Reviewerly/exHarmony huggingface dataset:
An Improved Geo-Textural Based Feature Extraction Vector For Offline Signature Verification

In the field of pattern recognition, automatic handwritten signature verification is of the essence. The uniqueness of each person’s signature makes it a preferred choice of human biometrics. However, the unavoidable side-effect is that they can be misused to feign data authenticity. In this paper, we present an improved feature extraction vector for offline signature verification system by combining features of grey level occurrence matrix (GLCM) and properties of image regions. In evaluating the performance of the proposed scheme, the resultant feature vector is tested on a support vector machine (SVM) with varying kernel functions. However, to keep the parameters of the kernel functions optimized, the sequential minimal optimization (SMO) and the least square method was used. Results of the study explained that the radial basis function (RBF) coupled with SMO best support the improved featured vector proposed.
Improved Two-Dimensional Quaternion Principal Component Analysis

The two-dimensional quaternion principal component analysis (2D-QPCA) is first improved into abstracting the features of quaternion matrix samples in both row and column directions, being the generalization ability, and with the components weighted by corresponding eigenvalues. The main components of the 2D-QPCA in row and column directions are defined as the solution of one optimal problem with orthogonality constraints on two variables. In advance, 2D-QPCA is innovatively armed with the generalization ability by applying the label information (if known) to generate the weight of each class of training set. With such generalization ability, 2D-QPCA may enlarge the variance of all projections of known and unknown samples. Different from the well-known PCA-like methods, the improved 2D-QPCA prefers to the components that have larger variances of projected samples and weight them with larger factors. The improved versions of 2D-QPCA are applied to the color face recognition and image reconstruction. The numerical results based on the real face data sets validate that the newly proposed method performs better than the state-of-the-art ones.
Mobile Blended Learning and Evaluation of Its Effectiveness on Students’ Learning Achievement

This study provides a new view on blended learning. It describes successful mobile blended learning, which is based on the combination of traditional, face-to-face classes and the use of mobile app, which was exploited as support for the retention and practice of new English words and phrases at the Faculty of Informatics and Management of the University of Hradec Kralove, Czech Republic. The purpose of this article is to evaluate the effectiveness of the use of mobile app on students’ learning achievement in the university Course of Practical English Language. The methodology is based on an experiment and statistical analysis of students’ achievement results. The findings of this study confirm that the use of mobile app is effective in learning new words and phrases, which is in line with other studies on this topic. In fact, the use of app in informal settings extends and enriches student’s learning environment and enhances student’s learning potential. Future research should continue in order to obtain more subject samples. It will be also important to see if the success rate of students who use the mobile app while studying changes or remains the same over time. In addition, the authors might research students’ motivation and attitude towards the use of mobile app.
Big data processing framework for manufacturing

Data analysis of manufacturing plays a vital part in the intelligent manufacturing service of Product-Service Systems (PSS). In order to solve the problem that, manufacturing companies can’t obtain valuable information from enterprise’s big data through traditional data analysis methods, this paper put forward a data processing architecture framework and introduce the predictive algorithm (Random Forest). Finally, a real-time prediction of quality under this framework which uses the random forest algorithm is given to verify the usefulness of the architecture framework.
A Preliminary Evaluation of Transferring the Approach Avoidance Task into Virtual Reality

Following our previous study, a new demonstrator was developed, which transfers the Approach Avoidance Task (AAT) into virtual reality (VR) to support the therapy of substance dependency diseases. This was done in consideration of past evaluation results in order to further increase the effectiveness of the training, for instance by improving usability, functionalities and graphics. A study was carried out with twenty-five people who were then asked to complete a questionnaire focusing on presence, involvement, realism, the possibility to act, and quality of interface. The results show that the use of the Leap Motion sensor as an interaction variant, compared to using controllers, performed best in most areas. Due to the simplified interaction and lower susceptibility to errors, this interaction variant, in combination with the new feature “simplified object interaction”, is to be preferred when calculating the cognitive bias.
Practical Eye Tracking with iTrace

The evolution and effort in designing and implementing iTrace, an infrastructure for integrating eye tracking into developer environments, is presented. The goal is to make eye tracking practical for various stakeholders in software engineering namely researchers, practitioners, and educators. An overview of iTrace and the general process involved in conducting an eye tracking study with human subjects using iTrace is presented in this tool demo paper. Upcoming features and ongoing plans for community involvement are also presented.
Superunsteerability as a quantifiable resource for random access codes assisted by Bell-diagonal states

We show how nonclassical correlations in local bipartite states can act as a resource for quantum information processing. Considering the task of quantum random access codes (RACs) through separable Bell-diagonal states, we demonstrate the advantage of superunsteerability over classical protocols assisted with two bits of shared randomness. We propose a measure of superunsteerability which quantifies nonclassicality beyond quantum steering and obtain its analytical expression for Bell-diagonal states in the context of two- and three-setting steering scenarios that are directly related to the quantum $2\ensuremath{\rightarrow}1$ and $3\ensuremath{\rightarrow}1$ RAC protocols, respectively. The maximal values of our quantifier yield the optimal quantum efficiency for both of the above protocols, thus showing that superunsteerability provides a precise characterization of the nonclassical resource for implementing RACs with separable Bell-diagonal class of states.
Open Educational Resources as a Trend of Modern Education

The article presents the results of the study of open educational resources in mathematical disciplines. The purpose of the article is to summarize the experience of teaching mathematical courses based on quantitative analysis of the corresponding open educational resources and determination of ways of using such resources in the conditions of Ukrainian universities. The definition of open educational resources and their specific features are specified, in particular, support of different formats for presentation of materials, their publication under conditions of an open license, provision of free access, the possibility of modification of materials by other users. The analysis of the number of open educational resources Coursera, Edx, Udemy, and INTUIT has revealed that the bulk of mathematical courses in relation to others is not large, which suggests a reduction in young people's interest in purely mathematical disciplines. At the same time, it was noted that the presence of such resources on the site of the university contributes to the formation of its positive image within the country and the world. The own experience of studying mathematical online courses on Analytical Geometry, Linear Algebra, Geometry and Mathematical Philosophy is described.
Multivariate Time Series Imputation with Variational Autoencoders

Multivariate time series with missing values are common in many areas, for instance in healthcare and finance. To face this problem, modern data imputation approaches should (a) be tailored to sequential data, (b) deal with high dimensional and complex data distributions, and (c) be based on the probabilistic modeling paradigm for interpretability and confidence assessment. However, many current approaches fall short in at least one of these aspects. Drawing on advances in deep learning and scalable probabilistic modeling, we propose a new deep sequential variational autoencoder approach for dimensionality reduction and data imputation. Temporal dependencies are modeled with a Gaussian process prior and a Cauchy kernel to reflect multi-scale dynamics in the latent space. We furthermore use a structured variational inference distribution that improves the scalability of the approach. We demonstrate that our model exhibits superior imputation performance on benchmark tasks and challenging real-world medical data.
Confidence Scoring Using Whitebox Meta-models with Linear Classifier Probes

We propose a novel confidence scoring mechanism for deep neural networks based on a two-model paradigm involving a base model and a meta-model. The confidence score is learned by the meta-model observing the base model succeeding/failing at its task. As features to the meta-model, we investigate linear classifier probes inserted between the various layers of the base model. Our experiments demonstrate that this approach outperforms multiple baselines in a filtering task, i.e., task of rejecting samples with low confidence. Experimental results are presented using CIFAR-10 and CIFAR-100 dataset with and without added noise. We discuss the importance of confidence scoring to bridge the gap between experimental and real-world applications.
Theoretical guarantees for sampling and inference in generative models with latent diffusions

We introduce and study a class of probabilistic generative models, where the latent object is a finite-dimensional diffusion process on a finite time interval and the observed variable is drawn conditionally on the terminal point of the diffusion. We make the following contributions: We provide a unified viewpoint on both sampling and variational inference in such generative models through the lens of stochastic control. We quantify the expressiveness of diffusion-based generative models. Specifically, we show that one can efficiently sample from a wide class of terminal target distributions by choosing the drift of the latent diffusion from the class of multilayer feedforward neural nets, with the accuracy of sampling measured by the Kullback-Leibler divergence to the target distribution. Finally, we present and analyze a scheme for unbiased simulation of generative models with latent diffusions and provide bounds on the variance of the resulting estimators. This scheme can be implemented as a deep generative model with a random number of layers.
On hybrid lagrangian-eulerian simulation methods

course Share on On hybrid lagrangian-eulerian simulation methods: practical notes and high-performance aspects Authors: Yuanming Hu MIT CSAIL MIT CSAILView Profile , Xinxin Zhang Beijing Film Academy Beijing Film AcademyView Profile , Ming Gao University of Pennsylvania University of PennsylvaniaView Profile , Chenfanfu Jiang University of Pennsylvania University of PennsylvaniaView Profile Authors Info & Claims SIGGRAPH '19: ACM SIGGRAPH 2019 CoursesJuly 2019 Article No.: 16Pages 1–246https://doi.org/10.1145/3305366.3328075Published:28 July 2019Publication History 7citation665DownloadsMetricsTotal Citations7Total Downloads665Last 12 Months77Last 6 weeks10 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Ultra-high temperature mechanical property test of C/C composites by a digital image correlation method based on an active laser illumination and background radiation suppressing method with multi-step filtering

Carbon/carbon (C/C) composites have been widely used in aerospace engineering because of their high temperature resistance. However, the commonly used non-contact deformation measurement method based on digital image correlation (DIC) still has some problems of speckle preparation and background radiation in an ultra-high temperature environment above 2000&#x00B0;C. An ultra-high temperature mechanical property test method based on active laser illumination and a background radiation suppressing method with multi-step filtering is proposed. In the proposed method, a laser with good coherence is used as the active illumination source to generate laser speckles that will not fall off or discolor as temperature increases. Also, a multi-step filtering device is devised with the combination of a spatial filter, plano&#x2013;convex lens, polarizing filter, and two different bandpass filters, which can successfully suppress the background radiation and retain the characteristics of laser speckle patterns at the same time. Tensile tests of C/C composite had been carried out at ultra-high temperatures of 2200&#x00B0;C, 2400&#x00B0;C, 2600&#x00B0;C, and 2800&#x00B0;C. The experimental results showed that high-quality laser speckle images were obtained and the deviations between the elastic moduli obtained by the proposed method and by the high temperature contact extensometer were all less than 7%.
A Comparison of Word-based and Context-based Representations for Classification Problems in Health Informatics

Distributed representations of text can be used as features when training a statistical classifier. These representations may be created as a composition of word vectors or as context-based sentence vectors. We compare the two kinds of representations (word versus context) for three classification problems: influenza infection classification, drug usage classification and personal health mention classification. For statistical classifiers trained for each of these problems, context-based representations based on ELMo, Universal Sentence Encoder, Neural-Net Language Model and FLAIR are better than Word2Vec, GloVe and the two adapted using the MESH ontology. There is an improvement of 2-4% in the accuracy when these context-based representations are used instead of word-based representations.
Convolution with even-sized kernels and symmetric padding

Compact convolutional neural networks gain efficiency mainly through depthwise convolutions, expanded channels and complex topologies, which contrarily aggravate the training process. Besides, 3x3 kernels dominate the spatial representation in these models, whereas even-sized kernels (2x2, 4x4) are rarely adopted. In this work, we quantify the shift problem occurs in even-sized kernel convolutions by an information erosion hypothesis, and eliminate it by proposing symmetric padding on four sides of the feature maps (C2sp, C4sp). Symmetric padding releases the generalization capabilities of even-sized kernels at little computational cost, making them outperform 3x3 kernels in image classification and generation tasks. Moreover, C2sp obtains comparable accuracy to emerging compact models with much less memory and time consumption during training. Symmetric padding coupled with even-sized convolutions can be neatly implemented into existing frameworks, providing effective elements for architecture designs, especially on online and continual learning occasions where training efforts are emphasized.
Image Anomaly Detection with Capsule Networks and Imbalanced Datasets

Image anomaly detection consists in finding images with anomalous, unusual patterns with respect to a set of normal data. Anomaly detection can be applied to several fields and has numerous practical applications, e.g. in industrial inspection, medical imaging, security enforcement, etc. However, anomaly detection techniques often still rely on traditional approaches such as one-class Support Vector Machines, while the topic has not been fully developed yet in the context of modern deep learning approaches. In this paper we propose an image anomaly detection system based on capsule networks under the assumption that anomalous data are available for training but their amount is scarce.
Deep Weakly-Supervised Learning Methods for Classification and Localization in Histology Images: A Survey

Using state-of-the-art deep learning (DL) models to diagnose cancer from histology data presents several challenges related to the nature and availability of labeled histology images, including image size, stain variations, and label ambiguity. In addition, cancer grading and the localization of regions of interest (ROIs) in such images normally rely on both image- and pixel-level labels, with the latter requiring a costly annotation process. Deep weakly-supervised object localization (WSOL) methods provide different strategies for low-cost training of DL models. Given only image-class annotations, these methods can be trained to simultaneously classify an image, and yield class activation maps (CAMs) for ROI localization. This paper provides a review of deep WSOL methods to identify and locate diseases in histology images, without the need for pixel-level annotations. We propose a taxonomy in which these methods are divided into bottom-up and top-down methods according to the information flow in models. Although the latter have seen only limited progress, recent bottom-up methods are currently driving a lot of progress with the use of deep WSOL methods. Early works focused on designing different spatial pooling functions. However, those methods quickly peaked in term of localization accuracy and revealed a major limitation, namely, – the under-activation of CAMs, which leads to high false negative localization. Subsequent works aimed to alleviate this shortcoming and recover the complete object from the background, using different techniques such as perturbation, self-attention, shallow features, pseudo-annotation, and task decoupling.&lt;br&gt;In the present paper, representative deep WSOL methods from our taxonomy are also evaluated and compared in terms of classification and localization accuracy using two challenging public histology datasets – one for colon cancer (GlaS), and a second, for breast cancer (CAMELYON16). Overall, the results indicate poor localization performance, particularly for generic methods that were initially designed to process natural images. Methods designed to address the challenges posed by histology data often use priors such as ROI size, or additional pixel-wise supervision estimated from a pre-trained classifier, allowing them to achieve better results. However, all the methods suffer from high false positive/negative localization. Classification performance is mainly affected by the model selection process, which uses either the classification or the localization metric. Finally, four key challenges are identified in the application of deep WSOL methods in histology, namely, – under-/over-activation of CAMs, sensitivity to thresholding, and model selection – and research avenues are provided to mitigate them. Our code is publicly available at &lt;a href='https://github.com/jeromerony/survey_wsl_histology'&gt;https://github.com/jeromerony/survey_wsl_histology&lt;/a&gt;
Mutual Influence-aware Runtime Learning of Self-adaptation Behavior

Self-adaptation has been proposed as a mechanism to counter complexity in control problems of technical systems. A major driver behind self-adaptation is the idea to transfer traditional design-time decisions to runtime and into the responsibility of systems themselves. To deal with unforeseen events and conditions, systems need creativity—typically realized by means of machine learning capabilities. Such learning mechanisms are based on different sources of knowledge. Feedback from the environment used for reinforcement purposes is probably the most prominent one within the self-adapting and self-organizing (SASO) systems community. However, the impact of other (sub-)systems on the success of the individual system’s learning performance has mostly been neglected in this context. In this article, we propose a novel methodology to identify effects of actions performed by other systems in a shared environment on the utility achievement of an autonomous system. Consider smart cameras (SC) as illustrating example: For goals such as 3D reconstruction of objects, the most promising configuration of one SC in terms of pan/tilt/zoom parameters depends largely on the configuration of other SCs in the vicinity. Since such mutual influences cannot be pre-defined for dynamic systems, they have to be learned at runtime. Furthermore, they have to be taken into consideration when self-improving their own configuration decisions based on a feedback loop concept, e.g., known from the SASO domain or the Autonomic and Organic Computing initiatives. We define a methodology to detect such influences at runtime, present an approach to consider this information in a reinforcement learning technique, and analyze the behavior in artificial as well as real-world SASO system settings.
Cross-Attention End-to-End ASR for Two-Party Conversations

We present an end-to-end speech recognition model that learns interaction between two speakers based on the turn-changing information. Unlike conventional speech recognition models, our model exploits two speakers' history of conversational-context information that spans across multiple turns within an end-to-end framework. Specifically, we propose a speaker-specific cross-attention mechanism that can look at the output of the other speaker side as well as the one of the current speaker for better at recognizing long conversations. We evaluated the models on the Switchboard conversational speech corpus and show that our model outperforms standard end-to-end speech recognition models.
Assessing the Effectiveness of Open Access Finding Tools

The open access (OA) movement seeks to ensure that scholarly knowledge is available to anyone with internet access, but being available for free online is of little use if people cannot find open versions. A handful of tools have become available in recent years to help address this problem by searching for an open version of a document whenever a user hits a paywall. This project set out to study how effective four of these tools are when compared to each other and to Google Scholar, which has long been a source of finding OA versions. To do this, the project used Open Access Button, Unpaywall, Lazy Scholar, and Kopernio to search for open versions of 1,000 articles. Results show none of the tools found as many successful hits as Google Scholar, but two of the tools did register unique successful hits, indicating a benefit to incorporating them in searches for OA versions. Some of the tools also include additional features that can further benefit users in their search for accessible scholarly knowledge.