In this paper, we study Hermitian linear complementary dual (abbreviated Hermitian LCD) rank metric codes. A class of Hermitian LCD generalized Gabidulin codes are constructed by qm-self-dual bases of Fq2m over Fq2. Moreover, the exact number of qm-self-dual bases of Fq2m over Fq2 is derived. As a consequence, an upper bound and a lower bound of the number of the constructed Hermitian LCD generalized Gabidulin codes are determined.

In recent decades, there has been a great deal of research into the problem of bilingual speech recognition - to develop a recognizer that can handle inter- and intra-sentential language switching between two languages. This paper presents our recent work on the development of a grammar-constrained, Mandarin-English bilingual Speech Recognition System (MESRS) for real world music retrieval. Two of the main difficult issues in handling the bilingual speech recognition systems for real world applications are tackled in this paper. One is to balance the performance and the complexity of the bilingual speech recognition system; the other is to effectively deal with the matrix language accents in embedded language. In order to process the intra-sentential language switching and reduce the amount of data required to robustly estimate statistical models, a compact single set of bilingual acoustic models derived by phone set merging and clustering is developed instead of using two separate monolingual models for each language. In our study, a novel Two-pass phone clustering method based on Confusion Matrix (TCM) is presented and compared with the log-likelihood measure method. Experiments testify that TCM can achieve better performance. Since potential system users' native language is Mandarin which is regarded as a matrix language in our application, their pronunciations of English as the embedded language usually contain Mandarin accents. In order to deal with the matrix language accents in embedded language, different non-native adaptation approaches are investigated. Experiments show that model retraining method outperforms the other common adaptation methods such as Maximum A Posteriori (MAP). With the effective incorporation of approaches on phone clustering and non-native adaptation, the Phrase Error Rate (PER) of MESRS for English utterances was reduced by 24.47% relatively compared to the baseline monolingual English system while the PER on Mandarin utterances was comparable to that of the baseline monolingual Mandarin system. The performance for bilingual utterances achieved 22.37% relative PER reduction.

This paper addresses a maximum likelihood method for source separation in the case of overdetermined mixtures corrupted by additive white Gaussian noise. We consider an approximate likelihood which is based on the Laplace approximation and develop a natural gradient adaptation algorithm to find a local maximum of the corresponding approximate likelihood. We present a detailed mathematical derivation of the algorithm using the Lie group invariance. Useful behavior of the algorithm is verified by numerical experiments.

This paper presents novel techniques for blind separation and blind extraction of instantaneously mixed binary sources, which are suitable for the case with less sensors than sources. First, a solvability analysis is presented for a general case. Necessary and sufficient conditions for recoverability of all or some part of sources are derived. A new deterministic blind separation algorithm is then proposed to estimate the mixing matrix and separate all sources efficiently in the noise-free or low noise level case. Next, using the Maximum Likelihood (ML) approach for robust estimation of centers of clusters, we have extended the algorithm for high additive noise case. Moreover, a new sequential blind extraction algorithm has been developed, which enables us not only to extract the potentially separable sources but also estimate their number. The sources can be extracted in a specific order according to their dominance (strength) in the mixtures. At last, simulation results are presented to illustrate the validity and high performance of the algorithms.

As scientific and technological resources are experiencing information overload, it is quite expensive to find resources that users are interested in exactly. The personalized recommendation system is a good candidate to solve this problem, but data sparseness and the cold starting problem still prevent the application of the recommendation system. Sparse data affects the quality of the similarity measurement and consequently the quality of the recommender system. In this paper, we propose a matrix factorization recommendation algorithm based on similarity calculation(SCMF), which introduces potential similarity relationships to solve the problem of data sparseness. A penalty factor is adopted in the latent item similarity matrix calculation to capture more real relationships furthermore. We compared our approach with other 6 recommendation algorithms and conducted experiments on 5 public data sets. According to the experimental results, the recommendation precision can improve by 2% to 9% versus the traditional best algorithm. As for sparse data sets, the prediction accuracy can also improve by 0.17% to 18%. Besides, our approach was applied to patent resource exploitation provided by the wanfang patents retrieval system. Experimental results show that our method performs better than commonly used algorithms, especially under the cold starting condition.

To find the exact items from the massive patent resources for users is a matter of great urgency. Although the recommender systems have shot this problem to a certain extent, there are still some challenging problems, such as tracking user interests and improving the recommendation quality when the rating matrix is extremely sparse. In this paper, we propose a novel method called Collaborative Filtering Auto-Encoder for the top-N recommendation. This method employs Auto-Encoders to extract the item's features, converts a high-dimensional sparse vector into a low-dimensional dense vector, and then uses the dense vector for similarity calculation. At the same time, to make the recommendation list closer to the user's recent interests, we divide the recommendation weight into time-based and recent similarity-based weights. In fact, the proposed method is an improved, item-based collaborative filtering model with more flexible components. Experimental results show that the method consistently outperforms state-of-the-art top-N recommendation methods by a significant margin on standard evaluation metrics.

We propose a Multiple Zones-based (M-Zone) routing protocol to discover node-disjoint multiplath routing efficiently and effectively in large-scale MANETs. Compared with single path routing, multipath routing can improve robustness, load balancing and throughput of a network. However, it is very difficult to achieve node-disjoint multipath routing in large-scale MANETs. To ensure finding node-disjoint multiple paths, the M-Zone protocol divides the region between a source and a destination into multiple zones based on geographical location and each path is mapped to a distinct zone. Performance analysis shows that M-Zone has good stability, and the control complexity and storage complexity of M-Zone are lower than those of the well-known AODVM protocol. Simulation studies show that the average end-to-end delay of M-Zone is lower than that of AODVM and the routing overhead of M-Zone is less than that of AODVM.

This paper presents our developed decoder which adopts the idea of statically optimizing part of the knowledge sources while handling the others dynamically. The lexicon, phonetic contexts and acoustic model are statically integrated to form a memory-efficient state network, while the language model (LM) is dynamically incorporated on the fly by means of extended tokens. The novelties of our approach for constructing the state network are (1) introducing two layers of dummy nodes to cluster the cross-word (CW) context dependent fan-in and fan-out triphones, (2) introducing a so-called "WI layer" to store the word identities and putting the nodes of this layer in the non-shared mid-part of the network, (3) optimizing the network at state level by a sufficient forward and backward node-merge process. The state network is organized as a multi-layer structure for distinct token propagation at each layer. By exploiting the characteristics of the state network, several techniques including LM look-ahead, LM cache and beam pruning are specially designed for search efficiency. Especially in beam pruning, a layer-dependent pruning method is proposed to further reduce the search space. The layer-dependent pruning takes account of the neck-like characteristics of WI layer and the reduced variety of word endings, which enables tighter beam without introducing much search errors. In addition, other techniques including LM compression, lattice-based bookkeeping and lattice garbage collection are also employed to reduce the memory requirements. Experiments are carried out on a Mandarin spontaneous speech recognition task where the decoder involves a trigram LM and CW triphone models. A comparison with HDecode of HTK toolkits shows that, within 1% performance deviation, our decoder can run 5 times faster with half of the memory footprint.

The security of P2P networks depends on building trust management among peers. However, current trust management models focus on preventing untrustworthy resources from spreading by malicious providers, but have few effects on reducing denial-of-service attacks of malicious consumers and free riding of selfish peers. Pointing to these problems, a bi-evaluation trust management model, called BiTrust, is proposed. In this model, the trustworthiness of a peer is divided into service and request trustworthiness. Service trustworthiness shows the resources reliability of providers, and request trustworthiness is used to deal with requests from consumers, which can keep away malicious consumers and encourage selfish peers to share resources. A generic method for evaluating service and request trustworthiness is described. Furthermore, the implementation strategies of the model are also depicted in this paper. The following analysis and simulation show that BiTrust is more effective on enhancing high-quality resources sharing among peers and more advanced in successful exchanges rate.

In a heterogeneous unreliable multiaccess network, wherein terminals share a common wireless channel with distinct error probabilities, existing works have shown that a persistent round-robin (RR-P) scheduling policy can be arbitrarily worse than the optimum in terms of Age of Information (AoI) under standard Automatic Repeat reQuest (ARQ). In this paper, practical Hybrid ARQ (HARQ) schemes which are widely-used in today's wireless networks are considered. We show that RR-P is very close to optimum with asymptotically many terminals in this case, by explicitly deriving tight, closed-form AoI gaps between optimum and achievable AoI by RR-P. In particular, it is rigorously proved that for RR-P, under HARQ models concerning fading channels (resp. finite-blocklength regime), the relative AoI gap compared with the optimum is within a constant of 6.4% (resp. 6.2% with error exponential decay rate of 0.5). In addition, RR-P enjoys the distinctive advantage of implementation simplicity with channel-unaware and easy-to-decentralize operations, making it favorable in practice. A further investigation considering constraint imposed on the number of retransmissions is presented. The performance gap is indicated through numerical simulations.

In this paper, a Many Integrated Core Architecture (MIC) accelerated parallel method of moment (MoM) algorithm is proposed to solve electromagnetic problems in practical applications, where MIC means a kind of coprocessor or accelerator in computer systems which is used to accelerate the computation performed by Central Processing Unit (CPU). Three critical points are introduced in this paper in detail. The first one is the design of the parallel framework, which ensures that the algorithm can run on distributed memory platform with multiple nodes. The hybrid Message Passing Interface (MPI) and Open Multi-Processing (OpenMP) programming model is designed to achieve the purposes. The second one is the out-of-core algorithm, which greatly breaks the restriction of MIC memory. The third one is the pipeline algorithm which overlaps the data movement with MIC computation. The pipeline algorithm successfully hides the communication and thus greatly enhances the performance of hybrid MIC/CPU MoM. Numerical result indicates that the proposed algorithm has good parallel efficiency and scalability, and twice faster performance when compared with the corresponding CPU algorithm.

Ontologies are regarded as the solution to data heterogeneity on the Semantic Web (SW), but they also suffer from the heterogeneity problem, which leads to the ambiguity of data information. Ontology Meta-Matching technique (OMM) is able to solve the ontology heterogeneity problem through aggregating various similarity measures to find the heterogeneous entities. Inspired by the success of Reinforcement Learning (RL) in solving complex optimization problems, this work proposes a RL-based OMM technique to address the ontology heterogeneity problem. First, we propose a novel RL-based OMM framework, and then, a neural network that is called evaluated network is proposed to replace the Q table when we choose the next action of the agent, which is able to reduce memory consumption and computing time. After that, to better guide the training of neural network and improve the accuracy of RL agent, we establish a memory bank to mine depth information during the evaluated network's training procedure, and we use another neural network that is called target network to save the historical parameters. The experiment uses the famous benchmark in ontology matching domain to test our approach's performance, and the comparisons among Deep Reinforcement Learning(DRL), RL and state-of-the-art ontology matching systems show that our approach is able to effectively determine high-quality alignments.

Multicarrier communications including orthogonal frequency-division multiplexing (OFDM) is a technique which has been adopted for various wireless applications. However, a major drawback to the widespread acceptance of OFDM is the high peak-to-mean envelope power ratio (PMEPR) of uncoded OFDM signals. Finding methods for construction of sequences with low PMEPR is an active research area. In this paper, by employing some new shortened and extended Golay complementary pairs as the seeds, we enlarge the family size of near-complementary sequences given by Yu and Gong. We also show that the new set of sequences we obtained is just a reversal of the original set. Numerical results show that the enlarged family size is almost twice of the original one. Besides, the Hamming distances of the binary near-complementary sequences are also analyzed.

The recent IEEE 802.16j-2009 adds multi-hop relay capabilities to IEEE 802.16 systems, which aims to offer improved coverage and capacity over single-hop radio access systems. In this paper, we point out several security issues, including non-authenticated privacy and key management messages, insecure relay multicast rekeying algorithm and insecure Multicast and Broadcast Rekeying Algorithm (MBRA), of the key management protocols in IEEE 802.16j-2009 and give some solutions. In particular, we propose a new Secure MBRA (SMBRA) based on identity and logical key tree to solve the security issues of MBRA. SMBRA can not only provide backward and forward secrecy of communications but also avoid key forgery. Furthermore, our theoretical analysis and simulation indicate that SMBRA is much more efficient and adequate, especially in a large group.