15248817
TEFE: A Time-Efficient Approach to Feature Extraction
With the rapid evolution of Internet applications, people all over the world are sharing pictures, videos and audios online, and thus, content-based analysis is often demanded. Test efficiency is crucial to the success of online information processing. One obstacle to high-speed testing is the time cost of feature extraction for test objects, particularly for objects with complex representation such as images, videos and audios. In this paper, we study the problem of reducing test time cost by extracting cheap but sufficient features. We propose the TEFE (time-efficient feature extraction) approach, which balances between the test accuracy and test time cost by extracting a proper subset of features for each test object. In the implementation, TEFE trains a sequence of support vector machines and classifies each test object cascadingly. Empirical study shows that TEFE is time efficient while holding a classification accuracy close to that of using all features. It also shows that the test time is linearly adjustable in TEFE.
16271421
Weak Evolvability Equals Strong Evolvability
A process for the purification of caffeine which comprises carrying out the following steps in any order; (a) recrystallization of caffeine from an aqueous alkaline solution of caffeine containing a reducing agent and (b) extraction of the caffeine from an aqueous alkaline solution of caffeine with a substantially water-immiscible solvent which is liquid under the conditions of the process.
11987571
Predicting Future Customers via Ensembling Gradually Expanded Trees
Our LAMDAer team has won the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2006 Data Mining Competition (open category) grand champion. This report presents our solution to the PAKDD 2006 Data Mining Competition. Following a brief description of the task, we discuss the difficulties of the task and explain the motivation of our solution. Then, we propose the Gradually Expanded Tree Ensemble (GetEnsemble) method, which handles the difficulties via ensembling expanded trees. We evaluated the proposed method and several other methods using AUC, and found the proposed method beats others in this task. Besides, we show how to obtain cues on which kind of second generation (2G) customers are likely to become third generation (3G) users with the proposed method.
14236359
Semi-naive Exploitation of One-Dependence Estimators
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, one-dependence estimators (ODEs) have been shown to be able to approximate the ground-truth attribute dependencies whilst keeping the effectiveness of probability estimation, and therefore leading to excellent performance. In previous studies, however, ODEs were exploited in simple ways, such as by averaging, for classification. In this paper, we propose a semi-naive exploitation of ODEs that fits a function of ODEs to pursue higher-order attribute dependencies. Extensive experiments show that the proposed SNODE approach can achieve better performance than many state-of-the-art Bayesian classifiers.
12506111
A Case Study in Integrating Multiple E-commerce Standards via Semantic Web Technology
Internet business-to-business transactions present great challenges in merging information from different sources. In this paper we describe a project to integrate four representative commercial classification systems with the Federal Cataloging System (FCS). The FCS is used by the US Defense Logistics Agency to name, describe and classify all items under inventory control by the DoD. Our approach uses the ECCMA Open Technical Dictionary (eOTD) as a common vocabulary to accommodate all different classifications. We create a semantic bridging ontology between each classification and the eOTD to describe their logical relationships in OWL DL. The essential idea is that since each classification has formal definitions in a common vocabulary, we can use subsumption to automatically integrate them, thus mitigating the need for pairwise mappings. Furthermore our system provides an interactive interface to let users choose and browse the results and more importantly it can translate catalogs that commit to these classifications using compiled mapping results.
6570934
Human appearance modeling for matching across video sequences
We present an appearance model for establishing correspondence between tracks of people which may be taken at different places, at different times or across different cameras. The appearance model is constructed by kernel density estimation. To incorporate structural information and to achieve invariance to motion and pose, besides color features, an additional feature of path-length is used. To achieve illumination invariance, two types of illumination insensitive color features are discussed: brightness color feature and RGB rank feature. The similarity between a test image and an appearance model is measured by the information gain or Kullback–Leibler distance. To thoroughly represent the information contained in a video sequence with as little data as possible, a key frame selection and matching scheme is proposed. Experimental results demonstrate the important role of the path-length feature in the appearance model and the effectiveness of the proposed appearance model and matching method.
8475658
Energy Minimization for Real-Time Data Gathering in Wireless Sensor Networks
This paper studies the challenging problem of energy minimization for data gathering over a multiple-sources single-sink communication substrate in wireless sensor networks by exploring the energy-latency tradeoffs using rate adaptation techniques. We consider a real-time scenario for mission-critical applications, where the data gathering must be performed within a specified latency constraint. We first propose an offline numerical optimization algorithm with performance analysis for a special case with a complete binary data gathering tree. Then, by discretizing the transmission time, we present a simple, distributed on-line protocol that relies only on the local information available at each sensor node. Extensive simulations were conducted for both long and short-range communication scenarios using two different source placement models. We used the baseline of transmitting all packets at the highest speed and shutting down the radios afterwards. Our simulation results show that compared with this baseline, up to 90% energy savings can be achieved by our techniques (both off-line and on-line), under different settings of several key system parameters
8755762
FEM Based Numerical Analysis on the Temperature Field in Grind-hardening
The grind-hardening is a new type of surface modification technology, which utilizes the dissipated grinding heat for hardening the surface layer of the workpiece. Currently, most of the researchers studying the grind-hardening have used Design of Experiments approach, by varying grind-hardening processes parameters with a great deal of experiments which has significant degree of uncertainty of the results. In this paper, temporal and spatial temperature distributions of the workpiece are simulated based on the FEM (finite element method). The simulated hardness penetration depth is deduced from the local temperature distribution and the time history of workpiece and its martensitic phase transformation conditions. The numerical simulation results were validated with experimental data. The results of this research indicate that the finite element method can be used in temperature field simulation in the grind-hardening technology. Thus the experiment cost and time in grind-hardening can be reduced by FEM simulation of the grind-hardening temperature field.
9006284
A workflow model based on fuzzy-timing Petri nets
Time information management in workflow has been recognized as one of the most significant tasks in workflow management. The uncertainties in time and time-related constraints in workflow models should be taken into consideration. Based on introducing time constraints on elements in fuzzy-timing Petri nets, this paper propose a new workflow model named fuzzy temporal workflow nets (FTWF-nets). The calculation of temporal elements in FTWF-nets is given. Then time modeling and time possibility analysis of temporal phenomena in FTWF-nets are investigated. Finally an example is given to illustrate how to use these methods. Research results show that FTWF-nets can be used to model time information in those workflows, which have time uncertainty and have time constraints on resources and activities, and analyze the time possibility on some constraints.