Reviewerly/exHarmony huggingface dataset:
Design and implementation of a deep recurrent model for prediction of readmission in urgent care using electronic health records

There has been a steady growth in machine learning research in healthcare, however, progress is difficult to measure because of the use of different cohorts, task definitions and input variables. To take the advantage of the availability and value of digital health data, we aim to predict unplanned readmissions to the intensive care unit (ICU)from a publicly available Critical Care dataset called Medical Information Mart for Intensive Care (MIMIC-III). In this research, we formulate a heterogeneous LSTM and CNN architecture specifically to create a model of readmission risk. Our proposed predictive framework outperformed all the benchmark classifiers such as support vector machine, random forest and logistic regression models on all performance measures (AUC, accuracy and precision)except on recall where random forest performed slightly better. Predictions from these models will help in resource planning and decrease mortality or length of stay in clinical care settings.
Cross-streamer wavefield interpolation using deep convolutional neural network

Seismic exploration in complex geological settings and shallow geological targets has led to a demand for higher spatial and temporal resolution in the final migrated image.Seismic data from conventional marine acquisition lacks near offset and wide azimuth data, which limits imaging in these settings.In addition, large streamer separation introduce aliasing of spatial frequencies across the streamers.A new marine survey configuration, known as TopSeis, was introduced in 2017 in order to address the shallow-target problem.However, introduction of near offset data has shown to be challenging for interpolation and regularization, using conventional methods.In this paper, we investigate deep learning as a tool for interpolation beyond spatial aliasing across the streamers, in the shot domain.The proposed method is based on imaging techniques from single-image super resolution (SISR).The model architecture consist of a deep convolutional neural network (CNN) and a periodic resampling layer for upscaling to the non-aliased wavefield.We demonstrate the performance of proposed method on representative broadband synthetic data and TopSeis field data from the Barents Sea.10.
Do security breaches matter? The shareholder puzzle

Abstract This article analyzes the effect of computer breaches on publicly traded equities from 2005 to 2017. An event study is performed and breaches analyzed conditioned on whether the breach announcement has been made in the mainstream media or through other channels. We find that in the period prior to the announcement date in the media, the mean abnormal return is negative, reflecting a likely leakage of information. In the period following the announcement date, the mean abnormal return is positive, often more than offsetting the previous declines. The findings have important implications for analysts, portfolio managers, institutional investors, and regulators.
A radiation avoiding algorithm of path optimization for radiation protection of workers and robots

In this paper, we propose a radiation avoiding algorithm of path optimization, aiming to provide a new technical support for radiation protection to professional personnel and robots engage in radiation works in limited and unknown environment. Based on Beetle Antennae Search (BAS) algorithm, radiation perceiving motion model and BAS-based radiation-avoiding algorithm are designed. In view of shortcomings of BAS for radiation avoiding, an improved radiation perceiving motion model and a local perceiving and target-attractive radiation avoiding path optimization algorithm are designed. The proposed algorithms have high computational efficiency and simple process. Besides, a graphical simulation program of the proposed algorithm is established. Comparison results of computer simulation prove effectiveness and feasibility of the proposed algorithms. Experimental results obtained in a simulated environment show that the proposed algorithm could be useful to meet the challenges of radiation avoiding applications that can be described as optimization problems.
Projection Distortion-based Object Tracking in Shader Lamp Scenarios

Shader lamp systems augment the real environment by projecting new textures on known target geometries. In dynamic scenes, object tracking maintains the illusion if the physical and virtual objects are well aligned. However, traditional trackers based on texture or contour information are often distracted by the projected content and tend to fail. In this paper, we present a model-based tracking strategy, which directly takes advantage from the projected content for pose estimation in a projector-camera system. An iterative pose estimation algorithm captures and exploits visible distortions caused by object movements. In a closed-loop, the corrected pose allows the update of the projection for the subsequent frame. Synthetic frames simulating the projection on the model are rendered and an optical flow-based method minimizes the difference between edges of the rendered and the camera image. Since the thresholds automatically adapt to the synthetic image, a complicated radiometric calibration can be avoided. The pixel-wise linear optimization is designed to be easily implemented on the GPU. Our approach can be combined with a regular contour-based tracker and is transferable to other problems, like the estimation of the extrinsic pose between projector and camera. We evaluate our procedure with real and synthetic images and obtain very precise registration results.
Continuous Control for High-Dimensional State Spaces: An Interactive Learning Approach

Deep Reinforcement Learning (DRL) has become a powerful methodology to solve complex decision-making problems. However, DRL has several limitations when used in real-world problems (e.g., robotics applications). For instance, long training times are required and cannot be accelerated in contrast to simulated environments, and reward functions may be hard to specify/model and/or to compute. Moreover, the transfer of policies learned in a simulator to the real-world has limitations (reality gap). On the other hand, machine learning methods that rely on the transfer of human knowledge to an agent have shown to be time efficient for obtaining well performing policies and do not require a reward function. In this context, we analyze the use of human corrective feedback during task execution to learn policies with high-dimensional state spaces, by using the D-COACH framework, and we propose new variants of this framework. D-COACH is a Deep Learning based extension of COACH (COrrective Advice Communicated by Humans), where humans are able to shape policies through corrective advice. The enhanced version of DCOACH, which is proposed in this paper, largely reduces the time and effort of a human for training a policy. Experimental results validate the efficiency of the D-COACH framework in three different problems (simulated and with real robots), and show that its enhanced version reduces the human training effort considerably, and makes it feasible to learn policies within periods of time in which a DRL agent do not reach any improvement.
Complexities of Implementation: Oxfam Australia's Experience in Piloting Blockchain

This paper describes one of Oxfam’s pilot projects exploring blockchain technology, focusing on the non-technological, institutional challenges faced by the organisation. There is an emerging literature on blockchain for social good, however, this predominantly focuses on the use cases and issues relating to applying the technology in international development projects. A gap in the literature exists regarding the non-technological aspects of blockchain projects both within the sector and more broadly. Addressing this gap is critically important as many of the promises of blockchain technology will only eventuate in their fullest when whole ecosystems are using the technology. To get there requires a transition period and it is this transition period that holds the key to success for organisations exploring the technology. This paper goes some way to addressing this gap. It does so by describing a specific case study and unpacking some of the organisational challenges associated with implementing a blockchain-based project in the international development and humanitarian sector. This has important implications for the sector as blockchain technology becomes ever more present as a tool capable of reducing inequality and addressing power imbalances. The Oxfam case study described in this paper highlights the difficulty many not-for-profits are having engaging with the technology. The lessons are drawn from a specific use case of a current pilot project using blockchain technology in a cash transfer preparedness project in a small island developing state. Although important and insightful, this paper does not focus on the specifics of the application of the technology but rather discusses the myriad non-technological challenges faced from Oxfam Australia’s perspective. These are categorised into three main areas: awareness and understanding of the technology, capacity constraints of in-house support services in providing relevant support for a nascent technology, and issues related to engaging in non-traditional partnerships. The paper concludes by recommending further areas of research and suggestions to develop practical tools and guidance to help the international development and humanitarian sector navigate this emerging technology.
Modal Resolution

Resolution-based provers for multimodal normal logics require pruning of the search space for a proof to ameliorate the inherent intractability of the satisfiability problem for such logics. We present a clausal modal-layered hyper-resolution calculus for the basic multimodal logic, which divides the clause set according to the modal level at which clauses occur to reduce the number of possible inferences. We show that the calculus is complete for the logics being considered. We also show that the calculus can be combined with other strategies. In particular, we discuss the completeness of combining modal layering with negative and ordered resolution and provide experimental results comparing the different refinements.
Multi-modal Representation Learning for Short Video Understanding and Recommendation

We study the task of short video understanding and recommendation which predicts the user's preference based on multimodal contents, including visual features, text features, audio features and user interactive history. In this paper, we present a multi-modal representation learning method to improve the performance of recommender systems. The method first converts multi-modal contents into vectors in the embedding space, and then concatenates these vectors as the input of a multi-layer perceptron to make prediction. We also propose a novel Key-Value Memory to map dense real-values into vectors, which could obtain more sufficient semantic in a nonlinear manner. Experimental results show that our representation significantly improves several baselines and achieves the superior performance on the dataset of ICME 2019 Short Video Understanding and Recommendation Challenge.
Post-Quantum Cryptography in Embedded Systems

Quantum computers that can run Shor's algorithm are expected to become available in the next decade. These algorithms can be used to break conventional digital signature schemes (e.g. RSA or ECDSA), which are widely used in embedded systems today. This puts these systems at risk when they are used in safety-relevant long-term applications such as automotive systems or critical infrastructures. To mitigate this risk, classical digital signature schemes used must be replaced by schemes secure against quantum computer based attacks.
Training contextual neural networks with rectifier activation functions: Role and adoption of sorting methods

Contextual neural networks are effective and very usable machine learning models being generalization of multilayer perceptron. They allow to solve classification problems with high accuracy while strongly limiting activity of connections between hidden neurons. Within this article we present novel study of properties of contextual neuronal networks with Hard and Exponential Rectifier activation functions and of their influence on behavior of the Generalized Error Backpropagation method. It is used to show how to optimize efficiency of the sorting phase of this algorithm when applied to train evaluated models. This considerably extends our previous related paper which was limited to analysis of contextual neuronal networks with Leaky Rectifier and Sigmoidal activation functions. This article includes wide description of contextual neural networks and generalized error backpropagation algorithm as well as the discussion of their connection with self-consistency paradigm, which is frequently used in quantum physics. Also the relation of the latter with sorting methods and considered rectifier functions during training of contextual neural networks is studied in details. Conclusions are backed up by the results of performed experiments. Reported outcomes of simulations confirm the ability of contextual neural networks to limit activity of connections between their neurons and – what is more important – indicate the detailed rules of selection of the most efficient sorting algorithm for updating scan-paths of contextual neurons that are using Hard and Exponential Rectifier activation functions. Presented results have considerable value both for research and practical applications – especially where the efficiency of training of contextual neural networks is crucial.
Performance Comparison of Naïve Bayes and Complement Naïve Bayes Algorithms

Big data is defined with 3 V which are volume, velocity and variety. It is hard to analyze, store and process this data because of its size and complexity. When traditional tools are used to analyze the data, execution time is too much. On the other hand, there are some tools and libraries to analyze and process the big data. As a result, it does not take too much time to analyze and process the data. For example; Hadoop is an open source library that allow the distributed computing for large datasets. Mahout is a library that allows machine learning, Hive allows querying and Kafka allows messaging. In this paper, Hadoop and Mahout are used and performance of Naïve Bayes and Complement Naïve Bayes Algorithms are compared based on average correctly classified instance percentage, average training time and average testing time with different size of the dataset. As a dataset, "20 Newsgroups" is used and size of the dataset is increased by scaling the dataset with 2, 4 and 8. As a result, datasets with the size of 37692, 75384 and 150768 are created. All experiments are carried on with all the datasets using different smoothing, weight and normalization parameters for 10 times and then, average of all the results are taken. After all the experiments, it is observed that performance of Naïve Bayes Algorithm is better than Complement Naïve Bayes Algorithm based on average training time. On the other hand, performance of Complement Naïve Bayes is better than the other based on average correctly classified instance percentage.
Automatic Feature Exploration and an Application in Defect Prediction

Many software engineering tasks heavily rely on hand-crafted software features, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , defect prediction, vulnerability discovery, software requirements, code review, and malware detection. Previous solutions to these tasks usually directly use the hand-crafted features or feature selection techniques for classification or regression, which usually leads to suboptimal results due to their lack of powerful representations of the hand-crafted features. To address the above problem, in this paper, we adopt the effort-aware just-in-time software defect prediction (JIT-SDP), which is a typical hand-crafted-feature-based task, as an example, to exploit new possible solutions. We propose a new model, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">neural forest</i> (NF), which uses the deep neural network and decision forest to build a holistic system for the automatic exploration of powerful feature representations that are used for the following classification. NF first employs a deep neural network to learn new feature representations from hand-crafted features. Then, a decision forest is connected after the neural network to perform classification and in the meantime, to guide the learning of feature representation. NF mainly aims at solving the challenging problem of combining the two different worlds of neural networks and decision forests in an end-to-end manner. When compared with previous state-of-the-art defect predictors and five designed baselines on six well-known benchmarks for within- and cross-project defect prediction, NF achieves significantly better results. The proposed NF model is generic to the classification problems which rely on the hand-crafted features.
Development of a Blockchain Taxonomy

Since its deployment in 2009 the Blockchain technology has been getting a lot of attention in research, media as well as various industries. In the beginning, only financial related use cases were discussed and implemented, as the first real life and well-known implementation was Bitcoin. Since then many different types of Blockchains evolved. However, it is still unclear what the basic characteristics of a Blockchain are and how those interact to form different types of Blockchains. With this research, we therefore propose a taxonomy of the Blockchain technology to help form a better understanding of the technology itself and guide future research on how to differentiate the multiple types.
Multi-path Fusion Network for High-Resolution Height Estimation from a Single Orthophoto

Height estimation from a single orthophoto is essential for reconstructing 3D scene models for navigation of unmanned aerial vehicles. It is particularly challenging to recover detailed object structures under various scales. In this paper, we propose a multi-path fusion network for generating high resolution height maps while preserving scene structures well. From multi-scale features that can be extracted by an efficient recursive refinement network, we introduce a multipath feature fusion module to combine these features and exploit information on different abstraction levels efficiently. We also design a residual up-sampling block to generate high-resolution height maps that well preserve structure details. Experimental results on two public datasets, Vaihingen and Potsdam, demonstrate that our method achieves better quantitative performance compared with previous techniques. Moreover, our results are much more visually pleasing because scene structures at different scales are well preserved.
Real-Time Vehicle Detection from Short-range Aerial Image with Compressed MobileNet

Vehicle detection from short-range aerial image faces challenges including vehicle blocking, irrelevant object interference, motion blurring, color variation etc., leading to the difficulty to achieve high detection accuracy and realtime detection speed.In this paper, benefiting from the recent development in MobileNet family network engineering, we propose a compressed MobileNet which is not only internally resistant to the above listed challenges but also gains the best detection accuracy/speed tradeoff when comparing with the original MobileNet.In a nutshell, we reduce the bottleneck architecture number during the feature map downsampling stage but add more bottlenecks during the feature map plateau stage, neither extra FLOPs nor parameters are thus involved but reduced inference time and better accuracy are expected.We conduct experiment on our collected 5-k short-range aerial images, containing six vehicle categories: truck, car, bus, bicycle, motorcycle, crowded bicycles and crowded motorcycles.Our proposed compressed MobileNet achieves 110 FPS (GPU), 31 FPS (CPU) and 15 FPS (mobile phone), 1.2 times faster and 2% more accurate (mAP) than the original MobileNet.
INVESTIGATION OF HISTOGRAM EQUALIZATION FILTER FOR CT SCAN IMAGE ENHANCEMENT

This piece of work investigates the application of histogram equalization method to clinical images for noise removal and efficient image enhancement without any information loss. Computed tomographic (CT) images of the abdomen bearing liver tumour are kept under study. Liver exhibits heterogeneous combination of intensities which makes it a challenging task to enhance the liver tumour embedded in the image. Distortion occurs due to the presence of quantum noise in the CT scans and important information of the image is suppressed. The methodology adopted in this paper comprises of two stages. Initially pixel based intensity transformation is adopted for de-noising the background of the image by the selection of appropriate threshold levels. The resultant image gives a noise free background and the foreground features are enhanced. In the next stage histogram equalization filters are applied to the transformed image. The equalization method which gives uniform image enhancement with lesser mean square error (MSE) and increased peak signal to noise ratio (PSNR) is supposed to be an effective method for efficient enhancement of the images. This study deals with the application of histogram equalization methods to CT images which can aid the radiologists for better visualization and diagnosis of the disease.
Data‐driven XGBoost‐based filter for target tracking

In recent years, the data-driven approach has been introduced in the field of target tracking as a powerful tool developing the end-to-end mapping relationship between input features and outputs. Typically, in data-driven methods, neural networks serve as a supplement of traditional Bayesian filters for improved estimation accuracy. However, these hybrid methods are somehow complicated to realise. In this study, inspired by the idea of direct-mapping from measurements to states, simpler method by developing a data-driven XGBoost-based Filter (DXGBF) is proposed. The DXGBF consists of four components, namely data generator, sliding window, centralisation strategy and XGBoost learner (XL). The data generator generates simulated data from the probabilistic model in the training phase. By intercepting the measurements, the sliding window enables DXGBF to track targets online. The centralisation strategy extracts the relevant kinematic information from different tracks that enables DXGBF to track randomly initialised targets. The XL is responsible for learning a function that mapping to estimate states. Simulation results show that the estimation accuracy of DXGBF is higher than those of Kalman filter, sampling importance resampling particle filter and the data-driven random-forest-based filter.
Information Placement in Virtual Reality

In this paper, we develop a technique for placing informational labels in complex Virtual Environments (VEs). The ability to effectively and efficiently present labels in VEs is valuable in Virtual Reality (VR) for many reasons, but the motivation is to bring us closer to a system that delivers information in VR in an optimal way without causing information overload. The novelty of this technique lies in the use of eye tracking as an accurate indicator of attention to identifying objects of interest. Labels associated with such objects of interest are revealed when the user attends to them. We conduct a series of experiments to evaluate label placement based on user attention. This investigation is a first step toward integrating attention into information placement strategies in VR.