Topic features are useful in improving text summarization. However, independency among topics is a strong restriction on most topic models, and alleviating this restriction can deeply capture text structure. This paper proposes a hybrid topic model to generate multi-document summaries using a combination of the Hidden Topic Markov Model (HTMM), the surface texture model and the topic transition model. Based on the topic transition model, regular topic transition probability is used during generating summary. This approach eliminates the topic independence assumption in the Latent Dirichlet Allocation (LDA) model. Meanwhile, the results of experiments show the advantage of the combination of the three kinds of models. This paper includes alleviating topic independency, and integrating surface texture and shallow semantic in documents to improve summarization. In short, this paper attempts to realize an advanced summarization system.

In this paper, we report our analysis of path loss variation characteristics in the low millimeter wave band based on measurement results obtained by using 26.365GHz. We analyze the fading scale of the measurement results by applying a wavelet analysis method. It is clarified that a fading of unique scale between the scale of fast fading and shadowing can be seen in the low millimeter band. Finally, it is shown that the variation characteristics can be evaluated by taking reflection paths at building walls into account, in addition to the ground waves and LOS paths.

Accuracy of a method for analyzing the interface defect properties; time-domain charge pumping method, is evaluated. The method monitors the charge pumping (CP) current in time domain, and thus we expect that it gives us a noble way to investigate the interface state properties. In this study, for the purpose of evaluating the accuracy of the method, the interface state density extracted from the time-domain data is compared with that measured using the conventional CP method. The results show that they are equal to each other for all measured devices with various defect densities, demonstrating that the time-domain CP method is sufficiently accurate for the defect density evaluation.

This letter presents a method to adaptively counter false data injection attacks (FDIAs) in wireless sensor networks, in which a fuzzy rule-based system detects FDIAs and chooses the most appropriate countermeasures. The method does not require en-route verification processes and manual parameter settings. The effectiveness of the method is shown with simulation results.

This paper presents a new methodology for realizing a Rice channel in BAN Over-The-Air (OTA) testing using a fading emulator with a dynamic phantom. For the proposed apparatus to be effective, the fading emulator must be provided with an appropriate K-factor that represents the actual propagation environment indoors. Further, an implementation of the Rice channel to the proposed fading emulator in a BAN situation is presented. Thereafter, a calibration method for the fading emulator to adjust the actual K-factor of the on-body Rice channel is advanced. This calibration method is validated by analyzing the variations in the instantaneous K-factor attributed to the arm-swinging motion. Finally, an experiment is conducted for a continuous human walking motion with the fading emulator using an arm-swinging dynamic phantom. The results show that the developed fading emulator allows BAN-OTA testing to replicate the actual Rice channel propagation environment with the consideration of the dynamic characteristics of human walking motion.

This paper presents a novel architecture for a fault-tolerant and dual modular redundancy (DMR) system using a checkpoint recovery approach. The architecture features exploitation of SRAM with simultaneous copy and instantaneous compare function. It can perform low-latency data copying between dual cores. Therefore, it can carry out fast backup and rollback. Furthermore, it can reduce the power consumption during data comparison process compared to the cyclic redundancy check (CRC). Evaluation results show that, compared with the conventional checkpoint/restart DMR, the proposed architecture reduces the cycle overhead by 97.8% and achieves a 3.28% low-latency execution cycle even if a one-time fault occurs when executing the task. The proposed architecture provides high reliability for systems with a real-time requirement.

A survey was conducted on the use of ICT by visually impaired people. Among 304 respondents, 81 used smartphones and 44, tablets. Blind people used feature phones at a higher rate and smartphones and tablets at lower rates than people with low vision. The most popular smartphone model was iPhone and the most popular tablet model was iPad. While almost all blind users used the speech output accessibility feature and only a few of them used visual features, low vision users used both visual features such as Zoom, Large text, and Invert colors and speech output at high rates both on smartphones and tablets. The most popular text entry methods were different between smartphones and tablets. For smartphones flick and numeric keypad input were popular among low vision users while voice input was the most popular among blind users. For tablets a software QWERTY keyboard was the most popular among both blind and low vision users. The advantages of smartphones were access to geographical information, quick Web browsing, voice input, and extensibility for both blind and low vision users, object recognition for blind users, and readability for low vision users. Tablets also work as a vision aid for people with low vision. The drawbacks of smartphones and tablets were text entry and touch operation difficulties and inaccessible apps for both blind and low vision users, problems in speech output for blind users, and problems in readability for low vision users. Researchers and makers of operating systems (OS) and apps should assume responsibility for solving these problems.

Fault localization is essential for conducting effective program repair. However, preliminary studies have shown that existing fault localization approaches do not take the requirements of automatic repair into account, and therefore restrict the repair performance. To address this issue, this paper presents the first study on designing fault localization approaches for automatic program repair, that is, we propose a fault localization approach using failure-related contexts in order to improve automatic program repair. The proposed approach first utilizes program slicing technique to construct a failure-related context, then evaluates the suspiciousness of each element in this context, and finally transfers the result of evaluation to automatic program repair techniques for performing repair on faulty programs. The experimental results demonstrate that the proposed approach is effective to improve automatic repair performance.

Recently, Hadoop has attracted much attention from engineers and researchers as an emerging and effective framework for Big Data. HDFS (Hadoop Distributed File System) can manage a huge amount of data with high performance and reliability using only commodity hardware. However, HDFS requires a single master node, called a NameNode, to manage the entire namespace (or all the i-nodes) of a file system. This causes the SPOF (Single Point Of Failure) problem because the file system becomes inaccessible when the NameNode fails. This also causes a bottleneck of efficiency since all the access requests to the file system have to contact the NameNode. Hadoop 2.0 resolves the SPOF problem by introducing manual failover based on two NameNodes, Active and Standby. However, it still has the efficiency bottleneck problem since all the access requests have to contact the Active in ordinary executions. It may also lose the advantage of using commodity hardware since the two NameNodes have to share a highly reliable sophisticated storage. In this paper, we propose a new HDFS architecture to resolve all the problems mentioned above.

We report the energy-efficient optical input interface using NbN superconducting nanowire-based optical-to-electrical (SN-OE) converters for a single-flux-quantum (SFQ) data processing system. The SN-OE converters with small active areas ranging from 1$, imes,$1 to 10$, imes,$10,$mu$m$^2$ were fabricated to improve the recovery time by reducing the kinetic inductance of the nanowire. The SN-OE with the smallest area of 1$, imes,$1 $mu$m$^2$ showed the recovery time of around 0.3 ns, while its detection efficiency for a single photon was reduced below 0.1% due to insufficient coupling efficiency with a single-mode optical fiber. However, the optical power dependence of the error rate of this device showed that the required optical power to achieve the error rate below $10^{-12}$ at 10 GHz operation is as large as 70 $mu$W, which is still one order of magnitude lower than semiconductor photo diodes. We also demonstrated the operation of the SN-OE converters combined with the SFQ readout circuit and confirmed the operating speed up to 77~MHz.

A compact composite right/left-handed transmission-line (CRLH TL) stub resonator is presented. The bandpass frequency of the resonator and the adjacent transmission-zeros are determined by the negative order resonance modes of the stub line. We demonstrate that these resonance frequencies can be arbitrarily controlled by using non-identical, unbalanced unit cells, leading to enhanced loaded-Q as well as unloaded-Q. We show that despite the presence of lumped element loss the unloaded-Q is enhanced by a factor of 2 compared to that of microstrip line as a result of nearly-zero group velocity. As a consequence, the loaded-Q can be increased without incurring significant insertion loss as in the case of conventional stub resonators on the same substrate. The physical mechanisms of the distinct features are discussed based on an equivalent dispersion diagram, a concept introduced to model general one-port CRLH TL used as a stub line.

Based on the work by Helleseth [1], for an odd prime p and an even integer n=2m, the cross-correlation values between two decimated m-sequences by the decimation factors 2 and 4pn/2-2 are derived. Their cross-correlation function is at most 4-valued, that is, $igg {rac{-1 pm p^{n/2}}{2}, rac{-1 + 3p^{n/2}}{2}, rac{-1 + 5p^{n/2}}{2} igg }$. From this result, for pm ≠ 2 mod 3, a new sequence family with family size 4N and the maximum correlation magnitude upper bounded by $rac{-1 + 5p^{n/2}}{2} simeq rac{5}{sqrt{2}}sqrt{N}$ is constructed, where $N = rac{p^n-1}{2}$ is the period of sequences in the family.

This paper presents a filtering method for efficient face image retrieval over large volume of face databases. The proposed method employs a new face image descriptor, called a cell-orientation vector (COV). It has a simple form: a 72-dimensional vector of integers from 0 to 8. Despite of its simplicity, it achieves high accuracy and efficiency. Our experimental results show that the proposed method based on COVs provides better performance than a recent approach based on identity-based quantization in terms of both accuracy and efficiency.

Displacement mapping has been widely used for adding geometric surface details to 3D mesh models. However, it requires sufficient tessellation of the mesh if fine details are to be represented. In this paper, we propose a method for applying the displacement mapping even on coarse models by using an augmented patch mesh. The patch mesh is a regularly tessellated flat square mesh, which is mapped onto the target area. Our method applies displacement mapping to the patch mesh for fitting it to the original mesh as well as for adding surface details. We generate a patch map, which stores three-dimensional displacements from the patch mesh to the original mesh. A displacement map is also provided for defining the new surface feature. The target area in the original mesh is then replaced with the patch mesh, and the patch mesh reconstructs the original shape using the patch map and the new surface detail is added using the displacement map. Our results show that our method conveniently adds surface features to various models. The proposed method is particularly useful if the surface features change dynamically since the original mesh is preserved and the separate patch mesh overwrites the target area at runtime.

In order to verify the channel sum-rate improvement by multi-user multiple-input multiple-output (MU-MIMO) transmission in distributed antenna systems (DASs), we investigate and compare the characteristics of channel sum-rates in both centralized antenna systems (CASs) and DASs under the effects of path loss, spatially correlated shadowing, correlated multi-path fading, and inter-cell interference. In this paper, we introduce two different types of functions to model the shadowing, auto-correlation and cross-correlation, and a typical exponential decay function to model the multi-path fading correlation. Thus, we obtain the distribution of the channel sum-rate and investigate its characteristics. Computer simulation results indicate that DAS can improve the performance of the channel sum-rate compared to CAS, even in the case under consideration. However, this improvement decreases as interference power increases. Moreover, the decrease in the channel sum-rate due to the increase in the interference power becomes slow under the effect of shadowing correlation. In addition, some other analyses on the shadowing correlation that occurs on both the transmit and receiver sides are provided. These analysis results show that the average channel sum-rate in a DAS without inter-cell interference considerably decreases because of the shadowing correlation. In contrast, there appears to be no change in the CAS. Furthermore, there are two different types of sum-rate changes in a DAS because of the difference in shadowing auto-correlation and cross-correlation.

Face recognition under variable illumination conditions is a challenging task. Numbers of approaches have been developed for solving the illumination problem. In this paper, we summarize and analyze some noteworthy issues in illumination processing for face recognition by reviewing various representative approaches. These issues include a principle that associates various approaches with a commonly used reflectance model and the shared considerations like contribution of basic processing methods, processing domain, feature scale, and a common problem. We also address a more essential question-what to actually normalize. Through the discussion on these issues, we also provide suggestions on potential directions for future research. In addition, we conduct evaluation experiments on 1) contribution of fundamental illumination correction to illumination insensitive face recognition and 2) comparative performance of various approaches. Experimental results show that the approaches with fundamental illumination correction methods are more insensitive to extreme illumination than without them. Tan and Triggs' method (TT) using L1 norm achieves the best results among nine tested approaches.

A workflow net (WF-net for short) is a Petri net which represents a workflow. There are two important subclasses of WF-nets: extended free choice (EFC for short) and well-structured (WS for short). It is known that most actual workflows can be modeled as EFC WF-nets; and acyclic WS is a subclass of acyclic EFC but has more analysis methods. A sound acyclic EFC WF-net may be transformed to an acyclic WS WF-net without changing the observable behavior of the net. Such a transformation is called refactoring. In this paper, we tackled a problem, named acyclic EFC WF-net refactorizability problem, that decides whether a given sound acyclic EFC WF-net is refactorable to an acyclic WS WF-net. We gave two sufficient conditions on the problem, and constructed refactoring procedures based on the conditions. Furthermore, we applied the procedures to a sample workflow, and confirmed usefulness of the procedures for the enhancement of the readability and the analysis power of acyclic EFC WF-nets.

The high efficiency video coding (HEVC) standard has significantly improved compression performance for many applications, including remote desktop and desktop sharing. Screen content video coding is widely used in applications with a high demand for real-time performance. HEVC usually introduces great computational complexity, which makes fast algorithms necessary to offset the limited computing power of HEVC encoders. In this study, a statistical analysis of several screen content sequences is first performed to better account for the completely different statistics of natural images and videos. Second, a fast coding unit (CU) splitting method is proposed, which aims to reduce HEVC intra coding computational complexity, especially in screen content coding. In the proposed scheme, CU size decision is made by checking the smoothness of the luminance values in every coding tree unit. Experiments demonstrate that in HEVC range extension standard, the proposed scheme can save an average of 29% computational complexity with 0.9% Bjøntegaard Delta rate (BD-rate) increase compared with HM13.0+RExt6.0 anchor for screen content sequences. For default HEVC, the proposed scheme can reduce encoding time by an average of 38% with negligible loss of coding efficiency.

Social recommendation systems that make use of the user's social information have recently attracted considerable attention. These recommendation approaches partly solve cold-start and data sparsity problems and significantly improve the performance of recommendation systems. The essence of social recommendation methods is to utilize the user's explicit social connections to improve recommendation results. However, this information is not always available in real-world recommender systems. In this paper, a solution to this problem of explicit social information unavailability is proposed. The existing user-item rating matrix is used to compute implicit social information, and then an ISRec (implicit social recommendation algorithm) which integrates this implicit social information and the user-item rating matrix for social recommendation is introduced. Experimental results show that our method performs much better than state-of-the-art approaches; moreover, complexity analysis indicates that our approach can be applied to very large datasets because it scales linearly with respect to the number of observations in the matrices.

In this paper, we propose a face representation approach using multi-orientation Log-Gabor local binary pattern (MOLGLBP) for realizing face recognition under facial expressions, illuminations and partial occlusions. Log-Gabor filters with different scales (frequencies) and orientations are applied on Y, I, and Q channel image in the YIQ color space respectively. Then Log-Gabor images of different orientations at the same scale are combined to form a multi-orientation Log-Gabor image (MOLGI) and two LBP operators are applied to it. For face recognition, histogram intersection metric is utilized to measure the similarity of faces. The proposed approach is evaluated on the CurtinFaces database and experiments demonstrate that the proposed approach is effectiveness against two simultaneous variations: expression & illumination, and illumination & occlusion.