pinecone/core-2020-05-10-deduplication huggingface dataset:
Accurate and robust image superresolution by neural processing of local image representations

The final publication is available at Springer via http://dx.doi.org/10.1007/11550822_78Proceedings of 15th International Conference, Warsaw, Poland, September 11-15, 2005.Model transformation is one of the pillars of Model-Driven Engineering (MDE). The increasing complexity of systems and modelling languages has dramatically raised the complexity and size of model transformations. Even though many transformation languages and tools have been proposed in the last few years, most of them are directed to the implementation phase of transformation development. However, there is a lack of cohesive support for the other phases of the transformation development, like requirements, analysis, design and testing.
In this paper, we propose a unified family of languages to cover the life-cycle of transformation development. Moreover, following an MDE approach, we provide tools to partially automate the progressive refinement of models between the different phases and the generation of code for specific transformation implementation languages.This work has been partially supported by grants BFI2003-07276 and TIN2004-04363-C03-03, and by PROFIT project FIT-330100-2004-9
Evolutionary algorithms for real-time artificial neural network training.

This paper reports on experiments investigating the use of Evolutionary Algorithms to train Artificial Neural Networks in real time. A simulated legged mobile robot was used as a test bed in the experiments. Since the algorithm is designed to be used with a physical robot, the population size was one and the recombination operator was not used. The algorithm is therefore rather similar to the original Evolutionary Strategies concept. The idea is that such an algorithm could eventually be used to alter the locomotive performance of the robot on different terrain types. Results are presented showing the effect of various algorithm parameters on system performance
Training neural networks using Taguchi methods: overcoming interaction problems.

Taguchi Methods (and other orthogonal arrays) may be used to train small Artificial Neural Networks very quickly in a variety of tasks. These include, importantly, Control Systems. Previous experimental work has shown that they could be successfully used to train single layer networks with no difficulty. However, interaction between layers precluded the successful reliable training of multi-layered networks. This paper describes a number of successful strategies which may be used to overcome this problem and demonstrates the ability of such networks to learn non-linear mappings
Discriminant parallel perceptrons

Proceedings of 22nd International Conference on Artificial Neural Networks, Lausanne, Switzerland, September 11-14, 2012The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-33266-1_70In this work we will apply Diffusion Maps (DM), a recent technique for dimensionality reduction and clustering, to build local models for wind energy forecasting. We will compare ridge regression models for K–means clusters obtained over DM features, against the models obtained for clusters constructed over the original meteorological data or principal components, and also against a global model. We will see that a combination of the DM model for the low wind power region and the global model elsewhere outperforms other options.With partial support of Spain’s CICyT, projects TIC 01–572, TIN2004–07676
Back-propagation as reinforcement in prediction tasks

The back-propagation (BP) training scheme is widely used for training network models in cognitive science besides its well known technical and biological short-comings. In this paper we contribute to making the BP training scheme more acceptable from a biological point of view in cognitively motivated prediction tasks overcoming one of its major drawbacks. Traditionally, recurrent neural networks in symbolic time series prediction (e. g. language) are trained with gradient decent based learning algorithms, notably with back-propagation (BP) through time. A major drawback for the biological plausibility of BP is that it is a supervised scheme in which a teacher has to provide a fully speci.ed target answer. Yet, agents in natural environments often receive a summary feed-back about the degree of success or failure only, a view adopted in reinforcement learning schemes. In this work we show that for simple recurrent networks in prediction tasks for which there is a probability interpretation of the network’s output vector, Elman BP can be reimplemented as a reinforcement learning scheme for which the expected weight updates agree with the ones from traditional Elman BP, using ideas from the AGREL learning scheme (van Ooyen and Roelfsema 2003) for feed-forward networks
Discovering predictive variables when evolving cognitive models

???The original publication is available at www.springerlink.com???. Copyright SpringerA non-dominated sorting genetic algorithm is used to evolve models of learning from di erent theories for multiple tasks. Correlation analysis is performed to identify parameters which affect performance on specific tasks; these are the predictive variables. Mutation is biased so that changes to parameter values tend to preserve values within the population's current range. Experimental results show that optimal models are evolved, and also that uncovering predictive variables is beneficial in improving the rate of convergence
Mathematical morphology and binary geodesy for robot navigation planning

A new method for obtaining the optimal path to robot navigation in
2-D environments is presented in this paper. To obtain the optimal path we use
mathematical morphology in binary worlds and the geodesic distance. The
navigation algorithm is based on the search for a path of minimum cost by
using the wave-front of the geodesic distance of the mathematical morphology.
The optimal path will be the one that minimize the direction changes of the
robot. The algorithm of optimal path will be applied in several and complex
2-D environments
Webservices Infrastructure for the Registration of Scientific Primary Data

Registration of scientific primary data, to make these data citable as a unique piece of work and not only a part of a publication, has always been an important issue. In the context of the project Publication and Citation of Scientific Primary Data funded by the German Research Foundation (DFG) the German National Library of Science and Technology (TIB) has become the first registration agency worldwide for scientific primary data. Registration has started for the field of earth science, but will be widened for other subjects in the future. This paper shall give an overview about the technical realization of this important usage field for a digital library
Deployment and connectivity repair of a sensor net with a flying robot

We consider multi-robot systems that include sensor nodes and aerial or ground robots networked together. Such networks are suitable for tasks such as large-scale environmental monitoring or for command and control in emergency situations. We present a sensor network deployment method using autonomous aerial vehicles and describe in detail the algorithms used for deployment and for measuring network connectivity and provide experimental data collected from field trials. A particular focus is on determining gaps in connectivity of the deployed network and generating a plan for repair, to complete the connectivity. This project is the result of a collaboration between three robotics labs (CSIRO, USC, and Dartmouth). Â© Springer-Verlag Berlin/Heidelberg 2006
Collective Inspection of Regular Structures using a Swarm of Miniature  Robots

We present a series of experiments concerned with the inspection of regular, engineered structures carried out using swarms of five to twenty autonomous, miniature robots, solely endowed with onboard, local sensors. Individual robot controllers are behaviorbased and the swarm coordination relies on a fully distributed control algorithm. The resulting collective behavior emerges from a combination of simple robot-robot interactions and the underlying environmental template. To estimate intrinsic advantages and limitations of the proposed control solution, we capture its characteristics at higher abstraction levels using nonspatial, microscopic and macroscopic probabilistic models. Although both types of models achieve only qualitatively correct predictions, they help us to shed light on the influence of the environmental template and control design choices on the considered nonspatial swarm metrics (inspection time and redundancy). Modeling results suggest that additional geometric details of the environmental structure should be taken into account for improving prediction accuracy and that the proposed control solution can be further optimized without changing its underlying architecture
System Architecture Analysis of Hybrid Watermarking Method

A hybrid watermark that consists of a robust part and a fragile part \ud
can be used to serve multiple purposes. The robust part can protect copyright \ud
information, the fragile part can detect tampering, and their combination enables identification of attacks encountered. This paper analyses an overlap and \ud
a non-overlap implementation of the robust and fragile parts in a hybrid system. \ud
The difference between the two implementation methods lies in the robust and \ud
fragile watermarks embedding positions. Embedding capacity, computational \ud
costs, watermark robustness, and tamper detection localization of the two implementations are analyzed. In addition, optimization issues of block size in the \ud
hybrid system are discussed
Two-Dimensional Fitting of Brightness Profiles in Galaxy Images with a Hybrid Algorithm

Fitting brightness profiles of galaxies in one dimension is frequently done because it suffices for some applications and is simple to implement, but many studies now resort to two-dimensional fitting, because many well-resolved, nearby galaxies are often poorly fitted by standard one-dimensional models. For the fitting we use a model based on de Vaucoleurs and exponential functions that is represented as a set of concentric generalized ellipses that fit the brightness profile of the image. In the end, we have an artificial image that represents the light distribution in the real image, then we make a comparison between such artificial image and the original to measure how close the model is to the real image. The problem can be seen as an optimization problem because we need to minimize the difference between the original optical image and the model, following a normalized Euclidean distance.
In this work we present a solution to such problem from a point of view of optimization using a hybrid algorithm, based on the combination of Evolution Strategies and the Quasi-Newton method. Results presented here show that the hybrid algorithm is very well suited to solve the problem, because it can find the solutions in almost all the cases and with a relatively low cost.status: publishe
Multi-level Semantic Analysis for Sports Video

There has been a huge increase in the utilization of video as one of the most preferred type of media due to its content richness for many significant applications including sports. To sustain an ongoing rapid growth of sports video, there is an emerging demand for a sophisticated content-based indexing system. Users recall video contents in a high-level abstraction while video is generally stored as an arbitrary sequence of audio-visual tracks. To bridge this gap, this paper will demonstrate the use of domain knowledge and characteristics to design the extraction of high-level concepts directly from audio-visual features. In particular, we propose a multi-level semantic analysis framework to optimize the sharing of domain characteristics
Fuzzy logic and neuro-fuzzy modelling of diesel spray penetration

This paper describes a comparative evaluation of two fuzzy-derived techniques for modelling fuel spray penetration in the cylinders of a diesel internal combustion engine. The first model is implemented using conventional fuzzy-based paradigm, where human expertise and operator knowledge were used to select the parameters for the system. The second model used an adaptive neuro-fuzzy inference system (ANFIS), where automatic adjustment of the system parameters is effected by a neural networks based on prior knowledge. Two engine operating parameters were used as inputs to the model, namely in-cylinder pressure and air density. Spray penetration length was modelled on the basis of these two inputs. The models derived using the two techniques were validated using test data that had not been used during training. The ANFIS model was shown to achieve an improved accuracy compared to a pure fuzzy model, based on conveniently selected parameters
A new inpainting method for highlights elimination by colour morphology

In this paper, we present a new application of the mathematical morphology:
a single-image approach for the automatic detection and elimination
of highlights in colour images. We use a 2D-histogram that allows us to relate
the achromatic and saturation signals of a colour image and to identify interior
brightness. To eliminate the highlights detected, we use an image-inpainting
method, by means of connected vectorial filters of the mathematical morphology.
This new filter operates exclusively on bright zones, reducing the high cost
of processing the connected filters and avoiding over-simplification. The new
method proposed here achieves good results, which are similar to those obtained
from other multimedia techniques, yet does not require either costly multiple-view systems or stereo images
Simulating artificial organisms with qualitative physiology

In this paper, we describe an approach to artificial life, which uses Qualitative Reasoning for the simulation of life within a 3D virtual environment. This system uses qualitative formalisms to describe both the physiology of a virtual creature and its environment. This approach has two main advantages: the possibility of representing integrated physiological functions at various levels of abstraction and the use of a common formalism for the simulation of internal (physiological) and external (environmental) processes. We illustrate this framework by revisiting early work in Artificial Life and providing these virtual life forms with a corresponding physiology, to obtain a complete living organism in virtual worlds
All Else Being Equal Be Empowered

The original publication is available at www.springerlink.com . Copyright Springer DOI : 10.1007/11553090_75The classical approach to using utility functions suffers from the drawback of having to design and tweak the functions on a case by case basis. Inspired by examples from the animal kingdom, social sciences and games we propose empowerment, a rather universal function, defined as the information-theoretic capacity of an agent???s actuation channel. The concept applies to any sensorimotoric apparatus. Empowerment as a measure reflects the properties of the apparatus as long as they are observable due to the coupling of sensors and actuators via the environment
Intelligence-based educational package on fluid mechanics

From the student feedback questionnaire, some students opined that the concepts of fluid mechanics are quite abstract and that they have difficulty in grasping the phenomena in real life situation. Hence, it demands some innovative learning methodologies to help arouse their interest. This paper depicts the development and implementation of an interactive teaching package on learning of basic fluid mechanics with a knowledge-based system approach. The prototype package is designed to guide engineering students in self-directed learning through the processes of interaction, reflection, and application, thus furnishing an opportunity of stimulating pedagogical environment. Diagnostic assessment is undertaken for every scenario of possible prompted answer on a specific topic, so as to evaluate the most probable shortfall or misconception of that particular student.Department of Civil and Environmental EngineeringSeries: Lecture notes in computer scienceAuthor name used in this publication: KwokWing Chau2004-2005 > Academic research: refereed > Publication in refereed journalpreprint_postprin
On Session Key Construction in Provably-Secure Key Establishment Protocols

We examine the role of session key construction in provably-secure key establishment protocols. We revisit an ID-based key establishment protocol due to Chen & Kudla (2003) and an ID-based protocol 2P-IDAKA due to McCullagh & Barreto (2005). Both protocols carry\ud
proofs of security in a weaker variant of the Bellare & Rogaway (1993) model where the adversary is not allowed to make any Reveal query. We advocate the importance of such a (Reveal) query as it captures the known-key security requirement. We then demonstrate that a small change to the way that session keys are constructed in both protocols results in these protocols being secure without restricting the adversary from asking the Reveal queries in most situations. We point out some errors in the existing proof for protocol 2P-IDAKA, and provide proof sketches for the improved Chen & Kudla's protocol. We conclude with a brief discussion on ways to construct session keys in key establishment protocols
A Practical Voter-Verifiable Election Scheme.

We present an election scheme designed to allow voters to verify that their vote is accurately included in the count. The scheme provides a high degree of transparency whilst ensuring the secrecy of votes. Assurance is derived from close auditing of all the steps of the vote recording and counting process with minimal dependence on the system components. Thus, assurance arises from verification of the election rather than having to place trust in the correct behaviour of components of the voting system. The scheme also seeks to make the voter interface as familiar as possible
Color-contrast landmark detection and encoding in outdoor images

International Conference on Computer Analysis of Images and Patterns (CAIP), 2005, Versalles (Francia)This paper describes a system to extract salient regions from an outdoor image and match them against a database of previously acquired landmarks. Region saliency is based mainly on color contrast, although intensity and texture orientation are also taken into account. Remarkably, color constancy is embedded in the saliency detection process through a novel color ratio algorithm that makes the system robust to illumination changes, so common in outdoor environments. A region is characterized by a combination of its saliency and its color distribution in chromaticity space. The newly acquired landmarks are compared with those already stored in a database, through a quadratic distance metric of their characterizations. Experimentation with a database containing 68 natural landmarks acquired with the system yielded good recognition results, in terms of both recall and rank indices. However, the discrimination between landmarks should be improved to avoid false positives, as suggested by the low precision index.This work was supported by the project 'Sistema reconfigurable para la navegación basada en visión de robots caminantes y rodantes en entornos naturales.' (00).Peer Reviewe
A power-efficient and scalable load-store queue design

The load-store queue (LQ-SQ) of modem superscalar processors is responsible for keeping the order of memory operations. As the performance gap between processing speed and memory access becomes worse, the capacity requirements for the LQ-SQ increase, and its design becomes a challenge due to its CAM structure. In this paper we propose an efficient load-store queue state filtering mechanism that provides a significant energy reduction (on average 35% in the LSQ and 3.5% in the whole processor), and only incurs a negligible performance loss of less than 0.6%
Extension of Ontologies Assisted by Automated Reasoning Systems

A method to extend ontologies with the assistance of automated
reasoning systems and preserving a kind of completeness with
respect to their associate conceptualizations is presented. The use of such
systems makes feasible the ontological insertion of new concepts, but it
is necessary to re-interpret the older ones with respect to new ontological
commitments.We illustrate the method extending a well-known ontology
about spatial relationships, the called Region Connection Calculus.Ministerio de Educación y Ciencia TIN2004-0388
Soft Computing and Geometrical Control for Computer Aided Driving

En: R. Moreno Díaz et al. (Eds): EUROCAST 2005. -- Computer Aided Systems Theory – Volume 3643/2005. -- 501-506 p.After having designed control systems for real autonomous cars in an urban environment using straight lines as reference [2], we are now trying to build a fuzzy control system based on clothoids [3], the curve used in roads and train tracks. This paper proposes a method based on soft computing and upgraded using genetic algorithms. Both sets of simulations are shown and comparedThis work was supported by the Spanish Ministry of Education and Science ISAAC\ud
project, DPI2002-04064-C05-02, and the Spanish Ministry of Development COPOS\ud
project, BOE 280, November 22, Res. 22778.Peer reviewe
A unifying framework for seed sensitivity and its application to subset
  seeds (Extended abstract)

We propose a general approach to compute the seed sensitivity, that can be
applied to different definitions of seeds. It treats separately three
components of the seed sensitivity problem - a set of target alignments, an
associated probability distribution, and a seed model - that are specified by
distinct finite automata. The approach is then applied to a new concept of
subset seeds for which we propose an efficient automaton construction.
Experimental results confirm that sensitive subset seeds can be efficiently
designed using our approach, and can then be used in similarity search
producing better results than ordinary spaced seeds
Application of PGA on optimization of distribution of shopping centers

In this study, the distribution of shopping centers is optimized in terms of realizing the shortest car-based shopping trips in an urban area. Modal split is performed between road and public traffic networks is calculated, and then the interaction between land-use and transportation in the context of choice of shopping destinations is modeled to build the optimal function. Parallel genetic algorithm (PGA) is applied to solve the optimal problem on distribution of the area of shopping centers. Several problems in application of PGA are discussed. A case study is undertaken in order to examine the effectiveness of this method.Department of Civil and Environmental EngineeringSeries: Lecture notes in computer scienceAuthor name used in this publication: Chun-Tian ChengAuthor name used in this publication: Kwokwing Chau2004-2005 > Academic research: refereed > Publication in refereed journalpreprint_postprin
Effects of team-based computer interaction: The media equation and game design considerations

The current paper applies media equation research to video game de-sign. The paper presents a review of the existing media equation research, de-scribes a specific study conducted by the authors, discusses how the findings of  the study can be used to inform future game design, and explores how other media equation findings might be incorporated into game design. The specific study, discussed in detail in the paper, explores the notion of team formation  between humans and computer team-mates. The results show that while highly experienced users will accept a computer as a team-mate, they tend to react more negatively towards the computer than to human teammates (a ‘Black  Sheep’ Effect