Reviewerly/exHarmony huggingface dataset:
Recurrent Deconvolutional Generative Adversarial Networks with Application to Video Generation

This paper proposes a novel model for video generation and especially makes the attempt to deal with the problem of video generation from text descriptions, i.e., synthesizing realistic videos conditioned on given texts. Existing video generation methods cannot be easily adapted to handle this task well, due to the frame discontinuity issue and their text-free generation schemes. To address these problems, we propose a recurrent deconvolutional generative adversarial network (RD-GAN), which includes a recurrent deconvolutional network (RDN) as the generator and a 3D convolutional neural network (3D-CNN) as the discriminator. The RDN is a deconvolutional version of conventional recurrent neural network, which can well model the long-range temporal dependency of generated video frames and make good use of conditional information. The proposed model can be jointly trained by pushing the RDN to generate realistic videos so that the 3D-CNN cannot distinguish them from real ones. We apply the proposed RD-GAN to a series of tasks including conventional video generation, conditional video generation, video prediction and video classification, and demonstrate its effectiveness by achieving well performance.
Tanji: a General-purpose Neural Network Accelerator with Unified Crossbar Architecture

The authors describe a neural network accelerator based on crossbar interconnect. The crossbar provides a uniform architectural mechanism to handle different communication patterns for convolutional, fully connected, and recurrent layers as well as off-chip communication.
Balancing Privacy and Accountability for Industrial Mortgage Management

Industrial mortgage enables companies to acquire loan for business venture or investment purposes by pledging their industrial assets to financial institutions. To prevent double-mortgage fraud of borrowers, information exchange among different financial institutions is necessary. On the other hand, it results in the privacy leakage of borrowers. In this article, we construct a blockchain-based accountable and privacy-preserving industrial mortgage scheme (BAPIM). BAPIM enables financial institutions to share the mortgage data of borrowers in an efficient and secure manner, that achieves the borrower identity privacy and accountability at the same time. Specifically, borrower identity is concealed on the blockchain by anonymous identity credential, while financial institutions can still uncover the identity of a misbehaving borrower if he pledges the same asset for multiple mortgages. We demonstrate that BAPIM achieves the desirable security properties and has high computational efficiency, so as to be suitable for the industrial mortgage management.
A Dependently Typed Multi-stage Calculus

We study a dependently typed extension of a multi-stage programming language à la MetaOCaml, which supports quasi-quotation and cross-stage persistence for manipulation of code fragments as first-class values and an evaluation construct for execution of programs dynamically generated by this code manipulation. Dependent types are expected to bring to multi-stage programming enforcement of strong invariant—beyond simple type safety—on the behavior of dynamically generated code. An extension is, however, not trivial because such a type system would have to take stages of types—roughly speaking, the number of surrounding quotations—into account. To rigorously study properties of such an extension, we develop $$\lambda ^{\text {MD}}$$, which is an extension of Hanada and Igarashi’s typed calculus $$\lambda ^{\triangleright \%}$$ with dependent types, and prove its properties including preservation, confluence, strong normalization for full reduction, and progress for staged reduction. Motivated by code generators that generate code whose type depends on a value from outside of the quotations, we argue the significance of cross-stage persistence in dependently typed multi-stage programming and certain type equivalences that are not directly derived from reduction rules.
Modeling Dengue Fever Cases by Using GSTAR(1;1) Model with Outlier Factor

Abstract Dengue fever is an endemic disease transmitted through the Aedes Aegypti mosquitos. Dengue virus can be transmitted from human hosts who have been infected by the virus to the mosquitoes to be transmitted back to other humans. So that, it is possible for the virus to be transmitted to several surrounding locations. Aedes Aegypti is one of the dengue mosquitoes that likes a warm climate and not too wet or dry. In addition, many un-expected factors can cause a significant increase in the number of dengue fever cases. So that the number of dengue fever cases can increase significantly far different from other data. An observation data that has different characteristics from others is called outlier. The existence of outliers can indicate individuals or groups that have very different behavior from the most of the individuals of the dataset. Outlier data in a data set are often encountered in various kinds of data analysis. Frequently, outliers are removed to improve accuracy of the estimators. But sometimes the presence of an outlier has a certain meaning, which explanation can be lost if the outlier is removed. In this paper, modeling dengue fever cases using GSTAR(1;1) with outlier factors was firstly proposed.
Nonparametric Bayesian Modeling and Estimation for Renewal Processes

ABSTRACT We propose a flexible approach to modeling for renewal processes. The model is built from a structured mixture of Erlang densities for the renewal process inter-arrival density. The Erlang mixture components have a common scale parameter, and the mixture weights are defined through an underlying distribution function modeled nonparametrically with a Dirichlet process (DP) prior. This model specification enables nonstandard shapes for the inter-arrival time density, including heavy tailed and multimodal densities. Moreover, the choice of the DP centering distribution controls clustering or declustering patterns for the point process, which can therefore be encouraged in the prior specification. Using the analytically available Laplace transforms of the relevant functions, we study the renewal function and the directly related K function, which can be used to infer about clustering or declustering patterns. From a computational point of view, the model structure is attractive as it enables efficient posterior simulation while properly accounting for the likelihood normalizing constant implied by the renewal process. A hierarchical extension of the model allows for the quantification of the impact of different levels of a factor. The modeling approach is illustrated with several synthetic datasets, earthquake occurrences data, and coal-mining disaster data.
Locality preserving partial least squares discriminant analysis for face recognition

We propose a locality preserving partial least squares discriminant analysis (LPPLSDA) which adds a locality preserving feature to the conventional partial least squares discriminant analysis(PLS-DA). The locality preserving feature captures the within group structural information via a similarity graph. The ability of LPPLS-DA to capture local structures allows it to be better suited for face recognition. We evaluate the performance of our proposed method on several benchmarked face databases which offer different levels of complexity in terms of sample size as well as image acquisition conditions. The experimental results indicate that, for each database used, the proposed method consistently outperformed the conventional PLS-DA method.
A Modelling Approach to Generate Representative UAV Trajectories Using PSO

We propose a trajectory generation algorithm (STGA) that represents realistically and stochastically trajectories followed by unmanned air vehicles (UAVs), in particular quadrotors UAVs. It is meant to be a tool for testing localization, state estimation and control algorithms. We propose to firstly model a number of representative flight scenarios. For each scenario, stochastic trajectories are generated. They follow a parametric non-linear model whose parameters are determined using a multi-objective evolutionary optimization method called particle swarm optimization (PSO). Numerical results are reported to verify feasibility in comparison to pure random unconstrained trajectory algorithm.
Thermal free entanglement of a <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e544" altimg="si21.svg"><mml:mi>Λ</mml:mi></mml:math> -type three-level atom and bimodal photons in an optical cavity

Quantum entanglement is usually affected by the temperature and surrounding environment, thus it has been observed at low temperatures. The creation and manipulation of entanglement at finite temperatures is of crucial interest. To fulfill creating robust free (distillable) entanglement at finite temperatures, in this article, thermally-induced free entanglement of a Λ-type three-level atom and bimodal photons amid an optical lossless cavity, is investigated. We assume that the system is in thermal equilibrium with an environment thus the probabilities for finding the system in either of its eigenvalues are associated with the Boltzmann distribution. Introducing a conserved (Casimir) operator, a standard and effective procedure is then developed to calculate analytically the eigenvalues and eigenstates of the Hamiltonian, and thereby, to compute the thermal density operator and its partial transpose over atomic states. To justify the behavior of free (distillable) atom–photon entanglement, the quantitative form of Peres–Horodecki criteria, i.e. the negativity, is calculated. The analytical calculations show that the negativity vanishes at zero temperature, approaches to its maximum at a specific temperature and subsequently decreases asymptotically to zero. Furthermore, it is also illustrated that the maximum is larger for stronger atom–photon coupling while it decreases for greater atom–photon detuning. In addition, it is deduced from the representative figures that how the atom–photon structure parameters influence on the negativity.
Denoising method based on intrascale correlation in nonsubsampled contourlet transform for synthetic aperture radar images

Compared with speckle noise, the targets in synthetic aperture radar (SAR) images have strong directionality. Since the target and noise are different in the directional sub-bands on the same scale of nonsubsampled contourlet transform (NSCT), there are obvious differences in characteristics of the NSCT coefficients. Taking advantage of the differences, a denoising method for SAR image based on intrascale correlation of NSCT is proposed. The coefficients in different directional sub-bands in NSCT field are analyzed and the distribution law of the difference between maximum and minimum coefficients on the same scale is presented. Then, a threshold-determining strategy is proposed for identifying noise from targets. Finally, the proposed method is compared with some state-of-the-art denoising methods. It is observed from the results that our method presents the best performance in balance of noises and edge-preserving.
Network representation learning with ensemble methods

This paper investigates network representation learning which involves network structures and labels. Most methods proposed so far try to utilize different kinds of network related data available in just one perfect model to learn the set of perfect embeddings, and then evaluate its performance comparing with other methods for downstream applications, such as node classification or link prediction. In this paper, we introduce ensemble learning to the study of network representation learning. In conventional scenario, ensemble methods train multiple individual learners and combine all their results about the same input to produce an improved one. Inspired by this, we try to train multiple individual models using network related data, and aggregate all the learned embeddings as the final network representations, to expect a performance boost for downstream applications. In order to learn good and diverse individual models, bootstrap sampling is used and different individual model structures are designed. Experimental results show that the idea of aggregating network embeddings really works, and can outperform existing excellent methods under specific experimental setups.
Replay Attack Detection Using Generalized Cross-Correlation of Stereo Signal

In this paper, we propose a replay attack detection method using the generalized cross-correlation (GCC) of a stereo signal for automatic speaker verification. In particular, this method focuses on a specific replay attack characteristics when speech is not active. In a genuine speaker case, when speech is not active, the maximum value of GCC is low since surrounding noise arrives from any direction. In contrast, in a replay attack case, even when the played speech is not active, the maximum value of GCC is high since recorded noise or electromagnetic noise is played by a loudspeaker for replay attack. Based on this assumption, two approaches of replay attack detection are introduced. One is to use the minimum value of GCC in short pauses. The other one is to use the average value of GCC in silent periods before the start point and after the end point of a target utterance. In experiments, it is confirmed that the proposed methods achieve low error rates without environmental restrictions.
Robust Keypoint Detection

In keypoint regression, models are trained to consume an image and produce the x, y coordinates of some entity, e.g., a person's nose or corner of a document. Typical methods for doing so include linear regression over deep features to directly produce x, y coordinates and training networks to regress a dense heatmap around each keypoint. Recently, several works have independently proposed using Fully Convolutional Networks (FCNs) to predict latent heatmaps, from which the continuous coordinates of the key points can be computed using a fully differentiable layer. This allows end-to-end training of convolutional neural networks with dense output to learn keypoint locations. We show that three of the these proposed methods are equivalent to interpreting the heatmap as an arrangement of point-masses and computing the Center of Mass (CoM) of the masses. The CoM is heavily influenced by outliers, which can lead to imprecise predictions. To fix this problem, we propose a new differentiable layer that computes the spatial median point of the latent heatmap, which is more robust to outliers. We demonstrate that our approach outperforms both strong baselines and a state-of-the-art regression function in synthetic experiments and on 5 real world keypoint regression datasets including faces, human pose, fashion, and document images.
Home Worlds: Situating Domestic Computing in Everyday Life Through a Study of DIY Home Repair

We draw on a qualitative study of homeowners who engage in do-it-yourself (DIY) home repair and introduce "home worlds" as a conceptual lens to understand how interactions with the home as a built environment (local and mechanical engagements with its material components) are situated in broad, diffuse, and constraint-rich social worlds. A "home worlds" perspective challenges domestic computing inquiries to adopt a broader orientation when considering a home's users and requirements; our case of home repair, in particular, provokes us to consider the home as embedded in communities and framed in relation to the precarious constraints of everyday life. We discuss how these insights open up new avenues for future research and design of collaborative domestic computing technologies. ?
Comparison of discriminant analysis methods on the sphere

Discriminant analysis for spherical data (directional data in general) has not been studied to a great degree and most papers focus on one distribution, the rotationally symmetric (or isotropic) von Mises–Fisher. This is the first paper on maximum likelihood discriminant analysis with spherical data that considers non-rotationally symmetric distributions, while the k-nearest neighbors (k-NN) algorithm is included as a model-free alternative. Extensive Monte Carlo simulations and experiments with numerous real data yield multiple conclusions regarding the algorithms’ predictive performance and computational cost. Maximum likelihood discriminant analysis using rotationally non-symmetric distributions performed satisfactorily and surprisingly enough, rotationally symmetric distributions performed well in some cases. Overall, the k-NN algorithm is suggested because it is non-parametric hence flexible, computationally efficient, scalable to large sample sizes and suitable for big data, and on average is on par or outperforms the other methods.
Experimentally attacking quantum money schemes based on quantum retrieval games

Abstract The concept of quantum money (QM) was proposed by Wiesner in the 1970s. Its main advantage is that every attempt to copy QM unavoidably leads to imperfect counterfeits. In the Wiesner’s protocol, quantum banknotes need to be delivered to the issuing bank for verification. Thus, QM requires quantum communication which range is limited by noise and losses. Recently, Bozzio et al . (2018) have demonstrated experimentally how to replace challenging quantum verification with a classical channel and a quantum retrieval game (QRG). This brings QM significantly closer to practical realisation, but still thorough analysis of the revised scheme QM is required before it can be considered secure. We address this problem by presenting a proof-of-concept attack on QRG-based QM schemes, where we show that even imperfect quantum cloning can, under some circumstances, provide enough information to break a QRG-based QM scheme.
Moving Targets Detection for Satellite-Based Surveillance Video

With the development of staring array cameras onboard, LEO satellites can work as surveillance cameras by monitoring a particular area for dozens of seconds. In this paper, a new method named as DBM (Detection Box Model) for moving targets detection is proposed for satellite based surveillance video. Compared with previous work done by other researchers by detecting moving target on roads only, DBM is a generalized method to detect moving targets in different size for the whole scene. DBM consist of two main steps: data preparation and detection of DBM. Our experiments with SkySat-1 data show that, the detection performance can reach a satisfactory level in terms of both precision rate and recall rate.
Inertial sensor-based human activity recognition via ensemble extreme learning machines optimized by quantum-behaved particle swarm

Sensor-based human activity recognition has a wide range of applications including caring the elderly, helping chronic patients, fitness, etc. As a new kind of single-layer feedforward network, extreme learning machine (ELM) has faster training speed and stronger generalization performance, which p rovides an effective technique for activity recognition. However, due to the random determination of input weights and hidden deviations, the ELM may converge to a local minimum in some cases. Therefore, in order to overcome the shortcomings of ELM and design a reliable and accurate recognition system, this paper proposes a multi-classifier recognition framework which utilizes extreme learning machines optimized by quantum-behaved particle swarm optimization (QPSO) as the base classifiers. The quantum-behaved particle swarm optimization was used to select the optimal parameters of base ELMs which are trained on different attribute characteristics. The proposed approach is assessed with two inertial sensor data sets. Comparative experiments with other optimization methods indicated that QPSO-ELM has better accuracy performance for inertial sensor-based human activityrecognition. The experiment showed that the proposed ensemble QPSO-ELM recognition method achieves an accuracy of 96.4% for recognizing six activities.
Deep Interpretable Mortality Model for Intensive Care Unit Risk Prediction

Estimating the mortality of patients plays a fundamental role in an intensive care unit (ICU). Currently, most learning approaches are based on deep learning models. However, these approaches in mortality prediction suffer from two problems: (i) the specificity of causes of death are not considered in the learning process due to the different diseases, and symptoms are mixed-used without diversification and localization; (ii) the learning outcome for the mortality prediction is not self-explainable for the clinicians. In this paper, we propose a Deep Interpretable Mortality Model (DIMM), which employs Multi-Source Embedding, Gated Recurrent Units (GRU), Attention mechanism and Focal Loss techniques to prognosticate mortality prediction. We intensified the mortality prediction by considering the different clinical measures, medical treatments and the heterogeneity of the disease. More importantly, for the first time, in this framework, we use a separate evidence-based interpreter named Highlighter to interpret the prediction model, which makes the prediction understandable and trustworthy to clinicians. We demonstrate that our approach achieves state-of-the-art performance in mortality prediction and can get an interpretable prediction on four different diseases.
Person re-identification with dictionary learning regularized by stretching regularization and label consistency constraint

Person re-identification (PRID) is a rather challenging task due to the ambiguity of visual appearance. In this paper, we develop a dictionary-based projection transformation learning approach, where the idea of metric learning and dictionary learning are introduced into a unified framework to make full use of their respective advantages. More specifically, to cope with the challenge caused by dramatic changes in visual appearance, we first project the image features of pedestrian into a discriminative subspace to make the same person from different views with the same coding coefficients. Moreover, we develop a new stretch regularization to make the distance between different pedestrian images larger than that of the same pedestrian images so as to reduce the similarity exhibited by different pedestrian images. Additionally, we develop a label consistency constraint and integrate it into the dictionary learning and then we obtain the ensemble learning model of identity discriminator and dictionary. As a result, the coding coefficient and the corresponding label are bridged and the supervision from the labeled samples is also better exploited. Experimental results on five popular person re-identification benchmarks indicate that the approach developed in this paper has higher identification performance than some state-of-the-art methods.