The access privileges in distributed systems can be effectively organized as a partial-order hierarchy that consists of distinct security classes, and the access rights are often designated with certain temporal restrictions. The time-bound hierarchical key assignment problem is to assign distinct cryptographic keys to distinct security classes according to their privileges so that users from a higher class can use their class key to derive the keys of lower classes, and these keys are time-variant with respect to sequentially allocated temporal units called time slots. In this paper, we present the involved principle, survey the state of the art, and particularly, look into two representative approaches to time-bound hierarchical key assignment for in-depth case studies.

Automated bug localization is an important issue in software engineering. In the last few decades, various proactive and reactive localization approaches have been proposed to predict the fault-prone software modules. However, most proactive or reactive approaches need source code information or software complexity metrics to perform localization. In this paper, we propose a reactive approach which considers only bug report information and historical revision logs. In our approach, the co-location relationships among bug reports are explored to improve the prediction accuracy of a state-of-the-art learning method. Studies on three open source projects reveal that the proposed scheme can consistently improve the prediction accuracy in all three software projects by nearly 11.6% on average.

Ant Colony Optimization (ACO) algorithms are a new branch of swarm intelligence. They have been applied to solve different combinatorial optimization problems successfully. Their performance is very promising when they solve small problem instances. However, the algorithms' time complexity increase and solution quality decrease for large problem instances. So, it is crucial to reduce the time requirement and at the same time to increase the solution quality for solving large combinatorial optimization problems by the ACO algorithms. This paper introduces a Local Search based ACO algorithm (LSACO), a new algorithm to solve large combinatorial optimization problems. The basis of LSACO is to apply an adaptive local search method to improve the solution quality. This local search automatically determines the number of edges to exchange during the execution of the algorithm. LSACO also applies pheromone updating rule and constructs solutions in a new way so as to decrease the convergence time. The performance of LSACO has been evaluated on a number of benchmark combinatorial optimization problems and results are compared with several existing ACO algorithms. Experimental results show that LSACO is able to produce good quality solutions with a higher rate of convergence for most of the problems.

A composite right/left-handed (CRLH) transmission line with demultiplexing property is proposed towards short-range functional wireless interconnects. The CRLH line is designed by analyzing dispersion relation of the microstrip line having a split-ring and a double-stub structure to realize frequency selective properties for leaky wave radiation. A prototype device is fabricated and estimated to study feasibility of the demultiplexing operation around ten GHz.

This paper focuses on the development of Cramer-Rao Bound (CRB) expressions for passive source location estimation in various Gaussian noise environments. The scenarios considered involve an unknown deterministic source signal with a short time duration, and additive general Gaussian noise. The mathematical derivation procedure presented is applicable to non-stationary Gaussian noise problems. Specifically, explicit closed-form CRB expressions are presented using the spectrum representation of the signal and noise for stationary Gaussian noise cases.

We try to use a computer algebra system Mathematica as a test case generation system. In test case generation, we generally need to solve equations and inequalities. The main reason why we take Mathematica is because it has a built-in function to solve equations and inequalities. In this paper, we deal with both black-box testing and white-box testing. First, we show two black-box test case generation procedures described in Mathematica. The first one is based on equivalence partitioning. Mathematica explicitly shows a case that test cases do no exist. This is an advantage in using Mathematica. The second procedure is a modification of the first one adopting boundary value analysis. For implementation of boundary value analysis, we give a formalization for it. Next, we show a white-box test case generation procedure. For this purpose, we also give a model for source programs. It is like a control flow graph model. The proposed procedure analyzes a model description of a program.

We have revealed that the electronic states in the electrodes give a significant influence to the electron transport in nano-electronic devices. We have theoretically investigated the time-evolution of electron transport from a two-dimensional electron gas (2DEG) to a quantum dot (QD), where 2DEG represents the electrode in the nano-electronic devices. We clearly showed that the coherent electron transport is remarkably modified depending on the initial electronic state in the 2DEG. The electron transport from the 2DEG to the QD is strongly enhanced, when the initial state of the electron in the 2DEG is localized below the QD. We have proposed that controlling the electronic state in the electrodes could realize a new concept device function without modifying the electrode structures; that achieves a new controllable state in future nano-electronic devices.

In this paper, we propose a robust room inverse filtering algorithm for speech dereverberation based on a kurtosis maximization. The proposed algorithm utilizes a new normalized kurtosis function that nonlinearly maps the input kurtosis onto a finite range from zero to one, which results in a kurtosis warping. Due to the kurtosis warping, the proposed algorithm provides more stable convergence and, in turn, better performance than the conventional algorithm. Experimental results are presented to confirm the robustness of the proposed algorithm.

This paper describes recent improvements to Synapse system for incremental learning of general context-free grammars (CFGs) and definite clause grammars (DCGs) from positive and negative sample strings. An important feature of our approach is incremental learning, which is realized by a rule generation mechanism called "bridging" based on bottom-up parsing for positive samples and the search for rule sets. The sizes of rule sets and the computation time depend on the search strategies. In addition to the global search for synthesizing minimal rule sets and serial search, another method for synthesizing semi-optimum rule sets, we incorporate beam search to the system for synthesizing semi-minimal rule sets. The paper shows several experimental results on learning CFGs and DCGs, and we analyze the sizes of rule sets and the computation time.

This letter proposes closed form solutions to the L2-sensitivity minimization of second-order state-space digital filters with real poles. We consider two cases of second-order digital filters: distinct real poles and multiple real poles. In case of second-order digital filters, we can express the L2-sensitivity of second-order digital filters by a simple linear combination of exponential functions and formulate the L2-sensitivity minimization problem by a simple polynomial equation. As a result, the minimum L2-sensitivity realizations can be synthesized by only solving a fourth-degree polynomial equation, which can be analytically solved.

A hash chain H for a one-way hash function h() is a sequence of hash values < v0, v1, ..., vn >, where v0 is a public value, vn a secret value, and vi = h(vi+1). A hash chain traversal T computes and outputs the hash chain H, returning vi in time period (called round) i for 1 ≤ i ≤ n. While previous hash chain traversal algorithms were designed to output all hash values vi (1 ≤ i ≤ n) in order, there are applications where every m-th hash value (i.e., vm, v2m, v3m, ...) is required to be output. We introduce a hash chain traversal algorithm that selectively outputs every m-th hash value efficiently. The main technique is a transformation from a hash chain traversal algorithm outputting every hash value into that outputting every m-th hash value. Compared with the direct use of previous hash chain traversal algorithms, our proposed method requires less memory storages and computational costs.

A dependency structure interprets modification relationships between words or phrases and is recognized as an important element in semantic information analysis. With the conventional approaches for extracting this dependency structure, it is assumed that the complete sentence is known before the analysis starts. For spontaneous speech data, however, this assumption is not necessarily correct since sentence boundaries are not marked in the data. Although sentence boundaries can be detected before dependency analysis, this cascaded implementation is not suitable for online processing since it delays the responses of the application. To solve these problems, we proposed a sequential dependency analysis (SDA) method for online spontaneous speech processing, which enabled us to analyze incomplete sentences sequentially and detect sentence boundaries simultaneously. In this paper, we propose an improved SDA integrating a labeling-based sentence boundary detection (SntBD) technique based on Conditional Random Fields (CRFs). In the new method, we use CRF for soft decision of sentence boundaries and combine it with SDA to retain its online framework. Since CRF-based SntBD yields better estimates of sentence boundaries, SDA can provide better results in which the dependency structure and sentence boundaries are consistent. Experimental results using spontaneous lecture speech from the Corpus of Spontaneous Japanese show that our improved SDA outperforms the original SDA with SntBD accuracy providing better dependency analysis results.

Differing from the long-term prediction used in the modern speech codec, the standard of the internet low bit rate codec (iLBC) independently encodes the residual of the linear predictive coding (LPC) frame by frame. In this paper, a complexity scalability design is proposed for the coding of the dynamic codebook search in the iLBC speech codec. In addition, a trade-off between the computational complexity and the speech quality can be achieved by dynamically setting the parameter of the proposed approach. Simulation results show that the computational complexity can be effectively reduced with imperceptible degradation of the speech quality.

Network monitoring systems that detect and analyze malicious activities as well as respond against them, are becoming increasingly important. As malwares, such as worms, viruses, and bots, can inflict significant damages on both infrastructure and end user, technologies for identifying such propagating malwares are in great demand. In the large-scale darknet monitoring operation, we can see that malwares have various kinds of scan patterns that involves choosing destination IP addresses. Since many of those oscillations seemed to have a natural periodicity, as if they were signal waveforms, we considered to apply a spectrum analysis methodology so as to extract a feature of malware. With a focus on such scan patterns, this paper proposes a novel concept of malware feature extraction and a distinct analysis method named "SPectrum Analysis for Distinction and Extraction of malware features (SPADE)". Through several evaluations using real scan traffic, we show that SPADE has the significant advantage of recognizing the similarities and dissimilarities between the same and different types of malwares.

System implementation for e-Government initiatives should be reliable. Unreliable system implementation could, on the one hand, be insufficient to fulfill basic system requirements, and more seriously on the other hand, break the trust of citizens on governments. The objective of this paper is to advocate the use of formal methods in general, the OTS/CafeOBJ method in particular in this paper, to help develop reliable system implementation for e-Government initiatives. An experiment with the OTS/CafeOBJ method on an e-Government messaging framework proposed for providing citizens with seamless public services is described to back up our advocation. Two previously not well-clarified problems of the framework and their potential harm realized in this experiment are reported, and possible ways of revisions to the framework are suggested as well. The revisions are proved to be sufficient for making the framework satisfy certain desired properties.

We have studied transmission property of electron in vertical MOSFET (V-MOSFET) from the viewpoint of quantum electro-dynamics. To obtain the intuitive picture of electron transmission property through channel of the V-MOSFET, we solve the time-dependent Schrodinger equation in real space by employing the split operator method. We injected an electron wave packet into the body of the V-MOSFET from the source, and traced the time-development of electron-wave function in the body and drain region. We successfully showed that the electron wave function propagates through the resonant states of the body potential. Our suggested approaches open the quantative and intuitive discussion for the carrier dynamics in the V-MOSFET on quantum limit.

We fabricated and examined current limiting effect for InP Gunn diodes with stable depletion layer mode operation of diodes for high efficiency Gunn oscillators. Current limiting at the cathode was achieved by a shallow Schottky barrier at the interface. We discussed fabrication procedure, the results for negative differential resistance and rf tests for InP Gunn diodes. It was shown that the fabricated Gunn diodes have the output power of 10.22 dBm at a frequency of 90.13 GHz. Its input voltage and corresponding current were 8.55 V and 252 mA, respectively.

The Distributed Denial of Service attack (DDoS) is one of the major threats to network security that exhausts network bandwidth and resources. Recently, an efficient approach Live Baiting was proposed for detecting the identities of DDoS attackers in web service using low state overhead without requiring either the models of legitimate requests nor anomalous behavior. However, Live Baiting has two limitations. First, the detection algorithm adopted in Live Baiting starts with a suspects list containing all clients, which leads to a high false positive probability especially for large web service with a huge number of clients. Second, Live Baiting adopts a fixed threshold based on the expected number of requests in each bucket during the detection interval without the consideration of daily and weekly traffic variations. In order to address the above limitations, we first distinguish the clients activities (Active and Non-Active clients during the detection interval) in the detection process and then further propose a new adaptive threshold based on the Change Point Detection method, such that we can improve the false positive probability and avoid the dependence of detection on sites and access patterns. Extensive trace-driven simulation has been conducted on real Web trace to demonstrate the detection efficiency of the proposed scheme in comparison with the Live Baiting detection scheme.

To handle coherent signals with unknown arrival angles, an adaptive beamforming method is proposed which can be applied to an arbitrary array. The proposed method efficiently solves a generalized eigenvalue problem to estimate the arrival angles of the desired coherent signal group, by exploiting the Brent method in conjunction with alternating maximization. We discuss the condition for the correct direction estimation without erroneously taking interference direction estimates for the desired ones. Simulation results show that the performance of the proposed beamformer is very similar to that of the beamformer with the exact composite steering vector (CSV).

A new adaptive algorithm is proposed by introducing some modifications to the recursive least squares (RLS) algorithm. Except for the noise variance, the proposed algorithm does not require any statistics or knowledge of the desired signal, thus, it is suitable for adaptive filtering for channel estimation in code division multiple access (CDMA) systems in cases where the standard RLS approach cannot be used. A theoretical analysis demonstrates the convergence of the proposed algorithm, and simulation results for CDMA channel estimation show that the proposed algorithm outperforms existing channel estimation schemes.