ppxscal/embedding_network huggingface dataset:
ACL2SIX: A Hint used to Integrate a Theorem Prover and an Automated Verification Tool
Authors: Jun Sawada, Erik Reeber
Venue: Proceedings of the Formal Methods in Computer Aided Design (2006)

We present a hardware verification environment that integrates the ACL2 theorem prover and SixthSense, an IBM internal formal verification tool. In this environment, SixthSense is invoked through an ACL2 function acl2six that makes use of a general-purpose external interface added to the ACL2 theorem prover. This interface allows decision procedures and modelcheckers to be connected to ACL2 by simply writing ACL2 functions. Our environment also exploits a unique approach to connect the logic of a general-purpose theorem prover with machine designs in VHDL without a language embedding. With an example of a pipelined multiplier, we show how our environment can be used to divide a large verification problem into a number of simpler problems, which can be verified using automated verification engines.
Discussion of Collaborative Learning Designs, Languages, Models and Tools - LICEF-Télé-université's Contribution
Authors: Gilbert Paquette
Venue: Proceedings of the Sixth IEEE International Conference on Advanced Learning Technologies (2006)

This short paper summarizes work at LICEF on Educational Modeling Languages, focusing on the design of collaborative units of learning.
A Hybrid System for Learning Sunspot Recognition and Classification
Authors: Trung Thanh Nguyen, Claire P. Willis, Derek J. Paddon, Hung Son Nguyen
Venue: Proceedings of the 2006 International Conference on Hybrid Information Technology - Volume 02 (2006)

Sunspots observation and classification are important tasks for solar astronomers. The activity of sunspots can give clues to the timing of solar flares and the solar weather in general. This paper describes a hybrid system for automatic sunspot recognition and classification. The system uses a combination of image processing and machine learning techniques to process and classify sunspot groups from digital satellite images. Sunspot data are extracted from daily images of the solar disk captured by the NASA SOHO/MDI satellite. The classification scheme attempted was the seven-class Modified Zurich scheme. The main components of the hybrid system are: 1) the image processor, 2) the feature extractor, 3) the clusterer, 4) the classification learner. Furthermore, the paper compares two clustering algorithms: hierarchical average-link and a density-based DBSCAN and examines their usefulness in dealing with sunspot data. The aim is to create clusters that closely match "natural" sunspot groups. Clustering is an important step in the process as the classification performance is dependent on it. In previous papers we have shown that by combining clustering with classification the overall results can be substantially improved.
Evaluating Dataflow and Pipelined Vector Processing Architectures for FPGA Co-processors
Authors: Michael Freeman
Venue: Proceedings of the 9th EUROMICRO Conference on Digital System Design (2006)

This paper describes the development of a FPGA based co-processor architecture for accelerating vector comparisons e.g. Euclidean distance. In this paper we will compare traditional pipelined and dataflow implementations, in terms of processing speed and area requirements. Processing performance will then be compared against a software implementation to evaluate possible speedup.
Structural and affective aspects of music from statistical audio signal analysis: Special Topic Section on Computational Analysis of Style
Authors: Shlomo Dubnov, Stephen McAdams, Roger Reynolds
Venue: Journal of the American Society for Information Science and Technology (2006)

Understanding and modeling human experience and emotional response when listening to music are important for better understanding of the stylistic choices in musical composition. In this work, we explore the relation of audio signal structure to human perceptual and emotional reactions. Memory, repetition, and anticipatory structure have been suggested as some of the major factors in music that might influence and possibly shape these responses. The audio analysis was conducted on two recordings of an extended contemporary musical composition by one of the authors. Signal properties were analyzed using statistical analyses of signal similarities over time and information theoretic measures of signal redundancy. They were then compared to Familiarity Rating and Emotional Force profiles, as recorded continually by listeners hearing the two versions of the piece in a live-concert setting. The analysis shows strong evidence that signal properties and human reactions are related, suggesting applications of these techniques to music understanding and music information-retrieval systems. &copy; 2006 Wiley Periodicals, Inc.
ATPG for Dynamic Burn-In Test in Full-Scan Circuits
Authors: Alfredo BENSO, Alberto BOSIO, Stefano DI CARLO, Giorgio DI NATALE, Paolo PRINETTO
Venue: Proceedings of the 15th Asian Test Symposium (2006)

Yield and reliability are two key factors affecting costs and profits in the semiconductor industry. Stress testing is a technique based on the application of higher than usual levels of stress to speed up the deterioration of electronic devices and increase yield and reliability. One of the standard industrial approaches for stress testing is high temperature burnin. This work proposes a full-scan circuit ATPG for dynamic burn-in. The goal of the proposed ATPG approach is to generate test patterns able to force transitions into each node of a full scan circuit to guarantee a uniform distribution of the stress during the dynamic burn-in test.
Predicting people's bidding behavior in negotiation
Authors: Ya'akov Gal, Avi Pfeffer
Venue: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems (2006)

This paper presents a statistical learning approach to predicting people's bidding behavior in negotiation. Our study consists multiple 2-player negotiation scenarios where bids of multi-valued goods can be accepted or rejected. The bidding task is formalized as a selection process in which a proposer player chooses a single bid to offer to a responder player from a set of candidate proposals. Each candidate is associated with features that affect whether not it is the chosen bid. These features represent social factors that affect people's play. We present and compare several algorithms for predicting the chosen bid and for learning a model from data. Data collection and evaluation of these algorithms is performed on both human and synthetic data sets. Results on both data sets show that an algorithm that reasons about dependencies between the features of candidate proposals is significantly more successful than an algorithm which assumes that candidates are independent. In the synthetic data set, this algorithm achieved near optimal performance. We also study the problem of inferring the features of a proposal given the fact that it was the chosen bid. A baseline importance sampling algorithm is first presented, and then compared with several approximations that attain much better performance.
Video object motion applications focusing on non-planar rotation
Authors: M. Garcia, H. Nicolas
Venue: Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1 (2003)

In this paper an object-based non-planar rotation estimation for video analysis and video coding is presented. This method is based on a non-planar rotation model which assumes that the moving object as a planar surface. It is also assumed the considered video objects are rigid and have been previously segmented for each key instant considered. The model can be easily adjusted by choosing a suitable region of support (block or arbitrary rigid region). The presented results indicate that the proposed technique is a suitable approach for motion estimation, which can be used for applications such as MPEG-4 block based hybrid coding or MPEG-4 object based video manipulations. Experimental results have been performed on real video sequences.
Synergy in the multi-local statistics of gradient directions in images
Authors: Alexandre J. Nasrallah, Lewis D. Griffin
Venue: Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop (2006)

Gestalt psychologists were able to establish a number of qualitative grouping rules which govern human visual perceptions. It is possible that natural image statistics underlie those grouping rules, specifically multi-local statistics. We define multi-local to mean local measurements made simultaneously at multiple locations. To assess whether multi- local interactions occur in natural images we have used information-theoretic methods, specifically interaction information. For example, we have measured triples of gradient directions, and computed the mutual information between a pair of gradient directions, and how the context of a third gradient direction affects that mutual information. If it increases, then measuring triples of gradient directions is synergetic. We find that triples of gradient directions show synergy for all of the following image classes: natural images, their phase randomized and whitened versions and Gaussian noise images. Further, we find that the mean power spectrum of image ensembles determines the dependencies between gradient directions.
Ayurinformatics- the application of bioinformatics in the ayurvedic system of medicine
Authors: L. S. Rath
Venue: Proceedings of the 9th International Conference on Information Technology (2006)

Presently medical research has experienced a paradigm shift from in vivo to in silico experimentation, a development that relies mostly upon bioinformatics. The key to future commercialization of sequence data from Human Genome Project will be to develop informatics technology that transforms this data into information that is useful for diagnosis and therapy. In this new wave of revolution and commercialization our traditional system of Ayurveda needs renovation. Therefore, modern Ayurveda and Siddha practitioners need training in Ayurinformatics to keep pace with the modern system of medicine.
Accurate Face Alignment using Shape Constrained Markov Network
Authors: Lin Liang, Fang Wen, Ying-Qing Xu, Xiaoou Tang, Heung-Yeung Shum
Venue: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (2006)

In this paper, we present a shape constrained Markov network for accurate face alignment. The global face shape is defined as a set of weighted shape samples which are integrated into the Markov network optimization. These weighted samples provide structural constraints to make the Markov network more robust to local image noise. We propose a hierarchical Condensation algorithm to draw the shape samples efficiently. Specifically, a proposal density incorporating the local face shape is designed to generate more samples close to the image features for accurate alignment, based on a local Markov network search. A constrained regularization algorithm is also developed to weigh favorably those points that are already accurately aligned. Extensive experiments demonstrate the accuracy and effectiveness of our proposed approach.
A Sniffer Based Approach to WS Protocols Conformance Checking
Authors: Pemadeep Ramsokul, Arcot Sowmya
Venue: Proceedings of the Proceedings of The Fifth International Symposium on Parallel and Distributed Computing (2006)

To reduce interoperability problems arising from ambiguous or incomplete web services protocol specifications, we have recently introduced a formal framework, which allows modelling and automatic verification of such protocols. However, interoperability problems can still occur due to incorrect implementations. In this paper, we introduce a sniffer based approach to check the conformance of a protocol's implementation to its specification; messages of the actual implementations are captured, processed and checked against the specification's formal model. We also briefly illustrate the application of our framework using a version of the WSAtomicTransaction protocol.
Periodic uncertainty in periodic spectral analysis of processes associated with periodic phenomena
Authors: L. B. White, P. J. Sherman
Venue: Proceedings of the Acoustics, Speech, and Signal Processing,1994. on IEEE International Conference - Volume 04 (1994)

This work addresses the problem of trying to take advantage of the nominally periodic nature of a diesel engine running at constant speed to obtain a time-varying spectral description of vibration data over the shaft period. It requires addressing a number of issues, most notably period estimation and removal of tonal components whose presence in a time-varying spectrum is redundant and undesirable. Use of a recently designed method to identify sinusoids, in conjunction with an adaptive tracking algorithm, results in significant improvement in simulations, and reasonably good improvement in the case of the diesel vibration data, especially when considering the complexity of the stochastic structure of this data.
A Low Complexity and Low Power SoC Design Architecture for Adaptive MAI Suppression in CDMA Systems
Authors: Yuanbin Guo, Joseph R. Cavallaro
Venue: Journal of VLSI Signal Processing Systems (2006)

In this paper, we propose a reduced complexity and power efficient System-on-Chip (SoC) architecture for adaptive interference suppression in CDMA systems. The adaptive Parallel-Residue-Compensation architecture leads to significant performance gain over the conventional interference cancellation algorithms. The multi-code commonality is explored to avoid the direct Interference Cancellation (IC), which reduces the IC complexity from $\mathcal{O}(K^2N)$ to $\mathcal{O}(KN)$ . The physical meaning of the complete versus weighted IC is applied to clip the weights above a certain threshold so as to reduce the VLSI circuit activity rate. Novel scalable SoC architectures based on simple combinational logic are proposed to eliminate dedicated multipliers with at least $10 \times$ saving in hardware resource. A Catapult C High Level Synthesis methodology is apply to explore the VLSI design space extensively and achieve at least $4 \times$ speedup. Multi-stage Convergence-Masking-Vector combined with clock gating is proposed to reduce the VLSI dynamic power consumption by up to $90 \%$
Probabilistic 3D Polyp Detection in CT Images: The Role of Sample Alignment
Authors: Zhuowen Tu, Xiang Sean Zhou, Luca Bogoni, Adrian Barbu, Dorin Comaniciu
Venue: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (2006)

Automatic polyp detection is an increasingly important task in medical imaging with virtual colonoscopy [15] being widely used. In this paper, we present a 3D object detection algorithm and show its application on polyp detection from CT images. We make the following contributions: (1) The system adopts Probabilistic Boosting Tree (PBT) to probabilistically detect polyps. Integral volume and 3D Haar filters are introduced to achieve fast feature computation. (2) We give an explicit convergence rate analysis for the AdaBoost algorithm [2] and prove that the error at each step \in t+1. is tightly bounded by the previous error \in t. (3) For a 3D polyp template, a generative model is defined. Given the bound and convergence analysis, we analyze the role of "sample alignment" in the template design and devise a robust and efficient algorithm for polyp detection. The overall system has been tested on 150 volumes and the results obtained are very encouraging.
Segmentation of Rat Cardiac Ultrasound Images with Large Dropout Regions
Authors: Xiaoning Qian, Hemant D. Tagare, Zhong Tao
Venue: Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop (2006)

Short-axis rat cardiac ultrasound images contain especially large regions of dropout which make it very difficult to segment the endocardium. Previous strategies, such as using shape priors, are not effective with such large dropout regions. This paper proposes a dropout modeling strategy, which can bridge large dropout regions and segment the endocardium when used along with shape priors. The segmentation is formulated as an active contour in a Maximum-APosteriori (M.A.P.) framework with explicit priors for the dropout function and shape. Further, the active contour is evolved by a strategy called tunneling descent. Tunneling descent is a deterministic evolution strategy which can escape from local minima. The combination of dropout modeling and tunneling descent gives active contours which can successfully segment rat cardiac ultrasound images. Experimental results comparing the performance of the new algorithm with manual segmentation and classical active contours are provided.
A comparative analysis of discretization methods for Medical Datamining with Naïve Bayesian classifier
Authors: Ranjit Abraham, Jay B.Simha, S. S. Iyengar
Venue: Proceedings of the 9th International Conference on Information Technology (2006)

Naive Bayes classifier has gained wide popularity as a probability-based classification method despite its assumption that attributes are conditionally mutually independent given the class label. This paper makes a study into discretization techniques to improve the classification accuracy of Naïve Bayes with respect to medical datasets. Our experimental results suggest that on an average, with Minimum Description Length (MDL) discretization the Naïve Bayes Classifier seems to be the best performer compared to popular variants of Naïve Bayes as well as some popular non-Naïve Bayes statistical classifiers.
The wavescalar architecture
Authors: Mark Oskin
Venue:  (2006)

Silicon technology will continue to provide an exponential increase in the availability of raw transistors. Effectively translating this resource into application performance, however, is an open challenge that conventional superscalar designs will not be able to meet. We present WaveScalar as a scalable alternative to conventional designs. WaveScalar is a dataflow instruction set and execution model designed for scalable, low-complexity, high-performance processors. Unlike previous dataflow machines, WaveScalar can efficiently provide the sequential memory semantics imperative languages require. To allow programmers to easily express parallelism, WaveScalar supports pthread-style, coarse-grain multithreading and dataflow-style, fine-grain threading. In addition, it permits blending the two styles within an application or even a single function. To execute WaveScalar programs, we have designed a scalable, tile-based processor architecture called the WaveCache. As a program executes, the WaveCache maps the program's instructions onto its array of processing elements (PEs). The instructions remain at their processing elements for many invocations, and as the working set of instructions changes, the WaveCache removes unused instructions and maps new instructions in their place. The instructions communicate directly with one-another over a scalable, hierarchical on-chip interconnect, obviating the need for long wires and broadcast communication. This thesis presents the WaveScalar instruction set and evaluates a simulated implementation based on current technology. For single-threaded applications, the WaveCache achieves performance on par with conventional processors, but in less area. For coarse-grain threaded applications, WaveCache performance scales with chip size over a wide range, and it outperforms a range of the multi-threaded designs. The WaveCache sustains 7-14 multiply-accumulates per cycle on fine-grain threaded versions of well-known kernels. Finally, we apply both styles of threading to an example application, equake from spec2000, and speed it up by 9&times; compared to the serial version.
Representing Extended Finite State Machines for SDL by A Novel Control Model of Discrete Event Systems
Authors: Peng Wang, Kai-Yuan Cai
Venue: Proceedings of the Sixth International Conference on Quality Software (2006)

This paper discusses EFSM for SDL and transforms EFSM into a novel control model of discrete event systems. We firstly propose a control model of discrete event systems, where the event set is made up of several conflicting pairs and control is implemented to select one event of the pair. Then we transform EFSM for SDL to the control model to clarify the control mechanism functioning in SDL flow graphs. This work views the EFSM for SDL in the perspective of supervisory control theory, and this contributes to the field of software cybernetics, which explores the theoretically justified interplay of software and the control.
Acceleration of cutting-plane and column generation algorithms: Applications to network design
Authors: Walid Ben-Ameur, José Neto
Venue: Networks (2007)

Most of integer, convex, and large-scale linear problems are solved using cutting plane and column generation algorithms. Therefore, to handle large-size problems and to reduce the computing times, it may be very useful to accelerate cutting plane algorithms. We show in this article that we can achieve this goal by choosing good separation points. Focus is given on problems for which we have an exact separation oracle. An in&ndash;out algorithm is proposed, and the convergence is proved under some general assumptions. Computational experiments related to three classes of problems, survivable network design, multicommodity flow problems, and random linear programs, clearly point out the savings of time allowed by the simple in&ndash;out approach proposed in this article. &copy; 2006 Wiley Periodicals, Inc. NETWORKS, Vol. 49(1), 3&ndash;17 2007
Cancellable Biometrics and Multispace Random Projections
Authors: Andrew Teoh Beng Jin
Venue: Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop (2006)

In this paper, a generic cancellable biometrics formulation is proposed by first transforming the raw biometrics data into a fixed length feature vector, then subsequently re-projecting the feature vector onto a sequence of random subspaces specified by the tokenised random vectors. Since random subspace is user-specific, the formulation can be extended to multiple random subspaces for different individuals to amplify the interclass variation whilst maintain the intra-class variation in biometrics verification setting. The privacy invasion and non-revocable problems in biometrics could be resolved by revocation of resulting feature through the random subspace replacement. This formulation furthermore enhances recognition effectiveness as arising from the Multispace Random Projections of biometric and external random inputs.
Yasper: a tool for workflow modeling and analysis
Authors: Kees van Hee, Olivia Oanea, Reinier Post, Lou Somers, Jan Martijn v. an der Werf
Venue: Proceedings of the Sixth International Conference on Application of Concurrency to System Design (2006)

This paper presents Yasper, a tool for modeling, analyzing and simulating workflow systems, based on Petri nets. Yasper puts Petri net modeling in the hands of business analysts and software architecture designers. They can specify systems in familiar terms (XOR choice, workflow, cases, roles, processing time and cost), and can directly run manual and automatic simulations on the resulting models to analyze correctness and performance. Yasper was designed to cooperate with other tools, such as Petri net analyzers, and off-the-shelf software for data (color) handling and forms handling.