pinecone/core-2020-05-10-deduplication huggingface dataset:
Manufacturing Reporting Knowledge Representation: A Case Study at STMicroelectronics

Part 1: Knowledge Discovery and SharingInternational audienceThis paper addresses the problem of manufacturing Reporting knowledge representation in manufacturing companies. An approach to characterize these knowledge is proposed. The solution is applied to the Reporting process at STMicroelectronics for capitalizing knowledge in the Wiki of the company and responding to users’ needs. In such an approach, the user participates throughout the knowledge representation definition process, even in choosing knowledge characteristics to represent it. For that aim, three dimensions are taken into consideration: the know What, the know Why and the know How
Integration of Maintenance in the Tactical Production Planning Process under Feasibility Constraint

Part 2: Knowledge Discovery and SharingInternational audienceThis paper deals with the problem of the joint optimization of the master production schedule and maintenance strategy for a manufacturing system. An efficient production planning and maintenance policy will allow to minimize the impacts of the potential random failures and will let the plan to be feasible. We present a modelisation where we take into account a feasibility constraint; the optimization problem is formulated as a linear program. We propose a heuristic algorithm to solve it and we show the impact of the feasibility constraint on different criteria
An Analogy between Bin Packing Problem and Permutation Problem: A New Encoding Scheme

Part 2: Knowledge Discovery and SharingInternational audienceThe bin packing problem aims to pack a set of items in a minimum number of bins, with respect to the size of the items and capacity of the bins. This is an NP-hard problem. Several approach methods have been developed to solve this problem. In this paper, we propose a new encoding scheme which is used in a hybrid resolution: a metaheuristic is matched with a list algorithm (Next Fit, First Fit, Best Fit) to solve the bin packing problem. Any metaheuristic can be used but in this paper, our proposition is implemented on a single solution based metaheuristic (stochastic descent, simulated annealing, kangaroo algorithm). This hybrid method is tested on literature instances to ensure its good results
Forward - Green Virtual Enterprises and their Breeding Environments: Sustainable Manufacturing, Logistics & Consumption

Green Virtual Enterprise Breeding Environments and their Forward-Green Virtual Enterprises, represent a promising paradigm to face the sustainable manufacturing, logistics and consumption challenges towards a Circular Economy. This paper explores the 'build-to-order supply chain management' paradigm and the customers involvement in sustainable supply chains to support the creation and operation of goal-oriented supply networks capable of responding to the two-sided market demands of the Circular Economy by means of sustainable offers by industry and sustainable demand by society.Griffith Sciences, Griffith School of EngineeringFull Tex
A Governance Reference Model for Virtual Enterprises

During its entire lifecycle the Virtual Enterprise (VE) partners should share a sort of assets and sensible information as well as execute intra- and inter- organizational business processes in a coordinated and secure way, mostly supported via computer networks. VE partners are however independent enterprises and have their own business strategies. Therefore, it is relevant to properly govern a VE in way to minimize conflicts among its partners and hence the risks for achieving the VE goals. This paper presents a VE governance reference model allowing the instantiation of more concrete governance models for given VEs. An important contribution of this research is the consideration of the economic dimension of governance, not covered in related works.Full Tex
Ontology-based workflow design for the coordination of homecare interventions

ICT are recognized as key opportunity to improve healthcare practices ,in the homecare domain particularly in relation to care interventions. However, despite all the advances accomplished in this field, a problem of coordination and continuity of personalized care remains. Given these needs, workflow management systems appear to be an appropriate tool for supporting the communication between the different stakeholders involved in the homecare ecosystem. Nevertheless, the specific characteristics of homecare processes make the design of such workflows a real challenge. In order to help with the conception of these workflows, we propose in this paper an approach based on ontology matching between homecare domain models and semantic representation of Business Process Modeling Notations (BPMN). This approach leads to the design of custom workflow coordinated interventions for a given patient according to his profile
An AAL collaborative system : the AAL4ALL and a mobile assistant case study

"15th IFIP WG 5.5 Working Conference on Virtual Enterprises, PRO-VE 2014, Amsterdam, The Netherlands, October 6-8, 2014"The areas of Ambient Assisted Living (AAL) and Intelligent Systems (IS) are in full development, but there are still some issues to be resolved. One issue is the myriad of user oriented solutions that are rarely built to interact or integrate with other systems available in the market. In this paper we present the AAL4ALL project and the UserAccess implementation, showing a novel approach towards virtual organizations, interoperability and certification. The aim of this project is to provide a collaborative network of services and devices that connect every user and product from other developers, building a heterogeneous ecosystem. Thus establishing an environment for collaborative care systems, which may be available to the users in from of safety services, comfort services and healthcare services.Project "AAL4ALL", co-financed by the European Community Fund FEDER, through COMPETE - Programa Operacional Factores de Competitividade (POFC). Foundation for Science and Technology (FCT), Lisbon, Portugal, through Project PEst-C/CTM/LA0025/2013 and the project PEst-OE/EEI/UI0752/2014.
Project CAMCoF - Context-aware Multimodal Communication Framework fund-ed by ERDF -European Regional Development Fund through the COMPETE Pro-gramme (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
An Adaptative Multi-Agent System to Co-Construct an Ontology from Texts with an Ontologist

Ontologies are one of the most used representations to model the domain knowledge. An ontology consists of a set of concepts connected by semantic relations. The construction and evolution of an ontology are complex and time-consuming tasks. This paper presents DYNAMO-MAS, an Adaptive Multi-Agent System (AMAS) that automates these tasks by co-constructing an ontology from texts with an ontologist. Terms and concepts of a given domain are agentified and they act, according to the AMAS approach, by solving the non cooperative situations they locally perceive at runtime. These agents cooperate to determine their position in the AMAS (that is the ontology) thanks to (i) lexical relations between terms, (ii) some adaptive mechanisms enabling addition, removing or moving of new terms, of concepts and of relations in the ontology as well as (iii) feedbacks from the ontologist about the propositions given by the AMAS. This paper focuses on the instantiation of the AMAS approach to this difficult problem. It presents the architecture of DYNAMO-MAS, and details the cooperative behaviors of the two types of agents we defined for ontology evolution. Finally evaluations made on three different ontologies are given in order to show the genericity of our solution
Minimum Partial-Matching and Hausdorff RMS-Distance under Translation: Combinatorics and Algorithms

We consider the RMS-distance (sum of squared distances between pairs of points) under translation between two point sets in the plane. In the Hausdorff setup, each point is paired to its nearest neighbor in the other set. We develop algorithms for finding a local minimum in near-linear time on the line, and in nearly quadratic time in the plane. These improve substantially the worst-case behavior of the popular ICP heuristics for solving this problem. In the partial-matching setup, each point in the smaller set is matched to a distinct point in the bigger set. Although the problem is not known to be polynomial, we establish several structural properties of the underlying subdivision of the plane and derive improved bounds on its complexity. In addition, we show how to compute a local minimum of the partial-matching RMS-distance under translation, in polynomial time
Rapid Task-Models Development Using Sub-models, Sub-routines and Generic Components

Whilst task models are perceived as critical artifacts within User Centered Design methods, task models development is often considered as a resource and time consuming activity. Structuring techniques can support handling issues such as reuse and scalability and can improve analysts’ productivity and the overall quality of models. In this paper we propose (based on the notation of the HAMSTERS project) several means to structure task models and present how they can be used in order to increase reusability and scalability in task models. Besides sub-models and sub-routines, generic components are described. These mechanisms are duly illustrated within a project for the ground segments of satellite missions. This paper shows, by example, how such elements look like and how both readability and quality of models is improved by their use
Multi-Target Regression via Random Linear Target Combinations

Multi-target regression is concerned with the simultaneous prediction of
multiple continuous target variables based on the same set of input variables.
It arises in several interesting industrial and environmental application
domains, such as ecological modelling and energy forecasting. This paper
presents an ensemble method for multi-target regression that constructs new
target variables via random linear combinations of existing targets. We discuss
the connection of our approach with multi-label classification algorithms, in
particular RA$k$EL, which originally inspired this work, and a family of recent
multi-label classification algorithms that involve output coding. Experimental
results on 12 multi-target datasets show that it performs significantly better
than a strong baseline that learns a single model for each target using
gradient boosting and compares favourably to multi-objective random forest
approach, which is a state-of-the-art approach. The experiments further show
that our approach improves more when stronger unconditional dependencies exist
among the targets
Policy search for path integral control

Path integral (PI) control defines a general class of control problems for which the optimal control computation is equivalent to an inference problem that can be solved by evaluation of a path integral over state trajectories. However, this potential is mostly unused in real-world problems because of two main limitations: first, current approaches can typically only be applied to learn open-loop controllers and second, current sampling procedures are inefficient and not scalable to high dimensional systems. We introduce the efficient Path Integral Relative-Entropy Policy Search (PI-REPS) algorithm for learning feedback policies with PI control. Our algorithm is inspired by information theoretic policy updates that are often used in policy search. We use these updates to approximate the state trajectory distribution that is known to be optimal from the PI control theory. Our approach allows for a principled treatment of different sampling distributions and can be used to estimate many types of parametric or non-parametric feedback controllers. We show that PI-REPS significantly outperforms current methods and is able to solve tasks that are out of reach for current methods
Fast Gaussian Pairwise Constrained Spectral Clustering

International audienceWe consider the problem of spectral clustering with partial supervision in the form of must-link and cannot-link constraints. Such pairwise constraints are common in problems like coreference resolution in natural language processing. The approach developed in this paper is to learn a new representation space for the data together with a dis-tance in this new space. The representation space is obtained through a constraint-driven linear transformation of a spectral embedding of the data. Constraints are expressed with a Gaussian function that locally reweights the similarities in the projected space. A global, non-convex optimization objective is then derived and the model is learned via gradi-ent descent techniques. Our algorithm is evaluated on standard datasets and compared with state of the art algorithms, like [14,18,31]. Results on these datasets, as well on the CoNLL-2012 coreference resolution shared task dataset, show that our algorithm significantly outperforms related approaches and is also much more scalable
Combination of One-Class Support Vector Machines for Classification with Reject Option

International audienceThis paper focuses on binary classification with reject op-tion, enabling the classifier to detect and abstain hazardous decisions. While reject classification produces in more reliable decisions, there is a tradeoff between accuracy and rejection rate. Two type of rejection are considered: ambiguity and outlier rejection. The state of the art mostly handles ambiguity rejection and ignored outlier rejection. The proposed approach, referred as CONSUM, handles both ambiguity and outliers detection. Our method is based on a quadratic constrained optimization formulation, combining one-class support vector machines. An adapta-tion of the sequential minimal optimization algorithm is proposed to solve the minimization problem. The experimental study on both artifi-cial and real world datasets exams the sensitivity of the CONSUM with respect to the hyper-parameters and demonstrates the superiority of our approach
Random forests with random projections of the output space for high
  dimensional multi-label classification

We adapt the idea of random projections applied to the output space, so as to
enhance tree-based ensemble methods in the context of multi-label
classification. We show how learning time complexity can be reduced without
affecting computational complexity and accuracy of predictions. We also show
that random output space projections may be used in order to reach different
bias-variance tradeoffs, over a broad panel of benchmark problems, and that
this may lead to improved accuracy while reducing significantly the
computational burden of the learning stage
Experimental Design in Dynamical System Identification: A Bandit-Based Active Learning Approach

International audienceThis study focuses on dynamical system identification, with the reverse modeling of a gene regulatory network as motivating appli-cation. An active learning approach is used to iteratively select the most informative experiments needed to improve the parameters and hidden variables estimates in a dynamical model given a budget for experiments. The design of experiments under these budgeted resources is formalized in terms of sequential optimization. A local optimization criterion (re-ward) is designed to assess each experiment in the sequence, and the global optimization of the sequence is tackled in a game-inspired setting, within the Upper Confidence Tree framework combining Monte-Carlo tree-search and multi-armed bandits. The approach, called EDEN for Experimental Design for parameter Estimation in a Network, shows very good performances on several re-alistic simulated problems of gene regulatory network reverse-modeling, inspired from the international challenge DREAM7
GMRF Estimation under Topological and Spectral Constraints

International audienceWe investigate the problem of Gaussian Markov random field selection under a non-analytic constraint: the estimated models must be compatible with a fast inference algorithm, namely the Gaussian belief propagation algorithm. To address this question, we introduce the *-IPS framework, based on iterative proportional scaling, which incrementally selects candidate links in a greedy manner. Besides its intrinsic sparsity-inducing ability, this algorithm is flexible enough to incorporate various spectral constraints, like e.g. walk summability, and topological constraints, like short loops avoidance. Experimental tests on various datasets, including traffic data from San Francisco Bay Area, indicate that this approach can deliver, with reasonable computational cost, a broad range of efficient inference models, which are not accessible through penalization with traditional sparsity-inducing norms
Hypernode Graphs for Spectral Learning on Binary Relations over Sets

National audienceWe introduce hypernode graphs as (weighted) binary relations between sets of nodes : a hypernode is a set of nodes, a hyperedge is a pair of hypernodes, and each node in a hypernode of a hyperedge is given a non ne-gative weight that represents the node contribution to the relation. Hypernode graphs model binary relations between sets of individuals while allowing to reason at the level of individuals. We present a spectral theory for hypernode graphs that allows us to introduce an unnormalized Laplacian and a smoothness semi-norm. In this framework, we are able to extend existing spec-tral graph learning algorithms to the case of hypernode graphs. We show that hypernode graphs are a proper extension of graphs from the expressive power point of view and from the spectral analysis point of view. Therefore hypernode graphs allow to model higher or-der relations while it has been shown in [1] that it is not the case for (classical) hypergraphs. In order to prove the capabilities of the model, we represent mul-tiple players games with hypernode graphs and intro-duce a novel method to infer skill ratings from the game outcomes. We show that spectral learning algorithms over hypernode graphs obtain competitive results with skill ratings specialized algorithms such as Elo duelling and TrueSkill
Mining Top-K Largest Tiles in a Data Stream

International audienceLarge tiles in a database are itemsets with the largest area which is defined as the itemset frequency in the database multiplied by its size. Mining these large tiles is an important pattern mining problem since tiles with a large area describe a large part of the database. In this paper, we introduce the problem of mining top-k largest tiles in a data stream under the sliding window model. We propose a candidate-based approach which summarizes the data stream and produces the top-k largest tiles efficiently for moderate window size. We also propose an approximation algorithm with theoretical bounds on the error rate to cope with large size windows. In the experiments with two real-life datasets, the approximation algorithm is up to hundred times faster than the candidate-based solution and the baseline algorithms based on the state-of-the-art solutions. We also investigate an application of large tile mining in computer vision and in emerging search topics monitorin
Fast estimation of the pattern frequency spectrum

Both exact and approximate counting of the number of frequent
patterns for a given frequency threshold are hard problems. Still,
having even coarse prior estimates of the number of patterns is useful, as
these can be used to appropriately set the threshold and avoid waiting
endlessly for an unmanageable number of patterns. Moreover, we argue
that the number of patterns for different thresholds is an interesting
summary statistic of the data: the pattern frequency spectrum.
To enable fast estimation of the number of frequent patterns, we adapt
the classical algorithm by Knuth for estimating the size of a search tree.
Although the method is known to be theoretically suboptimal, we demonstrate
that in practice it not only produces very accurate estimates, but
is also very efficient. Moreover, we introduce a small variation that can
be used to estimate the number of patterns under constraints for which
the Apriori property does not hold. The empirical evaluation shows that
this approach obtains good estimates for closed itemsets.
Finally, we show how the method, together with isotonic regression, can
be used to quickly and accurately estimate the frequency pattern spectrum:
the curve that shows the number of patterns for every possible
value of the frequency threshold. Comparing such a spectrum to one
that was constructed using a random data model immediately reveals
whether the dataset contains any structure of interest.status: publishe
An Algorithm for Grouping Lines Which Converge to Vanishing Points in Perspective Sketches of Polyhedra

We seek to detect the vanishing points implied by design sketches of engineering products. Adapting previous ap- proaches, developed in computer vision for analysis of vectorised photographic images, is unsatisfactory, as they do not allow for the inherent imperfection of sketches. Human perception seems not to be disturbed by such imperfections. Hence, we have de- signed and implemented a vanishing point detection algorithm which mimics the human perception process and tested it with perspective line drawings derived from engineering sketches of polyhedral objects. The new algorithm is fast, easily- implemented, returns the approximate locations of the main vanishing points and identifies those groups of lines in 2D which correspond to groups of parallel edges in the 3D object
AUTSEG: Automatic Test Set Generator for Embedded Reactive Systems

Part 2: Tools and FrameworksInternational audienceOne of the biggest challenges in hardware and software design is to ensure that a system is error-free. Small errors in reactive embedded systems can have disastrous and costly consequences for a project. Preventing such errors by identifying the most probable cases of erratic system behavior is quite challenging. In this paper, we introduce an automatic test set generator called AUTSEG. Its input is a generic model of the target system, generated using the synchronous approach. Our tool finds the optimal preconditions for restricting the state space of the model. It only works locally on significant subspaces. Our approach exhibits a simpler and efficient quasi-flattening algorithm than existing techniques and a useful compiled form to check security properties and reduce the combinatorial explosion problem of state space. To illustrate our approach, AUTSEG was applied to the case of a transportation contactless card
Data-aware picking for medical models

Medical doctors are often faced with the problem of selecting anchoring points in 3D space. These points are commonly used for measurement tasks, such as lengths of bones, or dimensions of pathological structures (e. g. tumors). Since previous research indicates that measurement tasks can be usually carried out more efficiently in VR environments than in desktop-based systems, we have concentrated on the development of selection tools for medical models. These models have a set of particularities such as the presence of semi-transparencies, and there is a lack of tools for measurement support for such models in VR environments. Our VR-based interaction technique uses the data to automatically generate candidate anchor points, and it is specially focused on the efficient selection of 3D points in datasets rendered using methods with semi-transparency such as Direct Volume Rendering. We will show that our method is effective, precise, and reduces the time and amount of movements required to set the anchor points as compared with other classical techniques based on clipping planes. We also provide a couple of improvements tailored to reduce the inherent imprecision of 3D devices due to hand vibration, and the flexibility in transfer function selection for anchor point definition.Peer ReviewedPostprint (author's final draft