ppxscal/embedding_network huggingface dataset:
Object Identification with Constraints
Authors: Steffen Rendle, Lars Schmidt-Thieme
Venue: Proceedings of the Sixth International Conference on Data Mining (2006)

Object identification aims at identifying different representations of the same object based on noisy attributes such as descriptions of the same product in different online shops or references to the same paper in different publications. Numerous solutions have been proposed for solving this task, almost all of them based on similarity functions of a pair of objects. Although today the similarity functions are learned from a set of labeled training data, the structural information given by the labeled data is not used. By formulating a generic model for object identification we show how almost any proposed identification model can easily be extended for satisfying structural constraints. Therefore we propose a model that uses structural information given as pairwise constraints to guide collective decisions about object identification in addition to a learned similarity measure. We show with empirical experiments on public and on real-life data that combining both structural information and attribute-based similarity enormously increases the overall performance for object identification tasks.
Web Information Retrieval Using Ontology for Children Based on their Lifestyles
Authors: Mika Nakaoka, Yukari Shirota, Katsumi Tanaka
Venue: Proceedings of the 21st International Conference on Data Engineering Workshops (2005)

Users of existing Web information retrieval systems need to input proper keywords through which the correct retrieval data can be found. It is, however, too difficult for children to identify appropriate keywords due to their lack of ability in generalizing concepts describing what they would like to search for: Therefore, a great deal of irrelevant data is included in the results. In the paper, we propose a "Kid's Lifestyle Ontology" so that the system can infer what they would like to search for to resolve ambiguities. The retrieval system can identify appropriate keywords that have strong relationships with concepts common to their lifestyles, and then the related search keywords can be ranked above other keywords. This is based on the episodic memories of children that help them discover other keywords. As a result, children can rejne their queries gradually. In the paper, we also propose a method of constructing the ontology incrementally and recursively.
Guest Editors' Introduction: Process Variation and Stochastic Design and Test
Authors: T. M. Mak, Sani Nassif
Venue: IEEE Design Test (2006)

As silicon manufacturing processes scale to and beyond the 65-nm node, process variations are consuming an increasingly larger portion of design and test budgets. Such variations play a significant part in subthreshold leakage and other important device performance metrics. The rise in inherent systematic and random nonuniformity as we scale our silicon devices to the level of atomic scaling will have far-reaching effects on every aspect of design, manufacturing, test, and overall reliability. This special issue explores this subject from different perspectives: process monitoring, testing, adaptive circuits, and architecture changes.
Formalising the L4 microkernel API
Authors: Rafal Kolanski, Gerwin Klein
Venue: Proceedings of the 12th Computing: The Australasian Theroy Symposium - Volume 51 (2006)

This paper gives an overview of a pilot project on the specification and verification of the L4 high-performance microkernel. Of the three aspects examined in the project, we describe one in more detail: the formalisation of the kernel's Application Programming Interface using the B Method. We conclude that machine-supported formal verification of software is at a turning point; that it is now feasible, and desirable, to formally verify production-quality operating systems.
FDI in Multivariate Process with Naive Bayesian Network in the Space of Discriminant Factors
Authors: Teodor Tiplica, Sylvain Verron, Abdessamad Kobi, Iulian Nastac
Venue: Proceedings of the International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce (2006)

The Naive Bayesian Network (NBN) classifier is an optimal classifier(in the sense of minimal classification error rate) in the case of independent descriptors or variables. The presence of dependencies between variables generally reduce his efficiency. In this article, we are proposing a new classification method named Naive Bayesian Network in the Space of Discriminants Factors (NBNSDF) which is based on the use of the NBN in the space of discriminants factors issue from a discriminant analysis. The discriminants factors are not correlated letting very efficient the use of the NBN. We found on simulated data that the NBNSDF method better detects and isolates faults in multivariate processes than the NBN in the case of strongly correlated variables.
A modular data analysis pipeline for the discovery of novel rna motifs
Authors: Daniel Ashlock, Dan Voytas
Venue:  (2006)

This dissertation presents a modular software pipeline that searches collections of RNA sequences for novel RNA motifs. In this case the motifs incorporate elements of primary and secondary structure. The motif search pipeline breaks up sets of RNA sequences into shortened segments of RNA primary sequence. The shortened segments are then folded to obtain low energy secondary structures. The distance estimation module of the pipeline then calculates distances between the folded bricks, and then analyzes the resulting distance matrices for patterns. An initial implementation of the pipeline is applied to synthetic and biological data sets. This implementation introduces a new distance measure for comparing RNA sequences based on structural annotation of the folded sequence as well as a new data analysis technique called non-linear projection. The modular nature of the pipeline is then used to explore the relationships between several different distance measures on random data, synthetic data, and a biological data set consisting of iron response elements. It is shown that the different distance measures capture different relationships between the RNA sequences. The non-linear projection algorithm is used to produce 2-dimensional projections of the distance matrices which are examined via inspection and k-means multiclustering. The pipeline is able to successfully cluster synthetic RNA sequences based only on primary sequence data as well as the iron response elements data set. The dissertation also presents a preliminary analysis of a large biological data set of HIV sequences.
Speculative pre-execution assisted by compiler (SPEAR)
Authors: Won W. Ro, Jean-Luc Gaudiot
Venue: Journal of Parallel and Distributed Computing (2006)

Speculative pre-execution achieves efficient data prefetching by running additional prefetching threads on spare hardware contexts. Various implementations for speculative pre-execution have been proposed, including compiler-based static approaches and hardware-based dynamic approaches. A static approach defines the p-thread at compile time and executes it as a stand-alone running thread. Therefore, it cannot efficiently take dynamic events into account and requires a higher fetch bandwidth. Conversely, a hardware approach is, by essence, able to dynamically make use of run-time information. However, it requires more complex hardware and also lacks global information on data and control flow. This paper proposes Speculative Pre-Execution Assisted by compileR (SPEAR), a pre-execution model which is a hybrid of the two approaches. It relies on a post-compiler to extract the p-thread code from program binaries and uses custom-designed hardware to execute the p-thread.
A Management System for Distributed Knowledge and Content Objects
Authors: Wernher Behrendt, Nitin Arora, Tobias Burger, Rupert Westenthaler
Venue: Proceedings of the Second International Conference on Automated Production of Cross Media Content for Multi-Channel Distribution (2006)

We present the results of a European research project which developed specifications for so-called Knowledge Content Objects (KCO) and for an attendant infrastructure, the Knowledge Content Carrier Architecture (KCCA). The work addresses the problem that while there are many standards for content and for meta data, there is at present, no suitable framework that enables organizations to manage knowledge alongside content, in a coherent manner. Our approach postulates the KCO as a common structural entity which can be recognized and manipulated by a KCCA enabled system.
Symbiotic Robotic Systems: Humans, Robots, and Smart Environments
Authors: Silvia Coradeschi, Alessandro Saffiotti
Venue: IEEE Intelligent Systems (2006)

A symbiotic robotic system consists of a robot, a human, and a (smart) environment that cooperate symbiotically to perform a task. While the possibility of cooperating with a smart environment and with humans might greatly simplify the execution of robotic tasks, it also presents new challenges that open new research directions. This article is part of a special issue on the Future of AI.
New 2/sup n/ discrete cosine transform algorithm using recursive filter structure
Authors: Wan-Chi Siu, Yuk-Hee Chan, Lap-Pui Chau
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1995. on International Conference - Volume 02 (1995)

The discrete cosine transform (DCT) is widely used in digital signal processing. It is always desirable to look for more efficient algorithms for the realization of the DCT. We generalize a formulation for converting a length-2/sup n/ DCT into n groups of equations, then apply a novel technique for its implementation. The sizes of the groups are 2/sup m/, for m=n-1,...,0. While their structures are extremely regular. The realization can then be converted into the simplest recursive filter form, which is of particularly simple for practical implementation. The filter structure is numerically stable, since it involves no division at all.
Packet-level diversity - from theory to practice: an 802.11-based experimental investigation
Authors: Evangelos Vergetis, Eric Pierce, Marc Blanco, Roch Guérin
Venue: Proceedings of the 12th annual international conference on Mobile computing and networking (2006)

Packet-level diversity, or distributing packet transmissions over multiple, diverse channels, offers benefits in improving communication performance and robustness to channel variations. Previous works have analyzed and quantified those benefits, and developed transmission policies to realize them. However, translating those benefits into practice still faces numerous challenges from uncertainty in the adequacy of the channel models used to develop policies, to implementation dificulties in realizing the precise transmission schedules they mandate. This work is a first step in assessing what remains of those benefits once confronted with such practical challenges. Our investigation is carried out over an 802.11 testbed, where diversity is realized through the different frequency bands available for transmissions between hosts and access points. We use the testbed to evaluate the impact of transmission policies, channel characteristics, channel correlation, and various end-system constraints that affect our ability to precisely control transmissions timing. Our investigation reveals that in spite of the many gaps that exist between theory and practice, packet-level diversity still provides a simple solution to improve transmission performance and robustness across a broad range of configurations.
Integrating Features from Different Sources for Music Information Retrieval
Authors: Tao Li, Mitsunori Ogihara, Shenghuo Zhu
Venue: Proceedings of the Sixth International Conference on Data Mining (2006)

Efficient and intelligent music information retrieval is a very important topic of the 21st century. With the ultimate goal of building personal music information retrieval systems, this paper studies the problem of identifying "similar" artists using both lyrics and acoustic data. In this paper, we present a clustering algorithm that integrates features from both sources to perform bimodal learning. The algorithm is tested on a data set consisting of 570 songs from 53 albums of 41 artists using artist similarity provided by All Music Guide. Experimental results show that the accuracy of artist similarity classifiers can be significantly improved and that artist similarity can be efficiently identified.
Improved Parameterized Complexity of the Maximum Agreement Subtree and Maximum Compatible Tree Problems
Authors: Vincent Berry, Francois Nicolas
Venue: IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) (2006)

Given a set of evolutionary trees on a same set of taxa, the maximum agreement subtree problem (MAST), respectively, maximum compatible tree problem (MCT), consists of finding a largest subset of taxa such that all input trees restricted to these taxa are isomorphic, respectively compatible. These problems have several applications in phylogenetics such as the computation of a consensus of phylogenies obtained from different data sets, the identification of species subjected to horizontal gene transfers and, more recently, the inference of supertrees, e.g., Trees Of Life. We provide two linear time algorithms to check the isomorphism, respectively, compatibility, of a set of trees or otherwise identify a conflict between the trees with respect to the relative location of a small subset of taxa. Then, we use these algorithms as subroutines to solve MAST and MCT on rooted or unrooted trees of unbounded degree. More precisely, we give exact fixed-parameter tractable algorithms, whose running time is uniformly polynomial when the number of taxa on which the trees disagree is bounded. The improves on a known result for MAST and proves fixed-parameter tractability for MCT.
A Comprehensive Empirical Study on Linear Subspace Methods for Facial Expression Analysis
Authors: Caifeng Shan, Shaogang Gong, Peter W. McOwan
Venue: Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop (2006)

Automatic facial expression analysis is a vital component of intelligent Human-Computer Interaction (HCI). In this paper, we present a extensive empirical study on linear subspace methods for facial expression analysis. Locality Preserving Projections (LPP) and Orthogonal Neighborhood Preserving Projections (ONPP) are first time applied to facial expression analysis. We systematically examine a number of linear subspace methods, and show that, in our comparative studies, the Supervised LPP (SLPP) is superior in supervised methods, while ONPP performs best in unsupervised learning.
An Adaptive Resource Scheduling Algorithm for Computational Grid
Authors: Tao Wang, Xing-she Zhou, Qiu-rang Liu, Zhi-yi Yang, Yun-lan Wang
Venue: Proceedings of the 2006 IEEE Asia-Pacific Conference on Services Computing (2006)

The emerging computational grid infrastructure consists of heterogeneous resource in widely distributed autonomous domains, which makes resource scheduling even more challenging. In this paper, we propose an adaptive resource scheduling algorithm for computational grid called BLBD (Based on Load Balancing and Demand). According to the computational grid job's personal resource requirements, the system load of computational grid nodes and the load balancing of computational grid system, the algorithm choose an appropriate node self-adaptively for computational grid job from the candidates. It has been successfully tested in NPU Campus Computational Grid environment. The experimental result shows that the new task scheduling algorithm can lead to significant performance gain for a variety of applications
Ways of working: a three-tiered interpretive model for video research
Authors: Donald W. Wortham, Sharon J. Derry
Venue: Proceedings of the 7th international conference on Learning sciences (2006)

In this "ways of working" paper, we describe a three-tier interpretive model for analyzing video data of social interaction. Since members of a research team often work from different theoretical perspectives, individual members' analyses of "what happened and why" are not always congruent. The model and process described here require making explicit methodological (and technological) links between layers of interpretation. Individual group members benefit because the connections between their analyses and data can be effectively warranted; the larger group benefits because the location of interpretive differences is more precisely exposed.
Multi-Scale Contour Extraction Based on Natural Image Statistics
Authors: Francisco J. Estrada, James H. Elder
Venue: Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop (2006)

Perceptual grouping of the complete boundaries of objects in natural images remains an unsolved problem in computer vision. The computational complexity of the problem and difficulties capturing global constraints limit the performance of current algorithms. In this paper we develop a coarse-to-fine Bayesian algorithm which addresses these constraints. Candidate contours are extracted at a coarse scale and then used to generate spatial priors on the location of possible contours at finer scales. In this way, a rough estimate of the shape of an object is progressively refined. The coarse estimate provides robustness to texture and clutter while the refinement process allows for the extraction of detailed object contours. The grouping algorithm is probabilistic and uses multiple grouping cues derived from natural scene statistics. We present a quantitative evaluation of grouping performance on the Berkeley Segmentation Database, and show that the multi-scale approach outperforms several single-scale contour extraction algorithms.
POS: A Practical Order Statistics Service forWireless Sensor Networks
Authors: Landon P. Cox Landon P. Cox, Miguel Castro, Antony Rowstron
Venue: Proceedings of the 26th IEEE International Conference on Distributed Computing Systems (2006)

We present the design and implementation of POS, an in-network service that computes accurate order statistics energy-efficiently. POS returns a stream of periodic samples from any order statistic. It initially computes the value of the order statistic and then periodically runs a validation protocol to determine whether the value is still valid. If not, it uses an optimized binary search to determine the new value and then resumes periodic validation. POS uses in-network aggregation and transmission suppression to reduce communication complexity. Results from both experiments on a mote testbed and simulations show that POS can compute order statistics accurately while consuming less energy than the best techniques to compute averages in common cases.
An approximation algorithm for scheduling malleable tasks under general precedence constraints
Authors: Klaus Jansen, Hu Zhang
Venue: ACM Transactions on Algorithms (TALG) (2006)

In this article, we study the problem of scheduling malleable tasks with precedence constraints. We are given m identical processors and n tasks. For each task the processing time is a function of the number of processors allotted to it. In addition, the tasks must be processed according to the precedence constraints. The goal is to minimize the makespan (maximum completion time) of the resulting schedule. The best previous approximation algorithm (that works in two phases) in Lep&egrave;re et al. [2002b] has a ratio 3 &plus; &sqrt;5&approx; 5.236. We develop an improved approximation algorithm with a ratio at most 100/43 &plus; 100(&sqrt;4349 &minus; 7)/2451 &approx; 4.730598. We also show that our resulting ratio is asymptotically tight.
Multiple Models Adaptive Decoupling Controller Using Dimension-By-DimensionTechnology
Authors: Xin Wang, Hui Yang
Venue: Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 1 (2006)

When multiple parameters jump simultaneously, a multiple models adaptive decoupling controller using Dimension-By-Dimension technology is presented to solve the problems such as too many models, long computing time and so on. To find the optimal parameter, it adopts one-dimension optimization method in series instead of multiple-dimension optimization method in parallel. At any time only one parameter is focused to find the optimal value and other parameters are kept constant, which can reduce the number of the system fixed models greatly. According to the switching index the best model is chosen and the controller is designed accordingly. By the choice of the weighting polynomial matrix, it not only places the poles of the closed loop system arbitrarily, but also decouples the system dynamically. At last the global convergence is obtained. In the simulation example, if the same number of the fixed models is used, system transient response and decoupling result are improved greatly.
A novel approach to studying cell communications signalling
Authors: Jasmina Panovska
Venue: Proceedings of the 37th conference on Winter simulation (2005)

In this paper we study the communication signals within a population of cells by extending the theory developed by Kummer and Ocone for the cell cycle. We show how the basic framework can be extrapolated from the cell level to the next organizational one, namely the population of cells. We define the basic quantities, such as the equivalent of the metabolic temperature and metabolic energy and we show how these are used to characterise the process of interest. We develop the basis for a non-equilibrium statistical theory for the system of cells and we establish the relationship between the variation in the local variables (eg. temperature and energy) and the variation in the cell concentration. The results from the theory are compared to those using the more classical approach of reaction-diffusion equations. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
A New Architecture of Data Access Middleware under Grid Environment
Authors: Qingyang Wang, Jingshu Chen, Xibin Gao, Wei Zhou, Baoping Yan
Venue: Proceedings of the 2006 IEEE Asia-Pacific Conference on Services Computing (2006)

Data sharing is one of the most important research areas in data grid. Distributed data resource and heterogeneous data schema bring difficulties to data Access and sharing. This article mainly focuses on how to deal with the heterogeneity of data schema and put forward a blueprint to solve the data access and sharing problem of heterogeneous physical data resources in the grid. To solve this problem we propose the SDB resource model which extracts three data layers from the physical data resource in order to facilitate the access and sharing of data resource.
Visualizing Distributions and Classification Accuracy
Authors: Dennis P. Groth
Venue: Proceedings of the conference on Information Visualization (2006)

Data mining is the search for novel, actionable information within data. It is important to note that the number of records in the data being analyzed is only one (and perhaps a small) factor in determining the complexity of a given data mining technique. Most complexity in data mining arises from the distribution of values contained in the data - not the number of records. In this paper we utilize straightforward histogram-based visualizations to gain insight into how the performance of a well-studied data mining technique, the naive-Bayes classifier, performs under various discretization schemes for both continuous and discrete values. The resulting visualization system provides users with a tool that describes the underlying model of the data used by the classifier. Exploratory visualizations of the distributions of training data can be selected based on expert domain knowledge and then combined to apply to the test data.