ppxscal/embedding_network huggingface dataset:
Error correction via a post-processor for continuous speech recognition
Authors: E. K. Ringger, J. F. Allen
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 01 (1996)

This paper presents a new technique for overcoming several types of speech recognition errors by post-processing the output of a continuous speech recognizer. The post-processor output contains fewer errors, thereby making interpretation by higher-level modules, such as a parser, in a speech understanding system more reliable. The primary advantage to the post-processing approach over existing approaches for overcoming SR errors lies in its ability to introduce options that are not available in the SR module's output. This work provides evidence for the claim that a modern continuous speech recognizer can be used successfully in "black-box" fashion for robustly interpreting spontaneous utterances in a dialogue with a human.
Adaptive, nonparametric markov models and information-theoretic methods for image restoration and segmentation
Authors: Ross T. Whitaker
Venue:  (2006)

The regularity in data fundamentally distinguishes itself from random noise. Describing this regularity in generic, yet powerful, ways is one of the key problems in signal processing. One way of capturing image regularity is by incorporating a priori information into the image model itself. Approaches extracting such prior information from training data have limited utility because of the lack of effective training sets for most applications. Unsupervised approaches that, typically, encode prior information via parametric models work best only when the data conforms to that model. Certain kinds of problems do not adhere to strict models, entailing unsupervised approaches to be adaptive. Statistical-inference methodologies that allow us to learn the underlying structure and variability in the data form important tools in adaptive signal processing.This dissertation presents an adaptive Markov-random-field (MRF) image model that automatically learns the local statistical dependencies via data-driven nonparametric techniques. We use this model to create adaptive algorithms for processing images. We incorporate prior information, when available, through optimal Bayesian frameworks. We enforce optimality criteria based on fundamental information-theoretic concepts that capture the functional dependence and information content in the data.We employ this adaptive-MRF framework for effectively solving several classic problems in image processing, computer vision, and medical image analysis. Inferring the statistical structure underlying corrupted images enables us to restore images without enforcing strong models on the signal. The restoration iteratively improves the predictability of pixel intensities from their neighborhoods, by decreasing their joint entropy. When the nature of noise is known, we present an effective empirical-Bayesian reconstruction strategy. We also present a method to optimally estimate the uncorrupted-signal statistics from the observed corrupted-signal statistics by minimizing a KL-divergence measure. We apply this adaptive-MRF framework to classify tissues in magnetic resonance (MR) images of the human brain by maximizing the mutual information between the classification labels and image data, capturing their mutual dependency. The generic formulation enables the method to adapt to different MR modalities, noise, inhomogeneities, and partial-voluming. We incorporate a priori information via probabilistic brain-tissue atlases. We use a similar strategy for texture segmentation, using fast threshold-dynamics-based level-set techniques for regularization.
Xen and co.: communication-aware CPU scheduling for consolidated xen-based hosting platforms
Authors: Sriram Govindan, Arjun R. Nath, Amitayu Das, Bhuvan Urgaonkar, Anand Sivasubramaniam
Venue: Proceedings of the 3rd international conference on Virtual execution environments (2007)

Recent advances in software and architectural support for server virtualization have created interest in using this technology in the design of consolidated hosting platforms. Since virtualization enables easier and faster application migration as well as secure co-location of antagonistic applications, higher degrees of server consolidation are likely to result in such virtualization-based hosting platforms (VHPs). We identify a key shortcoming in existing virtual machine monitors (VMMs) that proves to be an obstacle in operating hosting platforms, such as Internet data centers, under conditions of such high consolidation: CPU schedulers that are agnostic to the communication behavior of modern, multi-tier applications. We develop a new communication-aware CPU scheduling algorithm to alleviate this problem. We implement our algorithm in the Xen VMM and build a prototype VHP on a cluster of servers. Our experimental evaluation with realistic Internet server applications and benchmarks demonstrates the performance/cost benefits and the wide applicability of our algorithms. For example, the TPC-W benchmark exhibited improvements in average response times of up to 35% for a variety of consolidation scenarios. A streaming media server hosted on our prototype VHP was able to satisfactorily service up to 3.5 times as many clients as one running on the default Xen.
Introduction to the 2007 Electronic Government Track
Authors: Jochen Scholl
Venue: Proceedings of the 40th Annual Hawaii International Conference on System Sciences (2007)

For the study domain of e-Government, the past year unfolded as an extraordinarily successful one, in which the formation of professional digital and/or electronic government societies in both North America and Europe presented special highlights (for more information on the Digital Government Society of North America {DGSNA} and the European E-Government Society {EGOV-S} please see http://www.dgsociety.org/ and http://tinyurl.com/y4urfj ).
The benefit of using SIM application toolkit in the context of near field communication applications
Authors: Gerald Madlmayr, Oliver Dillinger, Josef Langer, Christoph Schaffer
Venue: Proceedings of the International Conference on the Management of Mobile Business (2007)

NFC is one of the most promising technologies in handsets for business applications like ticketing or payment. Actually those applications require a secure store for keeping sensitive data. Using the SIM card as a removable secure element for proximity applications is one option. We will elaborate a comprehensive study on the requirements and impacts of using the SIM card as a secure store for proximity applications, presenting the pros and cons of combining NFC with the SIM card in handsets. Based on these facts we worked out a concept and necessary processes for an NFC ecosystem based on a SAM. Hence we line out two different approaches for managing applications in the secure element. Finally the effects of this implementation on mobile network operators and application providers as well as consumers are summarized.
Can A Narrow Field of View Explain The Extremely Small Amount of Tactile Memory During Tactile Search for Change?
Authors: Takako Yoshida, Yuki Miyazaki, Kenji Yokoi, Hiromi Wake, Tenji Wake
Venue: Proceedings of the Second Joint EuroHaptics Conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems (2007)

In previous studies, we have assessed the amount of information that can be maintained in working memory with a "search for change task in both the visual and the somatosensory modality. We have reported a series of experiments that showed extremely limited amounts of tactile memory. Here, we tested the hypothesis that an extremely narrow field of view may explain our results. Using an eye movement contingent moving window system, an apparatus that actively narrows the visual field of view based on eye movements, we found that a narrow field view also dramatically reduced visual memory during active visual scanning. On the other hand, tactile search with both hands did not double the tactile memory compared to the search with one hand. These results indicate that the limited amounts of tactile memory observed previously can be attributed to a narrow field of view for one hand.
Estimating statistical aggregates on probabilistic data streams
Authors: T. S. Jayram, Andrew McGregor, S. Muthukrishnan, Erik Vee
Venue: Proceedings of the twenty-sixth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems (2007)

The probabilistic-stream model was introduced by Jayram et al. [20].It is a generalization of the data stream model that issuited to handling "probabilistic" data, where each item of the stream represents a probability distribution over a set of possible events. Therefore, a probabilistic stream determines a distribution over apotentially exponential number of classical "deterministic" streams where each item is deterministically one of the domain values. Designing efficient aggregation algorithms for probabilistic data is crucial for handling uncertainty in data-centric applications such as OLAP. Such algorithms are also useful in a variety of other setting including analyzing search engine traffic and aggregation in sensor networks. We present algorithms for computing commonly used aggregates ona probabilistic stream. We present the first one pass streaming algorithms for estimating the expected mean of a probabilistic stream, improving upon results in [20]. Next, we consider the problem of estimating frequency moments for probabilistic data. We propose a general approach to obtain unbiased estimators working over probabilistic data by utilizing unbiased estimators designed for standard streams. Applying this approach, we extend a classical data stream algorithm to obtain a one-pass algorithm for estimating F2, the second frequency moment. We present the first known streaming algorithms forestimating F0, the number of distinct items on probabilistic streams.Our work also gives an efficient one-pass algorithm for estimatingthe median of a probabilistic stream.
Shadow Elimination and Blinding Light Suppression for Interactive Projected Displays
Authors: Jay Summet, Matthew Flagg, Tat-Jen Cham, James M. Rehg, Rahul Sukthankar
Venue: IEEE Transactions on Visualization and Computer Graphics (2007)

A major problem with interactive displays based on front projection is that users cast undesirable shadows on the display surface. This paper demonstrates that shadows can be muted by redundantly illuminating the display surface using multiple projectors, all mounted at different locations. However, this technique alone does not eliminate shadows: Multiple projectors create multiple dark regions on the surface (penumbral occlusions) and cast undesirable light onto the users. These problems can be solved by eliminating shadows and suppressing the light that falls on occluding users by actively modifying the projected output. This paper categorizes various methods that can be used to achieve redundant illumination, shadow elimination, and blinding light suppression and evaluates their performance.
Visualizing endangered indigenous languages of French Polynesia with LEXUS
Authors: Gaby Cablitz, Jacquelijn Ringersma, Marc Kemps-Snijders
Venue: Proceedings of the 11th International Conference Information Visualization (2007)

This paper reports on the first results of the DOBES project "Towards a multimedia dictionary of the Marquesan and Tuamotuan languages of French Polynesia'. Within the framework of this project we are building a digital multimedia encyclopedic lexicon of the endangered Marquesan and Tuamotuan languages using a new tool, LEXUS. LEXUS is a web-based lexicon tool, targeted at linguists involved in language documentation. LEXUS offers the possibility to visualize language. It provides functionalities to include audio, video and still images to the lexical entries of the dictionary, as well as relational linking for the creation of a semantic network knowledge base. Further activities aim at the development of (1) an improved user interface in close cooperation with the speech community and (2) a collaborative workspace functionality which will allow the speech community to actively participate in the creation of lexica.
Spectral estimation based on the eigenanalysis of companion-like matrices
Authors: S.-E. Fotinea
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 01 (2000)

This paper presents a new state-space method for spectral estimation based on a companion matrix technique in order to estimate frequency, damping factor, amplitude and phase of exponential sinusoids. The new method, called CSE (companion matrix based spectral estimation), is compared against a previously proposed method called HTLS which is based on the use of total least squares. The latter lies among the most promising methods in the field of spectroscopy where accuracy of parameter estimation is of utmost importance. Experiments performed on a simulated NMR signal prove CSE to be more robust in terms of failure rate, especially for low signal to noise ratio.
Focalization in the Gulf of Mexico
Authors: Z.-H. Michalopoulou
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 06 (1996)

Matched-field processing (MFP) has been an active area of research in the last decade for estimating the location of a sound source and parameters related to the ocean channel. MFP yields estimates of the unknown parameters by matching the true received acoustic field to the theoretically predicted field calculated for sets of likely values for the unknown parameters. Successful demonstrations of the application of MFP to real data have been limited. Here, MFP is shown to provide both successful localization and focusing of the environment ('focalization') with real data collected in the Gulf of Mexico. The estimation is done by maximizing the correlation between the received and expected signals using simulated annealing.
A boosting approach for corporate failure prediction
Authors: Esteban Alfaro Cortés, Matías Gámez Martínez, Noelia García Rubio
Venue: Applied Intelligence (2007)

Predicting corporate failure is an important management science problem. This is a typical classification question where the objective is to determine which indicators are involved in the failure/success of a corporation. Despite the importance of this problem, until now only classical machine learning tools have been considered to tackle this classification task. The objective of this paper is twofold. On the one hand, we introduce novel discerning measures to rank independent variables in a generic classification task. On the other hand, we apply boosting techniques to improve the accuracy of a classification tree. We apply this methodology to a set of European firms, considering the usual predicting variables such as financial ratios, as well as including novel variables rarely used before in corporate failure prediction, such as firm size, activity and legal structure. We show that our approach decreases the generalization error about thirty percent with respect to the error produced with a classification tree. In addition, the most important ratios deal with profitability and indebtedness, as is usual in failure prediction studies.
Specifying Agent Beliefs for Authentication Systems
Authors: Ji Ma, Mehmet A. Orgun
Venue: Proceedings of the Fourth European Conference on Universal Multiservice Networks (2007)

The goal of an authentication system is to verify and authorise users, in order to protect restricted data and information. After authentication, two principals (people, computers, services) in a distributed system should be entitled to believe that they are com- municating with each other and not with intruders. Therefore it is important to express such beliefs precisely and to capture the reasoning that leads to them. In this paper, we focus on analysis of agent beliefs in dynamic environments in a formal framework based on a combination of a belief logic with a temporal logic. Through a well-known protocol example, that is, Kerberos, we dis- cuss how to express principal agent beliefs involved in authentica- tion protocols and the evolution of these beliefs as a consequence of communication, since trust is based on a series of observations of agents. Our approach is general and could be used for design- ing, verifying and implementing authentication protocols.
Rapid reaction Linux: Linux with low latency and high timing accuracy
Authors: Arnd C. Heursch, Helmut Rzehak
Venue: Proceedings of the 5th annual Linux Showcase & Conference - Volume 5 (2001)

Rapid Reaction Linux has been created at the University of German Federal Armed Forces in order to enhance realtime capabilities of the standard Linux kernel. Rapid Reaction Linux combines two well known patches to achieve this goal on the Intel x86 architecture using processors like Intel Pentium or any newer descendant. Rapid Reaction Linux combines the LOWLATENCY Patch [Molnar00], provided by Ingo Molnar, which has been found to reduce long latencies in the Linux kernel, with the UTIME Patch [Kansas97] of Kansas University that improves the precision of standard Linux timing services. Rapid Reaction Linux is not related to the well known KURT Linux [KURT98], except for the UTIME patch, both systems are relying on.
Computer graphics international 2005
Venue: Proceedings of the Computer Graphics International 2005 (2005)

Some applications require scenes mixing polygonal and volumetric objects and shadows make the scenes more realistic. This paper describes a shadow algorithm for mixed polygonal and volumetric data, including the generation of soft shadows for area light sources. Our volume shader leverages advanced graphics GPU for an accelerated and feasible solution. The shadow and soft shadow algorithm applies to all combinations of volumes and polygons, without any restriction on the geometric positioning and overlap of the volumes and polygons. For realistic rendering where we have a high albedo participating media, multiple scattering is significant. We extend our algorithm to handle both multiple forward scattering and back scattering with light attenuation. This constitutes a complete system for shadow generation and light propagation. 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
Large object space support for software distributed shared memory
Authors: Wang Leung Benny Cheung
Venue:  (2005)

Software Distributed Shared Memory (DSM) allows shared memory programming on machines with physically distributed memory. By introducing a virtual shared memory abstraction layer, DSM programs have close resemblance to sequential ones, exempting the need from inserting send and receive statements in the application code. Existing DSM systems can only support a shared memory space smaller than the hardware addressable virtual memory (VM) for a single process. This hinders scientific applications with large demand for shared memory from executing under DSM. Such problem has not so far been satisfactorily addressed in previous DSM studies. This study proposes a generic solution for supporting a shared object space larger than the VM addressable by the underlying hardware for a single process, and is implemented in the LOTS (Large ObjecT Space) DSM system. The challenge is to address more than 2n bytes of shared objects by an n-bit processor, while conforming to the process space utilization imposed by the operating system. In LOTS, shared objects to be accessed are swapped into VM from either the local hard disks or remote machines, while unused ones are swapped out to save process space. Unlike previous solutions in persistent storage systems, which required special compilers to generate and trap addresses for large objects, our solution is triggered automatically by user-level library code through the C++ operator overloading facility. No explicit program code, special compiler or operating system support is needed, preserving LOTS' good portability and programmability. This solution is also hardware-independent, making it generic and applicable to 32, 64 or 128-bit machines in extending the shared object space. In a large object space DSM, large objects can lead to I/O performance bottleneck and external fragmentation in VM. Thus we introduce segments, small but variable-sized chunks split from large objects to solve the problems. Contrary to some DSM systems, which split objects mechanically into fixed-size portions, LOTS splits objects into segments intelligently to increase the chance for each segment to fit the shared memory access patterns. This study also devises a new coherence protocol for large shared object space. Contrasting to traditional DSM, the local VM in LOTS cannot store all segments in the working set. We have to choose suitable segments to be swapped out to the disks. The decision can dramatically affect the number of disk accesses, but an exact prediction of the future memory access pattern is impossible. Our solution features a priority-based swapping protocol. It determines the swapping order of segments according to their access states, thereby reducing the amount of swaps between VM and the disks. A migrating-home-based mixed coherence protocol is also developed, treating lock and barrier-synchronized segments differently to minimize the number of network broadcast messages and bytes sent.
Quality of service renegotiation effect on ATM network performance
Authors: M. Ashibani, D. Mashao, B. Nleya
Venue: Proceedings of the The 8th International Conference on Communication Systems - Volume 01 (2002)

The bandwidth requirements for variable bit rate (VBR) traffic such as compressed video shows a dramatic variation over different time intervals. Allocating a bandwidth at the peak bit rate for the entire call duration will guarantee the quality of service (QoS) but this result in poor network resources utilization. We have proposed an alternative approach that renegotiates the allocated bandwidth and QoS during the lifetime of the connection. We present the results of a performance evaluation for the proposed renegotiation-based dynamic bandwidth allocation scheme. In particular, we determine the impact of the renegotiation frequency on the network performance.
On Parameterized Path and Chordless Path Problems
Authors: Yijia Chen, Jorg Flum
Venue: Proceedings of the Twenty-Second Annual IEEE Conference on Computational Complexity (2007)

We study the parameterized complexity of various path (and cycle) problems, the parameter being the length of the path. For example, we show that the problem of the existence of a maximal path of length k in a graph G is fixed-parameter tractable, while its counting version is #W[1]- complete. The corresponding problems for chordless (or induced) paths are W[2]-complete and #W[2]-complete respectively. With the tools developed in this paper we derive the NP-completeness of a related classical problem, thereby solving a problem due to Hedetniemi [17].