ppxscal/embedding_network huggingface dataset:
A Novel Algorithm for Fault Diagnosis of Analog Circuit with Tolerances Using Improved Binary-tree SVMs Based on SOMNN Clustering
Authors: Anna Wang, Junfang Liu, Hua Li, Feng Luan, Wenjing Yuan
Venue: Proceedings of the Third International Conference on Natural Computation - Volume 01 (2007)

In order to solving fault diagnosis of analog circuit with tolerances, noise, circuit nonlinearities and small sample sets, a novel multi-class classification algorithm which combined binary tree SVMs multi-classification based on self-organizing map nerve network (SOMNN) clustering roughly was proposed. The robustness characteristic of SOMNN based on the separability between pattern classes and support vector machine (SVM) based on the theory of statistic learning for the small sample set were integrated in the algorithm. The SOMNN was firstly applied to cluster layer by layer, by which structure of binary-tree SVMs multi-classifier for fault diagnosis was established, namely, the fault classes at each node of the tree were nailed down. Then according to the preprocess results of SOMNN, SVM were utilized to segment each decision node accurately. The simulation results show us that compared with the several existent multi-class classification methods, the current algorithm has high accuracy and speed.
A combinatorial procurement auction featuring bundle price revelation without free-riding
Authors: Robert W. Day, S. Raghavan
Venue: Decision Support Systems (2008)

Combinatorial auctions are currently becoming a common practice in industrial procurement, allowing bidders (sellers of goods and services in the procurement setting) to avoid the risk of selling good or service bundles that are incomplete, inefficient, or excessively expensive to deliver. Two major concerns in combinatorial auction design are the revelation or discovery of market price information over the course of the auction, and the inherent computational difficulty (NP-hardness) of the underlying ''winner-determination'' problem. In this paper we describe a new general auction format maintaining the benefits of the adaptive user-selection approach without the problems of free-riding, inefficiency, or distortionary linear prices. This auction format is particularly well-suited to the largest combinatorial auctions for which winner-determination is computationally tractable, because it provides bundle synergy information that is computable in polynomial time for all interactive phases.
Tracking mobile nodes using RF Doppler shifts
Authors: Branislav Kusy, Akos Ledeczi, Xenofon Koutsoukos
Venue: Proceedings of the 5th international conference on Embedded networked sensor systems (2007)

In this paper, we address the problem of tracking cooperative mobile nodes in wireless sensor networks. Aiming at a resource efficient solution, we advocate the use of sensors that maintain their location information and rely on the tracking service only when their locations change. In the proposed approach, the tracked node transmits a signal and infrastructure nodes measure the Doppler shifts of the transmitted signal. We show that Mica2 motes can measure RF Doppler shifts with 0.2 Hz accuracy corresponding to a 0.14 m/s error in relative speed estimates using radio inter-ferometric technique. The tracking problem is modeled as a non-linear optimization problem and an extended Kalman filter is used to solve it accurately assuming Gaussian measurement errors. However, this approach may fail if the tracked node changes its speed or direction. We propose to update the Kalman filter state by performing constrained least-squares optimization when a maneuver is detected. The combined approach achieves almost a 50% accuracy improvement over the Kalman filter alone when the mobile node changes its direction and speed frequently. We describe our proof-of-concept implementation of the tracking service and evaluate its performance experimentally and in simulation.
Who needs video summarization anyway?
Authors: Simone Santini
Venue: Proceedings of the International Conference on Semantic Computing (2007)

The purpose of video summarization, when used in an interface, is to extract from a video a limited number of key-frame that convey the meaning of the whole video at a glance. The development of cut detection and key-frame selection algorithm is based on the unspoken assumption that such meaning is conveyed not only by the sheer number of frames that is being presented, but that the comprehension of the video is increased by a careful choice of these frames. This paper presents an experiment that challenges this assumption. Two videos were synthetized using careful frame selection and using a uniform samplong of the video with the same number of frames, and the comprehension of two group of subjects was tested by asking them questions about the videos. The experiment revealed no difference between the comprehension gained by seeing the carefully selected key-frames and that gained by looking at the uniform sampling of the video.
Effective top-k computation in retrieving structured documents with term-proximity support
Authors: Mingjie Zhu, Shuming Shi, Mingjing Li, Ji-Rong Wen
Venue: Proceedings of the sixteenth ACM conference on Conference on information and knowledge management (2007)

Modern web search engines are expected to return top-k results efficiently given a query. Although many dynamic index pruning strategies have been proposed for efficient top-k computation, most of them are prone to ignore some especially important factors in ranking functions, e.g. term proximity (the distance relationship between query terms in a document). The inclusion of term proximity breaks the monotonicity of ranking functions and therefore leads to additional challenges for efficient query processing. This paper studies the performance of some existing top-k computation approaches using term-proximity-enabled ranking functions. Our investigation demonstrates that, when term proximity is incorporated into ranking functions, most existing index structures and top-k strategies become quite inefficient. According to our analysis and experimental results, we propose two index structures and their corresponding index pruning strategies: Structured and Hybrid, which performs much better on the new settings. Moreover, the efficiency of index building and maintenance would not be affected too much with the two approaches.
A CDD-based formal model for expert finding
Authors: Yupeng Fu, Rongjing Xiang, Yiqun Liu, Min Zhang, Shaoping Ma
Venue: Proceedings of the sixteenth ACM conference on Conference on information and knowledge management (2007)

Searching an organization's document repositories for experts is a frequently faced problem in intranet information management. This paper proposes a candidate-centered model which is referred as Candidate Description Document (CDD)-based retrieval model. The expertise evidence about an expert candidate scattered over repositories is mined and aggregated automatically to form a profile called the candidate's CDD, which represents his knowledge. We present the model from its foundations through its logical development and argue in favor of this model for expert finding. We devise and compare the different strategies for exploring a variety of expertise evidence. The experiments on TREC enterprise corpora demonstrate that the CDD-based model achieves significant and consistent improvement on performance through comparative studies with non-CDD methods.
A Pen-Based Interface for Real-Time Document Edition
Authors: J. Rodriguez, G. Sanchez, J. Llados
Venue: Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02 (2007)

This article presents a pen-based framework for man- ual edition of digital documents on tablet computers. In this system, the user draws certain proofreading symbols on the text parts to edit; some symbols can be accompa- nied by handwritten text. The input is interpreted and the corresponding editing action is executed in real time. The possibility that the input contains handwritten text is a nov- elty with respect to previous real-time systems that faced sketch-based edition, where usually text input is carried out via keyboard. Also, multimodal feedback mechanisms for error recovery are present. In this work we focus on the symbol recognition part. Different features are evaluated in recognition experiments using a support vector machine classifier. Experiments show that the symbol recognition is efficient enough for a real-time task and that the system can be used in real conditions with some experience.
Exponential Bidirectional Associative Memory Based on Small-world Architecture
Authors: Min Wang, Songcan Chen
Venue: Proceedings of the Third International Conference on Natural Computation - Volume 01 (2007)

Most of neural associative memory models have fully-connected structure. However, from both the neurobiological viewpoint and the hardware implementation perspective, it seems more reasonable to consider such networks with both predominantly local connectivity and sparsely global connectivity. Small- World Architecture (SWA) provides an interesting approach to implementing this design. Recently, Bohland et al. have introduced SWA into Hopfield network and verified the effectiveness of such structure. However, such an attention has not yet been paid to another important type of associative memories, i.e. Bidirectional Associative Memory (BAM). At the first glance, the introduction of SWA to BAM seems straightforward and easy. However, in fact, the randomly-rewiring procedure done for Hopfield network, cannot be directly applied to BAM or its variants because of their multi-layer structure and bidirectional associative mode. In this paper, we use an artful transformation to overcome the difficulty and propose a new exponential BAM model based on SWA, called SWeBAM. It is a brand-new extension to Hopfield network based on SWA in both associative mode and storage capacity. The experimental results demonstrate that with comparatively much fewer inter-neural connections, SWeBAM can obtain almost equivalent performances to the original exponential BAM (eBAM) in both storage capacity and error-correction capability. Moreover, owing to the introduction of SWA, SWeBAM can be realized more easily in hardware than eBAM.
Constructing Support Vector Machine Kernels from Orthogonal Polynomials for Face and Speaker Verification
Authors: Feng Zhou, Zhigang Fang, Jie Xu
Venue: Proceedings of the Fourth International Conference on Image and Graphics (2007)

This paper presents an alternative to construct support vector machine (SVM) kernels from orthogonal polynomials. After describing some knowledge about orthogonal polynomials, we construct kernels from orthogonal polynomials according to Mercer's condition. The elegant and fascinating characteristics of the orthogonal polynomials promise the minimum data redundancy in feature space and make it possible to represent the data with less support vectors. Experimental results show that the SVMs with orthogonal polynomial kernels outperform that with traditional kernels in terms of generalization power and less support vectors.
A High-Speed Algorithm for the Computer Generation of Fourier Transforms
Authors: H. Andrews
Venue: IEEE Transactions on Computers (1968)

Abstract An efficient algorithm is presented for performing the Fourier transformation operation on a digital computer. This algorithm completes the transform calculation with Nlog2N complex additions (complex additions include complex subtractions) and ½N [log2(N)-2]+1 complex multiplications. This is a considerable savings over a brute-force method of N2complex additions and multiplications, especially when considering the use of digital machines whose MULTIPLY time is much longer than ADD time.
Measuring Learning Difficulty Level by Comparing Ontologies
Authors: Dehai Zhang, Yao Zhu
Venue: Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 02 (2007)

It is a common practice that learners (human beings or intelligent agent) estimate the difficulty level of their learning tasks. In this paper, we present an ontology comparison based method to measure the difficulty level by comparing target knowledge the learner wants to learn and the knowledge the learner already has. Our method proposes a set of measures that compare the differences of ontologies from two perspectives, the structural and the conceptual. Based on the result of comparing two ontologies, we defined the learning difficulty formally, and gave an algorithm to compute it.
Query Processing in Distributed Database System
Authors: A. R. Hevner
Venue: IEEE Transactions on Software Engineering (1979)

Query processing in a distributed system requires the transmission f data between computers in a network. The arrangement of data transmissions and local data processing is known as a distribution strategy for a query. Two cost measures, response time and total time are used to judge the quality of a distribution strategy. Simple algorithms are presented that derive distribution strategies which have minimal response time and minimal total time, for a special class of queries. These optimal algorithms are used as a basis to develop a general query processing algorithm. Distributed query examples are presented and the complexity of the general algorithm is analyzed. The integration of a query processing subsystem into a distributed database management system is discussed.
Tutorial Series 10 LSI/VLSI Design Automation
Authors: T. C. Raymond
Venue: Computer (1981)

Advanced design aids will increase a designer's efficiency and reduce costs. As a result, a chip will contain more of a total system design.
Individual differences in sentence comprehension: A functional magnetic resonance imaging investigation of syntactic and lexical processing demands
Authors: Chantel S. Prat, Timothy A. Keller, Marcel Adam Just
Venue: Journal of Cognitive Neuroscience (2007)

Language comprehension is neurally underpinned by a network of collaborating cortical processing centers; individual differences in comprehension must be related to some set of this network's properties. This study investigated the neural bases of individual differences during sentence comprehension by examining the network's response to two variations in processing demands: reading sentences containing words of high versus low lexical frequency and having simpler versus more complex syntax. In a functional magnetic resonance imaging study, readers who were independently identified as having high or low working memory capacity for language exhibited three differentiating properties of their language network, namely, neural efficiency, adaptability, and synchronization. First, greater efficiency (defined as a reduction in activation associated with improved performance) was manifested as less activation in the bilateral middle frontal and right lingual gyri in high-capacity readers. Second, increased adaptability was indexed by larger lexical frequency effects in high-capacity readers across bilateral middle frontal, bilateral inferior occipital, and right temporal regions. Third, greater synchronization was observed in high-capacity readers between left temporal and left inferior frontal, left parietal, and right occipital regions. Synchronization interacted with adaptability, such that functional connectivity remained constant or increased with increasing lexical and syntactic demands in high-capacity readers, whereas low-capacity readers either showed no reliable differentiation or a decrease in functional connectivity with increasing demands. These results are among the first to relate multiple cortical network properties to individual differences in reading capacity and suggest a more general framework for understanding the relation between neural function and individual differences in cognitive performance.
Research on Distributed E-Commerce System Architecture
Authors: Xia Yang, Zhang Qiang, Xu Zhao, Zhang Ling
Venue: Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 02 (2007)

A new distributed E-Commerce architecture is put forward in this paper on the careful analysis of the existing shortcomings of the architecture of the present E-Commerce system. Different from the traditional E-Commerce system, the architecture makes a full use of the advantages of the new computer technology to distribute the E-Commerce events on several servers so as to raise systematic reliability, expansibility, stability, and maintenance. Based on the existing commercial systematic facility and technical platform, the system integrates multi-commercial activities of E-Commerce under the loose-coupled service programs so that the architecture of large scale distributed E-commerce system is formed.
A refined theorem concerning the conditioning of semidefinite programs
Authors: Ioannis K. Argyros
Venue: Journal of Applied Mathematics and Computing (2007)

Using a weaker version of the Newton-Kantorovich theorem [6] given by us in [3], we show how to refine the results given in [8] dealing with the analyzing of the effect of small perturbations in problem data on the solution. The new results are obtained under weaker hypotheses and the same computational cost as in [8].
Visual qualities of the Unified Modeling Language: Deficiencies and Improvements
Authors: Andrew Fish, Harald Storrle
Venue: Proceedings of the IEEE Symposium on Visual Languages and Human-Centric Computing (2007)

The Unified Modeling Language (UML) is a family of largely visual notations which has become the "lingua franca" of software engineering. In industrial settings the UML is used mainly as a means for facilitating communication between humans and so its visual qualities are decisive for its usefulness and practical value. However, it is rare to see any sort of analysis of the visual qualities and their effectiveness. Also, since the UML is continually evolving, some guidelines and criteria to help analyse any changes or additional proposed notations would be beneficial. Building on some basic frameworks and design criteria, we develop some principles for effective design, especially when considering languages with vague semantics. We use this to identify a number of problems of the UML that show up in the diagrammatic representation of models and propose improvements which overcome these difficulties. The methodology is applicable for analysing other complex languages with vague semantics in order to try to reduce human errors.
Algebraic Operations on Flexible Constraints Based on Knowledge Comparison
Authors: Z. M. Ma, Jiemin Liu, Li Yan
Venue: Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 04 (2007)

Knowledge used for helping in choice processes in artificial intelligence (AI) essentially consists of sets of constraints. Flexible constraints are extensively studied and have become attractive in AI. In this paper, we focus on algebraic operations on flexible constraints, which are very useful in knowledge management (e.g., knowledge integration). For this purpose, we identify the semantic relationship between flexible constraints. On this basis, we define the operations on flexible constraints. In particular, the notion of semantic inclusion degree is introduced to flexibly assess the semantic relationship between flexible constraints.
Multi-Objective Optimization for SVM Model Selection
Authors: C. Chatelain, S. Adam, Y. Lecourtier, L. Heutte, T. Paquet
Venue: Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 01 (2007)

In this paper, we propose a multi-objective optimiza- tion method for SVM model selection using the well known NSGA-II algorithm. FA and FR rates are the two crite- ria used to find the optimal hyperparameters of a set of SVM classifiers. The proposed strategy is applied to a digit/outlier discrimination task embedded in a more global information extraction system that aims at locating and recognizing numerical fields in handwritten incoming mail documents. Experiments conducted on a large database of digits and outliers show clearly that our method com- pares favorably with the results obtained by a state-of-the- art mono-objective optimization technique using the classi- cal Area Under ROC Curve criterion (AUC).
Optimal Investment Consumption Model with CIR Interest Rate
Authors: Shuping Wan
Venue: Proceedings of the Seventh International Conference on Intelligent Systems Design and Applications (2007)

The optimal investment consumption problem for a single riskless bond, a zero-coupon bond and a risky stock modeled by the CIR interest process has been established. The investment objective is maximizing the utility of his consumption and terminal wealth. By the stochastic dynamic programming principle, the HJB equation for the optimal solution is given. In the case of constant relative risk aversion utility, the analytic optimal trading strategies are derived. The results show that the optimal proportion allocated in the stock is a constant fraction, but the optimal proportion in the zero-coupon bond is time-variant. The optimal consumption rate is in a feedback form of the wealth and depends on the stochastic interest rate. A numerical example illustrating the results is presented.
Evaluation of Transcendental Functions on Imagine Architecture
Authors: Xiaobo Yan, Tao Tang, Yu Deng, Jing Du, Xuejun Yang
Venue: Proceedings of the 2007 International Conference on Parallel Processing (2007)

The fast and accurate evaluation of transcendental functions (e.g. exp, log, sin, and atan) is quite important in many domains. We implement a software inline function library that can be called from KernelC programming language to compute 8 typical functions on Imagine architecture. By exploiting some of the key features of Imagine architecture, we have been able to provide single precision transcendental functions that are very accurate yet can typically be evaluated to get 16 function values in between 18 and 43 clock cycles. In this paper, we also discuss the algorithms and implementation details of these functions.
Image Reconstruction using Microwave Tomography for Breast Cancer Detection on Distributed Memory Machine
Authors: Meilian Xu, Abas Sabouni, Parimala Thulasiraman, Sima Noghanian, Stephen Pistorius
Venue: Proceedings of the 2007 International Conference on Parallel Processing (2007)

Breast cancer, with the exception of lung cancer, is the leading cause of cancer deaths in women. It is also one of the few cancers that can be controlled by using asymptomatic screening method, followed by effective treatments. One recent screening modality under development, microwave tomography, uses the apparent dielectric property contrasts between different breast tissues at microwave frequencies. Microwave tomography uses a numerical model and the image reconstruction consists of iteratively searching the breast structures, applying the numerical model to the breast structures, and matching measured data with computation results of the model. This paper focuses on Finite-Difference Time-Domain (FDTD) for the numerical model and Genetic Algorithm (GA) for the iterative searches. FDTD and GA are time-consuming, yet they are data parallel in nature. In this paper, a parallel algorithm integrating GA and FDTD for detecting tumors using microwave tomography technique is presented. The algorithm is implemented on distributed memory machines usingMPI.