ppxscal/embedding_network huggingface dataset:
A Descend-Based Evolutionary Approach to Enhance Position Estimation in Wireless Sensor Networks
Authors: Vincent Tam, King-Yip Cheng, King-Shan Lui
Venue: Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence (2006)

Wireless sensor networks have wide applicability to many important applications including environmental monitoring and military applications. Typically with the absolute positions of only a small portion of sensors predetermined, localization works for the precise estimation of the remaining sensor positions on which most locationsensitive applications rely. Intrinsically, localization can be formulated as an unconstrained optimization problem based on various distance/path measures, for which most of the existing work focus on increasing its precision through different heuristic or mathematical techniques. In this paper, we propose to adapt an evolutionary approach, namely a micro-genetic algorithm (MGA), and its variant as postoptimizers to enhance the precision of existing localization methods including the Ad-hoc Positioning System. Our adapted MGA and its variants can easily be integrated into different localization methods. Besides, the prototypes of our evolutionary approach gained remarkable results on both uniform and anisotropic topologies of the simulation tests, thus prompting for many interesting directions for future investigation.
Switched chattering control of electrical servo-motors and backlash/friction attenuation
Authors: Y. Orlov, L. Aguilar, J. C. Cadiou
Venue: Proceedings of the 2003 International Conference on Physics and Control - Volume 4 (2003)

A switched control algorithm is developed to globally stabilize an electrical servo-motor operating under uncertainty conditions in the presence of complex nonlinear phenomena such as backlash, stick-slip motion, etc. Particularly, the chattering controller ensures robustness against external disturbances with the a priori known norm bounds, thus rejecting the influence of undesired backlash/friction effects on the motor. Stability analysis, given within the Lyapunov functions framework extended to discontinuous dynamic systems, additionally reveals a finite time convergence of the system trajectories to the equilibrium point. Performance issues of the controller are illustrated in a simulation study.
DSTree: A Tree Structure for the Mining of Frequent Sets from Data Streams
Authors: Carson Kai-Sang Leung, Quamrul I. Khan
Venue: Proceedings of the Sixth International Conference on Data Mining (2006)

With advances in technology, a flood of data can be produced in many applications such as sensor networks and Web click streams. This calls for efficient techniques for extracting useful information from streams of data. In this paper, we propose a novel tree structure, called DSTree (Data Stream Tree), that captures important data from the streams. By exploiting its nice properties, the DSTree can be easily maintained andmined for frequent itemsets as well as various other patterns like constrained itemsets.
Context-Aware Mobile Augmented Reality Architecture for Lifelong Learning
Authors: Jayfus T. Doswell
Venue: Proceedings of the Sixth IEEE International Conference on Advanced Learning Technologies (2006)

Virtual Instructor enabled Mobile Augmented Reality Systems (MARS) have the potential to provide continuous and autonomous instruction to human learners anytime, anyplace, and at any-pace. MARS based learning provides the advantage of a natural human-computer interface, flexible mobility, and context-aware instruction allowing learners to interact with their natural environment with augmented perceptual cues. These perceptual cues combining multi-modal animation, graphics, text, video, and voice along with empirical pedagogical techniques can elegantly orchestrate a mobile instructional tool that facilitates life-long learning. The challenge, however, is building a mobile instructional tool with capabilities for adapting to various learning environments ranging from traditional schools and outdoor learning to the workplace while also considering the cultural, geographical, and other contexts about the learner. This paper discusses a novel system/software architecture, CAARS, for developing context-aware mobile augmented reality instructional systems.
A Cross-Linguistic fMRI Study of Spectral and Temporal Cues Underlying Phonological Processing
Authors: Jack Gandour
Venue: Journal of Cognitive Neuroscience (2002)

It remains a matter of controversy precisely what kind of neural mechanisms underlie functional asymmetries in speech processing. Whereas some studies support speech-specific circuits, others suggest that lateralization is dictated by relative computational demands of complex auditory signals in the spectral or time domains. To examine how the brain processes linguistically relevant spectral and temporal information, a functional magnetic resonance imaging study was conducted using Thai speech, in which spectral processing associated with lexical tones and temporal processing associated with vowel length can be differentiated. Ten Thai and 10 Chinese subjects were asked to perform discrimination judgments of pitch and timing patterns presented in the same auditory stimuli under two different conditions: speech (Thai) and nonspeech (hums). In the speech condition, tasks required judging Thai tones (T) and vowel length (VL); in the nonspeech condition, homologous pitch contours (P) and duration patterns (D). A remaining task required listening passively to nonspeech hums (L). Only the Thai group showed activation in the left inferior prefrontal cortex in speech minus nonspeech contrasts for spectral (T vs. P) and temporal (VL vs. D) cues. Thai and Chinese groups, however, exhibited similar fronto-parietal activation patterns in nonspeech hums minus passive listening contrasts for spectral (P vs. L) and temporal (D vs. L) cues. It appears that lower level specialization for acoustic cues in the spectral and temporal domains cannot be generalized to abstract higher order levels of phonological processing. Regardless of the neural mechanisms underlying low-level auditory processing, our findings clearly indicate that hemispheric specialization is sensitive to language-specific factors.
Simulating scenarios for research on culture & cognition using a commercial role-play game
Authors: Rik Warren, David E. Diller, Alice Leung, William Ferguson, Janet L. Sutton
Venue: Proceedings of the 37th conference on Winter simulation (2005)

Most research on culture and cognition uses self-report tasks such as paper and pencil questionnaires. Such tasks are inexpensive, quick, and easy to score, but they are vulnerable to response bias and manipulation effects. Action-based or performance tasks can be more absorbing and permit more of someone's natural behavior to emerge but are rarer due to increased costs, lower experimenter control, and difficult logistics. Computer games can potentially regain the benefits of real performance and immersive play while retaining experimenter control and keeping costs low. Properly constructed, computer games can simulate action-demanding scenarios which embed opportunities for personality and culturally-conditioned behaviors to manifest themselves. This is especially true when computer-simulated non-player characters are included which exhibit carefully modeled behaviors. However, such simulations are not themselves panaceas. This paper examines some of the concepts we have tried, the challenges we have faced, and the lessons we have learned.
Interval neutrosophic sets and logic: theory and applications in computing
Authors: Rajshekhar Sunderraman
Venue:  (2005)

A neutrosophic set is a part of neutrosophy that studies the origin, nature, and scope of neutralities, as well as their interactions with different ideational spectra. The neutrosophic set is a powerful general formal framework that has been recently proposed. However, the neutrosophic set needs to be specified from a technical point of view. Here, we define the set-theoretic operators on an instance of a neutrosophic set, and call it an Interval Neutrosophic Set (INS). We prove various properties of INS, which are connected to operations and relations over INS. We also introduce a new logic system based on interval neutrosophic sets. We study the interval neutrosophic propositional calculus and interval neutrosophic predicate calculus. We also create a neutrosophic logic inference system based on interval neutrosophic logic. Under the framework of the interval neutrosophic set, we propose a data model based on the special case of the interval neutrosophic sets called Neutrosophic Data Model. This data model is the extension of fuzzy data model and paraconsistent data model. We generalize the set-theoretic operators and relation-theoretic operators of fuzzy relations and paraconsistent relations to neutrosophic relations. We propose the generalized SQL query constructs and tuple-relational calculus for Neutrosophic Data Model. We also design an architecture of Semantic Web Services agent based on the interval neutrosophic logic and do the simulation study.
Cryptanalysis of the new TTS scheme in CHES 2004
Authors: Jintai Ding, Dieter Schmidt, Zhijun Yin
Venue: International Journal of Information Security (2006)

We combine the method of searching for an invariant subspace of the unbalanced Oil and Vinegar signature scheme and the Minrank method to defeat the new TTS signature scheme, which was suggested for low-cost smart card applications at CHES 2004. We show that the attack complexity is less than 250.
Asymptotics in Empirical Risk Minimization
Authors: Leila Mohammadi, Sara van de Geer
Venue: The Journal of Machine Learning Research (2005)

In this paper, we study a two-category classification problem. We indicate the categories by labels Y=1 and Y=-1. We observe a covariate, or feature, X ∈ X ⊂ ℜd. Consider a collection {ha} of classifiers indexed by a finite-dimensional parameter a, and the classifier ha* that minimizes the prediction error over this class. The parameter a* is estimated by the empirical risk minimizer ân over the class, where the empirical risk is calculated on a training sample of size n. We apply the Kim Pollard Theorem to show that under certain differentiability assumptions, ân converges to a* with rate n-1/3, and also present the asymptotic distribution of the renormalized estimator.For example, let V0 denote the set of x on which, given X=x, the label Y=1 is more likely (than the label Y=-1). If X is one-dimensional, the set V0 is the union of disjoint intervals. The problem is then to estimate the thresholds of the intervals. We obtain the asymptotic distribution of the empirical risk minimizer when the classifiers have K thresholds, where K is fixed. We furthermore consider an extension to higher-dimensional X, assuming basically that V0 has a smooth boundary in some given parametric class.We also discuss various rates of convergence when the differentiability conditions are possibly violated. Here, we again restrict ourselves to one-dimensional X. We show that the rate is n-1 in certain cases, and then also obtain the asymptotic distribution for the empirical prediction error.
Analysis of spike statistics in neuronal systems with continuous attractors or multiple, discrete attractor states
Authors: Paul Miller
Venue: Neural Computation (2006)

Attractor networks are likely to underlie working memory and integrator circuits in the brain. It is unknown whether continuous quantities are stored in an analog manner or discretized and stored in a set of discrete attractors. In order to investigate the important issue of how to differentiate the two systems, here we compare the neuronal spiking activity that arises from a continuous (line) attractor with that from a series of discrete attractors. Stochastic fluctuations cause the position of the system along its continuous attractor to vary as a random walk, whereas in a discrete attractor, noise causes spontaneous transitions to occur between discrete states at random intervals. We calculate the statistics of spike trains of neurons firing as a Poisson process with rates that vary according to the underlying attractor network. Since individual neurons fire spikes probabilistically and since the state of the network as a whole drifts randomly, the spike trains of individual neurons follow a doubly stochastic (Poisson) point process.We compare the series of spike trains from the two systems using the autocorrelation function, Fano factor, and interspike interval (ISI) distribution. Although the variation in rate can be dramatically different, especially for short time intervals, surprisingly both the autocorrelation functions and Fano factors are identical, given appropriate scaling of the noise terms. Since the range of firing rates is limited in neurons, we also investigate systems for which the variation in rate is bounded by either rigid limits or because of leak to a single attractor state, such as the Ornstein-Uhlenbeck process. In these cases, the time dependence of the variance in rate can be different between discrete and continuous systems, so that in principle, these processes can be distinguished using second-order spike statistics.
Using Dependent Regions for Object Categorization in a Generative Framework
Authors: Gang Wang, Ye Zhang, Li Fei-Fei
Venue: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (2006)

"Bag of words" models have enjoyed much attention and achieved good performances in recent studies of object categorization. In most of these works, local patches are modeled as basic building blocks of an image, analogous to words in text documents. In most previous works using the "bag of words" models (e.g. [4, 20, 7]), the local patches are assumed to be independent with each other. In this paper, we relax the independence assumption and model explicitly the inter-dependency of the local regions. Similarly to previous work , we represent images as a collection of patches, each of which belongs to a latent "theme" that is shared across images as well as categories. We learn the theme distributions and patch distributions over the themes in a hierarchical structure [22]. In particular, we introduce a linkage structure over the latent themes to encode the dependencies of the patches. This structure enforces the semantic connections among the patches by facilitating better clustering of the themes. As a result, our models for object categories tend to be more discriminative than the ones obtained under the independent patch assumption. We show highly competitive categorization results on both the Caltech 4 and Caltech 101 object category datasets. By examining the distributions of the latent themes for each object category, we construct an object taxonomy using the 101 object classes from the Caltech 101 datasets.
Character Independent Font Recognition on a Single Chinese Character
Authors: Xiaoqing Ding, Li Chen, Tao Wu
Venue: IEEE Transactions on Pattern Analysis and Machine Intelligence (2007)

A novel algorithm for font recognition on a single unknown Chinese character, independent of the identity of the character, is proposed in this paper. We employ a wavelet transform on the character image and extract wavelet features from the transformed image. After a Box-Cox transformation and LDA (Linear Discriminant Analysis) process, the discriminating features for font recognition are extracted and classified through a MQDF (Modified Quadric Distance Function) classifier with only one prototype for each font class. Our experiments show that our algorithm can achieve a recognition rate of 90.28 percent on a single unknown character and 99.01 percent if five characters are used for font recognition. Compared with existing methods, all of which are based on a text block, our method can provide a higher recognition rate and is more flexible and robust, since it is based on a single unknown character. Additionally, our method demonstrates that it is possible to extract subtle yet discriminative signals embedded in a much larger noisy background.
Performance analysis of IEEE 802.15.4 and ZigBee for large-scale wireless sensor network applications
Authors: Mikko Kohvakka, Mauri Kuorilehto, Marko Hännikäinen, Timo D. Hämäläinen
Venue: Proceedings of the 3rd ACM international workshop on Performance evaluation of wireless ad hoc, sensor and ubiquitous networks (2006)

This paper analyses the performance of IEEE 802.15.4 Low-Rate Wireless Personal Area Network (LR-WPAN) in a large-scale Wireless Sensor Network (WSN) application. To minimize the energy consumption of the entire network and to allow adequate network coverage, IEEE 802.15.4 peer-to-peer topology is selected, and configured to a beacon-enabled cluster-tree structure. The analysis consists of models for CSMA-CA mechanism and MAC operations specified by IEEE 802.15.4. Network layer operations in a cluster-tree network specified by ZigBee are included in the analysis. For realistic results, power consumption measurements on an IEEE 802.15.4 evaluation board are also included. The performances of a device and a coordinator are analyzed in terms of power consumption and goodput. The results are verified with simulations using WIreless SEnsor NEtwork Simulator (WISENES). The results depict that the minimum device power consumption is as low as 73 μW, when beacon interval is 3.93 s, and data are transmitted at 4 min intervals. Coordinator power consumption and goodput with 15.36 ms CAP duration and 3.93 s beacon interval are around 370 μW and 34 bits/s
Multisensory Integration Sites Identified by Perception of Spatial Wavelet Filtered Visual Speech Gesture Information
Authors: Daniel E. Callan, Jeffery A. Jones, Kevin Munhall, Christian Kroos, Akiko M. Callan, Eric Vatikiotis-bateson
Venue: Journal of Cognitive Neuroscience (2004)

Perception of speech is improved when presentation of the audio signal is accompanied by concordant visual speech gesture information. This enhancement is most prevalent when the audio signal is degraded. One potential means by which the brain affords perceptual enhancement is thought to be through the integration of concordant information from multiple sensory channels in a common site of convergence, multisensory integration (MSI) sites. Some studies have identified potential sites in the superior temporal gyrus/sulcus (STG/S) that are responsive to multisensory information from the auditory speech signal and visual speech movement. One limitation of these studies is that they do not control for activity resulting from attentional modulation cued by such things as visual information signaling the onsets and offsets of the acoustic speech signal, as well as activity resulting from MSI of properties of the auditory speech signal with aspects of gross visual motion that are not specific to place of articulation information. This fMRI experiment uses spatial wavelet bandpass filtered Japanese sentences presented with background multispeaker audio noise to discern brain activity reflecting MSI induced by auditory and visual correspondence of place of articulation information that controls for activity resulting from the above-mentioned factors. The experiment consists of a low-frequency (LF) filtered condition containing gross visual motion of the lips, jaw, and head without specific place of articulation information, a midfrequency (MF) filtered condition containing place of articulation information, and an unfiltered (UF) condition. Sites of MSI selectively induced by auditory and visual correspondence of place of articulation information were determined by the presence of activity for both the MF and UF conditions relative to the LF condition. Based on these criteria, sites of MSI were found predominantly in the left middle temporal gyrus (MTG), and the left STG/S (including the auditory cortex). By controlling for additional factors that could also induce greater activity resulting from visual motion information, this study identifies potential MSI sites that we believe are involved with improved speech perception intelligibility.
Template-Based Development of Fault-Tolerant Embedded Software
Authors: Christian Buckl, Alois Knoll, Gerhard Schrott
Venue: Proceedings of the International Conference on Software Engineering Advances (2006)

Currently there are different approaches to develop fault-tolerant embedded software: implementing the system from scratch or using libraries respectively specialized hardware. By implementing from scratch the developer has all options concerning system design, the used programming language and hardware. But on the other hand the implementation is errorprone and time- and cost-intensive. The usage of libraries or specialized hardware reduces the design possibilities, while increasing the quality of the developed system and accelerating the development. We present a new technique for developing fault-tolerant systems that combines the advantages of these approaches. We suggest the implementation of reusable templates that solve different aspects of fault-tolerant systems, for example temporal synchronization. In addition we introduce a code generator that realizes a mapping of these templates into application-dependent source code.
Robust Stabilization for Uncertain Time-Delay Systems with Delay-Dependence
Authors: Pin-Lin Liu, Tung-Yi Lu, I-Chang Chen
Venue: Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 1 (2006)

The aim of this paper is to derive some sufficient stability conditions dependent of delay for stability. Based on Lyapunov-Krasovskii functional combining with LMI techniques, simple and delay-dependent robust exponential stability criteria are also derived. The results have been illustrated by given numerical examples. We believe that the proposed schemas are applicable to robust control design.
Efficient topology control for ad-hoc wireless networks with non-uniform transmission ranges
Authors: Xiang-Yang Li, Wen-Zhan Song, Yu Wang
Venue: Wireless Networks (2005)

Wireless network topology control has drawn considerable attention recently. Priori arts assumed that the wireless ad hoc networks are modeled by unit disk graphs (UDG), i.e., two mobile hosts can communicate as long as their Euclidean distance is no more than a threshold. However, practically, the networks are never so perfect as unit disk graphs: the transmission ranges may vary due to various reasons such as the device differences, the network control necessity, and the perturbation of the transmission ranges even the transmission ranges are set as the same originally. Thus, we assume that each mobile host has its own transmission range. The networks are modeled by mutual inclusion graphs (MG), where two nodes are connected iff they are within the transmission range of each other. Previously, no method is known for topology control when the networks are modeled as mutual inclusion graphs.The paper proposes the first distributed mechanism to build a sparse power efficient network topology for ad hoc wireless networks with non-uniform transmission ranges. We first extend the Yao structure to build a spanner with a constant length and power stretch factor for mutual inclusion graph. We then propose two efficient localized algorithms to construct connected sparse network topologies. The first structure, called extended Yao-Yao, has node degree at most O(log γ), where γ = maxu maxuv∈MG ru/rv. The second structure, called extended Yao and Sink, has node degree bounded by O(log γ), and is a length and power spanner. The methods are based on a novel partition strategy of the space surrounded each mobile host. Both algorithms have communication cost O(n) under a local broadcasting communication model, where each message has O(log n) bits.