representations are known to improve the performance of downstream learning tasks (e.g. classification) or applications such as contextual search and analogical reasoning. We apply word embedding techniques from natural language processing (NLP) to train embeddings for library packages ("library vectors"). Library vectors represent libraries by similar context of use as determined by import statements present in source code. Experimental results obtained from training such embeddings on three large open source software corpora reveals that library vectors capture semantically meaningful relationships among software libraries, such as the relationship between frameworks and their plug-ins and libraries commonly used together within ecosystems such as big data infrastructure projects (in Java), front-end and back-end web development frameworks (in JavaScript) and data science toolkits (in Python).
Address Space Layout Randomization Next Generation

Systems that are built using low-power computationally-weak devices, which force developers to favor performance over security; which jointly with its high connectivity, continuous and autonomous operation makes those devices specially appealing to attackers. ASLR (Address Space Layout Randomization) is one of the most effective mitigation techniques against remote code execution attacks, but when it is implemented in a practical system its effectiveness is jeopardized by multiple constraints: the size of the virtual memory space, the potential fragmentation problems, compatibility limitations, etc. As a result, most ASLR implementations (specially in 32-bits) fail to provide the necessary protection. In this paper we propose a taxonomy of all ASLR elements, which categorizes the entropy in three dimensions: (1) how, (2) when and (3) what; and includes novel forms of entropy. Based on this taxonomy we have created, ASLRA, an advanced statistical analysis tool to assess the effectiveness of any ASLR implementation. Our analysis show that all ASLR implementations suffer from several weaknesses, 32-bit systems provide a poor ASLR, and OS X has a broken ASLR in both 32- and 64-bit systems. This is jeopardizing not only servers and end users devices as smartphones but also the whole IoT ecosystem. To overcome all these issues, we present ASLR-NG, a novel ASLR that provides the maximum possible absolute entropy and removes all correlation attacks making ASLR-NG the best solution for both 32- and 64-bit systems. We implemented ASLR-NG in the Linux kernel 4.15. The comparative evaluation shows that ASLR-NG overcomes PaX, Linux and OS X implementations, providing strong protection to prevent attackers from abusing weak ASLRs.