Reviewerly/exHarmony huggingface dataset:
Quantum computation with machine-learning-controlled quantum stuff

We formulate the control over quantum matter, so as to perform arbitrary quantum computation, as an optimization problem. We then provide a schematic machine learning algorithm for its solution. Imagine a long strip of 'quantum stuff', endowed with certain assumed physical properties, and equipped with regularly spaced wires to provide input settings and to read off outcomes. After showing how the corresponding map from settings to outcomes can be construed as a quantum circuit, we provide a machine learning framework to tomographically 'learn' which settings implement the members of a universal gate set. To that end, we devise a loss function measuring how badly a proposed encoding has failed to implement a given circuit, and prove the existence of 'tomographically complete' circuit sets: should a given encoding minimize the loss function for each member of such a set, it also will for an arbitrary circuit. At optimum, arbitrary quantum gates, and thus arbitrary quantum programs, can be implemented using the stuff.
Trustworthy When Human and Bots Are Mingled

This paper is to identify conversational bots using blockchain technology, a first step to address trustworthy challenging when social media applications are mixed with human users and social bots. Internet persona or account user profile for social bots usually is hardly being used to distinguish conversational bots from other human users. PASS (Personal Archive Service System) using blockchain technology has built in the Proof of X mechanism. The usage of such built in feature into bot identification makes users aware of bot interaction which could mitigate the threat of disinformation by social bots. Moreover, in practice, we add feedback bot score, called syn points in the registration, verification and monitor lifecycle.
Computer Vision

Image segmentation is the task of associating pixels in an image with their respective object class labels. It has a wide range of applications in many industries including healthcare, transportation, robotics, fashion, home improvement, and tourism. Many deep learning-based approaches have been developed for image-level object recognition and pixel-level scene understanding - with the latter requiring a much denser annotation of scenes with a large set of objects. This tutorial provides an end-to-end pipeline for performing image segmentation using the state-of-art deep learning approaches and public datasets. The hands-on session will provide instructions for dataset customization, transformation, and training, validating, and testing segmentation models. The goal of this tutorial is to provide participants with a strong understanding of building image segmentation models for downstream applications.
Interpretable Topic Extraction and Word Embedding Learning Using Row-Stochastic DEDICOM

The DEDICOM algorithm provides a uniquely interpretable matrix factorization method for symmetric and asymmetric square matrices. We employ a new row-stochastic variation of DEDICOM on the pointwise mutual information matrices of text corpora to identify latent topic clusters within the vocabulary and simultaneously learn interpretable word embeddings. We introduce a method to efficiently train a constrained DEDICOM algorithm and a qualitative evaluation of its topic modeling and word embedding performance.
WeightGrad: Geo-Distributed Data Analysis Using Quantization for Faster Convergence and Better Accuracy

High network communication cost for synchronizing weights and gradients in geo-distributed data analysis consumes the benefits of advancement in computation and optimization techniques. Many quantization methods for weight, gradient or both have been proposed in recent years where weight-quantized model suffers from error related to weight dimension and gradient-quantized method suffers from slow convergence rate by a factor related to the gradient quantization resolution and gradient dimension. All these methods have been proved to be infeasible in terms of distributed training across multiple data centers all over the world. Moreover recent studies show that communicating over WANs can significantly degrade DNN model performance by upto 53.7x because of unstable and limited WAN bandwidth. Our goal in this work is to design a geo-distributed Deep-Learning system that (1) ensures efficient and faster communication over LAN and WAN and (2) maintain accuracy and convergence for complex DNNs with billions of parameters. In this paper, we introduce WeightGrad which acknowledges the limitations of quantization and provides loss-aware weight-quantized networks with quantized gradients for local convergence and for global convergence it dynamically eliminates insignificant communication between data centers while still guaranteeing the correctness of DNN models. Our experiments on our developed prototypes of WeightGrad running across 3 Amazon EC2 global regions and on a cluster that emulates EC2 WAN bandwidth show that WeightGrad provides 1.06% gain in top-1 accuracy, 5.36x speedup over baseline and 1.4x-2.26x over the four state-of-the-art distributed ML systems.
Codes over Trees

In graph theory, a tree is one of the more popular families of graphs with a wide range of applications in computer science as well as many other related fields. While there are several distance measures over the set of all trees, we consider here the one which defines the so-called tree distance, defined by the minimum number of edit operations, of removing and adding edges, in order to change one tree into another. From a coding theoretic perspective, codes over the tree distance are used for the correction of edge erasures and errors. However, studying this distance measure is important for many other applications that use trees and properties on their locality and the number of neighbor trees. Under this paradigm, the largest size of code over trees with a prescribed minimum tree distance is investigated. Upper bounds on these codes as well as code constructions are presented. A significant part of our study is dedicated to the problem of calculating the size of the ball of trees of a given radius. These balls are not regular and thus we show that while the star tree has asymptotically the smallest size of the ball, the maximum is achieved for the line tree.
Comparative Analysis and Performance Evaluation of Medical Image Compression Method for Telemedicine

In most of the telemedicine applications, the role of image compression techniques is important to deal with the medical images. This will be used for storage and transfer of data over a low bandwidth channel. One of the main drawbacks of the lossy compression algorithms is that it is not applicable for multimedia application while the overall quality of the image can be controlled to some extent. Due to the multiple properties of the Wavelet Transform over DCT, the wavelet based techniques (JPEG 2000) have produced better results compared to DCT based techniques (JPEG). Results of the above-noted techniques were compared and different parameters for medical images such as compression, MSE, SNR, PSNR analysis were carried out.
Optimal Learning of Joint Alignments with a Faulty Oracle

We consider the following problem, which is useful in applications such as joint image and shape alignment. The goal is to recover n discrete variables g <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> ∈ {0, ... , k - 1} (up to some global offset) given noisy observations of a set of their pairwise differences {(g <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> - g <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">j</sub> ) mod k}; specifically, with probability 1/k + δ for some δ > 0 one obtains the correct answer, and with the remaining probability one obtains a uniformly random incorrect answer. We consider a learning-based formulation where one can perform a query to observe a pairwise difference, and the goal is to perform as few queries as possible while obtaining the exact joint alignment. We provide an easy-to-implement, time efficient algorithm that performs O(n lg n/kδ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) queries, and recovers the joint alignment with high probability. We also show that our algorithm is optimal by proving a general lower bound that holds for all non-adaptive algorithms. Our work improves significantly the recent work by Chen and Candés [CC16], who view the problem as a constrained principal components analysis problem that can be solved using the power method. Specifically, our approach is simpler both in the algorithm and the analysis, and provides additional insights into the problem structure.
Graph Pooling in Graph Neural Networks with Node Feature Correlation

Because of the excellent performance of convolutional neural network in computer vision and natural language processing, we extend convolution operation to graph data and to define it as graph convolution. Different from node classification tasks, graph classification tasks need to pay attention to global information of graphs, which requires graph pooling mechanism to extract global information. Recently, Many researchers are devoted to the study of graph pooling and then proposed diversity of graph pooling models. However, in the graph classification tasks, these graph pooling methods are general and the graph classification accuracy still has room to improvement. Therefore, we propose the covariance pooling (CovPooling) to improve the classification accuracy of graph data sets. CovPooling uses node feature correlation to learn hierarchical representation of a graph. Our graph pooling utilizes node information and graph topology. Experiments show that our pooling module can be integrated into multiple graph convolution layers and achieve state-of-the-art performance in some datasets.
CP-Net: Channel Attention and Pixel Attention Network for Single Image Dehazing

An end-to-end channel attention and pixel attention network (CP-Net) is proposed to produce dehazed image directly in the paper. The CP-Net structure contains three critical components. Firstly, the double attention (DA) module consisting of channel attention (CA) and pixel attention (PA). Different channel features contain different levels of important information, and CA can give more weight to relevant information, so the network can learn more useful information. Meanwhile, haze is unevenly distributed on different pixels, and PA is able to filter out haze with varying weights for different pixels. It sums the outputs of the two attention modules to improve further feature representation which contributes to better dehazing result. Secondly, local residual learning and DA module constitute another important component, namely basic block structure. Local residual learning can transfer the feature information in the shallow part of the network to the deep part of the network through multiple local residual connections and enhance the expressive ability of CP-Net. Thirdly, CP-Net mainly uses its core component, DA module, to automatically assign different weights to different features to achieve satisfactory dehazing effect. The experiment results on synthetic datasets and real hazy images indicate that many state-of-the-art single image dehazing methods have been surpassed by the CP-Net both quantitatively and qualitatively.
Hybrid Attention Distribution and Factorized Embedding Matrix in Image Captioning

Self-attention mechanisms are widely used in current encoder-decoder frameworks of image-captioning tasks. These mechanisms use the transformer module as the basic unit to compute the similarity between query and key vectors, and normalize them to obtain the attention distribution matrix, weighting and reconstructing the rich semantic query vectors. However, a single attention distribution matrix cannot express more complex intrinsic relations between query and key vectors. Some very important intrinsic relations can thus become lost. We therefore propose a hybrid attention distribution (HAD) that allows multiple distributions to be reconstructed to express deeper internal relations and avoids a single shallow attention distribution. In addition, to reduce the number of model parameters, we factorize the word-embedding matrix. This effectively increases the training efficiency and prevents the metrics from decreasing. We extensively evaluate our proposals using the MS-COCO image-captioning dataset. Our results outperform existing state-of-the-art methods on some metrics.
Super-Resolution Based on Residual Learning and Optimized Phase Stretch Transform

High resolution infrared (IR) images are often required in military and industrial applications. Due to the limited properties of IR imaging sensors and camera lens, IR images exhibit poor spatial resolution with a blur phenomenon in the edge regions. In this correspondence, we develop a new super-resolution (SR)-IR image reconstruction method using the residual learning network in the wavelet domain (WRESNET) and optimized phase stretch transform (PST). Our algorithm first transforms the input low resolution (LR)-IR image into its low-frequency and high-frequency subbands using the discrete wavelet decomposition. Subsequently, we introduce the optimized PST to operate on the LR-IR image and extract the intrinsic edge structure. The PST behaves differently at low-frequency and high-frequency regions, thus capturing the intensity variations for edge detection. We incorporate the PST extracted edge map in the wavelet subbands to preserve the intrinsic structure of images. The resultant subbands are further refined based on the missing residuals obtained using the WRESNET. The proposed method is validated through quantitative and qualitative evaluations against the conventional and state-of-art SR methods. Results reveal that the proposed method outperforms the existing methods.
Similarity Measures for Learning in Lattice Based Biomimetic Neural Networks

This paper presents a novel lattice based biomimetic neural network trained by means of a similarity measure derived from a lattice positive valuation. For a wide class of pattern recognition problems, the proposed artificial neural network, implemented as a dendritic hetero-associative memory delivers high percentages of successful classification. The memory is a feedforward dendritic network whose arithmetical operations are based on lattice algebra and can be applied to real multivalued inputs. In this approach, the realization of recognition tasks, shows the inherent capability of prototype-class pattern associations in a fast and straightforward manner without need of any iterative scheme subject to issues about convergence. Using an artificially designed data set we show how the proposed trained neural net classifies a test input pattern. Application to a few typical real-world data sets illustrate the overall network classification performance using different training and testing sample subsets generated randomly.
Hierarchical mixture-of-experts models for count variables with excessive zeros

We propose a tree-structured hierarchical architecture for estimating count variables that exhibit both excessive zeros and over-dispersion, which is commonly observed in practice. The underlying statistical model is based on the Mixture-of-experts (MoE) model, a generalization of finite mixture regression models. With two levels of experts, the proposed model can efficiently model both excessive zeros and the long right-tail, controlled by two gating networks at different levels in the hierarchy. We develop the general form of the maximum likelihood estimator for the proposed model based on the EM algorithm with two latent variables, which allows a natural interpretation and convenient optimization for the likelihood function. As a numerical application, we apply the proposed model to a well-known real dataset and found that it shows superior performance and allows better interpretations compared to other existing alternatives.
Improving Abstractive Summarization via Dilated Convolution

Abstract In this paper, a sequence-to-sequence based hybrid neural network model is proposed for abstractive summarization. Our method utilizes Bi-directional Long Short-Term Memory (Bi-LSTM) and multi-level dilated convolutions (MDC) to capture the global semantic information and semantic-unit level information, respectively. In decoding phrase, our model generates words according to summary relevant information captured by attention mechanism. Experiment shows that this proposed model outperforms several strong baselines on both of Gigawords corpus and DUC-2004 task.
Range statistical resolution limit of two close-spaced targets based on the asymptotic distribution of GLRT

As the statistical resolution limit (SRL) can overcome the shortage of the typical Rayleigh limit, it has been paid much attention recently. There are two ways to define the SRL-one is based on the Cramer-Rao Bound (CRB), which will become complicated when there are unknown parameters; another is based on the hypothesis testing theory, which usually adopts the Taylor expansion and keeps one or two order approximation, so it will inevitably survive some errors. In this paper, we consider the latter way, where we apply the asymptotic distribution of the general likelihood rate (GLRT) to get the analysis expression of range SRL. By this way, we can easily find out the influence factors to the limit and compare the features of different waveforms.
Enhanced Security Approach Powered by Blockchain Technology with NFC to Prevent Fraudulence in Bank Letter of Credits

Letters of Credit (Letter-of-Credit) frauds are deceptive attempts against financial institutions, primarily by providing availing false/falsify documentation as a proof of shipment of goods when, either, inferior goods were shipped, or no delivery was made at all. Letter-of-Credit also maybe forged to be provided to the supplier to supply goods. Whereas parties can forfeit the Letter-of-Credit by someone with vast experience and expertise in finance commerce laws, and international laws and business processes. In this research, we aspire to come up with an option of incorporating NFC-enabled mobile application into Letter-of-credit. The application is powered by blockchain technology and would be crucial in helping the recipient of the Letter-of-credit to achieve easy verification of the legitimacy of the document. The idea of using blockchain to prevent Letter-of-Credit fraud may not be novel where some have already been put into reality. However, not much work done in combining an NFC enabled mobile application leveraging on blockchain technology to prevent Letter-of-Credits frauds. The study is intended for banks who are still in migration to paperless business and still depending on a physical Letter-of-Credit for their business transactions.
Performance Evaluation of Semantic Video Compression using Multi-cue Object Detection

Video compression becomes a very important task during real-time aerial surveillance scenarios where limited communication bandwidth and on-board storage greatly restrict air-to-ground and air-to-air communications. In these cases, efficient handling of video data is needed to ensure optimum storage, smoother video transmission, fast and reliable video analysis. Conventional video compression schemes were typically designed for human visual perception rather than automated video analytics. Information loss and artifacts introduced during image/video compression impose serious limitations on the performance of automated video analytics tasks. These limitations are further increased in aerial imagery due to complex background and small size of objects. In this paper, we describe and evaluate a salient region estimation pipeline for aerial imagery to enable adaptive bit-rate allocation during video compression. The salient regions are estimated using a multi-cue moving vehicle detection pipeline, which synergistically fuses complementary appearance and motion cues using deep learning-based object detection and flux tensor-based spatio-temporal filtering approaches. Adaptive compression results using the described multi-cue saliency estimation pipeline are compared against conventional MPEG and JPEG encoding in terms of compression ratio, image quality, and impact on automated video analytics operations. Experimental results on ABQ urban aerial video dataset [1] show that incorporation of contextual information enables high semantic compression ratios of over 2000:1 while preserving image quality for the regions of interest. The proposed pipeline enables better utilization of the limited bandwidth of the air-to-ground or air-to-air network links.
Evaluation of elastic modulus of carbon fiber reinforced polymers using an optical extensometer

Abstract The paper presents the results of application of different techniques for processing of digital image correlation data. The algorithms are described and applied for determination of elasticity modulus of carbon fiber reinforced polymer specimens tested by uniaxial tension. Two ways of evaluation of elongation along with two methods of elastic modulus calculation resulted in four techniques which have shown quite similar results with a scatter of 0.5-2%.
Intelligent Algorithm for Ceramic Decorative Pattern Style Transfer Based on CycleGAN

Image style conversion refers to a technique that uses an algorithm to learn the style of a famous painting and then applies it to another picture. Current generative adversarial networks have been widely used for image style conversion. However, Cycle Generative Adversarial Networks (CycleGAN) is not very clear in processing image textures. This paper proposes a method of adding a Local Binary Pattern (LBP) algorithm and adds a cyclic LBP algorithm to the generator that generates the adversarial network to improve the effectiveness of the cyclically created adversarial network to extract image texture feature content. Experimental results show that adding the LBP algorithm can improve the image quality and make the trueness and falseness between the input picture and the picture generated by cyclegan closer.
On the Number of Factorizations of Polynomials over Finite Fields

Motivated by coding applications, two enumeration problems are considered: the number of distinct divisors of a degree-m polynomial over F = GF(q), and the number of ways a polynomial can be written as a product of two polynomials of degree at most n over F. For the two problems, bounds are obtained on the maximum number of factorizations, and a characterization is presented for polynomials attaining that maximum. Finally, expressions are presented for the average and the variance of the number of factorizations, for any given m (resp., n).
Theoretical Justification of a Neural Network Approach to Combinatorial Optimization

Recently a new competition-based neural network approach for solving combinatorial optimization problems was proposed. The approach demonstrates excellent performance for a number of facility location problems, including the p-MiniSum, p-Defense-Sum and p-Hub problems. Here we provide a theoretical justification of the competition-based neural network approach. It is shown that the approach asymptotically converges to an optimal solution of the problem being solved. Finite-time approximation is also discussed.