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Originally developed for purely functional verification of software, native or host compiled simulation [6] has gained momentum, thanks to its considerable speedup compared to instruction set simulation (ISS). To obtain a performance model of the software, non-functional information is computed from the target binary code using low-level analysis and back-annotated into the highlevel code used to...
Native simulation is an interesting virtual prototyping candidate to speed-up architecture exploration and early software developments. It however does not provide out-of-the box non-functional information needed for software performance estimation. Annotating software with information is complex as highlevel codes and binary codes have different structures due to compiler optimizations. This work...
Constraint-based sequential pattern mining algorithms discover sequential patterns among from sequence data and the resultant sequential patterns satisfy a given constraint. For time stamped sequences duration and/or gap constraints can be applied to obtain corresponding constraint-based sequential patterns. One of the shortcomings of existing algorithms is the requirement to pre-specify a time window...
Symbolic time series analysis (STSA) is built upon the concept of symbolic dynamics that deals with discretization of dynamical systems in both space and time. The notion of STSA has led to the development of a pattern recognition tool in the paradigm of dynamic data-driven application systems (DDDAS), where a time series of sensor signals is partitioned to obtain a symbol sequence that, in turn,...
We propose a joint object pose estimation and categorization approach which extracts information about object poses and categories from the object parts and compositions constructed at different layers of a hierarchical object representation algorithm, namely Learned Hierarchy of Parts (LHOP) [7]. In the proposed approach, we first employ the LHOP to learn hierarchical part libraries which represent...
Effectively utilizing readily available auxiliary data to improve predictive performance on new modeling tasks is a key problem in data mining. In this research the goal is to transfer knowledge between sources of data, particularly when ground truth information for the new modeling task is scarce or is expensive to collect where leveraging any auxiliary sources of data becomes a necessity. Towards...
Very Fast Decision Tree (VFDT) in data stream mining has been widely studied for more than a decade. VFDT in essence can mine over a portion of an unbounded data stream at a time, and the structure of the decision tree gets updated whenever new data feed in; hence it can predict better upon the input of fresh data. Inherent from traditional decision trees that use information gains for tree induction,...
Object matching is a fundamental operation in data analysis. It typically requires the definition of a similarity measure between the classes of objects to be matched. Instead, we develop an approach which is able to perform matching by requiring a similarity measure only within each of the classes. This is achieved by maximizing the dependency between matched pairs of observations by means of the...
It is well known that the key of Bayesian classifier learning is to balance the two important issues, that is, the exploration of attribute dependencies in high orders for ensuring a sufficient flexibility in approximating the ground-truth dependencies, and the exploration of low orders for ensuring a stable probability estimate from limited training samples. By allowing one-order attribute dependencies,...
In a distributed estimation system, the fusion center receives the local estimates from sensors and fuses them to be an optimal estimation in terms of some criterion. Recently, the best linear unbiased estimation (BLUE) fusion was proposed to minimize the mean square error of the fused estimate, in which the weights to optimally combine the local estimates are determined by the covariance matrix of...
The purpose of this paper is to estimate the position of a human in the image frame and to use this information to diagnose falls. A nonholonomic locomotion model describes the displacement of the human due to the similarities between human and nonholonomic mobile robot displacements. To estimate the human position in the world frame, the principle of Receding Horizon Estimation (RHE) is extended...
Applying estimation of distribution algorithms (EDAs) to solve continuous problems is a significant and challenging task in the field of evolutionary computation. So far, various continuous EDAs have been developed based on different probability models. Initially, the EDAs based on a single Gaussian probability model are widely used but they have trouble in solving multimodal problems. Later EDAs...
In this paper, we describe PEGASUS, an open source peta graph mining library which performs typical graph mining tasks such as computing the diameter of the graph, computing the radius of each node and finding the connected components. as the size of graphs reaches several giga-, tera- or peta-bytes, the necessity for such a library grows too. To the best of our knowledge, PEGASUS is the first such...
Proximity ranking according to end-to-end network distances (e.g., Round-Trip Time, RTT) can reveal detailed proximity information, which is important in network management and performance diagnosis in distributed systems. However, to the best of our knowledge, there has been no similar work on this subject in the P2P computing field. We present a distributed rating method iRank, that enables proximity...
In this paper, a new distributed Kalman filter is proposed for state estimation of systems with acyclic digraph, namely acyclic systems. This method can be applied to a number of large-scale systems including sensor networks and formation flying missions. An acyclic system can be represented by an overlapping block-diagonal state space (OBDSS) model, which requires an extensive communication overhead...
We consider problems where multiple agents cooperate to control their individual state so as to optimize a common objective while communicating with each other to exchange state information. Since communication costs can be significant, we seek conditions under which communication of state information among nodes can be minimized while still ensuring that the optimization process converges. In prior...
The Two-Stage Algorithm (TSA) has been extensively used and adapted for the identification of block-oriented nonlinear systems including Hammerstein systems. This paper revisits an optimality result established by Bai in 1998 showing that the TSA provides the optimal estimation of a bilinearly parameterized Hammerstein system in the sense of a weighted nonlinear least-squares (LS) criterion formulated...
This paper proposes the chaos-genetic algorithm (CGA) based on the cat map in order to optimize a multidimensional and multimodal non-linear cost function for the seismic wavelet. The algorithm uses the initial sensitivity of the cat map to expand the scope of the search, and uses the ergodicity of the cat map to search the chaotic variables. Thus, reduces the data redundancy, maintains the diversity...
In this paper, we design a hybrid multi-objective algorithm using genetic and estimation of distribution based on design of Experiments. At first, we apply orthogonal design and uniform design to generate an initial population so that the population individual solutions scattered evenly in the feasible solutions space. Second, we proposed a new convergence criterion to check whether the distribution...
In this study, a novel clustering-based selection strategy of nondominated individuals for evolutionary multi-objective optimization is proposed. The new strategy partitions the nondominated individuals in current Pareto front adaptively into desired clusters. Then one representative individual will be selected in each cluster for pruning nondominated individuals. In order to evaluate the validity...
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