Reviewerly/exHarmony huggingface dataset:
Robust FIR State Estimation of Dynamic Processes Corrupted by Outliers

The outlier is a common issue in the design of state estimators for an industrial process. In this paper, a robust finite impulse response (FIR) filter is proposed for time-invariant state-space models with its noise following the Student's t distributions. A batch solution is first derived by maximizing the likelihood, and then, an equivalent iterative realization is given to provide a clearer insight into the FIR structure. It shows that the essence of the proposed approach is the convergence of the maximum likelihood estimates in horizon scale through iterations, and the state estimate at each sampling instant is independent of the degree-of-freedom (DOF) parameter of the Student's t distribution. Based on this, a modified algorithm that updates the DOF parameter in each iteration is further proposed. Applications to a moving target tracking example and a 3-DOF helicopter system demonstrate that the proposed methods can exhibit good immunity against outliers during the filtering.
NIPM-sWMF: Toward Efficient FPGA Design for High-Definition Large-Disparity Stereo Matching

Large disparity stereo matching is critical to the application of a stereo vision system especially for outdoor scenes. Nevertheless, how to efficiently design high accuracy large-disparity stereo matching on a field-programmable gate array (FPGA) is still a grand challenge. The computational complexity of previously proposed stereo matching is inevitably proportional to disparity range; hence their hardware designs become very inefficient when the disparity range is large. Motivated by the original PatchMatch and weighted median filtering (WMF) algorithms, this paper proposes a non-iterative PatchMatch and separable WMF (NIPM-sWMF) algorithm to significantly reduce the computational complexity of stereo matching and make it independent of disparity range. Moreover, we also propose a fully pipelined architecture design on FPGA that employs several hardware techniques to efficiently implement the proposed NIPM-sWMF. The disparity quality of the proposed NIPM-sWMF algorithm is evaluated on both KITTI2015 and Middlebury V3 stereo data sets, and the proposed architecture design is implemented and synthesized on Xilinx FPGA. Evaluation results demonstrate that the proposed NIPM-sWMF design on FPGA reaches the real-time performance of 1920 × 1080@60 Hz at the disparity range of 128, and can achieve almost the same disparity estimation accuracy, 4.5× processing throughput, while reducing the hardware cost of LUT, Register, DSP, and BRAM by 40%, 47%, 100%, and 68%, respectively, compared with the reference stereo matching design. Therefore, the proposed NIPM-sWMF design is an efficient way to address the challenge of large-disparity stereo matching.
Service Degradability Supported by Forecasting System in Optical Data Center Networks

A large-scale distributed scientific computing develops rapidly, making the interconnection of data centers (DCs) become the focus in both academic and industrial communities. Since the fiber transmission has the characteristics of high capacity, low power consumption, and strong reliability, establishing the optical data center network (ODCN)-where DCs are interconnected via optical backbone-is an inevitable trend. In ODCNs, the oversize inter-DC traffic demand suffers from the denial-of-service caused by network overload. Therefore, we propose a provident service degradation (service degradability) framework based on the traffic-demand forecasting system. This forecasting system helps us to precisely know when service degradability becomes necessary, together with the exact location of overload. As a result, service degradability can be adopted at appropriate times and locations. Specifically, we perform bandwidth degradability to overcome the overload of optical backbone, while utilizing computing-resource degradability if there is a DC overload. We simulate and analyze the performance of our prediction system and service degradability algorithm. The simulation results show that the system's prediction result well matches actual traffic demand, and the average absolute relative error is merely 0.0507; and compared with benchmarks, our algorithm has the better performance on maximizing service provider profit via mitigating network congestion.
Nonlinear Dimensionality Reduction for Discriminative Analytics of Multiple Datasets

Principal component analysis (PCA) is widely used for feature extraction and dimensionality reduction, with documented merits in diverse tasks involving high-dimensional data.PCA copes with one dataset at a time, but it is challenged when it comes to analyzing multiple datasets jointly.In certain data science settings however, one is often interested in extracting the most discriminative information from one dataset of particular interest (a.k.a.target data) relative to the other(s) (a.k.a.background data).To this end, this paper puts forth a novel approach, termed discriminative (d) PCA, for such discriminative analytics of multiple datasets.Under certain conditions, dPCA is proved to be least-squares optimal in recovering the latent subspace vector unique to the target data relative to background data.To account for nonlinear data correlations, (linear) dPCA models for one or multiple background datasets are generalized through kernelbased learning.Interestingly, all dPCA variants admit an analytical solution obtainable with a single (generalized) eigenvalue decomposition.Finally, substantial dimensionality reduction tests using synthetic and real datasets are provided to corroborate the merits of the proposed methods.
On the Duality Between Belief Networks and Feed-Forward Neural Networks

This paper addresses the duality between the deterministic feed-forward neural networks (FF-NNs) and linear Bayesian networks (LBNs), which are the generative stochastic models representing probability distributions over the visible data based on a linear function of a set of latent (hidden) variables. The maximum entropy principle is used to define a unique generative model corresponding to each FF-NN, called projected belief network (PBN). The FF-NN exactly recovers the hidden variables of the dual PBN. The large- N asymptotic approximation to the PBN has the familiar structure of an LBN, with the addition of an invertible nonlinear transformation operating on the latent variables. It is shown that the exact nature of the PBN depends on the range of the input (visible) data details for the three cases of input data range are provided. The likelihood function of the PBN is straightforward to calculate, allowing it to be used as a generative classifier. An example is provided in which a generative classifier based on the PBN has comparable performance to a deep belief network in classifying handwritten characters. In addition, several examples are provided that demonstrate the duality relationship, for example, by training networks from either side of the duality.
Detection of double compression in HEVC videos based on TU size and quantised DCT coefficients

With the advent of sophisticated and low-cost video editing software, digital videos are highly vulnerable to be tampered. The authenticity and integrity identification of digital videos is an urgent issue. In this study, an effective method to detect double High Efficiency Video Coding (HEVC) video compression with different quantisation parameter (QP) is proposed, which often occurs in the video tampering process. First, the effects of QP on the distributions of Discrete Cosine Transform (DCT) coefficients and Transform Unit (TU) size are analysed. Then a feature set including 17 features is derived from quantised DCT coefficients and TU size. It can characterize the statistical differences between single and double compressed videos. Finally, the Library for Support Vector Machine classifier is exploited to identify whether a given HEVC video has been double compressed or not. Experimental results demonstrate that the authors’ detection method has a good comprehensive performance.
Redundant-Signed-Digit-Based High Speed Elliptic Curve Cryptographic Processor

In this paper, a high speed elliptic curve cryptographic (ECC) processor for National Institute of Standards and Technology (NIST) recommended prime [Formula: see text] is proposed. The modular arithmetic components in the proposed ECC processor are highly optimized at both architectural level and circuit level. Redundant-signed-digit (RSD) arithmetic is adopted in the modular arithmetic components to avoid lengthy carry propagation delay. A high speed modular multiplier is designed based on an efficient segmentation and pipelining strategy. The clock cycle count is reduced as result of the segmentation, whereas operating frequency and throughput are significantly increased due to the pipelining. An optimized pipelined architecture for modular division is also presented which is suitable for the design of ECC processor using projective coordinates. The Joye’s double and add (DAA) algorithm based on [Formula: see text]-only common [Formula: see text] (co-[Formula: see text]) coordinate is adopted at the system level for its regular and efficient behavior. The proposed ECC processor is flexible and can be implemented using any field programmable gate array (FPGA) family or standard cell libraries. The proposed ECC processor executes a single elliptic curve (EC) point multiplication (PM) operation in 0.47[Formula: see text]ms at a maximum frequency of 327[Formula: see text]MHz on Virtex-6 FPGA. The implementation results demonstrate that the proposed ECC processor outperforms the other contemporary designs reported in the literature in terms of speed and [Formula: see text] metrics.
Handling Massive <i>N</i> -Gram Datasets Efficiently

Two fundamental problems concern the handling of large n -gram language models: indexing , that is, compressing the n -grams and associated satellite values without compromising their retrieval speed, and estimation , that is, computing the probability distribution of the n -grams extracted from a large textual source. Performing these two tasks efficiently is vital for several applications in the fields of Information Retrieval, Natural Language Processing, and Machine Learning, such as auto-completion in search engines and machine translation. Regarding the problem of indexing, we describe compressed, exact, and lossless data structures that simultaneously achieve high space reductions and no time degradation with respect to the state-of-the-art solutions and related software packages. In particular, we present a compressed trie data structure in which each word of an n -gram following a context of fixed length k , that is, its preceding k words, is encoded as an integer whose value is proportional to the number of words that follow such context. Since the number of words following a given context is typically very small in natural languages, we lower the space of representation to compression levels that were never achieved before, allowing the indexing of billions of strings. Despite the significant savings in space, our technique introduces a negligible penalty at query time. Specifically, the most space-efficient competitors in the literature, which are both quantized and lossy, do not take less than our trie data structure and are up to 5 times slower. Conversely, our trie is as fast as the fastest competitor but also retains an advantage of up to 65% in absolute space. Regarding the problem of estimation, we present a novel algorithm for estimating modified Kneser-Ney language models that have emerged as the de-facto choice for language modeling in both academia and industry thanks to their relatively low perplexity performance. Estimating such models from large textual sources poses the challenge of devising algorithms that make a parsimonious use of the disk. The state-of-the-art algorithm uses three sorting steps in external memory: we show an improved construction that requires only one sorting step by exploiting the properties of the extracted n -gram strings. With an extensive experimental analysis performed on billions of n -grams, we show an average improvement of 4.5 times on the total runtime of the previous approach.
Efficient Replica Migration Scheme for Distributed Cloud Storage Systems

With the wide adoption of large-scale internet services and big data, the cloud has become the ideal environment to satisfy the ever-growing storage demand. In this context, data replication has been touted as the ultimate solution to improve data availability and reduce access time. However, replica management systems usually need to migrate and create a large number of data replicas over time between and within data centers, incurring a large overhead in terms of network load and availability. In this paper, we propose CRANE, an effiCient Replica migrAtion scheme for distributed cloud Storage systEms. CRANE complements any replica placement algorithm by efficiently managing replica creation in geo-distributed infrastructures in order to (1) minimize the time needed to copy the data to the new replica location, (2) avoid network congestion, and (3) ensure the minimum desired availability for the data. Through simulation and experimental results, we show that CRANE provides a sub-optimal solution for the replica migration problem with lower computational complexity than its integer linear program formulation. We also show that, compared to OpenStack Swift, CRANE is able to reduce by up to 60 percent the replica creation and migration time and by up to 50 percent the inter-data center network traffic while ensuring the minimum required data availability.
Robust Causal Structure Learning with Some Hidden Variables

Summary We introduce a new method to estimate the Markov equivalence class of a directed acyclic graph (DAG) in the presence of hidden variables, in settings where the underlying DAG among the observed variables is sparse, and there are a few hidden variables that have a direct effect on many of the observed variables. Building on the so-called low rank plus sparse framework, we suggest a two-stage approach which first removes the effect of the hidden variables and then estimates the Markov equivalence class of the underlying DAG under the assumption that there are no remaining hidden variables. This approach is consistent in certain high dimensional regimes and performs favourably when compared with the state of the art, in terms of both graphical structure recovery and total causal effect estimation.
Robust Tracking with Model Mismatch for Fast and Safe Planning: An SOS Optimization Approach

In the pursuit of real-time motion planning, a commonly adopted practice is to compute trajectories by running a planning algorithm on a simplified, low-dimensional dynamical model, and then employ a feedback tracking controller that tracks such a trajectory by accounting for the full, high-dimensional system dynamics. While this strategy of planning with model mismatch generally yields fast computation times, there are no guarantees of dynamic feasibility, which hampers application to safety-critical systems. Building upon recent work that addressed this problem through the lens of Hamilton-Jacobi (HJ) reachability, we devise an algorithmic framework whereby one computes, offline, for a pair of "planner" (i.e., low-dimensional) and "tracking" (i.e., high-dimensional) models, a feedback tracking controller and associated tracking bound. This bound is then used as a safety margin when generating motion plans via the low-dimensional model. Specifically, we harness the computational tool of sum-of-squares (SOS) programming to design a bilinear optimization algorithm for the computation of the feedback tracking controller and associated tracking bound. The algorithm is demonstrated via numerical experiments, with an emphasis on investigating the trade-off between the increased computational scalability afforded by SOS and its intrinsic conservativeness. Collectively, our results enable scaling the appealing strategy of planning with model mismatch to systems that are beyond the reach of HJ analysis, while maintaining safety guarantees.
Dispersion Ratio based Decision Tree Model for Classification

In predictive tasks like classification, Information Gain (IG) based Decision Tree is very popularly used. However, IG method has some inherent problems like its preference towards choosing attributes with higher number of distinct values as the splitting attribute in case of nominal attributes and another problem is associated with imbalanced datasets. Most of the real-world datasets have many nominal attributes, and those nominal attributes may have many number of distinct values. In this paper, we have tried to point out these characteristics of the datasets while discussing the performance of our proposed approach. Our approach is a variant of the traditional Decision Tree model and uses a new technique called Dispersion_Ratio, a modification of existing Correlation Ratio (CR) method. The whole approach is divided into two phases - firstly, the dataset is discretised using a discretization module and secondly, the preprocessed dataset is used to build a Dispersion Ratio based Decision Tree model. The proposed method does not prefer the attributes with many unique values and indifferent about class distribution. It performs better than previously proposed CR based Decision Tree (CRDT) Model since an efficient discretization module has been added with it. We have evaluated the performance of our approach on some benchmark datasets from various domains to demonstrate the effectiveness of the proposed technique and also compared our model with Information Gain, Gain Ratio and Gini Index based models. Result shows that the proposed model outperforms other models in majority of the cases that we have considered in our experiment.
Distance transform network for shape analysis

Shape is known as an important source of information in object analyzes and has been studied for many years for this context. In the object classification task, several challenges such as variations in rotation and scale, noise, and degradation make the problem even harder. This paper proposes the Distance Transform Network (DTN), which combines the power of networks and the richness of information provided from Euclidean distance transform for shape analysis. First, a distance map is obtained by the application of the Euclidean distance transform on each contour. Thus, each radius of dilatation is modeled as a network. Then, degree measurements of the dynamic evolution network are used to characterize the contour. Finally, a robust feature vector is composed by characteristics of different radiuses of dilatation. The methodology was tested in seven benchmarks available databases, including two otolith and three sets containing shape of leaves species which presents challenging contours with a lot of intra-class variations. The results against literature methods show that the proposed DTN is effective for natural shapes classification according to the higher success rates obtained in all cases. The advantages of our approach include robustness to degradation and noise, and tolerance to variations in the shapes scale and orientation.
Enhancing semantic image retrieval with limited labeled examples via deep learning

With the rapid growth of the Internet, a large number of multi-modal objects such as images and their social tags can easily be downloaded from the Web. The use of such objects can improve training process in the presence of few or limited number of labeled images provided. In order to leverage these unlabeled and labeled multi-modal Web objects for enhancing the performance of unimodal image retrieval, we propose a novel approach called Semi-supervised Multi-concept Retrieval to semantic image retrieval via Deep Learning (SMRDL) in this paper. Differing from conventional methods that use multiple and independent concepts in a semantic multi-concept query, our proposed approach regards multiple concepts as a holistic scene for multi-concept scene learning of unimodal retrieval. In particular, we first train a multi-modal Convolutional Neural Network (CNN) as a concept classifier for images and texts, and then use it to annotate unlabeled Web images. For each of unlabeled images, we then obtain its most relevant concept annotations by using a new strategy of annotation promotion. Finally, we employ a unimodal visual CNN to train a concept classifier in visual modality, which uses both unlabeled and labeled examples for concept learning of unimodal retrieval. The results of our comprehensive experiments on two datasets of MIR Flickr 2011 and NUS-WIDE have shown that our proposed approach outperforms several state-of-the-art methods.
Unpacking the black box: Examining the (de)Gender categorization effect in human-machine communication

Although studies have explored the gender categorization effect in both face-to-face and mediated communication environments in relation to the use of gender-linked language, whether the effect still holds in the context of human-machine communication (HMC) remains unknown. To examine this question, in this study, we asked 245 participants to assign gender categories to targets after viewing transcripts of the targets' conversations with a chatbot and a human interlocutor. The results showed that the participants had a better-than-chance probability (68.98%) of correctly guessing the gender of the target based on the target-human conversation transcripts. However, the predictive power of the language cues decreased sharply to a less-than-chance level (42.86%) based on target-chatbot conversation transcripts. We also examined the roles that social media use and demographics played in the gender categorization processes in both computer-mediated communication and HMC contexts. Although far from conclusive, our results suggested that there were significant differences between the styles of conversation in the target-chatbot and target-human interlocutor transcripts. These findings imply that people use different approaches when communicating with human and non-human interlocutors.
Membership Privacy for Fully Dynamic Group Signatures

Group signatures present a compromise between the traditional goals of digital signatures and the need for signer privacy, allowing for the creation of unforgeable signatures in the name of a group which reveal nothing about the actual signer's identity beyond their group membership. An important consideration that is absent in prevalent models is that group membership itself may be sensitive information, especially if group membership is dynamic, i.e. membership status may change over time. We address this issue by introducing formal notions of membership privacy for fully dynamic group signature schemes, which can be easily integrated into the most expressive models of group signature security to date. We then propose a generic construction for a fully dynamic group signature scheme with membership privacy that is based on signatures with flexible public key (SFPK) and signatures on equivalence classes (SPSEQ). Finally, we devise novel techniques for SFPK to construct a highly efficient standard model scheme (i.e. without random oracles) that provides shorter signatures than even the non-private state-of-the-art from standard assumptions. This shows that, although the strictly stronger security notions we introduce have been completely unexplored in the study of fully dynamic group signatures so far, they do not come at an additional cost in practice.