ppxscal/embedding_network huggingface dataset:
A Context-Based Delegation Access Control Model for Pervasive Computing
Authors: Ramiro Liscano, Kaining Wang
Venue: Proceedings of the 21st International Conference on Advanced Information Networking and Applications Workshops - Volume 02 (2007)

This paper presents an architectural model for contextual-based delegation access control for pervasive computing, particularly access control mechanisms for ad hoc coalition scenarios. Coalition access control encompasses control mechanisms dealing with access between multiple heterogeneous entities from different security domains. For pervasive computing these coalitions are formed in an ad hoc manner and need to be sensitive to the context of the participating entities. An architecture model called the SIP Session-based Coalition Access Control Architecture (SCACA) is presented that extends the SIP signaling model in order to support long distance delegation access control. The result is an architecture that supports spontaneous ad hoc coalitions for a pervasive computing environment.
Improving the Public Health Information Network through Semantic Modeling
Authors: Parsa Mirhaji, Dean Allemang, Robert Coyne, S. Ward Casscells
Venue: IEEE Intelligent Systems (2007)

The US Centers for Disease Control and Prevention created the Public Health Information Network to advance information systems in public health organizations. PHIN provides functional requirements, capabilities, performance measures, and operational characteristics for public health information systems. The certification process is important to organizations, but PHIN barely satisfies its goals. Semantic modeling can improve PHIN in several ways, including organizing its information, streamlining the certification process, and facilitating interoperability with relevant federal and industrial frameworks.
Rainbow Color Map (Still) Considered Harmful
Authors: David Borland, Russell M. Taylor II
Venue: IEEE Computer Graphics and Applications (2007)

Despite much published research on its deficiencies, the rainbow color map is prevalent in the visualization community. The authors present survey results showing that the rainbow color map continues to appear in more than half of the relevant papers in IEEE Visualization Conference proceedings. Its use is encouraged by its selection as the default color map used in most visualization toolkits that the authors inspected. The visualization community must do better. In this article, the authors reiterate the characteristics that make the rainbow color map a poor choice, provide examples that clearly illustrate these deficiencies even on simple data sets, and recommend better color maps for several categories of display.
Packing Non-Returning A-Paths*
Authors: Gyula Pap&#x2020;
Venue: Combinatorica (2007)

Chudnovsky et al. gave a min-max formula for the maximum number of node-disjoint nonzero A-paths in group-labeled graphs [1], which is a generalization of Mader's theorem on node-disjoint A-paths [3]. Here we present a further generalization with a shorter proof. The main feature of Theorem 2.1 is that parity is &#x201c;hidden&#x201d; inside $$\ifmmode\expandafter\hat\else\expandafter\^\fi{v}$$, which is given by an oracle for non-bipartite matching.
Image feature evaluation for contents-based image retrieval
Authors: Adam Kuffner, Antonio Robles-Kelly
Venue: Proceedings of the HCSNet workshop on Use of vision in human-computer interaction - Volume 56 (2006)

This paper is concerned with feature evaluation for content-based image retrieval. Here we concentrate our attention on the evaluation of image features amongst three alternatives, namely the Harris corners, the maximally stable extremal regions and the scale invariant feature transform. To evaluate these image features in a content-based image retrieval setting, we have used the KD-tree algorithm. We use the KD-tree algorithm to match those features corresponding to the query image with those recovered from the images in the data set under study. With the matches at hand, we use a nearest neighbour approach to threshold the Euclidean distances between pairs of corresponding features. In this way, the retrieval is such that those features whose pairwise distances are small, "vote" for a retrieval candidate in the data-set. This voting scheme allows us to arrange the images in the data set in order of relevance and permits the recovery of measures of performance for each of the three alternatives. In our experiments, we focus in the evaluation of the effects of scaling and rotation in the retrieval performance.
Multi-Dimensional Range Query over Encrypted Data
Authors: Elaine Shi, John Bethencourt, T-H. Hubert Chan, Dawn Song, Adrian Perrig
Venue: Proceedings of the 2007 IEEE Symposium on Security and Privacy (2007)

We design an encryption scheme called Multi-dimensional Range Query over Encrypted Data (MRQED), to address the privacy concerns related to the sharing of network audit logs and various other applications. Our scheme allows a network gateway to encrypt summaries of network flows before submitting them to an untrusted repository. When network intrusions are suspected, an authority can release a key to an auditor, allowing the auditor to decrypt flows whose attributes (e.g., source and destination addresses, port numbers, etc.) fall within specific ranges. However, the privacy of all irrelevant flows are still preserved. We formally define the security for MRQED and prove the security of our construction under the decision bilinear Diffie-Hellman and decision linear assumptions in certain bilinear groups. We study the practical performance of our construction in the context of network audit logs. Apart from network audit logs, our scheme also has interesting applications for financial audit logs, medical privacy, untrusted remote storage, etc. In particular, we show that MRQED implies a solution to its dual problem, which enables investors to trade stocks through a broker in a privacypreserving manner.
A second-order HMM for high performance word and phoneme-based continuous speech recognition
Authors: J.-F. Mari, D. Fohr, J.-C. Junqua
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 01 (1996)

In the field of speech recognition by stochastic methods, it is conventional to pursue approaches using first-order-hidden Markov models (HMM1s). Despite the success of this approach, it is still worth investigating if some of the drawbacks of HMM1s can be overcome, e.g. by using higher-order Markov processes. In this paper, we show that second-order hidden Markov models (HMM2s) can yield high performances in the context of continuous speech recognition. We first present the underlying equations and complexity of HMM2s in the maximum likelihood estimation (MLE) paradigm. Then, we show that in a connected word recognition task, such as spelled name recognition over the telephone, HMM2s outperform HMM1s. In the field of phoneme-based continuous speech recognition, we show that context-independent HMM2s can achieve more than 69% phone accuracy.
Efficient signal enhancement scheme for adaptive antenna arrays in Nakagami multipath fading
Authors: Kyungseok Kim, Kicheol Tae
Venue: Proceedings of the The 8th International Conference on Communication Systems - Volume 01 (2002)

An efficient signal enhancement scheme for an adaptive antenna array under the Nakagami fading channel is proposed. The proposed signal enhancement scheme is a modified linear signal estimator with the Toeplitz matrix approximation (TMA) method. The underlying principle of the proposed signal enhancement scheme is to reduce the noise component using not only the Lagrangian method of the constrained minimization but also a signal-plus-noise subspace method. The TMA is also used because of its theoretical noise-free signal property. These functions greatly enhance the performance of an adaptive antenna array by removing the all undesired noise effects from the post-correlation received signal. The proposed signal enhancement scheme is applied with the Wiener maximal ratio combining (MRC) method which has been widely used as the conventional adaptive antenna array. Several computer simulation examples are provided for illustrating the effectiveness of the proposed scheme.
Parallel Graph Contraction with Applications to a Reconfigurable Parallel Architecture
Authors: Yuh-Dauh Lyuu, Eugen Schenfeld
Venue: Proceedings of the 1994 International Conference on Parallel Processing - Volume 03 (1994)

Communication plays a key role in the overall performance of a parallel system. In the general case, the communication network is required to transfer information from any set of sources to to any set of destinations. In practice, parallel applications have various communication needs. Many applications restrict their communication requests to a more limited number of possible connections between sources and destinations. The communication requests of parallel applications are represented by graphs. Parallel graph contraction has been used to embed these graphs into a reconfigurable parallel architecture. We review some of the previous work done in this area showing how various useful communication graphs (e.g., ring, kary trees, mesh, CCC, hypercube, etc.), can be contracted for an efficient embedding. We also present some results for the parallel contraction of other useful graphs such as de Bruijn, shuffle-exchange, mesh of trees, pyramid, and multi-stage communication structures. Lower bounds are proved for all graphs. A heuristic approach for parallel graph contraction is reviewed for irregular graphs based on simulated annealing.
Reinforcement learning in steady-state cellular genetic algorithms
Authors: Cin-Young Lee, E. K. Antonsson
Venue: Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02 (2002)

A novel cellular genetic algorithm is developed to address the issues of good mate selection. This is accomplished through reinforcement learning where good mating individuals attract and poor mating individuals repel. Adaptation of good mate choice occurs, thus leading to more efficient search. Results are presented for various test cases.
Fast nonlinear adaptive filtering using a partial window conjugate gradient algorithm
Authors: A. N. Birkett
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 06 (1996)

In this paper a modified form of the partial conjugate gradient algorithm is presented for use in nonlinear filtering using neural networks. The algorithm is based on using a gradient average window to provide a trade-off between convergence rate and complexity which, depending on the choice of averaging window, is (in both complexity and speed of convergence) intermediate between the conventional backpropagation (BP) algorithm and the Newton methods. An additional simplification is introduced by replacing the calculated optimum step size /spl alpha//sub k/ by a normalized step size /spl alpha/~, in the same manner as the normalized LMS algorithm. This new algorithm is applied to a cascaded neural network/nonlinear least mean squares structure for the identification of a nonlinear system. This proposed algorithm demonstrates improved convergence rates with even small choices of window size.
Preventing privilege escalation
Authors: Niels Provos, Markus Friedl, Peter Honeyman
Venue: Proceedings of the 12th conference on USENIX Security Symposium - Volume 12 (2003)

We introduce a system that eliminates the need to run programs in privileged process contexts. Using our system, programs run unprivileged but may execute certain operations with elevated privileges as determined by a configurable policy eliminating the need for suid or sgid binaries. We present the design and analysis of the "Systrace" facility which supports fine grained process confinement, intrusion detection, auditing and privilege elevation. It also facilitates the often difficult process of policy generation. With Systrace, it is possible to generate policies automatically in a training session or generate them interactively during program execution. The policies describe the desired behavior of services or user applications on a system call level and are enforced to prevent operations that are not explicitly permitted. We show that Systrace is efficient and does not impose significant performance penalties.
Algorithmic pricing via virtual valuations
Authors: Shuchi Chawla, Jason D. Hartline, Robert Kleinberg
Venue: Proceedings of the 8th ACM conference on Electronic commerce (2007)

Algorithmic pricing is the computational problem that sellers (e.g.,in supermarkets) face when trying to set prices for their items to maximize their profit in the presence of a known demand. Guruswami etal. (SODA, 2005) proposed this problem and gave logarithmic approximations (in the number of consumers) for the unit-demand and single-parameter cases where there is a specific set of consumers and their valuations for bundles are known precisely. Subsequently several versions of the problem have been shown to have poly-logarithmic in approximability. This problem has direct ties to the important open question of better understanding the Bayesian optimal mechanism in multi-parameter agent settings; however, for this purpose approximation factors logarithmic in the number of agents are inadequate. It is therefore of vital interest to consider special cases where constant approximations are possible. We consider the unit-demand variant of this pricing problem. Here a consumer has a valuation for each different item and their value for aset of items is simply the maximum value they have for any item in the set. Instead of considering a set of consumers with precisely known preferences, like the prior algorithmic pricing literature, we assume that the preferences of the consumers are drawn from a distribution. This is the standard assumption in economics; furthermore, the setting of a specific set of customers with specific preferences, which is employed in all of the prior work in algorithmic pricing, is a special case of this general Bayesian pricing problem, where there is a discrete Bayesian distribution for preferences specified by picking one consumer uniformly from the given set of consumers. Notice that the distribution over the valuations for the individual items that this generates is obviously correlated. Our work complements these existing works by considering the case where the consumer's valuations for the different items are independent random variables. Our main result is a constant approximation algorithm for this problem that makes use of an interesting connection between this problem and the concept of virtual valuations from the single-parameter Bayesian optimal mechanism design literature.
Classification of time delay estimates for robust speaker localization
Authors: N. Strobel
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06 (1999)

This paper proposes a solution to the problem of robust speaker localization under adverse acoustic conditions. The approach is based on the classification of time delay estimates. Two classification techniques are investigated in detail: maximum likelihood (ML) classification and classification based on histogram comparison. Their performance under adverse acoustic conditions is compared to outcomes obtained with the traditional approach which uses time delay estimates directly to infer speaker positions. Experiments indicate that the ML classification method provides little improvement over the traditional method. On the other hand, using histogram classification, we can improve the probability of correct speaker localization by more than 60% compared to either the traditional approach or the ML classification technique.
Strong equilibrium in cost sharing connection games
Authors: Amir Epstein, Michal Feldman, Yishay Mansour
Venue: Proceedings of the 8th ACM conference on Electronic commerce (2007)

In this work we study cost sharing connection games, where each player has a source and sink he would like to connect, and the cost of the edges is either shared equally (fair connection games) or in an arbitrary way (general connection games).We study the graph topologies that guarantee the existence of a strong equilibrium (where no coalition can improve the cost of eachof its members) regardless of the specific costs on the edges. Our main existence results are the following: (1) For a single source and sink we show that there is always a strong equilibrium (both for fair and general connection games). (2) For a single source multiple sinks we show that for a series parallel graph a strong equilibrium always exists (both for fair and general connection games). (3) For multi source and sink we show that an extension parallel graph always admits a strong equilibrium in fair connection games. As for the quality of the strong equilibrium we show that in any fair connection games the cost of a strong equilibrium is Θ(log n) from the optimal solution, where n is the number of players. (This should be contrasted with the Ω(n) price of anarchy for the same setting.) For single source general connection games and single source single sink fair connection games, we show that a strong equilibrium is always an optimal solution.
Theorizing in Information Systems Research: Some Preliminary Findings
Authors: Brent Furneaux, Michael Wade, Hossam Ali-Hassan
Venue: Proceedings of the 40th Annual Hawaii International Conference on System Sciences (2007)

Information systems (IS) research employs a wide range of theory, drawn from numerous disciplines. Over the course of the past three decades, the proliferation of theory has created considerable diversity, leading to calls for greater reflection on the nature of this diversity. Thus, this study seeks to examine the state of IS theorizing through a review of recent publications in one of the field's leading journals. The findings confirm the presence of considerable conceptual diversity within the field. A paradigmatic model is developed to frame the most salient relationships revealed in the research. The paper identifies common theories encountered in the field, along with key constructs, and the contexts under which these constructs are studied. The paper also suggests the presence of some relatively unexplored areas for potential future theorizing.
Congestion games with load-dependent failures: identical resources
Authors: Michal Penn, Maria Polukarov, Moshe Tennenholtz
Venue: Proceedings of the 8th ACM conference on Electronic commerce (2007)

We define a new class of games, congestion games with load-dependent failures (CGLFs), which generalizes the well-known class of congestion games, by incorporating the issue of resource failures into congestion games. In a CGLF, agents share a common set of resources, where each resource has a cost and a probability of failure. Each agent chooses a subset of the resources for the execution of his task, in order to maximize his own utility. The utility of an agent is the difference between his benefit from successful task completion and the sum of the costs over the resources he uses. CGLFs possess two novel features. It is the first model to incorporate failures into congestion settings, which results in a strict generalization of congestion games. In addition, it is the first model to consider load-dependent failures in such framework, where the failure probability of each resource depends on the number of agents selecting this resource. Although, as we show, CGLFs do not admit a potential function, and in general do not have a pure strategy Nash equilibrium, our main theorem proves the existence of a pure strategy Nash equilibrium in every CGLF with identical resources and nondecreasing cost functions.
A Python Module for Modeling and Control Design of Flexible Robots
Authors: Ryan W. Krauss, Wayne J. Book
Venue: Computing in Science and Engineering (2007)

This article discusses the creation of a Python module for object-oriented modeling and control design of flexible robots using the transfer matrix method (TMM). The authors overcame several theoretical hurdles to apply the TMM to practical flexible robots and have experimentally validated the Python module's modeling capabilities.
Requirements for Protection Methods of Personal Information in Vehicle Probing System
Authors: Michiko Izumi, Masaaki Sato, Hideki Sunahara
Venue: Proceedings of the 2007 International Symposium on Applications and the Internet Workshops (2007)

Nowadays, automobiles have a role as a sensor aggregation which is used to grasp the circumstance and its locational information. The value from sensors on each vehicle is called probe data. Organically-consolidated probe data which make social seminal information is deserving of societal expectation. These probe vehicle systems also known as floating car Data System (FCD) are developed under active promotion of cooperation among industry, academia, and government. In addition, for providing global social services over the Internet, lots of companies and general persons who use such services have drew attention to privacy, especially the management of personal data. In this paper, After providing a brief overview of general probe vehicle system, we sew up practical difficulties and threat analysis. Base on its analysis, we consider the requirements for envision a design and deployment of secure probe vehicle system, and inform the related activities at ISO.
Program Co-Chairs' Message
Authors: Hong Zhu
Venue: Proceedings of the 29th International Conference on Software Engineering Workshops (2007)

Welcome to the second International Workshop on Automation of Software Test (AST'06)! It has been our privilege to organize this workshop at the 29th International Conference on Software Engineering (ICSE'07) after the success of the first AST workshop at ICSE 2006 in Shanghai. As demonstrated by AST 2006 as well as the programme of this year, this unique workshop serves as a bridge to bring together researchers on software test automation from around the world.
A self-healing On-demand Geographic Path Routing Protocol for mobile ad-hoc networks
Authors: Venkata C. Giruka, Mukesh Singhal
Venue: Ad Hoc Networks (2007)

We present a self-healing On-demand Geographic Path Routing Protocol (OGPR) for mobile ad-hoc networks. OGPR is an efficient, stateless, and scalable routing protocol that inherits the best of the three well-known techniques for routing in ad-hoc networks, viz., greedy forwarding, reactive route discovery, and source routing. In OGPR protocol, source nodes utilize the geographic-topology information obtained during the location request phase to establish geographic paths to their respective destinations. Geographic paths decouple node ID's from the paths and are immune to changes in the network topology. Further, they help nodes avoid dead-ends due to greedy forwarding. To utilize geographic paths even in sparser networks, OGPR uses a path-healing mechanism that helps geographic paths adapt according to the network topology. We present extensions to OGPR protocol to cope with networks containing unidirectional links. Further, we present results from an extensive simulation study using GloMoSim. Simulation results show that OGPR achieves higher percentage packet delivery and lower control overhead, compared to a combination of GPSR+GLS protocols, AODV, and DSR under a wide range of network scenarios.