ppxscal/embedding_network huggingface dataset:
WSQoSX --- A QoS Architecture for Web Service Workflows
Authors: Rainer Berbner, Michael Spahn, Nicolas Repp, Oliver Heckmann, Ralf Steinmetz
Venue: Proceedings of the 5th international conference on Service-Oriented Computing (2007)

Web Services as a technology to enable distributed business processes gain in importance, especially in the area of Enterprise Application Integration (EAI) and Business Process Outsourcing (BPO). However, the support of Quality of Service (QoS) is crucial in this context. Without any guarantee regarding QoS, no enterprise is willing to rely on external Web Services within critical business processes. Thus, we designed and implemented the Web Service Quality of Service Architectural Extension (WSQoSX) as an integrated Web Service system with comprehensive QoS support [2, 4]. WSQoSX supports the assessment of Web Services to assure that only Web Services will be used in critical business processes that satisfy the requirements defined by the user. The selection and execution of a certain Web Service depends on its QoS-properties described by a Service Level Agreements (SLAs) document. The compliance with given SLAs is monitored by WSQoSX as well. In case of a Web Service not being able to fulfil the requirements, it can be replaced during runtime by selecting an alternative Web Service out of a pool of similar Web Services. Additionally, providers can register their Web Service offerings using the same Web-based interface, making WSQoSX a marketplace for Web Services.
Diagnosis Method for Gear Equipment by Sequential Fuzzy Neural Network
Authors: Xiong Zhou, Huaqing Wang, Peng Chen, Jingwei Song
Venue: Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II (2008)

This paper proposes a new method called "sequential fuzzy neural network" to diagnose fault of gear equipment automatically and precisely. Symptom parameters in time domain, by which fault of the gear equipment can be detected and distinguished, are selected according to its values calculated from the signals measured in each state of gear equipment. The probability density functions are translated to possibility distribution functions by possibility theory to express the relationship between the gear condition and the symptom parameters. The fuzzy neural networks proposed in this paper can sequentially distinguish fault types of gear equipment. Examples of practical diagnosis are shown to verify the efficiency of this method.
The-walk-in-the-city: a (no)ordinary image: an essay on creative technologies
Authors: Katerina Antonaki
Venue: Proceedings of the 3rd international conference on Digital Interactive Media in Entertainment and Arts (2008)

This is a study of the image; the image as an outcome of technological innovations; the image as a dominant element of communication; the visual image which coexists with us. The information-image. We perceive the existence of technology, the one which defines our everyday lives, which circulates ideas and data, which enhances our visuality and broadens our knowledge not as an abstract idea but via its apparatus which create new spaces of being -the space of flows of Manuel Castells [10], the non-spaces of Marc Auge [2], the digital space described by Nicholas Negroponte [27]- through its final outcomes which characterize our routine and which are 'naturally' enclosed in our surroundings. However embodiment is no simple matter, the body as it performed in everyday life is realized through its interactions with its environment, an environment populated by the material and cultural products of technosience [19]. I consider visual image one of the main products of technology and I believe that as technology have changed our vision, image has changed our visuality. In this essay I am trying to answer the following questions: Which are the crucial consequences of the domination of image on space? What effect does this constellation of images of diverse contents have on us? Is there any interaction? What disturbs our vision? Why? Which is the role of designer as creator of information images?
Mobile video stories
Authors: Jari Multisilta, Marjo Mäenpää
Venue: Proceedings of the 3rd international conference on Digital Interactive Media in Entertainment and Arts (2008)

The aim of this article is to test how different narrative structures work in mobile video storytelling applications for creative arts. Especially, we are interested in stories made with the mobile phone and for the mobile phone i.e. they are supposed to be viewed on the mobile phone. In addition, we present a new mobile social video service and demo platform MoViE that enables users to create mobile narrations and stories using narrative structures. Hypothesis is that it is possible to create a dramaturgically intensive and coherent story from various short mobile videos composed by several authors if only there is a story generator that composes the certain structure and order to the combination of mobile videos. User of a mobile phone with video camera works as an author, and several authors could produce a common narrative with one storyline that is composed by automatic story generator. In the empirical part of the article we apply a narrative structure based on jazz music as a matrix for the story generator and analyze the creation process of video clips. In this study, we use ethnomethodology as our research framework.
Measuring Web Accessibility by Estimating Severity of Barriers
Authors: Giorgio Brajnik
Venue: Proceedings of the 2008 international workshops on Web Information Systems Engineering (2008)

The paper addresses the issue of measuring web accessibility in such a way that differences in measurements reflect differences in the effectiveness experienced by disabled users. The paper presents the steps upon which a measuring methodology called MAMBO is based, and the data that are needed to compute the indexes, in addition to its conceptual rationale. An experimentation of MAMBO is then described, based on analysis of 14 accessibility reports; results are shown and discussed, including the effects that different severity judgments may have on the metric, how to estimate confidence intervals on the values, and how the metric can be used to estimate accessibility with respect to specific user groups.
Temporal metrics for software vulnerabilities
Authors: Ju An Wang, Fengwei Zhang, Min Xia
Venue: Proceedings of the 4th annual workshop on Cyber security and information intelligence research: developing strategies to meet the cyber security and information intelligence challenges ahead (2008)

It is widely recognized that metrics are important to information security. Metrics can be an effective tool for companies and information security professionals to measure, control, and improve their security control and mechanisms. However, common security metrics are often qualitative, subjective, and informal in the sense that they are lacking formal models and automated support. This paper discussed our work on temporal metrics for software vulnerabilities based on the Common Vulnerability Scoring System 2.0. A mathematical model is provided to calculate the severity and risk of a vulnerability, which is time dependent including exploitability, remediation level, and report confidence attributes of an information asset in a computing environment. A prototype of an automated tool, CVSSWizzard, is illustrated with examples.
Protecting Secret Keys with Fuzzy Fingerprint Vault Based on a 3D Geometric Hash Table
Authors: Sungju Lee, Daesung Moon, Seunghwan Jung, Yongwha Chung
Venue: Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II (2007)

Biometrics-based user authentication has several advantages over traditional password-based systems for standalone authentication applications such as home networks. This is also true for new authentication architectures known as crypto-biometric systems, where cryptography and biometrics are merged to achieve high security and user convenience at the same time. Recently, a cryptographic construct, called fuzzy vault, has been proposed for crypto-biometric systems. In this paper, we propose an approach to provide both the automatic alignment of fingerprint data and higher security by using a 3D geometric hash table. Based on the experimental results, we confirm that the proposed approach of using the 3D geometric hash table with the idea of the fuzzy vault can perform the fingerprint verification securely even with one thousand chaff data included.
A Decentralized Implementation of Mobile Ambients
Authors: Fabio Gadducci, Giacoma Valentina Monreale
Venue: Proceedings of the 4th international conference on Graph Transformations (2008)

We present a graphical implementation for finite processes of the mobile ambients calculus. Our encoding uses unstructured (i.e., non hierarchical) graphs and it is sound and complete with respect to the structural congruence of the calculus (that is, two processes are equivalent iff they are mapped into isomorphic graphs). With respect to alternative proposals for the graphical implementation of mobile ambients, our encoding distinguishes the syntactic structure of a process from the activation order of a process components. Our solution faithfully captures a basic feature of the calculus (ambients can be nested and reductions are propagated across ambient nesting) and it allows to model the reduction semantics via a graph transformation system containing just three rules.
Ensemble Neural Networks with Novel Gene-Subsets for Multiclass Cancer Classification
Authors: Jin-Hyuk Hong, Sung-Bae Cho
Venue: Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part II (2008)

Multiclass gene selection and classification of cancer are rapidly gaining attention in recent years, while conventional rank-based gene selection methods depend on predefined ideal marker genes that basically devised for binary classification. In this paper, we propose a novel gene selection method based on a gene's local class discriminability, which does not require any ideal marker genes for multiclass classification. An ensemble classifier with multiple NNs is trained with the gene subsets. The Global Cancer Map (GCM) cancer dataset is used to verify the proposed method for comparisons with the conventional approaches.
Enhanced random search based incremental extreme learning machine
Authors: Guang-Bin Huang, Lei Chen
Venue: Neurocomputing (2008)

Recently an incremental algorithm referred to as incremental extreme learning machine (I-ELM) was proposed by Huang et al. [G.-B. Huang, L. Chen, C.-K. Siew, Universal approximation using incremental constructive feedforward networks with random hidden nodes, IEEE Trans. Neural Networks 17(4) (2006) 879-892], which randomly generates hidden nodes and then analytically determines the output weights. Huang et al. [G.-B. Huang, L. Chen, C.-K. Siew, Universal approximation using incremental constructive feedforward networks with random hidden nodes, IEEE Trans. Neural Networks 17(4) (2006) 879-892] have proved in theory that although additive or RBF hidden nodes are generated randomly the network constructed by I-ELM can work as a universal approximator. During our recent study, it is found that some of the hidden nodes in such networks may play a very minor role in the network output and thus may eventually increase the network complexity. In order to avoid this issue and to obtain a more compact network architecture, this paper proposes an enhanced method for I-ELM (referred to as EI-ELM). At each learning step, several hidden nodes are randomly generated and among them the hidden node leading to the largest residual error decreasing will be added to the existing network and the output weight of the network will be calculated in a same simple way as in the original I-ELM. Generally speaking, the proposed enhanced I-ELM works for the widespread type of piecewise continuous hidden nodes.
Boundary conditions and phase transitions in neural networks. Theoretical results
Authors: Jacques Demongeot, Christelle Jézéquel, Sylvain Sené
Venue: Neural Networks (2008)

The purpose of this paper is to present some relevant theoretical results on the asymptotic behaviour of finite neural networks (on lattices) when they are subjected to fixed boundary conditions. This work focuses on two different topics of interest from the biological point of view. First, it exhibits a link between the possible updating iteration modes in these networks, whatever the number of dimensions is. It proves that the effects of boundary conditions on neural networks do not depend on the updating iteration mode under the hypothesis of synaptic weight symmetry. Thus, if the asymptotic behaviour admits phase transitions, these phase transitions are observable for many updating iteration modes (from synchrony to asynchrony). Then, it shows that boundaries have no significant impact on one-dimensional neural networks. In order to prove this property, we present a new general mathematical approach based on the use of a projectivity matrix in order to simplify the problem. This approach allows the theoretical study of the asymptotic dynamics and of the boundary influence in neural networks. We will also introduce the numerical tools generalising the method in order to study phase transitions in more complex cases.
Comments on "Nonlinear fixed charge transportation problem by spanning tree-based genetic algorithm" by Jung-Bok Jo, Yinzhen Li, Mitsuo Gen, Computers & Industrial Engineering (2007)
Authors: G. Kannan, P. Sasi Kumar, V. P. Vinay
Venue: Computers and Industrial Engineering (2008)

A spanning tree-based genetic algorithm approach for solving nonlinear fixed charge transportation problem proposed by Jo et al. [Jo, J. B., Li, Y., & Gen, M. (2007). Nonlinear fixed charge transportation problem by spanning tree-based genetic algorithm. Computers &Industrial Engineering. doi:10.1016/j.cie.2007.06.022] was recently published in Computers & Industrial Engineering journal. In this work, comments like calculation of total cost, indication of problem size are given for the published model of [Jo, J.B., Li, Y., & Gen, M. (2007). Nonlinear fixed charge transportation problem by spanning tree-based genetic algorithm. Computers &Industrial Engineering. doi:10.1016/j.cie.2007.06.022.].
Normed BPA vs. Normed BPP Revisited
Authors: Petr Janćar, Martin Kot, Zdeněk Sawa
Venue: Proceedings of the 19th international conference on Concurrency Theory (2008)

We present a polynomial-time algorithm deciding bisimilarity between a normed BPA process and a normed BPP process. This improves the previously known exponential upper bound by Černá, Křetínský, Ku¿era (1999). The algorithm relies on a polynomial bound for the "finite-state core" of the transition system generated by the BPP process. The bound is derived from the "prime form" of the underlying BPP system (where bisimilarity coincides with equality); we suggest an original algorithm for the respective transformation.
Faster Algorithm for the Set Variant of the String Barcoding Problem
Authors: Leszek Gąsieniec, Cindy Y. Li, Meng Zhang
Venue: Proceedings of the 19th annual symposium on Combinatorial Pattern Matching (2008)

A string barcoding problem is defined as to find a minimum set of substrings that distinguish between all strings in a given set of strings ${\cal S}$. In a biological sense the given strings represent a set of genomic sequences and the substrings serve as probes in a hybridisation experiment. In this paper, we study a variant of the string barcoding problem in which the substrings have to be chosen from a particular set of substrings of cardinality n. This variant can be also obtained from more general test set problem, see, e.g., [1] by fixing appropriate parameters. We present almost optimal $O(n|{\cal S}|\log^3 n)$-time approximation algorithm for the considered problem. Our approximation procedure is a modification of the algorithm due to Berman et al. [1] which obtains the best possible approximation ratio (1 + ln n), providing $NP\not\subseteq DTIME(n^{\log\log n})$. The improved time complexity is a direct consequence of more careful management of processed sets, use of several specialised graph and string data structures as well as tighter time complexity analysis based on an amortised argument.
Decision Support for Alzheimer's Patients in Smart Homes
Authors: Shuai Zhang, Sally McClean, Bryan Scotney, Xin Hong, Chris Nugent, Maurice Mulvenna
Venue: Proceedings of the 2008 21st IEEE International Symposium on Computer-Based Medical Systems (2008)

Assistive technology in smart homes for elderly people with Alzheimer's disease is needed to support 'aging in place'. In this paper, we propose a probabilistic learning approach to characterise behavioural patterns for multi-inhabitants in smart homes. Decision support is then provided to monitor and assist patients to complete activities of daily living (ADL). Reasoning is based on the learned profiles and partially observed low-level sensors information. Data are stored in the proposed snow-flake schema based on homeML (an XML based schema for representation of information within smart homes). A laboratory has been developed for studying activities of 'making drinks' for multiple users. Evaluations of our learning and decision support approach are carried out on both real and simulated data. The potential of our approach to support assistive living and home-health monitoring of Alzheimer's patients is demonstrated.
Partial Evaluation of PEPA Models for Fluid-Flow Analysis
Authors: Allan Clark, Adam Duguid, Stephen Gilmore, Mirco Tribastone
Venue: Proceedings of the 5th European Performance Engineering Workshop on Computer Performance Engineering (2008)

We present an application of partial evaluation to performance models expressed in the PEPA stochastic process algebra [1]. We partially evaluate the state-space of a PEPA model in order to remove uses of the cooperation and hiding operators and compile an arbitrary sub-model into a single sequential component. This transformation is applied to PEPA models which are not in the correct form for the application of the fluid-flow analysis for PEPA [2]. The result of the transformation is a PEPA model which is amenable to fluid-flow analysis but which is strongly equivalent [1] to the input PEPA model and so, by an application of Hillston's theorem, performance results computed from one model are valid for the other. We apply the method to a Markovian model of a key distribution centre used to facilitate secure distribution of cryptographic session keys between remote principals communicating over an insecure network.
Ant Colony Optimization for Energy-Efficient Broadcasting in Ad-Hoc Networks
Authors: Hugo Hernández, Christian Blum, Guillem Francès
Venue: Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence (2008)

In wireless ad-hoc networks, nodes are generally equipped with batteries, making energy a scarce resource. Therefore, power consumption of network operations is critical and subject to optimization. One of the fundamental problems in ad-hoc networks is broadcasting. In this work we consider the so-called minimum energy broadcast (MEB) problem, which can be stated as a combinatorial optimization problem. We develop an ant colony optimization algorithm for two scenarios: networks in which nodes are equipped with omni-directional, respectively directional, antennas. The results show that our algorithm consistently outperforms other methods for this problem.
Sequence Labeling with Reinforcement Learning and Ranking Algorithms
Authors: Francis Maes, Ludovic Denoyer, Patrick Gallinari
Venue: Proceedings of the 18th European conference on Machine Learning (2007)

Many problems in areas such as Natural Language Processing, Information Retrieval, or Bioinformatic involve the generic task of sequence labeling. In many cases, the aim is to assign a label to each element in a sequence. Until now, this problem has mainly been addressed with Markov models and Dynamic Programming.We propose a new approach where the sequence labeling task is seen as a sequential decision process. This method is shown to be very fast with good generalization accuracy. Instead of searching for a globally optimal label sequence, we learn to construct this optimal sequence directly in a greedy fashion. First, we show that sequence labeling can be modelled using Markov Decision Processes, so that several Reinforcement Learning (RL) algorithms can be used for this task. Second, we introduce a new RL algorithm which is based on the ranking of local labeling decisions.
Automated Digital Photo Classification by Tessellated Unit Block Alignment
Authors: Chuljin Jang, Hwan-Gue Cho
Venue: Proceedings of the 2008 International Conference on Convergence and Hybrid Information Technology (2008)

Since digital cameras are getting so popular nowadays,a number of digital photos are newly generated at every use of camera. To manage those photos efficiently it is important to reveal how much each photo pair is similar. In this paper we tessellate image into unit blocks and conduct 2D alignment extending to content-based similar region by using seed block pair ranked as high similarity. Through the method, we can distinguish whether photos are sharing the same object or background. Through an alignment, we can get a block region scoring best matching value on whole image. The result is less sensitive to transition or pause change of objects. In experiment, we show how our alignment method is applied to real photo and necessities for further research like photo clustering and massive photo management.
NICTA I2D2 Group at GeoCLEF 2006
Authors: Yi Li, Nicola Stokes, Lawrence Cavedon, Alistair Moffat
Venue: Evaluation of Multilingual and Multi-modal Information Retrieval: 7th Workshop of the Cross-Language Evaluation Forum, CLEF 2006, Alicante, Spain, September 20-22, 2006, Revised Selected Papers (2007)

We report on the experiments undertaken by the NICTA I2D2 Group as part of GeoCLEF 2006, as well as post-GeoCLEF evaluations and improvements to the submitted system. In particular, we used techniques to assign probabilistic likelihoods to geographic candidates for each identified geo-term, and a probabilistic IR engine. A normalisation process that adjusts term weights, so as to prevent expanded geo-terms from overwhelming non-geo terms, is shown to be crucial.
FIT: A Flexible, LIght-Weight, and Real-Time Scheduling System for Wireless Sensor Platforms
Authors: Wei Dong, Chun Chen, Xue Liu, Kougen Zheng, Rui Chu, Jiajun Bu
Venue: Proceedings of the 4th IEEE international conference on Distributed Computing in Sensor Systems (2008)

We propose FIT, a flexible, light-weight and real-time scheduling system for wireless sensor platforms. There are three salient features of FIT. First, its two-tier hierarchical framework supports customizable application-specific scheduling policies, hence FIT is very flexible. Second, FIT is light-weight in terms of minimizing thread number to reduce preemptions and memory consumption while at the same time ensuring system schedulability. We propose a novel Minimum Thread Scheduling Policy (MTSP) exploration algorithm within FIT to achieve this goal. Finally, FIT provides a detailed real-time schedulability analysis method to help check if application's temporal requirements can be met. We implemented FIT on MICAz motes, and carried out extensive evaluations. Results demonstrate that FIT is indeed flexible and light-weight for implementing real-time applications, at the same time, the schedulability analysis provided can predict the real-time behavior. FIT is a promising scheduling system for implementing complex real-time applications in sensor networks.