ppxscal/embedding_network huggingface dataset:
Analysis of Local Appearance-Based Face Recognition: Effects of Feature Selection and Feature Normalization
Authors: Hazim Kemal Ekenel, Rainer Stiefelhagen
Venue: Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop (2006)

In this paper, the effects of feature selection and feature normalization to the performance of a local appearance based face recognition scheme are presented. From the local features that are extracted using block-based discrete cosine transform, three feature sets are derived. These local feature vectors are normalized in two different ways; by making them unit norm and by dividing each coefficient to its standard deviation that is learned from the training set. The input test face images are then classified using four different distance measures: L1 norm, L2 norm, cosine angle and covariance between feature vectors. Extensive experiments have been conducted on the AR and CMU PIE face databases. The experimental results show the importance of using appropriate feature sets and doing normalization on the feature vector.
Reducing price fluctuation in continuous double auctions through pricing policy and shout improvement
Authors: Jinzhong Niu, Kai Cai, Simon Parsons, Elizabeth Sklar
Venue: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems (2006)

Auction mechanism design is a subfield of game theory dedicated to manipulating the rules of an auction so as to achieve specific goals. The complexity of the dynamics of auctions, especially continuous double auctions, makes it difficult to apply the usual analytic game-theoretic methods to do this. This paper takes an experimental approach, searching a parameterized space of possible auction types, and presents a new pricing policy for continuous double auctions. The paper further demonstrates how this policy, together with a shout improvement rule, helps to reduce the fluctuation of transaction prices in auctions involving agents with minimum intelligence while keeping the overall efficiency high.
A New Optimum Detection Scheme for Additive Watermarks Embedded in Spatial Domain
Authors: Maki Yoshida, Takaaki Fujita, Toru Fujiwara
Venue: Proceedings of the 2006 International Conference on Intelligent Information Hiding and Multimedia (2006)

A digital watermarking scheme permits embedding additional information into digital contents as a watermark so that the embedded information can be detected afterward. In detecting a watermark, there are two kinds of detection errors, a false positive error (FPE) and a false negative error (FNE). A detection scheme is said to be optimum if the FNE probability is minimized for a given FPE probability. In this paper, we present an optimum detection scheme for additive watermarks embedded in the spatial domain. The key idea of the proposed detection scheme is to use the difference between the brightness values. It is confirmed experimentally that the proposed scheme can control the FPE probability strictly.
SybilGuard: defending against sybil attacks via social networks
Authors: Haifeng Yu, Michael Kaminsky, Phillip B. Gibbons, Abraham Flaxman
Venue: ACM SIGCOMM Computer Communication Review (2006)

Peer-to-peer and other decentralized,distributed systems are known to be particularly vulnerable to sybil attacks. In a sybil attack,a malicious user obtains multiple fake identities and pretends to be multiple, distinct nodes in the system. By controlling a large fraction of the nodes in the system,the malicious user is able to "out vote" the honest users in collaborative tasks such as Byzantine failure defenses. This paper presents SybilGuard, a novel protocol for limiting the corruptive influences of sybil attacks.Our protocol is based on the "social network "among user identities, where an edge between two identities indicates a human-established trust relationship. Malicious users can create many identities but few trust relationships. Thus, there is a disproportionately-small "cut" in the graph between the sybil nodes and the honest nodes. SybilGuard exploits this property to bound the number of identities a malicious user can create.We show the effectiveness of SybilGuard both analytically and experimentally.
Development of Brain Mechanisms for Processing Orthographic and Phonologic Representations
Authors: James R. Booth, Douglas D. Burman, Joel R. Meyer, Darren R. Gitelman, Todd B. Parrish
Venue: Journal of Cognitive Neuroscience (2004)

Developmental differences in the neurocognitive networks for lexical processing were examined in 15 adults and 15 children (9- to 12-year-olds) using functional magnetic resonance imaging (fMRI). The lexical tasks involved spelling and rhyming judgments in either the visual or auditory modality. These lexical tasks were compared with nonlinguistic control tasks involving judgments of line patterns or tone sequences. The first main finding was that adults showed greater activation than children during the cross-modal lexical tasks in a region proposed to be involved in mapping between orthographic and phonologic representations. The visual rhyming task, which required conversion from orthography to phonology, produced greater activation for adults in the angular gyrus. The auditory spelling task, which required the conversion from phonology to orthography, also produced greater activation for adults in the angular gyrus. The greater activation for adults suggests they may have a more elaborated posterior heteromodal system for mapping between representational systems. The second main finding was that adults showed greater activation than children during the intra-modal lexical tasks in the angular gyrus. The visual spelling and auditory rhyming did not require conversion between orthography and phonology for correct performance but the adults showed greater activation in a system implicated for this mapping. The greater activation for adults suggests that they have more interactive convergence between representational systems during lexical processing.
A nonlinear fractional derivative model for large uni-axial deformation behavior of polyurethane foam
Authors: R. Deng, P. Davies, A. K. Bajaj
Venue: Signal Processing (2006)

In uni-axial quasi-static tests, flexible polyurethane foam undergoing large compressive loading and unloading deformation exhibits highly nonlinear and viscoelastic behavior. In particular, the response in the first cycle is observed to be significantly different from the response in subsequent cycles. In addition, the stresses in the loading paths are higher than those in unloading paths. This quasi-static response is modeled as an additive sum of a nonlinear elastic and a linear viscoelastic response. The nonlinear elastic behavior is modeled as a polynomial function of compressive strain, while the viscoelastic behavior is modeled as a parallel combination of five-parameter fractional derivative models. The focus of this paper is to develop a multi-element fractional derivative model that can capture the multi-cycle behavior. A parameter estimation procedure based on separating the contributions of the viscoelastic elements and the nonlinear elastic component is developed. This approach is applied to both simulation and experimental data. The combination of a nonlinear elastic component and a two-element fractional derivative model is found to predict the observed responses reasonably well. The results for two distinct foams from tests with different compression rates are compared and discussed.
Mapping moving landscapes by mining mountains of logs: novel techniques for dependency model generation
Authors: Mirko Steinle, Karl Aberer, Sarunas Girdzijauskas, Christian Lovis
Venue: Proceedings of the 32nd international conference on Very large data bases (2006)

Problem diagnosis for distributed systems is usually difficult. Thus, an automated support is needed to identify root causes of encountered problems such as performance lags or inadequate functioning quickly. The many tools and techniques existing today that perform this task rely usually on some dependency model of the system. However, in complex and fast evolving environments it is practically unfeasible to keep such a model up-to-date manually and it has to be created in an automatic manner. For high level objects this is in itself a challenging and less studied task. In this paper, we propose three different approaches to discover dependencies by mining system logs. Our work is inspired by a recently developed data mining algorithm and techniques for collocation extraction from the natural language processing field. We evaluate the techniques in a case study for Geneva University Hospitals (HUG) and perform large-scale experiments on production data. Results show that all techniques are capable of finding useful dependency information with reasonable precision in a real-world environment.
On the choice of wavelet filters for audio compression
Authors: P. Philippe, F. Moreau de Saint-Martin, L. Mainard
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1995. on International Conference - Volume 02 (1995)

We address the issue of choosing an optimal wavelet packets transform for audio compression. We present a comparison method based on a perceptual approach, which provides an entropic bit-rate for "transparent" coding of a given audio signal. The test with different wavelets leads to the conclusion that the most significant synthesis criterion for audio compression is the so-called "coding gain", while frequency selectivity, regularity and orthogonality seem less relevant.
Automated social network analysis as a tool for evaluating sociability
Authors: Kirk Job-Sluder
Venue: Proceedings of the 7th international conference on Learning sciences (2006)

Although the tools for mediating Internet-based learning communities have exploded in the last few years, our theoretical understanding of how to evaluate attempts to build online communities has lagged behind (Carroll, 2001). We used automated tools to build social network maps of an educational children's multi-user virtual environment using two modes of CMC. Internal "E-mail" was most frequently used within groups. Short "telegram" messages were frequently used within groups but also provided between-group interaction.
Forecasting Unstable Policy Enforcement
Authors: Javier Baliosian, Ann Devitt
Venue: Proceedings of the International Conference on Systems and Networks Communication (2006)

Policy-based network management (PBNM) is a promising but not yet delivering discipline aimed at automating network management decisions based on expert knowledge and strategic business objectives. One of the issues scarcely addressed in PBNM is the stability of the managed system as the result of the dynamic interaction between the "natural network behaviour and the autonomous management decision making. Yet this issue is central to the design of a self-management networking system comprised of autonomous entities making decisions driven by policies with often unknown consequences. Decisions made by one entity may change the context and configuration of other autonomous entities which may in turn react changing the context and configuration of the first entity triggering an unbounded chain of re-configuration actions. It is possible to model obligation policies and their constraints with finite state transducers (FST). It is also possible to learn patterns of recurrent behaviour using Bayesian networks (BN), a structurally similar kind of graph. The method presented in this paper analytically composes both finite state machines to derive predictions of the consequences of enforcing a given policy improving system stability.
On consistency and network latency in distributed interactive applications: a survey--part I
Authors: Declan Delaney, Tomás Ward, Seamus McLoone
Venue: Presence: Teleoperators and Virtual Environments (2006)

This paper is the first part of a two-part paper that documents a detailed survey of the research carried out on consistency and latency in distributed interactive applications (DIAs) in recent decades. Part I reviews the terminology associated with DIAs and offers definitions for consistency and latency. Related issues such as jitter and fidelity are also discussed. Furthermore, the various consistency maintenance mechanisms that researchers have used to improve consistency and reduce latency effects are considered. These mechanisms are grouped into one of three categories, namely time management, information management, and system architectural management. This paper presents the techniques associated with the time management category. Examples of such mechanisms include time warp, lock step synchronization, and predictive time management. The remaining two categories are presented in part II of the survey.
Bulk Transfer Capacity Estimation in IPv6 Networks
Authors: Robert Gardner, Francisco Garcia
Venue: Proceedings of the International Multi-Conference on Computing in the Global Information Technology (2006)

Bulk Transfer Capacity (BTC) is a measure of the sustainable data throughput on a network path that is subject to congestion control algorithms, such as those used by the Transmission Control Protocol (TCP). BTC is an important network performance metric because the vast majority of all traffic, typically around 95% of all packets, is conveyed by TCP. The performance of many Internet-based services is thus largely dependant on the BTC of the underlying network path. This paper proposes a novel tool, ReturnFlow6, for estimating the BTC of an IPv6 network path, conducted from a single point to a noninstrumented target. In order to verify the realistic operation of ReturnFlow6, a series of BTC measurements are conducted on a test network and a 'live' Intranet. The results are presented, analyzed and compared against the output of BTC estimation techniques for IPv4, permitting the relative advantages and disadvantages of each approach to be established.
Using formal concept analysis with an incremental knowledge acquisition system for web document management
Authors: Timothy J. Everts, Sung Sik Park, Byeong Ho Kang
Venue: Proceedings of the 29th Australasian Computer Science Conference - Volume 48 (2006)

It is necessary to provide a method to store Web information effectively so it can be utilised as a future knowledge resource. A commonly adopted approach is to classify the retrieved information based on its content. A technique that has been found to be suitable for this purpose is Multiple Classification Ripple-Down Rules (MCRDR). The MCRDR system constructs a classification knowledge base over time using an incremental learning process. This incremental method of acquiring classification knowledge suits the nature of Web information because it is constantly evolving and being updated. However, despite this advantage, the classification knowledge of the MCRDR system is not often utilised for browsing the classified information. This is because it does not directly organise the knowledge in a way that is suitable for browsing. As a result, often an alternate structure is utilised for browsing the information which is usually based on a user's abstract understanding of the information domain. This study investigated the feasibility of utilising the classification knowledge acquired through the use of the MCRDR system as a resource for browsing information retrieved from the WWW. A system was implemented that used the concept lattice-based browsing scheme of Formal Concept Analysis (FCA) to support the browsing of documents based on the MCRDR classification knowledge. The feasibility of utilising classification knowledge as a resource for browsing documents was evaluated statistically. This was achieved by comparing the concept lattice-based browsing approach to a standard one that utilises abstract knowledge of a domain as a resource for browsing the same documents.
Automatic Generation of Schedulings for Improving the Test Coverage of Systems-on-a-Chip
Authors: C. Helmstetter, F. Maraninchi, L. Maillet-Contoz, M. Moy
Venue: Proceedings of the Formal Methods in Computer Aided Design (2006)

SystemC is becoming a de-facto standard for the early simulation of Systems-on-a-chip (SoCs). It is a parallel language with a scheduler. Testing a SoC written in SystemC implies that we execute it, for some well chosen data. We are bound to use a particular deterministic implementation of the scheduler, whose specification is non-deterministic. Consequently, we may fail to discover bugs that would have appeared using another valid implementation of the scheduler. Current methods for testings SoCs concentrate on the generation of the inputs, and do not address this problem at all. We assume that the selection of relevant data is already done, and we generate several schedulings allowed by the scheduler specification. We use dynamic partial-order reduction techniques to avoid the generation of two schedulings that have the same effect on the system's behavior. Exploring alternative schedulings during testing is a way of guaranteeing that the SoC description, and in particular the embedded software, is scheduler-independent, hence more robust. The technique extends to the exploration of other non-fully specified aspects of SoC descriptions, like timing.
The role of process simulation and scheduling tools in the development and manufacturing of biopharmaceuticals
Authors: Demetri P. Petrides, Charles A. Siletti
Venue: Proceedings of the 36th conference on Winter simulation (2004)

Pharmaceutical manufacturers generally employ either organic synthesis or biotechnology. Each of these technologies has unique challenges, but batch process simulation and scheduling tools can facilitate process development in both. Batch process simulation is distinct from both traditional chemical process simulation and dynamic simulation and is uniquely suited to pharmaceutical processes. Furthermore, there is a close relationship between the data required for batch simulation and the data required for batch process scheduling. An example biopharmaceutical process illustrates how batch simulation can help improve a process. A scheduling example illustrates the relationship between batch simulation and scheduling.
Issues related to the computer realization of a multidisciplinary and multiobjective optimization system
Authors: Elina Madetoja, Kaisa Miettinen, Pasi Tarvainen
Venue: Engineering with Computers (2006)

Issues and novel ideas to be considered when developing computer realizations of complex multidisciplinary and multiobjective optimization systems are introduced. The aim is to discuss computer realizations that make possible both computationally efficient multidisciplinary analysis and multiobjective optimization of real world problems. We introduce software tools that make typically very time-consuming simulation processes more effective and, thus, enable even interactive multiobjective optimization with a real decision maker. In this paper, we first define a multidisciplinary and multiobjective optimization system and after that present an implementation overview of such problems including basic components participating in the solution process. Furthermore, interfaces and data flows between the components are described. A couple of important features related to the implementation are discussed in detail, for example, the usage of automatic differentiation. Finally, the ideas presented are illustrated with an industrial multiobjective optimization problem, when we describe numerical experiments related to quality properties in paper making.
Reducing the human overhead in text categorization
Authors: Arnd Christian König, Eric Brill
Venue: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining (2006)

Many applications in text processing require significant human effort for either labeling large document collections (when learning statistical models) or extrapolating rules from them (when using knowledge engineering). In this work, we describe away to reduce this effort, while retaining the methods' accuracy, by constructing a hybrid classifier that utilizes human reasoning over automatically discovered text patterns to complement machine learning. Using a standard sentiment-classification dataset and real customer feedback data, we demonstrate that the resulting technique results in significant reduction of the human effort required to obtain a given classification accuracy. Moreover, the hybrid text classifier also results in a significant boost in accuracy over machine-learning based classifiers when a comparable amount of labeled data is used.
Partial information use in uncertainty quantification
Authors: Daniel Berleant
Venue:  (2006)

Uncertainty exists frequently in our knowledge of the real world. Two forms of uncertainty are considered. One is variability coming from stochasticity. The other is epistemic uncertainty, also called 2nd order uncertainty and other names as well. Often it comes from ignorance or imprecision. In principle, this kind of uncertainty can be reduced by additional empirical data. Stochasticity is well studied in the field of probability theory. A variety of methods have been developed to address epistemic uncertainty. Some of these approaches are confidence limits, discrete convolutions, probabilistic arithmetic, Monte Carlo simulation, copulas, stochastic dominance, clouds, and distribution envelope determination. Belief and plausibility curves, upper and lower previsions, left and right envelopes and probability boxes designate an important type of representation for bounded uncertainty about distribution. Some methods combine probability theory and interval mathematics. Intervals have the potential for bounding the result of an operation. Discretization error coming from discretizing distributions may be bounded by intervals. Distribution envelope determination (DEnv) uses interval based analysis. If the dependency is not specified, result bounds will include the entire range of possible dependencies. These bounds will be wider than if a particular dependency is specified. I have worked on new algorithms to process the dependency relationships. Pearson correlation can be used to improve the results, for example. Also partial dependence information might be available in the form of unimodality or of probability over a specified area of a joint distribution. If this information is used in the calculation, more accurate results can be obtained than that without using this information. Another situation is uncertainty about the parameters of a distribution. All these topics are researched in this work. They are implemented in the software we call Statool. Based on the developed methods, uncertainty can be flexibly considered and added into models. This can make the model closer to real situations. One problem posed by Sandia National Laboratory is studied in this work. Other applications include Pert networks, decision models and others.
A RISC Processor with Redundant LNS Instructions
Authors: Mark G. Arnold
Venue: Proceedings of the 9th EUROMICRO Conference on Digital System Design (2006)

This paper presents Verilog code and FPGA synthesis results for a 32-bit RISC processor (RAWE) that uses the Dual Redundant Logarithmic Number System (DRLNS). A benchmark shows pure-software logarithmic arithmetic can be faster than floating point in some applications. An earlier processor (AWE) doubles that speed using hardware support for unsigned LNS values. Here, modest additional hardware allows the proposed RAWE to do signed multiply accumulate two to three times faster than AWE.