In a previous study, we proposed a technique to recommend candidate verbs for a method name so that developers can consistently use various verbs. In this study, we improve the rule extraction technique proposed in this previous study. Moreover, we confirm that the rank of each correct verb recommended by the new technique is higher than that by the previous technique.

The next generation high efficiency video coding (HEVC) standard achieves high performance by extending the encoding block to 64×64. There are some parallel tools to improve the efficiency for encoder and decoder. However, owing to the dependence of the current prediction block and surrounding block, parallel processing at CU level and Sub-CU level are hard to achieve. In this paper, focusing on the spatial motion vector prediction (SMVP) and temporal motion vector prediction (TMVP), parallel improvement for spatio-temporal prediction algorithms are presented, which can remove the dependency between prediction coding units and neighboring coding units. Using this proposal, it is convenient to process motion estimation in parallel, which is suitable for different parallel platforms such as multi-core platform, compute unified device architecture (CUDA) and so on. The simulation experiment results demonstrate that based on HM12.0 test model for different test sequences, the proposed algorithm can improve the advanced motion vector prediction with only 0.01% BD-rate increase that result is better than previous work, and the BDPSNR is almost the same as the HEVC reference software.

Heterogeneous hetworks (HetNets) have been introduced as an emerging technology in order to meet the increasing demand for mobile data. HetNets are a combination of multi-layer networks such as macrocells and small cells. In such networks, users may suffer significant cross-layer interference. To manage this interference, the 3rd Generation Partnership Project (3GPP) has introduced enhanced Inter-Cell Interference Coordination (eICIC) techniques. Almost Blank SubFrame (ABSF) is one of the time-domain techniques used in eICIC solutions. We propose a dynamically optimal Signal-to-Interference-and-Noise Ratio (SINR)-based ABSF framework to ensure macro user performance while maintaining small user performance. We also study cooperative mechanisms to help small cells collaborate efficiently in order to reduce mutual interference. Simulations show that our proposed scheme achieves good performance and outperforms the existing ABSF frameworks.

The advanced front-end (AFE) for automatic speech recognition (ASR) was standardized by the European Telecommunications Standards Institute (ETSI). The AFE provides speech enhancement realized by an iterative Wiener filter (IWF) in which a smoothed FFT spectrum over adjacent frames is used to design the filter. We have previously proposed robust time-varying complex Auto-Regressive (TV-CAR) speech analysis for an analytic signal and evaluated the performance of speech processing such as F0 estimation and speech enhancement. TV-CAR analysis can estimate more accurate spectrum than FFT, especially in low frequencies because of the nature of the analytic signal. In addition, TV-CAR can estimate more accurate speech spectrum against additive noise. In this paper, a time-invariant version of wide-band TV-CAR analysis is introduced to the IWF in the AFE and is evaluated using the CENSREC-2 database and its baseline script.

For the last few decades, learning with multiple kernels, represented by the ensemble kernel regressor and the multiple kernel regressor, has attracted much attention in the field of kernel-based machine learning. Although their efficacy was investigated numerically in many works, their theoretical ground is not investigated sufficiently, since we do not have a theoretical framework to evaluate them. In this paper, we introduce a unified framework for evaluating kernel regressors with multiple kernels. On the basis of the framework, we analyze the generalization errors of the ensemble kernel regressor and the multiple kernel regressor, and give a sufficient condition for the ensemble kernel regressor to outperform the multiple kernel regressor in terms of the generalization error in noise-free case. We also show that each kernel regressor can be better than the other without the sufficient condition by giving examples, which supports the importance of the sufficient condition.

Although sparse coding has emerged as an extremely powerful tool for texture and image classification, it neglects the relationship of coding coefficients from the same class in the training stage, which may cause a decline in the classification performance. In this paper, we propose a novel coding strategy named compact sparse coding for ground-based cloud classification. We add a constraint on coding coefficients into the objective function of traditional sparse coding. In this way, coding coefficients from the same class can be forced to their mean vector, making them more compact and discriminative. Experiments demonstrate that our method achieves better performance than the state-of-the-art methods.

This paper proposes a network clock system that detects degradation in the frequency accuracy of network clocks distributed across a network and finds the sources of the degradation. This system uses two factors to identify degradation in frequency accuracy and an algorithm that finds degradation sources by integrating and analyzing the evaluation results gathered from the entire network. Many frequency stability measurement systems have been proposed, and most are based on time synchronization protocols. These systems also realize avoidance of frequency degradation and identification of the sources of the degradation. Unfortunately, the use of time synchronization protocols is impractical if the service provider, such as NTT, has already installed a frequency synchronization system; the provider must replace massive amounts of equipment with new devices that support the time synchronization protocols. Considering the expenditure of installment, this is an excessive burden on service providers. Therefore, a new system that can detect of frequency degradation in network clocks and identify the degradation causes without requiring new equipment is strongly demanded. The proposals made here are implemented by the installation of new circuit cards in current equipment and installing a server that runs the algorithm. This proposed system is currently being installed in NTT's network.

This paper proposes a method that uses bistatic Multiple-Input Multiple-Output (MIMO) radar to locate living-bodies. In this method, directions of living-bodies are estimated by the MUltiple SIgnal Classification (MUSIC) method at the transmitter and receiver, where the Fourier transformed virtual Single-Input Multiple-Output (SIMO) channel matrix is used. Body location is taken as the intersection of the two directions. The proposal uses a single frequency and so has a great advantage over conventional methods that need a wide frequency band. Also, this method can be used in multipath-rich environments such as indoors. An experiment is performed in an indoor environment, and the MIMO channels yielded by various subject numbers and positions are measured. The result indicates that the proposed method can estimate multiple living-body locations with high accuracy, even in multipath environments.

Due to its on-demand and pay-as-you-go properties, cloud computing has become an attractive alternative for HPC applications. However, communication-intensive applications with complex communication patterns still cannot be performed efficiently on cloud platforms, which are equipped with MapReduce technologies, such as Hadoop and Spark. In particular, one major obstacle is that MapReduce's simple programming model cannot explicitly manipulate data transfers between compute nodes. Another obstacle is cloud's relatively poor network performance compared with traditional HPC platforms. The traditional Strassen's algorithm of square matrix multiplication has a recursive and complex pattern on the HPC platform. Therefore, it cannot be directly applied to the cloud platform. In this paper, we demonstrate how to make Strassen's algorithm with complex communication patterns “cloud-friendly”. By reorganizing Strassen's algorithm in an iterative pattern, we completely separate its computations and communications, making it fit to MapReduce programming model. By adopting a novel data/task parallel strategy, we solve Strassen's data dependency problems, making it well balanced. This is the first instance of Strassen's algorithm in MapReduce-style systems, which also matches Strassen's communication lower bound. Further experimental results show that it achieves a speedup ranging from 1.03× to 2.50× over the classical Θ(n3) algorithm. We believe the principle can be applied to many other complex scientific applications.

In this paper, we introduce a distributed power allocation strategy for random access, that has the capabilities of multipacket reception (MPR) and successive interference cancellation (SIC). The proposed random access scheme is suitable for machine-to-machine (M2M) communication application in fifth-generation (5G) cellular networks. A previous study optimized the probability distribution for discrete transmission power levels, with implicit limitations on the successful decoding of at most two packets from a single collision. We formulate the optimization problem for the general case, where a base station can decode multiple packets from a single collision, and this depends only on the signal-to-interference-plus-noise ratio (SINR). We also propose a feasible suboptimal iterative per-level optimization process; we do this by introducing relationships among the different discrete power levels. Compared with the conventional power allocation scheme with MPR and SIC, our method significantly improves the system throughput; this is confirmed by computer simulations.

In this letter, a new subband adaptive filter (SAF) which is robust against impulsive noise in system identification is presented. To address the vulnerability of adaptive filters based on the L2-norm optimization criterion to impulsive noise, the robust SAF (R-SAF) comes from the L1-norm optimization criterion with a constraint on the energy of the weight update. Minimizing L1-norm of the a posteriori error in each subband with a constraint on minimum disturbance gives rise to robustness against impulsive noise and the capable convergence performance. Simulation results clearly demonstrate that the proposal, R-SAF, outperforms the classical adaptive filtering algorithms when impulsive noise as well as background noise exist.

Sparsity-based methods have been recently applied to abnormal event detection and have achieved impressive results. However, most such methods suffer from the problem of dimensionality curse; furthermore, they also take no consideration of the relationship among coefficient vectors. In this paper, we propose a novel method called consistent sparse representation (CSR) to overcome the drawbacks. We first reconstruct each feature in the space spanned by the clustering centers of training features so as to reduce the dimensionality of features and preserve the neighboring structure. Then, the consistent regularization is added to the sparse representation model, which explicitly considers the relationship of coefficient vectors. Our method is verified on two challenging databases (UCSD Ped1 database and Subway batabase), and the experimental results demonstrate that our method obtains better results than previous methods in abnormal event detection.

Current software behavior models lack the ability to conduct semantic analysis. We propose a new model to detect abnormal behaviors based on a function semantic tree. First, a software behavior model in terms of state graph and software function is developed. Next, anomaly detection based on the model is conducted in two main steps: calculating deviation density of suspicious behaviors by comparison with state graph and detecting function sequence by function semantic rules. Deviation density can well detect control flow attacks by a deviation factor and a period division. In addition, with the help of semantic analysis, function semantic rules can accurately detect application layer attacks that fail in traditional approaches. Finally, a case study of RSS software illustrates how our approach works. Case study and a contrast experiment have shown that our model has strong expressivity and detection ability, which outperforms traditional behavior models.

In this paper, synchronization for uncertain fractional order chaotic systems is investigated. By using the fractional order extension of the Lyapunov stability criterion, a linear feedback controller and an adaptive controller are designed for synchronizing uncertain fractional order chaotic systems without and with unknown external disturbance, respectively. Quadratic Lyapunov functions are used in the stability analysis of fractional-order systems, and fractional order adaptation law is constructed to update design parameter. The proposed methods can guarantee that the synchronization error converges to zero asymptotically. Finally, illustrative examples are given to confirm the theoretical results.

With aggressive device scaling, timing failures have become more prevalent due to manufacturing defects and process variations. When timing failure occurs, it is important to take corrective actions immediately. Therefore, an efficient and fast diagnosis method is essential. In this paper, we propose a new diagnostic method using timing information. Our method approximately estimates all the segment delays of measured paths in a design, using inequality-constrained least squares methods. Then, the proposed method ranks the possible locations of delay defects based on the difference between estimated segment delays and the expected values of segment delays. The method works well for multiple delay defects as well as single delay defects. Experiment results show that our method yields good diagnostic resolution. With the proposed method, the average first hit rank (FHR), was within 7 for single delay defect and within 8 for multiple delay defects.

A high energy-efficiency and area-reduction switching scheme for a low-power successive approximation register (SAR) analog-to-digital converter (ADC) is presented. Based on the sequence initialization, monotonic capacitor switching procedure and multiple reference voltages, the average switching energy and total capacitance of the proposed scheme are reduced by 99.4% and 87.5% respectively, compared to the conventional architecture.

We propose efficient algorithms for Sorting k-Sets in Bins. The Sorting k-Sets in Bins problem can be described as follows. We are given numbered n bins with k balls in each bin. Balls in the i-th bin are numbered n-i+1. We can only swap balls between adjacent bins. Our task is to move all of the balls to the same numbered bins. For this problem, we give an efficient greedy algorithm with $rac{k+1}{4}n^2+O(k+n)$ swaps and provide a detailed analysis for k=3. In addition, we give a more efficient recursive algorithm using $rac{15}{16}n^2+O(n)$ swaps for k=3.

We present a method for recognition of acoustic events in conversation scenarios where speech usually overlaps with other acoustic events. While speech is usually considered the most informative acoustic event in a conversation scene, it does not always contain all the information. Non-speech events, such as a door knock, steps, or a keyboard typing can reveal aspects of the scene that speakers miss or avoid to mention. Moreover, being able to robustly detect these events could further support speech enhancement and recognition systems by providing useful information cues about the surrounding scenarios and noise. In acoustic event detection, state-of-the-art techniques are typically based on derived features (e.g. MFCC, or Mel-filter-banks) which have successfully parameterized the spectrogram of speech but reduce resolution and detail when we are targeting other kinds of events. In this paper, we propose a method that learns features in an unsupervised manner from high-resolution spectrogram patches (considering a patch as a certain number of consecutive frame features stacked together), and integrates within the deep neural network framework to detect and classify acoustic events. Superiority over both previous works in the field, and similar approaches based on derived features, has been assessed by statical measures and evaluation with CHIL2007 corpus, an annotated database of seminar recordings.

By investigating the properties that the offsets should satisfy, this letter presents a brief proof of general QAM Golay complementary sequences (GCSs) in Cases I-III constructions. Our aim is to provide a brief, clear, and intelligible derivation so that it is easy for the reader to understand the known Cases I-III constructions of general QAM GCSs.

Sharing data might bring the risk of disclosing the sensitive knowledge in it. Usually, the data owner may choose to sanitize data by modifying some items in it to hide sensitive knowledge prior to sharing. This paper focuses on protecting sensitive knowledge in the form of frequent itemsets by data sanitization. The sanitization process may result in side effects, i.e., the data distortion and the damage to the non-sensitive frequent itemsets. How to minimize these side effects is a challenging problem faced by the research community. Actually, there is a trade-off when trying to minimize both side effects simultaneously. In view of this, we propose a data sanitization method based on evolutionary multi-objective optimization (EMO). This method can hide specified sensitive itemsets completely while minimizing the accompanying side effects. Experiments on real datasets show that the proposed approach is very effective in performing the hiding task with fewer damage to the original data and non-sensitive knowledge.