ppxscal/embedding_network huggingface dataset:
Selected topics in video coding and computer vision
Authors: Xin Li
Venue:  (2007)

Video applications ranging from multimedia communication to computer vision have been extensively studied in the past decades. However, the emergence of new applications continues to raise questions that are only partially answered by existing techniques. This thesis studies three selected topics related to video: intra prediction in block-based video coding, pedestrian detection and tracking in infrared imagery, and multi-view video alignment. In the state-of-art video coding standard H.264/AVC, intra prediction is defined on the hierarchical quad-tree based block partitioning structure which fails to exploit the geometric constraint of edges. We propose a geometry-adaptive block partitioning structure and a new intra prediction algorithm named geometry-adaptive intra prediction (GAIP). A new texture prediction algorithm named geometry-adaptive intra displacement prediction (GAIDP) is also developed by extending the original intra displacement prediction (IDP) algorithm with the geometry-adaptive block partitions. Simulations on various test sequences demonstrate that intra coding performance of H.264/AVC can be significantly improved by incorporating the proposed geometry adaptive algorithms. In recent years, due to the decreasing cost of thermal sensors, pedestrian detection and tracking in infrared imagery has become a topic of interest for night vision and all weather surveillance applications. We propose a novel approach for detecting and tracking pedestrians in infrared imagery based on a layered representation of infrared images. Pedestrians are detected from the foreground layer by a Principle Component Analysis (PCA) based scheme using the appearance cue. To facilitate the task of pedestrian tracking, we formulate the problem of shot segmentation and present a graph matching-based tracking algorithm. Simulations with both OSU Infrared Image Database and WVU Infrared Video Database are reported to demonstrate the accuracy and robustness of our algorithms. Multi-view video alignment is a process to facilitate the fusion of non-synchronized multi-view video sequences for various applications including automatic video based surveillance and video metrology. In this thesis, we propose an accurate multi-view video alignment algorithm that iteratively aligns two sequences in space and time. To achieve an accurate sub-frame temporal alignment, we generalize the existing phase-correlation algorithm to 3-D case. We also present a novel method to obtain the ground-truth of the temporal alignment by using supplementary audio signals sampled at a much higher rate. The accuracy of our algorithm is verified by simulations using real-world sequences.
Handwritten Symbol Recognition by a Boosted Blurred Shape Model with Error Correction
Authors: Alicia Fornés, Sergio Escalera, Josep Lladós, Gemma Sánchez, Petia Radeva, Oriol Pujol
Venue: Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I (2007)

One of the major difficulties of handwriting recognition is the variability among symbols because of the different writer styles. In this paper we introduce the boosting of blurred shape models with error correction, which is a robust approach for describing and recognizing handwritten symbols tolerant to this variability. A symbol is described by a probability density function of blurred shape model that encodes the probability of pixel densities of image regions. Then, to learn the most distinctive features among symbol classes, boosting techniques are used to maximize the separability among the blurred shape models. Finally, the set of binary boosting classifiers is embedded in the framework of Error Correcting Output Codes (ECOC). Our approach has been evaluated in two benchmarking scenarios consisting of handwritten symbols. Compared with state-of-the-art descriptors, our method shows higher tolerance to the irregular deformations induced by handwritten strokes.
3D Display Using Passive Optical Scatterers
Authors: Shree K. Nayar, Vijay N. Anand
Venue: Computer (2007)

A new class of 3D displays uses a digital projector and a cloud of passive optical scatterers etched in a glass cube with laser-induced damage. These inexpensive devices can be used to display simple 3D objects, extend videogames to three dimensions, and create 3D avatars.
Cobra: a framework for continuous profiling and binary re-adaptation
Authors: Pen-Chung Yew, Wei-Chung Hsu
Venue:  (2008)

This thesis presents COBRA (Continuous Binary Re-Adaptation), a dynamic binary optimization framework, for single-threaded and multithreaded applications. It includes components for collective monitoring and dynamic profiling, profile and trace management, code optimization and code deployment. The monitoring component collects the hot branches and performance information from multiple working threads with the support of OS and the hardware performance monitors. It sends data to the dynamic profiler. The dynamic profiler accumulates performance bottleneck profiles such as cache miss information along with hot branch traces. The optimizer generates new optimized binary traces and stored them in the code cache. It is currently implemented on the Itaniurn 2 based CMP. SNIP and cc-NUMA systems. This thesis proposes new phase detection schemes and hardware support, especially for dynamic optimizations, that effectively identify and accurately predict program phases by exploiting program control flow information. The sampled Basic Block Vector (BBV)-based and sampled Hot Working Set (HWSET)-based program phase detection schemes are studied. We showed that Sampled HWSET-based program phase detection scheme had higher phase coverage and longer stable phase compared to sampled BBV-based program phase detection scheme. We also propose dynamic code region (DCR)-based program phase detection hardware for dynamic optimization systems. This thesis also proposes a persistent dynamic profile management scheme for continuous reoptimization. The code region based profile manager stores dynamic control flow information including hot paths and loops. It classifies them according to entropy calculated from the frequency vectors of taken branches and load latencies. The profile characterization and classification minimize the explosion of persistent runtime profiles and the overhead of profile collection for continuous reoptimization. Based on the COBRA framework, we implemented two dynamic compiler optimizations to reduce the impact of coherent memory accesses in OpenMP NAS parallel benchmarks. Using OpenMP NAS parallel benchmarks, we show how COBRA can adaptively choose appropriate optimizations according to observed changing runtime program behavior. The optimizations improve the performance of OpenMP NAS parallel benchmarks (BT, SP, LU, FT, MG, CG) up to 15% with an average of 4.79 on a 4-way Itanium 2 SMP server, and up to 68% with an average of 17.5% on a SGI Altix cc-NUMA system.
The SWAN biomedical discourse ontology
Authors: Paolo Ciccarese, Elizabeth Wu, Gwen Wong, Marco Ocana, June Kinoshita, Alan Ruttenberg, Tim Clark
Venue: Journal of Biomedical Informatics (2008)

Developing cures for highly complex diseases, such as neurodegenerative disorders, requires extensive interdisciplinary collaboration and exchange of biomedical information in context. Our ability to exchange such information across sub-specialties today is limited by the current scientific knowledge ecosystem's inability to properly contextualize and integrate data and discourse in machine-interpretable form. This inherently limits the productivity of research and the progress toward cures for devastating diseases such as Alzheimer's and Parkinson's. SWAN (Semantic Web Applications in Neuromedicine) is an interdisciplinary project to develop a practical, common, semantically structured, framework for biomedical discourse initially applied, but not limited, to significant problems in Alzheimer Disease (AD) research. The SWAN ontology has been developed in the context of building a series of applications for biomedical researchers, as well as in extensive discussions and collaborations with the larger bio-ontologies community. In this paper, we present and discuss the SWAN ontology of biomedical discourse. We ground its development theoretically, present its design approach, explain its main classes and their application, and show its relationship to other ongoing activities in biomedicine and bio-ontologies.
Hybridizing the cross-entropy method: An application to the max-cut problem
Authors: Manuel Laguna, Abraham Duarte, Rafael Martí
Venue: Computers and Operations Research (2009)

Cross-entropy has been recently proposed as a heuristic method for solving combinatorial optimization problems. We briefly review this methodology and then suggest a hybrid version with the goal of improving its performance. In the context of the well-known max-cut problem, we compare an implementation of the original cross-entropy method with our proposed version. The suggested changes are not particular to the max-cut problem and could be considered for future applications to other combinatorial optimization problems.
On the Performance of Stacked Generalization Classifiers
Authors: Mete Ozay, Fatos Tunay Vural
Venue: Proceedings of the 5th international conference on Image Analysis and Recognition (2008)

Stacked Generalization (SG) is an ensemble learning technique, which aims to increase the performance of individual classifiers by combining them under a hierarchical architecture. In many applications, this technique performs better than the individual classifiers. However, in some applications, the performance of the technique goes astray, for the reasons that are not well-known. In this work, the performance of Stacked Generalization technique is analyzed with respect to the performance of the individual classifiers under the architecture. This work shows that the success of the SG highly depends on how the individual classifiers share to learn the training set, rather than the performance of the individual classifiers. The experiments explore the learning mechanisms of SG to achieve the high performance. The relationship between the performance of the individual classifiers and that of SG is also investigated.
Energy-Aware Distributed Speech Recognition for Wireless Mobile Devices
Authors: Brian Delaney, Tajana Simunic, Nikil Jayant
Venue: IEEE Design Test (2005)

Editors' note: Severe limitations in computational, memory, and energy resources make implementing high-quality speech recognition in embedded devices a difficult challenge. In this article, the authors investigate the energy consumption of computation and communication in an embedded distributed speech recognition system and propose optimizations that reduce overall energy consumption while maintaining adequate quality of service for the end user.—Radu Marculescu, Carnegie Mellon University; and Petru Eles, Linköping University
Parameterized rough set model using rough membership and Bayesian confirmation measures
Authors: Salvatore Greco, Benedetto Matarazzo, Roman Słowiński
Venue: International Journal of Approximate Reasoning (2008)

A generalization of the original definition of rough sets and variable precision rough sets is introduced. This generalization is based on the concept of absolute and relative rough membership. Similarly to variable precision rough set model, the generalization called parameterized rough set model, is aimed at modeling data relationships expressed in terms of frequency distribution rather than in terms of a full inclusion relation used in the classical definition of rough sets. However, differently from the variable precision rough set model, one or more parameters modeling the degree to which the condition attribute values confirm the decision attribute value, are considered. The properties of this extended model are investigated and compared to the classical rough set model and to the variable precision rough set model.
ERIC: a generic rule-based framework for an affective embodied commentary agent
Authors: Martin Strauss, Michael Kipp
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1 (2008)

We present ERIC, an affective embodied agent for realtime commentary in many domains. The underlying architecture is rule-based, generic, and lightweight - based on Java/Jess modules. Apart from reasoning about dynamically changing events, the system can produce coherent natural language and non-verbal behaviour, based on a layered model of affect (personality, mood, emotion). We show how reasoning, template-based natural language generation and affective appraisal can be implemented within the same rule-based paradigm. To make the system domain independent we worked on two different domains, a virtual horse race and a multi-player tank battle game. We empirically evaluated the genericness of the system by measuring the effort it takes to change the domain, and discuss the results.
What Can and Cannot Be Found in Czech Spontaneous Speech Using Document-Oriented IR Methods -- UWB at CLEF 2007 CL-SR Track
Authors: Pavel Ircing, Josef Psutka, Jan Vavruška
Venue: Advances in Multilingual and Multimodal Information Retrieval: 8th Workshop of the Cross-Language Evaluation Forum, CLEF 2007, Budapest, Hungary, September 19-21, 2007, Revised Selected Papers (2008)

The paper presents an overview of the system build and experiments performed for the CLEF 2007 CL-SR track by the University of West Bohemia. We have concentrated on the monolingual experiments using the Czech collection only. The approach that was successfully employed by our team in the last year's campaign (simple tf.idf model with blind relevance feedback, accompanied with solid linguistic preprocessing) was used again but the set of performed experiments was broadened and a more detailed analysis of the results is provided.
A Tool for Supporting Feature-Driven Development
Authors: Marek Rychlý, Pavlína Tichá
Venue: Balancing Agility and Formalism in Software Engineering: Second IFIP TC 2 Central and East European Conference on Software Engineering Techniques, CEE-SET 2007, Poznan, Poland, October 10-12, 2007, Revised Selected Papers (2008)

This paper deals with the Featured Driven Development (FDD), an agile software development method. According to the requirement analysis for the FDD method application, an information system has been created providing all team members with instruments to follow the method. This tool has been implemented as a multi-user web-based application enabling creation of feature lists, planning a project, supporting cooperation among members of a feature-team, and tracking project progress in an illustrative way. To support project management and communication with customer representatives, a wide range of reporting features has been provided.
GARNET: A Graphical Attack Graph and Reachability Network Evaluation Tool
Authors: Leevar Williams, Richard Lippmann, Kyle Ingols
Venue: Proceedings of the 5th international workshop on Visualization for Computer Security (2008)

Attack graphs enable computation of important network security metrics by revealing potential attack paths an adversary could use to gain control of network assets. This paper presents GARNET (Graphical Attack graph and Reachability Network Evaluation Tool), an interactive visualization tool that facilitates attack graph analysis. It provides a simplified view of critical steps that can be taken by an attacker and of host-to-host network reachability that enables these exploits. It allows users to perform "what-if" experiments including adding new zero-day attacks, following recommendations to patch software vulnerabilities, and changing the attacker starting location to analyze external and internal attackers. Users can also compute and view metrics of assets captured versus attacker effort to compare the security of complex networks. For adversaries with three skill levels, it is possible to create graphs of assets captured versus attacker steps and the number of unique exploits required. GARNET is implemented as a Java application and is built on top of an existing C++ engine that performs reachability and attack graph computations. An initial round of user evaluations described in this paper led to many changes that significantly enhance usability.
The Impact of Noise in Spam Filtering: A Case Study
Authors: I. Cid, L. R. Janeiro, J. R. Méndez, D. Glez-Peña, F. Fdez-Riverola
Venue: Proceedings of the 8th industrial conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects (2008)

Unsolicited commercial e-mail (UCE), more commonly known as spam is a growing problem on the Internet. Every day people receive lots of unwanted advertising e-mails that flood their mailboxes. Fortunately, there are several approaches for spam filtering able to detect and automatically delete this kind of messages. However, spammers have adopted some techniques to reduce the effectiveness of these filters by introducing noise in their messages. This work presents a new pre-processing technique for noise identification and reduction, showing preliminary results when it is applied with a Flexible Bayes classifier. The experimental analysis confirms the advantages of using the proposed technique in order to improve spam filters accuracy.
Towards a System-Level Science Support
Authors: Tomasz Gubała, Marek Kasztelnik, Maciej Malawski, Marian Bubak
Venue: Proceedings of the 8th international conference on Computational Science, Part I (2008)

Recently, there is a growing need for an information technology solution to support a new methodology of scientific investigation, called system-level science. This paper presents a new approach to development and execution of collaborative applications. These applications are built as experiment plans with a notation based on the Ruby language. The virtual laboratory, which is an integrated system of dedicated tools and servers, provides a common space for planning, building, improving and performing in-silico experiments by a group of developers. The application is built with elements called gems which are available on the distributed Web- and Grid-based infrastructure. The process of application developments and the functionality of the virtual laboratory are demonstrated with a real-life example of the drug susceptibility ranking application from the HIV treatment domain.
Dissociating the effects of nitrous oxide on brain electrical activity using fixed order time series modeling
Authors: David T. J. Liley, Kate Leslie, Nicholas C. Sinclair, Martin Feckie
Venue: Computers in Biology and Medicine (2008)

A number of commonly used anesthetics, including nitrous oxide (N"2O), are poorly detected by current electroencephalography (EEG)-based measures of anesthetic depth such as the bispectral index. Based on a previously elaborated theory of electrocortical rhythmogenesis we developed a physiologically inspired method of EEG analysis that was hypothesized to be more sensitive in detecting and characterizing N"2O effect than the bispectral index, through its combined EEG estimates of cortical input and cortical state. By evaluating sevoflurane-induced loss of consciousness in the presence of low brain concentrations of N"2O in 38 elective surgical patients, N"2O was associated with a statistically significant reduction in the input the frontal cortex received from other cortical and subcortical areas. In contrast the bispectral index responded only to low, but not to high, concentrations of N"2O.
Images of Fluid Flow: Art and Physics by Students
Authors: J. Hertzberg, A. Sweetman
Venue: Journal of Visualization (2005)

In Spring 2003, a new experimental course on flow visualization was offered to a mixed class of Fine Arts Photography and Engineering students. Course content included fluid flow physics, history of photography with respect to the relationship of science and art, as well as flow visualization and photography techniques. Issues such as "What makes an image art? What makes an image scientific?" were addressed. The class focused on studio/laboratory experiences for mixed teams of students. In Spring 2004 these concepts were distilled into an engineering outreach experience for middle school girls. The spectacular images resulting from these experiments show that flow visualization can be both performed and appreciated by a broad spectrum of people. Thus flow visualization may represent a new bridge between scientists and non-scientists.
OD-MAC: An On-Demand MAC Protocol for Body Sensor Networks Based on IEEE 802.15.4
Authors: Dongheui Yun, Seong-eun Yoo, Daeyoung Kim, Dohyeun Kim
Venue: Proceedings of the 2008 14th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (2008)

A Body Sensor Network (BSN) is a network of sensors that sense vital signs of a human body. The vital signs can be divided into two categories based on significance to the life: critical and non-critical signs. The critical vital signs, or real-time messages (RTMs), should be transmitted within the deadlines, and they must not be collided. On the other hand, the non-critical signs, or non-real-time messages (NRTMs), are required to be delivered with the best effort. In this paper, an on-demand MAC (OD-MAC) is proposed for BSN to provide real-time transmission, collision avoidance, and energy efficiency. The protocol is developed on IEEE 802.15.4 standard, and some specifications are modified to support those requirements. The OD-MAC changes the superframe structure dynamically to schedule RTMs and NRTMs. Those messages request bandwidth, and the network coordinator adaptively changes beacon interval and allocates slots to the messages. For evaluating the performance, duty cycle according to required utilization, delivery ratio, and energy efficiency are analyzed with simulations using NS2.
Data Fusion Based on Neural Networks and Particle Swarm Algorithm and Its Application in Sugar Boiling
Authors: Yanmei Meng, Sijie Yan, Zhihong Tang, Yuanling Chen, Jingneng Liu
Venue: Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks (2008)

This paper utilizes data fusion algorithm to analyze the multi-sensors data, in order to obtain accurate and complete information in the sugar boiling process, and to improve the accuracy and reliability of decision-making. Proposing an improved particle swarm optimization method(BWPSO), compared with the basic particle swarm optimization, each particle adjust its position not only according to its own previous best solution and its group's previous best, but also its group's previous worst. This paper integrates BP neural network and particle swarm optimization, utilizes BWPSO algorithm to optimize BP neural network's connection weights, and exerts BWPSO's capability in global optimization and BP's advantage in local search sufficiently. The experimental results show that utilizing BWPSO to optimize BP can get faster convergence, higher precision and better effect.