Reviewerly/exHarmony huggingface dataset:
Case-base maintenance of a personalised and adaptive CBR bolus insulin recommender system for type 1 diabetes

People with type 1 diabetes must control their blood glucose level through insulin infusion either with several daily injections or with an insulin pump. However, estimating the required insulin dose is not easy. Recommender systems, mainly based on Case-Based Reasoning (CBR), are being developed to provide recommendations to users. These systems are designed to keep the experiences or cases of the user in a case-base, which requires maintenance to keep system’s response accurate and efficient. This paper proposes a case-base maintenance methodology that combines case-base redundancy reduction and attribute weight learning. Contrary to previous approaches designed for classification problems, the maintenance methodology presented in this paper deals with numerical recommendations. It can manage a potentially huge case-base due to the combinatorial derived from the number of attributes used to represent a case. The proposed approach has been tested using the UVA/PADOVA type 1 diabetes simulator and the results demonstrate that it can accomplish better levels of accuracy than other insulin recommender systems mentioned in the literature, when a large number of attributes is considered.
Hybrid SIFT Feature Extraction Approach for Indian Sign Language Recognition System Based on CNN

Indian sign language (ISL) is one of the most used sign languages in the Indian subcontinent. This research aims at developing a simple Indian sign language recognition system based on convolutional neural network (CNN). The proposed system needs webcam and laptop and hence can be used anywhere. CNN is used for image classification. Scale invariant feature transformation (SIFT) is hybridized with adaptive thresholding and Gaussian blur image smoothing for feature extraction. Due to unavailability of ISL dataset, a dataset of 5000 images, 100 images each for 50 gestures, has been created. The system is implemented and tested using python-based library Keras. The proposed CNN with hybrid SIFT implementation achieves 92.78% accuracy, whereas the accuracy of 91.84% was achieved for CNN with adaptive thresholding.
Representation Interpretation with Spatial Encoding and Multimodal Analytics

Representation learning models map data instances into a low-dimensional vector space, thus facilitating the deployment of subsequent models such as classification and clustering models, or the implementation of downstream applications such as recommendation and anomaly detection. However, the outcome of representation learning is difficult to be directly understood by users, since each dimension of the latent space may not have any specific meaning. Understanding representation learning could be beneficial to many applications. For example, in recommender systems, knowing why a user instance is mapped to a certain position in the latent space may unveil the user's interests and profile. In this paper, we propose an interpretation framework to understand and describe how representation vectors distribute in the latent space. Specifically, we design a coding scheme to transform representation instances into spatial codes to indicate their locations in the latent space. Following that, a multimodal autoencoder is built for generating the description of a representation instance given its spatial codes. The coding scheme enables indication of position with different granularity. The incorporation of autoencoder makes the framework capable of dealing with different types of data. Several metrics are designed to evaluate interpretation results. Experiments under various application scenarios and different representation learning models are conducted to demonstrate the flexibility and effectiveness of the proposed framework.
BGP with BGPsec: Attacks and Countermeasures

The BGP suffers from numerous security vulnerabilities, for example, fake routing updates incurring traffic hijacking and interception. The BGPsec protocol is supposed to fix these vulnerabilities by attesting routing updates. Although the BGP security problem has been extensively studied, the security of BGP with BGPsec is not well studied yet. We argue that even secured with BGPsec, BGP still has inherent security vulnerabilities. In particular, traffic can still be hijacked. In this article, we systematically study the vulnerabilities of BGP with BGPsec. We find that the protocol still cannot achieve the desired security guarantee of inter-domain routing. In particular, it is unable to ensure correct packet delivery on the Internet. We measure the impacts of the vulnerabilities by using a real data trace, and discuss enhancements to the design and the implementation of the secure BGP protocol, which allows BGP to achieve strong secure inter-domain routing.
Controlled teleportation of qubit states: Relation between teleportation faithfulness, controller's authority, and tripartite entanglement

We consider the controlled quantum teleportation performed on a single copy of entangled three-qubit pure states and show how the fidelity of teleportation and the control power are related to the tripartite entanglement measures, namely, three-tangle and genuine concurrence. We characterize the states with extreme properties, and use them to derive tight lower and upper bounds on both the teleportation fidelity and control power for a given amount of entanglement. Furthermore, we discuss the usefulness of these two quantities for experimental detection and/or quantification of tripartite entanglement. In particular, we present the results which imply that the control power cannot be considered as a candidate for a degree of three-qubit entanglement complementary to the three-tangle as conjectured recently [K. Jeong, J. Kim, and S. Lee, Phys. Rev. A 93, 032328 (2016)]. Furthermore, we prove a mutual relation between teleportation fidelity and control power which provides attainable limits of the controller's authority for existing controlled teleportation schemes. Although those relations are derived for pure states, their extensions for mixed states are also examined.
Three-dimensional digital image correlation method based on a light field camera

With the recent development of computer vision and photographic technology, many three-dimensional (3D) modelling methods have emerged. This paper introduces a new method that combines digital image correlation (DIC) with a light field camera to reconstruct the surface of an object. Before experimentation, the surface of the object was sprayed with a speckle pattern. The parameters of the light field camera, such as the micro-lens, focus, and lens distortion, were pre-calibrated. During the experiments, raw images of the samples were recorded by a light field camera, then each was extracted to two sub-aperture images. Using the digital image correlation method, feature point matching of the corresponding sub-images was executed and the 3D space coordinates of all of the matching points were calculated using the binocular vision principle with surface topography information. The experimental results demonstrate that the method can realise 3D reshape and 3D deformation measurements using a single camera. The system has the advantage of a simple structure, large depth of field, and wide measurement range for out-of-plane deformation. The light field camera is also applied to 3D-DIC.
Real time video image enhancement approach using particle swarm optimisation technique with adaptive cumulative distribution function based histogram equalization

Recent years, videos are playing an important role in different applications such as pattern recognition, security purpose, news analysis, weather digest, and video browser and so on. Due to the importance of video applications, the quality of the video must be improved for making effective results in video analysis process. This paper introduces the particle optimization with adaptive cumulative distribution based histogram enhancement technique (PACDHE) for improving the video quality. Initially the videos are collected, each incoming frame has been analyzed and noise present in the video frame is eliminated by applying the non-divisional median filter. After that, quality of video is enhanced iteratively by examining each pixel present in video frames using the optimized fitness and cumulative distribution function. This process is repeated continuously until to enhance the video frames contrast and quality. Then the performance of the system is analyzed by using CV online video database and the efficiency is examined in terms of peak signal to noise ratio (PSNR), Absolute Mean Brightness Error (AMBE) and Entropy. The experimental results of PSO are compared with genetic algorithm based approach and found that PSO outperforms the GA approach and the existing histogram equalization approach and the existing histogram equalization approaches.
Blind Quality Metric for Contrast-Distorted Images Based on Eigendecomposition of Color Histograms

Although contrast is a major issue in overall quality assessment of an image, existing contrast evaluators with a reasonable performance are currently scarce. Here, we propose a learning-based blind/no-reference (NR) image quality assessment (IQA) model, a dubbed histogram eigen-feature-based contrast score (HEFCS) for evaluating image contrast. This research seeks for the inter-relationship between contrast degradation and relevant image histogram features. We introduce “eigen-histograms,” which are the eigenvectors of the set of image patches' histograms. We found that the randomness of image eigen-histograms and the amplitude of corresponding eigenvalues can reliably reflect the changes in image contrast. Employing these characteristics leads to contrast-aware HEF vectors, which are used to compute the contrast score through a prediction model trained using support vector regression. Extensive analysis and cross validation are performed with five contrast relevant image databases, and the HEFCS performance results are compared with a collection of full-reference (FR), reduced-reference (RR), and NR measures. Despite its simplicity and low computational complexity, the HEFCS performs better than all competing NR-IQA models and also stands among the three best performers of FR and RR models.
Infrared super-resolution imaging using multi-scale saliency and deep wavelet residuals

Infrared (IR) imaging systems with low-density focal plane arrays produce images with poor spatial resolution. To address this limitation, super-resolution (SR) algorithms can be applied on IR-low resolution (LR) images. In this paper, we present a new SR technique based on the multi-scale saliency detection and the residuals learned by the deep convolutional neural network (CNN) in the wavelet domain (DWCNN). The input LR image is processed in the transformed domain by applying 2D discrete wavelet transform. It decomposes an image into its low-frequency and high-frequency subbands. The multi-scale saliency detection is used to extract small scale and large scale salient feature maps from the bicubic upscaled LR image. These maps are incorporated in the high-frequency subbands of the LR image. Furthermore, the low-frequency and high-frequency subands are refined using the residuals learned by the DWCNN in training phase. The proposed algorithm is compared with the conventional and state-of-the-art SR methods. Results indicate that our method yields good reconstruction quality with high peak signal to ratio, structural similarity and low blur indices. Besides, our method requires less computational time.
Attribute Reduction Based on Genetic Algorithm for the Coevolution of Meteorological Data in the Industrial Internet of Things

Due to the problem of attribute redundancy in meteorological data from the Industrial Internet of Things (IIoT) and the slow efficiency of existing attribute reduction algorithms, attribute reduction based on a genetic algorithm for the coevolution of meteorological data was proposed. The evolutionary population was divided into two subpopulations: one subpopulation used elite individuals to assist crossover operations to increase the convergence speed of the algorithm, and the other subpopulation balanced the population diversity in the evolutionary process by introducing a random population; these two subpopulations completed the evolutionary operations together. With the TSDPSO-AR algorithm and ARAGA algorithm, the attribute reduction operation for precipitation in meteorological data was performed. The results showed that the proposed algorithm maintained the diversity of the population during evolution, improved the reduction performance, and simplified the information system.
Noise reduction for near-infrared spectroscopy data using extreme learning machines

The near infrared (NIR) spectra technique is an effective approach to predict chemical properties and it is typically applied in petrochemical, agricultural, medical, and environmental sectors. NIR spectra are usually of very high dimensions and contain huge amounts of information. Most of the information is irrelevant to the target problem and some is simply noise. Thus, it is not an easy task to discover the relationship between NIR spectra and the predictive variable. However, this kind of regression analysis is one of the main topics of machine learning. Thus machine learning techniques play a key role in NIR based analytical approaches. Pre-processing of NIR spectral data has become an integral part of chemometrics modeling. The objective of the pre-processing is to remove physical phenomena (noise) in the spectra in order to improve the regression or classification model. In this work, we propose to reduce the noise using extreme learning machines which have shown good predictive performances in regression applications as well as in large dataset classification tasks. For this, we use a novel algorithm called C-PL-ELM, which has an architecture in parallel based on a non-linear layer in parallel with another non-linear layer. Using the soft margin loss function concept, we incorporate two Lagrange multipliers with the objective of including the noise of spectral data. Six real-life dataset were analyzed to illustrate the performance of the developed models. The results for regression and classification problems confirm the advantages of using the proposed method in terms of root mean square error and accuracy.
Challenges in RDF Validation

The RDF data model forms a cornerstone of the Semantic Web technology stack. Although there have been different proposals for RDF serialization syntaxes, the underlying simple data model enables great flexibility which allows it to be successfully employed in many different scenarios and to form the basis on which other technologies are developed. In order to apply an RDF-based approach in practice it is necessary to communicate the structure of the data that is being stored or represented. Data quality is of paramount importance for the acceptance of RDF as a data representation language and it must be enabled by the use of tools that can check if some data conforms to some specific structure. There have been several recent proposals for RDF validation languages like ShEx and SHACL. In this chapter, we describe both proposals and enumerate some challenges and trends that we foresee with regards to RDF validation. We devote more space to what we consider one of the main challenges, which is to compare ShEx and SHACL and to understand their underlying foundations. To that end, we propose an intermediate language and show how ShEx and SHACL can be converted to it.
Overcoming the underdetermination of specimens

Philosophers of science are well aware that theories are underdetermined by data. But what about the data? Scientific data are selected and processed representations or pieces of nature. What is useless context and what is valuable specimen, as well as how specimens are processed for study, are not obvious or predetermined givens. Instead, they are decisions made by scientists and other research workers, such as technicians, that produce different outcomes for the data. Vertebrate fossils provide a revealing case of this data-processing, because they are embedded in rock that often matches the fossils' color and texture, requiring an expert eye to judge where the fossil/context interface is. Fossil preparators then permanently define this interface by chiseling away the material they identify as rock. As a result, fossil specimens can emerge in multiple possible forms depending on the preparator's judgment, skill, and chosen tools. A prepared fossil then is not yet data but potential data, following Leonelli's (Philos Sci 82:810–821, 2015. https://doi.org/10.1086/684083 ) relational framework in which data are defined as evidence that scientists have used to support a proposed theory. This paper draws on ethnographic evidence to assess how scientists overcome this underdetermination of specimens, as potential data, in addition to the underdetermination of theories and of data, to successfully construct specimen-based knowledge. Among other strategies, paleontology maintains a division of labor between data-makers and theory-makers. This distinction serves to justify the omission of preparators' nonstandard, individualized techniques from scientific publications. This separation has benefits for both scientists and technicians; however, it restricts knowledge production by preventing scientists from understanding how the pieces of nature they study were processed into researchable specimens.
Identity theft in the internet age: Evidence from the U.S. states

This paper examines the determinants of identity theft, focusing especially on the influence of internet diffusion. Results, based on panel data across the U.S. states, show that a 10% in increase households with internet access would increase identity theft by about 9%, ceteris paribus. Other noteworthy findings point to states with greater corrupt activity having greater identity theft but greater police employment not having a significant deterrent impact. Dynamic panel regressions results reveal the presence of inertia in identity thefts. Some implications for policy are discussed.
Learning-Based Low-Complexity Reverse Tone Mapping With Linear Mapping

Although high dynamic range (HDR) display has become popular recently, the legacy content such as standard dynamic range (SDR) video is still in service and needs to be properly converted on HDR display devices. Therefore, it is desirable for HDR TV sets to have the capability of automatically converting input SDR video into HDR video, which is called reverse tone mapping (RTM). In this paper, we propose a novel learning-based low-complexity RTM scheme that not only expands the suppressed dynamic ranges (DR) of the SDR videos (or images), but also effectively restores lost detail in the SDR videos. Most existing conventional RTM schemes have focused on how to expand the DR of global contrast, resulting in limitations in recovering lost detail of SDR videos. On the other hand, the recent convolutional neural network-based approaches show promising results, but they are too complex to be applied on the users' devices in practice. In this paper, our learning-based RTM scheme is computationally simple but effective in recovering lost detail. To learn the SDR-to-HDR relation, training “SDR-HDR” images are first separated into their base layer components and detail layer components by applying a guided filter. The detail layer components of the “SDR-HDR” pairs are used to train the SDR-to-HDR mapping. The mapping matrices are computed based on kernel ridge regression. In the meantime, the global contrast of the base layers is expanded by a nonlinear function that suppresses darker regions and amplifies brighter regions to fit the full DR of a target HDR display. To verify the effectiveness of our learning-based RTM scheme, we performed subjective quality assessment for images and videos. The experimental results show that our RTM scheme outperforms the existing RTM scheme with the successful restoration of lost detail in SDR images.
Learning Discrete-Time Markov Chains Under Concept Drift

Learning under concept drift is a novel and promising research area aiming at designing learning algorithms able to deal with nonstationary data-generating processes. In this research field, most of the literature focuses on learning nonstationary probabilistic frameworks, while some extensions about learning graphs and signals under concept drift exist. For the first time in the literature, this paper addresses the problem of learning discrete-time Markov chains (DTMCs) under concept drift. More specifically, following a hybrid active/passive approach, this paper introduces both a family of change-detection mechanisms (CDMs), differing in the required assumptions and performance, for detecting changes in DTMCs and an adaptive learning algorithm able to deal with DTMCs under concept drift. The effectiveness of both the proposed CDMs and the adaptive learning algorithm has been extensively tested on synthetically generated experiments and real data sets.
Image Neural Style Transfer With Preserving the Salient Regions

Neural style transfer recently has become one of the most popular topics in academic research and industrial application. The existing methods can generate synthetic images by transferring different styles of some images to another given content images, but they mainly focus on learning low-level features of images with losses of content and style, leading to greatly alter the salient information of content images in the semantic level. In this paper, an improved scheme is proposed to keep the salient regions of the transferred image the same with that of content image. By adding the region loss calculated from a localization network, the synthetic image can almost keep the main salient regions consistent with that of original content image, which helps for saliency-based tasks such as object localization and classification. In addition, the transferred effect is more natural and attractive, avoiding simple texture overlay of the style image. Furthermore, our scheme can also extend to remain other semantic information (such as shape, edge, and color) of the image with the corresponding estimation networks.
Two or Few-Weight Trace Codes over ${\mathbb{F}_{q}}+u{\mathbb{F}_{q}}$

Let p be a prime number and q = p <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sup> for a positive integer s. For any positive divisor e of q - 1, we construct infinite families of codes C of size q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2m</sup> with few Lee-weight. These codes are defined as trace codes over the ring R = F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> + uF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> , u <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0. Using Gaussian sums, their Lee weight distributions are provided. In particular, when gcd(e, m) = 1, under the Gray map, the images of all codes in C are of two-weight over the finite field F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> , which meet the Griesmer bound. Moreover, when gcd(e, m) = 2, 3, or 4, all codes in C are of most five-weight codes.