pinecone/core-2020-05-10-deduplication huggingface dataset:
Paging Load Congestion Control in Cellular Mobile Networks

This paper addresses the requirement for a congestion control mechanism, in order to efficiently handle paging traffic over cellular networks. The standard simultaneous paging approach can easily result in congestion, for relatively small values of the offered load. Instead of differentiating paging capacity based on the incoming load, this approach is more oriented towards a limited-resource system. Considering that a paging channel usually experiences high and low utilization periods, it is proposed to differentiate the paging mechanism according to the processed load. Given medium or even mild overload conditions, several forms of sequential paging substantially decrease blocking probabilities, while presenting good delay behavior. Based on a queueing analysis, we are able to quantitatively estimate potential improvements, and pointout the basic points of such an adaptive scheme. © IFIP International Federation for Information Processing 2004
The Apriori Stochastic Dependency Detection (ASDD) algorithm for learning Stochastic logic rules

Apriori Stochastic Dependency Detection (ASDD) is an algorithm for fast induction of stochastic logic rules from a database of observations made by an agent situated in an environment. ASDD is based on features of the Apriori algorithm for mining association rules in large databases of sales transactions [1] and the MSDD algorithm for discovering stochastic dependencies in multiple streams of data [15]. Once these rules have been acquired the Precedence algorithm assigns operator precedence when two or more rules matching the input data are applicable to the same output variable. These algorithms currently learn propositional rules, with future extensions aimed towards learning first-order models. We show that stochastic rules produced by this algorithm are capable of reproducing an accurate world model in a simple predator-prey environment
Event-Driven grammars: Towards the integration of meta-modelling and graph transformation

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-540-30203-2_6Proceedings of Second International Conference, ICGT 2004, Rome, Italy, September 28–October 1, 2004In this work we introduce event-driven grammars, a kind of graph grammars that are especially suited for visual modelling environments generated by meta-modelling. Rules in these grammars may be triggered by user actions (such as creating, editing or connecting elements) and in its turn may trigger other user-interface events. Its combination with (non-monotonic) triple graph grammars allows constructing and checking the consistency of the abstract syntax graph while the user is building the concrete syntax model. As an example of these concepts, we show the definition of a modelling environment for UML sequence diagrams, together with event-driven grammars for the construction of the abstract syntax representation and consistency checking.This work has been partially sponsored by the Spanish Ministry of Science and Technology
(TIC2002-01948). The authors would like to thank the referees for their useful comment
Factorization of natural 4 × 4 patch distributions

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-540-30212-4_15Revised and Selected Papers of ECCV 2004 Workshop SMVP 2004, Prague, Czech Republic, May 16, 2004The lack of sufficient machine readable images makes impossible the direct computation of natural image 4 × 4 block statistics and one has to resort to indirect approximated methods to reduce their domain space. A natural approach to this is to collect statistics over compressed images; if the reconstruction quality is good enough, these statistics will be sufficiently representative. However, a requirement for easier statistics collection is that the method used provides a uniform representation of the compression information across all patches, something for which codebook techniques are well suited. We shall follow this approach here, using a fractal compression–inspired quantization scheme to approximate a given patch B by a triplet (D B , μ B , σ B ) with σ B the patch’s contrast, μ B its brightness and D B a codebook approximation to the mean–variance normalization (B – μ B )/σ B of B. The resulting reduction of the domain space makes feasible the computation of entropy and mutual information estimates that, in turn, suggest a factorization of the approximation of p(B) ≃ p(D B , μ B , σ B ) as p(D B , μ B , σ B ) ≃ p(D B )p(μ)p(σ)Φ(|| ∇ ||), with Φ being a high contrast correction.With partial support of Spain’s CICyT, TIC 01–57
Longest Motifs with a Functionally Equivalent Central Block

International audienceThis paper presents a generalization of the notion of longest repeats with a block of k don't care symbols introduced by [Crochemore et al., LATIN 2004] (for k fixed) to longest motifs composed of three parts: a first and last that parameterize match (that is, match via some symbol renaming, initially unknown), and a functionally equivalent central block. Such three-part motifs are called longest block motifs. Different types of functional equivalence, and thus of matching criteria for the central block are considered, which include as a subcase the one treated in [Crochemore et al., LATIN 2004] and extend to the case of regular expressions with no Kleene closure or complement operation. We show that a single general algorithmic tool that is a non-trivial extension of the ideas introduced in [Crochemore et al., LATIN 2004] can handle all the various kinds of longest block motifs defined in this paper. The algorithm complexity is, in all cases, in O(n log n)
Universal Convergence of Semimeasures on Individual Random Sequences

Solomonoff’s central result on induction is that the posterior of a universal semimeasure M converges rapidly and with probability 1 to the true sequence generating posterior μ, if the latter is computable. Hence, M is eligible as a universal sequence predictor in case of unknown μ. Despite some nearby results and proofs in the literature, the stronger result of convergence for all (Martin-Löf) random sequences remained open. Such a convergence result would be particularly interesting and natural, since randomness can be defined in terms of M itself. We show that there are universal semimeasures M which do not converge for all random sequences, i.e. we give a partial negative answer to the open problem. We also provide a positive answer for some non-universal semimeasures. We define the incomputable measure D as a mixture over all computable measures and the enumerable semimeasure W as a mixture over all enumerable nearly-measures. We show that W converges to D and D to μ on all random sequences. The Hellinger distance measuring closeness of two distributions plays a central role.This work was partially supported by the Swiss National Science Foundation (SNF
grant 2100-67712.02) and the Russian Foundation for Basic Research (RFBR grants
N04-01-00427 and N02-01-22001)
Prediction with expert advice by following the perturbed leader for general weights

When applying aggregating strategies to Prediction with Expert
Advice, the learning rate must be adaptively tuned. The natural
choice of square ( complexity/current loss) renders the analysis of Weighted Majority
derivatives quite complicated. In particular, for arbitrary weights
there have been no results proven so far. The analysis of the alternative
“Follow the Perturbed Leader” (FPL) algorithm from [KV03] (based
on Hannan’s algorithm) is easier. We derive loss bounds for adaptive
learning rate and both finite expert classes with uniform weights and
countable expert classes with arbitrary weights. For the former setup,
our loss bounds match the best known results so far, while for the latter
our results are new.This work was supported by SNF grant 2100-67712.02
On the convergence speed of MDL predictions for Bernoulli sequences

We consider the Minimum Description Length principle for online sequence prediction. If the underlying model class is discrete, then the total expected square loss is a particularly interesting performance measure: (a) this quantity is bounded, implying convergence with probability one, and (b) it additionally specifies a rate of convergence. Generally, for MDL only exponential loss bounds hold, as opposed to the linear bounds for a Bayes mixture. We show that this is even the case if the model class contains only Bernoulli distributions. We derive a new upper bound on the prediction error for countable Bernoulli classes. This implies a small bound (comparable to the one for Bayes mixtures) for certain important model classes. The results apply to many Machine Learning tasks including classification and hypothesis testing. We provide arguments that our theorems generalize to countable classes of i.i.d. models.This work was supported by SNF grant 2100-67712.02
Learning content sequencing in an educational environment according student needs

Proceeding of: Algorithmic learning theory, 15th International Conference on Algorithmic Learning Theory (ALT 04), Padova, Italy, October 2-5, 2004One of the most important issues in educational systems is to define effective teaching policies according to the students learning characteristics. This paper proposes to use the Reinforcement Learning (RL) model in order for the system to learn automatically sequence of contents to be shown to the student, based only in interactions with other students, like human tutors do. An initial clustering of the students according to their learning characteristics is proposed in order the system adapts better to each student. Experiments show convergence to optimal teaching tactics for different clusters of simulated students, concluding that the convergence is faster when the system tactics have been previously initialised
Forecasting time series by means of evolutionary algorithms

Proceeding of: 8th International Conference in Parallel Problem Solving from Nature - PPSN VIII , Birmingham, UK, September 18-22, 2004.The time series forecast is a very complex problem, consisting in predicting the behaviour of a data series with only the information of the previous sequence. There is many physical and artificial phenomenon that can be described by time series. The prediction of such phenomenon could be very complex. For instance, in the case of tide forecast, unusually high tides, or sea surges, result from a combination of chaotic climatic elements in conjunction with the more normal, periodic, tidal systems associated with a particular area. Too much variables influence the behaviour of the water level. Our problem is not only to find prediction rules, we also need to discard the noise and select the representative data. Our objective is to generate a set of prediction rules. There are many methods tying to achieve good predictions. In most of the cases this methods look for general rules that are able to predict the whole series. The problem is that usually the time series has local behaviours that dont allow a good level of prediction when using general rules. In this work we present a method for finding local rules able to predict only some zones of the series but achieving better level prediction. This method is based on the evolution of set of rules genetically codified, and following the Michigan approach. For evaluating the proposal, two different domains have been used: an artificial domain widely use in the bibliography (Mackey-Glass series) and a time series corresponding to a natural phenomenon, the water level in Venice Lagoon.Investigation supported by the Spanish Ministry of Science and Technology through the TRACER project under contract TIC2002-04498-C05-
On test functions for evolutionary multi-objective optimization

In order to evaluate the relative performance of optimization algorithms benchmark problems are frequently used. In the case of multi-objective optimization (MOO), we will show in this paper that most known benchmark problems belong to a constrained class of functions with piecewise linear Pareto fronts in the parameter space. We present a straightforward way to define benchmark problems with an arbitrary Pareto front both in the fitness and parameter spaces. Furthermore, we introduce a difficulty measure based on the mapping of probability density functions from parameter to fitness space. Finally, we evaluate two MOO algorithms for new benchmark problems. © Springer-Verlag 2004
An Approach for Assessment of Electronic Offers

Internet and mobile technology enable businesses to invent new business models by applying new forms of organization or offering new products and services. In order to assess these new business models there has to be a methodology that allows identifying advantages that are caused by electronic and mobile commerce. The proposed approach builds upon the theory of informational added values that provides a classification of gains produced by information work. This theory is extended by the definition of categories of technology inherent added values that result in informational added values. These informational added values can be perceived by users of information products and services and therefore be used to assess electronic offers. The relationship between technology inherent and informational added values will be clarified with examples of real business models. Furthermore, a classification of basic business model types will be provided.
Robotics in agriculture and forestry

In agriculture and forestry, robotics has made a substantial impact. Farmers are conscious of their need for automatic vehicle guidance to minimize damage to the growing zone of their soil. Automatic sensing, handling, and processing of produce are now commonplace, while there is substantial instrumentation and mechanization of livestock procedures. In forestry, legged harvesters have not yet seen great success in their application, but the automation of trimming and forwarding with simultaneous localization and mapping techniques will change the industry in the future. \ud
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Some impressive developments in walking forestry harvesters are presented, including machines targeted towards the difficult terrain of the Scandinavian forests. More-conventional cut-to-length harvesters are also highly automated, while operations such as delimbing must be carried out at speed. Before complete autonomous harvesting becomes possible, some of the localization and mapping techniques that are described must come to fruition. \ud
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The combination of machine vision with global positioning by satellite (GPS) allows a tractor to follow a row of crops, performing a headland turn at the end of the row. The history of a series of projects is outlined, leading to the present outcome that is in the process of being commercialized. Another project that is based on machine vision relates to the location of macadamia nuts. To select which trees should be propagated, it is necessary to attribute fallen nuts to the correct tree. Color sorting and grading of produce is not a matter of sensing alone, but involves a measure of produce handling that puts it at the fringe of robotics. \ud
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Automated milking parlours have proved their worth. However success has eluded some other projects described here, such as automated sheep-shearing and an automated abattoir. Another project is presented that literally sorts the sheep from the goats, using a swinging gate to separate different species using machine vision so that feral species are excluded from watering holes in the dry Australian outback. \ud
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Although robotics is making rapid inroads into these areas, they are still a fruitful source of application projects, some sufficiently demanding to require the development of new theoretical techniques. \u
A color constancy algorithm for the robust description of images collected from a mobile robot

Trabajo presentado al 9th CIARP celebrado en Puebla (Mexico) del 26 al 29 de octubre de 2004.In mobile robotics, it is necessary to have a robust and efficient way of describing the visual stream provided by a vision system to be used afterwards in tasks such as object recognition. Color histograms are a useful tool to capture and represent color properties of sets of images taken from a certain position. Since those images were obtained at different time and light conditions, their appearance have greatly changed, reducing the performance of the color descriptor. In this work, we develop a color constancy algorithm that copes with the color variation among sets of images taken from nearly the same place. We show that the performance of the color histogram descriptor rises after color constancy, becoming a more robust and useful color descriptor. In the results section, we support that claim with several sets of images of scenes belonging to different positions.This work was supported by the project 'Integration of robust perception, learning, and navigation systems in mobile robotics' (J-0929).Peer Reviewe
AmbieSense: a system and reference architecture for personalised and context-sensitive information services for mobile users

The purpose of AmbieSense is to provide personalised, context-sensitive information to the mobile user. It is about augmenting digital information to physical objects, rooms, and areas. The aim is to provide relevant information to the right user and situation. Digital content is distributed from the surroundings and onto your mobile phone. An ambient information environment is provided by a combination of context tag technology, a software platform to manage and deliver the information, and personal computing devices to which the information is served. This paper describes how the AmbieSense reference architecture has been defined and used in order to deliver information to the mobile citizen at the right time, place and situation. Information is provided via specialist content providers. The application area addresses the information needs of travellers and tourists
A relational model for object-oriented designs.

This paper develops a mathematical characterisation of object-oriented concepts by defining an observation-oriented semantics for an object-oriented language (OOL) with a rich variety of features including subtypes, visibility, inheritance, dynamic binding and polymorphism. The language is expressive enough for the specification of object-oriented designs and programs. We also propose a calculus based on this model to support both structural and behavioural refinement of object-oriented designs. We take the approach of the development of the design calculus based on the standard predicate logic in Hoare and Hes Unifying Theories of Programming (UTP). We also consider object reference in terms of object identity as values and mutually dependent methods
Timed patterns : TCOZ to timed automata.

The integrated logic-based modeling language, Timed Communicating Object Z (TCOZ), is well suited for presenting complete and coherent requirement models for complex real-time systems. However, the challenge is how to verify the TCOZ models with tool support, especially for analyzing timing properties. Specialized graph-based modeling technique, Timed Automata (TA), has powerful mechanisms for designing real-time models using multiple clocks and has well developed automatic tool support. One weakness of TA is the lack of high level composable graphical patterns to support systematic designs for complex systems. The investigation of possible links between TCOZ and TA may benefit both techniques. For TCOZ, TAs tool support can be reused to check timing properties. For TA, a set of composable graphical patterns can be defined based on the semantics of the TCOZ constructs, so that those patterns can be re-used in a generic way. This paper firstly defines the composable TA graphical patterns, and then presents sound transformation rules and a tool for projecting TCOZ specifications into TA. A case study of a railroad crossing system is demonstrated
On the generalized linear equivalence of functions over finite fields

In this paper we introduce the concept of generalized linear equivalence between functions defined over finite fields; this can be seen as an extension of the classical criterion of linear equivalence, and it is obtained by means of a particular geometric representation of the functions. After giving the basic definitions, we prove that the known equivalence relations can be seen as particular cases of the proposed generalized relationship and that there exist functions that are generally linearly equivalent but are not such in the classical theory. We also prove that the distributions of values in the Difference Distribution Table (DDT) and in the Linear Approximation Table (LAT) are invariants of the new transformation; this gives us the possibility to find some Almost Perfect Nonlinear (APN) functions that are not linearly equivalent (in the classical sense) to power functions, and to treat them accordingly to the new formulation of the equivalence criterion
MASPEGHI 2004 Mechanisms for Speialization, Generalization and Inheritance

Rapport de Recherche Projet OCL, N° I3S/RR-2004-15-FRInternational audienceMASPEGHI 2004 is the third edition of the MASPEGHI workshop. This year the organizers of both the ECOOP 2002 Inheritance Workshop and MASPEGHI 2003 came together to enlarge the scope of the workshop and to address new challenges. We succeeded in gathering a diverse group of researchers and practitioners interested in mechanisms for managing specialization and generalization of programming language components. The workshop contained a series of presentations with discussions as well as group work, and the interplay between the more than 22 highly skilled and inspiring people from many different communities gave rise to fruitful discussions and the potential for continued collaboration
Serial and parallel multilevel graph partitioning using fixed centers

31st Conference on Current Trends in Theory and Practice of Computer Science; Liptovsky Jan; Slovakia; 22 January 2005 through 28 January 2005We present new serial and parallel algorithms for multilevel graph partitioning. Our algorithm has coarsening, partitioning and uncoarsening phases like other multilevel partitioning methods. However, we choose fixed nodes which are at least a specified distance away from each other and coarsen them with their neighbor nodes in the coarsening phase using various heuristics. Using this algorithm, it is possible to obtain theoretically and experimentally much more balanced partitions with substantially decreased total edge costs between the partitions than other algorithms. We also developed a parallel method for the fixed centered partitioning algorithm. It is shown that parallel fixed centered partitioning obtains significant speedups compared to the serial case
Protective wrapping of off-the-shelf components

System designers using off-the-shelf components (OTSCs), whose internals they cannot change, often use add-on “wrappers” to adapt the OTSCs’ behaviour as required. In most cases, wrappers are used to change “functional” properties of the components they wrap. In this paper we discuss instead protective wrapping, the use of wrappers to improve the dependability – i.e., “non-functional” properties like availability, reliability, security, and/or safety – of a component and thus of a system. Wrappers can improve dependability by adding fault tolerance, e.g. graceful degradation, or error recovery mechanisms. We discuss the rational specification of such protective wrappers in view of system dependability requirements, and highlight some of the design trade-offs and uncertainties that affect system design with OTSCs and wrappers, and that differentiate it from other forms of fault-tolerant design