Reviewerly/exHarmony huggingface dataset:
Effect of the Sampling of a Dataset in the Hyperparameter Optimization Phase over the Efficiency of a Machine Learning Algorithm

Selecting the best configuration of hyperparameter values for a Machine Learning model yields directly in the performance of the model on the dataset. It is a laborious task that usually requires deep knowledge of the hyperparameter optimizations methods and the Machine Learning algorithms. Although there exist several automatic optimization techniques, these usually take significant resources, increasing the dynamic complexity in order to obtain a great accuracy. Since one of the most critical aspects in this computational consume is the available dataset, among others, in this paper we perform a study of the effect of using different partitions of a dataset in the hyperparameter optimization phase over the efficiency of a Machine Learning algorithm. Nonparametric inference has been used to measure the rate of different behaviors of the accuracy, time, and spatial complexity that are obtained among the partitions and the whole dataset. Also, a level of gain is assigned to each partition allowing us to study patterns and allocate whose samples are more profitable. Since Cybersecurity is a discipline in which the efficiency of Artificial Intelligence techniques is a key aspect in order to extract actionable knowledge, the statistical analyses have been carried out over five Cybersecurity datasets.
Wasserstein Adversarial Examples via Projected Sinkhorn Iterations

A rapidly growing area of work has studied the existence of adversarial examples, datapoints which have been perturbed to fool a classifier, but the vast majority of these works have focused primarily on threat models defined by $\ell_p$ norm-bounded perturbations. In this paper, we propose a new threat model for adversarial attacks based on the Wasserstein distance. In the image classification setting, such distances measure the cost of moving pixel mass, which naturally cover standard image manipulations such as scaling, rotation, translation, and distortion (and can potentially be applied to other settings as well). To generate Wasserstein adversarial examples, we develop a procedure for projecting onto the Wasserstein ball, based upon a modified version of the Sinkhorn iteration. The resulting algorithm can successfully attack image classification models, bringing traditional CIFAR10 models down to 3% accuracy within a Wasserstein ball with radius 0.1 (i.e., moving 10% of the image mass 1 pixel), and we demonstrate that PGD-based adversarial training can improve this adversarial accuracy to 76%. In total, this work opens up a new direction of study in adversarial robustness, more formally considering convex metrics that accurately capture the invariances that we typically believe should exist in classifiers. Code for all experiments in the paper is available at this https URL.
Hybrid multivariate pattern analysis combined with extreme learning machine for Alzheimer’s dementia diagnosis using multi-measure rs-fMRI spatial patterns

Background Early diagnosis of Alzheimer’s disease (AD) and Mild Cognitive Impairment (MCI) is essential for timely treatment. Machine learning and multivariate pattern analysis (MVPA) for the diagnosis of brain disorders are explicitly attracting attention in the neuroimaging community. In this paper, we propose a voxel-wise discriminative framework applied to multi-measure resting-state fMRI (rs-fMRI) that integrates hybrid MVPA and extreme learning machine (ELM) for the automated discrimination of AD and MCI from the cognitive normal (CN) state. Materials and methods We used two rs-fMRI cohorts: the public Alzheimer’s disease Neuroimaging Initiative database (ADNI2) and an in-house Alzheimer’s disease cohort from South Korea, both including individuals with AD, MCI, and normal controls. After extracting three-dimensional (3-D) patterns measuring regional coherence and functional connectivity during the resting state, we performed univariate statistical t-tests to generate a 3-D mask that retained only voxels showing significant changes. Given the initial univariate features, to enhance discriminative patterns, we implemented MVPA feature reduction using support vector machine-recursive feature elimination (SVM-RFE), and least absolute shrinkage and selection operator (LASSO), in combination with the univariate t-test. Classifications were performed by an ELM, and its efficiency was compared to linear and nonlinear (radial basis function) SVMs. Results The maximal accuracies achieved by the method in the ADNI2 cohort were 98.86% (p<0.001) and 98.57% (p<0.001) for AD and MCI vs. CN, respectively. In the in-house cohort, the same accuracies were 98.70% (p<0.001) and 94.16% (p<0.001). Conclusion From a clinical perspective, combining extreme learning machine and hybrid MVPA applied on concatenations of multiple rs-fMRI biomarkers can potentially assist the clinicians in AD and MCI diagnosis.
Cyber Security Breaches Survey 2020

This is the fifth annual survey of businesses and charities carried out by Ipsos Mori for the UK Government's Department for Digital, Culture, Media &amp; Sport (DCMS). And it will be a surprise to no-one that it finds a worsening situation in which threats have evolved and increased in frequency.
Learning Explainable Models Using Attribution Priors

Two important topics in deep learning both involve incorporating humans into the modeling process: Model priors transfer information from humans to a model by constraining the model's parameters; Model attributions transfer information from a model to humans by explaining the model's behavior. We propose connecting these topics with attribution priors (this https URL), which allow humans to use the common language of attributions to enforce prior expectations about a model's behavior during training. We develop a differentiable axiomatic feature attribution method called expected gradients and show how to directly regularize these attributions during training. We demonstrate the broad applicability of attribution priors ($\Omega$) by presenting three distinct examples that regularize models to behave more intuitively in three different domains: 1) on image data, $\Omega_{\textrm{pixel}}$ encourages models to have piecewise smooth attribution maps; 2) on gene expression data, $\Omega_{\textrm{graph}}$ encourages models to treat functionally related genes similarly; 3) on a health care dataset, $\Omega_{\textrm{sparse}}$ encourages models to rely on fewer features. In all three domains, attribution priors produce models with more intuitive behavior and better generalization performance by encoding constraints that would otherwise be very difficult to encode using standard model priors.
Verb Argument Structure Alternations in Word and Sentence Embeddings

Verbs occur in different syntactic environments, or frames. We investigate whether artificial neural networks encode grammatical distinctions necessary for inferring the idiosyncratic frame-selectional properties of verbs. We introduce five datasets, collectively called FAVA, containing in aggregate nearly 10k sentences labeled for grammatical acceptability, illustrating different verbal argument structure alternations. We then test whether models can distinguish acceptable English verb-frame combinations from unacceptable ones using a sentence embedding alone. For converging evidence, we further construct LaVA, a corresponding word-level dataset, and investigate whether the same syntactic features can be extracted from word embeddings. Our models perform reliable classifications for some verbal alternations but not others, suggesting that while these representations do encode fine-grained lexical information, it is incomplete or can be hard to extract. Further, differences between the word- and sentence-level models show that some information present in word embeddings is not passed on to the down-stream sentence embeddings.
Controllable Text-to-Image Generation

In this paper, we propose a novel controllable text-to-image generative adversarial network (ControlGAN), which can effectively synthesise high-quality images and also control parts of the image generation according to natural language descriptions. To achieve this, we introduce a word-level spatial and channel-wise attention-driven generator that can disentangle different visual attributes, and allow the model to focus on generating and manipulating subregions corresponding to the most relevant words. Also, a word-level discriminator is proposed to provide fine-grained supervisory feedback by correlating words with image regions, facilitating training an effective generator which is able to manipulate specific visual attributes without affecting the generation of other content. Furthermore, perceptual loss is adopted to reduce the randomness involved in the image generation, and to encourage the generator to manipulate specific attributes required in the modified text. Extensive experiments on benchmark datasets demonstrate that our method outperforms existing state of the art, and is able to effectively manipulate synthetic images using natural language descriptions.
What drives users’ participation in online social Q&amp;A communities? An empirical study based on social cognitive theory

Purpose The purpose of this paper is to examine some drivers of users’ participation in online social question-and-answer (Q&amp;A) communities based on social cognitive theory and then identify the underlying mechanism of this process. Design/methodology/approach This study developed a research model to test the proposed hypotheses, and an online survey was employed to collected data. Totally, 313 valid responses were collected, and partial least squares structural equation modeling was adopted to analyze these data. Findings This study empirically finds that the outcome expectations (personal outcome expectations and knowledge self-management outcome expectations) are positively related to participation in online social Q&amp;A communities. At the same time, users’ self-efficacy positively influences their participation behaviors. It can not only directly motivate users’ participation, but also indirectly promote participation behaviors through the two dimensions of outcome expectations. Besides, perceived expertise and perceived similarity are two positive and significant environmental elements affecting users’ participation. Originality/value This study extends the understanding about how participation behaviors will be motivated in the context of online social Q&amp;A communities. Drawing on the social cognitive theory, constructs were established based on the features of these communities. Meanwhile, some mediating effects in the motivating process were also discussed.
Link Prediction in Time-Evolving Criminal Network With Deep Reinforcement Learning Technique

The prediction of hidden or missing links in a criminal network, which represent possible interactions between individuals, is a significant problem. The criminal network prediction models commonly rely on Social Network Analysis (SNA) metrics. These models leverage on machine learning (ML) techniques to enhance the predictive accuracy of the models and processing speed. The problem with the use of classical ML techniques such as support vector machine (SVM), is the dependency on the availability of large dataset for training purpose. However, recent ground breaking advances in the research of deep reinforcement learning (DRL) techniques have developed methods of training ML models through self-generated dataset. In view of this, DRL could be applied to other domains with relatively smaller dataset such as criminal networks. Prior to this research, few, if any, previous works have explored the prediction of links within criminal networks that could appear and/or disappear over time by leveraging on DRL technique. Therefore, in this paper, the primary objective is to construct a time-based link prediction model (TDRL) by leveraging on DRL technique to train using a relatively small real-world criminal dataset that evolves over time. The experimental results indicate that the predictive accuracy of the DRL model trained on the temporal dataset is significantly better than other ML models that are trained only with the dataset at specific snapshot in time.
CATER: A diagnostic dataset for Compositional Actions & TEmporal Reasoning

Computer vision has undergone a dramatic revolution in performance, driven in large part through deep features trained on large-scale supervised datasets. However, much of these improvements have focused on static image analysis; video understanding has seen rather modest improvements. Even though new datasets and spatiotemporal models have been proposed, simple frame-by-frame classification methods often still remain competitive. We posit that current video datasets are plagued with implicit biases over scene and object structure that can dwarf variations in temporal structure. In this work, we build a video dataset with fully observable and controllable object and scene bias, and which truly requires spatiotemporal understanding in order to be solved. Our dataset, named CATER, is rendered synthetically using a library of standard 3D objects, and tests the ability to recognize compositions of object movements that require long-term reasoning. In addition to being a challenging dataset, CATER also provides a plethora of diagnostic tools to analyze modern spatiotemporal video architectures by being completely observable and controllable. Using CATER, we provide insights into some of the most recent state of the art deep video architectures.
Shadow Elimination Algorithm Using Color and Texture Features

Shadow detection and removal in real scene images are a significant problem for target detection. This work proposes an improved shadow detection and removal algorithm for urban video surveillance. First, the foreground is detected by background subtraction and the shadow is detected by HSV color space. Using local variance and OTSU method, we obtain the moving targets with texture features. According to the characteristics of shadow in HSV space and texture feature, the shadow is detected and removed to eliminate the shadow interference for the subsequent processing of moving targets. Finally, we embed our algorithm into C/S framework based on the HTML5 web socket protocol. Both the experimental and actual operation results show that the proposed algorithm is efficient and robust in target detection and shadow detection and removal under different scenes.
Automatic Segmentation of Multiple Organs on 3D CT Images by Using Deep Learning Approaches

This chapter focuses on modern deep learning techniques that are proposed for automatically recognizing and segmenting multiple organ regions on three-dimensional (3D) computed tomography (CT) images. CT images are widely used to visualize 3D anatomical structures composed of multiple organ regions inside the human body in clinical medicine. Automatic recognition and segmentation of multiple organs on CT images is a fundamental processing step of computer-aided diagnosis, surgery, and radiation therapy systems, which aim to achieve precision and personalized medicines. In this chapter, we introduce our recent works on addressing the issue of multiple organ segmentation on 3D CT images by using deep learning, a completely novel approach, instead of conventional segmentation methods originated from traditional digital image processing techniques. We evaluated and compared the segmentation performances of two different deep learning approaches based on 2D- and 3D deep convolutional neural networks (CNNs) without and with a pre-processing step. A conventional method based on a probabilistic atlas algorithm, which presented the best performance within the conventional approaches, was also adopted as a baseline for performance comparison. A dataset containing 240 CT scans of different portions of human bodies was used for training the CNNs and validating the segmentation performance of the learning results. A maximum number of 17 types of organ regions in each CT scan were segmented automatically and validated with the human annotations by using ratio of intersection over union (IoU) as the criterion. Our experimental results showed that the IoUs of the segmentation results had a mean value of 79% and 67% by averaging 17 types of organs that were segmented by the proposed 3D and 2D deep CNNs, respectively. All results using the deep learning approaches showed better accuracy and robustness than the conventional segmentation method that used the probabilistic atlas algorithm. The effectiveness and usefulness of deep learning approaches were demonstrated for multiple organ segmentation on 3D CT images.
Hybrid neural network with cost-sensitive support vector machine for class-imbalanced multimodal data

Although deep learning exhibits advantages in various applications involving multimodal data, it cannot effectively solve the class-imbalance problem. Herein, we propose a hybrid neural network with a cost-sensitive support vector machine (hybrid NN-CSSVM) for class-imbalanced multimodal data. We used a fused multiple-network structure obtained by extracting the features of different modality data, and used cost-sensitive support vector machines (SVMs) as a classifier. To alleviate the insufficiency of learning from minority-class data, our proposed cost-sensitive SVM loss function reflects different weights of misclassification errors from both majority and minority classes, by controlling cost parameters. Additionally, we present a theoretical setting of the cost parameters in our model. The proposed model is validated on real datasets that range from low to high imbalance ratios. By exploiting the complementary advantages of two architectures, the hybrid NN-CSSVM performs excellently, even with data having a minor-class proportion of only 2%.
Edge Heuristic GAN for Non-Uniform Blind Deblurring

Non-uniform blur, mainly caused by camera shake and motions of multiple objects, is one of the most common causes of image quality degradation. However, the traditional blind deblurring methods based on blur kernel estimation do not perform well on complicated non-uniform motion blurs. However, recent studies show that GAN-based approaches achieve impressive performance on deblurring tasks. In this letter, to further improve the performance of GAN-based methods on deblurring tasks, we propose an edge heuristic multi-scale generative adversarial network (GAN), which uses the coarse-to-fine scheme to restore clear images in an end-to-end manner. In particular, an edge-generated network is designed to generate sharp edges as auxiliary information to guide the deblurring process. Furthermore, We propose a hierarchical content loss function for deblurring tasks. Extensive experiments on different datasets show that our method achieves state-of-the-art performance in dynamic scene deblurring.
Versatile laser-free trapped-ion entangling gates

We present a general theory for laser-free entangling gates with trapped-ion hyperfine qubits, using either static or oscillating magnetic-field gradients combined with a pair of uniform microwave fields symmetrically detuned about the qubit frequency. By transforming into a 'bichromatic' interaction picture, we show that either σ^ϕ⊗σ^ϕ or σ^z⊗σ^z geometric phase gates can be performed. The gate basis is determined by selecting the microwave detuning. The driving parameters can be tuned to provide intrinsic dynamical decoupling from qubit frequency fluctuations. The σ^z⊗σ^z gates can be implemented in a novel manner which eases experimental constraints. We present numerical simulations of gate fidelities assuming realistic parameters.
Bayesian Gaussian Mixture Model for Robotic Policy Imitation

A common approach to learn robotic skills is to imitate a demonstrated policy. Due to the compounding of small errors and perturbations, this approach may let the robot leave the states in which the demonstrations were provided. This requires the consideration of additional strategies to guarantee that the robot will behave appropriately when facing unknown states. We propose to use a Bayesian method to quantify the action uncertainty at each state. The proposed Bayesian method is simple to set up, computationally efficient, and can adapt to a wide range of problems. Our approach exploits the estimated uncertainty to fuse the imitation policy with additional policies. It is validated on a Panda robot with the imitation of three manipulation tasks in the continuous domain using different control input/state pairs.
Research on Medical Question Answering System Based on Knowledge Graph

To meet the high-efficiency question answering needs of existing patients and doctors, this system integrates medical professional knowledge, knowledge graphs, and question answering systems that conduct man-machine dialogue through natural language. This system locates the medical field, uses crawler technology to use vertical medical websites as data sources, and uses diseases as the core entity to construct a knowledge graph containing 44,000 knowledge entities of 7 types and 300,000 entities of 11 kinds. It is stored in the Neo4j graph database, using rule-based matching methods and string-matching algorithms to construct a domain lexicon to classify and query questions. This system has specific practical value in the medical field knowledge graph and question answering system.
China's central bank digital currency and its impacts on monetary policy and payment competition: Game changer or regulatory toolkit?

After carrying out some preparatory work for several years, China is likely to issue central bank digital currency (CBDC). Built upon the relevant experience and research in other countries, China has been carefully designing its own version of digital currency and issuance mechanism with a purpose of not disrupting financial intermediation. Nevertheless, it still leaves some residual issues unanswered, in particular, the narrow bank concern and application scenarios. While this article proposes to use interest rates as a policy solution to mitigate the narrow bank concern, the ultimate success of CBDC still depends on market competition. China's CBDC, while initially being used as a regulatory toolkit to compete with other digital currencies, has potential to become a game changer once used in a large scale.
MDU-Net: multi-scale densely connected U-Net for biomedical image segmentation

Biomedical image segmentation plays a central role in quantitative analysis, clinical diagnosis, and medical intervention. In the light of the fully convolutional networks (FCN) and U-Net, deep convolutional networks (DNNs) have made significant contributions to biomedical image segmentation applications. In this paper, we propose three different multi-scale dense connections (MDC) for the encoder, the decoder of U-shaped architectures, and across them. Based on three dense connections, we propose a multi-scale densely connected U-Net (MDU-Net) for biomedical image segmentation. MDU-Net directly fuses the neighboring feature maps with different scales from both higher layers and lower layers to strengthen feature propagation in the current layer. Multi-scale dense connections, which contain shorter connections between layers close to the input and output, also make a much deeper U-Net possible. Besides, we introduce quantization to alleviate the potential overfitting in dense connections, and further improve the segmentation performance. We evaluate our proposed model on the MICCAI 2015 Gland Segmentation (GlaS) dataset. The three MDC improve U-Net performance by up to 1.8% on test A and 3.5% on test B in the MICCAI Gland dataset. Meanwhile, the MDU-Net with quantization obviously improves the segmentation performance of original U-Net.