The phenomenon of stochastic resonance (SR) in a mono-threshold-system-based detector (MTD) with additive background noise and multiplicative external noise is investigated. On the basis of maximum a posteriori probability (MAP) criterion, we deal with the binary signal transmission in four scenarios. The performance of the MTD is characterized by the probability of error detection, and the effects of system threshold and noise intensity on detectability are discussed in this paper. Similar to prior studies that focus on additive noises, along with increases in noise intensity, we also observe a non-monotone phenomenon in the multiplicative ways. However, unlike the case with the additive noise, optimal multiplicative noises all tend toward infinity for fixed additive noise intensities. The results of our model are potentially useful for the design of a sensor network and can help one to understand the biological mechanism of synaptic transmission.

Transfer learning extracts useful information from the related source domain and leverages it to promote the target learning. The effectiveness of the transfer was affected by the relationship among domains. In this paper, a novel multi-source transfer learning based on multi-similarity was proposed. The method could increase the chance of finding the sources closely related to the target to reduce the “negative transfer” and also import more knowledge from multiple sources for the target learning. The method explored the relationship between the sources and the target by multi-similarity metric. Then, the knowledge of the sources was transferred to the target based on the smoothness assumption, which enforced that the target classifier shares similar decision values with the relevant source classifiers on the unlabeled target samples. Experimental results demonstrate that the proposed method can more effectively enhance the learning performance.

In this paper, a logarithmic adaptive quantization projection (LAQP) algorithm for digital watermarking is proposed. Conventional quantization index modulation uses a fixed quantization step in the watermarking embedding procedure, which leads to poor fidelity. Moreover, the conventional methods are sensitive to value-metric scaling attack. The LAQP method combines the quantization projection scheme with a perceptual model. In comparison to some conventional quantization methods with a perceptual model, the LAQP only needs to calculate the perceptual model in the embedding procedure, avoiding the decoding errors introduced by the difference of the perceptual model used in the embedding and decoding procedure. Experimental results show that the proposed watermarking scheme keeps a better fidelity and is robust against the common signal processing attack. More importantly, the proposed scheme is invariant to value-metric scaling attack.

Distributed compressive video sensing (DCVS) has received considerable attention due to its potential in source-limited communication, e.g., wireless video sensor networks (WVSNs). Multi-hypothesis (MH) prediction, which treats the target block as a linear combination of hypotheses, is a state-of-the-art technique in DCVS. The common approach is under the supposition that blocks that are dissimilar from the target block are given lower weights than blocks that are more similar. This assumption can yield acceptable reconstruction quality, but it is not suitable for scenarios with more details. In this paper, based on the joint sparsity model (JSM), the authors present a Tikhonov-regularized MH prediction scheme in which the most similar block provides the similar common portion and the others blocks provide respective unique portions, differing from the common supposition. Specifically, a new scheme for generating hypotheses and a Euclidean distance-based metric for the regularized term are proposed. Compared with several state-of-the-art algorithms, the authors show the effectiveness of the proposed scheme when there are a limited number of hypotheses.

Identifying the infection sources in a network, including the sponsor of a network rumor, the servers that inject computer virus into a computer network, or the zero-patient in an infectious disease network, plays a critical role in limiting the damage caused by the infection. A two-source estimator is firstly constructed on basis of partitions of infection regions in this paper. Meanwhile, the two-source estimation problem is transformed into calculating the expectation of permitted permutations count which can be simplified to a single-source estimation problem under determined infection region. A heuristic algorithm is also proposed to promote the estimator to general graphs in a Breadth-First-Search (BFS) fashion. Experimental results are provided to verify the performance of our method and illustrate variations of error detection in different networks.

In this paper, we provide several methods to construct nonlinear resilient functions with multiple good cryptographic properties, including high nonlinearity, high algebraic degree, and non-existence of linear structures. Firstly, we present an improvement on a known construction of resilient S-boxes such that the nonlinearity and the algebraic degree will become higher in some cases. Then a construction of highly nonlinear t-resilient Boolean functions without linear structures is given, whose algebraic degree achieves n-t-1, which is optimal for n-variable t-resilient Boolean functions. Furthermore, we construct a class of resilient S-boxes without linear structures, which possesses the highest nonlinearity and algebraic degree among all currently known constructions.

The Virtual Machine Consolidation (VMC) algorithm is the core strategy of virtualization resource management software. In general, VMC efficiency dictates cloud datacenter efficiency to a great extent. However, all the current Virtual Machine (VM) consolidation strategies, including the Iterative Correlation Match Algorithm (ICMA), are not suitable for the dynamic VM consolidation of the level of physical servers in actual datacenter environments. In this paper, we propose two VM consolidation and placement strategies which are called standard Segmentation Iteration Correlation Combination (standard SICC) and Multi-level Segmentation Iteration Correlation Combination (multi-level SICC). The standard SICC is suitable for the single-size VM consolidation environment and is the cornerstone of multi-level SICC which is suitable for the multi-size VM consolidation environment. Numerical simulation results indicate that the numbers of remaining Consolidated VM (CVM), which are generated by standard SICC, are 20% less than the corresponding parameters of ICMA in the single-level VM environment with the given initial condition. The numbers of remaining CVMs of multi-level SICC are 14% less than the corresponding parameters of ICMA in the multi-level VM environment. Furthermore, the used physical servers of multi-level SICC are also 5% less than the used servers of ICMA under the given initial condition.

In this paper, the problem of lifetime extension of wireless sensor networks (WSNs) with redundant sensor nodes deployed in 3D vegetation-covered fields is modeled, which includes building communication models, network model and energy model. Generally, such a problem cannot be solved by a conventional method directly. Here we propose an Artificial Bee Colony (ABC) based optimal grouping algorithm (ABC-OG) to solve it. The main contribution of the algorithm is to find the optimal number of feasible subsets (FSs) of WSN and assign them to work in rotation. It is verified that reasonably grouping sensors into FSs can average the network energy consumption and prolong the lifetime of the network. In order to further verify the effectiveness of ABC-OG, two other algorithms are included for comparison. The experimental results show that the proposed ABC-OG algorithm provides better optimization performance.

Nowadays, malware is a serious threat to the Internet. Traditional signature-based malware detection method can be easily evaded by code obfuscation. Therefore, many researchers use the high-level structure of malware like function call graph, which is impacted less from the obfuscation, to find the malware variants. However, existing graph match methods rely on approximate calculation, which are inefficient and the accuracy cannot be effectively guaranteed. Inspired by the successful application of graph convolutional network in node classification and graph classification, we propose a novel malware similarity metric method based on graph convolutional network. We use graph convolutional network to compute the graph embedding vectors, and then we calculate the similarity metric of two graph based on the distance between two graph embedding vectors. Experimental results on the Kaggle dataset show that our method can applied to the graph based malware similarity metric method, and the accuracy of clustering application with our method reaches to 97% with high time efficiency.

In this paper, we consider the problem of bearing estimation for spatially distributed sources in unknown spatially-correlated noise. Assumed that the noise covariance matrix is centro-Hermitian, a differential denoising scheme is developed. Combined it with the classic DSPE algorithm, a differential denoising estimator is formulated. Its modified version is also derived. Exactly, the differential processing is first imposed on the covariance matrix of array outputs. The resulting differential signal subspace (DSS) is then utilized to weight array outputs. The noise components orthogonal to DSS are eliminated. Based on eigenvalue decomposition of the covariance matrix of weighted array outputs, the DSPE null spectrum is constructed. The asymptotic performance of the proposed bearing estimator is evaluated in a closed form. Moreover, in order to improve the performance of bearing estimation in case of low signal-to-noise ratio, a modified differential denoising estimator is proposed. Simulation results show the effectiveness of the proposed estimators under the low SNR case. The impacts of angular spread and number of sensors are also investigated.

In this paper, we present the channel estimation (CE) problem in the orthogonal frequency division multiplexing system with offset quadrature amplitude modulation (OFDM/OQAM). Most CE methods rely on the assumption of a low frequency selective channel to tackle the problem in a way similar to OFDM. However, these methods would result in a severe performance degradation of the channel estimation when the assumption is not quite inaccurate. Instead, we focus on estimating the channel impulse response (CIR) itself which makes no assumption on the degree of frequency selectivity of the channels. After describing the main idea of this technique, we present an iterative CE method that does not require zero-value guard symbols in the preamble and consequently improves the spectral efficiency. This is done by the iterative estimation of the unknown transmitted data adjacent to the preamble. Analysis and simulation results validate the efficacy of the proposed method in multipath fading channels.

The capacity of optical transport networks has been increasing steadily and the networks are becoming more dynamic, complex, and transparent. Though it is common to use worst case assumptions for estimating the quality of transmission (QoT) in the physical layer, over provisioning results in high margin requirements. Accurate estimation on the QoT for to-be-established lightpaths is crucial for reducing provisioning margins. Machine learning (ML) is regarded as one of the most powerful methodological approaches to perform network data analysis and enable automated network self-configuration. In this paper, an artificial neural network (ANN) framework, a branch of ML, to estimate the optical signal-to-noise ratio (OSNR) of to-be-established lightpaths is proposed. It takes account of both nonlinear interference between spectrum neighboring channels and optical monitoring uncertainties. The link information vector of the lightpath is used as input and the OSNR of the lightpath is the target for output of the ANN. The nonlinear interference impact of the number of neighboring channels on the estimation accuracy is considered. Extensive simulation results show that the proposed OSNR estimation scheme can work with any RWA algorithm. High estimation accuracy of over 98% with estimation errors of less than 0.5dB can be achieved given enough training data. ANN model with R=4 neighboring channels should be used to achieve more accurate OSNR estimates. Based on the results, it is expected that the proposed ANN-based OSNR estimation for new lightpath provisioning can be a promising tool for margin reduction and low-cost operation of future optical transport networks.

In this letter, we propose a power allocation scheme to optimize the ergodic secrecy rate of multiple-input single-output (MISO) fading wiretap channels with a probabilistic constraint, using the statistical channel state information (CSI) of the eavesdropper (CSI-E). The analytical expressions of the false secrecy probability are derived and used as constraints in the rate maximization problem. Moreover, we obtain a suboptimal solution by formulating the power allocation problem as a Rayleigh quotient problem.

In this letter, we present an automatic approach of objective singing performance evaluation for untrained singers by relating acoustic measurements to perceptual ratings of singing voice quality. Several acoustic parameters and their combination features are investigated to find objective correspondences of the perceptual evaluation criteria. Experimental results show relative strong correlation between perceptual ratings and the combined features and the reliability of the proposed evaluation system is tested to be comparable to human judges.

In this letter, we propose a novel technique to increase the visibility of the hazy image. Benefiting from the atmospheric scattering model and the invariance principle for scene structure, we formulate structure constraint equations that derive from two simulated inputs by performing gamma correction on the input image. Relying on the inherent boundary constraint of the scattering function, the expected scene albedo can be well restored via these constraint equations. Extensive experimental results verify the power of the proposed dehazing technique.

Relation extraction is one of the key basic tasks in natural language processing in which distant supervision is widely used for obtaining large-scale labeled data without expensive labor cost. However, the automatically generated data contains massive noise because of the wrong labeling problem in distant supervision. To address this problem, the existing research work mainly focuses on removing sentence-level noise with various sentence selection strategies, which however could be incompetent for disposing word-level noise. In this paper, we propose a novel neural framework considering both intra-sentence and inter-sentence relevance to deal with word-level and sentence-level noise from distant supervision, which is denoted as Sentence-Related Gated Piecewise Convolutional Neural Networks (SR-GPCNN). Specifically, 1) a gate mechanism with multi-head self-attention is adopted to reduce word-level noise inside sentences; 2) a soft-label strategy is utilized to alleviate wrong-labeling propagation problem; and 3) a sentence-related selection model is designed to filter sentence-level noise further. The extensive experimental results on NYT dataset demonstrate that our approach filters word-level and sentence-level noise effectively, thus significantly outperforms all the baseline models in terms of both AUC and top-n precision metrics.

Multi-hypothesis prediction technique, which exploits inter-frame correlation efficiently, is widely used in block-based distributed compressive video sensing. To solve the problem of inaccurate prediction in multi-hypothesis prediction technique at a low sampling rate and enhance the reconstruction quality of non-key frames, we present a resample-based hybrid multi-hypothesis scheme for block-based distributed compressive video sensing. The innovations in this paper include: (1) multi-hypothesis reconstruction based on measurements reorganization (MR-MH) which integrates side information into the original measurements; (2) hybrid multi-hypothesis (H-MH) reconstruction which mixes multiple multi-hypothesis reconstructions adaptively by resampling each reconstruction. Experimental results show that the proposed scheme outperforms the state-of-the-art technique at the same low sampling rate.

The differential fault analysis of SOSEMNAUK was presented in Africacrypt in 2011. In this paper, we improve previous work with algebraic techniques which can result in a considerable reduction not only in the number of fault injections but also in time complexity. First, we propose an enhanced method to determine the fault position with a success rate up to 99% based on the single-word fault model. Then, instead of following the design of SOSEMANUK at word levels, we view SOSEMANUK at bit levels during the fault analysis and calculate most components of SOSEMANUK as bit-oriented. We show how to build algebraic equations for SOSEMANUK and how to represent the injected faults in bit-level. Finally, an SAT solver is exploited to solve the combined equations to recover the secret inner state. The results of simulations on a PC show that the full 384 bits initial inner state of SOSEMANUK can be recovered with only 15 fault injections in 3.97h.

Aggregate signature (AS) schemes enable anyone to compress signatures under different keys into one. In sequential aggregate signature (SAS) schemes, the aggregate signature is computed incrementally by the sighers. Several trapdoor-permutation-based SAS have been proposed. In this paper, we give a constructions of SAS based on the first SAS scheme with lazy verification proposed by Brogle et al. in ASIACRYPT 2012. In Brogle et al.'s scheme, the size of the aggregate signature is linear of the number of the signers. In our scheme, the aggregate signature has constant length which satisfies the original ideal of compressing the size of signatures.

Irreversible thermal paints or temperature sensitive paints are a kind of special temperature sensor which can indicate the temperature grad by judging the color change and is widely used for off-line temperature measurement during aero engine test. Unfortunately, the hot gases flow within the engine during measuring always make the paint color degraded, which means a serious saturation reduction and contrast loss of the paint colors. This phenomenon makes it more difficult to interpret the thermal paint test results. Present contrast enhancement algorithms can significantly increase the image contrast but can't protect the hue feature of the paint images effectively, which always cause color shift. In this paper, we propose a color restoration method for thermal paint image. This method utilizes the atmospheric scattering model to restore the lost contrast and saturation information, so that the hue can be protected and the temperature can be precisely interpreted based on the image.