Reviewerly/exHarmony huggingface dataset:
Temporal Opinion Spam Detection by Multivariate Indicative Signals

Online consumer reviews reflect the testimonials of real people, unlike e.g., ads. As such, they have critical impact on potential consumers, and indirectly on businesses. Problematically, such financial incentives have created a market for spammers to fabricate reviews to unjustly promote or demote businesses, activities known as opinion spam (Jindal and Liu 2008). Most existing work on this problem have formulations based on static review data, with respective techniques operating in an offline fashion. Spam campaigns, however, are intended to make most impact during their course. Abnormal events triggered by spammers’ activities could be masked in the load of future events, which static analysis would fail to identify. In this work, we approach the opinion spam problem with a temporal formulation. Specifically, we monitor a list of carefully selected indicative signals of opinion spam over time and design efficient techniques to both detect and characterize abnormal events in real-time. Experiments on two different datasets show that our approach is fast, effective, and practical to be deployed in real-world systems.
Efficient spatio-temporal Gaussian regression via Kalman filtering

We study the non-parametric reconstruction of spatio-temporal dynamical processes via Gaussian Processes (GPs) regression from sparse and noisy data. GPs have been mainly applied to spatial regression where they represent one of the most powerful estimation approaches also thanks to their universal representing properties. Their extension to dynamical processes has been instead elusive so far since classical implementations lead to unscalable algorithms or require some sort of approximation. We propose a novel procedure to address this problem by coupling GPs regression and Kalman filtering. In particular, assuming space/time separability of the covariance (kernel) of the process and rational time spectrum, we build a finite-dimensional discrete-time state-space process representation amenable to Kalman filtering. With sampling over a finite set of fixed spatial locations, our major finding is that the current Kalman filter state represents a sufficient statistic to compute the minimum variance estimate of the process at any future time over the entire spatial domain. In machine learning, a representer theorem states that an important class of infinite-dimensional variational problems admits a computable and finite-dimensional exact solution. In view of this, our result can be interpreted as a novel Dynamic Representer Theorem for GPs. We then extend the study to situations where the spatial input locations set varies over time. The proposed algorithms are tested on both synthetic and real field data, providing comparisons with standard GP and truncated GP regression techniques.
Caricaturing as a general method to improve poor face recognition: Evidence from low-resolution images, other-race faces, and older adults.

There are multiple well-established situations in which humans' face recognition performance is poor, including for low-resolution images, other-race faces, and in older adult observers. Here we show that caricaturing faces-that is, exaggerating their appearance away from an average face-can provide a useful applied method for improving face recognition across all these circumstances. We employ a face-name learning task offering a number of methodological advantages (e.g., valid comparison of the size of the caricature improvement across conditions differing in overall accuracy). Across six experiments, we (a) extend previous evidence that caricaturing can improve recognition of low-resolution (blurred) faces; (b) show for the first time that caricaturing improves recognition and perception of other-race faces; and (c) show for the first time that caricaturing improves recognition in observers across the whole adult life span (testing older adults, M age = 71 years). In size, caricature benefits were at least as large where natural face recognition is poor (other-race, low resolution, older adults) as for the naturally best situation (own-race high-resolution faces in young adults). We discuss potential for practical applicability to improving face recognition in low-vision patients (age-related macular degeneration, bionic eye), security settings (police, passport control), eyewitness testimony, and prosopagnosia. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
Infants’ center bias in free viewing of real-world scenes

This study examines how salience and a center bias drive infants’ first fixation while looking at complex scenes. Adults are known to have a strong center bias, their first point of gaze is nearly always in the center of the scene. The center bias is likely to be a strategic bias, as looking towards the center minimizes the distance to other parts of the scene and important objects are often located at the center. In an experimental design varying salience regions of scenes and start positions we examined infants’ (N = 48, Age = 5–20-month-olds) first fixation after scene onset. The pre-registered hypothesis that infants also have a center bias while looking at real-world scenes was confirmed. The strength of the center bias is correlated with the saliency distribution such that the bias is weaker when the strongest salience is peripheral rather central. In the absence of clear salient regions there still was a strong center bias. These results suggests there is a competition between stimulus-driven factors and a center bias in steering attention from a young age onwards.
A gene expression programming model for economy growth using knowledge-based economy indicators

Purpose The purpose of this paper is to focus on modeling economy growth with indicators of knowledge-based economy (KBE) introduced by World Bank for a case study in Iran during 1993-2013. Design/methodology/approach First, for grouping and reducing the number of variables, Tukey method and the principal component analysis are used. Also for modeling, 67 per cent of data is used for training in the two approaches of ARDL bounds testing and gene expression programming (GEP) and 33 per cent of them for testing the models. Then, the result models are compared with fitness function and Akaike information criteria (AIC). Findings The GEP model with fitness 945.7461 for training data and 954.8403 for testing data from 1000 is better than ARDL bounds testing model with fitness 335.5479 from 1000. In addition, according to model comparison tools (AIC), the GEP model has an extremely larger weight in comparison with ARDL bounds model. Therefore, the GEP model is introduced for future use in academia. Practical implications Knowledge and information is one of the most basic sources of wealth in economists’ sight. Thus, using KBE indicators appears essential in economic growth regarding daily progress in knowledge processes and its different theories. It is also extremely important to determine an appropriate model for KBE indicators which play a highly important role in the allocation of the economic resources of the country in an optimal manner. Originality/value This paper introduced a novel expression for economy growth using KBE indicators. All the data and the indicators are extracted from Word Bank service between 1993 and 2013.
Prelocation image stitching method based on flexible and precise boresight adjustment using Risley prisms

A prelocation image stitching method is proposed for the scan imaging system using Risley prisms. By confining the feature-based stitching procedure to several prelocated overlap areas in multiple subregion images, the proposed method reduces the computational complexity of feature extraction and image registration. Experiment results and analysis have validated the feasibility and robustness of the overlap prelocation algorithm, which can enhance the image stitching efficiency by 21.89% at least and by 39.38% at most. In addition, the composite image obtained from the prelocation stitching procedure can achieve a large field of view while maintaining high resolution.
CRAFT: Lightweight Tweakable Block Cipher with Efficient Protection Against DFA Attacks

Traditionally, countermeasures against physical attacks are integrated into the implementation of cryptographic primitives after the algorithms have been designed for achieving a certain level of cryptanalytic security. This picture has been changed by the introduction of PICARO, ZORRO, and FIDES, where efficient protection against Side-Channel Analysis (SCA) attacks has been considered in their design. In this work we present the tweakable block cipher CRAFT: the efficient protection of its implementations against Differential Fault Analysis (DFA) attacks has been one of the main design criteria, while we provide strong bounds for its security in the related-tweak model. Considering the area footprint of round-based hardware implementations, CRAFT outperforms the other lightweight ciphers with the same state and key size. This holds not only for unprotected implementations but also when fault-detection facilities, side-channel protection, and their combination are integrated into the implementation. In addition to supporting a 64-bit tweak, CRAFT has the additional property that the circuit realizing the encryption can support the decryption functionality as well with very little area overhead.
El Constructivismo, según bases teóricas de César Coll

Constructivism is a theory, widely accepted and used, which states that the student does not passively acquire knowledge, but rather actively, which promotes meaningful learning and uses approaches that recognize the importance of using and questioning the mental models already present in students to improve their understanding and performance. With the constructivist teaching-learning approach, students develop metacognitive, cognitive, and socio-affective skills, achieving autonomy, which prepares them to address global challenges through inquiry, action, and reflection.
Collaborative Filtering Recommendation Model Based on k-means Clustering

In this age of information load, it becomes a herculean task for user to get the relevant things from vast number of information.This huge number of data demand specially designed Recommender system that can plays an important role in suggesting relevant information preferred by the users.From this point, this paper presents a modest approach to enhance prediction in MovieLens dataset with high scalability by applying user-based collaborative filtering methods on clustered data.The proposal consists of three consequence phases: preprocessing phase, similarity phase, prediction phase.The experimental results obtained conducting K-means clustering and correlation coefficient similarity measures against MovieLens datasets lead to an increase in the scalability of recommender system.
Nondestructive Visualization and Quantitative Characterization of Defects in Silicone Polymer Insulators Based on Laser Shearography

Effective defects detection method for silicone polymer insulators is desperately needed to eliminate defective insulators in factory examination and make early warnings for accidents of in-service insulators. This paper investigates a laser shearography method that reveals surface deformation anomalies induced by internal defects after applying thermal excitation. Samples with artificial defects of different sizes and depths in the silicone rubber sheath were tested. Both fringe patterns and phase distribution maps were generated after the experiments for defects identification by indicating the displacement derivative with respect to the shearing direction. The relationship between the defect parameters and the relative phase difference was discussed, and a parameter named detection sensitivity was defined to evaluate the system detection ability on different defects. Finally, Canny edge detection algorithm and proposed image gradient method were employed to quantitatively estimate the defect size, and the ability of identifying defects was also improved. With its noncontact testing, full-field measurement, real-time imaging, and visual results, laser speckle imaging would have quite promising applications in nondestructive testing of silicone polymer insulators and it can be extended for other composite materials insulating equipment.
Multi-Dimensional Residual Dense Attention Network for Stereo Matching

Very deep convolutional neural networks (CNNs) have recently achieved great success in stereo matching. It is still highly desirable to learn a robust feature map to improve ill-posed regions, such as weakly textured regions, reflective surfaces, and repetitive patterns. Therefore, we propose an end-to-end multi-dimensional residual dense attention network (MRDA-Net) in this paper, focusing on more comprehensive pixel-wise feature extraction. Our proposed network consists of two parts: the 2D residual dense attention net for feature extraction and the 3D convolutional attention net for matching. Our designed 2D residual dense attention net uses a dense network structure to fully exploit the hierarchical features from preceding convolutional layers and uses residual network structure to fuse low-level structure information and high-level semantic information. The 2D attention module of the net aims to adaptively recalibrate channel-wise features to be more concerned about informative features. Our proposed 3D convolutional attention net further expands attention mechanism for matching. The stacked hourglass module of the net is exploited to extract multi-scale context information as well as geometry information. The novel 3D attention module of the net aggregates hierarchical sub-cost volumes adaptively instead of manually and then achieves a comprehensive recalibrated cost volume for more correct disparity computation. The experiments demonstrate that our approach achieves the state-of-the-art accuracy on Scene Flow dataset and KITTI 2012 and KITTI 2015 Stereo datasets.
Combined Distinguishers to Enhance the Accuracy and Success of Side Channel Analysis

For the first time, the combination of mutual information analysis and correlation power analysis is proposed to enhance the accuracy and success rate of side channel analysis. Using the k-nearest-neighborhood (KNN) algorithm, correlation power analysis is combined with mutual information analysis to classify various possible keys to two classes of correct and wrong keys. The advantage of the combination of the distinguishers is two fold. First, the accuracy of the estimation is enhanced due to availability of multiple possible values for the correct key. Second, the number of measurements required to disclose the correct key is reduced by combining the distinguishers. The effectiveness of combined distinguisher is verified by extensive simulations. The number of measurements required to perform a side channel attack with a success rate of 90% is improved, respectively, by 20% and 49%, as compared to individual correlation power analysis and mutual information analysis.
ARSM: Augment-REINFORCE-Swap-Merge Estimator for Gradient Backpropagation Through Categorical Variables

To address the challenge of backpropagating the gradient through categorical variables, we propose the augment-REINFORCE-swap-merge (ARSM) gradient estimator that is unbiased and has low variance. ARSM first uses variable augmentation, REINFORCE, and Rao-Blackwellization to re-express the gradient as an expectation under the Dirichlet distribution, then uses variable swapping to construct differently expressed but equivalent expectations, and finally shares common random numbers between these expectations to achieve significant variance reduction. Experimental results show ARSM closely resembles the performance of the true gradient for optimization in univariate settings; outperforms existing estimators by a large margin when applied to categorical variational auto-encoders; and provides a try-and-see self-critic variance reduction method for discrete-action policy gradient, which removes the need of estimating baselines by generating a random number of pseudo actions and estimating their action-value functions.
Deep Semantic Matching with Foreground Detection and Cycle-Consistency

Establishing dense semantic correspondences between object instances remains a challenging problem due to background clutter, significant scale and pose differences, and large intra-class variations. In this paper, we present an end-to-end trainable network for learning semantic correspondences using only matching image pairs without manual keypoint correspondence annotations. To facilitate network training with this weaker form of supervision, we (1) explicitly estimate the foreground regions to suppress the effect of background clutter and (2) develop cycle-consistent losses to enforce the predicted transformations across multiple images to be geometrically plausible and consistent. We train the proposed model using the PF-PASCAL dataset and evaluate the performance on the PF-PASCAL, PF-WILLOW, and TSS datasets. Extensive experimental results show that the proposed approach achieves favorably performance compared to the state-of-the-art. The code and model will be available at https://yunchunchen.github.io/WeakMatchNet/ .
Extrinsic parameters calibration of multi-camera with non-overlapping fields of view using laser scanning

An extrinsic parameters calibration method of multi-cameras with non-overlapping fields of view (FOV) using laser scanning is presented. Firstly, two lasers are mounted on a multi-degree-of-freedom manipulator and can scan objects freely by the projected line-structured light. Then, controlling the movement of the manipulator, the line-structured light is projected into the field of view of one of the multi-cameras, and the light plane equation in the camera coordinate frame is calibrated by the target. The manipulator is moved several times in small amplitude to change the position of structured light in the field of vision of the camera and to continue to calibrate the light plane. The light plane equation of line-structured light in the manipulator coordinate frame are solved by the hand-eye calibration method. Secondly, with the help of the light planes, projected into the field of vision of other cameras to be calibrated, the light plane equation in the camera coordinate frame is calibrated, and the external parameters between the camera coordinate frame and the manipulator coordinate frame are calculated, so that the calibration of the external parameters of multiple cameras can be realized. The proposed method connects the non-overlapping multi-cameras by the laser scanning. It can effectively solve the problem of multi-camera extrinsic parameter calibration under the conditions of long working distance and complex environment light.
GTShark: genotype compression in large projects

Abstract Summary Nowadays large sequencing projects handle tens of thousands of individuals. The huge files summarizing the findings definitely require compression. We propose a tool able to compress large collections of genotypes almost 30% better than the best tool to date, i.e. squeezing human genotype to less than 62 KB. Moreover, it can also compress single samples in reference to the existing database achieving comparable results. Availability and implementation https://github.com/refresh-bio/GTShark. Supplementary information Supplementary data are available at Bioinformatics online.
Respiration Entrainment in Virtual Reality by Using a Breathing Avatar

Increasing evidence points to the role of interoception in body perception and in constructing the bodily self. Body ownership illusion (BOI) in virtual reality (VR) is a widely used paradigm to study body perception. However, existing research has focused mainly on exteroceptive sensory modalities, and studies on interoception and BOI remain scarce. The postulated mechanism of BOI is related to the multisensory integration of information and visuo-tactile or visuo-kinesthetic sensory conflict resolution. In this within-subjects experimental study, we introduced systematic visuo-interoceptive (visuo-respiratory) conflicts and tested if participants would resolve them by adjusting their respiration rate (RR). Participants observed a virtual breathing avatar body from the first-person perspective while their own RR was recorded. The VR system was connected to a respiration monitor. The avatar was first breathing for 60 seconds in accordance with the participant's RR; then, it was either slowing down or speeding up, each condition lasting for 180 seconds. The dependent variable was a change in participants' RR, expressed as a linear regression slope coefficient. Forty participants were included in each experimental condition, in a counterbalanced order. There was a change in RR in the predicted direction in both conditions. Participants' RR decreased on average by 0.48 breaths/minute and increased by 0.64 breaths/minute, leading to a change of 1.45 and 1.93 breaths/minute, respectively, over the entire timespan of the experiment. The difference between conditions was statistically significant (V = 192, p < 0.01). Because a change in RR of even 1 breath/minute is considered clinically significant, the results of this study—apart from demonstrating visuo-respiratory conflict resolution—may have an applied significance.
Recent Context-Aware LSTM for Clinical Event Time-Series Prediction

In this work, we propose a novel clinical event time-series model based on the long short-term memory architecture (LSTM) that can predict future event occurrences for a large number of different clinical events. Our model relies on two sources of information to predict future events. One source is derived from the set of recently observed clinical events. The other one is based on the hidden state space defined by the LSTM that aims to abstract past, more distant, patient information that is predictive of future events. We evaluate our proposed model on electronic health record (EHRs) data derived from MIMIC-III dataset. We show that the combination of the two sources of information implemented in our method leads to improved prediction performance compared to the models based on individual sources.
ZLiTE: Lightweight Clients for Shielded Zcash Transactions Using Trusted Execution

Cryptocurrencies record transactions between parties in a blockchain maintained by a peer-to-peer network. In most cryptocurrencies, transactions explicitly identify the previous transaction providing the funds they are spending, revealing the amount and sender/recipient pseudonyms. This is a considerable privacy issue. Zerocash resolves this by using zero-knowledge proofs to hide both the source, destination and amount of the transacted funds. To receive payments in Zerocash, however, the recipient must scan the blockchain, testing if each transaction is destined for them. This is not practical for mobile and other bandwidth constrained devices. In this paper, we build ZLiTE, a system that can support the so called “light clients”, which can receive transactions aided by a server equipped with a Trusted Execution Environment. Even with the use of a TEE, this is not a trivial problem. First, we must ensure that server processing the blockchain does not leak sensitive information via side channels. Second, we need to design a bandwidth efficient mechanism for the client to keep an up-to-date version of the witness needed in order to spend the funds they previously received.