Reviewerly/exHarmony huggingface dataset:
Augmented reality guidance in laparoscopic hepatectomy with deformable semi-automatic computed tomography alignment (with video)

An augmented reality (AR) application to help medical staff involved in interventional radiology (IR) learn how to properly use ceiling-suspended radiation shielding screens was created, and its utility was tested from the perspective of learner motivation.The distribution of scattered radiation in an angiography room was visualized with an AR application in three settings: when a ceiling-suspended radiation shielding screen is not used (incorrect); when there is a gap between the bottom edge of the shielding screen and the patient’s torso (incorrect); and when there is no gap between the bottom edge of the shielding screen and the patient’s torso (correct). This AR application was used by 33 medical staff, after which an Instructional Materials Motivation Survey (IMMS) based on the John Keller’s ARCS (four categories of Attention, Relevance, Confidence, and Satisfaction) Motivation Model, consisting of 36-items with responses on a 5-point (1–5) Likert scale, was conducted.The overall score was a high 4.67 ± 0.30 (mean ± standard deviation). Physician’s scores tended to be lower than those of other medical staff in the categories of Attention, Relevance, and Satisfaction (not statistically significant).The AR application to learn how to properly use ceiling-suspended radiation shielding screens was highly rated from the perspective of learner motivation.
Assessing Solution Quality of 3SAT on a Quantum Annealing Platform

When solving propositional logic satisfiability (specifically 3SAT) using quantum annealing, we analyze the effect the difficulty of different instances of the problem has on the quality of the answer returned by the quantum annealer. A high-quality response from the annealer in this case is defined by a high percentage of correct solutions among the returned answers. We show that the phase transition regarding the computational complexity of the problem, which is well-known to occur for 3SAT on classical machines (where it causes a detrimental increase in runtime), persists in some form (but possibly to a lesser extent) for quantum annealing.
Quality-Oriented Perceptual HEVC Based on the Spatiotemporal Saliency Detection Model

Perceptual video coding (PVC) can provide a lower bitrate with the same visual quality compared with traditional H.265/high efficiency video coding (HEVC). In this work, a novel H.265/HEVC-compliant PVC framework is proposed based on the video saliency model. Firstly, both an effective and efficient spatiotemporal saliency model is used to generate a video saliency map. Secondly, a perceptual coding scheme is developed based on the saliency map. A saliency-based quantization control algorithm is proposed to reduce the bitrate. Finally, the simulation results demonstrate that the proposed perceptual coding scheme shows its superiority in objective and subjective tests, achieving up to a 9.46% bitrate reduction with negligible subjective and objective quality loss. The advantage of the proposed method is the high quality adapted for a high-definition video application.
A case for deep learning in semantics: Response to Pater

Pater’s (2019) target article builds a persuasive case for establishing stronger ties between theoretical linguistics and connectionism (deep learning). This commentary extends his arguments to semantics, focusing in particular on issues of learning, compositionality, and lexical meaning.
Stacked ensemble extreme learning machine coupled with Partial Least Squares-based weighting strategy for nonlinear multivariate calibration

With its simple theory and strong implementation, extreme learning machine (ELM) becomes a competitive single hidden layer feed forward networks for nonlinear multivariate calibration in chemometrics. To improve the generalization and robustness of ELM further, stacked generalization is introduced into ELM to construct a modified ELM model called stacked ensemble ELM (SE-ELM). The SE-ELM is to create a set of sub-models by applying ELM repeatedly to different sub-regions of the spectra and then combine the predictions of those sub-models according to a weighting strategy. Three different weighting strategies are explored to implement the proposed SE-ELM, such as the Winner-takes-all (WTA) weighting strategy, the constraint non-negative least squares (CNNLS) weighing strategy and the partial least squares (PLS) weighting strategy. Furthermore, PLS is suggested to be selected as the optimal weighting method that can handle the multi-colinearity among the predictions yielded by all the sub-models. The experimental assessment of the three SE-ELM models with different weighting strategies is carried out on six real spectroscopic datasets and compared with ELM, back-propagation neural network (BPNN) and Radial basis function neural network (RBFNN), statistically tested by the Wilcoxon signed rank test. The obtained experimental results suggest that, in general, all the SE-ELM models are more robust and more accurate than traditional ELM. In particular, the proposed PLS-based weighting strategy is at least statistically not worse than, and frequently better than the other two weighting strategies, BPNN, and RBFNN.
STResNet_CF Tracker: The Deep Spatiotemporal Features Learning for Correlation Filter Based Robust Visual Object Tracking

Constructing a robust appearance model of the visual object is a crucial task for visual object tracking. Recently, more and more studies combine spatial feature with a temporal feature to improve the tracking performance. These methods successfully apply the features from spatial and temporal to address the problem for tracking. This paper presents a novel method for visual object tracking based on spatiotemporal feature combined with correlation filters. In this paper, the visual features of a target object are extracted from a spatial–temporal residual network (STResNet) appearance model with two sub-networks. The STResNet appearance model learns separately spatial feature and temporal feature, respectively, so that we can effectively utilize spatial context around the surrounding of the target object in each frame and the temporal relationship between successive frames to refine the appearance representation of the target object. Finally, our spatiotemporal fusion feature from STResNet appearance model is incorporated into the correlation filter for robust visual object tracking. The experimental results show that our method achieves similar or better performance compared with the other tracking methods based on convolutional neural networks or correlation filter.
Theoretical Analysis of Efficiency and Robustness of Softmax and Gap-Increasing Operators in Reinforcement Learning

In this paper, we propose and analyze conservative value iteration, which unifies value iteration, soft value iteration, advantage learning, and dynamic policy programming. Our analysis shows that algorithms using a combination of gap-increasing and max operators are resilient to stochastic errors, but not to non-stochastic errors. In contrast, algorithms using a softmax operator without a gap-increasing operator are less susceptible to all types of errors, but may display poor asymptotic performance. Algorithms using a combination of gap-increasing and softmax operators are much more effective and may asymptotically outperform algorithms with the max operator. Not only do these theoretical results provide a deep understanding of various reinforcement learning algorithms, but they also highlight the effectiveness of gap-increasing operators, as well as the limitations of traditional greedy value updates by the max operator.
Combination and application of machine learning and computational mechanics

训 练 数据 , 导致计算成本较高, 且结果比较依赖于k的取值 [14] .
Anisotropic diffusions of image processing from Perona–Malik on

Many reasons can be cited for the desire to harness the power of nonlinear anisotropic diffusion in image processing. Perona and Malik proposed one of the pioneering models which, while numerically viable, proves mathematically ill-posed. This discrepancy between its analytical properties and those of its numerical implementations spurred a significant amount of research in the past twenty years or so. An overview of the latter is the topic of this article.
Automated density-based counting of FISH amplification signals for HER2 status assessment

Automated image analysis can make quantification of FISH signals in histological sections more efficient and reproducible. Current detection-based methods, however, often fail to accurately quantify densely clustered FISH signals.We propose a novel density-based approach to quantifying FISH signals. Instead of detecting individual signals, this approach quantifies FISH signals in terms of the integral over a density map predicted by Deep Learning. We apply the density-based approach to the task of counting and determining ratios of ERBB2 and CEN17 signals and compare it to common detection-based and area-based approaches.The ratios determined by our approach were strongly correlated with results obtained by manual annotation of individual FISH signals (Pearson's r = 0.907). In addition, they were highly consistent with cutoff-scores determined by a pathologist (balanced concordance = 0.971). The density-based approach generally outperformed the other approaches. Its superiority was particularly evident in the presence of dense signal clusters.The presented approach enables accurate and efficient automated quantification of FISH signals. Since signals in clusters can hardly be detected individually even by human observers, the density-based quantification performs better than detection-based approaches.
Exploratory Analysis of Provenance Data Using R and the Provenance Package

The provenance of siliclastic sediment may be traced using a wide variety of chemical, mineralogical and isotopic proxies. These define three distinct data types: (1) compositional data such as chemical concentrations; (2) point-counting data such as heavy mineral compositions; and (3) distributional data such as zircon U-Pb age spectra. Each of these three data types requires separate statistical treatment. Central to any such treatment is the ability to quantify the `dissimilarity’ between two samples. For compositional data, this is best done using a logratio distance. Point-counting data may be compared using the chi-square distance, which deals better with missing components (zero values) than the logratio distance does. Finally, distributional data can be compared using the Kolmogorov–Smirnov and related statistics. For small datasets using a single provenance proxy, data interpretation can sometimes be done by visual inspection of ternary diagrams or age spectra. However, this no longer works for larger and more complex datasets. This paper reviews a number of multivariate ordination techniques to aid the interpretation of such studies. Multidimensional Scaling (MDS) is a generally applicable method that displays the salient dissimilarities and differences between multiple samples as a configuration of points in which similar samples plot close together and dissimilar samples plot far apart. For compositional data, classical MDS analysis of logratio data is shown to be equivalent to Principal Component Analysis (PCA). The resulting MDS configurations can be augmented with compositional information as biplots. For point-counting data, classical MDS analysis of chi-square distances is shown to be equivalent to Correspondence Analysis (CA). This technique also produces biplots. Thus, MDS provides a common platform to visualise and interpret all types of provenance data. Generalising the method to three-way dissimilarity tables provides an opportunity to combine several datasets together and thereby facilitate the interpretation of `Big Data’. This paper presents a set of tutorials using the statistical programming language R. It illustrates the theoretical underpinnings of compositional data analysis, PCA, MDS and other concepts using toy examples, before applying these methods to real datasets with the provenance package.
Labeled box-particle CPHD filter for multiple extended targets tracking

In multiple extended targets tracking, replacing traditional multiple measurements with a rectangular region of the nonzero volume in the state space inspired by the box-particle idea is exactly suitable to deal with extended targets, without distinguishing the measurements originating from the true targets or clutter. Based on our recent work on extended box-particle probability hypothesis density (ET-BP-PHD) filter, we propose the extended labeled box-particle cardinalized probability hypothesis density (ET-LBP-CPHD) filter, which relaxes the Poisson assumptions of the extended target probability hypothesis density (PHD) filter in target numbers, and propagates not only the intensity function but also cardinality distribution. Moreover, it provides the identity of individual target by adding labels to box-particles. The proposed filter can improve the precision of estimating target number meanwhile achieve targets' tracks. The effectiveness and reliability of the proposed algorithm are verified by the simulation results.
OptiPharm: An evolutionary algorithm to compare shape similarity

Virtual Screening (VS) methods can drastically accelerate global drug discovery processes. Among the most widely used VS approaches, Shape Similarity Methods compare in detail the global shape of a query molecule against a large database of potential drug compounds. Even so, the databases are so enormously large that, in order to save time, the current VS methods are not exhaustive, but they are mainly local optimizers that can easily be entrapped in local optima. It means that they discard promising compounds or yield erroneous signals. In this work, we propose the use of efficient global optimization techniques, as a way to increase the quality of the provided solutions. In particular, we introduce OptiPharm, which is a parameterizable metaheuristic that improves prediction accuracy and offers greater computational performance than WEGA, a Gaussian-based shape similarity method. OptiPharm includes mechanisms to balance between exploration and exploitation to quickly identify regions in the search space with high-quality solutions and avoid wasting time in non-promising areas. OptiPharm is available upon request via email.
Research on information retrieval model based on ontology

An information retrieval system not only occupies an important position in the network information platform, but also plays an important role in information acquisition, query processing, and wireless sensor networks. It is a procedure to help researchers extract documents from data sets as document retrieval tools. The classic keyword-based information retrieval models neglect the semantic information which is not able to represent the user’s needs. Therefore, how to efficiently acquire personalized information that users need is of concern. The ontology-based systems lack an expert list to obtain accurate index term frequency. In this paper, a domain ontology model with document processing and document retrieval is proposed, and the feasibility and superiority of the domain ontology model are proved by the method of experiment.
Feature Selection Method Based on Grey Wolf Optimization and Simulated Annealing

Background: Feature selection sometimes also known as attribute subset selection is a process in which optimal subset of features are elected with respect to target data by reducing dimensionality and removing irrelevant features. There will be 2n possible solutions for a dataset having n number of features that is difficult to solve by conventional attribute selection method. In such cases metaheuristic-based methods generally outruns the conventional methods. Objective: The main aim of this paper is to enhance the classification accuracy and minimize the number of selected features and error rate. Methods: To achieve the objective, a binary metaheuristic feature selection method bGWOSA based on grey wolf optimization and simulated annealing has been introduced. The proposed feature selection method uses simulated annealing for equalizing the trade-off between exploration and exploitation. The performance of the proposed binary feature selection method has been examined on the ten feature selection benchmark datasets taken from UCI repository and compared with binary cuckoo search, binary particle swarm optimization, binary grey wolf optimization, binary bat algorithm and binary hybrid whale optimization method. Results: The proposed feature selection method achieves the highest accuracy for the most of datasets compared to state-of-the-art. Further, from the experimental and statistical results, efficacy of the proposed feature selection method has been validated. Conclusion: Classification accuracy can be enhanced by employing feature selection methods. Moreover, performance can also be enhanced by tuning the control parameters of metaheuristic methods.
Initial Crypto-Asset Offerings (ICOs), Tokenization and Corporate Governance

This interdisciplinary paper by a mathematician and a legal counsel, both from the Paris 1 Panthéon-Sorbonne University, discusses the potential impacts of the so-called “initial coin offerings”, and of several developments based on distributed ledger technology (“DLT”), on corporate governance. While many academic papers focus mainly on the legal qualification of DLT and crypto-assets, and most notably in relation to the potential definition of the latter as securities/financial instruments, the authors analyze some of the use cases based on DLT technology and their potential for significant changes of the corporate governance analyses.The authors examine in depth the opportunities and risks raised by ICOs, tokenization and distributed ledger technology in terms of corporate governance. First the concepts of ICO and tokenization, and beyond the consequences of distributed ledger technology, will be discussed in the context of a corporate governance perspective; secondly the potential rights granted by different types of crypto-asset holders (whether utility tokens or security tokens) will be carefully analyzed; thirdly the possible governance of a decentralized and distributed organization trying to answer the previous questions will be addressed.
Autonomous Landing of a Micro Aerial Vehicle on a Moving Platform Using a Composite Landmark

In the existing vision-based autonomous landing systems for micro aerial vehicles (MAVs) on moving platforms, the limited range of landmark localization, the unknown measurement bias of the moving platform (such as wheel-slip or inaccurate calibration of encoders), and landing trajectory knotting seriously affect system performance. To overcome the above shortcomings, an autonomous landing system using a composite landmark is proposed in this paper. In the proposed system, a notched ring landmark and two-dimensional landmark are combined as an R2D landmark to provide visual localization over a wide range. In addition, the wheel-slip and imprecise calibration of encoders are modeled as the unknown measurement bias of the encoders and estimated online via an extended Kalman filter. The landing trajectory is planned by a solver as a convex quadratic programming problem in each control cycle. Meanwhile, an iterative algorithm for adding equality constraints is proposed and used to verify whether the planned trajectory is feasible or not. The simulation and actual landing experiment results verify the following: the visual localization with the R2D landmark has the advantages of wide localization range and high localization accuracy, the pose estimation result of the moving platform with unknown encoder measurement bias is continuous and accurate, and the proposed landing trajectory planning algorithm provides a continuous trajectory for reliable landing.
An Interpretable Fast Model for Predicting The Risk of Heart Failure

Lately, thanks to the huge amount of Electronic Health Records (EHR) data, deep learning models have been successfully applied to a variety of clinical prediction problems. Existing state-of-the-art clinical predicting models are usually built with recurrent neural network (RNN) and attention mechanism. However, such RNN based approaches mainly suffer from three limitations on clinical predictions, which if addressed would significantly widen their applicability. (i) Accuracy: The performance of RNN based models drops fast when the length of EHR sequences increases. (ii) Interpretability: The prediction results of RNN based models are hard to interpret due to the nature of deep models. (iii) Efficiency: The sequential property of RNN based models makes the parallelization of computation impossible, and accordingly hurts the efficiency of such models in practice. In this paper, we propose an efficient attention-based model to address the above three challenges simultaneously. In the context of heart failure prediction task, we demonstrate the interpretation capability of our model by visualizing relative connections between events and prediction result, and the high computational efficiency comparing to other baseline methods. Meanwhile, we show that the accuracy of our prediction model is comparable or better than those of other state-of-the-art prediction models in healthcare applications.
Secure and Efficient Searchable Public Key Encryption for Resource Constrained Environment Based on Pairings under Prime Order Group

Searchable public key encryption scheme is a key technique for protecting data confidentiality in today’s cloud environment. Specifically, public key encryption with conjunctive and disjunctive keyword search (PECDK) can provide flexible search options without sacrificing keywords security and thus attracts a lot of attention nowadays. However, the most effective PECDK scheme is based on the inner product encryption (IPE), which needs more time and space cost. In this paper, by utilizing the bilinear pairing with a prime order group, we propose an efficient PECDK scheme needing less time and storage consumption. The proposed scheme is proven to be secure under a rigorous security definition. The theoretical analysis and experimental results demonstrate that our proposed scheme can significantly improve the time and space efficiency over the state-of-the-art scheme.
Measuring the perceptual availability of phonological features during language acquisition using unsupervised binary stochastic autoencoders

Cory Shain, Micha Elsner. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
The SEOSS 33 dataset — Requirements, bug reports, code history, and trace links for entire projects

This paper provides a systematically retrieved dataset consisting of 33 open-source software projects containing a large number of typed artifacts and trace links between them. The artifacts stem from the projects' issue tracking system and source version control system to enable their joint analysis. Enriched with additional metadata, such as time stamps, release versions, component information, and developer comments, the dataset is highly suitable for empirical research, e.g., in requirements and software traceability analysis, software evolution, bug and feature localization, and stakeholder collaboration. It can stimulate new research directions, facilitate the replication of existing studies, and act as benchmark for the comparison of competing approaches. The data is hosted on Harvard Dataverse using DOI 10.7910/DVN/PDDZ4Q accessible via https://bit.ly/2wukCHc.