The growing trends in Internet usage for data and knowledge sharing calls for dynamic classification of web contents, particularly at the edges of the Internet. Rather than considering Linked Data as an integral part of Big Data, we propose Autonomous Decentralized Semantic-based Content Classifier (ADSCC) for dynamic classification of unstructured web contents, using Linked Data and web metadata in Content Delivery Network (CDN). The proposed framework ensures efficient categorization of URLs (even overlapping categories) by dynamically mapping the changing user-defined categories to ontologies' category/classes. This dynamic classification is performed by the proposed system that mainly involves three main algorithms/modules: Dynamic Mapping algorithm, Autonomous coordination-based Inference algorithm, and Context-based disambiguation. Evaluation results show that the proposed system achieves (on average), the precision, recall and F-measure within the 93-97% range.

Safety is the foremost requirement of avionics systems on aircraft. So far, avionics systems have evolved into an integrated system, i.e., integrated avionics system, and the derivative functions occur when the avionics systems are upgraded. However, the traditional safety analysis method is insufficient to be utilized in upgraded avionics systems due to these derivative functions. In this letter, a safety evaluation scheme is proposed to quantitatively evaluate the safety of the upgraded avionics systems. All the functions including the derivative functions can be traced and covered. Meanwhile, a set of safety issues based on different views is established to evaluate the safety capability from three layers, i.e., the mission layer, function layer and resource layer. The proposed scheme can be considered as an efficient scheme in the safety validation and verification in the upgraded avionics systems.

Distributed antenna systems (DASs) combined with multi-user multiple-input multiple-output (MU-MIMO) transmission techniques have recently attracted significant attention. To establish MU-MIMO DASs that have wide service areas, the use of a dynamic clustering scheme (CS) is necessary to reduce computation in precoding. In the present study, we propose a simple method for dynamic clustering to establish a single cell large-scale MU-MIMO DAS and investigate its performance. We also compare the characteristics of the proposal to those of other schemes such as exhaustive search, traditional location-based adaptive CS, and improved norm-based CS in terms of sum rate improvement. Additionally, to make our results more universal, we further introduce spatial correlation to the considered system. Computer simulation results indicate that the proposed CS for the considered system provides better performance than the existing schemes and can achieve a sum rate close to that of exhaustive search but at a lower computational cost.

Recently, the Services Oriented Architectures (SOA) have been recognized as the key to the integration and interoperability of different applications and systems that coexist in an organization. However, even though the use of SOA has increased, some applications are unable to use it. That is the case of mission critical information applications, whose requirements such as high reliability, non-stop operation, high flexibility and high performance are not satisfied by conventional SOA infrastructures. In this article we present a novel approach of combining SOA with Autonomous Decentralized Systems (ADS) in order to provide an infrastructure that can satisfy those requirements. We have named this infrastructure Autonomous Decentralized Service Oriented Architecture (ADSOA). We present the concept and architecture of ADSOA, as well as the Loosely Couple Delivery Transaction and Synchronization Technology for assuring the data consistency and high reliability of the application. Moreover, a real implementation and evaluation of the proposal in a mission critical information system, the Uniqueness Verifying Public Key Infrastructure (UV-PKI), is shown in order to prove its effectiveness.

In this paper, we propose an undirected model of learning systems, named max-min-degree neural network, to realize centralized-decentralized collaborative computing. The basic idea of the proposal is a max-min-degree constraint which extends a k-degree constraint to improve the communication cost, where k is a user-defined degree of neurons. The max-min-degree constraint is defined such that the degree of each neuron lies between kmin and kmax. Accordingly, the Boltzmann machine is a special case of the proposal with kmin=kmax=n, where n is the full-connected degree of neurons. Evaluations show that the proposal is much better than a state-of-the-art model of deep learning systems with respect to the communication cost. The cost of the above improvement is slower convergent speed with respect to data size, but it does not matter in the case of big data processing.

East Japan Railway Company has created new businesses such as life-style business and information technology business on the basis of railway business for sustainable growth. These businesses generate and provide synergy to one another effectively because each business is autonomous decentralized system based on diversified infrastructure. The infrastructure includes not just structure but management, technology, operation and maintenance: we call this “MTOMI Model.” The MTOMI Model is the key concept of JR East's businesses and can generate JR East's ecosystem.

Low-temperature bonding methods of optoelectronic chips, such as laser diodes (LD) and photodiode (PD) chips, have been the focus of much interest to develop highly functional and compact optoelectronic devices, such as microsensors and communication modules. In this paper, room-temperature bonding of the optoelectronic chips with Au thin film to coined Au stud bumps with smooth surfaces (Ra: 1.3nm) using argon and hydrogen gas mixture atmospheric-pressure plasma was demonstrated in ambient air. The die-shear strength was high enough to exceed the strength requirement of MIL-STD-883F, method 2019 (×2). The measured results of the light-current-voltage characteristics of the LD chips and the dark current-voltage characteristics of the PD chips indicated no degradation after bonding.

Research into closed-form Gaussian sum smoother has provided an attractive approach for tracking in clutter, joint detection and tracking (in clutter), and multiple target tracking (in clutter) via the probability hypothesis density (PHD). However, Gaussian sum smoother with nonlinear target model has particular nonlinear expressions in the backward smoothed density that are different from the other filters and smoothers. In order to extend the closed-form solution of linear Gaussian sum smoother to nonlinear model, two closed-form approximations for nonlinear Gaussian sum smoother are proposed, which use Gaussian particle approximation and unscented transformation approximation, separately. Since the estimated target number of PHD smoother is not stable, a heuristic approximation method is added. At last, the Bernoulli smoother and PHD smoother are simulated using Gaussian particle approximation and unscented transformation approximation, and simulation results show that the two proposed algorithms can obtain smoothed tracks with nonlinear models, and have better performance than filter.

An ordered successive interference cancellation (OSIC) scheme based on combined post-processing signal-to-interference-plus-noise ratio (PSINR) is proposed for multiple-input multiple-output (MIMO) systems with retransmission. For the OSIC procedures at the current transmission round, instead of reusing the PSINRs and decision statistics calculated for the previous transmission rounds, the proposed OSIC scheme newly calculates the combined PSINRs and combined decision statistics from the available receive signal vectors and channel matrices at every retransmission. Therefore, the proposed OSIC scheme utilizes all receive signal vectors and channel matrices obtained up to the current transmission round during the OSIC procedures. A low-complexity version of the proposed OSIC scheme is also proposed, and the low-complexity version recalculates the combined PSINRs and combined decision statistics from part of the available receive signal vectors and channel matrices. Simulation results verify that the proposed schemes achieve significantly better error performance than existing OSIC schemes based on the detection and combining process for MIMO systems with retransmission.

This paper presents a practical system which allows instructors to easily introduce 3D games utilizing smartphones in a classroom. The system consists of a PC server, a big screen and smartphone clients. The server provides 3D models, so no 3D authoring is needed when using this system. For an instructor, preparing slides of quiz-questions with the correct answers is all that is required when designing 3D games. According to a quiz specified by an instructor, this system constructs a corresponding 3D game scene. The answers students provide on their smartphones will be used to play this game. Everyone in the classroom can see this 3D game in real time on a big screen. The game illustrates how every student has reacted to a quiz. This system also introduces specialized queues for mobile interactions; a queue for commands from an instructor and a queue for data from students. The command queue has higher priority than the data queue; so that an instructor can control this system by sending commands with clicks on a smartphone. Previous studies have mostly provided specially designed teaching materials to instructors, often treating them as passive consultants. However, by using slides, already familiar to instructors, this system enables instructors to combine their own teaching materials with 3D games in the classroom. Moreover, 3D games are expected to further motivate students to actively participate in classroom activities. This system is evaluated in this paper.

In previous papers by the authors, a new scheme for diagnosis of stochastic discrete event systems, called sequence profiling (SP), is proposed. From given event logs, N-gram models that approximate the behavior of the target system are extracted. N-gram models are used for discovering discrepancy between observed event logs and the behavior of the system in the normal situation. However, when the target system is a distributed system consisting of several subsystems, event sequences from subsystems may be interleaved, and SP cannot separate the faulty event sequence from the interleaved sequence. In this paper, we introduce wildcard characters into event patterns. This contributes to removing the effect by subsystems which may not be related to faults.

We consider a bisimilarity control problem for transition systems. For this control problem, a necessary and sufficient condition for its solvability and a method for synthesizing a state feedback controller have been presented in the literature. However, the state of the system to be controlled is not necessarily observable. In this paper, we synthesize an observer-based output feedback controller for the bisimilarity control problem under a certain condition, and show that this output feedback controller is a solution to the control problem.

Recent development in network technology can realize the control of a remote plant by a digital controller. However, there is a delay caused by data transmission of control inputs and outputs. The delay degrades the control performance without taking it into consideration. In general, it is a difficult problem to identify the delay beforehand. We also assume that the plant's parameters have uncertainty. To solve the problem, we use reinforcement learning to achieve optimal digital control. First, we consider state feedback control. Next, we consider the case where the plant's outputs are observed, and apply reinforcement learning to output feedback control. Finally, we demonstrate by simulation that the proposed control method can search for the optimal gain and that it can adapt to the change of the delay.

In this paper, we propose a communication-efficient top-k continuous query processing method on distributed local nodes where data are horizontally partitioned. A designated coordinator server takes the role of issuing queries from users to local nodes and delivering the results to users. The final results are requested via a top-k subscription which lets local nodes know which data and updates need to be returned to users. Our proposed method makes use of the active previously posed queries to identify a small set of needed top-k subscriptions. In addition, with the pre-indexed nodes' skylines, the number of local nodes to be subscribed can be significantly reduced. As a result, only a small number of subscriptions are informed to a small number of local nodes resulting in lower communication overhead. Furthermore, according to dynamic data updates, we also propose a method that prevents nodes from reporting needless updates and also maintenance procedures to preserve the consistency. The results of experiments that measure the volume of transferred data show that our proposed method significantly outperforms the previously proposed methods.

One of the patterns that the design of parallel file systems has to solve stems from the difficulty of handling the metadata-intensive I/O generated by parallel applications accessing a single large directory. We demonstrate a middleware design called SFS to support existing parallel file systems for distributed and scalable directory service. SFS distributes directory entries over data servers instead of metadata servers to offer increased scalability and performance. Firstly, SFS exploits an adaptive directory partitioning based on extendible hashing to support concurrent and unsynchronized partition splitting. Secondly, SFS describes an optimization based on recursive split-ordering that emphasizes speeding up the splitting process. Thirdly, SFS applies a write-optimized index structure to convert slow, small, random metadata updates into fast, large, sequential writes. Finally, SFS gracefully tolerates stale mapping at the clients while maintaining the correctness and consistency of the system. Our performance results on a cluster of 32-servers show our implementation can deliver more than 250,000 file creations per second on average.

Verification of temporal logic properties plays a crucial role in proving the desired behaviors of continuous systems. In this paper, we propose an interval method that verifies the properties described by a bounded signal temporal logic. We relax the problem so that if the verification process cannot succeed at the prescribed precision, it outputs an inconclusive result. The problem is solved by an efficient and rigorous monitoring algorithm. This algorithm performs a forward simulation of a continuous-time dynamical system, detects a set of time intervals in which the atomic propositions hold, and validates the property by propagating the time intervals. In each step, the continuous state at a certain time is enclosed by an interval vector that is proven to contain a unique solution. We experimentally demonstrate the utility of the proposed method in formal analysis of nonlinear and complex continuous systems.

Analysis of security governed by dynamics of power systems, which we refer to as dynamic security analysis, is a primary but challenging task because of its hybrid nature, that is, nonlinear continuous-time dynamics integrated with discrete switchings. In this paper, we formulate this analysis problem as checking the reachability of a mathematical model representing dynamic performances of a target power system. We then propose a computational approach to the analysis based on the so-called RRT (Rapidly-exploring Random Tree) algorithm. This algorithm searches for a feasible trajectory connecting an initial state possibly at a lower security level and a target set with a desirable higher security level. One advantage of the proposed approach is that it derives a concrete control strategy to guarantee the desirable security level if the feasible trajectory is found. The performance and effectiveness of the proposed approach are demonstrated by applying it to two running examples on power system studies: single machine-infinite system and two-area system for frequency control problem.

Event-triggered and self-triggered control methods are an important control strategy in networked control systems. Event-triggered control is a method that the measured signal is sent to the controller (i.e., the control input is recomputed) only when a certain condition is satisfied. Self-triggered control is a method that the control input and the (non-uniform) sampling interval are computed simultaneously. In this paper, we propose new methods of event-triggered control and self-triggered control from the viewpoint of online optimization (i.e., model predictive control). In self-triggered control, the control input and the sampling interval are obtained by solving a pair of a quadratic programming (QP) problem and a mixed integer linear programming (MILP) problem. In event-triggered control, whether the control input is updated or not is determined by solving two QP problems. The effectiveness of the proposed methods is presented by numerical examples.

This paper investigates the consensus problem of heterogeneous uncertain multi-agent systems with jointly connected topology, where the considered systems are composed of linear first-order, second-order dynamics and nonlinear Euler-Lagrange (EL) dynamics. The consensus protocol is designed to converge the position and velocity states of the linear and nonlinear heterogeneous multi-agent systems under joint connected topology, and then the adaptive consensus protocol is also proposed for heterogeneous multi-agent systems with unknown parameters in the EL dynamics under jointly connected topology. Stability analysis for piecewise continuous functions induced by the jointly connection is presented based on Lyapunov function and Cauchy's convergence criteria. Finally, some simulation results are provided to verify the effectiveness of the proposed methods.

In PQCrypto 2013, Yasuda, Takagi and Sakurai proposed a new signature scheme as one of multivariate public key cryptosystems (MPKCs). This scheme (called YTS) is based on the fact that there are two isometry classes of non-degenerate quadratic forms on a vector space with a prescribed dimension. The advantage of YTS is its efficiency. In fact, its signature generation is eight or nine times faster than Rainbow of similar size. For the security, it is known that the direct attack, the IP attack and the min-rank attack are applicable on YTS, and the running times are exponential time for the first and the second attacks and sub-exponential time for the third attack. In the present paper, we give a new attack on YTS whose approach is to use the diagonalization of matrices. Our attack works in polynomial time and it actually recovers equivalent secret keys of YTS having 140-bits security against min-rank attack in around fifteen seconds.