pinecone/core-2020-05-10-deduplication huggingface dataset:
Dealing with multiple source spatio-temporal data in urban dynamics analysis

Capturing, representing, modelling and visualizing the dynamics of urban mobility have been attracting the interest of the research community recently.
One of the drivers for recent work in this area is the availability of large datasets representing many aspects of the urban dynamics. Applications for these studies are diverse and include urban planning, security, intelligent transportation systems and many others. Quite often, the proposed approaches are highly dependent on the data type. This paper describes the definition of a set of basic concepts for the representation and processing of spatio-temporal data, sufficiently flexible to deal with various types of mobility data and to support multiple forms of processing and visualization of the urban mobility. A place learning algorithm is also described to illustrate the flexibility of the proposed framework. Available results obtained by the integration of geometric and symbolic data reveal the adequacy of the proposed concepts, and uncover new possibilities for the fusion of heterogeneous datasets.Research group supported by FEDER Funds through the COMPETE and National Funds through FCT – Fundação para a Ciência e a Tecnologia under the Project: FCOMP-01-FEDER-0124-022674
Improving healthcare using cognitive computing based software: An application in emergency situation

The goal of this paper is to define the platform specifications dealing with medical information sharing both from research viewpoint and in terms of local health care. The purpose of this research work is based on doctor - patient relationships: VDS - Virtual Medical Doctor System. At this stage the platform is only used for scientific purposes. In particular, we assume the integrated design of the platform based on different levels (layers), which may be interconnected through the information flows (or links). The first level (the lower) involves the construction of a VDS platform. Based on this system it is possible to foresee a number of extensions such as social network for scientific research design, and risk analysis tool
A Stochastic Search on the Line-Based Solution to Discretized Estimation

Published version of a chapter in the book: Advanced Research in Applied Artificial Intelligence. Also available from the publisher at: http://dx.doi.org/10.1007/978-3-642-31087-4_77Recently, Oommen and Rueda [11] presented a strategy by which the parameters of a binomial/multinomial distribution can be estimated when the underlying distribution is nonstationary. The method has been referred to as the Stochastic Learning Weak Estimator (SLWE), and is based on the principles of continuous stochastic Learning Automata (LA). In this paper, we consider a new family of stochastic discretized weak estimators pertinent to tracking time-varying binomial distributions. As opposed to the SLWE, our proposed estimator is discretized , i.e., the estimate can assume only a finite number of values. It is well known in the field of LA that discretized schemes achieve faster convergence speed than their corresponding continuous counterparts. By virtue of discretization, our estimator realizes extremely fast adjustments of the running estimates by jumps, and it is thus able to robustly, and very quickly, track changes in the parameters of the distribution after a switch has occurred in the environment. The design principle of our strategy is based on a solution, pioneered by Oommen [7], for the Stochastic Search on the Line (SSL) problem. The SSL solution proposed in [7], assumes the existence of an Oracle which informs the LA whether to go “right” or “left”. In our application domain, in order to achieve efficient estimation, we have to first infer (or rather simulate ) such an Oracle. In order to overcome this difficulty, we rather intelligently construct an “Artificial Oracle” that suggests whether we are to increase the current estimate or to decrease it. The paper briefly reports conclusive experimental results that demonstrate the ability of the proposed estimator to cope with non-stationary environments with a high adaptation rate, and with an accuracy that depends on its resolution. The results which we present are, to the best of our knowledge, the first reported results that resolve the problem of discretized weak estimation using a SSL-based solution
A hierarchical learning scheme for solving the Stochastic Point Location problem

Published version of a chapter in the book: Advanced Research in Applied Artificial Intelligence. Also available from the publisher at: http://dx.doi.org/10.1007/978-3-642-31087-4_78This paper deals with the Stochastic-Point Location (SPL) problem. It presents a solution which is novel in both philosophy and strategy to all the reported related learning algorithms. The SPL problem concerns the task of a Learning Mechanism attempting to locate a point on a line. The mechanism interacts with a random environment which essentially informs it, possibly erroneously, if the unknown parameter is on the left or the right of a given point which also is the current guess. The first pioneering work [6] on the SPL problem presented a solution which operates a one-dimensional controlled Random Walk (RW) in a discretized space to locate the unknown parameter. The primary drawback of the latter scheme is the fact that the steps made are always very conservative. If the step size is decreased the scheme yields a higher accuracy, but the convergence speed is correspondingly decreased. In this paper we introduce the Hierarchical Stochastic Searching on the Line (HSSL) solution. The HSSL solution is shown to provide orders of magnitude faster convergence when compared to the original SPL solution reported in [6]. The heart of the HSSL strategy involves performing a controlled RW on a discretized space, which unlike the traditional RWs, is not structured on the line per se , but rather on a binary tree described by intervals on the line. The overall learning scheme is shown to be optimal if the effectiveness of the environment, p , is greater than the golden ratio conjugate [4] – which, in itself, is a very intriguing phenomenon. The solution has been both analytically analyzed and simulated, with extremely fascinating results. The strategy presented here can be utilized to determine the best parameter to be used in any optimization problem, and also in any application where the SPL can be applied [6]
Discretized Bayesian pursuit – A new scheme for reinforcement learning

Published version of a chapter in the book: Advanced Research in Applied Artificial Intelligence. Also available from the publisher at: http://dx.doi.org/10.1007/978-3-642-31087-4_79The success of Learning Automata (LA)-based estimator algorithms over the classical, Linear Reward-Inaction ( L RI )-like schemes, can be explained by their ability to pursue the actions with the highest reward probability estimates. Without access to reward probability estimates, it makes sense for schemes like the L RI to first make large exploring steps, and then to gradually turn exploration into exploitation by making progressively smaller learning steps. However, this behavior becomes counter-intuitive when pursuing actions based on their estimated reward probabilities. Learning should then ideally proceed in progressively larger steps, as the reward probability estimates turn more accurate. This paper introduces a new estimator algorithm, the Discretized Bayesian Pursuit Algorithm (DBPA), that achieves this. The DBPA is implemented by linearly discretizing the action probability space of the Bayesian Pursuit Algorithm (BPA) [1]. The key innovation is that the linear discrete updating rules mitigate the counter-intuitive behavior of the corresponding linear continuous updating rules, by augmenting them with the reward probability estimates. Extensive experimental results show the superiority of DBPA over previous estimator algorithms. Indeed, the DBPA is probably the fastest reported LA to date
Understanding business process models : the costs and benefits of structuredness

Previous research has put forward a number of properties of business process models that have an impact on their understandability. Two such properties are compactness and(block-)structuredness. What has not been sufficiently appreciated at this point is that these desirable properties may be at odds with one another. This paper presents the results of a two-pronged study aimed at exploring the trade-off between compactness and structuredness of process models. The first prong of the study is a comparative analysis of the complexity of a set of unstructured process models from industrial practice and of their corresponding structured versions. The second prong is an experiment wherein a cohort of students was exposed to semantically equivalent unstructured and structured process models. The key finding is that structuredness is not an absolute desideratum vis-a-vis for process model understandability. Instead, subtle trade-offs between structuredness and other model properties are at play
Abstracting modelling languages: A reutilization approach

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-31095-9_9Proceedings of 24th International Conference, CAiSE 2012, Gdansk, Poland, June 25-29, 2012Model-Driven Engineering automates the development of information systems. This approach is based on the use of Domain-Specific Modelling Languages (DSMLs) for the description of the relevant aspects of the systems to be built. The increasing complexity of the target systems has raised the need for abstraction techniques able to produce simpler versions of the models, but retaining certain properties of interest. However, developing such abstractions for each DSML from scratch is a time and resource consuming activity.

 Our solution to this situation is a number of techniques to build reusable abstractions that are defined once and can be reused over families of modelling languages sharing certain requirements. As a proof of concept, we present a catalogue of reusable abstractions, together with an implementation in the MetaDepth multi-level meta-modelling tool.Work funded by the Spanish Ministry of Economy and Competitivity (TIN2011-24139), and the R&D programme of Madrid Region (S2009/TIC-1650)
Gathering of Robots on Anonymous Grids without multiplicity detection

International audienceThe paper studies the gathering problem on grid networks. A team of robots placed at different nodes of a grid, have to meet at some node and remain there. Robots operate in Look-Compute-Move cycles; in one cycle, a robot perceives the current configuration in terms of occupied nodes (Look), decides whether to move towards one of its neighbors (Compute), and in the positive case makes the computed move instantaneously (Move). Cycles are performed asynchronously for each robot. The problem has been deeply studied for the case of ring networks. However, the known techniques used on rings cannot be directly extended to grids. Moreover, on rings, another assumption concerning the so-called multiplicity detection capability was required in order to accomplish the gathering task. That is, a robot is able to detect during its Look operation whether a node is empty, or occupied by one robot, or occupied by an undefined number of robots greater than one. In this paper, we provide a full characterization about gatherable configurations for grids. In particular, we show that in this case, the multiplicity detection is not required. Very interestingly, sometimes the problem appears trivial, as it is for the case of grids with both odd sides, while sometimes the involved techniques require new insights with respect to the well-studied ring case. Moreover, our results reveal the importance of a structure like the grid that allows to overcome the multiplicity detection with respect to the ring case
Towards a catalog of spreadsheet smells

Spreadsheets are considered to be the most widely used programming language in the world, and reports have shown that 90% of real-world spreadsheets contain errors. In this work, we try to identify spreadsheet smells, a concept adapted from software, which consists of a surface indication that usually corresponds to a deeper problem. Our smells have been integrated in a tool, and were computed for a large spreadsheet repository. Finally, the analysis of the results we obtained led to the refinement of our initial catalog
Dealing with dependencies among functional and non-functional requirements for impact analysis in web engineering

Due to the dynamic nature of the Web as well as its heterogeneous audience, web applications are more likely to rapidly evolve leading to inconsistencies among requirements during the development process. With the purpose to deal with these inconsistencies, web developers need to know dependencies among requirements considering that the understanding of these dependencies helps in better managing and maintaining web applications. In this paper, an algorithm has been defined and implemented in order to analyze dependencies among functional and nonfunctional requirements (in a goal-oriented approach) for understanding which is the impact derived from a change during the Model-Driven Web Engineering process. This Impact Analysis would support web developer in selecting requirements to be implemented ensuring that web applications finally satisfy the audience.This work has been partially supported by: Universidad Autónoma de Sinaloa (Mexico), Consejo Nacional de Ciencia y Tecnología Mexico (CONACYT), Programa Integral de Fortalecimiento Institucional (PIFI 2011), Programa de Mejoramiento del Profesorado (PROMEP) at Secretaría de Educación Pública, Mexico and MANTRA (GRE09-17) from the University of Alicante
A new Self-learning Algorithm for Dynamic Classification of Water Bodies

In many applications of remote sensing data land-water masks play an important role. In this context they can be a helpful orientation to distinguish dark areas (e.g. cloud shadows, topographic shadows, burned areas, coniferous forests) and water areas. However, water bodies cannot always be classified exactly on basis of available remote sensing data. This fact can be caused by a variety of different physical and biological factors (e.g. chlorophyll, suspended particles, surface roughness, turbid and shallow water and dynamic of water bodies) as well as atmospheric factors (e.g. haze and clouds). On the other hand the best available static water masks also show deficiencies. These are essentially caused by the fact that land-water masks represent only a temporal snapshot of the water bodies distributed worldwide and therefore these masks cannot reflect their dynamic behavior. This paper presents a dynamic self-learning water masking approach for AATSR remote sensing data in the context of integrating high-quality water masks in processing chains for deriving value-added remote sensing data products. As an advantage to conventional water masking algorithms, the proposed approach operates on basis of a static water mask as data base for deriving an optimized dynamic water mask. Significant research effort was spent to develop and validate a dynamic self-learning algorithm and a processing scheme for operational derivation of actual land-water masks as basis for operational interpretation of remote sensing data. Based on this concept actual activities and perspectives for contributions to operational monitoring systems will be presented
Warehouse design and planning: A mathematical programming approach

The dynamic nature of today's competitive markets compels organizations to an incessant reassessment in an effort to respond to continuous challenges. Therefore, warehouses as an important link in most supply chains, must be continually re-evaluated to ensure that they are consistent with both market's demands and management's strategies. A number of warehouse decision support models have been proposed in the literature but considerable difficulties in applying these models still remain, due to the large amount of information to be processed and to the large number of possible alternatives. In this paper we discuss a mathematical programming model aiming to support some warehouse management and inventory decisions. In particular a large mixed-integer nonlinear programming model (MINLP) is presented to capture the trade-offs among the different inventory and warehouse costs in order to achieve global optimal design satisfying throughput requirements.info:eu-repo/semantics/publishedVersio
An artificial fish swarm filter-based Method for constrained global optimization

Ana Maria A.C. Rocha, M. Fernanda P. Costa and Edite M.G.P. Fernandes, An Artificial Fish Swarm Filter-Based Method for Constrained Global Optimization, B. Murgante, O. Gervasi, S. Mirsa, N. Nedjah, A.M. Rocha, D. Taniar, B. Apduhan (Eds.), Lecture Notes in Computer Science, Part III, LNCS 7335, pp. 57–71, Springer, Heidelberg, 2012.An artificial fish swarm algorithm based on a filter methodology
for trial solutions acceptance is analyzed for general constrained
global optimization problems. The new method uses the filter set concept
to accept, at each iteration, a population of trial solutions whenever
they improve constraint violation or objective function, relative to the
current solutions. The preliminary numerical experiments with a wellknown
benchmark set of engineering design problems show the effectiveness
of the proposed method.Fundação para a Ciência e a Tecnologia (FCT
A derivative-free filter driven multistart technique for global optimization

A stochastic global optimization method based on a multistart strategy and a derivative-free filter local search for general constrained optimization is presented and analyzed. In the local search procedure, approximate descent directions for the constraint violation or the objective function are used to progress towards the optimal solution. The algorithm is able to locate all the local minima, and consequently, the global minimum of a multi-modal objective function. The performance of the multistart method is analyzed with a set of benchmark problems and a comparison is made with other methods.This work was financed by FEDER funds through COMPETE-Programa Operacional Fatores de Competitividade and by portuguese funds through FCT-Fundação para a Ciência e a Tecnologia within projects PEst-C/MAT/UI0013/2011 and FCOMP- 01-0124-FEDER-022674
Testing formula satisfaction

We study the query complexity of testing for properties defined by read once formulae, as instances of massively parametrized properties, and prove several testability and non-testability results. First we prove the testability of any property accepted by a Boolean read-once formula involving any bounded arity gates, with a number of queries exponential in \epsilon and independent of all other parameters. When the gates are limited to being monotone, we prove that there is an estimation algorithm, that outputs an approximation of the distance of the input from\ud
satisfying the property. For formulae only involving And/Or gates, we provide a more efficient test whose query complexity is only quasi-polynomial in \epsilon. On the other hand we show that such testability results do not hold in general for formulae over non-Boolean alphabets; specifically we construct a property defined by a read-once arity 2 (non-Boolean) formula over alphabets of size 4, such that any 1/4-test for it requires a number of queries depending on the formula size
Controlling ambiguities in legislative language

Legislative language exhibits some characteristics typical of languages of administration that are particularly prone to eliciting ambiguities.\ud
However, ambiguity is generally undesirable in legislative texts and can pose problems for the interpretation and application of codified law. In this paper, we demonstrate how methods of controlled natural languages can be applied to prevent ambiguities in legislative texts.\ud
We investigate what types of ambiguities are frequent in legislative language and therefore important to control, and we examine which ambiguities are already controlled by existing drafting guidelines. For those not covered by the guidelines, we propose additional control mechanisms.\ud
Wherever possible, the devised mechanisms reflect existing conventions and frequency distributions and exploit domain-specific means to make ambiguities explicit
Business success in software SMEs: Recommendations for future SPI studies

There is presently insufficient data regarding the relationship between software process improvement (SPI) and business success, a fact which may reduce process prioritisation in software development in practice. To assist future studies examining the relationship between SPI and business success, we developed a new two-phased approach to examining business. The first phase involves the elicitation of business objectives for the forthcoming year, with the second phase determining the extent of achievement of the recorded objectives. At EuroSPI 2011, we described the two-phased approach in detail and reported on the findings from deploying the first phase of the examination to software developing small- and medium-sized enterprises (software SMEs). In this follow-up paper, we report on the findings from the second phase of the investigation in the participating software SMEs, formulating an additional important new recommendation for future studies
Hacking the Natural Habitat: An in-the-wild study of smart homes, their development, and the people who live in them

Commercial home automation systems are becoming increasingly common, affording the opportunity to study technology-augmented homes in real world contexts. In order to understand how these technologies are being integrated into homes and their effects on inhabitants, we conducted a qualitative study involving smart home professionals who provide such technology, people currently in the process of planning or building smart homes, and people currently living in smart homes. We identified motivations for bringing smart technology into homes, and the phases involved in making a home smart. We also explored the varied roles of the smart home inhabitants that emerged during these phases, and several of the challenges and benefits that arise while living in a smart home. Based on these findings we propose open areas and new directions for smart home research
Cost-oriented recommendation model for e-commerce

Contemporary Web stores offer a wide range of products to e-customers. However, online sales are strongly dominated by a limited number of bestsellers whereas other, less popular or niche products are stored in inventory for a long time. Thus, they contribute to the problem of frozen capital and high inventory costs. To cope with this problem, we propose using information on product cost in a recommender system for a Web store. We discuss the proposed recommendation model, in which two criteria have been included: a predicted degree of meeting customer’s needs by a product and the product cost.
Euler diagram encodings

Euler Diagrams are a well-known visualisation of set-based relationships, used in many application areas and at the basis of more complex notations. We propose a static code for concrete Euler Diagrams, which enables efficient storage (vs. storage of concrete diagrams), and transformations preserving concrete-level structure, hence the viewer's mental map. We provide the theoretical underpinnings of the encoding, examples and deductions, and an indication of their utility. For use in an interactive setting, we provide algorithms to update the code upon curve addition and removal. Independently, we show that the code identifies minimal regions, enabling the computation of the abstract zone set