Reviewerly/exHarmony huggingface dataset:
A Unified Blockchain-Semantic Framework for Wireless Edge Intelligence Enabled Web 3.0

Web 3.0 enables user-generated contents and userselected authorities. With the help of decentralized wireless edge computing architectures, Web 3.0 allows users to read, write, and own contents. A core technology that enables Web 3.0 goals is blockchain, which provides security services by recording content in a decentralized and transparent manner. However, the explosion of on-chain recorded contents and the fast-growing number of users cause increasingly unaffordable computing and storage resource consumption. A promising paradigm is to analyze the semantic information of contents that can convey precisely the desired meanings without consuming many resources. In this article, we propose a unified blockchain-semantic ecosystems framework for wireless edge intelligence-enabled Web 3.0. Our framework consists of six key components to exchange semantic demands. We then introduce an Oracle-based proof of semantic mechanism to implement on-chain and off-chain interactions of Web 3.0 ecosystems on semantic verification algorithms while maintaining on-chain and off-chain service security. An adaptive Deep Reinforcement Learning-based sharding mechanism on Oracle is designed to improve interaction efficiency, which can facilitate Web 3.0 ecosystems to deal with varied semantic demands. Finally, a case study is presented to show that the proposed framework can dynamically adjust settings according to varied semantic demands.
Efficient 3-Party Distributed ORAM

Distributed Oblivious RAM (DORAM) protocols—in which parties obliviously access a shared location in a shared array—are a fundamental component of secure-computation protocols in the RAM model. We show here an efficient, 3-party DORAM protocol with semi-honest security for a single corrupted party. To the best of our knowledge, ours is the first protocol for this setting that runs in constant rounds, requires sublinear communication and linear work, and makes only black-box use of cryptographic primitives. Our protocol also appears to be concretely more efficient than existing solutions. As a building block of independent interest, we construct a 3-server distributed point function (DPF) with security against two colluding servers that is arguably simpler and has better concrete efficiency than prior work. We also show how to distribute the key-generation protocol of this DPF (in a black-box manner).
A novel adaptive wavelet threshold estimation based on hybrid particle swarm optimization for partial discharge signal denoising

Partial discharge (PD) of high voltage equipment detected by online monitoring is regarded as a key indicator of insulation status. Threshold estimation exerts an important influence on the denoising effects of PD signals after wavelet-based denoising. In order to improve the adaptive performance of wavelet-based denoising and reduce the distortion of denoised signal, an approach of hybrid particle swarm optimization (HPSO) adaptive wavelet threshold estimation (HPSOTE) is presented in this paper. To obtain the global optimum thresholds, a novel hybrid particle swarm optimization algorithm merging crossover mutation and chaos is proposed in HPSOTE which could solve the premature convergence problem of common threshold selection methods. Genetic algorithm (GA) and particle swarm optimization (PSO) algorithm are adopted to compare the effects with HPSOTE. The denoising results of simulated and actual PD signals show that HPSOTE has a faster convergence rate and more effective global optimization ability than the others and significantly improves the credibility and calculation speed. It gives better mean square error (MSE) and amplitude error performance. This HPSOTE can remove the noise effectively and has comparatively high application value in PD online monitoring for smart grid based on big data.
Local directional-structural pattern for person-independent facial expression recognition

Existing popular descriptors for facial expression recognition often suffer from inconsistent feature description, experiencing poor accuracies. We present a new local descriptor, local directional-structural pattern (LDSP), in this work to address this issue. Unlike the existing local descriptors using only the texture or edge information to represent the local structure of a pixel, the proposed LDSP utilizes the positional relationship of the top edge responses of the target pixel to extract more detailed structural information of the local texture. We further exploit such information to characterize expression-affiliated crucial textures while discarding the random noisy patterns. Moreover, we introduce a globally adaptive thresholding strategy to exclude futile flat patterns. Hence, LDSP offers a stable description of facial expressions with the explicit representation of the expression-affiliated features along with the exclusion of random futile textures. We visualize the efficacy of the proposed method in three folds. First, the LDSP descriptor possesses a moderate code-length owing to the exclusion of the futile patterns, yielding less computation time than other edge descriptors. Second, for person-independent expression recognition in benchmark datasets, LDSP demonstrates higher accuracy than existing descriptors and other state-of-the-art methods. Third, LDSP shows better performance than other descriptors against noise and low resolution, exhibiting its robustness under such uneven conditions.
Cryptocurrency regulation in the BriCs Countries and the eurasian economic union

This article presents the results of a comparative analysis of the legislative approaches to the regulation of cryptocurrencies in the BRICS countries and the Eurasian Economic Union. During the analysis, regulatory legal acts and draft laws, including material on judicial practice, of the Russian Federation, China, India, Brazil, the Republic of South Africa, the Republic of Belarus, Kazakhstan, Kyrgyzstan and Armenia were studied. The use of a comparative-legal method makes it possible to develop recommendations about the development of legislation on the circulation of cryptocurrencies within the BRICS countries. The assessment of the experience of EEU countries, and especially that of the Republic of Belarus, has great importance since the Russian Federation is a member country of both the EEU and BRICS. Comparative analysis was achieved by taking into account such key points as the existence of a regulatory framework, regulation of transaction taxation and counteracting the legalization of profits from crime. The results of the analysis are presented for each country separately and then systematized in the form of an analytical table. Based on the analysis, three approaches to the regulation of cryptocurrencies are identified: conservative, liberal and neutral. The study of the experiences of the BRICS and EEU countries allowed the authors to conclude that these countries need to formulate similar requirements for the regulation of cryptocurrencies so as to avoid the migration of investment and capital to other countries which have a more liberal approach.
Effects of a social-media-based support on premenstrual syndrome and physical activity among female university students in South Korea

Introduction: This study examined the effects of social-media-based support on premenstrual syndrome (PMS) and physical activity among female South Korean university students.Methods: This quasi-experimental study with an equivalent-control-group pretest–posttest design randomly assigned 64 female students with PMS to the experimental or control group. The experimental group received social-media-based support through a smartphone application, text messaging, and e-mail for one menstrual cycle between September and December, 2016. Descriptive and inferential statistics included a Chi-square test and independent and paired t-tests.Results: Significant differences emerged between the experimental and control groups in total PMS scores (p = .003), 14 premenstrual symptoms, and physical activity (p = .010).Conclusions: Female university students with PMS experienced decreased premenstrual symptoms and increased physical activity with social-media-based support, which could be an efficacious, accessible, and widely available nursing intervention to manage PMS and physical activity.
A Study of Various Text Augmentation Techniques for Relation Classification in Free Text

Data augmentation techniques have been widely used in visual recognition tasks as it is easy to generate new data by simple and straight forward image transformations. However, when it comes to text data augmentations, it is difficult to find appropriate transformation techniques which also preserve the contextual and grammatical structure of language texts. In this paper, we explore various text data augmentation techniques in text space and word embedding space. We study the effect of various augmented datasets on the efficiency of different deep learning models for relation classification in text.
Reconstruction of distorted underwater images using robust registration

Imaging through a fluctuating air-water surface is a challenging task since light rays bent by unknown amounts lead to complex geometric distortions. This paper presents a new algorithm to undistort dynamic refractive effects. An iterative robust registration algorithm is employed to overcome the structural turbulence of the waves of the frames by registering each frame to a reference frame. To get the high-quality reference frame, the image is reconstructed by the patches selected from the sequence frames. A blind deconvolution algorithm is also performed to improve the reference frame. Experiments show our method exhibits significant improvement over other methods.
mORAL

We address the open problem of reliably detecting oral health behaviors passively from wrist-worn inertial sensors. We present our model named mORAL (pronounced em oral) for detecting brushing and flossing behaviors, without the use of instrumented toothbrushes so that the model is applicable to brushing with still prevalent manual toothbrushes. We show that for detecting rare daily events such as toothbrushing, adopting a model that is based on identifying candidate windows based on events, rather than fixed-length timeblocks, leads to significantly higher performance. Trained and tested on 2,797 hours of sensor data collected over 192 days on 25 participants (using video annotations for ground truth labels), our brushing model achieves 100% median recall with a false positive rate of one event in every nine days of sensor wearing. The average error in estimating the start/end times of the detected event is 4.1% of the interval of the actual toothbrushing event.
Learning to Prove Theorems via Interacting with Proof Assistants.

Humans prove theorems by relying on substantial high-level reasoning and problem-specific insights. Proof assistants offer a formalism that resembles human mathematical reasoning, representing theorems in higher-order logic and proofs as high-level tactics. However, human experts have to construct proofs manually by entering tactics into the proof assistant. In this paper, we study the problem of using machine learning to automate the interaction with proof assistants. We construct CoqGym, a large-scale dataset and learning environment containing 71K human-written proofs from 123 projects developed with the Coq proof assistant. We develop ASTactic, a deep learning-based model that generates tactics as programs in the form of abstract syntax trees (ASTs). Experiments show that ASTactic trained on CoqGym can generate effective tactics and can be used to prove new theorems not previously provable by automated methods. Code is available at this https URL.
Performance Analysis of ANN and Naive Bayes Classification Algorithm for Data Classification

Classification is an important data mining technique with a wide range of applications to classify the various types of data existing in almost all areas of our lives. The purpose of this discovery study can be used to estimate the potential of having breast cancer by taking advantage of anthropometric data and collected routine blood analysis parameters. The study was performed using data from patients who were admitted to the clinic with the suspicion of breast cancer. The values of Age (years), BMI (kg/m2), Glucose (mg/dL), Insulin (µU/mL), HOMA, Leptin (ng/mL), Adiponectin (µg/mL), Resistin (ng/mL), MCP-1(pg/dL) were used. In our study, classification algorithms were applied to the data and they were asked to estimate the disease diagnosis. The classification performance of Artificial neural networks and Naive Bayes classifiers which were applied to data with 9 inputs and one output were calculated and theperformance results were compared. This article sheds light on the performance evaluation based on correct and incorrect data classification examples using ANN and Naive Bayes classification algorithm. When we look at the performances obtained, it is predicted that using the anthropometric data and the collected routine blood analysis parameters, the potential for diagnosing breast cancer is high using these data.
Live Data Visualization of IoT Sensors Using Augmented Reality (AR) and BIM

Live Data Visualization of IoT Sensors Using Augmented Reality (AR) and BIM Worawan Natephra and Ali Motamedi Pages 632-638 (2019 Proceedings of the 36th ISARC, Banff, Canada, ISBN 978-952-69524-0-6, ISSN 2413-5844) Abstract: Recently, the integration of Building Information Modeling (BIM) technology and Virtual and Augmented Reality (VR/AR) technologies during the operation and maintenance phase has been increasingly adopted. There has also been a surge of interest in the exploitation of Internet of Things (IoT) for constructed facilities, in the form of wireless networks connecting physical objects (e.g., sensing devices, facility assets, equipment, etc.). Recent IoT systems offer data management and visualization solutions. However, to improve issues such as safety and indoor comfort conditions for facilities, most of the existing IoT deployment do not benefit from the enriched digital representations of BIM and its graphical visualization capabilities. Although the integration of sensor data with BIM models has been investigated in academia, storing such real-time data in a standard and structured manner remains to be further investigated. This research aims to visualize live environmental data collected by IoT agents in the AR environment built upon existing BIM models. In our case study, the environmental data, such as indoor air temperature, light intensity, and humidity are captured by sensors connected to Arduino microcontrollers. Sensor reading are then stored in the BIM model and visualized in both the BIM platform and the AR application in a real-time manner. Keywords: BIM; sensors; thermal comfort; Augmented Reality; Arduino; Internet of Things (IoT); Visualization DOI: https://doi.org/10.22260/ISARC2019/0084 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
Evaluation of the effect of computer aided diagnosis system on breast ultrasound for inexperienced radiologists in describing and determining breast lesions

To investigate the effect of a computer-aided diagnosis (CAD) system on breast ultrasound (US) for inexperienced radiologists in describing and determining breast lesions.Between October 2015 to January 2017, 500 suspicious or probable benign lesions in 413 patients were reviewed. Five experienced readers retrospectively reviewed for each of 100 lesions according to the Breast Imaging Reporting and Data System (BI-RADS) lexicon and category, with CAD system (S-detectTM). The readers then made final decisions by combining CAD results to their US results. Using the nested experiment design, five inexperienced readers were asked to select the appropriate BI-RADS lexicons, categories, CAD results, and combination results for each of the 100 lesions, retrospectively. Diagnostic performance of experienced and inexperienced radiologists and CAD were assessed. For each case, agreements in the lexicons and categories were analyzed among the experienced reader, inexperienced reader and CAD.Indicators of the diagnostic performance for breast malignancy of the experienced group (AUC=0.83, 95%CI [0.80, 0.86]) were similar or higher than those of CAD (AUC = 0.79, 95%CI[0.74, 0.83], p=0.101), except for specificity. Conversely, indicators of diagnostic performance of inexperienced group (AUC=0.65, 95%CI[0.58, 0.71]) did not differ from or were lower than those of CAD(AUC=0.73, 95%CI[0.67, 0.78], p=0.013). Also, the diagnostic performance of the inexperienced group after combination with the CAD result was significantly improved (0.71, 95% CI [0.65, 0.77], p=0.001), whereas that of the experienced group did not change after combination with the CAD result, except for specificity and positive predictive value (PPV). Kappa values for the agreement of the categorization between CAD and each radiologist group were increased after applying the CAD result to their result of general US. Especially, the increase of the Kappa value was higher in the inexperienced group than in the experienced group. Also, for all the lexicons, the Kappa values between the experienced group and CAD were higher than those between the inexperienced group and CAD.By using the CAD system for classification of breast lesions, diagnostic performance of the inexperienced radiologists for malignancy was significantly improved, and better agreement was observed in lexicons between the experienced group and CAD than between the inexperienced group and CAD. CAD may be beneficial and educational for the inexperienced group.
Escaping from saddle points on Riemannian manifolds

We consider minimizing a nonconvex, smooth function $f$ on a Riemannian manifold $\mathcal{M}$. We show that a perturbed version of the gradient descent algorithm converges to a second-order stationary point for this problem (and hence is able to escape saddle points on the manifold). While the unconstrained problem is well-studied, our result is the first to prove such a rate for nonconvex, manifold-constrained problems. The rate of convergence depends as $1/\epsilon^2$ on the accuracy $\epsilon$, which matches a rate known only for unconstrained smooth minimization. The convergence rate also has a polynomial dependence on the parameters denoting the curvature of the manifold and the smoothness of the function.
Limitations of the Empirical Fisher Approximation.

Natural gradient descent, which preconditions a gradient descent update with the Fisher information matrix of the underlying statistical model, is a way to capture partial second-order information. Several highly visible works have advocated an approximation known as the empirical Fisher, drawing connections between approximate second-order methods and heuristics like Adam. We dispute this argument by showing that the empirical Fisher---unlike the Fisher---does not generally capture second-order information. We further argue that the conditions under which the empirical Fisher approaches the Fisher (and the Hessian) are unlikely to be met in practice, and that, even on simple optimization problems, the pathologies of the empirical Fisher can have undesirable effects.
On the Risk of Minimum-Norm Interpolants and Restricted Lower Isometry of Kernels.

We study the risk of minimum-norm interpolants of data in Reproducing Kernel Hilbert Spaces. Our upper bounds on the risk are of a multiple-descent shape for the various scalings of $d = n^{\alpha}$, $\alpha\in(0,1)$, for the input dimension $d$ and sample size $n$. Empirical evidence supports our finding that minimum-norm interpolants in RKHS can exhibit this unusual non-monotonicity in sample size; furthermore, locations of the peaks in our experiments match our theoretical predictions. Since gradient flow on appropriately initialized wide neural networks converges to a minimum-norm interpolant with respect to a certain kernel, our analysis also yields novel estimation and generalization guarantees for these over-parametrized models. At the heart of our analysis is a study of spectral properties of the random kernel matrix restricted to a filtration of eigen-spaces of the population covariance operator, and may be of independent interest.
A framework for evaluating correspondence between brain images using anatomical fiducials

Abstract Accurate spatial correspondence between template and subject images is a crucial step in neuroimaging studies and clinical applications like stereotactic neurosurgery. In the absence of a robust quantitative approach, we sought to propose and validate a set of point landmarks, anatomical fiducials (AFIDs), that could be quickly, accurately, and reliably placed on magnetic resonance images of the human brain. Using several publicly available brain templates and individual participant datasets, novice users could be trained to place a set of 32 AFIDs with millimetric accuracy. Furthermore, the utility of the AFIDs protocol is demonstrated for evaluating subject‐to‐template and template‐to‐template registration. Specifically, we found that commonly used voxel overlap metrics were relatively insensitive to focal misregistrations compared to AFID point‐based measures. Our entire protocol and study framework leverages open resources and tools, and has been developed with full transparency in mind so that others may freely use, adopt, and modify. This protocol holds value for a broad number of applications including alignment of brain images and teaching neuroanatomy.
Efficient Escrow-free CP-ABE with Constant Size Ciphertext and Secret Key for Big Data Storage in Cloud

Ciphertext-policy attribute-based encryption (CP-ABE) schemes are an appropriate cryptographic technique to enable privacy along with access control in the cloud, but the existing CP-ABE schemes do not directly apply for big data because they have the issue of long ciphertext and long secret key size (LC-LS). To address LC-LS, the constant size ciphertext and secret key (CSC-S) schemes proposed. However, the existing CSC-S schemes suffer from the key escrow security issue and efficiency issue. To address both simultaneously, the authors propose an efficient escrow-free CP-ABE with constant size ciphertext and secret key (EEF-CPABE) for big data storage in the Cloud. The EEF-CPABE scheme reduces the encryption and decryption computation overhead by designing CSC-S. Further, the data owner generates the decryption global key to decrypt the data along with user secret key which solves the key escrow issue. Security and performance analysis demonstrate that the EEF-CPABE scheme resists against authority, and chosen plain-text attacks and more efficient than CSC-S schemes.
Laser-free trapped-ion entangling gates with simultaneous insensitivity to qubit and motional decoherence

Laser-free entangling gates are a valuable technique for preparing entangled states in a manner that is robust against photon scattering, but with the price of increased vulnerability to qubit and motional decoherence. Building on a recent scheme that employs one rf and two microwave fields to mitigate sensitivity to qubit decoherence, it is now theoretically shown that motional decoherence can be simultaneously reduced by a clever choice of field frequencies and amplitudes.