Reviewerly/exHarmony huggingface dataset:
Optimal clustering with missing values

Missing values frequently arise in modern biomedical studies due to various reasons, including missing tests or complex profiling technologies for different omics measurements. Missing values can complicate the application of clustering algorithms, whose goals are to group points based on some similarity criterion. A common practice for dealing with missing values in the context of clustering is to first impute the missing values, and then apply the clustering algorithm on the completed data.We consider missing values in the context of optimal clustering, which finds an optimal clustering operator with reference to an underlying random labeled point process (RLPP). We show how the missing-value problem fits neatly into the overall framework of optimal clustering by incorporating the missing value mechanism into the random labeled point process and then marginalizing out the missing-value process. In particular, we demonstrate the proposed framework for the Gaussian model with arbitrary covariance structures. Comprehensive experimental studies on both synthetic and real-world RNA-seq data show the superior performance of the proposed optimal clustering with missing values when compared to various clustering approaches.Optimal clustering with missing values obviates the need for imputation-based pre-processing of the data, while at the same time possessing smaller clustering errors.
Rhetorically Controlled Encoder-Decoder for Modern Chinese Poetry Generation

Rhetoric is a vital element in modern poetry, and plays an essential role in improving its aesthetics.However, to date, it has not been considered in research on automatic poetry generation.In this paper, we propose a rhetorically controlled encoder-decoder for modern Chinese poetry generation.Our model relies on a continuous latent variable as a rhetoric controller to capture various rhetorical patterns in an encoder, and then incorporates rhetoricbased mixtures while generating modern Chinese poetry.For metaphor and personification, an automated evaluation shows that our model outperforms state-of-the-art baselines by a substantial margin, while a human evaluation shows that our model generates better poems than baseline methods in terms of fluency, coherence, meaningfulness, and rhetorical aesthetics.
A hybrid model for predicting human physical activity status from lifelogging data

One trend in the recent healthcare transformations is people are encouraged to monitor and manage their health based on their daily diets and physical activity habits. However, much attention of the use of operational research and analytical models in healthcare has been paid to the systematic level such as country or regional policy making or organisational issues. This paper proposes a model concerned with healthcare analytics at the individual level, which can predict human physical activity status from sequential lifelogging data collected from wearable sensors. The model has a two-stage hybrid structure (in short, MOGP-HMM) – a multi-objective genetic programming (MOGP) algorithm in the first stage to reduce the dimensions of lifelogging data and a hidden Markov model (HMM) in the second stage for activity status prediction over time. It can be used as a decision support tool to provide real-time monitoring, statistical analysis and personalized advice to individuals, encouraging positive attitudes towards healthy lifestyles. We validate the model with the real data collected from a group of participants in the UK, and compare it with other popular two-stage hybrid models. Our experimental results show that the MOGP-HMM can achieve comparable performance. To the best of our knowledge, this is the very first study that uses the MOGP in the hybrid two-stage structure for individuals’ activity status prediction. It fits seamlessly with the current trend in the UK healthcare transformation of patient empowerment as well as contributing to a strategic development for more efficient and cost-effective provision of healthcare.
CHiVE: Varying Prosody in Speech Synthesis with a Linguistically Driven Dynamic Hierarchical Conditional Variational Network

The prosodic aspects of speech signals produced by current text-to-speech systems are typically averaged over training material, and as such lack the variety and liveliness found in natural speech. To avoid monotony and averaged prosody contours, it is desirable to have a way of modeling the variation in the prosodic aspects of speech, so audio signals can be synthesized in multiple ways for a given text. We present a new, hierarchically structured conditional variational autoencoder to generate prosodic features (fundamental frequency, energy and duration) suitable for use with a vocoder or a generative model like WaveNet. At inference time, an embedding representing the prosody of a sentence may be sampled from the variational layer to allow for prosodic variation. To efficiently capture the hierarchical nature of the linguistic input (words, syllables and phones), both the encoder and decoder parts of the auto-encoder are hierarchical, in line with the linguistic structure, with layers being clocked dynamically at the respective rates. We show in our experiments that our dynamic hierarchical network outperforms a non-hierarchical state-of-the-art baseline, and, additionally, that prosody transfer across sentences is possible by employing the prosody embedding of one sentence to generate the speech signal of another.
Residual Pyramid Learning for Single-Shot Semantic Segmentation

Pixel-level semantic segmentation is a challenging task with a huge amount of computation, especially if the size of input is large.In the segmentation model, apart from the feature extraction, the extra decoder structure is often employed to recover spatial information.In this paper, we put forward a method for single-shot segmentation in a feature residual pyramid network (RPNet), which learns the main and residuals of segmentations by decomposing the label at different levels of residual blocks.Specifically speaking, we use the residual features to learn the edges and details, and the identity features to learn the main part of targets.At testing time, the predicted residuals are used to enhance the details of the top-level prediction.Residual learning blocks split the network into several shallow sub-networks which facilitates the training of the RPNet.We then evaluate the proposed method and compare it with recent stateof-the-art methods on CamVid and Cityscapes.The proposed single-shot segmentation based on RPNet achieves impressive results with high efficiency on pixel-level segmentation.
Input, Output and Construction Methods for Custom Fabrication of Room-Scale Deployable Pneumatic Structures

In this paper, we examine the future of designing room-scale deployable pneumatic structures that can be fabricated with interactive capabilities and thus be responsive to human input and environments. While there have been recent advances in fabrication methods for creating large-scale structures, they have mainly focused around creating passive structures. Hence in this work, we collectively tackle three main challenges that need to be solved for designing room-scale interactive deployable structures namely -- the input, output (actuation) and construction methods. First, we explore three types of sensing methods --- acoustic, capacitive and pressure --- in order to embed input into these structures. These sensing methods enable users to perform gestures such as knock, squeeze and swipe with specific parts of our fabricated structure such as doors, windows, etc. and make them interactive. Second, we explore three types of actuation mechanisms -- inflatable tendon drive, twisted tendon drive and roll bending actuator -- that are implemented at structural scale and can be embedded into our structures to enable a variety of responsive actuation. Finally, we provide a construction method to custom fabricate and assemble inter-connected pneumatic trusses with embedded sensing and actuation capability to prototype interactions with room-scale deployable structures. To further illustrate the collective (input, output and construction) usage of the system, we fabricated three exemplar interactive deployable structures -- a responsive canopy, an interactive geodesic dome and a portable table (Figures 1 and 2). These can be deployed from a compact deflated state to a much larger inflated state which takes on a desired form while offering interactivity.
Modern Family: A Revocable Hybrid Encryption Scheme Based on Attribute-Based Encryption, Symmetric Searchable Encryption and SGX

Secure cloud storage is considered as one of the most important issues that both businesses and end-users take into account before moving their private data to the cloud. Lately, we have seen some interesting approaches that are based either on the promising concept of Symmetric Searchable Encryption (SSE) or on the well-studied field of Attribute-Based Encryption (ABE). In this paper, we propose a hybrid encryption scheme that combines both SSE and ABE by utilizing the advantages of both these techniques. In contrast to many approaches, we design a revocation mechanism that is completely separated from the ABE scheme and solely based on the functionality offered by SGX.
Lexicographic and depth-sensitive margins in homogeneous and non-homogeneous deep models

With an eye toward understanding complexity control in deep learning, we study how infinitesimal regularization or gradient descent optimization lead to maximizing solutions in both homogeneous and non-homogeneous models, extending previous work that focused on infinitesimal regularization only in homogeneous models. To this end we study the limit of loss minimization with a diverging norm constraint (the constrained path), relate it to the limit of a margin path and characterize the resulting solution. For non-homogeneous ensemble models, which output is a sum of homogeneous sub-models, we show that this solution discards the shallowest sub-models if they are unnecessary. For homogeneous models, we show convergence to a lexicographic max-margin solution, and provide conditions under which max-margin solutions are also attained as the limit of unconstrained gradient descent.
SecCSL: Security Concurrent Separation Logic

We present SecCSL, a concurrent separation logic for proving expressive, data-dependent information flow security properties of low-level programs. SecCSL is considerably more expressive, while being simpler, than recent compositional information flow logics that cannot reason about pointers, arrays etc. To capture security concerns, SecCSL adopts a relational semantics for its assertions. At the same time it inherits the structure of traditional concurrent separation logics; thus SecCSL reasoning can be automated via symbolic execution. We demonstrate this by implementing SecC, an automatic verifier for a subset of the C programming language, which we apply to a range of benchmarks.
Automatically determining cause of death from verbal autopsy narratives

A verbal autopsy (VA) is a post-hoc written interview report of the symptoms preceding a person's death in cases where no official cause of death (CoD) was determined by a physician. Current leading automated VA coding methods primarily use structured data from VAs to assign a CoD category. We present a method to automatically determine CoD categories from VA free-text narratives alone.After preprocessing and spelling correction, our method extracts word frequency counts from the narratives and uses them as input to four different machine learning classifiers: naïve Bayes, random forest, support vector machines, and a neural network.For individual CoD classification, our best classifier achieves a sensitivity of.770 for adult deaths for 15 CoD categories (as compared to the current best reported sensitivity of.57), and.662 with 48 WHO categories. When predicting the CoD distribution at the population level, our best classifier achieves.962 cause-specific mortality fraction accuracy for 15 categories and.908 for 48 categories, which is on par with leading CoD distribution estimation methods.Our narrative-based machine learning classifier performs as well as classifiers based on structured data at the individual level. Moreover, our method demonstrates that VA narratives provide important information that can be used by a machine learning system for automated CoD classification. Unlike the structured questionnaire-based methods, this method can be applied to any verbal autopsy dataset, regardless of the collection process or country of origin.
Audio Steganalysis with Improved Convolutional Neural Network

Deep learning, especially the convolutional neural network (CNN), has enjoyed significant success in many fields, e.g., image recognition. Recently, CNN has successfully applied to multimedia steganalysis. However, the detection performance is still unsatisfactory. In this work, we propose an improved CNN-based method for audio steganalysis. Specifically, a special convolutional layer is first carefully designed, which could capture the minor steganographic noise. Then, a truncated linear unit is adapted to activate the output of shallow convolutional layer. In addition, we employ the average pooling to minimize the over-fitting risk. Finally, a parameter transfer strategy is adopted, aiming to boost the detection performance for the low embedding-rate cases. The experimental results evaluated on 30,000 audio clips verify the effectiveness of our method for a variety of embedding rates. Compared with the existing CNN-based steganalysis methods, our proposed method could achieve superior performance. To facilitate the reproducible research, the source code will be released at GitHub.
Supervised learning of an opto-magnetic neural network with ultrashort laser pulses

The explosive growth of data and its related energy consumption is pushing the need to develop energy-efficient brain-inspired schemes and materials for data processing and storage. Here, we demonstrate experimentally that Co/Pt films can be used as artificial synapses by manipulating their magnetization state using circularly-polarized ultrashort optical pulses at room temperature. We also show an efficient implementation of supervised perceptron learning on an opto-magnetic neural network, built from such magnetic synapses. Importantly, we demonstrate that the optimization of synaptic weights can be achieved using a global feedback mechanism, such that the learning does not rely on external storage or additional optimization schemes. These results suggest there is high potential for realizing artificial neural networks using optically-controlled magnetization in technologically relevant materials, that can learn not only fast but also energy-efficient.
Answering Range Queries Under Local Differential Privacy

Counting the fraction of a population having an input within a specified interval i.e. range count query is a fundamental database operation. Range count queries can also be used to compute other interesting statistics such as quantiles. The framework of differential privacy [6] (DP) is becoming a standard for privacy-preserving data analysis [1]. While many works address the problem of range counting queries in the trusted aggregation model, surprisingly, this problem has not been addressed specifically under untrusted aggregation (local DP [10]). In this work we study the problem of answering 1-dimensional range count queries under the constraint of LDP.
A Verifiable Fully Homomorphic Encryption Scheme for Cloud Computing Security

Performing smart computations in a context of cloud computing and big data is highly appreciated today. It allows customers to fully benefit from cloud computing capacities (such as processing or storage) without losing confidentiality of sensitive data. Fully homomorphic encryption (FHE) is a smart category of encryption schemes that enables working with the data in its encrypted form. It permits us to preserve confidentiality of our sensible data and to benefit from cloud computing capabilities. While FHE is combined with verifiable computation, it offers efficient procedures for outsourcing computations over encrypted data to a remote, but non-trusted, cloud server. The resulting scheme is called Verifiable Fully Homomorphic Encryption (VFHE). Currently, it has been demonstrated by many existing schemes that the theory is feasible but the efficiency needs to be dramatically improved in order to make it usable for real applications. One subtle difficulty is how to efficiently handle the noise. This paper aims to introduce an efficient and symmetric verifiable FHE based on a new mathematic structure that is noise free. In our encryption scheme, the noise is constant and does not depend on homomorphic evaluation of ciphertexts. The homomorphy of our scheme is obtained from simple matrix operations (addition and multiplication). The running time of the multiplication operation of our encryption scheme in a cloud environment has an order of a few milliseconds.
Annotations, Ontologies, and Whole Slide Images – Development of an Annotated Ontology-Driven Whole Slide Image Library of Normal and Abnormal Human Tissue

Digital pathology is today a widely used technology, and the digitalization of microscopic slides into whole slide images (WSIs) allows the use of machine learning algorithms as a tool in the diagnostic process. In recent years, "deep learning" algorithms for image analysis have been applied to digital pathology with great success. The training of these algorithms requires a large volume of high-quality images and image annotations. These large image collections are a potent source of information, and to use and share the information, standardization of the content through a consistent terminology is essential. The aim of this project was to develop a pilot dataset of exhaustive annotated WSI of normal and abnormal human tissue and link the annotations to appropriate ontological information.Several biomedical ontologies and controlled vocabularies were investigated with the aim of selecting the most suitable ontology for this project. The selection criteria required an ontology that covered anatomical locations, histological subcompartments, histopathologic diagnoses, histopathologic terms, and generic terms such as normal, abnormal, and artifact. WSIs of normal and abnormal tissue from 50 colon resections and 69 skin excisions, diagnosed 2015-2016 at the Department of Clinical Pathology in Linköping, were randomly collected. These images were manually and exhaustively annotated at the level of major subcompartments, including normal or abnormal findings and artifacts.Systemized nomenclature of medicine clinical terms (SNOMED CT) was chosen, and the annotations were linked to its codes and terms. Two hundred WSI were collected and annotated, resulting in 17,497 annotations, covering a total area of 302.19 cm2, equivalent to 107,7 gigapixels. Ninety-five unique SNOMED CT codes were used. The time taken to annotate a WSI varied from 45 s to over 360 min, a total time of approximately 360 h.This work resulted in a dataset of 200 exhaustive annotated WSIs of normal and abnormal tissue from the colon and skin, and it has informed plans to build a comprehensive library of annotated WSIs. SNOMED CT was found to be the best ontology for annotation labeling. This project also demonstrates the need for future development of annotation tools in order to make the annotation process more efficient.
Revisiting Feature Alignment for One-stage Object Detection

Recently, one-stage object detectors gain much attention due to their simplicity in practice. Its fully convolutional nature greatly reduces the difficulty of training and deployment compared with two-stage detectors which require NMS and sorting for the proposal stage. However, a fundamental issue lies in all one-stage detectors is the misalignment between anchor boxes and convolutional features, which significantly hinders the performance of one-stage detectors. In this work, we first reveal the deep connection between the widely used im2col operator and the RoIAlign operator. Guided by this illuminating observation, we propose a RoIConv operator which aligns the features and its corresponding anchors in one-stage detection in a principled way. We then design a fully convolutional AlignDet architecture which combines the flexibility of learned anchors and the preciseness of aligned features. Specifically, our AlignDet achieves a state-of-the-art mAP of 44.1 on the COCO test-dev with ResNeXt-101 backbone.
Out of Sight But Not Out of Mind: An Answer Set Programming Based Online Abduction Framework for Visual Sensemaking in Autonomous Driving

We demonstrate the need and potential of systematically integrated vision and semantics solutions for visual sensemaking (in the backdrop of autonomous driving). A general method for online visual sensemaking using answer set programming is systematically formalised and fully implemented. The method integrates state of the art in visual computing, and is developed as a modular framework usable within hybrid architectures for perception &amp; control. We evaluate and demo with community established benchmarks KITTIMOD and MOT. As use-case, we focus on the significance of human-centred visual sensemaking ---e.g., semantic representation and explainability, question-answering, commonsense interpolation--- in safety-critical autonomous driving situations.
Smooth Grad-CAM++: An Enhanced Inference Level Visualization Technique for Deep Convolutional Neural Network Models

Gaining insight into how deep convolutional neural network models perform image classification and how to explain their outputs have been a concern to computer vision researchers and decision makers. These deep models are often referred to as black box due to low comprehension of their internal workings. As an effort to developing explainable deep learning models, several methods have been proposed such as finding gradients of class output with respect to input image (sensitivity maps), class activation map (CAM), and Gradient based Class Activation Maps (Grad-CAM). These methods under perform when localizing multiple occurrences of the same class and do not work for all CNNs. In addition, Grad-CAM does not capture the entire object in completeness when used on single object images, this affect performance on recognition tasks. With the intention to create an enhanced visual explanation in terms of visual sharpness, object localization and explaining multiple occurrences of objects in a single image, we present Smooth Grad-CAM++ \footnote{Simple demo: http://35.238.22.135:5000/}, a technique that combines methods from two other recent techniques---SMOOTHGRAD and Grad-CAM++. Our Smooth Grad-CAM++ technique provides the capability of either visualizing a layer, subset of feature maps, or subset of neurons within a feature map at each instance at the inference level (model prediction process). After experimenting with few images, Smooth Grad-CAM++ produced more visually sharp maps with better localization of objects in the given input images when compared with other methods.