ppxscal/embedding_network huggingface dataset:
A study on kinematic trajectory calculations with different schemes using wind field of varying temporal frequency
Authors: M. Azadi, U. C. Mohanty, M. Mandal
Venue: Journal of Computational Methods in Sciences and Engineering (2006)

Computation of kinematic trajectories has got immense importance in assessing transport pathways of tracers and evaluating airflow patterns within meteorological systems. In a Lagrangian description of the transport of air pollutants, the kinematic trajectory of an air parcel is estimated using observed wind data or the output of a Numerical Weather Prediction (NWP) model. In this study, a non-hydrostatic mesoscale model is used to generate the meteorological data for a period, during which an active Western Disturbance (WD) crossed the Indian region. The 'true atmospheric trajectories (forward)' are constructed using model-simulated wind with 12 minutes time interval. The true trajectories are then compared to the trajectories that were constructed using simulated wind data with degraded temporal frequency of 36 min. and 1, 2, 3 and 6 hour. Five schemes including the well-known Petterssen scheme are used to calculate 72-hour forward trajectories. Comparison of the results using different schemes show that in terms of accuracy and computational efficiency, the midpoint method of integration (scheme-4) is comparatively better and the simplified Innocentini's scheme, which accounts for the spatial dependence of the wind field all along the particle path, gives results similar to the Petterssen scheme. Further, it is shown that when wind data are of low temporal frequency, use of smaller time steps will not improve the results.
Road Sign Analysis Using Multisensory Data
Authors: R. J. López-Sastre, S. Lafuente-Arroyo, P. Gil-Jiménez, P. Siegmann, S. Maldonado-Bascón
Venue: Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II (2007)

This paper deals with the problem of estimating the following road sign parameters: height, dimensions, visibility distance and partial occlusions. This work belongs to a framework whose main applications involve road sign maintenance, driver assistance, and inventory systems. From this paper we suggest a multisensory system composed from two cameras, a GPS receiver, and a distance measurement device, all of them installed in a car. The process consists of several steps which include road sign detection, recognition and tracking , and road signs parameters estimation. From some trigonometric properties, and a camera model, the information provided by the tracking subsystem and the distance measurement sensors, we estimate the road signs parameters. Results show that the described calculation methodology offers a correct estimation for all types of traffic signs.
A Distributed Wearable System Based on Multimodal Fusion
Authors: Il-Yeon Cho, John Sunwoo, Hyun-Tae Jeong, Yong-Ki Son, Hee-Joong Ahn, Dong-Woo Lee, Dong-Won Han, Cheol-Hoon Lee
Venue: Proceedings of the 3rd international conference on Embedded Software and Systems (2007)

Wearable computer can be worn anytime with the support of unrestricted communications and variety of services which provides maximum capability of information use. Key challenges in developing such wearable computers are level of comfort that users do not feel what they wear, easy and intuitive user interface and power management technique. This paper suggests a wearable system that consists of a wristwatch-type gesture recognition device and a personal mobile gateway that functional input/output modules can be freely plugged in and taken out. We describe our techniques implemented during our wearable system development: 1) multimodal fusion engine that recognizes voice and gesture simultaneously, 2) power management technique and 3) gesture recognition engine. Finally, we evaluate the performance of our multimodal fusion engine, and show the power consumption measurement data of our system built with the power management technique.
Understanding pointing problems in real world computing environments
Authors: Amy Hurst, Jennifer Mankoff, Scott E. Hudson
Venue: Proceedings of the 10th international ACM SIGACCESS conference on Computers and accessibility (2008)

Understanding how pointing performance varies in real world computer use and over time can provide valuable insight about how systems should accommodate changes in pointing behavior. Unfortunately, pointing data from individuals with pointing problems is rarely studied during real world use. Instead, it is most frequently evaluated in a laboratory where it is easier to collect and evaluate data. We developed a technique to collect and analyze real world pointing performance which we used to investigate the variance in performance of six individuals with a range of pointing abilities. Features of pointing performance we analyzed include metrics such as movement trajectories, clicking, and double clicking. These individuals exhibited high variance during both supervised and unsupervised (or real world) computer use across multiple login sessions. The high variance found within each participant highlights the potential inaccuracy of judging performance based on a single laboratory session.
Computing with Genetic Gates
Authors: Nadia Busi, Claudio Zandron
Venue: Proceedings of the 3rd conference on Computability in Europe: Computation and Logic in the Real World (2007)

We introduce Genetic Systems, a formalism inspired by genetic regulatory networks and suitable for modeling the interactions between the genes and the proteins, acting as regulatory products.The generation of new objects, representing proteins, is driven by genetic gates: a new object is produced when all the activator objects are available in the system, and no inhibitor object is available. Activators are not consumed by the application of such an evolution rule. Objects disappear because of degradation: each object is equipped with a lifetime, and the object decays when such a lifetime expires.We investigate the computational expressiveness of Genetic Systems: we show that they are Turing equivalent by providing an encoding of Random Access Machines in Genetic Systems.
Nonstationary Gaussian Process Regression Using Point Estimates of Local Smoothness
Authors: Christian Plagemann, Kristian Kersting, Wolfram Burgard
Venue: Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II (2008)

Gaussian processes using nonstationary covariance functions are a powerful tool for Bayesian regression with input-dependent smoothness. A common approach is to model the local smoothness by a latent process that is integrated over using Markov chain Monte Carlo approaches. In this paper, we demonstrate that an approximation that uses the estimated mean of the local smoothness yields good results and allows one to employ efficient gradient-based optimization techniques for jointly learning the parameters of the latent and the observed processes. Extensive experiments on both synthetic and real-world data, including challenging problems in robotics, show the relevance and feasibility of our approach.
What Are Possible Security Threats in Ubiquitous Sensor Network Environment?
Authors: Marie Kim, Youngjun Lee, Jaecheol Ryou
Venue: Proceedings of the 10th Asia-Pacific Network Operations and Management Symposium on Managing Next Generation Networks and Services (2007)

USN infrastructure means the logical and physical space which consists of all kinds of networks, which provides useful sensing information surrounding human being. It includes wireless sensor network, RFID, Mobile RFID, IP-USN, and wired sensor network. These kinds of network are sensing information providers for human beings and use different technologies to collect information from the environment and provide those information to information consumers. USN middleware provides USN infrastructure abstraction, complicated and integrated sensing information processing, Open Application Interface to the information consumer. The importance of USN middleware security lies in this role of USN middleware. It manages sensor networks and collects sensor data on the behalf of the sensing information consumer. Therefore a malicious consumer can disrupt the sensor networks through USN middleware and collect sensitive sensing information through USN middleware. Or, malicious sensor networks may provide contaminated sensing values to consumers through USN middleware. In this case, all information provided to consumers may be contaminated and destroyed. In this paper, we analyze possible security threats in USN environments. Based on this analysis, possible counter-threat measures can be taken.
A Granular Architecture for Dynamic Realtime Dialogue
Authors: Kristinn R. Thórisson, Gudny Ragna Jonsdottir
Venue: Proceedings of the 8th international conference on Intelligent Virtual Agents (2008)

We present a dialogue architecture that addresses perception, planning and execution of multimodal dialogue behavior. Motivated by realtime human performance and modular architectural principles, the architecture is full-duplex ("open-mic"); prosody is continuously analyzed and used for mixed-control turntaking behaviors (reactive and deliberative) and incremental utterance production. The architecture is fine-grain and highly expandable; we are currently applying it in more complex multimodal interaction and dynamic task environments. We describe here the theoretical underpinnings behind the architecture, compare it to prior efforts, discuss the methodology and give a brief overview of its current runtime characteristics.
Interactive Antarctica: a museum installation based on an augmented reality system
Authors: Caitilin de Bérigny Wall, Xiangyu Wang
Venue: Proceedings of the 3rd international conference on Digital Interactive Media in Entertainment and Arts (2008)

This paper considers the research work behind an interactive installation based on an Augmented Reality System (ARS). The installation will be a museum display, and will enable the participant to gesturally interact with data (digital image and sound) from the Antarctic. The ARS will allow a technological exploration of documentary and will considerably develop the contemporary status and understanding of the documentary genre in installation art. In this paper, we argue that the augmented reality installation presents a new mode of documentary experience, thus transforming the genre into a vastly new and complex ensemble. The fact that genre has always been fluid and mutable is widely understood; this paper extends that knowledge by showing the transformative possibilities when an ARS is adopted as the presentation vehicle.
Communicating Multiprocessor-Tasks
Authors: Jörg Dümmler, Thomas Rauber, Gudula Rünger
Venue: Languages and Compilers for Parallel Computing: 20th International Workshop, LCPC 2007, Urbana, IL, USA, October 11-13, 2007, Revised Selected Papers (2007)

The use of multiprocessor tasks (M-tasks) has been shown to be successful for mixed task and data parallel implementations of algorithms from scientific computing. The approach often leads to an increase of scalability compared to a pure data parallel implementation, but restricts the data exchange between M-tasks to the beginning or the end of their execution, expressing data or control dependencies between M-tasks.In this article, we propose an extension of the M-task model to communicating M-tasks (CM-tasks) which allows communication between M-tasks during their execution. In particular, we present and discuss the CM-task programming model, programming support for designing CM-task programs, and experimental results. Internally, a CM-task comprises communication and computation phases. The communication between different CM-tasks can exploit optimized communication patterns for the data exchange between CM-tasks, e.g., by using orthogonal realizations of the communication. This can be used to further increase the scalability of many applications, including time-stepping methods which use a similar task structure for each time step. This is demonstrated for solution methods for ordinary differential equations.
Workload adaptation in autonomic database management systems
Authors: Baoning Niu
Venue:  (2008)

Workload adaptation is a performance management process in which an autonomic database management system (DBMS) efficiently makes use of its resources by filtering or controlling the workload presented to it in order to meet its Service Level Objectives (SLOs). It is a challenge to adapt multiple workloads with complex resource requirements towards their performance goals while taking their business importance into account. This thesis studies approaches and techniques for workload adaptation. First we build a general framework for workload adaptation in autonomic DBMSs, which is composed of two processes, namely workload detection and workload control. The processes are in turn made up of four functional components - workload characterization, performance modeling, workload control, and system monitoring . We then implement a query scheduler that performs workload adaptation in a DBMS, as the test bed to prove the effectiveness of the framework. The query scheduler manages multiple classes of queries to meet their performance goals by allocating DBMS resources through admission control in the presence of workload fluctuation. The resource allocation plan is derived by maximizing the objective function that encapsulates the performance goals of all classes and their importance to the business. First-principle performance models are used to predict the performance under the new resource allocation plan. Experiments with IBM&reg; DB2&reg; are conducted to show the effectiveness of the framework. The effectiveness of the workload adaptation depends on the accuracy of the performance prediction. Finally we introduce a tracking filter (Kalman filter) to improve the accuracy of the performance prediction. Experimental results show that the approach is able to reduce the number of unpredicted SLO violations and prediction errors.
Placement Problem in an Industrial Environment
Authors: Shamshul Bahar Yaakob, Junzo Watada
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III (2008)

A problem of workers' evaluation and placement in an industrial environment is studied in this paper; an effect of workers' relationship on their placement is newly included in this paper. Evaluating workers' suitability is important when decision makers select proper candidates under various evaluation criteria among available human resources and jobs. For problems of this type, an analysis using a fuzzy number approach promises to be potentially effective. In order to make a more convincing and accurate decision, the relationship between jobs is included in the workers' assignment in an industrial environment. The fuzzy suitability evaluation is performed by means of aggregating the decision makers' fuzzy assessment. Examples of typical application are also presented: the results demonstrate that the workers' relationship is an important factor and our method is effective for the decision making process.
On the capacity of UWB-based wireless sensor networks
Authors: Yi Shi, Y. Thomas Hou
Venue: Computer Networks: The International Journal of Computer and Telecommunications Networking (2008)

Ultra-wideband (UWB) is a promising wireless communications technology and has great potential for emerging applications such as sensor networks. This paper studies the following fundamental problems for UWB-based sensor networks. For a given network instance, what is the maximum data rate (network capacity) that can be received at the base station (i.e., sink node)? What is the network capacity bound among arbitrary network instances? We show that these problems can be cast into a cross-layer formulation with joint consideration of routing, scheduling, power control, and rate assignment. For a given network instance, we find a closed-form network capacity as well as corresponding optimal routing, scheduling, power control, and rate assignment. We also find a network capacity bound among arbitrary network instances. Our results provide fundamental results for UWB-based sensor networks.
Variational Decomposition Model in Besov Spaces and Negative Hilbert-Sobolev Spaces
Authors: Min Li, Xiangchu Feng
Venue: Computational Intelligence and Security: International Conference, CIS 2006, Guangzhou, China, November 3-6, 2006, Revised Selected Papers (2007)

In this paper, we propose a new variational decomposition model which splits an image into two components: a first one containing the structure and a second one the texture or noise. Our decomposition model relies on the use of two semi-norms: the Besov semi-norm for the geometrical component, the negative Hilbert-Sobolev norms for the texture or noise. And the proposed model can be understood as generalizations of Daubechies-Teschke's model and have been motivated also by Lorenz's idea. And we illustrate our study with numerical examples for image decomposition and denoising.
A computational model for caenorhabditis elegans locomotory behavior: application to multi-worm tracking
Authors: Badrinath Roysam
Venue:  (2007)

We present a computational approach for modeling and quantifying the structure and dynamics of the nematode C. elegans observed by time-lapse microscopy. Complex worm movements are expressed in terms of three primitive patterns&mdash;peristaltic progression, deformation, and translation. Worm shape and conformation are decoupled. The model has been incorporated into algorithms for segmentation and simultaneous tracking of multiple worms in a field, some of which may be interacting in complex ways. A recursive Bayesian filter is used for tracking. Unpredictable behaviors associated with interactions are resolved using multiple-hypothesis tracking. The algorithm can track worms of diverse sizes and conformations (coiled/uncoiled) in the presence of imaging artifacts and clutter, even when worms overlap each other. Performance of algorithms was measured over 16 image sequences representing wild-type and uncoordinated mutants as a function of worm size, conformation, and the presence of worm overlaps. Overall detected tracking failures were 1.4%, undetected tracking failures were 0.4%, and segmentation errors were 1.1% of worm length. In cases when worms overlap, the method reduced undetected failures from 12% to 1.8%, and segmentation error from 12.2% to 6.4%. The method provides the basis for reliable morphometric and locomotory analysis of worm populations. The overall system was motivated by the need for quantitative analysis of the locomotory patterns of C. elegans. To that end a total of 20 videos featuring 10 different mutant types, some subtly different from wild-type worms, were processed and 839 worm tracks were extracted and quantified. A decision tree classifier was then used to identify the worm type. The average correct classification ratio, measured using the unbiased leave-one-out cross validation estimator, was 71%.
Automated Creation of Pattern Database Search Heuristics
Authors: Stefan Edelkamp
Venue: Model Checking and Artificial Intelligence: 4th Workshop, MoChArt IV, Riva del Garda, Italy, August 29, 2006, Revised Selected and Invited Papers (2007)

Pattern databases are dictionaries for heuristic estimates storing state-to-goal distances in state space abstractions. Their effectiveness is sensitive to the selection of the underlying patterns. Especially for multiple and additive pattern databases, the manual selection of patterns that leads to good exploration results is involved.For automating the selection process, greedy bin-packing has been suggested. This paper proposes genetic algorithms to optimize its output. Patterns are encoded as binary strings and optimized using an objective function that predicts the heuristic search tree size based on the distribution of heuristic values in abstract space.To reduce the memory requirements we construct the pattern databases symbolically. Experiments in heuristic search planning indicate that the total search efforts can be reduced significantly.
A framework for process-solution analysis in collaborative learning environments
Authors: Crescencio Bravo, Miguel A. Redondo, M. Felisa Verdejo, Manuel Ortega
Venue: International Journal of Human-Computer Studies (2008)

One of the most challenging aspects of computer-supported collaborative learning (CSCL) research is automation of collaboration and interaction analysis in order to understand and improve the learning processes. It is particularly necessary to look in more depth at the joint analysis of the collaborative process and its resulting product. In this article, we present a framework for comprehensive analysis in CSCL synchronous environments supporting a problem-solving approach to learning. This framework is based on an observation-abstraction-intervention analysis life-cycle and consists of a suite of analysis indicators, procedures for calculating indicators and a model of intervention based on indicators. Analysis indicators are used to represent the collaboration and knowledge building process at different levels of abstraction, and to characterize the solution built using models of the application domain, the problems to solve and their solutions. The analysis procedures combine analysis of actions and dialogue with analysis of the solution. In this way, the process and the solution are studied independently as well as together, enabling the detection of correlations between them. In order to exemplify and test the framework, the methodological process underlying the framework was followed to guide the implementation of the analysis subsystems of two existing CSCL environments. In addition, a number of studies have been conducted to evaluate the framework's approach, demonstrating that certain modes of collaborating and working imply particular types of solutions and vice versa.
Improving Performance of a Binary Classifier by Training Set Selection
Authors: Cezary Dendek, Jacek Mańdziuk
Venue: Proceedings of the 18th international conference on Artificial Neural Networks, Part I (2008)

In the paper a method of training set selection, in case of low data availability, is proposed and experimentally evaluated with the use of k-NN and neural classifiers. Application of proposed approach visibly improves the results compared to the case of training without postulated enhancements.Moreover, a new measure of distance between events in the pattern space is proposed and tested with k-NN model. Numerical results are very promising and outperform the reference literature results of k-NN classifiers built with other distance measures.