Abstract: As a prerequisite task in Natural language processing (NLP), Chinese word segmentation (CWS), is challenged by unknown words. Aiming to effectively detect Chinese unknown words, especially the low-frequency unknown words in unstructured microblog data, we modify the usage of Accessor variety (AV) to measure the context environments of core fragments and propose a novel variable, the Independence of strings, which is derived from the internal structure of segments. Our approach is unsupervised without using any manual materials. Due to the lack of manual resources of microblog-oriented unknown words extraction, we use sampling approach to assess the effectiveness of our method. Experimental results suggest our best system beats the baseline system as well as the state-of-the-art system by a significant improvement in F1-measure and the recall of low-frequency unknown words.
Abstract: To enhance the approximation ability of traditional Artificial neural network (ANN), by introducing the quantum rotation gates and the multi-qubits controlled-NOT gates to ANN, we proposed a Sequence input-based quantum-inspired neural network (SIQNN). In our model, the hidden nodes are composed of some multi-qubits controlled-NOT gates, the inputs are described by the multi-dimensional discrete qubits sequences, the output nodes are the traditional neurons. The model parameters include the rotation angles of quantum rotation gates in hide layer and the weights in output layer. The learning algorithms were derived by employing the Levenberg-Marquardt algorithm. Simulation results of predicting the runoff of the Hongjiadu Reservoir show that, the SIQNN is obviously superior to the ANN.
Abstract: A hyper-star is a graph consisting of the union of some hypercubes with at least one common vertex. The graph induced by a linearly separable Boolean function is a hyper-star. We obtain some properties of hyper-stars and give a decomposition algorithm of a hyperstar. We give a determination condition for a hyper-star. The determination condition yields an algorithm of constructing all hyper-stars of n vertices in time O(n3).
Abstract: The Quadratic assignment problem (QAP) is to assign a set of facilities to a set of locations with given distances between the locations and given flows between the facilities such that the sum of the products between flows and distances is minimized, which is a notoriously difficult NP-hard combinatorial optimization problem. A lot of heuristics have been proposed for the QAP problem, and some of them have proved to be efficient approximation algorithms for this problem. Ant colony optimization (ACO) is a general-purpose heuristic and usually considered as an approximation algorithms for NP-hard optimization problems. However, we know little about the performance of ACO for QAP from a theoretical perspective. This paper contributes to a theoretical understanding of ACO on the QAP problem. The worst-case bound on a simple ACO algorithm called (1+1) Max-min ant algorithm ((1+1) MMAA) for the QAP problem is presented. It is shown that a degenerate (1+1) MMAA finds an approximate solution on the QAP problem. Finally, we reveal that ACO can outperform the 2-exchange local search algorithm on an instance of the QAP problem.
Abstract: In various applications like personalized search and recommendation, full demographic information is a precondition for many applications' well performance, but such ideal dataset rarely exists in practical scenarios. What's worse, absence of key characteristics (e.g., baby's age in maternal and infant commodity recommendation) makes these applications struggle. We design a novel solution to solve the problem of time-dependent demographic prediction. The key insight behind our approach is, we leverage a Time-back-propagation (TBP) method to take the internal time correlation of historical behaviours into consideration and collect all available data to train a classifier, which is a mapping from user's historical behaviours to the demographic information. We demonstrate the effectiveness of our method through experiments of baby's age prediction. Our algorithm performs more balanced on each age group, and can predict Baby's age (BA) accurately in 78.2% on a real-world dataset with 2,058,909 items of a major E-commerce site.
Abstract: Cloud computing provides users with a great deal of flexibility and convenience. However, cloud computing also brings very serious security problems, especially for enterprise data security stored in the cloud. Once the data is outsourced to a third party, the data privacy has become a major problem, such as user authentication, integrity of data etc. and needs to be addressed very effectively. A mutual authentication scheme based on smartcard for cloud computing is proposed to solve the problem of which the illegal users access the resource of cloud servers and the legal users access the illegal cloud server. The scheme achieves mutual authentication by using hash functions to protect user privacy. Performance comparison shows that the proposed scheme is an efficient one.
Abstract: Based on the construction of a new distance-preserving Gray map from ((Fp + uFp)N, Gray distance) to (the corresponding Gray images in FppN, Hamming distance) and the calculation of Gray distances of (u-1)-constacyclic codes over Fp + uFp, a bound for the Hamming distances of a class of negacyclic codes with length pN over Fp is obtained, which is more tighter than Singleton bound. Further more, the exact Hamming distances of some p-ary negacyclic codes are determined from this bound, some of which cannot be got from Dinh's work published on Finite Fields and Their Applications in 2008.
Abstract: This paper presents a novel adaptive JPEG steganographic scheme in Discrete cosine transform (DCT) domain using Run-length statistical complexity(RSC). Unlike the traditional complexity measurements, RSC considers the change randomness of DCT coefficients in image blocks. The distortion functions in both side-informed and non-side-informed schemes are derived by RSC and minimized by Syndrome trellis coding (STC). Experimental results show superior performance of our scheme over a wide range of embedding rates at different image quality factors compared with previous three common steganographic schemes.
Abstract: To guarantee that electronic publications are accessible only to the authorized users via cloud, we propose an Efficient access control scheme (EACS) based on Attribute-based encryption (ABE), which is suitable for fine-grained access control. Compared with existing stateof-the-art schemes, EACS is more practical by following functions. Considering the factor that the user membership may change frequently, EACS has the capability of coping with dynamic membership efficiently. Arbitrary-State is also supported to facilitate the system management and improve efficiency. Besides, we prove in the standard model that the security of EACS is based on the Decisional Bilinear Diffie-Hellman assumption. To evaluate the practicality of EACS, we provide a detailed theoretical performance analysis and a simulation comparison with existing schemes. Both the theoretical analysis and the experimental results show that our proposal is efficient and practical for electronic publishing under cloud environment.
Abstract: This research focuses on the methods to improve the throughput and lower the power for low cost RSA coprocessors. We proposed the following optimized methods:1. A fast half-carry-save Montgomery modular multiplication algorithm suitable for hardware implementation; 2. A high-speed dual-core multiplier accumulator architecture to optimize the critical path; 3. Several lowpower optimization schemes for the RSA coprocessor. The design has been implemented with TSMC 90nm technology, and the experimental results show that the critical path is 2.71ns, the power consumption is just 9.76mW and the throughput can reach 381.57kbps. Compared with relative works, our design is featured by the minimal power and the best overall performance, thus it is most suitable for applications in low-power systems.
Abstract: With the increased snapshot operations (creation or deletion), the performance of storage system will degrade severely whether in Copy on write (COW) or Redirect on write (ROW). This paper presents a novel snapshot scheme which could use of storage resources effectively and keep the historical data accurately for SSDHDD-hybrid storage system. SSD (source volume) stores all the active data and executes read/write operations without responding to snapshot operations. In contrast, HDDs (snapshot volume) store the writing data sequentially in the form of logs and are responsible for backup and snapshot operations. In this way, the hybrid storage system can get the high throughput power via SSD even the snapshot functions are active and HDDs can keep pace with the high Input/Output operations per second (IOPS) capability of the SSD. Logs are combined and kept in a separate image volume by the controller. So, fine-grained Recovery point objective (RPO) and good Recovery time objective (RTO) can be achieved. Experiment results show that the prototype can achieve excellent I/O performance while snapshot operations are active and consume a few extra storage space.
Abstract: Regular expression matching is one of the key techniques for Deep packet inspection (DPI). Generally, the Deterministic finite automata (DFA) can process regular expression matching at a very high rate but memory inefficient. By contrast, the Non-deterministic finite automata (NFA) improves the memory efficiency significantly, at the cost of low speed. To meet the increasing demands on both throughput and memory scalability, we propose a novel schema to achieve fast regular expression with reasonable and controllable memory consumption. According to observations on matching real traffic, we design a Multi-Stride Indexing (MSI) table and divide each matching into two steps, toward the MSI table and the NFA respectively. the MSI table consumes a small fixed amount of on-chip memories(it is about 20KB for 2 stride level MSI table). After the most of unnecessary matching was eliminated via MSI table, we implement the fast-speed regular expression matching with NFA.
Abstract: Reliability of traditional electronic circuit is improved mainly by redundant fault-tolerant technology with large hardware resource consumption and limited fault self-repair capability. In complicated environment, electronic circuit faults appear easily. If on-site immediate repair is not implemented, normal running of electronic system will be directly affected. In order to solve these problems, Evolvable hardware (EHW) technology is widely used. The conventional EHW has some bottlenecks. The optimal design of Rectification circuit (RTC) is further researched on the basis of the previously proposed fault self-repair based on EHW and Reparation balance technology (RBT). Fault sets are selected by fault danger degree and fault coverage rate. The optimal designed RTC can completely repair faults in the fault set. Simulation results prove that it has higher self-repair capability and less hardware resource.
Abstract: This paper presents a novel neural network-based fault detection technique applicable to a class of nonlinear systems. The adaptive observer was designed for fault detection based on a single hidden layer feed-forward wavelet neural network. In order to guarantee network convergence, the network weights are updated according to a modified back-propagation algorithm, and the Lyapunov function is introduced to ensure stability. The proposed fault detection scheme was tested on the actuators of a typical spacecraft attitude control system, and the results demonstrated the effectiveness and feasibility of the proposed observer in detecting nonlinear system failure.
Abstract: A flexible and accurate frequency estimator is first proposed for frequency estimation of a complex sinusoid weighted with a rectangular window function in additive white Gaussian noise. This proposed frequency estimator can be operated in the application of an arbitrary length discrete Fourier transform where the original input data is padded with any zeroes, which makes it more flexible in practice. The proposed frequency estimator utilizes the maximum sample value and its two adjacent samples in the frequency domain to perform the fine frequency estimation with unbiased results obtained. Then a modified frequency estimator is proposed to estimate the frequency when the complex sinusoid signal is weighted with different nonrectangular window functions. Although the modified frequency estimator is nonanalytic and biased, it can still improve the estimation performance for certain applications. Simulation results demonstrate that both of the proposed frequency estimators are effective to achieve the high frequency estimation accuracy. And the root mean square errors of the proposed frequency estimators approach the Cramer-Rao bound when the signal-to-noise ratio is large enough to make the coarse frequency estimation work effectively.
Abstract: Conventional Multi-Bernoulli (MBer) filter assumes that the birth MBer Random finite set (RFS) is known a priori. However, this is not true for practical scenario. This paper proposes a novel extension of the MBer filter which eliminates the reliance of the prior birth MBer RFS and relaxes the limitation in new-born target appearance volume. The proposed filter classifies the measurements into survival measurements and birth measurements, and adaptively generates the birth MBer RFS using the birth measurements. The novel filtering equations that distinguish the persistent and new-born targets are derived. A Sequential Monte-Carlo (SMC) implementation of the proposed filter is given. Simulations are performed to verify the improvement in the performance of the proposed filter.
Abstract: The Instantaneous frequency (IF) is a basic concept in theory and application of signal processing. Wigner-Ville distribution (WVD) is a powerful tool in IF estimation, but it is sensitive to the noise. The Linear canonical transform (LCT) has been proved to be very powerful for non-stationary signal processing. A new definition of Wigner-Ville distribution associated with the linear canonical transform (WDL) has been put forward. In this case, this paper proposes a new IF estimation method which is suitable for processing non-stationary signals. This new method is useful in IF estimation. This new method is more accurate and antinoise than the one based on the WVD. Simulations demonstrated the derived results.
Abstract: A new prediction algorithm based on Empirical model decomposition (EMD) and Support vector machine (SVM) is put forward in this paper, and this algorithm solves the problem of the hydrogen atomic clock differences prediction, which is affected by the non-linearity and non-stability. The clock differences were decomposed into Intrinsic mode functions (IMF) and the residual series. The suitable kernel function and parameters were chosen to build the different SVM for predicting each IMF and the residual series. Each prediction result was summed to obtain the clock differences prediction. Results show that the EMD-SVM algorithm is effective compared with the linear regression and single SVM. The relative prediction error is reduced from 0.4327% to 0.2371%, and the dispersion is less than other methods.
Abstract: Roads as important artificial objects are the main body of modern traffic system, which provide many conveniences for human civilization. With the development of remote sensing and hyperspectral imaging technology, how to automatically and accurately extract road network from high-resolution multispectral satellite images has become a hot and challenging research topic of geographic information technology. In this paper, an automatic road extraction method from high-resolution multispectral satellite images is proposed by using multiple saliency features. Firstly, road edge is extracted by detecting local linear edge with Singular value decomposition (SVD). Secondly, road regions are constructed by K-means clustering after extracting the feature of background difference. Then road network is achieved by integrating multiple saliency features with Total variation (TV) based image fusion algorithm. Finally, the non-road parts and noises are removed from road network by optimizing multiple salient features with post-processing and morphological operations. The experimental results show that the proposed method can achieve a superior performance in completeness and correctness.
Abstract: A new detection method based on modified affinity propagation is proposed for detecting foreign particles in medical solution. The captured sequential images are used to generate moving trajectories of possible foreign particles. The external disturbances and background are almost removed by using a series of simple and effective image processing. The extracted potential targets, including foreign particles and possible residual noises, are partitioned into a number of valid clusters by modified affinity propagation clustering, in which the new similarity measure can better reflect the intrinsic link of linearstructured datasets and the temporal constraint enforces the temporal continuity of data points. The foreign particles can be easily recognized by analyzing the cluster structure according to the continuity and smoothness of moving targets trajectory. This algorithm combines modified affinity propagation method with the moving characteristics of different particles, and can achieve accurate partitions efficiently even though the clusters are badly adjacent. The experimental results indicate that the proposed algorithm can detect foreign particles effectively with high detection speed and accuracy.
Abstract: This paper proposes a High quality data hiding in halftone image based on block conjugate (HQDHBC). We construct Least mean square filters (LMSF), and using LMSFs to evaluate the quality of halftone images. This paper proposes block conjugate property, and this property can be applied to hide a secret pattern. Therefore a data hiding method based on block conjugate can be proposed, and in this method two images should be divided into multiple blocks of M×N pixels, and in the corresponding block if the first M×N-1 pixels are conjugate or identical, the last pixel can implement normal halftone method instead of date hiding methods. Experimental results show that the proposed HQDHBC can be applied in gray-scale and color error diffused halftone images, and through implementing this method, not only the quality of halftone images can be improved, but also the Correct decoding rate (CDR) of revealed pattern can be guaranteed.
Abstract: A directional sensor network consists of numerous small sensor nodes that have limited battery power and operate within a restricted sensing range angle. Coverage and connectivity, as two important issues, are widely studied in directional sensor networks. Different from conventional directional sensor nodes in previous studies, those in the present study could adjust their sensing ranges within a range of several values. This paper addresses the Connected adjusted-ranges directional cover (CARDC) problem. The purpose is to organize the directional sensor nodes into a group of connected covers, and assign activities to them, thereby maximizing the network lifetime. We propose a hybrid approach that combines column generation with a genetic algorithm to solve the CARDC problem. The genetic algorithm is utilized to settle the auxiliary problem of column generation, which has the ability to efficiently provide attractive columns for the master problem. Compared with pure integer linear programming formulations, the proposed genetic algorithm significantly improves the speed of column generation method, especially in large-scale networks. The effect variety of number of directions, number of power levels, and communication range on the network lifetime are also investigated.
Abstract: Mobile data offloading through third-party Femtocell access points (FAPs) is an emerging technology which is used to alleviate congestion in cellular network. We propose a distributed algorithm that combines college admissions game with auction theory to achieve the maximum social welfare of offloading and guarantee the Quality of service (QoS) of Mobile users (MUs). As the optimal problem is NP-hard, the college admissions game is developed to solve the sub-problem of matching between FAPs and MUs. The Second revenue sealed-bid auction with a reserve price (SRSARP) algorithm is developed to solve another sub-problem of matching between Wireless operators (WOs) and FAPs. The simulation results reveal that the distributed algorithm we proposed achieves the performance very close to that of the centralized method and significantly better than those of the random and Maxprice algorithms.
Abstract: Millimeter-wave (MMW) signals in 60GHz band have shown immense potential for accurate range estimation with precise time and multipath resolution. Nonline of sight (NLOS) propagation is a primary factor affecting the range precision. To improve range estimation, an Energy detector (ED) based normalized threshold algorithm which employs a Neural network (NN) is developed on the basis of NLOS identification. The maximum curl and standard deviation of the received energy block values are used to determine NLOS environment and the normalized thresholds for different Signal-to-noise ratios (SNRs). The effects of the channel and integration period are evaluated. Performance results are presented which show that the proposed approach provides better precision and is more robust than other solutions over a wide range of SNRs for the CM1.1 and CM2.1 channel models in the IEEE 802.15.3c standard.
Abstract: We investigate the network selection problem over heterogeneous networks. We formulate the decision making problem as a Markov decision process (MDP). Two Poisson processes are introduced to model user arrival and departure, and the change of the number of user in a specific network is described by Skellam Distribution. We apply the Total order preference by similarity to the ideal solution (TOPSIS) method to address the issue of state space explosion in MDP model. To acquire the optimal network selection scheme, the value iteration algorithm is employed to solve the MDP model. We take simulations in Matlab to prove the effectiveness of the proposed scheme. From the experiment results, it is found that our proposed scheme can reduce handover probability by 7.5% and 40.4% at the early stage of connection, compared with the Infinite MDP-based (IMDP) strategy and TOPSISonly strategy. It can also reduce blocking probability by 6.1% compared with IMDP, while maintaining desirable throughput for the target user.
Abstract: Network coding is promising to improve the throughput and robustness of video transmission over wireless networks. To address the security issue of network coding based Priority encoding transmission (PET), a special coding design against wiretapping is proposed with hierarchical security. The basic idea is first to randomize partial blocks of each layer and then to encode each layer with inter-layer overlapping coding. The method is characterized by quantifiable security for incremental decoding, and realizes scalability in confidentiality and video transmission, while incurring relatively low computational complexity and bandwidth overhead. A sufficient condition is also presented to design a stronger secure network coded PET system against the known-plaintext attacks. Analysis and evaluation show that the method can be efficient as a new paradigm to secure network coded PET system in realistic applications.
Abstract: One key of Multiple-input multiple-output (MIMO) Synthetic aperture radar (SAR) interferometry is the effective separation of the echoes originating from different transmissions. A multidimensional waveform design method for MIMO SAR interferometry is presented. By exploiting the wavenumber shift and Doppler-band shift phenomenon in the design of transmitted signals, the echo separation can be efficiently accomplished via the filtering operations in range-frequency domain and azimuth-spatial domain. Such waveform design can meet the requirement of MIMO SAR imaging on integrated cross interference level without reduce the swath width. Such waveform design also improves the coherence between the independent observations and enlarges the unambiguity range of height estimation. The results of the numerical simulations demonstrate the feasibility of the proposed waveform design method.
Abstract: We investigate the problem of direction finding for multiple Partially polarized (PP) electromagnetic signals, each of which possesses two spatial degrees of freedom. We use a uniform linear array of L sensors, where each consists of two co-located single-polarized elements. By exploiting the array geometry and its shift invariance property, we construct a set of data correlation sequences using the array output and its conjugate to transform the problem of direction finding to that of the estimation of complex sinusoid frequencies. The rank-2 signal correlation matrices of the PP signals become real-valued amplitudes of complex sinusoids, and thus, the rank-2K signal subspace of the array output transforms to the rank-K signal subspace of the constructed correlation sequences. We show that only L=K + 1 sensors are needed to resolve K PP signals by solving the problem of estimation of sinusoid frequencies using subspace-based methods. The proposed method is also applicable to the scenario, where both Completely polarized (CP) and PP signals coexist, without the need of any prior information on the degree of polarization of the signal.
Abstract: For Global navigation satellite system (GNSS), spoofing attack is extremely dangerous and destructive since it can lead GNSS receivers to generate misleading time and position information. Thus, reliable and timely spoofing detection and mitigation methods are of great importance. A new GNSS acquisition method with the capability of spoofing detection and mitigation is proposed in the paper. The proposed method can not only perform acquisition, but also detect and mitigate spoofing signals based on joint consistency detection of code and carrier Dopplers. We provide performance analysis and numerical simulation results to demonstrate the validity of the proposed method when there are one or even more spoofing signals. Successful acquisition probability of the authentic signal is compared to that achieved by traditional acquisition method. The proposed method can be easily implemented with only a little modification of current receiver acquisition module.