Entity descriptions have been exponentially growing in community-generated knowledge databases, such as DBpedia. However, many of those descriptions are not useful for identifying the underlying characteristics of their corresponding entities because semantically redundant facts or triples are included in the descriptions that represent the connections between entities without any semantic properties. Entity summarization is applied to filter out such non-informative triples and meaning-redundant triples and rank the remaining informative facts within the size of the triples for summarization. This study proposes an entity summarization approach based on pre-grouping the entities that share a set of attributes that can be used to characterize the entities we want to summarize. Entities are first grouped according to projected multilingual categories that provide the multi-angled semantics of each entity into a single entity space. Key facts about the entity are then determined through in-group-based rankings. As a result, our proposed approach produced summary information of significantly better quality (p-value =1.52×10-3 and 2.01×10-3 for the top-10 and -5 summaries, respectively) than the state-of-the-art method that requires additional external resources.

Authors previously studied the degradation of electrical contacts under the condition of various external micro-oscillations. They also developed a micro-sliding mechanism (MSM2), which causes micro-sliding and is driven by a piezoelectric actuator and elastic hinges. Using the mechanism, experimental results were obtained on the minimal sliding amplitude (MSA) required to make the electrical resistance fluctuate under various conditions. In this paper, to develop a more realistic model of input waveform than the previous one, Ts/2 is set as the rising or falling time, Tc as the flat time, and τ/2 as the duration in a sliding period T (0.25 s) of the input waveform. Using the Duhamel's integral method and an optimization method, the physical parameters of natural angular frequency ω0 (12000 s-1), damping ratio ζ (0.05), and rising and falling time Ts (1.3 or 1.2 ms) are obtained. Using the parameters and the MSA, the total acceleration of the input TA (=f(t)) and the displacement of the output x(t) are also obtained using the Fourier series expansion method. The waveforms x(t) and the experimental results are similar to each other. If the effective mass m, which is defined as that of the movable parts in the MSM2, is 0.1 kg, each total force TF (=2mTA) is estimated from TA and m. By the TF, the cases for 0.3 N/pin as frictional force or in impulsive as input waveform are more serious than the others. It is essential for the safety and the confidence of electrical contacts to evaluate the input waveform and the frictional force. The ringing waveforms of the output displacements x(t) are calculated at smaller values of Ts (1.0, 0.5, and 0.0 ms) than the above values (1.3 or 1.2 ms). When Ts is slightly changed from 1.3 or 1.2 ms to 1.0 ms, the ringing amplitude is doubled. For the degradation of electrical contacts, it is essential that Ts is reduced in a rectangular and impulsive input. Finally, a very simple wear model comprising three stages (I, II, and III) is introduced in this paper. Because Ts is much shorter in a rectangular or impulsive input than in a sinusoidal input, it is considered that the former more easily causes wear than the latter owing to a larger frictional force. Taking the adhesive wear in Stages I and III into consideration, the wear is expected to be more severe in the case of small damped oscillations owing to the ringing phenomenon.

Recent acoustic event classification research has focused on training suitable filters to represent acoustic events. However, due to limited availability of target event databases and linearity of conventional filters, there is still room for improving performance. By exploiting the non-linear modeling of deep neural networks (DNNs) and their ability to learn beyond pre-trained environments, this letter proposes a DNN-based feature extraction scheme for the classification of acoustic events. The effectiveness and robustness to noise of the proposed method are demonstrated using a database of indoor surveillance environments.

Blind deconvolution (BD) is the problem of restoring sharp images from blurry images when convolution kernels are unknown. While it has a wide range of applications and has been extensively studied, traditional shift-invariant (SI) BD focuses on uniform blur caused by kernels that do not spatially vary. However, real blur caused by factors such as motion and defocus is often nonuniform and thus beyond the ability of SI BD. Although specialized methods exist for nonuniform blur, they can only handle specific blur types. Consequently, the applicability of BD for general blur remains limited. This paper proposes a shift-variant (SV) BD method that models nonuniform blur using a field of kernels that assigns a local kernel to each pixel, thereby allowing pixelwise variation. This concept is realized as a Bayesian model that involves SV convolution with the field of kernels and smoothing of the field for regularization. A variational-Bayesian inference algorithm is derived to jointly estimate a sharp latent image and a field of kernels from a blurry observed image. Owing to the flexibility of the field-of-kernels model, the proposed method can deal with a wider range of blur than previous approaches. Experiments using images with nonuniform blur demonstrate the effectiveness of the proposed SV BD method in comparison with previous SI and SV approaches.

In current video encoding systems, the acquisition process is independent from the video encoding process. In order to compensate for the independence, pre-filters prior to the encoder are used. However, conventional pre-filters are designed under constraints on the temporal resolution, so they are not optimized enough in terms of coding efficiency. By relaxing the restriction on the temporal resolution of current video encoding systems, there is a good possibility to generate a video signal suitable for the video encoding process. This paper proposes a video generation method with an adaptive temporal filter that utilizes a temporally over-sampled signal. The filter is designed based on dynamic-programming. Experimental results show that the proposed method can reduce encoding rate on average by 3.01 [%] compared to the constant mean filter.

Recent research on the assessment of low probability of interception (LPI) radar waveforms is mainly based on limiting spectral properties of Wigner matrices. As the dimension of actual operating data is constrained by the sampling frequency, it is very urgent and necessary to research the finite theory of Wigner matrices. This paper derives a closed-form expression of the spectral cumulative distribution function (CDF) for Wigner matrices of finite sizes. The expression does not involve any derivatives and integrals, and therefore can be easily computed. Then we apply it to quantifying the LPI performance of radar waveforms, and the Kullback-Leibler divergence (KLD) is also used in the process of quantification. Simulation results show that the proposed LPI metric which considers the finite sample size and signal-to-noise ratio is more effective and practical.

With the in-depth development of service computing, it has become clear that when constructing service applications in an open dynamic network environment, greater attention must be paid to trustworthiness under the premise of functions' realization. Trustworthy computing requires theories for business process modeling in terms of both behavior and trustworthiness. In this paper, a calculus for ensuring the satisfaction of trustworthiness requirements in service-oriented systems is proposed. We investigate a calculus called QPi, for representing both the behavior and the trustworthiness property of concurrent systems. QPi is the combination of pi-calculus and a constraint semiring, which has a feature when problems with multi-dimensional properties must be tackled. The concept of the quantified bisimulation of processes provides us a measure of the degree of equivalence of processes based on the bisimulation distance. The QPi related properties of bisimulation and bisimilarity are also discussed. A specific modeling example is given to illustrate the effectiveness of the algebraic method.

We present a novel method to enable users to experience mobile interaction with digital content on external displays by embedding markers imperceptibly on the screen. Our method consists of two parts: marker embedding on external displays and marker detection. To embed markers, similar to previous work, we display complementary colors in alternating frames, which are selected by considering L*a*b color space in order to make the markers harder for humans to detect. Our marker detection process does not require mobile devices to be synchronized with the display, while certain constraints for the relation between camera and display update rate need to be fulfilled. In this paper, we have conducted three experiments. The results show 1) selecting complementary colors in the a*b* color plane maximizes imperceptibility, 2) our method is extremely robust when used with static contents and can handle animated contents up to certain optical flow levels, and 3) our method was proved to work well in case of small movements, but large movements can lead to loss of tracking.

Several applications for 3-D visualization require dense detection of correspondence for displacement estimation among heterogeneous multi-view images. Due to differences in resolution or sampling density and field of view in the images, estimation of dense displacement is not straight forward. Therefore, we propose a scale invariant polynomial expansion method that can estimate dense displacement between two heterogeneous views. Evaluation on heterogeneous images verifies accuracy of our approach.

Aiming at detecting pronunciation errors produced by second language learners and providing corrective feedbacks related with articulation, we address effective articulatory models based on deep neural network (DNN). Articulatory attributes are defined for manner and place of articulation. In order to efficiently train these models of non-native speech without such data, which is difficult to collect in a large scale, several transfer learning based modeling methods are explored. We first investigate three closely-related secondary tasks which aim at effective learning of DNN articulatory models. We also propose to exploit large speech corpora of native and target language to model inter-language phenomena. This kind of transfer learning can provide a better feature representation of non-native speech. Related task transfer and language transfer learning are further combined on the network level. Compared with the conventional DNN which is used as the baseline, all proposed methods improved the performance. In the native attribute recognition task, the network-level combination method reduced the recognition error rate by more than 10% relative for all articulatory attributes. The method was also applied to pronunciation error detection in Mandarin Chinese pronunciation learning by Japanese native speakers, and achieved the relative improvement up to 17.0% for detection accuracy and up to 19.9% for F-score, which is also better than the lattice-based combination.

The IEEE 802.11 wireless local area network (WLAN) is the most widely deployed communication standard in the world. Currently, the IEEE 802.11ax draft standard is one of the most advanced and promising among future wireless network standards. However, the suggested uplink-OFDMA (UL-OFDMA) random access method, based on trigger frame-random access (TF-R) from task group ax (TGax), does not yet show satisfying system performance. To enhance the UL-OFDMA capability of the IEEE 802.11ax draft standard, we propose a centralized contention-based MAC (CC-MAC) and describe its detailed operation. In this paper, we analyze the performance of CC-MAC by solving the Markov chain model and evaluating BSS throughput compared to other methods, such as DCF and TF-R, by computer simulation. Our results show that CC-MAC is a scalable and efficient scheme for improving the system performance in a UL-OFDMA random access situation in IEEE 802.11ax.

This paper deals with a problem to decide whether a given graph structure appears as a pattern in the structure of a given graph. A graph pattern is a triple p=(V,E,H), where (V,E) is a graph and H is a set of variables, which are ordered lists of vertices in V. A variable can be replaced with an arbitrary connected graph by a kind of hyperedge replacements. A substitution is a collection of such replacements. The graph pattern matching problem (GPMP) is the computational problem to decide whether or not a given graph G is obtained from a given graph pattern p by a substitution. In this paper, we show that GPMP for a graph pattern p and a graph G is solvable in polynomial time if the length of every variable in p is 2, p is of bounded treewidth, and G is connected.

Accurate optic disc localization and segmentation are two main steps when designing automated screening systems for diabetic retinopathy. In this paper, a novel optic disc detection approach based on saliency object detection and modified local intensity clustering model is proposed. It consists of two stages: in the first stage, the saliency detection technique is introduced to the enhanced retinal image with the aim of locating the optic disc. In the second stage, the optic disc boundary is extracted by the modified Local Intensity Clustering (LIC) model with oval-shaped constrain. The performance of our proposed approach is tested on the public DIARETDB1 database. Compared to the state-of-the-art approaches, the experimental results show the advantages and effectiveness of the proposed approach.

In both theoretical analysis and practical use for an antidictionary coding algorithm, an important problem is how to encode an antidictionary of an input source. This paper presents a proposal for a compact tree representation of an antidictionary built from a circular string for an input source. We use a technique for encoding a tree in the compression via substring enumeration to encode a tree representation of the antidictionary. Moreover, we propose a new two-pass universal antidictionary coding algorithm by means of the proposal tree representation. We prove that the proposed algorithm is asymptotic optimal for a stationary ergodic source.

In this letter, we integrate domain information into the original artificial bee colony algorithm to create a novel, neighbor-interactive bee colony algorithm. We use the Hamming distance measure to compute variable dependency between two binary variables and employ the Gini correlation coefficient to compute variable relation between integer variables. The proposed optimization method was evaluated by minimizing binary Ising models, integer Potts models, and trapped functions. Experimental results show that the proposed method outperformed the traditional artificial bee colony and other meta-heuristics in all the testing cases.

Asymmetric bilinear maps using Type-3 pairings are known to be advantageous in several points (e.g., the speed and the size of a group element) to symmetric bilinear maps using Type-1 pairings. Kremer and Mazaré introduce a symbolic model to analyze protocols based on bilinear maps, and show that the symbolic model is computationally sound. However, their model only covers symmetric bilinear maps. In this paper, we propose a new symbolic model to capture asymmetric bilinear maps. Our model allows us to analyze security of various protocols based on asymmetric bilinear maps (e.g., Joux's tripartite key exchange, and Scott's client-server ID-based key exchange). Also, we show computational soundness of our symbolic model under the decisional bilinear Diffie-Hellman assumption.

Scalar multiplication over higher degree rational point groups is often regarded as the bottleneck for faster pairing based cryptography. This paper has presented a skew Frobenius mapping technique in the sub-field isomorphic sextic twisted curve of Kachisa-Schaefer-Scott (KSS) pairing friendly curve of embedding degree 18 in the context of Ate based pairing. Utilizing the skew Frobenius map along with multi-scalar multiplication procedure, an efficient scalar multiplication method for KSS curve is proposed in the paper. In addition to the theoretic proposal, this paper has also presented a comparative simulation of the proposed approach with plain binary method, sliding window method and non-adjacent form (NAF) for scalar multiplication. The simulation shows that the proposed method is about 60 times faster than plain implementation of other compared methods.

In ProvSec 2014, Wang and Tanaka proposed a transformation which converts weakly existentially unforgeable (wEUF) signature schemes into strongly existentially unforgeable (sEUF) ones in the bounded leakage model. To obtain the construction, they combined leakage resilient (LR) chameleon hash functions with the Generalised Boneh-Shen-Waters (GBSW) transformation proposed by Steinfeld, Pieprzyk, and Wang. However, their transformation cannot be used in a more realistic model called continual leakage model since secret keys of LR chameleon hash functions cannot be updated. In this paper, we propose a transformation which can convert wEUF signature schemes into sEUF ones in the continual leakage model. To achieve our goal, we give a new definition of continuous leakage resilient (CLR) chameleon hash function and construct it based on the CLR signature scheme proposed by Malkin, Teranishi, Vahlis, and Yung. Although our CLR chameleon hash functions satisfy the property of strong collision-resistance, due to the existence of the updating algorithm, an adversary may find the kind of collisions such that messages are the same but randomizers are different. Hence, we cannot combine our chameleon hash functions with the GBSW transformation directly, or the sEUF security of the transformed signature schemes cannot be achieved. To solve this problem, we improve the original GBSW transformation by making use of the Groth-Sahai proof system and then combine it with CLR chameleon hash functions.

Identity-based encryption (IBE) is an advanced form of public key encryption and one of the most important cryptographic primitives. Of the many constructions of IBE schemes, the one proposed by Boneh and Boyen (in Eurocrypt 2004) is quite important from both practical and theoretical points of view. The scheme was standardized as IEEE P1363.3 and is the basis for many subsequent constructions. In this paper, we investigate its multi-challenge security, which means that an adversary is allowed to query challenge ciphertexts multiple times rather than only once. Since single-challenge security implies multi-challenge security, and since Boneh and Boyen provided a security proof for the scheme in the single-challenge setting, the scheme is also secure in the multi-challenge setting. However, this reduction results in a large security loss. Instead, we give tight security reduction for the scheme in the multi-challenge setting. Our reduction is tight even if the number of challenge queries is not fixed in advance (that is, the queries are unbounded). Unfortunately, we are only able to prove the security in a selective setting and rely on a non-standard parameterized assumption. Nevertheless, we believe that our new security proof is of interest and provides new insight into the security of the Boneh-Boyen IBE scheme.

We describe SyncSync, a time-of-arrival (ToA)-based localization method for smartphones. In general, ToA measurements show better precision than time-difference-of-arrival (TDoA) measurements, although ToA systems require a synchronization mechanism between anchor and mobile nodes. For this synchronization, we employ modulated LED light with an acoustic wave for time-of-flight distance measurements. These are detected by the smartphone's video camera and microphone. The time resolution in consumer video cameras is typically only a few tenths of a second, but by utilizing a CMOS image sensor's rolling shutter effect we obtain synchronization resolutions of a few microseconds, which is sufficient for precise acoustic ToA measurements. We conducted experiments to confirm operation of the system, and obtained ToA localization errors within 10mm. The characteristics of the error distributions for both TDoA and ToA measurements were explained by dilution of precision.