ppxscal/embedding_network huggingface dataset:
Design of Fair Scheduling Schemes for the QoS-Oriented Wireless LAN
Authors: Huei-Wen Ferng, Han-Yu Liau
Venue: IEEE Transactions on Mobile Computing (2009)

How to simultaneously achieve fairness and quality-of-service (QoS) guarantee in QoS-oriented wireless local area networks (LANs) is an important and challenging issue. Targeting at this goal and jointly taking priority setting, fairness, and cross-layer design into account, four scheduling schemes designed for the QoS-oriented wireless LAN mainly based on concepts of deficit count and allowance are proposed in this paper to provide better QoS and fairness. Using multiple deficit count to interframe space (IFS) and allowance to IFS mappings for different priorities, enhanced distributed deficit round robin (EDDRR) and enhanced distributed elastic round robin (EDERR) schemes are designed to reduce (or even eliminate) possible collisions, while EDDRR with backoff interval and EDERR with backoff interval schemes still keep the backoff procedure but dynamically adjust backoff intervals for nonfailure events (the events excluding collisions and failed transmissions) depending on the priority setting and deficit count or allowance with a cross-layer design. Through extensive numerical examples, we show that the proposed schemes outperform the closest scheduling schemes in the literature and exhibit much better QoS as well as station-level and flow-level fairness.
Dynamic Mining of Quantitative and Categorical Attributes with Skewed Support Distribution
Authors: M. R. Karthik, C. P. Saravana Kumar
Venue: Proceeding of the 2006 conference on Advances in Intelligent IT: Active Media Technology 2006 (2006)

Though many algorithms have focused on mining quantitative attributes with a uniform support distribution, very little work is done in mining quantitative attributes with skewed support distribution. Still the binning methods in these algorithms are not effective against skewed support distribution and noise data; mostly they are not dynamic. In this paper, two efficient algorithms are proposed: Support-distribution Based Binning (SBB) for binning quantitative attributes and Dynamic Rule Mining (DRM) for association rule computation. The performance of these algorithms is experimented with real-life data sets and results are analyzed.
Reference-dependent Qualitative Models for Decision Making under Uncertainty
Authors: Patrice Perny, Antoine Rolland
Venue: Proceeding of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy (2006)

The aim of this paper is to introduce and investigate a new family of purely qualitative models for decision making under uncertainty. Such models do not require any numerical representation and rely only on the definition of a preference relation over consequences and a relative likelihood relation on the set of events. Within this family, we focus on decision rules using reference levels in the comparison of acts. We investigate both the descriptive potential of such rules and their axiomatic foundations. We introduce in a Savage-like framework, a new axiom requiring that the Decision Maker's preference between two acts depends on the respective positions of their consequences relatively to reference levels. Under this assumption we determine the only possible form of the decision rule and characterize some particular instances of this rule under transitivity constraints.
Using immersive game-based virtual reality to teach fire-safety skills to children
Authors: Shana Smith, Emily Ericson
Venue: Virtual Reality (2009)

Virtual reality (VR) has been used both to simulate situations that are too dangerous to practice in real life and as a tool to help children learn. This study was conducted as part of a larger more comprehensive long-term research project which aims to combine the two techniques and demonstrate a novel application of the result, using immersive VR to help children learn about fire hazards and practice escape techniques. In the current study, a CAVE was used to immerse participants in a fire scene. To improve the children&#x2019;s motivation for learning over prior VR fire-safety training methods, game-like interface interaction techniques were used and students were encouraged to explore the virtual world. Rather than being passive viewers, as in prior related studies, the children were given full control to navigate through the virtual environment and to interact with virtual objects using a game pad and a 6DOF wand. Students identified home fire hazards with a partner and then practiced escaping from a simulated fire in the virtual environment. To test for improved motivation, a user study was completed. Results indicate that students were more engaged by the new game-like learning environment and that they reported that they found the experience fun and intriguing. Their enhanced enthusiasm for what is relatively standard fire-safety information demonstrates the promise of using game-based virtual environments for vital but otherwise tedious fire-safety skills training for children.
Building Learning Designs by Using an Automatic Planning Domain Generation: A State-Based Approach
Authors: Lluvia Morales, Luis Castillo, Juan Fernandez-Olivares, Arturo Gonzalez-Ferrer
Venue: Proceeding of the 2008 conference on STAIRS 2008: Proceedings of the Fourth Starting AI Researchers' Symposium (2008)

A Learning Design definition under the IMS-LD standard is a complex task for the instructor because it requires a lot of time, effort and previous knowledge of the students group over which will be defined the knowledge objectives. That is why, taking advantage from diffusion of learning objects labeling using IMS-MD standard in distance learning field, we have proposed to realize a knowledge engineering process, represented as an algorithm, with learning objects labels and user models to automaticaly define a domain that will be used by an intelligent planner to build a learning design. This learning design will be finally implemented in the ILIAS Learning Management System.
Secure kNN computation on encrypted databases
Authors: Wai Kit Wong, David Wai-lok Cheung, Ben Kao, Nikos Mamoulis
Venue: Proceedings of the 35th SIGMOD international conference on Management of data (2009)

Service providers like Google and Amazon are moving into the SaaS (Software as a Service) business. They turn their huge infrastructure into a cloud-computing environment and aggressively recruit businesses to run applications on their platforms. To enforce security and privacy on such a service model, we need to protect the data running on the platform. Unfortunately, traditional encryption methods that aim at providing "unbreakable" protection are often not adequate because they do not support the execution of applications such as database queries on the encrypted data. In this paper we discuss the general problem of secure computation on an encrypted database and propose a SCONEDB Secure Computation ON an Encrypted DataBase) model, which captures the execution and security requirements. As a case study, we focus on the problem of k-nearest neighbor (kNN) computation on an encrypted database. We develop a new asymmetric scalar-product-preserving encryption (ASPE) that preserves a special type of scalar product. We use APSE to construct two secure schemes that support kNN computation on encrypted data; each of these schemes is shown to resist practical attacks of a different background knowledge level, at a different overhead cost. Extensive performance studies are carried out to evaluate the overhead and the efficiency of the schemes.
Towards context-aware gesture enabled user interfaces
Authors: Ayman El-Sawah
Venue:  (2008)

Conventional graphical user interface techniques appear to be ill-suited for the kinds of interactive platforms that are required for future generations of computing devices. 3D graphics and immersive virtual reality applications require interactive 3D object manipulation and navigation. Perceptual user interfaces using speech and gestures are in high demand to provide a more natural human-computer interaction modality. The major challenge facing Perceptual user interfaces is the lack of a standard application programming interfaces capable of handling ambiguity and providing the means to include domain-specific knowledge about the context in which the user interface is used. In this dissertation, we study dynamic hand gestures, which are defined as a sequence of hand postures. We emphasize the generality of our dynamic gesture model, which is capable of recognizing essentially any dynamic hand gesture confined in a sequence of postures. Hand postures are static poses and are defined by an array of posture attributes. We use a generic definition hand postures capable of covering the space of hand postures at different levels of granularity and abstraction; and we timely monitor the posture variation as it unfolds within the dynamic gesture. We also study the role of context in gesture interpretation without making assumptions about a specific application. We view the hand-tracking and gesture-recognition subsystems as integral parts of a larger distributed and multi-user multi-service application, where gesture interpretation plays the role of resolving ambiguity of the recognized gesture. We identify the relevant aspects to hand gesture interpretation and we propose agent-based system architecture for gesture interpretation. We finally propose a framework for gesture-enabled system design, where context is placed in a middleware layer that interfaces with all sub modules in the system and plays a dialectic role and keeping the overall system stable.
Modeling and simulation for a 3D robot controlled by a pick and place application
Authors: Corbu Corina, Emil Pop, Monica Leba
Venue: Proceedings of the 10th WSEAS international conference on Automation & information (2009)

Our research begins from the possibilities to control a 3D robot. The paper presents the modeling and simulation module, component of the architecture of the control system for a 3D robot. Through direct kinematical analysis we have studied different methods of objective description, as well as the transformations necessary in order to carry out the task. The paper also presents the implementation of mathematical modeling and simulation to the 3D educational robot.
Signal processing: key element in designing an accurate machining forces measuring device
Authors: Mihaiela Iliescu, Magdalena Rosu, Paulina Spânu
Venue: Proceedings of the 8th WSEAS international conference on Signal processing, robotics and automation (2009)

When manufacturing parts, specially by machining process, it is often necessary to have reliable information on machining forces involved. So, systems and devices for measuring these forces have to be used, this paper presenting an innovative one, which, has also been patented. One of the most important aspects in designing such a device is represented by good signal processing, so that, for instance, specific calibrating equations to be appropriate determined. Checking obtained results into real machining conditions represents a final challenge.
An Effective Dimension Reduction Approach to Chinese Document Classification Using Genetic Algorithm
Authors: Zhishan Guo, Li Lu, Shijia Xi, Fuchun Sun
Venue: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II (2009)

Different kinds of methods have been proposed in Chinese document classification, while high dimension of feature vector is one of the most significant limits in these methods. In this paper, an important difference is pointed out between Chinese document classification and English document classification. Then an efficient approach is proposed to reduce the dimension of feature vector in Chinese document classification using Genetic Algorithm. Through merely choosing the set of much more "important" features, the proposed method significantly reduces the number of Chinese feature words. Experiments combining with several relative studies show that the proposed method has great effect on dimension reduction with little loss in correctly classified rate.
Real-time facial expression recognition using STAAM and layered GDA classifier
Authors: Jaewon Sung, Daijin Kim
Venue: Image and Vision Computing (2009)

This paper proposes a real-time person independent facial expression recognition in two parts: one is a model fitting part using a proposed stereo active appearance model (STAAM) and another is a person independent facial expression recognition using a layered generalized discriminant analysis (GDA) classifier. The STAAM fitting algorithm uses multiple calibrated perspective cameras to compute the 3D shape and rigid motion parameters. The use of calibration information reduces the number of model parameters, restricts the degree of freedom in the model parameters, and increases the accuracy and speed of fitting. The STAAM uses a modified simultaneous update fitting method that reduces the fitting computation greatly. Also, the layered GDA classifier combines 3D shape and 2D appearance to improve the recognition performance of person independent facial expressions. Experimental results show that (1) the STAAM shows a better fitting stability than the existing multiple-view AAM (MVAAM), (2) the modified simultaneous update algorithm accelerates the AAM fitting speed, and (3) the combination of the 3D shape and 2D appearance features using a layered GDA classifier improves the performance of facial expression recognition greatly.
Interfaces of high-k dielectrics on GaAs: Their common features and the relationship with Fermi level pinning (Invited Paper)
Authors: Matty Caymax, Guy Brammertz, Annelies Delabie, Sonja Sioncke, Dennis Lin, Marco Scarrozza, Geoffrey Pourtois, Wei-E Wang, Marc Meuris, Marc Heyns
Venue: Microelectronic Engineering (2009)

Numerous metal oxides have been studied worldwide as possible high-k gate dielectric candidates for MOS devices on alternative semiconductor materials (Ge, III/V compounds). We will discuss thermal and plasma-enhanced atomic layer deposition (ALD) of a few materials, HfO"2 and Al"2O"3. We will spend some attention to characteristic features of the growth process and specific growth precursors as this is known to influence strongly the quality of the layer bulk as well as the interface. Detailed electrical characterization of MOS capacitors build on such dielectric layers, before and after forming gas anneals, shows that these interface modifications can lead to a marked decrease of the smaller interface state peaks close to the edges of the bandgap, whereas the larger mid-gap peaks are barely touched. The results of atomistic modeling of the oxidation of a GaAs surface help to understand the origin of these mid-gap electronic states, which are responsible for the apparent pinning of the Fermi level.
Adobe Photoshop Elements 6 and Adobe Premiere Elements 4 Classroom in a Book Collection, 1st edition
Authors: Adobe Press
Venue:  (2007)

This collection combines Adobe Photoshop Elements 4.0 Classroom in a Book and Adobe Premiere Elements 6.0 Classroom in a Book into one affordable volume. Each chapter in these step-by-step, project-based guides contains a project that builds on the reader's growing knowledge of the program, while end-of-chapter review questions reinforce each lesson. Readers will learn how to use the programs' many new features, including Photomerge, Smart Albums, step-by-step editing assistance, and amazing photo-blending technology in Photoshop Elements 6. Premiere Elements 4 now includes the same Organizer that's included with Photoshop Elements, making it easy to visually tag video clips and photos to categorize by people, places, or events. Premiere Elements 4 also includes new Movie Themes, a new Sharing Center, world-class Adobe fonts for video titling, the ability to upload videos directly to YouTube, and more.Note from the publisher:Regrettably, we have discovered an issue on the disc for this product. 1) Lesson 2 has corrupt video clip. The elephant.avi is damaged midway through. There are good copies of this file in lessons 3, 8, and 10. Copy and replace the bad file with one of these.To request a replacement disc please contact ask@peachpit.com
Salience-driven Contextual Priming of Speech Recognition for Human-Robot Interaction
Authors: Pierre Lison, Geert-Jan Kruijff
Venue: Proceeding of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence (2008)

The paper presents an implemented model for priming speech recognition, using contextual information about salient entities. The underlying hypothesis is that, in human-robot interaction, speech recognition performance can be improved by exploiting knowledge about the immediate physical situation and the dialogue history. To this end, visual salience (objects perceived in the physical scene) and linguistic salience (objects, events already mentioned in the dialogue) are integrated into a single cross-modal salience model. The model is dynamically updated as the environment changes. It is used to establish expectations about which words are most likely to be heard in the given context. The update is realised by continuously adapting the word-class probabilities specified in a statistical language model. The paper discusses the motivations behind the approach, and presents the implementation as part of a cognitive architecture for mobile robots. Evaluation results on a test suite show a statistically significant improvement of salience-driven priming speech recognition (WER) over a commercial baseline system.
Polynomial chaos for multirate partial differential algebraic equations with random parameters
Authors: Roland Pulch
Venue: Applied Numerical Mathematics (2009)

In radio frequency applications, a multivariate model yields an efficient representation of signals with amplitude modulation and/or frequency modulation. Periodic boundary value problems of multirate partial differential algebraic equations (MPDAEs) have to be solved to reproduce the quasiperiodic signals. Typically, technical parameters appear in the system, which may exhibit some uncertainty. Substitution by random variables results in a corresponding stochastic model. We apply the technique of the generalised polynomial chaos to obtain according solutions. A Galerkin approach yields larger coupled systems of MPDAEs. We analyse the properties of the coupled systems with respect to the original formulations. Thereby, we focus on the case of frequency modulation, since the case of amplitude modulation alone is straightforward.
High dynamic range&mdash;a gateway for predictive ancient lighting
Authors: Alexandrino Gonçalves, Luís Magalhães, João Moura, Alan Chalmers
Venue: Journal on Computing and Cultural Heritage (JOCCH) (2009)

In the last few years, the number of projects involving historical reconstruction has increased significantly. Recent technologies have proven a powerful tool for a better understanding of our cultural heritage through which to attain a glimpse of the environments in which our ancestors lived. However, to accomplish such a purpose, these reconstructions should be presented to us as they may really have been perceived by a local inhabitant, according to the illumination and materials used back then and, equally important, the characteristics of the human visual system. The human visual system has a remarkable ability to adjust itself to almost all everyday scenarios. This is particularly evident in extreme lighting conditions, such as bright light or dark environments. However, a major portion of the visible spectra captured by our visual system cannot be represented in most display devices. High dynamic range imagery is a field of research which is developing techniques to correct such inaccuracies. This new viewing paradigm is perfectly suited for archaeological interpretation, since its high contrast and chromaticity can present us with an enhanced viewing experience, closer to what an inhabitant of that era may have seen. In this article we present a case study of the reconstruction of a Roman site. We generate high dynamic range images of mosaics and frescoes from one of the most impressive monuments in the ruins of Conimbriga, Portugal, an ancient city of the Roman Empire. To achieve the requisite level of precision, in addition to having a precise geometric 3D model, it is crucial to integrate in the virtual simulation authentic physical data of the light used in the period under consideration. Therefore, in order to create a realistic physical-based environment, we use in our lighting simulations real data obtained from simulated Roman luminaries of that time.
Investigating the Gap between Quantitative and Qualitative/Semi-quantitative Software Process Simulation Models: An Explorative Study
Authors: He Zhang
Venue: Proceedings of the International Conference on Software Process: Trustworthy Software Development Processes (2009)

Software Process Simulation Modeling (SPSM) research has increased in the past two decades. However, most of process models for simulation are quantitative, which require detailed understanding and accurate measurement. As the follow-up work to the previous studies in qualitative/semi-quantitative modeling of software process, this paper aims to investigate the equivalence and gap between quantitative and qualitative/semi-quantitative process modeling, to compare the characteristics and performance of these approaches by modeling and simulating a software evolution process, and further to establish the substantial linkage between them. Following the enhanced model conversion scheme developed in this paper, the reference quantitative continuous model and its counterpart models become comparable. The results present their different capabilities and interesting perspectives. 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
The Nonlinear Tensor Diffusion in Segmentation of Meaningful Biological Structures from Image Sequences of Zebrafish Embryogenesis
Authors: Olga Drblíková, Karol Mikula, Nadine Peyriéras
Venue: Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision (2009)

In this contribution we develop a strategy for segmentation of evolving biological structures in image sequences. Our approach is based on combination of nonlinear tensor diffusion image smoothing and subjective surface based image segmentation. Since the fine cell structure would restrain the evolving segmentation function to achieve a shape of meaningful biological structures, we have to smooth properly the images in the sequence. To that goal we apply the nonlinear tensor diffusion which enhances the connectivity of bordering structure lines and smoothes their inner parts. For the numerical implementations we use semi-implicit diamond-cell finite volume methods both for filtering and segmentation. We show application of the method in image segmentation of early stages of zebrafish embryogenesis.