ppxscal/embedding_network huggingface dataset:
Business-Driven Management of Policies in DiffServ Networks
Authors: Antonio Astorga, Javier Rubio-Loyola
Venue: Proceedings of the 2nd international conference on Autonomous Infrastructure, Management and Security: Resilient Networks and Services (2008)

This paper presents a framework to control the life cycle of enforceable policies aligned to business directives. The framework relies on three main aspects: the formalization of measurable business indicators underpinning the management cycle of policies, a holistic approach to conceal Quality of Service delivery with business alignment, and the use of policy-based management as the vehicle to control system behavior accordingly. The core contribution of this research lies in the network management area whose most widespread solutions for QoS delivery have been systematically decoupled from business value, although the research community recognizes business profit as the main motivation for any management solution. The ultimate goal of this research is to develop a management framework that allows exploiting business value in telecommunications infrastructures. As each application domain may have intrinsic peculiarities we propose to validate our approach in the context of DiffServ networks. Simulations will be conducted to evaluate and to optimize the performance of diverse business directives under different patterns of service invocations and patterns of inter-domain traffic exchange between autonomous systems. To the best of our knowledge no other approach has dealt with the above research area in such a holistic view.
Discovering Constrained Substructures in Bayesian Trees Using the E.M. Algorithm
Authors: Roman Filipovych, Eraldo Ribeiro
Venue: Proceedings of the 5th international conference on Image Analysis and Recognition (2008)

We present an Expectation-Maximization learning algorithm (E.M.) for estimating the parameters of partially-constrained Bayesian trees. The Bayesian trees considered here consist of an unconstrained subtree and a set of constrained subtrees. In this tree structure, constraints are imposed on some of the parameters of the parametrized conditional distributions, such that all conditional distributions within the same subtree share the same constraint. We propose a learning method that uses the unconstrained subtree to guide the process of discovering a set of relevant constrained substructures. Substructure discovery and constraint enforcement are simultaneously accomplished using an E.M. algorithm. We show how our tree substructure discovery method can be applied to the problem of learning representative pose models from a set of unsegmented video sequences. Our experiments demonstrate the potential of the proposed method for human motion classification.
Performance comparison between backpropagation algorithms applied to intrusion detection in computer network systems
Authors: Iftikhar Ahmad, M. A. Ansari, Sajjad Mohsin
Venue: Proceedings of the 7th WSEAS International Conference on Applied Computer and Applied Computational Science (2008)

In this paper a topology of neural network intrusion detection system is proposed on which different backpropagation algorithms are benchmarked. The proposed methodology uses sampled data from KddCup99 data set, an intrusion detection attacks database that is a standard for the evaluation of intrusion detection systems. The performance of backpropagation algorithms implemented in batch mode, is addressed. A comparative analysis of algorithms is made and then the most optimum solution is selected with respect to mean square error.
Detecting abnormal activities in video sequences
Authors: Angelo Chianese, Vincenzo Moscato, Antonio Picariello
Venue: Proceedings of the 2008 Ambi-Sys workshop on Ambient media delivery and interactive television (2008)

Automatically detecting suspicious human activities in restricted environments such as airports, parking lots and banks represents an open issue for the last generation surveillance systems. In this paper, we present an approach that allows to detect anomalies in a video sequence without any need of describing a priori "abnormal" activities. In particular, we first introduce a normal activities model based on the concept of elementary actions observable by means of image understanding procedures. We then provide an algorithm based on the use of decision trees that can quickly detect an abnormal situation as variation of currently processed activity with respect to normal patterns contained in the system knowledge base. Our preliminary experimental results on a dataset consisting of staged bank robbery videos show that our algorithm provides very encouraging results when compared to human reviewers.
Classifying Infrasound with Wavelet Networks
Authors: Wei Wang, ShiMin Wei, QiZheng Liao
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 4 - Volume 04 (2008)

Infrasound is ideally suited to provide essential information of these earthquakes without any invasive measures. This necessitates automatic processing of the data as the captured phenomena need to be sortedbefore further analysis can be undertaken. Extracting the physical characters of different signals by signal processing such as Fourier Transform and Wavelets Transform are generally employed to carry out the necessary expansion. This article reviews pattern recognition as it applies to earthquake prediction and discusses the concept of wavelet networks as a meansof seismic infrasound classification. An example is presented in which this approach was used for classifying preprocessed infrasound signals to identifyprecursory strong earthquake.
A Neural Network Approach to Similarity Learning
Authors: Stefano Melacci, Lorenzo Sarti, Marco Maggini, Monica Bianchini
Venue: Proceedings of the 3rd IAPR workshop on Artificial Neural Networks in Pattern Recognition (2008)

This paper presents a novel neural network model, called similarity neural network (SNN), designed to learn similarity measures for pairs of patterns. The model guarantees to compute a non negative and symmetric measure, and shows good generalization capabilities even if a very small set of supervised examples is used for training. Preliminary experiments, carried out on some UCI datasets, are presented, showing promising results.
Analog CMOS Circuits Implementing Neural Segmentation Model Based on Symmetric STDP Learning
Authors: Gessyca Maria Tovar, Eric Shun Fukuda, Tetsuya Asai, Tetsuya Hirose, Yoshihito Amemiya
Venue: Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part II (2008)

We proposed a neural segmentation model that is suitable for implementation in analog VLSIs using conventional CMOS technology. The model consists of neural oscillators mutually couple through synaptic connections. The model performs segmentation in temporal domain, which is equivalent to segmentation according to the spike timing difference of each neuron. Thus, the learning is governed by symmetric spike-timing dependent plasticity (STDP). We numerically demonstrate basic operations of the proposed model as well as fundamental circuit operations using a simulation program with integrated circuit emphasis (SPICE).
Classification of Hyperspectral Images Compressed through 3D-JPEG2000
Authors: Ian Blanes, Alaitz Zabala, Gerard Moré, Xavier Pons, Joan Serra-Sagristà
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III (2008)

Classification of hyperspectral images is paramount to an increasing number of user applications. With the advent of more powerful technology, sensed images demand for larger requirements in computational and memory capabilities, which has led to devise compression techniques to alleviate the transmission and storage necessities.Classification of compressed images is addressed in this paper. Compression takes into account the spectral correlation of hyperspectral images together with more simple approaches. Experiments have been performed on a large hyperspectral CASI image with 72 bands. Both coding and classification results indicate that the performance of 3d-DWT is superior to the other two lossy coding approaches, providing consistent improvements of more than 10 dB for the coding process, and maintaining both the global accuracy and the percentage of classified area for the classification process.
Bayes' Rule, Principle of Indifference, and Safe Distribution
Authors: Andrzej Piegat, Marek Landowski
Venue: Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing (2006)

Bayes' rule is the basis of probabilistic reasoning. It enables to surmount information gaps. However, it requires the knowledge of prior distributions of probabilistic variables. If this distribution is not known then, according to the principle of indifference, the uniform distribution has to be assumed. The uniform distribution is frequently and heavily criticized. The paper presents a safe distribution of probability density that can be often used instead of the uniform distribution to surmount information gaps. According to the authors' knowledge the concept of the safe distribution is new and unknown in the literature.
Effects of the Phonological Contents on Perceptual Speaker Identification
Authors: Kanae Amino, Takayuki Arai, Tsutomu Sugawara
Venue: Speaker Classification II: Selected Projects (2007)

It is known that the accuracy of perceptual speaker identification is dependent on the stimulus contents presented to the subjects. Two experiments were conducted in order to find out the effective sounds and to investigate the effects of the syllable structures on familiar speaker identification. The results showed that the nasal sounds were effective for identifying the speakers both in onset and coda positions, and coronal sounds were more effective than labial counterparts. The onset consonants were found to be important, and the identification accuracy was degraded in onsetless structures.
Grounding Mobile Force Feedback in the Real World
Authors: Mike Fraser, Paul Duff, Will Pearson
Venue: Proceedings of the 6th international conference on Haptics: Perception, Devices and Scenarios (2008)

We describe a prototype mobile force-only feedback system called `Limbot' constructed by placing a six degree-of-freedom force feedback system onto a two degree-of-freedom motorised platform. Existing mobile force-feedback platforms are typically designed to expand the virtual haptic workspace and support exploration of large haptic objects in the style of traditional Virtual Reality (VR) interfaces. Systems which focus on the VR paradigm typically preclude navigation of real environments because attention is primarily directed at the graphical display. In contrast, our device is unconstrained and designed for force-only explorations of real wide-area environments, for example as part of a location-based pervasive game. In order to achieve such wide-area exploration and navigation, our platform allows the user to couple and decouple the force feedback encoders to control the platform's wheels. The device's encoders switch between 'traditional' virtual exploration of a haptic object and as a real navigation controller for the base platform motors. Initial testing of our prototype highlights (i) that our positioning and mobility approaches require that we consider haptic perspective more closely; (ii) that the necessary speed and control of repositioning requires easily backdrivable base motors; and (iii) that our mobile force feedback experience is an unavoidably social experience in the real world, inspiring quick movements between haptic and robotic modes of operation.
Usability Evaluation of a Virtual Museum Interface
Authors: Athanasis Karoulis, Stella Sylaiou, Martin White
Venue: Informatica (2006)

The Augmented Representation of Cultural Objects (ARCO) system provides software and interface tools to museum curators to develop virtual museum exhibitions, as well as a virtual environment for museum visitors over the World Wide Web or in informative kiosks. The main purpose of the system is to offer an enhanced educative and entertaining experience to virtual museum visitors. In order to assess the usability of the system, two approaches have been employed: a questionnaire based survey and a Cognitive Walkthrough session. Both approaches employed expert evaluators, such as domain experts and usability experts. The result of this study shows a fair performance of the followed approach, as regards the consumed time, financial and other resources, as a great deal of usability problems has been uncovered and many aspects of the system have been investigated. The knowledge gathered aims at creating a conceptual framework for diagnose usability problems in systems in the area of Virtual Cultural Heritage.
Image-Based Interactive Exploration of Real-World Environments
Authors: Matthew Uyttendaele, Antonio Criminisi, Sing Bing Kang, Simon Winder, Richard Szeliski, Richard Hartley
Venue: IEEE Computer Graphics and Applications (2004)

This article describes an image-based acquisition and rendering system that enables users to explore remote real-world locations. The system is based on a lightweight, high-resolution, multisensor camera that can be used to quickly film a tour through a large environment such as a house or a garden. During the offline authoring phase, the raw video is processed to produce stabilized high-quality, high-dynamic-range video using a combination of novel techniques. These include a stitching algorithm to remove parallax in areas of image overlap, registration and blending algorithms to produce high dynamic range video from alternately exposed video frames, feature tracking to perform camera stabilization, and a video compression scheme that supports selective runtime decompression and random access. The final user experience is enhanced with multimedia elements such as overview maps, video textures, and sound.The user controls the viewpoint and location through a standard game controller, and the resulting user experience feels much like a computer game. The combination of high-resolution, continuous imagery with real-time interactivity provides viewers with an unprecedented sense of presence in interesting real world environments.
Circumventing Server Bottlenecks: Indirect Large-Scale P2P Data Collection
Authors: Di Niu, Baochun Li
Venue: Proceedings of the 2008 The 28th International Conference on Distributed Computing Systems (2008)

In most large-scale peer-to-peer (P2P) applications, it is necessary to collect vital statistics data — sometimes referred to as logs — from up to millions of peers. Traditional solutions involve sending large volumes of such data to centralized logging servers, which are not scalable. In addition, they may not be able to retrieve statistics data from departed peers in dynamic peer-to-peer systems. In this paper, we solve this dilemma through an indirect collection mechanism that distributes data using random network coding across the network, from which servers proactively pull such statistics. By buffering data in a decentralized fashion with only a small portion of peer resources, we show that our new mechanism provides a "buffering" zone and a "smoothing" factor to collect large volumes of statistics, with appropriate resilience to peer dynamics and scalability to a large peer population.
Creating and evaluating a video vocabulary for communicating verbs for different age groups
Authors: Xiaojuan Ma, Perry R. Cook
Venue: Proceedings of the 10th international ACM SIGACCESS conference on Computers and accessibility (2008)

Icons and digital images used in augmentative and alternative communication (AAC) are not as effective in illustrating verbs, especially for people with cognitive degeneration or impairment. Realistic videos have possible advantages for conveying verbs, as verified in our studies with young and old adults comparing single image, multiple images, animations, and video clips. Videos are especially more effective for verbs that show concrete movements or actions. Based on our studies, we propose rules for filming video verb representations, exploring possible visual cues and other factors that may affect people's perception and interpretation.
Regularized Alternating Least Squares Algorithms for Non-negative Matrix/Tensor Factorization
Authors: Andrzej Cichocki, Rafal Zdunek
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III (2007)

Nonnegative Matrix and Tensor Factorization (NMF/NTF) and Sparse Component Analysis (SCA) have already found many potential applications, especially in multi-way Blind Source Separation (BSS), multi-dimensional data analysis, model reduction and sparse signal/image representations. In this paper we propose a family of the modified Regularized Alternating Least Squares (RALS) algorithms for NMF/NTF. By incorporating regularization and penalty terms into the weighted Frobenius norm we are able to achieve sparse and/or smooth representations of the desired solution, and to alleviate the problem of getting stuck in local minima. We implemented the RALS algorithms in our NMFLAB/NTFLAB Matlab Toolboxes, and compared them with standard NMF algorithms. The proposed algorithms are characterized by improved efficiency and convergence properties, especially for large-scale problems.
Parallel Algorithms for Triangular Periodic Sylvester-Type Matrix Equations
Authors: Per Andersson, Robert Granat, Isak Jonsson, Bo Kågström
Venue: Proceedings of the 14th international Euro-Par conference on Parallel Processing (2008)

We present parallel algorithms for triangular periodic Sylves-ter-type matrix equations, conceptually being the third step of a periodic Bartels---Stewart-like solution method for general periodic Sylvester-type matrix equations based on variants of the periodic Schur decomposition. The presented algorithms are designed and implemented in the framework of the recently developed HPC library SCASY and are based on explicit blocking, 2-dimensional block cyclic data distribution and a wavefront-like traversal of the right hand side matrices. High performance is obtained by rich usage of level 3 BLAS operations. It is also demonstrated how several important key concepts of SCASY regarding communications and the treatment of quasi-triangular coefficient matrices are generalized to the periodic case. Some experimental results from a distributed memory Linux cluster demonstrate are also presented.
The Pictograph Chat Communicator II
Authors: Jun Munemori, Taro Fukuda, Moonyati Binti Mohd Yatid, Junko Itou
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III (2008)

In Japan and other countries, pictographs have widely spread to add nuance to mails in mobile phones. We have developed a pictograph chat system, which enables communication using nothing but pictographs. We have prepared 550 pictograph symbols. The system has a history tab, which can save and reuse all pictographs used in the previous chat lines. We applied the system for communication to 2 groups of foreign students and Japanese students. We have carried out experiments 4 times. We report the results of the experiments as below. (1) The average number of chat lines was 29 lines in 30 minutes. (2) The subjects understood 83% of the chat contents. (3) The history tab is used about 24% of all selected pictographs. (4) History tab was used frequently in relation to the number of chat lines.
Supporting Huge Address Spaces in a Virtual Machine for Java on a Cluster
Authors: Ronald Veldema, Michael Philippsen
Venue: Languages and Compilers for Parallel Computing: 20th International Workshop, LCPC 2007, Urbana, IL, USA, October 11-13, 2007, Revised Selected Papers (2007)

To solve problems that require far more memory than a single machine can supply, data can be swapped to disk in some manner, it can be compressed, and/or the memory of multiple parallel machines can be used to provide enough memory and storage space. Instead of implementing either functionality anew and specific for each application, or instead of relying on the operating system's swapping algorithms (which are inflexible, not algorithm-aware, and often limited in their fixed storage capacity), our solution is a Large Virtual Machine (LVM) that transparently provides a large address space to applications and that is more flexible and efficient than operating system approaches.LVM is a virtual machine for Java that is designed to support large address spaces for billions of objects. It swaps objects out to disk, compresses objects where needed, and uses multiple parallel machines in a Distributed Shared Memory (DSM) setting. The latter is the main focus of this paper. Allocation and collection performance is similar to well-known JVMs if no swapping is needed. With swapping and clustering, we are able to create a list containing 1.2×108 elements far faster than other JVMs. LVM's swapping is up to 10 times faster than OS-level swapping. A swap-aware GC algorithm helps by a factor of 3.
On Semantic Properties of Interestingness Measures for Extracting Rules from Data
Authors: Mondher Maddouri, Jamil Gammoudi
Venue: Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I (2007)

The extraction of IF-THEN rules from data is a promising task of data mining including both Artificial Intelligence and Statistics. One of the difficulties encountered is how to evaluate the relevance of the extracted rules? Many authors use statistical interestingness measures to evaluate the relevance of each rule (taken alone). Recently, few research works have done a synthesis study of the existing interestingness measures but their study presents some limits. In this paper, firstly, we present an overview of related works studying more than forty interestingness measures. Secondly, we establish a list of nineteen other interestingness measures not referenced by the related works. Then, we identify twelve semantic properties characterizing the behavior of interestingness measures. Finally, we did a theoretical study of sixty two interestingness measures by outlining their semantic properties. The results of this study are useful to the users of a data-mining system in order to help them to choose an appropriate measure.
Compressed Sensing and Time-Parallel Reduced-Order Modeling for Structural Health Monitoring Using a DDDAS
Authors: J. Cortial, C. Farhat, L. J. Guibas, M. Rajashekhar
Venue: Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007 (2007)

This paper discusses recent progress achieved in two areas related to the development of a Dynamic Data Driven Applications System (DDDAS) for structural and material health monitoring and critical event prediction. The first area concerns the development and demonstration of a sensor data compression algorithm and its application to the detection of structural damage. The second area concerns the prediction in near real-time of the transient dynamics of a structural system using a nonlinear reduced-order model and a time-parallel ODE (Ordinary Differential Equation) solver.
Microcomputers, Slotcars, and Education
Authors: Phillip McKerrow
Venue: IEEE Micro (1983)

Working models interfaced to microcomputers provide an inexpensive way of simulating real-world situations.