Reviewerly/exHarmony huggingface dataset:
A Cross-Database Study of Voice Presentation Attack Detection

Despite an increasing interest in speaker recognition technologies, a significant obstacle still hinders their wide deployment—their high vulnerability to spoofing or presentation attacks. These attacks can be easy to perform. For instance, if an attacker has access to a speech sample from a target user, he/she can replay it using a loudspeaker or a smartphone to the recognition system during the authentication process. The ease of executing presentation attacks and the fact that no technical knowledge of the biometric system is required to make these attacks especially threatening in practical application. Therefore, late research focuses on collecting data databases with such attacks and on development of presentation attack detection (PAD) Presentation Attack Detection (PAD) systems. In this chapter, we present an overview of the latest databases and the techniques to detect presentation attacks. We consider several prominent databases that contain bona fide and attack data, including ASVspoof 2015, ASVspoof 2017, AVspoof, voicePA, and BioCPqD-PA (the only proprietary database). Using these databases, we focus on the performance of PAD systems in the cross-database scenarioCross-database scenario or in the presence of "unknown" (not available during training) attacks, as these scenarios are closer to practice, when pretrained systems need to detect attacks in unforeseen conditions. We first present and discuss the performance of PAD systems based on handcrafted features and traditional Gaussian mixture model (GMM) classifiers. We then demonstrate whether the score fusion techniques can improve the performance of PADs. We also present some of the latest results of using neural networks for presentation attack detection. The experiments show that PAD systems struggle to generalize across databases and mostly unable to detect unknown attacksUnknown attacks, with systems based on neural networks demonstrating better performance compared to the systems based on handcrafted features.
Face hallucination through differential evolution parameter map learning with facial structure prior

Current learning based face hallucination approaches mainly focus on how to design a reasonable objective function, such as using different assumptions and incorporating different regularization terms, but do not give a reasonable way of selecting the model parameters. In this paper, we propose to exploit the facial structure prior to learn a parameter map based on differential evolution. Specifically, we claim that different position patches have different parameter settings because of their different statistical properties, and patches from the same position of different face images should have similar parameter settings. As a result, we first learn a parameter map for each training sample by leveraging an evolutionary algorithm based on differential evolution, and then fuse these learned parameter maps to an optimal parameter map for testing via mean-pooling strategy. Finally, we use the predicted parameter map to guide the co-occurrence relationship modeling in different regions of the input low-resolution (LR) face image. Experimental results demonstrate that, even without seeing the ground truth, results of proposed parameter map learning method are comparable to or better than those traditional unified parameter setting methods and some recently proposed deep learning methods.
Self-calibration of projective camera based on trajectory basis

To calibrate the projective camera, this paper presents a linear method for self-calibration of a projective camera based on the representing of deforming object by the trajectory basis. The trajectory basis can be modeled compactly in the domain of the Discrete Cosine Transform (DCT) basis vectors which can be predefined independent of the observed images, and the number of unknowns can be significantly reduced. At the same time, the camera self-calibration becomes a linear optimal problem, which can improve the robustness of the algorithm. The experiments with both simulate and real data show that the presented method can effectively calibrate the camera.
An efficient clustering and load balancing of distributed cloud data centers using graph theory

Summary The prime focus of the Cloud Service Providers is enhancing the service delivery performance of the distributed cloud data centers. The clustering and load balancing of distributed cloud data centers have significant impact on its service delivery performance. Hence, this paper models distributed cloud data center environment as a network graph and proposes a two‐phase cluster‐based load balancing (CLB) algorithm based on a graph model. The first phase proposes a Cloud Data Center Clustering algorithm to cluster the distributed cloud data centers based on their proximity. The second phase proposes a Client‐Cluster Assignment algorithm to perform uniform distribution of the client requests across the clusters to enable load balancing. To assess the performance, the proposed algorithms are compared with other K ‐constrained graph‐based clustering algorithms namely, graph‐based K ‐means and K ‐spanning tree algorithms on a simulated distributed cloud data center environment. The experimental results reveal that the proposed CLB algorithm outperforms the compared algorithms in terms of the average clustering time, load distribution, and fairness index and hence improves the service delivery performance of the distributed cloud data centers.
A modified level set algorithm based on point distance shape constraint for lesion and organ segmentation

The segmentation of organs and lesions from medical images is a challenging task due to the presents of noise, intensity inhomogeneity, blurry/weak boundaries. In this paper, a point distance shape constraint is proposed and incorporated in the level set framework for the segmentation of objects with various shapes.The proposed shape constraint is a linear combination of the Euclidean distance of user selected points. By selecting different numbers of points, it can generate different shape constraints and therefore is more flexible in dealing with different shapes. Then this shape constraint is incorporated into the variational level set framework. A convex relaxation is applied to get a convex model which can be efficiently solved by a primal-dual hybrid gradient algorithm.The proposed algorithm is tested on 60 CT images with the nodular type of hepatic cellular cancer (HCC), 100 ultrasound kidney images, 20 prostate MR images, 20 lumbar CT images and 100 transrectal ultrasound prostate images. The algorithms performance is evaluated using a number of metrics by comparison with expert delineations. The validation results show that, for five datasets mentioned previously, the average DSCs of the proposed algorithm are 95.6% ± 1.4%, 94.3% ± 3.1%, 91.3% ± 3.8%, 92.7% ± 1.5% and 94.4% ± 2.2% respectively. Both quantitative and qualitative evaluation confirm that the proposed method can provide more accurate segmentation than four state-of-the-art methods.The proposed point distance shape constraint segmentation model can accurately segment organs and lesions with a number of shapes in medical images.
Machine vision quality assessment for robust face detection

Machine vision quality (MVQ) assessment for face detection refers to image quality as judged by face detection algorithms. While perceptual image quality usually depends on human observers, MVQ depends on factors such as the face detection algorithm and performance measures such as recall and precision. We define the MVQ index as a weighted combination of the predicted precision and recall of the face detection algorithm and predict it using image features based on natural scene statistics. A filter bank framework is developed to achieve image enhancement of distorted images for face detection, where the enhancement operations can be optimized for either perceptual quality or MVQ. It is shown that MVQ can be effectively used to control the relative performance of recall and precision that can be achieved on enhanced images. For certain face detection algorithms, optimizing for MVQ instead of perceptual quality can lead to improved face detection performance. The MVQ is developed for three different face detection algorithms and the image enhancement framework is tested on a dataset based on the IDEAL-LIVE Distorted Face Database. A computationally efficient method to optimize for MVQ is designed by predicting the MVQ of enhanced images directly from features extracted from distorted images and filter parameters. The optimization framework is further enhanced by allowing for filter bank independent optimization of MVQ.
Median Filtering Forensics in Compressed Video

Median filtering has received extensive attention from forensics analyzers, as a common content-preserving, smoothing, and denoising manipulation. We propose a detection scheme for median filtering of video sequences in compressed domain based on the singular value decomposition of the process matrix , which approximates the median filtering. Projection over some of the eigenspaces of the process matrix gives a set of features of small dimension, even as small as three, making the proposed scheme a fast and suitable detector for video median filtering. The experimental evaluations show that the proposed method outperforms the state-of-the-art detectors of median filtering, and its edge becomes significant for localized detection over small blocks within frames. The new method can potentially be used as a detector of other editing processes.
A New Sparse Restricted Boltzmann Machine

Although existing sparse restricted Boltzmann machine (SRBM) can make some hidden units activated, the major disadvantage is that the sparseness of data distribution is usually overlooked and the reconstruction error becomes very large after the hidden unit variables become sparse. Different from the SRBMs which only incorporate a sparse constraint term in the energy function formula from the original restricted Boltzmann machine (RBM), an energy function constraint SRBM (ESRBM) is proposed in this paper. The proposed ESRBM takes into account the sparseness of the data distribution so that the learned features can better reflect the intrinsic features of data. Simulations show that compared with SRBM, ESRBM has smaller reconstruction error and lower computational complexity, and that for supervised learning classification, ESRBM obtains higher accuracy rates than SRBM, classification RBM, and Softmax classifier.
Criminal Event Ontology Population and Enrichment using Patterns Recognition from Text

Thousands of criminal events are reported in newspapers and social networks every day. They describe violent acts that include actors, places, times, causes and any information concerning them. Verbal and nominal phrases are used to characterize and expose criminal events, which employ an important variety of natural language structures in the newspapers. In addition, causes, times and spaces of criminal events, use linguistic phrases to represent them in text. All of them need to be extracted as a pattern recognition process in order to extract criminal events from text and the information that concerns them. The extracted events, as a knowledge base, are very useful for information retrieval tasks. Therefore, this paper presents an approach based on pattern recognition in order to extract criminal events from Spanish text, by populating and enriching an ontology model. Ontology population and enrichment involve the instantiation of criminal events and their cause relationships. An evaluation process is carried out with a set of manually tagged newspapers with categories of specific events, and shows promising results.
Tailoring density ratio weight for covariate shift adaptation

In many real-world applications, the performance of machine learning models is often significantly degraded because of the covariate shift problem. Appropriately dealing with this problem remains an important challenge. A common way for covariate shift adaptation is to estimate the density ratio weight from unlabeled source and target data and then learn a final hypothesis by directly minimizing the weighted empirical loss. This approach may result in a poor final hypothesis for the target domain by overweighting a few training examples that carry large loss. The problem stems from separating the estimation of density ratio weight from the learning of the final hypothesis. In this paper, an Adaptively Weighted Structural Risk Minimization (AWSRM) framework is developed for addressing this problem. In the proposed framework, the raw weights are first estimated from any density ratio estimation technique, and then the tailored weights and the target model are obtained by simultaneously minimizing the weighted structural risk and the discrepancy between the tailored weights and the raw weights. Comprehensive experimental studies on both synthetic and real-world data sets demonstrate that the AWSRM framework outperforms existing state-of-the-art covariate shift adaptation methods in terms of prediction accuracy.
Recursive Maximum Likelihood Algorithm for Dependent Observations

A recursive maximum-likelihood algorithm (RML) is proposed that can be used when both the observations and the hidden data have continuous values and are statistically dependent between different time samples. The algorithm recursively approximates the probability density functions of the observed and hidden data by analytically computing the integrals with respect to the state variables, where the parameters are updated using gradient steps. A full convergence proof is given, based on the ordinary differential equation approach, which shows that the algorithm converges to a local minimum of the Kullback-Leibler divergence between the true and the estimated parametric probability density functions-a result that is useful even for a miss-specified parametric model. Compared to other RML algorithms proposed in the literature, this contribution extends the state-space model and provides a theoretical analysis in a nontrivial statistical model that was not analyzed so far. We further extend the RML analysis to constrained parameter estimation problems. Two examples, including nonlinear state-space models, are given to highlight this contribution.
IMSSAP: After‐school interactive mobile learning student support application

Abstract Traditional methods intended for education are no longer suitable for it, since they are unable to meet the students’ learning needs. M‐learning is becoming a global interest. Various studies were conducted in many countries such as South Korea, Taiwan, UK, Malaysia, Serbia, European Union, and Saudi Arabia investigating student's readiness to adopt M‐learning. Our study aims to introduce a new mobile bite‐sized learning tool and to explore student's readiness to adopt M‐learning and to investigate the motivational and acceptance factors related to it. We introduced our learning tool (IMSSAP), after‐school interactive M‐learning student support Application, to university students from different majors in Turkey. Analysis of the questionnaire data revealed that an easy to use tool with social media collaboration and sharing capabilities appears to be the vital factors that will motivate students to adopt M‐learning. An improvement in the relation between the students, the teachers and the academic institution was obvious. Student's performance got much better added to better analytical and critical skills advancement. We have compared IMSSAP with another learning tool in the same class for clarity and reliability of the results. Highlights: A cloud‐based mobile application built with Google‐Firebase platform in the model of Backend as a Service (BaaS). Bite‐sized learning methodology used to fulfill student's needs in a social circle era. Lightweight learning tool created for knowledge sharing and a learning process monitoring facility.
Rescaled Boosting in Classification

Boosting is a learning scheme that combines weak learners to produce a strong composite learner, with the underlying intuition that one can obtain accurate learner by combining “rough” ones. This paper aims at developing a new boosting strategy, called rescaled boosting (RBoosting), to accelerate the numerical convergence rate and, consequently, improve learning performances of the original boosting. Our studies show that RBoosting possesses the almost optimal numerical convergence rate in the sense that, up to a logarithmic factor, it can reach the minimax nonlinear approximation rate. We then use RBoosting to tackle classification problems and deduce corresponding statistical consistency and tight generalization error estimates. A series of theoretical and experimental results shows that RBoosting outperforms boosting in terms of generalization.
A crowdsourced system for robust eye tracking

Eye tracking is widely used in modern intelligent applications, such as HCI, somatosensory game and fatigue driving. Traditional eye tracking system based on Haar-like features or external hardware, which is loss of accuracy and complicated. It is obviously that human gaze point is related to head pose. However, the label of head pose in most dataset is ambiguous. So in this paper, we propose a crowdsourced system which can collect large-scale dataset for eye tracking. For better performance, we leverage head guidance point and random dot instead of fixed dot as the concern when capture frames from camera. And different illumination, poses and persons also considered for robust performance. And we propose a two-phase CNN training strategy for combining head pose and eye angles. The proposed CNN architecture can reduce the overfitting when we train eye tracking models with head pose directly. The experimental results show that our proposed method can perform well in eye tracking.
Deep F-Measure Maximization in Multi-label Classification: A Comparative Study

In recent years several novel algorithms have been developed for maximizing the instance-wise F $$_\beta $$ -measure in multi-label classification problems. However, so far, such algorithms have only been tested in tandem with shallow base learners. In the deep learning landscape, usually simple thresholding approaches are implemented, even though it is expected that such approaches are suboptimal. In this article we introduce extensions of utility maximization and decision-theoretic methods that can optimize the F $$_\beta $$ -measure with (convolutional) neural networks. We discuss pros and cons of the different methods and we present experimental results on several image classification datasets. The results illustrate that decision-theoretic inference algorithms are worth the investment. While being more difficult to implement compared to thresholding strategies, they lead to a better predictive performance. Overall, a decision-theoretic inference algorithm based on proportional odds models outperforms the other methods. Code related to this paper is available at: https://github.com/sdcubber/f-measure .
An alternative polynomial-sized formulation and an optimization based heuristic for the reviewer assignment problem

Peer review systems are based on evaluating a scholarly work, referred to as a proposal, by experts in that field. In such a system, we consider the reviewer assignment problem, i.e., the problem of assigning proposals to reviewers under the assumption that each reviewer returns her preferences using ordinal rankings. Motivated by the problem defined in Cook et al. (Management Science, 51:655–661, 2005), we focus on reviewer assignments so as to maximize the total number of pairwise comparisons of proposals while ensuring a balanced coverage of distinct pairs of proposals. We propose an alternative mixed integer linear programming formulation for the reviewer assignment problem. In contrast to the optimization model proposed by Cook et al. (2005), the size of our formulation is polynomial in the input size. We present a semidefinite programming relaxation of our optimization model. Furthermore, we propose an optimization based heuristic approach, in which an optimal solution of the linear programming relaxation or the semidefinite programming relaxation of our optimization model is rounded in a straightforward fashion, followed by a local improvement scheme based on pairwise exchanges of proposals. Our computational results illustrate the effectiveness of our optimization model and our heuristic approach.
Scalable and Flexible Unsupervised Feature Selection

Recently, graph-based unsupervised feature selection algorithms (GUFS) have been shown to efficiently handle prevalent high-dimensional unlabeled data. One common drawback associated with existing graph-based approaches is that they tend to be time-consuming and in need of large storage, especially when faced with the increasing size of data. Research has started using anchors to accelerate graph-based learning model for feature selection, while the hard linear constraint between the data matrix and the lower-dimensional representation is usually overstrict in many applications. In this letter, we propose a flexible linearization model with anchor graph and ℓ21 -norm regularization, which can deal with large-scale data sets and improve the performance of the existing anchor-based method. In addition, the anchor-based graph Laplacian is constructed to characterize the manifold embedding structure by means of a parameter-free adaptive neighbor assignment strategy. An efficient iterative algorithm is developed to address the optimization problem, and we also prove the convergence of the algorithm. Experiments on several public data sets demonstrate the effectiveness and efficiency of the method we propose.
Anomaly Detection in Surveillance Videos

Automated anomaly detection is a useful task that can aid investigations and detect crimes. To this end, we present a model that can be used as a tool for anomaly detection in surveillance videos. Following an unsupervised approach, we use an autoencoder model trained to minimize the reconstruction error between the input and the generated output. We also augment the training of the auto-encoder with supervision in the form of user ratings per frame; higher user ratings reflect normal behaviour that the model is expected to faithfully reconstruct. On the other hand, lower rated frames are suspected to be anomalous. We analyze the output of the autoencoder on a standard dataset as well as two of our datasets that we have made public. We study the behavior of reconstruction error with and without supervision as well as the temporal coherence of the reconstruction error. Additionally, we use Grad-CAM to highlight potentially anomalous regions in the input. Finally, we discuss the problem of constructing summaries based on anomalous segments using heuristic approaches as well as a graph-theoretic formulation of determining a ranked list of maximum weighted cliques. We also make available in a single tool, our auto-encoder model as well as the anomaly summarizer.