ppxscal/embedding_network huggingface dataset:
A System for Historic Document Image Indexing and Retrieval Based on XML Database Conforming to MPEG7 Standard
Authors: Wafa Maghrebi, Anis Borchani, Mohamed A. Khabou, Adel M. Alimi
Venue: Graphics Recognition. Recent Advances and New Opportunities: 7th International Workshop, GREC 2007, Curitiba, Brazil, September 20-21, 2007. Selected Papers (2008)

We present a novel image indexing and retrieval system based on object contour description. Extended curvature scale space (CSS) descriptors composed of both local and global features are used to represent and index concave and convex object shapes. These features are size, rotation, and translation invariant. The index is saved into an XML database conforming to the MPEG7 standard. Our system contains a graphical user interface that allows a user to search a database using either sample or user-drawn shapes. The system was tested using two image databases: the Tunisian National Library (TNL) database containing 430 color and gray-scale images of historic documents, mosaics, and artifacts; and the Squid dataset containing 1100 contour images of fish. Recall and precision rates of 94% and 87%, respectively, were achieved on the TNL database and 71% and 86% on the Squid database. Average response time to a query is about 2.55 sec on a 2.66 GHz Pentium-based computer with 256 Mbyte of RAM.
Monitoring SIP Traffic Using Support Vector Machines
Authors: Mohamed Nassar, Radu State, Olivier Festor
Venue: Proceedings of the 11th international symposium on Recent Advances in Intrusion Detection (2008)

We propose a novel online monitoring approach to distinguish between attacks and normal activity in SIP-based Voice over IP environments. We demonstrate the efficiency of the approach even when only limited data sets are used in learning phase. The solution builds on the monitoring of a set of 38 features in VoIP flows and uses Support Vector Machines for classification. We validate our proposal through large offline experiments performed over a mix of real world traces from a large VoIP provider and attacks locally generated on our own testbed. Results show high accuracy of detecting SPIT and flooding attacks and promising performance for an online deployment are measured.
Letters: Adaptive RBF neural-networks control for a class of time-delay nonlinear systems
Authors: Qing Zhu, Shumin Fei, Tianping Zhang, Tao Li
Venue: Neurocomputing (2008)

A control scheme combined with backstepping, radius basis function (RBF) neural networks and adaptive control is proposed for the stabilization of nonlinear system with input and state delay. By using state transformation, the original system is converted to the system without input delay. The RBF neural network is employed to estimate the unknown continuous function. The controller is designed for the converted system so that the closed-loop system is bounded. According to the relation between the original system and the converted one, the state of the original system is proved to be bounded. The control scheme ensures that the closed-loop system is semi-globally uniformly ultimately bounded.
Building a Value-Centric e-Government Service Framework Based on a Business Model Perspective
Authors: Chien-Chih Yu
Venue: Proceedings of the 7th international conference on Electronic Government (2008)

The main objective of e-government is to create public values by delivering public services to citizens, businesses, and government agencies. Since business models have been noted as proper means to illustrate the concept and methods of value proposition and creation, the aims of this paper is at proposing a value-centric e-government service framework based on the business model perspective for guiding and ensuring successful development, management, and delivery of e-government systems and services. Four adapted value-centric business model perspectives include public beneficiaries, government internal organization and process, government service chain, and society and national environments. Identified generic e-government service functions encompass profile management, security and trust management, information navigation and search, transaction and payment, participation and collaboration, personalization and customization, as well as learning and knowledge management.
Modulation Classification of Analog and Digital Signals Using Neural Network and Support Vector Machine
Authors: Cheol-Sun Park, Dae Young Kim
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III (2007)

Most of the algorithms proposed in the literature deal with the problem of digital modulation classification and consider classic probabilistic or decision tree classifiers. In this paper, we compare and analyze the performance of 2 neural network classifiers and 3 support vector machine classifiers (i.e. 1-v-r type, 1-v-1 type and DAG type multi-class classifier). This paper also deals with the modulation classification problems of classifying both analog and digital modulation signals in military and civilian communications applications. A total of 7 statistical signal features are extracted and used to classify 9 modulation signals. It is known that the existing technology is able to classify reliably (accuracy ¿ 90%) only at SNR above 10dB when a large range of modulation types including both digital and analog is being considered. Numerical simulations were conducted to compare performance of classifiers. Results indicated an overall success rate of over 95% at the SNR of 10dB in all classifiers. Especially, it was shown that 3 support vector machine classifiers can achieve the probabilities of correct classification (Pcc) of 96.0%, 97.3% and 97.8% at the SNR of 5dB, respectively.
The impact of radio signal strength on the design of wireless sensor networks
Authors: Lionel M. Ni
Venue:  (2007)

Radio signal strength (RSS) is an attractive property since it can be obtained without additional hardware. Based on various signal propagation models that describe the relationship between RSS and distance, RSS can be employed in network connectivity, localization, and link quality issues in wireless sensor networks (WSNs). However, the deterministic propagation models, in which RSS is only determined by distance, are far from reality. We carefully design a series of experiments using MICA2 nodes in real environments to investigate the parameters besides distance&mdash;frequency, variation of transceivers, antenna orientation, battery voltage, temporal-spatial properties of environment, and environmental dynamics. All the parameters contribute to RSS irregularity, which can highly affect the design of localization and topology control algorithms in wireless sensor networks. We derive the log-normal error model for RSS-based distance estimation from the underlying propagation models and find out that the popular noisy disk error model always underestimates distance error in localization. A quality-based localization algorithm is proposed to handle the large error in RSS-based distance estimation. In WSNs, topology control achieves energy-efficiency by turning off redundant nodes, while still satisfying the given network requirement. We first propose several connectivity-based topology control algorithms, which assume that links are either connected or disconnected. As our experiments show that, besides the connected and disconnected region, a large number of links reside in the transitional region with fluctuating link quality. We then propose a link-quality-based topology control algorithm, which employ opportunistic transmission to catch the best transmission opportunities on transitional links. Our simulations demonstrate that opportunistic transmission can significantly improve energy-efficiency in topology control with low communication overhead. To the best of our knowledge, we are the first to consider link quality and apply opportunistic communication in topology control for WSNs.
XCL: The Extensible Characterisation Language --- One Step towards an Automatic Evaluation of Format Conversions
Authors: Jan Schnasse, Sebastian Beyl, Elona Chudobkaite, Volker Heydegger, Manfred Thaller
Venue: Proceedings of the 12th European conference on Research and Advanced Technology for Digital Libraries (2008)

Today file format specifications are formulated in natural languages. A programmer who wants to decode, encode or render the information contained in a file has to read through the specification before translating it into the terms of a programming language. The maintainer of the format usually eases that process by the deployment of libraries for the format. While this is a well proven process the translation from one format into another format is often an errorprone undertaking, nevertheless. For content holders format conversion is one strategy to assure long term access to their digital resources. However, currently there is still no standardised automatic procedure for the evaluation of format conversions available. Mainly in the case where format conversion is used as a strategy for long time preservation of digital content, this is a serious gap. With the Extensible Characterisation Languages (XCL) we want to address the problem of automatic evaluation of format conversions.
Combining structure and function-based descriptors for component retrieval in software digital libraries
Authors: Yuhanis Yusof, Omer F. Rana
Venue: Integrated Computer-Aided Engineering (2008)

In software development, it is often desirable to re-use existing software components. Currently, a number of repositories of components are available - generally including program source code, but finding the ones that can be re-used for an application is a challenging task. Program source code may be viewed as a form of data, containing both structure and function; it is therefore important to make use of this information in representing programs in the repository. We propose to combine the functional and structural information to facilitate software component search and retrieval. The proposed model reveals how functional and structural descriptors are identified and combined into a single representation. The functional descriptors are identified by extracting selected terms from program source code and a weighting scheme is adopted to differentiate the importance of terms. Structural descriptors that comprise of information generated based on structural relationships, such as design patterns and software metrics, are extracted from a program to be added as program descriptors. In order to retrieve components that are relevant to a given query, the use of similarity measurement based on the vector model and data distribution are investigated. The experiments undertaken on program retrieval indicate that the use of a combination of functional and structural descriptors is better than using functional descriptors on their own. Furthermore, programs retrieved using the proposed approach are less complex and easy to maintain.
Segmentation of Pathologic Hearts in Long-Axis Late-Enhancement MRI
Authors: Cybèle Ciofolo, Maxim Fradkin
Venue: Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I (2008)

We propose a new method to segment long-axis cardiac MR images acquired with a late-enhancement protocol. Detecting the myocardium boundaries is difficult in these images because healthy myocardium appears dark while the intensity of enhanced areas ranges from gray to white, depending on the myocardial damage. In this context, geometrical template deformation, alternated with the update of a damaged tissue map, allows us to include abnormal myocardium parts in the final segmentation. The template and map are initialized using short-axis images and the deformation parameters are adapted according to the type of enhancement pattern. Good segmentation results are obtained on a database of real pathologic heart images presenting various types of abnormal myocardium tissues.
Assessment of the Impact of Sensor Failure in the Recognition of Activities of Daily Living
Authors: Xin Hong, Chris Nugent, Maurice Mulvenna, Sally Mcclean, Bryan Scotney, Steven Devlin
Venue: Proceedings of the 6th international conference on Smart Homes and Health Telematics (2008)

Smart Homes offer improved living conditions and levels of independence for the elderly population who require support with both physical and cognitive functions. Within these environments sensing technologies provide a key facility to monitor the behaviour of the person and their interactions with the living environment. In this paper we investigate the effects of sensor failures on the 'trust' of activity inference processing. We introduce a sensor evidence reasoning network which has been developed for ADL recognition along with the ability of handling uncertainty that may occur at a sensor level. Details of the initial experiments which have been conducted in the assessment of ADLs in a smart laboratory environment using this model are presented. Finally, we present the findings from the analysis on experimental and simulation data taking into consideration the impact of sensor failure on the overall stage of inference processing.
One-Time Programs
Authors: Shafi Goldwasser, Yael Tauman Kalai, Guy N. Rothblum
Venue: Proceedings of the 28th Annual conference on Cryptology: Advances in Cryptology (2008)

In this work, we introduce one-time programs, a new computational paradigm geared towards security applications. A one-time program can be executed on a single input, whose value can be specified at run time. Other than the result of the computation on this input, nothing else about the program is leaked. Hence, a one-time program is like a black box function that may be evaluated once and then "self destructs." This also extends to k-time programs, which are like black box functions that can be evaluated k times and then self destruct.One-time programs serve many of the same purposes of program obfuscation, the obvious one being software protection, but also including applications such as temporary transfer of cryptographic ability. Moreover, the applications of one-time programs go well beyond those of obfuscation, since one-time programs can only be executed once (or more generally, a limited number of times) while obfuscated programs have no such bounds. For example, one-time programs lead naturally to electronic cash or token schemes: coins are generated by a program that can only be run once, and thus cannot be double spent.Most significantly, the new paradigm of one-time computing opens new avenues for conceptual research. In this work we explore one such avenue, presenting the new concept of "one-time proofs," proofs that can only be verified once and then become useless and unconvincing.All these tasks are clearly impossible using software alone, as any piece of software can be copied and run again, enabling the user to execute the program on more than one input. All our solutions employ a secure memory device, inspired by the cryptographic notion of interactive oblivious transfer protocols, that stores two secret keys (k 0,k 1). The device takes as input a single bit b ¿ {0,1}, outputs k b , and then self destructs. Using such devices, we demonstrate that for every input length, any standard program (Turing machine) can be efficiently compiled into a functionally equivalent one-time program. We also show how this memory device can be used to construct one-time proofs. Specifically, we show how to use this device to efficiently convert a classical witness for any NP statement, into "one-time proof" for that statement.
Authoring 744: Writing Descriptions to Create Content
Authors: Jose M. Martinez, Francisco Moran
Venue: IEEE MultiMedia (2003)

The MPEG-4 1 and MPEG-7 2 standards provide new ways for authoring, transporting, accessing, retrieving, and interacting with multimedia content. MPEG-4 is the first standard that supports interactive and complex audio-visual presentations, while MPEG-7 standardizes the description of various types of multimedia content.
A set of SAS macros for calculating and displaying adjusted odds ratios (with confidence intervals) for continuous covariates in logistic B-spline regression models
Authors: Martin Gregory, Hanno Ulmer, Karl P. Pfeiffer, Stefan Lang, Alexander M. Strasak
Venue: Computer Methods and Programs in Biomedicine (2008)

In clinical and epidemiologic research to investigate dose-response associations, non-parametric spline regression has long been proposed as a powerful alternative to conventional parametric regression approaches, since no underlying assumptions of linearity have to be fulfilled. For logistic spline models, however, to date, little standard statistical software is available to estimate any measure of risk, typically of interest when quantifying the effects of one or more continuous explanatory variable(s) on a binary disease outcome. In the present paper, we propose a set of SAS macros which perform non-parametric logistic regression analysis with B-spline expansions of an arbitrary number of continuous covariates, estimating adjusted odds ratios with respective confidence intervals for any given value with respect to a supplied reference value. Our SAS codes further allow to graphically visualize the shape of the association, retaining the exposure variable under consideration in its initial, continuous form while concurrently adjusting for multiple confounding factors. The macros are easily to use and can be implemented quickly by the clinical or epidemiological researcher to flexibly investigate any dose-response association of continuous exposures with the risk of binary disease outcomes. We illustrate the application of our SAS codes by investigating the effect of body-mass index on risk of cancer incidence in a large, population-based male cohort.
Relative places
Authors: Maureen Donnelly
Venue: Applied Ontology (2005)

Newton distinguishes between absolute and relative places. Both types of places endure through time and may be occupied by various objects at various times. But unlike absolute places, each relative place stands in fixed spatial relations with one or more reference objects. Relative places with independent reference objects (e.g., a ship and the earth) may move relative to one another. Relative places, not absolute places, are used to locate objects and track their movements in common-sense reasoning and in disciplines such as biology, engineering, and geology. The purpose of this paper is to develop a formal theory for reasoning about relative places and their changing relations to both other places and to material objects.
Diagrammatic Knowledge-Based Tools for Complex Multi-dynamic Processes
Authors: Ronald R. Grau, Peter C. Cheng
Venue: Proceedings of the 5th international conference on Diagrammatic Representation and Inference (2008)

This paper describes the initial work in a program of research that aims to create diagrammatic tools for the modeling and discovery of an extremely challenging domain: Complex multi-dynamic (CMD) processes. Diagrams play a central role in the approach because they provide a range of representational, cognitive and computational properties that may yield potential solutions to the challenges posed by CMD processes. Motivation for this work comes from previous research on the use of diagrams in computational scientific discovery [2], [3] and the potential of diagrams for conducting knowledge acquisition [4].
Tactic-based learning for collective learning systems
Authors: Peter Bock
Venue:  (2008)

Tactic-Based Learning is a new selection policy for statistical learning systems that has been tested with a Collective Learning Automaton which solves a simple, but representative problem. Current selection policies respond to immature stimuli that do not yet have high-confidence responses associated with them by selecting responses randomly. Albeit unbiased, this policy ignores any confident information already acquired for other well-trained stimuli. To exploit this confident information, Tactic-Based Learning hypothesizes that in the absence of a sufficiently confident response to a given stimulus, selecting a confident response to a different, but nonetheless well-trained stimulus is a better strategy than selecting a random response. Tactic-Based Learning does not require any feature comparison in search of an appropriate response. Preliminary results show that Tactic-Based Learning significantly accelerates learning and reduces error, especially when several stimuli share the same response, i.e. , when broad domain generalization is possible. Tactic-Based Learning reduces the use of pseudo-random number generators in the response selection process. Additionally, Tactic-Based Learning assists the recovery of learning performance when the problem evolves over time.
Plenary lecture III: spectral structure of the equilateral triangle
Authors: Brian J. McCartin
Venue: Proceedings of the American Conference on Applied Mathematics (2008)

Lame's formulas for the eigenvalues and eigenfunctions of the Laplacian on an equilateral triangle with Dirichlet and Neumann boundary conditions will be reviewed and then extended to the Robin boundary condition. The eigenfunctions will be shown to form a complete orthonormal system. Various properties of the spectrum and modal functions will be explored.
Precise Information Extraction from Text Based on Two-Level Concept Lattice
Authors: Zhao-man Zhong, Zong-tian Liu, Yan Guan
Venue: Proceedings of the 2008 International Symposiums on Information Processing (2008)

Aiming at the problems of high cost of time consumption and low precision of IE (information extraction) in IE systems based on concept lattice, a novel mechanism of two-level concept lattice-based IE is put forward. The structure concept lattice is the logical storage of semantic structure of documents, and the content concept lattice is used to store content information of documents. The paper gives the formal descriptions of structure and content concept lattice and analyses the time complexity of them. The consequence shows our method has obvious advantages concerning the time consumption and the precision of content extraction compared with existing IE methods.
Joint effect analysis of phase noise, carrier frequency offset, Doppler spread and nonlinear amplifier on the performance of OFDM system
Authors: Mohammad Mahmoud, Mona Riad
Venue: Proceedings of the 7th WSEAS International Conference on Application of Electrical Engineering (2008)

In this paper, we provide an exact closed-form expression of the effective signal-to-noise ratio (SNR) for orthogonal frequency division multiplexing (OFDM) performance under the combined effect of phase noise, frequency offset, and amplifier nonlinearities of the soft envelope limiter (SEL). This formula describes explicitly the various parameters with the system performance. After deriving the effective SNR, we evaluate the bit-error-rate (BER) for slowly fading Rayleigh channel for QPSK and 16QAM modulation formats. We also obtain the minimum total degradation (TD) versus phase noise rate for different values of frequency offset rate as a criterion of performance evaluation. We show that the degradation is increased as the phase noise levels and frequency offset levels are increased. Even worse results are obtained for 16QAM modulation format compared with QPSK modulation format.
What should the agent know?: the challenge of capturing human knowledge
Authors: Emma Norling
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3 (2008)

Reports of applications that include agent-based models of human behaviour tend to focus on the applications themselves and the success of the modelling exercise. They give little (if any) information on the process used to construct the models. Those who attempt to construct models of human behaviour quickly realise that this is a non-trivial task. This paper presents a methodological approach to eliciting knowledge for BDI-based models of human behaviour.