Reviewerly/exHarmony huggingface dataset:
Predicting particle accelerator failures using binary classifiers

Particle accelerator failures lead to unscheduled downtime and lower reliability. Although simple to mitigate while they are actually happening such failures are difficult to predict or identify beforehand. In this work we propose using machine learning approaches to predict machine failures via beam current measurements before they actual occur. To demonstrate this technique in this paper we examine beam pulses from the Oakridge Spallation Neutron Source (SNS). By evaluating a pulse against a set of common classification techniques we show that accelerator failure can be identified prior to actually failing with almost 80% accuracy. We also show that tuning classifier parameters and using pulse properties for refining datasets can further lead to almost 92% accuracy in classification of bad pulses. Most importantly, in the paper we establish there is information about the failure encoded in the pulses prior to it, so we also present a list of feasible next steps for increasing pulse classification accuracy.
Uncertainty model for a traceable stereo-photogrammetry system

Through the computational modelling and experimental verification of a stereo-photogrammetry system, the expanded uncertainty on form measurement was estimated and was found to be 32μm, 12μm and 29μm for a 95% confidence interval (coverage factors of k = 3.2, 2.0 and 2.0 respectively) in the x, y and z axis respectively. The contribution of systematic offsets in the system properties was also investigated, demonstrating the complex distortions of the measurement volume that result from these systematic errors. Additionally, a traceable method of applying a scale factor to the reconstruction was demonstrated using a laser interferometer and gauge block. The relative standard uncertainty on the size of the measurements was estimated to be 0.007% corresponding to length measurement uncertainties of around 7μm over a 100 mm range. Finally, the residuals from a linear fit of the scale factor were found to exhibit behaviour that would be expected to result from small offsets in the system properties. The outcome of this work is a better understanding of the propagation of uncertainty through the stereo-photogrammetry system as well as highlighting key influence factors that must be addressed in future work.
Effects of coupling and phase imperfections in programmable photonic hexagonal waveguide meshes

We present a study of the effect of imperfections on the transmission and crosstalk in programmable photonic meshes with feedback loops consisting of tunable couplers and phase shifters. The many elements in such a mesh can generate a multitude of parasitic paths when the couplers and phase shifters deviate even slightly from their nominal value. Performing Monte Carlo simulations, we show that small stochastic imperfections in the phase and coupling ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m1"> <mml:mrow> <mml:mo form="prefix">&lt;</mml:mo> <mml:mn>1.0</mml:mn> <mml:mi>%</mml:mi> </mml:mrow> </mml:math> ) can introduce unwanted interferences and resonances and significantly deteriorate the frequency response of the circuit. We also demonstrate that, in the presence of imperfections, the programming strategy of the unused couplers can reduce effects of such parasitics.
Single Image Dehazing via NIN-DehazeNet

Single image dehazing has always been a challenging problem in the field of computer vision. Traditional image defogging methods use manual features. With the development of artificial intelligence, the defogging method based on deep learning has developed rapidly. In this paper, we propose a novel image defogging approach called NIN-DehazeNet for single image. This method estimates the transmission map by NIN-DehazeNet combining Network-in-Network with MSCNN(Single Image Dehazing via Multi-Scale Convolutional Neural Networks). In the test stage, we estimate the transmission map of the input hazy image based on the trained model, and then generate the dehazed image using the estimated atmospheric light and computed transmission map. Extensive experiments have shown that the proposed algorithm overperformance traditional methods.
An Underwater Image Enhancement Method for Different Illumination Conditions Based on Color Tone Correction and Fusion-Based Descattering

In the shallow-water environment, underwater images often present problems like color deviation and low contrast due to light absorption and scattering in the water body, but for deep-sea images, additional problems like uneven brightness and regional color shift can also exist, due to the use of chromatic and inhomogeneous artificial lighting devices. Since the latter situation is rarely studied in the field of underwater image enhancement, we propose a new model to include it in the analysis of underwater image degradation. Based on the theoretical study of the new model, a comprehensive method for enhancing underwater images under different illumination conditions is proposed in this paper. The proposed method is composed of two modules: color-tone correction and fusion-based descattering. In the first module, the regional or full-extent color deviation caused by different types of incident light is corrected via frequency-based color-tone estimation. And in the second module, the residual low contrast and pixel-wise color shift problems are handled by combining the descattering results under the assumption of different states of the image. The proposed method is experimented on laboratory and open-water images of different depths and illumination states. Qualitative and quantitative evaluation results demonstrate that the proposed method outperforms many other methods in enhancing the quality of different types of underwater images, and is especially effective in improving the color accuracy and information content in badly-illuminated regions of underwater images with non-uniform illumination, such as deep-sea images.
Big Data Analytics of Twitter Data and Its Application for Physician Assistants: Who Is Talking About Your Profession in Twitter?

In the current era of big data, huge volumes of a wide variety of valuable data—which may be of different veracity—are easily generated or collected at a high velocity in various real-life applications. A rich source of complex big data is social networking sites (e.g., Twitter, Facebook, LinkedIn), in which many people are connected with each other. For many of these creators of social network data (i.e., users on the social networking sites), it is not unusual for them to have hundreds or even thousands of friends or connections. Among these friends or connections, some of them care about you as an individual user or friend, while some others care or talk about your profession. It is interesting to know how many of your friends or connections know about your profession, understand it, and talk about it. This chapter presents a system for big data analytics of Twitter data. In particular, we design a system to allow non-computer experts to extract interesting information from the social network (especially, Twitter) data. To demonstrate the practicality of the system, we also conduct a case study to demonstrate how the system helps physician assistants (PAs) to find interesting pattern from tweets about their profession.
Label-dependent session types

Session types have emerged as a typing discipline for communication protocols. Existing calculi with session types come equipped with many different primitives that combine communication with the introduction or elimination of the transmitted value. We present a foundational session type calculus with a lightweight operational semantics. It fully decouples communication from the introduction and elimination of data and thus features a single communication reduction, which acts as a rendezvous between senders and receivers. We achieve this decoupling by introducing label-dependent session types, a minimalist value-dependent session type system with subtyping. The system is sufficiently powerful to simulate existing functional session type systems. Compared to such systems, label-dependent session types place fewer restrictions on the code. We further introduce primitive recursion over natural numbers at the type level, thus allowing to describe protocols whose behaviour depends on numbers exchanged in messages. An algorithmic type checking system is introduced and proved equivalent to its declarative counterpart. The new calculus showcases a novel lightweight integration of dependent types and linear typing, with has uses beyond session type systems.
Estimating the Required Training Dataset Size for Transmitter Classification Using Deep Learning

Despite the recent surge in the application of deep learning to wireless communication problems, very little is known about the required training dataset size to solve difficult problems with acceptable accuracy, including the problem of transmitter classification. Many researchers use rules-of-thumb to find out how much training data is needed for certain classification or identification tasks. For the artificial neural network (ANN) research, these rules of thumb may suffice, however, for convolutional neural networks (CNN), a class of deep neural networks, these rules of thumb may not hold, and researchers are often left to Figure out the training dataset size needed for accurate classification. In this paper, we investigate the correlation between training dataset size and classification accuracy for transmitter classification applications by investigating whether the rules-of-thumb used in ANN research applies in CNN-based transmitter classification tasks. We predict classification performance of a CNN-based architecture given a dataset size using a power law model and the Levenberg-Marquardt algorithm. We use the chi-squared goodness-of-fit test to validate our predicted model. Our results show that we can predict classification accuracy for larger training dataset sizes with different experimental scenarios with at least 97.5% accuracy. We also compare our scheme with similar prior works in wireless transmitter classification. Finally, we propose a rule-of-thumb for the required training dataset size in transmitter classification using CNNs.
Privacy-Preserving Diverse Keyword Search and Online Pre-Diagnosis in Cloud Computing

With the development of Mobile Healthcare Monitoring Network (MHMN), patients’ data collected by body sensors not only allows patients to monitor their health or make online pre-diagnosis but also enables clinicians to make proper decisions by utilizing data mining technique. However, sensitive data privacy is still a major concern. In this article, we propose practical techniques for searching and making online pre-diagnosis over encrypted data. First, we propose a new Diverse Keyword Searchable Encryption (DKSE) scheme which supports multi-dimension digital vectors range query and textual multi-keyword ranked search to gain a broad range of applications in practice. In addition, a framework called PRIDO based on the DKSE is designed to protect patients’ personal data in data mining and online pre-diagnosis. According to the PRIDO framework, we achieve privacy-preserving naïve Bayesian and decision tree classifiers and discuss its potential applications in actual deployments. Security analysis proves that patients’ data privacy can be well protected without loss of data confidentiality, and performance evaluation demonstrates the efficiency and accuracy in the diverse keyword search, data mining, and disease pre-diagnosis, respectively.
Learning Regional Attraction for Line Segment Detection

This paper presents regional attraction of line segment maps, and hereby poses the problem of line segment detection (LSD) as a problem of region coloring. Given a line segment map, the proposed regional attraction first establishes the relationship between line segments and regions in the image lattice. Based on this, the line segment map is equivalently transformed to an attraction field map (AFM), which can be remapped to a set of line segments without loss of information. Accordingly, we develop an end-to-end framework to learn attraction field maps for raw input images, followed by a squeeze module to detect line segments. Apart from existing works, the proposed detector properly handles the local ambiguity and does not rely on the accurate identification of edge pixels. Comprehensive experiments on the Wireframe dataset and the YorkUrban dataset demonstrate the superiority of our method. In particular, we achieve an F-measure of 0.831 on the Wireframe dataset, advancing the state-of-the-art performance by 10.3 percent.
Spatial Correspondence With Generative Adversarial Network: Learning Depth From Monocular Videos

Depth estimation from monocular videos has important applications in many areas such as autonomous driving and robot navigation. It is a very challenging problem without knowing the camera pose since errors in camera-pose estimation can significantly affect the video-based depth estimation accuracy. In this paper, we present a novel SC-GAN network with end-to-end adversarial training for depth estimation from monocular videos without estimating the camera pose and pose change over time. To exploit cross-frame relations, SC-GAN includes a spatial correspondence module which uses Smolyak sparse grids to efficiently match the features across adjacent frames, and an attention mechanism to learn the importance of features in different directions. Furthermore, the generator in SC-GAN learns to estimate depth from the input frames, while the discriminator learns to distinguish between the ground-truth and estimated depth map for the reference frame. Experiments on the KITTI and Cityscapes datasets show that the proposed SC-GAN can achieve much more accurate depth maps than many existing state-of-the-art methods on monocular videos.
Outcomes-based appropriation of context-aware ubiquitous technology across educational levels

This review study investigates the appropriation of sensing technology in context-aware ubiquitous learning (CAUL) in the fields of sciences, engineering, and humanities. 40 empirical studies with concrete learning outcomes across mandatory and higher education have been systematically reviewed and thematically analyzed with an outcomes-based teaching and learning approach. Four derived themes have been found to describe the design and implementation of CAUL, including learner-centeredness, technological facilitation, learning ecology, and research evaluation. The learning processes enabled by context-aware sensing technology have been explicated, revealing specific ways to apply new technologies in formal and informal environments. The analysis based on intended learning outcomes suggest that more efforts should be directed to fostering competence in analyzing and creating in mandatory education, and to creating in tertiary settings. Finally, unequal distribution of CAUL implementation across world regions calls for more technological appropriation in Southeast Asia and Africa. Specific suggestions on how to improve CAUL are also provided to better prepare learners in the twenty-first century.
Correlation Net: Spatiotemporal multimodal deep learning for action recognition

This paper describes a network that captures multimodal correlations over arbitrary timestamps. The proposed scheme operates as a complementary, extended network over a multimodal convolutional neural network (CNN). Spatial and temporal streams are required for action recognition by a deep CNN, but overfitting reduction and fusing these two streams remain open problems. The existing fusion approach averages the two streams. Here we propose a correlation network with a Shannon fusion for learning a pre-trained CNN. A Long-range video may consist of spatiotemporal correlations over arbitrary times, which can be captured by forming the correlation network from simple fully connected layers. This approach was found to complement the existing network fusion methods. The importance of multimodal correlation is validated in comparison experiments on the UCF-101 and HMDB-51 datasets. The multimodal correlation enhanced the accuracy of the video recognition results.
Does corruption grease or sand the wheels of bank profitability in Ghana?

This paper investigates the effect of corruption on bank profitability in Ghana using bank-level dataset spanning 2008 to 2017.By employing the system Generalized Method of Moments (GMM) technique, the study finds a significant negative relationship between corruption and bank profitability.This supports the "sand the wheels" view on corruption and controverts the "grease the wheels" view, which hypothesizes that corruption boost firm performance.Controlling for bankspecific and macroeconomic factors, the findings further reveal that, while bank size, capital adequacy, and inflation have a significant positive effect on profitability, management efficiency and monetary policy rate negatively and significantly drive bank profits.The study discusses key implications for policy.
Teacher competence in utilizing digital media literacy in education

Abstract This study aims to determine the responses and competencies of teachers in digital media literacy. This research is a descriptive study using survey methods. The study sample was 112 Indonesian language teachers in West Sumatra. The instrument of this study used a questionnaire. The results of the study show that most teachers have used digital media to assist in the learning process. However, the digital media used is still limited and not yet diverse. The use of digital media in learning has positive and negative effects. In order for maximum positive effects to be obtained and negative effects to be avoided, teacher competence is needed to make it happen. Based on the results of this study it is recommended that teachers can keep up with the times by utilizing digital media in learning, both to obtain material so that the use of learning media, or support the learning methods used.
New Non-Binary Quantum Codes from Cyclic Codes Over Product Rings

For any odd prime p, and a divisor ℓ of p, we consider Ie to be the set of all divisors of p - 1, which are less than or equal to ℓ. In this letter, we construct quantum codes from cyclic codes over F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> and F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ℓ</sub> , where S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ℓ</sub> = Π <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i∈Iℓ</sub> R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> , for R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> = (Fp[u])/(〈u <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i+1</sup> -u〉). For that, first we construct linear codes and a Gray map over R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ℓ</sub> . Using this construction, we study cyclic codes over R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ℓ</sub> , and then extend that over F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ℓ</sub> . We also give a Gray map over F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ℓ</sub> . Then, using necessary and sufficient condition of dual containing property for cyclic codes, we construct quantum MDS codes. It is observed that the quantum codes constructed are new in the literature.
Neural architecture search for image saliency fusion

Saliency detection methods proposed in the literature exploit different rationales, visual clues, and assumptions, but there is no single best saliency detection algorithm that is able to achieve good results on all the different benchmark datasets. In this paper we show that fusing different saliency detection algorithms together by exploiting neural network architectures makes it possible to obtain better results. Designing the best architecture for a given task is still an open problem since the existing techniques have some limits with respect to the problem formulation, to the search space, and require very high computational resources. To overcome these problems, in this paper we propose a three-step fusion approach. In the first step, genetic programming techniques are exploited to combine the outputs of existing saliency algorithms using a set of provided operations. Having a discrete search space allows us a fast generation of the candidate solutions. In the second step, the obtained solutions are converted into backbone Convolutional Neural Networks (CNNs) where operations are all implemented with differentiable functions, allowing an efficient optimization of the corresponding parameters (in a continuous space) by backpropagation. In the last step, to enrich the expressiveness of the initial architectures, the networks are further extended with additional operations on intermediate levels of the processing that are once again efficiently optimized through backpropagation. Extensive experimental evaluations show that the proposed saliency fusion approach outperforms the state-of-the-art on the MSRAB dataset and it is able to generalize to unseen data of different benchmark datasets.
Wear Fault Diagnosis of Aeroengines Based on Broad Learning System and Ensemble Learning

An aircraft engine (aeroengine) operates in an extremely harsh environment, causing the working state of the engine to constantly change. As a result, the engine is prone to various kinds of wear faults. This paper proposes a new intelligent method for the diagnosis of aeroengine wear faults based on oil analysis, in which broad learning system (BLS) and ensemble learning models are introduced and integrated into the bagging-BLS model, in which 100 sub-BLS models are established, which are further optimized by ensemble learning. Experiments are conducted to verify the proposed method, based on the analysis of oil data, in which the random forest and single BLS algorithms are used for comparison. The results show that the output accuracy of the proposed method is stable (at 0.988), showing that the bagging-BLS model can improve the accuracy and reliability of engine wear fault diagnosis, reflecting the development trend of fault diagnosis in implementing intelligent technology.
User Data Sharing Frameworks: A Blockchain-Based Incentive Solution

Currently, there is no universal method to track who shared what, with whom, when and for what purposes in a verifiable way to create an individual incentive for data owners. A platform that allows data owners to control, delete, and get rewards from sharing their data would be an important enabler of user data-sharing. We propose a usable blockchain- and smart contracts-based framework that allows users to store research data locally and share without losing control and ownership of it. We have created smart contracts for building automatic verification of the conditions for data access that also naturally supports building up a verifiable record of the provenance, incentives for users to share their data and accountability of access. The paper presents a review of the existing work of research data sharing, the proposed blockchain-based framework and an evaluation of the framework by measuring the transaction cost for smart contracts deployment. The results show that nodes responded quickly in all tested cases with a befitting transaction cost.