ppxscal/embedding_network huggingface dataset:
Model-Based Covert Timing Channels: Automated Modeling and Evasion
Authors: Steven Gianvecchio, Haining Wang, Duminda Wijesekera, Sushil Jajodia
Venue: Proceedings of the 11th international symposium on Recent Advances in Intrusion Detection (2008)

The exploration of advanced covert timing channel design is important to understand and defend against covert timing channels. In this paper, we introduce a new class of covert timing channels, called model-based covert timing channels, which exploit the statistical properties of legitimate network traffic to evade detection in an effective manner. We design and implement an automated framework for building model-based covert timing channels. Our framework consists of four main components: filter, analyzer, encoder, and transmitter. The filter characterizes the features of legitimate network traffic, and the analyzer fits the observed traffic behavior to a model. Then, the encoder and transmitter use the model to generate covert traffic and blend with legitimate network traffic. The framework is lightweight, and the overhead induced by model fitting is negligible. To validate the effectiveness of the proposed framework, we conduct a series of experiments in LAN and WAN environments. The experimental results show that model-based covert timing channels provide a significant increase in detection resistance with only a minor loss in capacity.
Using Mapping Relations to Semi Automatically Compose Web Services
Authors: Marwan Sabbouh, Jeffrey L. Higginson, Caleb Wan, Scott R. Bennett
Venue: Proceedings of the 2008 IEEE Congress on Services - Part I (2008)

This paper accomplishes the automatic composition of web services by leveraging semantics in XML Schemas, and by automatically generating aligned ontologies from mappings between XML schemas. Hence, we propose to encode the import of schemas and their mappings in HTML, which when combined with JavaScript libraries enable a web user to write mashups that hide coding and ontological complexities and achieve automated data mediation
Electrical characteristic modeling and simulation of PEMFC based on least-squares parameter estimation
Authors: Wei Dong, Lu Yong-jun, Chu Lei-min
Venue: Proceedings of the 8th WSEAS International Conference on Robotics, Control and Manufacturing Technology (2008)

The electrical characteristic model of Proton exchange membrane fuel cell (PEMFC) is built according to the theories of electrochemistry, hydrokinetics, energetic and the structure of PEMFC and so on. Through simulating with experimental data, the results show that this mathematical model can reflect the cell's electrical characteristic commendably in the middle and high electricity density. In order to reduce the complexity of this model and improve the degree of fitting in the low electricity density, a method based on the least-squares parameter identification is introduced to estimate the unknown parameters in the model. The results of simulation and experiment show that this method can improve the accuracy of model greatly. So this algorithm is effective and superior, it is of practical value in the PEMFC control system modeling.
Proceedings of the workshop on Real-world wireless sensor networks
Venue: European Conference on Computer Systems (2008)

It is our greatest pleasure to welcome you to the third Workshop on Real-World Wireless Sensor Networks (REALWSN) in the city of Glasgow. We want to thank you all -- authors, attendees and members of the technical program committee -- for making this event possible. As the name of the workshop suggests, REALWSN is a forum for people interested in real world issues in the fascinating research area of wireless sensor networks. Deploying real sensor networks is a challenging task since in most cases the behaviour of the deployed network differs substantially from the behaviour of the same network in the simulator. The main objective of REALWSN is to bring together researchers and practitioners to boost the state of the art in this exciting field. This year the program consists of 14 full papers carefully selected by the program committee from 34 submissions. The technical program covers topics from hardware and software development to real-world sensor network applications.
On the Behaviour of Information Measures for Test Selection
Authors: Danielle Sent, Linda C. Gaag
Venue: Proceedings of the 11th conference on Artificial Intelligence in Medicine (2007)

In diagnostic decision-support systems, a test-selection facility serves to select tests that are expected to yield the largest decrease in the uncertainty about a patient's diagnosis. For capturing diagnostic uncertainty, often an information measure is used. In this paper, we study the Shannon entropy, the Gini index, and the misclassification error for this purpose. We argue that for a large range of values, the first derivative of the Gini index can be regarded as an approximation of the first derivative of the Shannon entropy. We also argue that the differences between the derivative functions outside this range can explain different test sequences in practice. We further argue that the misclassification error is less suited for test-selection purposes as it is likely to show a tendency to select tests arbitrarily. Experimental results from using the measures with a real-life probabilistic network in oncology support our observations.
Real Time Colour Image Segmentation with Non-Symmetric Gaussian Membership Functions
Authors: Omid Sojodishijani, Vahid Rostami, Abd Rahman Ramli
Venue: Proceedings of the 2008 Fifth International Conference on Computer Graphics, Imaging and Visualisation (2008)

Segmentation and also partitioning image into disjoint subsets are one of the most important stages in many computer vision applications. In this paper the new algorithm for dividing colour image into homogeneous regions is proposed. In this algorithm we use histogram estimation in 3 channels - RED, GREEN and BLUE - to create adaptive membership functions that update dynamically when the entire image is scanned. These triple membership functions characterise each region in image. For producing the proper estimation of histograms, the Non-Symmetric Gaussian Function (NSGF) is proposed. To achieve real time implementation, the algorithm scans the image in raster fashion. Finally, the performance of algorithm is shown in the result of applying this algorithm to natural and synthetic noisy images.
An analysis of data corruption in the storage stack
Authors: Lakshmi N. Bairavasundaram, Andrea C. Arpaci-Dusseau, Remzi H. Arpaci-Dusseau, Garth R. Goodson, Bianca Schroeder
Venue: ACM Transactions on Storage (TOS) (2008)

An important threat to reliable storage of data is silent data corruption. In order to develop suitable protection mechanisms against data corruption, it is essential to understand its characteristics. In this article, we present the first large-scale study of data corruption. We analyze corruption instances recorded in production storage systems containing a total of 1.53 million disk drives, over a period of 41 months. We study three classes of corruption: checksum mismatches, identity discrepancies, and parity inconsistencies. We focus on checksum mismatches since they occur the most. We find more than 400,000 instances of checksum mismatches over the 41-month period. We find many interesting trends among these instances, including: (i) nearline disks (and their adapters) develop checksum mismatches an order of magnitude more often than enterprise-class disk drives, (ii) checksum mismatches within the same disk are not independent events and they show high spatial and temporal locality, and (iii) checksum mismatches across different disks in the same storage system are not independent. We use our observations to derive lessons for corruption-proof system design.
Message Quality for Ambient System Security
Authors: Ciarán Bryce
Venue: Proceedings of the 7th international conference on Ad-hoc, Mobile and Wireless Networks (2008)

In ambient systems, a principal may be a physical object whose identity does not convey useful information for taking security decisions. Thus, establishing a trusted channel with a device depends more on the device being able to demonstrate what is does, rather than who it is. This paper proposes a security model that allows a principal to establish the intent of an adversary and to make the adversary prove its trustworthiness by furnishing proof of current and past behavior.
Spike-Timing Dependent Plasticity in Recurrently Connected Networks with Fixed External Inputs
Authors: Matthieu Gilson, David B. Grayden, J. Leo Hemmen, Doreen A. Thomas, Anthony N. Burkitt
Venue: Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part I (2007)

This paper investigates spike-timing dependent plasticity (STDP) for recurrently connected weights in a network with fixed external inputs (homogeneous Poisson pulse trains). We use a dynamical system to model the network activity and predict its asymptotic evolution, which turns out to qualitatively depend on the learning parameters and the correlation structure of the inputs. Our predictions are supported by numerical simulations of Poisson neuron networks in general cases as well as for certain cases when using Integrate-And-Fire (IF) neurons.
Palmprint Recognition by Applying Wavelet Subband Representation and Kernel PCA
Authors: Murat Ekinci, Murat Aykut
Venue: Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition (2007)

This paper presents a novel Daubechies-based kernel Principal Component Analysis (PCA) method by integrating the Daubechies wavelet representation of palm images and the kernel PCA method for palmprint recognition. The palmprint is first transformed into the wavelet domain to decompose palm images and the lowest resolution subband coefficients are chosen for palm representation. The kernel PCA method is then applied to extract non-linear features from the subband coefficients. Finally, weighted Euclidean linear distance based NN classifier and support vector machine (SVM) are comparatively performed for similarity measurement. Experimental results on PolyU Palmprint Databases demonstrate that the proposed approach achieves highly competitive performance with respect to the published palmprint recognition approaches.
An Open Mobile Identity Tool: An Architecture for Mobile Identity Management
Authors: Konstantin Hyppönen
Venue: Proceedings of the 5th European PKI workshop on Public Key Infrastructure: Theory and Practice (2008)

We present an architecture for a flexible and open mobile electronic identity tool, which can work as a replacement for numerous ID cards and licenses. In addition, it can be used in various payment and user authentication scenarios. The tool is mobile phone based and uses a security element (e.g., a SIM card) for storing sensitive identity information. We follow the design for privacy principles, such as minimisation of data collection and informed consent of the user. The tool can be implemented using currently available handset technology.
Polynomial-Time Construction of Linear Network Coding
Authors: Kazuo Iwama, Harumichi Nishimura, Mike Paterson, Rudy Raymond, Shigeru Yamashita
Venue: Proceedings of the 35th international colloquium on Automata, Languages and Programming, Part I (2008)

Constructing k independent sessions between k source-sink pairs with the help of a linear operation at each vertex is one of the most standard problems in network coding. For an unbounded k, this is known to be NP-hard. Very recently, a polynomial-time algorithm was given for k = 2 [Wang and Shroff, ISIT 2007], but was open for a general (constant) k. This paper gives a polynomial-time algorithm for this problem under the assumption that the size of the finite field for the linear operations is bounded by a fixed constant.
On the Linkability of Some Group Signature Schemes
Authors: Hung-Min Sun, Her-Tyan Yeh Tzonelih Hwang
Venue: Informatica (2003)

A group signature scheme is a digital signature scheme that allows a group member to sign messages anonymously on behalf of the group. Recently, Tseng and Jan proposed two group signature schemes based on self-certified and ID-based public keys respectively. However, these two schemes were shown to be insecure against forgery due to Joye et al. Later, Sun et al. showed that Tseng and Jan's self-certified group signature scheme is linkable. In this paper, we first point out that the proposed linking equation, which is used to check the linkability of Tseng and Jan's self-certified scheme, cannot work because the inverse problem of RSA is hard. A repaired linking equation is consequently proposed to fix this problem. Then, we show that Tseng and Jan's ID-based scheme is still linkable because given any two valid group signatures it is easy to decide whether these two group signatures are generated by the same group member or not.
An Implementation of a Free-Variable Tableaux for KLM Preferential Logic P of Nonmonotonic Reasoning: The Theorem Prover FreeP 1.0
Authors: Laura Giordano, Valentina Gliozzi, Nicola Olivetti, Gian Luca Pozzato
Venue: Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing (2007)

We present FreeP 1.0, a theorem prover for the KLM preferential logic P of nonmonotonic reasoning. FreeP 1.0 is a SICStus Prolog implementation of a free-variables, labelled tableau calculus for P, obtained by introducing suitable modalities to interpret conditional assertions. The performances of FreeP 1.0 are promising. FreeP 1.0 can be downloaded at http://www.di.unito.it/~pozzato/FreeP1.0 .
How Do People Seek Knowledge in Information System Projects: A New Perspective from Social Network Theory
Authors: Chenghong Zhang, Yunjie (Calvin) Xu, Cheng Zhang
Venue: Proceedings of the 2008 The Third International Multi-Conference on Computing in the Global Information Technology (iccgi 2008) (2008)

Why does a project team member prefer some colleagues to others in knowledge seeking? Past literature suggests that the physical accessibility of a knowledge source, the knowledge quality of the source and relational concerns are important to such choice. Our social network analysis of an Information Systems (IS) project team indicates that job interdependence and rank have a significant impact on knowledge sourcing frequency. In contrast, personal relationship and the knowledge quality of source have no significant effect. This finding suggests both the importance of structural factors in knowledge sourcing and the context-dependent nature of knowledge sourcing behavior.
Software tool for naval surface warfare simulation and training
Authors: Sergiu Dascalu, Sermsak Buntha, Daniela Saru, Narayan Debnath
Venue: Journal of Computational Methods in Sciences and Engineering (2006)

Determining optimal operations involves performing complex tasks by navy commanders. In this paper we present a software tool with four main functions that facilitate commanders' tasks in naval surface warfare. First, a human-computer interaction function presents battlefield information using information-rich graphical symbols. Second, a command and control function allows commanders to achieve centralized control of naval warfare operation in battlefield situations. Third, an interception function is available to determine the optimum course for intercepting a particular enemy target ship. Fourth, an escape route planner can be invoked to automatically calculate routes that minimize detection by enemy platforms. The paper presents background information on naval surface warfare, excerpts of the proposed software tool's model, and details of the tool's user interface. A brief survey of related projects as well as several pointers to future work are also included.
Optimizing partitioned global address space programs for cluster architectures
Authors: Katherine A. Yelick
Venue:  (2007)

Unified Parallel C (UPC) is an example of a partitioned global address space language for high performance parallel computing. This programming model enables application to be written in a shared memory style, but still allows the programmer to control data layout and the assignment of work to processors. An open question is whether programs written in simple style, with fine-grained accesses and blocking communication, can achieve performance approaching that of hand-optimized code, especially for cluster environments with high network latencies. This dissertation proposes an optimization framework for UPC that automates the transformations currently performed manually by programmers. The goals of the optimizations are twofold: not only do we seek to aggregate fine-grained remote accesses to reduce the number and volume of message traffic, but we also want to overlap communication with computation to hide network latency. The framework first applies communication vectorization and strip-mining to optimize regular array accesses in loops. For irregular fine-grained accesses, we apply a partial redundancy elimination framework that also generates split-phase communication. The last phase targets the blocking bulk transfers in the program by utilizing runtime support to automatically schedule them and achieve overlap. Message aggregation is performed as part of the scheduling to further reduce the communication overhead. Finally, we present several techniques for optimizing the serial performance of a UPC program, in order to reduce both the overhead of UPC-specific constructs and our source-to-source translation. The optimization framework has been implemented in the Berkeley UPC compiler, and has been tested on a number of supercomputer clusters. The optimizations are validated on a variety of benchmarks exhibiting different communication patterns, from bulk synchronous benchmarks to dynamic shared data structure code. Experimental results reveal that our framework offers comparable performance to aggressive manual optimization, and can achieve significant speedup compared to the fine-grained and blocking communication code that programmers find much easier to implement. Our framework is completely transparent to the user, and therefore improves productivity by freeing programmers from the details of communication management.
Collisional polarizability correlation functions: A step towards rotational-translational coupling
Authors: W. Głaz
Venue: Journal of Computational Methods in Sciences and Engineering (2004)

A theory of the spectral distribution of the molecular time correlation function of the collision induced polarizabilities active in the collision induced light scattering has been developed. Within the framework of this approach a new concept of the averaging procedure is proposed, which - unlike the usually applied assumptions of total separation of the angle dependent and the translational contributions - partially includes the coupling between the rotational and translational degrees of freedom due to anisotropic intermolecular interactions. Introduction of the rotational spectral functions, valid for different frequency ranges, in order to produce a numerically treatable method of deriving collisional spectral profiles is discussed. The final results of the theory are formulated in a fashion providing a solid base for numerical calculations capable of interpreting data obtained in various fields of physical, environmental and astrophysical sciences.
The Role of Internal States in the Emergence of Motivation and Preference: A Robotics Approach
Authors: Carlos Herrera, Alberto Montebelli, Tom Ziemke
Venue: Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction (2007)

In order to explain and model emotion we need to attend to the role internal states play in the generation of behavior. We argue that motivational and perceptual roles emerge from the dynamical interaction between physiological processes, sensory-motor processes and the environment. We investigate two aspects inherent to emotion appraisal and response which rely on physiological process: the ability to categorize relations with the environment and to modulate response generating different action tendencies.
Forecasting of Government's Financial Educational Fund by Using Neural Networks Model
Authors: Kai Li
Venue: Proceedings of the 2008 Second International Conference on Genetic and Evolutionary Computing (2008)

Forecasting method using neural networks has been advocated as an alternative to traditional statistical forecasting in recent years. The paper built a feed-forward neural network model to forecast the values of government’s financial educational fund (GFEF) in year 2010. On the basis of data processing, the structure of neural networks was given. The algorithm that adopted as a learning phase in the model was a fast one differing from that of the steep decent algorithm. The forecasts obtained from neural networks model were compared with the data forecasting by experts, and the error curve and the auto-adjusting curve of learning rate were also illustrated. The results show that the model was very effective.
Lloyd-Max's Algorithm Implementation in Speech Coding Algorithm Based on Forward Adaptive Technique
Authors: Jelena Nikolic, Zoran Peric
Venue: Informatica (2008)

In this paper a detail analysis of speech coding algorithm based on forward adaptive technique is carried out. We consider an algorithm that works on frame-by-frame basis, where a frame consists of a certain number of speech samples. Buffering frame-by-frame an estimation of the gain defined as squared root of the frame variance is enabled. The information about the gain (side information) and the code book of a nonadaptive quantizer, which is designed for the unit variance case of the input signal, are further used when designing an adaptive quantizer. In such a way better quantizer adaptation to the varying input statistics is provided. Observe that the goal of this paper is to investigate the preference that for the wide range of variance change could be achieved when implementing in the forward adaptive speech coding algorithm, the recently developed effective method for the Lloyd-Max's algorithm initialization, which provides optimal Lloyd-Max's quantizer performances for the unit variance case of the input signal. We destine to consider the speech coding algorithm based on forward adaptive technique since the backward adaptation provides SQNR (signal to quantization noise ratio) within 1 dB of the forward adaptation. We provide theoretical and experimental results (performances of our algorithm) which are compared with the optimal results. Additionally, we discuss the performances of speech coding schemes designed according to G. 711 standard and we point out the benefits that can be achieved by using our algorithm. Finally, in order to find better solution for implementation of the proposed algorithm in practice we consider the performances of our algorithm when log-uniform as well as uniform scalar quantizer are used for gain quantizing.
Graphic Drawing Tools for Network Traffic Simulation
Authors: Shingo Nomoto, Kensuke Fukuda, Minoru Uehara, Hideki Mori
Venue: Proceedings of the 2nd international conference on Network-Based Information Systems (2008)

We propose a visualization tool for better understanding AS-level Internet Topology. Traditional graph drawing tools do not focus on the AS-level policy, thereby restricting depiction of the direction of traffic flow. The proposed tool provides information about the relationship of AS links, as well as routing tables for nodes.