ppxscal/embedding_network huggingface dataset:
Embedded Malware Detection Using Markov n-Grams
Authors: M. Zubair Shafiq, Syed Ali Khayam, Muddassar Farooq
Venue: Proceedings of the 5th international conference on Detection of Intrusions and Malware, and Vulnerability Assessment (2008)

Embedded malware is a recently discovered security threat that allows malcode to be hidden inside a benign file. It has been shown that embedded malware is not detected by commercial antivirus software even when the malware signature is present in the antivirus database. In this paper, we present a novel anomaly detection scheme to detect embedded malware. We first analyze byte sequences in benign files to show that benign files' data generally exhibit a 1-st order dependence structure. Consequently, conditional n-grams provide a more meaningful representation of a file's statistical properties than traditional n-grams. To capture and leverage this correlation structure for embedded malware detection, we model the conditional distributions as Markov n -grams. For embedded malware detection, we use an information-theoretic measure, called entropy rate, to quantify changes in Markov n-gram distributions observed in a file. We show that the entropy rate of Markov n-grams gets significantly perturbed at malcode embedding locations, and therefore can act as a robust feature for embedded malware detection. We evaluate the proposed Markov n-gram detector on a comprehensive malware dataset consisting of more than 37,000 malware samples and 1,800 benign samples of six well-known filetypes. We show that the Markov n-gram detector provides better detection and false positive rates than the only existing embedded malware detection scheme.
Sliding Mode Control for Missile Electro-hydraulic Servo System Using Recurrent Fuzzy Neural Network
Authors: Huafeng He, Yunfeng Liu, Xiaogang Yang
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks (2007)

The position tracking control of a missile electro-hydraulic servo system is studied. Since the dynamics of the system are highly nonlinear and have large extent of model uncertainties, such as big changes in parameters and external disturbance, a design method of sliding mode control (SMC) using recurrent fuzzy neural network (RFNN) is proposed. First a SMC system, which is insensitive to uncertainties including parameter variations and external disturbance, is introduced. Then, to overcome the problems with SMC, such as the assumption of known uncertainty bounds and the chattering phenomena in the control signal, an RFNN is introduced in conventional SMC. An RFNN bound observer is utilized to adjust the uncertainty bounds in real time. Simulation results verify the validity of the proposed approach.
Online, Dynamic, and Distributed Embeddings of Approximate Ultrametrics
Authors: Michael Dinitz
Venue: Proceedings of the 22nd international symposium on Distributed Computing (2008)

The theoretical computer science community has traditionally used embeddings of finite metrics as a tool in designing approximation algorithms. Recently, however, there has been considerable interest in using metric embeddings in the context of networks to allow network nodes to have more knowledge of the pairwise distances between other nodes in the network. There has also been evidence that natural network metrics like latency and bandwidth have some nice structure, and in particular come close to satisfying an ¿-three point condition or an ¿-four point condition. This empirical observation has motivated the study of these special metrics, including strong results about embeddings into trees and ultrametrics. Unfortunately all of the current embeddings require complete knowledge about the network up front, and so are less useful in real networks which change frequently. We give the first metric embeddings which have both low distortion and require only small changes in the structure of the embedding when the network changes. In particular, we give an embedding of semimetrics satisfying an ¿-three point condition into ultrametrics with distortion (1 + ¿)logn + 4 and the property that any new node requires only O(n 1/3) amortized edge swaps, where we use the number of edge swaps as a measure of "structural change". This notion of structural change naturally leads to small update messages in a distributed implementation in which every node has a copy of the embedding. The natural offline embedding has only (1 + ¿)logn distortion but can require ¿(n) amortized edge swaps per node addition. This online embedding also leads to a natural dynamic algorithm that can handle node removals as well as insertions.
Consecutive block minimization is 1.5-approximable
Authors: Salim Haddadi, Zoubir Layouni
Venue: Information Processing Letters (2008)

We present a polynomial-time heuristic for the Consecutive Block Minimization Problem such that the solutions generated do not differ from optimal by more than 50%.
Supporting Agent Organizations
Authors: Estefania Argente, Javier Palanca, Gustavo Aranda, Vicente Julian, Vicente Botti, Ana Garcia-Fornes, Agustin Espinosa
Venue: Proceedings of the 5th international Central and Eastern European conference on Multi-Agent Systems and Applications V (2007)

Organizational Theory concepts have been adapted to improve, extend and facilitate Agent Organizations modeling. Thus, three basic organizational topologies from which other more complex organizations can be composed have been identified. Moreover, this organizational modeling has been implemented in a specific MAS platform (SPADE) making use of Multiuser Conference technology. We have also defined several services for SPADE that ease the implementation of organization dynamics, such as member access and agent interaction control.
Translation of Common Information Model to Web Ontology Language
Authors: Marta Majewska, Bartosz Kryza, Jacek Kitowski
Venue: Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007 (2007)

This paper presents a brief overview of the work on translation of Common Information Model (CIM) to Web Ontology Language (OWL) standard. The main motivation for the work is given, along with discussion of major issues faced during this work. The paper contains also comparison of existing approaches to conversion of CIM to OWL and presents the CIM2OWL tool that performs the conversion of CIM schema and allows convertion of CIM instances - representing for instance configurations of particular systems - to OWL individuals.
Person tracking for ambient camera selection in complex sports environments
Authors: Nikos Katsarakis, Aristodemos Pnevmatikakis, John Soldatos
Venue: Proceedings of the 3rd international conference on Digital Interactive Media in Entertainment and Arts (2008)

As pervasive computing technologies are gradually penetrating sport, we are witnessing a proliferating number of research systems that can track athletes during training and/or in competition. Indeed, athlete tracking is a particularly challenging research tasks, which is also a key enabler for a wide range of applications such as ambient personalized broadcasting. In this paper we survey person tracking systems for sport applications and illustrate their limitations for realistic sports environments. As a real-life example we present the robust and high-performance person tracking system developed at the Athens Information Technology, and explain its inability to deal with occlusions, interlacing, adverse and changing light conditions and mostly the strain of the athletes, which are very common in high activity athletic scenes. We also present techniques for dealing with these challenges, along with an architecture for building ambient camera selection environments for broadcasting purposes. These techniques form the basis for the person tracking and ambient camera selection systems that are developed in the scope of the my-e-Director 2012 EC project, which is working towards a realistic prototype system for the London 2012 Olympics.
Towards a Management Paradigm with a Constrained Benchmark for Autonomic Communications
Authors: Frank Chiang, Robin Braun
Venue: Computational Intelligence and Security: International Conference, CIS 2006, Guangzhou, China, November 3-6, 2006, Revised Selected Papers (2007)

This paper describes a management paradigm to give effect to autonomic activation, monitoring and control of services or products in the future converged telecommunications networks. It suggests an architecture that places the various management functions into a structure that can then be used to select those functions which may yield to autonomic management, as well as guiding the design of the algorithms. The validation of this architecture, with particular focus on service configuration, is done via a genetic algorithm -- Population Based Incremental Learning (PBIL). Even with this centralized adaptation strategy, the simulation results show that the proposed architecture and benchmark can be applied to this constrained benchmark, produces effective convergence performance in terms of finding nearly optimal configurations under multiple constraints.
Bayesian Oil Spill Segmentation of SAR Images Via Graph Cuts
Authors: Sónia Pelizzari, José M. Bioucas-Dias
Venue: Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II (2007)

This paper extends and generalizes the Bayesian semi-supervised segmentation algorithm [1] for oil spill detection using SAR images. In the base algorithm on which we build on, the data term is modeled by a finite mixture of Gamma distributions. The prior is an M-level logistic Markov Random Field enforcing local continuity in a statistical sense. The methodology proposed in [1] assumes two classes and known smoothness parameter. The present work removes these restrictions. The smoothness parameter controlling the degree of homogeneity imposed on the scene is automatically estimated and the number of used classes is optional. Semi-automatic estimation of the class parameters is also implemented. The maximum a posteriori (MAP) segmentation is efficiently computed via the ¿¿ expansion algorithm [2], a recent graph-cut technique, The effectiveness of the proposed approach is illustrated with simulated (Gaussian or Gamma data term and M-level logistic classes) and real ERS data.
Iterative Reinforcement Cross-Domain Text Classification
Authors: Di Zhang, Gui-Rong Xue, Yong Yu
Venue: Proceedings of the 4th international conference on Advanced Data Mining and Applications (2008)

Traditional text classification techniques are based on a basic assumption that the underlying distributions of training and test data should be identical. However, in many real world applications, this assumption is not often satisfied. Labeled training data are expensive, but there may be some labeled data available in a different but related domain from test data. Therefore, how to make use of labeled data from a different domain to supervise the classification becomes a crucial task. In this paper, we propose a novel algorithm for cross-domain text classification using reinforcement learning. In our algorithm, the training process is iteratively reinforced by making use of the relations between documents and words. Empirically, our method is an effective and scalable approach for text categorization when the training and test data are from different but related domains. The experimental results show that our algorithm can achieve better performance than several state-of-art classifiers.
Incremental Knowledge Representation Based on Visual Selective Attention
Authors: Minho Lee, Sang-Woo Ban
Venue: Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part II (2008)

Knowledge-based clustering and autonomous mental development remains a high priority research topic, among which the learning techniques of neural networks are used to achieve optimal performance. In this paper, we present a new framework that can automatically generate a relevance map from sensory data that can represent knowledge regarding objects and infer new knowledge about novel objects. The proposed model is based on understating of the visual what pathway in our brain. A bottom-up attention model can selectively decide salient object areas. Color and form features for a selected object are generated by a sparse coding mechanism by a convolution neural network (CNN). Using the extracted features by the CNN as inputs, the incremental knowledge representation model, called the growing fuzzy topology adaptive resonant theory (TART) network, makes clusters for the construction of an ontology map in the color and form domains. The clustered information is relevant to describe specific objects, and the proposed model can automatically infer an unknown object by using the learned information. Experimental results with real data have demonstrated the validity of this approach.
Session-Based Distributed Programming in Java
Authors: Raymond Hu, Nobuko Yoshida, Kohei Honda
Venue: Proceedings of the 22nd European conference on Object-Oriented Programming (2008)

This paper demonstrates the impact of integrating session types and object-oriented programming, through their implementation in Java. Session types provide high-level abstraction for structuring a series of interactions in a concise syntax, and ensure type-safe communications between distributed peers. We present the first full implementation of a language and runtime for session-based distributed programming featuring asynchronous message passing, delegation, and session subtyping and interleaving, combined with class downloading and failure handling. The compilation-runtime framework of our language effectively maps session abstraction onto underlying transports and guarantees communication safety through static and dynamic session type checking. We have implemented two alternative mechanisms for performing distributed session delegation and prove their correctness. Benchmark results show session abstraction can be realised with low runtime overhead.
A Novel RGBW Pixel for LED Displays
Authors: Neveen Shlayan, Rama Venkat, Paolo Ginobbi, Glenn Mercier
Venue: Proceedings of the 2008 19th International Conference on Systems Engineering (2008)

In display technology, pixels are composed of three primary colors: red, green, and blue (RGB). Using the primary colors provides the ability to generate variety of hues including achromatic point (AP) (white light). This concept is applied in Light emitting diode (LED) displays where each pixel consists of RGB LEDs. Although using compact RGB light in LED displays provides a wide color gamut, many issues arise due to dissimilarities in optical and physical characteristics of LEDs such as: uniform spatial light mixing and distribution of AP, and AP maintenance and thermal management. Power consumption is also a major concern in LED displays. In this paper, the issues with current RGB pixel architecture such as non-uniform mixing and distribution of AP, energy efficiency and a solution to these issues by introducing a new pixel configuration consisting of red, green, blue and white (RGBW) are discussed. Experimental results as well as theoretical analysis of the new configuration are presented and discussed.
Strong Secrecy for Wireless Channels (Invited Talk)
Authors: João Barros, Matthieu Bloch
Venue: Proceedings of the 3rd international conference on Information Theoretic Security (2008)

It is widely accepted by the information security community that a secrecy criterion based solely on minimizing the rate at which an eavesdropper extracts bits from a block of noisy channel outputs is too weak a concept to guarantee the confidentiality of the protected data. Even if this rate goes to zero asymptotically (i.e. for sufficiently large codeword length), vital information bits can easily be leaked to an illegitimate receiver. In contrast, many of the recent results in information-theoretic security for wireless channel models with continuous random variables rely on this weak notion of secrecy, even though previous work has shown that it is possible to determine the ultimate secrecy rates for discrete memoryless broadcast channels under a stronger secrecy criterion -- namely one which bounds not the rate but the total number of bits obtained by the eavesdropper. Seeking to bridge the existing gap between fundamental cryptographic requirements and ongoing research in wireless security, we present a proof for the secrecy capacity of Gaussian broadcast channels under the strong secrecy criterion. As in the discrete memoryless case, the secrecy capacity is found to be the same as in the weaker formulation. The extension to fading channels is shown to be straightforward.
Neuro-Identifier-Based Tracking Control of Uncertain Chaotic System
Authors: Wen Tan, Fuchun Sun, Yaonan Wang, Shaowu Zhou
Venue: Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II (2008)

A novel neuro-identifier-based tracking control of uncertain nonlinear chaotic system is presented. The algorithm is divided into two contributions. First, a dynamic neural networks is used to identify the unknown chaos, then a dynamic adaptive state feedback controller based on neuro-identifier is derived to direct the unknown chaotic system into desired reference model trajectories. Moreover, the identification error and trajectory error is theoretically verified to be bounded and converge to zero Computer simulations are shown to demonstrate the effectiveness of this proposed methodology.
Towards a Versatile Contract Model to Organize Behavioral Specifications
Authors: Philippe Collet, Alain Ozanne, Nicolas Rivierre
Venue: Proceedings of the 33rd conference on Current Trends in Theory and Practice of Computer Science (2007)

The dependability of component-based systems mainly relies on the ability to guarantee the safe collaboration of components. Many specification formalisms can then be used and we argue that such specifications should be organized through an appropriate contract model so that guarantees and possible violations can be better exploited. In this paper, we propose a versatile contract model that explicitly reifies the assumptions and guarantees of some behavioral specifications on component assemblies. We briefly illustrate the integration of executable assertions and we detail how Behavior Protocols can be integrated in the contract model.
Inventing Wellness Systems for Aging in Place
Authors: Eric Dishman
Venue: Computer (2004)

Intel's Proactive Health lab emerged from an anthropological study of households that had been early adopters of broadband technology. Almost every study participant over age 40 asked for technology to help with the care of aging parents. As the worldwide population over age 65 doubles in the next 20 years, the caregiving needs of this population will become an ever greater part of our personal lives and social healthcare costs.The lab is applying digital home technologies to the development of "aging in place" personal health systems. These applications of wireless sensors, adaptive interfaces, real-time data capture, and context-aware feedback provide a rigorous testbed for digital home technologies. They also support an alternative to the costly "mainframe" healthcare that dominates current medical science and practice. Ultimately, aging-in-place research supports fundamental new ways of understanding both aging and disease processes to help us all better manage our health.
Designing Fault-Tolerant Component Based Applications with a Model Driven Approach
Authors: Brahim Hamid, Ansgar Radermacher, Agnes Lanusse, Christophe Jouvray, Sébastien Gérard, François Terrier
Venue: Proceedings of the 6th IFIP WG 10.2 international workshop on Software Technologies for Embedded and Ubiquitous Systems (2008)

The requirement for higher reliability and availability of systems is continuously increasing even in domains not traditionally strongly involved in such issues. Solutions are expected to be efficient, flexible, reusable on rapidly evolving hardware and of course at low cost. Model driven approaches can be very helpful for this purpose. In this paper, we propose a study associating model-driven technology and component-based development. This work is illustrated by the realization of a use case from aerospace industry that has fault-tolerance requirements: a launch vehicle.UML based modeling is used to capture application structure and related non-functional requirements thanks to the profiles CCM (CORBA Component Model) and QoS FT (Quality of Service and Fault Tolerance). The application model is enriched with infrastructure component dedicated to fault-tolerance. From this model we generate CCM descriptor files which in turns are used to build bootcode (static deployment) which instantiates, configures and connects components. Within this process, component replication and FT properties are declaratively specified at model level and are transparent for the component implementation.
Ink flow control by multiple models in an offset lithographic printing process
Authors: Cristofer Englund, Antanas Verikas
Venue: Computers and Industrial Engineering (2008)

A multiple model-based controller has been developed aiming at controlling the ink flow in the offset lithographic printing process. The control system consists of a model pool of four couples of inverse and direct models. Each couple evaluates a number of probable control signals and the couple, generating the most suitable control signal is used to control the printing press, at that moment. The developed system has been tested at a newspaper printing shop during normal production. The results show that the developed modelling and control system is able to drive the output of the printing press to the desired target levels.
Trust and Norms in the Context of Computer Security: A Logical Formalization
Authors: Emiliano Lorini, Robert Demolombe
Venue: Proceedings of the 9th international conference on Deontic Logic in Computer Science (2008)

In this paper we present a logical model of trust in which trust is conceived as an expectation of the truster about some properties of the trustee. A general typology of trust is presented. We distinguish trust in the trustee's action from trust in the trustee's disposition (motivational or normative disposition); positive trust from negative trust. A part of the paper is devoted to the formalization of security properties and to the analysis of their relationships with trust.
Evolutionary Canonical Particle Swarm Optimizer --- A Proposal of Meta-optimization in Model Selection
Authors: Hong Zhang, Masumi Ishikawa
Venue: Proceedings of the 18th international conference on Artificial Neural Networks, Part I (2008)

We proposed Evolutionary Particle Swarm Optimization (EPSO) which provides a new paradigm of meta-optimization for model selection in swarm intelligence. In this paper, we extend the technique of online evolutionary computation of EPSO to Canonical Particle Swarm Optimizer (CPSO), and propose Evolutionary Canonical Particle Swarm Optimizer (ECPSO) for optimizing CPSO. In order to effectually evaluate the performance of CPSO, a temporally cumulative fitness function of the best particle is adopted in ECPSO as the behavioral representative for entire swarm. Applications of the proposed method to a suite of 5-dimensional benchmark problems well demonstrate the effectiveness. Our experimental results clearly indicate that (1) the proper parameter sets in CPSO for solving various optimization problems are not unique; (2) the values of parameters in them are quite different from that of the original CPSO; (3) the search performance of the optimized CPSO is superior to that of the original CPSO, and to that of RGA/E except for the result to the Rastrigin's benchmark problem.