pinecone/core-2020-05-10-deduplication huggingface dataset:
On The Joint Modeling of The Behavior of Social Insects and Their Interaction With Environment by Taking Into Account Physical Phenomena Like Anisotropic Diffusion

This work takes place in the framework of GEODIFF project (funded by CNRS) and deals with the general issue of the social behavior modeling of pest insects with a particular focus on Bark Beetles. Bark Beetles are responsible for pine trees devastation in North America since 2005. In order to stem the problem and to apply an adapted strategy, one should be able to predict the evolution of the population of Bark Beetles. More precisely, a model taking into account a given population of insects (a colony) interacting with its environment, the forest ecosystem, would be very helpful. In a previous work, we aimed to model diffusive phenomenons across the environment using a simple reactive Multi-agent System. Bark beetle use pheromones as a support for recruitment of other bark beetles in the neighborhood in order to achieve a mass attack over a tree. They are first attracted by the ethanol or other phytopheromones emitted by a sick, stressed or dead tree and reinforce the presence of other individuals amongst the targeted tree. Both ethanol and semiochemicals are transported through the forest thanks to the wind, thermic effects and this advection phenomenon is modulated by the topology of the environment, tree and other obstacles distribution. In other words, the environment is involved in the process of a bark beetle attack. The first modeling we used to tackle our objective was not spatially explicit as long as free space propagation only was taken into account (isotropic phenomenon) with no constraint imposed by the environment such as wind. This article is intended to take into account such physical phenomenons and push the modeling one step further by providing predictions driven by measures provided by a Geographical Information System
An approach for a negotiation model inspired on social networks

Supporting group decision-making in ubiquitous contexts is a complex
task that needs to deal with a large amount of factors to be successful. Here
we propose an approach for a negotiation model to support the group decisionmaking
process specially designed for ubiquitous contexts. We propose a new
look into this problematic, considering and defining strategies to deal with important
points such as the type of attributes in the multi-criteria problem and
agents' reasoning. Our model uses a social networking logic due to the type of
communication employed by the agents as well as to the type of relationships
they build as the interactions occur. Our approach intends to support the ubiquitous
group decision-making process in a similar way to the real process, which
simultaneously preserves the amount and quality of intelligence generated in
face-to-face meetings and is adapted to be used in a ubiquitous context.This work is part-funded by ERDF - European Regional Development Fund through
the COMPETE Programme (operational programme for competitiveness) and by
National Funds through the FCT - Fundação para a Ciência e a Tecnologia (Portuguese
Foundation for Science and Technology) within project FCOMP-01-0124-
FEDER-028980 (PTDC/EEISII/1386/2012) and SFRH/BD/89697/2012.info:eu-repo/semantics/publishedVersio
Copulas based on Marshall–Olkin machinery

International audienceWe present a general construction principle for copulas that is inspired by the celebrated Marshall–Olkin exponential model. From this general construction method we derive special sub–classes of copulas that could be useful in different situations and recall their main properties. Moreover, we discuss possible estimation strategy for the proposed copulas. The presented results are expected to be useful in the construction of stochastic models for lifetimes (e.g. in reliability theory) or in credit risk models
Assessing the robustness of parsimonious predictions for gene neighborhoods from reconciled phylogenies

International audienceThe availability of a large number of assembled genomes opens the way to study the evolution of syntenic character within a phylogenetic context. The DeCo algorithm, recently introduced by Bérard et al. allows the computation of parsimonious evolutionary scenarios for gene adjacencies, from pairs of reconciled gene trees. Following the approach pioneered by Sturmfels and Pachter, we describe how to modify the DeCo dynamic programming algorithm to identify classes of cost schemes that generates similar parsimonious evolutionary scenarios for gene adjacencies, as well as the robustness to changes to the cost scheme of evolutionary events of the presence or absence of specific ancestral gene adjacencies. We apply our method to six thousands mammalian gene families, and show that computing the robustness to changes to cost schemes provides new and interesting insights on the evolution of gene adjacencies and the DeCo model
Ontology-based fuzzy-syllogistic reasoning

28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2015; Seoul; South Korea; 10 June 2015 through 12 June 2015We discuss the Fuzzy-Syllogistic System (FSS) that consists of the well-known 256 categorical syllogisms, namely syllogistic moods, and Fuzzy- Syllogistic Reasoning (FSR), which is an implementation of the FSS as one complex approximate reasoning mechanism, in which the 256 moods are interpreted as fuzzy inferences. Here we introduce a sample application of FSR as ontology reasoner. The reasoner can associate up to 256 possible fuzzyinferences with truth ratios in [0,1] for every triple concept relationship of the ontology. We further discuss a transformation technique, by which the truth ratio of a fuzzy-inference can increase, by adapting the fuzzy-quantifiers of a fuzzy-inference to the syllogistic logic of the sample propositions
Handling Regulatory Goal Model Families as Software Product Lines

International audienceGoal models can capture the essence of legal and regulation statements and many of their relationships, enabling compliance analysis. However, current goal modeling approaches do not scale well when handling large regulations with many variable parts that depend on different aspects of regulated organizations. In this paper, we propose a tool-supported approach that integrates the Goal-oriented Requirement Language and feature modeling to handle regulatory goal model families. We show how they can be organized as a Software Product Line (SPL), ensuring the consistency of the SPL as a whole, and providing an adapted derivation process associated to a feature model configuration. The proposed approach is also evaluated on large generated SPLs with results suggesting its capability to address scalability concerns
Verification and validation of UML artifact-centric business process models

This paper presents a way of checking the correctness of artifact-centric business process models defined using the BAUML framework.\ud
To ensure that these models are free of errors, we propose an approach to verify (i.e. there are no internal mistakes) and to validate them (i.e. the model complies with the business requirements). This approach is based on translating these models into logic and then encoding the desirable properties as satisfiability problems of derived predicates. In this way, we can then use a tool to check if these properties are fulfilledPeer ReviewedPostprint (author's final draft
Deriving artefact-centric interfaces for overloaded web services

We present a novel framework and algorithms for the analysis of Web service interfaces to improve the efficiency of application integration in wide-spanning business networks. Our approach addresses the notorious issue of large and overloaded operational signatures, which are becoming increasingly prevalent on the Internet and being opened up for third-party service aggregation. Extending upon existing techniques used to refactor service interfaces based on derived artefacts of applications, namely business entities, we propose heuristics for deriving relations between business entities, and in turn, deriving permissible orders in which operations are invoked. As a result, service operations are refactored on business entity CRUD which then leads to behavioural protocols generated, thus supportive of fine-grained and flexible service discovery, composition and interaction. A prototypical implementation and analysis of web services, including those of commercial logistic systems (Fedex), are used to validate the algorithms and open up further insights into service interface synthesis
Learning a hidden Markov model-based hyper-heuristic

A simple model shows how a reasonable update scheme for the probability vector by which a hyper-heuristic chooses the next heuristic leads to neglecting useful mutation heuristics. Empirical evidence supports this on the MaxSat, TravelingSalesman, PermutationFlowshop and VehicleRoutingProblem problems. A new approach to hyper-heuristics is proposed that addresses this problem by modeling and learning hyper-heuristics by means of a hidden Markov Model. Experiments show that this is a feasible and promising approach.status: publishe
A warped kernel improving robustness in Bayesian optimization via random embeddings

International audienceThis works extends the Random Embedding Bayesian Optimization approach by integrating a warping of the high dimensional subspace within the covariance kernel. The proposed warping, that relies on elementary geometric considerations, allows mitigating the drawbacks of the high extrinsic dimensionality while avoiding the algorithm to evaluate points giving redundant information. It also alleviates constraints on bound selection for the embedded domain, thus improving the robustness, as illustrated with a test case with 25 variables and intrinsic dimension 6
A clustering based approach to reduce feature redundancy

This document is the Accepted Manuscript version of the following paper: Cordeiro de Amorim, R.,and Mirkin, B., ???A clustering based approach to reduce feature redundancy???, in Proceedings, Andrzej M. J. Skulimowski and Janusz Kacprzyk, eds., Knowledge, Information and Creativity Support Systems: Recent Trends, Advances and Solutions, Selected papers from KICSS???2013 - 8th International Conference on Knowledge, Information, and Creativity Support Systems, Krak??w, Poland, 7-9 November 2013. ISBN 978-3-319-19089-1, e-ISBN 978-3-319-19090-7. Available online at doi: 10.1007/978-3-319-19090-7. ?? Springer International Publishing Switzerland 2016.Research effort has recently focused on designing feature weighting clustering algorithms. These algorithms automatically calculate the weight of each feature, representing their degree of relevance, in a data set. However, since most of these evaluate one feature at a time they may have difficulties to cluster data sets containing features with similar information. If a group of features contain the same relevant information, these clustering algorithms set high weights to each feature in this group, instead of removing some because of their redundant nature. This paper introduces an unsupervised feature selection method that can be used in the data pre-processing step to reduce the number of redundant features in a data set. This method clusters similar features together and then selects a subset of representative features for each cluster. This selection is based on the maximum information compression index between each feature and its respective cluster centroid. We present an empirical validation for our method by comparing it with a popular unsupervised feature selection on three EEG data sets. We find that our method selects features that produce better cluster recovery, without the need for an extra user-defined parameter
Cheap and Cheerful: Trading Speed and Quality for Scalable Social Recommenders

International audienceRecommending appropriate content and users is a critical feature of on-line social networks. Computing accurate recommendations on very large datasets can however be particularly costly in terms of resources , even on modern parallel and distributed infrastructures. As a result, modern recommenders must generally trade-off quality and computational cost to reach a practical solution. This trade-off has however so far been largely left unexplored by the research community, making it difficult for practitioners to reach informed design decisions. In this paper, we investigate to which extent the additional computing costs of advanced recommendation techniques based on supervised classifiers can be balanced by the gains they bring in terms of quality. In particular , we compare these recommenders against their unsupervised counterparts , which offer lightweight and highly scalable alternatives. We propose a thorough evaluation comparing 11 classifiers against 7 lightweight recommenders on a real Twitter dataset. Additionally, we explore data grouping as a method to reduce computational costs in a distributed setting while improving recommendation quality. We demonstrate how classifiers trained using data grouping can reduce their computing time by 6 while improving recommendations up to 22% when compared with lightweight solutions
Similitude: Decentralised Adaptation in Large-Scale P2P Recommenders

International audienceDecentralised recommenders have been proposed to deliver privacy-preserving, personalised and highly scalable on-line recommendations. Current implementations tend, however, to rely on a hard-wired similarity metric that cannot adapt. This constitutes a strong limitation in the face of evolving needs. In this paper, we propose a framework to develop dynamically adaptive decentralised recommendation systems. Our proposal supports a decentralised form of adaptation, in which individual nodes can independently select, and update their own recommendation algorithm, while still collectively contributing to the overall system's mission
Concise server-wide causality management for eventually consistent data stores

Large scale distributed data stores rely on optimistic replication to scale and remain highly available in the face of net work partitions. Managing data without coordination results in eventually consistent data stores that allow for concurrent data updates. These systems often use anti-entropy mechanisms (like Merkle Trees) to detect and repair divergent data versions across nodes. However, in practice hash-based data structures are too expensive for large amounts of data and create too many false conﬂicts. Another aspect of eventual consistency is detecting write conﬂicts. Logical clocks are often used to track data causality, necessary to detect causally concurrent writes on the same key. However, there is a nonnegligible metadata overhead per key, which also keeps growing with time, proportional with the node churn rate. Another challenge is deleting keys while respecting causality: while the values can be deleted, perkey metadata cannot be permanently removed without coordination. Weintroduceanewcausalitymanagementframeworkforeventuallyconsistentdatastores,thatleveragesnodelogicalclocks(BitmappedVersion Vectors) and a new key logical clock (Dotted Causal Container) to provides advantages on multiple fronts: 1) a new eﬃcient and lightweight anti-entropy mechanism; 2) greatly reduced per-key causality metadata size; 3) accurate key deletes without permanent metadata.(undefined
Heterogeneous Resource Selection for Arbitrary HPC Applications in the Cloud

International audienceCloud infrastructures offer a wide variety of resources to choose from. However, most cloud users ignore the potential benefits of dynamically choosing cloud resources among a wide variety of VM instance types with different con-figuration/cost tradeoffs. We propose to automate the choice of resources that should be assigned to arbitrary non-interactive applications. During the first executions of the application, the system tries various resource configurations and builds a custom performance model for this application. Thereafter, cloud users can specify their execution time or financial cost constraints, and let the system automatically select the resources which best satisfy this constraint
Logic programming and artificial neural networks in breast cancer detection

About 90% of breast cancers do not cause or are capable of producing death if detected at an early stage and treated properly. Indeed, it is still not known a specific cause for the illness. It may be not only a beginning, but also a set of associations that will determine the onset of the disease. Undeniably, there are some factors that seem to be associated with the boosted risk of the malady. Pondering the present study, different breast cancer risk assessment models where considered. It is our intention to develop a hybrid decision  support system under a formal framework based on Logic Programming for knowledge representation and reasoning, complemented with an approach to computing centered on Artificial Neural Networks, to evaluate the risk of  developing breast cancer and the respective Degree-of-Confidence that one has on such a happening.This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope UID/CEC/00319/2013
Groups whose word problem is a Petri net language

There has been considerable interest in exploring the connections between the word problem of a finitely generated group as a formal language and the algebraic structure of the group. However, there are few complete characterizations that tell us precisely which groups have their word problem in a specified class of languages. We investigate which finitely generated groups have their word problem equal to a language accepted by a Petri net and give a complete classification, showing that a group has such a word problem if and only if it is virtually abelian
Free space interference experiments with single photons and single ions

Trapped ion crystals have proved to be one of the most viable physical
implementations of quantum registers and a promising candidate for a scalable
realization of quantum networks. The latter will require the development of an
efficient interface between trapped ions and photons. We describe two research
directions that are currently investigated to realize such photonic quantum
interfaces in free space using high numerical aperture optics. The first
approach investigates how strong focusing of light onto a single ion can
increase the interaction strength to achieve efficient interaction between a
photon and the ion. The second approach uses a probabilistic measurement on
scattered photons to generate entanglement between two ions that could be used
to distribute information in a quantum network. For both approaches a higher
numerical aperture would increase the efficiency of the interface.Comment: to appear as a book chapter in a compilation "Engineering the
  Atom-Photon Interaction" published by Springer in 2015, edited by A.
  Predojevic and M. W. Mitchel
Towards Quantum Repeaters with Solid-State Qubits: Spin-Photon
  Entanglement Generation using Self-Assembled Quantum Dots

In this chapter we review the use of spins in optically-active InAs quantum
dots as the key physical building block for constructing a quantum repeater,
with a particular focus on recent results demonstrating entanglement between a
quantum memory (electron spin qubit) and a flying qubit (polarization- or
frequency-encoded photonic qubit). This is a first step towards demonstrating
entanglement between distant quantum memories (realized with quantum dots),
which in turn is a milestone in the roadmap for building a functional quantum
repeater. We also place this experimental work in context by providing an
overview of quantum repeaters, their potential uses, and the challenges in
implementing them.Comment: 51 pages. Expanded version of a chapter to appear in "Engineering the
  Atom-Photon Interaction" (Springer-Verlag, 2015; eds. A. Predojevic and M. W.
  Mitchell
Enhancing declarative process models with DMN decision logic

Modeling dynamic, human-centric, non-standardized and knowledge-intensive business processes with imperative process modeling approaches is very challenging. Declarative process modeling approaches are more appropriate for these processes, as they offer the run-time flexibility typically required in these cases. However, by means of a realistic healthcare process that falls in the aforementioned category, we demonstrate in this paper that current declarative approaches do not incorporate all the details needed. More specifically, they lack a way to model decision logic, which is important when attempting to fully capture these processes. We propose a new declarative language, Declare-R-DMN, which combines the declarative process modeling language Declare-R with the newly adopted OMG standard Decision Model and Notation. Aside from supporting the functionality of both languages, Declare-R-DMN also creates bridges between them. We will show that using this language results in process models that encapsulate much more knowledge, while still offering the same flexibility
Role and task recommendation and social tagging to enable social business process management

Traditional Business Process Management (BPM) poses a number of limitations for the management of ad-hoc processes, where the execution paths are not designed a priori and evolve during enactment. Social BPM, which predicates to integrate social software into the BPM lifecycle, has emerged as an answer to such limitations. This paper presents a framework for social BPM in which social tagging is used to capture process knowledge emerging during the enactment and design of the processes. Process knowledge concerns both the type of activities chosen to fulfil a certain goal and the skills and experience of users in executing specific tasks. Such knowledge is exploited by recommendation tools to support the design and enactment of future process instances. We first provide an overview of our framework, introducing the concepts of role and task recommendations, which are supported by social tagging. These mechanisms are then elaborated further by an example. Eventually, we discuss a prototype of our framework enabling collaborative process design and execution
Enterprise architecture for business network planning: A capability-based approach

<i>Enterprise Architecture (EA)</i> has been used for planning business and IT systems capturing different aspects, including services, processes, resources, and data. To date, it has mostly been coordinated by single organisations, even if external interactions with outside organizations play an important role in developing an EA. This paper provides insights about the role of EA in business network planning through the development of a method to conceptualize a multi-partner network, which reflects new affordances opened by a digitally connected world, where shared interactions and dependencies across organizations, through business networks, are converging into <i>cohesive network businesses</i>. We present a five stage approach to adopt EA for business network planning by illustrating how <i>novation requirements</i> can be defined in integrated scenario models specifying how local roles and their set of skills (capabilities) can be substituted, extended etc. at the level of the network, such that models retain their compact form. Our method benefits from extensive insights observed through the <i>eGovernment One-Stop Shop</i> adopted by Australian governments (Department of Human Services and MyGov at the federal level, Service NSW and One-Stop Shop Implementation Office in Queensland Government) and also from the upstream petroleum oil and natural gas industry. This approach establishes important correspondences between the (internal) operation planning of an organization and (external) business network planning