Survivability is the capability of a system to provide its services in a timely manner even after intrusion and compromise occur. In this paper, we focus on the quantitative analysis of survivability of virtual machine (VM) based intrusion tolerant system in the presence of Byzantine failures due to malicious attacks. Intrusion tolerant system has the ability of a system to continuously provide correct services even if the system is intruded. This paper introduces a scheme of the intrusion tolerant system with virtualization, and derives the success probability for one request by a Markov chain under the environment where VMs have been intruded due to a security hole by malicious attacks. Finally, in numerical experiments, we evaluate the performance of VM-based intrusion tolerant system from the viewpoint of survivability.

The crossbar-based switch fabric is widely used in today's high performance switches, due to its internally nonblocking and simply implementation properties. Usually there are two main switching architectures for crossbar-based switch fabric: internally bufferless crossbar switch and crosspoint buffered crossbar switch. As internally bufferless crossbar switch requires a complex centralized scheduler which limits its scalability to high speeds, crosspoint buffered crossbar switch has gained more attention because of its simpler distributed scheduling algorithm and better switching performance. However, almost all the scheduling algorithms proposed previously for crosspoint buffered crossbar switch either have unsatisfactory scheduling performance under non-uniform traffic patterns or show poor service fairness between input traffic flows. In order to overcome the disadvantages of existing algorithms, in this paper we propose two novel high performance scheduling algorithms named MCQF_RR and IMCQF_RR for crosspoint buffered crossbar switches. Both algorithms have a time complexity of O(log N), where N is the number of input/output ports of the switch. MCQF_RR takes advantage of the combined weight information about queue length and service waiting time of input queues to perform scheduling. In order to further reduce the scheduling complexity and make it feasible for high speed switches, IMCQF_RR uses the compressed queue length information instead of original queue length information to schedule cells in input VOQs. Simulation results show that our novel scheduling algorithms MCQF_RR and IMCQF_RR can demonstrate excellent delay performance comparable to existing high performance scheduling algorithms under both uniform and non-uniform traffic patterns, while maintain good service fairness performance under severe non-uniform traffic patterns.

The conventional non-negative matrix factorization (NMF)-based speech enhancement is accomplished by updating iteratively with the prior knowledge of the clean speech and noise spectra bases. With the probabilistic estimation of whether the speech is present or not in a certain frame, this letter proposes a speech enhancement algorithm incorporating the speech presence probability (SPP) obtained via noise estimation to the NMF process. To take advantage of both the NMF-based and statistical model-based approaches, the final enhanced speech is achieved by applying a statistical model-based filter to the output of the SPP weighted NMF. Objective evaluations using perceptual evaluation of speech quality (PESQ) on TIMIT with 20 noise types at various signal-to-noise ratio (SNR) levels demonstrate the superiority of the proposed algorithm over the conventional NMF and statistical model-based baselines.

A compact dual-band MIMO (Multiple-Input-Multiple-Output) antenna system for LTE33 band, LTE 2300 and ISM 2.4GHz applications is presented. The whole system consists of four identical folded 3-D IFAs (inverted F antenna) mounted on nonmetallic cuboids. By using the radiation pattern diversity of the antenna elements, higher than 15dB isolation among the antenna elements and low correlation coefficient (CC<0.3) are achieved without any specially designed decoupling structures. Its gain and radiation pattern are elucidated.

In this paper, a self-recoverable, frequency-aware and cost-effective robust latch (referred to as RFC) is proposed in 45nm CMOS technology. By means of triple mutually feedback Muller C-elements, the internal nodes and output node of the latch are self-recoverable from single event upset (SEU), i.e. particle striking induced logic upset, regardless of the energy of the striking particle. The proposed robust latch offers a much wider spectrum of working clock frequency on account of a smaller delay and insensitivity to high impedance state. The proposed robust latch performs with lower costs regarding power and area than most of the compared latches. SPICE simulation results demonstrate that the area-power-delay product is 73.74% saving on average compared with previous radiation hardened latches.

In this paper, we investigate the fault-tolerant Hamiltonian problems of crossed cubes with a faulty path. More precisely, let P denote any path in an n-dimensional crossed cube CQn for n ≥ 5, and let V(P) be the vertex set of P. We show that CQn-V(P) is Hamiltonian if |V(P)|≤n and is Hamiltonian connected if |V(P)| ≤ n-1. Compared with the previous results showing that the crossed cube is (n-2)-fault-tolerant Hamiltonian and (n-3)-fault-tolerant Hamiltonian connected for arbitrary faults, the contribution of this paper indicates that the crossed cube can tolerate more faulty vertices if these vertices happen to form some specific types of structures.

Nonnegative matrix factorization (NMF) is an unsupervised technique to represent nonnegative data as linear combinations of nonnegative bases, which has shown impressive performance for source separation. However, its source separation performance degrades when one signal can also be described well with the bases for the interfering source signals. In this paper, we propose a discriminative NMF (DNMF) algorithm which exploits the reconstruction error for the interfering signals as well as the target signal based on target bases. The objective function for training the bases is constructed so as to yield high reconstruction error for the interfering source signals while guaranteeing low reconstruction error for the target source signals. Experiments show that the proposed method outperformed the standard NMF and another DNMF method in terms of both the perceptual evaluation of speech quality score and signal-to-distortion ratio in various noisy environments.

This paper proposes fast repairing methods that uses hierarchical software defined network controllers for recovering from massive failure in a large-scale IP over a wavelength-division multiplexing network. The network consists of multiple domains, and slave controllers are deployed in each domain. While each slave controller configures transport paths in its domain, the master controller manages end-to-end paths, which are established across multiple domains. For fast repair of intra-domain paths by the slave controllers, we define the optimization problem of path configuration order and propose a heuristic method, which minimizes the repair time to move from a disrupted state to a suboptimal state. For fast repair of end-to-end path through multiple domains, we also propose a network abstraction method, which efficiently manages the entire network. Evaluation results suggest that fast repair within a few minutes can be achieved by applying the proposed methods to the repairing scenario, where multiple links and nodes fail, in a 10,000-node network.

Mobile virtual network operators (MVNOs) are mobile operators without their own infrastructure or government issued spectrum licenses. They purchase spectrum resources from primary mobile network operators (MNOs) to provide communication services under their own brands. MVNOs are expected to play an important role in mobile network markets, as this will increase the competition in retail markets and help to meet the demand of niche markets. However, with the rapidly increasing demand of mobile data traffic, efficient utilization of the limited spectrum resources owned by MVNOs has become an important issue. We propose here a resource sharing mechanism between MVNOs against the background of network functions virtualization (NFV). The proposed mechanism enables MVNOs to improve their quality of service (QoS) by sharing spectrum resources with each other. A nash bargaining solution based decision strategy is also devised to ensure the fairness of resource sharing. Extensive numerical evaluation results validate the effectiveness of the proposed models and mechanisms.

In a multi-tenant data center, nodes and links of tenants' virtual networks (VNs) share a single component of the physical substrate network (SN). The failure of a single SN component can thereby cause the simultaneous failures of multiple nodes and links in a single VN; this complex of failures must significantly disrupt the services offered on the VN. In the present paper, we clarify how the fault tolerance of each VN is affected by a single SN failure, especially from the perspective of VN allocation in the SN. We propose a VN allocation model for multi-tenant data centers and formulate a problem that deals with the bandwidth loss in a single VN due a single SN failure. We conduct numerical simulations (with the setting that has 1.7×108bit/s bandwidth demand on each VN, (denoted by Ci)). When each node in each VN is scattered and mapped to an individual physical server, each VN can have the minimum bandwidth loss (5.3×102bit/s (3.0×10-6×Ci)) but the maximum required bandwidth between physical servers (1.0×109bit/s (5.7×Ci)). The balance between the bandwidth loss and the required physical resources can be optimized by assigning every four nodes of each VN to an individual physical server, meaning that we minimize the bandwidth loss without over-provisioning of core switches.

Most error correction interfaces for speech recognition applications on smartphones require the user to first mark an error region and choose the correct word from a candidate list. We propose a simple multimodal interface to make the process more efficient. We develop Long Context Match (LCM) to get candidates that complement the conventional word confusion network (WCN). Assuming that not only the preceding words but also the succeeding words of the error region are validated by users, we use such contexts to search higher-order n-grams corpora for matching word sequences. For this purpose, we also utilize the Web text data. Furthermore, we propose a combination of LCM and WCN (“LCM + WCN”) to provide users with candidate lists that are more relevant than those yielded by WCN alone. We compare our interface with the WCN-based interface on the Corpus of Spontaneous Japanese (CSJ). Our proposed “LCM + WCN” method improved the 1-best accuracy by 23%, improved the Mean Reciprocal Rank (MRR) by 28%, and our interface reduced the user's load by 12%.

Template tracking has been extensively studied in Computer Vision with a wide range of applications. A general framework is to construct a parametric model to predict movement and to track the target. The difference in intensity between the pixels belonging to the current region and the pixels of the selected target allows a straightforward prediction of the region position in the current image. Traditional methods track the object based on the assumption that the relationship between the intensity difference and the region position is linear or non-linear. They will result in bad tracking performance when just one model is adopted. This paper proposes a method, called as Mixture Hyperplanes Approximation, which is based on finite mixture of generalized linear regression models to perform robust tracking. Moreover, a fast learning strategy is discussed, which improves the robustness against noise. Experiments demonstrate the performance and stability of Mixture Hyperplanes Approximation.

Isosurface extraction is one of the most popular techniques for visualizing scalar volume data. However, volume data contains infinitely many isosurfaces. Furthermore, a single isosurface might contain many connected components, or contours, with each representing a different object surface. Hence, it is often a tedious and time-consuming manual process to find and extract contours that are interesting to users. This paper describes a novel method for automatically extracting salient contours from volume data. For this purpose, we propose a contour gradient tree (CGT) that contains the information of salient contours and their saliency magnitude. We organize the CGT in a hierarchical way to generate a sequence of contours in saliency order. Our method was applied to various medical datasets. Experimental results show that our method can automatically extract salient contours that represent regions of interest in the data.

In this paper we consider two non-parametric estimation methods for software reliability assessment without specifying the fault-detection time distribution, where the underlying stochastic process to describe software fault-counts in the system testing is given by a non-homogeneous Poisson process. The resulting data-driven methodologies can give the useful probabilistic information on the software reliability assessment under the incomplete knowledge on fault-detection time distribution. Throughout examples with real software fault data, it is shown that the proposed methods provide more accurate estimation results than the common parametric approach.

With aggressive device scaling, timing failures have become more prevalent due to manufacturing defects and process variations. When timing failure occurs, it is important to take corrective actions immediately. Therefore, an efficient and fast diagnosis method is essential. In this paper, we propose a new diagnostic method using timing information. Our method approximately estimates all the segment delays of measured paths in a design, using inequality-constrained least squares methods. Then, the proposed method ranks the possible locations of delay defects based on the difference between estimated segment delays and the expected values of segment delays. The method works well for multiple delay defects as well as single delay defects. Experiment results show that our method yields good diagnostic resolution. With the proposed method, the average first hit rank (FHR), was within 7 for single delay defect and within 8 for multiple delay defects.

A new modeling approach for the non-homogeneous Poisson processes (NHPPs) based software reliability modeling is proposed to describe the stochastic behavior of software fault-detection processes, of which the failure rate is not monotonic. The fundamental idea is to apply the Marshall-Olkin distribution to the software fault-detection time distribution. The applicability of Marshall-Olkin distribution in software reliability modeling is studied. The data fitting abilities of the proposed NHPP-based software reliability model is compared with the existing typical ones through real software project data analysis.

In this study, we investigate whether copula modeling contributes to the improvement of reliability evaluation in a cascading failure-occurrence environment. In particular, as a basic problem, we focus on a 2-unit parallel system whose units may fail dependently each other. As a result, the reliability assessment of the system by using the maximal copula provides more accurate evaluation than the traditional Weibull analysis, if the degree of dependency between two units are high. We show this result by using several simulation studies.

This letter introduces an Energy-Aware LED Light System (EA-LLS) that provides adequate illumination to users according to the analysis of the sun's position, the user's movement, and various environmental factors, without sun illumination detection sensors. This letter presents research using algorithms and scenarios. We propose an EA-LLS that offers not only On/Off and dimming control, but dimming control through daylight, space, and user behavior analysis.

In this paper we discuss the system failure probability of a k-out-of-n system considering common-cause failures. The conventional implicit technique is first introduced. Then the failure probabilities are formulated when the independence between common-cause failure events is assumed. We also provide algorithms to enumerate all the cut sets and the minimal cut sets, and to calculate the system failure probability. These methods are extendable to the case of systems with non-identical components. We verify the effectiveness of our method by comparison with the exact solution obtained by numerical calculation.

A new algorithm for separating mass spectra into individual substances for explosives detection is proposed. In the field of mass spectrometry, separation methods, such as principal-component analysis (PCA) and independent-component analysis (ICA), are widely used. All components, however, have no negative values, and the orthogonality condition imposed on components also does not necessarily hold in the case of mass spectra. Because these methods allow negative values and PCA imposes an orthogonality condition, they are not suitable for separation of mass spectra. The proposed algorithm is based on probabilistic latent-component analysis (PLCA). PLCA is a statistical formulation of non-negative matrix factorization (NMF) using KL divergence. Because PLCA imposes the constraint of non-negativity but not orthogonality, the algorithm is effective for separating components of mass spectra. In addition, to estimate the components more accurately, a sparsity constraint is applied to PLCA for explosives detection. The main contribution is industrial application of the algorithm into an explosives-detection system. Results of an experimental evaluation of the algorithm with data obtained in a real railway station demonstrate that the proposed algorithm outperforms PCA and ICA. Also, results of calculation time demonstrate that the algorithm can work in real time.