In this paper we propose an improved algorithm for k-concealment, which has been proposed as an alternative to the well-known k-anonymity model. k-concealment achieves similar privacy goals as k-anonymity; it proposes to generalize records in a table in such a way that each record is indistinguishable from at least k-1 other records, while achieving higher utility than k-anonymity. However, its computation is quite expensive in particular when dealing with large datasets containing massive records due to its high computational complexity. To cope with this problem, we propose neighbor lists, where for each record similar records are stored. Neighbor lists are constructed in advance, and can also be efficiently constructed by mapping each record to a point in a high-dimensional space and using appropriate multidimensional indexes. Our proposed scheme successfully decreases the execution time from O(kn2) to O(k2n+knlogn), and it can be practically applied to databases with millions of records. The experimental evaluation using a real dataset reveals that the proposed scheme can achieve the same level of utility as k-concealment while maintaining the efficiency at the same time.

This paper presents a parallelization method utilizing dead time to implement higher precision feedback control systems in multicore processors. The feedback control system is known to be difficult to parallelize, and it is difficult to deal with the dead time in control systems. In our method, the dead time is explicitly represented as delay elements. Then, these delay elements are distributed to the overall systems with equivalent transformation so that the system can be simulated or executed in parallel pipeline operation. In addition, we introduce a method of delay-element addition for parallelization. For a spring-mass-damper model with a dead time, parallel execution of the model using our technique achieves 3.4 times performance acceleration compared with its sequential execution on an ideal four-core simulation and 1.8 times on a cycle-accurate simulator of a four-core embedded processor as a threaded application on a real-time operating system.

Research on emotion recognition using electroencephalogram (EEG) of subjects listening to music has become more active in the past decade. However, previous works did not consider emotional oscillations within a single musical piece. In this research, we propose a continuous music-emotion recognition approach based on brainwave signals. While considering the subject-dependent and changing-over-time characteristics of emotion, our experiment included self-reporting and continuous emotion annotation in the arousal-valence space. Fractal dimension (FD) and power spectral density (PSD) approaches were adopted to extract informative features from raw EEG signals and then we applied emotion classification algorithms to discriminate binary classes of emotion. According to our experimental results, FD slightly outperformed PSD approach both in arousal and valence classification, and FD was found to have the higher correlation with emotion reports than PSD. In addition, continuous emotion recognition during music listening based on EEG was found to be an effective method for tracking emotional reporting oscillations and provides an opportunity to better understand human emotional processes.

For the maintenance of a business system, developers must understand the business rules implemented in the system. One type of business rules defines computational business rules; they represent how an output value of a feature is computed from the valid inputs. Unfortunately, understanding business rules is a tedious and error-prone activity. We propose a program-dependence analysis technique tailored to understanding computational business rules. Given a variable representing an output, the proposed technique extracts the conditional statements that may affect the computation of the output. To evaluate the usefulness of the technique, we conducted an experiment with eight developers in one company. The results confirm that the proposed technique enables developers to accurately identify conditional statements corresponding to computational business rules. Furthermore, we compare the number of conditional statements extracted by the proposed technique and program slicing. We conclude that the proposed technique, in general, is more effective than program slicing.

As the electricity rates during peak hours are higher, this paper proposes a design for an ultrabook to automatically shift the charging period to an off-peak period. In addition, this design sets an upper limit for the battery which thus protects the battery and prevents it from remaining in a continued state of both high temperature and high voltage. This design uses both a low-power embedded controller (EC) and the fuzzy logic controller (FLC) control method as the main control techniques together with real time clock (RTC) ICs. The sensing value of the EC and the presetting of parameters are used to control the conversion of the AC/DC module. This user interface design allows the user to set not only the peak/off-peak period but also the upper use limit of the battery.

Nonnegative component representation (NCR) is a mid-level representation based on nonnegative matrix factorization (NMF). Recently, it has attached much attention and achieved encouraging result for action recognition. In this paper, we propose a novel hierarchical dictionary learning strategy (HDLS) for NMF to improve the performance of NCR. Considering the variability of action classes, HDLS clusters the similar classes into groups and forms a two-layer hierarchical class model. The groups in the first layer are disjoint, while in the second layer, the classes in each group are correlated. HDLS takes account of the differences between two layers and proposes to use different dictionary learning methods for this two layers, including the discriminant class-specific NMF for the first layer and the discriminant joint dictionary NMF for the second layer. The proposed approach is extensively tested on three public datasets and the experimental results demonstrate the effectiveness and superiority of NCR with HDLS for large-scale action recognition.

Phishing attacks target financial returns by luring Internet users to exposure their sensitive information. Phishing originates from e-mail fraud, and recently it is also spread by social networks and short message service (SMS), which makes phishing become more widespread. Phishing attacks have drawn great attention due to their high volume and causing heavy losses, and many methods have been developed to fight against them. However, most of researches suffered low detection accuracy or high false positive (FP) rate, and phishing attacks are facing the Internet users continuously. In this paper, we are concerned about feature engineering for improving the classification performance on phishing web pages detection. We propose a novel anti-phishing framework that employs feature engineering including feature selection and feature extraction. First, we perform feature selection based on genetic algorithm (GA) to divide features into critical features and non-critical features. Then, the non-critical features are projected to a new feature by implementing feature extraction based on a two-stage projection pursuit (PP) algorithm. Finally, we take the critical features and the new feature as input data to construct the detection model. Our anti-phishing framework does not simply eliminate the non-critical features, but considers utilizing their projection in the process of classification, which is different from literatures. Experimental results show that the proposed framework is effective in detecting phishing web pages.

Due to the periodic recovery of virtual machines regardless of whether malicious intrusions exist, proactive recovery-based Intrusion Tolerant Systems (ITSs) are being considered for mission-critical applications. However, the virtual replicas can easily be exposed to attacks during their working period, and additionally, proactive recovery-based ITSs are ineffective in eliminating the vulnerability of exposure time, which is closely related to service availability. To address these problems, we propose a novel hybrid recovery-based ITS in this paper. The proposed method utilizes availability-driven recovery and dynamic cluster resizing. The availability-driven recovery method operates the recovery process by both proactive and reactive ways for the system to gain shorter exposure times and higher success rates. The dynamic cluster resizing method reduces the overhead of the system that occurs from dynamic workload fluctuations. The performance of the proposed ITS with various synthetic and real workloads using CloudSim showed that it guarantees higher availability and reliability of the system, even under malicious intrusions such as DDoS attacks.

Changing attitudes toward energy security and energy conservation have led to the introduction of distributed power systems such as photovoltaic, gas-cogeneration, biomass, water, and wind power generators. The mass installation of distributed energy generators often causes instability in the voltage and frequency of the power grid. Moreover, the power quality of distributed power grids can become degraded when system faults or the activation of highly loaded machines cause rapid changes in power load. To avoid such problems and maintain an acceptable power quality, it is important to detect the source of these rapid changes. To address these issues, next-generation power grids that can detect the fault location have been proposed. Fault location demands accurate time synchronization. Conventional techniques use the Global Positioning System (GPS) and/or IEEE 1588v2 for time synchronization. However, both methods have drawbacks — GPS cannot be used in indoor situations, and the installation cost of IEEE 1588v2 devices is high. In this paper, a time synchronization technique using the broadcast function of an Ethernet Passive Optical Network (EPON) system is proposed. Experiments show that the proposed technique is low-cost and useful for smart grid applications that use time synchronization in EPON-based next-generation power grids.

Secret sharing is a method of information protection for security. The information is divided into n shares and reconstructed from any k shares, but no knowledge of the information is revealed from k-1 shares. Physical layer security is a method of achieving favorable reception conditions at the destination terminal in wireless communications. In this study, we propose a security enhancement technique for wireless packet communications. The technique uses secret sharing and physical layer security to exchange a secret encryption key. The encryption key for packet information is set as the secret information in secret sharing, and the secret information is divided into n shares. Each share is located in the packet header. The base station transmits the packets to the destination terminal by using physical layer security based on precoded multi-antenna transmission. With this transmission scheme, the destination terminal can receive more than k shares without error and perfectly recover the secret information. In addition, an eavesdropper terminal can receive less than k-1 shares without error and recover no secret information. In this paper, we propose a protection technique using secret sharing based on systematic Reed-Solomon codes. The technique establishes an advantageous condition for the destination terminal to recover the secret information. The evaluation results by numerical analysis and computer simulation show the validity of the proposed technique.

Cloud storage has become popular and is being used to hold important data. As a result, availability to become important; cloud storage providers should allow users to upload or download data even if some part of the system has failed. In this paper, we discuss distributed cloud storage that is robust against failures. In distributed cloud storage, multiple replicas of each data chunk are stored in the virtual storage at geographically different locations. Thus, even if one of the virtual storage systems becomes unavailable, users can access the data chunk from another virtual storage system. In distributed cloud storage, the placement of the virtual storage system is important; if the placement of the virtual cloud storage system means that a large number of virtual storages are possible could become unavailable from a failure, a large number of replicas of each data chunk should be prepared to maintain availability. In this paper, we propose a virtual storage placement method that assures availability with a small number of replicas. We evaluated our method by comparing it with three other methods. The evaluation shows that our method can maintain availability while requiring only with 60% of the network costs required by the compared methods.

With the development of silicon-based Nano-photonics, Optical Network on Chip (ONoC) is, due to its high bandwidth and low latency, becoming an important choice for future multi-core networks. As a key ONoC technology, the arbitration scheme should provide differential arbitration service with high throughput and low latency for various types and priorities of traffic in CMPs. In this work, we propose a fast hierarchical arbitration scheme based on multi-level priority QoS. First, given multi-priority data buffer queue, arbiters provide differential transmissions with fair service for all nodes and guarantee the max-transmit-delay and min-communication-bandwidth for all queues. Second, arbiter adopts the transmit bound resource reservation scheme to reserve time slots for all nodes fairly, thereby achieving a throughput of 100%. Third, we propose fast arbitration with a layout of fast optical arbitration channels (FOACs) to reduce the arbitration period, thereby reducing packet transmitting delay. Simulation results show that with our hierarchical arbitration scheme, all nodes are allocated almost equal service access probability under various traffic patterns; thus, the min-communication-bandwidth and max-transmit-delay is guaranteed to be 5% and 80 cycles, respectively, under the overload demands. This scheme improves throughput by 17% compared to FeatherWeight under a self-similar traffic pattern and decreases arbitration delay by 15% compare to 2-pass arbitration, incurring a total power overhead of 5%.

This paper reports an assessment of the feasibility and the practicality of a creation support system for contemporary dance e-learning. We developed a Body-part Motion Synthesis System (BMSS) that allows users to create choreographies by synthesizing body-part motions to increase the effect of learning contemporary dance choreography. Short created choreographies can be displayed as animation using 3DCG characters. The system targets students who are studying contemporary dance and is designed to promote the discovery learning of contemporary dance. We conducted a series of evaluation experiments for creating contemporary dance choreographies to verify the learning effectiveness of our system as a support system for discovery learning. As a consequence of experiments with 26 students who created contemporary dances, we verified that BMSS is a helpful creation training tool to discover new choreographic methods, new dance movements, and new awareness of their bodies.

We propose an impact and high-pitch noise-suppression method based on spectral entropy. Spectral entropy takes a large value for flat spectral amplitude and a small value for spectra with several lines. We model the impact noise as a flat spectral signal and its damped oscillation as a high-pitch periodic signal consisting of spectra with several lines. We discriminate between the current noise situations by using spectral entropy and adaptively change the noise-suppression parameters used in a zero phase-based impact-noise-suppression method. Simulation results show that the proposed method can improve the perceptual evaluation of the speech quality and speech-recognition rate compared to conventional methods.

Mathematical formulae play an important role in many scientific domains. Regardless of the importance of mathematical formula search, conventional keyword-based retrieval methods are not sufficient for searching mathematical formulae, which are structured as trees. The increasing number as well as the structural complexity of mathematical formulae in scientific articles lead to the necessity for large-scale structure-aware formula search techniques. In this paper, we formulate three types of measures that represent distinctive features of semantic similarity of math formulae, and develop efficient hash-based algorithms for the approximate calculation. Our experiments using NTCIR-11 Math-2 Task dataset, a large-scale test collection for math information retrieval with about 60-million formulae, show that the proposed method improves the search precision while also keeps the scalability and runtime efficiency high.

With the rapid merger of healthcare business and information technology, more healthcare institutions and medical practices are sharing information. Since these records often contain patients' sensitive personal information, Healthcare Information Systems (HISs) should be properly designed to manage these records in a secure manner. We propose a novel security design for the HIS complying with the security and privacy rules. The proposed system defines protocols to ensure secure delivery of medical records over insecure public networks and reliable management of medical record in the remote server without incurring excessive costs to implement services for security. We demonstrate the practicality of the proposed system through a security analysis and performance evaluation.

This paper presents an efficient subverted symmetric encryption scheme, which outputs a random initialization vector (IV). Compared with the available scheme of the same kind in the literature, our attack provides a saboteur (big brother) with much faster recovery of a key used in a victim's symmetric encryption scheme. Our result implies that care must be taken when a symmetric encryption scheme with a random IV such as randomized CBC is deployed.

This paper proposes a novel object tracking method via online boosting. The on-line boosting technique is combined with local features to treat tracking as a keypoint matching problem. First, We improve matching reliability by exploiting the statistical repeatability of local features. In addition, we propose 2D scale-rotation invariant quasi-keypoint matching to further improve matching efficiency. Benefiting from SURF feature's statistical repeatability and the complementary quasi-keypoint matching technique, we can easily find reliable matching pairs and thus perform accurate and stable tracking. Experimental results show that the proposed method achieves better performance compared with previously reported trackers.

This work presents an object tracking framework which is based on integration of Deformable Part based Models (DPMs) and Dynamic Conditional Random Fields (DCRF). In this framework, we propose a DCRF based novel way to track an object and its details on multiple resolutions simultaneously. Meanwhile, we tackle drastic variations in target appearance such as pose, view, scale and illumination changes with DPMs. To embed DPMs into DCRF, we design specific temporal potential functions between vertices by explicitly formulating deformation and partial occlusion respectively. Furthermore, temporal transition functions between mixture models bring higher robustness to perspective and pose changes. To evaluate the efficacy of our proposed method, quantitative tests on six challenging video sequences are conducted and the results are analyzed. Experimental results indicate that the method effectively addresses serious problems in object tracking and performs favorably against state-of-the-art trackers.