We propose a speech analysis-synthesis and deep neural network (DNN)-based text-to-speech (TTS) synthesis framework using Gaussian mixture model (GMM)-based approximation of full-band spectral envelopes. GMMs have excellent properties as acoustic features in statistic parametric speech synthesis. Each Gaussian function of a GMM fits the local resonance of the spectrum. The GMM retains the fine spectral envelope and achieve high controllability of the structure. However, since conventional speech analysis methods (i.e., GMM parameter estimation) have been formulated for a narrow-band speech, they degrade the quality of synthetic speech. Moreover, a DNN-based TTS synthesis method using GMM-based approximation has not been formulated in spite of its excellent expressive ability. Therefore, we employ peak-picking-based initialization for full-band speech analysis to provide better initialization for iterative estimation of the GMM parameters. We introduce not only prediction error of GMM parameters but also reconstruction error of the spectral envelopes as objective criteria for training DNN. Furthermore, we propose a method for multi-task learning based on minimizing these errors simultaneously. We also propose a post-filter based on variance scaling of the GMM for our framework to enhance synthetic speech. Experimental results from evaluating our framework indicated that 1) the initialization method of our framework outperformed the conventional one in the quality of analysis-synthesized speech; 2) introducing the reconstruction error in DNN training significantly improved the synthetic speech; 3) our variance-scaling-based post-filter further improved the synthetic speech.

In this paper, we propose a novel single-template strategy based on a mean template set and locally/globally weighted dynamic time warping (LG-DTW) to improve the performance of online signature verification. Specifically, in the enrollment phase, we implement a time series averaging method, Euclidean barycenter-based DTW barycenter averaging, to obtain a mean template set considering intra-user variability among reference samples. Then, we acquire a local weighting estimate considering a local stability sequence that is obtained analyzing multiple matching points of an optimal match between the mean template and reference sets. Thereafter, we derive a global weighting estimate based on the variable importance estimated by gradient boosting. Finally, in the verification phase, we apply both local and global weighting methods to acquire a discriminative LG-DTW distance between the mean template set and a query sample. Experimental results obtained on the public SVC2004 Task2 and MCYT-100 signature datasets confirm the effectiveness of the proposed method for online signature verification.

How to restore virtual network against substrate network failure (e.g. link cut) is one of the key challenges of network virtualization. The traditional virtual network recovery (VNR) methods are mostly based on the idea of centralized control. However, if multiple virtual networks fail at the same time, their recovery processes are usually queued according to a specific priority, which may increase the average waiting time of users. In this letter, we study distributed virtual network recovery (DVNR) method to improve the virtual network recovery efficiency. We establish exclusive virtual machine (VM) for each virtual network and process recovery requests of multiple virtual networks in parallel. Simulation results show that the proposed DVNR method can obtain recovery success rate closely to centralized VNR method while yield ~70% less average recovery time.

In this paper, we address the problem of analyzing topics, included in a social video group, to improve the retrieval performance of videos. Unlike previous methods that focused on an individual visual aspect of videos, the proposed method aims to leverage the “mutual reinforcement” of heterogeneous modalities such as tags and users associated with video on the Internet. To represent multiple types of relationships between each heterogeneous modality, the proposed method constructs three subgraphs: user-tag, video-video, and video-tag graphs. We combine the three types of graphs to obtain a heterogeneous graph. Then the extraction of latent features, i.e., topics, becomes feasible by applying graph-based soft clustering to the heterogeneous graph. By estimating the membership of each grouped cluster for each video, the proposed method defines a new video similarity measure. Since the understanding of video content is enhanced by exploiting latent features obtained from different types of data that complement each other, the performance of visual reranking is improved by the proposed method. Results of experiments on a video dataset that consists of YouTube-8M videos show the effectiveness of the proposed method, which achieves a 24.3% improvement in terms of the mean normalized discounted cumulative gain in a search ranking task compared with the baseline method.

Tweakable block cipher (TBC) is an extension of conventional block cipher. We study how to build a TBC based on generalized Feistel structure (GFS), a classical block cipher construction. While known dedicated TBC proposals are based on substitution-permutation network (SPN), GFS has not been used for building TBC. In particular, we take 64-bit GFS block cipher TWINE and try to make it tweakable with a minimum change. To find a best one from a large number of candidates, we performed a comprehensive search with a help of mixed integer linear programming (MILP) solver. As a result, our proposal TWINE is quite efficient, has the same number of rounds as TWINE with extremely simple tweak schedule.

The network load is increasing due to the spread of content distribution services. Caching is recognized as a technique to reduce the peak network load by storing popular content into memories of users. Coded caching is a new caching approach based on a carefully designed content placement to create coded multicasting opportunities. Coded caching schemes in single-rate networks are evaluated by the tradeoff between the size of memory and that of delivered data. For considering the network with multiple transmission rates, it is crucial how to operate multicast. In multicast delivery, a sender must communicate to intended receivers at a rate that is available to all receivers. Multicast scheduling method of determining rates to deliver are evaluated by throughput and delay in multi-rate wireless networks. In this paper, we discuss coded caching in the multi-rate wireless networks. We newly define a measure for evaluating the coded caching scheme as coded caching delay and propose a new coded caching scheme. Also, we compare the proposed coded caching scheme with conventional coded caching schemes and show that the proposed scheme is suitable for multi-rate wireless networks.

For Free and Open Source Software (FOSS), identifying the copyright notices is important. However, both the collaborative manner of FOSS project development and the large number of source files increase its difficulty. In this paper, we aim at automatically identifying the copyright notices in source files based on machine learning techniques. The evaluation experiment shows that our method outperforms FOSSology, the only existing method based on regular expression.

In emergency communication systems research, aerial wireless relay networks (AWRNs) using multicopter unmanned aerial vehicles (UAVs) have been proposed. The main issue of the AWRNs is how to minimize the delay time of packet transmissions since it is not easy to supply many multicopters to cover a wide area. In this paper, we review the flight schemes and their delay time for the AWRNs. Furthermore, the network has specific issues such as multicopters' drops due to their battery capacity depletion and inclination of moving multicopters. The inclination of multicopters affects the received power, and the communication range changes based on the inclination as well. Therefore, we clarify the effect of these issues on the delay time.

The volume of digital information is growing at an extremely fast pace which, in turn, exacerbates the need of efficient mechanisms to find the presence of a pattern in an input text or a set of input strings. Combining the processing power of Graphics Processing Unit (GPU) with matching algorithms seems a natural alternative to speedup the string-matching process. This work proposes a Parallel Rabin-Karp implementation (PRK) that encompasses a fast-parallel prefix-sums algorithm to maximize parallelization and accelerate the matching verification. Given an input text T of length n and p patterns of length m, the proposed implementation finds all occurrences of p in T in O(m+q+n/τ+nm/q) time, where q is a sufficiently large prime number and τ is the available number of threads. Sequential and parallel versions of the PRK have been implemented. Experiments have been executed on p≥1 patterns of length m comprising of m=10, 20, 30 characters which are compared against a text string of length n=227. The results show that the parallel implementation of the PRK algorithm on NVIDIA V100 GPU provides speedup surpassing 372 times when compared to the sequential implementation and speedup of 12.59 times against an OpenMP implementation running on a multi-core server with 128 threads. Compared to another prominent GPU implementation, the PRK implementation attained speedup surpassing 37 times.

The flooding DDoS attack is a serious problem these days. In order to detect the flooding DDoS attack, the survival approaches and the mitigation approaches have been investigated. Since the survival approach occurs the burden on the victims, the mitigation approach is mainly studied. As for the mitigation approaches, to detect the flooding DDoS attack, the conventional schemes using the bloom filter, machine learning, and pattern analyzation have been investigated. However, those schemes are not effective to ensure the high accuracy (ACC), the high true positive rate (TPR), and the low false positive rate (FPR). In addition, the data size and calculation time are high. Moreover, the performance is not effective from the fluctuant attack packet per second (pps). In order to effectively detect the flooding DDoS attack, we propose the lightweight detection using bloom filter against flooding DDoS attack. To detect the flooding DDoS attack and ensure the high accuracy, the high true positive rate, and the low false positive rate, the dec-all (decrement-all) operation and the checkpoint are flexibly changed from the fluctuant pps in the bloom filter. Since we only consider the IP address, all kinds of flooding attacks can be detected without the blacklist and whitelist. Moreover, there is no complexity to recognize the attack. By the computer simulation with the datasets, we show our scheme achieves an accuracy of 97.5%. True positive rate and false positive rate show 97.8% and 6.3%, respectively. The data size for processing is much small as 280bytes. Furthermore, our scheme can detect the flooding DDoS attack at once in 11.1sec calculation time.

Pilot contamination is addressed in massive multiple-input multiple-output (MIMO) uplink. The main ideas of pilot decontamination are twofold: One is to design transmission timing of pilot sequences such that the pilot transmission periods in different cells do not fully overlap with each other, as considered in previous works. The other is joint channel and data estimation via approximate message-passing (AMP) for bilinear inference. The convergence property of conventional AMP is bad in bilinear inference problems, so that adaptive damping was required to help conventional AMP converge. The main contribution of this paper is a modification of the update rules in conventional AMP to improve the convergence property of AMP. Numerical simulations show that the proposed AMP outperforms conventional AMP in terms of estimation performance when adaptive damping is not used. Furthermore, it achieves better performance than state-of-the-art methods based on subspace estimation when the power difference between cells is small.

Change-point detection is the problem of finding points of time when a probability distribution of samples changed. There are various related problems, such as estimating the number of the change-points and estimating magnitude of the change. Though various statistical models have been assumed in the field of change-point detection, we particularly deal with i.p.i.d. (independent-piecewise-identically-distributed) sources. In this paper, we formulate the related problems in a general manner based on statistical decision theory. Then we derive optimal estimators for the problems under the Bayes risk principle. We also propose efficient algorithms for the change-point detection-related problems in the i.p.i.d. sources, while in general, the optimal estimations requires huge amount of calculation in Bayesian setting. Comparison of the proposed algorithm and previous methods are made through numerical examples.

It is well known that the large-scale access of wind power to the power system will affect the economic and environmental objectives of power generation scheduling, and also bring new challenges to the traditional deterministic power generation scheduling because of the intermittency and randomness of wind power. In order to deal with these problems, a multiobjective optimization dispatch method of wind-thermal power system is proposed. The method can be described as follows: A multiobjective interval power generation scheduling model of wind-thermal power system is firstly established by describing the wind speed on wind farm as an interval variable, and the minimization of fuel cost and pollution gas emission cost of thermal power unit is chosen as the objective functions. And then, the optimistic and pessimistic Pareto frontiers of the multi-objective interval power generation scheduling are obtained by utilizing an improved normal boundary intersection method with a normal boundary intersection (NBI) combining with a bilevel optimization method to solve the model. Finally, the optimistic and pessimistic compromise solutions is determined by a distance evaluation method. The calculation results of the 16-unit 174-bus system show that by the proposed method, a uniform optimistic and pessimistic Pareto frontier can be obtained, the analysis of the impact of wind speed interval uncertainty on the economic and environmental indicators can be quantified. In addition, it has been verified that the Pareto front in the actual scenario is distributed between the optimistic and pessimistic Pareto front, and the influence of different wind power access levels on the optimistic and pessimistic Pareto fronts is analyzed.

In this paper, we aim to develop technologies for the circuit fault diagnosis and propose a formulation of a measure of a test pattern for the circuit fault diagnosis. Given a faulty circuit, the fault diagnosis is to deduce locations of faults that had occurred in the circuit. The fault diagnosis is executed in software before the failure analysis by which engineers inspect physical defects, and helps to improve the manufacturing process which yielded faulty circuits. The heart of the fault diagnosis is to distinguish between candidate faults by using test patterns, which are applied to the circuit-under-diagnosis (CUD), and thus test patterns that can distinguish as many faults as possible need to be generated. This fact motivates us to consider the test pattern measure based on the number of fault-pairs that become distinguished by a test pattern. To the best of the authors' knowledge, that measure requires the computational time of complexity order O(NF2), where NF denotes the number of candidate faults. Since NF is generally large for real industrial circuits, the computational time of the measure is long even when a high-performance computer is used. The formulation proposed in this paper makes it possible to calculate the measure in the computational complexity of O(NF log NF), and thus that measure is useful for the test pattern selection in the fault diagnosis. In computational experiments, the effectiveness of the formulation is demonstrated as samples of computational times of the measure calculated by the traditional and the proposed formulae and thorough comparisons between several greedy heuristics which are based on the measure.

The spreading technique can improve system performance since it mitigates the influence of deeply faded subcarrier channels. Proposals for implementing orthogonal frequency division multiplexing (OFDM) systems include frequency symbol spreading (FSS) based on the Walsh-Hadamard transform (WHT) and the discrete Fourier transform (DFT). In a single carrier frequency division multiplexing (SC-FDMA), good performance is obtained by the interleaved subcarrier allocation. Moreover, in a multiple-input multiple-output (MIMO), interleaving the operation of the different transmit antennas is also effective. By combining these techniques, in this paper, we propose the different antenna interleaved allocation with the full and divided WHT/DFT spreading for a high time resolution carrier interferometry (HTRCI) MIMO-OFDM.

A spatially “Mt. Fuji” coupled (SFC) low-density parity-check (LDPC) ensemble is a modified version of the spatially coupled (SC) LDPC ensemble. Its decoding error probability in the waterfall region has been studied only in an experimental manner. In this paper, we theoretically analyze it over the binary erasure channel by modifying the expected graph evolution (EGE) and covariance evolution (CE) that have been used to analyze the original SC-LDPC ensemble. In particular, we derive the initial condition modified for the SFC-LDPC ensemble. Then, unlike the SC-LDPC ensemble, the SFC-LDPC ensemble has a local minimum on the solution of the EGE and CE. Considering the property of it, we theoretically expect the waterfall curve of the SFC-LDPC ensemble is steeper than that of the SC-LDPC ensemble. In addition, we also confirm it by numerical experiments.

In recent years, with the improvement of health awareness, people have paid more and more attention to proper meal. Existing research has shown that a proper meal can help people prevent lifestyle diseases such as diabetes. In this research, by attaching sensors to the tableware, the information during the meal can be captured, and after processing and analyzing it, the meal information, such as time and sequence of meal, can be obtained. This paper introduces how to use supervised learning and multi-instance learning to deal with meal information and a detailed comparison is made. Three supervised learning algorithms and two multi-instance learning algorithms are used in the experiment. The experimental results showed that although the supervised learning algorithms have achieved good results in F-score, the multi-instance learning algorithms have achieved better results not only in accuracy but also in F-score.

Low Probability of Intercept (LPI) radar waveform has complex and diverse modulation schemes, which cannot be easily identified by the traditional methods. The research on intrapulse modulation LPI radar waveform recognition has received increasing attention. In this paper, we propose an automatic LPI radar waveform recognition algorithm that uses a multi-resolution fusion convolutional neural network. First, signals embedded within the noise are processed using Choi-William Distribution (CWD) to obtain time-frequency feature images. Then, the images are resized by interpolation and sent to the proposed network for training and identification. The network takes a dual-channel CNN structure to obtain features at different resolutions and makes features fusion by using the concatenation and Inception module. Extensive simulations are carried out on twelve types of LPI radar waveforms, including BPSK, Costas, Frank, LFM, P1~P4, and T1~T4, corrupted with additive white Gaussian noise of SNR from 10dB to -8dB. The results show that the overall recognition rate of the proposed algorithm reaches 95.1% when the SNR is -6dB. We also try various sample selection methods related to the recognition task of the system. The conclusion is that reducing the samples with SNR above 2dB or below -8dB can effectively improve the training speed of the network while maintaining recognition accuracy.

In recent years, agreement technologies have garnered interest among agents in the field of multi-agent systems. Automated negotiation is one of the agreement technologies, in which agents negotiate with each other to make an agreement so that they can solve conflicts between their preferences. Although most agents keep their own preferences private, it is necessary to estimate the opponent's preferences to obtain a better agreement. Therefore, opponent modeling is one of the most important elements in automated negotiating strategy. A frequency model is widely used for opponent modeling because of its robustness against various types of strategy while being easy to implement. However, existing frequency models do not consider the opponent's proposal speed and the transition of offers. This study proposes a novel frequency model that considers the opponent's behavior using two main elements: the offer ratio and the weighting function. The offer ratio stabilizes the model against changes in the opponent's offering speed, whereas the weighting function takes the opponent's concession into account. The two experiments conducted herein show that our proposed model is more accurate than other frequency models. Additionally, we find that the agent with the proposed model performs with a significantly higher utility value in negotiations.

LSTM network have shown to outperform in facial expression recognition of video sequence. In view of limited representation ability of single-layer LSTM, a hierarchical attention model with enhanced feature branch is proposed. This new network architecture consists of traditional VGG-16-FACE with enhanced feature branch followed by a cross-layer LSTM. The VGG-16-FACE with enhanced branch extracts the spatial features as well as the cross-layer LSTM extracts the temporal relations between different frames in the video. The proposed method is evaluated on the public emotion databases in subject-independent and cross-database tasks and outperforms state-of-the-art methods.