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.

We perform error probability analysis of the uncoded OFDM fixed gain Amplify-and-Forward (AF) relaying system with subcarrier permutation (SCP). Two SCP schemes, named: the best-to-best SCP (BTB SCP) and the best-to-worst SCP (BTW SCP) are considered. Closed-form expressions for the bit error rate (BER) of the above SCP methods are derived. Numerical results manifest that these SCP schemes may outperform one another, depending on the average channel conditions of the links involved. That is, BTB SCP is better at low signal-to-noise ratio (SNR) values, while BTW SCP prevails in the medium and high SNR regime. Thus, it could be concluded that OFDM AF relaying systems may switch from the BTB SCP to BTW SCP in order to achieve optimum BER performance. Moreover, using the derived end-to-end SNR probability density functions (PDF), tight upper bounds for the ergodic capacities of both SCP schemes are obtained.

In this paper, we describe a verification environment which is based on a constrained random layered testbench using SystemVerilog OOP. As SystemVerilog OOP technique does not allow multiple inheritance, we adopt SystemC to design components of a verification environment which employ multiple inheritance. Then SystemC design unit is linked to a SystemVerilog-based verification environment using SystemVerilog DPI and ModelSim macro. Employing multiple inheritance of SystemC makes the design phase of verification environment simple and easy through source code reusability without corruption due to multi-level single inheritance.

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.

In this paper, we propose four new general constructions of LCZ/ZCZ sequence sets based on interleaving technique and affine transformations. A larger family of LCZ/ZCZ sequence sets with longer period are generated by these constructions, which are more flexible among the selection of the alphabet size, the period of the sequences and the length of LCZ/ZCZ, compared with those generated by the known constructions. Especially, two families of the newly constructed sequences can achieve or almost achieve the theoretic bound.

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.

In this paper, we show that the termination and the innermost termination properties are decidable for the class of term rewriting systems (TRSs for short) all of whose dependency pairs are right-linear and right-shallow. We also show that the innermost termination is decidable for the class of TRSs all of whose dependency pairs are shallow. The key observation common to these two classes is as follows: for every TRS in the class, we can construct, by using the dependency-pairs information, a finite set of terms such that if the TRS is non-terminating then there is a looping sequence beginning with a term in the finite set. This fact is obtained by modifying the analysis of argument propagation in shallow dependency pairs proposed by Wang and Sakai in 2006. However we gained a great benefit that the resulted procedures do not require any decision procedure of reachability problem used in Wang's procedure for shallow case, because known decidable classes of reachability problem are not larger than classes discussing in this paper.

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.

The traditional cryptographic hash functions are sensitive to even one-bit difference of the input message. While multimedia data always undergo compression or other signal processing operations, which lead to the unsuitability of multimedia authentication using cryptographic hash. The image hashing has emerged recently which captures visual essentials for robust image authentication. In this paper, we give a comprehensive survey of image hashing. We present an overview of various image hashing schemes and discuss their advantages and limitations in terms of security, robustness, and discrimination under different types of operations on the image.

In key-recovery methods using smart cards, a user can recover the disk encryption key in cooperation with the system administrator, even if the user has lost the smart card including the disk encryption key. However, the disk encryption key is known to the system administrator in advance in most key-recovery methods. Hence user's disk data may be read by the system administrator. Furthermore, if the disk encryption key is not known to the system administrator in advance, it is difficult to achieve a key authentication. In this paper, we propose a scheme which enables to recover the disk encryption key when the user's smart card is lost. In our scheme, the disk encryption key is not preserved anywhere and then the system administrator cannot know the key before key-recovery phase. Only someone who has a user's smart card and knows the user's password can decrypt that user's disk data. Furthermore, we measured the processing time required for user authentication in an experimental environment using a virtual machine monitor. As a result, we found that this processing time is short enough to be practical.

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.

Mobile devices, such as cellular phones and car navigation systems, are essential to daily life. People acquire necessary information and preferred content over communication networks anywhere, anytime. However, usability issues arise from the simplicity of user interfaces themselves. Thus, a recommendation of content that is adapted to a user's preference and situation will help the user select content. In this paper, we describe a method to realize such a system using Bayesian networks. This user-adapted mobile system is based on a user model that provides recommendation of content (i.e., restaurants, shops, and music that are suitable to the user and situation) and that learns incrementally based on accumulated usage history data. However, sufficient samples are not always guaranteed, since a user model would require combined dependency among users, situations, and contents. Therefore, we propose the LK method for modeling, which complements incomplete and insufficient samples using knowledge data, and CPT incremental learning for adaptation based on a small number of samples. In order to evaluate the methods proposed, we applied them to restaurant recommendations made on car navigation systems. The evaluation results confirmed that our model based on the LK method can be expected to provide better generalization performance than that of the conventional method. Furthermore, our system would require much less operation than current car navigation systems from the beginning of use. Our evaluation results also indicate that learning a user's individual preference through CPT incremental learning would be beneficial to many users, even with only a few samples. As a result, we have developed the technology of a system that becomes more adapted to a user the more it is used.

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.

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.

Due to the development and popularization of high-definition televisions, digital video cameras, Blu-ray discs, digital broadcasting, IP television and so on, it plays an important role to identify and quantify video quality degradations. In this paper, we propose SV-CIELAB which is an objective video quality assessment (VQA) method using a spatio-velocity contrast sensitivity function (SV-CSF). In SV-CIELAB, motion information in videos is effectively utilized for filtering unnecessary information in the spatial frequency domain. As the filter to apply videos, we used the SV-CSF. It is a modulation transfer function of the human visual system, and consists of the relationship among contrast sensitivities, spatial frequencies and velocities of perceived stimuli. In the filtering process, the SV-CSF cannot be directly applied in the spatial frequency domain because spatial coordinate information is required when using velocity information. For filtering by the SV-CSF, we obtain video frames separated in spatial frequency domain. By using velocity information, the separated frames with limited spatial frequencies are weighted by contrast sensitivities in the SV-CSF model. In SV-CIELAB, the criteria are obtained by calculating image differences between filtered original and distorted videos. For the validation of SV-CIELAB, subjective evaluation experiments were conducted. The subjective experimental results were compared with SV-CIELAB and the conventional VQA methods such as CIELAB color difference, Spatial-CIELAB, signal to noise ratio and so on. From the experimental results, it was shown that SV-CIELAB is a more efficient VQA method than the conventional methods.

Entanglement is expected to play a crucial role in the next-generation quantum communication systems. This paper reviews recent quantum communication experiments over optical fiber using 1.5-µm telecom-band entangled photon pairs. After describing the telecom-band entanglement sources based on spontaneous parametric processes, we review three quantum communication experiments using entangled photons: a long-distance entanglement distribution, an entanglement-based quantum key distribution, and an entanglement swapping.

We exhibit a simple procedure to find how classical signals should be processed in cluster-state quantum computation. Using stabilizers characterizing a cluster state, we can easily find a precise classical signal-flow that is required in performing cluster-state computation.

With the increasing number of crimes and accidents in which children are becoming involved, there is a growing demand for devices to safeguard children's security by detecting their locations on their way to and from school. This paper proposes a system that uses an IEEE802.15.4-standard network to detect children's locations. To overcome the susceptibility of radio interference from nearby wireless LANs, frequency division multiplexing is applied to this IEEE802.15.4-based network, toward improving data acquisition from terminal units. The effectiveness of the system was field-tested with elementary school students who used about 400 IEEE 802.15.4-compliant terminal units. An experiment verified that the use of frequency division multiplexing in an environment where radio interference by wireless LANs is strong allowed the network to double the success rate of information communication from terminal units relative to that without frequency division multiplexing. In the experiment for detecting elementary schoolers' arrival at and departure from school, the terminal detection rate was 99% and the terminal detection rate on the designated school routes was 90%. These results prove the effectiveness of the system in detecting locations.

Inductive theorem proving plays an important role in the field of formal verification of systems. The rewriting induction (RI) is a method for inductive theorem proving proposed by Reddy. In order to obtain successful proofs, it is very important to choose appropriate contexts (such as in which direction each equation should be oriented) when applying RI inference rules. If the choice is not appropriate, the procedure may diverge or the users have to come up with several lemmas to prove together with the main theorem. Therefore we have a good reason to consider parallel execution of several instances of the rewriting induction procedure, each in charge of a distinguished single context in search of a successful proof. In this paper, we propose a new procedure, called multi-context rewriting induction, which efficiently simulates parallel execution of rewriting induction procedures in a single process, based on the idea of the multi-completion procedure. By the experiments with a well-known problem set, we discuss the effectiveness of the proposed procedure when searching along various contexts for a successful inductive proof.

In this letter, a novel method is proposed for carrier-frequency offset (CFO) estimation for multiple users in orthogonal frequency division multiple access (OFDMA) uplink with the generalized carrier assignment scheme (GCAS). The base station (BS) is equipped with multiple antennas, and each user's CFO can be estimated by the ESPRIT-like method that utilizes the rotation invariance of the space-domain snapshot matrix. The method is still effective even in fully loaded system with all subcarriers allocated to users. Simulation results illustrate the high performance of the proposed algorithm.