Nowadays, with the development of online social networks (OSN), a mass of online social information has been generated in OSN, which has triggered research on social recommendation. Collaborative filtering, as one of the most popular techniques in social recommendation, faces several challenges, such as data sparsity, cold-start users and prediction quality. The motivation of our work is to deal with the above challenges by effectively combining collaborative filtering technology with social information. The trust relationship has been identified as a useful means of using social information to improve the quality of recommendation. In this paper, we propose a trust-based recommendation approach which uses GlobalTrust (GT) to represent the trust value among users as neighboring nodes. A matrix factorization based on singular value decomposition is used to get a trust network built on the GT value. The recommendation results are obtained through a modified random walk algorithm called GlobalTrustWalker. Through experiments on a real-world sparser dataset, we demonstrate that the proposed approach can better utilize users' social trust information and improve the recommendation accuracy on cold-start users.

We propose a time-frequency interleave (TFI) structure of single carrier (SC) frequency domain equalization (FDE) to combat spectral nulls of wireless channels. Permuted copies of block data are transmitted in the TFI-FDE, providing the same diversity order as maximal-ratio receiver combining. The spectral nulls are compensated by uncorrelated spectral components of the same channel. It shows 4 dB diversity gains at BER of 10-2 over an indoor channel. The TFI-FDE is computationally-efficient in combination with fast Fourier transform. This TFI-FDE fits SC systems with single antenna. It needs no channel state information at the transmitter.

In this paper, we propose a highly efficient mobile visual search algorithm. For descriptor extraction process, we propose a low complexity feature detection which utilizes the detected local key points of the coarse octaves to guide the scale space construction and feature detection in the fine octave. The Gaussian and Laplacian operations are skipped for the unimportant area, and thus the computing time is saved. Besides, feature selection is placed before orientation computing to further reduce the complexity of feature detection by pre-discarding some unimportant local points. For the image retrieval process, we design a high-performance reranking method, which merges both the global descriptor matching score and the local descriptor similarity score (LDSS). In the calculating of LDSS, the tf-idf weighted histogram matching is performed to integrate the statistical information of the database. The results show that the proposed highly efficient approach achieves comparable performance with the state-of-the-art for mobile visual search, while the descriptor extraction complexity is largely reduced.

This paper describes the multiple access performance of parallel combinatory spread spectrum (PC/SS) communication systems in nonfading and Rayleigh fading multipath channels. The PC/SS systems can provide the high-speed data transmission capability by transmitting multiple pseudo-noise sequences out of a pre-assigned sequence set. The performance is evaluated in terms of average bit error rate (BER) by numerical computation. In nonfading white gaussian channel, the PC/SS systems are superior to conventional direct sequence spread spectrum (DS/SS) systems under the identical spreading factor condition. In Rayleigh fading channel, the performance of the PC/SS system without diversity is poorer than that of the DS/SS system. By including the explicit and implicit diversity, the performance of the PC/SS system becomes better than that of conventional DS/SS systems. A longer spreading sequence is assignable to a PC/SS system having the spreading factor equal to that in the conventional DS/SS system. Hence, the error control coding is easily. It is found that the PC/SS systems including diversity and Reed-Solomon coding improves the multiple access performance.

In this paper, we propose an Adaptive Stochastic Collocation Method for block-based Statistical Static Timing Analysis (SSTA). A novel adaptive method is proposed to perform SSTA with delays of gates and interconnects modeled by quadratic polynomials based on Homogeneous Chaos expansion. In order to approximate the key atomic operator MAX in the full random space during timing analysis, the proposed method adaptively chooses the optimal algorithm from a set of stochastic collocation methods by considering different input conditions. Compared with the existing stochastic collocation methods, including the one using dimension reduction technique and the one using Sparse Grid technique, the proposed method has 10x improvements in the accuracy while using the same order of computation time. The proposed algorithm also shows great improvement in accuracy compared with a moment matching method. Compared with the 10,000 Monte Carlo simulations on ISCAS85 benchmark circuits, the results of the proposed method show less than 1% error in the mean and variance, and nearly 100x speeds up.

In this paper, we propose a Modified nested sparse grid based Adaptive Stochastic Collocation Method (MASCM) for block-based Statistical Static Timing Analysis (SSTA). The proposed MASCM employs an improved adaptive strategy derived from the existing Adaptive Stochastic Collocation Method (ASCM) to approximate the key operator MAX during timing analysis. In contrast to ASCM which uses non-nested sparse grid and tensor product quadratures to approximate the MAX operator for weakly and strongly nonlinear conditions respectively, MASCM proposes a modified nested sparse grid quadrature to approximate the MAX operator for both weakly and strongly nonlinear conditions. In the modified nested sparse grid quadrature, we firstly construct the second order quadrature points based on extended Gauss-Hermite quadrature and nested sparse grid technique, and then discard those quadrature points that do not contribute significantly to the computation accuracy to enhance the efficiency of the MAX approximation. Compared with the non-nested sparse grid quadrature, the proposed modified nested sparse grid quadrature not only employs much fewer collocation points, but also offers much higher accuracy. Compared with the tensor product quadrature, the modified nested sparse grid quadrature greatly reduced the computational cost, while still maintains sufficient accuracy for the MAX operator approximation. As a result, the proposed MASCM provides comparable accuracy while remarkably reduces the computational cost compared with ASCM. The numerical results show that with comparable accuracy MASCM has 50% reduction in run time compared with ASCM.

In digital transmission and storage systems, sequences must have attributes that comply with the physical characteristics of the channel. These channel constraints can often be satisfied through constrained sequence coding techniques which avoid use of sequences that violate the given channel constraints. In the design of a constrained code, it is usually helpful to consider the PSD of the encoded sequence in order to ensure that PSD requirements are met, and to obtain an indication of bandwidth, response at dc, average power peaks, and other spectral characteristics of interest. In this paper, we introduce an approach for the construction of finite-state sequential machine (FSSM) modeled encoders to satisfy spectral requirements. This approach involves construction of either a Mealy or a Moore FSSM to represent the encoder, and evaluation of the state transition probabilities and codeword values such that the PSD of the designed code meets a predefined spectral shape. Examples in this paper demonstrate the usefulness of this approach.

Novel joint motion-compensated interpolation using eight-neighbor block motion vectors (8J-MCI) is presented. The proposed method uses bi-directional motion estimation (BME) to obtain the motion vector field of the interpolated frame and adopts motion vectors of the interpolated block and its 8-neighbor blocks to jointly predict the target block. Since the smoothness of the motion vector filed makes the motion vectors of 8-neighbor blocks quite close to the true motion vector of the interpolated block, the proposed algorithm has the better fault-tolerancy than traditional ones. Experiments show that the proposed algorithm outperforms the motion-aligned auto-regressive algorithm (MAAR, one of the state-of-the-art frame rate up-conversion (FRUC) schemes) in terms of the average PSNR for the test image sequence and offers better subjective visual quality.

Wireless communication security has become a hot topic in recent years. The directional modulation (DM) is a promising secure communication technique that has attracted attentions of many researchers. Several different frequency diverse arrays (FDAs) are used to obtain the direction-range-dependent DM signals in previous literatures. However, most of them are not ideal enough to obtain a nonperiodic dot-shaped secure area. In this paper, the symmetrical multi-carrier frequency diverse array with logarithmical frequency increment, named the symmetrical-multilog-FDA, is used to obtain the direction-range-dependent DM signals that are normal at the desired locations while disordered at other locations. Based on the symmetrical-multilog-FDA, we derive the closed-form expression of baseband-weighted vector using the artificial-noise-aided zero-forcing approach. Compared with previous schemes, the proposed scheme can obtain a more fine-focusing nonperiodic dot-shaped secure area at the desired location. In addition, it can achieve a point-to-multipoint secure communication for multiple cooperative receivers at different locations.

In recent years we have witnessed an increasing demand to process queries on large datasets in Non-ordered Discrete Data Spaces (NDDS). In particular, one type of query in an NDDS, called box queries, is used in many emerging applications including error corrections in bioinformatics and network intrusion detection in cybersecurity. Effective indexing methods are necessary for efficiently processing queries on large datasets in disk. However, most existing NDDS indexing methods were not designed for box queries. Several recent indexing methods developed for box queries on a large NDDS dataset in disk are based on the popular data-partitioning approach. Unfortunately, a space-partitioning based indexing scheme, which is more effective for box queries in an NDDS, has not been studied before. In this paper, we propose a novel indexing method based on space-partitioning, called the BINDS-tree, for supporting efficient box queries on a large NDDS dataset in disk. A number of effective strategies such as node split based on minimum span and cross optimal balance, redundancy reduction utilizing a singleton dimension inheritance property, and a space-efficient structure for the split history are incorporated in the constructing algorithm for the BINDS-tree. Experimental results demonstrate that the proposed BINDS-tree significantly improves the box query I/O performance, comparing to that of the state-of-the-artdata-partitioning based NDDS indexing method.

As an important extension of high-efficiency video coding (HEVC), screen content coding (SCC) includes various new coding modes, such as Intra Block Copy (IBC), Palette-based coding (Palette), and Adaptive Color Transform (ACT). These new tools have improved screen content encoding performance. This paper proposed a novel and fast algorithm by classifying Code Units (CUs) as text CUs or non-text CUs. For text CUs, the Intra mode was skipped in the compression process, whereas for non-text CUs, the IBC mode was skipped. The current CU depth range was then predicted according to its adjacent left CU depth level. Compared with the reference software HM16.7+SCM5.4, the proposed algorithm reduced encoding time by 23% on average and achieved an approximate 0.44% increase in Bjøntegaard delta bit rate and a negligible peak signal-to-noise ratio loss.

Community detection is a pivotal task in data mining, and users' emotional behaviors have an important impact on today's society. So it is very significant for society management or marketing strategies to detect emotional communities in social networks. Based on the emotional homophily of users in social networks, it could confirm that users would like to gather together to form communities according to emotional similarity. This paper exploits multivariate emotional behaviors of users to measure users' emotional similarity, then takes advantage of users' emotional similarity as edge weight to remodel an emotional network and detect communities. The detailed process of detecting emotional communities is as follows: 1) an emotional network is constructed and emotional homophily in experimental dataset is verified; 2) both CNM and BGLL algorithms are employed to detect emotional communities in emotional network, and emotional characters of each community are analyzed; 3) in order to verify the superiority of emotional network for detecting emotional communities, 1 unweighted network and 3 other weighted and undirected networks are constructed as comparison. Comparison experiments indicate that the emotional network is more suitable for detecting emotional communities, the users' emotional behaviors are more similar and denser in identical communities of emotional network than the contrastive networks' communities.

A novel secure spatial modulation (SM) scheme based on dynamic multi-parameter weighted-type fractional Fourier transform (WFRFT), abbreviated as SMW, is proposed. Each legitimate transmitter runs WFRFT on the spatially modulated super symbols before transmit antennas, the parameters of which are dynamically updated using the transmitting bits. Each legitimate receiver runs inverse WFRFT to demodulate the received signals, the parameters of which are also dynamically generated using the recovered bits with the same updating strategies as the transmitter. The dynamic update strategies of WFRFT parameters are designed. As a passive eavesdropper is ignorant of the initial WFRFT parameters and the dynamic update strategies, which are indicated by the transmitted bits, it cannot recover the original information, thereby guaranteeing the communication security between legitimate transmitter and receiver. Besides, we formulate the maximum likelihood (ML) detector and analyze the secrecy capacity and the upper bound of BER. Simulations demonstrate that the proposed SMW scheme can achieve a high level of secrecy capacity and maintain legitimate receiver's low BER performance while deteriorating the eavesdropper's BER.

The performance of parallel combinatory spread spectrum (PC/SS) communication systems in the frequency-nonselective, slowly Rayleigh fading channel is studied. Performance is evaluated by symbol error rate using numerical computation. To overcome the performance degradation caused by fading, we also studied the effects of selection diversity and Reed-Solomon coding applied to the PC/SS system. As a result, a remarkable improvement in error rate performance is achieved with Reed-Solomon coding and diversity technique. The coding rate for the maximum coding gain is almost a half of that in the additive white gaussian noise channel.

Avoiding runway incursions is a significant challenge and a top priority in aviation. Due to all causes of runway incursions belong to human factors, runway incursion prevention systems should remove human from the system operation loop as much as possible. Although current runway incursion prevention systems have made big progress on how to obtain accurate and sufficient information of aircraft/vehicles, they cannot predict and detect runway incursions as early as experienced air traffic controllers by using the same surveillance information, and cannot give explicit instructions and/or suggestions to prevent runway incursions like real air traffic controllers either. In one word, human still plays an important position in current runway incursion prevention systems. In order to remove human factors from the system operation loop as much as possible, this paper proposes a new type of runway incursion prevention system based on logic-based reasoning. The system predicts and detects runway incursions, then gives explicit instructions and/or suggestions to pilots/drivers to avoid runway incursions/collisions. The features of the system include long-range prediction of incidents, explicit instructions and/or suggestions, and flexible model for different policies and airports. To evaluate our system, we built a simulation system, and evaluated our system using both real historical scenarios and conventional fictional scenarios. The evaluation showed that our system is effective at providing earlier prediction of incidents than current systems, giving explicit instructions and/or suggestions for handling the incidents effectively, and customizing for specific policies and airports using flexible model.

Paragraph segmentation is a text segmentation task. Iikura et al. achieved excellent results on paragraph segmentation by introducing focal loss to Bidirectional Encoder Representations from Transformers. In this study, we investigated paragraph segmentation on Daily News and Novel datasets. Based on the approach proposed by Iikura et al., we used auxiliary loss to train the model to improve paragraph segmentation performance. Consequently, the average F1-score obtained by the approach of Iikura et al. was 0.6704 on the Daily News dataset, whereas that of our approach was 0.6801. Our approach thus improved the performance by approximately 1%. The performance improvement was also confirmed on the Novel dataset. Furthermore, the results of two-tailed paired t-tests indicated that there was a statistical significance between the performance of the two approaches.

LDCs system with finite-rate error-free feedback is proposed in this letter. The optimal transmission codeword is selected at the receiver and the codeword index is sent to the transmitter. A simple random search algorithm is introduced for codebook generation. Moreover, the max-min singular value criterion is adopted for codeword selection. Simulation results showed that, with only 3-4 feedback bits, the low-complexity Zero-Forcing receiver can approach the Maximum-Likelihood (ML) performance.

Quasi cyclic LDPC (QC-LDPC) codes consisting of circulant permutation matrices (CPM-QC-LDPC) are one of the most attractive types of LDPC codes due to their many advantages. In this paper, we mainly do some research on CPM-QC-LDPC codes. We first propose a two-stage decoding scheme mainly based on parity check matrix transform (MT), which can efficiently improve the bit error rate performance. To optimize the tradeoff between hardware implementation complexity and decoding performance, an improved method that combines our proposed MT scheme with the existing CPM-RID decoding scheme is presented. An experiment shows that both schemes can improve the bit error rate (BER) performance. Finally, we show that the MT decoding mechanism can be applied to other types of LDPC codes. We apply the MT scheme to random LDPC codes and show that it can efficiently lower the error floor.

We address the problem of multiuser co-channel interference scheduling in multicell interference-limited networks. Our target is to optimize the network capacity under the SIR-balanced power control policy. Since it's difficult to optimize the original problem, we derive a new problem which maximizes the lower bound of the network capacity. Based on the analysis of this new problem, we propose an interference matched scheduling algorithm. This algorithm considers the caused co-channel interference and the channel conditions to schedule the "matched" users at the same time. We prove that this interference matched scheduling algorithm optimizes the lower bound of the network capacity for any arbitrary numbers of cells and users. Moreover, this scheduling method is low-complexity and can be implemented in a fully distributed fashion. Simulation results reveal that the performance of the proposed algorithm achieves near optimal capacity, even though it does not optimize the network capacity directly. Finally, the proposed algorithm holds a great gain over formerly proposed round robin and power matched scheduling method, especially when the scale of the network is large.

With the gradually increase of the application of new energy in microgrids, Electric Spring (ES), as a new type of distributed compensation power electronic device has been widely studied. The Generalized Electric Spring (G-ES) is an improved topology, and the space limitation problem in the traditional topology is solved. Because of the mode of G-ES use in the power grid, a reasonable solution to the voltage loss of the critical section feeder is needed. In this paper, the voltage balance equation based on the feedforward compensation coefficient is established, and a two cascade control strategy based on the equation is studied. The first stage of the two cascade control strategy is to use communication means to realize the allocation of feedforward compensation coefficients, and the second stage is to use the coefficients to realize feedforward fixed angle control. Simulation analysis shows that the proposed control strategy does not affect the control accuracy of the critical load (CL), and effectively improves the operational range of the G-ES.