ppxscal/embedding_network huggingface dataset:
Fluent VTK Extractor
Authors: Raymond C. Maple, Ryan Osterday, Rhonda Vickery
Venue: Proceedings of the HPCMP Users Group Conference (2006)

A Fluent User Defined Function (UDF) to extract and save portions of a time-accurate simulation is presented. This UDF, called the Fluent VTK Extractor (FVE) makes use of the Visualization Toolkit (VTK) library to manipulate unstructured grid data and perform file IO. The FVE UDF is applied to three unsteady flow problems. Data storage reductions of up to 99% and runtime reductions of up to 15% are observed.
Overlay Node Placement: Analysis, Algorithms and Impact on Applications
Authors: Sabyasachi Roy, Himabindu Pucha, Zheng Zhang, Y. Charlie Hu, Lili Qiu
Venue: Proceedings of the 27th International Conference on Distributed Computing Systems (2007)

Overlay routing has emerged as a promising approach to improving performance and reliability of Internet paths. To fully realize the potential of overlay routing under the constraints of deployment costs in terms of hardware, network connectivity and human effort, it is critical to carefully place infrastructure overlay nodes to balance the trade-off between performance and resource constraints. In this paper, we investigate approaches to perform intelligent placement of overlay nodes to facilitate (i) resilient routing and (ii) TCP performance improvement.We formulate objective functions to accurately capture application behavior: reliability and TCP performance, and develop several placement algorithms, which offer a wide range of trade-offs in complexity and required knowledge of the clientserver location and traffic load. Using simulations on synthetic and real Internet topologies, and PlanetLab experiments, we demonstrate the effectiveness of the placement algorithms and objective functions developed, respectively. We conclude that an approach, hybrid of random and greedy approaches, provides the best tradeoff between computational efficiency and accuracy. We also uncover the fundamental challenge in simultaneously optimizing for reliability and TCP performance, and propose a simple unified algorithm to achieve the same.
Comparison of multi-channel adaptive MLSE equalizers using different channel tracking strategies
Authors: Jiunn-Tsair Chen
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 05 (1999)

In order to gain insights on equalization design in wireless mobile communication systems, we compare the performance of several multi-channel MLSE equalizers which adaptively track fast-fading channels. Commonly-used channel tracking schemes, decision-directed recursive least square (DD/RLS), per-survivor processing recursive least square (PSP/RLS) and other reduced-complexity MLSE algorithms are considered. Simulation results that illustrate the performance of the equalizers working with various channel tracking schemes are presented.
Research and Implementation of Design Pattern-Oriented Model Transformation
Authors: Xue-Bin Wang, Quan-Yuan Wu, Huai-Min Wang, Dian-Xi Shi
Venue: Proceedings of the International Multi-Conference on Computing in the Global Information Technology (2007)

Design pattern describes a repeatedly presenting issue during software designing, as well the solution to it. Applying design pattern enables developers to reuse it to solve a specified designing issue. Applying and maintaining design pattern in models is a duplicating work. In order to automatically apply design patterns in models, we propose a design pattern oriented model transformation approach. In this paper, we designed a set of atom mappings and pattern mappings, as well the combination of them, the model transformation code template. Such a template can generate XSLT based model transformation code, and enable automatically applying design pattern in software models. A case study is provided to demonstrate our approach.
K-Means+ID3: A Novel Method for Supervised Anomaly Detection by Cascading K-Means Clustering and ID3 Decision Tree Learning Methods
Authors: Shekhar R. Gaddam Vir V. Phoha
Venue: IEEE Transactions on Knowledge and Data Engineering (2007)

In this paper, we present "K-Means+ID3,” a method to cascade k-Means clustering and the ID3 decision tree learning methods for classifying anomalous and normal activities in a computer network, an active electronic circuit, and a mechanical mass-beam system. The k-Means clustering method first partitions the training instances into k clusters using Euclidean distance similarity. On each cluster, representing a density region of normal or anomaly instances, we build an ID3 decision tree. The decision tree on each cluster refines the decision boundaries by learning the subgroups within the cluster. To obtain a final decision on classification, the decisions of the k-Means and ID3 methods are combined using two rules: 1) the Nearest-neighbor rule and 2) the Nearest-consensus rule. We perform experiments on three data sets: 1) Network Anomaly Data (NAD), 2) Duffing Equation Data (DED), and 3) Mechanical System Data (MSD), which contain measurements from three distinct application domains of computer networks, an electronic circuit implementing a forced Duffing Equation, and a mechanical system, respectively. Results show that the detection accuracy of the K-Means+ID3 method is as high as 96.24 percent at a false-positive-rate of 0.03 percent on NAD; the total accuracy is as high as 80.01 percent on MSD and 79.9 percent on DED.
A new generic surface micromachining module for MEMS hermetic packaging at temperatures below 200&#x00a0;&#x00b0;C
Authors: R. Rico Hell&#x00ed;n, J.-P. Celis, K. Baert, C. Van Hoof, A. Witvrouw
Venue: Microsystem Technologies (2007)

This paper presents the different processing steps of a new generic surface micromachining module for MEMS hermetic packaging at temperatures around 180&#x00a0;&#x00b0;C based on nickel plating and photoresist sacrificial layers. The advantages of thin film caps are the reduced thickness and area consumption and the promise of being a low-cost batch process. Moreover, sealing happens by a reflow technique, giving the freedom of choosing the pressure and atmosphere inside the cavity. Sacrificial etch holes are situated above the device allowing shorter release times compared to the state-of-the-art. With the so-called over-plating process, small etch holes can be created in the membrane without the need of expensive lithography tools. The etch holes in the membrane have been shown to be sufficiently small to block the sealing material to pass through, but still large enough to enable an efficient release.
A source code linearization technique for detecting plagiarized programs
Authors: Jeong-Hoon Ji, Gyun Woo, Hwan-Gue Cho
Venue: Proceedings of the 12th annual SIGCSE conference on Innovation and technology in computer science education (2007)

It is very important to detect plagiarized programs in the field of computer science education. Therefore, many tools and algorithms have been developed for this purpose. Generally, these tools are operated in two phases. In phase 1, a program plagiarism detecting tool generates an intermediate representation from a given program set. The intermediate representation should reflect the structural characterization of the program. Most tools use the parse tree or token sequence by intermediate representation. In phase 2, the program looks for plagiarized material and evaluates the similarity of two programs. It is helpful to announce the plagiarized metarials between two programs to the instructor. In this paper, we present the static tracing method in order to improve program plagiarism detection accuracy. The static tracing method statically executes a program at the syntax-level and then extracts predefined keywords according to the order of the executed functions. The result of experiment proves this method can detect plagiarism more effectively than the previously released plagiarism detecting method.
UBCA: Utility-Based Clustering Architecture for Peer-to-Peer Systems
Authors: Brent Lagesse, Mohan Kumar
Venue: Proceedings of the 27th International Conference on Distributed Computing Systems Workshops (2007)

Peer-to-Peer (P2P) systems are currently used in a variety of applications. File sharing applications and ad hoc networking have fueled the usage of these systems. P2P systems generate new challenges in scalability, fairness, and quality of service. Current systems often approach these challenges through incentive-based solutions and structured system design. Incentive-based solutions appeal to the self-interested nature of peers by utilizing payment or penalty to encourage peers to contribute to the system. System design principles, which attempt to improve performance through protocols and system-algorithms, include distributed hash tables and graph-theoretic designs. These approaches have seen some success, but also result in new problems such as overhead costs of authenticity/ security for incentives, increased centralization, and decreased ability to handle dynamic peers. We introduce Utility-Based Clustering Architecture, (UBCA) designed to improve quality of service through the use of implicit incentives. UBCA runs on peers and groups them into logical clusters in real time, based on mutual utility gained as a result of the grouping. Simulation studies show with a high confidence that UBCA exhibits improved bandwidth and latency per access.
Build One, Get One Free: Leveraging the Coexistence of Multiple P2P Overlay Networks
Authors: Balasubramaneyam Maniymaran, Marin Bertier, Anne-Marie Kermarrec
Venue: Proceedings of the 27th International Conference on Distributed Computing Systems (2007)

Many different P2P overlay networks providing various functionalities, targeting specific applications, have been proposed in the past five years. It is now reasonable to consider that multiple overlays may be deployed over a large set of nodes so that the most appropriate overlay might be chosen depending on the application. A physical peer may then host several instances of logical peers belonging to different overlay networks. In this paper, we show that the coexistence of a structured P2P overlay and an unstructured one may be leveraged so that, by building one, the other is automatically constructed as well1. More specifically, we show that the randomness provided by an unstructured gossip-based overlay can be used to build the routing table of a structured P2P overlay and the randomness in the numerical proximity links in the structured networks provides the random peer sampling required by gossip-based unstructured overlays. In this paper, we show that maintaining the leaf set of Pastry and the proximity links of an unstructured overlay is enough to build the complete overlays. Simulation results comparing our approach with both a Pastry-like system and a gossip-based unstructured overlay show that we significantly reduce the overlay maintenance overhead without sacrificing the performance.
Tracking with Prescribed Transient Behavior for Nonlinear Systems of Known Relative Degree
Authors: Achim Ilchmann, Eugene P. Ryan, Philip Townsend
Venue: SIAM Journal on Control and Optimization (2007)

Tracking of a reference signal (assumed bounded with essentially bounded derivative) is considered in the context of a class $\Sigma_{\rho}$ of multi-input, multi-output dynamical systems, modelled by functional differential equations, affine in the control and satisfying the following structural assumptions: (i) arbitrary&mdash;but known&mdash;relative degree $\rho \ge 1; (ii) the &ldquo;high-frequency gain&rdquo; is sign definite&mdash;but possibly of unknown sign. The class encompasses a wide variety of nonlinear and infinite-dimensional systems and contains (as a prototype subclass) all finite-dimensional, linear, $m$-input, $m$-output, minimum-phase systems of known strict relative degree. The first control objective is tracking, by the output $y$, with prescribed accuracy: given $\lambda >0$ (arbitrarily small), determine a feedback strategy which ensures that, for every reference signal $r$ and every system of class $\Sigma_{\rho}$, the tracking error $e=y-r$ is ultimately bounded by &lgr; (that is, $\|e(t)\| < \lambda$ for all $t$ sufficiently large). The second objective is guaranteed output transient performance: the tracking error is required to evolve within a prescribed performance funnel $\mathcal{F}_\varphi$ (determined by a function &Jgr;). Both objectives are achieved using a filter in conjunction with a feedback function of the tracking error, the filter states, and the funnel parameter &Jgr;.
Statistical model order reduction for interconnect circuits considering spatial correlations
Authors: Jeffrey Fan, Ning Mi, Sheldon X.-D. Tan, Yici Cai, Xianlong Hong
Venue: Proceedings of the conference on Design, automation and test in Europe (2007)

In this paper, we propose a novel statistical model order reduction technique, called statistical spectrum model order reduction (SS-MOR) method, which considers both intra-die and inter-die process variations with spatial correlations. The SSMOR generates order-reduced variational models based on given variational circuits. The reduced model can be used for fast statistical performance analysis of interconnect circuits with variational input sources, such as power grid and clock networks. The SSMOR uses statistical spectrum method to compute the variational moments and Monte Carlo sampling method with the modified Krylov subspace reduction method to generate the variational reduced models. To consider spatial correlations, we apply orthogonal decomposition to map the correlated random variables into independent and uncorrelated variables. Experimental results show that the proposed method can deliver about 100x speedup over the pure Monte Carlo projection-based reduction method with about 2% of errors for both means and variances in statistical transient analysis.
Automating three modes of evolution for object-oriented software architectures
Authors: Lance Tokuda, Don Batory
Venue: Proceedings of the 5th conference on USENIX Conference on Object-Oriented Technologies & Systems - Volume 5 (1999)

Architectural evolution is a costly yet unavoidable consequence of a successful application. One method for reducing cost is to automate aspects of the evolutionary cycle when possible. Three kinds of architectural evolution in object-oriented systems are: schema transformations, the introduction of design pattern microarchitectures, and the hot-spot-driven-approach. This paper shows that all three can be viewed as transformations applied to an evolving design. Further, the transformations are automatable with refactorings -- behavior-preserving program transformations. A comprehensive list of refactorings used to evolve large applications is provided and an analysis of supported schema transformations, design patterns, and hot-spot meta patterns is presented. Refactorings enable the evolution of architectures on an if-needed basis reducing unnecessary complexity and inefficiency.
Evaluating the accuracy of an unlexicalized statistical parser on the PARC DepBank
Authors: Ted Briscoe, John Carroll
Venue: Proceedings of the COLING/ACL on Main conference poster sessions (2006)

We evaluate the accuracy of an unlexicalized statistical parser, trained on 4K treebanked sentences from balanced data and tested on the PARC DepBank. We demonstrate that a parser which is competitive in accuracy (without sacrificing processing speed) can be quickly tuned without reliance on large in-domain manually-constructed treebanks. This makes it more practical to use statistical parsers in applications that need access to aspects of predicate-argument structure. The comparison of systems using DepBank is not straightforward, so we extend and validate DepBank and highlight a number of representation and scoring issues for relational evaluation schemes.
Efficient Symbolic Sensitivity based Parasitic-Inclusive Optimization in Layout Aware Analog Circuit Synthesis
Authors: Huiying Yang, Ranga Vemuri
Venue: Proceedings of the 20th International Conference on VLSI Design held jointly with 6th International Conference: Embedded Systems (2007)

High-performance circuit optimization and synthesis should consider parasitic effects. This paper introduces techniques for parasitic estimation and fast parasitic optimization based on symbolic sensitivity analysis. An effective framework to incorporate parasitic modeling and optimization is presented in order to account for parasitic effects during synthesis. In this paper we primarily focus on using efficient symbolic sensitivity analysis based on element-coefficient diagrams (ECD) to evaluate the dominant parasitic effects so as to eliminate insignificant parasitics. An ECD is the cancellation-free and per-coefficient term generation version of determinant decision diagrams (DDDs). In this paper, parasitic-aware analog circuit synthesis methodology is proposed. The accuracy and efficiency of the parasitic-inclusive optimization have been demonstrated.
Controlling the parallel layer perceptron complexity using a multiobjective learning algorithm
Authors: D. A. G. Vieira, J. A. Vasconcelos, W. M. Caminhas
Venue: Neural Computing and Applications (2007)

This paper deals with the parallel layer perceptron (PLP) complexity control, bias and variance dilemma, using a multiobjective (MOBJ) training algorithm. To control the bias and variance the training process is rewritten as a bi-objective problem, considering the minimization of both training error and norm of the weight vector, which is a measure of the network complexity. This method is applied to regression and classification problems and compared with several other training procedures and topologies. The results show that the PLP MOBJ training algorithm presents good generalization results, outperforming traditional methods in the tested examples.
2DCCA: A Novel Method for Small Sample Size Face Recognition
Authors: Cai-rong Zou, Ning Sun, Zhen-hai Ji, Li Zhao
Venue: Proceedings of the Eighth IEEE Workshop on Applications of Computer Vision (2007)

In the traditional Canonical Correlation Analysis (CCA) based face recognition methods, the size of sample is always smaller than the dimension of sample. This problem is so called the Small Sample Size (SSS) problem. In order to solve this problem, a new supervised learning method called Two-dimensional CCA (2DCCA) is developed in this paper. Different from traditional CCA method, 2DCCA directly extracts the features from image matrix rather than matrix-tovector transformation. In practice, the covariance matrix extracted by 2DCCA is always full rank. Hence the Small Sample Size (SSS) problem can be effectively dealt with by this new developed method. The theory foundation of 2DCCA method is firstly developed, and the construction method for the class-membership matrix Y which is used to precisely represent the relationship between samples and classes in the 2DCCA framework is then clarified. Simultaneously, the analytic form of the generalized inverse of such class-membership matrix is derived. From our experiment results on face recognition, we clearly find that not only the SSS problem can be effectively solved, but also better recognition performance than several other CCA based methods has been achieved.
Proving Approximate Implementations for Probabilistic I/O Automata
Authors: Sayan Mitra, Nancy Lynch
Venue: Electronic Notes in Theoretical Computer Science (ENTCS) (2007)

In this paper we introduce the notion of approximate implementations for Probabilistic I/O Automata (PIOA) and develop methods for proving such relationships. We employ a task structure on the locally controlled actions and a task scheduler to resolve nondeterminism. The interaction between a scheduler and an automaton gives rise to a trace distribution-a probability distribution over the set of traces. We define a PIOA to be a (discounted) approximate implementation of another PIOA if the set of trace distributions produced by the first is close to that of the latter, where closeness is measured by the (resp. discounted) uniform metric over trace distributions. We propose simulation functions for proving approximate implementations corresponding to each of the above types of approximate implementation relations. Since our notion of similarity of traces is based on a metric on trace distributions, we do not require the state spaces nor the space of external actions of the automata to be metric spaces. We discuss applications of approximate implementations to verification of probabilistic safety and termination.
Multimodal dynamics: self-supervised learning in perceptual and motor systems
Authors: Whitman Richards, Howard Shrobe
Venue:  (2006)

This thesis presents a self-supervised framework for perceptual and motor learning based upon correlations in different sensory modalities. The brain and cognitive sciences have gathered an enormous body of neurological and phenomenological evidence in the past half century demonstrating the extraordinary degree of interaction between sensory modalities during the course of ordinary perception. We develop a framework for creating artificial perceptual systems that draws on these findings, where the primary architectural motif is the cross-modal transmission of perceptual information to enhance each sensory channel individually. We present self-supervised algorithms for learning perceptual grounding, intersensory influence, and sensory-motor coordination, which derive training signals from internal cross-modal correlations rather than from external supervision. Our goal is to create systems that develop by interacting with the world around them, inspired by development in animals. We demonstrate this framework with: (1) a system that learns the number and structure of vowels in American English by simultaneously watching and listening to someone speak. The system then cross-modally clusters the correlated auditory and visual data. It has no advance linguistic knowledge and receives no information outside of its sensory channels. This work is the first unsupervised acquisition of phonetic structure of which we are aware, outside of that done by human infants. (2) a system that learns to sing like a zebra finch, following the developmental stages of a juvenile zebra finch. It first learns the song of an adult male and then listens to its own initially nascent attempts at mimicry through an articulatory synthesizer. In acquiring the birdsong to which it was initially exposed, this system demonstrates self-supervised sensorimotor learning. It also demonstrates afferent and efferent equivalence---the system learns motor maps with the same computational framework used for learning sensory maps. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)