Reviewerly/exHarmony huggingface dataset:
An adaptive term proximity based rocchio’s model for clinical decision support retrieval

In order to better help doctors make decision in the clinical setting, research is necessary to connect electronic health record (EHR) with the biomedical literature. Pseudo Relevance Feedback (PRF) is a kind of classical query modification technique that has shown to be effective in many retrieval models and thus suitable for handling terse language and clinical jargons in EHR. Previous work has introduced a set of constraints (axioms) of traditional PRF model. However, in the feedback document, the importance degree of candidate term and the co-occurrence relationship between a candidate term and a query term. Most methods do not consider both of these factors. Intuitively, terms that have higher co-occurrence degree with a query term are more likely to be related to the query topic.In this paper, we incorporate original HAL model into the Rocchio's model, and propose a new concept of term proximity feedback weight. A HAL-based Rocchio's model in the query expansion, called HRoc, is proposed. Meanwhile, we design three normalization methods to better incorporate proximity information to query expansion. Finally, we introduce an adaptive parameter to replace the length of sliding window of HAL model, and it can select window size according to document length.Based on 2016 TREC Clinical Support medicine dataset, experimental results demonstrate that the proposed HRoc and HRoc_AP models superior to other advanced models, such as PRoc2 and TF-PRF methods on various evaluation metrics. Among them, compared with the Proc2 and TF-PRF models, the MAP of our model is increased by 8.5% and 12.24% respectively, while the F1 score of our model is increased by 7.86% and 9.88% respectively.The proposed HRoc model can effectively enhance the precision and the recall rate of Information Retrieval and gets a more precise result than other models. Furthermore, after introducing self-adaptive parameter, the advanced HRoc_AP model uses less hyper-parameters than other models while enjoys an equivalent performance, which greatly improves the efficiency and applicability of the model and thus helps clinicians to retrieve clinical support document effectively.
Towards Blockchain-Based E-Voting Systems

Electronic voting is one of the most challenging cryptographic problems, since the developed system should guarantee strong and sometimes contrasting security properties. Blockchain technology can be of help providing for free some important guarantees such as the immutability and transparency of the votes using a distributed ledger. In this paper we propose a blockchain based e-voting system, which is lightweight, since it does not rely on strong cryptographic primitives, and efficient, since it improves over previous proposals in terms of both execution time and associated cost for the required infrastructure. We provide the description of a proof of concept system together with the cost and performance analysis.
A scalable motion planner for high-dimensional kinematic systems

Sampling-based algorithms are known for their ability to effectively compute paths for high-dimensional robots in relatively short times. The same algorithms, however, are also notorious for poor-quality solution paths, particularly as the dimensionality of the system grows. This work proposes a new probabilistically complete sampling-based algorithm, XXL, specially designed to plan the motions of high-dimensional mobile manipulators and related platforms. Using a novel sampling and connection strategy that guides a set of points mapped on the robot through the workspace, XXL scales to realistic manipulator platforms with dozens of joints by focusing the search of the robot’s configuration space to specific degrees of freedom that affect motion in particular portions of the workspace. Simulated planning scenarios with the Robonaut2 platform and planar kinematic chains confirm that XXL exhibits competitive solution times relative to many existing works while obtaining execution-quality solution paths. Solutions from XXL are of comparable quality to cost-aware methods even though XXL does not explicitly optimize over any particular criteria, and are computed in an order of magnitude less time. Furthermore, observations about the performance of sampling-based algorithms on high-dimensional manipulator planning problems are presented that reveal a cautionary tale regarding two popular guiding heuristics used in these algorithms, indicating that a nearly random search may outperform the state-of-the-art when defining such heuristics is known to be difficult.
ytree: A Python package for analyzing merger trees

The formation of cosmological structure is dominated, especially on large scales, by the force of gravity.In the early Universe, matter is distributed homogeneously, with only small fluctuations about the average density.Overdense regions undergo gravitational collapse to form bound structures, called halos, which will host galaxies within them.Halos grow via accretion of the surrounding material and by merging with other halos.This process of merging to form increasingly massive halos is naturally conceptualized as an inverted tree, where small branches connect up to continually larger ones, leading eventually to a trunk.
Type-1 possibilistic fuzzy forecasting functions

Type-1 Fuzzy Functions (T1FFs) were developed by Turksen as an alternative fuzzy inference system (FIS) and have been commonly used in forecasting problems. The main advantages of T1FFs are that they are free of rules and easy to implement. Thus, they have recently been an attractive tool for researchers. T1FFs start with clustering the inputs using the fuzzy c-means (FCM) clustering algorithm. Later, the degrees of membership and its nonlinear transformations are included into the input matrix for each cluster. Thus, as many input matrices are obtained as the number of clusters. Finally, the outputs are combined using the degrees of membership of the new observations. Because gathering objects in the same cluster as homogeneously as possible is an important task for a clustering algorithm, the possibilistic FCM is adapted to T1FFs in order to overcome the FCM's limitations in the proposed method. We used 14 financial datasets and a beer consumption dataset to verify the forecasting performance of the proposed method. For example, the proposed method outperformed the other selected forecasting methods for the Taiwan Stock Exchange time-series datasets in terms of the mean of root mean square errors.
Almost minimum error discrimination of N-ary weak coherent states by Jaynes-Cummings Hamiltonian dynamics

Quantum state discrimination of coherent states has been one of important problems in quantum information processing. Recently, R. Han et al. showed that minimum error discrimination of two coherent states can be nearly done by using Jaynes-Cummings Hamiltonian. In this paper, based on the result of R. Han et al., we propose the methods where minimum error discrimination of more than two weak coherent states can be nearly performed. Specially, we construct models which can do almost minimum error discrimination of three and four coherent states. Our result can be applied to quantum information processing of various coherent states.
Pedestrian Detection Based on Deep Learning

While it is a hot issue whether cars could drive by themselves in emergent situations without any kind of human interference, pedestrian detection is the key technology in autonomous driving cars. Though current pedestrian detection technologies have come to a point in which they are accurate in normal conditions and surroundings, existent systems are inaccurate in harsh situations, such as when there are too many pedestrians, when there is too much light or when it is too dark, or when it is raining or snowing heavily. This problem may be solved by integrating deep learning and combining a new type of local pattern with the RGB raw image as input, instead of using just the RGB image as input. We will introduce a new type of local pattern called Triangular Patterns, which is effective in extracting more detailed and stable features from local regions. Here in this paper, we propose a pedestrian detection system in which deep learning is used, along with combining the RGB raw image with Triangular Patterns for input.
Model of Artificial Neural Networks in Predictions of Corn Productivity in an Effort to Overcome Imports in Indonesia

Abstract Indonesian people still consume a lot of corn as a staple food. Corn productivity based on data from the Indonesian Central Bureau of Statistics from 2005 to 2015 is not stable. Therefore this study was conducted to determine the right prediction model to see the level of corn productivity in Indonesia for the coming year, with the hope that the government has a reference to continue to work to improve corn productivity to remain stable in order to meet the needs and minimize corn imports. This study uses data on corn productivity in 2005-2015 sourced from the Indonesian Central Bureau of Statistics. The algorithm used to determine the prediction model is the Backpropagation artificial neural network. This algorithm is able to predict times series data. Based on this algorithm, the training and testing process is carried out using 5 network architecture models, namely 5-25-1, 5-43-1, 5-76-1, 5-78-1 and 7-128-1. The best architecture obtained from the 5 models is 5-25-1 with 88% accuracy percentage and MSE value 0, 00992433.
A robust and efficient convolutional deep learning framework for age‐invariant face recognition

Abstract Extensive research has been carried out in the past on face recognition, face detection, and age estimation. However, age‐invariant face recognition (AIFR) has not been explored that thoroughly. The facial appearance of a person changes considerably over time that results in introducing significant intraclass variations, which makes AIFR a very challenging task. Most of the face recognition studies that have addressed the ageing problem in the past have employed complex models and handcrafted features with strong parametric assumptions. In this work, we propose a novel deep learning framework that extracts age‐invariant and generalized features from facial images of the subjects. The proposed model trained on facial images from a minor part (20–30%) of lifespan of subjects correctly identifies them throughout their lifespan. A variety of pretrained 2D convolutional neural networks are compared in terms of accuracy, time, and computational complexity to select the most suitable network for AIFR. Extensive experimental results are carried out on the popular and challenging face and gesture recognition network ageing dataset. The proposed method achieves promising results and outperforms the state‐of‐the‐art AIFR models by achieving an accuracy of 99%, which proves the effectiveness of deep learning in facial ageing research.
LS-SVR as a Bayesian RBF Network

We show theoretical similarities between the least squares support vector regression (LS-SVR) model with a radial basis functions (RBFs) kernel and maximum a posteriori (MAP) inference on Bayesian RBF networks with a specific Gaussian prior on the regression weights. Although previous articles have pointed out similar expressions between those learning approaches, we explicitly and formally state the existing correspondences. We empirically demonstrate our result by performing computational experiments with standard regression benchmarks. Our findings open a range of possibilities to improve LS-SVR by borrowing strength from well-established developments in Bayesian methodology.
Multi-Frame Based Homography Estimation for Video Stitching in Static Camera Environments

In this paper, a multi-frame based homography estimation method is proposed for video stitching in static camera environments. A homography that is robust against spatio-temporally induced noise can be estimated by intervals, using feature points extracted during a predetermined time interval. The feature point with the largest blob response in each quantized location bin, a representative feature point, is used for matching a pair of video sequences. After matching representative feature points from each camera, the homography for the interval is estimated by random sample consensus (RANSAC) on the matched representative feature points, with their chances of being sampled proportional to their numbers of occurrences in the interval. The performance of the proposed method is compared with that of the per-frame method by investigating alignment distortion and stitching scores for daytime and noisy video sequence pairs. It is shown that alignment distortion in overlapping regions is reduced and the stitching score is improved by the proposed method. The proposed method can be used for panoramic video stitching with static video cameras and for panoramic image stitching with less alignment distortion.
Fraud detection in credit card transaction using neural networks

Banking transactions, such as online transactions, credit card transactions and the mobile wallet, are gaining popularity. People are shopping more and more using credit cards. Credit cards have become a necessity, to the virtual world, for digitized and paperless transactions. Millions of online transactions take place every day and all of these transactions are subject to various types of fraud. There are many techniques developed to analyze, detect and prevent credit card fraud, these techniques are no longer sufficient for current needs. In recent years, several studies have used machine Learning techniques to find solutions to this problem. In this paper, we present a comparative study of different techniques of machine learning, especially neural network, applied to the same dataset. Our analysis aims to propose a comprehensive guide to choose the best techniques for credit card fraud detection.
Suboptimal adaptive Kalman filtering based on the proportional control of prior error covariance

For linear time-invariant systems, this paper develops a new kind of adaptive Kalman filter to deal with Kalman filtering problems troubled by unknown/inaccurate process noise covariance. The limitation of Kalman filter is that its performance would deteriorate or even degrade if the accurate noise statistics could not be obtained in advance. To reduce or mitigate the negative influence caused by unknown/mismatched process noise covariance, this work elaborates a novel covariance control scheme in which the prior error covariance is recursively regulated with the proportional form of feedback information: the posterior sequence is first evaluated as online feedback to constitute a closed-loop structure for covariance propagation process, and then a proportional gain is employed to amplify the feedback term and fasten the converging of the estimated covariance parameter; note that, the new approach is relatively more independent of the parameter of process noise covariance and, therefore, the Kalman theory's rigorous dependency on accurate process noise covariance could be relaxed significantly. The mathematical properties and sub-optimality of the new covariance control scheme are discussed in detail as well as some practical considerations. The advantage of this newly developed method in filtering accuracy, adaptability and simplicity are illustrated with an object tracking scenario.
RIDNet: Recursive Information Distillation Network for Color Image Denoising

Color image denoising is more challenging in effectiveness when compared with the grayscale one. Most existing methods play a certain role in efficiency or flexibility, but lack robustness to handle various noise levels, especially the severe noise. This keeps them away from being practically applied to color image denoising. To address this issue, we propose a robust CNN based denoiser, namely Recursive Information Distillation Network (RIDNet), to handle the denoising task at high noise levels. The proposed RIDNet simultaneously keeps the efficiency and flexibility by introducing the information distillation module and merging a tunable noise level map as the input, respectively. Experiment results on Additive White Gaussian Noise (AWGN) images demonstrate that our method outperforms most of the state-of-the-art color image denoisers.
Resolution enhancement for inverse synthetic aperture radar images using a deep residual network

Abstract A framework for inverse synthetic aperture radar (ISAR) image resolution enhancement using a deep residual network is proposed. Our framework directly learns an end‐to‐end mapping in the form of a deep residual network (ResNet) between the input low‐resolution (LR) images and the output high‐resolution (HR) images with respect to the point spread function (PSF). In our network, residual blocks without batch normalization (BN) layers are applied to retain ISAR image contrast, and parametric rectified linear unit (PReLU) which can adaptively learn the negative coefficients is used as the activation function. The complex‐valued ISAR imagery is divided into two real‐valued channels to preserve phase information implicitly. Experimental results show that the proposed method can obtain higher quality HR images compared with traditional sparsity‐driven methods while freeing itself from a prior model for radar echoes and a complicated parameter estimation process.
Reservoir computing with two-bit input task using dipole-coupled nanomagnet array

Reservoir computing (RC), which utilizes physical phenomena, is a candidate for low energy consumption recurrent neural networks. Recently, we proposed RC which uses a dipole-coupled nanomagnet array as a reservoir, which applies the direction of the static magnetic moment of each nanomagnet as a node state in the array. It is possible to learn binary tasks with one-bit input data using this reservoir. Moreover, more complex tasks such as motion detection utilize multi-bit input data. This study simulates a two-bit binary input RC by employing the dipole coupled nanomagnet array as a reservoir. We apply a binary task with two-bit input data, which requires a memory capacity and non-linear computing capability of the reservoir. Due to the simulations with a macrospin model at a temperature of 0 K, this RC was able to learn these binary tasks by utilizing 24 nanomagnets.
Acceleration-Level Obstacle Avoidance of Redundant Manipulators

Avoiding obstacle during path planning is a crucial issue in redundant manipulators. In this study, the obstacle avoidance of redundant manipulators, which is investigated at acceleration level, is presented. Specifically, a new acceleration-level inequality is designed and formulated, which is proven to be capable of avoiding obstacle. By combining the end-effector planning requirement (formulated as equality constraint) and incorporating the physical limits (formulated as bound constraints), the acceleration-level obstacle avoidance (ALOA) scheme for redundant manipulators is proposed. Such scheme is transformed into a quadratic program and is computed by a numerical algorithm. Simulation results under the PA10 manipulator with different obstacles further validate the effective performance of the proposed ALOA scheme.
Automatic generation of meteorological briefing by event knowledge guided summarization model

In recent years, frequent meteorological disasters have brought great suffering to people. The meteorological briefing is an effective way to realize the real-time perception of extreme meteorological events, which is of great significance for decision-makers to formulate plans and provide timely assistance. Traditional meteorological briefings primarily rely on physical sensors for data collection and are organized manually. However, such an approach has the disadvantages of rigid content, high cost, and poor real-time performance. As an emerging lightweight social sensor, social networks can respond to real world events in a timely and comprehensive manner, which also makes up for the shortcomings of the traditional methods. In this paper, we present an event knowledge guided summarization (EKGS) model to automatically summarize weibo posts in the meteorological domain. Our model consists of two modules: a summary generation module and an event knowledge guidance module. The event knowledge guidance module is used to guide and constrain the content generated by the summary generation module, so that it can generate the content with core knowledge of specific events, which are 14 types of extreme meteorological events defined by the China Meteorological Administration (CMA). Compared to other baseline models, our EKGS model achieves the best test results on all metrics. In addition, we construct an automatic meteorological briefing generation framework based on the EKGS model, which has been applied as an online service to the meteorological briefing overview module of the CMA Public Meteorological Service Center.
The 2nd 3D Face Alignment in the Wild Challenge (3DFAW-Video): Dense Reconstruction From Video

3D face alignment approaches have strong advantages over 2D with respect to representational power and robustness to illumination and pose. Over the past few years a number of research groups have made rapid advances in dense 3D alignment from 2D video and obtained impressive results. How these various methods compare is relatively unknown. Previous benchmarks addressed sparse 3D alignment and single image 3D reconstruction. No commonly accepted evaluation protocol exists for dense 3D face reconstruction from video with which to compare them. The 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> 3D Face Alignment in the Wild from Videos (3DFAW-Video) Challenge extends the previous 3DFAW 2016 competition to the estimation of dense 3D facial structure from video. It presented a new large corpora of profile-to-profile face videos recorded under different imaging conditions and annotated with corresponding high-resolution 3D ground truth meshes. In this paper we outline the evaluation protocol, the data used, and the results. 3DFAW-Video is to be held in conjunction with the 2019 International Conference on Computer Vision, in Seoul, Korea.
SlingShot - Automated Threat Detection and Incident Response in Multi Cloud Storage Systems

Cyber-attacks against cloud storage infrastructure e.g. Amazon S3 and Google Cloud Storage, have increased in recent years. One reason for this development is the rising adoption of cloud storage for various purposes. Robust counter-measures are therefore required to tackle these attacks especially as traditional techniques are not appropriate for the evolving attacks. We propose a two-pronged approach to address these challenges in this paper. The first approach involves dynamic snapshotting and recovery strategies to detect and partially neutralize security events. The second approach builds on the initial step by automatically correlating the generated alerts with cloud event log, to extract actionable intelligence for incident response. Thus, malicious activities are investigated, identified and eliminated. This approach is implemented in SlingShot, a cloud threat detection and incident response system which extends our earlier work - CSBAuditor, which implements the first step. The proposed techniques work together in near real time to mitigate the aforementioned security issues on Amazon Web Services (AWS) and Google Cloud Platform (GCP). We evaluated our techniques using real cloud attacks implemented with static and dynamic methods. The average Mean Time to Detect is 30 seconds for both providers, while the Mean Time to Respond is 25 minutes and 90 minutes for AWS and GCP respectively. Thus, our proposal effectively tackles contemporary cloud attacks.