ppxscal/embedding_network huggingface dataset:
Diagnosis of Transistor Shorts in Logic Test Environment
Authors: Yoshinobu Higami, Kewal K. Saluja, Hiroshi Takahashi, Sin-ya Kobayashi, Yuzo Takamatsu
Venue: Proceedings of the 15th Asian Test Symposium (2006)

For deep-submicron technology based LSIs, conventional stuck-at fault model is no longer sufficient for fault test and diagnosis. In this paper we present a method of fault diagnosis for transistor shorts in combinational and full-scan circuits under logic test environment. Description of a short requires a very large number of physical parameters, and hence it is difficult, if not impossible, to describe precisely the behavior of transistor shorts. Therefore, we define two types of transistor short models and we develop algorithms to address the diagnostic problem. A novelty of our algorithms is that they use conventional stuck-at fault simulation methodologies to diagnose transistor level shorts. We conduct experiments on benchmark circuits to demonstrate the effectiveness of our method.
Tracking multiple maneuvering point targets using multiple filter bank in infrared image sequence
Authors: M. A. Zaveri, U. B. Desai, S. N. Merchant
Venue: Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1 (2003)

Performance of any tracking algorithm depends upon the model selected to capture the target dynamics. In real world applications, no a priori knowledge about the target motion is available. Moreover, it could be a maneuvering target. The proposed method is able to track maneuvering or nonmaneuvering multiple point targets with large motion (/spl mnplus/20 pixels) using multiple filter bank in an IR image sequence in the presence of clutter and occlusion due to clouds. The use of multiple filters is not new, but the novel idea here is that it uses single-step decision logic to switch over between filters. Our approach does not use any a priori knowledge about maneuver parameters, nor does it exploit a parameterized nonlinear model for the target trajectories. This is in contrast to: (i) interacting multiple model (IMM) filtering which required the maneuver parameters, and (ii) extended Kalman filter (EKF) or unscented Kaiman filter (UKF), both of which require a parameterized model for the trajectories. We compared our approach for target tracking with IMM filtering using EKF and UKF for non-linear trajectory models. UKF uses the nonlinearity of the target model, where as a first order linearization is used in case of EKF. RMS for the predicted position error (RMS-PPE) obtained using our proposed methodology is significantly less in case of highly maneuvering target.
Introducing Time into RDF
Authors: Claudio Gutierrez, Carlos A. Hurtado, Alejandro Vaisman
Venue: IEEE Transactions on Knowledge and Data Engineering (2007)

The Resource Description Framework (RDF) is a metadata model and language recommended by the W3C. This paper presents a framework to incorporate temporal reasoning into RDF, yielding temporal RDF graphs. We present a semantics for these kinds of graphs which includes the notion of temporal entailment and a syntax to incorporate this framework into standard RDF graphs, using the RDF vocabulary plus temporal labels. We give a characterization of temporal entailment in terms of RDF entailment and show that the former does not yield extra asymptotic complexity with respect to nontemporal RDF graphs. We also discuss temporal RDF graphs with anonymous timestamps, providing a theoretical framework for the study of temporal anonymity. Finally, we sketch a temporal query language for RDF, along with complexity results for query evaluation that show that the time dimension preserves the tractability of answers.
Computing real roots of a polynomial in Chebyshev series form through subdivision
Authors: John E. Boyd
Venue: Applied Numerical Mathematics (2006)

An arbitrary polynomial of degree N, fN (x), can always be represented as a truncated Chebyshev polynomial series ("Chebyshev form"). This representation is much better conditioned than the usual "power form" of a polynomial. We describe two families of algorithms for finding the real roots of fN in Chebyshev form. We briefly review existing companion matrix methods--robust, but relatively expensive. We then describe a broad family of new rootfinders employing subdivision. These new methods partition the canonical interval, x ∈ [-1, 1], into Ns subintervals and approximate fN by a low degree Chebyshev interpolant on each subdomain. We derive a rigorous error bound that allows tight control of the error in these local approximations. Because the cost of companion matrix methods grows as the cube of the degree, it is much less expensive for N > 50 to calculate the roots of many low degree polynomials, one polynomial on each subdivision, than to directly compute the roots of a single polynomial of high degree.
Visualization of News Distribution in Blog Space
Authors: Yasufumi Takama, Akio Matsumura, Tomoki Kajinami
Venue: Proceedings of the 2006 IEEE/WIC/ACM international conference on Web Intelligence and Intelligent Agent Technology (2006)

This paper proposes a visualization method of News distribution in Blog space. Recently, Blog is becoming one of the important information resources on the Web, from which subjective data and trend information could be obtained. On the other hand, online news site is another information resource, which reports latest events in the world. This paper focuses on the combination of both resources, and proposes a method for visualizing how news is mentioned and distributed in Blog space. The target objects to be visualized are news articles, Blog entries, and Blog sites. The paper defines news to news relationship, news to entry relationship, entry to entry relationship, and entry to site relationship, based on which Blog space is visualized on 2D space by interactive information visualization system. This paper shows with a small data set that news distribution in Blog space can be visualized from multiple viewpoints.
New Horn Revision Algorithms
Authors: Judy Goldsmith, Robert H. Sloan
Venue: The Journal of Machine Learning Research (2005)

A revision algorithm is a learning algorithm that identifies the target concept, starting from an initial concept. Such an algorithm is considered efficient if its complexity (in terms of the measured resource) is polynomial in the syntactic distance between the initial and the target concept, but only polylogarithmic in the number of variables in the universe. We give efficient revision algorithms in the model of learning with equivalence and membership queries. The algorithms work in a general revision model where both deletion and addition revision operators are allowed. In this model one of the main open problems is the efficient revision of Horn formulas. Two revision algorithms are presented for special cases of this problem: for depth-1 acyclic Horn formulas, and for definite Horn formulas with unique heads.
MACCS: enabling communications for mobile workers within healthcare environments
Authors: Michael J. Sammon, Lynne Shapiro Brotman, Ed Peebles, Dorée Duncan Seligmann
Venue: Proceedings of the 8th conference on Human-computer interaction with mobile devices and services (2006)

As wireless communications systems become more ubiquitous, enterprise workers are becoming more and more mobile. Addressing mobility in the enterprise has recently become a pressing concern for many corporations. In particular, there is a growing component of mobile workers whose job tasks require them to be mobile within their local workspace. These workers sometimes do not have a desk or phone and frequently use their hands in performing required tasks; they typically referred to as "corridor cruisers" or "campus roamers". One class of workers that fall under this category is healthcare professionals (e.g. nurses). Communication enabling these workers usually involves an expensive proposition: equipping them with a mobile/wireless phone, PDA or a paging device. Our goal was to see if we could address the communications needs of healthcare workers by using a small, inexpensive, wearable, hands-free audio device (a wireless headset) along with a speech interface to an intelligent agent. In this paper we present the results of an industrial user study in a real world healthcare environment of our Mobile Access to Converged Communications System (MACCS) which empowers mobile workers with a hands-free voice interface to manage their communications. In addition we also discuss the design, implementation and deployment of MACCS.
Distributed constructionism
Authors: Mitchel Resnick
Venue: Proceedings of the 1996 international conference on Learning sciences (1996)

This paper introduces the concept of distributed constructionism, building on previous research on constructionism and on distributed cognition. It focuses particularly on the use of computer networks to support students working together on design and construction activities, and it argues that these types of activities are particularly effective in supporting the development of knowledge-building communities. The paper describes three main categories of distributed constructionist activities: discussing constructions, sharing constructions, and collaborating on constructions. In each category, it describes ongoing research projects at the MIT Media Lab and discusses how these projects support new ways of thinking and learning.
The Möbius function of a composition poset
Authors: Bruce E. Sagan, Vincent Vatter
Venue: Journal of Algebraic Combinatorics: An International Journal (2006)

We determine the Möbius function of a poset of compositions of an integer. In fact, we give two proofs of this formula, one using an involution and one involving discrete Morse theory. This composition poset turns out to be intimately connected with subword order, whose Möbius function was determined by Björner. We show that, using a generalization of subword order, we can obtain both Björner's results and our own as special cases.
Blind wideband source separation
Authors: M. Najar, M. A. Lagunas, I. Bonet
Venue: Proceedings of the Acoustics, Speech, and Signal Processing,1994. on IEEE International Conference - Volume 04 (1994)

This paper deals with the general problem of separating two independent wideband sources when they are mixed by unknown filters. In order to solve this problem, a backward framework is proposed which is composed of two different stages. The first one consists of two linear predictors devoted to improve the source separation, whitening the input signals. Their coefficients are calculated applying the LMS algorithm, minimizing the mean squared errors between the predicted signals and the output of the separation network. The second stage is formed by decoupling filters that have to be blindly estimated imposing an independence criterion to the outputs.
An interpretation of the effort function through the mathematical formalism of exponential informetric process
Authors: Thierry Lafouge, Agnieszka Smolczewska
Venue: Information Processing and Management: an International Journal (2006)

Statistical distributions in the production or utilization of information are most often studied in the framework of Lotkaian informetrics. In this article, we show that an Information Production Process (IPP), traditionally characterized by Lotkaian distributions, can be fruitfully studied using the effort function, a concept introduced in an earlier article to define an Exponential informetric Process. We thus propose replacing the concept of Lotkaian distribution by the logarithmic effort function. In particular, we show that an effort function defines an Exponential Informetric Process if its asymptotic behavior is equivalent to the logarithmic function β. Log(x) with β > 1, which is the effort function of a Lotkaian distribution.
Comparison of recent methods for inference of variable influence in neural networks
Authors: Stavros Papadokonstantakis, Argyrios Lygeros, Sven P. Jacobsson
Venue: Neural Networks (2006)

Neural networks (NNs) belong to 'black box' models and therefore 'suffer' from interpretation difficulties. Four recent methods inferring variable influence in NNs are compared in this paper. The methods assist the interpretation task during different phases of the modeling procedure. They belong to information theory (ITSS), the Bayesian framework (ARD), the analysis of the network's weights (GIM), and the sequential omission of the variables (SZW). The comparison is based upon artificial and real data sets of differing size, complexity and noise level. The influence of the neural network's size has also been considered. The results provide useful information about the agreement between the methods under different conditions. Generally, SZW and GIM differ from ARD regarding the variable influence, although applied to NNs with similar modeling accuracy, even when larger data sets sizes are used. ITSS produces similar results to SZW and GIM, although suffering more from the 'curse of dimensionality'.
A programming paradigm for machine learning, with a case study of Bayesian networks
Authors: Lloyd Allison
Venue: Proceedings of the 29th Australasian Computer Science Conference - Volume 48 (2006)

Inductive programming is a new machine learning paradigm which combines functional programming for writing statistical models and information theory to prevent overfitting, Type-classes specify general properties that models must have. Many statistical models, estimators and operators have polymorphic types. Useful operators combine models, and estimators, to form new ones; Functional programmings's compositional style of programming is a great advantage in this domain, Complementing this, information theory provides a compositional measure of the complexity of a model from its parts.Inductive programming is illustrated by a case study of Bayesian networks, Networks are built from classification- (decision-) trees. Trees are built from partioning functions and models on data-spaces. Trees, and hence networks, are general as a natural consequence of the method. Discrete and continious variables, and missing values are handled by the networks. Finally the Bayesian networks are applied to a challenging data set on lost persons.
A Generative-Discriminative Hybrid Method for Multi-View Object Detection
Authors: Dong-Qing Zhang, Shih-Fu Chang
Venue: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (2006)

We present a novel discriminative-generative hybrid approach in this paper, with emphasis on application in multiview object detection. Our method includes a novel generative model called Random Attributed Relational Graph (RARG) which is able to capture the structural and appearance characteristics of parts extracted from objects. We develop new variational learning methods to compute the approximation of the detection likelihood ratio function. The variaitonal likelihood ratio function can be shown to be a linear combination of the individual generative classifiers defined at nodes and edges of the RARG. Such insight inspires us to replace the generative classifiers at nodes and edges with discriminative classifiers, such as support vector machines, to further improve the detection performance. Our experiments have shown the robustness of the hybrid approach - the combined detection method incorporating the SVM-based discriminative classifiers yields superior detection performances compared to prior works in multiview object detection.
Traffic Scheduling to Prolong the Lifetime of Sensor Networks
Authors: Yantao Pan
Venue: Proceedings of the 2006 IEEE/WIC/ACM international conference on Web Intelligence and Intelligent Agent Technology (2006)

Sensor networks are expected to be used for spatial cognition in harsh environments. When an event happens, there will be several sensors detect it and send their reports to a sink, but these data are neither integrated nor reliable. Therefore, it is reasonable to make use of data fusion on intermediate nodes to achieve deeper knowledge on an event and cut down the total traffic at the same time. However, a node needs to decide in what order it send packets to its neighbors, which is called traffic scheduling. In sensor networks with data fusion, bad traffic scheduling results in remarkable delay, because an intermediate node must wait for all incoming data to arrive before it can perform data fusion. In order to prolong the network lifetime, we apply traffic scheduling to reduce the energy that consumed by such idle listening.
Plagiarism in e-Learning Systems: Identifying and Solving the Problem for Practical Assignments
Authors: Emil Marais, Ursula Minnaar, David Argles
Venue: Proceedings of the Sixth IEEE International Conference on Advanced Learning Technologies (2006)

A big part of life long learning is the move from residential lectures to distance education. Distance education falls under the multi-modal policy of the teaching institution and thereby a change in student contact. The lecturer facilitating the distance education course is also faced with a problem where the quality and originality of submitted assignments need to be checked. This has always been a difficult task, as going through practical assignments and looking for similarities is a tedious job. Software checkers are available, but as yet, have not been integrated into popular online e-learning systems. If closer contact and warning to students are given at an early stage the problem is minimized as they know they are being closely monitored. As will be shown in this article, plagiarism is a current problem with online practical submissions. We will also show how this problem can be minimized through the integration of plagiarism checking tools and other checking methods into e-learning systems.
The Evolution of a Test Process for Spacecraft Software
Authors: Deborah A. Clancy, Brenda A. Clyde, M. Annette Mirantes
Venue: Proceedings of the 2nd IEEE International Conference on Space Mission Challenges for Information Technology (2006)

The Johns Hopkins University Applied Physics Laboratory formed an embedded software group in 2001. In addition to defining the process for developing and testing flight software, the group had to quickly apply those new processes to a series of four different spacecraft missions from 2001 through the present. The paper describes the evolution of the software testing approach for embedded flight software during this time. After a brief description of the four spacecraft missions, this paper presents our initial approach to testing and how that approach changed with each mission, as our resources were overextended and our schedules were compressed. The final section of the paper presents the changes that we believe had the most significant impact on our testing efforts and our proposed test approach for the next mission.
Experiences from the annual 24-hour software engineering competition at University of Sk&ouml;vde: how to use simulation for detecting good software engineers
Authors: Per M. Gustavsson, Anna Persson, Tomas Planstedt, Christoffer Brax, Madeleine Norstedt Larsson
Venue: Proceedings of the 37th conference on Winter simulation (2005)

In the presentation the five-year experiences from the yearly 24 hours software engineering competition held at University of Sk&ouml;vde is presented. The outline for the competition is briefed and architectural and design issues are in focus. The competition is divided into two phases where the first is to develop the software the second is too compete with it towards each other in a distributed simulation. The systems that the students build are C2 systems for, air traffic control, ambulance control, focused logistic. Presented and elaborated together with the solutions. The work has evolved new ideas in how simulation can be used to enhance the planning, learning and combining gameengines architectures with military simulation architectures. HLA versus CORBA versus OpenSIS as interoperability distribution mechanisms are discussed. The application (Scenarios) is presented as well as the presentation techniques in 2D and 3D to visualize the competition within the competition for the audience.
Application of Grid to GRAPES Meso-Scale Model
Authors: Xuesheng Yang, Weimin Zhang, Yongzhu Liu, Dehui Chen
Venue: Proceedings of the Second International Conference on Semantics, Knowledge, and Grid (2006)

Considering the fact that the computing resources and NWP model developers within the framework of the China Meteorological Administration (CMA) are distributed widespread across the countries, and that the NWP systems very much depend on datasharing, remote collaborative research, heavy computation, massive data and intensive services, the Meso-scale Model Application Grid (MMAG) has been established based on the grid technology. Ultimately this platform has aggregated the distributed computing resources and NWP software at CAMS, NMIC, STI, GRMC and GXMB. The GRAPES Meso-scale model runs routinely on MMAG and users can access the NWP products at the designated website. It also provides a remote collaborative research environment for the geographical distributed scientists to make contributions to the improvement of the GRAPES.
Fast, automatic, procedure-level performance tuning
Authors: Zhelong Pan, Rudolf Eigenmann
Venue: Proceedings of the 15th international conference on Parallel architectures and compilation techniques (2006)

This paper presents an automated performance tuning solution, which partitions a program into a number of tuning sections and finds the best combination of compiler options for each section. Our solution builds on prior work on feedback-driven optimization, which tuned the whole program, instead of each section. Our key novel algorithm partitions a program into appropriate tuning sections. We also present the architecture of a system that automates the tuning process; it includes several pre-tuning steps that partition and instrument the program, followed by the actual tuning and the post-tuning assembly of the individually-optimized parts. Our system, called PEAK, achieves fast tuning speed by measuring a small number of invocations of each code section, instead of the whole-program execution time, as in common solutions. Compared to these solutions PEAK reduces tuning time from 2.19 hours to 5.85 minutes on average, while achieving similar program performance. PEAK improves the performance of SPEC CPU2000 FP benchmarks by 12% on average over GCC O3, the highest optimization level, on a Pentium IV machine.
ImprovingWeb-based Image Search via Content Based Clustering
Authors: Nadav Ben-Haim, Boris Babenko, Serge Belongie
Venue: Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop (2006)

Current image search engines on the web rely purely on the keywords around the images and the filenames, which produces a lot of garbage in the search results. Alternatively, there exist methods for content based image retrieval that require a user to submit a query image, and return images that are similar in content. We propose a novel approach named ReSPEC (Re-ranking Sets of Pictures by Exploiting Consistency), that is a hybrid of the two methods. Our algorithm first retrieves the results of a keyword query from an existing image search engine, clusters the results based on extracted image features, and returns the cluster that is inferred to be the most relevant to the search query. Furthermore, it ranks the remaining results in order of relevance.