pinecone/core-2020-05-10-deduplication huggingface dataset:
Variable-size rectangle covering

LNCS v. 5573 is conference proceedings of the 3rd International Conference, COCOA 2009In wireless communication networks, optimal use of the directional antenna is very important. The directional antenna coverage (DAC) problem is to cover all clients with the smallest number of directional antennas. In this paper, we consider the variable-size rectangle covering (VSRC) problem, which is a transformation of the DAC problem. There are n points above the base line y=0 of the two-dimensional plane. The target is to cover all these points by minimum number of rectangles, such that the dimension of each rectangle is not fixed but the area is at most 1, and the bottom edge of each rectangle is on the base line y=0. In some applications, the number of rectangles covering any position in the two-dimensional plane is bounded, so we also consider the variation when each position in the plane is covered by no more than two rectangles. We give two polynomial time algorithms for finding the optimal covering. Further, we propose two 2-approximation algorithms that use less running time (O(nlogn) and O(n)). © 2009 Springer Berlin Heidelberg.postprin
Collaborative ocean resource interoperability - multi-use of ocean data on the semantic web

Earth Observations (EO) collect various characteristics of the objective environment using sensors which often have different measuring, spatial and temporal coverage. Making individual observational data interoperable becomes equally important when viewed in the context of its expensive and time-consuming EO operations. Interoperability will improve reusability of existing observations in both the broader context, and with other observations. As a demonstration of the potential offered by semantic web technology, we have used the National Oceanography Centre Southampton’s Ferrybox project (where suites of environmental sensors installed on commercial ships collect near real time data) to set up an ontology based reference model of a Collaborative Ocean, where relevant oceanographic resources, such as sensors and observations, can be semantically annotated by their stakeholders to produce RDF format metadata to facilitate data/resource interoperability in a distributed environment. We have also demonstrated an infrastructure where common semantic management activities are supported, including ontology management, semantic annotation, storage, and reuse (navigating, inference and query). Once the method and infrastructure are adopted by other related oceanographic projects to describe their resources and move their metadata onto the semantic web, it would be possible to see better interoperability within the Collaborative Ocean initiative to facilitate multiuse of ocean data, as well as making more EO data available on the semantic web
Video, semantics and the sensor web

This talk will present a snapshot of some of the current projects underway in the CLARITY centre which contribute to the proposition of the sensor web. In particular we focus on lifelogging, tennis, cycling and environmental water quality monitoring as examples of sensor webs. The then present a summary of approaches taken to identifying the presence or absence of groups of semantic features, in video. The annual TRECVid activity has been benchmarking the effectiveness of various  approaches since 2001 and we will examine what is the performance of these detectors, what are the trends in this area, and what is the state of the art. We will discover that the performance of individual detectors varies widely depending on the nature of the semantic feature, the quality of training data and its dependence on other detectors. There is a strong parallel between this and the way that sensors (environmental, physiological, etc.) which make up the sensor web, can also have poor accuracy levels when used in isolation but whose individual performances can be improved when used in combination
FO(ID) as an extension of DL with rules

acceptance rate=18%There are many interesting Knowledge Representation questions surrounding rule languages for the Semantic Web.  The most basic one is of course: which kind of rules should be used and how do they integrate with existing Description Logics?  Similar questions have already been addressed in the field of Logic Programming, where one particular answer has been provided by the language of FO(ID).  FO(ID) is an extension of first-order logic with a rule-based representation for inductive definitions.  By offering  a general integration of first-order logic and Logic Programs, it also induces a particular way of extending Description Logics with rules.  The goal of this paper is to investigate this integration and discover whether there are interesting extensions of DL with rules that can be arrived at by imposing appropriate restrictions on the highly expressive FO(ID).status: publishe
Polynomial Algorithm for Submap Isomorphism: Application to searching patterns in images

International audienceIn this paper, we address the problem of searching for a pattern in a plane graph, i.e., a planar drawing of a planar graph. To do that, we propose to model plane graphs with 2-dimensional combinatorial maps, which provide nice data structures for modelling the topology of a subdivision of a plane into nodes, edges and faces. We define submap isomorphism, we give a polynomial algorithm for this problem, and we show how this problem may be used to search for a pattern in a plane graph. First experimental results show the validity of this approach to efficiently search for patterns in images
Deriving information requirements from responsibility models

This is a conference paper from CAiSE 2009: http://www.lirmm.fr/caise/ . CAiSE Proceedings are published in the Lecture Notes in Computer Science Series by Springer. This paper was published in Advanced Information Systems Engineering, 21st International Conference, CAiSE 2009, Amsterdam, The Netherlands, June 8-12, 2009, edited by Pascal van Eck, Jaap Gordijn and Roel Wieringa Volume 5565, 2009, DOI: 10.1007/978-3-642-02144-2 http://www.springerlink.com/content/978-3-642-02143-5/#section=72414&page=1&locus=0This paper describes research in understanding the require-\ud
ments for complex information systems that are constructed from one or\ud
more generic COTS systems. We argue that, in these cases, behavioural\ud
requirements are largely de ned by the underlying system and that the\ud
goal of the requirements engineering process is to understand the in-\ud
formation requirements of system stakeholders. We discuss this notion\ud
of information requirements and propose that an understanding of how\ud
a socio-technical system is structured in terms of responsibilities is an\ud
e ective way of discovering this type of requirement. We introduce the\ud
idea of responsibility modelling and show, using an example drawn from\ud
the domain of emergency planning, how a responsibility model can be\ud
used to derive information requirements for a system that coordinates\ud
the multiple agencies dealing with an emergency
Verifying action semantics specifications in UML behavioral models

MDD and MDA approaches require capturing the behavior of UML models in sufficient detail so that the models can be automatically implemented/executed in the production environment. With this purpose, Action Semantics (AS) were added to the UML specification as the fundamental unit of behavior specification. Actions are the basis for defining the fine-grained behavior of operations, activity diagrams, interaction diagrams and state machines. Unfortunately, current proposals devoted to the verification of behavioral schemas tend to skip the analysis of the actions they may include. The main goal of this paper is to cover this gap by presenting several techniques aimed at verifying AS specifications. Our techniques are based on the static analysis of the dependencies between the different actions included in the\ud
behavioral schema. For incorrect specifications, our method returns a meaningful feedback that helps repairing the inconsistency.Peer ReviewedPostprint (published version
A method for the definition of metrics over i* models

The i* framework has been widely adopted by the information systems community for goal- and agent-oriented modeling and analysis. One of its potential benefits is the assessment of the properties of the modeled sociotechnical system. In this respect, the definition and evaluation of metrics may\ud
play a fundamental role. We are interested in porting to the i* framework metrics that have been already defined and validated in other domains. After some experimentation with i* metrics in this context, the complexity inherent to their definition has driven us to build a method for defining them. In this paper, we present the resulting method, iMDFM, which is structured into 4 steps: domain analysis, domain metrics analysis, metrics formulation and framework update. We apply our approach to an existing suite of metrics for measuring\ud
business processes performance and drive some observations from this experiment.Peer ReviewedPostprint (author’s final draft
Automated Context-Aware Service Selection for Collaborative Systems

This is the author's final draft of the paper published as Lecture Notes in Computer Science, 2009, 5565, pp. 261-274, and presented at the Advanced Information Systems Engineering 21st International Conference, CAiSE 2009.  The original publication is available at www.springerlink.com.  Doi: 10.1007/978-3-642-02144-2_23Service-Oriented Architecture (SOA) can provide a paradigm for constructing context-aware collaboration systems. Particularly, the promise of inexpensive context-aware collaboration devices and context-awareness for supporting the selection of suitable services at run-time have provoked growing adoptation of SOA in collaborative systems. In this paper, we introduce an approach for selecting the most suitable service within a SOA based collaboration system, where suitability depends on the user’s context. The approach includes context modelling, generation of context-aware selection criteria and a suitable service selection methodology
Configurable process models : experiences from a municipality case study

Configurable process models integrate different variants of\ud
a business process into a single model. Through configuration users of such models can then combine the variants to derive a process model optimally fitting their individual needs. While techniques for such models were suggested in previous research, this paper presents a case study in which these techniques were extensively tested on a real-world scenario. We gathered information from four Dutch municipalities on registration processes executed on a daily basis. For each process we identified variations among municipalities and integrated them into a single, configurable process model, which can be executed in the YAWL workflow environment. We then evaluated the approach through interviews with organizations that support municipalities in organizing and executing their processes. The paper reports on both the feedback of the interviewed partners and our own observations during the model creation
Business process modeling : current issues and future challenges

Business process modeling has undoubtedly emerged as a popular and relevant practice in Information Systems. Despite being an actively researched field, anecdotal evidence and experiences suggest that the focus of the research community is not always well aligned with the needs of industry. The main aim of this paper is, accordingly, to explore the current issues and the future challenges in business process modeling, as perceived by three key stakeholder groups (academics, practitioners, and tool vendors). We present the results of a global Delphi study with these three groups of stakeholders, and discuss the findings and their implications for research and practice. Our findings suggest that the critical areas of concern are standardization of modeling approaches, identification of the value proposition of business process\ud
modeling, and model-driven process execution. These areas are also expected to persist as business process modeling roadblocks in the future
WiSeKit: A Distributed Middleware to Support Application-level Adaptation in Sensor Network

International audienceApplications for Wireless Sensor Networks (WSNs) are being spread to areas in which the contextual parameters modeling the environment are changing over the application lifespan. Whereas software adaptation has been identified as an effective approach for addressing context-aware applications, the existing work on WSNs fails to support context-awareness and mostly focuses on developing techniques to re-program the whole sensor node rather than reconfiguring a particular portion of the sensor application software. Therefore, enabling adaptivity in the higher layers of a WSN architecture such as the middleware and application layers, beside the consideration in the lower layers, becomes of high importance. In this paper, we propose a distributed component-based middleware approach, named WiSeKit, to enable adaptation and reconfiguration of WSN applications. In particular, this proposal aims at providing an abstraction to facilitate development of adaptive WSN applications. As resource availability is the main concern of WSNs, the preliminary evaluation shows that our middleware approach promises a lightweight, fine-grained and communication-efficient model of application adaptation with a very limited memory and energy overhead
Fault-tolerant aggregation by flow updating

Data aggregation plays an important role in the design of scalable systems, allowing the determination of meaningful system-wide properties to direct the execution of distributed applications. In the particular case of wireless sensor networks, data collection is often only practicable if aggregation is performed. Several aggregation algorithms have been proposed in the last few years, exhibiting different properties in terms of accuracy, speed and communication tradeoffs. Nonetheless, existing approaches are found lacking in terms of fault tolerance. In this paper, we introduce a novel fault-tolerant averaging based data aggregation algorithm. It tolerates substantial message loss (link failures), while competing algorithms in the same class can be affected by a single lost message. The algorithm is based on manipulating flows (in the graph theoretical sense), that are updated using idempotent messages, providing it with unique robustness capabilities. Furthermore, evaluation results obtained by comparing it with other averaging approaches have revealed that it outperforms them in terms of time and message complexity
Experimental analysis of insertion costs in a naïve dynamic MDF-tree

Similarity search is a widely employed technique in Pattern Recognition. In order to speed up the search many indexing techniques have been proposed. However, the majority of the proposed techniques are static, that is, a fixed training set is used to build up the index. This characteristic becomes a major problem when these techniques are used in dynamic (interactive) systems. In these systems updating the training set is necessary to improve its performance. In this work, we explore the surprising efficiency of a naïve algorithm that allows making incremental insertion in a previously known index: the MDF-tree.Partially supported by the Pascal Network of Excellence
Median graph computation by means of a genetic approach based on minimum common supergraph and maximum common subraph

Trabajo presentado al 4th IbPRIA celebrado en Portugal del 10 al 12 de junio de 2009.Given a set of graphs, the median graph has been theoretically presented as a useful concept to infer a representative of the set. However, the computation of the median graph is a highly complex task and its practical application has been very limited up to now. In this work we present a new genetic algorithm for the median graph computation. A set of experiments on real data, where none of the existing algorithms for the median graph computation could be applied up to now due to their computational complexity, show that we obtain good approximations of the median graph. Finally, we use the median graph in a real nearest neighbour classification showing that it leaves the box of the only-theoretical concepts and demonstrating, from a practical point of view, that can be a useful tool to represent a set of graphs.This work was supported by the project 'Grup de recerca consolidat - VIS' (2009SGR-00937).Peer Reviewe
Index of balanced accuracy: a performance measure for skewed class distributions

This paper introduces a new metric, named Index of Balanced Accuracy, for evaluating learning processes in two-class imbalanced domains. The method combines an unbiased index of its overall accuracy and a measure about how dominant is the class with the highest individual accuracy rate. Some theoretical examples are conducted to illustrate the benefits of the new metric over other well-known performance measures. Finally, a number of experiments demonstrate the consistency and validity of the evaluation method here propose
Melodic track identification in MIDI files considering the imbalanced context

In this paper, the problem of identifying the melodic track of a MIDI file in imbalanced scenarios is addressed. A polyphonic MIDI file is a digital score that consists of a set of tracks where usually only one of them contains the melody and the remaining tracks hold the accompaniment. This leads to a two-class imbalance problem that, unlike in previous work, is managed by over-sampling the melody class (the minority one) or by under-sampling the accompaniment class (the majority one) until both classes are the same size. Experimental results over three different music genres prove that learning from balanced training sets clearly provides better results than the standard classification proces
Genetic Algorithm in Ab Initio Protein Structure Prediction Using Low Resolution Model: A Review

Proteins are sequences of amino acids bound into a linear chain that  adopt a specific folded three-dimensional (3D) shape. This specific folded shape  enables proteins to perform specific tasks. The protein structure prediction (PSP)  by ab initio or de novo approach is promising amongst various available computational  methods and can help to unravel the important relationship between sequence  and its corresponding structure. This article presents the ab initio protein  structure prediction as a conformational search problem in low resolution model  using genetic algorithm. As a review, the essence of twin removal, intelligence in  coding, the development and application of domain specific heuristics garnered  from the properties of the resulting model and the protein core formation concept  discussed are all highly relevant in attempting to secure the best solution.Griffith Sciences, School of Information and Communication TechnologyFull Tex
Recognition of Users' Activities using Constraint Satisfaction

Ideally designed software allow users to explore and pursue interleaving plans, making it challenging to automatically recognize user interactions. The recognition algorithms presented use constraint satisfaction techniques to compare user interaction histories to a set of ideal solutions. We evaluate these algorithms on data obtained from user interactions with a commercially available pedagogical software, and find that these algorithms identified users’ activities with 93% accuracy.Engineering and Applied Science
Creating user profiles from a command-line interface: a statistical approach

Proceeding of: 17th International Conference on User Modeling, Adaptation, and Personalization (UMAP), Trento, Italy, June 22-26 2009.Knowledge about computer users is very beneficial for assisting them, predicting their future actions or detecting masqueraders. In this paper, an approach for creating and recognizing automatically the behavior profile of a user from the commands (s)he types in a commandline interface, is presented. Specifically, in this research, a computer user behavior is represented as a sequence of UNIX commands. This sequence is transformed into a distribution of relevant subsequences in order to find out a profile that defines its behavior. Then, statistical methods are used for recognizing a user from the commands (s)he types. The experiment results, using 2 different sources of UNIX command data, show that a system based on our approach can efficiently recognize a UNIX user. In addition, a comparison with a HMM-base method is done. Because a user profile usually changes constantly, we also propose a method to keep up to date the created profiles using an age-based mechanism.Publicad
What have the neighbours ever done for us? A collaborative filtering perspective

Collaborative filtering (CF) techniques have proved to be a powerful and popular component of modern recommender systems.
Common approaches such as user-based and item-based methods generate predictions from the past ratings of users by combining two separate ratings components: a base estimate, generally based on the average rating of the target user or item, and a neighbourhood estimate, generally
based on the ratings of similar users or items. The common assumption
is that the neighbourhood estimate gives CF techniques a considerable
edge over simpler average-rating techniques. In this paper we examine
this assumption more carefully and demonstrate that the influence of
neighbours can be surprisingly minor in CF algorithms, and we show
how this has been disguised by traditional approaches to evaluation,
which, we argue, have limited progress in the field