This paper shows a way to derive positive definite kernels from edit distances. It is well-known that, if a distance d is negative definite, e^-λd is positive definite for any λ > 0. This property provides us the opportunity to apply useful techniques of kernel multivariate analysis to the features of data captured by means of the distance. However, the known instances of edit distance are not always negative definite. Even worse, it is usually not easy to examine whether a given instance of edit distance is negative definite. This paper introduces alignment kernels to present an alternative means to derive kernels from edit distance. The most important advantage of the alignment kernel consists in its easy-to-check sufficient condition for the positive definiteness. In fact, when we surveyed edit distances for strings, trees and graphs, all but one are instantly verified to meet the condition and therefore proven to be positive definite.

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Recently, fuzzy set theory has been widely employed in building portfolio selection models where uncertainty plays a role. In these models, future security returns are generally taken for fuzzy variables and mathematical models are then built to maximize the investment profit according to a given risk level or to minimize a risk level based on a fixed profit level. Based on existing works, this paper proposes a portfolio selection model based on fuzzy birandom variables. Two original contributions are provided by the study: First, the concept of technical analysis is combined with fuzzy set theory to use the security returns as fuzzy birandom variables. Second, the fuzzy birandom Value-at-Risk (VaR) is used to build our model, which is called the fuzzy birandom VaR-based portfolio selection model (FBVaR-PSM). The VaR can directly reflect the largest loss of a selected case at a given confidence level and it is more sensitive than other models and more acceptable for general investors than conventional risk measurements. To solve the FBVaR-PSM, in some special cases when the security returns are taken for trapezoidal, triangular or Gaussian fuzzy birandom variables, several crisp equivalent models of the FBVaR-PSM are derived, which can be handled by any linear programming solver. In general, the fuzzy birandom simulation-based particle swarm optimization algorithm (FBS-PSO) is designed to find the approximate optimal solution. To illustrate the proposed model and the behavior of the FBS-PSO, two numerical examples are introduced based on investors' different risk attitudes. Finally, we analyze the experimental results and provide a discussion of some existing approaches.

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In collaborative software developments, many change processes implementing change requests are executed concurrently by different workers. The fact that the workers do not have sufficient information about the others' work and complicated dependencies among artifacts can lead to unexpected inconsistencies among the artifacts impacted by the changes. Most previous studies concentrated only on concurrent changes and considered them separately. However, even when the changes are not concurrent, inconsistencies may still happen if a worker does not recognize the impact of the changes made by other workers on his changes or the impact of his changes on other workers' changes. In addition, the changes in a change process are related to each other through their common target of realizing the change request and the dependencies among the changed artifacts. Therefore, to handle inconsistencies more effectively, we concentrate on both concurrent and non-concurrent changes, and the context of a change, i.e. the change process containing the change, rather than the ongoing changes only. In this paper, we present an inconsistency awareness mechanism and a Change Support Workflow Management System (CSWMS) that realizes this mechanism. By monitoring the progress of the change processes and the ongoing changes in the client workspaces, CSWMS can notify the workers of a (potential) inconsistency in advance along with the context of the inconsistency, that is, the changes causing the inconsistency and the change processes containing these changes. Based on the information provided by CSWMS, the workers can detect and resolve inconsistencies more easily and quickly. Therefore, our research can contribute to building a safer and more efficient collaborative software development environment.

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With the increasing demand for geographic information and position information, the geographic information system (GIS) has come to be widely used in city planning, utilities management, natural resource environments, land surveying, etc. While most GIS maps use vector data to represent geographic information more easily and in greater detail, a GIS vector map can be easily copied, edited, and illegally distributed, like most digital data. This paper presents an invisible, blind, secure, and robust watermarking method that provides copyright protection of GIS vector digital maps by means of arc length distribution. In our method, we calculate the arc lengths of all the polylines/polygons in a map and cluster these arc lengths into a number of groups. We then embed a watermark bit by changing the arc length distribution of a suitable group. For greater security and robustness, we use a pseudo-random number sequence for processing the watermark and embed the watermark multiple times in all maps. Experimental results verify that our method has good invisibility, security, and robustness against various geometric attacks and that the original map is not needed in the watermark extraction process.

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KCipher-2 is a word-oriented stream cipher and an ISO/IEC 18033 standard. It is listed as a CRYPTREC cryptographic algorithm for Japanese governmental use. It consists of two feedback shift registers and a non-linear function. The size of each register in KCipher-2 is 32 bits and the non-linear function mainly applies 32-bit operations. Therefore, it can be efficiently implemented as software. SNOW-family stream ciphers are also word-oriented stream ciphers, and their high performance has already been demonstrated.We propose optimised implementations of KCipher-2 and compare their performance to that of the SNOW-family and other eSTREAM portfolios. The fastest algorithm is SNOW 2.0 and KCipher-2 is the second fastest despite the complicated irregular clocking mechanism. However, KCipher-2 is the fastest of the feasible algorithms, as SNOW 2.0 has been shown to have a security flaw. We also optimise the hardware implementation for the Virtex-5 field-programmable gate array (FPGA) and show two implementations. The first implementation is a rather straightforward optimisation and achieves 16,153 Mbps with 732 slices. In the second implementation, we duplicate the non-linear function using the structural advantage of KCipher-2 and we achieve 17,354 Mbps with 813 slices. Our implementation of KCipher-2 is around three times faster than those of the SNOW-family and efficiency, which is evaluated by “Throughput/Area (Mbps/slice)”, is 3.6-times better than that of SNOW 2.0 and 8.5-times better than that of SNOW 3G. These syntheses are performed using Xilinx ISE version 12.4.

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We propose a new method that accelerates asynchronous Byzantine Fault Tolerant (BFT) protocols designed on the principle of state machine replication. State machine replication protocols ensure consistency among replicas by applying operations in the same order to all of them. A naive way to determine the application order of the operations is to repeatedly execute the BFT consensus to determine the next executed operation, but this may introduce inefficiency caused by waiting for the completion of the previous execution of the consensus protocol. To reduce this inefficiency, our method allows parallel execution of the consensuses while keeping consistency of the consensus results at the replicas. In this paper, we also prove the correctness of our method and experimentally compare it with the existing method in terms of latency and throughput. The evaluation results show that our method makes a BFT protocol three or four times faster than the existing one when some machines or message transmissions are delayed.

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Checkpoint-rollback recovery, which is a universal method for restoring distributed systems after faults, requires a sophisticated snapshot algorithm especially if the systems are large-scale, since repeatedly taking global snapshots of the whole system requires unacceptable communication cost. As a sophisticated snapshot algorithm, a partial snapshot algorithm has been introduced that takes a snapshot of a subsystem consisting only of the nodes that are communication-related to the initiator instead of a global snapshot of the whole system. In this paper, we modify the previous partial snapshot algorithm to create a new one that can take a partial snapshot more efficiently, especially when multiple nodes concurrently initiate the algorithm. Experiments show that the proposed algorithm greatly reduces the amount of communication needed for taking partial snapshots.

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How do global warming and agriculture influence each other? It is possible to answer the question by searching knowledge about the relationship between global warming and agriculture. As exemplified by this question, strong demands exist for searching relationships between objects. Mining knowledge about relationships on Wikipedia has been studied. However, it is desired to search more diverse knowledge about relationships on the Web. By utilizing the objects constituting relationships mined from Wikipedia, we propose a new method to search images with surrounding text that include knowledge about relationships on the Web. Experimental results show that our method is effective and applicable in searching knowledge about relationships. We also construct a relationship search system named “Enishi” based on the proposed new method. Enishi supplies a wealth of diverse knowledge including images with surrounding text to help users to understand relationships deeply, by complementarily utilizing knowledge from Wikipedia and the Web.

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The traditional reinforcement learning (RL) methods can solve Markov Decision Processes (MDPs) online, but these learning methods cannot effectively use a priori knowledge to guide the learning process. The exploration of the optimal policy is time-consuming and does not employ the information about specific issues. To tackle the problem, this paper proposes heuristic function negotiation (HFN) as an online learning framework. The HFN framework extends MDPs and introduces heuristic functions. HFN changes the state-action dual layer structure of traditional RL to the triple layer structure, in which multiple heuristic functions can be set to meet the needs required to solve the problem. The HFN framework can use different algorithms to let the functions negotiate to determine the appropriate action, and adjust the impact of each function according to the rewards. The HFN framework introduces domain knowledge by setting heuristic functions and thus speeds up the problem solving of MDPs. Furthermore, user preferences can be reflected in the learning process, which improves the flexibility of RL. The experiments show that, by setting reasonable heuristic functions, the learning results of the HFN framework are more efficient than traditional RL. We also apply HFN to the air combat simulation of unmanned aerial vehicles (UAVs), which shows that different function settings lead to different combat behaviors.

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Regularized forward selection is viewed as a method for obtaining a sparse representation in a nonparametric regression problem. In regularized forward selection, regression output is represented by a weighted sum of several significant basis functions that are selected from among a large number of candidates by using a greedy training procedure in terms of a regularized cost function and applying an appropriate model selection method. In this paper, we propose a model selection method in regularized forward selection. For the purpose, we focus on the reduction of a cost function, which is brought by appending a new basis function in a greedy training procedure. We first clarify a bias and variance decomposition of the cost reduction and then derive a probabilistic upper bound for the variance of the cost reduction under some conditions. The derived upper bound reflects an essential feature of the greedy training procedure; i.e., it selects a basis function which maximally reduces the cost function. We then propose a thresholding method for determining significant basis functions by applying the derived upper bound as a threshold level and effectively combining it with the leave-one-out cross validation method. Several numerical experiments show that generalization performance of the proposed method is comparable to that of the other methods while the number of basis functions selected by the proposed method is greatly smaller than by the other methods. We can therefore say that the proposed method is able to yield a sparse representation while keeping a relatively good generalization performance. Moreover, our method has an advantage that it is free from a selection of a regularization parameter.

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To help student nurses learn to transfer patients from a bed to a wheelchair, this paper proposes a system for automatic skill evaluation in nurses' training for this task. Multiple Kinect sensors were employed, in conjunction with colored markers attached to the trainee's and patient's clothing and to the wheelchair, in order to measure both participants' postures as they interacted closely during the transfer and to assess the correctness of the trainee's movements and use of equipment. The measurement method involved identifying body joints, and features of the wheelchair, via the colors of the attached markers and calculating their 3D positions by combining color and depth data from two sensors. We first developed an automatic segmentation method to convert a continuous recording of the patient transfer process into discrete steps, by extracting from the raw sensor data the defining features of the movements of both participants during each stage of the transfer. Next, a checklist of 20 evaluation items was defined in order to evaluate the trainee nurses' skills in performing the patient transfer. The items were divided into two types, and two corresponding methods were proposed for classifying trainee performance as correct or incorrect. One method was based on whether the participants' relevant body parts were positioned in a predefined spatial range that was considered ‘correct’ in terms of safety and efficacy (e.g., feet placed appropriately for balance). The second method was based on quantitative indexes and thresholds for parameters describing the participants' postures and movements, as determined by a Bayesian minimum-error method. A prototype system was constructed and experiments were performed to assess the proposed approach. The evaluation of nurses' patient transfer skills was performed successfully and automatically. The automatic evaluation results were compared with evaluation by human teachers and achieved an accuracy exceeding 80%.

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Soccer player tracking and labeling suffer from the similar appearance of the players in the same team, especially in long-shot scenes where the faces and the numbers of the players are too blurry to identify. In this paper, we propose an efficient multi-player tracking system. The tracking system takes the detection responses of a human detector as inputs. To realize real-time player detection, we generate a spatial proposal to minimize the scanning scope of the detector. The tracking system utilizes the discriminative appearance models trained using the online Boosting method to reduce data-association ambiguity caused by the appearance similarity of the players. We also propose to build an online learned player recognition model which can be embedded in the tracking system to approach online player recognition and labeling in tracking applications for long-shot scenes by two stages. At the first stage, to build the model, we utilize the fast k-means clustering method instead of classic k-means clustering to build and update a visual word vocabulary in an efficient online manner, using the informative descriptors extracted from the training samples drawn at each time step of multi-player tracking. The first stage finishes when the vocabulary is ready. At the second stage, given the obtained visual word vocabulary, an incremental vector quantization strategy is used to recognize and label each tracked player. We also perform importance recognition validation to avoid mistakenly recognizing an outlier, namely, people we do not need to recognize, as a player. Both quantitative and qualitative experimental results on the long-shot video clips of a real soccer game video demonstrate that, the proposed player recognition model performs much better than some state-of-the-art online learned models, and our tracking system also performs quite effectively even under very complicated situations.

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The results of fishery investigations are used to estimate the catch size, times fish are caught, and future stock in the fish culture industry. In Tokoro, Japan, scallop farms are located on gravel and sand seabed. Seabed images are necessary to visually estimate the number of scallops of a particular farm. However, there is no automatic technology for measuring resources quantities and so the current investigation technique is the manual measurement by experts. We propose a method to extract scallop areas from images of sand seabed. In the sand field, we can see only the shelly rim because the scallop is covered with sand and opens and closes its shell while it is alive and breathing. We propose a method to extract the shelly rim areas under varying illumination, extract the scallop areas using the shelly rims based on professional knowledge of the sand field, explain the results, and evaluate the method's effectiveness.

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In this paper, we study partial words in relation with pcodes, compatibility, and containment. First, we introduce C_⊂(L), the set of all partial words contained by elements of L, and C_⊃(L), the set of all partial words containing elements of L, for a set L of partial words. We discuss the relation between C(L), the set of all partial words compatible with elements of the set L, C_⊂(L), and C_⊃(L). Next, we consider the condition for C(L), C_⊂(L), and C_⊃(L) to be a pcode when L is a pcode. Furthermore, we introduce some classes of pcodes. An infix pcode and a comma-free pcode are defined, and the inclusion relation among these classes is established.

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Optical Network-on-Chip (ONoC) is a promising emerging technology, which can solve the bottlenecks faced by electrical on-chip interconnection. However, the existing proposals of ONoC are mostly built on fixed topologies, which are not flexible enough to support various applications. To make full use of the limited resource and provide a more efficient approach for resource allocation, RONoC (Reconfigurable Optical Network-on-Chip) is proposed in this letter. The topology can be reconfigured to meet the requirement of different applications. An 8×8 nonblocking router is also designed, together with the communication mechanism. The simulation results show that the saturation load of RONoC is 2 times better than mesh, and the energy consumption is 25% lower than mesh.

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In analysis of general-purpose problems which involves many factors from different viewpoints, an important challenge is to acquire different opinions and distributed modeling templates from multiple remote experts, and to aggregate these templates. In order to deal with this problem, we developed the Distributed Cooperative Modeling System (DCMS) by integrating our achievements [1]-[5]. The paper introduces how to analyze a complex problem using DCMS, with distributed templates from multiple experts, historical templates based on statistical data, and trend templates deduced from historical data, with the example of analyzing “diversification of Macao industries”

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To reduce perforamnce degradations of LR-WPANs due to interference from WLANs, this letter proposes a protocol to allow a piconet to switch an operating channel to an interference-free channel only for transmitting beacon frames. The proposed method does not only increase network performances because of hgh reliability of the beacon frames, but also increase overerall channel utilizations because of using even interfered-channels.

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In this paper, a simple yet efficient texture representation is proposed for texture classification by exploring the joint statistics of local quantized patterns (jsLQP). In order to combine information of different domains, the Gaussian derivative filters are first employed to obtain the multi-scale gradient responses. Then, three feature maps are generated by encoding the local quantized binary and ternary patterns in the image space and the gradient space. Finally, these feature maps are hybridly encoded, and their joint histogram is used as the final texture representation. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art LBP based and even learning based methods for texture classification.

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In this paper, we introduce a biologically-motivated model to detect image saliency. The model employs an isophote based operator to detect potential structure and global saliency information related to each pixel, which are then combined with integral image to build up final saliency maps. We show that the proposed model outperforms seven state-of-the-art saliency detectors in experimental studies.

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We describe a novel idea to improve machine translation by combining multiple candidate translations and extra translations. Without manual work, extra translations can be generated by identifying and hybridizing the syntactic equivalents in candidate translations. Candidate and extra translations are then combined on sentence level for better general translation performance.

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