Reviewerly/exHarmony huggingface dataset:
Word Embeddings for Semantic Resemblance of Substantial Text Data: A Comparative Study

Extraction of semantic resemblance from text dataKabir, Kazi Lutful is an important task in the field of text mining. Out of several approaches in this direction, strategies based on distributional semantics are found to be reasonably effective. A number of suchAlam, Fardina Fathmiul semantic word embeddings of considerably high quality are publicly available. The aim of this article is to compare a few of those both qualitatively and quantitatively and find which one is moreIslam, Anika Binte suitable for dealing with a large amount of text data. The techniques considered have also been contrasted as superior to traditional semantic analyses.
Localized Interactive Instance Segmentation

In current interactive instance segmentation works, the user is granted a free hand when providing clicks to segment an object; clicks are allowed on background pixels and other object instances far from the target object. This form of interaction is highly inconsistent with the end goal of efficiently isolating objects of interest. In our work, we propose a clicking scheme wherein user interactions are restricted to the proximity of the object. In addition, we propose a novel transformation of the user-provided clicks to generate a weak localization prior on the object which is consistent with image structures such as edges, textures etc. We demonstrate the effectiveness of our proposed clicking scheme and localization strategy through detailed experimentation in which we raise state-of-the-art on several standard interactive segmentation benchmarks.
Exploration and Research on English Linguistics Teaching Based on the Perspective of Constructivism

In the current education system in China, the English major is an important part of the education system. As the exchanges between China’s and foreign countries have deepened, the importance of English education has become more prominent. Therefore, it is of great signifcance to improve students’ ability to use spoken English. However, as far as the teaching model in China’s current universities and colleges is concerned, there are still many problems, and there is an urgent need for optimization and rectifcation. Based on this, this paper is based on the theory of constructivism, and deeply explores the solution to the problem of the current China’s English linguistics teaching model and hope this paper can provide a meaningful reference for China’s English education.
E-Learning resource reuse, based on bilingual ontology annotation and ontology mapping

Explicit semantic modelling of the knowledge has an important role in e-Learning.Explicit semantic relations may help users of educational content to understand the domain or to navigate overall e-Learning system.Links to similar content are important for finding additional learning resources.Explicit links between concepts are useful for easy comprehension and understanding the subject.Today international languages (as English and Russian) will propose the highest quality learning content, but others prefer to use learning resources, information and knowledge, presented in his native language.Ontologies are more and more frequently used in e-Learning systems, to support almost all e-Learning tasks and resource reuse.*Author for correspondence e-Learning standards, learners, learning content, learning process, and more, are modeled, using ontologies in many projects and in such a way personalization, flexibility, and resource reuse are achieved [1].But almost all ontologies developed and tested in e-Learning projects use lexicalization only in English language.These ontologies are not usable for describing and annotation of non-English learning content.The semantic representation of metadata in almost all-natural languages are needed to support search and delivery of the most suitable resources for every learner and learning tack.Recently, the increased usefulness of ontologies for practical applications has led to building some ontologies that use other language as a base human language (for representing labels, comments, textual definitions in ontologies) [2][3][4].But most ontologies in e-learning projects continue to use English-based lexicalization.
306.2: Analyzing network of organ sales and trafficking using web scraping data.

Background: Many countries have been enforcing domestic law against kidney sales to make sure that vulnerable people are not misused. Concurrently, the transnational transplant tourism has been on the rise and organ trafficking cases have been identified worldwide. Thus, several regional hubs are emerging in most part of the world including South Asia, Central America, Middle East, and East Asia. The South Asian hub comprised of India, Pakistan, Nepal, and Bangladesh is one of the most discussed ones. This is a complex network of organ trade that includes a complex relationship among the buyers, sellers, and brokers at the international borders. These brokers mostly arrange buyers and sellers from different countries and mobilize them to another country for surgery so that they can bypass the domestic laws. However, there have been no scientific studies thus far to identify these networks and patterns of transactions. Moreover, this field lacks scientific tools and empirical data that can help understand the extent of the organ trade problems. This paper aims to develop a web base algorithm to gather data from newspaper articles from the regional hubs of organ trade and analyze the data. Methods: We use a web scraping method to collect articles from regional newspaper websites based on very carefully selected search terms: kidney + “trafficking,” “trade,” “buying,” “selling,” “market,” “bazaar,” “sales,” and “illegal transplant.” To execute this search actions, we use Python libraries: Selenium, NLTK, GeoText, Requests, and BeautifulSoup. We extracted article fragments from the gathered universe which contain locations (cities, states, countries) and variations on buying, selling, surgery (synonyms/word forms) to identify networks between the three mentioned in the articles. After removing the duplicates, we check identified networks by hand. Results: We have collected more than 4 thousand unique new paper articles and after analyzing them we have found approximately 200 unique mentions of networks of buyers, sellers, and surgery countries. The results help to clearly separate the country which has most nodes of either buyers, sellers, or surgeries or a combination of them. We find that while most surgeries are taken place in India, the sellers are mainly from Nepal and Bangladesh. However, the buyers are mostly from South Asian economies without a significant pattern, there are also buyers from the Middle East and Gulf countries. The surgeries have also taken place in Sri Lanka and Thailand. Conclusions: The results support our initial hypothesis that there are strong networks of organ trade comprised of transnational actors. This machine learning approach to complement the scarce data on organ trade appears to be a promising tool and can be expanded to analyze networks of similar illicit trades around the world. This research was funded by National Science Foundations (NSF).
NeuronUnityIntegration2.0. A Unity Based Application for Motion Capture and Gesture Recognition

NeuronUnityIntgration2.0 (demo video is avilable at http://tiny.cc/u1lz6y) is a plugin for Unity which provides gesture recognition functionalities through the Perception Neuron motion capture suit. The system offers a recording mode, which guides the user through the collection of a dataset of gestures, and a recognition mode, capable of detecting the recorded actions in real time. Gestures are recognized by training Support Vector Machines directly within our plugin. We demonstrate the effectiveness of our application through an experimental evaluation on a newly collected dataset. Furthermore, external applications can exploit NeuronUnityIntgration2.0's recognition capabilities thanks to a set of exposed API.
A Journey Towards Fully Immersive Media Access

Universal media access (UMA) as proposed almost two decades ago is now reality. We can generate, distribute, share, and consume any media content, anywhere, anytime, and with/on any device. A technical breakthrough was the adaptive streaming over HTTP resulting in the standardization of MPEG Dynamic Adaptive Streaming over HTTP (DASH), which is now successfully deployed in a plethora of environments. The next big thing in adaptive media streaming is virtual reality applications, and specifically, omnidirectional (360-degree) media streaming, which is currently built on top of the existing adaptive streaming ecosystems. This tutorial provides a detailed overview of adaptive streaming of both traditional and omnidirectional media. The tutorial focuses on the basic principles and paradigms for adaptive streaming as well as on already deployed content generation, distribution, and consumption workflows. Additionally, the tutorial provides insights into standards and emerging technologies in the adaptive streaming space. Finally, the tutorial includes the latest approaches for immersive media streaming enabling six Degrees of Freedom (6DoF) DASH through Point Cloud Compression (PCC) and concludes with open research issues and industry efforts in this domain.
All your data will be held against you: secondary use of data from personal genomics and wearable tech

Personal data is everywhere. Individuals leave data trails through their use of various technologies, often without their knowledge or consent. This data can be repurposed by businesses for the purposes of marketing or other secondary research. However, it is also being used by other entities, such as the pharmaceutical and insurance industries, and it is beginning to be used by law enforcement in the context of legal proceedings and criminal investigations. This is an exploratory chapter, which discusses the impact of electronic contracts and privacy policies on individuals’ rights in their personal data. This discussion is centred on two examples: direct-to-consumer genetic tests (DTC or personal genomics); and wearable devices. Both these industries are examples of new technologies that have created new markets and both involve the collection and processing of consumers’ data that has the potential to be reused for a wide range of secondary purposes. Often contracts and privacy policies are relied upon by businesses to govern their relationships with consumers and also to allow for reuse of such data and currently, business practices may often not be in compliance with the EU’s General Data Protection Regulation (GDPR).
Online Meta-Learning on Non-convex Setting.

The online meta-learning framework is designed for the continual lifelonglearning setting. It bridges two fields: meta-learning which tries to extractprior knowledge from existing tasks for fast learning of future tasks, andonline-learning which focuses on the sequential setting in which problems arerevealed one by one. In this paper, we generalize the original framework fromconvex to non-convex setting, and introduce the local regret as the alternativeperformance measure. We then apply this framework to stochastic settings, andshow theoretically that it enjoys a logarithmic local regret, and is robust toany hyperparameter initialization. The empirical test on a real-world taskdemonstrates its superiority compared with traditional methods.
Spatio-Temporal Residual Networks for Slide Transition Detection in Lecture Videos

In this paper, we present an approach for detecting slide transitions in lecture videos by introducing the spatio-temporal residual networks.Given a lecture video which records the digital slides, the speaker, and the audience by multiple cameras, our goal is to find keyframes where slide content changes.Since temporal dependency among video frames is important for detecting slide changes, 3D Convolutional Networks has been regarded as an efficient approach to learn the spatio-temporal features in videos.However, 3D ConvNet will cost much training time and need lots of memory.Hence, we utilize ResNet to ease the training of network, which is easy to optimize.Consequently, we present a novel ConvNet architecture based on 3D ConvNet and ResNet for slide transition detection in lecture videos.Experimental results show that the proposed novel ConvNet architecture achieves the better accuracy than other slide progression detection approaches.
Personalized Reviews Based on Aspect Analysis and Polarity

Before e-commerce site users make a decision to purchase a product, first of all, they read the reviews on the sites. Based on the votes which website owners receive from users that read reviews, the site owners choose the best reviews. Nevertheless, it has recently been found that redundant information may be contained in such reviews. Thus, it has been recommended that review selection is made based on coverage (i.e., the number of entity aspects) enveloped by the reviewers. This paper focuses on additional improving the topmost review group through the use of customization criteria. This driven by the relevance of product aspects may differ among users, and users prefer to pay attention to the aspects that are more relevant to them. The main aim of this work is to give priority to the individual preferences and sentiments of users in terms of reviews. This is achieved through the selection of a personalized top review set by Personalized Aspect Analysis Model (PAAM), which covers reviews about aspects that are of more relevance to the user. The effectiveness of the proposed approach on computing PAAM with high-level coverage, top quality, and relevance of the subjects that are of importance to the customer are demonstrated through the experimental evaluation.
The development of an ontology model for early identification of children with specific learning disabilities

&lt;span lang="EN-US"&gt;Ontology-based knowledge representation is explored in special education environment as not much attention has been given to the area of specific learning disabilities such as dyslexia, dysgraphia and dyscalculia. Therefore, this paper aims to capture the knowledge in special education domain, represent the knowledge using ontology-based approach and make it efficient for early identification of children who might have specific learning disabilities. In this paper, the step-by-step development process of the ontology is presented by following the five phases of ontological engineering approach, which consists of specification, conceptualization, formalization, implementation, and maintenance. The details of the ontological model’s content and structure is built and the applicability of the ontology for early identification and recommendation is demonstrated.&lt;/span&gt;
Play, Pause, and Skip: Touch Screen Media Player Interaction on the Move

Our active lifestyles see us playing, pausing, and skipping through life all the while our phones are in our hands. For many, completing the daily grind requires regular audio and visual media accompaniment and for this we interact with our phones as we skip, run, and jump. In this respect, a unique form of digital library is our mobile media player of choice. These media players serve as both the interface for listening to and watching these audio and visual media as well as the media library and storage. We argue therefore that the interface design considerations of the media library as well as the media interaction require user centered investigation. We tested button placement variations and analyzed the user preferences as well as user interaction with these mobile media player prototypes while on the move. Early insights suggest users prefer what they are most accustomed to, yet issues of accuracy with interface designs that are unfamiliar require further investigation.
Trajectory Tracking with MPC by Maximizing the Autocorrelation of Heading Angle

In this paper, a novel quadratic finite horizon cost function was proposed to solve the trajectory tracking problem of a wheeled mobile robot. This cost function accounts for the correlation of past heading angle state of the kinematic model of a wheeled mobile robot to smooth out the trajectory following behavior. A first-order polynomial is fitted to the past heading angles and future heading angles in the control horizon are predicted with this first-order polynomial; when the cost function is minimized with model predictive control the mobile robot makes a compromise between predicted heading angles and the future states on the reference path. It has been shown that with the use of the proposed model predictive control, the autocorrelation of the heading angle signal of a wheeled mobile robot is maximized. Thus, a smooth trajectory tracking behavior is achieved on the reference path.
Teleportation improvement by noiseless linear amplification

We address de-Gaussification of continuous variables Gaussian states by optimal non-deterministic noiseless linear amplifier (NLA) and analyze in details the properties of the amplified states. In particular, we investigate the entanglement content and the non-Gaussian character for the class of non-Gaussian entangled state obtained by using NL-amplification of two-mode squeezed vacua (twin-beam, TWB). We show that entanglement always increases, whereas improved EPR correlations are observed only when the input TWB has low energy. We then examine a Braunstein-Kimble-like protocol for the teleportation of coherent states, and compare the performances of TWB-based teleprotation with those obtained using NL-amplified resources. We show that teleportation fidelity and security may be improved for a large range of NLA parameters (gain and threshold).
Docria: Processing and Storing Linguistic Data with Wikipedia

The availability of user-generated content has increased significantly over time. Wikipedia is one example of a corpora which spans a huge range of topics and is freely available. Storing and processing these corpora requires flexible documents models as they may contain malicious and incorrect data. Docria is a library which attempts to address this issue by providing a solution which can be used with small to large corpora, from laptops using Python interactively in a Jupyter notebook to clusters running map-reduce frameworks with optimized compiled code. Docria is available as open-source code.
Estimation of Emotions Evoked by Images Based on Multiple Gaze-based CNN Features

This paper presents a method for estimating emotions evoked by watching images based on multiple visual features considering relationship with gaze information. The proposed method obtains multiple visual features from multiple middle layers of a Convolutional Neural Network. Then the proposed method newly derives their gaze-based visual features maximizing correlation with gaze information by using Discriminative Locality Preserving Canonical Correlation Analysis. The final estimation result is calculated by integrating multiple estimation results obtained from these gaze-based visual features. Consequently, successful emotion estimation becomes feasible by using such multiple estimation results which correspond to different semantic levels of target images.
A Dirichlet Process Mixture Model for Non-Ignorable Dropout

Longitudinal cohorts are a valuable resource for studying HIV disease progression; however, dropout is common in these studies.Subjects often fail to return for visits due to disease progression, loss to follow-up, or death.When dropout depends on unobserved outcomes, data are missing not at random, and results from standard longitudinal data analyses can be biased.Several methods have been proposed to adjust for non-ignorable dropout; however, many of these approaches rely on parametric assumptions about the distribution of dropout times and the functional form of the relationship between the outcome and dropout time.More flexible approaches may be needed when the distribution of dropout times does not follow a known distribution or violates proportional hazards assumptions, or when the relationship between the outcome and dropout times does not have a simple polynomial form.We propose a Bayesian semi-parametric Dirichlet process mixture model to flexibly model the relationship between dropout time and the outcome and show that more accurate inference can be obtained by nonparametrically modeling the distribution of subject-specific effects as well as the distribution of dropout times.Results from simulation studies as well as an application to a longitudinal HIV cohort study database illustrate the strengths of our Bayesian semi-parametric approach.
Decoherence Induced by a Spin Chain: Role of Initial Prepared States

We study the decoherence process induced by a spin chain environment on a central spin consisting of R spins and we apply it on the dynamics of quantum correlations (QCs) of three interacting qubits. To see the impact of the initially prepared state of the spin chain environment on the decoherence process, we assume the spin chain environment prepared in two main ways, namely, either the ground state or the vacuum state in the momentum space. We develop a general heuristic analysis when the spin chain environment is prepared in these states, to understand the decoherence process against the physical parameters. We show that the decoherence process is mainly determined by the choice of the initially prepared state, the number of spins of the chain, the coupling strength, the anisotropy parameter and the position from the quantum critical point. In fact, in the strong coupling regime, the decoherence process does not appear for the environment prepared in the vacuum state and it behaves oscillatory in the case of evolution from the ground state. On the other hand, in the weak coupling regime and far from the quantum critical point, decoherence induced by the ground state is weaker than that of the vacuum state. Finally, we show that QCs are completely shielded from decoherence in the case of evolution from the W state and obey the same dynamics as the decoherence factors for the GHZ state.
Bayesian nonstationary Gaussian process modeling: the BayesNSGP package for R

In spite of the diverse literature on nonstationary spatial modeling and approximate Gaussian process (GP) methods, there are no general approaches for conducting fully Bayesian inference for moderately sized nonstationary spatial data sets on a personal laptop. For statisticians and data scientists who wish to learn about spatially-referenced data and conduct posterior inference and prediction with appropriate uncertainty quantification, the lack of such approaches and corresponding software is a significant limitation. In this paper, we develop methodology for implementing formal Bayesian inference for a general class of nonstationary GPs. Our novel approach uses pre-existing frameworks for characterizing nonstationarity in a new way that is applicable for small to moderately sized data sets via modern GP likelihood approximations. Posterior sampling is implemented using flexible MCMC methods, with nonstationary posterior prediction conducted as a post-processing step. We demonstrate our novel methods on two data sets, ranging from several hundred to several thousand locations, and compare our methodology with related statistical methods that provide off-the-shelf software. All of our methods are implemented in the freely available BayesNSGP software package for R.