ppxscal/embedding_network huggingface dataset:
Grouping Distributed Stream Query Services by Operator Similarity and Network Locality
Authors: Sangeetha Seshadri, Bhuvan Bamba, Brian F. Cooper, Vibhore Kumar, Ling Liu, Karsten Schwan, Gong Zhang
Venue: Proceedings of the 2008 IEEE Congress on Services - Part I (2008)

Distributed stream query services must simultaneously process a large number of complex, continuous queries with stringent performance requirements while utilizing distributed processing resources. In this paper we present the design and evaluation of a distributed stream query service that achieves massive scalability, a key design principle for such systems, by taking advantage of the opportunity to reuse the same distributed operator for multiple and different concurrent queries. We present concrete techniques that utilize the well-defined semantics of CQL-style queries to reduce the cost of query deployment and duplicate processing thereby increasing system throughput and scalability. Our system exhibits several unique features, including : (1) a 'reuse lattice' to encode both operator similarity and network locality using a uniform data structure; (2) techniques to generate an optimized query grouping plan in the form of 'relaxed operators' to capitalize on reuse opportunities while taking into account multiple run-time variations, such as network locality, data rates, and operator lifetime; and (3) techniques to modify operator semantics at runtime to facilitate reuse. Evaluation of our service-oriented design and techniques under realistic workloads shows that stream queries relaxed and grouped using our approach operate efficiently without a priori knowledge of workload, and offer an order of magnitude improvement in performance over existing approaches.
Concepts and Fields of Relational Justice
Authors: Pompeu Casanovas, Marta Poblet
Venue: Computable Models of the Law: Languages, Dialogues, Games, Ontologies (2008)

This paper intends to introduce and explore the broad conceptual background of relational justice according to the current state of the art. Relational Justice (RJ) is defined as the justice produced through cooperative behavior, agreement, negotiation, or dialogue among actors in a post-conflict situation. We found concepts stemming from at least thirty different fields, going from behavioral sciences (neurology, brain sciences, primatology, social psychology, etc.) to criminology, jurisprudence, and philosophy. One of these contributing fields is Artificial Intelligence (AI), which uses several techniques to grasp the practical knowledge of negotiators and mediators and builds tools to support both negotiation and mediation processes. However, contrary to the legal ontologies field, there are no developed ontologies of Relational Justice yet representing the conceptual richness of the domain.
Designing Digital Systems? SSI And MSI Vs. LSI And VLSI
Authors: Arno Peels
Venue: IEEE Micro (1987)

Digital system design with LSI and VLSI circuits can be better understood and performed if one can define an implementation-independent abstract processor, map processes on it, and proceed by steps, in a top-down fashion, from this abstraction to realization.
Design of a Digital Library System for Large-Scale Evaluation Campaigns
Authors: Marco Dussin, Nicola Ferro
Venue: Proceedings of the 12th European conference on Research and Advanced Technology for Digital Libraries (2008)

This work describes the effort of designing and developing a Digital Library System (DLS) able to manage the different types of information resources produced during a large-scale evaluation campaign and to support the different stages of it. We discuss, in particular, the design of DIRECT, a DLS developed to assist the work of the actors of international evaluation campaigns.
Applications of Semantic Web Methodologies and Techniques to Biology and Bioinformatics
Authors: Paolo Romano, Andrea Splendiani
Venue: Reasoning Web: 4th International Summer School 2008, Venice, Italy, September 7-11, 2008, Tutorial Lectures (2008)

Semantic Web technologies are appealing for biomedical researchers since they promise to solve many of the daily problems they face while accessing and integrating biological information that is distributed over the Internet and managed by using tools which are extremely heterogeneous and largely not compatible. On the other hand, the complexity of biomedical information and its heterogeneity, together with the need of keeping current production services steadily up and running, make the transition from current semantic-less to future semantic-aware services a huge problem.In this paper, authors present the characteristics of biomedical information that make adoption of semantic web technologies both desirable and complex at the same time. They then present the tools and the applications that have been developed so far, including biomedical ontologies, RDF/OWL data stores, query systems and semantic-aware tools and browsers. Finally, they present community efforts and the perspectives that can be sought for short- and mid-term developments in the field.
An Efficient Device Authentication Protocol Using Bioinformatic
Authors: Yoon-Su Jeong, Bong-Keun Lee, Sang-Ho Lee
Venue: Computational Intelligence and Security: International Conference, CIS 2006, Guangzhou, China, November 3-6, 2006, Revised Selected Papers (2007)

The personalization of computing environments is one of the key aspects of ubiquitous computing, and such personalization requires isolated computing environments for better security and stability. In this paper, we propose a mutual authentication method using biometric information of each device users in a situation where device A communicates with device B which is in heterogenous environment. Proposed scheme is designed to be agile in a peer-to-peer environment. We provide analysis results against security attacks and number of required procedures for device authentication.
A Fast and Key-Efficient Reduction of Chosen-Ciphertext to Known-Plaintext Security
Authors: Ueli Maurer, Johan Sjödin
Venue: Proceedings of the 26th annual international conference on Advances in Cryptology (2007)

Motivated by the quest for reducing assumptions in security proofs in cryptography, this paper is concerned with designing efficient symmetric encryption and authentication schemes based on any weak pseudorandom function (PRF) which can be much more efficiently implemented than PRFs. Damgård and Nielsen (CRYPTO '02) have shown how to construct an efficient symmetric encryption scheme based on any weak PRF that is provably secure against chosen-plaintext attacks. The main ingredient is a range-extension construction for weak PRFs. By using well-known techniques, they also showed how their scheme can be made secure against the stronger chosen-ciphertext attacks.The results of our paper are three-fold. First, we give a range-extension construction for weak PRFs that is optimal within a large and natural class of reductions (especially all known today). Second, we propose a construction of a regular PRF from any weak PRF. Third, these two results imply a (for long messages) much more efficient chosen-ciphertext secure encryption scheme than the one proposed by Damgård and Nielsen. The results also give answers to open questions posed by Naor and Reingold (CRYPTO '98) and by Damgård and Nielsen.
Ontology-Based User Modelling Personalization: Analyzing the Requirements of a Semantic Learning Portal
Authors: Liana Razmerita, Miltiadis D. Lytras
Venue: Proceedings of the 1st world summit on The Knowledge Society: Emerging Technologies and Information Systems for the Knowledge Society (2008)

In last decade a number of design approaches have been adopted towards high performance e-learning systems. In this context Users/Learners modelling is a key milestone towards the specification of characteristics and features of learners that potentially can modify the flow of learning. In this article we are introducing an Ontology-Based User Modelling framework and and we show how it can be used in the context of a Semantic Learning Portal. The main contribution of the article relates to the detailed analysis for the design variables and requirements for Semantic Learning Applications towards adoption of services, personalization and management of personal profiles and identities of learners.
Intersection Signal Control Approach Based on PSO and Simulation
Authors: Yun Wei, Qing Shao, Yin Han, Bingquan Fan
Venue: Proceedings of the 2008 Second International Conference on Genetic and Evolutionary Computing (2008)

This paper applies fuzzy theory and machine learning in the process of intersection signal control. It provides a fuzzy traffic signal control approach based on Particle Swarm Optimization for intersection signal control. Through fuzzy classifying traffic flow in under control intersection and adjacent intersection, this paper puts decision schemes of signal control in different conditions as rule-set into knowledge-database. It applies PSO to improve the rule-sets in traffic signal control process, so the control model has the self-learning ability. After programming the simulation program of this control model and simulating, this paper compares the control effect of this new approach with the traditional fuzzy control method. The result of simulating illustrates that the effect of the model is obviously better than the traditional ones.
Properties of Symmetric Incentive Compatible Auctions
Authors: Xiaotie Deng, Kazuo Iwama, Qi Qi, Aries Wei Sun, Toyotaka Tasaka
Venue: Proceedings of the 13th Annual international conference on Computing and Combinatorics (2007)

We formalize the definition of symmetric auctions to study fundamental properties of incentive compatible auction protocols. We characterize such auction protocols for those with a fixed number of items to sell and study some important properties of those with an indefinite number of sales.
A Simulation Study of Routing Performance in Realistic Urban Scenarios for MANETs
Authors: Gianni A. Caro, Frederick Ducatelle, Luca M. Gambardella
Venue: Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence (2008)

We study in simulation the performance of two MANET routing algorithms in an urban environment. The two algorithms, AODV and AntHocNet, are representative of two different approaches and design methodologies. AODV is a state-of-the-art algorithm following a purely reactive approach. AntHocNet is based on Ant Colony Optimization and integrates proactive and reactive mechanisms. We investigate the usefulness of the different approaches they adopt when confronted with the peculiarities of urban environments and real-world applications. At this aim we define a detailed and realistic simulation setup in terms of radio propagation, constrained node mobility, and data traffic.
Adaptive spacetime meshing for discontinuous Galerkin methods
Authors: Shripad Thite
Venue: Computational Geometry: Theory and Applications (2009)

Spacetime-discontinuous Galerkin (SDG) finite element methods are used to solve hyperbolic spacetime partial differential equations (PDEs) to accurately model wave propagation phenomena arising in important applications in science and engineering. Tent Pitcher is a specialized algorithm, invented by Ungor and Sheffer (2000) and extended by Erickson et al. (2005) to construct an unstructured simplicial (d+1)-dimensional spacetime mesh over an arbitrary d-dimensional space domain. Tent Pitcher is an advancing front algorithm that incrementally adds groups of elements to the evolving spacetime mesh. It supports an accurate, local, and parallelizable solution strategy by interleaving mesh generation with an SDG solver. When solving nonlinear PDEs, previous versions of Tent Pitcher must make conservative worst-case assumptions about the physical parameters which limit the duration of spacetime elements. Thus, these algorithms create a mesh with many more elements than necessary. In this paper, we extend Tent Pitcher to give the first spacetime meshing algorithm suitable for efficient simulation of nonlinear phenomena using SDG methods. We adapt the duration of spacetime elements to changing physical parameters due to nonlinear response. Given a triangulated 2-dimensional Euclidean space domain M corresponding to time t=0 and initial and boundary conditions of the underlying hyperbolic spacetime PDE, we construct an unstructured tetrahedral mesh in the spacetime domain E^2xR. For every target time T>=0, our algorithm meshes the spacetime volume Mx[0,T] with a bounded number of non-degenerate tetrahedra. A recent extension of Tent Pitcher due to Abedi et al. (2004) adapts the spatial size of spacetime elements in 2Dxtime to a posteriori estimates of numerical error. Our extension of Tent Pitcher retains the ability to perform adaptive refinement and coarsening of the mesh. We thus obtain the first adaptive nonlinear Tent Pitcher algorithm to build spacetime meshes in 2Dxtime.
Realizability of the Axiom of Choice in HOL. (An Analysis of Krivine's Work)
Authors: Christophe Raffalli, Frédéric Ruyer
Venue: Fundamenta Informaticae (2008)

This paper is an introduction to recent works in realizability, mainly Krivine's work to realize the dependent choice axiom. We also show how to improve programs extracted from classical proofs by distinguishing formulas with and without algorithmic contents.
Developing Flexible Software Using Agent-Oriented Software Engineering
Authors: Hisham Mubarak
Venue: IEEE Software (2008)

Software agents are associated with modern industrial software. In various application fields such as e-business, telecommunications, logistics, and industrial automation, software based on agents seems to make the challenges in modern applications easier to overcome. However, it's often unclear what a software agent is and why problems can be better solved with it. Is it concerning a technology, an architecture, or a programming concept? This article presents a general introduction to software agent concepts from the software engineer's perspective, explaining the theoretical basis and clarifying to which type of problem software agents might represent a reasonable approach.
SALT - Semantically Annotated $\mbox{\LaTeX}$ for Scientific Publications
Authors: Tudor Groza, Siegfried Handschuh, Knud Möller, Stefan Decker
Venue: Proceedings of the 4th European conference on The Semantic Web: Research and Applications (2007)

Machine-understandable data constitutes the foundation for the Semantic Web. This paper presents a viable way for authoring and annotating Semantic Documents on the desktop. In our approach, the PDF file format is the container for document semantics, being able to store both the content and the related metadata in a single file. To achieve this, we provide a framework (SALT - Semantically Annotated $\mbox{\LaTeX}$), that extends the $\mbox{\LaTeX}$ writing environment and supports the creation of metadata for scientific publications. SALT allows the author to create metadata concurrently, i.e. while in the process of writing a document. We discuss some of the requirements which have to be met when developing such a support for creating semantic documents. In addition, we describe a usage scenario to show the feasability and benefit of our approach.
Diagnostic analysis of a small-scale incinerator by the Garson index
Authors: Jeng-Chung Chen, Wei-Hsin Chen
Venue: Information Sciences: an International Journal (2008)

The formation of PCDD/Fs (dioxins/furans) due to incomplete combustion in solid waste incinerators has caused tremendous public concern. Consequently, more stringent standards for combustion and emission control have been implemented in order to mitigate the formation of these substances. This change in regulations will inevitably result in shutting down many small-scale incinerators because of the expense incurred in retrofitting such systems. Yet there is still an acute need for building small-scale incinerators for the purposes of disease control, environmental sanitation, and financial savings in rural areas and remote communities. For this reason, it is still worthwhile to pursue an optimal management strategy for small-scale incinerators. Through using the Garson index derived by a neural networks model, we can identify which operating factor is the one most influential to combustion status. Research findings clearly indicate that supplementing the auxiliary fuel via an on/off control unit is not an ideal method of maintaining a stable combustion evidenced by its relatively lower Garson index. Therefore the control of the auxiliary fuel system must be properly upgraded in order to improve its handling of the combustion unit. The results also show that the amount of waste in batch-charging and the lowest temperature of the primary chamber during the previous feeding are critical operating factors in this type of incinerator; controlling the charging amount per each feed around 30kg is optimal for mitigating the variance of combustion status in the small-scale incinerator.
Variable step-size LMS algorithm with a quotient form
Authors: Shengkui Zhao, Zhihong Man, Suiyang Khoo, Hong Ren Wu
Venue: Signal Processing (2009)

An improved robust variable step-size least mean square (LMS) algorithm is developed in this paper. Unlike many existing approaches, we adjust the variable step-size using a quotient form of filtered versions of the quadratic error. The filtered estimates of the error are based on exponential windows, applying different decaying factors for the estimations in the numerator and denominator. The new algorithm, called more robust variable step-size (MRVSS), is able to reduce the sensitivity to the power of the measurement noise, and improve the steady-state performance for comparable transient behavior, with negligible increase in the computational cost. The mean convergence, the steady-state performance and the mean step-size behavior of the MRVSS algorithm are studied under a slow time-varying system model, which can be served as guidelines for the design of MRVSS algorithm in practical applications. Simulation results are demonstrated to corroborate the analytic results, and to compare MRVSS with the existing representative approaches. Superior properties of the MRVSS algorithm are indicated.
Exploring relationships between products characteristics and B2C interaction in electronic commerce
Authors: Karin Axelsson
Venue: Journal of Theoretical and Applied Electronic Commerce Research (2008)

The main purpose of the paper is to explore and discuss the influence of product type on customer interaction in electronic commerce. We have conducted two qualitative case studies in distance selling companies to inductively explore how the product characteristics of garments and music-CDs influence customer interaction. We apply a product classification scheme built on product theory to characterize and classify these products. By classifying product types according to this scheme we focus certain aspects; e.g., in which situation the customer uses the product and, thus, experiences potential satisfaction. By applying product theory to the electronic commerce context we are expanding this theory into a new area. Knowledge about product characteristics' influence on customer interaction in various phases of electronic commerce enriches and specifies the existing product theory. In order to put this theory contribution into practice, a set of focal questions with the aim to increase product understanding in an electronic commerce setting is formulated. The questions highlight product related issues that are important to consider when deciding communication media in electronic commerce.
Random Forests Identification of Gas Turbine Faults
Authors: Manolis Maragoudakis, Euripides Loukis, Panagiotis-Prodromos Pantelides
Venue: Proceedings of the 2008 19th International Conference on Systems Engineering (2008)

In the present paper, Random Forests are used in a critical and at the same time non trivial problem concerning the diagnosis of Gas Turbine blading faults, portraying promising results. Random forests-based fault diagnosis is treated as a Pattern Recognition problem, based on measurements and feature selection. Two different types of inserting randomness to the trees are studied, based on different theoretical assumptions. The classifier is compared against other Machine Learning algorithms such as Neural Networks, Classification and Regression Trees, Naive Bayes and K-Nearest Neighbor. The performance of the prediction model reaches a level of 97% in terms of precision and recall, improving the existing state-of-the-art levels achieved by Neural Networks by a factor of 1.5%-2%. Furthermore, emphasis is given on the pre-processing phase, where feature selection and outliers identification is carried out, in order to provide the basis of a high performance automated diagnostic system. The conclusions derived are of more general interest and applicability.
Finding Optimal Refueling Policies in Transportation Networks
Authors: Shieu-Hong Lin
Venue: Proceedings of the 4th international conference on Algorithmic Aspects in Information and Management (2008)

We study the combinatorial properties of optimal refueling policies, which specify the transportation paths and the refueling operations along the paths to minimize the total transportation costs between vertices. The insight into the structure of optimal refueling policies leads to an elegant reduction of the problem of finding optimal refueling policies into the classical shortest path problem, which ends in simple and more efficient algorithms for finding optimal refueling policies.
Replacement strategies to preserve useful diversity in steady-state genetic algorithms
Authors: Manuel Lozano, Francisco Herrera, José Ramón Cano
Venue: Information Sciences: an International Journal (2008)

In this paper, we propose a replacement strategy for steady-state genetic algorithms that considers two features of the candidate chromosome to be included into the population: a measure of the contribution of diversity to the population and the fitness function. In particular, the proposal tries to replace an individual in the population with worse values for these two features. In this way, the diversity of the population becomes increased and the quality of the solutions gets better, thus preserving high levels of useful diversity. Experimental results show the proposed replacement strategy achieved significant performance for problems with different difficulties, with regards to other replacement strategies presented in the literature.
Eating Data Is Good for Your Immune System: An Artificial Metabolism for Data Clustering Using Systemic Computation
Authors: Erwan Martelot, Peter J. Bentley, R. Beau Lotto
Venue: Proceedings of the 7th international conference on Artificial Immune Systems (2008)

Previous work suggests that innate immunity and representations of tissue can be useful when combined with artificial immune systems. Here we provide a new implementation of tissue for AIS using systemic computation, a new model of computation and corresponding computer architecture based on a systemics world-view and supplemented by the incorporation of natural characteristics. We show using systemic computation how to create an artificial organism, a program with metabolism that eats data, expels waste, clusters cells based on the nature of its food and emits danger signals suitable for an artificial immune system. The implementation is tested by application to a standard machine learning set and shows excellent abilities to recognise anomalies in its diet.