ppxscal/embedding_network huggingface dataset:
The Application of BIST-Aided Scan Test for Real Chips
Authors: Hideaki Konishi, Michiaki Emori, Takahisa Hiraide
Venue: Proceedings of the 15th Asian Test Symposium (2006)

It is common to use ATPG of scan-based design for high fault coverage in LSI testing. However, significant increase in test cost is caused in accordance with increasing design complexity. We proposed a new method, BIST-Aided Scan Test (BAST), to reduce test cost in 2003[1]. Since then, we applied this method for about 200 chips, and the result is very successful to reduce test cost with less design flow impact.
Learning from positive and unlabeled examples
Authors: François Denis, Rémi Gilleron, Fabien Letouzey
Venue: Theoretical Computer Science (2005)

In many machine learning settings, labeled examples are difficult to collect while unlabeled data are abundant. Also, for some binary classification problems, positive examples which are elements of the target concept are available. Can these additional data be used to improve accuracy of supervised learning algorithms? We investigate in this paper the design of learning algorithms from positive and unlabeled data only. Many machine learning and data mining algorithms, such as decision tree induction algorithms and naive Bayes algorithms, use examples only to evaluate statistical queries (SQ-like algorithms). Kearns designed the statistical query learning model in order to describe these algorithms. Here, we design an algorithm scheme which transforms any SQ-like algorithm into an algorithm based on positive statistical queries (estimate for probabilities over the set of positive instances) and instance statistical queries (estimate for probabilities over the instance space). We prove that any class learnable in the statistical query learning model is learnable from positive statistical queries and instance statistical queries only if a lower bound on the weight of any, target concept f can be estimated in polynomial time. Then, we design a decision tree induction algorithm POSC4.5, based on C4.5, that uses only positive and unlabeled examples and we give experimental results for this algorithm. In the case of imbalanced classes in the sense that one of the two classes (say the positive class) is heavily underrepresented compared to the other class, the learning problem remains open. This problem is challenging because it is encountered in many real-world applications.
Sharing sensor networks
Authors: Manabu Isomura, Till Riedel, Christian Decker, Michael Beigl, Hiroki Horiuchi
Venue: Proceedings of the 26th IEEE International ConferenceWorkshops on Distributed Computing Systems (2006)

Many industrial applications rely on sensors and sensor networks residing on machinery, transport containers or in the environment. For distributed processes in such domains the sharing of those sensor networks is crucial. This paper introduces a peer-to-peer (P2P) architecture for sharing services provided by existing sensor networks with any Internet based application. The differences between the sensor networks are abstracted using a service-oriented approach. The proposed technology is able to build up a global sensor Internet across multiple domain boundaries. Our implementation is evaluated with 300+ sensor nodes organized in a P2P network across continents.
Multi-class ROC analysis from a multi-objective optimisation perspective
Authors: Richard M. Everson, Jonathan E. Fieldsend
Venue: Pattern Recognition Letters (2006)

The receiver operating characteristic (ROC) has become a standard tool for the analysis and comparison of classifiers when the costs of misclassification are unknown. There has been relatively little work, however, examining ROC for more than two classes. Here we discuss and present an extension to the standard two-class ROC for multi-class problems. We define the ROC surface for the Q-class problem in terms of a multi-objective optimisation problem in which the goal is to simultaneously minimise the Q(Q-1) misclassification rates, when the misclassification costs and parameters governing the classifier's behaviour are unknown. We present an evolutionary algorithm to locate the Pareto front-the optimal trade-off surface between misclassifications of different types. The use of the Pareto optimal surface to compare classifiers is discussed and we present a straightforward multi-class analogue of the Gini coefficient. The performance of the evolutionary algorithm is illustrated on a synthetic three class problem, for both k-nearest neighbour and multi-layer perceptron classifiers.
Preserving the Volume of Fluid Using Multi-phase Flow Approach
Authors: Roman Durikovic, Katsuhiro Numata
Venue: Proceedings of the conference on Information Visualization (2006)

Methods to animate multiphase fluids are rarely researched, although such phenomena are common in our daily lives. The main focus of this research is to develop a suitable method to create realistic animation of multiphase flows, i.e. flow of fluid mixtures. Our method is based on Navier-Stokes equations, a set of physical equations that describe motion of fluid. A Marker-and-Cell (MAC) grid is used to solve the equations on discrete computational domain. To simulate several fluids, we propose the modification of Volume-of-Fluid (VOF) representation of fluid and integrate it into the multiphase-fluid approach. A mixture of fluids is treated as a single fluid having variable density and viscosity. This scheme allows two or more fluids having different densities and viscosities to be simulated simultaneously.
Adaptive demodulation of phase modulated signals using hidden Markov models
Authors: L. B. White, J. D. Ross
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1993. ICASSP-93 Vol 4., 1993 IEEE International Conference on - Volume 04 (1993)

The authors address the problem of estimating the phase sequence of a phase modulated signal in additive Guassian white noise by using hidden Markov model methods. Either digital (i.e., PSK) or analog modulated signals may be considered within this framework. Unknown and varying channel gains may be incorporated. Good estimation and adaptation performance for each method is demonstrated.
Leveraging Optical Technology in Future Bus-based Chip Multiprocessors
Authors: Nevin Kirman, Meyrem Kirman, Rajeev K. Dokania, Jose F. Martinez, Alyssa B. Apsel, Matthew A. Watkins, David H. Albonesi
Venue: Proceedings of the 39th Annual IEEE/ACM International Symposium on Microarchitecture (2006)

Although silicon optical technology is still in its formative stages, and the more near-term application is chip-to-chip communication, rapid advances have been made in the development of on-chip optical interconnects. In this paper, we investigate the integration of CMOS-compatible optical technology to on-chip cache-coherent buses in future CMPs. While not exhaustive, our investigation yields a hierarchical opto-electrical system that exploits the advantages of optical technology while abiding by projected limitations. Our evaluation shows that, for the applications considered, compared to an aggressive all-electrical bus of similar power and area, significant performance improvements can be achieved using an opto-electrical bus. This performance improvement is largely dependent on the application's bandwidth demand and on the number of implemented wavelengths per optical waveguide. We also present a number of critical areas for future work that we discover in the course of our research.
CreateASoft Inc.: Simcad Pro 7.1
Authors: Hosni Adra
Venue: Proceedings of the 37th conference on Winter simulation (2005)

Next generation simulation: Simcad Pro 7.1, brings to the simulation market unprecedented functionality by allowing users to map multiple products through shared processes while mapping out the interconnectivity of multiple value stream maps. Simcad Pro 7.1 can connect live to barcode readers and RFID tags providing unprecedented "virtual camera" capability and dynamic initial state loading. Control and monitor improvements with statistical comparison of current, future and past states. Value stream maps are dynamically linked to the process flow map. Every aspect of Simcad Pro 7.1 is customized to the specific needs of the user and is not limited to predefined building blocks. Model building and scenario analysis are accomplished through a "code free" graphical user interface. Fully customizable, Simcad Pro 7.1 applies to multiple industries, from banking and healthcare, to manufacturing and distribution including document flow, disaster planning, automation, supply chain logistics and highly custom job-shop environments. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
Adaptive control variates for American option pricing
Authors: Sam Ehrlichman, Shane G. Henderson
Venue: Proceedings of the 37th conference on Winter simulation (2005)

Recently, Kim and Henderson (2004) have proposed a scheme for variance reduction in Monte Carlo simulations that uses stochastic optimization to compute an effective control variate. We apply this technique to the problem of American option pricing. While our work is similar in spirit to work by Bolia and Juneja (2005), the nonlinear procedure allows us to consider a more flexible class of approximating functions for our control variate; in particular, we are freed from having to make a particular choice of basis function parameterization up front.
CURE for cubes: cubing using a ROLAP engine
Authors: Konstantinos Morfonios, Yannis Ioannidis
Venue: Proceedings of the 32nd international conference on Very large data bases (2006)

Data cube construction has been the focus of much research due to its importance in improving efficiency of OLAP. A significant fraction of this work has been on ROLAP techniques, which are based on relational technology. Existing ROLAP cubing solutions mainly focus on "flat" datasets, which do not include hierarchies in their dimensions. Nevertheless, the nature of hierarchies introduces several complications into cube construction, making existing techniques essentially inapplicable in a significant number of real-world applications. In particular, hierarchies raise three main challenges: (a) The number of nodes in a cube lattice increases dramatically and its shape is more involved. These require new forms of lattice traversal for efficient execution. (b) The number of unique values in the higher levels of a dimension hierarchy may be very small; hence, partitioning data into fragments that fit in memory and include all entries of a particular value may often be impossible. This requires new partitioning schemes. (c) The number of tuples that need to be materialized in the final cube increases dramatically. This requires new storage schemes that remove all forms of redundancy for efficient space utilization. In this paper, we propose CURE, a novel ROLAP cubing method that addresses these issues and constructs complete data cubes over very large datasets with arbitrary hierarchies. CURE contributes a novel lattice traversal scheme, an optimized partitioning method, and a suite of relational storage schemes for all forms of redundancy. We demonstrate the effectiveness of CURE through experiments on both real-world and synthetic datasets. Among the experimental results, we distinguish those that have made CURE the first ROLAP technique to complete the construction of the cube of the highest-density dataset in the APB-1 benchmark (12 GB). CURE was in fact quite efficient on this, showing great promise with respect to the potential of the technique overall.
On extensions of a theorem of Baxter
Authors: J. S. Geronimo, A. Martínez-Finkelshtein
Venue: Journal of Approximation Theory (2006)

We combine the Riemann-Hilbert approach with the techniques of Banach algebras to obtain an extension of Baxter's Theorem for polynomials orthogonal on the unit circle. This is accomplished by using the link between the negative Fourier coefficients of the scattering function and the coefficients in the recurrence formula satisfied by these polynomials.
Toward adaptive information fusion in multimodal systems
Authors: Sharon Oviatt
Venue: Proceedings of the 2005 NICTA-HCSNet Multimodal User Interaction Workshop - Volume 57 (2006)

Techniques for information fusion are at the heart of multimodal system design. To develop new user-adaptive approaches for multimodal fusion, our lab has investigated the stability and basis of major individual differences that have been documented in users' multimodal integration patterns. In this talk, I summarized the following: (1) there are large individual differences in users' dominant speech and pen multimodal integration pattern, such that individual users can be classified as either simultaneous or sequential integrators (Oviatt, 1999; Oviatt et al., 2003), (2) users' dominant integration pattern can be identified almost immediately (i.e., upon first interaction with computer), and it remains highly consistent over a session (Oviatt et al., 2003; Oviatt et al., 2005b), (3) users' dominant integration pattern also remains stable across their lifespan (Oviatt et al., 2003; Oviatt et al., 2005b), (4) users' dominant integration pattern is highly resistant to change, even when they are given strong selective reinforcement or explicit instructions to switch patterns (Oviatt et al., 2003; Oviatt et al., 2005a), (5) when users encounter cognitive load (e.g., due to increasing task difficulty, or system recognition errors), their dominant multimodal integration pattern entrenches or becomes "hypertimed," (Oviatt et al., 2003; Oviatt et al., 2004), and (6) users' distinctive integration patterns appear to derive from enduring differences in basic reflective-impulsive cognitive style (Oviatt et al., 2005b).
UOFC-AINet: A Fuzzy Immune Network for Unsupervised Optimal Clustering
Authors: Li Liu, Wenbo Xu
Venue: Proceedings of the International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce (2006)

Inspired by biological immunity mechanism, a novel immune network model named as UOFC-AINet was proposed to specifically perform unsupervised optimal fuzzy clustering. The bottom layer of UOFC-AINet generated optimal centroids of clusters with given cluster number and network parameters, which were controlled by the top layer of UOFC-AINet. Unlike aiNet immune network for data analysis, each antibody in the UOFC-AINet immune network was encoded by a possible solution and optimal antibodies in the network were evolved according to objective function of fuzzy clustering. Based on the clone, mutation, network suppression and influx of new cells, the UOFC-AINet network is capable of maintaining local optima solutions, exploring the global optima and dynamically set number of clusters and parameters of the immune network. The algorithm was described theoretically and compared with similar approaches experimentally. The results of experiments were evaluated with validity measures and visualized by PCA and fuzzy Sammon mapping.
On computing the coefficients of bivariate holonomic formal series
Authors: P. Massazza, R. Radicioni
Venue: Theoretical Computer Science (2005)

In this work, we study the problem of computing the coefficients of holonomic formal series in two commuting variables. Given a formal series φ(x, y) = Σn,k ≥ 0 cnkxnyk specified by a holonomic system Σj=0d1 pj(x, y)∂xjφ = 0 and Σj=0d2qj(x, y)∂yjφ = 0 with a suitable finite set of intial conditions {[xayb]φ(x, y)}, we show that the coefficient [xiyj]φ(x, y) can be computed in time O(i + j) under the uniform cost criterion.
Cumulus cloud synthetic rendering techniques and their evaluations
Authors: Manil Maskey, Timothy S. Newman
Venue: Machine Graphics Vision International Journal (2005)

Three new techniques for synthesizing realistic renderings of cumulus clouds are introduced and evaluated. The techniques utilize variations of the Perlin Noise and Koch fractals to achieve a reasonable cloud-like shape and texture. To evaluate the quality of renderings produced by the techniques, three classes of texture features are considered using cluster quality measures. Rendering quality is also evaluated versus real images using shape and texture features.
One Class Classification Methods Based Non-Relevance Feedback Document Retrieval
Authors: Takashi Onoda, Hiroshi Murata, Seiji Yamada
Venue: Proceedings of the 2006 IEEE/WIC/ACM international conference on Web Intelligence and Intelligent Agent Technology (2006)

We applied active learning techniques based on Support Vector Machine for evaluating documents each iteration, which is called relevance feedback. Our proposed approach has been very useful for document retrieval with relevance feedback experimentally. However, the initial retrieved documents, which are displayed to a user, sometimes don't include relevant documents. In order to solve this problem, we propose a new feedback method using information of non-relevant documents only. We named this method non-relevance feedback document retrieval. The non-relevance feedback document retrievals are based on One Class Support Vector Machine and Support Vector Data Description. Our experimental results show that One Class Support Vector Machine based method can retrieve relevant documents efficiently using information of non-relevant documents only.
Explicit modeling of semantics associated with composite states in UML statecharts
Authors: Zhaoxia Hu, Sol M. Shatz
Venue: Automated Software Engineering (2006)

UML statecharts are used for describing dynamic aspects of system behavior. The work presented here extends a general Petri net-based methodology to support formal modeling of UML statecharts. The approach focuses on the specific task of generating explicit transition models associated with the hierarchical structure of statechart. We introduce a state-transition notation to serve as an intermediate model for conversion of UML statecharts, and in particular, the complexity of composite states, to other target specifications. By defining a process for deriving, from UML statecharts, a state-transition notation that can serve as an intermediate state machine model, we seek to deepen understanding of modeling practices and help bridge the gap between model development and model analysis. This work covers all of the primary issues associated with the hierarchical structure of composite states, including entry and exit transitions, transition priorities, history states, and event dispatching. Thus, the results provide an important step forward toward the goal of modeling increasingly complex semantics of UML statecharts.
An expectation maximization approach multiple model filtering with application to tracking and scheduling
Authors: Nader Namazi
Venue:  (2006)

This dissertation study focuses on the development of a technique for model dynamic system detection and state estimation of maneuverable targets in radar tracking applications. The new technique incorporates the Expectation and Maximization algorithm with that of the Interacting Multiple Model (IMM) estimator. The main feature of this technique is its ability to estimate the state of a dynamic system with several behavior modes, which can change from one to another. The IMM-EM estimator general structure consists of a bank of Kalman filters for the state cooperating with a filter for the parameters. The IMM-EM estimator is a suboptimal hybrid filter that has an excellent compromise between performance and complexity. Its complexity is nearly linear in the number of models. The IMM-EM algorithm has three major properties: it is recursive, modular, and has fixed computational requirements per cycle. In each cycle, it consists of three major steps: filtering, combination and model detection. Adaptive radar systems can use IMM-EM technique in order to adjust their scan rate. Lowing the scan rate for non-maneuvering targets and increasing the scan rate for maneuvering targets. As the result, adaptive radar systems can increase their fidelity throughput by adjusting their scan rate. As an extended application of the IMM-EM technique, a novel methodology is developed for the computation of the radar scan rate based on the target model dynamics.
The design discussion area: a collaborative learning tool in support of learning from problem-solving and design activities
Authors: Janet L. Kolodner, Kristine Nagel
Venue: Proceedings of the 1999 conference on Computer support for collaborative learning (1999)

The Design Discussion Area (DDA) supports student reports to each other about their design efforts as they are learning science from design activities. Its scaffolding was designed to support both the writing of well-formed stories about their design efforts and discussion about each design effort. At their best, these discussions are a source of ideas for other students; a venue for talking science, using evidence in support of claims, getting advice, and providing explanations that others can understand; and a vehicle for some of the reflection necessary to turn one's experiences into well-formed and well-indexed cases in one's memory. DDA was trialed with 200 students; it was easy for students to use but it wasn't as straightforward to integrate into classroom activities as we had supposed and didn't provide as useful scaffolding as we had imagined.
Abnormal Event Detection in Video Using N-cut Clustering
Authors: Chun-Ku Lee, Meng-Fen Ho, Wu-Sheng Wen, Chung-Lin Huang
Venue: Proceedings of the 2006 International Conference on Intelligent Information Hiding and Multimedia (2006)

This paper introduces an unusual event detection scheme in various video scenes. The proposed method finds out the video clips that are most different from the others based on the similarity measure. Each video clip is represented by the motion magnitude and direction histograms and color histogram. Without searching key-frames, we calculate the similarity matrix by using \chi^2 difference or chamfer difference as the similarity measure of features in different clips. Finally, we apply n-cut clustering. Clusters with low self-similarity value are reported as unusual events.
2005 Special Issue: Beyond emotion archetypes: Databases for emotion modelling using neural networks
Authors: Roddy Cowie, Ellen Douglas-Cowie, Cate Cox
Venue: Neural Networks (2005)

There has been rapid development in conceptions of the kind of database that is needed for emotion research. Familiar archetypes are still influential, but the state of the art has moved beyond them. There is concern to capture emotion as it occurs in action and interaction ('pervasive emotion') as well as in short episodes dominated by emotion, and therefore in a range of contexts, which shape the way it is expressed. Context links to modality-different contexts favour different modalities. The strategy of using acted data is not suited to those aims, and has been supplemented by work on both fully natural emotion and emotion induced by various technique that allow more controlled records. Applications for that kind of work go far beyond the 'trouble shooting' that has been the focus for application: 'really natural language processing' is a key goal. The descriptions included in such a database ideally cover quality, emotional content, emotion-related signals and signs, and context. Several schemes are emerging as candidates for describing pervasive emotion. The major contemporary databases are listed, emphasising those which are naturalistic or induced, multimodal, and influential.