Reviewerly/exHarmony huggingface dataset:
Fast Blind Quality Assessment of DIBR-Synthesized Video Based on High-High Wavelet Subband

Free-viewpoint video, as the development direction of the next-generation video technologies, uses the depth-image-based rendering (DIBR) technique for the synthesis of video sequences at viewpoints, where real captured videos are missing. As reference videos at multiple viewpoints are not available, a blind reliable real-time quality metric of the synthesized video is needed. Although no-reference quality metrics dedicated to synthesized views successfully evaluate synthesized images, they are not that effective when evaluating synthesized video due to additional temporal flicker distortion typical only for video. In this paper, a new fast no-reference quality metric of synthesized video with synthesis distortions is proposed. It is guided by the fact that the DIBR-synthesized images are characterized by increased high frequency content. The metric is designed under the assumption that the perceived quality of DIBR-synthesized video can be estimated by quantifying the selected areas in the high-high wavelet subband. The threshold is used to select the most important distortion sensitive regions. The proposed No-Reference Morphological Wavelet with Threshold (NR_MWT) metric is computationally extremely efficient, comparable to PSNR, as the morphological wavelet transformation uses very short filters and only integer arithmetic. It is completely blind, without using machine learning techniques. Tested on the publicly available dataset of synthesized video sequences characterized by synthesis distortions, the metric achieves better performances and higher computational efficiency than the state-of-the-art metrics dedicated to DIBR-synthesized images and videos.
Bitcoin, Litecoin, and the Euro: an annualized volatility analysis

Purpose This paper aims to empirically investigate the volatility of Bitcoin, Litecoin and the Euro. Design/methodology/approach The authors use quantitative methodologies to assess the annualized volatility of two cryptocurrencies and one international fiat currency. The exchange rate of the currencies is monitored on a daily basis using 1,460 observations from January 1, 2014 to December 31, 2017. The models used include the augmented Dickey–Fuller test, Akaike Information Criteria, autocorrelation function and exchange rate changes determining which currency is the most volatile. Findings The findings indicate, based on the statistical measures used, including the standard deviation of selected currencies and annualized volatility, that Litecoin is more volatile than Bitcoin and the Euro and that Bitcoin is more volatile than the Euro. This furthers previous research on cryptocurrency volatility. Originality/value The paper provides compelling evidence about the volatility of Litecoin and Bitcoin. The volatility of cryptocurrencies is furthered with data that are more current. The findings are important for investors, financial markets and central banks.
Tokenized Securities and Commercial Real Estate

The following research investigates the application of security tokenization to commercial real estate assets. Primary research through interviews was conducted to uncover some of the most salient use cases and blockchain benefits for the space. The report explores three domains of blockchain application to real estate: (1) the application of blockchain to securities issuance and trading, (2) the application of blockchain to the real estate investment value chain, and (3) the application of blockchain to the representation of the physical assets themselves. Overall, we find that the value creation provided by tokenization can come in several layers, with some standalone benefits emerging by applying tokenization to each of the three domains in isolation. However, significant synergies can arise from combining these layers. As integration increases, additional features become possible. Our conclusion offers a general framework that can be used to perform future research on the tokenization of other types of assets and their related securities.
PDE-Based Group Equivariant Convolutional Neural Networks

We present a PDE-based framework that generalizes Group equivariant Convolutional Neural Networks (G-CNNs). In this framework, a network layer is seen as a set of PDE-solvers where geometrically meaningful PDE-coefficients become the layer's trainable weights. Formulating our PDEs on homogeneous spaces allows these networks to be designed with built-in symmetries such as rotation in addition to the standard translation equivariance of CNNs. Having all the desired symmetries included in the design obviates the need to include them by means of costly techniques such as data augmentation. We will discuss our PDE-based G-CNNs (PDE-G-CNNs) in a general homogeneous space setting while also going into the specifics of our primary case of interest: roto-translation equivariance. We solve the PDE of interest by a combination of linear group convolutions and non-linear morphological group convolutions with analytic kernel approximations that we underpin with formal theorems. Our kernel approximations allow for fast GPU-implementation of the PDE-solvers, we release our implementation with this article in the form of the LieTorch extension to PyTorch, available at https://gitlab.com/bsmetsjr/lietorch . Just like for linear convolution a morphological convolution is specified by a kernel that we train in our PDE-G-CNNs. In PDE-G-CNNs we do not use non-linearities such as max/min-pooling and ReLUs as they are already subsumed by morphological convolutions. We present a set of experiments to demonstrate the strength of the proposed PDE-G-CNNs in increasing the performance of deep learning based imaging applications with far fewer parameters than traditional CNNs.
High-resolution T2-FLAIR and non-contrast CT brain atlas of the elderly

Abstract Normative brain atlases are a standard tool for neuroscience research and are, for example, used for spatial normalization of image datasets prior to voxel-based analyses of brain morphology and function. Although many different atlases are publicly available, they are usually biased with respect to an imaging modality and the age distribution. Both effects are well known to negatively impact the accuracy and reliability of the spatial normalization process using non-linear image registration methods. An important and very active neuroscience area that lacks appropriate atlases is lesion-related research in elderly populations (e.g. stroke, multiple sclerosis) for which FLAIR MRI and non-contrast CT are often the clinical imaging modalities of choice. To overcome the lack of atlases for these tasks and modalities, this paper presents high-resolution, age-specific FLAIR and non-contrast CT atlases of the elderly generated using clinical images.
How Developers Choose Names

The names of variables and functions serve as implicit documentation and are instrumental for program comprehension. But choosing good meaningful names is hard. We perform a sequence of experiments in which a total of 334 subjects are required to choose names in given programming scenarios. The first experiment shows that the probability that two developers would select the same name is low: in the 47 instances in our experiments the median probability was only 6.9 percent. At the same time, given that a specific name is chosen, it is usually understood by the majority of developers. Analysis of the names given in the experiment suggests a model where naming is a (not necessarily cognizant or serial) three-step process: (1) selecting the concepts to include in the name, (2) choosing the words to represent each concept, and (3) constructing a name using these words. A followup experiment, using the same experimental setup, then checked whether using this model explicitly can improve the quality of names. The results were that names selected by subjects using the model were judged by two independent judges to be superior to names chosen in the original experiment by a ratio of two-to-one. Using the model appears to encourage the use of more concepts and longer names.
Identifying Banking Transaction Descriptions via Support Vector Machine Short-Text Classification Based on a Specialized Labelled Corpus

Short texts are omnipresent in real-time news, social network commentaries, etc. Traditional text representation methods have been successfully applied to self-contained documents of medium size.However, information in short texts is often insufficient, due, for example, to the use of mnemonics, which makes them hard to classify.Therefore, the particularities of specific domains must be exploited.In this article we describe a novel system that combines Natural Language Processing techniques with Machine Learning algorithms to classify banking transaction descriptions for personal finance management, a problem that was not previously considered in the literature.We trained and tested that system on a labelled dataset with real customer transactions that will be available to other researchers on request.Motivated by existing solutions in spam detection, we also propose a short text similarity detector to reduce training set size based on the Jaccard distance.Experimental results with a two-stage classifier combining this detector with a SVM indicate a high accuracy in comparison with alternative approaches, taking into account complexity and computing time.Finally, we present a use case with a personal finance application, CoinScrap, which is available at Google Play and App Store.
Thermal Brownian Motion of Skyrmion for True Random Number Generation

The true random number generators (TRNGs) have received extensive attention because of their wide applications in information transmission and encryption. The true random numbers generated by TRNG are typically applied to the encryption algorithm or security protocol of the information security core. Recently, TRNGs have also been employed in emerging stochastic computing paradigm for reducing power consumption. Roughly speaking, TRNG can be divided into circuits-based, e.g., oscillator sampling or directly noise amplifying; and quantum physics-based, e.g., photoelectric effect. The former generally requires a large area and has a large power consumption, whereas the latter is intrinsic random but is more difficult to implement and usually requires additional post-processing circuitry. Very recently, magnetic skyrmion has become a promising candidate for implementing TRNG because of their nanometer size, high stability, and intrinsic thermal Brownian motion dynamics. In this work, we propose a TRNG based on continuous skyrmion thermal Brownian motion in a confined geometry at room temperature. True random bitstream can be easily obtained by periodically detecting the relative position of the skyrmion without the need for additional current pulses. More importantly, we implement a probability-adjustable TRNG, in which a desired ratio of 0 and 1 can be acquired by adding an anisotropy gradient through voltage-controlled magnetic anisotropy (VCMA) effect. The behaviors of the skyrmion-based TRNG are verified by using micromagnetic simulations. The National Institute of Standards and Technology (NIST) test results demonstrate that our proposed random number generator is TRNG with good randomness. Our research provides a new perspective for efficient TRNG realization.
An Unordered Image Stitching Method Based on Binary Tree and Estimated Overlapping Area

Aiming at the complex computation and time-consuming problem during unordered image stitching, we present a method based on the binary tree and the estimated overlapping areas to stitch images without order in this paper. For image registration, the overlapping areas between input images are estimated, so that the extraction and matching of feature points are only performed in these areas. For image stitching, we build a model of the binary tree to stitch each two matched images without sorting. Compared to traditional methods, our method significantly reduces the computational time of matching irrelevant image pairs and improves the efficiency of image registration and stitching. Moreover, the stitching model of the binary tree proposed in this paper further reduces the distortion of the panorama. Experimental results show that the number of extracted feature points in the estimated overlapping area is approximately 0.3∼0.6 times of that in the entire image by using the same method, which greatly reduces the computational time of feature extraction and matching. Compared to the exhaustive image matching method, our approach only takes about 1/3 of the time to find all matching images.
Stochastic quasi-gradient methods: variance reduction via Jacobian sketching

Abstract We develop a new family of variance reduced stochastic gradient descent methods for minimizing the average of a very large number of smooth functions. Our method—JacSketch—is motivated by novel developments in randomized numerical linear algebra, and operates by maintaining a stochastic estimate of a Jacobian matrix composed of the gradients of individual functions. In each iteration, JacSketch efficiently updates the Jacobian matrix by first obtaining a random linear measurement of the true Jacobian through (cheap) sketching, and then projecting the previous estimate onto the solution space of a linear matrix equation whose solutions are consistent with the measurement. The Jacobian estimate is then used to compute a variance-reduced unbiased estimator of the gradient. Our strategy is analogous to the way quasi-Newton methods maintain an estimate of the Hessian, and hence our method can be seen as a stochastic quasi-gradient method . Our method can also be seen as stochastic gradient descent applied to a controlled stochastic optimization reformulation of the original problem, where the control comes from the Jacobian estimates. We prove that for smooth and strongly convex functions, JacSketch converges linearly with a meaningful rate dictated by a single convergence theorem which applies to general sketches. We also provide a refined convergence theorem which applies to a smaller class of sketches, featuring a novel proof technique based on a stochastic Lyapunov function . This enables us to obtain sharper complexity results for variants of JacSketch with importance sampling. By specializing our general approach to specific sketching strategies, JacSketch reduces to the celebrated stochastic average gradient (SAGA) method, and its several existing and many new minibatch, reduced memory, and importance sampling variants. Our rate for SAGA with importance sampling is the current best-known rate for this method, resolving a conjecture by Schmidt et al. (Proceedings of the eighteenth international conference on artificial intelligence and statistics, AISTATS 2015, San Diego, California, 2015). The rates we obtain for minibatch SAGA are also superior to existing rates and are sufficiently tight as to show a decrease in total complexity as the minibatch size increases. Moreover, we obtain the first minibatch SAGA method with importance sampling.
A Deep Learning Method to Detect Foreign Objects for Inspecting Power Transmission Lines

Image online monitoring technology has been widely used in transmission lines inspection, but the intelligent and efficient foreign object detection still has a gap with the ideal.In this paper, we propose a deep learning method to detect invading foreign objects for power transmission line inspection.Specifically, we design our network based on the regression strategy with oriented bounding boxes to accurately predict spatial location and orientation angle of foreign objects, as well as their categories in cluttered backgrounds.Moreover, an easy yet effective Scale Histogram Matching method is proposed to be applied to the publicly available dataset, allowing useful patterns to be exploited to detect tiny foreign objects during the pretraining procedure and boosting detection performance even with limited annotated samples.Besides, we construct an image dataset that contains common foreign objects in transmission line scenarios to evaluate proposed methods, on which experiment results show our full model achieves accuracy with 88.1% mean Average Precision (mAP).Additionally, the efficient and compact network structure allows our network to run in real-time, which provides possibilities for practical use.
Self-Diagnosis of Multiphase Flow Meters through Machine Learning-Based Anomaly Detection

Measuring systems are becoming increasingly sophisticated in order to tackle the challenges of modern industrial problems. In particular, the Multiphase Flow Meter (MPFM) combines different sensors and data fusion techniques to estimate quantities that are difficult to be measured like the water or gas content of a multiphase flow, coming from an oil well. The evaluation of the flow composition is essential for the well productivity prediction and management, and for this reason, the quantification of the meter measurement quality is crucial. While instrument complexity is increasing, demands for confidence levels in the provided measures are becoming increasingly more common. In this work, we propose an Anomaly Detection approach, based on unsupervised Machine Learning algorithms, that enables the metrology system to detect outliers and to provide a statistical level of confidence in the measures. The proposed approach, called AD4MPFM (Anomaly Detection for Multiphase Flow Meters), is designed for embedded implementation and for multivariate time-series data streams. The approach is validated both on real and synthetic data.
Unconventional Applications of Superconducting Nanowire Single Photon Detectors

Superconducting nanowire single photon detectors are becoming a dominant technology in quantum optics and quantum communication, primarily because of their low timing jitter and capability to detect individual low-energy photons with high quantum efficiencies. However, other desirable characteristics, such as high detection rates, operation in cryogenic and high magnetic field environments, or high-efficiency detection of charged particles, are underrepresented in literature, potentially leading to a lack of interest in other fields that might benefit from this technology. We review the progress in use of superconducting nanowire technology in photon and particle detection outside of the usual areas of physics, with emphasis on the potential use in ongoing and future experiments in nuclear and high energy physics.
Weak Measurement of a Superconducting Qubit Reconciles Incompatible Operators

Traditional uncertainty relations dictate a minimal amount of noise in incompatible projective quantum measurements. However, not all measurements are projective. Weak measurements are minimally invasive methods for obtaining partial state information without projection. Recently, weak measurements were shown to obey an uncertainty relation cast in terms of entropies. We experimentally test this entropic uncertainty relation with strong and weak measurements of a superconducting transmon qubit. A weak measurement, we find, can reconcile two strong measurements' incompatibility, via backaction on the state. Mathematically, a weak value---a preselected and postselected expectation value---lowers the uncertainty bound. Hence we provide experimental support for the physical interpretation of the weak value as a determinant of a weak measurement's ability to reconcile incompatible operations.
Semi-Supervised Learning with Data Augmentation for End-to-End ASR

In this paper, we apply Semi-Supervised Learning (SSL) along with Data Augmentation (DA) for improving the accuracy of End-to-End ASR. We focus on the consistency regularization principle, which has been successfully applied to image classification tasks, and present sequence-to-sequence (seq2seq) versions of the FixMatch and Noisy Student algorithms. Specifically, we generate the pseudo labels for the unlabeled data on-the-fly with a seq2seq model after perturbing the input features with DA. We also propose soft label variants of both algorithms to cope with pseudo label errors, showing further performance improvements. We conduct SSL experiments on a conversational speech data set with 1.9kh manually transcribed training data, using only 25% of the original labels (475h labeled data). In the result, the Noisy Student algorithm with soft labels and consistency regularization achieves 10.4% word error rate (WER) reduction when adding 475h of unlabeled data, corresponding to a recovery rate of 92%. Furthermore, when iteratively adding 950h more unlabeled data, our best SSL performance is within 5% WER increase compared to using the full labeled training set (recovery rate: 78%).
A Blockchain-Based Consent Platform for Active Assisted Living: Modeling Study and Conceptual Framework

Recent advancements in active assisted living (AAL) technologies allow older adults to age well in place. However, sensing technologies increase the complexity of data collection points, making it difficult for users to consent to data collection. One possible solution for improving transparency in the consent management process is the use of blockchain, an immutable and timestamped ledger.This study aims to provide a conceptual framework based on technology aimed at mitigating trust issues in the consent management process.The consent management process was modeled using established methodologies to obtain a mapping of trust issues. This mapping was then used to develop a conceptual framework based on previous monitoring and surveillance architectures for connected devices.In this paper, we present a model that maps trust issues in the informed consent process; a conceptual framework capable of providing all the necessary underlining technologies, components, and functionalities required to develop applications capable of managing the process of informed consent for AAL, powered by blockchain technology to ensure transparency; and a diagram showing an instantiation of the framework with entities comprising the participants in the blockchain network, suggesting possible technologies that can be used.Our conceptual framework provides all the components and technologies that are required to enhance the informed consent process. Blockchain technology can help overcome several privacy challenges and mitigate trust issues that are currently present in the consent management process of data collection involving AAL technologies.
Mapping the OWASP Top Ten to Blockchain

The OWASP Top Ten List is a widely-recognized tool for identifying vulnerabilities in web applications. Since blockchain-specific security guidance is currently lacking, mapping existing frameworks, such as OWASP, to the blockchain can help in the identification of potential vulnerabilities in blockchain systems. While the OWASP Top Ten List is designed to describe the vulnerabilities that web application developers face, nine of ten of the OWASP vulnerabilities also apply to blockchain systems. The exception, XML External Entities (XXE), is not applicable due to the lack of use of XML in blockchain.
Quantum noise extraction from the interference of laser pulses in an optical quantum random number generator

We propose a method for quantum noise extraction from the interference of laser pulses with random phase. Our technique is based on the calculation of a parameter, which we called the quantum reduction factor, and which allows for the determination of the contributions of quantum and classical noises with the assumption that classical fluctuations exhibit Gaussian distribution. To the best of our knowledge, the concept of quantum reduction factor is introduced for the first time. We use such an approach to implement the post-processing-free optical quantum random number generator with the random bit generation rate of 2 Gbps.