Reviewerly/exHarmony huggingface dataset:
Making linear prediction perform like maximum likelihood in Gaussian autoregressive model parameter estimation

A two-stage method for the parameter estimation of Gaussian autoregressive models is proposed. The proposed first stage is an improved version of the conventional forward-backward prediction method and can be interpreted as its weighted version with the weights derived from the arithmetic mean of the log-likelihood functions for different conditioning cases. The weighted version is observed to perform better than the conventional forward-backward prediction method and other linear prediction based methods (correlation method, covariance method, Burg's method etc.) in terms of attained likelihood value. The proposed second stage uses the estimate of the first stage as the initial condition and approximates the highly non-linear log-likelihood function with a quadratic function around the initial estimate. The optimization of the quadratic cost function yields the optimal perturbation vector that locally maximizes the likelihood in the vicinity of the initial condition. The proposed method is compared with other methods and it has been observed that the likelihood value attained at the end of two-stages is almost identical to the value attained by higher complexity numerical-search based optimization tools in a wide range of experiments. The maximum likelihood-like performance at a significantly lower implementation cost makes the proposed method especially valuable for the applications with short data-records and limited computational resources.
sPortfolio: Stratified Visual Analysis of Stock Portfolios

Quantitative Investment, built on the solid foundation of robust financial theories, is at the center stage in investment industry today. The essence of quantitative investment is the multi-factor model, which explains the relationship between the risk and return of equities. However, the multi-factor model generates enormous quantities of factor data, through which even experienced portfolio managers find it difficult to navigate. This has led to portfolio analysis and factor research being limited by a lack of intuitive visual analytics tools. Previous portfolio visualization systems have mainly focused on the relationship between the portfolio return and stock holdings, which is insufficient for making actionable insights or understanding market trends. In this paper, we present sPortfolio, which, to the best of our knowledge, is the first visualization that attempts to explore the factor investment area. In particular, sPortfolio provides a holistic overview of the factor data and aims to facilitate the analysis at three different levels: a Risk-Factor level, for a general market situation analysis; a Multiple-Portfolio level, for understanding the portfolio strategies; and a Single-Portfolio level, for investigating detailed operations. The system's effectiveness and usability are demonstrated through three case studies. The system has passed its pilot study and is soon to be deployed in industry.
Accountable Outsourcing Location-Based Services With Privacy Preservation

With the enhancement of the positioning function of mobile devices and the upgrade of communication networks, location-based service (LBS) has become an important application of mobile devices. Among the numerous researches on location privacy preservation, cloud-based location privacy preservation has become a hot topic, but it undoubtedly brings new problems such as data confidentiality and user privacy disclosure. This paper proposes an accountable outsourced LBS privacy-preserving scheme. In the outsourcing scenario, in order to make users interact with cloud server to obtain query data, firstly we construct location hierarchical index and attribute hierarchical index based on Bloom Filter, and secondly we divide one region into atomic regions using Hilbert Curve, both of which ensure the privacy of query and improve the efficiency of query. At last, we realize the sharing of encrypted data among different users by accountable proxy re-encryption (APRE) technology, which can effectively suppress the abuse of proxy re-encryption key. We demonstrate the correctness of the proposed scheme through security analysis, and show the effectiveness of the scheme through performance analysis.
Development and implementation of a dynamically updated big data intelligence platform from electronic health records for nasopharyngeal carcinoma research

To develop a big data intelligence platform for secondary use of electronic health records (EHRs) data to facilitate research for nasopharyngeal cancer (NPC).This project was launched in 2015 and carried out by the cooperation of an academic cancer centre and a technology company. Patients diagnosed with NPC at Sun Yat-sen University Cancer Centre since January 2008 were included in the platform. Standard data elements were established to defined 981 variables for the platform. For each patient, data from 13 EHRs systems were extracted, integrated, structurized and normalized. Eight functional modules were constructed for the platform to facilitate the investigators to identify eligible patients, establish research projects, conduct statistical analysis, track the follow-up, search literature, etc.From January 2008 to December 2018, 54,703 patients diagnosed with NPC were included. Of these patients, 39,058 (71.4%) were male, and 15,645 (28.6%) were female; median age was 47 (interquartile range, 39-55) years. Of 981 variables, 341 were obtained from data structurization and normalization, of which 68 were generated by interacting multiple data sources via well-defined logical rules. The average precision rate, recall rate and F-measure for 341 variables were 0.97 ± 0.024, 0.92 ± 0.030, and 0.94 ± 0.027 respectively. The platform is regularly updated every seven days to include new patients and add new data for existing patients. Up to now, eight big data-driven retrospective studies have been published from the platform.Our big data intelligence platform demonstrates the feasibility of integrating EHRs data of routine healthcare, and offers an important perspective on real-world study of NPC. The continued efforts may be focus on data sharing among multiple hospitals and publicly releasing of data files.Our big data intelligence platform is the first disease-specific data platform for NPC research. It incorporates comprehensive EHRs data from routine healthcare, which can facilitate real-world study of NPC in risk stratification, decision-making and comorbidities management.
Video Question Answering via Knowledge-based Progressive Spatial-Temporal Attention Network

Visual Question Answering (VQA) is a challenging task that has gained increasing attention from both the computer vision and the natural language processing communities in recent years. Given a question in natural language, a VQA system is designed to automatically generate the answer according to the referenced visual content. Though there recently has been much intereset in this topic, the existing work of visual question answering mainly focuses on a single static image, which is only a small part of the dynamic and sequential visual data in the real world. As a natural extension, video question answering (VideoQA) is less explored. Because of the inherent temporal structure in the video, the approaches of ImageQA may be ineffectively applied to video question answering. In this article, we not only take the spatial and temporal dimension of video content into account but also employ an external knowledge base to improve the answering ability of the network. More specifically, we propose a knowledge-based progressive spatial-temporal attention network to tackle this problem. We obtain both objects and region features of the video frames from a region proposal network. The knowledge representation is generated by a word-level attention mechanism using the comment information of each object that is extracted from DBpedia. Then, we develop a question-knowledge-guided progressive spatial-temporal attention network to learn the joint video representation for video question answering task. We construct a large-scale video question answering dataset. The extensive experiments based on two different datasets validate the effectiveness of our method.
ARCHANGEL: Tamper-Proofing Video Archives Using Temporal Content Hashes on the Blockchain

We present ARCHANGEL; a novel distributed ledger based system for assuring the long-term integrity of digital video archives. First, we describe a novel deep network architecture for computing compact temporal content hashes (TCHs) from audio-visual streams with durations of minutes or hours. Our TCHs are sensitive to accidental or malicious content modification (tampering) but invariant to the codec used to encode the video. This is necessary due to the curatorial requirement for archives to format shift video over time to ensure future accessibility. Second, we describe how the TCHs (and the models used to derive them) are secured via a proof-of-authority blockchain distributed across multiple independent archives. We report on the efficacy of ARCHANGEL within the context of a trial deployment in which the national government archives of the United Kingdom, Estonia and Norway participated.
Recursive Context-Aware Lexical Simplification

This paper presents a novel architecture for recursive context-aware lexical simplification, REC-LS, that is capable of (1) making use of the wider context when detecting the words in need of simplification and suggesting alternatives, and (2) taking previous simplification steps into account.We show that our system outputs lexical simplifications that are grammatically correct and semantically appropriate, and outperforms the current state-of-theart systems in lexical simplification.
YouTube as a Media in English Language Teaching (ELT) Context: Teaching Procedure Text

Media is the one of tools that can help teacher in teaching and learning process in a class, especially in EFL classrooms. There are two kinds of media, such as visual and audio.In this article, the writer wants to use video as media in teaching-learning process, especially videos in YouTube. YouTube isn’t only have about fun videos, in YouTube can also use as learning media, in there we also can find so many education videos in every level, start from beginner, intermediate, and also for advanced level. In line with that, the writer wants to give video about how to make or do something or the other word we can say it “procedure”. The learning outcome is to make the students know about procedure text, especially in how to make and how to identify. Then, this article also wants to know YouTube is useful or not as learning media if we make it to support us in teaching and learning process.
Multi-Input Multi-Output Sequence Labeling for Joint Extraction of Fact and Condition Tuples from Scientific Text

Condition is essential in scientific statement.Without the conditions (e.g., equipment, environment) that were precisely specified, facts (e.g., observations) in the statements may no longer be valid.Existing ScienceIE methods, which aim at extracting factual tuples from scientific text, do not consider the conditions.In this work, we propose a new sequence labeling framework (as well as a new tag schema) to jointly extract the fact and condition tuples from statement sentences.The framework has (1) a multi-output module to generate one or multiple tuples and (2) a multi-input module to feed in multiple types of signals as sequences.It improves F1 score relatively by 4.2% on BioNLP2013 and by 6.2% on a new bio-text dataset for tuple extraction.
CUNI Submission for Low-Resource Languages in WMT News 2019

This paper describes the CUNI submission to the WMT 2019 News Translation Shared Task for the low-resource languages: Gujarati-English and Kazakh-English.We participated in both language pairs in both translation directions.Our system combines transfer learning from different high-resource language pair followed by training on backtranslated monolingual data.Thanks to the simultaneous training in both directions, we can iterate the backtranslation process.We are using the Transformer model in a constrained submission.
Learning by active nonlinear diffusion

This article proposes an active learning method for high-dimensional data, based on intrinsic data geometries learned through diffusion processes on graphs. Diffusion distances are used to parametrize low-dimensional structures on the dataset, which allow for high-accuracy labelings with only a small number of carefully chosen training labels. The geometric structure of the data suggests regions that have homogeneous labels, as well as regions with high label complexity that should be queried for labels. The proposed method enjoys theoretical performance guarantees on a general geometric data model, in which clusters corresponding to semantically meaningful classes are permitted to have nonlinear geometries, high ambient dimensionality, and suffer from significant noise and outlier corruption. The proposed algorithm is implemented in a manner that is quasilinear in the number of unlabeled data points, and exhibits competitive empirical performance on synthetic datasets and real hyperspectral remote sensing images.
Tackling Long-Tailed Relations and Uncommon Entities in Knowledge Graph Completion

For large-scale knowledge graphs (KGs), recent research has been focusing on the large proportion of infrequent relations which have been ignored by previous studies.For example few-shot learning paradigm for relations has been investigated.In this work, we further advocate that handling uncommon entities is inevitable when dealing with infrequent relations.Therefore, we propose a meta-learning framework that aims at handling infrequent relations with few-shot learning and uncommon entities by using textual descriptions.We design a novel model to better extract key information from textual descriptions.Besides, we also develop a novel generative model in our framework to enhance the performance by generating extra triplets during the training stage.Experiments are conducted on two datasets from real-world KGs, and the results show that our framework 1 outperforms previous methods when dealing with infrequent relations and their accompanying uncommon entities.
Multi-Head Attention with Diversity for Learning Grounded Multilingual Multimodal Representations

With the aim of promoting and understanding the multilingual version of image search, we leverage visual object detection and propose a model with diverse multi-head attention to learn grounded multilingual multimodal representations.Specifically, our model attends to different types of textual semantics in two languages and visual objects for finegrained alignments between sentences and images.We introduce a new objective function which explicitly encourages attention diversity to learn an improved visual-semantic embedding space.We evaluate our model in the German-Image and English-Image matching tasks on the Multi30K dataset, and in the Semantic Textual Similarity task with the English descriptions of visual content.Results show that our model yields a significant performance gain over other methods in all of the three tasks.
Linguistically-Driven Strategy for Concept Prerequisites Learning on Italian

We present a new concept prerequisite learning method for Learning Object (LO) ordering that exploits only linguistic features extracted from textual educational resources.The method was tested in a cross-and indomain scenario both for Italian and English.Additionally, we performed experiments based on a incremental training strategy to study the impact of the training set size on the classifier performances.The paper also introduces ITA-PREREQ, to the best of our knowledge the first Italian dataset annotated with prerequisite relations between pairs of educational concepts, and describe the automatic strategy devised to build it.
A Baseline for Multi-Label Image Classification Using an Ensemble of Deep Convolutional Neural Networks

Recent studies on multi-label image classification have focused on designing more complex architectures of deep neural networks such as the use of attention mechanisms and region proposal networks. Although performance gains have been reported, the backbone deep models of the proposed approaches and the evaluation metrics employed in different works vary, making it difficult to compare fairly. Moreover, due to the lack of properly investigated baselines, the advantage introduced by the proposed techniques are often ambiguous. To address these issues, we make a thorough investigation of the mainstream deep convolutional neural network architectures for multi-label image classification and present a strong baseline. With the use of proper data augmentation techniques and model ensembles, the basic deep architectures can achieve better performance than many existing more complex ones on three benchmark datasets, providing great insight for the future studies on multi-label image classification.
Pitfalls in the Evaluation of Sentence Embeddings

Deep learning models continuously break new records across different NLP tasks.At the same time, their success exposes weaknesses of model evaluation.Here, we compile several key pitfalls of evaluation of sentence embeddings, a currently very popular NLP paradigm.These pitfalls include the comparison of embeddings of different sizes, normalization of embeddings, and the low (and diverging) correlations between transfer and probing tasks.Our motivation is to challenge the current evaluation of sentence embeddings and to provide an easy-to-access reference for future research.Based on our insights, we also recommend better practices for better future evaluations of sentence embeddings.
R-Transformer Network Based on Position and Self-Attention Mechanism for Aspect-Level Sentiment Classification

Aspect-level sentiment classification (ASC) is a research hotspot in natural language processing, which aims to infer the sentiment polarity of a particular aspect in an opinion sentence.There are three main influence factors in the aspect-level sentiment classification: the semantic information of the context; the interaction information of the context and aspect; the position information between the aspect and the context.Some researchers have proposed way to solve aspect-level sentiment classification.However, previous work mainly used the average vector of the aspect to calculate the attention score of the context, which introduced the influence of noise words.Moreover, these attention-based approaches simply used relative positions to calculate positional information for contextual and aspect terms and did not provided better semantic information.Based on these above questions, in this paper, we propose the PSRTN model.Firstly, obtaining the position-aware influence propagate between words and aspects by Gaussian kernel and generating the influence vector for each context word.Secondly, capturing global and local information of the context by the R-Transformer, and using the self-attention mechanism to obtain the keywords in the aspect.Finally, context representation of a particular aspect is generated for classification.In order to evaluate the validity of the model, we conduct experiments on SemEval2014 and Twitter.The results show that the accuracy of the PSRTN model can reach 83.8%, 80.9%, and 75.1% on three data sets, respectively.
Cycle-Consistent Domain Adaptive Faster RCNN

Traditional object detection methods always assume both of the training and test data follow the same distribution, but this cannot always be guaranteed in the real world. Domain adaptive methods are proposed to handle this situation. However, existing methods generally ignore the semantic alignment at feature level when they try to align data distributions between source and target domains. In this paper, we propose a novel unsupervised cross-domain object detection method, named Cycle-consistent domain Adaptive Faster RCNN (CA-FRCNN). A couple of Generative Adversarial Nets (GAN) are used to make the features from two domains consistent at both data distribution level and semantic level. Specifically, features from source domain are transformed to the target domain. Then they are aligned with features from target domain. At the same time, target features are handled with similar operations. Furthermore, a cycle-consistent loss is optimized to guarantee that the semantic information is preserved before and after the style translations. In the end, identity module is used to make the feature in source domain equivalent to the reconstructed feature output by the source generator, whose input is from the source domain. For the feature in the target domain, similar identity is required. Experiments on multiple datasets show that our method performs better than previous state-of-the-art methods.
Image registration using two-layer cascade reciprocal pipeline and context-aware dissimilarity measure

We present a method based on a robust two-layer cascade reciprocal pipeline (TCPR) and the context-aware dissimilarity measure (CADM) for feature-based image registration. We first build the two-layer pipeline to find the optimal correspondence and to achieve the image transformation. In the first layer, the neighborhood structures of point sets are used to find and register reliable feature point sets. In the second layer, we create the point-to-point neighborhood structure to recover the remaining inliers for image registration. The CADM with scaling, translation and rotation invariance is then designed for dissimilarity evaluation. Extensive experiments regarding feature matching, image registration and retrieval on real and synthesized images demonstrate that our method can achieve a better accuracy–efficiency tradeoff compared with ten state-of-the-art methods.
HOBBIT: A platform for benchmarking Big Linked Data

An increasing number of solutions aim to support the steady increase of the number of requirements and requests for Linked Data at scale. This plethora of solutions leads to a growing need for objective means that facilitate the selection of adequate
SRNet: Structured Relevance Feature Learning Network From Skeleton Data for Human Action Recognition

In recent years, human action recognition based on skeleton information has recently drawn increasing attention with published large-scale skeleton datasets.The most crucial factors for this task line in two aspects: the intra-frame representation for joint co-occurrences and the inter-frame representation for skeletons' temporal evolution.The most effective ways focus on spontaneous feature extraction by using deep learning.However, they ignore the structure information of skeleton joints and the correlation between two different skeleton joints for human action recognition.In this paper, we do not simply treat the joints position information as unordered points.Instead, we propose a novel data reorganizing strategy to represent the global and local structure information of human skeleton joints.Meanwhile, we also employ the data mirror to increase the relationship between skeleton joints.Based on this design, we proposed an end-toend multi-dimensional CNN network (SRNet) to fully consider the spatial and temporal information to learn the feature extraction transform function.Specifically, in this CNN network, we employ different convolution kernels on different dimensions to learn skeleton representation to make the most of human structural information to generate robust features.Finally, we compare with other state-of-the-art on action recognition datasets like NTU RGB+D, PKU-MMD, SYSU, UT-Kinect, and HDM05.The experimental results also demonstrate the superiority of our method.