ppxscal/embedding_network huggingface dataset:
User Authentication Mechanism Using Java Card for Personalized IPTV Services
Authors: Youn-Kyoung Park, Sun-Hee Lim, Okyeon Yi, Sangjin Lee, Soo-Hyung Kim
Venue: Proceedings of the 2008 International Conference on Convergence and Hybrid Information Technology (2008)

Internet Protocol Television (IPTV), becoming a popular business model, provides interactive and personalized service. IPTV provides identification, authentication and personalized services though Set-top box (STB) by a unique hardware identifier. This is based on box-level identification, which is inconsistent with IPTV's main intention of providing personalized services. The goal of the proposed system is to provide an opportunity to use the flexible user-centric authentication mechanism through Java Card applets in the IPTV application server and 3G Wideband Code Division Multiple Access (W-CDMA) networks. When a viewer watching IPTV is successfully authenticated, an authentication token allows the subscriber to conveniently use services which are adaptable to personal preference anywhere at anytime. In addition, this paper presents effectiveness and security analysis for verifying the proposal.
Predictability Estimating for Room Impulse Response
Authors: Zihou Meng, Lu Dai
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 5 - Volume 05 (2008)

To estimate the predictability of a room impulse response, the concept of concentrated space, used to describe the signal with energy concentrated in some region both in time and frequency domain, is introduced. The predictability is analyzed based on the dimension analysis of the concentrated space and estimated with the optimum prediction method using the prolate spheroidal functions. A psychoacoustic experiment is carried out to investigate the relationship between the listening impression and the theoretical predictability.
The application monitor aspect pattern
Authors: Roberta Coelho, Ayla Dantas, Uirá Kulesza, Walfredo Cirne, Arndt von Staa, Carlos Lucena
Venue: Proceedings of the 2006 conference on Pattern languages of programs (2006)

Modern applications are typically complex, multithreaded, distributed, and often should provide real-time responses and small-footprint. Due to such characteristics, most often, it is hard to understand the behavior of such systems and consequently detect the root causes of performance or reliability problems. In order to collect information about system's runtime behavior - operations' performance, internal threads status - the system developer is required to instrument the target application (and sometimes also its execution platform). Such monitoring code which allows the developer to reason about the code execution is not localized in a single application module; it must be included in many modules. As a consequence, the monitoring concern tends to be scattered across multiple application/platform modules and tangled with other application concerns. The Application Monitor pattern supports the separate definition of monitoring-related functionalities concerns through the use of aspect-oriented programming. It decouples such concerns from the implementation of application-specific concerns, which in turn improves the system reusability and maintainability.
Domain Theory and the Causal Structure of Space-Time
Authors: Keye Martin, Prakash Panangaden
Venue: Proceedings of the 4th conference on Computability in Europe: Logic and Theory of Algorithms (2008)

We prove that a globally hyperbolic spacetime with its causality relation is a bicontinuous poset whose interval topology is the manifold topology. From this one can show that from only a countable dense set of events and the causality relation, it is possible to reconstruct a globally hyperbolic spacetime in a purely order theoretic manner. The ultimate reason for this is that globally hyperbolic spacetimes belong to a category that is equivalent to a special category of domains called interval domains. We obtain a mathematical setting in which one can study causality independently of geometry and differentiable structure, and which also suggests that spacetime emerges from something discrete.
Brave Induction
Authors: Chiaki Sakama, Katsumi Inoue
Venue: Proceedings of the 18th international conference on Inductive Logic Programming (2008)

This paper considers the following induction problem. Given the background knowledge B and an observation O, find a hypothesis H such that a consistent theory B ¿ H has a minimal model satisfying O. We call this type of induction brave induction. Brave induction is different from explanatory induction in ILP, which requires that O is satisfied in every model of B ¿ H. Brave induction is useful for learning disjunctive rules from observations, or learning from the background knowledge containing indefinite or incomplete information. We develop an algorithm for computing brave induction, and extend it to induction in answer set programming.
Investigating five key predictive text entry with combined distance and keystroke modelling
Authors: Mark D. Dunlop, Michelle Montgomery Masters
Venue: Personal and Ubiquitous Computing (2008)

This paper investigates text entry on mobile devices using only five-keys. Primarily to support text entry on smaller devices than mobile phones, this method can also be used to maximise screen space on mobile phones. Reported combined Fitt's law and keystroke modelling predicts similar performance with bigram prediction using a five-key keypad as is currently achieved on standard mobile phones using unigram prediction. User studies reported here show similar user performance on five-key pads as found elsewhere for novice nine-key pad users.
Mistaken Driven and Unconditional Learning of NTC
Authors: Taeho Jo, Malrey Lee
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks (2007)

This paper attempts to evaluate machine learning based approaches to text categorization including NTC without decomposing it into binary classification problems, and presents another learning scheme of NTC. In previous research on text categorization, state of the art approaches have been evaluated in text categorization, decomposing it into binary classification problems. With such decomposition, it becomes complicated and expensive to implement text categorization systems, using machine learning algorithms. Another learning scheme of NTC mentioned in this paper is unconditional learning where weights of words stored in its learning layer are updated whenever each training example is presented, while its previous learning scheme is mistake driven learning, where weights of words are updated only when a training example is misclassified. This research will find advantages and disadvantages of both learning schemes by comparing them with each other
A DFT-Based Algorithm for n-Order Singular State Space Systems
Authors: George E. Antoniou_res
Venue: Informatica (2003)

The discrete Fourier transform (DFT) is used for determining the coefficients of a transfer function for n-order singular linear systems, Ex (n)=&Sigma; i=1 nA ix (n&minus;1)+Bu, where E may be singular. The algorithm is straight forward and easily can be implemented. Three step-by-step examples illustrating the application of the algorithm are presented. Research supported by Margaret and Herman Sokol Faculty Fellow Award and MSU2002 FSIP.
Accelerating configuration interaction calculations for nuclear structure
Authors: Philip Sternberg, Esmond G. Ng, Chao Yang, Pieter Maris, James P. Vary, Masha Sosonkina, Hung Viet Le
Venue: Proceedings of the 2008 ACM/IEEE conference on Supercomputing (2008)

One of the emerging computational approaches in nuclear physics is the configuration interaction (CI) method for solving the many-body nuclear Hamiltonian in a sufficiently large single-particle basis space to obtain exact answers - either directly or by extrapolation. The lowest eigenvalues and corresponding eigenvectors for very large, sparse and unstructured nuclear Hamiltonian matrices are obtained and used to evaluate additional experimental quantities. These matrices pose a significant challenge to the design and implementation of efficient and scalable algorithms for obtaining solutions on massively parallel computer systems. In this paper, we describe the computational strategies employed in a state-of-the-art CI code MFDn (Many Fermion Dynamics - nuclear) as well as techniques we recently developed to enhance the computational efficiency of MFDn. We will demonstrate the current capability of MFDn and report the latest performance improvement we have achieved. We will also outline our future research directions.
Statistical Surface Realisation of Portuguese Referring Expressions
Authors: Daniel Bastos Pereira, Ivandré Paraboni
Venue: Proceedings of the 6th international conference on Advances in Natural Language Processing (2008)

Natural Language Generation systems usually require substantial knowledge about the structure of the target language in order to perform the final task in the generation process --- the mapping from semantic representation to text known as surface realisation. Designing knowledge bases of this kind, typically represented as sets of grammar rules, may however become a costly, labour-intensive enterprise. In this work we take a statistical approach to surface realisation in which no linguistic knowledge is hard-coded, but rather trained automatically from large corpora. Results of a small experiment in the generation of referring expressions show significant levels of similarity between our (computer-generated) text and those produced by humans, besides the usual benefits commonly associated with statistical NLP such as low development costs, domain- and language-independency.
Decision-Based Hybrid Image Watermarking in Wavelet Domain Using HVS and Neural Networks
Authors: Hung-Hsu Tsai
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III (2007)

This paper presents a Decision-based Hybrid Image Watermarking (DHIW) technique, based on the Human Visible System (HVS) and an Artificial Neural Network (ANN), for image copyright protection in wavelet domain. In [1], an image watermarking technique, called the IWNN technique, utilizes an ANN to extract watermarks without original images. However, the IWNN technique performs poorly for highly complicated image textures because the generalization capability of neural networks is powerfully effective in dealing with smooth image textures. Therefore, the PAIW method is proposed to enhance the IWNN technique, which uses the spatial information associated with wavelet-transformed images. The DHIW technique takes advantages of these two techniques by using a decision preprocessor. Experimental results prove that the DHIW technique remarkably outperforms other existing schemes.
Multiple-Instance Learning Improves CAD Detection of Masses in Digital Mammography
Authors: Balaji Krishnapuram, Jonathan Stoeckel, Vikas Raykar, Bharat Rao, Philippe Bamberger, Eli Ratner, Nicolas Merlet, Inna Stainvas, Menahem Abramov, Alexandra Manevitch
Venue: Proceedings of the 9th international workshop on Digital Mammography (2008)

We propose a novel multiple-instance learning (MIL) algorithm for designing classifiers for use in computer aided detection (CAD). The proposed algorithm has 3 advantages over classical methods. First, unlike traditional learning algorithms that minimize the candidate level misclassification error, the proposed algorithm directly optimizes the patient-wise sensitivity. Second, this algorithm automatically selects a small subset of statistically useful features. Third, this algorithm is very fast, utilizes all of the available training data (without the need for cross-validation etc.), and requires no human hand tuning or intervention. Experimentally the algorithm is more accurate than state of the art support vector machine (SVM) classifier, and substantially reduces the number of features that have to be computed.
Case-Based Reasoning Adaptation for High Dimensional Solution Space
Authors: Ying Zhang, Panos Louvieris, Maria Petrou
Venue: Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development (2007)

Case-Based Reasoning (CBR) is a methodology that reuses the solutions of previous similar problem to solve new problems. Adaptation is one of the most difficult parts of CBR cycle, especially, when the solution space with multi-dimension. This paper discusses the adaptation of high dimensional solution space and proposes a possible approach for it. Visualisation induced Self Organising Map (ViSOM) is used to map the problem space and solution space first, then a BackPropagation (BP) network is applied to analyse the relations between these two maps. A simple military scenario is used as case study for evaluation.
Voice over IP Security: Networking Technology: IP Communications, 1 edition
Authors: Patrick Park
Venue:  (2008)

Voice over IP Security Security best practices derived from deep analysis of the latest VoIP network threats Patrick Park VoIP security issues are becoming increasingly serious because voice networks and services cannot be protected from recent intelligent attacks and fraud by traditional systems such as firewalls and NAT alone. After analyzing threats and recent patterns of attacks and fraud, consideration needs to be given to the redesign of secure VoIP architectures with advanced protocols and intelligent products, such as Session Border Controller (SBC). Another type of security issue is how to implement lawful interception within complicated service architectures according to government requirements. Voice over IP Security focuses on the analysis of current and future threats, the evaluation of security products, the methodologies of protection, and best practices for architecture design and service deployment. This book not only covers technology concepts and issues, but also provides detailed design solutions featuring current products and protocols so that you can deploy a secure VoIP service in the real world with confidence. Voice over IP Security gives you everything you need to understand the latest security threats and design solutions to protect your VoIP network from fraud and security incidents. Patrick Park has been working on product design, network architecture design, testing, and consulting for more than 10 years. Currently Patrick works for Cisco as a VoIP test engineer focusing on security and interoperability testing of rich media collaboration gateways. Before Patrick joined Cisco, he worked for Covad Communications as a VoIP security engineer focusing on the design and deployment of secure network architectures and lawful interception (CALEA). Patrick graduated from the Pusan National University in South Korea, where he majored in computer engineering. Understand the current and emerging threats to VoIP networks Learn about the security profiles of VoIP protocols, including SIP, H.323, and MGCP Evaluate well-known cryptographic algorithms such as DES, 3DES, AES, RAS, digital signature (DSA), and hash function (MD5, SHA, HMAC) Analyze and simulate threats with negative testing tools Secure VoIP services with SIP and other supplementary protocols Eliminate security issues on the VoIP network border by deploying an SBC Configure enterprise devices, including firewalls, Cisco Unified Communications Manager, Cisco Unified Communications Manager Express, IP phones, and multilayer switches to secure VoIP network traffic Implement lawful interception into VoIP service environments This IP communications book is part of the Cisco Press Networking Technology Series. IP communications titles from Cisco Press help networking professionals understand voice and IP telephony technologies, plan and design converged networks, and implement network solutions for increased productivity. Category: NetworkingIP Communication Covers: VoIP Security
Greedy rule generation from discrete data and its use in neural network rule extraction
Authors: Koichi Odajima, Yoichi Hayashi, Gong Tianxia, Rudy Setiono
Venue: Neural Networks (2008)

This paper proposes a GRG (Greedy Rule Generation) algorithm, a new method for generating classification rules from a data set with discrete attributes. The algorithm is ''greedy'' in the sense that at every iteration, it searches for the best rule to generate. The criteria for the best rule include the number of samples and the size of subspaces that it covers, as well as the number of attributes in the rule. This method is employed for extracting rules from neural networks that have been trained and pruned for solving classification problems. The classification rules are extracted from the neural networks using the standard decompositional approach. Neural networks with one hidden layer are trained and the proposed GRG algorithm is applied to their discretized hidden unit activation values. Our experimental results show that neural network rule extraction with the GRG method produces rule sets that are accurate and concise. Application of GRG directly on three medical data sets with discrete attributes also demonstrates its effectiveness for rule generation.
Reversible Flowchart Languages and the Structured Reversible Program Theorem
Authors: Tetsuo Yokoyama, Holger Bock Axelsen, Robert Glück
Venue: Proceedings of the 35th international colloquium on Automata, Languages and Programming, Part II (2008)

Many irreversible computation models have reversible counterparts, but these are poorly understood at present. We introduce reversible flowcharts with an assertion operator and show that any reversible flowchart can be simulated by a structured reversible flowchart using only three control flow operators. Reversible flowcharts are r- Turing-complete, meaning that they can simuluate reversible Turing machines without garbage data. We also demonstrate the injectivization of classical flowcharts into reversible flowcharts. The reversible flowchart computation model provides a theoretical justification for low-level machine code for reversible microprocessors as well as high-level block-structured reversible languages. We give examples for both such languages and illustrate them with a lossless encoder for permutations given by Dijkstra.
Network robot systems
Authors: Alberto Sanfeliu, Norihiro Hagita, Alessandro Saffiotti
Venue: Robotics and Autonomous Systems (2008)

This article introduces the definition of Network Robot Systems (NRS) as is understood in Europe, USA and Japan. Moreover, it describes some of the NRS projects in Europe and Japan and presents a summary of the papers of this Special Issue.
CARISMA: Context-Aware Reflective mIddleware System for Mobile Applications
Authors: Licia Capra Wolfgang Emmerich, Cecilia Mascolo
Venue: IEEE Transactions on Software Engineering (2003)

Mobile devices, such as mobile phones and personal digital assistants, have gained wide-spread popularity. These devices will increasingly be networked, thus enabling the construction of distributed applications that have to adapt to changes in context, such as variations in network bandwidth, battery power, connectivity, reachability of services and hosts, etc. In this paper, we describe CARISMA, a mobile computing middleware which exploits the principle of reflection to enhance the construction of adaptive and context-aware mobile applications. The middleware provides software engineers with primitives to describe how context changes should be handled using policies. These policies may conflict. We classify the different types of conflicts that may arise in mobile computing and argue that conflicts cannot be resolved statically at the time applications are designed, but, rather, need to be resolved at execution time. We demonstrate a method by which policy conflicts can be handled; this method uses a microeconomic approach that relies on a particular type of sealed-bid auction. We describe how this method is implemented in the CARISMA middleware architecture and sketch a distributed context-aware application for mobile devices to illustrate how the method works in practice. We show, by way of a systematic performance evaluation, that conflict resolution does not imply undue overheads, before comparing our research to related work and concluding the paper.
Contour Tracking When Two Gray-Level Discontinuities Are Close to Each Other
Authors: Marcello Demi, Elisabetta Bianchini, Francesco Faita, Vincenzo Gemignani
Venue: Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications (2008)

Vascular measurements are indispensable to quantify important indexes of cardiovascular risk and image processing systems are needed to automatically track the vascular structures through sequences of echographic images. Given a starting contour c n on frame f n , a contour tracking algorithm is generally based on the application of a mathematical operator at the points of c n and on an iterative procedure which brings such points to the respective points of the contour c n + 1 on the subsequent frame f n + 1. In this paper, the performances of a mathematical operator which looks for similar regional gray level distributions are compared to those of an edge detection operator. The paper shows that when two or more gray-level discontinuities are present and close to each other, as in the case of arteries, both operators should be used sequentially.
Optimal simplification of polygonal chains for subpixel-accurate rendering
Authors: Lilian Buzer
Venue: Computational Geometry: Theory and Applications (2009)

For a given polygonal chain, we study the min-# problem, which consists of finding an approximate and ordered subchain with a minimum number of vertices under a given approximation criterion. We propose the first near-linear time algorithm for the min-# problem that ensures optimality in the number of vertices and that retains the shape of the input polygonal chain at the same time. Previous algorithms with near-linear time performance produced geometrical inconsistencies and former methods used to preserve the features of the original chain required a quadratic time complexity. When we set the approximation error equal to the pixel pitch, our simplification criterion guarantees that the rendering of the simplification lies at most half a pixel away from the original chain, contrary to other methods that do not offer a direct control over the rendering. Thus, we avoid producing visible topological inconsistencies. Moreover, our method is based on basic data structures, which leads to a simple and efficient implementation.
Mobile sensor localization and navigation using RF doppler shifts
Authors: Isaac Amundson, Xenofon Koutsoukos, Janos Sallai
Venue: Proceedings of the first ACM international workshop on Mobile entity localization and tracking in GPS-less environments (2008)

Many wireless sensor network applications require knowledge of node placement in order to make sense of sensor data in a spatial context. Networks of mobile sensors require position updates for navigation through the sensing region. The global positioning system is able to provide localization information, however in many situations it cannot be relied on, and alternative localization methods are required. We propose a technique for the localization and navigation of a mobile robot that uses the Doppler-shift in frequency observed by stationary sensor nodes. Our experimental results show that, by using observed RF Doppler shifts, a robot is able to navigate through a sensing region with an average localization error of 1.68 meters.