Microblog data contains rich information of real-world events with great commercial values, so microblog-oriented natural language processing (NLP) tasks have grabbed considerable attention of researchers. However, the performance of microblog-oriented Chinese Word Segmentation (CWS) based on deep neural networks (DNNs) is still not satisfying. One critical reason is that the existing microblog-oriented training corpus is inadequate to train effective weight matrices for DNNs. In this paper, we propose a novel active learning method to extend the scale of the training corpus for DNNs. However, due to a large amount of partially overlapped sentences in the microblogs, it is difficult to select samples with high annotation values from raw microblogs during the active learning procedure. To select samples with higher annotation values, parameter λ is introduced to control the number of repeatedly selected samples. Meanwhile, various strategies are adopted to measure the overall annotation values of a sample during the active learning procedure. Experiments on the benchmark datasets of NLPCC 2015 show that our λ-active learning method outperforms the baseline system and the state-of-the-art method. Besides, the results also demonstrate that the performances of the DNNs trained on the extended corpus are significantly improved.

In this letter, we propose a generalized quadrature spatial modulation technique (GQSM) which offers additional bits per channel use (bpcu) gains and a low complexity greedy detector algorithm, structured orthogonal matching pursuit (S-OMP)- GQSM, based on compressive sensing (CS) framework. Simulation results show that the bit error rate (BER) performance of the proposed greedy detector is very close to maximum likelihood (ML) and near optimal detectors based on convex programming.

With the popularity of cloud computing services, outsourcing computation has entered a period of rapid development. Modular exponentiation is one of the most expensive operations in public key cryptographic systems, but the current outsourcing algorithms for modular exponentiations (MExps) with single server are inefficient or have small checkability. In this paper, we propose an efficient and fully verifiable algorithm for outsourcing multiple MExps with single untrusted server where the errors can be detected by an outsourcer with a probability of 1. The theory analysis and experimental evaluations also show that the proposed algorithm is the most efficient one compared with the previous work. Finally, we present the outsourcing schemes of digital signature algorithm (DSA) and attribute based encryption (ABE) as two applications of the proposed algorithm.

We present a GPU (graphics processing unit) accelerated stochastic algorithm implementation for simulating biochemical reaction networks using the latest NVidia architecture. To effectively utilize the massive parallelism offered by the NVidia Pascal hardware, we apply a set of performance tuning methods and guidelines such as exploiting the architecture's memory hierarchy in our algorithm implementation. Based on our experimentation results as well as comparative analysis using CPU-based implementations, we report our initial experiences on the performance of modern GPUs in the context of scientific computing.

A cograph (complement reducible graph) is a graph which can be generated by disjoint union and complement operations on graphs, starting with a single vertex graph. Cographs arise in many areas of computer science and are studied extensively. With the goal of developing an effective data mining method for graph structured data, in this paper we introduce a graph pattern expression, called a cograph pattern, which is a special type of cograph having structured variables. Firstly, we show that a problem whether or not a given cograph pattern g matches a given cograph G is NP-complete. From this result, we consider the polynomial time learnability of cograph pattern languages defined by cograph patterns having variables labeled with mutually different labels, called linear cograph patterns. Secondly, we present a polynomial time matching algorithm for linear cograph patterns. Next, we give a polynomial time algorithm for obtaining a minimally generalized linear cograph pattern which explains given positive data. Finally, we show that the class of linear cograph pattern languages is polynomial time inductively inferable from positive data.

This paper proposes a classification method of second-language-learner utterances for interactive computer-assisted language learning systems. This classification method uses three types of bilingual evaluation understudy (BLEU) scores as features for a classifier. The three BLEU scores are calculated in accordance with three subsets of a learner corpus divided according to the quality of utterances. For the purpose of overcoming the data-sparseness problem, this classification method uses the BLEU scores calculated using a mixture of word and part-of-speech (POS)-tag sequences converted from word sequences based on a POS-replacement rule according to which words are replaced with POS tags in n-grams. Experiments of classifying English utterances by Japanese demonstrated that the proposed classification method achieved classification accuracy of 78.2% which was 12.3 points higher than a baseline with one BLEU score.

In this paper, we propose a Mobile Edge Internet of Things (MEIoT) architecture by leveraging the fiber-wireless access technology, the cloudlet concept, and the software defined networking framework. The MEIoT architecture brings computing and storage resources close to Internet of Things (IoT) devices in order to speed up IoT data sharing and analytics. Specifically, the IoT devices (belonging to the same user) are associated to a specific proxy Virtual Machine (VM) in the nearby cloudlet. The proxy VM stores and analyzes the IoT data (generated by its IoT devices) in real-time. Moreover, we introduce the semantic and social IoT technology in the context of MEIoT to solve the interoperability and inefficient access control problem in the IoT system. In addition, we propose two dynamic proxy VM migration methods to minimize the end-to-end delay between proxy VMs and their IoT devices and to minimize the total on-grid energy consumption of the cloudlets, respectively. Performance of the proposed methods is validated via extensive simulations.

Considering that different people are different in their linguistic preference and in order to determine the consensus state when using Computing with Words (CWW) for supporting consensus decision making, this paper first proposes an interval composite scale based 2-tuple linguistic model, which realizes the process of translation from word to interval numerical and the process of retranslation from interval numerical to word. Second, this paper proposes an interval composite scale based personalized individual semantics model (ICS-PISM), which can provide different linguistic representation models for different decision-makers. Finally, this paper proposes a consensus decision making model with ICS-PISM, which includes a semantic translation and retranslation phase during decision process and determines the consensus state of the whole decision process. These models proposed take into full consideration that human language contains vague expressions and usually real-world preferences are uncertain, and provide efficient computation models to support consensus decision making.

This paper studies a simultaneous wireless information and power transfer (SWIPT) system in which the transmitter not only sends data and energy to many types of wireless users, such as multiple information decoding users, multiple hybrid power-splitting users (i.e., users with a power-splitting structure to receive both information and energy), and multiple energy harvesting users, but also prevents information from being intercepted by a passive eavesdropper. The transmitter is equipped with multiple antennas, whereas all users and the eavesdropper are assumed to be equipped with a single antenna. Since the transmitter does not have any channel state information (CSI) about the eavesdropper, artificial noise (AN) power is maximized to mask information as well as to interfere with the eavesdropper as much as possible. The non-convex optimization problem is formulated to minimize the transmit power satisfying all signal-to-interference-plus-noise (SINR) and harvested energy requirements for all users so that the remaining power for generating AN is maximized. With perfect CSI, a semidefinite relaxation (SDR) technique is applied, and the optimal solution is proven to be tight. With imperfect CSI, SDR and a Gaussian randomization algorithm are proposed to find the suboptimal solution. Finally, numerical performance with respect to the maximum SINR at the eavesdropper is determined by a Monte-Carlo simulation to compare the proposed AN scenario with a no-AN scenario, as well as to compare perfect CSI with imperfect CSI.

This paper proposes a novel method of reducing channel state information (CSI) feedback by using transmit antenna selection for downlink multiuser multiple input multiple output (DL-MU-MIMO) transmission in dense distributed antenna systems. It is widely known that DL-MU-MIMO transmission achieves higher total bit-rate by mitigating inter-user interference based on pre-coding techniques. The pre-coding techniques require CSI between access point (AP) and multiple users. However, overhead for CSI acquisition degrades the transmission efficiency of DL-MU-MIMO transmission. In the proposed CSI feedback reduction method, AP first selects the antenna set that maximizes the received power at each user, second it skips the sequence of CSI feedback for users whose signal to interference power ratio is larger than a threshold, and finally it performs DL-MU-MIMO transmission to multiple users by using the selected antenna set. To clarify the proposed method, we evaluate it by computer simulations in an indoor scenario. The results show that the proposed method can offer higher transmission efficiency than the conventional DL-MU-MIMO transmission with the usual CSI feedback method.

Recently, H-bridge pulse width modulation (PWM) micro-stepping motor drivers have been widely used for 3-D printers, robots, and medical instruments. Differently from a simple PWM motor driver circuit, the H-bridge PWM micro-stepping motor driver circuit can generate radio frequency (RF) electromagnetic interference (EMI) noises of up to several hundred MHz frequencies, due to digital interface circuits and a high-performance CPU. For medical instrument systems, the minimization of EMI noises can assure operating safety and greatly reduce the chance of malfunction between instruments. This work proposes a passive-filter configuration-based circuit design for reducing up-to-several-hundred-MHz EMI noises generated from the H-bridge PWM micro-stepping motor driver circuit. More specifically, the proposed RF EMI reduction approach consists of proper passive filter design, shielding in motor wires, and common ground design in the print circuit board. The proposed passive filter configuration design is validated through the overall reduction of EMI noises at RF band. Finally, the proposed EMI reduction approach is tested experientially through a prototype and about 16 dB average reduction of RF EMI noises is demonstrated.

In this paper, we use the MCA (Multi-Cache Approximation) algorithm to numerically determine cache hit probability in a multi-cache network. We then analytically obtain performance metrics for Content-Centric networking (CCN). Our analytical model contains multiple routers, multiple repositories (e.g., storage servers), and multiple entities (e.g., clients). We obtain three performance metrics: content delivery delay (i.e., the average time required for an entity to retrieve a content through a neighboring router), throughput (i.e., number of contents delivered from an entity per unit of time), and availability (i.e., probability that an entity can successfully retrieve a content from a network). Through several numerical examples, we investigate how network topology affects the performance of CCN. A notable finding is that content caching becomes more beneficial in terms of content delivery time and availability (resp., throughput) as distance between the entity and the requesting repository narrows (resp., widens).

In wireless sensor networks, the on-off attacker nodes can present good behaviors and then opportunistically and selectively behave badly to compromise the network. Such misbehaving nodes are usually difficult to be spotted by the network system in a short term. To address this issue, in this study, we propose a reputation scheme to mitigate the on-off attack. In addition, a penalty module is properly designed so that the reputation scheme can effectively respond to the on-off misbehaviors and make such nodes quickly detected by the system, hence the minimization of their influence. We confirm the feasibility and effectiveness of the proposed scheme through simulation tests.

To aim to achieve a high-performance computation for microwave simulations with low cost, small size machine and low energy consumption, a method of the FDTD dedicated computer has been investigated. It was shown by VHDL logical circuit simulations that the FDTD dedicated computer with a dataflow architecture has much higher performance than that of high-end PC and GPU. Then the remaining task of this work is large scale computations by the dedicated computer, since microwave simulations for only 18×18×Z grid space (Z is the number of girds for z direction) can be executed in a single FPGA at most. To treat much larger numerical model size for practical applications, this paper considers an implementation of a domain decomposition method operation of the FDTD dedicated computer in a single FPGA.

The aim of this paper is to apply automatic jigsaw puzzle solvers, which are methods of assembling jigsaw puzzles, to the field of information security. Encryption-then-Compression (EtC) systems have been considered for the user-controllable privacy protection of digital images in social network services. Block scrambling-based encryption schemes, which have been proposed to construct EtC systems, have enough key spaces for protecting brute-force attacks. However, each block in encrypted images has almost the same correlation as that of original images. Therefore, it is required to consider the security from different viewpoints from number theory-based encryption methods with provable security such as RSA and AES. In this paper, existing jigsaw puzzle solvers, which aim to assemble puzzles including only scrambled and rotated pieces, are first reviewed in terms of attacking strategies on encrypted images. Then, an extended jigsaw puzzle solver for block scrambling-based encryption scheme is proposed to solve encrypted images including inverted, negative-positive transformed and color component shuffled blocks in addition to scrambled and rotated ones. In the experiments, the jigsaw puzzle solvers are applied to encrypted images to consider the security conditions of the encryption schemes.

This paper discusses a mode for pseudorandom functions (PRFs) based on the hashing mode of Lesamnta-LW and the domain extension called Merkle-Damgård with permutation (MDP). The hashing mode of Lesamnta-LW is a plain Merkle-Damgård iteration of a block cipher with its key size half of its block size. First, a PRF mode is presented which produces multiple independent PRFs with multiple permutations and initialization vectors if the underlying block cipher is a PRP. Then, two applications of the PRF mode are presented. One is a PRF with minimum padding. Here, padding is said to be minimum if the produced message blocks do not include message blocks only with the padded sequence for any non-empty input message. The other is a vector-input PRF using the PRFs with minimum padding.

The performance of secret-sharing (SS)-based multiparty computation (MPC) has recently increased greatly, and several efforts to implement and use it have been put into practice. Authentication of clients is one critical mechanism for implementing SS-based MPC successfully in practice. We propose a password-based authentication protocol for SS-based MPC. Our protocol is secure in the presence of secure channels, and it is optimized for practical use with SS-based MPC in the following ways. Threshold security: Our protocol is secure in the honest majority, which is necessary and sufficient since most practical results on SS-based MPC are secure in the same environment. Establishing distinct channels: After our protocol, a client has distinct secure and two-way authenticated channels to each server. Ease of implementation: Our protocol consists of SS, operations involving SS, and secure channels, which can be reused from an implementation of SS-based MPC. Furthermore, we implemented our protocol with an optimization for the realistic network. A client received the result within 2 sec even when the network delay was 200 ms, which is almost the delay that occurs between Japan and Europe.

This paper presents a new GPU-based rasterization algorithm for Boolean operations that handles arbitary closed polygons. We construct an efficient data structure for interoperation of CPU and GPU and propose a fast GPU-based contour extraction method to ensure the performance of our algorithm. We then design a novel traversing strategy to achieve an error-free calculation of intersection point for correct Boolean operations. We finally give a detail evaluation and the results show that our algorithm has a higher performance than exsiting algorithms on processing polygons with large amount of vertices.

In this letter, robust sparse signal recovery is considered in the presence of heavy-tailed impulsive noise. Two Bayesian approaches are developed where a Bayesian framework is constructed by utilizing the Laplace distribution to model the noise. By rewriting the noise-fitting term as a reweighted quadratic function which is optimized in the sparse signal space, the Type I Maximum A Posteriori (MAP) approach is proposed. Next, by exploiting the hierarchical structure of the sparse prior and the likelihood function, we develop the Type II Evidence Maximization approach optimized in the hyperparameter space. The numerical results verify the effectiveness of the proposed methods in the presence of impulsive noise.

This paper newly proposes a fast computation technique on the method of image Green's function for p-characteristic calculations, when a plane wave with the transverse wavenumber p is incident on a periodic rough surface having perfect conductivity. In the computation of p-characteristics, based on a spectral domain periodicity of the periodic image Green's function, the image integral equation for a given incidence p maintains the same form for other particular incidences except for the excitation term. By means of a quadrature method, such image integral equations lead to matrix equations. Once the first given matrix equation is performed by a solution procedure as calculations of its matrix elements and its inverse matrix, the other matrix equations for other particular incidences no longer need such a solution procedure. Thus, the total CPU time for the computation of p-characteristics is largely reduced in complex shaped surface cases, huge roughness cases or large period cases.