pinecone/core-2020-05-10-deduplication huggingface dataset:
Enriching Task Models with Usability and User Experience Evaluation Data

Evaluation results focusing on usability and user experience are often difficult to be taken into account during an iterative design process. This is due to the fact that evaluation exploits concrete artefacts (prototype or system) while design and development are based on more abstract descriptions such as task models or software models. As concrete data cannot be represented, evaluation results are just discarded. This paper addresses the problem of discrepancy between abstract view of task models and concrete data produced in evaluations by first, describing the requirements for a task modelling notation: (a) representation of data for each individual participant, (b) representation of aggregated data for one evaluation as well as (c) several evaluations and (d) the need to visualize multi-dimensional data from the evaluation as well as the interactive system gathered during runtime. Second: by showing how the requirements were integrated in a task modelling tool. Using an example from an experimental evaluation possible usages of the tool are demonstrated
Perceived impacts of artificial intelligence and responses to positive behaviour change intervention

Artificial intelligence (AI) technologies have a great potential to aid not only in promoting tourism products and services, but also in influencing responsible travel behaviour to support sustainability. The effectiveness of using AI for positive behaviour change interventions depends on consumers’ attitudes toward AI. This study found three underlying views of AI impacts: Beneficial AI, Destructive AI, and Risky AI. Based on these, three consumer segments were identified: The Laggards, The Aficionados, and The Realists. The first two segments hold opposing views: the former averaging higher in negative impacts, while the latter in positive impacts of AI. The Realists are aware of both benefits and risks of AI. These segments differ in their intention to follow recommendations from AI. It is suggested that mainstream consumers, those belonging to The Realists, are likely to respond positively to AI systems recommending responsible behaviour, signifying the positive role of AI in sustainable tourism
Proof-carrying plans

It is becoming increasingly important to verify safety and security of AI applications. While declarative languages (of the kind found in automated planners and model checkers) are traditionally used for verifying AI systems, a big challenge is to design methods that generate verified executable programs. A good example of such a “verification to implementation” cycle is given by automated planning languages like PDDL, where plans are found via a model search in a declarative language, but then interpreted or compiled into executable code in an imperative language. In this paper, we show that this method can itself be verified. We present a formal framework and a prototype Agda implementation that represent PDDL plans as executable functions that inhabit types that are given by formulae describing planning problems. By exploiting the well-known Curry-Howard correspondence, type-checking then automatically ensures that the generated program corresponds precisely to the specification of the planning problem.Postprin
IDEA: an interactive dialogue translation\ud
demo system using Furhat robots

We showcase IDEA, an Interactive DialoguE trAnslation\ud
system using Furhat robots, whose novel contributions are: (i) it is a web\ud
service-based application combining translation service, speech recognition\ud
service and speech synthesis service; (ii) it is a task-oriented hybrid\ud
machine translation system combining statistical and neural machine\ud
learning methods for domain-specific named entity (NE) recognition\ud
and translation; and (iii) it provides user-friendly interactive interface\ud
using Furhat robot with speech input, output, head movement and facial\ud
emotions. IDEA is a case-study demo which can efficiently and accurately\ud
assist customers and agents in different languages to reach an agreement\ud
in a dialogue for the hotel booking
PathGAN: visual scanpath prediction with generative adversarial networks

We introduce PathGAN, a deep neural network for visual scanpath prediction trained on adversarial examples. A visual scanpath is defined as the sequence of fixation points over an image defined by a human observer with its gaze. PathGAN is composed of two parts, the generator and the discriminator. Both parts extract features from images using off-the-shelf networks, and train recurrent layers to generate or discriminate scanpaths accordingly. In scanpath prediction, the stochastic nature of the data makes it very difficult to generate realistic predictions using supervised learning strategies, but we adopt adversarial training as a suitable alternative. Our experiments prove how PathGAN improves the state of the art of visual scanpath prediction on the iSUN and Salient360! datasets
Health care and migration : what data can tell us of the hard-to-measure impact of migrants on the European health systems

Nowadays migration is one of the key issues in the international as well as in the European political and public debate. It represents a key challenge for modern societies and, together with the adaptation of welfare, has been extensively investigated in social sciences. One of the most compelling challenges consists in the adaptation of health systems to migration’s new needs. The European Union (EU) presents among its member states (MS) highly differentiated situations in terms of healthcare provision models, contribution systems, and integration policies adopted towards foreigners. Compared to other countries with a longer migratory tradition, the differences in access and use of health systems by intra-EU migrants and migrants from third countries are still considerable within EU MS, and further diversified based on migrants’ legal status. Starting from the traditional types of healthcare systems, a more specific purpose will be to establish and measure a systematic relationship between the costs and performance of health systems, migratory care demand, and the migrants’ contribution to the European systems
Spanish named entity recognition in the biomedical domain

Named Entity Recognition in the clinical domain and in languages different from English has the difficulty of the absence of complete dictionaries, the informality of texts, the polysemy of terms, the lack of accordance in the boundaries of an entity, the scarcity of corpora and of other resources available. We present a Named Entity Recognition method for poorly resourced languages. The method was tested with Spanish radiology reports and compared with a conditional random fields system.Peer ReviewedPostprint (author's final draft
Multipurpose Urban Sensing Equipment—An EPS@ISEP 2018 Project

This paper describes the development of a Multi-purpose Urban Sensing Equipment, named Billy, designed by a multinational and multidisciplinary team enrolled in the European Project Semester (EPS) at Instituto Superior de Engenharia do Porto (ISEP). The project is set to design, develop and test an interactive billboard in compliance with the relevant EU regulation and the allocated budget. The Team benefited from the different background, multidisciplinary skills and the newly acquired skills of the members, like marketing, sustainability and design ethics, in activities both inside and outside of the University. The challenge was to design a multi-purpose urban sensing and displaying equipment to inform citizens of nearby environmental conditions. The Team decided to design a system to monitor and display the temperature, humidity, air pressure and air quality of leisure areas, featured with a proximity detection sensor for energy saving. Billy will not only monitor and display this local information, but also the air quality determined by other billboards placed in other locations, creating a distributed urban sensing network. The system has been successfully prototyped and tested using the ESPduino Wi-Fi enabled micro-controller, different sensors and displays (screen and map-based). The results show not only that the prototype functions according to derived specifications and design, but that the team members were able to learn, together and from each other, how to solve this multidisciplinary problem.info:eu-repo/semantics/publishedVersio
Vertical Farming—An EPS@ISEP 2018 Project

This paper summarises the joint efforts of a multinational group of six undergraduate students cooperating within the European Project Semester (EPS) conducted at the Instituto Superior de Engenharia do Porto (ISEP). The EPS@ISEP initiative, made available as a part of the Erasmus+ international students exchange programme, employs the principles of problem-based learning, facing students with—albeit downscaled—real-life scenarios and tasks they may encounter in their future professional practice. Participation in the project initiative outclasses most of the traditional courses through a wide spawn of its learning outcomes. Participants acquire not only hard skills necessary for an appropriate execution of the project, but also broaden their understanding of the approached problem through detailed scientific, management, marketing, sustainability, and ethics analysis—all in the atmosphere of multicultural and interdisciplinary collaboration. The team under consideration, based on personal preferences and predispositions, chose the topic of vertical farming and, in particular, to design a domestic indoor gardening solution, appropriate for space efficient incubation of plants. The paper portrays the process, from research, analysis, formulation of the idea to the design, development and testing of a minimum viable proof of concept prototype of the “Vereatable” solution.info:eu-repo/semantics/publishedVersio
Water Intellibuoy—An EPS@ISEP 2018 Project

This paper reports the collaborative learning experience of a team of five Erasmus students who participated in EPS@ISEP—the European Project Semester (EPS) at Instituto Superior de Engenharia do Porto (ISEP)—during the spring of 2018. EPS@ISEP is a project-based learning capstone programme for third and fourth year engineering, product design and business students, focussing on teamwork and multidisciplinary problem solving as well as on the development of sustainable and ethical practices. In this context, the Team developed a drifting intelligent buoy to monitor the water quality of urban water spaces. Motivated by the desire to build an intelligent buoy for urban water bodies, the Team conducted several scientific, technical, sustainability, marketing, ethical and deontological analyses. Based on the findings, it has derived the requirements, designed the structure and functional system, selected the list of components and providers and assembled a proof of concept prototype. The result is Aquality, an intelligent drifting buoy prototype, designed for private sustainable pools. Aquality monitors the quality of the pool water by measuring its temperature and turbidity, while interfacing with the user through a mobile application. Considering the EPS@ISEP learning experience, the Team valued the knowledge and skills acquired, and, particularly, the collaborative learning and working component of the project, i.e., working together towards one goal while maintaining high motivation and cohesion.info:eu-repo/semantics/publishedVersio
The Oslo Opera House – Condition Analysis and Proposal for Cleaning, Protection and Maintenance of Exterior Marble

The photo-oxidative degradation of the polymers applied in the past onto the marble slabs of the Oslo Opera House (OOH) led to the yellowing of the coatings and/or to the formation of insoluble fractions of polymer. Despite the high reduction of the b* parameter (yellowness) observed after the mechanical abrasion, this cleaning treatment cannot be recommended for further application, due to the unacceptable removal of structural material during the abrasion. Cleaning of historic masonry has progressed significantly in recent years, thanks to methods that rely on nanoscience. Therefore, in this research project instead of using organic solvents exhibiting toxicity and poor performance, advanced nanostructured fluids, such as micelles solutions (MC) and microemulsions (ME), embedded in nanogels with industrial solvents were tested aiming at the efficient removal of the hydrophobic coatings from Carrara marble slabs. These nanogels are amphiphilic-based formulations using a system of water, oil and surfactant. However, lack of reproducibility characterized the application of these advanced nanogels, due to partly removal of the unevenly distributed polymer products. Therefore a three-step cleaning methodology was adopted including: the introduction of a chelating agent capable of dissolving the hard film created by the applied coatings, thus allowing any subsequent MC and ME solutions applied in the second step, to swell the polymeric chains and to facilitate their removal, which was further completed by suitable oxidants. Optical and scanning electron microscopy coupled with EDX, along with colorimetric measurements proved that the three-step cleaning tests applied to yellowed samples can successfully remove the colour discoloration
Deep Reinforcement Learning in Strategic Board Game Environments

In this paper we propose a novel Deep Reinforcement Learning (DRL) algorithm that uses the concept of “action-dependent state features”, and exploits it to approximate the Q-values locally, employing a deep neural network with parallel Long Short Term Memory (LSTM) components, each one responsible for computing an action-related Q-value. As such, all computations occur simultaneously, and there is no need to employ “target” networks and experience replay, which are techniques regularly used in the DRL literature. Moreover, our algorithm does not require previous training experiences, but trains itself online during game play. We tested our approach in the Settlers Of Catan multi-player strategic board game. Our results confirm the effectiveness of our approach, since it outperforms several competitors, including the state-of-the-art jSettler heuristic algorithm devised for this particular domain
Self Hyper-parameter Tuning for Stream Recommendation Algorithms

E-commerce platforms explore the interaction between users and digital content – user generated streams of events – to build and maintain dynamic user preference models which are used to make meaningful recommendations. However, the accuracy of these incremental models is critically affected by the choice of hyper-parameters. So far, the incremental recommendation algorithms used to process data streams rely on human expertise for hyper-parameter tuning. In this work we apply our Self Hyper-Parameter Tuning (SPT) algorithm to incremental recommendation algorithms. SPT adapts the Melder-Mead optimisation algorithm to perform hyper-parameter tuning. First, it creates three models with random hyper-parameter values and, then, at dynamic size intervals, assesses and applies the Melder-Mead operators to update their hyper-parameters until the models converge. The main contribution of this work is the adaptation of the SPT method to incremental matrix factorisation recommendation algorithms. The proposed method was evaluated with well-known recommendation data sets. The results show that SPT systematically improves data stream recommendations.info:eu-repo/semantics/publishedVersio
Behavior of Five-Pad Tilting–Pad Journal Bearings with Different Pivot Stiffness

In this paper, two different five-shoe tilting-pad journal bearings, namely rocker-backed pivot and spherical pivot with different pivot stiffness have been characterized with different working conditions. For the spherical pivot bearing, elastic shims and displacement restriction component are introduced to recognize the role of the variable stiffness of pivot. The copper is used for the material of the shim. An analysis of the dynamic behavior of the two bearings using a thermo-elasto-hydrodynamic model is presented first. This model considers the flexibility for both pad as well as pivot and a simple thermal model only for the fluid film temperature to accurately calculate the performance of bearings. The model also accounts for pivot stiffness of pads. The predicted dynamic coefficients of the two bearings were compared with the experimentally measured ones. The results show that the pivot stiffness or the pivot flexibility plays an important role in the dynamic coefficients estimation
Dynamic Characteristics of a Non-symmetric Tilting Pad Journal Bearing

Because tilting-pad journal bearings are more stable and efficient than conventional bearings, they have been commonly applied to many rotating machinery applications. Most of the studies about steady state and dynamic characteristics of tilting-pad journal bearings are usually evaluated by means of thermo hydrodynamic models assuming nominal dimensions for the bearing. However machining errors could lead to actual bearing geometry and dimensions different from the nominal ones. In particular for tilting-pad journal bearing the asymmetry of the bearing geometry is the principal cause of unexpected behavior. In this paper a theoretical analysis on dynamic characteristics of a five-pad tilting-pad journal bearing is investigated with non-nominal geometry, that is, different thickness for each pad. The dynamic coefficients of a five-pad tilting-pad journal bearing with a nominal diameter of 100 mm, length-to-diameter ratio (L/D) of 0.7 are evaluated versus rotor rotational speed, load direction and static load. Then, the analytical results of the non-nominal bearing are compared to those of a bearing having nominal (i.e. ideal) geometry
A Lightweight Multilevel Markup Language for Connecting Software Requirements and Simulations

[Context] Simulation is a powerful tool to validate specified requirements especially for complex systems that constantly monitor and react to characteristics of their environment. The simulators for such systems are complex themselves as they simulate multiple actors with multiple interacting functions in a number of different scenarios. To validate requirements in such simulations, the requirements must be related to the simulation runs. [Problem] In practice, engineers are reluctant to state their requirements in terms of structured languages or models that would allow for a straightforward relation of requirements to simulation runs. Instead, the requirements are expressed as unstructured natural language text that is hard to assess in a set of complex simulation runs. Therefore, the feedback loop between requirements and simulation is very long or non-existent at all. [Principal idea] We aim to close the gap between requirements specifications and simulation by proposing a lightweight markup language for requirements. Our markup language provides a set of annotations on different levels that can be applied to natural language requirements. The annotations are mapped to simulation events. As a result, meaningful information from a set of simulation runs is shown directly in the requirements specification. [Contribution] Instead of forcing the engineer to write requirements in a specific way just for the purpose of relating them to a simulator, the markup language allows annotating the already specified requirements up to a level that is interesting for the engineer. We evaluate our approach by analyzing 8 original requirements of an automotive system in a set of 100 simulation runs
My Employer’s Prestige, My Prestige

Employer branding is an essential component that attracts potential candidates to companies. Social media, particularly employer rating platforms, provide many opportunities to present a company’s employer brand. Individuals use these platforms to collect information and evaluations about potential employers and companies could utilise these platforms to present themselves favourably. Based on social capital theory, this study examined the variables of support and benefit as reasons why\ud
individuals share information about their employers on employer rating platforms. The influence of demographic factors on the use of these platforms was also investigated. Data was collected from 309 respondents via an online survey, and analysed using\ud
the t-test, Spearman’s correlation, and analysis of variance (ANOVA) with the least significant difference (LSD) method. Only descriptive statistics, distribution of responses, and statistically significant results are presented
High-Performance Modelling and Simulation for Big Data Applications

This open access book is the final compendium of case studies emanated from the 4-year COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications” (cHiPSet). Funded by the European Commission from 2015, cHiPSet has created a sustainable reference network linking applied research in High Performance Computing (HPC) and Modelling & Simulation to tangibly address Big Data challenges.\ud
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cHiPSet has enabled research partnerships for dozens of academics and industry practitioners located in 34 COST countries, as well as in Australia, Belarus, Brazil, China, Russia, and the USA. As a cooperation framework, cHiPSet has reached out to new audiences such as ICT professionals, commercial software developers, and the general public. At a time when Big Data has become a common household term, cHiPSet has strived to become a knowledge hub where data-driven HPC meets Modelling & Simulation.\ud
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cHiPSet has also endeavoured to use and exploit results through Open Science practices, i.e., open access publication, open access to data repositories, and open-source software development. A testament to this philosophy, this compendium is set to become a required reference for the fast-changing fields of HPC, Big Data, and Modelling & Simulation
Forecasting Cryptocurrency Value by Sentiment Analysis: An HPC-Oriented Survey of the State-of-the-Art in the Cloud Era

This chapter surveys the state-of-the-art in forecasting cryptocurrency value by Sentiment Analysis. Key compounding perspectives of current challenges are addressed, including blockchains, data collection, annotation, and filtering, and sentiment analysis metrics using data streams and cloud platforms. We have explored the domain based on this problem-solving metric perspective, i.e., as technical analysis, forecasting, and estimation using a standardized ledger-based technology. The envisioned tools based on forecasting are then suggested, i.e., ranking Initial Coin Offering (ICO) values for incoming cryptocurrencies, trading strategies employing the new Sentiment Analysis metrics, and risk aversion in cryptocurrencies trading through a multi-objective portfolio selection. Our perspective is rationalized on the perspective on elastic demand of computational resources for cloud infrastructures
Big Data Processing, Analysis and Applications in Mobile\ud
Cellular Networks

When coupled with spatio-temporal context, location-based\ud
data collected in mobile cellular networks provide insights into patterns of human activity, interactions, and mobility. Whilst uncovered patterns have immense potential for improving services of telecom providers as\ud
well as for external applications related to social wellbeing, its inherent massive volume make such ‘Big Data’ sets complex to process. A significant number of studies involving such mobile phone data have been\ud
presented, but there still remain numerous open challenges to reach technology readiness. They include efficient access in privacy-preserving manner,\ud
high performance computing environments, scalable data analytics, innovative data fusion with other sources–all finally linked into the applications ready for operational mode. In this chapter, we provide a broad\ud
overview of the entire workflow from raw data access to the final applications and point out the critical challenges in each step that need to be addressed to unlock the value of data generated by mobile cellular\ud
networks