In the model of no-dictionary searchable symmetric encryption (SSE) schemes, the client does not need to keep the list of keywords W. In this paper, we first show a generic method to transform any passively secure SSE scheme to a no-dictionary SSE scheme such that the client can verify search results even if w ∉ W. In particular, it takes only O(1) time for the server to prove that w ∉ W. We next present a no-dictionary SSE scheme such that the client can hide even the search pattern from the server.

We propose a method for accurately identifying people using temporal and spatial changes in local movements measured from video sequences of body sway. Existing methods identify people using gait features that mainly represent the large swinging of the limbs. The use of gait features introduces a problem in that the identification performance decreases when people stop walking and maintain an upright posture. To extract informative features, our method measures small swings of the body, referred to as body sway. We extract the power spectral density as a feature from local body sway movements by dividing the body into regions. To evaluate the identification performance using our method, we collected three original video datasets of body sway sequences. The first dataset contained a large number of participants in an upright posture. The second dataset included variation over the long term. The third dataset represented body sway in different postures. The results on the datasets confirmed that our method using local movements measured from body sway can extract informative features for identification.

Software defect prediction (SDP) plays a significant part in allocating testing resources reasonably, reducing testing costs, and ensuring software quality. One of the most widely used algorithms of SDP models is Naive Bayes (NB) because of its simplicity, effectiveness and robustness. In NB, when a data set has continuous or numeric attributes, they are generally assumed to follow normal distributions and incorporate the probability density function of normal distribution into their conditional probabilities estimates. However, after conducting a Kolmogorov-Smirnov test, we find that the 21 main software metrics follow non-normal distribution at the 5% significance level. Therefore, this paper proposes an improved NB approach, which estimates the conditional probabilities of NB with kernel density estimation of training data sets, to help improve the prediction accuracy of NB for SDP. To evaluate the proposed method, we carry out experiments on 34 software releases obtained from 10 open source projects provided by PROMISE repository. Four well-known classification algorithms are included for comparison, namely Naive Bayes, Support Vector Machine, Logistic Regression and Random Tree. The obtained results show that this new method is more successful than the four well-known classification algorithms in the most software releases.

We propose a new context-adaptive arithmetic coding (CAAC) scheme for lossless bit rate reduction of parametric stereo (PS) in enhanced aacPlus. Based on the probability analysis of stereo parameters indexes in PS, we propose a stereo band-dependent CAAC scheme for PS. We also propose a new coding structure of the scheme which is simple but effective. The proposed scheme has normal and memory-reduced versions, which are superior to the original and conventional schemes and guarantees significant bit rate reduction of PS. The proposed scheme can be an alternative to the original PS coding scheme at low bit rate, where coding efficiency is very important.

The present paper introduces a novel type of structured ternary sequences having a zero-correlation zone (zcz) for both periodic and aperiodic correlation functions. The cross-correlation function and the side lobe of the auto-correlation function of the proposed sequence set are zero for phase shifts within the zcz. The proposed zcz sequence set can be generated from an arbitrary pair of an Hadamard matrix of order lh and a binary/ternary perfect sequence of length lp. The sequence set of order 0 is identical to the r-th row of the Hadamard matrix. For m ≥ 0, the sequence set of order (m+1) is constructed from the sequence set of order m by sequence concatenation and interleaving. The sequence set has lp subsets of size 2lh. The periodic correlation function and the aperiodic correlation function of the proposed sequence set have a zcz from -(2m+1-1) to 2m+1-1. The periodic correlation function and the aperiodic correlation function of the sequences of the i-th subset and k-th subset have a zcz from -2m+2-(lh+1)((j-k) mod lp) to -2m+2-(lh+1)((j-k) mod lp). The proposed sequence is suitable for a heterogeneous wireless network, which is one of the candidates for the fifth-generation mobile networks.

Pseudorandom number generators are required to generate pseudorandom numbers which have good statistical properties as well as unpredictability in cryptography. An m-sequence is a linear feedback shift register sequence with maximal period over a finite field. M-sequences have good statistical properties, however we must nonlinearize m-sequences for cryptographic purposes. A geometric sequence is a sequence given by applying a nonlinear feedforward function to an m-sequence. Nogami, Tada and Uehara proposed a geometric sequence whose nonlinear feedforward function is given by the Legendre symbol, and showed the period, periodic autocorrelation and linear complexity of the sequence. Furthermore, Nogami et al. proposed a generalization of the sequence, and showed the period and periodic autocorrelation. In this paper, we first investigate linear complexity of the geometric sequences. In the case that the Chan-Games formula which describes linear complexity of geometric sequences does not hold, we show the new formula by considering the sequence of complement numbers, Hasse derivative and cyclotomic classes. Under some conditions, we can ensure that the geometric sequences have a large linear complexity from the results on linear complexity of Sidel'nikov sequences. The geometric sequences have a long period and large linear complexity under some conditions, however they do not have the balance property. In order to construct sequences that have the balance property, we propose interleaved sequences of the geometric sequence and its complement. Furthermore, we show the periodic autocorrelation and linear complexity of the proposed sequences. The proposed sequences have the balance property, and have a large linear complexity if the geometric sequences have a large one.

Binary sparse representation based on arbitrary quality metrics and its applications are presented in this paper. The novelties of the proposed method are twofold. First, the proposed method newly derives sparse representation for which representation coefficients are binary values, and this enables selection of arbitrary image quality metrics. This new sparse representation can generate quality metric-independent subspaces with simplification of the calculation procedures. Second, visual saliency is used in the proposed method for pooling the quality values obtained for all of the parts within target images. This approach enables visually pleasant approximation of the target images more successfully. By introducing the above two novel approaches, successful image approximation considering human perception becomes feasible. Since the proposed method can provide lower-dimensional subspaces that are obtained by better image quality metrics, realization of several image reconstruction tasks can be expected. Experimental results showed high performance of the proposed method in terms of two image reconstruction tasks, image inpainting and super-resolution.

In this letter, we consider the harvested-energy fairness problem in cognitive multicast systems with simultaneous wireless information and power transfer. In the cognitive multicast system, a cognitive transmitter with multi-antenna sends the same information to cognitive users in the presence of licensed users, and cognitive users can decode information and harvest energy with a power-splitting structure. The harvested-energy fairness problem is formulated and solved by using two proposed algorithms, which are based on semidefinite relaxation with majorization-minimization method, and sequential parametric convex approximation with feasible point pursuit technique, respectively. Finally, the performances of the proposed solutions and baseline schemes are verified by simulation results.

Once a bug is reported, it is a major concern whether or not the bug is resolved (closed) soon. This paper examines seven metrics quantifying the amount of clues to the early close of reported bugs through a case study. The results show that one of the metrics, the similarity to already-closed bug reports, is strongly related to early-closed bugs.

An important problem in mathematics and data science, given two or more metric spaces, is obtaining a metric of the product space by aggregating the source metrics using a multivariate function. In 1981, Borsík and Doboš solved the problem, and much progress has subsequently been made in generalizations of the problem. The triangle inequality is a key property for a bivariate function to be a metric. In the metric aggregation, requesting the triangle inequality of the resulting metric imposes the subadditivity on the aggregating function. However, in some applications, such as the image matching, a relaxed notion of the triangle inequality is useful and this relaxation may enlarge the scope of the aggregators to include some natural superadditive functions such as the harmonic mean. This paper examines the aggregation of two semimetrics (i.e. metrics with a relaxed triangle inequality) by the harmonic mean is studied and shows that such aggregation weakly preserves the relaxed triangle inequalities. As an application, the paper presents an alternative simple proof of the relaxed triangle inequality satisfied by the robust Jaccard-Tanimoto set dissimilarity, which was originally shown by Gragera and Suppakitpaisarn in 2016.

We investigate linear complementary dual (LCD) rank-metric codes in this paper. We construct a class of LCD generalized Gabidulin codes by a self-dual basis of an extension field over the base field. Moreover, a class of LCD MRD codes, which are obtained by Cartesian products of a generalized Gabidulin code, is constructed.

In this paper, we present the effects of finite superstrates and asymmetrical grounds on the performance of high gain superstrate antennas. First, when the source of a superstrate antenna is located at an edge of a ground plane, that is, an asymmetric ground plane, the gain of the superstrate antenna can be made to match the gain of the superstrate antenna with a symmetrical ground plane using the PEC (E-plane asymmetric) or the AMC wall (H-plane asymmetric) near the edge. Second, the gain of the superstrate antenna, which has a ground plane with dimensions sufficiently close to infinite, is found to be roughly proportional to the reflection magnitude of a partially reflective surface (PRS). It is found that when the square ground size has a finite dimension of two wavelengths or less, the reflection magnitude of the PRS should have the optimum value for achieving maximum gain. Finally, the gain of the superstrate antenna is studied when the ground plane differs from a PRS. For the above three cases, the performances of the superstrate antenna are verified and compared by analysis, full-wave simulation, and measurement.

In this study, the electromagnetic scatterings from conducting bodies have been investigated via a surface equivalence theorem. When one formulates equivalent electric and magnetic currents from geometrical optics (GO) reflected field in the illuminated surface and GO incident field in the shadowed surface, it has been found that the asymptotically derived radiation fields are found to be the same as those formulated from physical optics (PO) approximation.

In this study, we developed a new orthogonal partition concept for asymmetric orthogonal arrays and used it for the construction of orthogonal arrays for the first time. Permutation matrices and the Kronecker product were also successfully and skillfully used as our main tools. Hence, a new general iterative construction method for asymmetric orthogonal arrays of high strength was developed, and some new infinite families of orthogonal arrays of strength 3 and several new orthogonal arrays of strength 4, 5, and 6 were obtained.

An improved radiometric calibration algorithm by extending the Mitsunaga and Nayar least-square minimization based algorithm with two major ideas is presented. First, a noise & outlier removal procedure based on the analysis of brightness transfer function is included for improving the algorithm's capability on handling noise and outlier in least-square estimation. Second, an alternative minimization formulation based on weighted least square is proposed to improve the weakness of least square minimization when dealing with biased distribution observations. The performance of the proposed algorithm with regards to two baseline algorithms is demonstrated, i.e. the classical least square based algorithm proposed by Mitsunaga and Nayar and the state-of-the-art rank minimization based algorithm proposed by Lee et al. From the results, the proposed algorithm outperforms both baseline algorithms on both the synthetic dataset and the dataset of real-world images.

The accurate assessment of infants' pain is important for understanding their medical conditions and developing suitable treatment. Pediatric studies reported that the inadequate treatment of infants' pain might cause various neuroanatomical and psychological problems. The fact that infants can not communicate verbally motivates increasing interests to develop automatic pain assessment system that provides continuous and accurate pain assessment. In this paper, we propose a new set of pain facial activity features to describe the infants' facial expression of pain. Both dynamic facial texture feature and dynamic geometric feature are extracted from video sequences and utilized to classify facial expression of infants as pain or no pain. For the dynamic analysis of facial expression, we construct spatiotemporal domain representation for texture features and time series representation (i.e. time series of frame-level features) for geometric features. Multiple facial features are combined through both feature fusion and decision fusion schemes to evaluate their effectiveness in infants' pain assessment. Experiments are conducted on the video acquired from NICU infants, and the best accuracy of the proposed pain assessment approaches is 95.6%. Moreover, we find that although decision fusion does not perform better than that of feature fusion, the False Negative Rate of decision fusion (6.2%) is much lower than that of feature fusion (25%).

The combination of large-scale antenna arrays and simultaneous wireless information and power transfer (SWIPT), which can provide enormous increase of throughput and energy efficiency is a promising key in next generation wireless system (5G). This paper investigates efficient transceiver design to minimize transmit power, subject to users' required data rates and energy harvesting, in large-scale SWIPT system where the base station utilizes a very large number of antennas for transmitting both data and energy to multiple users equipped with time-switching (TS) or power-splitting (PS) receive structures. We first propose the well-known semidefinite relaxation (SDR) and Gaussian randomization techniques to solve the minimum transmit power problems. However, for these large-scale SWIPT problems, the proposed scheme, which is based on conventional SDR method, is not suitable due to its excessive computation costs, and a consensus alternating direction method of multipliers (ADMM) cannot be directly applied to the case that TS or PS ratios are involved in the optimization problem. Therefore, in the second solution, our first step is to optimize the variables of TS or PS ratios, and to achieve simplified problems. After then, we propose fast algorithms for solving these problems, where the outer loop of sequential parametric convex approximation (SPCA) is combined with the inner loop of ADMM. Numerical simulations show the fast convergence and superiority of the proposed solutions.

Individuals can be identified by features extracted from an electrocardiogram (ECG). However, irregular palpitations due to stress or exercise decrease the identification accuracy due to distortion of the ECG waveforms. In this letter, we propose a human identification scheme based on the frequency spectrums of an ECG, which can successfully extract features and thus identify individuals even while exercising. For the proposed scheme, we demonstrate an accuracy rate of 99.8% in a controlled experiment with exercising subjects. This level of accuracy is achieved by determining the significant features of individuals with a random forest classifier. In addition, the effectiveness of the proposed scheme is verified using a publicly available ECG database. We show that the proposed scheme also achieves a high accuracy with this public database.

Topic modeling as a well-known method is widely applied for not only text data mining but also multimedia data analysis such as video data analysis. However, existing models cannot adequately handle time dependency and multimodal data modeling for video data that generally contain image information and speech information. In this paper, we therefore propose a novel topic model, sequential symmetric correspondence hierarchical Dirichlet processes (Seq-Sym-cHDP) extended from sequential conditionally independent hierarchical Dirichlet processes (Seq-CI-HDP) and sequential correspondence hierarchical Dirichlet processes (Seq-cHDP), to improve the multimodal data modeling mechanism via controlling the pivot assignments with a latent variable. An inference scheme for Seq-Sym-cHDP based on a posterior representation sampler is also developed in this work. We finally demonstrate that our model outperforms other baseline models via experiments.

This paper describes the design and experiment results of a 25Gbps vertical-cavity surface emitting laser (VCSEL) driver circuit for a multi channel optical transmitter. To compensate for the non-linearity of the VCSEL and achieve high speed data rate communication, an asymmetric pre-emphasis technique is proposed for the VCSEL driver. An asymmetric pre-emphasis signal can be created by adjusting the duty ratio of the emphasis signal. The VCSEL driver adopts a double cascode connection that can apply a drive current from a high voltage DC bias and feed-forward compensation that can enhance the band-width for common-cathode VCSEL. For the design of the optical module structure, a two-tier low temperature co-fired ceramics (LTCC) package is adopted to minimize the wire bonding between the signal pad on the LTCC and the anode pad on the VCSEL. This structure and circuit reduces the simulated deterministic jitter from 12.7 to 4.1ps. A test chip was fabricated with the 65-nm standard CMOS process and demonstrated to work as an optical transmitter. An experimental evaluation showed that this VCSEL driver with asymmetric pre-emphasis reduced the total deterministic jitter up to 8.6ps and improved the vertical eye opening ratio by 3% compared with symmetric pre-emphasis at 25Gbps with a PRBS=29-1 test signal. The power consumption of the VCSEL driver was 3.0mW/Gbps/ch at 25Gbps. An optical transmitter including the VCSEL driver achieved 25-Gbps, 4-ch fully optical links.