ppxscal/embedding_network huggingface dataset:
Comprehensive depiction of configuration-dependent performance anomalies in distributed server systems
Authors: Christopher Stewart, Ming Zhong, Kai Shen, Thomas O'Neill
Venue: Proceedings of the 2nd conference on Hot Topics in System Dependability - Volume 2 (2006)

Distributed server systems allow many configuration settings and support various application workloads. Often performance anomalies, situations where actual performance falls below expectations, only manifest under particular runtime conditions. This paper presents a new approach to examine a large space of potential runtime conditions and to comprehensively depict the conditions under which performance anomalies are likely to occur. In our approach, we derive our performance expectations from a hierarchy of sub-models in which each submodel can be independently adjusted to consider new runtime conditions. We then produce a representative set of measured runtime condition samples (both normal and abnormal)with carefully chosen sample size and anomaly error threshold. Finally, we employ decision tree based classification to produce an easy-to-interpret depiction of the entire space of potential runtime conditions. Our depictions can be used to guide the avoidance of anomaly-inducing system configurations and it can also assist root cause diagnosis and performance debugging. We present preliminary experimental results with a real J2EE middleware system.
Efficient nonlinear distortion analysis of RF circuits
Authors: Dani Tannir, Roni Khazaka
Venue: Proceedings of the conference on Design, automation and test in Europe (2007)

Nonlinear distortion, typically defined using the third order intercept point (IP3), is one of the key figures of merit that are critical in the design of RF communication circuits. The calculation of IP3 is typically based on analytical approaches such as Volterra Series which are very complex and difficult to apply to circuits of arbitrary complexity, or on simulation based methods which require multi-tone inputs and thus result in a very high CPU cost. In this paper a new method based on the computation of the circuit moments is proposed. The new approach uses the circuit moments in order to numerically compute the Volterra kernels. This automates the process of numerically obtaining such kernels for any circuit and results in an efficient approach for the computation of IP3.
A Rate Distortion Method for Beamforming in RF Image Formation
Authors: Robert J. Bonneau
Venue: Proceedings of the 35th Applied Imagery and Pattern Recognition Workshop (2006)

Conventional RF image formation relies on a fixed waveform set that is based largely on obtaining maximum resolution for a given amount of bandwidth present in a waveform. However, the correlation process for a given waveform set varies widely depending on the cross correlation properties of the waveform and the geometry of the aperture interrogating the object to be imaged. We propose a method that maximizes quality of the imagery being reconstructed based by first using an orthogonal basis to minimize the unwanted correlation response for the waveform. We then shape the frequency and temporal correlation response of the waveform for a given target using a rate distortion criteria and demonstrate the performance of the method.
The tv-l(1) model: theory, computation, and applications
Authors: Donald Goldfarb
Venue:  (2006)

In digital image processing, images are treated as two dimensional signals and are processed using signal processing techniques. This thesis focuses on a signal decomposition technique that partitions an input image into components with different geometric scales. This multiscale decomposition technique is based on the minimization of an energy that is a weighted sum of a total variation and an L1 distance (TV-L 1). Although the minimization of the L1-fidelity term penalizes the intensity difference between the input and an output images, the decomposition of the input image by the TV-L1 model is solely determined by the geometric scales of the features (homogeneous parts) in the image, independent of the feature intensity. This important property was previously observed by Alliney, Nikolova, Chan and Esedoglu. Here we present in Chapter 2 a rigorous proof of this property that is based on a newly established equivalence between the minimizations of the TV-L1 energy and a sequence of geometric energies. Furthermore, we use this property to obtain other properties, including geometric and morphological invariance, of the TV-L 1 model and to discuss new computational methods. To reveal the differences between the TV-L1 model and other TV-based models for image decomposition, we present the related ROF, Meyer, and Vese-Osher models and formulate all four TV-based models as second-order cone programs (SOCPs). We used an SOCP interior-point method, combined with nested dissection to reduce computational complexity, to numerically solve the SOCPs corresponding to these models and obtained solutions for both 1D and 2D examples. Our tests show that the decomposition by the TV -L1 model is qualitatively different from those produced by the other TV based models and, therefore, is very unique. Because of its special properties shown both analytically and numerically, the TV-L1 model quickly finds applications in medical imaging and computer vision. In the first application, cDNA microarray images with inhomogeneous background are processed by the TV-L 1 model to have the large-scale inhomogeneous background separated from the useful foreground cDNA spots. In the second application, a multiplicative version of the TV-L1 model is applied to face images with varying illumination conditions. The resulting quotient images are successfully used for face recognition.
Joint diagonalization via subspace fitting techniques
Authors: A.-J. van der Veen
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 05 (2001)

Joint diagonalization problems of Hermitian or non-Hermitian matrices occur as the final parameter estimation step in several blind source separation problems such as ACMA, JADE, PARAFAC, and SOBI. Previous approaches have been Jacobi iteration schemes and alternating projections. Here we show how the joint diagonalization problem can be formulated as a (weighted) subspace fitting problem so that it can be solved using the efficient Gauss-Newton optimization algorithm proposed for that problem. Since a good initial point is usually available, the algorithm converges very fast.
Teaching data structures to students who are blind
Authors: Matt Calder, Robert F. Cohen, Jessica Lanzoni, Neal Landry, Joelle Skaff
Venue: Proceedings of the 12th annual SIGCSE conference on Innovation and technology in computer science education (2007)

We present our work in assisting students who are blind to understand fundamental data structures. We have developed a system called PLUMB EXTRA3 (EXploring data sTRuctures using Audible Algorithm Animation) that conveys an algorithm animation using audio cues and speech. This extends our earlier work on presenting graphs to users who are blind.
TOM, a new temporal neural net architecture for speech signal processing
Authors: S. Durand
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 06 (1996)

The neural net model TOM (temporal organization map) that we present in the paper is a new connectionist approach whose time representation is different from the one in classical temporal connectionist models. The architecture is neurobiologically inspired and is dedicated to sensory problems involving a temporal dimension. The basic idea of the TOM model is the propagation of an activity throughout the network whose elements are organized according to a map architecture. This propagation leads to a triggering of a sequence detection. We have applied this new kind of architecture to a spoken digit recognition problem. The results draw near to the results of the best hidden Markov model (HMM) techniques. The interest of such an architecture is its genericity and the possibility to merge several data flows in order to improve the classical performances of neural nets.
On the Automatic Generation of Test Programs for Path-Delay Faults in Microprocessor Cores
Authors: P. Bernardi, M. Grosso, E. Sanchez, M. Sonza Reorda
Venue: Proceedings of the 12th IEEE European Test Symposium (2007)

Delay testing is mandatory for guaranteeing the correct behavior of today's high-performance microprocessors. Several methodologies have been proposed to tackle this issue resorting to additional hardware or to software self-test techniques. Software techniques are particularly promising as they resort to Assembly programs in normal mode of operation, without requiring circuit modifications; however, the problem of generating effective and efficient test programs for path-delay fault detection is still open. This paper presents an innovative approach for the generation of path-delay self-test programs for microprocessors, based on an evolutionary algorithm and on ad-hoc software simulation/hardware emulation heuristic techniques. Experimental results show how the proposed methodology allows generating suitable test programs in reasonable times.
Nonuniform Compression in Databases with Haar Wavelet
Authors: S. Chen, A. Nucci
Venue: Proceedings of the 2007 Data Compression Conference (2007)

Data synopsis is a lossy compressed representation of data stored into databases that helps the query optimizer to speed up the query process, e.g. time to retrieve the data from the database. An efficient data synopsis must provide accurate information about the distribution of data to the query optimizer at any point in time. Due to the fact that some data will be queried more often than others, a good data synopsis should consider the use of nonuniform accuracy, e.g. provide better approximation of data that are queried the most. Although, the generation of data synopsis is a critical step to achieve a good approximation of the initial data representation, data synopsis must be updated over time when dealing with time varying data. In this paper, we introduce new Haar wavelet synopses for nonuniform accuracy and time-varying data that can be generated in linear time and space, and updated in sublinear time. The efficiency of our new data synopses is validated against other linear methods by using both synthetic and real data sets.
Internet-based desktops in Tcl/Tk: collaborative and recordable
Authors: Amit Khetawat, Hemang Lavana, Franc Brglez
Venue: Proceedings of the 6th conference on Annual Tcl/Tk Workshop, 1998 - Volume 6 (1998)

This paper addresses issues that arise when a peer group, distributed across several time zones, uses the Internet to configure and execute distributed desktop-based applications and tasks. The paper provides solutions and Tcl/Tk implementations to support (1) peer-to-peer communication/control of distributed software and computing resources over the Internet; (2) recording and playback of interactive execution of Tcl/Tk applications and collaborative sessions. The summary of 540 Internet-based experiments, each relying on RecordTaker and PlaybackMaker to record, playback, and execute ReubenDesktop configurations from local, cross-state, and cross-country servers, demonstrates the effectiveness of the proposed concepts and implementation.
Selection of Optimal Computing Platforms through the Suitability Measure
Authors: Shean T. McMahon, Isaac D. Scherson
Venue: Proceedings of the Sixth International Symposium on Parallel and Distributed Computing (2007)

Selection of spaceborne computing platforms requires balance among several competing factors. Traditional performance analysis techniques are ill-suited for this purpose due to their overriding concern with runtime. The suitability measure is a new approach that quantifies the match between a computing platform and a program. It analyzes a program at the opcode and control flow levels, and compares this to a machine's capability to support the unique characteristics of the program. In this paper we develop the suitability measure and a series of program analysis methods. Experimental results confirm that machines that provide a better match to the program yield a higher suitability score. We prove that loops provide the only contribution to the suitability value, and also that the number of loop iterations is irrelevant, leading to the conclusion that a single pass through a loop is sufficient to derive a suitability value.
Case Study: Soft Error Rate Analysis in Storage Systems
Authors: Brian Mullins, Hossein Asadi, Mehdi B. Tahoori, David Kaeli, Kevin Granlund, Rudy Bauer, Scott Romano
Venue: Proceedings of the 25th IEEE VLSI Test Symmposium (2007)

Soft errors due to cosmic particles are a growing reliability threat for VLSI systems. In this paper we analyze the soft error vulnerability of FPGAs used in storage systems. Since the reliability requirements of these high performance storage subsystems are very stringent, the reliability of the FPGA chips used in the design of such systems plays a critical role in the overall system reliability. We validate the projections produced by our analytical model by using field error rates obtained from actual field failure data of a large FPGA-based design used in the Logical Unit Module board of a commercial storage system. This comparison confirms that the projections obtained from our analytical tool are accurate (there is an 81% overlap in FIT rate range obtained with our analyticalmodeling framework and the field failure data studied).
Fast online video image sequence recognition with statistical methods
Authors: M. Schuster
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 06 (1996)

In this paper a fast method to recognize image sequences is presented. It is based on a discrete statistical model consisting of a vector quantizer and a special probabilistic neural net giving an estimation for the a posteriori probability P(SEQUENCE|DATA), which allows to classify image sequences without applying rules depending on the content of the sequence. The simple feature extraction also allows the classification with discrete hidden Markov models. As an application we present results from a test conducted for the classification of various gestures done by human beings in front of a video camera for both classification methods, which gave promising recognition results in real time.
Monte Carlo filtering and smoothing with application to time-varying spectral estimation
Authors: A. Doucet
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 02 (2000)

We develop methods for performing filtering and smoothing in nonlinear non-Gaussian dynamical models. The methods rely on a particle cloud representation of the filtering distribution which evolves through time using importance sampling and resampling ideas. In particular, novel techniques are presented for generation of random realisations from the joint smoothing distribution and for MAP estimation of the state sequence. Realisations of the smoothing distribution are generated in a forward-backward procedure, while the MAP estimation procedure can be performed in a single forward pass of the Viterbi algorithm applied to a discretised version of the state space. An application to spectral estimation for time-varying autoregressions is described.
Privacy, accuracy, and consistency too: a holistic solution to contingency table release
Authors: Boaz Barak, Kamalika Chaudhuri, Cynthia Dwork, Satyen Kale, Frank McSherry, Kunal Talwar
Venue: Proceedings of the twenty-sixth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems (2007)

The contingency table is a work horse of official statistics, the format of reported data for the US Census, Bureau of Labor Statistics, and the Internal Revenue Service. In many settings such as these privacy is not only ethically mandated, but frequently legally as well. Consequently there is an extensive and diverse literature dedicated to the problems of statistical disclosure control in contingency table release. However, all current techniques for reporting contingency tables fall short on at leas one of privacy, accuracy, and consistency (among multiple released tables). We propose a solution that provides strong guarantees for all three desiderata simultaneously. Our approach can be viewed as a special case of a more general approach for producing synthetic data: Any privacy-preserving mechanism for contingency table release begins with raw data and produces a (possibly inconsistent) privacy-preserving set of marginals. From these tables alone-and hence without weakening privacy--we will find and output the "nearest" consistent set of marginals. Interestingly, this set is no farther than the tables of the raw data, and consequently the additional error introduced by the imposition of consistency is no more than the error introduced by the privacy mechanism itself. The privacy mechanism of [20] gives the strongest known privacy guarantees, with very little error. Combined with the techniques of the current paper, we therefore obtain excellent privacy, accuracy, and consistency among the tables. Moreover, our techniques are surprisingly efficient. Our techniques apply equally well to the logical cousin of the contingency table, the OLAP cube.
Pit Pattern Classification of Zoom-Endoscopical Colon Images Using DCT and FFT
Authors: Michael Hafner, Leonhard Brunauer, Hannes Payer, Robert Resch, Friedrich Wrba, Alfred Gangl, Andreas Vecsei, Andreas Uhl
Venue: Proceedings of the Twentieth IEEE International Symposium on Computer-Based Medical Systems (2007)

This work presents a classification approach for images taken from magnifying colonoscopy. Classification is done according to the pit pattern scheme. Images are not classified directly in the proposed classifier. Instead, they are transformed to a frequency domain using discrete cosine or Fourier transformation. Feature selection is optimized using a genetic algorithm, the actual classification is done using standard methods from statistical pattern recognition (a Bayes normal classifier).
Computation-performance control for DCT-based video coding
Authors: I. Ismaeil
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 04 (2000)

In this paper, we propose a flexible framework for DCT-based video encoding that yields very good computation performance tradeoffs. Each of the encoding components features a set of parameters that can be used to control its computational complexity and performance. A sequence of optimum parameter sets have been designed to obtain encoders with varying degrees of computational complexity. A computation control mechanism is proposed within the encoding framework to allow the encoding algorithm to adapt to the available computational resources. This will allow the encoder to run in real time on machines with different computing power levels, while also achieving the best possible reproduction quality. The proposed framework was applied to MPEG-2 and H.263 encoding. Our experimental results show that excellent speed-performance tradeoffs as well as accurate computation control can be obtained using the proposed method.
Experiments for a Collaborative Haptic Virtual Reality
Authors: Mee Young Sung, Yonghee Yoo, Kyungkoo Jun, Nam-Joong Kim, Jinseok Chae
Venue: Proceedings of the 16th International Conference on Artificial Reality and Telexistence--Workshops (2006)

In this paper, we present preliminary results of our ongoing networked virtual reality project by discussing the implementation and performance of a networked haptic game. In this game, online players feel like handling a real basketball since they are able to feel the sense of touch by using haptic interfaces. One of challenging issues in this implementation is haptic data transmission over the Internet to allow online multi-user play. Since haptic information is extremely sensitive to delay and jitter, the provision of timely transmission is critical. We carry out experiments to compare the performance of the haptic data transmission when using UDP (User Datagram Protocol) and TCP (Transmission Control Protocol), and observe that neither UDP nor TCP is able to satisfy QoS (Quality of Service) requirements of the haptic transmission. As a further study, we plan to investigate some alternative haptic data transmission schemes.
Realizing XML Driven Algorithm Visualization
Authors: Thomas Naps, Myles McNally, Scott Grissom
Venue: Electronic Notes in Theoretical Computer Science (ENTCS) (2007)

In this paper we describe work in progress on JHAVE-II, a new generation of the client-server based algorithm visualization system JHAVE. We believe this to be the first algorithm visualization system to be totally XML driven. We describe the XML scripting language visualization authors can use with JHAVE-II to define the sequence of graphical snapshots, integrated pop-up questions, synchronized pseudocode, and supplemental information that comprise a particular algorithm visualization. JHAVE-II then uses these scripts to render visualizations and support student exploration of algorithms.
Embedding the Evidence Information in Guideline Representation Languages
Authors: Alime Ozturk, Katharina Kaiser, Patrick Martini, Silvia Miksch
Venue: Proceedings of the Twentieth IEEE International Symposium on Computer-Based Medical Systems (2007)

Clinical practice guidelines are widely used to support medical staff in treatment planning and decision-making, whereas, the classification of different recommendations in the CPGs are one of the most important information sources to use. However, there is a lack of consensus amongst guideline developers, regarding those classification schemes. To address this problem, we mapped the different graded and ungraded evidence information used by different guideline developing organizations into our meta schema. In this paper we describe how guideline representation languages, such as Asbru and PROforma can be extended to model our meta schema.
Forecasting time series with a new architecture for polynomial artificial neural network
Authors: E. Gómez-Ramírez, K. Najim, E. Ikonen
Venue: Applied Soft Computing (2007)

Polynomial artificial neural networks (PANN) have been shown to be powerful for forecasting nonlinear time series. The training time is small compared to the time used by other algorithms of artificial neural networks and the capacity to compute relations between the inputs and outputs represented by every term of the polynomial. In this paper a new structure of polynomial is presented that improves the performance of this type of network considering only non-integers exponents. The architecture adaptation uses genetic algorithm (GA) to find the optimal architecture for every example. Some examples of sunspots and chaotic time series are presented.
A high resolution time frequency representation with significantly reduced cross-terms
Authors: A. K. Ozdemir
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 02 (2000)

A novel algorithm is proposed for efficiently smoothing the slices of the Wigner distribution by exploiting the developed relation between the Radon transform of the ambiguity function and the fractional Fourier transformation (Odzemir and Ankan, 1999). The main advantage of the new algorithm is its ability to suppress cross-term interference on chirp-like auto-components without any detrimental effect to the auto-components. For a signal with N samples, the computational complexity of the algorithm is O(N log N) flops for each smoothed slice of the Wigner distribution.