Reviewerly/exHarmony huggingface dataset:
<scp>Swat</scp>: A system for detecting salient Wikipedia entities in texts

Abstract We study the problem of entity salience by proposing the design and implementation of Swat , a system that identifies the salient Wikipedia entities occurring in an input document. Swat consists of several modules that are able to detect and classify on‐the‐fly Wikipedia entities as salient or not, based on a large number of syntactic, semantic, and latent features properly extracted via a supervised process, which has been trained over millions of examples drawn from the New York Times corpus. The validation process is performed through a large experimental assessment, eventually showing that Swat improves known solutions over all publicly available datasets. We release Swat via an API that we describe and comment in the paper to ease its use in other software.
Sum–product graphical models

This paper introduces a probabilistic architecture called sum–product graphical model (SPGM). SPGMs represent a class of probability distributions that combines, for the first time, the semantics of probabilistic graphical models (GMs) with the evaluation efficiency of sum–product networks (SPNs): Like SPNs, SPGMs always enable tractable inference using a class of models that incorporate context specific independence. Like GMs, SPGMs provide a high-level model interpretation in terms of conditional independence assumptions and corresponding factorizations. Thus, this approach provides new connections between the fields of SPNs and GMs, and enables a high-level interpretation of the family of distributions encoded by SPNs. We provide two applications of SPGMs in density estimation with empirical results close to or surpassing state-of-the-art models. The theoretical and practical results demonstrate that jointly exploiting properties of SPNs and GMs is an interesting direction of future research.
Resource Mention Extraction for MOOC Discussion Forums

In discussions hosted on discussion forums for massive online open courses (MOOCs), references to online learning resources are often of central importance.They contextualize the discussion, anchoring the discussion participants' presentation of the issues and their understanding.However, they are usually mentioned in free text, without appropriate hyperlinking to their associated resource.Automated learning resource mention hyperlinking and categorization will facilitate discussion and searching within the MOOC forums and also benefit the contextualization of such resources across disparate views.We propose the novel problem of learning resource mention identification in MOOC forums, i.e., to identify resource mentions in discussions and classify them into pre-defined resource types.As this is a novel task with no publicly available data, we first contribute a large-scale labeled dataset-dubbed the forum resource mention (FoRM) dataset-to facilitate our current research and future research on this task.The FoRM contains over 10 000 real-world forum threads in collaboration with Coursera, with more than 23 000 manually labeled resource mentions.We then formulate this task as a sequence tagging problem and investigate solution architectures to address the problem.Importantly, we identify two major challenges that hinder the applications of sequence tagging models to the task: 1) the diversity of resource mention expression and (2) long-range contextual dependencies.We address these challenges by incorporating character-level and thread context information into an LSTM-CRF model.First, we incorporate a character encoder to address the out-of-vocabulary problem caused by the diversity of mention expressions.Second, to address the context dependency challenge, we encode thread contexts using an RNN-based context encoder and apply the attention mechanism to selectively leverage useful context information during sequence tagging.The experiments on FoRM show that the proposed method improves the baseline deep sequence tagging models notably, significantly bettering performance on instances that exemplify two challenges.
A reformed grasshopper optimization with genetic principle for securing medical data

Cloud computing is an emerging computing technology that uses the internet and central remote servers to sustain data and applications. Using cloud computing, users can access database resources through internet from anywhere without distressing about the management of actual resources. This concept can be used almost in all fields including the healthcare sector. In such situations, data mining and their effective investigation from cloud are more demanding due to the privacy in data preservation. Consequently, the privacy and security of the information system is under major risk because of security attacks like Known Plain text attack (KPA) and Cipher Text attack (CPA). To manage these issues, this paper describes privacy preservation approach with data sanitization and data restoration process for securing medical data. Moreover, several researchers have proposed enhancements to the restoration process, since it causes a low accuracy. As a solution to this crisis, this paper uses a hybrid algorithm known as Grasshopper Optimization with Genetic Algorithm (GOAGA) for the data sanitization and restoration process. Further, the performance of this hybrid approach is compared with other conventional approaches in terms of the sanitization and restoration effectiveness, convergence, statistical and key sensitivity analysis and thus the governance of the progressed approach is validated.
Pyramid Quantile Regression

We describe a Bayesian model for simultaneous linear quantile regression at several specified quantile levels. More specifically, we propose to model the conditional distributions by using random probability measures, known as quantile pyramids, introduced by Hjort and Walker. Unlike many existing approaches, this framework allows us to specify meaningful priors on the conditional distributions, while retaining the flexibility afforded by the nonparametric error distribution formulation. Simulation studies demonstrate the flexibility of the proposed approach in estimating diverse scenarios, generally outperforming other competitive methods. We also provide conditions for posterior consistency. The method is particularly promising for modeling the extremal quantiles. Applications to extreme value analysis and in higher dimensions are also explored through data examples. Supplemental material for this article is available online.
Contract design for purchasing private data using a biased differentially private algorithm

Personal information and other types of private data are valuable for both data owners and institutions interested in providing targeted and customized services that require analyzing such data. In this context, privacy is sometimes seen as a commodity: institutions (data buyers) pay individuals (or data sellers) in exchange for private data. In this study, we examine the problem of designing such data contracts, through which a buyer aims to minimize his payment to the sellers for a desired level of data quality, while the latter aim to obtain adequate compensation for giving up a certain amount of privacy. Specifically, we use the concept of differential privacy and examine a model of linear and nonlinear queries on private data. We show that conventional algorithms that introduce differential privacy via zero-mean noise fall short for the purpose of such transactions as they do not provide sufficient degree of freedom for the contract designer to negotiate between the competing interests of the buyer and the sellers. Instead, we propose a biased differentially private algorithm which allows us to customize the privacy-accuracy tradeoff for each individual. We use a contract design approach to find the optimal contracts when using this biased algorithm to provide privacy, and show that under this combination the buyer can achieve the same level of accuracy with a lower payment as compared to using the unbiased algorithms, while incurring lower privacy loss for the sellers.
A theoretically motivated method for automatically evaluating texts for gist inferences

We developed a method to automatically assess texts for features that help readers produce gist inferences. Following fuzzy-trace theory, we used a procedure in which participants recalled events under gist or verbatim instructions. Applying Coh-Metrix, we analyzed written responses in order to create gist inference scores (GISs), or seven variables converted to Z scores and averaged, which assess the potential for readers to form gist inferences from observable text characteristics. Coh-Metrix measures reflect referential cohesion and deep cohesion, which increase GIS because they facilitate coherent mental representations. Conversely, word concreteness, hypernymy for nouns and verbs (specificity), and imageability decrease GIS, because they promote verbatim representations. Also, the difference between abstract verb overlap among sentences (using latent semantic analysis) and more concrete verb overlap (using WordNet) should enhance coherent gist inferences, rather than verbatim memory for specific verbs. In the first study, gist condition responses scored nearly two standard deviations higher on GIS than did the verbatim condition responses. Predictions based on GIS were confirmed in two text analysis studies of 50 scientific journal article texts and 50 news articles and editorials. Texts from the Discussion sections of psychology journal articles scored significantly higher on GIS than did texts from the Method sections of the same journal articles. News reports also scored significantly lower than editorials on the same topics from the same news outlets. GIS proved better at discriminating among texts than did alternative formulae. In a behavioral experiment with closely matched text pairs, people randomly assigned to high-GIS versions scored significantly higher on knowledge and comprehension.
Examining the use and non-use of mobile payment systems for merchant payments in India

Many countries across the globe are engaged in efforts to promote a cashless society. In India, there is a strong top-down push by the government and the private sector for mobile payments. In this work, we examine the benefits and pitfalls people perceive in using mobile payments for customer-merchant transactions. Through interviews with 19 customers and 15 merchants across rural, periurban, and urban India, we explore people's awareness about mobile payment systems, perceived trade-offs among different payment methods, and the security, privacy, and utility barriers in its adoption and use. Overall, we found that customers were interested in adopting mobile payments for referral rewards and sign-up incentives, but were hesitant to regularly use them, whereas merchants saw mobile payments as an unnecessary burden for their business. We discuss the nuanced views of customers and merchants, and offer recommendations to address the barriers in the use of mobile payments for customer-to-merchant transactions.
Scalable and Interpretable One-Class SVMs with Deep Learning and Random Fourier Features

One-class support vector machine (OC-SVM) for a long time has been one of the most effective anomaly detection methods and extensively adopted in both research as well as industrial applications. The biggest issue for OC-SVM is yet the capability to operate with large and high-dimensional datasets due to optimization complexity. Those problems might be mitigated via dimensionality reduction techniques such as manifold learning or autoencoder. However, previous work often treats representation learning and anomaly prediction separately. In this paper, we propose autoencoder based one-class support vector machine (AE-1SVM) that brings OC-SVM, with the aid of random Fourier features to approximate the radial basis kernel, into deep learning context by combining it with a representation learning architecture and jointly exploit stochastic gradient descent to obtain end-to-end training. Interestingly, this also opens up the possible use of gradient-based attribution methods to explain the decision making for anomaly detection, which has ever been challenging as a result of the implicit mappings between the input space and the kernel space. To the best of our knowledge, this is the first work to study the interpretability of deep learning in anomaly detection. We evaluate our method on a wide range of unsupervised anomaly detection tasks in which our end-to-end training architecture achieves a performance significantly better than the previous work using separate training. Code related to this paper is available at: https://github.com/minh-nghia/AE-1SVM .
Can adversarial networks hallucinate occluded people with a plausible aspect?

When you see a person in a crowd, occluded by other persons, you miss visual information that can be used to recognize, re-identify or simply classify him or her. You can imagine its appearance given your experience, nothing more. Similarly, AI solutions can try to hallucinate missing information with specific deep learning architectures, suitably trained with people with and without occlusions. The goal of this work is to generate a complete image of a person, given an occluded version in input, that should be a) without occlusion b) similar at pixel level to a completely visible people shape c) capable to conserve similar visual attributes (e.g. male/female) of the original one. For the purpose, we propose a new approach by integrating the state-of-the-art of neural network architectures, namely U-nets and GANs, as well as discriminative attribute classification nets, with an architecture specifically designed to de-occlude people shapes. The network is trained to optimize a Loss function which could take into account the aforementioned objectives. As well we propose two datasets for testing our solution: the first one, occluded RAP, created automatically by occluding real shapes of the RAP dataset created by Li et al. (2016) (which collects also attributes of the people aspect); the second is a large synthetic dataset, AiC, generated in computer graphics with data extracted from the GTA video game, that contains 3D data of occluded objects by construction. Results are impressive and outperform any other previous proposal. This result could be an initial step to many further researches to recognize people and their behavior in an open crowded world.
Forward-Secure Data Outsourcing Based on Revocable Attribute-Based Encryption

In cloud-based storage services, Ciphertext-Policy Attribute Based Encryption (CP-ABE) has been emerging as a promising outsourcing solution to provide data confidentiality protection and fine-grained data access management without relying on the Cloud. CP-ABE schemes enable data owner to encrypt data under a desired access structure before outsourcing it to the Cloud. Only those who own a certified attribute set, which matches with the access structure, can decrypt the encrypted data. Despite the advantages over conventional encryption algorithms, attribute and user revocations of CP-ABE schemes remain challenging. Firstly, existing revocation mechanisms cannot thoroughly solve the problem as they give rise to security issues and increases computational, communication overheads. Secondly, forward secrecy, in which revoked users are unable to decrypt data shared in the past, is not rigorously taken into account. In this paper, we investigate these problems and design two CP-ABE schemes that allow both efficient user and attribute revocations. The forward secrecy requirement of the proposed schemes is guaranteed by integrating re-encryption techniques. To demonstrate the feasibility of the proposals, we evaluate their performance and provide a security analysis.
A DASH-based Efficient Throughput and Buffer Occupancy-based Adaptation Algorithm for Smooth Multimedia Streaming

Today, the dynamic network environment poses severe challenging issues to multimedia streaming services that account for an enormous part of network traffic all over the world. Dynamic adaptive streaming over HTTP (DASH) facilitates seamless video playback by allowing for dynamic adjustment of the video bitrate to the ongoing network situation. Despite several attempts, there is still a challenge to design solutions which use DASH to adjust video delivery to the dynamic network environment and achieve high user quality of experience levels. This paper presents a novel DASH-based throughput and buffer occupancy-based adaptation (TBOA) algorithm to provide an improved streaming experience for remote users. TBOA was compared against alternative solutions such as FDASH and SFTM in single- and multiple-client scenarios. Testing results show how TBOA selects higher video bitrates while performing fewer video bitrate switches and reduces the risk of buffer underrun in comparison with the competitors.
Median Activation Functions for Graph Neural Networks

Graph neural networks (GNNs) have been shown to replicate convolutional neural networks’ (CNNs) superior performance in many problems involving graphs. By replacing regular convolutions with linear shift-invariant graph filters (LSI-GFs), GNNs take into account the (irregular) structure of the graph and provide meaningful representations of network data. However, LSI-GFs fail to encode local nonlinear graph signal behavior, and so do regular activation functions, which are nonlinear but pointwise. To address this issue, we propose median activation functions with support on graph neighborhoods instead of individual nodes. A GNN architecture with a trainable multirresolution version of this activation function is then tested on synthetic and real-word datasets, where we show that median activation functions can improve GNN capacity with marginal increase in complexity.
Development and application of spatial carrier interferometry for whole field real-time investigation of temperatures in liquid media

This paper presents interferometric measurement of dynamic phenomena in transparent media. The method is based on differential spatial carrier interferometry that provides interesting features for experimental fluid mechanics. The method is particularly suitable for progressively changing phenomena. Temperature is automatically evaluated from a single interferogram, and a differential approach allows to decrease dynamic range of the measurement. As a consequence, in case of high temporal sampling of the phenomena, no spatial phase unwrapping is needed. Otherwise, the spatial unwrapping is performed on low fringe density phase maps and the fringe order is unambiguously determined. The interferometric method was experimentally tested by measuring dynamically varying temperature fields in water. The inspected phenomenon was a convection of water layers within a glass tank, where a heat sink was positioned at the bottom of the tank. Interferometric and temperature measurements were simultaneously conducted. Temperature was measured using four thermocouples to verify the reliability of the method. Results of both approaches are in a good agreement.
Multipatch Unbiased Distance Non-Local Adaptive Means With Wavelet Shrinkage

Many existing non-local means (NLM) methods either use Euclidean distance to measure the similarity between patches, or compute weight ωij only once and keep it unchanged during the subsequent denoising iterations, or use only the structure information of the denoised image to update weight ωij . These may lead to the limited denoising performance. To address these issues, this paper proposes the non-local adaptive means (NLAM) for image denoising. NLAM treats weight ωij as an optimization variable and iteratively updates its value. We then introduce three unbiased distances, namely, pixel-pixel, patch-patch, and coupled unbiased distances. These unbiased distances are more robust to measure the image pixel/patch similarity than Euclidean distance. Using the coupled unbiased distance, we propose the unbiased distance non-local adaptive means (UD-NLAM). Because UD-NLAM uses only a single patch size to compute weight ωij , we introduce multipatch UD-NLAM (MUD-NLAM) to adapt different noise levels. To further improve denoising performance, we then propose a new denoising method called MUD-NLAM with wavelet shrinkage (MUD-NLAM-WS). Experimental results show that the proposed NLAM, UD-NLAM, and MUD-NLAM outperform existing NLM methods, and MUD-NLAM-WS achieves a better performance than the state-of-the-art denoising methods.
The Asymptotic Spectrum of LOCC Transformations

We study the exact, non-deterministic conversion of multipartite pure quantum states into one-another via local operations and classical communication (LOCC) and asymptotic entanglement transformation under such channels. In particular, we consider the maximal number of copies of any given target state that can be extracted exactly from many copies of any given initial state as a function of the exponential decay in the success probability, known as the converse error exponent. We give a formula for the optimal rate presented as an infimum over the asymptotic spectrum of LOCC conversion. A full understanding of exact asymptotic extraction rates between pure states in the converse regime thus depends on a full understanding of this spectrum. We present a characterization of spectral points and use it to describe the spectrum in the bipartite case. This leads to a full description of the spectrum and thus an explicit formula for the asymptotic extraction rate between pure bipartite states, given a converse error exponent. This extends the result on entanglement concentration in [1], where the target state is fixed as the Bell state. In the limit of vanishing converse error exponent, the rate formula provides an upper bound on the exact asymptotic extraction rate between two states, when the probability of success goes to 1. In the bipartite case, we prove that this bound holds with equality.
A Collaborative Visual Tracking Architecture for Correlation Filter and Convolutional Neural Network Learning

Visual object tracking has achieved remarkable progress in recent years and has been broadly applied in intelligent transportation systems such as autonomous vehicles and drones to monitor and analyze the behavior of specific targets. One typical tracking approach is the discriminative tracker, which branches into two main categories: the correlation filter (CF) and the convolutional neural network (CNN). However, most of the current researches consider both categories as two separate techniques and only rely on one of them. Thus, a dense cooperation between the CF and the CNN still remains less discovered and the question of how to effectively join both techniques to further boost the tracking performance is still open. To address this issue, in this paper, we propose a collaborative architecture which incorporates models constructed with both techniques and dynamically aggregates their response maps for target inference. By an alternating optimization, both models are learned on each other's errors to persistently improve the classification power of the whole tracker. For further efficiency, we present a faster solver for our utilized CF and an analytical solution for dynamic model weighting. Through experiments on standard benchmarks, we reveal the influence of key factors on the joint learning architecture and show that it outperforms the state-of-the-art approaches.