In this work, we provide the first practical secure email filtering scheme based on homomorphic encryption. Specifically, we construct a secure naïve Bayesian filter (SNBF) using the Paillier scheme, a partially homomorphic encryption (PHE) scheme. We first show that SNBF can be implemented with only the additive homomorphism, thus eliminating the need to employ expensive fully homomorphic schemes. In addition, the design space for specialized hardware architecture realizing SNBF is explored. We utilize a recursive Karatsuba Montgomery structure to accelerate the homomorphic operations, where multiplication of 2048-bit integers are carried out. Through the experiment, both software and hardware versions of the SNBF are implemented. On software, 104-105x runtime and 103x storage reduction are achieved by SNBF, when compared to existing fully homomorphic approaches. By instantiating the designed hardware for SNBF, a further 33x runtime and 1919x power reduction are achieved. The proposed hardware implementation classifies an average-length email in under 0.5s, which is much more practical than existing solutions.

In this paper, we propose a method for enhancing the visibility of omnidirectional spherical images by enlarging the foreground and compressing the background without provoking a sense of visual incompatibility by using a simplified spring model.

Two new families of balanced almost binary sequences with a single zero element of period L=2q are presented in this letter, where q=4d+1 is an odd prime number. These sequences have optimal autocorrelation value or optimal autocorrelation magnitude. Our constructions are based on cyclotomy and Chinese Remainder Theorem.

This paper proposes a consensus-based subgradient method under a common constraint set with switching undirected graphs. In the proposed method, each agent has a state and an auxiliary variable as the estimates of an optimal solution and accumulated information of past gradients of neighbor agents. We show that the states of all agents asymptotically converge to one of the optimal solutions of the convex optimization problem. The simulation results show that the proposed consensus-based algorithm with accumulated subgradient information achieves faster convergence than the standard subgradient algorithm.

This paper proposes a novel distributed proximal minimization algorithm for constrained optimization problems over fixed strongly connected networks. At each iteration, each agent updates its own state by evaluating a proximal operator of its objective function under a constraint set and compensating the unbalancing due to unidirectional communications. We show that the states of all agents asymptotically converge to one of the optimal solutions. Numerical results are shown to confirm the validity of the proposed method.

In this paper, based on the policy of model predictive control, a new method of predictive pinning control is proposed for the consensus problem of multi-agent systems. Pinning control is a method that the external control input is added to some agents (pinning nodes), e.g., leaders. By the external control input, consensus to a certain target value (not the average of the initial states) and faster consensus are achieved. In the proposed method, the external control input is calculated by the controller node connected to only pinning nodes. Since the states of all agents are required in calculation of the external control input, communication delays must be considered. The proposed algorithm includes not only calculation of the external control input but also delay compensation. The effectiveness of the proposed method is presented by a numerical example.

Electrophoretic deposition (EPD) usingpolydimethylsiloxane(PDMS)-based organic-inorganic hybrid materials as binders can be used to prepare alumina-binder composites on metal substrates. Herein, we investigated the deposition mechanism of PDMS-based polymers. The composition and porosity of EPD composites can be controlled by adjusting the EPD condition, and shape of alumina particles.

Cloud computing has attracted a growing number of enterprises to move their business to the cloud because of the associated operational and cost benefits. Improving availability is one of the major concerns of cloud application owners because modern applications generally comprise a large number of components and failures are common at scale. Fault tolerance enables an application to continue operating properly when failure occurs, but fault tolerance strategy is typically employed for the most important components because of financial concerns. Therefore, identifying important components has become a critical research issue. To address this problem, we propose a failure-sensitive structure-based component ranking approach (FSCRank), which integrates component failure impact and application structure information into component importance evaluation. An iterative ranking algorithm is developed according to the structural characteristics of cloud applications. The experimental results show that FSCRank outperforms the other two structure-based ranking algorithms for cloud applications. In addition, factors that affect application availability optimization are analyzed and summarized. The experimental results suggest that the availability of cloud applications can be greatly improved by implementing fault tolerance strategy for the important components identified by FSCRank.

The k-edge-connectivity augmentation problem for a specified set of vertices (kECA-SV for short) is defined by “Given a graph G=(V, E) and a subset Γ ⊆ V, find a minimum set E' of edges such that G'=(V, E ∪ E') has at least k edge-disjoint paths between any pair of vertices in Γ.” Let σ be the edge-connectivity of Γ (that is, G has at least σ edge-disjoint paths between any pair of vertices in Γ). We propose an algorithm for (σ+1)ECA-SV which is done in O(|Γ|) maximum flow operations. Then the time complexity is O(σ2|Γ||V|+|E|) if a given graph is sparse, or O(|Γ||V||BG|log(|V|2/|BG|)+|E|) if dense, where |BG| is the number of pairs of adjacent vertices in G. Also mentioned is an O(|V||E|+|V|2 log |V|) time algorithm for a special case where σ is equal to the edge-connectivity of G and an O(|V|+|E|) time one for σ ≤ 2.

We describe the preparation of an α-phenyl-4'-(diphenylamino)stilbene (TPA) single crystal and the evaluation of its hole transport property. Based on the characterization using optical microscopy, polarizing microscopy, and X-ray diffraction, a large-scale TPA single crystal of dimensions 7.0×0.9×0.8mm is successfully synthesized using a solution method based on the solubility and supersolubility curves of the TPA. Notably, the current in the long-axis direction is larger than those in the short-axis and thickness directions (i(long) > i(short) > i(thickness)), which reveals the anisotropic charge transfer of the TPA single crystal. The observed anisotropic conductivity is well explained by the orientation of the triphenylamine unit in the TPA single crystal. Furthermore, the activation energy of the long-axis direction in the TPA single crystal is lower than that of the short-axis in TPA and all the axes in the α-phenyl-4'-[bis(4-methylphenyl)amino]stilbene single crystal reported in our previous study.

Word order is one of the most significant differences between the Chinese and Vietnamese. In the phrase-based statistical machine translation, the reordering model will learn reordering rules from bilingual corpora. If the bilingual corpora are large and good enough, the reordering rules are exact and coverable. However, Chinese-Vietnamese is a low-resource language pair, the extraction of reordering rules is limited. This leads to the quality of reordering in Chinese-Vietnamese machine translation is not high. In this paper, we have combined Chinese dependency relation and Chinese-Vietnamese word alignment results in order to pre-order Chinese word order to be suitable to Vietnamese one. The experimental results show that our methodology has improved the machine translation performance compared to the translation system using only the reordering models of phrase-based statistical machine translation.

The gap states of tetratetracontane (C44H90; TTC), which is a model oligomer of polyethylene, was examined by using high-sensitivity UV photoemission spectroscopy (HS-UPS). The high sensitivity enabled us to directly observe the weak gap states distributed in the HOMO-LUMO gap from the valence band top to 3.0 eV below the vacuum level. On the basis of the density-of-states derived from UPS results, the tribocharging nature of polyethylene was discussed in comparison with our previous result for nylon-6,6 film.

This paper investigates energy-efficient resource allocation problem for the wireless power transfer (WPT) enabled multi-user massive multiple-input multiple-output (MIMO) systems. In the considered systems, the sensor nodes (SNs) are firstly powered by WPT from the power beacon (PB) with a large scale of antennas. Then, the SNs use the harvested energy to transmit the data to the base station (BS) with multiple antennas. The problem of optimizing the energy efficiency objective is formulated with the consideration of maximum transmission power of the PB and the quality of service (QoS) of the SNs. By adopting fractional programming, the energy-efficient optimization problem is firstly converted into a subtractive form. Then, a joint power and time allocation algorithm based on the block coordinate descent and Dinkelbach method is proposed to maximize energy efficiency. Finally, simulation results show the proposed algorithm achieves a good compromise between the spectrum efficiency and total power consumption.

This letter proposes a new face sketch recognition method. Given a query sketch and face photos in a database, the proposed method first synthesizes pseudo sketches by computing the locality sensitive histogram and dense illumination invariant features from the resized face photos, then extracts discriminative features by computing histogram of averaged oriented gradients on the query sketch and pseudo sketches, and finally find a match with the shortest cosine distance in the feature space. It achieves accuracy comparable to the state-of-the-art while showing much more robustness than the existing face sketch recognition methods.

Saliency detection is widely used in many vision tasks like image retrieval, compression and person re-identification. The deep-learning methods have got great results but most of them focused more on the performance ignored the efficiency of models, which were hard to transplant into other applications. So how to design a efficient model has became the main problem. In this letter, we propose parallel feature network, a saliency model which is built on convolution neural network (CNN) by a parallel method. Parallel dilation blocks are first used to extract features from different layers of CNN, then a parallel upsampling structure is adopted to upsample feature maps. Finally saliency maps are obtained by fusing summations and concatenations of feature maps. Our final model built on VGG-16 is much smaller and faster than existing saliency models and also achieves state-of-the-art performance.

In this letter, a construction of asymmetric Gaussian integer zero correlation zone (ZCZ) sequence sets is presented based on interleaving and filtering. The proposed approach can provide optimal or almost optimal single Gaussian integer ZCZ sequence sets. In addition, arbitrary two sequences from different sets have inter-set zero cross-correlation zone (ZCCZ). The resultant sequence sets can be used in the multi-cell QS-CDMA system to reduce the inter-cell interference and increase the transmission data.

In this letter, a physical-layer network coding (PNC) scheme based on continuous phase modulation (CPM) signal using the titled-phase model, i.e., TIP-CPM-PNC, is presented, and the combined titled-phase state trellis for the superimposed CPM signal in TIP-CPM-PNC is discussed. Simulation results show that the proposed scheme with low decoding complexity can achieve the same error performance as CPM-PNC using the traditional-phase model.

Spectral graph theory gives an algebraic approach to the analysis of the dynamics of a network by using the matrix that represents the network structure. However, it is not easy for social networks to apply the spectral graph theory because the matrix elements cannot be given exactly to represent the structure of a social network. The matrix element should be set on the basis of the relationship between persons, but the relationship cannot be quantified accurately from obtainable data (e.g., call history and chat history). To get around this problem, we utilize the universality of random matrices with the feature of social networks. As such a random matrix, we use the normalized Laplacian matrix for a network where link weights are randomly given. In this paper, we first clarify that the universality (i.e., the Wigner semicircle law) of the normalized Laplacian matrix appears in the eigenvalue frequency distribution regardless of the link weight distribution. Then, we analyze the information propagation speed by using the spectral graph theory and the universality of the normalized Laplacian matrix. As a result, we show that the worst-case speed of the information propagation changes up to twice if the structure (i.e., relationship among people) of a social network changes.

Modern file systems, such as ext4, btrfs, and XFS, are evolving and enable the introduction of new features to meet ever-changing demands and improve reliability. File system developers are struggling to eliminate all software bugs, but the operating system community points out that file systems are a hotbed of critical software bugs. This paper analyzes the code coverage of xfstests, a widely used suite of file system tests, on three major file systems (ext4, btrfs, and XFS). The coverage is 72.34%, and the uncovered code runs into 23,232 lines of code. To understand why the code coverage is low, the uncovered code is manually examined line by line. We identified three major causes, peculiar to file systems, that hinder higher coverage. First, covering all the features is difficult because each file system provides a wide variety of file-system specific features, and some features can be tested only on special storage devices. Second, covering all the execution paths is difficult because they depend on file system configurations and internal on-disk states. Finally, the code for maintaining backward-compatibility is executed only when a file system encounters old formats. Our findings will help file system developers improve the coverage of test suites and provide insights into fostering the development of new methodologies for testing file systems.

This paper presents a formal approach for generating train timetables in a mesoscopic level that is more concrete than the macroscopic level, where each station is simply expressed in a black-box, and more abstract than the microscopic level, where the infrastructure in each station-area is expressed in detail. The accuracy of generated timetable and the computational effort for the generation is a trade-off. In this paper, we design a formal mesoscopic modeling language by analyzing real railways, for example Tazawako-line as the first step of this work. Then, we define the constraint formulae for generating train timetables with the help of SMT (Satisfiability Module Theories)-Solver, and explain our tool RW-Solver that is an implementation of the constraint formulae. Finally, we demonstrate how RW-Solver with the help of SMT-Solver can be used for generating timetables in a case study of Tazawako-line.