ppxscal/embedding_network huggingface dataset:
In-Network Outlier Detection in Wireless Sensor Networks
Authors: Joel Branch, Boleslaw Szymanski, Chris Giannella, Ran Wolff, Hillol Kargupta
Venue: Proceedings of the 26th IEEE International Conference on Distributed Computing Systems (2006)

To address the problem of unsupervised outlier detection in wireless sensor networks, we develop an algorithm that (1) is flexible with respect to the outlier definition, (2) works in-network with a communication load proportional to the outcome, and (3) reveals its outcome to all sensors. We examine the algorithm's performance using simulation with real sensor data streams. Our results demonstrate that the algorithm is accurate and imposes a reasonable communication load and level of power consumption.
Leveraging .NET meta-programming components from F#: integrated queries and interoperable heterogeneous execution
Authors: Don Syme
Venue: Proceedings of the 2006 workshop on ML (2006)

Language-integrated meta-programming and extensible compilation have been recurring themes of programming languages since the invention of LISP. A recent real-world application of these techniques is the use of small meta-programs to specify database queries, as used in the Microsoft LINQ extensions for .NET. It is important that .NET languages such as F# are able to leverage the functionality provided by LINQ and related components for heterogeneous execution, both for pragmatic reasons and as a first step toward applying more disciplined, formal approaches to these problems. This paper explores the use of a modest meta-programming extension to F# to access and leverage the functionality of LINQ and other components. We do this by demonstrating an implementation of language integrated SQL queries using the LINQ/SQLMetal libraries. We also sketch two other applications: the execution of data-parallel quoted F# programs on a GPU via the Accelerator libraries, and dynamic native-code compilation via LINQ.
Acceleration of narrow band method and its application to topology-adaptive 3D geometrical modeling
Authors: Shuntaro Yui, Kenji Hara, Hongbin Zha, Tsutomu Hasegawa
Venue: Systems and Computers in Japan (2006)

A method for increasing the speed of surface shape evolution called the fast narrow band method and a topology-adaptive geometrical modeling method that uses the fast narrow band method are proposed. Although there have been many reports of the use of the level set method as an effective method for topology-adaptive geometrical modeling, the methods suffers from the problem that the computational cost is very large. The narrow band method has been widely used as the most general method for increasing the speed of the level set method, but it has not been found to sufficiently reduce the computational cost. The proposed fast narrow band method retains the precision of the narrow band method while reducing the computational cost by using distance information to increase the efficiency of the search process. Experiments are performed on scenes containing objects with holes and scenes containing multiple objects, and a five-fold increase in speed is achieved over the Hermes algorithm, which was previously the fastest implementation of the level set method. &copy; 2006 Wiley Periodicals, Inc. Syst Comp Jpn, 37(11): 68&ndash;78, 2006; Published online in Wiley InterScience (). DOI 10.1002&sol;scj.20130
Soft computing techniques for case knowledge extraction in cbr system development
Authors: Simon Shiu
Venue:  (2006)

The performance of a case-based reasoning (CBR) system depends on its problem-solving quality, efficiency and competence. In a case base, a case can be defined as a piece of contextual and specific knowledge. The more the cases, the better the competence (coverage) of the problem domain, and therefore larger CBR systems tend to provide better solutions than the smaller ones. However, this is not always true because not all the cases collected in the system are useful for problem solving. For example, cases may be in conflict with each other; many cases may be redundant because of their close similarity; some cases may be noises in the system because they are not offering any help in the problem solving, and sometimes may even cause confusion. Another important aspect of CBR system is its efficiency (or speed) in providing helps. The purpose of this research is to examine closely these two aspects, and develop feasible computational techniques that will facilitate the development of CBR systems. This research question leads us to think deeply what constitute the problem solving ability of a CBR system; and also how to strike a balance between efficiency and problem-solving quality. Furthermore, in many real world situations, data and information collected are always incomplete, uncertain and vague, thus, the use of soft computing principles to achieve tractability, robustness and low solution cost is inevitable. Having the above understanding in mind, we then built up a set of soft computing based techniques for the extraction of case knowledge from data. They aim at (i) removing the redundancy and noises; (ii) reducing the size of the case base; and (iii) preserving the problem solving ability (or competence in CBR terminology). The developed algorithms deal with the processes of feature selection and reduction; similarity learning among features; case selection and case generation; and competence model development. Specific concepts and techniques, like approximate reducts; GA-based case-matching; redefined case coverage and reachability measurement; boundary cases with NN guiding principle; fast rough set-based feature reduction; rough LVQ based case generation; fuzzy integral-based case base competence model, are developed, tested and compared with traditional methods such as KPCA and SVM. The experimental results are very promising, and support our objective of trying to develop a compact and competent CBR system through case knowledge extraction.
Robust array processing detectors in dependent noise
Authors: M. Ketel, L. Kurz
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1993. ICASSP-93 Vol 4., 1993 IEEE International Conference on - Volume 04 (1993)

The theory of m-interval partitioning is extended and applied to the problem of sequentially detecting weak signals in time-dependent noise by using an array of sensors. Each sample vector of the data is transformed, via a suboptimum projection, into a single variable with univariate distribution upon which the sequential test is applied. The recursive evaluation of the projected data can reduce the complexity and the time to decision of the sequential detector considerably. The resultant array processor is easily implemented and is adaptable to slowly changing noise conditions.
Repair, Revision, and Complexity in Syntactic Analysis: An Electrophysiological Differentiation
Authors: Edith Kaan, Tamara Y. Swaab
Venue: Journal of Cognitive Neuroscience (2003)

One of the core aspects of human sentence processing is the ability to detect errors and to recover from erroneous analysis through revision of ambiguous sentences and repair of ungrammatical sentences. In the present study, we used event-related potentials (ERPs) to help identify the nature of these processes by directly comparing ERPs to complex ambiguous sentence structures with and without grammatical violations, and to simpler unambiguous sentence structures with and without grammatical violations. In ambiguous sentences, preference of syntactic analysis was manipulated such that in one condition, the structures agreed with the preferred analysis, and in another condition, a nonpreferred but syntactically correct analysis (garden path) was imposed. Nonpreferred ambiguous structures require revision, whereas ungrammatical structures require repair. We found that distinct ERPs reflected different characteristics of syntactic processing. Specifically, our results are consistent with the idea that a positivity with a posterior distribution across the scalp (posterior P600) is an index of syntactic processing difficulty, including repair and revision, and that a frontally distributed positivity (frontal P600) is related to ambiguity resolution and/or to an increase in discourse level complexity.
Generation of relevance maps and navigation in a digital book
Authors: Katsuhiro Ikeda, Kozo Sugiyama, Isamu Watanabe, Kazuo Misue
Venue: Proceedings of the 2006 Asia-Pacific Symposium on Information Visualisation - Volume 60 (2006)

This paper describes how to design a digital book for a new science called 'Knowledge Science'. We prepare several types of navigation facilities for browsing the book. Specially, we generate relevance maps by mining useful information from large volume of textual data and visualizing it. We evaluate the quality of the generated relevance maps through questionnaires to the authors. Also, we evaluate the usability of prepared navigation facilities through questionnaires to the users.
Learning the unified kernel machines for classification
Authors: Steven C. H. Hoi, Michael R. Lyu, Edward Y. Chang
Venue: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining (2006)

Kernel machines have been shown as the state-of-the-art learning techniques for classification. In this paper, we propose a novel general framework of learning the Unified Kernel Machines (UKM) from both labeled and unlabeled data. Our proposed framework integrates supervised learning, semi-supervised kernel learning, and active learning in a unified solution. In the suggested framework, we particularly focus our attention on designing a new semi-supervised kernel learning method, i.e., Spectral Kernel Learning (SKL), which is built on the principles of kernel target alignment and unsupervised kernel design. Our algorithm is related to an equivalent quadratic programming problem that can be efficiently solved. Empirical results have shown that our method is more effective and robust to learn the semi-supervised kernels than traditional approaches. Based on the framework, we present a specific paradigm of unified kernel machines with respect to Kernel Logistic Regresions (KLR), i.e., Unified Kernel Logistic Regression (UKLR). We evaluate our proposed UKLR classification scheme in comparison with traditional solutions. The promising results show that our proposed UKLR paradigm is more effective than the traditional classification approaches.
Agent based consumer choice model of broadband internet services incorporating geographical limitations of technologies
Authors: Conrad Mark Fonseca
Venue: Proceedings of the 37th conference on Winter simulation (2005)

Telecommunications providers are seeking to capitalise on the growth potential of broadband internet services in Australia. Choice of technology roll-out by providers must balance technology roll-out cost, technology limitations of broadband speed decreasing with distance from source, and consumer's demand for internet services requiring increasing broadband speeds over time. Evans & Peck and Alcatel Australia jointly developed a consumer choice model which allows scenario analysis on the potential impact to providers over the next 10 years. Critical to the consumer choice behaviour in the model were the geographical aspects of consumer location in relation to technology sources (eg. telephone exchange) which ultimately setup a choice matrix of technologies and providers for that particular consumer. A user interface was created in which a map of the geographical area of a single telephone exchange could be overlaid with both the location characteristics of households (density and consumer type)and of technologies coverage. 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
On the graphical analysis of the electronic structure of ferromagnetic clusters of medium size
Authors: José Ramón Alvarez Collado
Venue: Journal of Molecular Graphics (2004)

In a previous work, a theoretical approach, suitable to describe systems having a large number of fermions, was proposed, and results for ferromagnetic surface clusters of medium size (100-1000 atoms) were presented. The aim of the present contribution is to complete this previous work. Several significant theoretical and technical details, omitted previously, are provided here. The obtained spin orbitals are analyzed, by studying their symmetry, energy, and d contribution properties.
Risk Prediction in ERP Projects: Classification of Reengineered Business Processes
Authors: M. S. Camara, L. Kermad, A. El Mhamedi
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)

Companies have been implementing ERPs for almost ten years now and the same implementation problems continue to occur. More and more, ERP project's technological challenges are accompanied by critical dimensions related to the management of the transformational effects of these projects on the organization [17]. Indeed, risk management in this kind of project is not very structured and misses specific methods. In this article, we propose a structured methodology for risk management particularly in the BPR (Business Process Reengineering) sub-project of ERP implementation. The final aim of our work is to decease appreciably resistance to organizational change which poses the greatest obstacle to business process reengineering efforts. We propose to adapt OSR (Optimal Set Reduction), a risk management method used in software development,for the risk management in ERP implementation. We address specially the risk related to the changes in enterprise processes in the ERP project. To achieve this objective we propose to define business process change metrics starting from business process complexity metrics that exist in the literature.
A bijection between permutations and floorplans, and its applications
Authors: Eyal Ackerman, Gill Barequet, Ron Y. Pinter
Venue: Discrete Applied Mathematics (2006)

A floorplan represents the relative relations between modules on an integrated circuit. Floorplans are commonly classified as slicing, mosaic, or general. Separable and Baxter permutations are classes of permutations that can be defined in terms of forbidden subsequences. It is known that the number of slicing floorplans equals the number of separable permutations and that the number of mosaic floorplans equals the number of Baxter permutations [B. Yao, H. Chen, C.K. Cheng, R.L. Graham, Floorplan representations: complexity and connections, ACM Trans. Design Automation Electron. Systems 8(1) (2003) 55-80]. We present a simple and efficient bijection between Baxter permutations and mosaic floorplans with applications to integrated circuits design. Moreover, this bijection has two additional merits: (1) It also maps between separable permutations and slicing floorplans; and (2) it suggests enumerations of mosaic floorplans according to various structural parameters.
Using Coupled Hidden Markov Models to Model Suspect Interactions in Digital Forensic Analysis
Authors: Nathan Brewer, Nianjun Liu, Olivier De Vel, Terry Caelli
Venue: Proceedings of the International Workshop on on Integrating AI and Data Mining (2006)

This paper expands the use of Hidden Markov Models in Digital Forensics by using Coupled Hidden Markov Models to investigate interactions between multiple suspects in forensic cases. This paper compares the output of a coupled hidden Markov model to a similarly trained single-chain hidden Markov models and to expert knowledge in a simulated digital forensic case with two suspects. In the situation modelled, there was a notable improvement in the accuracy of the coupled model compared to single chain models. This demonstrates that there is some form of interaction between suspects in this digital forensic case, and that this interaction can be effectively modelled by a coupled hidden Markov model.
Analyzing DICOM and non-DICOM Features in Content-Based Medical Image Retrieval: A Multi-Layer Approach
Authors: Antonio da Luz,  Jr., Daniel D. Abdala, Aldo V. Wangenheim, Eros Comunello
Venue: Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems (2006)

This paper presents a hybrid approach to perform content-based retrieval on medical image databases. It takes advantage of a pre-processed case base that is batch updated. DICOM information is used to perform pre-filtering to speed up the retrieval process and a image processing knowledge base is used to dynamically reconfigure the most appropriated image processing procedures to perform the image feature extraction. It shows that pre-filtering can speed up considerably the retrieval process and also that some image features produce very similar results what leads to future work on defining the needed digital image processing knowledge base.
Demotion-based exclusive caching through demote buffering: design and evaluations over different networks
Authors: Jiesheng Wu, Pete Wyckoff, Dhabaleswar K. Panda
Venue: Proceedings of the international workshop on Storage network architecture and parallel I/Os (2003)

Multi-level buffer cache architecture has been widely deployed in today's multiple-tier computing environments. However, caches in different levels are inclusive. To make better use of these caches and to achieve the expected performance commensurate to the aggregate cache size, exclusive caching has been proposed. Demotion-based exclusive caching [1] introduces a DEMOTE operation to transfer blocks discarded by a upper level cache to a lower level cache. In this paper, we propose a DEMOTE buffering mechanism over storage networks to reduce the visible costs of DEMOTE operations and provide more flexibility for optimizations. We evaluate the performance of DEMOTE buffering using simulations across both synthetic and real-life workloads on three different networks and protocol layers (TCP/IP on Fast Ethernet, IBNice on InfiniBand, and VAPI on InfiniBand). Our results show that DEMOTE buffering can effectively hide demotion costs. A maximum speedup of 1.4x over the original DEMOTE approach is achieved for some workloads. Speedups in the range of 1.08--1.15x are achieved for two real-life workloads. The vast performance gains results from overlapping demotions and other activities, reduced communication operations and high utilization of the network bandwidth.
Electroencephalographic Activity in a Flanker Interference Task Using Japanese Orthography
Authors: Shuhei Yamaguchi, Genya Toyoda, Jiang Xu, Shotai Kobayashi, Avishai Henik
Venue: Journal of Cognitive Neuroscience (2002)

The neural activities for color word interference effects were investigated using event-related brain potentials (ERPs) recorded in a flanker-type interference task. Kanji words (Japanese morphograms) and kana words (Japanese phonograms) were used as the flanker stimuli to obtain insights about hemispheric specialization for processing two types of Japanese orthographies. Interference effects in reaction time were larger when kanji words were presented in the left visual field and when kana words were in the right visual field. ERPs were modulated by the incongruent flankers, which generated a negative ERP component with the different onset and offset depending on flanker attributes. Consistent with the behavioral data, the interference-related negativity was observed for kanji words presented in the left visual field and for kana words in the right visual field. The negativity distributed maximally over the fronto-central site. The early part of the negativity distributed strongly over the frontal midline area, whereas it extended bilaterally over the frontal area in the late phase. The present results support the view of preferential processing of kanji in the right hemisphere and that of kana in the left hemisphere. The temporal profile of scalp topographies for the interference-related neural activity suggests that the medial and dorsolateral prefrontal regions may be involved in maintaining attentional set and conflict resolution.
Nonlinear phase estimators based on the Kullback distance
Authors: J. M. N. Leitao, J. M. F. Moura
Venue: Proceedings of the Acoustics, Speech, and Signal Processing,1994. on IEEE International Conference - Volume 04 (1994)

This paper considers the design of phase estimators by combining concepts of stochastic nonlinear filtering and information theory. To propagate the involved probability density functions, adequate finite representations are needed. This is accomplished in this work by adopting minimum Kullback (1978) distance criteria. Applied to the important and paradigmatic cyclic phase estimation problem, our approach leads to consistent and systematic design methods. The resulting simple and parallelizable structure outperforms the commonly used extended Kalman-Bucy filter in tracking and acquisition situations. These features make the developed nonlinear filter suited to digital communications (carrier synchronization).
Reaching Out to Shape Our Future
Authors: Deborah M. Cooper
Venue: Computer (2006)

The Society's 2006 president reports on this year's accomplishments.
Ozone decomposition of the second order in aqueous solutions
Authors: J. Biazar, M. Tango, R. Islam
Venue: Applied Mathematics and Computation (2006)

In this article the problem of mass transfer of ozone of the second order from a gaseous phase into an aqueous phase has been studied. And Adomian decomposition method is employed to derive an analytical approximation to the solutions of the system of differential equations governing on the problem. Some parametric studies have been included. The effects of the temperature and hydroxyl ion reaction order to the solutions are illustrated by some plots.
Higher-order statistics based blind estimation of non-Gaussian bidimensional moving average models
Authors: M'hamed Bakrim, Driss Aboutajdine
Venue: Signal Processing (2006)

In this paper, four batches least squares linear approaches are developed for non-minimum phase bidimensional non-Gaussian moving average (MA) models identification. A relationship between autocorrelation and cumulant sequences is established. One of the proposed methods is cumulant based. The others exploit both autocorrelation and mth-order cumulants (m>2). Three of these proposed methods are obtained by transforming Brillinger-Rosenblatt's non-linear equation into linear one using the Tugnait's closed-form solution. We also generalize the 2-D version of Giannakis-Mendel's method to mth-order cumulant. The simulation results show that one of the three autocorrelation and cumulants based methods gives the best estimates in free-noise environments, but in a Gaussian noisy case, the cumulant-based one is more adequate when large data are available. We also show the usefulness of the relationship to improve the estimates of the autocorrelation-based method in colored noise environment.