ppxscal/embedding_network huggingface dataset:
Assessing Nonlinear Granger Causality from Multivariate Time Series
Authors: Xiaohai Sun
Venue: Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II (2008)

A straightforward nonlinear extension of Granger's concept of causality in the kernel framework is suggested. The kernel-based approach to assessing nonlinear Granger causality in multivariate time series enables us to determine, in a model-free way, whether the causal relation between two time series is present or not and whether it is direct or mediated by other processes. The trace norm of the so-called covariance operator in feature space is used to measure the prediction error. Relying on this measure, we test the improvement of predictability between time series by subsampling-based multiple testing. The distributional properties of the resulting p-values reveal the direction of Granger causality. Experiments with simulated and real-world data show that our method provides encouraging results.
An Application of Gaussian Mixtures: Colour Segmenting for the Four Legged League Using HSI Colour Space
Authors: Naomi Henderson, Robert King, Richard H. Middleton
Venue: RoboCup 2007: Robot Soccer World Cup XI (2008)

In the colour coded environment of the RoboCup 4 Legged League it is crucial to extract as much colour information as possible from an image without error. To do this requires hours of manual YUV pixel mapping and testing to ensure robustness under all possible lighting conditions. The YUV colour space is a very convenient standard for transmission of video data, but for colour classification and segmentation it suffers from being non-intuitive and sensitive to changes in lighting. Alternatively, colour classification principles can be applied in an HSI colour space; one of the convenient characteristics of the HSI colour space is that the hue value, H, represents the colour wavelength information. From this concept it is easier to separate and label colour regions in an automated process as the theoretical hue and colour wavelength relationship is known. By fitting a Gaussian model using mixtures to HSI histograms we can generate boundaries of colour classes in HSI colour space.
Wavelet based fault detection in analog VLSI circuits using neural networks
Authors: P. Kalpana, K. Gunavathi
Venue: Applied Soft Computing (2008)

This paper deals with a new method of testing analog VLSI circuits, using wavelet transform for analog circuit response analysis and artificial neural networks (ANN) for fault detection. Pseudo-random patterns generated by Linear Feedback Shift Register (LFSR) are used as input test patterns. The wavelet coefficients obtained for the fault-free and faulty cases of the circuits under test (CUT) are used to train the neural network. Two different architectures, back propagation and probabilistic neural networks are trained with the test data. To minimize the neural network architecture, normalization and principal component analysis are done on the input data before it is applied to the neural network. The proposed method is validated with two IEEE benchmark circuits, namely, the operational amplifier and state variable filter.
Soliton automata with constant external edges
Authors: Miklós Krész
Venue: Information and Computation (2008)

Soliton automata are the mathematical models of certain possible molecular switching devices. Both from theoretical and practical point of view, it is a central question to describe soliton automata with constant external edges. Extending a result of Dassow and Jurgensen, we characterize soliton automata in this special case.
Designation of harmonic estimation ANN model using experimental data obtained from different produced induction motors
Authors: H. Selcuk Nogay, Yasar Birbir
Venue: Proceedings of the 9th WSEAS International Conference on Neural Networks (2008)

Artificial Neural Network (ANN) technique has been used for the prediction of voltage THD (Total Harmonic Distortion), mainly from input and output measurements of three phase, squirrel cage induction motors fed from a pulse width modulation inverter voltage supply. The induction motors have different construction, different power and produced by different firm. A sinusoidal pulse-width modulation (SPWM) inverter feeding three-phase induction motors were tested up to first thirty harmonic voltage components at different loads. The results show that the artificial neural network model produces reliable estimates of voltage THD.
Architectural-Level Risk Analysis Using UML
Authors: Katerina Goseva-Popstojanova, Ahmed Hassan, Ajith Guedem Walid Abdelmoez, Diaa Eldin M. Nassar, Hany Ammar, Ali Mili
Venue: IEEE Transactions on Software Engineering (2003)

Risk assessment is an essential part in managing software development. Performing risk assessment during the early development phases enhances resource allocation decisions. In order to improve the software development process and the quality of software products, we need to be able to build risk analysis models based on data that can be collected early in the development process. These models will help identify the high-risk components and connectors of the product architecture, so that remedial actions may be taken in order to control and optimize the development process and improve the quality of the product. In this paper, we present a risk assessment methodology which can be used in the early phases of the software life cycle. We use the Unified Modeling Language (UML) and commercial modeling environment Rational Rose Real Time (RoseRT) to obtain UML model statistics. First, for each component and connector in software architecture, a dynamic heuristic risk factor is obtained and severity is assessed based on hazard analysis. Then, a Markov model is constructed to obtain scenarios risk factors. The risk factors of use cases and the overall system risk factor are estimated using the scenarios risk factors. Within our methodology, we also identify critical components and connectors that would require careful analysis, design, implementation, and more testing effort. The risk assessment methodology is applied on a pacemaker case study.
Aspect weaving in standard Java class libraries
Authors: Alex Villazón, Walter Binder, Philippe Moret
Venue: Proceedings of the 6th international symposium on Principles and practice of programming in Java (2008)

Aspect-oriented programming (AOP) has been successfully applied to application code thanks to techniques such as Java bytecode instrumentation. Unfortunately, with current technology, such as AspectJ, aspects cannot be woven into standard Java class libraries. This restriction is particularly unfortunate for aspects that would benefit from a complete bytecode coverage, such as profiling or debugging aspects. In this paper we present an adaptation of the popular AspectJ weaver that is able to weave aspects also into standard Java class libraries. We evaluate our approach with existing profiling aspects, which now cover all bytecode executing in the virtual machine. In addition, we present a new aspect for memory leak detection that also benefits from our approach.
Maintaining Data Dependencies Across BPEL Process Fragments
Authors: Rania Khalaf, Oliver Kopp, Frank Leymann
Venue: Proceedings of the 5th international conference on Service-Oriented Computing (2007)

Continuous process improvement (CPI) may require a BPEL process to be split amongst different participants. In this paper, we enable splitting standard BPEL - without any extensions or new middleware. We present a solution that uses a BPEL process, partition information, and results of data analysis to produce a BPEL process for each participant. The collective behavior of these participant processes recreates the control and data flow of the non-split process. Previous work presented process splitting using a variant of BPEL where data flow is modeled explicitly using `data links'. We reuse the control flow aspect from that work, focusing in this paper on maintaining the data dependencies in standard BPEL.
ALICIA
Authors: Marco Paleari, Brian Duffy, Benoit Huet
Venue: Proceedings of the 7th international conference on Intelligent Virtual Agents (2007)

One of the most important social ability for effective social interaction with people is the capacity to understand, feel and ultimately express emotions. In this paper we present an architecture, based on the BDI paradigm, employing a three layered approach and coupling an emotion engine which simulates the generation of affective states based on Scherer's component process theory and influences decision making.
BehaviorSim: A Learning Environment for Behavior-Based Agent
Authors: Fasheng Qiu, Xiaolin Hu
Venue: Proceedings of the 10th international conference on Simulation of Adaptive Behavior: From Animals to Animats (2008)

Behavior-based control is one of the fundamental control paradigms for autonomous agents to achieve adaptive behavior in a dynamical environment. Existing work has mainly focused on the research aspect rather than on the learning and educational aspect. This paper presents an effort to develop a learning environment for behavior-based agents. It allows educators and students to develop and exercise behavior-based control by setting up entities, behaviors, and behavior networks without involving significant programming effort. Specification of behavior-based agent system is presented and demonstrative examples are provided.
Coordination Via Types in an Event-Based Framework
Authors: Gianluigi Ferrari, Roberto Guanciale, Daniele Strollo, Emilio Tuosto
Venue: Proceedings of the 27th IFIP WG 6.1 international conference on Formal Techniques for Networked and Distributed Systems (2007)

We propose a novel approach to service choreography through a typed process calculus that features an event notification paradigm for coordinating distributed components (e.g., services). Basically, the type system expresses coordination policies for handling the events spawn in a network so that distributed components react to events when the type of their public interface is "compatible" with (the policies expressed by) the types of signals.Remarkably, the type system can naturally handle multi-party sessions, as shown in the formalisation of the OpenID protocol which requires multi-party sessions for handling user identities.
Natural Language Specification of Performance Trees
Authors: Lei Wang, Nicholas J. Dingle, William J. Knottenbelt
Venue: Proceedings of the 5th European Performance Engineering Workshop on Computer Performance Engineering (2008)

The accessible specification of performance queries is a key challenge in performance analysis. To this end, we seek to combine the intuitive aspects of natural language query specification with the expressive power and flexibility of the Performance Tree formalism. Specifically, we present a structured English grammar for Performance Trees, and use it to implement a Natural Language Query Builder (NLQB) for the Platform Independent Petri net Editor (PIPE). The NLQB guides users in the construction of performance queries in an iterative fashion, presenting at each step a range of natural language alternatives that are appropriate in the query context. We demonstrate our technique in the specification of performance queries on a model of a hospital's Accident and Emergency department.
DSA Signature Scheme Immune to the Fault Cryptanalysis
Authors: Maciej Nikodem
Venue: Proceedings of the 8th IFIP WG 8.8/11.2 international conference on Smart Card Research and Advanced Applications (2008)

In this paper we analyse the Digital Signature Algorithm (DSA) and its immunity to the fault cryptanalysis that takes advantage of errors inducted into the private key a. The focus of our attention is on the DSA scheme as it is a widely adopted by the research community, it is known to be vulnerable to this type of attack, but neither sound nor effective modifications to improve its immunity have been proposed. In our paper we consider a new way of implementing the DSA that enhances its immunity in the presence of faults. Our proposal ensures that inducting errors into the private key has no benefits since the attacker cannot deduce any information about the private key given erroneous signatures. The overhead of our proposal is similar to the overhead of obvious countermeasure based on signature verification. However, our modification generates fewer security issues.
The Hybrid Principal Component Analysis Based on Wavelets and Moving Median Filter
Authors: Cheng-Lin Wen, Shao-Hui Fan, Zhi-Guo Chen
Venue: Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks (2007)

The data obtained from any process may be corrupted with noise and outliers which may lead to false-alarm when applying conventional PCA to process monitoring. To overcome the above mentioned limitations of conventional PCA, an approach is developed by combining the ability of wavelets and moving median filter with PCA. This method utilizes the quality of wavelets and moving median filter to preprocess the data to eliminate noise and outliers. At last, this method is applied to fault detection and has a good effect which proves the method is effective and feasible.
Distinctiveness in multimodal behaviors
Authors: Maurizio Mancini, Catherine Pelachaud
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1 (2008)

While talking, people may move heavily their arms around, remain expressionless, or even display subtle facial movements... These differences may arise from personality, cultural, social factors and many more. In the present work, we are interested in defining a schema that characterizes distinctiveness in behaviors. Distinctiveness encompasses behaviors differences regarding (i) shape (which signals are performed) and (ii) quality (expressivity of movement, the way in which movements are performed). Thus, we aim to define embodied conversational agents (ECAs) that, given their communicative intention and behaviors tendencies definition, present distinctive behaviors.
Automatic Program and Control in Network-Manufacturing System
Authors: Sun Huilai, Zhao Fangfang, Lin Shuzhong, Qi Xiangyang, Yang Xianghong, Qi Jiangbo, Liu Haihua
Venue: Proceedings of the 2008 ISECS International Colloquium on Computing, Communication, Control, and Management - Volume 02 (2008)

Networked Manufacturing can improve the strength and market competitiveness of manufacturing group. And the automatic program is one of key technologies. In the network Manufacturing System, design file was PLT format that realized by AutoCAD through networks collaboration to design, analyze and exchange. CAD module and CAM module were established through which the VC program dealt with design file, extracted manufacturing data and control symbol, programmed numerical manufacturing program. Modularization design ideology was adopted in software programming. And it improved the manufacturing reliability and shortened development manufacturing cycle. Experiments indicated that the automatic Program and control system was operating well and satisfied with the design requirements.
Effectiveness of global features for automatic medical image classification and retrieval - The experiences of OHSU at ImageCLEFmed
Authors: Jayashree Kalpathy-Cramer, William Hersh
Venue: Pattern Recognition Letters (2008)

In 2006 and 2007, Oregon Health and Science University (OHSU) participated in the automatic image annotation task for medical images at ImageCLEF, an annual international benchmarking event that is part of the cross language evaluation forum (CLEF). The goal of the automatic annotation task was to classify 1000 test images based on the image retrieval in medical applications (IRMA) code, given a set of 10,000 training images. There were 116 distinct classes in 2006 and 2007. We evaluated the efficacy of a variety of primarily global features for this classification task. These included features based on histograms, gray level correlation matrices and the gist technique. A multitude of classifiers including k-nearest neighbors, two-level neural networks, support vector machines, and maximum likelihood classifiers were evaluated. Our official error rates for the 1000 test images were 26% in 2006 using the flat classification structure. The error count in 2007 was 67.8 using the hierarchical classification error computation based on the IRMA code in 2007. Confusion matrices as well as clustering experiments were used to identify visually similar classes. The use of the IRMA code did not help us in the classification task as the semantic hierarchy of the IRMA classes did not correspond well with the hierarchy based on clustering of image features that we used. Our most frequent misclassification errors were along the view axis. Subsequent experiments based on a two-stage classification system decreased our error rate to 19.8% for the 2006 dataset and our error count to 55.4 for the 2007 data.
Universal families of permutation groups
Authors: William M. Kantor
Venue: Journal of Algebraic Combinatorics: An International Journal (2008)

For several families ¿ of finite transitive permutation groups it is shown that each finite group is isomorphic to a 2-point stabilizer of infinitely many members of ¿.
On Feature Extraction Capabilities of Fast Orthogonal Neural Networks
Authors: Bartłomiej Stasiak, Mykhaylo Yatsymirskyy
Venue: Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II (2007)

The paper investigates capabilities of fast orthogonal neural networks in a feature extraction task for classification problems. Neural networks with an architecture based on the fast cosine transform, type II and IV are built and applied for extraction of features used as a classification base for a multilayer perceptron. The results of the tests show that adaptation of the neural network allows to obtain a better transform in the feature extraction sense as compared to the fast cosine transform. The neural implementation of both the feature extractor and the classifier enables integration and joint learning of both blocks.
Formal Security Analysis of Electronic Software Distribution Systems
Authors: Monika Maidl, David Oheimb, Peter Hartmann, Richard Robinson
Venue: Proceedings of the 27th international conference on Computer Safety, Reliability, and Security (2008)

Software distribution to target devices like factory controllers, medical instruments, vehicles or airplanes is increasingly performed electronically over insecure networks. Such software often implements vital functionality, and so the software distribution process can be highly critical, both from the safety and the security perspective. In this paper, we introduce a novel software distribution system architecture with a generic core component, such that the overall software transport from the supplier to the target device is an interaction of several instances of this core component communicating over insecure networks. The main advantage of this architecture is reduction of development and certification costs. The second contribution of this paper describes the validation and verification of the proposed system. We use a mix of formal methods, more precisely the AVISPA tool, and the Common Criteria (CC) methodology, to achieve high confidence in the security of the software distribution system at moderate costs.
Tree-Based Indirect Encodings for Evolutionary Development of Neural Networks
Authors: Jan Drchal, Miroslav Šnorek
Venue: Proceedings of the 18th international conference on Artificial Neural Networks, Part II (2008)

This paper focuses on TWEANN (Topology and Weight Evolving Artificial Neural Network) methods based on indirect developmental encodings. TWEANNs are Evolutionary Algorithms (EAs) which evolve both topology and parameters (weights) of neural networks. Indirect developmental encoding is an approach inspired by multi-cellular organisms' development from a single cell (zygote) known from Nature. The possible benefits of such encoding can be seen in Nature: for example, human genome consists of roughly 30 000 genes, which describe more than 20 billion neurons, each linked to as many as 10 000 others. In this work we examine properties of known tree-based indirect developmental encodings: Cellular Encoding and Edge Encoding. Well known Genetic Programming is usualy used to evolve tree structures. We have employed its successors: Gene Expression Programming (GEP) and Grammatical Evolution (GE) to optimize the trees. The combination of well designed developmental encoding and proper optimization method should bring compact genomes able to describe large-scale, modular neural networks. We have compared GE and GEP using a benchmark and found that GE was able to find solution about 7 times faster then GEP. On the other hand GEP solutions were more compact.
Adaptive Tracking Control for the Output PDFs Based on Dynamic Neural Networks
Authors: Yang Yi, Tao Li, Lei Guo, Hong Wang
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks (2007)

In this paper, a novel adaptive tracking control strategy is established for general non-Gaussian stochastic systems based on two-step neural network models. The objective is to control the conditional PDF of the system output to follow a given target function by using dynamic neural network models. B-spline neural networks are used to model the dynamic output probability density functions (PDFs), then the concerned problem is transferred into the tracking of given weights corresponding to the desired PDF. The dynamic neural networks with undetermined parameters are employed to identify the nonlinear relationships between the control input and the weights. To achieve control objective, an adaptive state feedback controller is given to estimate the unknown parameters and control the nonlinear dynamics.
What You Expect Is What You See
Authors: Dirkjan Joor, Wilco Beekhuizen, Lidwien Wijngaert, Pascal Ijegalu
Venue: Proceedings of the 6th European conference on Changing Television Environments (2008)

In this paper an experiment was conducted to measure the effect of framing a high definition television (HDTV) clip. One group of participants was told they were watching a brand new HDTV clip while the other group was told they were watching a digital DVD clip. Both groups were in fact watching the same (low) quality DVD clip. After watching this clip the beliefs of participants and their viewing experience was measured through a questionnaire. A significant more positive viewing experience for people framed to watch the HDTV clip was found. This means participants were unable to discriminate properly between digital and high definition signals and were influenced by the frame set for them. This effect has been shown in many different situations and now it has been established for watching HD-quality television as well. The results still indicate that the HDTV-frame is already associated with a high quality viewing experience, which may influence the selling strategy and/or adoption speed of this technology.
A Factory Pattern in Fortran 95
Authors: Viktor K. Decyk, Henry J. Gardner
Venue: Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007 (2007)

This paper discusses the concept and application of software design patterns in Fortran 95-based scientific programming. An example of a factory pattern is given together with a context in Particle in Cell plasma simulation.