MathML is a standard markup language for describing math expressions. MathML consists of two sets of elements: Presentation Markup and Content Markup. The former is widely used to display math expressions in Web pages, while the latter is more suited to the calculation of math expressions. In this letter, we focus on the former and consider classifying Presentation MathML expressions. Identifying the classes of given Presentation MathML expressions is helpful for several applications, e.g., Presentation to Content MathML conversion, text-to-speech, and so on. We propose a method for classifying Presentation MathML expressions by using multilayer perceptron. Experimental results show that our method classifies MathML expressions with high accuracy.

The deep neural named entity recognition model automatically learns and extracts the features of entities and solves the problem of the traditional model relying heavily on complex feature engineering and obscure professional knowledge. This issue has become a hot topic in recent years. Existing deep neural models only involve simple character learning and extraction methods, which limit their capability. To further explore the performance of deep neural models, we propose two character feature learning models based on convolution neural network and long short-term memory network. These two models consider the local semantic and position features of word characters. Experiments conducted on the CoNLL-2003 dataset show that the proposed models outperform traditional ones and demonstrate excellent performance.

This paper proposes a semi-supervised source separation method for stereophonic music signals containing multiple recorded or processed signals, where synthesized music is focused on the stereophonic music. As the synthesized music signals are often generated as linear combinations of many individual source signals and their respective mixing gains, phase or phase difference information between inter-channel signals, which represent spatial characteristics of recording environments, cannot be utilized as acoustic clues for source separation. Non-negative Tensor Factorization (NTF) is an effective technique which can be used to resolve this problem by decomposing amplitude spectrograms of stereo channel music signals into basis vectors and activations of individual music source signals, along with their corresponding mixing gains. However, it is difficult to achieve sufficient separation performance using this method alone, as the acoustic clues available for separation are limited. To address this issue, this paper proposes a Cepstral Distance Regularization (CDR) method for NTF-based stereo channel separation, which involves making the cepstrum of the separated source signals follow Gaussian Mixture Models (GMMs) of the corresponding the music source signal. These GMMs are trained in advance using available samples. Experimental evaluations separating three and four sound sources are conducted to investigate the effectiveness of the proposed method in both supervised and semi-supervised separation frameworks, and performance is also compared with that of a conventional NTF method. Experimental results demonstrate that the proposed method yields significant improvements within both separation frameworks, and that cepstral distance regularization provides better separation parameters.

Code smells are indicators of design flaws or problems in the source code. Various tools and techniques have been proposed for detecting code smells. These tools generally detect a large number of code smells, so approaches have also been developed for prioritizing and filtering code smells. However, lack of empirical data detailing how developers filter and prioritize code smells hinders improvements to these approaches. In this study, we investigated ten professional developers to determine the factors they use for filtering and prioritizing code smells in an open source project under the condition that they complete a list of five tasks. In total, we obtained 69 responses for code smell filtration and 50 responses for code smell prioritization from the ten professional developers. We found that Task relevance and Smell severity were most commonly considered during code smell filtration, while Module importance and Task relevance were employed most often for code smell prioritization. These results may facilitate further research into code smell detection, prioritization, and filtration to better focus on the actual needs of developers.

In X-ray computed tomography, scattered X-rays are generally removed by using a post-patient collimator located in front of the detector. In this paper, we show that the scattered X-rays have the potential to improve the estimation accuracy of the attenuation coefficient in computed tomography. In order to clarify the problem, we simplified the geometry of the computed tomography into a thin cylinder composed of a homogeneous material so that only one attenuation coefficient needs to be estimated. We then conducted a Monte Carlo numerical experiment on improving the estimation accuracy of attenuation coefficient by measuring the scattered X-rays with several dedicated toroidal detectors around the cylinder in addition to the primary X-rays. We further present a theoretical analysis to explain the experimental results. We employed a model that uses a T-junction (i.e., T-junction model) to divide the photon transport into primary and scattered components. This division is processed with respect to the attenuation coefficient. Using several T-junction models connected in series, we modeled the case of several scatter detectors. The estimation accuracy was evaluated according to the variance of the efficient estimator, i.e., the Cramer-Rao lower bound. We confirmed that the variance decreases as the number of scatter detectors increases, which implies that using scattered X-rays can reduce the irradiation dose for patients.

The ultrasound image segmentation is a crucial task in many clinical applications. However, the ultrasound image is difficult to segment due to image inhomogeneity caused by the ultrasound imaging technique. In this paper, to deal with image inhomogeneity with considering ultrasound image properties the Local Rayleigh Distribution Fitting (LRDF) energy term is introduced into the traditional level set method newly. While the curve evolution equation is derived for energy minimization, and self-driven uterus contour is achieved on the ultrasound images. The experimental segmentation results on synthetic images and in-vivo ultrasound images present that the proposed approach is effective and accurate, with the Dice Score Coefficient (DSC) of 0.95 ± 0.02.

We present a modular way of implementing adaptive decisions in performing scientific simulations. The proposed method employs modern software engineering mechanisms to allow for better software management in scientific computing, where software adaptation has often been implemented manually by the programmer or by using in-house tools, which complicates software management over time. By applying the aspect-oriented programming (AOP) paradigm, we consider software adaptation as a separate concern and, using popular AOP constructs, implement adaptive decision separately from the original code base, thereby improving software management. We demonstrate the effectiveness of our approach with applications to stochastic simulation software.

Applying Software Defined Network (SDN) technology to wireless networks are attracting much attention. Our previous study proposed a channel utilization method based on SDN/OpenFlow technology to improve the channel utilization efficiency of the multi-channel wireless backhaul network (WBN). However, since control messages are inherently transmitted with data traffic on a same channel in WBN, it inevitably degrades the network capacity. Specifically, the amount of control messages for collecting statistical information of each flow (FlowStats) linearly increases with the number of ongoing flows, thereby being the dominant overhead for backhaul networks. In this paper, we propose a new method that prevents the increase of control traffic while retaining the network performance of the previous method. Our proposed method uses statistical information of each interface (PortStats) instead of per-flow information (FlowStats), and handles multiple flows on the interface together if possible. Otherwise, to handle individual flow, we propose a way to estimate per-flow information without introducing extra control messages. Finally, we show that the proposed method offers the same performance with the previous method, while greatly reducing the amount of control traffic.

Because mobile users demand a high quality and energy-friendly video delivery service that efficiently uses wireless resources, we introduce an energy-efficient video delivery system by applying moving route navigation and playout buffer control based on the mobile throughput history data. The proposed system first determines the optimal travel route to achieve high-speed and energy-efficient communications. Then when a user enters a high throughput area, our system temporarily extends the video playout buffer size, and the user aggressively downloads video segments via a high-speed and energy-efficient wireless connection until the extended buffer is filled. After leaving this area, the user consumes video segments from the extended buffer in order to keep smooth video playback without wireless communications. We carry out computer simulations, laboratory and field experiments and confirm that the proposed system can achieve energy-efficient mobile video delivery.

This paper discusses the collection of sensor data for power distribution systems. In current power distribution systems, this is usually performed solely by the Remote Terminal Unit (RTU) which is located at the root of a power distribution network. The recent rise of distributed power sources, such as photovoltaic generators, raises the demand to increase the frequency of data collection because the output of these distributed generators varies quickly depending on the weather. Increasing data collection frequency in turn requires shortening the time required for data collection. The paper proposes the use of aggregation points for this purpose. An aggregation point can collect sensor data concurrently with other aggregation points as well as with the RTU. The data collection time can be shortened by having the RTU receive data from aggregation points, instead of from all sensors. This approach then poses the problem of finding the optimal location of aggregation points. To solve this problem, the paper proposes a Mixed Integer Linear Problem (MILP) formulation of the problem. The MILP problem can then be solved with off-the-shelf mathematical optimization software. The results of experiments show that the proposed approach is applicable to rather large scale power distribution systems.

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.

Location information is meaningful information for future ITS (Intelligent Transport Systems) world. Especially, the accuracy of the information is required because the accuracy decides the quality of ITS services. For realization of high precision positioning, Kinematic positioning technique has been attracting attention. The Kinematic positioning requires the configuration of many positioning parameters. However, the configuration is difficult because optimal parameter differs according to user's environment. In this paper, we will propose an estimation method of optimal parameter according to the environment. Further, we will propose an elimination method of unreliable positioning results. Hereby, we can acquire extensively only the reliable positioning results. By using the actual vehicle traveling data, the ability and the applicable range of the proposed method will be shown. The result will show that our proposed method improves the acquision rate of reliable positioning results and mitigates the acquision rate of the unreliable positioning results.

Modeling user activities on the Web is a key problem for various Web services, such as news article recommendation and ad click prediction. In our work-in-progress paper[1], we introduced an approach that summarizes each sequence of user Web page visits using Paragraph Vector[3], considering users and URLs as paragraphs and words, respectively. The learned user representations are used among the user-related prediction tasks in common. In this paper, on the basis of analysis of our Web page visit data, we propose Backward PV-DM, which is a modified version of Paragraph Vector. We show experimental results on two ad-related data sets based on logs from Web services of Yahoo! JAPAN. Our proposed method achieved better results than those of existing vector models.

In order to verify the correctness of functional requirements, we have been developing a verification method of the correctness of functional requirements specification using the Requirements Frame model. In this paper, we propose a verification method of non-functional requirements specification in terms of time-response requirements written with a natural language. We established a verification method by extending the Requirements Frame model. We have also developed a prototype system based on the method using Java. The extended Requirements Frame model and the verification method will be illustrated with examples.

Bug report summarization has been explored in past research to help developers comprehend important information for bug resolution process. As text mining technology advances, many summarization approaches have been proposed to provide substantial summaries on bug reports. In this paper, we propose an enhanced summarization approach called TSM by first extending a semantic model used in AUSUM with the anthropogenic and procedural information in bug reports and then integrating the extended semantic model with the shallow textual information used in BRC. We have conducted experiments with a dataset of realistic software projects. Compared with the baseline approaches BRC and AUSUM, TSM demonstrates the enhanced performance in achieving relative improvements of 34.3% and 7.4% in the F1 measure, respectively. The experimental results show that TSM can effectively improve the performance.

This paper proposes MRO-PUF, a new architecture for ring-oscillator-based physically unclonable functions (PUFs) with enhanced resistance against machine learning attacks. In the proposed PUF, an instantaneous output value of a ring oscillator is used as a response, whereas the most existing PUFs directly use propagation delays to determine the response. Since the response of the MRO-PUF is non-linear and discontinuous as the delay of the ring oscillator increases, the prediction of the response by machine learning attacks is difficult. Through the performance evaluation of the MRO-PUF with simulations, it achieves 15 times stronger resistance against machine learning attacks using a support vector machine compared to the existing ones such as an arbiter PUF and a bistable ring PUF. The MRO-PUF also achieves a sufficient level of the basic performance of PUFs in terms of uniqueness and robustness.

A penta-band antenna based on the mu-negative transmission line is presented for radio frequency (RF) energy harvesting application. The antenna utilizes five radiation modes; two quarter wavelength resonances, three quarter wavelength resonance, zeroth order resonance, and first order resonance. The parasitic radiating strip antenna generates quarter wavelength resonance radiation. The dual band antenna based on two unit cell mu-negative (MNG) transmission line gives birth to the zeroth order resonance (ZOR) mode and the first order resonance (FOR) mode. The parasitic radiating strip and dual band antenna based on two unit mu-negative (MNG) transmission line are magnetically coupled by a feed monopole with gap. This feed monopole, simultaneously, radiates at quarter and three quarter wavelength resonance frequency to cover the other bands. The multi-mode coupling mechanism of this penta-band antenna is well modeled by our derived equivalent circuit. The measured radiation efficiencies are more than 87% over the entire penta-band.

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.

For the reconstruction of the feedback polynomial of a synchronous scrambler placed after a convolutional encoder, the existing algorithms require the prior knowledge of a dual word of the convolutional code. To address the case of a dual word being unknown, a new algorithm for the reconstruction of the feedback polynomial based on triple correlation characteristic of an m-sequence is proposed. First, the scrambled convolutional code sequence is divided into bit blocks; the product of the scrambled bit blocks with a dual word is proven to be an m-sequence with the same period as the synchronous scrambler. Second, based on the triple correlation characteristic of the generated m-sequence, a dual word is estimated; the generator polynomial of the generated m-sequence is computed by two locations of the triple correlation peaks. Finally, the feedback polynomial is reconstructed using the generator polynomial of the generated m-sequence. As the received sequence may contain bit errors, a method for detecting triple correlation peaks based on the constant false-alarm criterion is elaborated. Experimental results show that the proposed algorithm is effective. Ulike the existing algorithms available, there is no need to know a dual word a priori and the reconstruction result is more accurate. Moreover, the proposed algorithm is robust to bit errors.

A problem of global stabilization of a class of approximately feedback linearized systems is considered. A new system structural feature is the presence of non-trivial diagonal terms along with nonlinearity, which has not been addressed by the previous control results. The stability analysis reveals a new relationship between the time-varying rates of system parameters and system nonlinearity along with our controller. Two examples are given for illustration.