Reviewerly/exHarmony huggingface dataset:
Monitoring Resilience in a Rook-managed Containerized Cloud Storage System

Distributed cloud storage solutions are currently gaining high momentum in industry and academia. The enterprise data volume growth and the recent tendency to move as much as possible data to the cloud is strongly stimulating the storage market growth. In this context, and as a main requirement for cloud native applications, it is of utmost importance to guarantee resilience of the deployed applications and the infrastructure. Indeed, with failures frequently occurring, a storage system should quickly recover to guarantee service availability. In this paper, we focus on containerized cloud storage, proposing a resilience monitoring solution for the recently developed Rook storage operator. While, Rook brings storage systems into a cloud-native container platform, in this paper we design an additional module to monitor and evaluate the resilience of the Rook-based system. Our proposed module is validated in a production environment, with software components generating a constant load and a controlled removal of system elements to evaluate the self-healing capability of the storage system. Failure recovery time revealed to be 41 and 142 seconds on average for a 32GB and a 215GB object storage device respectively.
Dependent relevance determination for smooth and structured sparse regression

In many problem settings, parameter vectors are not merely sparse but dependent in such a way that non-zero coefficients tend to cluster together. We refer to this form of dependency as region sparsity. Classical sparse regression methods, such as the lasso and automatic relevance determination (ARD), which model parameters as independent a priori, and therefore do not exploit such dependencies. Here we introduce a hierarchical model for smooth, region-sparse weight vectors and tensors in a linear regression setting. Our approach represents a hierarchical extension of the relevance determination framework, where we add a transformed Gaussian process to model the dependencies between the prior variances of regression weights. We combine this with a structured model of the prior variances of Fourier coefficients, which eliminates unnecessary high frequencies. The resulting prior encourages weights to be region-sparse in two different bases simultaneously. We develop Laplace approximation and Monte Carlo Markov Chain (MCMC) sampling to provide efficient inference for the posterior. Furthermore, a two-stage convex relaxation of the Laplace approximation approach is also provided to relax the inevitable non-convexity during the optimization. We finally show substantial improvements over comparable methods for both simulated and real datasets from brain imaging.
“That spelling tho”: A sociolinguistic study of the nonstandard form of though in a corpus of Reddit comments

Abstract Tho , the nonstandard spelling of though which was proposed by American spelling reformers in the 19th century, is making a comeback. In 2013, internet memes such as that backflip tho gave a boost to the shortened form. This sociolinguistic study investigates the use of tho in RedditGender, a 19 million-word corpus of comments posted by 1044 Reddit users. First, concordance lines generated from the whole corpus were analyzed in order to compare the use of tho with the meme and the standard spelling. Then, regression analysis was conducted with a sample of the corpus, containing only complete cases. Results show that tho is rarely used in the meme construction that contributed to popularize it, and that it appears more often as an adverb than as a conjunction. They also seem to indicate that the use of tho is correlated with gender and race. Most frequent users are black males. This suggests that the shortened spelling is not simply a way to save time when typing, and that it is not semantically equivalent to though . It seems to be a marker of affiliation with a social group and of familiarity with internet subcultures.
The Nuts and Bolts of Conceptual Blending: Multidomain Concept Creation with Divago

We revisit Divago, one of the first computational systems based on conceptual blending theory, along with an integrated and extended description of the main aspects that characterise it. Our tour around this framework includes revisiting past publications that report work related to the Divago architecture since the initial sketch presented in the late 1990s, up to publications reporting experiments in concept creation, including a detailed description of a more mature version introduced in 2005. Additionally, we report ongoing research work aimed at adding new relevant features to the system.
Multi-view facial capture using binary spherical gradient illumination

High resolution facial capture has received significant attention in computer graphics due to its application in the creation of photorealistic digital humans for various applications ranging from film and VFX to games and VR. Here, the state of the art method for high quality acquisition of facial geometry and reflectance employs polarized spherical gradient illumination [Ghosh et al. 2011; Ma et al. 2007]. The technique has had a significant impact in facial capture for film VFX, recently receiving a Technical Achievement award from the Academy of Motion Picture Arts and Sciences [Aca 2019]. However, the method imposes a few constraints due to the employment of polarized illumination, and requires the camera viewpoints to be located close to the equator of the LED sphere for appropriate diffuse-specular separation for multiview capture [Ghosh et al. 2011]. The employment of polarization for reflectance separation also reduces the amount of light available for exposures and requires double the number of photographs (in cross and parallel polarization states), increasing the capture time and the number of photographs required for each face scan.
The Influence of Mobile Augmented Reality-Based Sandbox Games on Chinese Characters Learning

With the rapid development of augmented reality technology in the field of education, the availability of learning methods based on mobile AR technology has been verified. However, there is little research on the impact of mobile AR on Chinese character learning. This paper designed and developed a mobile AR Chinese characters sandbox game. Compared with the traditional Chinese characters learning applications, the game is more interesting and effective. The game combines 3D touch interaction, 2D interface interaction, image recognition-based AR interaction and so on. The game can intelligently recommend suitable learning content to learners. In the game, participants write Chinese characters by interacting with the real environment, while changing the virtual environment. The results of this study show that the game has a positive impact on learners’ learning of Chinese characters. It can greatly improve learners’ learning interest and motivation.
The ALOS Dataset for Advert Localization in Outdoor Scenes

The rapid increase in the number of online videos provides the marketing and advertising agents ample opportunities to reach out to their audience. One of the most widely used strategies is product placement, or embedded marketing, wherein new advertisements are integrated seamlessly into existing advertisements in videos. Such strategies involve accurately localizing the position of the advert in the image frame, either manually in the video editing phase, or by using machine learning frameworks. However, these machine learning techniques and deep neural networks need a massive amount of data for training. In this paper, we propose and release the first large-scale dataset of advertisement billboards, captured in outdoor scenes. We also benchmark several state-of-the-art semantic segmentation algorithms on our proposed dataset.
High Dimensional Inference With Random Maximum A-Posteriori Perturbations

This paper presents a new approach, called perturb-max, for high-dimensional statistical inference in graphical models that is based on applying random perturbations followed by optimization. This framework injects randomness into maximum a-posteriori (MAP) predictors by randomly perturbing the potential function for the input. A classic result from extreme value statistics asserts that perturb-max operations generate unbiased samples from the Gibbs distribution using high-dimensional perturbations. Unfortunately, the computational cost of generating so many high-dimensional random variables can be prohibitive. However, when the perturbations are of low dimension, sampling the perturb-max prediction is as efficient as MAP optimization. This paper shows that the expected value of perturb-max inference with low dimensional perturbations can be used sequentially to generate unbiased samples from the Gibbs distribution. Furthermore the expected value of the maximal perturbations is a natural bound on the entropy of such perturb-max models. A measure concentration result for perturb-max values shows that the deviation of their sampled average from its expectation decays exponentially in the number of samples, allowing effective approximation of the expectation.
Steerable ePCA: Rotationally Invariant Exponential Family PCA

In photon-limited imaging, the pixel intensities are affected by photon count noise. Many applications require an accurate estimation of the covariance of the underlying 2-D clean images. For example, in X-ray free electron laser (XFEL) single molecule imaging, the covariance matrix of 2-D diffraction images is used to reconstruct the 3-D molecular structure. Accurate estimation of the covariance from low-photon-count images must take into account that pixel intensities are Poisson distributed, hence the classical sample covariance estimator is highly biased. Moreover, in single molecule imaging, including in-plane rotated copies of all images could further improve the accuracy of covariance estimation. In this paper we introduce an efficient and accurate algorithm for covariance matrix estimation of count noise 2-D images, including their uniform planar rotations and possibly reflections. Our procedure, steerable ePCA, combines in a novel way two recently introduced innovations. The first is a methodology for principal component analysis (PCA) for Poisson distributions, and more generally, exponential family distributions, called ePCA. The second is steerable PCA, a fast and accurate procedure for including all planar rotations when performing PCA. The resulting principal components are invariant to the rotation and reflection of the input images. We demonstrate the efficiency and accuracy of steerable ePCA in numerical experiments involving simulated XFEL datasets and rotated face images from Yale Face Database B.
Interpreting and Extending the Guided Filter via Cyclic Coordinate Descent

The guided filter (GF) is a widely used smoothing tool in computer vision and image processing. However, to the best of our knowledge, few papers investigate the mathematical connection between this filter and the least-squares optimization. In this paper, we first interpret the guided filter as the cyclic coordinate descent (CCD) solver of a least-squares objective function. This discovery implies an extension approach to generalize the guided filter since we can change the least-squares objective function and define new filters as the first pass iteration of the CCD solver of modified objective functions. In addition, referring to the iterative minimizing procedure of the CCD, we can derive new rolling filtering schemes. So, we are reasonable to say that our discovery not only reveals an approach to design new GF-like filters adapting to specific requirements of applications but also offers thorough explanations for two rolling filtering schemes of the guided filter as well as the method to extend them. Experiments prove our new proposed filters and rolling filtering schemes could produce state-of-the-art results.
Journals on Information Literacy: Citation Analysis and Social Impact Metrics

Library and information science as a discipline is undergoing rapid and extensive change, driven particularly by new areas of research. Citation analysis and information literacy are widely researched and have a rich background of growth. Publish or Perish, Buzzsumo.com, altmetrics.com, and textalyser.net have been used to analyze all metrics associated with citation analysis, social networking, and keyword analysis.The hypothesis of this study is: “There is no significant difference in the citation pattern of the three prominent citation journals: Journal of Information Literacy, Communications in Information Literacy and Nordic Journal of Information Literacy in Higher Education”.The hypotheses of the study were tested by assessment of the degree to which each of the journals met set of objective criteria. 697 papers from the three journals were analyzed and the results showed that the citation metrics of the Journal of Information Literacy had the highest correspondence with the criteria. The information linked to the Journals of Information Literacy was moderately posted on social networking sites (SNS) and the highest frequency of the keywords: “Information”; “Information literacy”; and “Information literacy instruction” were found in one, two and three-word searches. Some recommendations have also been offered to improve SNS presence and for further research in this field.
-NUMBERS IN FIELDS OF FORMAL POWER SERIES OVER FINITE FIELDS

In the field $\mathbb{K}$ of formal power series over a finite field $K$ , we consider some lacunary power series with algebraic coefficients in a finite extension of $K(x)$ . We show that the values of these series at nonzero algebraic arguments in $\mathbb{K}$ are $U$ -numbers.
Development of Mixed Reality Robot Control System Based on HoloLens

By using HoloLens, a wearable mixed reality device, and Unity3D, a 3D real-time driving engine, to simulate the robot kinematics and dynamics control in a mixed reality environment, we can test the performance of the control algorithm in virtual space before the physical simulation of the robot control algorithm. This paper first introduces the method of constructing a parameter-adjustable manipulator model with arbitrary degrees of freedom using Unity3D, and then introduces the method of using C# programming to implement the inverse kinematics simulation of the manipulator, and establishes a six-degree-of-freedom manipulator inverse kinematics control model to verify the feasibility of this method. Finally, we use the classic one-stage inverted pendulum model to verify the feasibility of robot dynamics control in Unity3D. This method changes the situation that the existing simulation method is difficult to interact with the real environment. Users can use the virtual robot model to verify the feasibility of the control scheme in complex environments.
Design and Implementation of a Disambiguity Framework for Smart Voice Controlled Devices

With about 100 million people using it recently, SVCD(Smart Voice Controlled Device) are becoming demotic. Whether at home or in an office, usually, multiple appliances are under the control of a single SVCD and several people may manipulate an SVCD simultaneously. However, present SVCD fails to handle them appropriately. In this paper, we propose a novel framework for SVCD to eliminate orders’ ambiguity for single user or multi-user. We also design an algorithm combining Word2Vec and emotion detection for the device to wipe off ambiguity. Finally, we apply our framework into a virtual smart home scene and the performance of it indicates that our strategy resolves the problems commendably.
Roles of cataloguing librarians in the digital era

Purpose This study aims to explore the roles and skills of Omani academic cataloguing librarians in facilitating access to various kinds of traditional and digital information resources and challenges confronting them in fulfilling that task. Design/methodology/approach Data for this research were gathered from 37 academic libraries in the country through a questionnaire sent to their cataloguing librarians. Google Docs format was used for cumulating and presenting quantitative data, whilst content analysis was utilized for gathering the qualitative data in specific themes according to the research questions. Findings The obvious findings are that Omani academic cataloguers still perform the traditional cataloguing functions, including copy and original cataloguing, and maintain authority files. Moreover, more than half of the respondents have the skills required for working in groups and are familiar with cataloguing tools such as RDA rules and BIBFRAME. However, cataloguers lack familiarity with computer languages (XML, PMH, OAI, RDF) and metadata. Practical implications The research concludes with suggestions to libraries for improving librarians’ skills and performance through such as provision of proficient training and establishment of a collaborative body. Originality/value This research addresses an area of interest to most librarians. However, few researchers in the authors’ region have focused on this topic. The study, therefore, aims to contribute to filling this gap through assessing whether cataloguers in Oman have the skills and roles required for organizing various information resources in the digital era.
Learning Identity-Aware Face Features Across Poses Based on Deep Siamese Networks

Face recognition is an important biometric due to its non-intrusive collection of data that can be applied to surveillance systems. However, the human pose is unconstrained under surveillance, and there is only one frontal face available in the gallery in most scenarios, and these two factors challenge face recognition performance. A goodly amount of research has been published to solve this problem. Specially, deep learning-based methods learn generic models between poses to synthesize different poses of a given face, however, generic synthesis models can lose the face identity while warping the face, which deteriorates the discriminative capability of learned features. In this paper, we proposed the deep Siamese networks to learn identity-aware and pose-invariant features, adding contrastive loss to the face synthesis model to preserve the face identity while synthesizing the face. In addition, we trained various face synthesis models with different target poses as supervisory signals, the learned pose-invariant features were incorporated by another Siamese network, resulting in deeper pose-invariant and identity-aware features. The proposed network is free of landmark estimation and face pose, and it is in real time. We tested the proposed algorithm in the FERET and CAS-PEAL datasets, and experimental results demonstrated that our network achieved superior performance to that of recently published algorithms for cross-pose face recognition, especially the 2D deep learning-based algorithms.
Trust, Resilience and Interpretability of AI Models

In this tutorial, we present our recent work on building trusted, resilient and interpretable AI models by combining symbolic methods developed for automated reasoning with connectionist learning methods that use deep neural networks. The increasing adoption of artificial intelligence and machine learning in systems, including safety-critical systems, has created a pressing need for developing scalable techniques that can be used to establish trust over their safe behavior, resilience to adversarial attacks, and interpretability to enable human audits. This tutorial is comprised of three components: review of techniques for verification of neural networks, methods for using geometric invariants to defend against adversarial attacks, and techniques for extracting logical symbolic rules by reverse engineering machine learning models. These techniques form the core of TRINITY: Trusted, Resilient and Interpretable AI framework being developed at SRI. In this tutorial, we identify the key challenges in building the TRINITY framework, and report recent results on each of these three fronts.
Multi-label Text Classification Based on Sequence Model

In the multi-label text classification problem, the category labels are frequently related in the semantic space. In order to enhance the classification performance, using the correlation between labels and using the Encoder in the seq2seq model and the Decoder model with the attention mechanism, a multi-label text classification method based on sequence generation is proposed. First, the Encoder encodes the word vector in the text to form a semantic coding vector. Then, the LSTM neural network in the Decoder stage is utilized to process the dependency of the category label sequence to consider the correlation between the category labels, and the attention mechanism is added to calculate the probability of attention distribution. Highlight the effect of key input on the output, and improve the missing semantic problem caused by the input too long, and finally output the predicted label category. The experimental results show that our model is better than the existing model after considering the label correlation.
Construction Method of Sentiment Lexicon Based on Word2vec

In natural language processing, sentiment lexicon is an effective method for sentiment analysis of text. Aiming at the shortcomings of existing sentiment lexicon in emotional and semantic expression, a method of constructing domain sentiment lexicon based on word2vec is proposed. Based on the existing Chinese affective dictionaries, this method uses TFIDF to measure the importance of vocabulary and the expression of word vectors in semantics to form the selection criteria and classification basis of seed vocabulary, and then forms a sentiment lexicon by category judgment. The comparative experiments show that this method has played a positive role in improving the accuracy of sentiment words classification and the construction of sentiment lexicon.
Computing Exact Solutions of Consensus Halving and the Borsuk-Ulam Theorem

We study the problem of finding an exact solution to the consensus halving problem. While recent work has shown that the approximate version of this problem is PPA-complete [Filos-Ratsikas and Goldberg, 2018; Filos-Ratsikas and Goldberg, 2018], we show that the exact version is much harder. Specifically, finding a solution with n agents and n cuts is FIXP-hard, and deciding whether there exists a solution with fewer than n cuts is ETR-complete. We also give a QPTAS for the case where each agent's valuation is a polynomial.Along the way, we define a new complexity class BU, which captures all problems that can be reduced to solving an instance of the Borsuk-Ulam problem exactly. We show that FIXP subseteq BU subseteq TFETR and that LinearBU = PPA, where LinearBU is the subclass of BU in which the Borsuk-Ulam instance is specified by a linear arithmetic circuit.
Research-Based Text Extraction with SVM Distinct Stages Framework Data Mining Approach

Today, with due to new revolution on the biomedical domain combined to data mining applications, research on biomedical text applications in the context of information extraction are becoming very essential. Biomedical applications are unstructured in nature, making it difficult to utilize data mining and discovery techniques to retrieve needed documents. This article introduces a based Support Vector Machine (SVM) Information Extraction from the Biomedical domain by adopting the use of text mining framework. Three main structures (Text gathering, Text Pre-processing, Data Analysis) are used in the purpose to give input/output text Pre-processing in different phases. Finally, by increasing the precision and recall rate of information retrieval, good framework performances are obtained on the Biomedical documents.