ppxscal/embedding_network huggingface dataset:
Objective speech quality measure for cellular phone
Authors: K. H. Lam, O. C. Au, C. C. Chan, K. F. Hui, S. F. Lau
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 01 (1996)

Cellular phone network speech quality monitoring is a regular task performed by the cellular service providers. Objective speech quality measures are needed in such tasks to provide a reasonably accurate estimate of subjective quality of the network. We performed an experiment to collect real distorted data, conducted a survey to obtain subjective quality measure of the collected speech samples and studied the statistical correlation of 32 objective speech quality measures with the subjective measures. Four of the objective measures were found to be good. Synchronization was found to be important.
A flexible approach to evaluating soft conditions with unequal preferences in fuzzy databases: Research Articles
Authors: Gloria Bordogna, Gabriella Pasi
Venue: International Journal of Intelligent Systems (2007)

A flexible model for evaluating soft query with unequal preferences in fuzzy databases is proposed. We assume that conditions with unequal preferences have an exclusive meaning like in the request &ldquo;find a holiday accommodation such that big apartments are preferred to high rating hotels.&rdquo; In this case it is assumed that the aggregator of the soft conditions is an implicit OR. Conversely, conditions with unequal importance have an inclusive meaning, like in the query &ldquo;find a house to rent that is cheap (most important), big (important), new (fairly important).&rdquo; In this case the implicit aggregator is an AND. What we propose in this article is to model preferences as modifiers of the semantics of the evaluation function of the conditions. Because the soft conditions are aggregated by an OR, the more a soft condition is preferred, the more its evaluation function tolerates a greater undersatisfaction of the soft condition. The proposed approach is formalized by considering two alternative semantics of the evaluation function: the first semantics defines the evaluation function by means of a generalized fuzzy inclusion measure, and the second one as a generalized similarity measure. These functions are parameterized so that their modification is simply achieved by tuning the functions' parameters. &copy; 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 665&ndash;689, 2007.
Deformation styles for spline-based skeletal animation
Authors: Sven Forstmann, Jun Ohya, Artus Krohn-Grimberghe, Ryan McDougall
Venue: Proceedings of the 2007 ACM SIGGRAPH/Eurographics symposium on Computer animation (2007)

We present a novel skinned skeletal animation system based on spline-aligned deformations for providing high quality and fully designable deformations in real-time. Our ambition is to allow artists the easy creation of abstract, pose-dependent deformation behaviors that might directly be assigned to a large variety of target objects simultaneously. To achieve this goal, we introduce the usage of deformation styles and demonstrate their applicability by our animation system. We therefore enhance spline-skinned skeletal animation with two sweep-based free-form-deformation (FFD) variants. The two FFD variants are pose-dependent, driven by three textures and three curves, which can be designed by the artist. As the three textures are similar to height-maps, their creation is very intuitive. Once designed, the deformation styles can be directly applied to any number of targets for imitating material behaviors of cloth, metal or even muscles. Our GPU based implementation shows promising results for real-time usage, as about 30 Million vertices per second can be animated. The basic spline-skinning even reaches more than twice the speed and gets close to the performance of skeletal subspace deformation (SSD). Furthermore, our method can easily be combined along with other existing deformation techniques as pose space deformation or SSD.
An Empirical Model for Quantifying Security Based on Services
Authors: Emmanouil Serrelis, Nikos Alexandris
Venue: Proceedings of the International Multi-Conference on Computing in the Global Information Technology (2007)

This paper presents a novel approach for quantifying the security of specific services as well as that of a whole organisation. This is done by reducing the security of any service to five selected security factors which can be measured by posing questions with objective answers. The security of any specific service is the weighted average of those five factors, whereas the overall security is the weighted average of the services.
Streamwise feature selection
Authors: Lyle H. Ungar
Venue:  (2006)

In streamwise feature selection, new features are sequentially considered for addition to a predictive model. When the space of potential features is large, streamwise feature selection offers many advantages over traditional feature selection methods, which assume that all features are known in advance. Features can be generated dynamically, focusing the search for new features on promising subspaces, and overfitting can be controlled by dynamically adjusting the threshold for adding features to the model. In contrast to traditional forward feature selection algorithms such as stepwise regression in which at each step all possible features are evaluated and the best one is selected, streamwise feature selection only evaluates each feature once when it is generated. We describe information-investing and &alpha;-investing, two adaptive complexity penalty methods for streamwise feature selection which dynamically adjust the threshold on the error reduction required for adding a new feature. These two methods give false discovery rate style guarantees against overfitting. They differ from standard penalty methods such as AIC, BIC and RIC, which always drastically over- or under-fit in the limit of infinite numbers of non-predictive features. Empirical results show that strearnwise regression is competitive with (on small data sets) and superior to (on large data sets) much more compute-intensive feature selection methods such as stepwise regression, and allows feature selection on problems with millions of potential features. When doing feature selection in multiple simultaneous regressions, one can "borrow strength" across the different regressions to get a more sensitive criterion for deciding which features to include in which regressions. We use information theory to derive the Multiple Inclusion Criterion (MIC), an efficient coding scheme, in a stepwise or strearnwise feature selection. Each feature can be added to none; some, or all of the regression models. Experiments show that the MIC approach is useful for selecting a small set of features when predicting multiple responses from the same set of potential features.
Speaker identification security improvement by means of speech watermarking
Authors: Marcos Faundez-Zanuy, Martin Hagmüller, Gernot Kubin
Venue: Pattern Recognition (2007)

This paper presents a security enhanced speaker identification system based on speech signal watermarking. Our proposed system can detect several situations where a playback speech, a synthetically generated speech, or a hacker trying to imitate the speech is fooling the biometric system. It is also suitable for forensic experts, who sometimes have to demonstrate in front of a court that a digital recording has neither been manipulated nor edited. In addition, we demonstrate that this watermark can coexist simultaneously with biometric speaker identification based on Gaussian mixture models (GMM), minimizing the mutual effects.
Information Technology for Development Introduction to Mini-track at HICSS 40
Authors: Sajda Qureshi, Peter Wolcott, Maung Sein
Venue: Proceedings of the 40th Annual Hawaii International Conference on System Sciences (2007)

Information Technology for Development (ITD) is the application and development of Information Technology infrastructures to bring about economic, social and human development in emerging and developed economies. Contributions of research in ITD over twenty years have been to the management of economies through the implementation of IT infrastructures to stimulate national development. Examples of such research include the use of indicators such as gross domestic product and human development indices to assess the effect of IT technologies and infrastructures on national development. Such research provides insight for policy makers seeking to increase business investment in their regions. It also provides guidelines for businesses seeking to implement and use IT infrastructures to support their sourcing strategies.
Retaining undergraduate women in science, math, and engineering: a model program
Authors: C. B. Muller
Venue: Proceedings of the Frontiers in Education Conference,1997. on 27th Annual Conference. Teaching and Learning in an Era of Change. - Volume 01 (1997)

This paper describes the retention strategies incorporated into the Women in Science Project at Dartmouth College, USA, presents data on increases in the number of undergraduate women graduating with majors in science, math and engineering since the project's inception, evaluates findings of specific interventions, suggests key elements contributing to the project's success and describes how other universities have designed programs modeled on the Women in Science Project.
Heterogeneous systems on chip and systems in package
Authors: I. O'Connor, B. Courtois, K. Chakrabarty, N. Delorme, M. Hampton, J. Hartung
Venue: Proceedings of the conference on Design, automation and test in Europe (2007)

This paper discusses several forms of heterogeneity in systems on chip and systems in package. A means to distinguish the various forms of heterogeneity is given, with an estimation of the maturity of design and modeling techniques with respect to various physical domains. Industry-level MEMS integration, and more prospective microfluidic biochip systems are considered at both technological and EDA levels. Finally, specific flows for signal abstraction heterogeneity in RF SiP and for functional co-verification are discussed.
Restricted total least squares solutions for hyperspectral imagery
Authors: B. Sirkeci
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 01 (2000)

Hyperspectral image processing is a pixel-by-pixel approach to the detection and localization of features by spectral analysis techniques. Usually, partial knowledge about the feature, noise, and clutter spectra are provided, and the problem is to 'unmix' each pixel, or to estimate the relative concentrations of the reference spectra on a per pixel basis. A popular method of linear spectral unmixing for hyperspectral imagery is linear least squares. Linear least square approaches are appropriate when observational errors predominate and are inappropriate when significant modeling errors are present. The least square approach has some disadvantages, especially in cases with few, poorly known references or significant reference variation throughout an image. In this article, the restricted total least squares (RTLS) approach is presented and evaluated on experimental data. Although the proposed RTLS require more calculations than linear least squares, its relative error performance is much better.
A vector Taylor series approach for environment-independent speech recognition
Authors: P. J. Moreno
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 02 (1996)

In this paper we introduce a new analytical approach to environment compensation for speech recognition. Previous attempts at solving analytically the problem of noisy speech recognition have either used an overly-simplified mathematical description of the effects of noise on the statistics of speech or they have relied on the availability of large environment-specific adaptation sets. Some of the previous methods required the use of adaptation data that consists of simultaneously-recorded or "stereo" recordings of clean and degraded speech. In this work we introduce the use of a vector Taylor series (VTS) expansion to characterize efficiently and accurately the effects on speech statistics of unknown additive noise and unknown linear filtering in a transmission channel. The VTS approach is computationally efficient. It can be applied either to the incoming speech feature vectors, or to the statistics representing these vectors. In the first case the speech is compensated and then recognized; in the second case HMM statistics are modified using the VTS formulation. Both approaches use only the actual speech segment being recognized to compute the parameters required for environmental compensation. We evaluate the performance of two implementations of VTS algorithms using the CMU SPHINX-II system on the 100-word alphanumeric CENSUS database and on the 1993 5000-word ARPA Wall Street Journal database. Artificial white Gaussian noise is added to both databases. The VTS approaches provide significant improvements in recognition accuracy compared to previous algorithms.
A novel drie fabrication process development for SOI-based MEMS devices
Authors: J. Li, Q. X. Zhang, A. Q. Liu
Venue: Proceedings of the Symposium on Design, Test, Integration and Packaging of MEMS/MOEMS (2003)

This paper reports a novel deep reactive ion etching (RIE) fabrication process for silicon-on-insulator (SOI) based Micro-electromechanical systems (MEMS) devices. The notching effect and stiction problems that widely exist in SOI based micro fabrication process are solved regardless of the feature sizes by introducing space oxide thin film deposition combined with lateral oxide etching. Dry chemical release is also realized by employing the notching effect. Highspeed optical switch is fabricated by using this new process.
Approximating K-means-type Clustering via Semidefinite Programming
Authors: Jiming Peng, Yu Wei
Venue: SIAM Journal on Optimization (2007)

One of the fundamental clustering problems is to assign $n$ points into $k$ clusters based on minimal sum-of-squared distances (MSSC), which is known to be NP-hard. In this paper, by using matrix arguments, we first model MSSC as a so-called 0-1 semidefinite programming (SDP) problem. We show that our 0-1 SDP model provides a unified framework for several clustering approaches such as normalized k-cut and spectral clustering. Moreover, the 0-1 SDP model allows us to solve the underlying problem approximately via the linear programming and SDP relaxations. Second, we consider the issue of how to extract a feasible solution of the original 0-1 SDP model from the optimal solution of the relaxed SDP problem. By using principal component analysis, we develop a rounding procedure to construct a feasible partitioning from a solution of the relaxed problem. In our rounding procedure, we need to solve a K-means clustering problem in $\Re^{k-1}$, which can be done in $O(n^{k^2-2k+2})$ time. In case of biclustering, the running time of our rounding procedure can be reduced to $O(n\log n)$. We show that our algorithm provides a 2-approximate solution to the original problem. Promising numerical results for biclustering based on our new method are reported.
Semi-blind channel identification for W-CDMA by orthogonal transmit diversity
Authors: T. H. Li
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 05 (2000)

A novel semi-blind channel identification scheme is proposed for 3G W-CDMA communication systems by using orthogonal transmit diversity where two transmit antennas and one receive antenna are employed. After decomposing the de-spread signals into two independent signals, an equivalent SIMO channel model is obtained. Semi-blind channel identification is then achieved by applying both the cross-relation method and pilot symbols to the derived channel model. This scheme allows one to perform semi-blind channel identification without increasing both the cost and complexity of mobile stations. Simulation results are presented to evaluate the proposed semi-blind channel identification method.
Face Recognition by Combination of RBF Neural Networks Using Dempster-Shafer Theory
Authors: S. Thakur, J. K. Sing, D. K. Basu, M. Nasipuri, M. Kundu
Venue: Proceedings of the International Conference on Computing: Theory and Applications (2007)

This paper presents an approach to face recognition based on Dempster-Shafer (DS) theory of evidence, which combines the evidences of two radial basis function (RBF) neural networks. The degrees of belief of the two RBF neural networks for classification of an image have been estimated using two different feature vectors derived from images of the ORL face database. Then these degrees of belief have been combined using DS theory to improve the overall recognition rates. The average recognition rates of the proposed method have been found to be 83.78%, 88.08%, 97.10%, 98.06% and 97.75%, in 10 different experimental runs of 3, 4, 5, 6 and 7 training images out of 10 images per individual, respectively. The proposed method is found to be better than some of the existing methods.
Cluster analysis of heterogeneous rank data
Authors: Ludwig M. Busse, Peter Orbanz, Joachim M. Buhmann
Venue: Proceedings of the 24th international conference on Machine learning (2007)

Cluster analysis of ranking data, which occurs in consumer questionnaires, voting forms or other inquiries of preferences, attempts to identify typical groups of rank choices. Empirically measured rankings are often incomplete, i.e. different numbers of filled rank positions cause heterogeneity in the data. We propose a mixture approach for clustering of heterogeneous rank data. Rankings of different lengths can be described and compared by means of a single probabilistic model. A maximum entropy approach avoids hidden assumptions about missing rank positions. Parameter estimators and an efficient EM algorithm for unsupervised inference are derived for the ranking mixture model. Experiments on both synthetic data and real-world data demonstrate significantly improved parameter estimates on heterogeneous data when the incomplete rankings are included in the inference process.
Classification of radar returns using Wigner-Ville distribution
Authors: P. K. Kumar
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 06 (1996)

This paper attempts to analyze and classify the radar returns from a simulated ground based radar using Wigner-Ville distribution (WVD). Stochastic modeling methods like AR and ARMA have earlier been applied for the classification problem. However they suffer from inaccuracies such as showing inexact spectral widths, which is a prime factor to distinguish weather returns from birds, non-adaptability to time-varying clutter spectra, etc., despite being prominent in resolving spectral peaks. The paper proposes WVD as an alternative tool for the classification of radar returns, since it is a powerful technique for time-varying spectra. The analysis shows promising results especially from the view point of spectral widths, though faced with the problem of cross-spectral components. Examples of WVD applied to the simulated radar signal are presented to illustrate the advantages of this method.
Design and implementation of a routing control platform
Authors: Matthew Caesar, Donald Caldwell, Nick Feamster, Jennifer Rexford, Aman Shaikh, Jacobus van der Merwe
Venue: Proceedings of the 2nd conference on Symposium on Networked Systems Design & Implementation - Volume 2 (2005)

The routers in an Autonomous System (AS) must distribute the information they learn about how to reach external destinations. Unfortunately, today's internal Border Gateway Protocol (iBGP) architectures have serious problems: a "full mesh" iBGP configuration does not scale to large networks and "route reflection" can introduce problems such as protocol oscillations and persistent loops. Instead, we argue that a Routing Control Platform (RCP) should collect information about external destinations and internal topology and select the BGP routes for each router in an AS. RCP is a logically-centralized platform, separate from the IP forwarding plane, that performs route selection on behalf of routers and communicates selected routes to the routers using the unmodified iBGP protocol. RCP provides scalability without sacrificing correctness. In this paper, we present the design and implementation of an RCP prototype on commodity hardware. Using traces of BGP and internal routing data from a Tier-1 backbone, we demonstrate that RCP is fast and reliable enough to drive the BGP routing decisions for a large network. We show that RCP assigns routes correctly, even when the functionality is replicated and distributed, and that networks using RCP can expect comparable convergence delays to those using today's iBGP architectures.
Embedding Linux to track concealed weapons
Authors: Alexander R. Perry, John F. Sturtz, David Walsh, Brian Whitecotton
Venue: Proceedings of the 5th annual Linux Showcase & Conference - Volume 5 (2001)

A football-sized array of magnetic sensors can detect blades, knives, handguns and vehicles in the vicinity. Immediate analysis can show the concealed target as a red blob that is superimposed on a video image. This paper reviews why Linux was the only viable platform, describes the software and summarizes the algorithms being used.
A robust LSP encoding scheme for noisy channel
Authors: Junchen Du
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 02 (1996)

A new speech LSP parameters coding scheme for noisy channels is proposed. In this scheme, 5 out of the 10 LSPs are scalar quantized. Non-linear prediction is used for coding the other 5 LSPs and the prediction errors are vector quantized. The 5 LSPs are chosen in the structure so that the channel error will not affect more than two frame LSPs. This new scheme can support various levels of channel coding. In noiseless channels it needs 27 bits/frame at 1 dB average spectral distortion with a frame size 30 ms. With additional 4 bits/frame to protect a part of the 27 bits, a (15,11) Hamming code achieves 1 dB average distortion at the error rate 0.1%.
Integration of mobility and intrusion detection for wireless ad hoc networks: Research Articles
Authors: Bo Sun, Kui Wu, Yang Xiao, Ruhai Wang
Venue: International Journal of Communication Systems (2007)

One of the main challenges in building intrusion detection systems (IDSs) for mobile ad hoc networks (MANETs) is to integrate mobility impacts and to adjust the behaviour of IDSs correspondingly. In this paper, we first introduce two different approaches, a Markov chain-based approach and a Hotelling's T2 test based approach, to construct local IDSs for MANETs. We then demonstrate that nodes' moving speed, a commonly used parameter in tuning IDS performances, is not an effective metric to tune IDS performances under different mobility models. To solve this problem, we further propose an adaptive scheme, in which suitable normal profiles and corresponding proper thresholds can be selected adaptively by each local IDS through periodically measuring its local link change rate, a proposed unified performance metric. We study the proposed adaptive mechanism at different mobility levels, using different mobility models such as random waypoint model, random drunken model, and obstacle mobility model. Simulation results show that our proposed adaptive scheme is less dependent on the underlying mobility models and can further reduce false positive ratio. Copyright &copy; 2006 John Wiley & Sons, Ltd.