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  • An expectation operator for belief functions in the Dempster–Shafer theory* * Presented at the 11th Workshop on Uncertainty Processing (WUPES'18), Třeboň, Czech Republic, June 6–9, 2018.View all notes
    Int. J. Gen. Syst. (IF 2.259) Pub Date : 2019-09-02
    Prakash P. Shenoy

    The main contribution of this paper is a new definition of expected value of belief functions in the Dempster–Shafer (D–S) theory of evidence. Our definition shares many of the properties of the expectation operator in probability theory. Also, for Bayesian belief functions, our definition provides the same expected value as the probabilistic expectation operator. A traditional method of computing expected of real-valued functions is to first transform a D–S belief function to a corresponding probability mass function, and then use the expectation operator for probability mass functions. Transforming a belief function to a probability function involves loss of information. Our expectation operator works directly with D–S belief functions. Another definition is using Choquet integration, which assumes belief functions are credal sets, i.e. convex sets of probability mass functions. Credal sets semantics are incompatible with Dempster's combination rule, the center-piece of the D–S theory. In general, our definition provides different expected values than, e.g. if we use probabilistic expectation using the pignistic transform or the plausibility transform of a belief function. Using our definition of expectation, we provide new definitions of variance, covariance, correlation, and other higher moments and describe their properties.

    更新日期:2020-02-18
  • Inexact Block Coordinate Descent Algorithms for Nonsmooth Nonconvex Optimization
    IEEE Trans. Signal Process. (IF 5.230) Pub Date : 2019-12-11
    Yang Yang; Marius Pesavento; Zhi-Quan Luo; Björn Ottersten

    In this paper, we propose an inexact block coordinate descent algorithm for large-scale nonsmooth nonconvex optimization problems. At each iteration, a particular block variable is selected and updated by inexactly solving the original optimization problem with respect to that block variable. More precisely, a local approximation of the original optimization problem is solved. The proposed algorithm has several attractive features, namely, i) high flexibility, as the approximation function only needs to be strictly convex and it does not have to be a global upper bound of the original function; ii) fast convergence, as the approximation function can be designed to exploit the problem structure at hand and the stepsize is calculated by the line search; iii) low complexity, as the approximation subproblems are much easier to solve and the line search scheme is carried out over a properly constructed differentiable function; iv) guaranteed convergence of a subsequence to a stationary point, even when the objective function does not have a Lipschitz continuous gradient. Interestingly, when the approximation subproblem is solved by a descent algorithm, convergence of a subsequence to a stationary point is still guaranteed even if the approximation subproblem is solved inexactly by terminating the descent algorithm after a finite number of iterations. These features make the proposed algorithm suitable for large-scale problems where the dimension exceeds the memory and/or the processing capability of the existing hardware. These features are also illustrated by several applications in signal processing and machine learning, for instance, network anomaly detection and phase retrieval. To promote reproducible research, the simulation code is available at https://github.com/optyang/BSCA .

    更新日期:2020-02-18
  • Multiple Bayesian Filtering as Message Passing
    IEEE Trans. Signal Process. (IF 5.230) Pub Date : 2020-01-09
    Giorgio M. Vitetta; Pasquale Di Viesti; Emilio Sirignano; Francesco Montorsi

    In this manuscript, a general method for deriving filtering algorithms that involve a network of interconnected Bayesian filters is proposed. This method is based on the idea that the processing accomplished inside each of the Bayesian filters and the interactions between them can be represented as message passing algorithms over a proper graphical model. The usefulness of our method is exemplified by developing new filtering techniques, based on the interconnection of a particle filter and an extended Kalman filter, for conditionally linear Gaussian systems. Numerical results for two specific dynamic systems evidence that the devised algorithms can achieve a better complexity-accuracy tradeoff than marginalized particle filtering and multiple particle filtering.

    更新日期:2020-02-18
  • Distributions and Power of Optimal Signal-Detection Statistics in Finite Case
    IEEE Trans. Signal Process. (IF 5.230) Pub Date : 2020-01-16
    Hong Zhang; Jiashun Jin; Zheyang Wu

    For detecting weak and sparse signals by a set of $n$ input $p$ -values, the Higher Criticism (HC) type statistics, the Berk-Jones (B-J) type statistics, and the phi-divergence statistics have the equivalent asymptotic optimality as $n$ goes to infinity. However, they can have significantly different performance in practical data analysis, where $n$ is always finite and even very small. To address this problem in a broader context, this paper introduces a general family of goodness-of-fit statistics, called the gGOF, which unifies a broad signal-detection statistics including these optimal ones. Efficient and accurate analytical calculations for the distributions of the gGOF statistics are provided under arbitrary i.i.d. continuous models of the null and the alternative hypotheses. Based on that, a systematic power study reveals that in finite case, the number of signals is often more relevant than the signal proportion. The HC and the reverse HC have advantages for relatively sparser and denser signals, respectively, while the B-J is more robust. A general framework is given to apply the gGOF into data analysis based on the generalized linear models. An application to the SNP-set based genome-wide association study (GWAS) for Crohn's disease shows that these optimal statistics have a good potential for detecting novel disease genes with weak SNP effects. The calculations have been implemented into an R package SetTest and published on the CRAN.

    更新日期:2020-02-18
  • Gridless Parameter Estimation for One-Bit MIMO Radar With Time-Varying Thresholds
    IEEE Trans. Signal Process. (IF 5.230) Pub Date : 2020-01-29
    Feng Xi; Yijian Xiang; Shengyao Chen; Arye Nehorai

    We investigate the one-bit MIMO (1b-MIMO) radar that performs one-bit sampling with a time-varying threshold in the temporal domain and employs compressive sensing in the spatial and Doppler domains. The goals are to significantly reduce the hardware cost, energy consumption, and amount of stored data. The joint angle and Doppler frequency estimations from noisy one-bit data are studied. By showing that the effect of noise on one-bit sampling is equivalent to that of sparse impulsive perturbations, we formulate the one-bit $\ell _1$ -regularized atomic-norm minimization (1b-ANM-L1) problem to achieve gridless parameter estimation with high accuracy. We also develop an iterative method for solving the 1b-ANM-L1 problem via the alternating direction method of multipliers. The Cram $\acute{\text{e}}$ r-Rao bound (CRB) of the 1b-MIMO radar is analyzed, and the analytical performance of one-bit sampling with two different threshold strategies is discussed. Numerical experiments are presented to show that the 1b-MIMO radar can achieve high-resolution parameter estimation with a largely reduced amount of data.

    更新日期:2020-02-18
  • Min-Max Metric for Spectrally Compatible Waveform Design Via Log-Exponential Smoothing
    IEEE Trans. Signal Process. (IF 5.230) Pub Date : 2020-01-23
    Wen Fan; Junli Liang; Hing Cheung So; Guangshan Lu

    To ensure the proper functioning of active sensing systems in the presence of interferences from other electromagnetic equipment in a spectrally crowded environment, we devise four new solutions for spectrally compatible waveform design based on the min-max metric, namely, minimum modulus dynamic range, min-max spectral shape, minimum weighted peak sidelobe level, and minimum similarity. To address the resultant nonconvex and nonsmooth optimization problems, a unified algorithm framework is proposed. That is, we first approximate the min-max metric by using the “log-exponential smoothing” technique, then apply majorization-minimization to smooth and simplify the approximate optimization formulations, and finally use the Karush-Kuhn-Tucker theory to tackle the majorized problems. Besides, we develop an adaptive approximation parameter selection scheme, which monotonically decreases the approximation error at each iteration. The proposed algorithms are computationally efficient as they can be realized via fast Fourier transform. Finally, numerical examples are presented to demonstrate their excellent performance.

    更新日期:2020-02-18
  • Prospect Theoretic Utility Based Human Decision Making in Multi-Agent Systems
    IEEE Trans. Signal Process. (IF 5.230) Pub Date : 2020-01-30
    Baocheng Geng; Swastik Brahma; Thakshila Wimalajeewa; Pramod K. Varshney; Muralidhar Rangaswamy

    This paper studies human decision making via a utility based approach in a binary hypothesis testing framework that includes the consideration of individual behavioral disparity. Unlike rational decision makers who make decisions so as to maximize their expected utility, humans tend to maximize their subjective utilities, which are usually distorted due to cognitive biases. We use the value function and the probability weighting function from prospect theory to model human cognitive biases and obtain their subjective utility function in decision making. First, we show that the decision rule which maximizes the subjective utility function reduces to a likelihood ratio test (LRT). Second, to capture the unreliable nature of human decision making behavior, we model the decision threshold of a human as a Gaussian random variable, whose mean is determined by his/her cognitive bias, and the variance represents the uncertainty of the agent while making a decision. This human decision making framework under behavioral biases incorporates both cognitive biases and uncertainties. We consider several decision fusion scenarios that include humans. Extensive numerical results are provided throughout the paper to illustrate the impact of human behavioral biases on the performance of the decision making systems.

    更新日期:2020-02-18
  • Bayesian Cooperative Localization Using Received Signal Strength With Unknown Path Loss Exponent: Message Passing Approaches
    IEEE Trans. Signal Process. (IF 5.230) Pub Date : 2020-01-29
    Di Jin; Feng Yin; Carsten Fritsche; Fredrik Gustafsson; Abdelhak M. Zoubir

    We propose a Bayesian framework for the received-signal-strength-based cooperative localization problem with unknown path loss exponent. Our purpose is to infer the marginal posterior of each unknown parameter: the position or the path loss exponent. This probabilistic inference problem is solved using message passing algorithms that update messages and beliefs iteratively. For numerical tractability, we combine the variable discretization and Monte-Carlo-based numerical approximation schemes. To further improve computational efficiency, we develop an auxiliary importance sampler that updates the beliefs with the help of an auxiliary variable. An important ingredient of the proposed auxiliary importance sampler is the ability to sample from a normalized likelihood function. To this end, we develop a stochastic sampling strategy that mathematically interprets and corrects an existing heuristic strategy. The proposed message passing algorithms are analyzed systematically in terms of computational complexity, demonstrating the computational efficiency of the proposed auxiliary importance sampler. Various simulations are conducted to validate the overall good performance of the proposed algorithms.

    更新日期:2020-02-18
  • Blind Over-the-Air Computation and Data Fusion via Provable Wirtinger Flow
    IEEE Trans. Signal Process. (IF 5.230) Pub Date : 2020-01-29
    Jialin Dong; Yuanming Shi; Zhi Ding

    Over-the-air computation (AirComp) shows great promise to support fast data fusion in Internet-of-Things (IoT) networks. AirComp typically computes desired functions of distributed sensing data by exploiting superposed data transmission in multiple access channels. To overcome its reliance on channel state information (CSI), this work proposes a novel blind over-the-air computation (BlairComp) without requiring CSI access, particularly for low complexity and low latency IoT networks. To solve the resulting non-convex optimization problem without the initialization dependency exhibited by the solutions of a number of recently proposed efficient algorithms, we develop a Wirtinger flow solution to the BlairComp problem based on random initialization . We establish the global convergence guarantee of Wirtinger flow with random initialization for BlairComp problem, which enjoys a model-agnostic and natural initialization implementation for practitioners with theoretical guarantees. Specifically, in the first stage of the algorithm, the iteration of randomly initialized Wirtinger flow given sufficient data samples can enter a local region that enjoys strong convexity and strong smoothness within a few iterations. We also prove the estimation error of BlairComp in the local region to be sufficiently small. We show that, at the second stage of the algorithm, its estimation error decays exponentially at a linear convergence rate.

    更新日期:2020-02-18
  • Low-Complexity Methods for Estimation After Parameter Selection
    IEEE Trans. Signal Process. (IF 5.230) Pub Date : 2020-01-29
    Nadav Harel; Tirza Routtenberg

    Statistical inference of multiple parameters often involves a preliminary parameter selection stage. The selection stage has an impact on subsequent estimation, for example by introducing a selection bias. The post-selection maximum likelihood (PSML) estimator is shown to reduce the selection bias and the post-selection mean-squared-error (PSMSE) compared with conventional estimators, such as the maximum likelihood (ML) estimator. However, the computational complexity of the PSML is usually high due to the multi-dimensional exhaustive search for a global maximum of the post-selection log-likelihood (PSLL) function. Moreover, the PSLL involves the probability of selection that, in general, does not have an analytical form. In this paper, we develop new low-complexity post-selection estimation methods for a two-stage estimation after parameter selection architecture. The methods are based on implementing the iterative maximization by parts (MBP) approach, which is based on the decomposition of the PSLL function into “easily-optimized” and complicated parts. The proposed second-best PSML method applies the MBP-PSML algorithm with a pairwise probability of selection between the two highest-ranked parameters w.r.t. the selection rule. The proposed SA-PSML method is based on using stochastic approximation (SA) and Monte Carlo integrations to obtain a non-parametric estimation of the gradient of the probability of selection and then applying the MBP-PSML algorithm on this approximation. For low-complexity performance analysis, we develop the empirical post-selection Cram $\acute{\text{e}}$ r-Rao-type lower bound. Simulations demonstrate that the proposed post-selection estimation methods are tractable and reduce both the bias and the PSMSE, compared with the ML estimator, while only requiring moderate computational complexity.

    更新日期:2020-02-18
  • Complement for two-way alternating automata
    Acta Inform. (IF 1.042) Pub Date : 2020-02-17
    Viliam Geffert, Christos A. Kapoutsis, Mohammad Zakzok

    Abstract We consider the problem of converting a two-way alternating finite automaton (2AFA) with n states to a 2AFA accepting the complement of its language. Complementing is trivial for halting2AFAs, by swapping the roles of existential and universal decisions and the roles of accepting and rejecting states. However, since 2AFAs do not have resources to detect infinite loops by counting executed steps, it was not known whether the cost of complementing is polynomial in n in the general case. Here we shall show that 2AFAs can be complemented by using \(O(n^7)\) states.

    更新日期:2020-02-18
  • Efficient maximum clique computation and enumeration over large sparse graphs
    VLDB J. (IF 1.973) Pub Date : 2020-02-15
    Lijun Chang

    Abstract This paper studies the problem of maximum clique computation (MCC) over sparse graphs, as large real-world graphs are usually sparse. In the literature, the problem of MCC over sparse graphs has been studied separately and less extensively than its dense counterpart—MCC over dense graphs—and advanced algorithmic techniques that are developed for MCC over dense graphs have not been utilized in the existing MCC solvers for sparse graphs. In this paper, we design an algorithm \(\mathsf {MC\text {-}BRB}\) for sparse graphs which transforms an instance of MCC over a large sparse graph G to instances of k-clique finding (KCF) over dense subgraphs of G, each of which can be computed by the existing MCC solvers for dense graphs. To further improve the efficiency, we then develop a new branch-reduce-&-bound framework for KCF over dense graphs by proposing light-weight reducing techniques and leveraging the advanced branching and bounding techniques that are used in the existing MCC solvers for dense graphs. In addition, we also design an ego-centric algorithm \(\mathsf {MC\text {-}EGO}\) for heuristically computing a near-maximum clique in near-linear time, and we extend our \(\mathsf {MC\text {-}BRB}\) algorithm to enumerate all maximum cliques. Finally, we parallelize our algorithms to exploit multiple CPU cores. We conduct extensive empirical studies on large real graphs and demonstrate the efficiency and effectiveness of our techniques.

    更新日期:2020-02-18
  • Secure Transmission Design in HARQ Assisted Cognitive NOMA Networks
    IEEE Trans. Inform. Forensics Secur. (IF 6.211) Pub Date : 2020-01-20
    Zhongwu Xiang; Weiwei Yang; Yueming Cai; Zhiguo Ding; Yi Song

    In this paper, we design a secure transmission scheme in hybrid automatic repeat request (HARQ) assisted cognitive non-orthogonal multiple access (NOMA) networks, where a security-required user (SRU) is paired with a quality of service (QoS)-sensitive user (QSU) to perform NOMA. To elaborate, the QoS requirement of the QSU is guaranteed by a cognitive power allocation scheme, while the HARQ technique is employed to mitigate the successive interference cancellation (SIC) errors and improve the secrecy performance of the SRU. For reducing information leakage, a randomized retransmission NOMA (RR-NOMA) scheme is designed, where the retransmitted signals are generated from independent randomized codebooks. In this scheme, the closed-form expressions for the connection outage probability (COP), the average number of transmission (ANT), the secrecy outage probability (SOP), and effective secrecy throughput (EST) of the SRU are derived. In addition, as benchmarks, the performance analyses for the fixed retransmission (FR-NOMA) scheme and the randomized retransmission orthogonal multiple access (RR-OMA) scheme are also provided. Results show a trade-off between SOP and COP or EST, which is denoted by security-reliability trade-off (SRT) or security-efficiency trade-off (SET). Furthermore, simulation results show that the HARQ technique improves SRT and the RR-NOMA scheme achieves better SET than the FR-NOMA scheme in the low SOP region. We further conduct asymptotic analysis in the RR-NOMA, FR-NOMA and RR-OMA schemes. Asymptotic results demonstrate that the three schemes achieve the same ANT and the RR-NOMA scheme obtains better secrecy performance than the RR-OMA scheme and equal secrecy performance to the FR-NOMA scheme in terms of both EST and SOP.

    更新日期:2020-02-14
  • Distributed Linear Estimation Via a Roaming Token
    IEEE Trans. Signal Process. (IF 5.230) Pub Date : 2020-01-09
    Lucas Balthazar; João Xavier; Bruno Sinopoli

    We present an algorithm for the problem of linear distributed estimation of a parameter in a network where a set of agents are successively taking measurements. The approach considers a roaming token in a network that carries the estimate, and jumps from one agent to another in its vicinity according to the probabilities of a Markov chain. When the token is at an agent it records the agent's local information. We analyze the proposed algorithm and show that it is consistent and asymptotically optimal, in the sense that its mean-square-error (MSE) rate of decay approaches the centralized one as the number of iterations increases. We show these results for a scenario where the network changes over time, and we consider two different sets of assumptions on the network instantiations: (I) they are i.i.d. and connected on the average, or (II) that they are deterministic and strongly connected for every finite time window of a fixed size. Simulations show our algorithm is competitive with consensus+innovations and diffusion type of algorithms, achieving a smaller MSE at each iteration in all considered scenarios.

    更新日期:2020-02-14
  • Target Detection With Imperfect Waveform Separation in Distributed MIMO Radar
    IEEE Trans. Signal Process. (IF 5.230) Pub Date : 2020-01-06
    Pu Wang; Hongbin Li

    This paper considers target detection in distributed multiple-input multiple-output (MIMO) radar with imperfect waveform separation at local receivers. The problem is formulated as a binary composite hypothesis testing problem, where target residuals due to imperfect waveform separation are explicitly modeled as a subspace component in the alternative hypothesis, while disturbances including the clutter and thermal noise are present under both hypotheses. Under assumptions of fluctuating and non-fluctuating target amplitude over a scan, e.g., Swerling models, we particularly consider a distributed hybrid-order Gaussian (DHOG) signal model and develop the generalized likelihood ratio test (GLRT) which relies on the maximum likelihood (ML) estimation of the target amplitude and the residual covariance matrix under the alternative hypothesis. The Cramér-Rao bounds (CRBs) on estimating the target amplitude and residual subspace covariance matrix are derived. Simulation results in both local and distributed scenarios confirm the effectiveness of the proposed GLRT and show improved performance in terms of receiver operating characteristic (ROC) by exploiting the existence of target residual component.

    更新日期:2020-02-14
  • Distributed Sequential Detection: Dependent Observations and Imperfect Communication
    IEEE Trans. Signal Process. (IF 5.230) Pub Date : 2019-11-25
    Shan Zhang; Prashant Khanduri; Pramod K. Varshney

    In this paper, we consider the problem of distributed sequential detection using wireless sensor networks in the presence of imperfect communication channels between the sensors and the fusion center. Sensor observations are assumed to be spatially dependent. Moreover, the channel noise can be dependent and non-Gaussian. We propose a copula-based distributed sequential detection scheme that takes the spatial dependence into account. More specifically, each local sensor runs a memory-less truncated sequential test repeatedly and sends its binary decisions to the fusion center synchronously. The fusion center fuses the received messages using a copula-based sequential test. To this end, we first propose a centralized copula-based sequential test and show its asymptotic optimality and time efficiency. We then show the asymptotic optimality and time efficiency of the proposed distributed scheme. We also show that by suitably designing the local thresholds and the truncation window, the local probabilities of false alarm and miss detection of the proposed memory-less truncated local sequential tests satisfy the pre-specified error probabilities. Numerical experiments are conducted to demonstrate the effectiveness of our approach.

    更新日期:2020-02-14
  • Hyperspectral Super-Resolution With Coupled Tucker Approximation: Recoverability and SVD-Based Algorithms
    IEEE Trans. Signal Process. (IF 5.230) Pub Date : 2020-01-15
    Clémence Prévost; Konstantin Usevich; Pierre Comon; David Brie

    We propose a novel approach for hyperspectral super-resolution, that is based on low-rank tensor approximation for a coupled low-rank multilinear (Tucker) model. We show that the correct recovery holds for a wide range of multilinear ranks. For coupled tensor approximation, we propose two SVD-based algorithms that are simple and fast, but with a performance comparable to the state-of-the-art methods. The approach is applicable to the case of unknown spatial degradation and to the pansharpening problem.

    更新日期:2020-02-14
  • Scalable and Robust Community Detection With Randomized Sketching
    IEEE Trans. Signal Process. (IF 5.230) Pub Date : 2020-01-10
    Mostafa Rahmani; Andre Beckus; Adel Karimian; George K. Atia

    This article explores and analyzes the unsupervised clustering of large partially observed graphs. We propose a scalable and provable randomized framework for clustering graphs generated from the stochastic block model. The clustering is first applied to a sub-matrix of the graph's adjacency matrix associated with a reduced graph sketch constructed using random sampling. Then, the clusters of the full graph are inferred based on the clusters extracted from the sketch using a correlation-based retrieval step. Uniform random node sampling is shown to improve the computational complexity over clustering of the full graph when the cluster sizes are balanced. A new random degree-based node sampling algorithm is presented which significantly improves upon the performance of the clustering algorithm even when clusters are unbalanced. This framework improves the phase transitions for matrix-decomposition-based clustering with regard to computational complexity and minimum cluster size, which are shown to be nearly dimension-free in the low inter-cluster connectivity regime. A third sampling technique is shown to improve balance by randomly sampling nodes based on spatial distribution. We provide analysis and numerical results using a convex clustering algorithm based on matrix completion.

    更新日期:2020-02-14
  • Maximum Total Complex Correntropy for Adaptive Filter
    IEEE Trans. Signal Process. (IF 5.230) Pub Date : 2020-01-23
    Guobing Qian; Shiyuan Wang; Herbert H. C. Iu

    Nowadays, complex Correntropy has been widely used for adaptive filtering in the complex domain. Compared with the second order statistics methods, the complex correntropy based algorithms have shown the superiority in the non-Gaussian noise, especially the impulsive noise. However, the current complex correntropy based adaptive filtering algorithms have not taken the input noise into consideration, and the performances will be deteriorated when the input signals are also corrupted by the noise. In this article, we focus on the errors-in-variables (EIV) model and propose an adaptive algorithm based on the maximum total complex correntropy (MTCC). More importantly, we present the local stability analysis and derive the theoretical weight error power. Simulation results confirm the validity of the theoretical analysis and illustrate the superior performance of the propose algorithm in the EIV cases.

    更新日期:2020-02-14
  • Asynchronous Blind Network-Assisted Diversity Multiple Access
    IEEE Trans. Signal Process. (IF 5.230) Pub Date : 2020-01-21
    Naeem Akl; Ahmed Tewfik

    We present a blind collision resolution algorithm in slow fading channels based on retransmission diversity. The algorithm neither assumes packet nor symbol synchronization of the different users and it does not demand estimates of the arrival times of the collided signals. The proposed scheme works independently of the relative alignment of the packets, so it can also resolve synchronous collisions. The decoding complexity does not scale with the packet size and thus does not burden the receiver. In the blocking mode, the algorithm achieves high throughput and low queuing delay similar to synchronous network division multiple access (NDMA) protocols. In the non-blocking mode, there is longer queuing delay of the packets before transmission, but the throughput is still high due to faster accumulation of the buffered packets at the transmitters.

    更新日期:2020-02-14
  • Khaos: An Adversarial Neural Network DGA With High Anti-Detection Ability
    IEEE Trans. Inform. Forensics Secur. (IF 6.211) Pub Date : 2019-12-18
    Xiaochun Yun; Ji Huang; Yipeng Wang; Tianning Zang; Yuan Zhou; Yongzheng Zhang

    A botnet is a network of remote-controlled devices that are infected with malware controlled by botmasters in order to launch cyber attacks. To evade detection, the botmaster frequently changes the domain name of his Command and Control (C&C) server. Notice that most of these types of domain names are generated by domain generation algorithms (DGAs). In this paper, we propose Khaos, a novel DGA with high anti-detection ability based on neural language models and the Wasserstein Generative Adversarial Network (WGAN). The key insight of our research is that real domain names are composed of readable syllables and acronyms, and thus we can arrange syllables and acronyms using neural language models to mimic real domain names. In Khaos, we first find the most common ${n}$ -grams in real domain names, then tokenize these domain names into ${n}$ -grams, and finally synthesize new domain names after learning arrangements of ${n}$ -grams from real domain names. We carry out experiments using a variety of state-of-the-art DGA detection approaches: the statistics-based, the distribution-based, the LSTM-based and the graph-based detection approach. Our experimental results show that the average distance for detecting Khaos under the distribution-based detection approach is 0.64, the AUCs of Khaos under the statistics-based and the LSTM-based detection approach are 0.76 and 0.57, respectively, and the precision of Khaos under the graph-based detection approach is 0.68. Our work proves that the existing detection approaches have big troubles in detecting Khaos, and Khaos has better anti-detection ability than state-of-the-art DGAs. In addition, we find that training the existing detection approach on a dataset including the domain names generated by Khaos can improve its detection ability.

    更新日期:2020-02-11
  • Cycle Age-Adversarial Model Based on Identity Preserving Network and Transfer Learning for Cross-Age Face Recognition
    IEEE Trans. Inform. Forensics Secur. (IF 6.211) Pub Date : 2019-12-18
    Lingshuang Du; Haifeng Hu; Yongbo Wu

    Age variations bring a large challenge for face recognition tasks. Existing Cross-Age Face Recognition (CAFR) methods have two limitations. Firstly, many CAFR approaches require both age labels and identity labels for training. However, it is difficult to collect images under a large age span from each individual. Secondly, many works are based on the assumption that age and identity information are independent of each other, which may not satisfy various conditions. In this paper, a Cycle Age-Adversarial Model (CAAM) is proposed for CAFR, which only uses the age labels for training without considering independence hypothesis. CAAM includes two different branch networks. Firstly, the branch of Age-robust Feature Extracting Model (AFEM) is designed to adaptively learn age-invariant features by adversarial learning scheme, which includes an age discriminator network and a feature generator network. The age discriminator network is trained to discriminate the age information, and the generator extracts age-invariant features through adversarial learning with discriminator. Secondly, a branch of the Identity Preserving Network (IPN) is proposed to keep identity information, which introduces Unsupervised Identity Loss (UIL) to enlarge the inter-class distance, and decrease the loss of identity information in the learning process. Finally, the features of the two branches are cyclically optimized through minmizing Feature Consistency Loss (FCL), which integrates age invariance learning and identity discrimination learning into final feature representation. Different from existing CAFR networks, our adversarial learning strategy for age-robust feature learning can be generalized to other attributes including pose and expression. Moreover, we introduce cycle optimization strategy to merge the advantages of two branch networks, which is a novel strategy to fuse multi-task features. Extensive CAFR experiments performed on the benchmark MORPH Album2, CACD-VS and Cross Age LFW databases demonstrate the effectiveness and superiority of CAAM.

    更新日期:2020-02-11
  • Identification of VoIP Speech With Multiple Domain Deep Features
    IEEE Trans. Inform. Forensics Secur. (IF 6.211) Pub Date : 2019-12-18
    Yuankun Huang; Bin Li; Mauro Barni; Jiwu Huang

    Identifying whether a phone call comes from VoIP (Voice over Internet Protocol) is a challenging but less-investigated audio forensic issue. As shown in a previous study, existing feature based methods do not work well. In this paper, we propose a robust data-driven approach, called CNN-MLS (convolutional neural network based multi-domain learning scheme), to distinguish VoIP calls from mobile phone calls. To better explore the differences between VoIP and mobile phone calls, we first process data with high-pass filtering, and then extract deep features from both temporal domain and spectral domain. Two CNN architectures are designed for accepting data from respective domains, and some tricks such as auxiliary classifiers and individual subnet training are used for accelerating network convergence. The deep features are finally fused in a classification module for identifying the phone call type. The proposed method is evaluated on VPCID (VoIP Phone Call Identification Database) dataset, under various testing conditions. We pay particular attention to tests on data belonging to a source mismatched with the training sources. Experimental results show that, compared with existing methods, our method can achieve satisfactory and better accuracy on two-second-long inputs, implying that an alert may be activated shortly after a VoIP call is made.

    更新日期:2020-02-11
  • On the Secrecy Performance and Power Allocation in Relaying Networks With Untrusted Relay in the Partial Secrecy Regime
    IEEE Trans. Inform. Forensics Secur. (IF 6.211) Pub Date : 2019-12-16
    Diana Pamela Moya Osorio; Hirley Alves; Edgar Eduardo Benitez Olivo

    Recently, three useful secrecy metrics based on the partial secrecy regime were proposed to analyze secure transmissions on wireless systems over quasi-static fading channels, namely: generalized secrecy outage probability, average fractional equivocation, and average information leakage. These metrics were devised from the concept of fractional equivocation, which is related to the decoding error probability at the eavesdropper, so as to provide a comprehensive insight on the practical implementation of wireless systems with different levels of secrecy requirements. Considering the partial secrecy regime, in this paper we examine the secrecy performance of an amplify-and-forward relaying network with an untrusted relay node, where a destination-based jamming is employed to enable secure transmissions. In this regard, a closed-form approximation is derived for the generalized secrecy outage probability, and integral-form expressions are obtained for the average fractional equivocation and the average information leakage rate. Additionally, equal and optimal power allocation schemes are investigated and compared for the three metrics. From this analysis, we show that different power allocation approaches lead to different system design criteria. The obtained expressions are validated via Monte Carlo simulations.

    更新日期:2020-02-11
  • Audio Steganography Based on Iterative Adversarial Attacks Against Convolutional Neural Networks
    IEEE Trans. Inform. Forensics Secur. (IF 6.211) Pub Date : 2020-01-03
    Junqi Wu; Bolin Chen; Weiqi Luo; Yanmei Fang

    Recently, convolutional neural networks (CNNs) have demonstrated superior performance on digital multimedia steganalysis. However, some studies have noted that most CNN-based classifiers can be easily fooled by adversarial examples, which form slightly perturbed inputs to a target network according to the gradients. Inspired by this phenomenon, we first introduce a novel steganography method based on adversarial examples for digital audio in the time domain. Unlike related methods for image steganography, such as [1] – [4] , which are highly dependent on some existing embedding costs, the proposed method can start from a flat or even a random embedding cost and then iteratively update the initial costs by exploiting the adversarial attacks until satisfactory security performances are obtained. The extensive experimental results show that our method significantly outperforms the existing nonadaptive and adaptive steganography methods and achieves state-of-the-art results. Moreover, we also provide experimental results to investigate why the proposed embedding modifications seem evenly located at all audio segments despite their different content complexities, which is contrary to the content adaptive principle widely employed in modern steganography methods.

    更新日期:2020-02-11
  • Physical Layer Security Protocol for Poisson Channels for Passive Man-in-the-Middle Attack
    IEEE Trans. Inform. Forensics Secur. (IF 6.211) Pub Date : 2020-01-03
    Masahito Hayashi; Ángeles Vázquez-Castro

    In this work, we focus on the classical optical channel having Poissonian statistical behavior and propose a novel secrecy coding-based physical layer protocol. Our protocol is different but complementary to both (computationally secure) quantum immune cryptographic protocols and (information theoretically secure) quantum cryptographic protocols. Specifically, our (information theoretical) secrecy coding protocol secures classical digital information bits at photonic level exploiting the random nature of the Poisson channel. It is known that secrecy coding techniques for the Poisson channel based on the classical one-way wiretap channel (introduced by Wyner in 1975) ensure secret communication only if the mutual information to the eavesdropper is smaller than that to the legitimate receiver. In order to overcome such a strong limitation, we introduce a two-way protocol that always ensures secret communication independently of the conditions of legitimate and eavesdropper channels. We prove this claim showing rigorous comparative derivation and analysis of the information theoretical secrecy capacity of the classical one-way and of the proposed two-way protocols. We also show numerical calculations that prove drastic gains and strong practical potential of our proposed two-way protocol to secure information transmission over optical channels.

    更新日期:2020-02-11
  • Location-Based PVO and Adaptive Pairwise Modification for Efficient Reversible Data Hiding
    IEEE Trans. Inform. Forensics Secur. (IF 6.211) Pub Date : 2020-01-03
    Tong Zhang; Xiaolong Li; Wenfa Qi; Zongming Guo

    Pixel-value-ordering (PVO) is an efficient technique of reversible data hiding (RDH). By PVO, the maximum and minimum in each cover image block are first predicted and then modified to embed data. Actually, many PVO-based methods are essentially based on high-dimensional histogram modification. For these methods, a two-dimensional (2D) prediction-error histogram (PEH) is first generated and then modified based on a 2D mapping. However, these methods have two drawbacks. On one hand, the generated 2D PEH is irregular so that it is difficult to design suitable histogram modification strategy. On the other hand, the employed 2D mapping is empirically designed, and thus the embedding performance is far from optimal. Based on these considerations, a new PVO-based RDH scheme is proposed in this paper. By considering both pixel value orders and pixel locations, a new predictor is proposed so that the generated 2D PEH is regular in shape and suitable for reversible embedding. Moreover, instead of manually designing 2D mappings, to optimize the embedding performance, a self-learning mechanism is proposed to adaptively select the 2D mapping according to the image content. With the new predictor and the self-learning mechanism for 2D mapping selection, the proposed method works well with a good marked image quality, e.g., the PSNR of the image Lena is as high as 61.53 dB for an embedding capacity of 10 000 bits. Besides, compared with some state-of-the-art RDH methods, the superiority of the proposed method is experimentally verified.

    更新日期:2020-02-11
  • Analysis of Moving Target Defense Against False Data Injection Attacks on Power Grid
    IEEE Trans. Inform. Forensics Secur. (IF 6.211) Pub Date : 2019-07-15
    Zhenyong Zhang; Ruilong Deng; David K. Y. Yau; Peng Cheng; Jiming Chen

    Recent studies have considered thwarting false data injection (FDI) attacks against state estimation in power grids by proactively perturbing branch susceptances. This approach is known as moving target defense (MTD). However, despite of the deployment of MTD, it is still possible for the attacker to launch stealthy FDI attacks generated with former branch susceptances. In this paper, we prove that, an MTD has the capability to thwart all FDI attacks constructed with former branch susceptances only if (i) the number of branches $l$ in the power system is not less than twice that of the system states $n$ (i.e., $l \geq 2n$ , where $n + 1$ is the number of buses); (ii) the susceptances of more than $n$ branches, which cover all buses, are perturbed. Moreover, we prove that the state variable of a bus that is only connected by a single branch (no matter it is perturbed or not) can always be modified by the attacker. Nevertheless, in order to reduce the attack opportunities of potential attackers, we first exploit the impact of the susceptance perturbation magnitude on the dimension of the stealthy attack space , in which the attack vector is constructed with former branch susceptances. Then, we propose that, by perturbing an appropriate set of branches, we can minimize the dimension of the stealthy attack space and maximize the number of covered buses. Besides, we consider the increasing operation cost caused by the activation of MTD. Finally, we conduct extensive simulations to illustrate our findings with IEEE standard test power systems.

    更新日期:2020-02-11
  • A Two-Dimensional Vectorized Secure Transmission Scheme for Wireless Communications
    IEEE Trans. Inform. Forensics Secur. (IF 6.211) Pub Date : 2019-11-20
    Renyong Wu; Chao Yuan; Ning Zhang; Kim-Kwang Raymond Choo

    Wireless communication security can be enhanced by leveraging the characteristics of the physical (PHY) layer, where discriminatory scrambling can be employed at the symbol level to either improve the signal quality of the legitimate receiver or degrade that of the eavesdroppers. However, in the presence of multiple passive eavesdroppers with more antennas than the transmitter, the transmitted signals can still be reliably separated from random disturbances. To mitigate this challenge, we propose a two-dimensional vectorized secure transmission scheme. Unlike existing transmission schemes (where the same data symbol is sent over each antenna with a predesigned complex weight at a time), in the proposed scheme, a sequence of data symbols in a predefined order (named symbol vector) is first pre-superposed through pre-coding with a random complex matrix prior to been sent as a vector in parallel over each transmitting antenna at a time. As a result, physical randomness of the legitimate channel is introduced into the received signals at passive eavesdroppers in the pre-coding procedure. Moreover, to ensure that transmitted data symbols can be recovered in the right order, each symbol vector is sent repeatedly, according to the principle of maximum entropy. To ensure the intended receiver can recover the transmitted symbol vector, the random pre-coding matrices are selected such that a linear constraint imposed by the CSI of the legitimate channel is satisfied. In addition, an extended maximum likelihood (ML) detection method is developed for the desired receiver while the random pre-coding matrices are not required to be transmitted. We then analyze its security based on the signal detection theory to demonstrate that the intended receiver can recover the transmitted symbol vectors while the eavesdroppers are not capable of doing so. We also evaluate the performance of the proposed scheme to demonstrate its effectiveness.

    更新日期:2020-02-11
  • Machine Learning-Based Delay-Aware UAV Detection and Operation Mode Identification Over Encrypted Wi-Fi Traffic
    IEEE Trans. Inform. Forensics Secur. (IF 6.211) Pub Date : 2019-12-16
    Amir Alipour-Fanid; Monireh Dabaghchian; Ning Wang; Pu Wang; Liang Zhao; Kai Zeng

    The consumer unmanned aerial vehicle (UAV) market has grown significantly over the past few years. Despite its huge potential in spurring economic growth by supporting various applications, the increase of consumer UAVs poses potential risks to public security and personal privacy. To minimize the risks, efficiently detecting and identifying invading UAVs is in urgent need for both invasion detection and forensics purposes. Aiming to complement the existing physical detection mechanisms, we propose a machine learning-based framework for fast UAV identification over encrypted Wi-Fi traffic. It is motivated by the observation that many consumer UAVs use Wi-Fi links for control and video streaming. The proposed framework extracts features derived only from packet size and inter-arrival time of encrypted Wi-Fi traffic, and can efficiently detect UAVs and identify their operation modes. In order to reduce the online identification time, our framework adopts a re-weighted $\ell _{1}$ -norm regularization, which considers the number of samples and computation cost of different features. This framework jointly optimizes feature selection and prediction performance in a unified objective function. To tackle the packet inter-arrival time uncertainty when optimizing the trade-off between the detection accuracy and delay, we utilize maximum likelihood estimation (MLE) method to estimate the packet inter-arrival time. We collect a large number of real-world Wi-Fi data traffic of eight types of consumer UAVs and conduct extensive evaluation on the performance of our proposed method. Evaluation results show that our proposed method can detect and identify tested UAVs within 0.15–0.35s with high accuracy of 85.7–95.2%. The UAV detection range is within the physical sensing range of 70m and 40m in the line-of-sight (LoS) and non-line-of-sight (NLoS) scenarios, respectively. The operation mode of UAVs can be identified with high accuracy of 88.5–98.2%.

    更新日期:2020-02-11
  • Deep and Ordinal Ensemble Learning for Human Age Estimation From Facial Images
    IEEE Trans. Inform. Forensics Secur. (IF 6.211) Pub Date : 2020-01-13
    Jiu-Cheng Xie; Chi-Man Pun

    Some recent work treats age estimation as an ordinal ranking task and decomposes it into multiple binary classifications. However, a theoretical defect lies in this type of methods: the ignorance of possible contradictions in individual ranking results. In this paper, we partially embrace the decomposition idea and propose the Deep and Ordinal Ensemble Learning with Two Groups Classification (DOEL 2groups ) for age prediction. An important advantage of our approach is that it theoretically allows the prediction even when the contradictory cases occur. The proposed method is characterized by a deep and ordinal ensemble and a two-stage aggregation strategy. Specifically, we first set up the ensemble based on Convolutional Neural Network (CNN) techniques, while the ordinal relationship is implicitly constructed among its base learners. Each base learner will classify the target face into one of two specific age groups. After achieving probability predictions of different age groups, then we make aggregation by transforming them into counting value distributions of whole age classes and getting the final age estimation from their votes. Moreover, to further improve the estimation performance, we suggest to regard the age class at the boundary of original two age groups as another age group and this modified version is named the Deep and Ordinal Ensemble Learning with Three Groups Classification (DOEL 3groups ). Effectiveness of this new grouping scheme is validated in theory and practice. Finally, we evaluate the proposed two ensemble methods on controlled and wild aging databases, and both of them produce competitive results. Note that the DOEL 3groups shows the state-of-the-art performance in most cases.

    更新日期:2020-02-11
  • Operator-in-the-Loop Deep Sequential Multi-Camera Feature Fusion for Person Re-Identification
    IEEE Trans. Inform. Forensics Secur. (IF 6.211) Pub Date : 2019-12-04
    K. L. Navaneet; Ravi Kiran Sarvadevabhatla; Shashank Shekhar; R. Venkatesh Babu; Anirban Chakraborty

    Given a target image as query, person re-identification systems retrieve a ranked list of candidate matches on a per-camera basis. In deployed systems, a human operator scans these lists and labels sighted targets by touch or mouse-based selection. However, classical re-id approaches generate per-camera lists independently. Therefore, target identifications by operator in a subset of cameras cannot be utilized to improve ranking of the target in remaining set of network cameras. To address this shortcoming, we propose a novel sequential multi-camera re-id approach. The proposed approach can accommodate human operator inputs and provides early gains via a monotonic improvement in target ranking. At the heart of our approach is a fusion function which operates on deep feature representations of query and candidate matches. We formulate an optimization procedure custom-designed to incrementally improve query representation. Since existing evaluation methods cannot be directly adopted to our setting, we also propose two novel evaluation protocols. The results on two large-scale re-id datasets (Market-1501, DukeMTMC-reID) demonstrate that our multi-camera method significantly outperforms baselines and other popular feature fusion schemes. Additionally, we conduct a comparative subject-based study of human operator performance. The superior operator performance enabled by our approach makes a compelling case for its integration into deployable video-surveillance systems.

    更新日期:2020-02-11
  • Practical Privacy-Preserving Face Authentication for Smartphones Secure Against Malicious Clients
    IEEE Trans. Inform. Forensics Secur. (IF 6.211) Pub Date : 2020-01-27
    Jong-Hyuk Im; Seong-Yun Jeon; Mun-Kyu Lee

    We propose a privacy-preserving face authentication system for smartphones that guarantees security against malicious clients. Using the proposed system, a face feature vector is stored on a remote server in encrypted form. To guarantee security against an honest-but-curious server who may try to learn the private feature vector, we perform a Euclidean distance-based matching score computation on encrypted feature vectors using homomorphic encryption. To provide security against malicious clients, we adopt a blinding technique. We implement the proposed system on a mobile client and a desktop server. Through an experiment with real-world participants, we demonstrate that secure face verification can be completed in real time (within 1.3 s) even when a smartphone is involved, with an Equal Error Rate (EER) of 3.04%. In further experiments with two public face datasets, CFP and ORL, face verification is completed in approximately 1 s with EER of 1.17% and 0.37%, respectively. Our system is two orders of magnitude faster than previous privacy-preserving face verification method with the same security assumptions and functionalities. To achieve this secure real-time computation, we improve the Catalano-Fiore transformation which converts a linear homomorphic encryption scheme into a quadratic scheme, and parallelize the decryption procedure of our system.

    更新日期:2020-02-11
  • Hardware-Assisted MMU Redirection for In-Guest Monitoring and API Profiling
    IEEE Trans. Inform. Forensics Secur. (IF 6.211) Pub Date : 2020-01-27
    Shun-Wen Hsiao; Yeali S. Sun; Meng Chang Chen

    With the advance of hardware, network, and virtualization technologies, cloud computing has prevailed and become the target of security threats such as the cross virtual machine (VM) side channel attack, with which malicious users exploit vulnerabilities to gain information or access to other guest virtual machines. Among the many virtualization technologies, the hypervisor manages the shared resource pool to ensure that the guest VMs can be properly served and isolated from each other. However, while managing the shared hardware resources, due to the presence of the virtualization layer and different CPU modes (root and non-root mode), when a CPU is switched to non-root mode and is occupied by a guest machine, a hypervisor cannot intervene with a guest at runtime. Thus, the execution status of a guest is like a black box to a hypervisor, and the hypervisor cannot mediate possible malicious behavior at runtime. To rectify this, we propose a hardware-assisted VMI (virtual machine introspection) based in-guest process monitoring mechanism which supports monitoring and management applications such as process profiling. The mechanism allows hooks placed within a target process (which the security expert selects to monitor and profile) of a guest virtual machine and handles hook invocations via the hypervisor. In order to facilitate the needed monitoring and/or management operations in the guest machine, the mechanism redirects access to in-guest memory space to a controlled, self-defined memory within the hypervisor by modifying the extended page table (EPT) to minimize guest and host machine switches. The advantages of the proposed mechanism include transparency, high performance, and comprehensive semantics. To demonstrate the capability of the proposed mechanism, we develop an API profiling system (APIf) to record the API invocations of the target process. The experimental results show an average performance degradation of about 2.32%, far better than existing similar systems.

    更新日期:2020-02-11
  • Multi-Stage Feature Constraints Learning for Age Estimation
    IEEE Trans. Inform. Forensics Secur. (IF 6.211) Pub Date : 2020-01-27
    Min Xia; Xu Zhang; Wan’an Liu; Liguo Weng; Yiqing Xu

    The biometric information contained in a face image is affected by many factors such as living environment, racial differences, and genetic diversity, this complexity leads to the nonstationary of the age estimation. In order to reduce the overlap of face features between adjacent ages and improve the accuracy of age prediction, a multi-stage feature constraints learning method is proposed for face age estimation. The proposed method gradually refines the feature through three feature constraint stages. In each stage, the algorithm continuously updates the feature center of its corresponding age range, and minimizes the distance between each age feature and feature center of the corresponding age range through feature constraint. Feature constraint makes the feature distances between different individuals in the same age feature space smaller and decrease the overlap areas between adjacent age range feature spaces. Meanwhile, the feature distance of different age range feature space is enlarged. The proposed network efficiently merges the features of three stages and optimizes the mapping of feature maps to an ordered binary comparison space. Experiments show that the proposed method is able to effectively improve the discrimination between different age features, and hence to improve the accuracy of face age estimation. In addition, the proposed algorithm is simple enough to achieve fast face age estimation.

    更新日期:2020-02-11
  • DCAP: A Secure and Efficient Decentralized Conditional Anonymous Payment System Based on Blockchain
    IEEE Trans. Inform. Forensics Secur. (IF 6.211) Pub Date : 2020-01-27
    Chao Lin; Debiao He; Xinyi Huang; Muhammad Khurram Khan; Kim-Kwang Raymond Choo

    Blockchain, a distributed ledger technology, can potentially be deployed in a wide range of applications. Among these applications, decentralized payment systems (e.g. Bitcoin) have been one of the most mature blockchain applications with widespread adoption. While the early designs (e.g. Bitcoin) are often the currency of choice by cybercriminals (e.g., in ransomware incidents), they only provide pseudo-anonymity, in the sense that anyone can deanonymize Bitcoin transactions by using information in the blockchain. To strengthen the privacy protection of decentralized payment systems, a number of solutions such as Monero and Zerocash have been proposed. However, completely Decentralized Anonymous Payment (DAP) systems can be criminally exploited, for example in online extortion and money laundering activities. Recognizing the importance of regulation, we present a novel definition of Decentralized Conditional Anonymous Payment (DCAP) and describe the corresponding security requirements. In order to construct a concrete DCAP system, we first design a Condition Anonymous Payment (CAP) scheme (based on our proposed signature of knowledge), whose security can be demonstrated under the defined formal semantic and security models. To demonstrate utility, we compare the performance of our proposal with that of Zerocash under the same parameters and testing environment.

    更新日期:2020-02-11
  • Hidden Electricity Theft by Exploiting Multiple-Pricing Scheme in Smart Grids
    IEEE Trans. Inform. Forensics Secur. (IF 6.211) Pub Date : 2020-01-09
    Yang Liu; Ting Liu; Hong Sun; Kehuan Zhang; Pengfei Liu

    With the development of demand response technologies, the pricing scheme in smart grids is moving from flat pricing to multiple pricing (MP), which facilitates the energy saving at the consumer side. However, the flexible pricing policy may be exploited for the stealthy reduction of utility bills. In this paper, we present a hidden electricity theft (HET) attack by exploiting the emerging MP scheme. The basic idea is that attackers can tamper with smart meters to cheat the utility that some electricity is consumed under a lower price. To construct the HET attack, we propose an optimization problem aiming at maximizing the attack profits while evading current detection methods, and design two algorithms to conduct the attack on smart meters. Moreover, we disclose and exploit several new vulnerabilities of smart meters to demonstrate the feasibility of HET attacks. To protect smart grids against HET attacks, we propose several defense and detection countermeasures, including selective protection on smart meters, limiting the attack cycle, and updating the billing mechanism. Extensive experiments on a real data set demonstrate that the attack could cause high economic losses, and the proposed countermeasures could effectively mitigate the attack’s impact at a low cost.

    更新日期:2020-02-11
  • Adaptive Convolution Local and Global Learning for Class-Level Joint Representation of Facial Recognition With a Single Sample Per Data Subject
    IEEE Trans. Inform. Forensics Secur. (IF 6.211) Pub Date : 2020-01-13
    Meng Yang; Wei Wen; Xing Wang; Linlin Shen; Guangwei Gao

    Due to the absence of training samples and intraclass variation, the extraction of discriminative facial features and construction of powerful classifiers have bottlenecks in improving the performance of facial recognition (FR) with a single sample per data subject (SSPDS). In this paper, we propose to learn regional adaptive convolution features that are locally and globally discriminative to facial identity and robust to facial variation. Then, a novel class-level joint representation framework is presented to exploit the distinctiveness and class-level commonality of different facial features. In the proposed class-level joint representation with regional adaptive convolution features (CJR-RACF), both discriminative facial features that are robust to facial variations and powerful representations for classification with generic facial variations have been fully exploited. Furthermore, the gallery discrimination is extracted by our proposed weight-embedded supervision in the training phase (denoted by CJR-RACF w ), which is conducive to more specific features for FR with SSPDS. CJR-RACF and CJR-RACF w have been evaluated on several popular databases, including the large-scale CMU Multi-PIE, LFW, Megaface, and VGGFace datasets. Experimental results demonstrate the much higher robustness and effectiveness of the proposed methods compared to the state-of-the-art methods.

    更新日期:2020-02-11
  • Homogeneous and Heterogeneous Feed-Forward XOR Physical Unclonable Functions
    IEEE Trans. Inform. Forensics Secur. (IF 6.211) Pub Date : 2020-01-20
    S. V. Sandeep Avvaru; Ziqing Zeng; Keshab K. Parhi

    Physical unclonable functions (PUFs) are hardware security primitives that are used for device authentication and cryptographic key generation. Standard XOR PUFs typically contain multiple standard arbiter PUFs as components, and are more secure than standard arbiter PUFs or feed-forward (FF) arbiter PUFs (FF PUFs). This paper proposes design of feed-forward XOR PUFs (FFXOR PUFs) where each component PUF is a FF PUF. Various homogeneous and heterogeneous FFXOR PUFs are presented and evaluated in terms of four fundamental properties of PUFs: uniqueness, attack-resistance, reliability and randomness. Certain key issues pertaining to XOR PUFs such as their vulnerability to machine learning attacks and instability in responses are investigated. Other important challenges like the lack of uniqueness in FF PUFs and the asymmetry in FPGA arbiter PUFs are addressed and it is shown that FFXOR PUFs can naturally overcome these problems. It is shown that heterogeneous FFXOR PUFs (i.e., FFXOR PUFs with non-identical components) can be resilient to state-of-the-art machine learning attacks. We also present systematic reliability analysis of FFXOR PUFs and demonstrate that soft-response thresholding can be used as an effective countermeasure to overcome the degraded reliability bottleneck. Observations from simulations are further verified through hardware implementation of 64-bit FFXOR PUFs on Xilinx Artix-7 FPGA.

    更新日期:2020-02-11
  • Compressive Privacy Generative Adversarial Network
    IEEE Trans. Inform. Forensics Secur. (IF 6.211) Pub Date : 2020-01-20
    Bo-Wei Tseng; Pei-Yuan Wu

    Machine learning as a service (MLaaS) has brought much convenience to our daily lives recently. However, the fact that the service is provided through cloud raises privacy leakage issues. In this work we propose the compressive privacy generative adversarial network (CPGAN), a data-driven adversarial learning framework for generating compressing representations that retain utility comparable to state-of-the-art, with the additional feature of defending against reconstruction attack. This is achieved by applying adversarial learning scheme to the design of compression network (privatizer), whose utility/privacy performances are evaluated by the utility classifier and the adversary reconstructor, respectively. Experimental results demonstrate that CPGAN achieves better utility/privacy trade-off in comparison with the previous work, and is applicable to real-world large datasets.

    更新日期:2020-02-11
  • Functional Analysis Attacks on Logic Locking
    IEEE Trans. Inform. Forensics Secur. (IF 6.211) Pub Date : 2020-01-20
    Deepak Sirone; Pramod Subramanyan

    Logic locking refers to a set of techniques that can protect integrated circuits (ICs) from counterfeiting, piracy and malicious functionality changes by an untrusted foundry. It achieves these goals by introducing new inputs, called key inputs, and additional logic to an IC such that the circuit produces the correct output only when the key inputs are set to specific values. The correct values of the key inputs are kept secret from the untrusted foundry and programmed after manufacturing and before distribution, thus rendering piracy, counterfeiting and malicious design changes infeasible. The security of logic locking relies on the assumption that the untrusted foundry cannot infer the correct values of the key inputs by analysis of the circuit. In this paper, we introduce a new attack on state-of-the-art logic locking schemes which invalidates the above assumption. We propose F unctional A nalysis attacks on L ogic L ocking algorithms (abbreviated as FALL attacks). FALL attacks have two stages. Their first stage is dependent on the locking algorithm and involves analyzing structural and functional properties of locked circuits to identify a list of potential locking keys. The second stage is algorithm agnostic and introduces a powerful addition to SAT-based attacks called key confirmation . Key confirmation can identify the correct key from a list of alternatives and works even on circuits that are resilient to the SAT attack. In comparison to past work, the FALL attack is more practical as it can often succeed (90% of successful attempts in our experiments) by only analyzing the locked netlist, without requiring oracle access to an unlocked circuit. Our experimental evaluation shows that FALL attacks are able to defeat 65 out of 80 (81%) circuits locked using Stripped-Functionality Logic Locking (SFLL-HD).

    更新日期:2020-02-11
  • Compressive Sensing-Based Adaptive Active User Detection and Channel Estimation: Massive Access Meets Massive MIMO
    IEEE Trans. Signal Process. (IF 5.230) Pub Date : 2020-01-16
    Malong Ke; Zhen Gao; Yongpeng Wu; Xiqi Gao; Robert Schober

    This paper considers massive access in massive multiple-input multiple-output (MIMO) systems and proposes an adaptive active user detection and channel estimation scheme based on compressive sensing. By exploiting the sporadic traffic of massive connected user equipments and the virtual angular domain sparsity of massive MIMO channels, the proposed scheme can support massive access with dramatically reduced access latency. Specifically, we design non-orthogonal pseudo-random pilots for uplink broadband massive access, and formulate the active user detection and channel estimation as a generalized multiple measurement vector compressive sensing problem. Furthermore, by leveraging the structured sparsity of the uplink channel matrix, we propose an efficient generalized multiple measurement vector approximate message passing (GMMV-AMP) algorithm to realize joint active user detection and channel estimation based on a spatial domain or an angular domain channel model. To jointly exploit the channel sparsity present in both the spatial and the angular domains for enhanced performance, a Turbo-GMMV-AMP algorithm is developed for detecting the active users and estimating their channels in an alternating manner. Finally, an adaptive access scheme is proposed, which adapts the access latency to guarantee reliable massive access for practical systems with unknown channel sparsity level. Additionally, the state evolution of the proposed GMMV-AMP algorithm is derived to predict its performance. Simulation results demonstrate the superiority of the proposed active user detection and channel estimation schemes compared to several baseline schemes.

    更新日期:2020-02-11
  • Quickly Finding the Best Linear Model in High Dimensions via Projected Gradient Descent
    IEEE Trans. Signal Process. (IF 5.230) Pub Date : 2020-01-06
    Yahya Sattar; Samet Oymak

    We study the problem of finding the best linear model that can minimize least-squares loss given a dataset. While this problem is trivial in the low-dimensional regime, it becomes more interesting in high-dimensions where the population minimizer is assumed to lie on a manifold such as sparse vectors. We propose projected gradient descent (PGD) algorithm to estimate the population minimizer in the finite sample regime. We establish linear convergence rate and data-dependent estimation error bounds for PGD. Our contributions include: 1) The results are established for heavier tailed subexponential distributions besides subgaussian and allows for an intercept term. 2) We directly analyze the empirical risk minimization and do not require a realizable model that connects input data and labels. The numerical experiments validate our theoretical results.

    更新日期:2020-02-11
  • Multi-UAV Interference Coordination via Joint Trajectory and Power Control
    IEEE Trans. Signal Process. (IF 5.230) Pub Date : 2020-01-16
    Chao Shen; Tsung-Hui Chang; Jie Gong; Yong Zeng; Rui Zhang

    Recently, unmanned aerial vehicles (UAVs) have found growing applications in wireless communications and sensor networks. One of the key challenges for UAV-based wireless networks lies in managing the strong cross-link interference caused by the line-of-sight dominated propagation conditions. In this article, we address this challenge by studying a UAV-enabled interference channel (UAV-IC), where each of the $K$ UAVs communicates with its associated ground terminal. To exploit the new degree of freedom of UAV mobility, we formulate a joint trajectory and power control (TPC) problem for maximizing the aggregate sum rate of the UAV-IC for a given flight interval, under practical constraints on the UAV flying speed, altitude, and collision avoidance. These constraints couple the TPC variables across different time slots and UAVs, leading to a challenging large-scale and non-convex optimization problem. We show that the optimal TPC solution follows the fly--hover--fly strategy, based on which the problem can be handled first by finding optimal hovering locations followed by solving a dimension-reduced TPC problem. For the reduced TPC problem, we propose a successive convex approximation algorithm. To further reduce the computation time, we develop a parallel TPC algorithm that is efficiently implementable over multi-core CPUs. We also propose a segment-by-segment method that decomposes the TPC problem into sequential TPC subproblems each with a smaller problem dimension. Simulation results demonstrate the superior computation time efficiency of the proposed algorithms, and also show that the UAV-IC can yield higher network sum rate than the benchmark orthogonal schemes.

    更新日期:2020-02-11
  • Massive MIMO Radar for Target Detection
    IEEE Trans. Signal Process. (IF 5.230) Pub Date : 2020-01-17
    Stefano Fortunati; Luca Sanguinetti; Fulvio Gini; Maria Sabrina Greco; Braham Himed

    Since the seminal paper by Marzetta from 2010, the Massive MIMO paradigm in communication systems has changed from being a theoretical scaled-up version of MIMO, with an infinite number of antennas, to a practical technology. Its key concepts have been adopted in the 5G new radio standard and base stations, where 64 fully-digital transceivers have been commercially deployed. Motivated by these recent developments, this paper considers a co-located MIMO radar with $M_T$ transmitting and $M_R$ receiving antennas and explores the potential benefits of having a large number of virtual spatial antenna channels $N=M_TM_R$ . Particularly, we focus on the target detection problem and develop a robust Wald-type test that guarantees certain detection performance, regardless of the unknown statistical characterization of the disturbance. Closed-form expressions for the probabilities of false alarm and detection are derived for the asymptotic regime $N\rightarrow \infty$ . Numerical results are used to validate the asymptotic analysis in the finite system regime with different disturbance models. Our results imply that there always exists a sufficient number of antennas for which the performance requirements are satisfied, without any a-priori knowledge of the disturbance statistics. This is referred to as the Massive MIMO regime of the radar system.

    更新日期:2020-02-11
  • Sparse Bayesian DOA Estimation Using Hierarchical Synthesis Lasso Priors for Off-Grid Signals
    IEEE Trans. Signal Process. (IF 5.230) Pub Date : 2020-01-20
    Jie Yang; Yixin Yang

    Within the conventional sparse Bayesian learning (SBL) framework, only Gaussian scale mixtures have been adopted to model sparsity-inducing priors that guarantee the exact inverse recovery. In light of the relative scarcity of formal SBL tools in enforcing a proper sparsity profile of signal vectors, we explore the use of hierarchical synthesis lasso (HSL) priors for representing the same small subset of features among multiple responses. We outline a viable approximation to this particular choice of sparse prior, leading to tractable marginalization over all weights and hyperparameters. We then discuss how the statistical variables of the hierarchical Bayesian model can be estimated via an adaptive updating formula, and include a refined one dimensional searching procedure to extraordinarily improve the direction of arrival (DOA) estimation performance when take the off-grid DOAs into account. Using these modifications, we show that exploiting HSL priors are very helpful in encouraging sparseness. Numerical simulations also verify the superiority of the proposal in terms of convergence speed and root mean squared estimation error, as compared to the traditional and more recent sparse Bayesian algorithms.

    更新日期:2020-02-11
  • Efficient and Unambiguous Two-Target Resolution via Subarray-Based Four-Channel Monopulse
    IEEE Trans. Signal Process. (IF 5.230) Pub Date : 2020-01-17
    Shengbin Luo Wang; Zhen-Hai Xu; Xiao Yang; Zhongren Li; Guoyu Wang

    The four-channel monopulse (FCM) technique enables a phased array radar (PAR) to track the angular location of two targets with reduced complexity and thus has been extensively investigated in the fields of radar multitarget tracking. However, the FCM technique has limited practical use because it was originally designed for a four-channel monopulse radar and is suitable only for a PAR configured with a rectangular planar array. In addition, the FCM technique is unable to resolve two targets when they have identical azimuthal or elevation angles due to angular ambiguity. In this paper, we propose a subarray-based FCM (SFCM) method to achieve an efficient, unambiguous, and fast two-target resolution, which is applicable to a PAR with a regular shape, i.e., circular arrays, elliptical arrays, and regular-octagonal arrays. Specifically, the proposed SFCM method consists of two stages: coarse estimation and precise estimation. Coarse estimation is performed by an inscribed rectangular array to initially estimate the angles of the two targets, and then precise estimation is achieved by a closed-form solution. Moreover, the proposed SFCM method can avoid the angular ambiguity issue via coordinate rotation. Furthermore, we prove that the optimal rotation angle is $45^\circ$ for minimizing the estimation error. In the performance analysis, we evaluate the effectiveness, computational complexity and Doppler effect for the SFCM method. Numerical results demonstrate that the proposed method outperforms its counterpart FCM method in accuracy, applicability, and robustness.

    更新日期:2020-02-11
  • Suboptimal Low Complexity Joint Multi-Target Detection and Localization for Non-Coherent MIMO Radar With Widely Separated Antennas
    IEEE Trans. Signal Process. (IF 5.230) Pub Date : 2020-01-21
    Wei Yi; Tao Zhou; Yue Ai; Rick S. Blum

    In this article, the problem of simultaneously detecting and localizing multiple targets in homogeneous noise environment is considered for non-coherent multiple-input multiple-output (MIMO) radar with widely separated antennas. By assuming that the a prior knowledge of target number is available, an optimal solution to this problem is presented first. It is essentially a maximum-likelihood (ML) estimator searching the parameters of interest in a high-dimensional state space. However, the complexity of this solution increases exponentially with the number $G$ of targets. Besides, if the number of targets is unknown, a multi-hypothesis testing strategy to verify all the possible hypotheses on target number is required, which further complicates this method. In order to devise computationally feasible methods for practical applications, we split the high-dimensional maximization into $G$ disjoint sub-optimization problems by sequentially detecting targets and then clearing their interference for the subsequent detection of remaining targets. In this way, we further propose two fast and robust suboptimal solutions which allow to trade performance for a much lower implementation complexity. In addition, the multi-hypothesis testing is no longer required when target number is unknown. Simulation results show that the proposed algorithms can correctly detect and accurately localize multiple targets even when targets lie in the same range bins. Experimental data recorded by three small radars are also provided to demonstrate the efficacy of the proposed algorithms.

    更新日期:2020-02-11
  • On the Performance of Splitting Receiver With Joint Coherent and Non-Coherent Processing
    IEEE Trans. Signal Process. (IF 5.230) Pub Date : 2020-01-21
    Yanyan Wang; Wanchun Liu; Xiangyun Zhou; Guanghui Liu

    In this article, we revisit a recently proposed receiver design, named the splitting receiver, which jointly uses coherent and non-coherent processing for signal detection. By considering an improved signal model for the splitting receiver as compared to the original study in the literature, we conduct a performance analysis on the achievable data rate under Gaussian signaling and obtain a fundamentally different result on the performance gain of the splitting receiver over traditional receiver designs that use either coherent or non-coherent processing alone. Specifically, the original study ignored the antenna noise and concluded on a 50% gain in achievable data rate in the high signal-to-noise ratio (SNR) regime. In contrast, we include the antenna noise in the signal model and show that the splitting receiver improves the achievable data rate by a constant gap in the high SNR regime. This represents an important correction of the theoretical understanding on the performance of the splitting receiver. In addition, we examine the maximum-likelihood detection and derive a low-complexity detection rule for the splitting receiver for practical modulation schemes. Our numerical results give further insights into the conditions under which the splitting receiver achieves significant gains in terms of either achievable data rate or detection error probability.

    更新日期:2020-02-11
  • Watson–Crick quantum finite automata
    Acta Inform. (IF 1.042) Pub Date : 2020-02-10
    Debayan Ganguly, Kingshuk Chatterjee, Kumar Sankar Ray

    Abstract One-way quantum finite automata are reversible in nature, which greatly reduces its accepting property. In fact, the set of languages accepted by one-way quantum finite automata is a proper subset of regular languages. In this paper, we replace the tape head of one-way quantum finite automata with DNA double strand and name the model Watson–Crick quantum finite automata. The non-injective complementarity relation of Watson–Crick automata introduces non-determinism in the quantum model. We show that this introduction of non-determinism increases the computational power of one-way quantum finite automata significantly. Watson–Crick quantum finite automata can accept all regular languages and also accepts some languages which are not accepted by any multi-head deterministic finite automata. Exploiting the superposition property of quantum finite automata, we show that Watson–Crick quantum finite automata accept the language L = {ww|w ∈ {a, b}*}.

    更新日期:2020-02-10
  • Spatio-temporal modeling of PM2.5 concentrations with missing data problem: a case study in Beijing, China
    Int. J. Geograph. Inform. Sci. (IF 3.545) Pub Date : 2019-09-13
    Qiang Pu; Eun-Hye Yoo

    One of the major challenges in conducting epidemiological studies of air pollution and health is the difficulty of estimating the degree of exposure accurately. Fine particulate matter (PM2.5) concentrations vary in space and time, which are difficult to estimate in rural, suburban and smaller urban areas due to the sparsity of the ground monitoring network. Satellite retrieved aerosol optical depth (AOD) has been increasingly used as a proxy of ground PM2.5 observations, although it suffers from non-trivial missing data problems. To address these issues, we developed a multi-stage statistical model in which daily PM2.5 concentrations can be obtained with complete spatial coverage. The model consists of three stages – an inverse probability weighting scheme to correct non-random missing patterns of AOD values, a spatio-temporal linear mixed effect model to account for the spatially and temporally varying PM2.5-AOD relationships, and a gap-filling model based on the integrated nested Laplace approximation-stochastic partial differential equations (INLA-SPDE). Good model performance was achieved from out-of-sample validation as shown in R2 of 0.93 and root mean square error of 9.64 μg/m3. The results indicated that the multi-stage PM2.5 prediction model proposed in the present study yielded highly accurate predictions, while gaining computational efficiency from the INLA-SPDE.

    更新日期:2020-02-07
  • A fast candidate viewpoints filtering algorithm for multiple viewshed site planning
    Int. J. Geograph. Inform. Sci. (IF 3.545) Pub Date : 2019-09-16
    Yiwen Wang; Wanfeng Dou

    The aim of site planning based on multiple viewshed analysis is to select the minimum number of viewpoints that maximize visual coverage over a given terrain. However, increasingly high-resolution terrain data means that the number of terrain points will increase rapidly, which will lead to rapid increases in computational requirements for multiple viewshed site planning. In this article, we propose a fast Candidate Viewpoints Filtering (CVF) algorithm for multiple viewshed site planning to lay a foundation for viewpoint optimization selection. Firstly, terrain feature points are selected as candidate viewpoints. Then, these candidate viewpoints are clustered and those belonging to each cluster are sorted according to the index of viewshed contribution (IVC). Finally, the candidate viewpoints with relatively low viewshed contribution rate are removed gradually using the CVF algorithm, through which, the viewpoints with high viewshed contribution are preserved and the number of viewpoints to be preserved can be controlled by the number of clusters. To evaluate the effectiveness of our CVF algorithm, we compare it with the Region Partitioning for Filtering (RPF) and Simulated Annealing (SA) algorithms. Experimental results show that our CVF algorithm is a substantial improvement in both computational efficiency and total viewshed coverage rate.

    更新日期:2020-02-07
  • Free and open source GIS in South America: political inroads and local advocacy
    Int. J. Geograph. Inform. Sci. (IF 3.545) Pub Date : 2019-09-18
    Sterling Quinn

    Geographical information systems (GIS) practitioners worldwide enjoy a growing array of free and open source software (FOSS) options. This software has expanded the accessibility of GIS in economically developing countries while fostering local technical expertise. This article reviews FOSS GIS uptake and advocacy in South America, especially how it relates to a climate of political friendliness toward FOSS in the region. The use or absence of FOSS GIS is assessed in public-facing web maps in South America, first at the national government level, and then at the provincial level using Argentina as a country of study. Local technical support groups and software development initiatives surrounding FOSS GIS in South America are then summarized. Finally, three case studies are presented of notable efforts to build FOSS GIS technical communities at the local level: the FOSSGIS Brasil online magazine, the Geoinquietos Argentina professional network, and the FOSS.4GIS.GOV conference in Brazil. A study of the leaders, dynamics, and practices of these groups can inform others in similar circumstances around the world who are trying to promote FOSS GIS adoption, development, skills, and services.

    更新日期:2020-02-07
  • Integrated edge detection and terrain analysis for agricultural terrace delineation from remote sensing images
    Int. J. Geograph. Inform. Sci. (IF 3.545) Pub Date : 2019-08-22
    Wen Dai; Jiaming Na; Nan Huang; Guanghui Hu; Xin Yang; Guoan Tang; Liyang Xiong; Fayuan Li

    Agricultural terraces are important for agricultural production and soil-and-water conservation. They comprise treads and risers that require manual construction and maintenance. If managed improperly, risers will collapse, causing soil loss, gully erosion, and cultivation threats. However, mapping terrace risers remains a challenge. This study presents a novel approach to automatically map terrace risers by combining remote sensing images and digital elevation models (DEMs). First, a terraced hillslope was extracted via a hill-shading method and edges in the image were detected using a Canny edge detector. Next, the DEM was used to generate the contour direction, and edges along this direction were searched and coded as candidate terrace risers via directional detection. Finally, the results of directional detection and the edge image obtained from the Canny detector were overlaid to backtrack complete terrace risers. The approach was validated using four study areas with different topographic characteristics in the Loess Plateau, China. The results verify that the approach achieves outstanding performance and robustness in mapping terrace risers. The precision, recall, and F-measure were 90.81%–97.57%, 88.53%–94.10%, and 90.13%–95.80%, respectively. This approach is flexible and applicable with freely available images and DEM sources.

    更新日期:2020-02-07
  • Migration pattern of Yellow-throated buntings revealed by isotope-based geographic assignment
    Int. J. Geograph. Inform. Sci. (IF 3.545) Pub Date : 2019-10-11
    Chang-Yong Choi; Hyun-Young Nam; Jong-Gil Park; Gi-Chang Bing

    Migratory birds have evolved diverse migration strategies in response to a variety of factors, but information about the detailed migration patterns of Asian songbirds is not yet available. To understand the short-distance migration pattern of declining Yellow-throated Buntings (Emberiza elegans) in East Asia, we analyzed stable isotopes from the outermost tail feathers (rectrices) of individual buntings collected in Korea and Japan. Temporal changes in feather hydrogen (δ2Hf) and oxygen (δ18Of) isotopic values at stopover islands suggested that northern populations start migration earlier than southern populations, especially in autumn. Latitudinal gradient in δ2Hf values of three wintering populations implied that northern breeders wintered farther north than southern breeders. The migration pattern of this bunting, known as Type II chain migration, was also inferred from hydrogen isotope-based geographic assignments of feather growth origins. Our data demonstrate that stable isotope analysis may help to bridge current knowledge gaps in songbird migration despite coarsely mapped isoscapes and as-of-yet undetermined isotope calibration functions in Asia.

    更新日期:2020-02-07
  • CostMAP: an open-source software package for developing cost surfaces using a multi-scale search kernel
    Int. J. Geograph. Inform. Sci. (IF 3.545) Pub Date : 2019-10-17
    Brendan Hoover; Sean Yaw; Richard Middleton

    Cost surfaces are a crucial aspect of route optimization and least cost path (LCP) calculations and are used in awide range of disciplines including computer science, landscape ecology, and energy-infrastructure modeling. Linear features present akey weakness to traditional routing calculations along cost surfaces because they cannot identify whether moving from acell to its adjacent neighbors constitutes crossing alinear barrier (increased cost) or following acorridor (reduced cost). Following and avoiding linear features can drastically change predicted routes. We introduce an approach to address this adjacency issue using asearch kernel that identifies these critical barriers and corridors. We have built this approach into anew Java-based open-source software package– CostMAP (cost surface multi-layer aggregation program)– which calculates cost surfaces and cost networks using the search kernel. CostMAP allows users to input multiple GIS data layers and to set weights and rules for developing aweighted-cost network. We compare CostMAP performance with traditional cost surface approaches and show significant performance gains– both following corridors and avoiding barriers– by modeling the movement of alarge terrestrial animal– the Baird’s Tapir (Tapirus bairdii)– in amovement ecology framework and by modeling pipeline routing for carbon capture and storage (CCS).

    更新日期:2020-02-07
  • An evaluation of compression algorithms applied to moving object trajectories
    Int. J. Geograph. Inform. Sci. (IF 3.545) Pub Date : 2019-10-24
    Yoran E. Leichsenring; Fabiano Baldo

    The amount of spatiotemporal data collected by gadgets is rapidly growing, resulting in increasing costs to transfer, process and store it. In an attempt to minimize these costs several algorithms were proposed to reduce the trajectory size. However, to choose the right algorithm depends on a careful analysis of the application scenario. Therefore, this paper evaluates seven general purpose lossy compression algorithms in terms of structural aspects and performance characteristics, regarding four transportation modes: Bike, Bus, Car and Walk. The lossy compression algorithms evaluated are: Douglas-Peucker (DP), Opening-Window (OW), Dead-Reckoning (DR), Top-Down Time-Ratio (TS), Opening-Window Time-Ratio (OS), STTrace (ST) and SQUISH (SQ). Pareto Efficiency analysis pointed out that there is no best algorithm for all assessed characteristics, but rather DP applied less error and kept length better-preserved, OW kept speed better-preserved, ST kept acceleration better-preserved and DR spent less execution time. Another important finding is that algorithms that use metrics that do not keep time information have performed quite well even with characteristics time-dependent like speed and acceleration. Finally, it is possible to see that DR had the most suitable performance in general, being among the three best algorithms in four of the five assessed performance characteristics.

    更新日期:2020-02-07
  • A deep learning architecture for semantic address matching
    Int. J. Geograph. Inform. Sci. (IF 3.545) Pub Date : 2019-10-24
    Yue Lin; Mengjun Kang; Yuyang Wu; Qingyun Du; Tao Liu

    Address matching is a crucial step in geocoding, which plays an important role in urban planning and management. To date, the unprecedented development of location-based services has generated a large amount of unstructured address data. Traditional address matching methods mainly focus on the literal similarity of address records and are therefore not applicable to the unstructured address data. In this study, we introduce an address matching method based on deep learning to identify the semantic similarity between address records. First, we train the word2vec model to transform the address records into their corresponding vector representations. Next, we apply the enhanced sequential inference model (ESIM), a deep text-matching model, to make local and global inferences to determine if two addresses match. To evaluate the accuracy of the proposed method, we fine-tune the model with real-world address data from the Shenzhen Address Database and compare the outputs with those of several popular address matching methods. The results indicate that the proposed method achieves a higher matching accuracy for unstructured address records, with its precision, recall, and F1 score (i.e., the harmonic mean of precision and recall) reaching 0.97 on the test set.

    更新日期:2020-02-07
  • Spatial interpolation of marine environment data using P-MSN
    Int. J. Geograph. Inform. Sci. (IF 3.545) Pub Date : 2019-11-05
    Bingbo Gao; Maogui Hu; Jinfeng Wang; Chengdong Xu; Ziyue Chen; Haimei Fan; Haiyuan Ding

    When a marine study area is large, the environmental variables often present spatially stratified non-homogeneity, violating the spatial second-order stationary assumption. The stratified non-homogeneous surface can be divided into several stationary strata with different means or variances, but still with close relationships between neighboring strata. To give the best linear-unbiased estimator for those environmental variables, an interpolated version of the mean of the surface with stratified non-homogeneity (MSN) method called point mean of the surface with stratified non-homogeneity (P-MSN) was derived. P-MSN distinguishes the spatial mean and variogram in different strata and borrows information from neighboring strata to improve the interpolation precision near the strata boundary. This paper also introduces the implementation of this method, and its performance is demonstrated in two case studies, one using ocean color remote sensing data, and the other using marine environment monitoring data. The predictions of P-MSN were compared with ordinary kriging, stratified kriging, kriging with an external drift, and empirical Bayesian kriging, the most frequently used methods that can handle some extent of spatial non-homogeneity. The results illustrated that for spatially stratified non-homogeneous environmental variables, P-MSN outperforms other methods by simultaneously improving interpolation precision and avoiding artificially abrupt changes along the strata boundaries.

    更新日期:2020-02-07
  • A comprehensive framework for studying diffusion patterns of imported dengue with individual-based movement data
    Int. J. Geograph. Inform. Sci. (IF 3.545) Pub Date : 2019-11-18
    Haiyan Tao; Keli Wang; Li Zhuo; Xuliang Li; Qiuping Li; Yuan Liu; Yong Xu

    International communication and global cooperation have greatly accelerated the worldwide spread of dengue fever, increasing the impact of imported cases on dengue outbreaks in non-naturally endemic areas. Existing studies mostly focus on describing the quantitative relationship between imported cases and local transmission but ignore the space-time diffusion mode of imported cases under the influence of individual mobility. In this paper, we propose a comprehensive framework at a fine scale to establish the disease transmission network and a mathematical model, which constructs ‘source-sink’ links between the imported and indigenous cases on a regular grid with a spatial resolution of 1 km to explore the diffusion pattern and spatiotemporal heterogeneity of imported cases. An application to Guangzhou, China, reveals the main flow and transmission path of imported cases under the influence of human movement and identifies the spatiotemporal distribution of transmission speed according to the time lag of each source-sink link. In addition, we demonstrate that using individual-based movement data and socio-economic factors to study human mobility and imported cases can help to understand the driving forces of dengue spread. Our research provides a comprehensive framework for the analysis of early dengue transmission patterns with benefits to similar urban applications.

    更新日期:2020-02-07
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