Reviewerly/exHarmony huggingface dataset:
Joint Weighted Tensor Schatten $p$ -Norm and Tensor $l_p$ -Norm Minimization for Image Denoising

In the traditional non-local similar patch-based denoising algorithms, the image patches are firstly flatted into a vector. The structure information within the image patches is ignored; however, the spatial layout information can be used for improving the denoising performance. To solve this problem, this paper treats the image patches as matrices and proposes a low-rank tensor recovery model for image denoising, and thus it makes full use of spatial information within the image. Meanwhile, the proposed model can realize joint weighted tensor Schatten p-norm and tensor l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> -norm minimization, which has two advantages: 1) it can deal with zero mean Gaussian noise, impulse noise, and any other noise that can be approximated by mixing these two kinds of noise and 2) the employed norms require relatively weak incoherence conditions than l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> norm and nuclear norm, and thus they are more robust against outliers and noise. The experimental results show that the proposed algorithm outperforms other state-of-the-art denoising algorithms in both visual perception quality and quantitative measures.
Ranking résumés automatically using only résumés: A method free of job offers

With the success of the electronic recruitment, now it is easier to find a job offer and apply for it. However, due to this same success, nowadays, human resource managers tend to receive high volumes of applications for each job offer. These applications turn into large quantities of documents, known as résumés or curricula vitae, that need to be processed quickly and correctly. To reduce the time necessary to process the résumés, human resource managers have been working with the scientific community to create systems that automate their ranking. Until today, most of these systems are based on the comparison of job offers and résumés. Nevertheless, this comparison is impossible to do in data sets where job offers are no longer available, as it happens in this work. We present two methods to rank résumés that do not use job offers or any semantic resource, unlike existing state-of-the-art systems. The methods are based on what we call Inter-Résumé Proximity, which is the lexical similarity between only résumés sent by candidates in response to the same job offer. Besides, we propose the use of Relevance Feedback, at general and lexical levels to improve the ranking of résumés. Relevance Feedback is applied using techniques based on similarity coefficients and vocabulary scoring. All the methods have been tested on a large corpus of 171 real selection processes, which correspond to more than 14,000 résumés. The developed methods can rank correctly, in average, 93% of the résumés sent to each job posting. The outcomes presented here show that it is not necessary to use job offers or semantic resources to provide high quality results. Furthermore, we observed that résumés have particular characteristics that as ensemble, work as a facial composite and provide more information about the job posting than the job offer. This certainly will change how systems analyze and rank résumés.
Feasibility of using the Omnyx digital pathology system for cytology practice

Whole slide imaging systems have focused mostly on surgical pathologic evaluation. However, for pathology laboratories to become fully digital, cytology slides will also need to be digitized, managed, viewed, and analyzed. Our aim was to determine the feasibility of using the Omnyx whole slide imaging system for various purposes in cytology.Our institution implemented the Omnyx digital pathology system, which was tested for feasibility and not implemented for clinical use in cytology. Glass slides (n = 18), scanned using various whole slide scanners, were uploaded into the Omnyx system. The system was evaluated for its feasibility with cytology case management, digital slide navigation and annotation, telecytology, and cytologic-histologic correlation.The Omnyx software was able to manage cases similar to a laboratory information system. Users were able to electronically distribute, search, and sort the clinical cases. A graphic dashboard approach and virtual slide tray is available for end users to evaluate cases. The ability to create custom folders and drag-and-drop images into these folders fulfilled clinical, quality assurance, education, and research needs. Innovative tools for digital slide navigation and annotation (eg, auto-pan, adding text to annotation, hiding annotations, image coregistration) offered innovative methods to work with slides. Omnyx also provided a mechanism for sharing images with others to perform teleconsultation.We have demonstrated the feasibility of using the Omnyx whole slide imaging system for various purposes in cytology practice. The application supported, not only case management, but also the ability to perform cytologic-histologic correlation and telecytology. The viewer offered many features that improved digital slide navigation and annotation.
Optical Hyperspectral Image Cryptosystem Based on Affine Transform and Fractional Fourier Transform

An encryption algorithm for hyperspectral data in fractional Fourier domain is designed. Firstly, the original hyperspectral image is separated into single bands and then each pair of bands are regarded as the real and imaginary part of a complex function by using an affine transform. Subsequently, the complex functions are encoded and transformed in fractional Fourier domain (FrFT). The parameters in affine transform and FrFT serve as the key of the encryption system. The proposed encryption scheme can not only protect the image information in spatial domains but also the spectrum information in spectral domains. Various experiments are given to demonstrate the validity and capability of the proposed encryption scheme.
An Efficient Essential Secret Image Sharing Scheme Using Derivative Polynomial

As a popular technology in information security, secret image sharing is a method to guarantee the secret image’s security. Usually, the dealer would decompose the secret image into a series of shadows and then assign them to a number of participants, and only a quorum of participants could recover the secret image. Generally, it is assumed that every participant is equal. Actually, due to their position in many practical applications, some participants are given special privileges. Therefore, it is desirable to give an approach to generate shadows with different priorities shadows. In this paper, an efficient essential secret image sharing scheme using a derivative polynomial is proposed. Compared with existing related works, our proposed scheme can not only create the same-sized shadows with smaller size but also removes the concatenation operation in the sharing phase. Theoretical analysis and simulations confirm the security and effectiveness of the proposed scheme.
Testing Pediatric Acuity With an iPad: Validation of “Peekaboo Vision” in Malawi and the UK

Purpose: To evaluate two builds of the digital grating acuity test, “Peekaboo Vision” (PV), in young (6–60 months) populations in two hospital settings (Malawi and United Kingdom). Methods: Study 1 evaluated PV in Blantyre, Malawi (N = 58, mean age 33 months); study 2 evaluated an updated build in Glasgow, United Kingdom (N = 60, mean age 44 months). Acuities were tested-retested with PV and Keeler Acuity Cards for Infants (KACI). Bland-Altman techniques were used to compare results and repeatability. Child engagement was compared between groups. Study 2 included test-time comparison. Results: Study 1 (Malawi): The mean difference between PV and KACI was 0.02 logMAR with 95% limits of agreement (LoA) of 0.33 to 0.37 LogMAR. On test-retest, PV demonstrated 95% LoA of −0.283 to 0.198 logMAR with coefficient of repeatability (CR) 0.27. KACI demonstrated 95% LoA of −0.427 to 0.323 logMAR, and larger CR was 0.37. PV evidenced higher engagement scores than KACI (P = 0.0005). Study 2 (UK): The mean difference between PV and KACI was 0.01 logMAR; 95% LoA was −0.413 to 0.437 logMAR. Again, on test-retest, PV had narrower LoA (−0.344 to 0.320 logMAR) and lower CR (0.32) versus KACI, with LoA −0.432 to 0.407 logMAR, CR 0.42. The two tests did not differ in engagement score (P = 0.5). Test time was ∼1 minute shorter for PV (185 vs. 251 s, P = 0.0021). Conclusions: PV gives comparable results to KACI in two pediatric populations in two settings, with benefits in repeatability indices and test duration. Translational Relevance: Leveraging tablet technology extends reliable infant acuity testing to bedside, home, and rural settings, including areas where traditional equipment cannot be financed.
Application of rank metric codes in digital image watermarking

In this paper, we propose a new digital image watermarking algorithm where the resistance against attacks is studied using error correcting codes. Using the well known Lattice QIM in the spatial domain, we propose to use a different kind of error correcting codes called rank metric codes. These codes are already well used in cryptography and communications for network coding but not used yet in the context of watermarking. In this article, we show how this metric permits to correct errors with a specific structure and is adapted to specific image attacks when combined with a watermarking technique. In particular, we describe a rank metric code family called Gabidulin codes analogous to the well known Reed–Solomon codes. If one considers a rank code over a finite field extension, then any codeword has a matrix representation. One can decode the original message if the matrix rank of the detected codeword is small enough. We propose a study to validate the concept of rank metric in watermarking applications. First, we introduce a theoretically invariant method to luminance additive constant change. After combining the Lattice QIM method and rank metric codes, we add a multi-detection strategy on the damaged images with controlled luminance distortions. Then, using a block-based watermarking approach, we show how the proposed association can also be robust to an image distortion we called content erasure or copy-paste. The proposed approach completes other watermarking strategies against attacks with random errors such as JPEG compression.
Improving Catheter Segmentation &amp; Localization in 3D Cardiac Ultrasound Using Direction-Fused Fcn

Fast and accurate catheter detection in cardiac catheterization using harmless 3D ultrasound (US) can improve the efficiency and outcome of the intervention. However, the low image quality of US requires extra training for sonographers to localize the catheter. In this paper, we propose a catheter detection method based on a pre-trained VGG network, which exploits 3D information through re-organized cross-sections to segment the catheter by a shared fully convolutional network (FCN), which is called a Direction-Fused FCN (DF-FCN). Based on the segmented image of DF-FCN, the catheter can be localized by model fitting. Our experiments show that the proposed method can successfully detect an ablation catheter in a challenging ex-vivo 3D US dataset, which was collected on the porcine heart. Extensive analysis shows that the proposed method achieves a Dice score of 57.7%, which offers at least an 11.8% improvement when compared to state-of the-art instrument detection methods. Due to the improved segmentation performance by the DF-FCN, the catheter can be localized with an error of only 1.4 mm.
Automated Tool for Resume Classification Using Sementic Analysis

Recruitment in the IT sector has been on the rise in recent times.Software companies are on the hunt to recruit raw talent right from the colleges through job fairs.The process of allotment of projects to the new recruits is a manual affair, usually carried out by the Human Resources department of the organization.This process of project allotment to the new recruits is a costly affair for the organization as it relies mostly on human effort.In the recent times, software companies round the world are leveraging the advances in machine learning and Artificial intelligence in general to automate routine tasks in the enterprise to increase the productivity.In the paper, we discuss the design and implementation of a resume classifier application which employs an ensemble learning based voting classifier to classify a profile of a candidate into a suitable domain based on his interest, work-experience and expertise mentioned by the candidate in the profile.The model employs topic modelling techniques to introduce a new domain to the list of domains upon failing to achieve the threshold value of confidence for the classification of the candidate profile.The Stack-Overflow REST APIs are called for the profiles which fail on the confidence threshold test set in the application.The topics returned by the APIs are subjected to topic modelling to obtain a new domain, on which the voting classifier is retrained after a fixed interval to improve the accuracy of the model.Overall, emphasis is laid out on building a dynamic machine learning automation tool which is not solely dependent on the training data in allotment of projects to the new recruits.We extended our previous work withnew learning model that has the ability to classify the resumes with better accuracy and support more new domains.
Word embeddings for application in geosciences: development, evaluation, and examples of soil-related concepts

Abstract. A large amount of descriptive information is available in geosciences. This information is usually considered subjective and ill-favoured compared with its numerical counterpart. Considering the advances in natural language processing and machine learning, it is possible to utilise descriptive information and encode it as dense vectors. These word embeddings, which encode information about a word and its linguistic relationships with other words, lay on a multidimensional space where angles and distances have a linguistic interpretation. We used 280 764 full-text scientific articles related to geosciences to train a domain-specific language model capable of generating such embeddings. To evaluate the quality of the numerical representations, we performed three intrinsic evaluations: the capacity to generate analogies, term relatedness compared with the opinion of a human subject, and categorisation of different groups of words. As this is the first attempt to evaluate word embedding for tasks in the geosciences domain, we created a test suite specific for geosciences. We compared our results with general domain embeddings commonly used in other disciplines. As expected, our domain-specific embeddings (GeoVec) outperformed general domain embeddings in all tasks, with an overall performance improvement of 107.9 %. We also presented an example were we successfully emulated part of a taxonomic analysis of soil profiles that was originally applied to soil numerical data, which would not be possible without the use of embeddings. The resulting embedding and test suite will be made available for other researchers to use and expand upon.
Utterance Generation With Variational Auto-Encoder for Slot Filling in Spoken Language Understanding

Slot filling must be trained using human-labeled data that are expensive and only a limited amount of labeled utterances are readily available for learning. Data generation methods can help increase the size of the dataset and make variations to the training dataset by means of emerging new instances. We propose a novel labeled utterance generation algorithm to augment training data. Our hypothesis is that words in an utterance can be separated into the two parts, namely, slot values that are instances of slot types and the accompanying contexts. Our model aims to generate utterances that are diverse combinations of slot values and contexts that can appear together. To create various utterances containing a given condition, our deep generative model uses a conditional variational auto-encoder architecture. We conduct experiments on various slot filling datasets, specifically airline travel information systems (ATIS), Snips, and MIT Corpus. A quantitative analysis shows that the application of data augmentation using the proposed model improves the F1 score for slot filling. We also demonstrate that our labeled utterance generation model yields more desirable utterances.
A Differential Evolution Approach to Optimize Weights of Dynamic Time Warping for Multi-Sensor Based Gesture Recognition

In this research, we present a differential evolution approach to optimize the weights of dynamic time warping for multi-sensory based gesture recognition. Mainly, we aimed to develop a robust gesture recognition method that can be used in various environments. Both a wearable inertial sensor and a depth camera (Kinect Sensor) were used as heterogeneous sensors to verify and collect the data. The proposed approach was used for the calculation of optimal weight values and different characteristic features of heterogeneous sensor data, while having different effects during gesture recognition. In this research, we studied 27 different actions to analyze the data. As finding the optimal value of the data from numerous sensors became more complex, a differential evolution approach was used during the fusion and optimization of the data. To verify the performance accuracy of the presented method in this study, a University of Texas at Dallas Multimodal Human Action Datasets (UTD-MHAD) from previous research was used. However, the average recognition rates presented by previous research using respective methods were still low, due to the complexity in the calculation of the optimal values of the acquired data from sensors, as well as the installation environment. Our contribution was based on a method that enabled us to adjust the number of depth cameras and combine this data with inertial sensors (multi-sensors in this study). We applied a differential evolution approach to calculate the optimal values of the added weights. The proposed method achieved an accuracy 10% higher than the previous research results using the same database, indicating a much improved accuracy rate of motion recognition.
Acting as ethical and responsible digital citizens: The teacher’s key role

The global information society was eagerly awaited in the 1970s and joyously celebrated in the 1980s, but as the new millennium loomed closer, it began to be viewed with distrust and trepidation.In the 21st century, it's a fact of life that's impossible to escape or ignore.And it poses a growing concern for educators: the issue of digital citizenship.
Format-Compliant Selective Secret 3-D Object Sharing Scheme

This paper presents a new method of secret three-dimensional object sharing (S3DOS), which allows sharing of three-dimensional (3-D) objects, while preserving its file format by selectively encrypting a 3-D object in order to sufficiently protect the visual nature of the content. This scheme, named format-compliant selective (k, n) S3DOS, modifies selected bits of the vertices of a 3-D object to protect the visual content by inducing geometrical distortions. These distortions are controlled thanks to the application of a degradation level at the beginning of the sharing process. To reconstruct the secret 3-D object, at least k shared 3-D objects among n have to be combined in order to remove the geometrical distortions and recover the exact original 3-D object. Experimental results are presented and evaluated to showcase the feasibility of the proposed scheme.
Hybrid Rendering for Real-Time Ray Tracing

This chapter describes the rendering pipeline developed for PICA PICA, a real-time ray tracing experiment featuring self-learning agents in a procedurally assembled world. PICA PICA showcases a hybrid rendering pipeline in which rasterization, compute, and ray tracing shaders work together to enable real-time visuals approaching the quality of offline path tracing. The design behind the various stages of such a pipeline is described, including implementation details essential to the realization of PICA PICA’s hybrid ray tracing techniques. Advice on implementing the various ray tracing stages is provided, supplemented by pseudocode for ray traced shadows and ambient occlusion. A replacement to exponential averaging in the form of a reactive multi-scale mean estimator is also included. Even though PICA PICA’s world is lightly textured and small, this chapter describes the necessary building blocks of a hybrid rendering pipeline that could then be specialized for any AAA game. Ultimately, this chapter provides the reader with an overall good design to augment existing physically based deferred rendering pipelines with ray tracing, in a modular fashion that is compatible across visual styles.
Deep Learning Research: Scientometric Assessment of Global Publications Output during 2004 -17

The paper provides a quantitative and qualitative description of deep learning research using bibliometric indicators covering global research publications published during 14-year period 2004-17. Global deep learning research registered 106.76% high growth per annum, and averaged 7.99 citations per paper. Top 10 countries world- over dominate the research field with their 99.74% global publications share and more than 100% global citations share. China ranks the top with the highest (29.25%) global publications share, followed by USA (26.46%), U.K. (6.40%), etc. during the period. Canada tops in relative citation index (5.30). International collaboration has been a major driver of research in the subject with 14.96% to 53.76% of national-level share of top 10 countries output appeared as international collaborative publications. Computer Science is one of the most popular areas of research in deep learning research (76.85% share). The study identifies top 50 most productive organizations and 50 most productive authors and top 20 most productive journals reporting deep learning research and 118 highly cited papers with 100+ citations per paper.
A Coarse-to-Fine Registration Strategy for Multi-Sensor Images with Large Resolution Differences

Automatic image registration for multi-sensors has always been an important task for remote sensing applications. However, registration for images with large resolution differences has not been fully considered. A coarse-to-fine registration strategy for images with large differences in resolution is presented. The strategy consists of three phases. First, the feature-base registration method is applied on the resampled sensed image and the reference image. Edge point features acquired from the edge strength map (ESM) of the images are used to pre-register two images quickly and robustly. Second, normalized mutual information-based registration is applied on the two images for more accurate transformation parameters. Third, the final transform parameters are acquired through direct registration between the original high- and low-resolution images. Ant colony optimization (ACO) for continuous domain is adopted to optimize the similarity metrics throughout the three phases. The proposed method has been tested on image pairs with different resolution ratios from different sensors, including satellite and aerial sensors. Control points (CPs) extracted from the images are used to calculate the registration accuracy of the proposed method and other state-of-the-art methods. The feature-based preregistration validation experiment shows that the proposed method effectively narrows the value range of registration parameters. The registration results indicate that the proposed method performs the best and achieves sub-pixel registration accuracy of images with resolution differences from 1 to 50 times.