ppxscal/embedding_network huggingface dataset:
T-MAESTRO and its authoring tool: using adaptation to integrate entertainment into personalized t-learning
Authors: Marta Rey-López, Rebeca P. Díaz-Redondo, Ana Fernández-Vilas, José J. Pazos-Arias, Martín López-Nores, Jorge García-Duque, Alberto Gil-Solla, Manuel Ramos-Cabrer
Venue: Multimedia Tools and Applications (2008)

Interactive Digital TV opens new learning possibilities where new forms of education are needed. On the one hand, the combination of education and entertainment is essential to boost the participation of viewers in TV learning (t-learning), overcoming their typical passiveness. On the other hand, researchers broadly agree that in order to prevent the learner from abandoning the learning experience, it is necessary to take into account his/her particular needs and preferences by means of a personalized experience. Bearing this in mind, this paper introduces a new approach to the conception of personalized t-learning: edutainment and entercation experiences. These experiences combine TV programs and learning contents in a personalized way, with the aim of using the playful nature of TV to make learning more attractive and to engage TV viewers in learning. This paper brings together our work in constructing edutainment/entercation experiences by relating TV and learning contents. Taking personalization one step further, we propose the adaptation of learning contents by defining A-SCORM (Adaptive-SCORM), an extension of the ADL SCORM standard. Over and above the adaptive add-ons, this paper focuses on two fundamental entities for the proposal: (1) an Intelligent Tutoring System, called T-MAESTRO, which constructs the t-learning experiences by applying semantic knowledge about the t-learners; and (2) the authoring tool which allow teachers to create adaptive courses with a minimal technical background.
Computation of reference Bayesian inference for variance components in longitudinal studies
Authors: Miao-Yu Tsai, Chuhsing K. Hsiao
Venue: Computational Statistics (2008)

Generalized linear mixed models (GLMMs) have been applied widely in the analysis of longitudinal data. This model confers two important advantages, namely, the flexibility to include random effects and the ability to make inference about complex covariances. In practice, however, the inference of variance components can be a difficult task due to the complexity of the model itself and the dimensionality of the covariance matrix of random effects. Here we first discuss for GLMMs the relation between Bayesian posterior estimates and penalized quasi-likelihood (PQL) estimates, based on the generalization of Harville's result for general linear models. Next, we perform fully Bayesian analyses for the random covariance matrix using three different reference priors, two with Jeffreys' priors derived from approximate likelihoods and one with the approximate uniform shrinkage prior. Computations are carried out via the combination of asymptotic approximations and Markov chain Monte Carlo methods. Under the criterion of the squared Euclidean norm, we compare the performances of Bayesian estimates of variance components with that of PQL estimates when the responses are non-normal, and with that of the restricted maximum likelihood (REML) estimates when data are assumed normal. Three applications and simulations of binary, normal, and count responses with multiple random effects and of small sample sizes are illustrated. The analyses examine the differences in estimation performance when the covariance structure is complex, and demonstrate the equivalence between PQL and the posterior modes when the former can be derived. The results also show that the Bayesian approach, particularly under the approximate Jeffreys' priors, outperforms other procedures.
Optimization of checkpointing-related I/O for high-performance parallel and distributed computing
Authors: Rajagopal Subramaniyan, Eric Grobelny, Scott Studham, Alan D. George
Venue: The Journal of Supercomputing (2008)

Checkpointing, the process of saving program/application state, usually to a stable storage, has been the most common fault-tolerance methodology for high-performance applications. The rate of checkpointing (how often) is primarily driven by the failure rate of the system. If the checkpointing rate is low, fewer resources are consumed but the chance of high computational loss is increased and vice versa if the checkpointing rate is high. It is important to strike a balance, and an optimum rate of checkpointing is required. In this paper, we analytically model the process of checkpointing in terms of mean-time-between-failure of the system, amount of memory being checkpointed, sustainable I/O bandwidth to the stable storage, and frequency of checkpointing. We identify the optimum frequency of checkpointing to be used on systems with given specifications thereby making way for efficient use of available resources and maximum performance of the system without compromising on the fault-tolerance aspects. Further, we develop discrete-event models simulating the checkpointing process to verify the analytical model for optimum checkpointing. Using the analytical model, we also investigate the optimum rate of checkpointing for systems of varying resource levels ranging from small embedded cluster systems to large supercomputers.
The on-line cross entropy method for unsupervised data exploration
Authors: Ying Wu, Colin Fyfe
Venue: WSEAS Transactions on Mathematics (2007)

We investigate the use of the new Cross Entropy method as a tool for exploratory data analysis. We show how this method can be used to perform linear projections such as principal component analysis, exploratory projection pursuit and canonical correlation analysis. We further go on to show how topology preserving mappings can be created usin the cross entropy method. We also show how the cross entropy method can be used to train deep architecture nets which are one of the main current research directions for creating true artificial intelligence. Finally we show how the cross entropy method can be used to optimize parameters for latent variable models.
Learn Access 2003 VBA With The Smart Method, 1st edition
Authors: Mike Smart
Venue:  (2007)

This unique book provides the easiest possible route to mastering professional VBA programming techniques for Microsoft Access. Both absolute beginners and IT professionals will love this book as it is crammed with great tips and insights into building quality data centric application . No previous exposure to VBA is assumed but, to get the most out of the book, you should already be quite competent with interactive Access. The format of the book is quite special, every lesson has been designed to fit onto two facing A4 pages so that you learn Access VBA in many small, easily digested, focused lessons. The book was originally only available for students taking The Smart Method's popular Access courses. Now it is available to all, both as a self-study guide and also for teachers wishing to use our unique teaching method to run their own Access VBA courses. The emphasis throughout the book is on solid, professional coding practices and techniques. Students are gently introduced to a set of design rules which summarise best-practice for developing Access applications. Needless technical jargon is avoided and everything you need is explained in a simple and no-nonsense way. Instead of simple code snippets the book takes on the task of developing a high-quality Access application entirely from scratch. By the end of the course the student has a working and complete application that can be used as the basis for real-world commercial projects.
Automatic request categorization in internet services
Authors: Abhishek B. Sharma, Ranjita Bhagwan, Monojit Choudhury, Leana Golubchik, Ramesh Govindan, Geoffrey M. Voelker
Venue: ACM SIGMETRICS Performance Evaluation Review (2008)

Modeling system performance and workload characteristics has become essential for efficiently provisioning Internet services and for accurately predicting future resource requirements on anticipated workloads. The accuracy of these models benefits substantially by differentiating among categories of requests based on their resource usage characteristics. However, categorizing requests and their resource demands often requires significantly more monitoring infrastructure. In this paper, we describe a method to automatically differentiate and categorize requests without requiring sophisticated monitoring techniques. Using machine learning, our method requires only aggregate measures such as total number of requests and the total CPU and network demands, and does not assume prior knowledge of request categories or their individual resource demands. We explore the feasibility of our method on the .Net PetShop 4.0 benchmark application, and show that it works well while being lightweight, generic, and easily deployable.
Monocular 3D tracking of articulated human motion in silhouette and pose manifolds
Authors: Feng Guo, Gang Qian
Venue: Journal on Image and Video Processing (2008)

This paper presents a robust computational framework for monocular 3D tracking of human movement. The main innovation of the proposed framework is to explore the underlying data structures of the body silhouette and pose spaces by constructing low-dimensional silhouettes and poses manifolds, establishing intermanifold mappings, and performing tracking in such manifolds using a particle filter. In addition, a novel vectorized silhouette descriptor is introduced to achieve low-dimensional, noise-resilient silhouette representation. The proposed articulated motion tracker is view-independent, self-initializing, and capable of maintaining multiple kinematic trajectories. By using the learned mapping from the silhouette manifold to the pose manifold, particle sampling is informed by the current image observation, resulting in improved sample efficiency. Decent tracking results have been obtained using synthetic and real videos.
Estimation of interchannel time difference in frequency subbands based on nonuniform discrete Fourier transform
Authors: Bo Qiu, Yadong Lu, Jun Yang
Venue: EURASIP Journal on Audio, Speech, and Music Processing (2008)

Binaural cue coding (BCC) is an efficient technique for spatial audio rendering by using the side information such as interchannel level difference (ICLD), interchannel time difference (ICTD), and interchannel correlation (ICC). Of the side information, the ICTD plays an important role to the auditory spatial image. However, inaccurate estimation of the ICTD may lead to the audio quality degradation. In this paper, we develop a novel ICTD estimation algorithm based on the nonuniform discrete Fourier transform (NDFT) and integrate it with the BCC approach to improve the decoded auditory image. Furthermore, a new subjective assessment method is proposed for the evaluation of auditory image widths of decoded signals. The test results demonstrate that the NDFT-based scheme can achieve much wider and more externalized auditory image than the existing BCC scheme based on the discrete Fourier transform (DFT). It is found that the present technique, regardless of the image width, does not deteriorate the sound quality at the decoder compared to the traditional scheme without ICTD estimation.
Ada and software engineering education: one professor's experiences
Authors: John W. McCormick
Venue: Proceedings of the 2008 ACM annual international conference on SIGAda annual international conference (2008)

How do you select a programming language for your project? Few developers have the luxury of coding the same design in multiple languages to compare language merits. For over twenty years my undergraduate students have implemented the same large (10-15K lines), multi-tasking, real-time embedded system. In one 15 week semester, student teams specify, design, and implement software to control a substantial model railroad layout. Students implement everything from device drivers for custom I/O hardware to high-level decision making algorithms. Student teams have implemented the project in both Ada and C. This paper describes the course, the laboratory, the project, and an analysis of the results achieved with each of the implementation languages.
Fast over-land atmospheric correction of visible and near-infrared satellite images
Authors: A. S. L. Nunes, A. R. S. Marcal, R. A. Vaughan
Venue: International Journal of Remote Sensing (2008)

A rapid atmospheric correction method is proposed to be used for visible and near-infrared satellite sensor images over land. The method is based on a simplified use of a radiative transfer code (RTC), which is used only a priori, to generate Look-Up-Tables (LUTs) of the estimated surface reflectance. A typical scenario and ranges of values for the main atmospheric correction parameters are initially established. Each image pixel is treated as a slight deviation from the reference scenario defined by the vector of the typical values for the parameters. The assumption of the parameter's independence allows the use of one-dimensional LUTs. The method is suitable for near real-time processing or whenever a large number of data are to be handled rapidly. The operator intervention is minimal, and the computation time involved in the correction of a whole image is about 1000 times shorter than the full use of the base RTC. A test is performed with advanced very-high-resolution radiometer (AVHRR) visible and near-infrared data, using the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) RTC as the base code. The accuracy of the proposed method was compared with the standard use of the 6S RTC over the same dataset with resulting root mean square errors of 0.0114 and 0.0104 for AVHRR bands 1 and 2 for the estimated surface reflectance, respectively.
The existence of countably many positive solutions for nonlinear singular m-point boundary value problems on the half-line
Authors: Sihua Liang, Jihui Zhang
Venue: Journal of Computational and Applied Mathematics (2008)

In this paper, by introducing a new operator, improving and generating a p-Laplace operator for some p>1, we study the existence of countably many positive solutions for nonlinear boundary value problems on the half-line (@f(u^'))^'+a(t)f(u(t))=0,0R is the increasing homeomorphism and positive homomorphism and @f(0)=0. We show the sufficient conditions for the existence of countably many positive solutions by using the fixed-point index theory and a new fixed-point theorem in cones.
I-SSA: Interaction-Situated Semantic Alignment
Authors: Manuel Atencia, Marco Schorlemmer
Venue: Proceedings of the OTM 2008 Confederated International Conferences, CoopIS, DOA, GADA, IS, and ODBASE 2008. Part I on On the Move to Meaningful Internet Systems: (2008)

We tackle semantic heterogeneity in multi-agent communication by looking at semantics related to interaction in order to avoid dependency on a priori semantic agreements. Our underlying claim is that semantic alignment is often relative to the particular interaction in which agents are engaged, and that in such cases the interaction state should be taken into account and brought into the alignment mechanism. We provide a formal foundation along with an alignment mechanism and protocol based on it.
Taming verification hardness: an efficient algorithm for testing subgraph isomorphism
Authors: Haichuan Shang, Ying Zhang, Xuemin Lin, Jeffrey Xu Yu
Venue: Proceedings of the VLDB Endowment (2008)

Graphs are widely used to model complicated data semantics in many applications. In this paper, we aim to develop efficient techniques to retrieve graphs, containing a given query graph, from a large set of graphs. Considering the problem of testing subgraph isomorphism is generally NP-hard, most of the existing techniques are based on the framework of filtering-and-verification to reduce the precise computation costs; consequently various novel feature-based indexes have been developed. While the existing techniques work well for small query graphs, the verification phase becomes a bottleneck when the query graph size increases. Motivated by this, in the paper we firstly propose a novel and efficient algorithm for testing subgraph isomorphism, QuickSI. Secondly, we develop a new feature-based index technique to accommodate QuickSI in the filtering phase. Our extensive experiments on real and synthetic data demonstrate the efficiency and scalability of the proposed techniques, which significantly improve the existing techniques.
A proxy caching system for MPEG-4 video streaming with a quality adaptation mechanism
Authors: Yoshiaki Taniguchi, Naoki Wakamiya, Masayuki Murata
Venue: WSEAS TRANSACTIONS on COMMUNICATIONS (2007)

In this paper, we propose, design, implement, and evaluate a proxy caching system for MPEG- 4 video streaming services for heterogeneous users. In our system, a video stream is divided into blocks for efficient use of the cache buffer and the bandwidth. A proxy retrieves a block from a server, deposits it in its local cache buffer, and provides a requesting client with the block in time. It maintains the cache with limited capacity by replacing unnecessary cached blocks with a newly retrieved one. It also prefetches video blocks that are expected to be required in the near future. In addition, the proxy server adjusts the quality of block appropriately, because clients are heterogeneous, in terms of the available bandwidth, end-system performance, and so on. Through evaluations, we proved that our proxy caching system could provide users with a continuous and high-quality video streaming service in a heterogeneous environment.
VCNI - Applying value theory to contextual needs identification in proactive elderly care
Authors: Soe-Tsyr Yuan, I-Chien Lin
Venue: Expert Systems with Applications: An International Journal (2009)

This paper presents a novel needs-identification system (named VCNI) that is a context-aware and value-perspective for proactive elderly care (that considers certain quality elements as consumer participation, caregiver involvement, and continuous quality improvement). We apply the value theories and principles and devise a novel method (conflict resolution algorithm) aiming for allocating an agent team with the least value conflict fashion so as to discover the contextualized mission that could fulfill the needs of the elderly. With the context information reported by pervasive components, this system can proactively provide the directions of the individualized assistance required for the elderly in maintaining an independent lifestyle and reduce possible health-care costs. To show the feasibility of the methodology proposed in the paper, a prototype system has been implemented for justifying the capability of the system's needs identification from the macro and micro perspectives. Moreover, our system also exemplifies a future way of service provision on electronic care.
Reexamining the impact of information technology investment on productivity using regression tree and multivariate adaptive regression splines (MARS)
Authors: Myung Ko, Kweku-Muata Osei-Bryson
Venue: Information Technology and Management (2008)

As health care costs increased significantly in the 1990s, investments in information technology (IT) in the health care industry have also increased continuously in order to improve the quality of patient care and to respond to government pressure to reduce costs. Several studies have investigated the impact of IT on productivity with mixed conclusions. In this paper, we revisit this issue and re-examine the impact of investments in IT on hospital productivity using two data mining techniques, which allowed us to explore interactions between the input variables as well as conditional impacts. The results of our study indicated that the relationship between IT investment and productivity is very complex. We found that the impact of IT investment is not uniform and the rate of IT impact varies contingent on the amounts invested in the IT Stock, Non-IT Labor, Non-IT Capital, and possibly time.
Remote sensing of ambient particles in Delhi and its environs: estimation and validation
Authors: N. Kumar, A. Chu, A. Foster
Venue: International Journal of Remote Sensing (2008)

Recent advances in atmospheric remote sensing offer a unique opportunity to compute indirect estimates of air quality, particularly for developing countries that lack adequate spatial-temporal coverage of air pollution monitoring. The present research establishes an empirical relationship between satellite-based aerosol optical depth (AOD) and ambient particulate matter (PM) in Delhi and its environs. The PM data come from two different sources. Firstly, a field campaign was conducted to monitor airborne particles≤2.5 µm and≤10 µm in aerodynamic diameter (PM2.5 and PM10 respectively) at 113 spatially dispersed sites from July to December 2003 using photometric samplers. Secondly, data on eight hourly PM10 and total suspended particulate (TSP) matter, collected using gravimetric samplers, from 2000 to 2005 were acquired from the Central Pollution Control Board (CPCB). The aerosol optical depths were estimated from MODIS data, acquired from NASA's Goddard Space Flight Center Earth Sciences Distributed Active Archive Center from 2000 to 2005. Both the PM and AOD data were collocated by time and space: PM mass±150 min of AOD time, and ±2.5 and 5 km radius (separately) of the centroid of the AOD pixel for the 5 and 10 km AOD, respectively. The analysis here shows that PM correlates positively with the 5 km AOD; a 1% change in the AOD explains 0.52%±0.20% and 0.39%±0.15% changes in PM2.5 within 45 and 150 min intervals (of AOD data) respectively. At a coarser spatial resolution, however, the relationship between AOD and PM is relatively weak. But, the relationship turns significantly stronger when monthly estimates are analysed over a span of six years (2000 to 2005), especially for the winter months, which have relatively stable meteorological conditions.