Reviewerly/exHarmony huggingface dataset:
The Airbus Air Traffic Control Speech Recognition 2018 Challenge: Towards ATC Automatic Transcription and Call Sign Detection

In this paper, we describe the outcomes of the challenge organized and run by Airbus and partners in 2018 on Air Traffic Control (ATC) speech recognition.The challenge consisted of two tasks applied to English ATC speech: 1) automatic speechto-text transcription, 2) call sign detection (CSD).The registered participants were provided with 40 hours of speech along with manual transcriptions.Twenty-two teams submitted predictions on a five hour evaluation set.ATC speech processing is challenging for several reasons: high speech rate, foreignaccented speech with a great diversity of accents, noisy communication channels.The best ranked team achieved a 7.62% Word Error Rate and a 82.41%CSD F1-score.Transcribing pilots' speech was found to be twice as harder as controllers' speech.Remaining issues towards solving ATC ASR are also discussed in the paper.
A Survey of Anonymity of Cryptocurrencies

The concept of virtual currencies is an emerging, and perhaps unexpected development in the modern financial world. Bitcoin can be regarded as the first successful virtual currency, followed by many other implementations. Analogous to paper currencies, it is apparent that privacy and anonymity are two pivotal considerations that affect the adoption of virtual currencies by users. However, many studies have identified several problems associated with the privacy and anonymity of Bitcoin. Consequently, a large number of attempts have been made to address these issues, yet it has been proven that many such solutions do not provide an acceptable level of anonymity. This survey presents an account of the level of anonymity achieved through those and attempts to provide a comparative evaluation across different constructions.
An Exploratory Study on Visual Exploration of Model Simulations by Multiple Types of Experts

Experts in different domains rely increasingly on simulation models of complex processes to reach insights, make decisions, and plan future projects. These models are often used to study possible trade-offs, as experts try to optimise multiple conflicting objectives in a single investigation. Understanding all the model intricacies, however, is challenging for a single domain expert. We propose a simple approach to support multiple experts when exploring complex model results. First, we reduce the model exploration space, then present the results on a shared interactive surface, in the form of a scatterplot matrix and linked views. To explore how multiple experts analyse trade-offs using this setup, we carried out an observational study focusing on the link between expertise and insight generation during the analysis process. Our results reveal the different exploration strategies and multi-storyline approaches that domain experts adopt during trade-off analysis, and inform our recommendations for collaborative model exploration systems.
Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spaces

In order to integrate uncertainty estimates into deep time-series modelling, Kalman Filters (KFs) (Kalman et al., 1960) have been integrated with deep learning models, however, such approaches typically rely on approximate inference tech- niques such as variational inference which makes learning more complex and often less scalable due to approximation errors. We propose a new deep approach to Kalman filtering which can be learned directly in an end-to-end manner using backpropagation without additional approximations. Our approach uses a high-dimensional factorized latent state representation for which the Kalman updates simplify to scalar operations and thus avoids hard to backpropagate, computationally heavy and potentially unstable matrix inversions. Moreover, we use locally linear dynamic models to efficiently propagate the latent state to the next time step. The resulting network architecture, which we call Recurrent Kalman Network (RKN), can be used for any time-series data, similar to a LSTM (Hochreiter & Schmidhuber, 1997) but uses an explicit representation of uncertainty. As shown by our experiments, the RKN obtains much more accurate uncertainty estimates than an LSTM or Gated Recurrent Units (GRUs) (Cho et al., 2014) while also showing a slightly improved prediction performance and outperforms various recent generative models on an image imputation task.
Denoising images corrupted with impulse, Gaussian, or a mixture of impulse and Gaussian noise

In this paper, a cascade of stages is used to denoise images corrupted with Gaussian noise, impulse noise or a mixture of the two. The proposed method is based on removing the abnormal values in the initial stage to help remove the noisy small components in the next subsequent stages. The initial stage is based on an intensity difference technique, while the subsequent stages are based on principle component analysis. Most of the noisy small components are removed in the first subsequent stage and then the smaller ones, which represent the remaining noise, are removed in the next stages. At the end, a new restored version is attained and compared with the results of well-known methods. Simulation experiments illustrate that the proposed method is very fast and delivers superior performance either in terms of Peak Signal to Noise Ratio (PSNR) or visual image quality, particularly in restoring images corrupted by a combination of Gaussian and impulse noise.
Remote sensing image registration based on local structural information and global constraint

In the process of remote sensing image registration, a robust point-matching algorithm is very important to get better results. However, it may be difficult to find enough correct correspondences when remote sensing images exhibit various noises and local deformations. Although existing methods can remove the outliers, many inliers will be removed at the same time. We propose a point-matching method for remote sensing image registration based on phase congruency normalized cross-correlation and global constraint. First, the classical synthetic aperture radar scale-invariant feature transform algorithm is used to obtain the initial correspondences. Then, the feature distance and phase consistency normalized cross-correlation are combined to remove the outliers in the local neighborhood blocks of the initial correspondences. Finally, the proposed method focuses on increasing the number of inliers and raising the correct ratio by a global constraint. The experimental results compared with several state-of-the-art algorithms show that our method can get more correspondences with higher accuracy.
Moving-Object Tracking Algorithm Based on PCA-SIFT and Optimization for Underground Coal Mines

In view of the complex and changeable environment in underground coal mines, an improved algorithm based on the principal component analysis-scale invariant feature transform (PCA-SIFT) and mean shift is proposed to address the issues for which existing tracking algorithms are not adequate; for example, when differentiating between moving targets and the background, the tracking in the case of moving objects (e.g., confusion between foreground and background) is not optimal.This results in poor resolution and the inability to deal with very dusty conditions, scale change, and rotation.The proposed feature target tracking model was developed using the scale invariance property of the PCA-SIFT feature-extraction algorithm.Finally, the mean-shift method was used to track moving objects.The experimental results showed that the optimized algorithm for tracking moving objects was significantly better and more robust than the existing algorithm.
Face Liveness Detection Using Thermal Face-CNN with External Knowledge

Face liveness detection is important for ensuring security. However, because faces are shown in photographs or on a display, it is difficult to detect the real face using the features of the face shape. In this paper, we propose a thermal face-convolutional neural network (Thermal Face-CNN) that knows the external knowledge regarding the fact that the real face temperature of the real person is 36~37 degrees on average. First, we compared the red, green, and blue (RGB) image with the thermal image to identify the data suitable for face liveness detection using a multi-layer neural network (MLP), convolutional neural network (CNN), and C-support vector machine (C-SVM). Next, we compared the performance of the algorithms and the newly proposed Thermal Face-CNN in a thermal image dataset. The experiment results show that the thermal image is more suitable than the RGB image for face liveness detection. Further, we also found that Thermal Face-CNN performs better than CNN, MLP, and C-SVM when the precision is slightly more crucial than recall through F-measure.
Data-adaptive low-rank modeling and external gradient prior for single image super-resolution

Image super-resolution (SR) is a challenging task which aims to recover the high-resolution (HR) images from the degraded low-resolution (LR) observations. To address this ill-posed problem, properly exploiting the image prior is of great importance. In this paper, we propose a data-adaptive low-rank (DLR) model. Rather than directly assuming that the rank of a group of similar patches is low, the DLR model imposes the low-rank property on the residual of the grouped patches. In addition, the shape of the patches in our DLR model is adapted to the contents of images, so that the dissimilar pixels in a group of patches can be largely reduced. In order to further boost the performance, an external gradient prior (EGP), which is learned externally to capture gradient information, is combined with DLR to form a joint prior. When solving the DLR-based and the joint-prior-based minimization problems, the split Bregman method is adopted to speed up the convergence. The extensive experimental results show that our algorithms outperform many state-of-the-art single image SR methods in terms of both objective and subjective qualities.
Exploring the Role of Trust in Blockchain Adoption: An Inductive Approach

While interest in blockchain technology and applications increases, research studying the role of trust as an element that leads potential users and consumers to adopt and accept the technology remains scarce.This study conducts acceptance research that expands beyond traditional acceptance models and explores the role of trust from the user/consumer perspective.It provides comprehensive insights from the user/consumer angle and a deeper understanding of the role of trust in blockchain adoption.Using an inductive research approach that builds theory from qualitative empirical data, this paper identifies trust as a critical benefit of blockchain technology and applications, encompassing both functional (economic and system-/ process-related) as well as emotional benefits (social and personal).As trust spans across functional and emotional benefit dimensions, this study suggests that trust is a key driver for user/consumer adoption of blockchain technology and applications.
Robustly Safe Compilation

Secure compilers generate compiled code that withstands many target-level attacks such as alteration of control flow, data leaks or memory corruption. Many existing secure compilers are proven to be fully abstract, meaning that they reflect and preserve observational equivalence. Fully abstract compilation is strong and useful but, in certain cases, comes at the cost of requiring expensive runtime constructs in compiled code. These constructs may have no relevance for security, but are needed to accommodate differences between the source and target languages that fully abstract compilation necessarily needs. As an alternative to fully abstract compilation, this paper explores a different criterion for secure compilation called robustly safe compilation or RSC. Briefly, this criterion means that the compiled code preserves relevant safety properties of the source program against all adversarial contexts interacting with the compiled program. We show that RSC can be proved more easily than fully abstract compilation and also often results in more efficient code. We also develop two illustrative robustly-safe compilers and, through them, illustrate two different proof techniques for establishing that a compiler attains RSC. Based on these, we argue that proving RSC can be simpler than proving fully abstraction.
Flag additivity in quantum resource theories

Quantum resource theories offer a powerful framework for studying various phenomena in quantum physics. Although considerable effort has been devoted to developing a unified framework of resource theories, there are few common properties that hold for all quantum resources. In this paper, we fill this gap by introducing the flag additivity based on the tensor product structure and the flag basis for the general quantum resources. To illustrate the usefulness of flag additivity, we show that flag additivity can be used to derive other nontrivial properties in quantum resource theories, e.g., strong monotonicity, convexity, and full additivity.
A Hitchhiker's Guide to Statistical Comparisons of Reinforcement Learning Algorithms

Consistently checking the statistical significance of experimental results is the first mandatory step towards reproducible science. This paper presents a hitchhiker's guide to rigorous comparisons of reinforcement learning algorithms. After introducing the concepts of statistical testing, we review the relevant statistical tests and compare them empirically in terms of false positive rate and statistical power as a function of the sample size (number of seeds) and effect size. We further investigate the robustness of these tests to violations of the most common hypotheses (normal distributions, same distributions, equal variances). Beside simulations, we compare empirical distributions obtained by running Soft-Actor Critic and Twin-Delayed Deep Deterministic Policy Gradient on Half-Cheetah. We conclude by providing guidelines and code to perform rigorous comparisons of RL algorithm performances.
A Local Feature Descriptor Based on Oriented Structure Maps with Guided Filtering for Multispectral Remote Sensing Image Matching

Multispectral image matching plays a very important role in remote sensing image processing and can be applied for registering the complementary information captured by different sensors. Due to the nonlinear intensity difference in multispectral images, many classic descriptors designed for images of the same spectrum are unable to work well. To cope with this problem, this paper proposes a new local feature descriptor termed histogram of oriented structure maps (HOSM) for multispectral image matching tasks. This proposed method consists of three steps. First, we propose a new method based on local contrast to construct the structure guidance images from the multispectral images by transferring the significant contours from source images to results, respectively. Second, we calculate oriented structure maps with guided image filtering. In details, we first construct edge maps by the progressive Sobel filters to extract the common structure characteristics from the multispectral images, and then we compute the oriented structure maps by performing the guided filtering on the edge maps with the structure guidance images constructed in the first step. Finally, we build the HOSM descriptor by calculating the histogram of oriented structure maps in a local region of each interest point and normalize the feature vector. The proposed HOSM descriptor was evaluated on three commonly used datasets and was compared with several state-of-the-art methods. The experimental results demonstrate that the HOSM descriptor can be robust to the nonlinear intensity difference in multispectral images and outperforms other methods.
Reinforcement Learning to Diversify Top-N Recommendation

In this paper, we study how to recommend both accurate and diverse top-N recommendation, which is a typical instance of the maximum coverage problem. Traditional approaches are to treat the process of constructing the recommendation list as a problem of greedy sequential items selection, which are inevitably sub-optimal. In this paper, we propose a reinforcement learning and neural networks based framework – Diversify top-N Recommendation with Fast Monte Carlo Tree Search (Div-FMCTS) – to optimize the diverse top-N recommendations in a global view. The learning of Div-FMCTS consists of two stages: (1) searching for better recommendation with MCTS; (2) generalizing those plans with the policy and value neural networks. Due to the difficulty of searching over extremely large item permutations, we propose two approaches to speeding up the training process. The first approach is pruning the branches of the search tree by the structure information of the optimal recommendations. The second approach is searching over a randomly chosen small subset of items to quickly harvest the fruits of searching in the generalization with neural networks. Its effectiveness has been proved both empirically and theoretically. Extensive experiments on four benchmark datasets have demonstrated the superiority of Div-FMCTS over state-of-the-art methods.
Using blog‐like documents to investigate software practice: Benefits, challenges, and research directions

Abstract Background An emerging body of research is using grey literature to investigate software practice. One frequently occurring type of grey literature is the blog post. Whilst there are prospective benefits to using grey literature and blog posts to investigate software practice, there are also concerns about the quality of such material. Objectives To identify and describe the benefits and challenges to using blog‐like content to investigate software practice, and to scope directions for further research. Methods We conduct a review of previous research, mainly within software engineering, to identify benefits, challenges, and directions and use that review to complement our experiences of using blog posts in research. Results and Conclusion We identify and organise benefits and challenges of using blog‐like documents in software engineering research. We develop a definition of the type of blog‐like document that should be of (more) value to software engineering researchers. We identify and scope several directions in which to progress research into and with blog‐like documents. We discuss similarities and differences in secondary and primary studies that use blog‐like documents and similarities and differences between the use of blog‐like documents and the use of already established research methods, eg, interview and survey.
Robust digital watermarking for color images in combined DFT and DT-CWT domains

Image watermarking focuses on hiding secret data into the cover image imperceptibly to protect the copyright of the original image. In this paper, we propose a new framework of robust digital watermarking for color images using combined embedding techniques of Discrete Fourier Transform (DFT) and Dual Tree Complex Wavelet Transform (DTCWT). The cover image is first divided into Y, U and V channels. The Y channel is then transformed by DFT and partitioned into the ring shapes. With an embedding key, we generate pseudo-random patterns to represent the watermark. These patterns are also transformed and partitioned. The watermark represented by the selection of patterns is then embedded into the rings of the DFT coefficients. We further embed a rectification watermark into the U channel, in which DTCWT is applied to achieve a capability of geometric distortion resilience. On the recipient's side, the detection and extraction of watermark can be successfully done. Compared with previous schemes, the proposed method is better on preserving the image quality. Meanwhile, the robustness against typical attacks is also stronger.
Improved Secure Integer Comparison via Homomorphic Encryption

Secure integer comparison has been one of the first problems introduced in cryptography, both for its simplicity to describe and for its applications. The first formulation of the problem was to enable two parties to compare their inputs without revealing the exact value of those inputs, also called the Millionaires’ problem [45]. The recent rise of fully homomorphic encryption has given a new formulation to this problem. In this new setting, one party blindly computes an encryption of the boolean $$(a<b)$$ given only ciphertexts encrypting a and b. In this paper, we present new solutions for the problem of secure integer comparison in both of these settings. The underlying idea for both schemes is to avoid decomposing the integers in binary in order to improve the performances. On the one hand, our fully homomorphic based solution is inspired by [9], and makes use of the fast bootstrapping techniques developed by [12, 14, 23] to obtain scalability for large integers while preserving high efficiency. On the other hand, our solution to the original Millionaires’ problem is inspired by the protocol of [10], based on partially homomorphic encryption. We tweak their protocol in order to minimize the number of interactions required, while preserving the advantage of comparing non-binary integers. Both our techniques provide efficient solutions to the problem of secure integer comparison for large (even a-priori unbounded in our first scenario) integers with minimum interactions.
A Fitts' law study of pupil dilations in a head-mounted display

Head-mounted displays offer full control over lighting conditions. When equipped with eye tracking technology, they are well suited for experiments investigating pupil dilation in response to cognitive tasks, emotional stimuli, and motor task complexity, particularly for studies that would otherwise have required the use of a chinrest, since the eye cameras are fixed with respect to the head. This paper analyses pupil dilations for 13 out of 27 participants completing a Fitts' law task using a virtual reality headset with built-in eye tracking. The largest pupil dilation occurred for the condition subjectively rated as requiring the most physical and mental effort. Fitts' index of difficulty had no significant effect on pupil dilation, suggesting differences in motor task complexity may not affect pupil dilation.
Universal Barcode Detector via Semantic Segmentation

Barcodes are used in many commercial applications, thus fast and robust reading is important. There are many different types of barcodes, some of them look similar while others are completely different. In this paper we introduce new fast and robust deep learning detector based on semantic segmentation approach. It is capable of detecting barcodes of any type simultaneously both in the document scans and in the wild by means of a single model. The detector achieves state-of-the-art results on the ArTe-Lab 1D Medium Barcode Dataset with detection rate 0.995. Moreover, developed detector can deal with more complicated object shapes like very long but narrow or very small barcodes. The proposed approach can also identify types of detected barcodes and performs at real-time speed on CPU environment being much faster than previous state-of-the-art approaches.