Reviewerly/exHarmony huggingface dataset:
Dilated Saliency U-Net for White Matter Hyperintensities Segmentation Using Irregularity Age Map

White matter hyperintensities (WMH) appear as regions of abnormally high signal intensity on T2-weighted magnetic resonance image (MRI) sequences. In particular, WMH have been noteworthy in age-related neuroscience for being a crucial biomarker for all types of dementia and brain aging processes. The automatic WMH segmentation is challenging because of their variable intensity range, size and shape. U-Net tackles this problem through the dense prediction and has shown competitive performances not only on WMH segmentation/detection but also on varied image segmentation tasks. However, its network architecture is high complex. In this study, we propose the use of Saliency U-Net and Irregularity map (IAM) to decrease the U-Net architectural complexity without performance loss. We trained Saliency U-Net using both: a T2-FLAIR MRI sequence and its correspondent IAM. Since IAM guides locating image intensity irregularities, in which WMH are possibly included, in the MRI slice, Saliency U-Net performs better than the original U-Net trained only using T2-FLAIR. The best performance was achieved with fewer parameters and shorter training time. Moreover, the application of dilated convolution enhanced Saliency U-Net by recognizing the shape of large WMH more accurately through multi-context learning. This network named Dilated Saliency U-Net improved Dice coefficient score to 0.5588 which was the best score among our experimental models, and recorded a relatively good sensitivity of 0.4747 with the shortest training time and the least number of parameters. In conclusion, based on our experimental results, incorporating IAM through Dilated Saliency U-Net resulted an appropriate approach for WMH segmentation.
Towards Conversational Syntax for Domain-Specific Languages using Chatbots.

Traditionally, users have interacted with computers through graphical or command line interfaces.However, these may still be too technical for certain users, or cumbersome to use in some scenarios (e.g., in mobility).To tackle this issue, recent advances in natural language (NL) processing have boosted the proliferation of chatbots: programs whose user interface is NL and are frequently integrated within social networks.In this paper, we explore the usage of NL as concrete syntax for domainspecific modelling languages, and propose an approach to automate the creation of modelling chatbots that converse with users to assist them in building domain-specific models.As chatbots are deployed on social networks, modelling becomes collaborative.We provide an implementation of our approach on top of Google's DialogFlow, and illustrate its usefulness on the basis of a case study to build and deploy streaming data applications using a conversational interface.
Testing the predictive strength of the comparative method: an ongoing experiment on unattested words in Western Kho‐Bwa languages

&#x0D; &#x0D; &#x0D; Although it is well‐known to most historical linguists that the comparative method could in principle be used to predict hitherto unobserved words in genetically related languages, the task of word prediction is rarely discussed in the linguistic literature. Here, we introduce 'reflex retrodiction' as a new task for historical linguistics and report on an ongoing experiment in which we use a computer‐assisted workflow to retrodict reflexes for so far unobserved words in eight varieties of Western Kho‐Bwa (a subgroup of Sino‐Tibetan). Since, at the time of writing this report, the experiment is still ongoing, we do not report concrete results, but instead provide an estimate of our expectations by testing the performance of the computational part of our workflow on existing language data. Our results suggest that reflex retrodiction has the potential of becoming a useful tool for historically oriented fieldwork.&#x0D; &#x0D; &#x0D;
Not So Fast: Analyzing the Performance of WebAssembly vs. Native Code

All major web browsers now support WebAssembly, a low-level bytecode intended to serve as a compilation target for code written in languages like C and C++. A key goal of WebAssembly is performance parity with native code; previous work reports near parity, with many applications compiled to WebAssembly running on average 10% slower than native code. However, this evaluation was limited to a suite of scientific kernels, each consisting of roughly 100 lines of code. Running more substantial applications was not possible because compiling code to WebAssembly is only part of the puzzle: standard Unix APIs are not available in the web browser environment. To address this challenge, we build Browsix-Wasm, a significant extension to Browsix that, for the first time, makes it possible to run unmodified WebAssembly-compiled Unix applications directly inside the browser. We then use Browsix-Wasm to conduct the first large-scale evaluation of the performance of WebAssembly vs. native. Across the SPEC CPU suite of benchmarks, we find a substantial performance gap: applications compiled to WebAssembly run slower by an average of 45% (Firefox) to 55% (Chrome), with peak slowdowns of 2.08x (Firefox) and 2.5x (Chrome). We identify the causes of this performance degradation, some of which are due to missing optimizations and code generation issues, while others are inherent to the WebAssembly platform.
Neural Logic Reinforcement Learning

Deep reinforcement learning (DRL) has achieved significant breakthroughs in various tasks. However, most DRL algorithms suffer a problem of generalising the learned policy, which makes the policy performance largely affected even by minor modifications of the training environment. Except that, the use of deep neural networks makes the learned policies hard to be interpretable. To address these two challenges, we propose a novel algorithm named Neural Logic Reinforcement Learning (NLRL) to represent the policies in reinforcement learning by first-order logic. NLRL is based on policy gradient methods and differentiable inductive logic programming that have demonstrated significant advantages in terms of interpretability and generalisability in supervised tasks. Extensive experiments conducted on cliff-walking and blocks manipulation tasks demonstrate that NLRL can induce interpretable policies achieving near-optimal performance while showing good generalisability to environments of different initial states and problem sizes.
Mobile language learning applications for Arabic speaking migrants – a usability perspective

Abstract Usability testing with mobile applications (apps) plays an important role in determining the ease of use of an app as well as in informing their design and development. The purpose of this research is to investigate the usability of three language learning apps with a sample of Arabic speaking migrants who have recently arrived to Sweden. In addition, We surveyed available apps for the target group on both Android and Apple market stores and used a framework for categorizing language learning apps to guide the design and development of our own app. The outcomes show that in order for users to engage in an app, there are a number of obstacles that need to be overcome to make an app motivating enough to use, such as lack of variation in functionality, instructions and feedback. Our app, Minclusion, was developed from the input obtained from the usability studies.
Let’s Take a Walk on Superpixels Graphs: Deformable Linear Objects Segmentation and Model Estimation

While robotic manipulation of rigid objects is quite straightforward, coping with deformable objects is an open issue. More specifically, tasks like tying a knot, wiring a connector or even surgical suturing deal with the domain of Deformable Linear Objects (DLOs). In particular the detection of a DLO is a non-trivial problem especially under clutter and occlusions (as well as self-occlusions). The pose estimation of a DLO results into the identification of its parameters related to a designed model, e.g. a basis spline. It follows that the stand-alone segmentation of a DLO might not be sufficient to conduct a full manipulation task. This is why we propose a novel framework able to perform both a semantic segmentation and b-spline modeling of multiple deformable linear objects simultaneously without strict requirements about environment (i.e. the background). The core algorithm is based on biased random walks over the Region Adiacency Graph built on a superpixel oversegmentation of the source image. The algorithm is initialized by a Convolutional Neural Networks that detects the DLO’s endcaps. An open source implementation of the proposed approach is also provided to easy the reproduction of the whole detection pipeline along with a novel cables dataset in order to encourage further experiments.
GreyCat: Efficient what-if analytics for data in motion at scale

Over the last few years, data analytics shifted from adescriptive era, confined to the explanation of past events, to the emergence of predictive techniques. Nonetheless, existing predictive techniques still fail to effectively explore alternative futures, which continuously diverge from current situations when exploring the effects of what-if decisions. Enabling prescriptive analytics therefore calls for the design of scalable systems that can cope with the complexity and the diversity of underlying data models. In this article, we address this challenge by combining graphs and time series within a scalable storage system that can organize a massive amount of unstructured and continuously changing data into multi-dimensional data models, called Many-Worlds Graphs. We demonstrate that our open source implementation, GreyCat, can efficiently fork and update thousands of parallel worlds composed of millions of timestamped nodes, such as what-if exploration.
Geoscience analysis on Twitter

Social media is increasingly being used to share near-real-time analysis of emergent and sometimes hazardous geological events. Such open discussion can drive new research directions and collaborations for geoscientists.
AEkNN: An AutoEncoder kNN–Based Classifier With Built-in Dimensionality Reduction

High dimensionality tends to be a challenge for most machine learning tasks, including classification.There are different classification methodologies, of which instance-based learning is one.One of the best known members of this family is the k-nearest neighbors (kNNs) algorithm.Its strategy relies on searching a set of nearest instances.In high-dimensional spaces, the distances between examples lose significance.Therefore, kNN, in the same way as many other classifiers, tends to worsen its performance as the number of input variables grows.In this study, AEkNN, a new kNN-based algorithm with built-in dimensionality reduction, is presented.Aiming to obtain a new representation of the data, having a lower dimensionality but with more informational features, AEkNN internally uses autoencoders.From this new vector of features the computed distances should be more significant, thus providing a way to choose better neighbors.An experimental evaluation of the new proposal is conducted, analyzing several configurations and comparing them against the original kNN algorithm and classical dimensionality reduction methods.The obtained conclusions demonstrate that AEkNN offers better results in predictive and runtime performance.
Mutual information inspired feature selection using kernel canonical correlation analysis

This paper proposes a filter-based feature selection method by combining the measurement of kernel canonical correlation analysis (KCCA) with the mutual information (MI)-based feature selection method, named mRMJR-KCCA. The mRMJR-KCCA maximizes the relevance between the feature candidate and the target class labels and simultaneously minimizes the joint redundancy between the feature candidate and the already selected features in the view of KCCA. To improve the computation efficiency, we adopt the Incomplete Cholesky Decomposition to approximate the kernel matrix in implementing the KCCA in mRMJR-KCCA for larger-size datasets. The proposed method is experimentally evaluated on 13 classification-associated datasets. Compared with certain popular feature selection methods, the experimental results demonstrate the better performance of the proposed mRMJR-KCCA.
Online Probabilistic Goal Recognition over Nominal Models

This paper revisits probabilistic, model-based goal recognition to study the implications of the use of nominal models to estimate the posterior probability distribution over a finite set of hypothetical goals. Existing model-based approaches rely on expert knowledge to produce symbolic descriptions of the dynamic constraints domain objects are subject to, and these are assumed to produce correct predictions. We abandon this assumption to consider the use of nominal models that are learnt from observations on transitions of systems with unknown dynamics. Leveraging existing work on the acquisition of domain models via learning for Hybrid Planning we adapt and evaluate existing goal recognition approaches to analyze how prediction errors, inherent to system dynamics identification and model learning techniques have an impact over recognition error rates.
Predicting Credit Card Transaction Fraud Using Machine Learning Algorithms

Credit card fraud is a wide-ranging issue for financial institutions, involving theft and fraud committed using a payment card.In this paper, we explore the application of linear and nonlinear statistical modeling and machine learning models on real credit card transaction data.The models built are supervised fraud models that attempt to identify which transactions are most likely fraudulent.We discuss the processes of data exploration, data cleaning, variable creation, feature selection, model algorithms, and results.Five different supervised models are explored and compared including logistic regression, neural networks, random forest, boosted tree and support vector machines.The boosted tree model shows the best fraud detection result (FDR = 49.83%) for this particular data set.The resulting model can be utilized in a credit card fraud detection system.A similar model development process can be performed in related business domains such as insurance and telecommunications, to avoid or detect fraudulent activity.
Credit Risk Prediction in an Imbalanced Social Lending Environment Based on XGBoost

The credit data of small enterprises are imbalanced, which leads to the failure of the general classification models in predicting the credit performance directly. In order to reduce the negative impact of imbalanced data set on model, we construct a credit rating model based on K-XGBoost from the perspective of a hybrid model, which is a new hybrid model proposed by this paper combining the advantages of both KMeans++ and XGBoost. And we construct XGBoost credit rating models based on SMOTE series algorithms from the perspective of random oversampling. According to empirical analysis, the indicators of K-XGBoost model are superior to the XGBoost model of dataset balanced by the Borderline SMOTE algorithm, and can reduce the unnecessary costs by accelerating modeling.In order to meet the needs of commercial banks for risk control and market share, we construct a three-class credit rating model based on XGBoost, which divides customers into stable good customers, unstable good customers and bad customers. When the loan policy is tightened, only stable customers will be the target customer group. Empirical research shows that the above three models have good performance and strong generalization ability.
Classification of Microcalcification Clusters in Digital Mammograms Using a Stack Generalization Based Classifier

This paper presents a machine learning based approach for the discrimination of malignant and benign microcalcification (MC) clusters in digital mammograms. A series of morphological operations was carried out to facilitate the feature extraction from segmented microcalcification. A combination of morphological, texture, and distribution features from individual MC components and MC clusters were extracted and a correlation-based feature selection technique was used. The clinical relevance of the selected features is discussed. The proposed method was evaluated using three different databases: Optimam Mammography Image Database (OMI-DB), Digital Database for Screening Mammography (DDSM), and Mammographic Image Analysis Society (MIAS) database. The best classification accuracy ( 95 . 00 ± 0 . 57 %) was achieved for OPTIMAM using a stack generalization classifier with 10-fold cross validation obtaining an A z value equal to 0 . 97 ± 0 . 01 .
Pixelwise Estimation of Signal-Dependent Image Noise Using Deep Residual Learning

In traditional image denoising, noise level is an important scalar parameter which decides how much the input noisy image should be smoothed. Existing noise estimation methods often assume that the noise level is constant at every pixel. However, real-world noise is signal dependent, or the noise level is not constant over the whole image. In this paper, we attempt to estimate the precise and pixelwise noise level instead of a simple global scalar. To the best of our knowledge, this is the first work on the problem. Particularly, we propose a deep convolutional neural network named "deep residual noise estimator" (DRNE) for pixelwise noise-level estimation. We carefully design the architecture of the DRNE, which consists of a stack of customized residual blocks without any pooling or interpolation operation. The proposed DRNE formulates the process of noise estimation as pixel-to-pixel prediction. The experimental results show that the DRNE can achieve better performance on nonhomogeneous noise estimation than state-of-the-art methods. In addition, the DRNE can bring denoising performance gains in removing signal-dependent Gaussian noise when working with recent deep learning denoising methods.
Quasi-Periodic WaveNet Vocoder: A Pitch Dependent Dilated Convolution Model for Parametric Speech Generation

In this paper, we propose a quasi-periodic neural network (QPNet) vocoder with a novel network architecture named pitch-dependent dilated convolution (PDCNN) to improve the pitch controllability of WaveNet (WN) vocoder. The effectiveness of the WN vocoder to generate high-fidelity speech samples from given acoustic features has been proved recently. However, because of the fixed dilated convolution and generic network architecture, the WN vocoder hardly generates speech with given F0 values which are outside the range observed in training data. Consequently, the WN vocoder lacks the pitch controllability which is one of the essential capabilities of conventional vocoders. To address this limitation, we propose the PDCNN component which has the time-variant adaptive dilation size related to the given F0 values and a cascade network structure of the QPNet vocoder to generate quasi-periodic signals such as speech. Both objective and subjective tests are conducted, and the experimental results demonstrate the better pitch controllability of the QPNet vocoder compared to the same and double sized WN vocoders while attaining comparable speech qualities. Index Terms: WaveNet, vocoder, quasi-periodic signal, pitch-dependent dilated convolution, pitch controllability
SINGLE SHOT APPEARANCE MODEL (SSAM) FOR MULTI-TARGET TRACKING

An appearance model plays a crucial rule in multi-target tracking. In traditional approaches, the two steps of appearance modeling i.e visual representation and statistically similarity measure are modeled separately. Visual representation is achieved either through hand-crafted features or deep features and statically similarity is measure through a cross entropy loss function. A loss function based on crossentropy (KL-divergence, mutual information) find closely related probability distribution for the targets. However, if the targets have similar visual representation, it ends up mixing the targets. To tackle this problem, we come up with a synergetic appearance model named Single Shot Appearance Model (SSAM) based on Siamese neural network. The network is trained with a contrastive loss function for finding the similarity between different targets in a single shot. The input to the network is two target patches and based on their similarity, a contrastive score is output by the network. The proposed model is evaluated on accumulative dissimilarity metric on three datasets. Quantitatively, promising results are achieved against three baseline methods.
T2-ETS-IE: A Type-2 Evolutionary Takagi–Sugeno Fuzzy Inference System With the Information Entropy-Based Pruning Technique

We introduce a new nonlinear system identification technique, leveraging the benefits of the Type-2 Evolutionary Takagi-Sugeno (T2-ETS) fuzzy system. The major advantage of our proposed system identification technique is mainly due to its ability to learn-from-scratch while accommodating the footprint-of-uncertainties (FoUs). To support its mission to achieve a reasonably high prediction accuracy for uncertain nonlinear dynamic systems, we also introduce a new type reduction method to convert Type-2 fuzzy systems into their Type-1 counterparts. As a part of its efficient pruning strategy, the proposed system incorporates the concept of information entropy to avoid over fitting, which is a highly undesirable issue in modeling. We demonstrate the effectiveness of our system identification technique in achieving a delicate balance between minimizing the complexity of the acquired fuzzy model and maximizing the prediction accuracy. To highlight the efficacy of our algorithm, we employ a set of challenging pH neutralization data, known for its substantial nonlinearity, in addition to the dynamics of a nonlinear mechanical system. We conclude our research by conducting a rigorous comparative study to quantify the relative merits of our proposed technique with respect to the previous ETS algorithm (as its predecessor), the well-known KM-type reduction technique, and the higher-order discrete transfer functions, widely implemented in most conventional mathematical modeling techniques.
Accelerating Deep Unsupervised Domain Adaptation with Transfer Channel Pruning

Deep unsupervised domain adaptation (UDA) has recently received increasing attention from researchers. However, existing methods are computationally intensive due to the computation cost of Convolutional Neural Networks (CNN) adopted by most work. To date, there is no effective network compression method for accelerating these models. In this paper, we propose a unified Transfer Channel Pruning (TCP) approach for accelerating UDA models. TCP is capable of compressing the deep UDA model by pruning less important channels while simultaneously learning transferable features by reducing the cross-domain distribution divergence. Therefore, it reduces the impact of negative transfer and maintains competitive performance on the target task. To the best of our knowledge, TCP is the first approach that aims at accelerating deep UDA models. TCP is validated on two benchmark datasets Office-31 and ImageCLEF-DA with two common backbone networks VGG16 and ResNet50. Experimental results demonstrate that TCP achieves comparable or better classification accuracy than other comparison methods while significantly reducing the computational cost. To be more specific, in VGG16, we get even higher accuracy after pruning 26% floating point operations (FLOPs); in ResNet50, we also get higher accuracy on half of the tasks after pruning 12% FLOPs. We hope that TCP will open a new door for future research on accelerating transfer learning models.