Subspace methods for recovering rigid motion I: Algorithm and implementation

As an observer moves and explores the environment, the visual stimulation in his/her eye is constantly changing. Somehow he/she is able to perceive the spatial layout of the scene, and to discern his/her movement through space. Computational vision researchers have been trying to solve this problem for a number of years with only limited success. It is a difficult problem to solve because the optical flow field is nonlinearly related to the 3D motion and depth parameters.Here, we show that the nonlinear equation describing the optical flow field can be split by an exact algebraic manipulation to form three sets of equations. The first set relates the flow field to only the translational component of 3D motion. Thus, depth and rotation need not be known or estimated prior to solving for translation. Once the translation has been recovered, the second set of equations can be used to solve for rotation. Finally, depth can be estimated with the third set of equations, given the recovered translation and rotation.The algorithm applies to the general case of arbitrary motion with respect to an arbitrary scene. It is simple to compute, and it is plausible biologically. The results reported in this article demonstrate the potential of our new approach, and show that it performs favorably when compared with two other well-known algorithms.     

CAR-Miner: An efficient algorithm for mining class-association rules

Building a high accuracy classifier for classification is a problem in real applications. One high accuracy classifier used for this purpose is based on association rules. In the past, some researches showed that classification based on association rules (or class-association rules – CARs) has higher accuracy than that of other rule-based methods such as ILA and C4.5. However, mining CARs consumes more time because it mines a complete rule set. Therefore, improving the execution time for mining CARs is one of the main problems with this method that needs to be solved. In this paper, we propose a new method for mining class-association rule. Firstly, we design a tree structure for the storage frequent itemsets of datasets. Some theorems for pruning nodes and computing information in the tree are developed after that, and then, based on the theorems, we propose an efficient algorithm for mining CARs. Experimental results show that our approach is more efficient than those used previously.     

Decision-making coordination and efficient reasoning techniques for feature-based configuration

Software Product Lines is a contemporary approach to software development that exploits the similarities and differences within a family of systems in a particular domain of interest in order to provide a common infrastructure for deriving members of this family in a timely fashion, with high-quality standards, and at lower costs.In Software Product Lines, feature-based product configuration is the process of selecting the desired features for a given software product from a repository of features called a feature model. This process is usually carried out collaboratively by people with distinct skills and interests called stakeholders. Collaboration benefits stakeholders by allowing them to directly intervene in the configuration process. However, collaboration also raises an important side effect, i.e., the need of stakeholders to cope with decision conflicts. Conflicts arise when decisions that are locally consistent cannot be applied globally because they violate one or more constraints in the feature model.Unfortunately, current product configuration systems are typically single-user-based in the sense that they do not provide means to coordinate concurrent decision-making on the feature model. As a consequence, configuration is carried out by a single person that is in charge of representing the interests of all stakeholders and managing decision conflicts on their own. This results in an error-prone and time-consuming process that requires past decisions to be revisited continuously either to correct misinterpreted stakeholder requirements or to handle decision conflicts. Yet another challenging issue related to configuration problems is the typically high computational cost of configuration algorithms. In fact, these algorithms frequently fall into the category of NP-hard and thus can become intractable in practice.In this paper, our goal is two-fold. First, we revisit our work on Collaborative Product Configuration (CPC) in which we proposed an approach to describe and validate collaborative configuration scenarios. We discuss how collaborative configuration can be described in terms of a workflow-like plan that safely guides stakeholders during the configuration process. Second, we propose a preliminary set of reasoning algorithms tailored to the feature modelling domain that can be used to provide automated support for product configuration. In addition, we compare empirically the performance of the proposed algorithms to that of a general-purpose solution. We hope that the insights provided in this paper will encourage other researchers to develop new algorithms in the near future.     

StatApriori: an efficient algorithm for searching statistically significant association rules

Searching statistically significant association rules is an important but neglected problem. Traditional association rules do not capture the idea of statistical dependence and the resulting rules can be spurious, while the most significant rules may be missing. This leads to erroneous models and predictions which often become expensive. The problem is computationally very difficult, because the significance is not a monotonic property. However, in this paper, we prove several other properties, which can be used for pruning the search space. The properties are implemented in the StatApriori algorithm, which searches statistically significant, non-redundant association rules. Empirical experiments have shown that StatApriori is very efficient, but in the same time it finds good quality rules.     

Learning decision rules for energy efficient building control

While rule based control (RBC) is current practice in most building automation systems that issue discrete control signals, recent simulation studies suggest that advanced, optimization based control methods such as hybrid model predictive control (HMPC) can potentially outperform RBC in terms of energy efficiency and occupancy comfort. However, HMPC requires a more complex IT infrastructure and numerical optimization in the loop, which makes commissioning, operation of the building, and error handling significantly more involved than in the rule based setting. In this paper, we suggest an automated RBC synthesis procedure for binary decisions that extracts prevalent information from simulation data with HMPC controllers. The result is a set of simple decision rules that preserves much of the control performance of HMPC. The methods are based on standard machine learning algorithms, in particular support vector machines (SVMs) and adaptive boosting (AdaBoost). We consider also the ranking and selection of measurements which are used for a decision and show that this feature selection is useful in both complexity reduction and reduction of investment costs by pruning unnecessary sensors. The suggested methods are evaluated in simulation for six different case studies and shown to maintain the performance of HMPC despite a tremendous reduction in complexity.     

Wavelet domain association rules for efficient texture classification

The wavelet domain association rules method is proposed for efficient texture characterization. The concept of association rules to capture the frequently occurring local intensity variation in textures. The frequency of occurrence of these local patterns within a region is used as texture features. Since texture is basically a multi-scale phenomenon, multi-resolution approaches such as wavelets, are expected to perform efficiently for texture analysis. Thus, this study proposes a new algorithm which uses the wavelet domain association rules for texture classification. Essentially, this work is an extension version of an early work of the Rushing et al. [10], [11], where the generation of intensity domain association rules generation was proposed for efficient texture characterization. The wavelet domain and the intensity domain (gray scale) association rules were generated for performance comparison purposes. As a result, Rushing et al. [10], [11] demonstrated that intensity domain association rules performs much more accurate results than those of the methods which were compared in the Rushing et al. work. Moreover, the performed experimental studies showed the effectiveness of the wavelet domain association rules than the intensity domain association rules for texture classification problem. The overall success rate is about 97%.     

Partial state observability recovering for linear systems by additional sensor implementation

This paper deals with the problem of additional sensor location in order to recover the observability of any given part of the state for structured linear systems. The proposed method is based on a graph-theoretic approach and assumes only the knowledge of the system’s structure. We first provide new graphical necessary and sufficient conditions for the generic partial observability. Then, we study the location of additional sensors in order to satisfy the latter conditions. We provide necessary and sufficient requirements to be satisfied by these additional sensors and all their possible locations. The proposed solution is simple to implement because it is based on well-known algorithms, usually used for finding successors and predecessors of vertex subsets or on computation of maximal linkings in a digraph. All the used algorithms have polynomial complexity orders.     

An efficient cluster and decomposition algorithm for mining association rules

Conventional algorithms for mining association rules operate in a combination of smaller large itemsets. This paper presents a new efficient which combines both the cluster concept and decomposition of larger candidate itemsets, while proceeds from mining the maximal large itemsets down to large 1-itemsets, named cluster-decomposition association rule (CDAR). First, the CDAR method creates some clusters by reading the database only once, and then clustering the transaction records to the kth cluster, where the length of a record is k. Then, the large k-itemsets are generated by contrasts with the kth cluster only, unlike the combination concept that contrasts with the entire database. Experiments with real-life databases show that CDAR outperforms Apriori, a well-known and widely used association rule.     

State and input observability recovering by additional sensor implementation: A graph-theoretic approach

This paper deals with the problem of additional sensor location in order to recover the state and input observability for structured linear systems. The proposed method is based on a graph-theoretic approach and assumes only the knowledge of the system’s structure. It allows one to provide the minimal number of the required sensors and either their pertinent location or a necessary and sufficient condition which allows one to check if a sensor location is adequate or not. We obtain a sensor placement procedure based on classical and well-known graph theory algorithms, which have polynomial complexity orders.     

An implementation base for efficient data structuring

The specification of data structure in higher-level languages is isolated from the related specifications of data allocation and binding of names. Structure specification is claimed to be the definition of the accessing (addressing) function for items having the structure. Conventional techniques for data structure isolation in higher-level languages are examined and are found to suffer from a lack of clarity and efficiency. The means by which data structure accessors may be defined in Bliss, the specification of their association with named, allocated storage, and their automatic invocation by reference to the named storage only, are discussed. An example is presented which illustrates their efficient implementation and their utility for separating the activities, of data structure programming and algorithmic programming.This work was supported by the Advanced Research Projects Agency of the Office of the Secretary of Defense (F44620-70-C-0107) and is monitored by the Air Force Office of Scientific Research.     

CAVLCU: an efficient GPU-based implementation of CAVLC

The Journal of Supercomputing - CAVLC (Context-Adaptive Variable Length Coding) is a high-performance entropy method for video and image compression. It is the most commonly used entropy method in...     

高效的关联规则挖掘算法研究

目前已经提出了许多用于高效地发现大规模数据库中的关联规则的算法,但都是对关联规则中满足最小支持度的频繁项集的研究,没有对频繁项集中如何高效地计算得到满足最小置信度的关联规则进行研究.针对这种情况,提出了一种高效关联规则的挖掘算法EA,解决了在挖掘关联规则过程中如何高效挖掘满足最小置信度的关联规则问题.     

一种高效的关联规则增量更新算法

对挖掘关联规则中FUP算法的关键思想以及性能进行了研究,提出了改进的FUP算法SFUP。该算法充分利用原有挖掘结果中候选频繁项集的支持数,能有效减少对数据库的重复扫描次数,并通过实验对这两种算法进行比较,结果充分说明了SFUP算法的效率要明显优于FUP算法。     

XML-based genetic programming framework: design philosophy, implementation, and applications

We present the design philosophy, implementation, and various applications of an XML-based genetic programming (GP) framework (XGP). The key feature of XGP is the distinct representation of genetic programs as DOM parsing trees featuring corresponding flat XML text. XGP contributes to the achievements of: (i) fast prototyping of GP by using the standard built-in API of DOM parsers for manipulating the genetic programs, (ii) human readability and modifiability of the genetic representations, (iii) generic support for the representation of the grammar of a strongly typed GP using W3C-standardized XML schema; (iv) inherent inter-machine migratability of the text-based genetic representation (i.e., the XML text) in the distributed implementations of GP.     

Geometry-based efficient rata coordination for manipulators with kinematic redundancy

The redundancy resolution problem for kinematically redundant serial chain manipulators is addressed. In this article we present a generalization of the geometry-based rate allocation algorithm, developed initially for only a minimum norm solution, to obtain the optimal joint rate solution for any specified objective function, with or without weightage. This generalization is made possible through a physial interpretation of the common pseudoinverse-based gradient solution scheme, and by developing a modified formulation for the objective function as a minimum criterion not with respect to the origin of the joint rate space, but with respect to another point in the joint rate space represented by the gradient of the specified objective. Application examples of the algorithm including procedures of solution are demonstrated using 7R manipulators with two generic types of geometry. Moreover, a closed form optimal solution for the class of 7R anthropomorphic arms is also given. © 1995 John Wiley & Sons, Inc.     

Progressive partition miner: an efficient algorithm for mining general temporal association rules

We explore a new problem of mining general temporal association rules in publication databases. In essence, a publication database is a set of transactions where each transaction T is a set of items of which each item contains an individual exhibition period. The current model of association rule mining is not able to handle the publication database due to the following fundamental problems, i.e., 1) lack of consideration of the exhibition period of each individual item and 2) lack of an equitable support counting basis for each item. To remedy this, we propose an innovative algorithm progressive-partition-miner (abbreviated as PPM) to discover general temporal association rules in a publication database. The basic idea of PPM is to first partition the publication database in light of exhibition periods of items and then progressively accumulate the occurrence count of each candidate 2-itemset based on the intrinsic partitioning characteristics. Algorithm PPM is also designed to employ a filtering threshold in each partition to early prune out those cumulatively infrequent 2-itemsets. The feature that the number of candidate 2-itemsets generated by PPM is very close to the number of frequent 2-itemsets allows us to employ the scan reduction technique to effectively reduce the number of database scans. Explicitly, the execution time of PPM is, in orders of magnitude, smaller than those required by other competitive schemes that are directly extended from existing methods. The correctness of PPM is proven and some of its theoretical properties are derived. Sensitivity analysis of various parameters is conducted to provide many insights into Algorithm PPM.     

高性能OCSP系统的设计与实现

在对在线证书状态协议(OCSP)深入分析的基础上,针对目前OCSP协议并没有规定响应器用来检索证书状态的信息源以及大量请求到达OCSP响应器易造成其崩溃等问题,利用CA证书库作为OCSP响应器的信息源并采用Hash表缓存机制、预签名技术和多线程机制,提出了一种高性能OCSP系统的实现方案.该方案极大提高了OCSP响应的正确性和及时性.     

Removing partial parametrization for efficient implementation of functional languages

This paper describes a program transformation technique for functional languages called removing partial parametrization. By transforming functional programs into equivalent ones without partial parametrization, each function is applied to the same number of arguments as its formal parameters. A new method of improving the efficiency of the implementation of functional language is to design the compiler according to the features of the program without partial parametrization. We have used this method in the environment-based implementation of the functional programming language LK. Programs run considerably faster and consume less memory space than traditional ones.