ppxscal/embedding_network huggingface dataset:
Automatic Design of ANNs by Means of GP for Data Mining Tasks: Iris Flower Classification Problem
Authors: Daniel Rivero, Juan Rabuñal, Julián Dorado, Alejandro Pazos
Venue: Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I (2007)

This paper describes a new technique for automatically developing Artificial Neural Networks (ANNs) by means of an Evolutionary Computation (EC) tool, called Genetic Programming (GP). This paper also describes a practical application in the field of Data Mining. This application is the Iris flower classification problem. This problem has already been extensively studied with other techniques, and therefore this allows the comparison with other tools. Results show how this technique improves the results obtained with other techniques. Moreover, the obtained networks are simpler than the existing ones, with a lower number of hidden neurons and connections, and the additional advantage that there has been a discrimination of the input variables. As it is explained in the text, this variable discrimination gives new knowledge to the problem, since now it is possible to know which variables are important to achieve good results.
On Complex Networks in Software: How Agent---Orientation Effects Software Structures
Authors: Jan Sudeikat, Wolfgang Renz
Venue: Proceedings of the 5th international Central and Eastern European conference on Multi-Agent Systems and Applications V (2007)

Software-Engineering provides techniques to ease handling the essential complexity of software. A number of engineering paradigms and architectures have been devised and each generation claims to relieve future development efforts. But to date little is known about how different development approaches affect the underlying implemenation structures, making their contributions arguable. Recently, the statistical analysis of large---scale modular software systems --- represented as directed graphs --- revealed complex system characteristics, namely scale---free and small---world phenomena. In this paper, we argue that the exhibited network characteristics reflect utilized design approaches and apply graph analysis to examine the structural differences imposed by the utilization of Agent---Oriented Software Engineering. As this novel development paradigm proposes autonomous and pro---active entities as an atomic design and development metaphor for complicated and inherently distributed software systems, an initial analysis and comparison of graphs abstracting both agent--- and object---oriented system designs reveals structural differences which suggest that agent autonomy influences the resulting underlying implementation structures.
Automatic Image Analysis of Histopathology Specimens Using Concave Vertex Graph
Authors: Lin Yang, Oncel Tuzel, Peter Meer, David J. Foran
Venue: Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I (2008)

Automatic image analysis of histopathology specimens would help the early detection of blood cancer. The first step for automatic image analysis is segmentation. However, touching cells bring the difficulty for traditional segmentation algorithms. In this paper, we propose a novel algorithm which can reliably handle touching cells segmentation. Robust estimation and color active contour models are used to delineate the outer boundary. Concave points on the boundary and inner edges are automatically detected. A concave vertex graph is constructed from these points and edges. By minimizing a cost function based on morphological characteristics, we recursively calculate the optimal path in the graph to separate the touching cells. The algorithm is computationally efficient and has been tested on two large clinical dataset which contain 207 images and 3898 images respectively. Our algorithm provides better results than other studies reported in the recent literature.
Fast and Robust Way of Learning the Fourier Series Neural Networks on the Basis of Multidimensional Discrete Fourier Transform
Authors: Krzysztof Halawa
Venue: Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing (2006)

The calculation method for weights of orthogonal Fourier series neural networks on the grounds of multidimensional discrete Fourier transform is presented. The method proposed represents high speed of operation and outlier robustness. It allows easy reduction of network structure following its training process. The paper presents also the ways of applying the method to modelling of dynamic controlled systems. It is very easy to prepare a program which would allow to use the procedure proposed. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
Evaluating the Effects of the Environmental Context-of-Use on Mobile Website Usability
Authors: Alexandros S. Tsiaousis, George M. Giaglis
Venue: Proceedings of the 2008 7th International Conference on Mobile Business (2008)

Environmental conditions arguably influence mobile website usability, especially as users on the move may be exposed to fluctuating contextual conditions. Since the aspects of context cannot be readily simulated in a mobile usability study, the development of theories and models explaining how the context of use may affect usability has yet to be sufficiently addressed. In this paper we propose a model hypothesizing on the effects of environmental distractions on the usability of mobile sites. Environmental distractions are categorized into auditory, visual and social and we suggest both direct and moderated effects of these distractions on efficiency, effectiveness, and perceived satisfaction. The paper contributes to the mobile HCI literature by developing a concrete set of empirically testable hypotheses and a method for their subsequent assessment. A preliminary test (N=30) was conducted to investigate our main hypotheses. Results confirmed that environmental distractions have a direct effect on mobile website usability.
Conservative Extensions in the Lightweight Description Logic $\mathcal{EL}$
Authors: Carsten Lutz, Frank Wolterinst
Venue: Proceedings of the 21st international conference on Automated Deduction: Automated Deduction (2007)

We bring together two recent trends in description logic (DL): lightweight DLs in which the subsumption problem is tractable and conservative extensions as a central tool for formalizing notions of ontology design such as refinement and modularity. Our aim is to investigate conservative extensions as an automated reasoning problem for the basic tractable DL $\mathcal{EL}$. The main result is that deciding (deductive) conservative extensions is ExpTime-complete, thus more difficult than subsumption in $\mathcal{EL}$, but not more difficult than subsumption in expressive DLs. We also show that if conservative extensions are defined model-theoretically, the associated decision problem for $\mathcal{EL}$ is undecidable.
Flexible Elastoresistive Tactile Sensor for Minimally Invasive Surgery
Authors: Pauwel Goethals, Mauro M. Sette, Dominiek Reynaerts, Hendrik Brussel
Venue: Proceedings of the 6th international conference on Haptics: Perception, Devices and Scenarios (2008)

In minimally invasive surgery, tactile feedback is lacking. An elastoresistive tactile sensor is designed to feel inside the body of the patient. The sensor is thin, flexible, robust, cheap, and has a simple structure. It has 16×16 elements, a spatial resolution of 1 mm and a bandwidth of 78 Hz. Despite a large hysteresis and non-linear behaviour, the sensor is very well suited for a qualitative measurement of the pressure distribution, with a high resolution in position, force and time.
Evaluation Measures for TCBR Systems
Authors: M. A. Raghunandan, Nirmalie Wiratunga, Sutanu Chakraborti, Stewart Massie, Deepak Khemani
Venue: Proceedings of the 9th European conference on Advances in Case-Based Reasoning (2008)

Textual-case based reasoning (TCBR) systems where the problem and solution are in free text form are hard to evaluate. In the absence of class information, domain experts are needed to evaluate solution quality, and provide relevance information. This approach is costly and time consuming. We propose three measures that can be used to compare alternate TCBR system configurations, in the absence of class information. The main idea is to quantify alignment as the degree to which similar problems have similar solutions. Two local measures capture this information by analysing similarity between problem and solution neighbourhoods at different levels of granularity, whilst a global measure achieves the same by analyzing similarity between problem and solution clusters. We determine the suitability of the proposed measures by studying their correlation with classifier accuracy on a health and safety incident reporting task. Strong correlation is observed with all three approaches with local measures being slightly superior over the global one.
An Interactive Algorithm for Blurred Medical Image Segmentation Based on Curve Fitting
Authors: Wenming Huang
Venue: Proceedings of the 2008 Second International Conference on Genetic and Evolutionary Computing (2008)

This paper presents a user steered segmentation algorithm based on the radial basis function (RBF) curve fitting for blurred medical image caused by the continuity of organism, which results in the difficulties of algorithm based on the radial basis function (RBF) curve fitting for blurred medical image caused by the continuity of organism, which results in the difficulties of satisfactory automatic segmentation. The algorithm begins with the feature knots selection from the user interested area, followed by the reconstruction of an implicit surface, whose iso-line is the segmented areas, in 3D by using RBF to interpolate these knots. Two acceleration methods are also presented for the bounding box of the feature points and mathematical morphology operators. Lots of experiments show that the approach is efficient for dealing with very blurred and noisy medial images. The interpolation knots should be selected adaptively such that the fine details can be represented well. The accuracy of the segmentation is mainly determined by the position of the knots.
Improving the Responsiveness of NSGA-II in Dynamic Environments Using an Adaptive Mutation Operator --- A Case Study
Authors: Alvaro Gomes, C. Henggeler Antunes, A. Gomes Martins
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part I (2008)

This paper presents a comparative analysis of the results obtained with two different implementations of the NSGA-II genetic algorithm in the framework of load management activities in electric power systems. The multiobjective real-world problem deals with the identification and the selection of suitable control strategies to be applied to groups of electric loads aimed at reducing maximum power demand, maximize profits and minimize user discomfort. It is shown that the algorithm performance is improved when the NSGA-II mutation operator is adaptively changed to incorporate information about the results of the search process and transfer this "knowledge" to the population.
Intuitionistic Fuzzy Histograms of an Image
Authors: Ioannis K. Vlachos, George D. Sergiadis
Venue: Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing (2007)

This paper proposes an automated approach for constructing the intuitionistic fuzzy histograms (IF-histograms) of a gray-scale digital image, based on the notion of intuitionistic fuzzy numbers (IF-numbers). A method for constructing parametric IF-numbers from their fuzzy counterparts using intuitionistic fuzzy generators (IFGs) is also presented, using an entropic optimization criterion. Finally, experimental results demonstrate the ability of the proposed approach to obtain efficiently the IF-histograms of gray-scale images.
Classification of Suspected Liver Metastases Using fMRI Images: A Machine Learning Approach
Authors: Moti Freiman, Yifat Edrei, Yehonatan Sela, Yitzchak Shmidmayer, Eitan Gross, Leo Joskowicz, Rinat Abramovitch
Venue: Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I (2008)

This paper presents a machine-learning approach to the interactive classification of suspected liver metastases in fMRI images. The method uses fMRI-based statistical modeling to characterize colorectal hepatic metastases and follow their early hemodynamical changes. Changes in hepatic hemodynamics are evaluated from $T_2^*$-W fMRI images acquired during the breathing of air, air-CO 2, and carbogen. A classification model is build to differentiate between tumors and healthy liver tissues. To validate our method, a model was built from 29 mice datasets, and used to classify suspicious regions in 16 new datasets of healthy subjects or subjects with metastases in earlier growth phases. Our experimental results on mice yielded an accuracy of 78% with high precision (88%). This suggests that the method can provide a useful aid for early detection of liver metastases.
Plan Repair in Hybrid Planning
Authors: Julien Bidot, Bernd Schattenberg, Susanne Biundo
Venue: Proceedings of the 31st annual German conference on Advances in Artificial Intelligence (2008)

We present a domain-independent approach to plan repair in a formal framework for hybrid planning. It exploits the generation process of the failed plan by retracting decisions that led to the failed plan fragments. They are selectively replaced by suitable alternatives, and the repaired plan is completed by following the previous generation process as close as possible. This way, a stable solution is obtained, i.e. a repair of the failed plan that causes minimal perturbation.
Threshold accepting trained principal component neural network and feature subset selection: Application to bankruptcy prediction in banks
Authors: V. Ravi, C. Pramodh
Venue: Applied Soft Computing (2008)

This paper proposes an application of new principal component neural network (PCNN) architecture to bankruptcy prediction problem in commercial banks. Further, a new feature subset selection (FSS) algorithm is proposed. In this architecture, the hidden layer is completely replaced by what is referred to as a 'principal component layer'. This layer consists of a few selected principal components that perform the function of hidden nodes. Moreover, this study proposes an algorithm based on the threshold accepting (TA) meta-heuristic to train the PCNN. The architecture reduces the number of weights by a great number as there are no formal connections between the input layer and the principal component layer. The efficacy of the algorithm is tested on the Spanish banks dataset and Turkish banks dataset. The results showed high generalization power of PCNN in the 10-fold cross-validation and also the feature subsets selected in each of the examples showed high discriminating power. PCNN is also compared with PCA-TANN and PCA-BPNN, which have PCA as the preprocessor and have one hidden layer each. Further comparisons are also made with TANN and BPNN. All these classifiers are compared with respect to the AUC (area under the receiver operating characteristic (ROC) curve) criterion. ROC curve is drawn for each classifier with sensitivity on the X-axis and one-specificity on the Y-axis. Based on the experiments conducted, it is inferred that the proposed PCNN hybrids outperformed other classifiers in terms of AUC. It is also observed that the proposed feature subset selection algorithm is very stable and powerful.
Technical Communication: Modeling of vocal tracts based on polynomials
Authors: M.R. Karami Mollaei, Malihah Hassani
Venue: Computers and Electrical Engineering (2008)

A great problem in speech processing is to represent the shape and characteristics of the vocal tracts. This task is normally done by using an acoustics tube model, based on the calculation of the area function would be performed. The area function can be found from sagittal function determined by sagittal cub. In this paper, an optimization based method is presented to model the vocal tracts and their relationship with the fundamental frequencies of vowels and consonants. We present two models which are simple polynomials that need the minimum number of parameters. We will show that these models have good performance in experiments.
Eliminating the Gap between the High and Low Students through Meta-cognitive Strategy Instruction
Authors: Min Chi, Kurt Vanlehn
Venue: Proceedings of the 9th international conference on Intelligent Tutoring Systems (2008)

One important goal of Intelligent Tutoring Systems (ITSs) is to bring students up to the same level of mastery. We showed that an ITS teaching a domain-independent problem-solving strategy indeed closed the gap between High and Low learners, not only in the domain where it was taught (probability) but also in a second domain where it was not taught (physics). The strategy includes two main components: one is solving problems via Backward-Chaining (BC) from goals to givens, named the BC-strategy, and the other is drawing students' attention on the characteristics of each individual domain principle, named the principle-emphasis skill. Evidence suggests that the Low learners transferred the principle-emphasis skill to physics while the High learners seemingly already had such skill and thus mainly transferred the other skill, the BC-strategy. Surprisingly, the former learned just as effectively as the latter in physics. We concluded that the effective element of the taught strategy seemed not to be the BC-Strategy, but the principle-emphasis skill.
Increasing Expressiveness of Composite Events Using Parameter Contexts
Authors: Indrakshi Ray, Wei Huang
Venue: Proceedings of the 12th East European conference on Advances in Databases and Information Systems (2008)

The event-condition-action paradigm (also known as triggers or ECA rules) gives a database "active" capabilities --- the ability to react automatically to changes in the database or the environment. Events can be primitive or composite. Primitive events cannot be decomposed. Different types of primitive events can be composed using event composition operators to form composite events. When a composite event occurs, it is possible that many instances of some constituent primitive event occurs. The context determines which of these primitive events should be considered for evaluating the composite event. Researchers have identified four different contexts, namely, recent, chronicle, continuous, and cumulative that can be associated with a composite event. Associating a single context with a complex composite event is often times not adequate. Many real-world scenarios cannot be expressed if a composite event is associated with a single context. To solve this problem, we need to associate different contexts for the various constituent primitive events. We show how this can be done by providing a formal semantics for associating contexts with primitive events. Finally, we give algorithms for event detection that implement these semantics.
A lab implementation of SYN flood attack and defense
Authors: Dongqing Yuan, Jiling Zhong
Venue: Proceedings of the 9th ACM SIGITE conference on Information technology education (2008)

A "denial-of-service" attack is characterized by an explicit attempt by attackers to prevent legitimate users of a service from using that service. SYN flood attack is one of the most common types of DoS. In this lab, we model and simulate a real world network, and we launch a SYN attack against our web server. Through this, we study the nature of the attack and investigate the effectiveness of several approaches in defending against SYN attack. This lab will allow students to anatomize the SYN flooding attack and defense in the lab environment and obtain data and parameters of DoS resilience capability.
Activity-aware computing: modeling of human activity and behavior
Authors: Nigel Davies, Peter J. Downey
Venue:  (2008)

With our society becoming increasingly mobile and devices that are small, inexpensive and wireless, we are transitioning from an age of desktop computing to an age where computers are used in all aspects of life and leisure. Ubiquitous Computing is largely concerned with the progression of computers from stationary desktop environments to environments where computers and sensors are integrated with objects and every aspect of our daily life, often in an invisible way. This dissertation investigates an important problem in Ubiquitous Computing: detecting domestic activities using ubiquitously deployed sensors from data sets of limited size. The dissertation assumes that home environments in the next 20 years will support a wide range of sensing technologies that are built in smart appliances and the surrounding environment (e.g. RFID tags and readers, accelerometers, temperature sensors etc.). The dissertation also assumes that there will be an abundance of embedded CPU power in the environment that will enable fast and efficient spectral analysis and feature extraction from sensor signals. Using efficient wireless technologies such as the new Bluetooth Wibree protocol, these devices will be able to communicate their sensed data in an efficient way. Two approaches are presented for domestic activity recognition from wireless sensors. The first approach is rule-based and logical in nature and is suitable when sensor data is not present for training. Importantly, fuzzy distributions model the uncertainty and variability in expert knowledge. The second approach is probabilistic in nature and learns by observation without human intervention. This approach uses Bayesian Learning and is optimized to deal with sparse data sets (with hundreds of sensor readings and few instances of activities). Further, a case study is presented in which activity recognition optimizes energy consumption for wireless PC cards that results in significant energy savings. This dissertation concludes by highlighting major and minor results. A summary of the author's future and current research efforts is presented including the application of activity recognition in medical interventions and resource allocation problems.
Dichotomy Method toward Interactive Testing-Based Fault Localization
Authors: Ji-Rong Sun, Zhi-Shu Li, Jian-Cheng Ni
Venue: Proceedings of the 4th international conference on Advanced Data Mining and Applications (2008)

A dichotomy method is presented to perform on test-based fault localization. First the test information itself is optimized from three aspects: searching scope localization using slice technique, redundant test case removal, and test suite reduction with nearest series. Secondly, the diagnosis matrix is set up according to the optimized test information and each code is prioritized accordingly. Thirdly, dichotomy method is iteratively applied to an interactive process for seeking the bug: the searching scope is cut in two by the checkpoint cp, which is of the highest priority; If cp is wrong, the bug is found; else we will ignore the codes before/after it according to the result of cp. Finally, we conduct three studies with Siemens suite of 132 program mutants. Our method scores 0.85 on average, which means we only need to check less than 15% of the program before finding out a bug.