pinecone/core-2020-05-10-deduplication huggingface dataset:
PowerOnt: An Ontology-based Approach for Power Consumption Estimation in Smart Homes

The impact of electricity consumption in buildings on the overall energy budget of European and North-American states is steadily growing and requires solutions for achieving a more sustainable development. Smart metering and energy management system can be hardly afforded by residential homes, for their cost and the required granularity. Empowering smart homes with suitable power consumption models allows to estimate, in real-time, the current home consumption on the basis of currently active devices. In this paper we introduce an ontology based power consumption model (PowerOnt) for smart homes, and we discuss some application use cases where the proposed approach may successfully be exploited. Results show that the modeling approach has the potential to support energy efficiency measures into residential homes with few or no metering device
IoT Meets Caregivers: a Healthcare Support System in Assisted Living Facilities

This paper presents a system that exploits the synergy between wearable/mobile technology and smart caring environments to support caregivers in Assisted Living Facilities (ALFs) for persons with physical and cognitive disabilities. In particular, this healthcare support system allows caregivers to be automatically alerted of potentially hazardous situations that happen to the inhabitants while these are alone. The design stemmed from six system requirements derived from the results of three focus groups conducted with 30 caregivers of different ALFs in Northern Ital
DIMCloud: a distributed framework for district energy simulation and management

To optimize energy consumption, it is needed to monitor real-time data and simulate all energy flows. In a city district context, energy consumption data usually come from many sources and encoded in different formats. However, few models have been proposed to trace the energy behavior of city districts and handle related data. In this article, we introduce DIMCloud, a model for heterogeneous data management and integration at district level, in a pervasive computing context. Our model, by means of an ontology, is able to register the relationships between different data sources of the district and to disclose the sources locations using a publish-subscribe design pattern. Furthermore, data sources are published as Web Services, abstracting the underlying hardware from the user's point-of-vie
EYES : a novel overtaking assistance system for vehicular networks

Developments in the ITS area are received with great expectation by both consumers and industry. Despite their huge potential benefits, ITS solutions suffer from the slow pace of adoption by manufacturers. In this paper we propose EYES, an ITS system that aims at helping drivers in overtaking. The system autonomously creates a network of the devices running EYES, and provides drivers with a video feed from the vehicle located just ahead, thus presenting a better view of any vehicles coming from the opposite direction and the road ahead. This is specially useful when the front view of the driver is blocked by large vehicles, and thus the decision whether to overtake can be taken based on the visuals provided by the application. We have validated EYES, the proposed overtaking assistance system, in both indoor and realistic scenarios involving vehicular network, and preliminary results allow being optimistic about its effectiveness and applicability
Consensus Clustering Using kNN Mode Seeking

Published version. Source at <a href=http://doi.org/10.1007/978-3-319-19665-7_15>http://doi.org/10.1007/978-3-319-19665-7_15</a>.In this paper we present a novel clustering approach which combines two modern strategies, namely consensus clustering, and two stage clustering as represented by the mean shift spectral clustering algorithm. We introduce the recent kNN mode seeking algorithm in the consensus clustering framework, and the information theoretic kNN Cauchy Schwarz divergence as foundation for spectral clustering. In combining these frameworks, two well known problematic issues are directly bypassed; the kernel bandwidth choice of the kernel density based mean shift and the computational complexity of the mean shift iterations. We demonstrate experiments on both real and synthetic data as a proof of concept for our contributions
Declaratively solving Google Code Jam problems with Picat

In this paper we present several examples of solving algorithmic problems
from the Google Code Jam programming contest with Picat programming language
using declarative techniques: constraint logic programming and tabled logic
programming. In some cases the use of Picat simplifies the implementation
compared to conventional imperative programming languages, while in others it
allows to directly convert the problem statement into an efficiently solvable
declarative problem specification without inventing an imperative algorithm
Implementation and performance of probabilistic inference pipelines

In order to handle real-world problems, state-of-the-art probabilistic logic and learning frameworks, such as ProbLog, reduce the expensive inference to an efficient Weighted Model Counting. To do so ProbLog employs a sequence of transformation steps, called an \emph{inference pipeline}. Each step in the probabilistic inference pipeline is called a \emph{pipeline component}. The choice of the mechanism to implement a component can be crucial to the performance of the system. In this paper we describe in detail different ProbLog pipelines. Then we perform a empirical analysis to determine which components have a crucial impact on the efficiency. Our results show that the Boolean formula conversion is the crucial component in an inference pipeline.  
Our main contributions are the thorough analysis of ProbLog inference pipelines and the introduction of new pipelines, one of which performs very well on our benchmarks.status: publishe
An overview of the quality of service in bluetooth communications in healthcare

Currently, the general public requires devices getting faster and great performance, that is, devices ensuring a better quality of service. One way to achieve these goals is through the use of devices supported by the mobile com-puting with tools to help the search for information. Bluetooth technology is an open standard for wireless communication allowing the transmission of data and information between electronic devices within walking distance, with minimum resource expenditures, safe and rapid transition of data. So, the Bluetooth tech-nology was initially designed to support simple network devices and personal devices such as mobile phones, PDAs and computers, but quickly it were discov-ered other applications in several areas. In this article, it will be performed a lit-erature review on the topic, with the goal to understand how the Bluetooth tech-nology can benefit increases in the Quality of Service and the presentation of some actual and potential biomedical applications.(undefined
Reducing Stress and Fuel Consumption Providing Road Information

The porceeding at: 6th International Symposium on Ambient Intelligence (ISAmI 2015), took place in June,3-5,  2015, in Salamanca (Spain).In this paper, we propose a solution to reduce the stress level of the driver, minimize fuel consumption and improve safety. The system analyzes the driving and driver workload during the trip. If it discovers an area where the stress increases and the driving style is worse from the point of view of energy efficiency, a photo is taken and is saved along with its location in a shared database. On the other hand, the solution warns the user when is approaching a region where the driving is difficult (high fuel consumption and stress) using the shared database. In this case, the proposal shows on the screen of the mobile device the image captured previously of the area. The aim is that driver knows in advance the driving environment. Therefore, he or she may adjust the vehicle speed and the driver workload decreases. Data Envelopment Analysis is used to estimate the efficiency of driving and driver workload in each area. We employ this method because there is no preconceived form on the data in order to calculate the efficiency and stress level. A validation experiment has been conducted with 6 participants who made 96 driving tests in Spain. The system reduces the slowdowns (38 %), heart rate (4.70 %), and fuel consumption (12.41 %). The proposed solution is implemented on Android mobile devices and does not require the installation of infrastructure on the road. It can be installed on any model of vehicle.The research leading to these results has received funding from the “HERMES-
SMART DRIVER” project TIN2013-46801-C4-2-R within the Spanish “Plan Nacional de
I + D+I” under the Spanish Ministerio de Economía y Competitividad and from the Spanish Ministerio de Economía y Competitividad funded projects (co-financed by the Fondo Europeo de Desarrollo Regional (FEDER)) IRENE (PT-2012-1036-370000), COMINN (IPT-2012-0883-430000) and REMEDISS (IPT-2012-0882-430000) within the INNPACTO programPublicad
Step count and classification using sensor information fusion

In order to suppress the GNSS (Global Navigation Satellite System) limitation to track persons in indoor or in dense environments, a pedestrian inertial navigation system can be used. However, this type of systems have huge location estimation errors due to the Pedestrian Dead Reckoning (PDR) characteristics and the use of low-cost inertial sensors. To suppress some of these errors we propose a system that uses several sensors spread in person's body combined with information fusion techniques. Information fusion techniques provide lighter algorithms implementations, to count and classify the type of step, to run in mobile devices. Thus, improving pedestrian inertial navigation systems accuracy.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/EEI-SII/1386/2012). Ricardo also acknowledge FCT for the
support of his work through the PhD grant SFRH/DB/70248/2010.info:eu-repo/semantics/publishedVersio
Non-conventional digital signatures and their implementations – A review

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-19713-5_36The current technological scenario determines a profileration
of trust domains, which are usually defined by validating the digital
identity linked to each user. This validation entails critical assumptions
about the way users’ privacy is handled, and this calls for new methods
to construct and treat digital identities. Considering cryptography,
identity management has been constructed and managed through conventional
digital signatures. Nowadays, new types of digital signatures
are required, and this transition should be guided by rigorous evaluation
of the theoretical basis, but also by the selection of properly verified software
means. This latter point is the core of this paper. We analyse the
main non-conventional digital signatures that could endorse an adequate
tradeoff betweeen security and privacy. This discussion is focused on
practical software solutions that are already implemented and available
online. The goal is to help security system designers to discern identity
management functionalities through standard cryptographic software libraries.This work was supported by Comunidad de Madrid (Spain) under the project S2013/ICE-3095-CM (CIBERDINE) and the Spanish Government project TIN2010-19607
Using smart cards for authenticating in public services: A comparative study

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-19713-5_37Smart cards are well-known tamper-resistant devices, and
as such they represent an excellent platform for implementing strong
authentication. Many services requesting high levels of security rely on
smart cards, which provide a convenient security token due to their portability.
This contribution analyses two Spanish smart card deployments
intended to be used for accessing eGoverment services, comparing their
respective contents and capabilities.This work has been partially supported by Comunidad de Madrid (Spain) under the project S2013/ICE-3095-CM (CIBERDINE)
On the difficult tradeoff between security and privacy: Challenges for the management of digital identities

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-19713-5_39The deployment of security measures can lead in many occasions
to an infringement of users’ privacy. Indeed, nowadays we have
many examples about surveillance programs or personal data breaches
in online service providers. In order to avoid the latter problem, we need
to establish security measures that do not involve a violation of privacy
rights. In this communication we discuss the main challenges when conciliating
information security and users’ privacy.This work was supported by Comunidad de Madrid (Spain) under the project S2013/ICE-3095-CM (CIBERDINE)
On the Stability of Polynomial Interpolation Using Hierarchical Sampling

International audienceMotivated by the development of non-intrusive methods for high dimensional para-metric PDE's, we study the stability of a sparse high dimensional polynomial interpolation procedure introduced in [6]. A key aspect of this procedure is its hierarchical structure: the sampling set is progressively enriched together with the polynomial space. The evaluation points are selected from a grid obtained by tensorization of a univariate sequence. The Lebesgue constant that quantifies the stability of the resulting interpolation operator depends on the choice of this sequence. Here we study the-Leja sequence, obtained by the projection of Leja sequences on the complex unit circle , with initial value 1, onto [−1, 1]. For this sequence, we prove cubic growth in the number of points for the Lebesgue constant of the multivariate interpolation operator, independently of the number of variable and of the shape of the polynomial space
Predicting Comprehension from Students’ Summaries

International audienceComprehension among young students represents a key component of their formation throughout the learning process. Moreover, scaffolding students as they learn to coherently link information, while organically construct- ing a solid knowledge base, is crucial to students’ development, but requires regular assessment and progress tracking. To this end, our aim is to provide an automated solution for analyzing and predicting students’ comprehension levels by extracting a combination of reading strategies and textual complexity factors from students’ summaries. Building upon previous research and enhancing it by incorporating new heuristics and factors, Support Vector Machine classification models were used to validate our assumptions that automatically identified reading strategies, together with textual complexity indices applied on students’ summaries, represent reliable estimators of comprehension
Predicting students' emotions using machine learning techniques

Detecting students' real-time emotions has numerous benefits, such as helping lecturers understand their students' learning behaviour and to address problems like confusion and boredom, which undermine students' engagement. One way to detect students' emotions is through their feedback about a lecture. Detecting students' emotions from their feedback, however, is both demanding and time-consuming. For this purpose, we looked at several models that could be used for detecting emotions from students' feedback by training seven different machine learning techniques using real students' feedback. The models with a single emotion performed better than those with multiple emotions. Overall, the best three models were obtained with the CNB classiffier for three emotions: amused, bored and excitement
Adapting feedback types according to students’ affective states

Affective states play a significant role in students’ learning behaviour. Positive affective states can enhance learning, while negative ones can inhibit it. This paper describes the development of an affective state reasoner that is able to adapt the feedback type according to students’ affective states in order to evoke positive affective states and as such improve their learning experience. The reasoner relies on a dynamic Bayesian network trained with data gathered in a series of ecologically valid Wizard-of-Oz studies, where the effect of feedback on students’ affective states was investigated
Affect matters: exploring the impact of feedback during mathematical tasks in an exploratory environment

We describe a Wizard-of-Oz study that investigates the impact of different types of feedback on students’ affective states. Our results indicate the importance of matching carefully the affective state with appropriate feedback in order to help students transition into more positive states. For example when students were confused affect boosts and specific instruction seem to be effective in helping students to be in flow again. We discuss this and other effective ways to and implications for the development of our system and the field in general
Off the beaten path: The impact of adaptive content sequencing on student navigation in an open social student modeling interface

© Springer International Publishing Switzerland 2015. One of the original goals of intelligent educational systems is to guide every student to the most appropriate educational content. Exploring both knowledge-based and social guidance approaches in past work, we learned that each of these approaches has weak sides. In this paper we follow the idea of combining social guidance with more traditional knowledge-based guidance to support more optimal content navigation. We proposed a greedy sequencing approach that maximizes student’s level of knowledge and tested it in a classroom. Results indicated that this approach positively impacts students’ navigation
Carotenoid analysis of Cassava genotypes roots (Manihot Esculenta Crantz) cultivated in Southern Brazil using chemometric tools

Manihot esculenta roots rich in -carotene are an important staple food for populations with risk of vitamin A deficiency. Cassava genotypes with high pro-vitamin A activity have been identified as a strategy to reduce the prevalence of deficiency of this vitamin, In this study, the metabolomics characterization focusing on the carotenoid composition of ten cassava genotypes cultivated in southern Brazil by UV-visible scanning spectrophotometry and reverse phase-high performance liquid chromatography was performed. The data set was used for the construction of a descriptive model by chemometric analysis. The genotypes of yellow roots were clustered by the higher concentrations of cis--carotene and lutein. Inversely, cream roots genotypes were grouped precisely due to their lower concentrations of these pigments, as samples rich in lycopene differed among the studied genotypes. The analytical approach (UV-Vis, HPLC, and chemometrics) used showed to be efficient for understanding the chemodiversity of cassava genotypes, allowing to classify them according to important features for human health and nutrition.(undefined
Regular varieties of automata and coequations

In this paper we use a duality result between equations and coequations for automata, proved by Ballester-Bolinches, Cosme-Llópez, and Rutten to characterize nonempty classes of deterministic automata that are closed under products, subautomata, homomorphic images, and sums. One characterization is as classes of automata defined by regular equations and the second one is as classes of automata satisfying sets of coequations called varieties of languages. We show how our results are related to Birkhoff's theorem for regular varieties
Fusion for free: efficient algebraic effect handlers

Algebraic effect handlers are a recently popular approach for modelling side-effects that separates the syntax and semantics of effectful operations. The shape of syntax is captured by functors, and free monads over these functors denote syntax trees. The semantics is captured by algebras, and effect handlers pass these over the syntax trees to interpret them into a semantic domain.
This approach is inherently modular: different functors can be composed to make trees with richer structure. Such trees are interpreted by applying several handlers in sequence, each removing the syntactic constructs it recognizes. Unfortunately, the construction and traversal of intermediate trees is painfully inefficient and has hindered the adoption of the handler approach.
This paper explains how a sequence of handlers can be fused into one, so that multiple tree traversals can be reduced to a single one and no intermediate trees need to be allocated. At the heart of this optimization is keeping the notion of a free monad abstract, thus enabling a change of representation that opens up the possibility of fusion. We demonstrate how the ensuing code can be inlined at compile time to produce efficient handlers.status: publishe
Notions of bidirectional computation and entangled state monads

Bidirectional transformations (bx) support principled consistency maintenance
between data sources. Each data source corresponds to one perspective on a
composite system, manifested by operations to 'get' and 'set' a view of the
whole from that particular perspective. Bx are important in a wide range of
settings, including databases, interactive applications, and model-driven
development. We show that bx are naturally modelled in terms of mutable state;
in particular, the 'set' operations are stateful functions. This leads
naturally to considering bx that exploit other computational effects too, such
as I/O, nondeterminism, and failure, all largely ignored in the bx literature
to date. We present a semantic foundation for symmetric bidirectional
transformations with effects. We build on the mature theory of monadic
encapsulation of effects in functional programming, develop the equational
theory and important combinators for effectful bx, and provide a prototype
implementation in Haskell along with several illustrative examples
High Order Finite Difference Schemes for the Heat Equation Whose
  Convergence Rates are Higher Than Their Truncation Errors

Typically when a semi-discrete approximation to a partial differential
equation (PDE) is constructed a discretization of the spatial operator with a
truncation error $\tau$ is derived. This discrete operator should be
semi-bounded for the scheme to be stable. Under these conditions, the
Lax--Ricchtmyer equivalence theorem assures that the scheme converges and that
the error will be, at most, of the order of $\| \tau \|$. In most cases, the
error is in indeed of the order of $\| \tau \|$.
  We demonstrate that for the Heat equation stable schemes can be constructed,
whose truncation errors are $\tau$, however, the actual errors are much
smaller. This gives more degrees of freedom in the design of schemes which can
make them more efficient (more accurate or compact) than standard schemes. In
some cases, the accuracy of the schemes can be further enhanced using
post-processing procedures.Comment: 9 pages, 5 figure