Reviewerly/exHarmony huggingface dataset:
Image super‐resolution using conditional generative adversarial network

Recently, extensive studies on a generative adversarial network (GAN) have made great progress in single image super-resolution (SISR). However, there still exists a significant difference between the reconstructed high-frequency and the real high-frequency details. To address this issue, this study presents an SISR approach based on conditional GAN (SRCGAN). SRCGAN includes a generator network that generates super-resolution (SR) images and a discriminator network that is trained to distinguish the SR images from ground-truth high-resolution (HR) ones. Specifically, the discriminator network uses the ground-truth HR image as a conditional variable, which guides the network to distinguish the real images from the SR images, facilitating training a more stable generator model than GAN without this guidance. Furthermore, a residual-learning module is introduced into the generator network to solve the issue of detail information loss in SR images. Finally, the network is trained in an end-to-end manner by optimizing a perceptual loss function. Extensive evaluations on four benchmark datasets including Set5, Set14, BSD100, and Urban100 demonstrate the superiority of the proposed SRCGAN over state-of-the-art methods in terms of PSNR, SSIM, and visual effect.
Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations.

Natural-gradient methods enable fast and simple algorithms for variational inference, but due to computational difficulties, their use is mostly limited to \emph{minimal} exponential-family (EF) approximations. In this paper, we extend their application to estimate \emph{structured} approximations such as mixtures of EF distributions. Such approximations can fit complex, multimodal posterior distributions and are generally more accurate than unimodal EF approximations. By using a \emph{minimal conditional-EF} representation of such approximations, we derive simple natural-gradient updates. Our empirical results demonstrate a faster convergence of our natural-gradient method compared to black-box gradient-based methods with reparameterization gradients. Our work expands the scope of natural gradients for Bayesian inference and makes them more widely applicable than before.
cwl_eval

We present a tool ("cwl_eval") which unifies many metrics typically used to evaluate information retrieval systems using test collections. In the CWL framework metrics are specified via a single function which can be used to derive a number of related measurements: Expected Utility per item, Expected Total Utility, Expected Cost per item, Expected Total Cost, and Expected Depth. The CWL framework brings together several independent approaches for measuring the quality of a ranked list, and provides a coherent user model-based framework for developing measures based on utility (gain) and cost. Here we outline the CWL measurement framework; describe the cwl_eval architecture; and provide examples of how to use it. We provide implementations of a number of recent metrics, including Time Biased Gain, U-Measure, Bejewelled Measure, and the Information Foraging Based Measure, as well as previous metrics such as Precision, Average Precision, Discounted Cumulative Gain, Rank-Biased Precision, and INST. By providing state-of-the-art and traditional metrics within the same framework, we promote a standardised approach to evaluating search effectiveness.
Strain measurement during tensile testing using deep learning-based digital image correlation

This paper describes a novel non-contact strain measurement method defined as deep learning based on digital image correlation (DDIC). In particular, it is very difficult to measure directly displacement of gauge length during tensile testing of thin films. Therefore, we obtained the image data continuously to observe the behavior of the material during tensile testing. The sequential image data obtained at a specific position is assigned to a multi-channel input to train the deep neural network. As a result, the multi-channel image is composed of sequential images obtained along the time domain. Since these images have a correlation with each other along the time domain in each pixel, the neural network learns displacement, including temporal information. The DDIC method originates from a 3D convolutional neural network, which can extract both the spatial and the temporal domain features at the same time. A 3D convolutional filter is used as the feature extraction part of the network in order to effectively learn the input data. The deep learning is an end-to-end learning method and the deformation between two images can be measured regardless of the parameters for the nonlinear deformation of the images. An elastic modulus of 118 GPa, 0.2% yield strength of 941 MPa, ultimate tensile strength of 1108 MPa and fracture strain of 0.02414 are estimated by applying the DDIC method during a tensile test of BeCu thin film. The results of the DDIC method are compared with the displacement sensor data and digital image correlation data.
PCGAN: Partition-Controlled Human Image Generation

Human image generation is a very challenging task since it is affected by many factors. Many human image generation methods focus on generating human images conditioned on a given pose, while the generated backgrounds are often blurred. In this paper, we propose a novel Partition-Controlled GAN to generate human images according to target pose and background. Firstly, human poses in the given images are extracted, and foreground/background are partitioned for further use. Secondly, we extract and fuse appearance features, pose features and background features to generate the desired images. Experiments on Market-1501 and DeepFashion datasets show that our model not only generates realistic human images but also produce the human pose and background as we want. Extensive experiments on COCO and LIP datasets indicate the potential of our method.
DSCOVR: Randomized Primal-Dual Block Coordinate Algorithms for Asynchronous Distributed Optimization

Machine learning with big data often involves large optimization models. For distributed optimization over a cluster of machines, frequent communication and synchronization of all model parameters (optimization variables) can be very costly. A promising solution is to use parameter servers to store different subsets of the model parameters, and update them asynchronously at different machines using local datasets. In this paper, we focus on distributed optimization of large linear models with convex loss functions, and propose a family of randomized primal-dual block coordinate algorithms that are especially suitable for asynchronous distributed implementation with parameter servers. In particular, we work with the saddle-point formulation of such problems which allows simultaneous data and model partitioning, and exploit its structure by doubly stochastic coordinate optimization with variance reduction (DSCOVR). Compared with other first-order distributed algorithms, we show that DSCOVR may require less amount of overall computation and communication, and less or no synchronization. We discuss the implementation details of the DSCOVR algorithms, and present numerical experiments on an industrial distributed computing system.
Learning to Learn from Web Data Through Deep Semantic Embeddings

In this paper we propose to learn a multimodal image and text embedding from Web and Social Media data, aiming to leverage the semantic knowledge learnt in the text domain and transfer it to a visual model for semantic image retrieval. We demonstrate that the pipeline can learn from images with associated text without supervision and perform a thorough analysis of five different text embeddings in three different benchmarks. We show that the embeddings learnt with Web and Social Media data have competitive performances over supervised methods in the text based image retrieval task, and we clearly outperform state of the art in the MIRFlickr dataset when training in the target data. Further we demonstrate how semantic multimodal image retrieval can be performed using the learnt embeddings, going beyond classical instance-level retrieval problems. Finally, we present a new dataset, InstaCities1M, composed by Instagram images and their associated texts that can be used for fair comparison of image-text embeddings.
Neural-symbolic computing: An effective methodology for principled integration of machine learning and reasoning

Current advances in Artificial Intelligence and machine learning in general, and deep learning in particular have reached unprecedented impact not only across research communities, but also over popular media channels. However, concerns about interpretability and accountability of AI have been raised by influential thinkers. In spite of the recent impact of AI, several works have identified the need for principled knowledge representation and reasoning mechanisms integrated with deep learning-based systems to provide sound and explainable models for such systems. Neural-symbolic computing aims at integrating, as foreseen by Valiant, two most fundamental cognitive abilities: the ability to learn from the environment, and the ability to reason from what has been learned. Neural-symbolic computing has been an active topic of research for many years, reconciling the advantages of robust learning in neural networks and reasoning and interpretability of symbolic representation. In this paper, we survey recent accomplishments of neural-symbolic computing as a principled methodology for integrated machine learning and reasoning. We illustrate the effectiveness of the approach by outlining the main characteristics of the methodology: principled integration of neural learning with symbolic knowledge representation and reasoning allowing for the construction of explainable AI systems. The insights provided by neural-symbolic computing shed new light on the increasingly prominent need for interpretable and accountable AI systems.
GKC: A Reasoning System for Large Knowledge Bases

This paper introduces GKC, a resolution prover optimized for search in large knowledge bases. The system is built upon a shared memory graph database Whitedb, enabling it to solve multiple different queries without a need to repeatedly parse or load the large parsed knowledge base from the disk. Due to the relatively shallow and simple structure of most of the literals in the knowledge base, the indexing methods used are mostly hash-based. While GKC performs well on large problems from the TPTP set, the system is built for use as a core system for developing a toolset of commonsense reasoning functionalities.
Characterization and Learning of Causal Graphs with Latent Variables from Soft Interventions

The challenge of learning the causal structure underlying a certain phenomenon is undertaken by connecting the set of conditional independences (CIs) readable from the observational data, on the one side, with the set of corresponding constraints implied over the graphical structure, on the other, which are tied through a graphical criterion known as d-separation (Pearl, 1988). In this paper, we investigate the more general scenario where multiple observational and experimental distributions are available. We start with the simple observation that the invariances given by CIs/d-separation are just one special type of a broader set of constraints, which follow from the careful comparison of the different distributions available. Remarkably, these new constraints are intrinsically connected with do-calculus (Pearl, 1995) in the context of soft-interventions. We introduce a novel notion of interventional equivalence class of causal graphs with latent variables based on these invariances, which associates each graphical structure with a set of interventional distributions that respect the do-calculus rules. Given a collection of distributions, two causal graphs are called interventionally equivalent if they are associated with the same family of interventional distributions, where the elements of the family are indistinguishable using the invariances obtained from a direct application of the calculus rules. We introduce a graphical representation that can be used to determine if two causal graphs are interventionally equivalent. We provide a formal graphical characterization of this equivalence. Finally, we extend the FCI algorithm, which was originally designed to operate based on CIs, to combine observational and interventional datasets, including new orientation rules particular to this setting.
M-Net: A Novel U-Net With Multi-Stream Feature Fusion and Multi-Scale Dilated Convolutions for Bile Ducts and Hepatolith Segmentation

Automatically segmenting bile ducts and hepatolith in abdominal CT scans is helpful to assist hepatobiliary surgeons for minimally invasive surgery.High-deformation characteristics of bile ducts and small-size characteristics of hepatolith make this segmentation task challenging.To the best of our knowledge, we make the first attempt to simultaneously segment bile ducts and hepatolith in this paper.Inspired by U-Net, a novel two-dimensional end-to-end fully convolutional network named M-Net is designed to implement this segmentation task.The M-Net is composed of four streams involving two encoder-decoder processes.Multi-scale dilated convolutions are designed to extract abundant semantic features and multi-scale context information at different scales.To make full advantages of multi-scale feature maps, a multi-stream feature fusion strategy is proposed to transfer the most abundant semantic features produced in the first stream to the other streams.To further improve the segmentation performance, a novel loss function is defined to focus the M-Net on hard pixels (difficultly distinguished) in the edges of bile ducts and hepatolith, which is based on the online bootstrapped method and cross entropy.By discarding pixels (easy to distinguish) with higher probability of class, the decline of loss is focused on hard pixels so that the training become more efficient and directional.Experimental results indicate that our proposed M-Net is superior to the state-of-the-art deep-learning methods for simultaneously segmenting bile ducts and hepatolith in the abdominal CT scans.The M-Net can simultaneously segment bile ducts and hepatolith in abdominal CT scans at a high performance with 98.678% Recall, 84.427% Precision, 89.831% DICE and 90.998% F1-score for bile ducts, and 99.894% Recall, 55.132% Precision, 71.248% DICE and 71.051% F1-score for hepatolith.
Robust Speech Steganography Using Differential SVD

The speech signal is different from the typical audio in terms of spectral bandwidth, intensity distribution, and signal continuity, thus how to achieve high imperceptibility and strong robustness for speech steganography is a big challenge. In this paper, we present a speech steganography scheme based on the parity-segmented method and the differential singular value decomposition (SVD). The selected discrete cosine transform (DCT) coefficients are divided into two segments according to parity order. In this way, the energy of the paired segments is approximately equal, therefore the changes in the singular values caused by data embedding are reduced, and high imperceptibility is achieved. Unlike the common SVD-based steganography, the differential SVD scheme can effectively remove the impact of amplitude scaling attack by embedding the secret message into the difference between the singular values. Experimental results show that the proposed method achieves high imperceptibility and strong robustness while resisting the state-of-the-art steganalytic methods.
Page Cache Attacks

We present a new side-channel attack that targets one of the most fundamental software caches in modern computer systems: the operating system page cache. The page cache is a pure software cache that contains all disk-backed pages, including program binaries, shared libraries, and other files. On Windows, dynamic pages are also part of this cache and can be attacked as well, e.g., data, heap, and stacks. Our side channel permits unprivileged monitoring of accesses to these pages of other processes, with a spatial resolution of 4kB and a temporal resolution of 2µs on Linux (≤6.7 measurements per second), and 466ns on Windows 10 (≤223 measurements per second). We systematically analyze the side channel by demonstrating different hardware-agnostic local attacks, including a sandbox-bypassing high-speed covert channel, an ASLR break on Windows 10, and various information leakages that can be used for targeted extortion, spam campaigns, and more directly for UI redressing attacks. We also show that, as with hardware cache attacks, we can attack the generation of temporary passwords on vulnerable cryptographic implementations. Our hardware-agnostic attacks can be mitigated with our proposed security patches, but the basic side channel remains exploitable via timing measurements. We demonstrate this with a remote covert channel exfiltrating information from a colluding process through innocuous server requests.
Multi-Agent Architecture for Peer-to-Peer Electricity Trading based on Blockchain Technology

The security of smart grids is put at risk due to their automation and remote access features. Blockchain technology can be used as a distributed ledger where data is stored and all the data transactions between the different entities of a smart grid are signed to protect them from such attacks. This paper proposes a multi-agent system (MAS) that combines smart contracts and blockchain to enable Peer-to-Peer electricity trading in a MicroGrid (MG) scenario, without the need for human intervention. The use of blockchain technology helps reduce transaction costs and allows to make micro transactions in the proposed market. Blockchain also improves the security of the platform because all the involved actors can be certain about the authorship of the information produced in the system. Finally, the use of a MAS and the possibility of negotiating between the agents helps obtain an optimal state in the system in which the costs of energy are minimal and the local production of energy is profitable.
Improved separated-parameter calibration method for binocular vision measurements with a large field of view

Large field-of-view (FOV) calibration is indispensable to ensure the accuracy of vision measurement systems for large aviation components. We propose an improved separated-parameter calibration method for large-FOV binocular vision measurements with a high flexibility and accuracy. Firstly, the camera parameters are separately calibrated according to the sub-area features of image. Subsequently, based on the spatial-calibration accuracy, a stereoscopic calibration object is devised. The mean error of the proposed method is experimentally obtained as 0.13 mm for a FOV of 2.0 m × 1.5 m. Its feasibility and effectiveness for the measurement in the field is validated by workshop calibration.
A Survey on Robot Path Planning using Bio-inspired Algorithms

The path planning problem has been an essential topic in autonomous robotics. There are many new path planning methods that have been developed, and those using bioinspired algorithms attract extensive attention. This paper analyzes several bio-inspired algorithms and surveys recent developments of robot path planning. Swarm intelligence, evolutionary algorithms, and neurodynamics are three primary robot path planning methods that are focused on in this survey. In addition, the pros and cons of these three methods are summarized and analyzed.
COMPUTER VISION FOR HUMAN-MACHINE INTERACTION-REVIEW

The paper is a review on the computer vision that is helpful in the interaction between the human and the machines. The computer vision that is termed as the subfield of the artificial intelligence and the machine learning is capable of training the computer to visualize, interpret and respond back to the visual world in a similar way as the human vision does. Nowadays the computer vision has found its application in broader areas such as the heath care, safety security, surveillance etc. due to the progress, developments and latest innovations in the artificial intelligence, deep learning and neural networks. The paper presents the enhanced capabilities of the computer vision experienced in various applications related to the interactions between the human and machines involving the artificial intelligence, deep learning and the neural networks.
Mutual information for unsupervised deep learning image registration

Current unsupervised deep learning-based image registration methods are trained with mean squares or nor- malized cross correlation as a similarity metric. These metrics are suitable for registration of images where a linear relation between image intensities exists. When such a relation is absent knowledge from conventional image registration literature suggests the use of mutual information. In this work we investigate whether mutual information can be used as a loss for unsupervised deep learning image registration by evaluating it on two datasets: breast dynamic contrast-enhanced MR and cardiac MR images. The results show that training with mutual information as a loss gives on par performance compared with conventional image registration in contrast enhanced images, and the results show that it is generally applicable since it has on par performance compared with normalized cross correlation in single-modality registration.
Token-Based Access Control

Traditional centralized access control has some shortcomings in robustness, trustworthiness and circulation.Blockchains have the advantages of fault tolerance and trust.Smart contracts have the characteristics of automatic execution and flexible expansion.Tokens can well record credential information and transfer easily.In this paper, blockchain, smart contract and token are integrated and applied to access control to solve the shortcomings of traditional access control.First, access control, blockchain, smart contract and token are briefly described.Second, this paper proposes a solution by giving the general data structure of access control token, elaborating the equivalence, split, merge and verification algorithms of access control token, and explaining the system architecture of token-based access control.Last, this paper uses a token-based access control simulation system to verify that token-based access control has certain comparative advantages in robustness, trustworthiness, circulation, concurrency and so on.
Data Valuation using Reinforcement Learning

A method includes obtaining a set of training samples. During each of a plurality of training iterations, the method also includes sampling a batch of training samples from the set of training samples. The method includes, for each training sample in the batch of training samples, determining, using a data value estimator, a selection probability. The selection probability for the training sample is based on estimator parameter values of the data value estimator. The method also includes selecting, based on the selection probabilities of each training sample, a subset of training samples from the batch of training samples, and determining, using a predictor model with the subset of training samples, performance measurements. The method also includes adjusting model parameter values of the predictor model based on the performance measurements, and updating the estimator parameter values of the data value estimator based on the performance measurements.