Fast computation of singular value decomposition (SVD) is of great interest in various machine learning tasks. Recently, SVD methods based on randomized linear algebra have shown significant speedup in this regime. For processing large-scale data, computing systems with accelerators like GPUs have become the mainstream approach. In those systems, access to the input data dominates the overall process time; therefore, it is needed to design an out-of-core algorithm to dispatch the computation into accelerators. This paper proposes an accurate two-pass randomized SVD, named block randomized SVD (BRSVD), designed for matrices with a slow-decay singular spectrum that is often observed in image data. BRSVD fully utilizes the power of modern computing system architectures and efficiently processes large-scale data in a parallel and out-of-core fashion. Our experiments show that BRSVD effectively moves the performance bottleneck from data transfer to computation, so that outperforms existing randomized SVD methods in terms of speed with retaining similar accuracy.

A limited number of types of sound event occur in an acoustic scene and some sound events tend to co-occur in the scene; for example, the sound events “dishes” and “glass jingling” are likely to co-occur in the acoustic scene “cooking.” In this paper, we propose a method of sound event detection using graph Laplacian regularization with sound event co-occurrence taken into account. In the proposed method, the occurrences of sound events are expressed as a graph whose nodes indicate the frequencies of event occurrence and whose edges indicate the sound event co-occurrences. This graph representation is then utilized for the model training of sound event detection, which is optimized under an objective function with a regularization term considering the graph structure of sound event occurrence and co-occurrence. Evaluation experiments using the TUT Sound Events 2016 and 2017 detasets, and the TUT Acoustic Scenes 2016 dataset show that the proposed method improves the performance of sound event detection by 7.9 percentage points compared with the conventional CNN-BiGRU-based detection method in terms of the segment-based F1 score. In particular, the experimental results indicate that the proposed method enables the detection of co-occurring sound events more accurately than the conventional method.

Some non-acoustic modalities have the ability to reveal certain speech attributes that can be used for synthesizing speech signals without acoustic signals. This study validated the use of ultrasonic Doppler frequency shifts caused by facial movements to implement a silent speech interface system. A 40kHz ultrasonic beam is incident to a speaker's mouth region. The features derived from the demodulated received signals were used to estimate the speech parameters. A nonlinear regression approach was employed in this estimation where the relationship between ultrasonic features and corresponding speech is represented by deep neural networks (DNN). In this study, we investigated the discrepancies between the ultrasonic signals of audible and silent speech to validate the possibility for totally silent communication. Since reference speech signals are not available in silently mouthed ultrasonic signals, a nearest-neighbor search and alignment method was proposed, wherein alignment was achieved by determining the optimal pair of ultrasonic and audible features in the sense of a minimum mean square error criterion. The experimental results showed that the performance of the ultrasonic Doppler-based method was superior to that of EMG-based speech estimation, and was comparable to an image-based method.

The choreography realization problem is a design challenge for systems based on service-oriented architecture. In our previous studies, we studied the problem on a case where choreography was given by one or two scenarios and was expressed by an acyclic relation of events; we introduced the notion of re-constructibility as a property of acyclic relations to be satisfied. However, when choreography is defined by multiple scenarios, the resulting behavior cannot be expressed by an acyclic relation. An event structure is composed of an acyclic relation and a conflict relation. Because event structures are a generalization of acyclic relations, a wider class of systems can be expressed by event structures. In this paper, we propose the use of event structures to express choreography, introduce the re-constructibility of event structures, and show a necessary condition for an event structure to be re-constructible.

Boolean networks (BNs) are considered as popular formal models for the dynamics of gene regulatory networks. There are many different types of BNs, depending on their updating scheme (synchronous, asynchronous, deterministic, or non-deterministic), such as Classical Random Boolean Networks (CRBNs), Asynchronous Random Boolean Networks (ARBNs), Generalized Asynchronous Random Boolean Networks (GARBNs), Deterministic Asynchronous Random Boolean Networks (DARBNs), and Deterministic Generalized Asynchronous Random Boolean Networks (DGARBNs). An important long-term behavior of BNs, so-called attractor, can provide valuable insights into systems biology (e.g., the origins of cancer). In the previous paper [1], we have studied properties of attractors of GARBNs, their relations with attractors of CRBNs, also proposed different algorithms for attractor detection. In this paper, we propose a new algorithm based on SAT-based bounded model checking to overcome inherent problems in these algorithms. Experimental results prove the effectiveness of the new algorithm. We also show that studying attractors of GARBNs can pave potential ways to study attractors of ARBNs.

The accurate calculation of the inductance is the most basic problem of the inductor design. In this paper, the core flux density distribution and leakage flux in core window and winding of core-type inductor are analyzed by finite element analysis (FEA) firstly. Based on it, an improved magnetic equivalent circuit with high accuracy flux density distribution (iMEC) is proposed for a single-phase core-type inductor. Depend on the geometric structure, two leakage paths of the core window are modeled. Furthermore, the iMEC divides the magnetomotive force of the winding into the corresponding core branch. It makes the core flux density distribution consistent with the FEA distribution to improve the accuracy of the inductance. In the iMEC, flux density of the core leg has an error less than 5.6% compared to FEA simulation at 150A. The maximum relative error of the inductance is less than 8.5% and the average relative error is less than 6% compared to the physical prototype test data. At the same time, due to the high computational efficiency of iMEC, it is very suitable for the population-based optimization design.

This study focuses on computer based foreign language vocabulary learning systems. Our objective is to automatically build vocabulary decks with desired levels of relative difficulty relations. To realize this goal, we exploit the fact that word frequency is a good indicator of vocabulary difficulty. Subsequently, for composing the decks, we pose two requirements as uniformity and diversity. Namely, the difficulty level of the cards in the same deck needs to be uniform enough so that they can be grouped together and difficulty levels of the cards in different decks need to be diverse enough so that they can be grouped in different decks. To assess uniformity and diversity, we use rank-biserial correlation and propose an iterative algorithm, which helps in attaining desired levels of uniformity and diversity based on word frequency in daily use of language. In experiments, we employed a spaced repetition flashcard software and presented users various decks built with the proposed algorithm, which contain cards from different content types. From users' activity logs, we derived several behavioral variables and examined the polyserial correlation between these variables and difficulty levels across different word classes. This analysis confirmed that the decks compiled with the proposed algorithm induce an effect on behavioral variables in line with the expectations. In addition, a series of experiments with decks involving varying content types confirmed that this relation is independent of word class.

This paper describes graph-based Wave Function Collapse algorithm for procedural content generation. The goal of this system is to enable a game designer to procedurally create key content elements in the game level through simple association rule input. To do this, we propose a graph-based data structure that can be easily integrated with a navigation mesh data structure in a three-dimensional world. With our system, if the user inputs the minimum association rule, it is possible to effectively perform procedural content generation in the three-dimensional world. The experimental results show that the Wave Function Collapse algorithm, which is a texture synthesis algorithm, can be extended to non-grid shape content with high controllability and scalability.

We propose a deep learning-based model for classifying pathological voices using a convolutional neural network and a feedforward neural network. The model uses combinations of heterogeneous parameters, including mel-frequency cepstral coefficients, linear predictive cepstral coefficients and higher-order statistics. We validate the accuracy of this model using the Massachusetts Eye and Ear Infirmary (MEEI) voice disorder database and the Saarbruecken Voice Database (SVD). Our model achieved an accuracy of 99.3% for MEEI and 75.18% for SVD. This model achieved an accuracy that is 7.18% higher than that of competitive models in previous studies.

A multi-band wireless local area network (WLAN) enables flexible use of multiple frequency bands. To efficiently monitor radio resources in multi-band WLANs, a distributed-sensing system that employs a number of stations (STAs) is considered to alleviate sensing constraints at access points (APs). This paper examines the distributed sensing that expands the sensing coverage area and monitors multiple object channels by employing STA-based sensing. To avoid issuing unnecessary reports, each STA autonomously judges whether it should make a report by comparing the importance of its own sensing result and that of the overheard report. We address how to efficiently collect the necessary sensing information from a large number of STAs. We propose a reactive reporting scheme that is highly scalable by the number of STAs to collect such sensing results as the channel occupancy ratio. Evaluation results show that the proposed scheme keeps the number of reports low even if the number of STAs increases. Our proposed sensing scheme provides large sensing coverage.

This paper proposes a dual-polarized metasurface that utilizes multi-layer ceramic capacitors (MLCCs) for radar cross-section (RCS) reduction in the 28GHz band of the quasi-millimeter band. MLCCs are very small in size; therefore, miniaturization of the unit cell structure of the metamaterial can be expected, and the MLCCs can be periodically loaded onto a narrow object. First, the MLCC structure was modeled as a basic structure, and the effective permeability of the MLCC was determined to investigate the influence of the arrangement direction on MLCC interaction. Next, the unit cell structure of the dual-polarized metasurface was designed for an MLCC set on a dielectric substrate. By analyzing the infinite periodic structure and finite structure, the monostatic reduction characteristics, oblique incidence characteristics, and dual-polarization characteristics of the proposed metasurface were evaluated. In the case of the MLCCs arranged in the same direction, the monostatic RCS reduction was approximately 30dB at 29.8GHz, and decreased when the MLCCs were arranged in a checkerboard pattern. The monostatic RCS reductions for the 5 × 5, 10 × 10, and 20 × 20 divisions were roughly the same, i.e., 10.8, 9.9, and 10.3dB, respectively. Additionally, to validate the simulated results, the proposed dual-polarized metasurface was fabricated and measured. The measured results were found to approximately agree with the simulated results, confirming that the RCS can be reduced for dual-polarization operation.

This paper presents an analysis of the relationship between noise and bandwidth in visible light communication (VLC) systems. In the past few years, pre-emphasis and post-equalization techniques were proposed to extend the bandwidth of VLC systems. However, these bandwidth extension techniques also influence noise and sensitivity of the VLC systems. In this paper, first, we build a system model of VLC transceivers and circuit models of pre-emphasis and post-equalization. Next, we theoretically compare the bandwidth and noise of three different transceiver structures comprising a single pre-emphasis circuit, a single post-equalization circuit and a combination of pre-emphasis and post-equalization circuits. Finally, we validate the presented theoretical analysis using experimental results. The result shows that for the same resonant frequency, and for high signal-to-noise ratio (S/N), VLC systems employing post-equalization or pre-emphasis have the same bandwidth extension ability. Therefore, a transceiver employing both the pre-emphasis and post-equalization techniques has a bandwidth √2 times the bandwidth of the systems employing only the pre-emphasis or post-equalization. Based on the theoretical analysis of noise, the VLC system with only active pre-emphasis shows the lowest noise, which is a good choice for low-noise systems. The result of this paper may provide a new perspective of noise and sensitivity of the bandwidth extension techniques in VLC systems.

Femtocell structures can offer better voice and data exchange in cellular networks. However, interference in such networks poses a major challenge in the practical development of cellular communication. To tackle this issue, an advanced interference mitigation scheme for Line-Of-Sight (LOS) femtocell networks in indoor environments is proposed in this paper. Using a femtocell management system (FMS) that controls all femtocells in a service area, the aggressor femtocells are identified and then the transmitted beam patterns are adjusted using the linear array antenna equipped in each femtocell to mitigate the interference contribution to the neighbouring femtocells. Prior to that, the affected users are switched to the femtocells that provide better throughput levels to avoid increasing the outage probability. This paper considers different femtocell deployment indexes to verify and justifies the feasibility of the findings in different density areas. Relative to fixed and adaptive power control schemes, the proposed scheme achieves approximately 5% spectral efficiency (SE) improvement, about 10% outage probability reduction, and about 7% Mbps average user throughput improvement.

We address the security issue of RSA with implicitly related keys in this paper. Informally, we investigate under what condition is it possible to efficiently factorize RSA moduli in polynomial time given implicit relation of the related private keys that certain portions of bit pattern are the same. We formulate concrete attack scenarios and propose lattice-based cryptanalysis by using lattice reduction algorithms. A subtle lattice technique is adapted to represent an unknown private key with the help of known implicit relation. We analyze a simple case when given two RSA instances with the known amount of shared most significant bits (MSBs) and least significant bits (LSBs) of the private keys. We further extend to a generic lattice-based attack for given more RSA instances with implicitly related keys. Our theoretical results indicate that RSA with implicitly related keys is more insecure and better asymptotic results can be achieved as the number of RSA instances increases. Furthermore, we conduct numerical experiments to verify the validity of the proposed attacks.

5G network will serve billions of people worldwide in the near future and protecting human privacy from being violated is one of its most important goals. In this paper, we carefully studied the 5G authentication protocols (namely 5G AKA and EAP-AKA') and a location sniffing attack exploiting 5G authentication protocols vulnerability is found. The attack can be implemented by an attacker through inexpensive devices. To cover this vulnerability, a fix scheme based on the existing PKI mechanism of 5G is proposed to enhance the authentication protocols. The proposed scheme is successfully verified with formal methods and automatic verification tool TAMARIN. Finally, the communication overhead, computational cost and storage overhead of the scheme are analyzed. The results show that the security of the fixed authentication protocol is greatly improved by just adding a little calculation and communication overhead.

This paper introduces a new multi-satellite multi-beam system with single frequency reuse; it uses the MIMO (Multi Input Multi Output) technique to improve the frequency efficiency as the satellite communication band is limited. MIMO is the one of the most important approaches to improve the spectral efficiency in support of broadband communications. Since it is difficult to achieve high spectral efficiency by simply combining conventional MIMO satellite techniques, i.e. combining a multi-beam system with single frequency reuse with a multiple satellite system, this paper proposes transmitter pre-coding and receiver equalization techniques to enhance the channel capacity even under time/frequency asynchronous conditions. A channel capacity comparison shows that the proposed system is superior to conventional alternatives.

Blockchain is a distributed ledger system composed of a P2P network and is used for a wide range of applications, such as international remittance, inter-individual transactions, and asset conservation. In Blockchain systems, tamper resistance is enhanced by the property of transaction that cannot be changed or deleted by everyone including the creator of the transaction. However, this property also becomes a problem that unintended transaction created by miss operation or secret key theft cannot be corrected later. Due to this problem, once an illegal transaction such as theft occurs, the damage will expand. To suppress the damage, we need countermeasures, such as detecting illegal transaction at high speed and correcting the transaction before approval. However, anomaly detection in the Blockchain at high speed is computationally heavy, because we need to repeat the detection process using various feature quantities and the feature extractions become overhead. In this paper, to accelerate anomaly detection, we propose to cache transaction information necessary for extracting feature in GPU device memory and perform both feature extraction and anomaly detection in the GPU. We also propose a conditional feature extraction method to reduce computation cost of anomaly detection. We employ anomaly detection using K-means algorithm based on the conditional features. When the number of users is one million and the number of transactions is 100 millions, our proposed method achieves 8.6 times faster than CPU processing method and 2.6 times faster than GPU processing method that does not perform feature extraction on the GPU. In addition, the conditional feature extraction method achieves 1.7 times faster than the unconditional method when the number of users satisfying a given condition is 200 thousands out of one million.

While existing inference engines solved real world problems using probabilistic knowledge representation, one challenging task is to efficiently utilize the representation under a situation of uncertainty during conflict resolution. This paper presents a new approach to straightforwardly combine a rule-based system (RB) with a probabilistic graphical inference framework, i.e., naïve Bayesian network (BN), towards probabilistic argumentation via a so-called probabilistic assumption-based argumentation (PABA) framework. A rule-based system (RB) formalizes its rules into defeasible logic under the assumption-based argumentation (ABA) framework while the Bayesian network (BN) provides probabilistic reasoning. By knowledge integration, while the former provides a solid testbed for inference, the latter helps the former to solve persistent conflicts by setting an acceptance threshold. By experiments, effectiveness of this approach on conflict resolution is shown via an example of liver disorder diagnosis.

Social media channels, such as Facebook, Twitter, and Instagram, have altered our world forever. People are now increasingly connected than ever and reveal a sort of digital persona. Although social media certainly has several remarkable features, the demerits are undeniable as well. Recent studies have indicated a correlation between high usage of social media sites and increased depression. The present study aims to exploit machine learning techniques for detecting a probable depressed Twitter user based on both, his/her network behavior and tweets. For this purpose, we trained and tested classifiers to distinguish whether a user is depressed or not using features extracted from his/her activities in the network and tweets. The results showed that the more features are used, the higher are the accuracy and F-measure scores in detecting depressed users. This method is a data-driven, predictive approach for early detection of depression or other mental illnesses. This study's main contribution is the exploration part of the features and its impact on detecting the depression level.

System logs record system states and significant events at various critical points to help debug performance issues and failures. Therefore, the rapid and accurate detection of the system log is crucial to the security and stability of the system. In this paper, proposed is a novel attention-based neural network model, which would learn log patterns from normal execution. Concretely, our model adopts a GRU module with attention mechanism to extract the comprehensive and intricate correlations and patterns embedded in a sequence of log entries. Experimental results demonstrate that our proposed approach is effective and achieve better performance than conventional methods.