Seam carving, which preserves semantically important image content during resizing process, has been actively researched in recent years. This paper proposes a novel forensic technique to detect the trace of seam carving. We exploit the energy bias and noise level of images under analysis to reliably unveil the evidence of seam carving. Furthermore, we design a detector investigating the relationship among neighboring pixels to estimate the inserted seams. Experimental results from a large set of test images indicates the superior performance of the proposed methods for both seam carving and seam insertion.

News articles usually represent a biased viewpoint on contentious issues, potentially causing social problems. To mitigate this media bias, we propose a novel framework for predicting orientation of a news article by analyzing social user behaviors in Twitter. Highly active users tend to have consistent behavior patterns in social network by retweeting behavior among users with the same viewpoints for contentious issues. The bias ratio of highly active users is measured to predict orientation of users. Then political orientation of a news article is predicted based on the bias ratio of users, mutual retweeting and opinion analysis of tweet documents. The analysis of user behavior shows that users with the value of 1 in bias ratio are 88.82%. It indicates that most of users have distinctive orientation. Our prediction method based on orientation of users achieved 88.6% performance in accuracy. Experimental results show significant improvements over the SVM classification. These results show that proposed detection method is effective in social network.

Linear dynamical systems are basic state space models literally dealing with underlying system dynamics on the basis of linear state space equations. When the model is employed for time-series data analysis, the system identification, which detects the dimension of hidden state variables, is one of the most important tasks. Recently, it has been found that the model has singularities in the parameter space, which implies that analysis for adverse effects of the singularities is necessary for precise identification. However, the singularities in the models have not been thoroughly studied. There is a previous work, which dealt with the simplest case; the hidden state and the observation variables are both one dimensional. The present paper extends the setting to general dimensions and more rigorously reveals the structure of singularities. The results provide the asymptotic forms of the generalization error and the marginal likelihood, which are often used as criteria for the system identification.

We review our recent achievements in monolithic 3D-ICs and flexible electronics based on single-grain Si TFTs that are fabricated inside a single-grain with a low-temperature process. Based on pulsed-laser crystallization and submicron sized cavities made in the substrate, amorphous-Si precursor film was converted into poly-Si having grains that are formed on predetermined positions. Using the method called µ-Czochralski process and LPCVD a-Si precursor film, two layers of the SG Si TFT layers with the grains having a diameter of 6µm were vertically stacked with a maximum process temperature of 550°C. Mobility for electrons and holes were 600cm2/Vs and 200cm2/Vs, respectively. As a demonstration of monolithic 3D-ICs, the two SG-TFT layers were successfully implemented into CMOS inverter, 3D 6T-SRAM and single-grain lateral PIN photo-diode with in-pixel amplifier. The SG Si TFTs were applied to flexible electronics. In this case, the a-Si precursor was prepared by doctor-blade coating of liquid-Si based on pure cyclopentasilane (CPS) on a polyimide (PI) substrate with maximum process temperature of 350°C. The µ-Czochralski process provided location-controlled Si grains with a diameter of 3µm and mobilities of 460 and 121cm2/Vs for electrons and holes, respectively, were obtained. The devices on PI were transferred to a plastic foil which can operate with a bending diameter of 6mm. Those results indicate that the SG TFTs are attractive for their use in both monolithic 3D-ICs and flexible electronics.

A lot of work has been conducted on time series classification and similarity search over the past decades. However, the classification of a time series with high accuracy is still insufficient in applications such as ubiquitous or sensor systems. In this paper, a novel textual approximation of a time series, called TAX, is proposed to achieve high accuracy time series classification. l-TAX, an extended version of TAX that shows promising classification accuracy over TAX and other existing methods, is also proposed. We also provide a comprehensive comparison between TAX and l-TAX, and discuss the benefits of both methods. Both TAX and l-TAX transform a time series into a textual structure using existing document retrieval methods and bioinformatics algorithms. In TAX, a time series is represented as a document like structure, whereas l-TAX used a sequence of textual symbols. This paper provides a comprehensive overview of the textual approximation and techniques used by TAX and l-TAX

With the distributed data mining technique having been widely used in a variety of fields, the privacy preserving issue of sensitive data has attracted more and more attention in recent years. Our major concern over privacy preserving in distributed data mining is the accuracy of the data mining results while privacy preserving is ensured. Corresponding to the horizontally partitioned data, this paper presents a new hybrid algorithm for privacy preserving distributed data mining. The main idea of the algorithm is to combine the method of random orthogonal matrix transformation with the proposed secure multi-party protocol of matrix product to achieve zero loss of accuracy in most data mining implementations.

Graph pattern mining has played important roles in network analysis and information retrieval. However, temporal characteristics of networks have not been estimated sufficiently. We propose time graph pattern mining as a new concept of graph mining reflecting the temporal information of a network. We conduct two case studies of time graph pattern mining: extensively discussed topics on blog sites and a book recommendation network. Through examination of case studies, we ascertain that time graph pattern mining has numerous possibilities as a novel means for information retrieval and network analysis reflecting both structural and temporal characteristics.

This paper investigates a signal detection method with a RAKE combiner for the case wherein the receiving signals use the same spreading code. In the case where multiple user interference with the same spreading code (MUI-SC) occurs, blind channel estimation is difficult and as far as we know has not been investigated. To tackle the issue of MUI-SC, we propose two blind channel estimation methods based on the multimodulus algorithm (MMA), i.e., MMA-IQ and MMA-I methods. When a one dimensional modulation scheme, such as differential binary phase-shift keying (DBPSK), is used, the output of the MMA-IQ channel estimation method can, under MUI-SC, have two states. The first state is that the channel estimate corresponds to a channel response for one of the received signals, and the second state is that the channel estimate corresponds to combined channel responses for two of the received signals. This is because the MMA-IQ uses two degrees of freedom (both axes in the IQ-plane), however one DBPSK signal uses only one degree of freedom. In the case of the second state, it is possible to detect two signals/packets at once. However, in the MMA-IQ, the receiver has to recognize the state of the channel estimate before the signal detection, thus we also propose a state recognition method. In the MMA-I channel estimation method, only the I-axis is used thus the channel estimate always corresponds the case with one signal. Numerical results show that the average number of detected packets of the MMA-IQ is more than that of the MMA-I in high signal-to-noise power ratio case. In addition, several aspects of the MMA-I and MMA-IQ based RAKE signal detection methods are shown.

This letter proposes a novel intrusion tolerant system consisting of several virtual machines (VMs) that refresh the target system periodically and by live migration, which monitors the many features of the VMs to identify and replace exhausted VMs. The proposed scheme provides adequate performance and dependability against denial of service (DoS) attacks. To show its efficiency and security, we conduct experiments on the CSIM20 simulator, which showed 22% improvement in a normal situation and approximately 77.83% improvement in heavy traffic in terms of the response time compared to that reported in the literature. We measure and compare the response time. The result show that the proposed scheme has shorter response time and maintains than other systems and supports services during the heavy traffic.

The availability of more and more Web services provides great varieties for users to design service processes. However, there are situations that services or service processes cannot meet users' requirements in functional QoS dimensions (e.g., translation quality in a machine translation service). In those cases, composing Web services and human tasks is expected to be a possible alternative solution. However, analysis of such practical efforts were rarely reported in previous researches, most of which focus on the technology of embedding human tasks in software environments. Therefore, this study aims at analyzing the effects of composing Web services and human activities using a case study in the domain of language service with large scale experiments. From the experiments and analysis, we find out that (1) service implementation variety can be greatly increased by composing Web services and human activities for satisfying users' QoS requirements; (2) functional QoS of a Web service can be significantly improved by inducing human activities with limited cost and execution time provided certain quality of human activities; and (3) multiple QoS attributes of a composite service are affected in different ways with different quality of human activities.

We propose a parameterized multisurface fitting method for multi-frame super-resolution (SR) processing. A parameter assumed for the unknown high-resolution (HR) pixel is used for multisurface fitting. Each surface fitted at each low-resolution (LR) pixel is an expression of the parameter. Final SR result is obtained by fusing the sampling values from these surfaces in the maximum a posteriori fashion. Experimental results demonstrate the superiority of the proposed method.

This paper proposes the state observer design for feedforward nonlinear systems with delayed output. It is shown that by using the Lyapunov-Krasovskii functional, the proposed design method ensures the asymptotic stability of estimation error for an arbitrarily large output delay. Finally, an illustrative example is given in order to show the effectiveness of our design method.

In this paper, a new evolutionary approach to recommender systems is presented. The aim of this work is to develop a new recommendation method that effectively adapts and immediately responds to the user's preference. To this end, content-based filtering is judiciously utilized in conjunction with interactive evolutionary computation (IEC). Specifically, a fitness-based truncation selection and a feature-wise crossover are devised to make full use of desirable properties of promising items within the IEC framework. Moreover, to efficiently search for proper items, the content-based filtering is modified in cooperation with data grouping. The experimental results demonstrate the effectiveness of the proposed approach, compared with existing methods.

In this letter, we propose a simple but effective technique that improves statistical model-based voice activity detection (VAD) by both reducing computational complexity and increasing detection accuracy. The improvements are made by applying Taylor series approximations to the exponential and logarithmic functions in the VAD algorithm based on an in-depth analysis of the algorithm. Experiments performed on a smartphone as well as on a desktop computer with various background noises confirm the effectiveness of the proposed technique.

In this paper, we study the impact of opportunistic user scheduling on the outage probability of cognitive radio (CR) multiple-input multiple-output (MIMO) systems in the high power region where the peak transmit power constraint is higher than the peak interference constraint. The primary contributions of this paper are the derivation of exact closed-form expressions of the proposed scheduled CR-MIMO systems for outage probability and asymptotic analysis to quantify the diversity order and signal to noise ratio (SNR) gain. Through exact analytical results, we provide the achievable outage probability of the proposed scheduled systems as a function of SNR. Also, through asymptotic analysis, we show that the scheduled CR-MIMO systems provide some diversity order gain over the non-scheduled CR-MIMO systems which comes from multi-user diversity (MUD). Also, the SNR gain of the proposed scheduled systems is identical to that of the non-scheduled CR-MIMO systems.

We have already proposed a service discovery scheme using mobile agents for mobile ad hoc networks where node positions in the network and the network topology change frequently. Mobile agents autonomously migrate among nodes and then perform a given task at a node. In the service discovery scheme using mobile agents, mobile agents collect and disseminate services in the network so it is most important how the mobile agents migrate in the network. Therefore, we propose two types of mobile agent migration mechanisms in this paper. One is that mobile agents migrate to the nodes at which other mobile agents do not stay, the other is that mobile agents migrate to the nodes to which mobile agents can disseminate a lot of service information. Finally, we conducted simulation experiments to investigate the performance of the proposed migration mechanisms with respect to the service dissemination time and rate.

Let G be a graph with a single source w, assigned a positive integer called the supply. Every vertex other than w is a sink, assigned a nonnegative integer called the demand. Every edge is assigned a positive integer called the capacity. Then a spanning tree T of G is called a spanning distribution tree if the capacity constraint holds when, for every sink v, an amount of flow, equal to the demand of v, is sent from w to v along the path in T between them. The spanning distribution tree problem asks whether a given graph has a spanning distribution tree or not. In the paper, we first observe that the problem is NP-complete even for series-parallel graphs, and then give a pseudo-polynomial time algorithm to solve the problem for a given series-parallel graph G. The computation time is bounded by a polynomial in n and D, where n is the number of vertices in G and D is the sum of all demands in G.

For battery based real-time embedded systems, high performance to meet their real-time constraints and energy efficiency to extend battery life are both essential. Real-Time Dynamic Voltage Scaling (RT-DVS) has been a key technique to satisfy both requirements. This paper presents EccEDF (Enhanced ccEDF), an efficient algorithm based on ccEDF. ccEDF is one of the most simple but efficient RT-DVS algorithms. Its simple structure enables it to be easily and intuitively coupled with a real-time operating system without incurring any significant cost. ccEDF, however, overlooks an important factor in calculating the available slacks for reducing the operating frequency. It calculates the saved utilization simply by dividing the slack by the period without considering the time needed to run the task. If the elapsed time is considered, the maximum utilization saved by the slack on completion of the task can be found. The proposed EccEDF can precisely calculate the maximum unused utilization with consideration of the elapsed time while keeping the structural simplicity of ccEDF. Further, we analytically establish the feasibility of EccEDF using the fluid scheduling model. Our simulation results show that the proposed algorithm outperforms ccEDF in all simulations. A simulation shows that EccEDF consumes 27% less energy than ccEDF.

Attenuated and delayed versions of the pulse signal overlap in multipath propagation. Previous algorithms can resolve them only if signal sampling is ideal, but fail to resolve two counterparts with non-ideal sampling. In this paper, we propose a novel method which can resolve the general types of non-ideally sampled pulse signals in the time domain via Taylor Series Expansion (TSE) and estimate multipath signals' precise time delays and amplitudes. In combination with the CLEAN algorithm, the overlapped pulse signal parameters are estimated one by one through an iteration method. Simulation results verify the effectiveness of the proposed method.

This paper proposes an adaptive marker coding (AMC) for correction of insertion/deletion/substitution errors. Unlike the conventional marker codings which select marker-bit values deterministically, the AMC adaptively reverses the first and last bits of each marker as well as bits surrounding the marker. Decoding is based on a forward-backward algorithm which takes into account the dependency of bit-values around the marker. Evaluation shows that, for a channel with insertion/deletion error probability 1.8×10-2, the decoded BER of existing marker coding of rate 9/16 is 4.25×10-3, while that of the proposed coding with the same code rate is 1.73×10-3.