ppxscal/embedding_network huggingface dataset:
Fuzzy Classifier with Probabilistic IF-THEN Rules
Authors: Hexin Lv, Bin Zhu, Yongchuan Tang
Venue: Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing (2007)

The typical fuzzy classifier consists of rules each one describing one of the classes. This paper presents a new fuzzy classifier with probabilistic IF-THEN rules. A learning algorithm based on the gradient descent method is proposed to identify the probabilistic IF-THEN rules from the training data set. This new fuzzy classifier is finally applied to the well-known Wisconsin breast cancer classification problem, and a compact, interpretable and accurate probabilistic IF-THEN rule base is achieved.
Exclusive Strategy for Generalization Algorithms in Micro-data Disclosure
Authors: Lei Zhang, Lingyu Wang, Sushil Jajodia, Alexander Brodsky
Venue: Proceeedings of the 22nd annual IFIP WG 11.3 working conference on Data and Applications Security (2008)

When generalization algorithms are known to the public, an adversary can obtain a more precise estimation of the secret table than what can be deduced from the disclosed generalization result. Therefore, whether a generalization algorithm can satisfy a privacy property should be judged based on such an estimation. In this paper, we show that the computation of the estimation is inherently a recursive process that exhibits a high complexity when generalization algorithms take a straightforward inclusive strategy. To facilitate the design of more efficient generalization algorithms, we suggest an alternative exclusive strategy, which adopts a seemingly drastic approach to eliminate the need for recursion. Surprisingly, the data utility of the two strategies are actually not comparable and the exclusive strategy can provide better data utility in certain cases.
Object-Oriented Implementation of Software for Solving Ordinary Differential Equations
Authors: Kjell Gustafsson
Venue: Scientific Programming (1993)

We describe an object-oriented implementation of numerical integration methods for solving ordinary differential equations. Software components that are common to many different integration methods have been identified and implemented in such a way that they can be reused. This facilitates the design of a uniform user interface and makes the task of implementing a new integration method fairly modest. The sharing of code in this type of implementation also allows for less subjective comparisons of the result from different integration methods.
Constructions for strictly cyclic 3-designs and applications to optimal OOCs with λ=2
Authors: Tao Feng, Yanxun Chang, Lijun Ji
Venue: Journal of Combinatorial Theory Series A (2008)

In this paper we give some recursive constructions for strictly cyclic 3-designs. Using these constructions we have some infinite families of strictly cyclic Steiner quadruple systems and optimal optical orthogonal codes with weight 4 and index 2. As corollaries, many known constructions for strictly cyclic Steiner quadruple systems and optimal optical orthogonal codes are unified. We also notice that there does not exist an optimal (n,4,2)-OOC for any n=0 (mod24). Thus we introduce the concept of strictly cyclic maximal packing quadruple systems to deal with the cases of n=0 (mod24) for (n,4,2)-OOCs. By our recursive constructions, some infinite families are also given on strictly cyclic maximal packing quadruple systems.
Enabling dynamic spectrum allocation in cognitive radio networks
Authors: William A. Arbaugh
Venue:  (2007)

The last decade has witnessed the proliferation of innovative wireless technologies, such as Wi-Fi, wireless mesh networks, operating in unlicensed bands. Due to the increasing popularity and the wide deployments of these technologies, the unlicensed bands become overcrowded. The wireless devices operating in these bands interfere with each other and hurt the overall performance. To support fast growths of wireless technologies, more spectrums are required. However, as most "prime" spectrum has been allocated, there is no spectrum available to expand these innovative wireless services. Despite the general perception that there is an actual spectral shortage, the recent measurement results released by the FCC (Federal Communications Commission) show that on average only 5% of the spectrum from 30MHz to 30 GHz is used in the US. This indicates that the inefficient spectrum usage is the root cause of the spectral shortage problem. Therefore, this dissertation is focused on improving spectrum utilization and efficiency in tackling the spectral shortage problem to support ever-growing user demands for wireless applications. This dissertation proposes a novel concept of dynamic spectrum allocation, which adaptively divides available spectrum into non-overlapping frequency segments of different bandwidth considering the number of potentially interfering transmissions and the distribution of traffic load in a local environment. The goals are (1) to maximize spectrum efficiency by increasing parallel transmissions and reducing co-channel interferences, and (2) to improve fairness across a network by balancing spectrum assignments. Since existing radio systems offer very limited flexibility, cognitive radios, which can sense and adapt to radio environments, are exploited to support such a dynamic concept. We explore two directions to improve spectrum efficiency by adopting the proposed dynamic allocation concept. First, we build a cognitive wireless system called KNOWS to exploit unoccupied frequencies in the licensed TV bands. KNOWS is a hardware-software platform that includes new radio hardware, a spectrum-aware MAC (medium access control) protocol and an algorithm for implementing the dynamic spectrum allocation. We show that KNOWS accomplishes a remarkable 200% throughput gain over systems based on fixed allocations in common cases. Second, we enhance Wireless LANs (WLANs), the most popular network setting in unlicensed bands, by proposing a dynamic channelization structure and a scalable MAC design. Through analysis and extensive simulations, we show that the new channelization structure and the scalable MAC design improve not only network capacity but per-client fairness by allocating channels of variable width for access points in a WLAN. As a conclusion, we believe that our proposed concept of dynamic spectrum allocation lays down a solid foundation for building systems to efficiently use the invaluable spectrum resource.
WebIDS: A Cooperative Bayesian Anomaly-Based Intrusion Detection System for Web Applications (Extended Abstract)
Authors: Nathalie Dagorn
Venue: Proceedings of the 11th international symposium on Recent Advances in Intrusion Detection (2008)

This paper presents WebIDS, a learning-based anomaly detection system for Web applications aiming at improving the decision process, reducing the number of false positives, and achieving distributed detection.
A Modified Simulated Annealing Algorithm for the Quadratic Assignment Problem
Authors: Alfonsas Misevičius
Venue: Informatica (2003)

The quadratic assignment problem (QAP) is one of the well-known combinatorial optimization problems and is known for its various applications. In this paper, we propose a modified simulated annealing algorithm for the QAP - M-SA-QAP. The novelty of the proposed algorithm is an advanced formula of calculation of the initial and final temperatures, as well as an original cooling schedule with oscillation, i.e., periodical decreasing and increasing of the temperature. In addition, in order to improve the results obtained, the simulated annealing algorithm is combined with a tabu search approach based algorithm. We tested our algorithm on a number of instances from the library of the QAP instances - QAPLIB. The results obtained from the experiments show that the proposed algorithm appears to be superior to earlier versions of the simulated annealing for the QAP. The power of M-SA-QAP is also corroborated by the fact that the new best known solution was found for the one of the largest QAP instances - THO150.
Using adaptive consultation of experts to improve convergence rates in multiagent learning
Authors: Greg Hines, Kate Larson
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3 (2008)

We present a regret-based multiagent learning algorithm which is provably guaranteed to converge (during self-play) to the set of Nash equilibrium in a wide class of games. Our algorithm, FRAME, consults experts in order to obtain strategy suggestions for agents. If the experts provide effective advice for the agent, then the learning process will quickly reach a desired outcome. If, however, the experts do not provide good advice, then the agents using our algorithm are still protected. We further expand our algorithm so that agents learn, not only how to play against the other agents in the environment, but also which experts are providing the most effective advice for the situation at hand.
Operator-Centric and Adaptive Intrusion Detection
Authors: Ulf E. Larson, Stefan Lindskog, Dennis K. Nilsson, Erland Jonsson
Venue: Proceedings of the 2008 The Fourth International Conference on Information Assurance and Security (2008)

An intrusion detection system should support the operator of the system. Thus, in addition to producing alerts, it should allow for easy insertion of new detection algorithms. It should also support dynamic selection and de-selection of detection algorithms, and it should adjust its resource consumption to the current need. Such a system would allow the operator to easily extend the system when new detection algorithms become available. It would also allow the operator to maintain a low-cost monitoring baseline and perform more extensive monitoring when it is required. In this paper we propose an architecture for intrusion detection which aims at providing the operator with this support. The architecture uses a modular design to promote a high degree of flexibility. This supports creation of an environment in which state-of-the-art intrusion detection algorithmseasily can be inserted. The modular design also allows for detection algorithms to be enabled and disabled when required. Additionally, the architecture uses a sensor reconfiguration mechanism to affect the amount of data collected. When a detection algorithm is enabled ordisabled, the sensor providing the input data to the algorithm is correspondingly reconfigured. This implies a minimum of excess collected data. To illustrate the feasibility of the architecture, we provide a proof-of-concept supporting monitoring of users for insider detection and webserver monitoring for intrusion attempts.
Trust-Rated Authentication for Domain-Structured Distributed Systems
Authors: Ralph Holz, Heiko Niedermayer, Peter Hauck, Georg Carle
Venue: Proceedings of the 5th European PKI workshop on Public Key Infrastructure: Theory and Practice (2008)

We present an authentication scheme and new protocol for domain-based scenarios with inter-domain authentication. Our protocol is primarily intended for domain-structured Peer-to-Peer systems but is applicable for any domain scenario where clients from different domains wish to authenticate to each other. To this end, we make use of Trusted Third Parties in the form of Domain Authentication Servers in each domain. These act on behalf of their clients, resulting in a four-party protocol. If there is a secure channel between the Domain Authentication Servers, our protocol can provide secure authentication. To address the case where domains do not have a secure channel between them, we extend our scheme with the concept of trust-rating. Domain Authentication Servers signal security-relevant information to their clients (pre-existing secure channel or not, trust, ...). The clients evaluate this information to decide if it fits the security requirements of their application.
Real-Time Head Tracker Using Color, Stereovision and Ellipse Fitting in a Particle Filter
Authors: Bogdan Kwolek
Venue: Informatica (2004)

This paper proposes the use of a particle filter combined with color, depth information and shape features as an efficient and effective way to deal with tracking a head on the basis of image stream coming from a mobile stereovision camera. The head is modeled in the 2D image domain by an ellipse. The color distribution within interior of the ellipse is represented by a color histogram. The color histogram is dynamically updated over time. The length of the ellipse's minor axis is determined on the basis of depth information. The particles representing the candidate ellipses are weighted in each time step in respect of intensity gradient near the edge of the ellipse and matching score of the color histograms representing the interior of an ellipse surrounding the tracked object and currently analyzed one. The proposed algorithm can track a head reliably in cases of temporal occlusions as well as varying illumination conditions by dealing with multiple hypotheses for the pose. Experimental results obtained on long image sequences show the feasibility of our approach to perform tracking a head undergoing complex changes of shape and appearance against a varying background. The tracker has been evaluated in experiments consisting in face tracking with a real mobile agent.
Metadata Extraction from Semi-structured Email Documents
Authors: Ashok Sharma, B. D. Chaudhary, M. M. Gore
Venue: Proceedings of the 2008 The Third International Multi-Conference on Computing in the Global Information Technology (iccgi 2008) (2008)

This paper presents metadata extraction technique from email documents. Emails are characterized in terms of keywords that are extracted from body of the mail using frequency, average similarity and term discrimination value measures. The email metadata is defined as a Document Type Definition (DTD) in Extensible Markup Language (XML) that captures the structure as well as content characterizing keywords with their attributes (weights). A PERL application has been designed and implemented to extract keywords with their attributes (weights) and generate XML document for email metadata. Practical applications of metadata extraction technique are also discussed briefly.
Note: Quadruple systems with independent neighborhoods
Authors: Zoltan Füredi, Dhruv Mubayi, Oleg Pikhurko
Venue: Journal of Combinatorial Theory Series A (2008)

A 4-graph is odd if its vertex set can be partitioned into two sets so that every edge intersects both parts in an odd number of points. Letb(n)=max@a{@a(n-@a3)+(n-@a)(@a3)}=(12+o(1))(n4) denote the maximum number of edges in an n-vertex odd 4-graph. Let n be sufficiently large, and let G be an n-vertex 4-graph such that for every triple xyz of vertices, the neighborhood N(xyz)={w:wxyz@?G} is independent. We prove that the number of edges of G is at most b(n). Equality holds only if G is odd with the maximum number of edges. We also prove that there is @e>0 such that if the 4-graph G has minimum degree at least (1/2-@e)(n3), then G is 2-colorable. Our results can be considered as a generalization of Mantel's theorem about triangle-free graphs, and we pose a conjecture about k-graphs for larger k as well.
Collision Attacks on AES-Based MAC: Alpha-MAC
Authors: Alex Biryukov, Andrey Bogdanov, Dmitry Khovratovich, Timo Kasper
Venue: Proceedings of the 9th international workshop on Cryptographic Hardware and Embedded Systems (2007)

Message Authentication Code construction Alred and its AES-based instance Alpha-MAC were introduced by Daemen and Rijmen in 2005. We show that under certain assumptions about its implementation (namely that keyed parts are perfectly protected against side-channel attacks but bulk hashing rounds are not) one can efficiently attack this function. We propose a side-channel collision attack on this MAC recovering its internal state just after 29 measurements in the known-message scenario which is to be compared to 40 measurements required by collision attacks on AES in the chosen-plaintext scenario. Having recovered the internal state, we mount a selective forgery attack using new 4 to 1 round collisions working with negligible memory and time complexity.
Error-Entropy Minimization for Dynamical Systems Modeling
Authors: Jernej Zupanc
Venue: Proceedings of the 18th international conference on Artificial Neural Networks, Part I (2008)

Recent publications have presented many successful usages of elements from information theory in adaptive systems training. Error-entropy has been proven to outperform mean squared error as a cost function in many artificially generated data sets, but still few applications to real world data have been published. In this paper, we design a neural network trained with error-entropy minimization criterion and use it for dynamical systems modeling on artificial as well as real world data. Performance of this neural network is compared against the mean squared error driven approach in terms of computational complexity, parameter optimization, and error probability densities.
Centralized Indirect Control of an Anaerobic Digestion Bioprocess Using Recurrent Neural Identifier
Authors: Ieroham S. Baruch, Rosalba Galvan-Guerra, Boyka Nenkova
Venue: Proceedings of the 13th international conference on Artificial Intelligence: Methodology, Systems, and Applications (2008)

The paper proposed to use a Recurrent Neural Network Model (RNNM) and a dynamic Backpropagation learning for centralized identification of an anaerobic digestion bioprocess, carried out in a fixed bed and a recirculation tank of a wastewater treatment system. The anaerobic digestion bioprocess represented a distributed parameter system, described by partial differential equations. The analytical model is simplified to a lumped ordinary system using the orthogonal collocation method, applied in three collocation points, generating data for the neural identification. The obtained neural state and parameter estimations are used to design an indirect sliding mode control of the plant. The graphical simulation results of the digestion wastewater treatment indirect control exhibited a good convergence and precise reference tracking.
Criteria for conceptual and operational notions of complexity
Authors: Dominique Chu
Venue: Artificial Life (2008)

While complex systems have been studied now for more than two decades, there still is no agreement on what complexity actually is. This lack of a definition might be a problem when asking questions about the evolution of complexity. In this article criteria against which candidate measures of complexity can be assessed are discussed. The main conclusion of this article is that because of the absence of a basic consensus on what complexity is, there is no criterion that can be used to decide whether or not a proposed measure actually measures complexity. The main recommendation is to abandon complexity as a formal notion; instead, research into the evolution of complexity should use well-understood proxy notions (as is sometimes done in the literature). For the time being “complexity” should remain an informal notion. Research into evolutionary trends of these proxy notions might eventually lead to an emergent community consensus on what complexity is.
Level Learning Set: A Novel Classifier Based on Active Contour Models
Authors: Xiongcai Cai, Arcot Sowmya
Venue: Proceedings of the 18th European conference on Machine Learning (2007)

This paper presents a novel machine learning algorithm for pattern classification based on image segmentation and optimisation techniques employed in active contour models and level set methods. The proposed classifier, named level learning set (LLS), has the ability to classify general datasets including sparse and non sparse data. It moves developments in vision segmentation into general machine learning by utilising and extending level set-based active contour models from the field of computer vision to construct decision boundaries in any feature space. This model has advantages over traditional classifiers in its ability to directly construct complex decision boundaries, and in better knowledge representation. Various experimental results including comparisons to existing machine learning algorithms are presented, and the advantages of the proposed approach are discussed.
Simultaneous mapping and navigation for skid steered mobile robot
Authors: Tomas Neuzil
Venue: Proceedings of the 7th WSEAS International Conference on Signal Processing, Robotics and Automation (2008)

This paper presents extended kalman filter based algorithm for simultaneous localisation and mapping. This algorithm was designed for use with skid steer mobile robot platform. Also models of laser proximity sensor and mobile platform are presented and their properties are specified.
Interval rough mereology and description logic: An approach to formal treatment of imprecision in the Semantic Web ontologies
Authors: Pavel Klinov, Julia M. Taylor, Lawrence J. Mazlack
Venue: Web Intelligence and Agent Systems (2008)

Learning and maintaining ontologies play critically important roles in the Semantic Web. Ontologies provide formal and explicit semantics to available knowledge and make possible shared understanding of a domain. A great deal of knowledge in the Semantic Web involves different sorts of imprecision. Common examples of imprecise knowledge include various statistical hypotheses, vagueness and ambiguities of natural language and many others. Consequently, ontologies must be expressive enough to handle imprecision. This paper presents an approach to formal handling of granularity as a special kind of imprecision in ontologies. It is based on Soft Computing techniques, in particular, Rough Set and Fuzzy Set theories. The approach also makes heavy use of Rough Mereology - a recently proposed paradigm for approximate reasoning - to compute degrees of imprecision of ontological concepts, relationships and assertions. It is demonstrated how the proposed mereological approach can be used together with the framework for representing uncertainty in Description Logic - family of popular logics for specifying formal ontologies. Finally, the paper introduces interval computations to the area of representing and reasoning with ontologies and shows how intervals can formally capture the imprecision incurred by computing rough inclusion (mereological) functions.
Efficient Method for Periodic Task Scheduling with Storage Requirement Minimization
Authors: Karine Deschinkel, Sid-Ahmed-Ali Touati
Venue: Proceedings of the 2nd international conference on Combinatorial Optimization and Applications (2008)

In this paper, we study the general problem of one-dimensional periodic task scheduling under storage requirement, irrespective of machine constraints. We have already presented in [9] a theoretical framework that allows an optimal optimization of periodic storage requirement in a periodic schedule. This problem is used to optimize processor register usage in embedded systems. Our storage optimization problem being NP-complete [8], solving an exact integer linear programming formulation is too expensive in practice. In this article, we propose an efficient two-steps heuristic using model's properties that allows fast resolution times while providing nearly optimal results. This method includes the resolution of a integer linear program with a totally unimodular constraints matrix in first step, then the resolution of a linear assignment problem. Our solution has been implemented and included inside a compiler for embedded processors.