ppxscal/embedding_network huggingface dataset:
Evaluating Membership Functions for Fuzzy Discrete SVM
Authors: Carlotta Orsenigo, Carlo Vercellis
Venue: Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory (2007)

A vast majority of classification problems are characterized by an intrinsic vagueness in the knowledge of the class label associated to each example. In this paper we propose a classifier based on fuzzy discrete support vector machines, that takes as input a binary classification problem together with a membership value for each example, and derives an optimal separation rule by solving a mixed-integer optimization problem. We consider different methods for computing the membership function: some are based on a metric defined in the attribute space; some derive the membership function from a scoring generated by a probabilistic classifier; others make use of frequency voting by an ensemble classifier. To evaluate the overall accuracy of the fuzzy discrete SVM, and to investigate the effect of the alternative membership functions, computational tests have been performed on benchmark datasets. They show that fuzzy discrete SVM is an accurate classification method capable to generate robust rules and to smooth out the effect of outliers.
Using Feature Distribution Methods in Ensemble Systems Combined by Fusion and Selection-Based Methods
Authors: Laura E. Santana, Anne M. Canuto, João C. Xavier,  Jr.
Venue: Proceedings of the 18th international conference on Artificial Neural Networks, Part I (2008)

The main prerequisite for the efficient use of ensemble systems is that the base classifiers should be diverse among themselves. One way of increasing diversity is through the use of feature distribution methods in ensemble systems. In this paper, an investigation of the use of feature distribution methods among the classifiers of ensemble systems will be performed. In this investigation, five different methods of data distribution will be used. These ensemble systems will use six existing combination methods, in which four of them are fusion-based methods and the remaining two are selection-based methods. As a result, it is aimed to detect which ensemble systems are more suitable to use feature distribution among the classifier.
Recent Hybrid Techniques for the Multi-Knapsack Problem
Authors: Carlos Diego Rodrigues, Philippe Michelon, Manoel B. Campêlo
Venue: Proceedings of the 14th international conference on Principles and Practice of Constraint Programming (2008)

The cooperation between the Mathematical Programming (MP) and Constraint Programming (CP) paradigms has come to focus in the recent years. The impossibility of solving bigger instances alone and the apparently complementarity between the two approaches pushes towards further researches on hybrid methods. We can define a linear binary optimization problem as (P) { min c.x : Ax ≤ b, x ¿ B n }, where x is a binary variable, A ¿ R m ×n is the constraint matrix, b ¿ R m is the right-hand side vector and c ¿ R n is the objective function.
Shape-Based Human Activity Recognition Using Independent Component Analysis and Hidden Markov Model
Authors: Md. Zia Uddin, J. J. Lee, T. -S. Kim
Venue: Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence (2008)

In this paper, a novel human activity recognition method is proposed which utilizes independent components of activity shape information from image sequences and Hidden Markov Model (HMM) for recognition. Activities are represented by feature vectors from Independent Component Analysis (ICA) on video images and based on these features, recognition is achieved by trained HMMs of activities. Our recognition performance has been compared to the conventional method where Principle Component Analysis (PCA) is typically used to derive activity shape features. Our results show that superior recognition is achieved with our proposed method especially for activities (e.g., skipping) that cannot be easily recognized by the conventional method.
Publish-Subscribe Information Delivery with Substring Predicates
Authors: Ioannis Aekaterinidis, Peter Triantafillou
Venue: IEEE Internet Computing (2007)

The content-based publish-subscribe (pub-sub) paradigm for system design is becoming increasingly popular, offering unique benefits for many data-intensive applications. Coupled with peer-to-peer technology, it can serve as a central building block for developing data-dissemination applications deployed over a large-scale network infrastructure. A key open problem in creating large-scale content-based pub-sub infrastructures relates to efficiently and accurately matching subscriptions with substring predicates to incoming events. This work addresses this issue.
SLA-Based Advance Reservations with Flexible and Adaptive Time QoS Parameters
Authors: Marco A. Netto, Kris Bubendorfer, Rajkumar Buyya
Venue: Proceedings of the 5th international conference on Service-Oriented Computing (2007)

Utility computing enables the use of computational resources and services by consumers with service obligations and expectations defined in Service Level Agreements (SLAs). Parallel applications and workflows can be executed across multiple sites to benefit from access to a wide range of resources and to respond to dynamic runtime requirements. A utility computing provider has the difficult role of ensuring that all current SLAs are provisioned, while concurrently forming new SLAs and providing multiple services to numerous consumers. Scheduling to satisfy SLAs can result in a low return from a provider's resources due to trading off Quality of Service (QoS) guarantees against utilisation. One technique is to employ advance reservations so that an SLA aware scheduler can properly manage and schedule its resources. To improve system utilisation we exploit the principle that some consumers will be more flexible than others in relation to the starting or completion time, and that we can juggle the execution schedule right up until each execution starts. In this paper we present a QoS scheduler that uses SLAs to efficiently schedule advance reservations for computation services based on their flexibility. In our SLA model users can reduce or increase the flexibility of their QoS requirements over time according to their needs and resource provider policies. We introduce our scheduling algorithms, and show experimentally that it is possible to use flexible advance reservations to meet specified QoS while improving resource utilisation.
A Framework for CFG-Based Static Program Analysis of Ada Programs
Authors: Raul Fechete, Georg Kienesberger, Johann Blieberger
Venue: Proceedings of the 13th Ada-Europe international conference on Reliable Software Technologies (2008)

The control flow graph is the basis for many code optimisation and analysis techniques. We introduce a new framework for the construction of powerful CFG-based representations of arbitrary Ada programs. The generated data holds extensive information about the original Ada source, such as visibility, package structure and type definitions and provides means for complete interprocedural analysis. We use ASIS-for-GNAT as an interface to the Ada environment and extract the needed information in a single traversal of the provided syntax trees. In addition, further refinement of the resulting data structures is done.
Design and Implementation of an Adaptive Neuro-controller for Trajectory Tracking of Nonholonomic Wheeled Mobile Robots
Authors: Francisco García-Córdova, Antonio Guerrero-González, Fulgencio Marín-García
Venue: Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II (2007)

A kinematic adaptive neuro-controller for trajectory tracking of nonholonomic mobile robots is proposed. The kinematic adaptive neuro-controller is a real-time, unsupervised neural network that learns to control a nonholonomic mobile robot in a nonstationary environment, which is termed Self-Organization Direction Mapping Network (SODMN), and combines associative learning and Vector Associative Map (VAM) learning to generate transformations between spatial and velocity coordinates. The transformations are learned in an unsupervised training phase, during which the robot moves as a result of randomly selected wheel velocities. The robot learns the relationship between these velocities and the resulting incremental movements. The neural network requires no knowledge of the geometry of the robot or of the quality, number, or configuration of the robot's sensors. The efficacy of the proposed neural architecture is tested experimentally by a differentially driven mobile robot.
Dissemination of Information with Fair Load Distribution in Self-organizing Grids
Authors: Agostino Forestiero, Carlo Mastroianni, Giandomenico Spezzano
Venue: Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence (2008)

This paper presents an ant-inspired algorithm for building a self-organizing information system of a Grid. Ant-inspired mobile agents travel the Grid through P2P (peer-to-peer) interconnections and disseminate "descriptors", i.e., metadata information about available Grid resources. Descriptors are reorganized and spatially sorted over the Grid, thus facilitating resource management and discovery. Moreover, agents distribute descriptors so as to respect the different capabilities of Grid hosts: hosts with higher storage capacity are assigned a larger number of descriptors than low capacity hosts. The effectiveness of the presented algorithm is assessed by event-driven simulation which proves that the simple operations performed by mobile agents successfully achieve both descriptor reorganization and fair load distribution.
Computation of Positive Realizations of Singular MIMO Hybrid Linear Systems
Authors: Sajewski Lukasz, Tadeusz Kaczorek
Venue: Proceedings of the 2008 19th International Conference on Systems Engineering (2008)

The realization problem for singular positive multi-input and multi-output (MIMO) 2D linear hybrid systems is addressed. A method based on the state variable diagram for finding positive realizations of a given improper transfer matrix is proposed. Sufficient conditions for the existence of a positive realization of a given improper transfer matrix are established. A procedure for computation of a positive realization is proposed.
Mercury: a platform for the execution of multiagent systems
Authors: Marc-Philippe Huget
Venue: Proceedings of the 8th international conference on New technologies in distributed systems (2008)

JADE and to a lesser extent MadKit are the two most used platforms for the execution of multiagent. This is because these two platforms exist for a long time and are simple to use. In this paper, we present a new proposal called Mercury so as to complete the above mentionned platforms. We describe here the platform architecture, its features related to organization, communication and environment and finally an example.
Flash Disk Opportunity for Server Applications
Authors: Jim Gray, Bob Fitzgerald
Venue: Queue (2008)

Future flash-based disks could provide breakthroughs in IOPS, power, reliability, and volumetric capacity when compared with conventional disks. NAND flash densities have been doubling each year since 1996. Samsung announced that its 32-gigabit NAND flash chips would be available in 2007. This is consistent with Chang-gyu Hwang's flash memory growth model1 that NAND flash densities will double each year until 2010. Hwang recently extended that 2003 prediction to 2012, suggesting 64 times the current density250 GB per chip. This is hard to credit, but Hwang and Samsung have delivered 16 times since his 2003 article when 2-GB chips were just emerging. So, we should be prepared for the day when a flash drive is a terabyte(!). As Hwang points out in his article, mobile and consumer applications, rather than the PC ecosystem, are pushing this technology.
An FPGA Run-Time Parameterisable Log-Normal Random Number Generator
Authors: Pedro Echeverría, David B. Thomas, Marisa López-Vallejo, Wayne Luk
Venue: Proceedings of the 4th international workshop on Reconfigurable Computing: Architectures, Tools and Applications (2008)

Monte Carlo financial simulation relies on the generation of random variables with different probability distribution functions. These simulations, particularly the random number generator (RNG) cores, are computationally intensive and are ideal candidates for hardware acceleration. In this work we present an FPGA based Log-normal RNG ideally suited for financial Monte Carlo simulations, as it is run-time parameterisable and compatible with variance reduction techniques. Our architecture achieves a throughput of one sample per cycle with a 227.6 MHz clock on a Xilinx Virtex-4 FPGA.
Global Search Method for Parallel Machine Scheduling
Authors: Hyun Joon Shin
Venue: Proceedings of the 3rd international conference on Algorithmic Aspects in Information and Management (2007)

This paper presents a guided multi-restart search (GMRS) algorithm for scheduling parallel machines in terms of global optimum. GMRS consists of a strategic guided local search phase and a phase that generates a beneficial restart point using the information acquired during the local search. The experimental results show that the proposed algorithm considerably improves the solution within a reasonable time. 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
Privacy, Value and Control Issues in Four Mobile Business Applications
Authors: Benjamin D. Renegar, Katina Michael, M. G. Michael
Venue: Proceedings of the 2008 7th International Conference on Mobile Business (2008)

This paper presents four case studies that explore the adoption and acceptance of mobile technologies and services within the context of the privacy-value-control (PVC) trichotomy. The technologies studied include: the mobile phone, electronic toll payment tags, e-passports, and loyalty card programs. The study shows that despite the potential barriers to adoption in each of the depicted cases, the applications were embraced with great success soon after their introduction. An understanding of why these mobile innovations succeeded in spite of the concerns surrounding them will serve to help practitioners understand other issues currently plaguing emerging technologies like radio-frequency identification (RFID) tags and transponders. The contribution of this paper is not only in its usage of secondary sources to support case development and subsequent cross-case analysis but on the importance of emphasizing the value proposition to the consumer to ensure the success of an innovation. The PVC trichotomy emphasizes the need to harmonize privacy, value and control.
Automating Software Testing Using Program Analysis
Authors: Patrice Godefroid, Peli de Halleux, Aditya V. Nori, Sriram K. Rajamani, Wolfram Schulte, Nikolai Tillmann, Michael Y. Levin
Venue: IEEE Software (2008)

During the last 10 years, code inspection for standard programming errors has largely been automated with static code analysis. During the next 10 years, we expect to see similar progress in automating testing, and specifically test generation, thanks to advances in program analysis, efficient constraint solvers, and powerful computers. Three new tools from Microsoft combine techniques from static program analysis, dynamic analysis, model checking, and automated constraint solving while targeting different application domains.
Assessing Classification Accuracy in the Revision Stage of a CBR Spam Filtering System
Authors: José Ramón Méndez, Carlos González, Daniel Glez-Peña, Florentino Fdez-Riverola, Fernando Díaz, Juan Manuel Corchado
Venue: Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development (2007)

In this paper we introduce a quality metric for characterizing the solutions generated by a successful CBR spam filtering system called SpamHunting. The proposal is denoted as relevant information amount rate and it is based on combining estimations about relevance and amount of information recovered during the retrieve stage of a CBR system. The results obtained from experimentation show how this measure can successfully be used as a suitable complement for the classifications computed by our SpamHunting system. In order to evaluate the performance of the quality estimation index, we have designed a formal benchmark procedure that can be used to evaluate any accuracy metric. Finally, following the designed test procedure, we show the behaviour of the proposed measure using two well-known publicly available corpus.
Finite-Time Boundedness Analysis of Uncertain Neural Networks with Time Delay: An LMI Approach
Authors: Yanjun Shen, Lin Zhu, Qi Guo
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks (2007)

This paper considers the problem of finite-time boundedness (FTB) of the general delayed neural networks with norm-bounded parametric uncertainties. The concept of FTB for time delay system is extended first. Then, based on the Lyapunov function and linear matrix inequality (LMI) technique, some delay-dependent criteria are derived to guarantee FTB. The conditions can be reduced to a feasibility problem involving linear matric inequalities (LMIs). Finally, two examples are given to demonstrate the validity of the proposed methodology.
Towards an Efficient Implementation of Tree Automata Completion
Authors: Emilie Balland, Yohan Boichut, Thomas Genet, Pierre-Etienne Moreau
Venue: Proceedings of the 12th international conference on Algebraic Methodology and Software Technology (2008)

Term Rewriting Systems (TRSs) are now commonly used as a modeling language for applications. In those rewriting based models, reachability analysis, i.e. proving or disproving that a given term is reachable from a set of input terms, provides an efficient verification technique. Using a tree automata completion technique, it has been shown that the non reachability of a term t can be verified by computing an over-approximation of the set of reachable terms and proving that t is not in the over-approximation. Since the verification of real programs gives rise to rewrite models of significant size, efficient implementations of completion are essential. We present in this paper a TRS transformation preserving the reachability analysis by tree automata completion. This transformation makes the completion implementation based on rewriting techniques possible. Thus, the reduction of a term to a state by a tree automaton is fully handled by rewriting. This approach has been prototyped in Tom, a language extension which adds rewriting primitives to Java. The first experiments are very promising relative to the state-of-the-art tool Timbuk.
Parameterized Algorithms and Hardness Results for Some Graph Motif Problems
Authors: Nadja Betzler, Michael R. Fellows, Christian Komusiewicz, Rolf Niedermeier
Venue: Proceedings of the 19th annual symposium on Combinatorial Pattern Matching (2008)

We study the NP-complete Graph Motif problem: given a vertex-colored graph G = (V,E) and a multiset M of colors, does there exist an S ¿ V such that G[S] is connected and carries exactly (also with respect to multiplicity) the colors in M? We present an improved randomized algorithm for Graph Motif with running time O(4.32|M|·|M|2·|E|). We extend our algorithm to list-colored graph vertices and the case where the motif G[S] needs not be connected. By way of contrast, we show that extending the request for motif connectedness to the somewhat "more robust" motif demands of biconnectedness or bridge-connectedness leads to W[1]-complete problems. Actually, we show that the presumably simpler problems of finding (uncolored) biconnected or bridge-connected subgraphs are W[1]-complete with respect to the subgraph size. Answering an open question from the literature, we further show that the parameter "number of connected motif components" leads to W[1]-hardness even when restricted to graphs that are paths.
An application of supervised and unsupervised learning approaches to telecommunications fraud detection
Authors: Constantinos S. Hilas, Paris As. Mastorocostas
Venue: Knowledge-Based Systems (2008)

This paper investigates the usefulness of applying different learning approaches to a problem of telecommunications fraud detection. Five different user models are compared by means of both supervised and unsupervised learning techniques, namely the multilayer perceptron and the hierarchical agglomerative clustering. One aim of the study is to identify the user model that best identifies fraud cases. The second task is to explore different views of the same problem and see what can be learned form the application of each different technique. All data come from real defrauded user accounts in a telecommunications network. The models are compared in terms of their performances. Each technique's outcome is evaluated with appropriate measures.
Augmented Display of Anatomical Names of Bronchial Branches for Bronchoscopy Assistance
Authors: Shunsuke Ota, Daisuke Deguchi, Takayuki Kitasaka, Kensaku Mori, Yasuhito Suenaga, Yoshinori Hasegawa, Kazuyoshi Imaizumi, Hirotsugu Takabatake, Masaki Mori, Hiroshi Natori
Venue: Proceedings of the 4th international workshop on Medical Imaging and Augmented Reality (2008)

This paper presents a method for an automated anatomical labeling of bronchial branches (ALBB) for augmented display of its result for bronchoscopy assistance. A method for automated ALBB plays an important role for realizing an augmented display of anatomical names of bronchial branches. The ALBB problem can be considered as a problem that each bronchial branch is classified into the bronchial name to which it belongs. Therefore, the proposed method constructs classifiers that output anatomical names of bronchial branches by employing the machine-learning approach. The proposed method consists of four steps: (a) extraction of bronchial tree structures from 3D CT datasets, (b) construction of classifiers using the multi-class AdaBoost technique, (c) automated classification of bronchial branches by using the constructed classifiers, and (d) an augmented display of anatomical names of bronchial branches. We applied the proposed method to 71 cases of 3D CT datasets. We evaluated the ALBB results by leave-one-out scheme. The experimental results showed that the proposed method could assign correct anatomical names to bronchial branches of 90.1% up to segmental lobe branches. Also, we confirmed that an augmented display of the ALBB results was quite useful to assist bronchoscopy.
Multi-document Summarization Based on Cluster Using Non-negative Matrix Factorization
Authors: Sun Park, Ju-Hong Lee, Deok-Hwan Kim, Chan-Min Ahn
Venue: Proceedings of the 33rd conference on Current Trends in Theory and Practice of Computer Science (2007)

In this paper, a new summarization method, which uses non-negative matrix factorization (NMF) and K-means clustering, is introduced to extract meaningful sentences from multi-documents. The proposed method can improve the quality of document summaries because the inherent semantics of the documents are well reflected by using the semantic features calculated by NMF and the sentences most relevant to the given topic are extracted efficiently by using the semantic variables derived by NMF. Besides, it uses K-means clustering to remove noises so that it can avoid the biased inherent semantics of the documents to be reflected in summaries. We perform detail experiments with the well-known DUC test dataset. The experimental results demonstrate that the proposed method has better performance than other methods using the LSA, the Kmeans, and the NMF.