ppxscal/embedding_network huggingface dataset:
SEGMENTATION OF MULTIPLE HUMAN OBJECTS IN VIDEO SEQUENCES
Authors: Hsin-Tai Yang, Chen-Sen Ouyang, Li-Ming Huang, Shie-Jue Lee
Venue: Applied Artificial Intelligence (2007)

This article presents a method for segmentation of multiple human objects in a video stream. Given the base frame of a video stream, a fuzzy self-clustering technique is used to group similar pixels of the frame into a set of segments. Combinations of segments are checked if candidate face regions can be formed using chrominance values and the face shape feature. The existence of a face within each candidate face region is investigated by searching for possible constellations of eyes-mouth triangles and verifying each eyes-mouth combination based on the predefined template and a set of criteria. By referring to the position and orientation of a face, the corresponding body is found. Then rough foreground and background are formed. Finally, human objects in the base frame and the remaining frames of the video stream are located by a fuzzy neural network, which is trained by a SVD-based hybrid learning algorithm. From experimental results, we have found that our system can locate human objects more accurately and efficiently than other systems.
Learning multiple linear manifolds with self-organizing networks
Authors: Huicheng Zheng, Pádraig Cunningham, Alexey Tsymbal
Venue: International Journal of Parallel, Emergent and Distributed Systems (2007)

Learning multiple linear manifolds permits one to deal with multiple variations incurred in target objects. The adaptive-subspace self-organizing map (ASSOM) proposed by Kohonen can learn a set of ordered subspaces, i.e. linear manifolds passing through the origin, but not those shifted away from the origin. The linear manifold self-organizing map (LMSOM) proposed in this paper considers offsets of linear manifolds from the origin and aims to learn linear manifolds by minimizing a projection error function in a gradient-descent fashion. At each learning step, the winning module and its neighbours update basis vectors as well as offset vectors of their manifolds towards the negative gradient of the error function. Experiments show that the LMSOM can learn clusters aligned on linear manifolds shifted away from the origin and separate them accordingly. The LMSOM is applied to handwritten digit recognition and shows promising results.
SMS based natural language interface to yellow pages directory
Authors: Sunil Kumar Kopparapu, Akhilesh Srivastava, Arun Pande
Venue: Proceedings of the 4th international conference on mobile technology, applications, and systems and the 1st international symposium on Computer human interaction in mobile technology (2007)

Yellow Pages are directories that source information about various commercial organizations like their addresses, phone contact and other details. These are very useful and are used by individual and other business houses. Until recently, the only way to access these yellow pages directory information was to physically look into a huge hard-copy directory, which was not only laborious but also time consuming and required the user to be familiar with the organization of the directory. More recently, there have been IVR based contact centers that have been set up which can be used by the users to query information. While it is easier than browsing through the physical directory, it still has several pitfalls. The time spent on trying to get the information is quite long and at the end of enquiry one is not sure if one will get the information that one is looking for. In this paper, we propose a novel interface which enables accessing the yellow pages directory information on the mobile phone by sending a short message service (SMS). The central idea of the proposed method is to avoid any constraint on the way the user can query the yellow pages directory except that it be in natural English. The system, which uses natural language processing (NLP) techniques, understands the intent of the query and intelligently searches the yellow pages directory to retrieve information. This retrieved information is then sent back to the user in the form of a SMS.
Using eclipse in the classroom
Authors: Dwight Deugo
Venue: ACM SIGCSE Bulletin (2008)

In this Tips & Techniques session, I will describe what to teach students about Eclipse, while remaining in the contexts of existing courses, such as CS1/CS2, in higher-level courses such as Quality Assurance, and in project courses.
Proceedings of the 2008 international workshop on Comparison and versioning of software models
Venue: International Conference on Software Engineering (2008)

Modern software development approaches, especially those which follow a model-driven way of development, make heavy use of models during the whole development process. In this context models are being evolved, simplified and/or extended, over a longer period of time. This kind of model evolution leads to the need of keeping different versions of models, of comparing them, of measuring their (un)similarity, and of merging different models into one. Conventional techniques for document comparison, document retrieval, and document reuse, which have been developed in the context of version and configuration management systems, are primarily aimed at textual documents. They make use of a large body of knowledge developed over the years in the context of version management systems. They are not directly applicable to models, especially if they come as visual documents like UML diagrams. For models written in visual languages the foundations, algorithms, and tools are not so elaborated yet. The aim of the ICSE 2008 workshop on Comparison and Versioning of Software Models (CVSM08) is to establish the state of the art in the area of comparing and versioning of models. It follows similar events at the German Software Engineering Conference (SE 2007) in March 2007 in Hamburg, Germany (http://pi.informatik.uni-siegen.de/gi/fg211/VVUM07/), and at the Nordic Workshop on Model Driven Software Engineering (NW-MoDE'07) in June 2007 in Ronneby, Sweden (http://www.ituniv.se/~miroslaw/node.htm). 14 contributions were submitted in response to the call. Each paper was assessed by at least three members of the program committee who agreed to accept 8 papers for this workshop. These papers hopefully will raise fruitful and inspiring discussions during the meeting. They will be presented in three sessions which are planned to provide enough time for extensive discussions. The final session will be dedicated to the discussion of a road map for future research in this area.
A Simple Proof of Miller-Yu Theorem
Authors: Laurent Bienvenu, Wolfgang Merkle, Alexander Shen
Venue: Fundamenta Informaticae (2008)

A few years ago a nice criterion of Martin-L&ouml;f randomness in terms of plain (neither prefix nor monotone) Kolmogorov complexity was found (among many other results, it is published in [5]). In fact Martin-L&ouml;f came rather close to the formulation of this criterion around 1970 (see [4] and [7], p. 98); a version of it that involves both plain and prefix complexity1 was proven by Gacs in 1980 ([2], remark after corollary 5.4 on p. 391). We provide a simple proof of this criterion that uses only elementary arguments very close to the original proof of Levin-Schnorr criterion of randomness (1973) in terms of monotone complexity ([3, 6]).
EasiTPQ: QoS-Based Topology Control in Wireless Sensor Network
Authors: Wei Liu, Li Cui, Xiaoguang Niu, Wei Liu
Venue: Journal of Signal Processing Systems (2008)

With the rapid development of wireless sensor networks (WSNs), the requirements for quality of service (QoS) are growing, particularly in applications where real time imaging, video or audio communications are involved. The system needs to meet both optimized energy consumption design metric and to satisfy certain QoS requirements simultaneously from long view. Different from most of the existing works which deal with resource allocation (e.g., scheduling or buffering) or routing strategy to achieve QoS, we extend QoS support to the topology control layer by introducing a number of active nodes distributed in a gradient fashion based on their logical distances to the sink node. In this paper, a novel topology control algorithm, namely EasiTPQ (Easy QoS-based topology control) is proposed to improve QoS performance in wireless sensor networks. Simulation results show that data loss rate and latency are averagely improved by 55 and 45%, respectively.
An extension on robust directed projection of points onto point clouds
Authors: Ming-Cui Du, Yu-Shen Liu
Venue: Computer-Aided Design (2008)

Azariadis and Sapidis [Azariadis PN, Sapidis NS. Drawing curves onto a cloud of points for point-based modelling. Computer-Aided Design 2005;37(1):109-22] introduced a novel method of point directed projection (DP) onto a point cloud along an associated projection vector. This method is essentially based on an idea of least sum of squares by making use of a weight function for bounding the influence of noise. One problem with their method is the lack of robustness for outliers. Here, we present a simple, robust, and efficient algorithm: robust directed projection (RDP) to guide the DP computation. Our algorithm is based on a robust statistical method for outlier detection: least median of squares (LMS). In order to effectively approximate the LMS optimization, the forward search technique is utilized. The algorithm presented here is better suited to detect outliers than the DP approach and thus finds better projection points onto the point cloud. One of the advantages of our algorithm is that it automatically ignores outliers during the directed projection phase.
Using neural networks for prediction of central venous pressure during open-heart surgery
Authors: Miljenko Križmaric, Peter Kokol, Mirt Kamenik, Dušanka Mičetic-Turk
Venue: Proceedings of the 4th WSEAS International Conference on Applied Informatics and Communications (2004)

In this paper, we describe a use of Neural Networks (NN) of formal neurons for predictions of Central Venous Pressure (CVP) during open-heart surgery. As input variables in the system of neural network we use primary non-invasive parameters and in the second simulation we added invasive physiological parameters of patient. CVP is a pressure in the right atrium of the heart, which is measured in very invasive way with catheter, which is placed directly into the heart. We assume that value of CVP can be predicted with only non-invasive parameters. At first we teach NN only with non-invasive input parameters captured from anesthesia machine, ECG monitor and pulse oximeter. In the second part of simulation we added invasive blood pressure (IBP) to non-invasive input parameters, which is measured in radial artery. Neural network has showed 75% correlation between central venous pressure (CVP) and non-invasive parameters. When we consider additional invasive parameter as neural network input, then success rate increase to 80%.
VEA-bility Security Metric: A Network Security Analysis Tool
Authors: Melanie Tupper, A. Nur Zincir-Heywood
Venue: Proceedings of the 2008 Third International Conference on Availability, Reliability and Security (2008)

In this work, we propose a novel quantitative security metric, VEA-bility, which measures the desirability of different network configurations. An administrator can then use the VEA-bility scores of different configurations to configure a secure network. Based on our findings, we conclude that the VEA-bility can be used to accurately estimate the comparative desirability of a specific network configuration. This information can then be used to explore alternate possible configurations and allows an administrator to select one among the given options. These tools are important to network administrators as they strive to provide secure, yet functional, network configurations.
Independent Component Analysis of Event-related Functional Near-infrared Spectroscopy (fNIRS)
Authors: Yun Jiao, Zhenyu Zhou, Hui Wang, Hongyu Yang, Zongcai Ruan, Hui Gong, Zuhong Lu
Venue: Proceedings of the 2008 International Conference on BioMedical Engineering and Informatics - Volume 02 (2008)

Functional near-infrared spectroscopy (fNIRS) is a non-invasive method for studying functional activation via monitoring changes of the hemodynamic properties in brain and event-related experimental in examining cognitive processes is very useful but much more flexible in data analysis. To validate the usefulness of independent component analysis (ICA) of event-related fNRIS data, a “high-motivation” GKT (Guilty Knowledge Test) task was applied. A very efficient method of maximization suited called FastICA was applied to maximize the contrast function in practice. Compared with the fMRI studies of ICA, the oxy-HB (oxygenated hemoglobin) signals can be decomposed and the active areas can be found in emotional behavior. The time courses and active regions of the event-related ICA components were consistent across trials. ICA approach delivers valid fNIRS amplitude estimations and enables the analysis of event-related fNIRS data series which are highly relevant in particular for cognitive fNIRS studies.
The MERIS Case 2 water algorithm
Authors: R. Doerffer, H. Schiller
Venue: International Journal of Remote Sensing (2007)

The paper describes the case 2 coastal water algorithm, which has been developed for the ground processor of the Medium Resolution Imaging Spectrometer (MERIS). The spectrometer is operated on board the earth observation satellite ENVISAT of the European Space Agency ESA. The algorithm derives the inherent optical properties (IOP) (1) absorption coefficient of phytoplankton pigment, (2) absorption coefficient of gelbstoff and total suspended matter after bleaching the phytoplankton pigment fraction and (3) the scattering coefficient of total suspended matter (TSM). The IOPs of (1) and (3) are also converted into the concentrations of chlorophyll a and TSM dry weight. The algorithm is based on a neural network (NN), which relates the bi-directional water leaving radiance reflectances with these IOPs. The network is trained with simulated reflectances. The bio-optical model used for the simulations is based on a large data set collected mainly in European waters. One special feature is the combination of a forward NN and a backward NN, which allows testing if the measured spectrum is within the scope of the training set. The comparison with independent test data sets, in situ observations and with the case 1 water algorithm used for MERIS show a good agreement.
Restricted unimodular chordal graphs
Authors: Uri N. Peled, Julin Wu
Venue: Journal of Graph Theory (1999)

A chordal graph is called restricted unimodular if each cycle of its vertex-clique incidence bipartite graph has length divisible by 4. We characterize these graphs within all chordal graphs by forbidden induced subgraphs, by minimal relative separators, and in other ways. We show how to construct them by starting from block graphs and multiplying vertices subject to a certain restriction, which leads to a linear-time recognition algorithm. We show how they are related to other classes such as distance-hereditary chordal graphs and strongly chordal graphs. &copy; 1999 John Wiley & Sons, Inc. J Graph Theory 30: 121&ndash;136, 1999
A code provenance management tool for ip-aware software development
Authors: Ya Bin Dang, Ping Cheng, Lin Luo, Adrian Cho
Venue: Companion of the 30th international conference on Software engineering (2008)

In this paper we introduce a code provenance management tool which can track the provenance of source code and generate provenance reports to facilitate best practices for managing intellectual property (IP) throughout the software development lifecycle. We present the IP-aware process supported by the tool and explain how it reduces both the cost and the risk of code IP management. Results from three pilots are analyzed to illustrate the effectiveness of cost and risk reduction.
Regularized simultaneous model selection in multiple quantiles regression
Authors: Hui Zou, Ming Yuan
Venue: Computational Statistics Data Analysis (2008)

Simultaneously estimating multiple conditional quantiles is often regarded as a more appropriate regression tool than the usual conditional mean regression for exploring the stochastic relationship between the response and covariates. When multiple quantile regressions are considered, it is of great importance to share strength among them. In this paper, we propose a novel regularization method that explores the similarity among multiple quantile regressions by selecting a common subset of covariates to model multiple conditional quantiles simultaneously. The penalty we employ is a matrix norm that encourages sparsity in a column-wise fashion. We demonstrate the effectiveness of the proposed method using both simulations and an application of gene expression data analysis.
Error-driven active learning in growing radial basis function networks for early robot learning
Authors: Qinggang Meng, Mark Lee
Venue: Neurocomputing (2008)

In this paper, we describe a new error-driven active learning approach to self-growing radial basis function networks for early robot learning. There are several mappings that need to be set up for an autonomous robot system for sensorimotor coordination and transformation of sensory information from one modality to another, and these mappings are usually highly nonlinear. Traditional passive learning approaches usually cause both large mapping errors and nonuniform mapping error distribution compared to active learning. A hierarchical clustering technique is introduced to group large mapping errors and these error clusters drive the system to actively explore details of these clusters. Higher level local growing radial basis function subnetworks are used to approximate the residual errors from previous mapping levels. Plastic radial basis function networks construct the substrate of the learning system and a simplified node-decoupled extended Kalman filter algorithm is presented to train these radial basis function networks. Experimental results are given to compare the performance among active learning with hierarchical adaptive RBF networks, passive learning with adaptive RBF networks and hierarchical mixtures of experts, as well as their robustness under noise conditions.
Requirements Elicitation with and for Older Adults
Authors: Helena Sustar, Ulrike Pfeil, Panayiotis Zaphiris
Venue: IEEE Software (2008)

This installment of the Requirments column addresses how to apply user-centered design methods to design interactive systems for the elderly. A case study (with its successes and weaknesses) showed that a need exists for more creative and participatory design approaches for this population.
A New Live Streaming Media Architecture for Peer-to-Peer Network
Authors: JiuJun Cheng, ChunGang Yan, Kuifei Yu, Jian Ma
Venue: Proceedings of the 2008 International Conference on Multimedia and Ubiquitous Engineering (2008)

In P2P network, live streaming media distribution in a large scale P2P network and the impact on the real-time caused by the dynamicity of peers are still not well resolved. In order to address this problem, a relative stable P2P network architecture for live streaming media is presented. Our approach is that the architecture combines tree with clusters, and some super-peers are reserved in the clusters for peers’ joining and departure, which is likely to be significant in a highly dynamic environment. In addition, the maximum accepted connection of each super-peer and its average disposing delay are adopted to select the optimal father peer. The experiment results show that the new architecture assures the improvement of real-time and live streaming media distribution in the large-scale P2P network, and at the same time, improves the scalability of the P2P network.
Exponential shadow maps
Authors: Thomas Annen, Tom Mertens, Hans-Peter Seidel, Eddy Flerackers, Jan Kautz
Venue: Proceedings of graphics interface 2008 (2008)

Rendering high-quality shadows in real-time is a challenging problem. Shadow mapping has proved to be an efficient solution, as it scales well for complex scenes. However, it suffers from aliasing problems. Filtering the shadow map alleviates aliasing, but unfortunately, native hardware-accelerated filtering cannot be applied, as the shadow test has to take place beforehand. We introduce a simple approach to shadow map filtering, by approximating the shadow test using an exponential function. This enables us to pre-filter the shadow map, which in turn allows for high quality hardware-accelerated filtering. Compared to previous filtering techniques, our technique is faster, consumes less memory and produces less artifacts.
The Definitive Guide to Terracotta: Cluster the JVM for Spring, Hibernate and POJO Scalability
Authors: Terracotta,  Inc.
Venue: The Definitive Guide (2008)

Get the definitive guide on all the fundamentals of Terracotta as well as user secrets, recipes, and prepackaged frameworks. Written by Terracotta CTO Ari Zilka and his team, The Definitive Guide to Terracotta: Cluster the JVM for Spring, Hibernate and POJO Scalability covers the following: High Availability (HA) nth degree scaling and clustering for traditional J2EE and Java EE 5 applications (using Seam or other application) as well as Springbased enterprise applications Everyday Terracotta using its prepackaged frameworks and integration recipes, including configuration and customization for your application tuning, no matter the scale Power user secrets available, including config modules, customized advanced performance tuning, SDLC, Maven, and more What youll learn See how Terracotta works fundamentally, and the user pieces and parts necessary for using Terracotta and its open source options. Learn and apply case studies involving distributed cache, Hibernate, Master/Worker, and HTTP Session. Understand thread coordination and advanced performance tuning. Use more advanced case studies involving Spring, POJOs, FOO, and more. Configure and create your own modules using the software development and deployment life cycle. Who is this book for? This definitive book from the Terracotta team is for both developers and architects who want to learn the whats, wheres, whens, and whys of the Terracotta scaling engine.
Technology as a Catalyst for School Communities: Beyond Boxes and Bandwidth
Authors: Burns Mary, Dimock K. Victoria
Venue:  (2007)

Technology as a Catalyst for School Communities presents case studies that illustrate how administrators and teachers can work together to find solutions to the best ways to integrate technology in the classroom.
Assessing clinical trial eligibility with logic expression queries
Authors: D. W. Lonsdale, C. Tustison, C. G. Parker, D. W. Embley
Venue: Data Knowledge Engineering (2008)

This paper introduces a system that processes clinical trials using a combination of natural language processing and database techniques. We process web-based clinical trial recruitment pages to extract semantic information reflecting eligibility criteria for potential participants. From this information we then formulate a query that can match criteria against medical data in patient records. The resulting system reflects a tight coupling of web-based information extraction, natural language processing, medical informatic approaches to clinical knowledge representation, and large-scale database technologies. We present an evaluation of the system and future directions for further system development.