Reviewerly/exHarmony huggingface dataset:
Detecting topological edge states with the dynamics of a qubit

We consider the Su-Schrieffer-Heeger (SSH) chain, which has 0, 1, or 2 topological edge states depending on the ratio of the hopping parameters and the parity of the chain length. We couple a qubit to one edge of the SSH chain and a semi-infinite undimerized chain to the other, and evaluate the dynamics of the qubit. By evaluating the decoherence rate of the qubit we can probe the edge states of the SSH chain. The rate shows strong even-odd oscillations with the number of sites reflecting the presence or absence of edge states. Hence, the qubit acts as an efficient detector of the topological edge states of the SSH model. This can be generalized to other topological systems.
Emotions Are Universal: Learning Sentiment Based Representations of Resource-Poor Languages Using Siamese Networks

Machine learning approaches in sentiment analysis principally rely on the abundance of resources. To limit this dependence, we propose a novel method called Siamese Network Architecture for Sentiment Analysis (SNASA) to learn representations of resource-poor languages by jointly training them with resource-rich languages using a siamese network. SNASA model consists of twin Bi-directional Long Short-Term Memory Recurrent Neural Networks (Bi-LSTM RNN) with shared parameters joined by a contrastive loss function, based on a similarity metric. The model learns the sentence representations of resource-poor and resource-rich language in a common sentiment space by using a similarity metric based on their individual sentiments. The model, hence, projects sentences with similar sentiment closer to each other and the sentences with different sentiment farther from each other. Experiments on large-scale datasets of resource-rich languages - English and Spanish and resource-poor languages - Hindi and Telugu reveal that SNASA outperforms the state-of-the-art sentiment analysis approaches based on distributional semantics, semantic rules, lexicon lists and deep neural network representations without shared parameters.
CAPTOR

Nowadays, the evolution of deep learning and cloud service significantly promotes neural network based mobile applications. Although intelligent and prolific, those applications still lack certain flexibility: For classification tasks, neural networks are generally trained online with vast classification targets to cover various utilization contexts. However, only partial classes are practically tested due to individual mobile user preference and application specificity. Thus the unneeded classes cause considerable computation and communication cost. In this work, we propose CAPTOR - a class-level reconfiguration framework for Convolutional Neural Networks (CNNs). By identifying the class activation preference of convolutional filters through feature interest visualization and gradient analysis, CAPTOR can effectively cluster and adaptively prune the filters associated with unneeded classes. Therefore, CAPTOR enables class-level CNN reconfiguration for network model compression and local deployment on mobile devices. Experiment shows that, CAPTOR can reduce computation load for VGG-16 by up to 40.5% and 37.9% energy consumption with ignored loss of accuracy. For AlexNet, CAPTOR also reduces computation load by up to 42.8% and 37.6% energy consumption with less than 3% loss in accuracy.
New Algorithm of Response Curve for Fitting HDR Image

Based on the process of generating HDR images from LDR image sequences with different light exposures in the same scene, a new fitting method of camera response curves is proposed to solve the problem that the boundary of the fitting algorithm of camera response curves will be blurred and it is difficult to determine and verify the accuracy of the fitting curves. The optimal response curve is fitted by increasing LDR images step by step through considering the pixel value and texture characteristics. In order to validate the fitting effect of curves, we compare the photographed images and the real images in different time intervals on the basis of HDR images and response curves. We use RGB and gray image experiments to compare the current mainstream algorithms and the accuracy of our proposed algorithm can reach 96%, which has robustness.
ASSERT: Anti-Spoofing with Squeeze-Excitation and Residual neTworks

We present JHU's system submission to the ASVspoof 2019 Challenge: Anti-Spoofing with Squeeze-Excitation and Residual neTworks (ASSERT). Anti-spoofing has gathered more and more attention since the inauguration of the ASVspoof Challenges, and ASVspoof 2019 dedicates to address attacks from all three major types: text-to-speech, voice conversion, and replay. Built upon previous research work on Deep Neural Network (DNN), ASSERT is a pipeline for DNN-based approach to anti-spoofing. ASSERT has four components: feature engineering, DNN models, network optimization and system combination, where the DNN models are variants of squeeze-excitation and residual networks. We conducted an ablation study of the effectiveness of each component on the ASVspoof 2019 corpus, and experimental results showed that ASSERT obtained more than 93% and 17% relative improvements over the baseline systems in the two sub-challenges in ASVspooof 2019, ranking ASSERT one of the top performing systems. Code and pretrained models will be made publicly available.
Precision optical path alignment system for point diffraction interferometer based on image information

In a point diffraction interferometer, the existence of alignment error between an objective convergent spot and a diffraction pinhole can lead to wavefront error and diffraction intensity reduction. Meanwhile, the contrast of the point diffraction interferograms probably decreases in this procedure. These changes will have significant influence on its inspection precision. A precise alignment system of an optical path for a point diffraction interferometer is proposed in this paper. First, diffraction theory is used to analyze the mathematical relationships of alignment error to diffraction wavefront error and numerical aperture and wavefront error to pinhole size. Then, according to the requirement, the scheme of an optical path alignment system is designed. In this stage, alignment images as well as intensities of a reflected and diffracted beam from the point diffraction plate will be acquired. In addition, an image processing algorithm for measuring alignment error is designed, and a mathematical model between quantities of measurement and control is constructed. Finally, implementation and experiment of this method are also introduced. Misalignment situations, including lateral translation, longitudinal defocus, and tilt error, are well eliminated, and the quality of interferograms is also improved. From the results, it can be concluded that the system is of desirable precision and efficiency.
LAEO-Net++: Revisiting People Looking at Each Other in Videos

Capturing the 'mutual gaze' of people is essential for understanding and interpreting the social interactions between them. To this end, this paper addresses the problem of detecting people Looking At Each Other (LAEO) in video sequences. For this purpose, we propose LAEO-Net++, a new deep CNN for determining LAEO in videos. In contrast to previous works, LAEO-Net++ takes spatio-temporal tracks as input and reasons about the whole track. It consists of three branches, one for each character's tracked head and one for their relative position. Moreover, we introduce two new LAEO datasets: UCO-LAEO and AVA-LAEO. A thorough experimental evaluation demonstrates the ability of LAEO-Net++ to successfully determine if two people are LAEO and the temporal window where it happens. Our model achieves state-of-the-art results on the existing TVHID-LAEO video dataset, significantly outperforming previous approaches. Finally, we apply LAEO-Net++ to a social network, where we automatically infer the social relationship between pairs of people based on the frequency and duration that they LAEO, and show that LAEO can be a useful tool for guided search of human interactions in videos.
A Universal, Rapid and Accurate Measurement for Bend Tubes Based on Multi-View Vision

Non-circular-cross-section tubes are widely used as heat exchange components throughout the industry, including in fields such as aerospace, automobiles, and vessels.Tube manufacturing in these fields is a highly complex process; to manufacture the needed tubes and guarantee a precise and stress-free assembly, the geometric parameters should be measured during manufacturing and before assembly.However, current studies and manufacturing instrumentation focus on tubes of circular cross section.This study proposes an extraction method for the tubular 3D skeleton of non-circular-cross-section tubes based on multi-view vision.Then, non-circular-cross-section tubes can be measured based on the proposed method.The experimental results show that the average deviation does not exceed 0.3 mm with 3σ reliability and the measuring time is fewer than 5 s.The accuracy and efficiency of this method are sufficient to satisfy the requirements for industrial applications.
An Improved Back-Propagation Neural Network for the Prediction of College Students’ English Performance

The global economic boom has greatly boosted the need for communication be-tween different cultures and difference countries. The effective communication requires good command of foreign languages, especially English. This paper highlights the necessity to predict the English performance of college students, and sums up the types and features of neural network (NN) models. On this ba-sis, the backpropagation (BP) NN was selected to predict the English perfor-mance of college students. The Spearman’s R correlation test was conducted to analyze how the English performance is affected by the following factors: the score in National College Entrance Examination (NCEE), gender, age and learn-ing attitude. Then, the improved BPNN was adopted to predict the English per-formance of college students. The results show that the NCEE score has the greatest impact on English performance, followed in descending order by learn-ing attitude and gender, while age does not greatly affect English scores; the im-proved BPNN achieved a desirable effect in predicting the English performance of college students. The research findings shed new lights on college English teachers and learners.
FINANCIAL INCLUSION, MOBILE PHONE DIFFUSION, AND ECONOMIC GROWTH; EVIDENCE FROM AFRICA

Whether finance enhances growth has continued to be a major debate among academics in the bids to drive economic growth and development. Hitherto, empirical studies focusing on Africa in addressing these issues for the expected regional sustainable growth are rare. This study applied structural equation modelling to simultaneously analyse mobile phones diffusion, financial inclusion, and economic growth in a panel of 32 African countries over the period from 2004 to 2016. The results provided evidence that financial inclusion affects economic growth via mobile phones. This study has implication for managing the deployment of mobile phones by for finance and growth relevance in Africa.Keywords: Financial inclusion, mobile phones diffusion, economic growth, structural equation modellingJEL Classifications: B26; C58; G21DOI: https://doi.org/10.32479/ijefi.8256
Semi-supervised VAE-GAN for Out-of-Sample Detection Applied to MRI Quality Control

Discriminative deep learning models have shown remarkable success in many medical image analysis applications. However, their success is limited in problems that involve learning from imbalanced and heterogeneous datasets. Generative models parameterized using deep learning models can resolve this problem by characterizing the distribution of well-represented classes, a step enabling the identification of samples that were improbably generated from that distribution. This paper proposes a semi-supervised out-of-sample detection framework based on a 3D variational autoencoder-based generative adversarial network (VAE-GAN). The proposed framework relies on a high-level similarity metric and invariant representations learned by a semi-supervised discriminator to evaluate the generated images. The encoded latent representations were constrained according to user-defined properties through a jointly trained predictor network. Anomaly samples are detected using learned similarity scores and/or scores from an online one-class neural network. The high performance of the proposed methods is confirmed via a novel application to the automatic quality control of structural MR images.
Group, Extract and Aggregate: Summarizing a Large Amount of Finance News for Forex Movement Prediction

Incorporating related text information has proven successful in stock market prediction.However, it is a huge challenge to utilize texts in the enormous forex (foreign currency exchange) market because the associated texts are too redundant.In this work, we propose a BERT-based Hierarchical Aggregation Model to summarize a large amount of finance news to predict forex movement.We firstly group news from different aspects: time, topic and category.Then we extract the most crucial news in each group by the SOTA extractive summarization method.Finally, we conduct interaction between the news and the trade data with attention to predict the forex movement.The experimental results show that the category based method performs best among three grouping methods and outperforms all the baselines.Besides, we study the influence of essential news attributes (category and region) by statistical analysis and summarize the influence patterns for different currency pairs.
ISBI Challenge 2019: Convolution Neural Networks for B-ALL Cell Classification

We Participated in International Symposium on Biomedical Imaging (ISBI) 2019 challenge: Classification of Normal versus Malignant Cells in B-ALL White Blood Cancer Microscopic Images. Acute Lymphoblastic Leukemia (ALL) is a cancer of the lymphoid line of blood cells characterized by the development of large numbers of immature lymphocytes. In this paper, we present a Convolutional Neural Networks (CNNs) based solution for the challenge. We designed our solution with pretrained MobileNetV2 architecture as base classifier. Also, we employ transfer learning as the amount of labeled data is limited and ensemble the trained base classifier variants to reduce the generalization error on prospective data. With our final solution we secured second place in the challenge with F1 score of 0.8947.
Application of Nipple Aspirate Fluid miRNA Profiles for Early Breast Cancer Detection and Management

The authors of the recently published review, "Why the Gold Standard Approach by Mammography Demands Extension by Multiomics [...].
AE-OT: A NEW GENERATIVE MODEL BASED ON EXTENDED SEMI-DISCRETE OPTIMAL TRANSPORT

Generative adversarial networks (GANs) have attracted huge attention due to its capability to generate visual realistic images. However, most of the existing models suffer from the mode collapse or mode mixture problems. In this work, we give a theoretic explanation of the both problems by Figalli’s regularity theory of optimal transportation maps. Basically, the generator compute the transportation maps between the white noise distributions and the data distributions, which are in general discontinuous. However, DNNs can only represent continuous maps. This intrinsic conflict induces mode collapse and mode mixture. In order to tackle the both problems, we explicitly separate the manifold embedding and the optimal transportation; the first part is carried out using an autoencoder to map the images onto the latent space; the second part is accomplished using a GPU-based convex optimization to find the discontinuous transportation maps. Composing the extended OT map and the decoder, we can finally generate new images from the white noise. This AE-OT model avoids representing discontinuous maps by DNNs, therefore effectively prevents mode collapse and mode mixture.
Graph inference learning for semi-supervised classification

In this work, we address the semi-supervised classification of graph data, where the categories of those unlabeled nodes are inferred from labeled nodes as well as graph structures. Recent works often solve this problem with the advanced graph convolution in a conventional supervised manner, but the performance could be heavily affected when labeled data is scarce. Here we propose a Graph Inference Learning (GIL) framework to boost the performance of node classification by learning the inference of node labels on graph topology. To bridge the connection of two nodes, we formally define a structure relation by encapsulating node attributes, between-node paths and local topological structures together, which can make inference conveniently deduced from one node to another node. For learning the inference process, we further introduce meta-optimization on structure relations from training nodes to validation nodes, such that the learnt graph inference capability can be better self-adapted into test nodes. Comprehensive evaluations on four benchmark datasets (including Cora, Citeseer, Pubmed and NELL) demonstrate the superiority of our GIL when compared with other state-of-the-art methods in the semi-supervised node classification task.
Hybrid image segmentation method based on anisotropic Gaussian kernels and adjacent graph region merging

Image segmentation is a key technique in image analysis for object identification. In this paper, a hybrid segmentation method is proposed, which is based on the Anisotropic Gaussian Kernel (ANGK) edge detector and region adjacent graph (RAG) merging algorithm. An anisotropic directional derivative filter is constructed by angled ANGK to detect the edge contour of original images. Based on the gradient magnitude pattern of the edge contour from ANGK processing, watershed transform is adopted to produce initial partition (coarse segmentation result). Finally, combined with the RAG region merging algorithm, the proposed method performs fine segmentation by merging similar fragmented regions (initial partition) iteratively. Additionally, statistic similarity measure and shape cost function in merging cost are also introduced. They provide quantitative criteria for region merging, which have critical influences on the detection result. A series of experiments are conducted to evaluate the performance of this method, and a preferable localization accuracy as well as noise robustness is proved. Compared with conventional edge and region based methods, the proposed one has a superior segmentation effect. Another advantage is that the problem of oversegmentation can be solved effectively.
A Natural Language Process-Based Framework for Automatic Association Word Extraction

Word association, revealing mental representations and connections of human, has been widely studied in psychology. However, the scale of available associative cue-response words is severely restricted due to the traditional manually collecting methodology. Meanwhile, with the tremendous success in Natural Language Process (NLP) tasks, an extremely large amount of plain texts can be easily acquired. This suggests an insight about the potential to find association words automatically from the text corpus instead of manually collection. As an original attempt, this paper takes a small step toward proposing a deep learning based framework for automatic association word extraction. The framework mainly consists of two stages of association word detection and machine association network construction. In particular, attention mechanism based Reading Comprehension (RC) algorithm is explored to find valuable association words automatically. To validate the value of the extracted association words, the correlation coefficient between semantic similarities of machine and human association words is introduced as an effective measurement for evaluating association consistence. The experiments are conducted on two text datasets from which together about 20k association words, more than the existing largest human association word dataset, are finally derived. The experiment further verifies that the machine association words are generally consistent with human association words with respect to semantic similarity, which highlights the promising utilization of the machine association words in the future researches of both psychology and NLP.
Improved Transient Data Compression Algorithm Based on Wavelet Spectral Quantization Models

In this work a dedicated encoding algorithm is presented for current and voltage transient signals digitalized from electrical networks. New models for dynamic bit allocation based on adaptive and fixed spectral envelope estimation models for transformed coefficients are proposed. The fixed profiles adjust the number of bits to be used by the wavelet transform decomposition level, which is used in the transformed vector quantization. The models proposed to estimate the adaptive spectral envelope have the transformed spectrum segmented by subbands or by decomposition levels of the wavelet transform. The dynamic bit allocation is implemented according to the signal spectral behavior. The quantized coefficient vector is encoded using an entropy coder, and it is asynchronously packaged in a compressed file. A parametric configuration for optimization algorithm is also proposed for the fixed spectral profile models in order to improve performance. Simulation results are presented using a signal data bank with a set of reported events of electric power networks. Performance comparisons with other work are also presented.
An Approach of Secure Two-Way-Pegged Multi-sidechain

As a temper-resistant ledger, blockchain ensures integrity of transaction information among trust-less participants in peer to peer network. Thus, blockchain has attracted enormous research interests in the past decade due to its prior application in cryptocurrency, financial auditing, supply chain management, etc. However, blockchain scalability limitations has impeded blockchain technology from large scale commercial applications. Since blockchain is low in throughput, blockchain in incapable of handling large scale asset transfer. To tackle this problem, in this paper, we propose a secure multi-sidechain system. The proposed approach can transfer assets simultaneously to increase throughput. In addition, security of assets during transfer was also ensured by implementing firewall property. Detailed adversarial analysis shows this proposed approach can (1) prevent double spending and transaction ordering dependence attacks during asset transfer, (2) protect mainchain from sidechain’s catastrophic failure, (3) apply multi-sidechain model with different functions in each sidechain.