The maximal linear forest problem is to find, given a graph G = (V, E), a maximal subset of V that induces a linear forest. Three parallel algorithms for this problem are presented. The first one is randomized and runs in O(log n) expected time using n2 processors on a CRCW PRAM. The second one is deterministic and runs in O(log 2n) timeusing n4 processors on an EREW PRAM. The last one is deterministic and runs in O(log 5n) time using n3 processors on an EREW PRAM. The results put the problem in the class NC.

The notion of alternating context-free grammar (ACFG for short) was introduced by Moriya in 1989. In this paper, we study the relationships between some complexity classes and the classes of languages generated by restricted types of ACFG's. Two restricted types of ACFG's considered are linear ACFG's and ε-free ACFG's. For an ACFG G, let Lleft (G) denote the language of terminal strings generated by leftmost derivations in G. Let {Lleft (G)G is an ε-free ACFG} and ACFLlinear{Lleft (G)G is a linear ACFG's}. The main results of the present paper are as follows:(1) the class of languages that are log-space many-one reducible to languages in ACFLlinear is equivalent to P, and(2) the class of languages that are log-space many-one reducible to languages in is equivalent to PSPACE.

Montgomery algorithm has demonstrated its effectiveness in applications like cryptosystems. Most of the existing works on finding the Montgomery inverse of an element over the Galois field are based on the software implementation, which is then extended to derive the scalable hardware architecture. In this work, we consider a fundamental change at the algorithmic level and eliminate the potential problems in hardware implementation which makes the resulting modified Montgomery inverse algorithm over GF(2m) very suitable for hardware realization. Due to its structural simplicity, the modified algorithm can be easily mapped onto a high-speed and possibly low-complexity circuit. Experimental results show that our development can achieve both the area and speed advantages over the previous work when the inversion operation over GF(2m) is under consideration and the improvement becomes more significant when we increase the value of m as in the applications of cryptosystems. The salient property of our development sustains the high-speed operation as well as low hardware complexity over a wide range of m for commercial cryptographic applications and makes it suitable for both the scalable architecture and direct hardware implementation.

Cross-view action recognition is a challenging research field for human motion analysis. Appearance-based features are not credible if the viewpoint changes. In this paper, a new framework is proposed for cross-view action recognition by topic based knowledge transfer. First, Spatio-temporal descriptors are extracted from the action videos and each video is modeled by a bag of visual words (BoVW) based on the codebook constructed by the k-means cluster algorithm. Second, Latent Dirichlet Allocation (LDA) is employed to assign topics for the BoVW representation. The topic distribution of visual words (ToVW) is normalized and taken to be the feature vector. Third, in order to bridge different views, we transform ToVW into bilingual ToVW by constructing bilingual dictionaries, which guarantee that the same action has the same representation from different views. We demonstrate the effectiveness of the proposed algorithm on the IXMAS multi-view dataset.

As the mainstream approach in the field of machine translation, neural machine translation (NMT) has achieved great improvements on many rich-source languages, but performance of NMT for low-resource languages ae not very good yet. This paper uses data enhancement technology to construct Mongolian-Chinese pseudo parallel corpus, so as to improve the translation ability of Mongolian-Chinese translation model. Experiments show that the above methods can improve the translation ability of the translation model. Finally, a translation model trained with large-scale pseudo parallel corpus and integrated with soft context data enhancement technology is obtained, and its BLEU value is 39.3.

It is a well-known and useful problem to find a matching in a given graph G whose size is at most a given parameter k and whose weight is maximized (over all matchings of size at most k in G). In this paper, we consider two natural extensions of this problem. One is to find t disjoint matchings in a given graph G whose total size is at most a given parameter k and whose total weight is maximized, where t is a (small) constant integer. Previously, only the special case where t=2 was known to be fixed-parameter tractable. In this paper, we show that the problem is fixed-parameter tractable for any constant t. When t=2, the time complexity of the new algorithm is significantly better than that of the previously known algorithm. The other is to find a set of vertex-disjoint paths each of length 1 or 2 in a given graph whose total length is at most a given parameter k and whose total weight is maximized. As interesting applications, we further use the algorithms to speed up several known approximation algorithms (for related NP-hard problems) whose approximation ratio depends on a fixed parameter 0<ε<1 and whose running time is dominated by the time needed for exactly solving the problems on graphs in which each connected component has at most ε-1 vertices.

In this paper, we investigate a novel preamble channel estimation (CE) method based on the compressed sensing (CS) theory in the orthogonal frequency division multiplexing system with offset quadrature amplitude modulation (OQAM/OFDM) over a frequency selective fading channel. Most of the preamble based CE methods waste power by deploying the pilots in all the subcarriers. Inspired by the CS theory, we focus on using many fewer pilots than one of traditional CE methods and realize accurate reconstruction of the channel response. After describing and analyzing the concept of OQAM/OFDM and its traditional CE methods, we propose a novel channel estimation method based on CS that requires fewer pilots in the preamble, and we design the corresponding preamble pattern to meet the requirements of CS. Simulation results validate the efficiency and superior performance of the proposed method in wireless channel.

A maximal l-diameter tree cover of a graph G(V,E) is a spanning subgraph C(V,EC) of G such that each connected component of C is a tree, C contains no path with more than l edges, and adding any edge in EEC to C yields either a path of length l1 or a cycle. For every function f from positive integers to positive integers, the maximal f-diameter tree cover prolem (MDTC(f) problem for short) is to find a maximal f(n)-diameter tree cover of G, given an n-node graph G. In this paper, we give two parallel algorithms for the MDTC(f) problem. The first algorithm can be implemented in time O(TMSP(n,f(n))log2n) using polynomial number of processors on an EREW PRAM, where TMSP(n,f(n) is the time needed to find a maximal set of vertex disjoint paths of length f(n) in a given n-node graph using polynomial number of processors on an EREW PRAM. We then show that if suitable restrictions are imposed on the input graph and/or on the magnitude of f, then TMSP(n,f(n))O(logkn) for some constant k and thus, for such cases, we obtain an NC algorithm for the MDTC(f) problem. The second algorithm runs in time O(n log2n/{f(n)1}) using polynomial number of processors on an EREW PRAM. Thus if f(n)Ω(n/logkn) for some kO, we obtain an NC algorithm for the MDTC(f) problem.

In this paper, a clustering-based method is proposed for automatically constructing a multi-input Takagi-Sugeno (TS) fuzzy model where only the input-output data of the identified system are available. The TS fuzzy model is automatically generated by the process of structure identification and parameter identification. In the structure identification step, a clustering method is proposed to provide a systematic procedure to partition the input space so that the number of fuzzy rules and the shapes of fuzzy sets in the premise part are determined from the given input-output data. In the parameter identification step, the recursive least-squares algorithm is applied to choose the parameter values in the consequent part from the given input-output data. Finally, two examples are used to illustrate the effectiveness of the proposed method.

In this letter, a distributed TDMA-based data gathering scheme for wireless sensor networks, called DTDGS, is proposed in order to avoid transmission collisions, achieve high levels of power conservation and improve network lifetime. Our study is based on corona-based network division and a distributed TDMA-based scheduling mechanism. Different from a centralized algorithm, DTDGS does not need a centralized gateway to assign the transmission time slots and compute the route for each node. In DTDGS, each node selects its transmission slots and next-hop forwarding node according to the information gathered from neighbor nodes. It aims at avoiding transmission collisions and balancing energy consumption among nodes in the same corona. Compared with previous data gathering schemes, DTDGS is highly scalable and energy efficient. Simulation results show high the energy efficiency of DTDGS.

Estimation of distribution algorithms (EDAs), since they were introduced, have been successfully used to solve discrete optimization problems and hence proven to be an effective methodology for discrete optimization. To enhance the applicability of EDAs, researchers started to integrate EDAs with discretization methods such that the EDAs designed for discrete variables can be made capable of solving continuous optimization problems. In order to further our understandings of the collaboration between EDAs and discretization methods, in this paper, we propose a quality measure of discretization methods for EDAs. We then utilize the proposed quality measure to analyze three discretization methods: fixed-width histogram (FWH), fixed-height histogram (FHH), and greedy random split (GRS). Analytical measurements are obtained for FHH and FWH, and sampling measurements are conducted for FHH, FWH, and GRS. Furthermore, we integrate Bayesian optimization algorithm (BOA), a representative EDA, with the three discretization methods to conduct experiments and to observe the performance difference. A good agreement is reached between the discretization quality measurements and the numerical optimization results. The empirical results show that the proposed quality measure can be considered as an indicator of the suitability for a discretization method to work with EDAs.

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.

Some diffusive and recurrence properties of Lorentz Lattice Gas Cellular Automata (LLGCA) have been expensively studied in terms of the densities of some of the left/right static/flipping mirrors/rotators. In this paper, for any combination S of these well known scatters, we study the computational complexity of the following problem which we call PERIODICITY on the S-model: given a finite configuration that distributes only those scatters in S, whether a particle visits the starting position periodically or not. Previously, the flipping mirror model and the occupied flipping rotator model have been shown unbounded, i.e. the process is always diffusive [17]. On the other hand, PERIODICITY is shown PSPACE-complete in the unoccupied flipping rotator model [21]. In this paper, we show that PERIODICITY is PSPACE-compete in any S-model that is neither occupied, unbounded, nor static. Particularly, we prove that PERIODICITY in any unoccupied and bounded model containing flipping mirror is PSPACE-complete.

Modern mobile devices are equipped with a variety of tools and services, and handle increasing amounts of sensitive information. In the same trend, the number of vulnerabilities exploiting mobile devices are also augmented on a daily basis and, undoubtedly, popular mobile platforms, such as Android and iOS, represent an alluring target for malware writers. While researchers strive to find alternative detection approaches to fight against mobile malware, recent reports exhibit an alarming increase in mobile malware exploiting victims to create revenues, climbing towards a billion-dollar industry. Current approaches to mobile malware analysis and detection cannot always keep up with future malware sophistication [2],[4]. The aim of this work is to provide a structured and comprehensive overview of the latest research on mobile malware detection techniques and pinpoint their benefits and limitations.

We propose a timing driven gate duplication algorithm for the technology independent phase. Our algorithm is a generalization of the gate duplication strategy suggested in [3]. Our technique gets a more global view by duplicating multiple gates at a time. We compare the minimum circuit delay obtained by SIS with the delay obtained by using our gate duplication. Results show that up to 11% improvement in delay can be obtained. Our algorithm does not have an adverse effect on the overall synthesis time, indicating that gate duplication is an efficient strategy for timing optimization.

The impact of intersymbol interference (ISI) on single carrier frequency domain equalization with multiple input multiple output (MIMO-SCFDE) systems is severe. Most existing channel equalization methods fail to solve it completely. In this paper, given the disadvantages of the error propagation and the gap from matched filter bound (MFB), we creatively introduce a decision feedback equalizer with frequency-domain bidirectional noise prediction (DFE-FDBiNP) to tackle intersymbol interference (ISI) in MIMO-SCFDE systems. The equalizer has two-part equalizer, that is the normal mode and the time-reversal mode decision feedback equalization with noise prediction (DFE-NP). Equal-gain combining is used to realize a greatly simplified and low complexity diversity combining. Analysis and simulation results validate the improved performance of the proposed method in quasi-static frequency-selective fading MIMO channel for a typical urban environment.

This paper deals with the maximum-weight 2-path packing problem (M2PP), which is the problem of computing a set of vertex-disjoint paths of length 2 in a given edge-weighted complete graph so that the total weight of edges in the paths is maximized. Previously, Hassin and Rubinstein gave a randomized cubic-time approximation algorithm for M2PP which achieves an expected ratio of - ε ≈ 0.5223 - ε for any constant ε > 0. We refine their algorithm and derandomize it to obtain a deterministic cubic-time approximation algorithm for the problem which achieves a better ratio (namely, 0.5265 - ε for any constant ε>0).

Collective activity recognition plays an important role in high-level video analysis. Most current feature representations look at contextual information extracted from the behaviour of nearby people. Every person needs to be detected and his pose should be estimated. After extracting the feature, hierarchical graphical models are always employed to model the spatio-temporal patterns of individuals and their interactions, and so can not avoid complex preprocessing and inference operations. To overcome these drawbacks, we present a new feature representation method, called attribute-based spatio-temporal (AST) descriptor. First, two types of information, spatio-temporal (ST) features and attribute features, are exploited. Attribute-based features are manually specified. An attribute classifier is trained to model the relationship between the ST features and attribute-based features, according to which the attribute features are refreshed. Then, the ST features, attribute features and the relationship between the attributes are combined to form the AST descriptor. An objective classifier can be specified on the AST descriptor and the weight parameters of the classifier are used for recognition. Experiments on standard collective activity benchmark sets show the effectiveness of the proposed descriptor.