Reviewerly/exHarmony huggingface dataset:
Effect of Bitcoin fee on transaction-confirmation process

In Bitcoin system, transactions are prioritized according to transaction fees. Transactions without fees are given low priority and likely to wait for confirmation. Because the demand of micro payment in Bitcoin is expected to increase due to low remittance cost, it is important to quantitatively investigate how transactions with small fees of Bitcoin affect the transaction-confirmation time. In this paper, we analyze the transaction-confirmation time by queueing theory. We model the transaction-confirmation process of Bitcoin as a priority queueing system with batch service, deriving the mean transaction-confirmation time. Numerical examples show how the demand of transactions with low fees affects the transaction-confirmation time. We also consider the effect of the maximum block size on the transaction-confirmation time.
Multi-User Multi-Keyword Rank Search Over Encrypted Data in Arbitrary Language

Multi-keyword rank searchable encryption (MRSE) returns the top-k results in response to a data user's request of multi-keyword search over encrypted data, and hence provides an efficient way for preserving data privacy in cloud storage systems while without loss of data usability. Many existing MRSE systems are constructed based on an algorithm which we term as k-nearest neighbor for searchable encryption (KNN-SE). Unfortunately, KNN-SE has a number of shortcomings, which limit its practical applications. In this paper, we propose a new MRSE system which overcomes almost all the defects of the KNN-SE based MRSE systems. Specifically, our new system does not require a predefined keyword set and supports keywords in arbitrary languages, is a multi-user system which supports flexible search authorization and time-controlled revocation, and it achieves better data privacy protection since even the cloud server is not able to tell which documents are the top-k results returned to a data user. We also conduct extensive experiments to demonstrate the efficiency of the new system.
Analyzing randomized search heuristics via stochastic domination

Apart from few exceptions, the mathematical runtime analysis of evolutionary algorithms is mostly concerned with expected runtimes, occasionally augmented by tail bounds. In this work, we argue that stochastic domination is a notion that should be used more frequently in this area. Stochastic domination allows to formulate much more informative performance guarantees, it allows to decouple the algorithm analysis into the true algorithmic part of detecting a domination statement and the probability-theoretical part of deriving the desired probabilistic guarantees from this statement, and it helps finding simpler and more natural proofs. As particular results, we prove a variant of the fitness level theorem which shows that the runtime of the search heuristic is dominated by a sum of independent geometric random variables, we prove the first tail bounds for several classic runtime problems, and we give a short and natural proof for Witt's result that the runtime of any (μ,p) mutation-based algorithm on any function with unique optimum is subdominated by the runtime of a variant of the (1+1) EA on the OneMax function. As side-products, we determine the fastest unbiased (1+1) algorithm for the LeadingOnes benchmark problem, both in the general case and when restricted to static mutation operators, and we prove a Chernoff-type tail bound for sums of independent coupon collector distributions.
CINEMA: Efficient and Privacy-Preserving Online Medical Primary Diagnosis With Skyline Query

Online medical primary diagnosis system, which can provide convenient medical decision support through applying mobile communication and data analysis technology, has been considered as a promising approach to improve the quality of healthcare service. However, it still faces many severe challenges on the privacy of users' health information and the accuracy of diagnosis result, which deter the wide adoption of online medical primary diagnosis system. In this paper, we propose an efficient and privacy-preserving online medical primary diagnosis (CINEMA) framework. Within CINEMA framework, users can access online medical primary diagnosing service accurately without divulging their medical data. Specifically, based on fast secure permutation and comparison technique, the encrypted user's query is directly operated at the service provider (SP) without decryption, and the diagnosis result can only be decrypted by the user, meanwhile, the diagnosis model in SP can also be protected. Through extensive analysis, we show that CINEMA can ensure that user's health information and healthcare SP's diagnosis model are kept confidential, and has significantly reduce computation and communication overhead. In addition, performance evaluations via implementing CINEMA demonstrate its effectiveness in term of the real environment.
One-Network Adversarial Fairness

There is currently a great expansion of the impact of machine learning algorithms on our lives, prompting the need for objectives other than pure performance, including fairness. Fairness here means that the outcome of an automated decisionmaking system should not discriminate between subgroups characterized by sensitive attributes such as gender or race. Given any existing differentiable classifier, we make only slight adjustments to the architecture including adding a new hidden layer, in order to enable the concurrent adversarial optimization for fairness and accuracy. Our framework provides one way to quantify the tradeoff between fairness and accuracy, while also leading to strong empirical performance.
SwapGAN: A Multistage Generative Approach for Person-to-Person Fashion Style Transfer

Fashion style transfer has attracted significant attention because it both has interesting scientific challenges and it is also important to the fashion industry. This paper focuses on addressing a practical problem in fashion style transfer, person-to-person clothing swapping, which aims to visualize what the person would look like with the target clothes worn on another person instead of dressing them physically. This problem remains challenging due to varying pose deformations between different person images. In contrast to traditional nonparametric methods that blend or warp the target clothes for the reference person, in this paper we propose a multistage deep generative approach named SwapGAN that exploits three generators and one discriminator in a unified framework to fulfill the task end-to-end. The first and second generators are conditioned on a human pose map and a segmentation map, respectively, so that we can simultaneously transfer the pose style and the clothes style. In addition, the third generator is used to preserve the human body shape during the image synthesis process. The discriminator needs to distinguish two fake image pairs from the real image pair. The entire SwapGAN is trained by integrating the adversarial loss and the mask-consistency loss. The experimental results on the DeepFashion dataset demonstrate the improvements of SwapGAN over other existing approaches through both quantitative and qualitative evaluations. Moreover, we conduct ablation studies on SwapGAN and provide a detailed analysis about its effectiveness.
Data Augmentation for X-Ray Prohibited Item Images Using Generative Adversarial Networks

Recognizing prohibited items automatically is of great significance for intelligent X-ray baggage security screening.Convolutional neural networks (CNNs), with the support of big training data, have been verified as the powerful models capable of reliably detecting the expected objects in images.Therefore, building a specific CNN model working reliably on prohibited item detection also requires large amounts of labeled item image data.Unfortunately, the current X-ray baggage image database is not big enough in count and diversity for CNN model training.In this paper, we propose a novel method for X-ray prohibited item data augmentation using generative adversarial networks (GANs).The prohibited items are first extracted from X-ray baggage images using a K-nearest neighbor matting scheme.Then, the poses of the obtained item images are estimated using a space rectangular coordinate system and categorized into four or eight classes for constructing a training database.For generating the realistic samples reliably, different GAN models are evaluated using Frechet Inception Distance scores, and some important tips of handling GAN training in X-ray prohibited item image generation are reported.Finally, to verify whether the generated images belong to its corresponding class or not, a cross-validation scheme based on a CNN model is implemented.The experimental results show that most of the generated images can be classified correctly by the CNN model.This implies that the generated prohibited item images can be used as the extended samples to augment an X-ray image database.
Neural Models of Text Normalization for Speech Applications

Machine learning, including neural network techniques, have been applied to virtually every domain in natural language processing. One problem that has been somewhat resistant to effective machine learning solutions is text normalization for speech applications such as text-to-speech synthesis (TTS). In this application, one must decide, for example, that 123 is verbalized as one hundred twenty three in 123 pages but as one twenty three in 123 King Ave. For this task, state-of-the-art industrial systems depend heavily on hand-written language-specific grammars. We propose neural network models that treat text normalization for TTS as a sequence-to-sequence problem, in which the input is a text token in context, and the output is the verbalization of that token. We find that the most effective model, in accuracy and efficiency, is one where the sentential context is computed once and the results of that computation are combined with the computation of each token in sequence to compute the verbalization. This model allows for a great deal of flexibility in terms of representing the context, and also allows us to integrate tagging and segmentation into the process. These models perform very well overall, but occasionally they will predict wildly inappropriate verbalizations, such as reading 3 cm as three kilometers. Although rare, such verbalizations are a major issue for TTS applications. We thus use finite-state covering grammars to guide the neural models, either during training and decoding, or just during decoding, away from such “unrecoverable” errors. Such grammars can largely be learned from data.
A 3D-CNN and LSTM Based Multi-Task Learning Architecture for Action Recognition

Multi-task learning (MTL) is a machine learning method to share knowledge for multiple related machine learning tasks via learning those tasks jointly. It has been shown to be capable of effectively improving the generalization capability of each single task (learning just one task at a time). In this paper, we propose a novel MTL architecture that first combines 3D convolutional neural networks (3D CNN) plus the long short-term memory (LSTM) networks together with the MTL mechanism, tailored to information sharing of video inputs. We split each video into several clips and apply the hybrid deep model of 3D CNN and LSTM to extract the sequential features of those video clips. Therefore, our MTL model can share visual knowledge based on those video-clip features among different categories more efficiently. We evaluate our method on three popular public action recognition video datasets. The experimental results show that our novel MTL method can efficiently share detailed information in video clips among multiple action categories and outperforms other multi-task methods.
Single Image Blind Deblurring Using Multi-Scale Latent Structure Prior

Blind image deblurring is a challenging problem in computer vision, which aims to restore both the blur kernel and the latent sharp image from only a blurry observation.Inspired by the prevalent self-example prior in image superresolution, in this paper, we observe that a coarse enough image down-sampled from a blurry observation is approximately a low-resolution version of the latent sharp image.We prove this phenomenon theoretically and define the coarse enough image as a latent structure prior of the unknown sharp image.Starting from this prior, we propose to restore sharp images from the coarsest scale to the finest scale on a blurry image pyramid, and progressively update the prior image using the newly restored sharp image.These coarse-to-fine priors are referred to as Multi-Scale Latent Structures (MSLS).Leveraging the MSLS prior, our algorithm comprises two phases: 1) we first preliminarily restore sharp images in the coarse scales; 2) we then apply a refinement process in the finest scale to obtain the final deblurred image.In each scale, to achieve lower computational complexity, we alternately perform a sharp image reconstruction with fast local self-example matching, an accelerated kernel estimation with error compensation, and a fast non-blind image deblurring, instead of computing any computationally expensive non-convex priors.We further extend the proposed algorithm to solve more challenging non-uniform blind image deblurring problem.Extensive experiments demonstrate that our algorithm achieves competitive results against the state-of-the-art methods with much faster running speed.Index Terms-Blind image deblurring,
Developing cybersecurity education and awareness programmes for small- and medium-sized enterprises (SMEs)

Purpose: An essential component of an organisation's cybersecurity strategy is building awareness and education of online threats, and how to protect corporate data and services. This research article focuses on this topic and proposes a high-level programme for cybersecurity education and awareness to be used when targeting Small-to-Medium-sized Enterprises/Businesses (SMEs/SMBs) at a city-level. We ground this programme in existing research as well as unique insight into an ongoing city-based project with similar aims. Findings: We find that whilst literature can be informative at guiding education and awareness programmes, it may not always reach real-world programmes. On the other hand, existing programmes, such as the one we explored, have great potential but there can also be room for improvement. Knowledge from each of these areas can, and should, be combined to the benefit of the academic and practitioner communities. Originality/value: The study contributes to current research through the outline of a high-level programme for cybersecurity education and awareness targeting SMEs/SMBs. Through this research, we engage in a reflection of literature in this space, and present insights into the advances and challenges faced by an on-going programme. These analyses allow us to craft a proposal for a core programme that can assist in improving the security education, awareness and training that targets SMEs/SMBs.
Efficient Human Activity Recognition Solving the Confusing Activities Via Deep Ensemble Learning

The ubiquity of smartphones and their rich set of on-board sensors has created many exciting new opportunities, where smartphones are used as powerful computing platforms to sense and analyze pervasive data.One important application of mobile sensing is activity recognition based on smartphone inertial sensors, which is a fundamental building block for a variety of scenarios, such as indoor pedestrian tracking, mobile health care, and smart cities.Although many approaches have been proposed to address the human activity recognition problem, several challenges are still present: 1) people's motion modes are very different for different individuals; 2) there is only a very limited amount of training data; 3) human activities can be arbitrary and complex, thus handcrafted feature engineering often fails to work; and 4) the recognition accuracy tends to be limited due to confusing activities.To tackle those challenges, in this paper, we propose a human activity recognition framework based on convolutional neural networks (CNNs) with two convolutional layers using the smartphone-based accelerometer, gyroscope, and magnetometer.To solve the confusion between highly similar activities like going upstairs and walking, this paper presents a novel ensemble model of CNN to further improve the identification accuracy.The extensive experiments have been conducted using 235 977 sensory samples from a total of 100 subjects.The results have shown that the classification accuracy of the proposed model can be up to 96.11%, which proves the effectiveness of the proposed model.
Renormalized Circuit Complexity

We propose a modification to Nielsen's circuit complexity for Hamiltonian simulation using the Suzuki-Trotter (ST) method, which provides a network like structure for the quantum circuit. This leads to an optimized gate counting linear in the geodesic distance and spatial volume, unlike in the original proposal. The optimized ST iteration order is correlated with the error tolerance and plays the role of an anti-de Sitter radial coordinate. The density of gates is shown to be monotonic with the tolerance and a holographic interpretation using path-integral optimization is given.
SemEval-2019 Task 8: Fact Checking in Community Question Answering Forums

We present SemEval-2019 Task 8 on Fact Checking in Community Question Answering Forums, which features two subtasks.Subtask A is about deciding whether a question asks for factual information vs. an opinion/advice vs. just socializing.Subtask B asks to predict whether an answer to a factual question is true, false or not a proper answer.We received 17 official submissions for subtask A and 11 official submissions for Subtask B. For subtask A, all systems improved over the majority class baseline.For Subtask B, all systems were below a majority class baseline, but several systems were very close to it.The leaderboard and the data from the competition can be found at http://competitions. codalab.org/competitions/20022.
Blended coarse gradient descent for full quantization of deep neural networks

Quantized deep neural networks (QDNNs) are attractive due to their much lower memory storage and faster inference speed than their regular full-precision counterparts. To maintain the same performance level especially at low bit-widths, QDNNs must be retrained. Their training involves piecewise constant activation functions and discrete weights; hence, mathematical challenges arise. We introduce the notion of coarse gradient and propose the blended coarse gradient descent (BCGD) algorithm, for training fully quantized neural networks. Coarse gradient is generally not a gradient of any function but an artificial ascent direction. The weight update of BCGD goes by coarse gradient correction of a weighted average of the full-precision weights and their quantization (the so-called blending), which yields sufficient descent in the objective value and thus accelerates the training. Our experiments demonstrate that this simple blending technique is very effective for quantization at extremely low bit-width such as binarization. In full quantization of ResNet-18 for ImageNet classification task, BCGD gives 64.36% top-1 accuracy with binary weights across all layers and 4-bit adaptive activation. If the weights in the first and last layers are kept in full precision, this number increases to 65.46%. As theoretical justification, we show convergence analysis of coarse gradient descent for a two-linear-layer neural network model with Gaussian input data and prove that the expected coarse gradient correlates positively with the underlying true gradient.
PARABANK: Monolingual Bitext Generation and Sentential Paraphrasing via Lexically-Constrained Neural Machine Translation

We present PARABANK, a large-scale English paraphrase dataset that surpasses prior work in both quantity and quality. Following the approach of PARANMT (Wieting and Gimpel, 2018), we train a Czech-English neural machine translation (NMT) system to generate novel paraphrases of English reference sentences. By adding lexical constraints to the NMT decoding procedure, however, we are able to produce multiple high-quality sentential paraphrases per source sentence, yielding an English paraphrase resource with more than 4 billion generated tokens and exhibiting greater lexical diversity. Using human judgments, we also demonstrate that PARABANK’s paraphrases improve over PARANMT on both semantic similarity and fluency. Finally, we use PARABANK to train a monolingual NMT model with the same support for lexically-constrained decoding for sentence rewriting tasks.
Learning to Self-Train for Semi-Supervised Few-Shot Classification

Few-shot classification (FSC) is challenging due to the scarcity of labeled training data (e.g. only one labeled data point per class). Meta-learning has shown to achieve promising results by learning to initialize a classification model for FSC. In this paper we propose a novel semi-supervised meta-learning method called learning to self-train (LST) that leverages unlabeled data and specifically meta-learns how to cherry-pick and label such unsupervised data to further improve performance. To this end, we train the LST model through a large number of semi-supervised few-shot tasks. On each task, we train a few-shot model to predict pseudo labels for unlabeled data, and then iterate the self-training steps on labeled and pseudo-labeled data with each step followed by fine-tuning. We additionally learn a soft weighting network (SWN) to optimize the self-training weights of pseudo labels so that better ones can contribute more to gradient descent optimization. We evaluate our LST method on two ImageNet benchmarks for semi-supervised few-shot classification and achieve large improvements over the state-of-the-art.
Cryptocurrency adoption: current stage, opportunities, and open challenges

In this research, a Systematic Literature Review (SLR) is performed to identify the carried-out research topics related to cryptocurrency adoption and models that have been used as well as the current adoption challenges that need to be overcome in future studies.
Label enhanced and patch based deep learning for phase retrieval from single frame fringe pattern in fringe projection 3D measurement

We propose a label enhanced and patch based deep learning phase retrieval approach which can achieve fast and accurate phase retrieval using only several fringe patterns as training dataset. To the best of our knowledge, it is the first time that the advantages of the label enhancement and patch strategy for deep learning based phase retrieval are demonstrated in fringe projection. In the proposed method, the enhanced labeled data in training dataset is designed to learn the mapping between the input fringe pattern and the output enhanced fringe part of the deep neural network (DNN). Moreover, the training data is cropped into small overlapped patches to expand the training samples for the DNN. The performance of the proposed approach is verified by experimental projection fringe patterns with applications in dynamic fringe projection 3D measurement.