Reviewerly/exHarmony huggingface dataset:
TXtract: Taxonomy-Aware Knowledge Extraction for Thousands of Product Categories

Extracting structured knowledge from product profiles is crucial for various applications in e-Commerce. State-of-the-art approaches for knowledge extraction were each designed for a single category of product, and thus do not apply to real-life e-Commerce scenarios, which often contain thousands of diverse categories. This paper proposes TXtract, a taxonomy-aware knowledge extraction model that applies to thousands of product categories organized in a hierarchical taxonomy. Through category conditional self-attention and multi-task learning, our approach is both scalable, as it trains a single model for thousands of categories, and effective, as it extracts category-specific attribute values. Experiments on products from a taxonomy with 4,000 categories show that TXtract outperforms state-of-the-art approaches by up to 10% in F1 and 15% in coverage across all categories.
Emergence of Syntax Needs Minimal Supervision

This paper is a theoretical contribution to the debate on the learnability of syntax from a corpus without explicit syntax-specific guidance. Our approach originates in the observable structure of a corpus, which we use to define and isolate grammaticality (syntactic information) and meaning/pragmatics information. We describe the formal characteristics of an autonomous syntax and show that it becomes possible to search for syntax-based lexical categories with a simple optimization process, without any prior hypothesis on the form of the model.
Basic Linguistic Resources and Baselines for Bhojpuri, Magahi and Maithili for Natural Language Processing

Corpus preparation for low-resource languages and for development of human language technology to analyze or computationally process them is a laborious task, primarily due to the unavailability of expert linguists who are native speakers of these languages and also due to the time and resources required. Bhojpuri, Magahi, and Maithili, languages of the Purvanchal region of India (in the north-eastern parts), are low-resource languages belonging to the Indo-Aryan (or Indic) family. They are closely related to Hindi, which is a relatively high-resource language, which is why we make our comparisons with Hindi. We collected corpora for these three languages from various sources and cleaned them to the extent possible, without changing the data in them. The text belongs to different domains and genres. We calculated some basic statistical measures for these corpora at character, word, syllable, and morpheme levels. These corpora were also annotated with parts-of-speech (POS) and chunk tags. The basic statistical measures were both absolute and relative and were meant to give an indication of linguistic properties such as morphological, lexical, phonological, and syntactic complexities (or richness). The results were compared with a standard Hindi corpus. For most of the measures, we tried to keep the size of the corpus the same across the languages so as to avoid the effect of corpus size, but in some cases it turned out that using the full corpus was better, even if sizes were very different. Although the results are not very clear, we try to draw some conclusions about the languages and the corpora. For POS tagging and chunking, the BIS tagset was used to manually annotate the data. The sizes of the POS tagged data are 16067, 14669 and 12310 sentences, respectively for Bhojpuri, Magahi and Maithili. The sizes for chunking are 9695 and 1954 sentences for Bhojpuri and Maithili, respect
SECOND ORDER DERIVATIVE CROSS DIAGONAL MATRIX APPROACH FOR CBIR

In this paper, we propose two descriptors based on second-order derivative tetra patterns for efficient content based image retrieval (CBIR). The proposed descriptors initially encode the first-order derivative along 00 and 900 over the center pixel and also on each of the sampling pixel or referred pixel of a 3x3 window. These derivatives derive ternary directions. This paper computes the relationship between the first-order ternary derivative of center pixel and each referred pixel. This process derives the second-order derivative tetra pattern ( ) for each referred pixel. This paper then computes unit for each 3x3window of tetra patterns. This paper initially computes the histograms of individual color planes of HSV model. The proposed first descriptor integrates individual histograms of HSV plane with , and this derives color based ( ). To reduce the bin size of , this paper divides the 3x3 into the cross and diagonal units and derives the second-order derivative cross and diagonal matrix ( ). The GLCM features are computed on . The proposed descriptors and are tested with popular databases and also compared with the recent methods of CBIR.
Breast cancer classification using parametric free thresholding adjacency statistics based Fibonacci patterns

According to the American cancer society, the average risk of women getting diagnosed with breast cancer during their life is 13%. The World Health Organization also reports that the number of cancer cases is projected to rise to 19.3 million by 2025. Recent research works point out that physicians can only diagnose cancer with 79% accuracy while machine learning procedures achieve 91% accuracy or more. The current challenges are early cancer detection and the efficient and accurate diagnosis of histopathology tissue samples. Several Deep Learning breast cancer classification models have been developed to assist medical practitioners. However, these methods are data hungry and require thousands of training image samples, often coupled with data augmentation to achieve satisfactory results with long training hours. In this paper, we propose a machine learning classification model by integrating the Parameter free Thresholding Adjacency Statistics (PFTAS) with Fibonacci-p patterns for breast cancer detection. Computer simulations on BreakHis cancer datasets in comparison to other machine learning and deep learning-based methods show that (i) the presented method helps eliminate dependence on large training data and data augmentation, (ii) robustness to noise and background stains, and (iii) lightweight model easy to train and deploy.
An Efficiency-Driven Approach For Real-Time Optical Flow Processing On Parallel Hardware

This article tackles the entire lifecycle of an algorithm: from its design to its implementation. It exhibits a method for making efficient choices at algorithm design time knowing the characteristics of the underlying hardware target. As of today, computing the optical flow of a stream of images is still a demanding task. In the meantime, the use of Graphics Processing Units (GPU) has become mainstream and allows substantial gains in processing frame rate. In this paper, we focus on a specific variational method (CLG [1]) where linear systems have to be solved. They depend on two parameters $\alpha$ and $\rho$. To efficiently solve the problem, we look at convergence speed with respect to the model's parameters. We benchmark usual linear solvers with preconditioners to identify the fastest in terms of convergence per iteration. We then show that once implemented on GPUs, the most efficient solver changes depending on the model parameters. For $640 \times 480$ images, with the right choice of solver and parameters, our implementation can solve the system with relative $10 e^{-8}$ accuracy in 15 ms on a Titan V GPU. All the results are aggregated on a 30-image set to increase confidence in their extendability.
Unsupervised anomaly detection via deep metric learning with end-to-end optimization

We investigate unsupervised anomaly detection for high-dimensional data and introduce a deep metric learning (DML) based framework. In particular, we learn a distance metric through a deep neural network. Through this metric, we project the data into the metric space that better separates the anomalies from the normal data and reduces the effect of the curse of dimensionality for high-dimensional data. We present a novel data distillation method through self-supervision to remedy the conventional practice of assuming all data as normal. We also employ the hard mining technique from the DML literature. We show these components improve the performance of our model and significantly reduce the running time. Through an extensive set of experiments on the 14 real-world datasets, our method demonstrates significant performance gains compared to the state-of-the-art unsupervised anomaly detection methods, e.g., an absolute improvement between 4.44% and 11.74% on the average over the 14 datasets. Furthermore, we share the source code of our method on Github to facilitate further research.
Novel Algorithm for Service Composition and QOS Enhancements in Composite Computing Services

The swift progress in the computing set up by providing services has caused the sudden increase in the amount of the services that are operationally similar. The dissimilarity in such services are observed based on the characteristics standards of their service quality. Consequently a thought-provoking issue in the perspective of the service-configuration is based on the choosing the accurate client services according to the user demands for which the acquired compound services guarantees the non-computational demands on the ground of comprehensive service quality constraints. The procedure carried out in the paper presents the novel algorithm for the service composition/configuration for enhancing the quality of service of the compound /composite computing services. The algorithm focusses in improve the service configuration feasibility and optimality and minimize the configuration time. The algorithm is totally based on the limits in the configuration strategies that relies on the Pareto-Dominance and the quality of service decomposition (PD-QOSD). The quantity of the service arrangements is expected to increase according to the service count and quality of service characteristics in case of PD and whereas the QOSD takes care of the feasibility of the arrangements during the high restrictions imposed by users on the constraints of the comprehensive quality of service. The validation outcomes prove that the novel algorithm developed function efficiently on the basis of the time required for composition, the optimality, as well as the search space.
[Neural mechanisms of basic functions of cognitive control].

Cognitive control refers to the brain functions that regulate variously specific mental activities in terms of task goal, forming the basis of goal-guided behaviors. In the last decade, our team devoted to investigating the neural mechanisms of basic functions of cognitive control, i.e., monitoring, controlling, and switching. We published a series of papers on the temporal course of monitoring initiating cognitive control and its mechanisms, the influential scope of controlling and new controlling mechanisms, brain networks related to controlling efficiency, brain hubs and neural dynamic encoding of switching. This paper reviews the related studies and further extracts their theoretical significance. In the future, more attention should be paid on causal studies, studies on functional implementation of cognitive control, and transfer-application studies, by which we expect to deeply elucidate neural mechanisms of cognitive control.
CasualStereo: Casual Capture of Stereo Panoramas with Spherical Structure-from-Motion

Hand-held capture of stereo panoramas involves spinning the camera in a roughly circular path to acquire a dense set of views of the scene. However, most existing structure-from-motion pipelines fail when trying to reconstruct such trajectories, due to the small baseline between frames. In this work, we evaluate the use of spherical structure-from-motion for reconstructing handheld stereo panorama captures. The spherical motion constraint introduces a strong regularization on the structure-from-motion process which mitigates the small-baseline problem, making it well-suited to the use case of stereo panorama capture with a handheld camera. We demonstrate the effectiveness of spherical structure-from-motion for casual capture of high-resolution stereo panoramas and validate our results with a user study.
ZeroShotCeres: Zero-Shot Relation Extraction from Semi-Structured Webpages

In many documents, such as semi-structured webpages, textual semantics are augmented with additional information conveyed using visual elements including layout, font size, and color. Prior work on information extraction from semi-structured websites has required learning an extraction model specific to a given template via either manually labeled or distantly supervised data from that template. In this work, we propose a solution for open-domain relation extraction from webpages with a previously unseen template, including from websites with little overlap with existing sources of knowledge for distant supervision and websites in entirely new subject verticals. Our model uses a graph neural network-based approach to build a rich representation of text fields on a webpage and the relationships between them, enabling generalization to new templates. Experiments show this approach provides a 31% F1 gain over a baseline for zero-shot extraction in a new subject vertical.
Evasive subspaces.

Let $V$ denote an $r$-dimensional vector space over $\mathbb{F}_{q^n}$, the finite field of $q^n$ elements. Then $V$ is also an $rn$-dimension vector space over $\mathbb{F}_q$. An $\mathbb{F}_q$-subspace $U$ of $V$ is $(h,k)_q$-evasive if it meets the $h$-dimensional $\mathbb{F}_{q^n}$-subspaces of $V$ in $\mathbb{F}_q$-subspaces of dimension at most $k$. The $(1,1)_q$-evasive subspaces are known as scattered and they have been intensively studied in finite geometry, their maximum size has been proved to be $\lfloor rn/2 \rfloor$ when $rn$ is even or $n=3$. We investigate the maximum size of $(h,k)_q$-evasive subspaces, study two duality relations among them and provide various constructions. In particular, we present the first examples, for infinitely many values of $q$, of maximum scattered subspaces when $r=3$ and $n=5$. We obtain these examples in characteristics $2$, $3$ and $5$.
Latviešu valodas apguvēju korpusa datu ieguves un apstrādes metodoloģijas izstrāde

Popularity of learning Latvian as a foreign language is increasing. Latvian as a foreign language is being taught not only in the higher educational institutions of Latvia, but also in more than 20 universities outside Latvia (Šalme 2008; Šalme 2011; Laizāne 2019). Therefore, corpus-based and corpus-driven teaching materials are crucial for the international students that acquire Latvian both in Latvia and abroad. Since September 2018 the project Development of Learner Corpus of Latvian: methods, tools and applications have been carried out. During the project, based on the already existing experience of designing Learner Corpus of Latvian (LaVA), a corpus of students’ essays with different language backgrounds will be created. The newly developed corpus will be publicly available. Although the corpus creation pipeline includes text collection, digitization, and morphological and error annotation; this article will cover just the first phases of the creation of the corpus – the development of a methodology for data collection and digitization. The agreement form with data subject about data inclusion into the corpus and the metadata (gender, age, mother tongue, language proficiency level) collection form were developed. Guidelines for teachers on preferred topics have been prepared. The corpus is built on an integrated multifunctional platform that provides a single interface for uploading, annotating and search. Moreover, the web platform can also be used for storing scanned copies of essays, comparing texts entered by two independent digitizers, correcting texts, and error-annotated texts and making inter-annotator agreement. At least 1000 essays on different topics from students with different language backgrounds are planned to be included in the LaVA corpus. For data collection, multiple universities and language teachers have been contacted and have agreed to support the corpus creation process by providing it with their students’ previously developed assignments. Collected essays with metadata are handwritten; therefore, they need to be digitized for further data processing steps. The digitization is carried out in three steps: 1) scanning of the assignments and essays, 2) metadata input, 3) text rewriting in digital format. Scanned images of the assignments help to validate data correctness if such concern arises. Metadata is entered manually.
Imaginative nature of children’s play as an informative and creative resource

Introduction. In the modern world, due to the intensive development of high technology, there are many changes that penetrate into the childhood and the world of children’s play. An increasing number of children’s games appear to have a computer format. Playing them, the child begins to alienate from reality, to lose skills in cognitive and creative activities, relying on the help of high technology. One of the reasons of (negative) personality transformations is blocking of functioning of imagination in computer games. The purpose of the article is to study the imaginative nature of children’s play and its potentials, having a cognitive and creative orientation. Materials and Methods. This problem is considered on the basis of phenomenological analysis and direct observation of children’s games. The results of the study showed that the child through immersion in the world of direct play, receives impulses to the knowledge of the world, creativity and moral manifestations. The source of the above is the imagination, which performs the role of instinct, cultural consciousness and reason in human existence. Through imagining, the child imagines, creates and interprets what is being played, expanding the horizons of his being. Discussion and Conclusion. The study of imagination allowed for a demarcation line between direct and computer children’s games. Their key difference lies in the functioning of the imagination, which manifests itself freely in direct games, and in computer – is blocked by high technology and software algorithms. The current situation actualizes the idea of returning to the children’s game, based on direct interaction and communication.
Magic Bounce: Playful Interaction on Superelastic Display

We present an interactive system using a superelastic surface on which to bounce 3D objects. Ellipsoids are especially used as interface owing to the bounce back movement on the surface. Touching information of the objects on the surface including the depth is detected and used for application that is projected on the surface. Several interactive contents are demonstrated as examples of possible interactions using this system.
Anticipatory memory for regular and random patterns.

Real-world scenes elicit anticipatory representation in long-term memory (LTM) and working memory (WM) resulting in boundary extension (BE). Would the same results hold for nonscene patterns of objects? In Experiment 1A (LTM-paradigm), 15 regular or 15 random object-patterns were sequentially presented (10 s each); a boundary-rating-task followed. Both pattern-types elicited BE. Surprisingly, regularity (greater pattern predictability) did not enhance BE. Instead, random patterns more consistently yielded BE. Perhaps random object-groupings enhance BE (e.g., more scene-like). Objects within regular patterns were colored to create object-groupings; this increased BE compared to regular patterns without color groups, but only in 1 of the 2 tests (Experiment 1B, LTM-paradigm). In Experiment 2 (WM-paradigm), a briefly presented pattern, regular or random, was followed by a 2-s masked interval and a boundary-rating-task on each trial. Here, results reversed: patterns now elicited boundary restriction (whether or not a concurrent task had been presented to prevent verbalization). Under time pressure, without real-world layout and meaning, pattern elements may not have cohered into a unified expanse, failing to elicit BE and resulting in loss of peripheral content. We propose 2 classes of visual content that may determine anticipatory representation: content that embellishes space versus content that defines space. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Shaking Hands in Virtual Space: Recovery in Redirected Walking for Direct Interaction between Two Users

Various studies have been conducted to realize realistic direct interaction in the virtual environment. In this study, we focus on a situation wherein two users using the same physical space explore the same virtual environment using redirected walking (RDW) technology. For two users to meet each other in a virtual environment to realize realistic direct interaction, they must simultaneously meet each other in physical space. However, if the RDW algorithm is applied to each user independently, the relative positions and orientations of the two users can be significantly different in the virtual and physical spaces. We present a recovery algorithm that adjusts the relative position and orientation such that they become the same in the two spaces. Our recovery algorithm uses either modified subtle RDW techniques or overt recovery techniques in three cases depending on the relative position and orientation of the two users. Once the recovered state is reached, the two users can go forward to meet each other and directly interact in the virtual and physical spaces simultaneously. Based on the experiment results, we can confirm that the application of our recovery technology to the system increases the user’s satisfaction in usability and the presence of coexistence in the virtual environment with other users.
APPLICATION EXPERT SYSTEM DIAGNOSES CHICKENPOX DISEASE IN CHILDREN USING CASE BASED REASONING METHOD

Chickenpox is an infectious disease which can affect anyone, especially those who have not been immunized. Diagnosis and treatment of chickenpox requires expert staff, but it is still difficult to find experts in this field of disease. To overcome these problems, an expert system application can be built using the Case Based Reasoning (CBR) method. This study uses the CBR method to analyze 9 (nine) symptoms to diagnose 6 (six) types of chickenpox. The diagnostic process using the CBR method is done by calculating the similarity value between old cases with new case. Old cases are a number of records that have occurred in the past and have been proven true. The diagnosis result is disease in old case that has the highest similarity value with the tested case. Application can be used to diagnose children chickenpox by displaying the results of diagnosis, consists of disease name, similarity value and solution based on disease that has the highest similarity value with old cases.
The Effects of Grit on the Deliberate Practice and Goal Commitment of Students in Physical Education Classes

The purpose of this study was to investigate the effects of students’ grit on their deliberate practice and goal commitment in Physical Education classes and the effects of deliberate practice on their goal commitment. For this purpose, the investigator set middle and high school students in Gyeonggi-do as the population and selected 319 through convenience sampling. Collected data was put to the multiple regression analysis with the PASW Ver 18.0 statistical program. The findings were as follows: First, perseverance of effort, a subfactor of grit, had statistically significant effects on the deliberate practice of students in Physical Education classes, but consistency of interest had no significant effects on it; secondly, deliberate practice had statistically significant impacts on their goal commitment in Physical Education classes; and finally, perseverance of effort, a subfactor of grit, had statistically significant influences on the goal commitment of students in Physical Education classes, but consistency of interest had no significant influences on it.