pinecone/core-2020-05-10-deduplication huggingface dataset:
Pervasive interventions to increase pro-environmental awareness, consciousness, and learning at the workplace

Börner, D., Kalz, M., Ternier, S., & Specht, M. (2013). Pervasive interventions to increase pro-environmental awareness, consciousness, and learning at the workplace. In D. Hernández-Leo et al. (Eds.), Scaling up Learning for Sustained Impact. Proceedings of the 8th European Conference on Technology Enhanced Learning (EC-TEL 2013), LNCS 8095 (pp. 57-70). Berlin Heidelberg, Germany: Springer. 
[The final publication is available at link.springer.com/chapter/10.1007/978-3-642-40814-4_6]This paper reports about pervasive interventions at a university campus to increase the pro-environmental awareness, consciousness, and learning of employees. Based on an assessment of the research gaps in this problem area we present results and design implications from three intervention iterations. While in the first intervention the focus was on increasing awareness through information distribution with ambient learning displays on the campus, the second iteration provided personalised feedback to employees with the help of a
sensor network and different client applications. The third iteration then implemented a game-based learning concept. Results reveal that these approaches are effective on different levels and that a combination of these elements can lead to increased pro-environmental consciousness, learning and hopefully a sustained behaviour change of employees.SURFnet Innovatieregeling Duurzaamheid & IC
etiquetAR: Tagging Learning Experiences

Proocedings of: 8th European Conference on Technology Enhanced Learning: Scaling Up Learning for Sustained Impact (EC-TEL 2013). Paphos, Cyprus, September 17-21, 2013etiquetAR is an authoring tool for supporting the design and enactment of mobile context-based learning experiences based on QR tags. etiquetAR enables creating, managing, and sharing personalized QR tags attachable to any object or physical geographical location. Tags are digital layers of contextualized information that transforms any physical space into a digitally augmented learning environment. This demonstration paper presents etiquetAR first working prototype of this application. In particular, the paper details: (1) how etiquetAR web-based application can be used to edit a tag, associate different resources to it, and relate resources information to a particular profile for adaptive learning experiences; and (2) how users can access and contribute to the information hidden in the tags using the mobile-based application. This demonstration will show the audience how etiquetAR is a simple tool designed to encourage practitioners to create their own tag-based learning experiences.This work has been partially funded by the Spanish Ministry of Economy and Competitiveness with the EEE project (TIN2011-28308-C03-01 and TIN2011-28308-C03-03) and by the eMadrid project (S2009/TIC-1650) funded by the Regional Government of Madrid. The authors would also like to especially thank the members of the research groups GAST (Universidad Carlos III de Madrid) for their contributions and ideas.Publicad
Transforming the Campus into a Digitally Augmented Learning Space Using etiquetAR

Proceedings of: 8th European Conference on Technology Enhanced Learning: Scaling Up Learning for Sustained Impact (EC-TEL 2013). Paphos, Cyprus, September 17-21, 2013etiquetAR is a web-authoring application designed towards learning purposes to support practitioners in the design of activities based on QR codes. This poster illustrates how etiquetAR QR codes were used to augment a Univer-sity Campus as a support for a gamified tag-based learning activity. The QR codes generated change its behavior depending on the profile of the student in-teracting with the tag. The results show the successful application of etiquetAR in a real educational context.Publicad
Engaging end-users in the collaborative development of domain-specific modelling languages

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-40840-3_16Proceedings of 10th International Conference, CDVE 2013, Alcudia, Mallorca, Spain, September 22-25, 2013Domain-Specific Modelling Languages (DSMLs) are high-level languages specially designed to perform tasks in a particular domain. When developing DSMLs, the participation of end-users is normally limited to providing domain knowledge and testing the resulting language prototypes. Language developers, which are perhaps not domain experts, are therefore in control of the language development and evolution. This may cause misinterpretations which hamper the development process and the quality of the DSML. Thus, it would be beneficial to promote a more active participation of end-users in the development process of DSMLs. While current DSML workbenches are mono-user and designed for technical experts, we present a process and tool support for the example-driven, collaborative construction of DSMLs in order to engage end-users in the creation of their own languages.This work was funded by the Spanish Ministry of Economy and Competitivity (project “Go Lite” TIN2011-24139), the R&D programme of the Madrid Region (project “e-Madrid” S2009/TIC-1650) and the European Commission under the ICT Policy Support Programme, grant no.
317859
Model and Feature Selection in Hidden Conditional Random Fields with Group Regularization

Proceedings of: 8th International Conference on Hybrid Artificial Intelligence Systems (HAIS 2013). Salamanca, September 11-13, 2013.Sequence classification is an important problem in computer vision, speech analysis or computational biology. This paper presents a new training strategy for the Hidden Conditional Random Field sequence classifier incorporating model and feature selection. The standard Lasso regularization employed in the estimation of model parameters is replaced by overlapping group-L1 regularization. Depending on the configuration of the overlapping groups, model selection, feature selection,or both are performed. The sequence classifiers trained in this way have better predictive performance. The application of the proposed method in a human action recognition task confirms that fact.This work was supported in part by Projects MINECO TEC2012-37832-C02-01, CICYT TEC2011-28626-C02-02, CAM CONTEXTS (S2009/TIC-1485)Publicad
Monitoring mental fatigue through the analysis of keyboard and mouse interaction patterns

In our living, we often have a sense of being tired due to a mental or physical work, plus a feeling of performance degradation even in the accomplishment of simple tasks. However, these mental states are often not consciously felt or are ignored, an attitude that may result in human failures, errors and even in the occurrence of health problems or on a decrease in the quality of life. States of fatigue may be detected with a close monitoring of some indicators, such as productivity, performance or even the health states. In this work it is proposed a model and a prototype to detect and monitor fatigue based on some of these items. We focus specifically on mental fatigue, a key factor in an individual’s performance. With this approach we aim to develop leisure and work context-aware environments that may improve the quality of life and the individual performance of any human being
Information fusion for context awareness in intelligent environments

The development of intelligent environments requires handling of data
perceived from users, received from environments and gathered from objects.
Such data is often used to implement machine learning tasks in order to predict
actions or to anticipate needs and wills, as well as to provide additional context
in applications. Thus, it is often needed to perform operations upon collected
data, such as pre-processing, information fusion of sensor data, and manage
models from machine learning. These machine learning models may have
impact on the performance of platforms and systems used to obtain intelligent
environments. In this paper, it is addressed the issue of the development of
middleware for intelligent systems, using techniques from information fusion
and machine learning that provide context awareness and reduce the impact of
information acquisition on both storage and energy efficiency. This discussion
is presented in the context of PHESS, a project to ensure energetic sustainability,
based on intelligent agents and multi-agent systems, where these techniques
are applied
Clinical careflows aided by uncertainty representation models

Serie : Lecture notes in computer science, ISSN 0302-9743, vol. 8073Choosing an appropriate support for Clinical Decision Support Systems is a complicated task, and dependent on the domain in which the system will intervene. The development of wide solutions, which are transversal to different clinical specialties, is impaired by the existence of complex decision moments that reflect the uncertainty and imprecision that are often present in these processes. The need for solutions that combine the relational nature of declarative knowledge with other models, capable of handling that uncertainty, is a necessity that current systems may be faced with. Following this line of thought, this work introduces an ontology for the representation of Clinical Practice Guidelines, with a case-study regarding colorectal cancer. It also presents two models, one based on Bayesian Networks, and another one on Artificial Neural Networks, for colorectal cancer prognosis. The objective is to observe how well these two ways of obtaining and representing knowledge are complementary, and how the machine learning models perform, attending to the available information.This work is funded by National Funds through the FCT Fundacao para a Ciencia e a Tecnologia (Portuguese Foundation for Science and Technology) within project PEst-OE/EEI/UI0752/2011. The work of Tiago Oliveira is supported by a doctoral grant by FCT (SFRH/BD/85291/2012)
Measuring digital crime investigation capacity to guide international
  crime prevention strategies

This work proposes a method for the measurement of a country's digital
investigation capacity and saturation for the assessment of future capacity
expansion. The focus is on external, or international, partners being a factor
that could negatively affect the return on investment when attempting to expand
investigation capacity nationally. This work concludes with the argument that
when dealing with digital crime, target international partners should be a
consideration in expansion, and could potentially be a bottleneck of
investigation requests.Comment: 7 pages, 3 figures, Presented at FutureTech 201
Multi-dimensional auctions under information asymmetry for costs and qualities

This paper discusses the design of a novel multi-dimensional mechanism which allows a principal to procure a single project or an item from multiple suppliers through a two-step payment. The suppliers are capable of producing different qualities at costs which cannot exceed a certain value and the mechanism balances between the costs faced by the suppliers and the benefit the principal achieves from higher qualities. Iniatially, the principal implements a standard second score auction and allocates the project to a single supplier based its reported cost and quality, while then it elicits truthful reporting of the quality by issuing a symmetric secondary payment after observing the winner’s production. We then provide an alternate mechanism in which the principal issues an asymmetric secondary payment which rewards agents for producing higher qualities, while it penalises them for producing lower qualities than they reported. We prove that for both mechanisms truthful revelation of costs and qualities is a dominant strategy (weakly for costs) and that they are immune to combined misreporting of both qualities and costs. We also show that the mechanisms are individually rational, and that the optimal payments received by the winners of the auctions are equal to the payment issued by the standard second score auction.
Geoconservation, concept of

[Excerto] The exceptional scientific value of certain geodiversity elements justifies the need to implement proper measures in order
to assure their conservation. Obviously, not all geodiversity elements should be envisaged for conservation. Most
elements have no particular value, and a vast variety of geological resources are exploited everyday to satisfy the needs
of modern societies. This means that accurate methods should be used to select exceptional sites that need to be
protected and conserved, i.e., geosites. It should be noted that geological heritage (or geoheritage) encompasses in situ
occurrences of notable geodiversity elements (minerals, fossils, rocks, soils, and landforms) and ex situ elements usually
integrating museum collections...info:eu-repo/semantics/publishedVersio
Geoconservation, history of

[Excerto] The word "geoconservation" was probably used for the first time in Tasmania (Australia) in the beginning of the 1990s
(Sharples 1993). Sharples, a pioneer of Australian geoconservation, reports that during the period of 1993-1994, the
Forestry Commission of Tasmania prepared several reports with preliminary inventories of landforms in the state forests
of Tasmania in order to facilitate "the conservation of Earth systems ('Geoconservation')" (Sharples 1993). However,
initiatives to protect particular geological and geomorphological features started centuries before, during the seventeenth
century. The first example of the protection of geological features dates back to 1668, concerning the protection of the
Baumannshöhle cave in the Harz Mountains in Germany (Grube 1994)...info:eu-repo/semantics/publishedVersio
Security-Informed Safety: If it's not secure, it's not safe

Traditionally, safety and security have been treated as separate disciplines, but this position is increasingly becoming untenable and stakeholders are beginning to argue that if it’s not secure, it’s not safe. In this paper we present some of the work we have been doing on “security-informed safety”. Our approach is based on the use of structured safety cases and we discuss the impact that security might have on an existing safety case. We also outline a method we have been developing for assessing the security risks associated with an existing safety system such as a large-scale critical infrastructure
Multi-interval discretization of continuous attributes for label ranking

Label Ranking (LR) problems, such as predicting rankings of financial analysts, are becoming increasingly important in data mining. While there has been a significant amount of work on the development of learning algorithms for LR in recent years, pre-processing methods for LR are still very scarce. However, some methods, like Naive Bayes for LR and APRIORI-LR, cannot deal with real-valued data directly. As a make-shift solution, one could consider conventional discretization methods used in classification, by simply treating each unique ranking as a separate class. In this paper, we show that such an approach has several disadvantages. As an alternative, we propose an adaptation of an existing method, MDLP, specifically for LR problems. We illustrate the advantages of the new method using synthetic data. Additionally, we present results obtained on several benchmark datasets. The results clearly indicate that the discretization is performing as expected and in some cases improves the results of the learning algorithms. © 2013 Springer-Verlag.This work was partially supported by Project Best-Case, which is co-financed by the North Portugal Regional Operational Programme (ON.2 - O Novo Norte), under the National Strategic Reference Framework (NSRF), through the European Regional Development Fund (ERDF)
Generalizing from example clusters

We consider the following problem: Given a set of data and one or
more examples of clusters, find a clustering of the whole data set that is consistent with the given clusters. This is essentially a semi-supervised clustering problem, but it differs from previously studied semi-supervised clustering settings in significant ways. Earlier work has shown that none of the existing methods for semi-supervised clustering handle this problem well.We identify two reasons for this, which are related to the default metric learning methods not working well in this situation, and to overfitting behavior. We investigate the latter in more detail and propose a new method that explicitly guards against overfitting. Experimental results confirm that the new method generalizes much better. Several other problems identified here remain open.status: publishe
Clustering based active learning for evolving data streams

Data labeling is an expensive and time-consuming task. Choosing which labels to use is increasingly becoming important. In the active learning setting, a classifier is trained by asking for labels for only a small fraction of all instances. While many works exist that deal with this issue in non-streaming scenarios, few works exist in the data stream setting. In this paper we propose a new active learning approach for evolving data streams based on a pre-clustering step, for selecting the most informative instances for labeling. We consider a batch incremental setting: when a new batch arrives, first we cluster the examples, and then, we select the best instances to train the learner. The clustering approach allows to cover the whole data space avoiding to oversample examples from only few areas. We compare our method w.r.t. state of the art active learning strategies over real datasets. The results highlight the improvement in performance of our proposal. Experiments on parameter sensitivity are also reported
Improving business process models using observed behavior

Process-aware information systems (PAISs) can be configured using a reference process model, which is typically obtained via expert interviews. Over time, however, contextual factors and system requirements may cause the operational process to start deviating from this reference model. While a reference model should ideally be updated to remain aligned with such changes, this is a costly and often neglected activity. We present a new process mining technique that automatically improves the reference model on the basis of the observed behavior as recorded in the event logs of a PAIS. We discuss how to balance the four basic quality dimensions for process mining (fitness, precision, simplicity and generalization) and a new dimension, namely the structural similarity between the reference model and the discovered model. We demonstrate the applicability of this technique using a real-life scenario from a Dutch municipality
Zipper-based attribute grammars and their extensions

Lecture Notes in Computer Science Volume 8129, 2013.Attribute grammars are a suitable formalism to express complex software language analysis and manipulation algorithms, which rely on multiple traversals of the underlying syntax tree. Recently, Attribute Grammars have been extended with mechanisms such as references and high-order and circular attributes. Such extensions provide a powerful modular mechanism and allow the specification of complex fix-point computations. This paper defines an elegant and simple, zipper-based embedding of attribute grammars and their extensions as first class citizens. In this setting, language specifications are defined as a set of independent, off-the-shelf components that can easily be composed into a powerful, executable language processor. Several real examples of language specification and processing programs have been implemented in this setting
Boilerplates for reconfigurable systems: a language and its semantics

Boilerplates are simplified, normative English texts,intended to capture software requirements in a controlled way. This paper proposes a pallet of boilerplates as a requirements modelling language for reconfigurable systems, i.e., systems structured in different modes of execution among which they can dynamically commute. The language semantics is given as an hybrid logic, in an institutional setting. The mild use made of the theory of institutions, which, to a large extent, may be hidden from the working software engineer, not only provides a rigorous and generic semantics, but also paves the way to tool-supported validation.FC
Multiple intermediate structure deforestation by shortcut fusion

Lecture Notes in Computer Science Volume 8129, 2013.Shortcut fusion is a well-known optimization technique for functional programs. Its aim is to transform multi-pass algorithms into single pass ones, achieving deforestation of the intermediate structures that multi-pass algorithms need to construct. Shortcut fusion has already been extended in several ways. It can be applied to monadic programs, maintaining the global effects, and also to obtain circular and higher-order programs. The techniques proposed so far, however, only consider programs defined as the composition of a single producer with a single consumer. In this paper, we analyse shortcut fusion laws to deal with programs consisting of an arbitrary number of function compositions.FCT -Fundação para a Ciência e a Tecnologia(FCOMP-01-0124-FEDER-022701
Universal knowledge-seeking agents for stochastic environments

We define an optimal Bayesian knowledge-seeking agent, KL-KSA, designed for countable hypothesis classes of stochastic environments and whose goal is to gather as much information about the unknown world as possible. Although this agent works for arbitrary countable classes and priors, we focus on the especially interesting case where all stochastic computable environments are considered and the prior is based on Solomonoff’s universal prior. Among other properties, we show that KL-KSA learns the true environment in the sense that it learns to predict the consequences of actions it does not take. We show that it does not consider noise to be information and avoids taking actions leading to inescapable traps. We also present a variety of toy experiments demonstrating that KL-KSA behaves according to expectation
Dimension-adaptive bounds on compressive FLD Classification

Efficient dimensionality reduction by random projections (RP) gains popularity, hence the learning guarantees achievable in RP spaces are of great interest. In finite dimensional setting, it has been shown for the compressive Fisher Linear Discriminant (FLD) classifier that forgood generalisation the required target dimension grows only as the log of the number of classes and is not adversely affected by the number of projected data points. However these bounds depend on the dimensionality d of the original data space. In this paper we give further guarantees that remove d from the bounds under certain conditions of regularity on the data density structure. In particular, if the data density does not fill the ambient space then the error of compressive FLD is independent of the ambient dimension and depends only on a notion of ‘intrinsic dimension'
Bayesian methods for low-rank matrix estimation: short survey and
  theoretical study

The problem of low-rank matrix estimation recently received a lot of
attention due to challenging applications. A lot of work has been done on
rank-penalized methods and convex relaxation, both on the theoretical and
applied sides. However, only a few papers considered Bayesian estimation. In
this paper, we review the different type of priors considered on matrices to
favour low-rank. We also prove that the obtained Bayesian estimators, under
suitable assumptions, enjoys the same optimality properties as the ones based
on penalization.Comment: Corrected version of a paper published in the proceedings of ALT 201