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While requirements elicitation remains a key success factor for software projects, there is little empirical research on the elicitation methods. We focus on fit-gap analysis, a requirements elicitation technique that is common in practice, but hardly studied in requirements engineering research. Fit-gap analysis is a method for matching software products with the needs of customers, with the aim to identify needs that are supported as fits, and needs that are not as gaps. Through a grounded theory investigation of recording transcripts from fit-gap analysis sessions, we provide empirical knowledge about this elicitation technique. We determine and discuss the different categories of the topics contained in a fit-gap analysis. Additionally, as a first step toward assisting analysts in processing and exploring their analyses, we build and share a set of keywords and phrases that can help automatically identify those categories within the transcripts. We conduct an experiment for early validation, involving both students and practitioners, that determines the relative perceived importance of the identified fit-gap categories. Finally, we derive implications for research in the field that include our perspective on how tooling can assist analysts in fit-gap analysis.

The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears. Funding Agencies|National Key R&D Program of China [2018YFB1801101]; National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61960206006, 61901109]; Frontiers Science Center for Mobile Information Communication and Security; High Level Innovation and Entrepreneurial Research Team Program in Jiangsu; High Level Innovation and Entrepreneurial Talent Introduction Program in Jiangsu; National Postdoctoral Program for Innovative Talents [BX20180062]; Research Fund of National Mobile Communications Research Laboratory, Southeast University [2020B01]; Fundamental Research Funds for the Central UniversitiesFundamental Research Funds for the Central Universities [2242020R30001]

In everyday life, we often observe and learn from interactions between other individuals—so-called third-party encounters. As robots are poised to become an increasingly familiar presence in our daily lives, third-party encounters between other people and robots might offer a valuable approach to influence people’s behaviors and attitudes towards robots. Here, we conducted an online experiment where participants (n = 48) watched videos of human—robot dyads interacting in a cooperative or competitive manner. Following this observation, we measured participants’ behavior and attitudes towards the human and robotic agents. First, participants played a game with the agents to measure whether their behavior was affected by their observed encounters. Second, participants’ attitudes toward the agents were measured before and after the game. We found that the third-party encounters influenced behavior during the game but not attitudes towards the observed agents. Participants showed more effort towards robots than towards humans, especially when the human and robot agents were framed as competitive in the observation phase. Our study suggests that people’s behaviors towards robots can be shaped by the mere observation of third-party encounters between robots and other people.

In 2016, the Flemish Government set the transition to a circular economy (CE) as one of its priorities. Adopting the paradigm of circularity in, amongst others, the building sector has the potential to create more sustainable buildings, where the use of new resources and the production of waste is minimised by closing material flows. However, circular building implies radical changes at different levels: from organisational changes within the sector to new design methods. To support and guide stakeholders in this transition to a CE in the Flemish building sector, different organisations developed design support tools and methods. However, due to the lack of overview of the available tools and their purpose, it is unclear for the stakeholders which tool could fit best for their context. Therefore, this research analyses the potential of the identified tools for circular building practice. Further, the possible impact potential of the tools in a circular design process is assessed by reviewing the needs of circular building stakeholders and by comparing that need with the tools. The tools and needs are classified per building design aspect and per design stage. Subsequently, conclusions can be drawn on opportunities to develop new design support tools for circular buildings and on opportunities to improve existing tools.

Publisher Copyright: © 2021 The Authors. Published by Elsevier B.V. Amik@ is an innovative project that connects people with and without intellectual disability (ID), in cherished and safe environments, through a digital platform that strongly follows digital inclusion principles. The Information, Communication and Technology (ICT) solution aims to promote interpersonal relationships and social participation of people with ID, in which life and learning experiences, provided by face-to-face social interaction, brings benefits to participants. A literature review was made on needs of individuals with ID, common attitudes towards people with ID, patterns of use of digital media by people with ID and guidelines that must be followed when developing solutions for improving accessibility of these groups to the digital world. Considering the evidence found, the technical and functional requirements of the solution were defined. Ongoing and future work includes coding the app, piloting and giving massive access to target groups. publishersversion published

The introduction of more renewable energy sources into the energy system increases the variability and weather dependence of electricity generation. Power system simulations are used to assess the adequacy and reliability of the electricity grid over decades, but often become computational intractable for such long simulation periods with high technical detail. To alleviate this computational burden, we investigate the use of outlier detection algorithms to find periods of extreme renewable energy generation which enables detailed modelling of the performance of power systems under these circumstances. Specifically, we apply the Maximum Divergent Intervals (MDI) algorithm to power generation time series that have been derived from ERA5 historical climate reanalysis covering the period from 1950 through 2019. By applying the MDI algorithm on these time series, we identified intervals of extreme low and high energy production. To determine the outlierness of an interval different divergence measures can be used. Where the cross-entropy measure results in shorter and strongly peaking outliers, the unbiased Kullback-Leibler divergence tends to detect longer and more persistent intervals. These intervals are regarded as potential risks for the electricity grid by domain experts, showcasing the capability of the MDI algorithm to detect critical events in these time series. For the historical period analysed, we found no trend in outlier intensity, or shift and lengthening of the outliers that could be attributed to climate change. By applying MDI on climate model output, power system modellers can investigate the adequacy and possible changes of risk for the current and future electricity grid under a wider range of scenarios.

Streaming services offer access to huge amounts of movie and video collections, resulting in the need for intuitive interaction designs. Yet, most current interfaces are focused on targeted search, neglecting support for interactive data exploration and prioritizing speed over experience. We present the MovieWall, a new interface that complements such designs by enabling users to randomly browse large movie collections. A pilot study proved the feasibility of our approach. We confirmed this observation with a detailed evaluation of an improved design, which received overwhelmingly positive subjective feedback; 80% of the subjects enjoyed using the application and even more stated that they would use it again. The study also gave insight into concrete characteristics of the implementation, such as the benefit of a clustered visualization.

This work was supported by the National Funds through Fundacao para a Ciencia e a Tecnologia (FCT) under Grant UIDB/50021/2020 and Grant UIDB/00066/2020. This paper introduces a new topology for a DC-DC converter with bipolar output and high voltage gain. The topology was designed with the aim to use only one active power switch. Besides the bipolar multiport output and high voltage gain this converter has another important feature, namely, it has a continuous input current. Due to the self-balancing bipolar outputs, the proposed topology is suitable for bipolar DC microgrids. Indeed, the topology balancing capability can achieve the two symmetrical voltage poles of bipolar DC microgrids. Furthermore, it is possible to create a midpoint in the output of the converter that can be directly connected to the ground of the DC power supply, avoiding common-mode leakage currents in critical applications such as transformerless grid-connect PV systems. The operating principle of the proposed topology will be supported by mathematical analysis. To validate and verify the characteristics of the presented topology, several experimental results are shown. publishersversion published

This chapter delves into the theme of control and censorship from the viewpoint of people involved in social movements. Harry Halpin, a cybersecurity and social activist, discusses in stark relief the challenges activists face in repressive regimes: in this case Syria. His chapter focuses on the efforts of Bassel Safadi, an activist involved in Syria’s revolution, to use mobile technologies and the Internet to communicate between activists and the population in the early days of the Syrian revolution despite the control measures imposed by the Syrian regime. Halpin describes in intricate and intimate detail the attempts of Safadi to establish free and safe means of communication using existing mobile technologies, such as free software applications. When the limitations of commercial mobile technologies become apparent, Safadi and Halpin discuss what alternative, independent mobile networks might and need look like. Safadi’s activism eventually led to his death, but the chapter wants to keep his dreams and work alive. It tells a story that is at once a guide and an inspiration to all those interested in creating free communication infrastructures, which enable users to overcome state controls and censorship in the quest for access to knowledge.

To ensure the sustainability of pharmacy practice and provide health for all, pharmacy as a profession must embrace the digital transformation that has been changing healthcare at a rapid pace. The International Pharmaceutical Federation (FIP) has conducted a global study on digital health in pharmacy education to describe the readiness, adaptability, and responsiveness of pharmacy education programmes to train the current and future pharmaceutical workforce on digital health and to identify the knowledge and skill gaps of the existing pharmaceutical workforce with regard to digital health. An online survey was distributed to collect feedback from academics, pharmacy schools, pharmacists, and pharmacy students. The findings showed that a large proportion of pharmacy schools do not offer any digital health education, and the skillsets and knowledge of how to apply digital health technologies to solve existing clinical problems and improve care have been identified as a gap. The future of pharmacy and pharmaceutical sciences is digital and exciting. A digitally enabled and agile pharmaceutical workforce will capitalise on the benefits of digital health to serve the higher purpose of providing good health and wellbeing for all, leaving no one behind. Therefore, pharmacy and pharmaceutical sciences education should act now., peer-reviewed

This paper proposes a classification of three aspects of argument strength based on philosophical insights, in particular Aristotle’s distinction between logic, dialectic and rhetoric. It is then argued that when developing or evaluating gradual accounts of argument strength it is essential to be explicit about which aspect of argument strength is modelled and about the adopted interpretation of the arguments and their relations in abstract or bipolar argumentation frameworks. The underlying aim is to encourage a principled development and evaluation of (principles for) gradual argumentation semantics.

Our research is developing flexible strategies for forming and routing future platoons of automated urban logistics vehicles. We propose the notion of compensational platooning using automated negotiation between agents representing vehicles. After the vehicles reach the end of a common route, an agent can propose part of its route along with a monetary value to platoon partners for further together-travel. If negotiation is successful, a new platoon is formed and follows the proposed route. If the compensation is too small or the route proposed oversteps the agent's limitations, the offer is rejected and the vehicles continue their travel separately. A contribution of this paper is a negotiation strategy that proposes compensation based on beliefs of what the opponent's payment threshold would be. In doing so, the bid with the highest acceptance likelihood is calculated, keeping negotiations short and effective. Our model is tested on a synthetic network and a real urban example. We show that by using negotiation, vehicles can identify mutually beneficial new routes that a centralised/distributed approach would not find, with utility improvements of up to 8%.

Incidents like the crash of Lion Air Flight 610 in 2018 challenge the design of reliable and secure cyber-physical systems that operate in the real-world and have to cope with unpredictable external phenomena and error-prone technology. We argue that their design needs to guarantee minimal machine consciousness, which expresses that these systems must operate with full awareness of (the state of) their components and the environment. The concept emerged from our recent effort to develop a computational model for conscious behavior in robots, based on the theory of automata. Making systems ‘minimal machine conscious’ leads to more trustworthy systems, as it strengthens their behavioral flexibility in varying environments and their resilience to operation and cooperation failures of their components and as a whole. The notion of minimal machine consciousness has the potential to become one of the defining attributes of Industry 4.0.

Publisher Copyright: © 2013 IEEE. As technology and artificial intelligence conquer a place under the spotlight in the automotive world, driver drowsiness monitoring systems have sparked much interest as a way to increase safety and avoid sleepiness-related accidents. Such technologies, however, stumble upon the observation that each driver presents a distinct set of behavioral and physiological manifestations of drowsiness, thus rendering its objective assessment a non-trivial process. The AUTOMOTIVE project studied the application of signal processing and machine learning techniques for driver-specific drowsiness detection in smart vehicles, enabled by immersive driving simulators. More broadly, comprehensive research on biometrics using the electrocardiogram (ECG) and face enables the continuous learning of subject-specific models of drowsiness for more efficient monitoring. This paper aims to offer a holistic but comprehensive view of the research and development work conducted for the AUTOMOTIVE project across the various addressed topics and how it ultimately brings us closer to the target of improved driver drowsiness monitoring. publishersversion published

Publisher Copyright: © 2021 The Authors. Published by Elsevier B.V. Copyright: Copyright 2021 Elsevier B.V., All rights reserved. Telehealth has been referred has an innovative solution for COPD patient treatment, minimizing the costs related with medical assistance while improving the patient care support and health outcomes. We have designed an innovative telehealth program of pulmonary rehabilitation (PR) that aims to increase long term adherence of patient to physical activity (PA), self-care management of health condition and facilitation of social participation following an approach of community-based intervention, taking account the patient preferences. Therefore, our telehealth program combine education sessions, exercise training and follow up of health outcomes using a mobile application and a wrist oximeter, through bluetooth technology, for facilitating outdoor exercise and social participation of people living with COPD. publishersversion published

The well-known Disjoint Paths problem is to decide if a graph contains k pairwise disjoint paths, each connecting a different terminal pair from a set of k distinct pairs. We determine, with an exception of two cases, the complexity of the Disjoint Paths problem for H-free graphs. If k is fixed, we obtain the k -Disjoint Paths problem, which is known to be polynomial-time solvable on the class of all graphs for every k≥ 1. The latter does no longer hold if we need to connect vertices from terminal sets instead of terminal pairs. We completely classify the complexity of k -Disjoint Connected Subgraphs for H-free graphs, and give the same almost-complete classification for Disjoint Connected Subgraphs for H-free graphs as for Disjoint Paths.

Bravo J.M. (2021) Forecasting Longevity for Financial Applications: A First Experiment with Deep Learning Methods. In: Kamp M. et al. (eds) Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2021. Communications in Computer and Information Science, vol 1525. Springer, Cham. https://doi.org/10.1007/978-3-030-93733-1_17 Forecasting longevity is essential in multiple research and policy areas, including the pricing of life insurance contracts, the valuation of capital market solutions for longevity risk management, and pension policy. This paper empirically investigates the predictive performance of Recurrent Neural Networks (RNN) with Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) architectures in jointly modeling and multivariate time series forecasting of age-specific mortality rates at all ages. We fine-tune the three hidden layers GRU and LSTM model’s hyperparameters for time series forecasting and compare the model’s forecasting accuracy with that produced by traditional Generalised Age-Period-Cohort (GAPC) stochastic mortality models. The empirical results suggest that the two RNN architectures generally outperform the GAPC models investigated in both the male and female populations, but the results are sensitive to the accuracy criteria. The empirical results also show that the RNN-GRU network slightly outperforms the RNN with an LSTM architecture and can produce mortality schedules that capture relatively well the dynamics of mortality rates across age and time. Further investigations considering other RNN architectures, calibration procedures, and sample datasets are necessary to confirm the superiority of RNN in forecasting longevity. authorsversion published

Project E!11439 Ears are a particularly difficult region of the human face to model, not only due to the non-rigid deformations existing between shapes but also to the challenges in processing the retrieved data. The first step towards obtaining a good model is to have complete scans in correspondence, but these usually present a higher amount of occlusions, noise and outliers when compared to most face regions, thus requiring a specific procedure. Therefore, we propose a complete pipeline taking as input unordered 3D point clouds with the aforementioned problems, and producing as output a dataset in correspondence, with completion of the missing data. We provide a comparison of several state-of-the-art registration and shape completion methods, concluding on the best choice for each of the steps. publishersversion published

Unmanned aerial vehicle (UAV)-assisted device-to-device (D2D) communications can be deployed flexibly thanks to UAVs' agility. By exploiting the direct D2D interaction supported by UAVs, both the user experience and network performance can be substantially enhanced at public events. However, the continuous moving of D2D users, limited energy and flying time of UAVs are impediments to their applications in real-time. To tackle this issue, we propose a novel model based on deep reinforcement learning in order to find the optimal solution for the energy-harvesting time scheduling in UAV-assisted D2D communications. To make the system model more realistic, we assume that the UAV flies around a central point, the D2D users move continuously with random walk model and the channel state information encountered during each time slot is randomly time-variant. Our numerical results demonstrate that the proposed schemes outperform the existing solutions. The associated energy efficiency game can be solved in less than one millisecond by an off-the-shelf processor using trained neural networks. Hence our deep reinforcement learning techniques are capable of solving real-time resource allocation problems in UAV-assisted wireless networks.

Robotic process automation (RPA) is a novel technology that automates tasks by interacting with other software through their respective user interfaces. The technology has received substantial business attention because of its potential for rapid automation of process-driven tasks that would otherwise require tedious manual labor. This article explores the dichotomy between the practical reality of symbolic RPA, which requires handcrafting robots using process models and rulesets, and the promise of intelligent RPA, which relies on artificial intelligence technology to implement intelligent robots. Our research is based on a scholarly literature review as well as an interview study to derive and discuss challenges for this transition. We found that issues such as the lack of training data, human bias in data, compliance issues with transfer learning, poor explainability of robot decisions, and job-security-induced fear of AI robots all need to be addressed to enable the transition from symbolic to intelligent RPA.

We consider the classical problems (Edge) Steiner Tree and Vertex Steiner Tree after restricting the input to some class of graphs characterized by a small set of forbidden induced subgraphs. We show a dichotomy for the former problem restricted to \((H_1,H_2)\)-free graphs and a dichotomy for the latter problem restricted to H-free graphs. We find that there exists an infinite family of graphs H such that Vertex Steiner Tree is polynomial-time solvable for H-free graphs, whereas there exist only two graphs H for which this holds for Edge Steiner Tree. We also find that Edge Steiner Tree is polynomial-time solvable for \((H_1,H_2)\)-free graphs if and only if the treewidth of the class of \((H_1,H_2)\)-free graphs is bounded (subject to \(\mathsf{P}\ne \mathsf{NP}\)). To obtain the latter result, we determine all pairs \((H_1,H_2)\) for which the class of \((H_1,H_2)\)-free graphs has bounded treewidth.

For several medical treatments, it is possible to observe transcriptional variations in gene expressions between responders and non-responders. Modelling the correlation between such variations and the patient’s response to drugs as a system of Ordinary Differential Equations could be invaluable to improve the efficacy of treatments and would represent an important step towards personalized medicine. Two main obstacles lie on this path: (i) the number of genes is too large to straightforwardly analyze their interactions; (ii) defining the correct parameters for the mathematical models of gene interaction is a complex optimization problem, even when a limited number of genes is involved. In this paper, we propose a novel approach to creating mathematical models able to explain patients’ response to treatment from transcriptional variations. The approach is based on: (i) a feature selection algorithm, set to identify a minimal set of gene expressions that are highly correlated with treatment outcome, (ii) a state-of-the-art evolutionary optimizer, Covariance Matrix Adaptation Evolution Strategy, applied to finding the parameters of the mathematical model characterizing the relationship between gene expressions and patient responsiveness. The proposed methodology is tested on real-world data describing responsiveness of asthma patients to Omalizumab, a humanized monoclonal antibody that binds to immunoglobulin E. In this case study, the presented approach is shown able to identify 5 genes (out of 28,402) that are transcriptionally relevant to predict treatment outcomes, and to deliver a compact mathematical model that is able to explain the interaction between the different genes involved.

Funding Information: Mara Guerreiro thanks the VASelfCare funders (01/2018 – 03/2020; LISBOA -01-0145-FEDER -024250, 02/SAICT/2016; https://vaselfcare.rd.ciencias.ulisboa.pt) and the project team. Vânia Brissos and Mara Guerreiro’s enrolment in the StartUp Research program was supported by Câmara Municipal de Oeiras grants. Funding Information: Mara Guerreiro thanks the VASelfCare funders (01/2018 - 03/2020; LISBOA-01-0145-FEDER-024250, 02/SAICT/2016; https://vaselfcare.rd.ciencias.ulisboa.pt) and the project team. V?nia Brissos and Mara Guerreiro's enrolment in the StartUp Research program was supported by C?mara Municipal de Oeiras grants. Publisher Copyright: © 2021 The Authors. Published by Elsevier B.V. Diabetes represents a tremendous burden at a global scale. Changing and sustaining desirable health behaviours, such as engaging in a healthy diet, physical activity and taking prescribed medication, is key to achieve better health outcomes in people with type 2 diabetes (T2D). This paper describes the first stage of a plan to translate an innovative a mobile application to facilitate self-care of older adults with T2D from bench to daily life. The project plan emerged in the context of the StartUp Research program for scientists, held by ITQB NOVA and NOVA SBE. PMBOK was selected as the project management framework; we briefly explain its knowledge areas - scope, time, quality, human resources, stakeholders' management, communications, costs, procurement and risks- in relation to VASelfCare T2D. publishersversion published

For a graph class C, the C -Edge-Deletion problem asks for a given graph G to delete the minimum number of edges from G in order to obtain a graph in C. We study the C -Edge-Deletion problem for C the class of interval graphs and other related graph classes. It follows from Courcelle’s Theorem that these problems are fixed parameter tractable when parameterized by treewidth. In this paper, we present concrete FPT algorithms for these problems. By giving explicit algorithms and analyzing these in detail, we obtain algorithms that are significantly faster than the algorithms obtained by using Courcelle’s theorem.

We give an algorithmic and lower bound framework that facilitates the construction of subexponential algorithms and matching conditional complexity bounds. It can be applied to intersection graphs of similarly-sized fat objects, yielding algorithms with running time 2(O(n1-1/d)) for any fixed dimension d >= 2 for many well-known graph problems, including INDEPENDENT SET, r-DOMINATING SET for constant r, and STEINER TREE. For most problems, we get improved running times compared to prior work; in some cases, we give the first known subexponential algorithm in geometric intersection graphs. Additionally, most of the obtained algorithms are representation-agnostic, i.e., they work on the graph itself and do not require the geometric representation. Our algorithmic framework is based on a weighted separator theorem and various treewidth techniques. The lower bound framework is based on a constructive embedding of graphs into d-dimensional grids, and it allows us to derive matching 2(Omega(n1-1/d)) lower bounds under the exponential time hypothesis even in the much more restricted class of d-dimensional induced grid graphs.

Carrasquinha, E., Santinha, J., Mongolin, A., Lisitskiya, M., Ribeiro, J., Cardoso, F., Matos, C., Vanneschi, L., & Papanikolaou, N. (2020). Regularization techniques in radiomics: A case study on the prediction of pCR in breast tumours and the axilla. In P. Cazzaniga, D. Besozzi, I. Merelli, & L. Manzoni (Eds.), Computational Intelligence Methods for Bioinformatics and Biostatistics: 16th International Meeting, CIBB 2019, Revised Selected Papers (pp. 271-281). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12313 LNBI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-63061-4_24 Clinicians have shown an increasing interest in quantitative imaging for precision medicine. Imaging features can extract distinct phenotypic differences of tumours, potentially they can be used as a non-invasive prognostic tool and contribute for a better prediction of pathological Complete Response (pCR). However, the high-dimensional nature of the data brings many constraints, for which several approaches have been considered, with regularization techniques in the cutting-edge research front. In this work, classic lasso, ridge and the recently proposed priority-lasso are applied to high-dimensional imaging data, regarding a binary outcome. A breast cancer dataset, with radiomics, clinical and pathological information as features, was used. The application of sparsity techniques to the dataset enabled the selection of relevant features extracted in MRI of breast cancer patients, in order to identify the accuracy of those features and predict the pCR in the breast and the axilla. authorsversion published

Buildings’ obsolescence and inefficient use can be prevented by designing general and adaptable plan layouts. General plan layouts accommodate different needs without being altered, while adaptable plan layouts can be easily altered thanks to, for example, demountable walls. A design-support method to quantify the generality and adaptability of plan layouts is the Spatial Assessment of Generality and Adaptability (SAGA) method Herthogs (Doctoral thesis. Vrije Universiteit Brussel, Brussels, 2016 [1]). To the knowledge of the authors, SAGA has not been used yet in a real design assignment. To understand how the method can support architectural decisions in a real design process, we applied an adapted version of SAGA in the transformation of a Brussels row house. In this paper, we describe the method’s relevance in validating the architect’s intuition by comparing the results of the method on the initial and future states of the house. Secondly, we evaluate the method’s added-value to guide the optimization of the plan layout, by comparing the future state with three alternative plan layouts. In this case, the architect considers the quantitative assessment as useful to evaluate and improve his architectural design, but the results are hardly interpretable without prior expertise. In conclusion, the method has potential in validating design choices fostering general and adaptable plan layouts. Depending on the expertise of the assessor, it can also support the optimization of the plan. In the future, implementing automatic checks or suggestions could bypass this reliance on expertise.

Bravo, J. M., & Ayuso, M. (2020). Previsões de mortalidade e de esperança de vida mediante combinação bayesiana de modelos: Uma aplicação à população portuguesa. [Mortality and life expectancy forecasts using Bayesian model combinations: An application to the Portuguese population]. RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao, 2020(E40), 128-144. https://doi.org/10.17013/risti.40.128-145 In this paper we forecast age-specific mortality rates and life expectancy measures using an adaptive Bayesian Model Ensemble of heterogeneous models. The procedure involves both the selection of the model confidence set and the determination of optimal weights. Model-averaged Bayesian credible prediction intervals are derived accounting for both the uncertainty arising from model error and parameter uncertainty. The models are calibrated to Portuguese population data in the period 1960-2018 and age range 60-125 and forecasted to 2050. The results anticipate a continuous increase in both period and cohort life expectancy at adult ages and signal significant implicit taxes and subsidies between generations, which distort consumption, saving and investment decisions and challenge intergenerational fairness. publishersversion published

This study is motivated by the need to restore part of the Nepal water distribution network that was destroyed by the Gorkha and Dolakha earthquakes in April and May 2015. The problem consists of two hierarchical subproblems: locating water taps to ensure a good coverage of the population, and connecting these water taps to water sources by means of a pipe distribution network. Both subproblems are subject to a variety of accessibility and technical constraints that make the problem unique and highly complex. Namely, because Nepal is highly mountainous, elevations must be taken into account in the distance calculations, and the distribution network is gravity-fed, meaning that pumps are not used. The problem is solved by means of a two-phase matheuristic: the first subproblem is a constrained location–allocation problem which is solved exactly by integer linear programming, while the second subproblem is tackled by means of a cluster-first, tree-second heuristic. Several variants of the heuristic are developed and compared. The network design problem is of very large scale, being solved on a graph with as many as 29,900 vertices and 75,200 arcs. Tests are performed on real-world data, obtained by satellite imagery, from the Suspa Kshemawati and Lapilang communities in the Dolakha district. Extensive computational results confirm the effectiveness of the proposed methodology and enable an identification of the best parameter settings and algorithmic tactical choices.

Bakurov, I., Castelli, M., Fontanella, F., Scotto Di Freca, A., & Vanneschi, L. (2022). A novel binary classification approach based on geometric semantic genetic programming. Swarm and Evolutionary Computation, 69(March), 1-12. [101028]. https://doi.org/10.1016/j.swevo.2021.101028 ------Funding Information: This work was supported by national funds through the FCT (Fundação para a Ciência e a Tecnologia) by the projects GADgET (DSAIPA/DS/0022/2018), BINDER (PTDC/CCIINF/29168/2017), and AICE (DSAIPA/DS/0113/2019). Mauro Castelli acknowledges the financial support from the Slovenian Research Agency (research core funding no. P5-0410). Geometric semantic genetic programming (GSGP) is a recent variant of genetic programming. GSGP allows the landscape of any supervised regression problem to be transformed into a unimodal error surface, thus it has been applied only to this kind of problem. In a previous paper, we presented a novel variant of GSGP for binary classification problems that, taking inspiration from perceptron neural networks, uses a logistic-based activation function to constrain the output value of a GSGP tree in the interval [0,1]. This simple approach allowed us to use the standard RMSE function to evaluate the train classification error on binary classification problems and, consequently, to preserve the intrinsic properties of the geometric semantic operators. The results encouraged us to investigate this approach further. To this aim, in this paper, we present the results from 18 test problems, which we compared with those achieved by eleven well-known and widely classification schemes. We also studied how the parameter settings affect the classification performance and the use of the -score function to deal with imbalanced data. The results confirmed the effectiveness of the proposed approach. authorsversion published

In this study, we evaluated one of the modern automated steganalysis tools, Stegdetect, to study its false negative rates when analysing a bulk of images. In so doing, we used JPHide method to embed a randomly generated messages into 2000 JPEG images. The aim of this study is to help digital forensics analysts during their investigations by means of providing an idea of the false negative rates of Stegdetect. This study found that (1) the false negative rates depended largely on the tool&rsquo, s sensitivity values, (2) the tool had a high false negative rate between the sensitivity values from 0.1 to 3.4 and (3) the best sensitivity value for detection of JPHide method was 6.2. It is therefore recommended that when analysing a huge bulk of images forensic analysts need to take into consideration sensitivity values to reduce the false negative rates of Stegdetect.

A locally threshold testable language L is a language with the property that for some non negative integers k and l and for some word u from L, a word v belongs to L if and only if (1) the prefixes [suffixes] of length k-1 of words u and v coincide, (2) the numbers of occurrences of every factor of length k in both words u and v are either the same or greater than l-1. A deterministic finite automaton is called locally threshold testable if the automaton accepts a locally threshold testable language for some l and k. New necessary and sufficient conditions for a deterministic finite automaton to be locally threshold testable are found. On the basis of these conditions, we modify the algorithm to verify local threshold testability of the automaton and to reduce the time complexity of the algorithm. The algorithm is implemented as a part of the $C/C ^{++}$ package TESTAS. \texttt{http://www.cs.biu.ac.il/$\sim$trakht/Testas.html}., 11 pages, 4 figures

Short Message Service (SMS) constitutes one of the most used communication medium. It has become an integral part of people's lives and like other communication media, {SMS} texts have been used for propagating spam messages. Despite the fact that a broad range of spam techniques have been proposed to reduce the frequency of such incidents, many difficulties are still present due to text ambiguity; there, the same words can be used in seemingly similar texts which makes it more difficult to identify spam messages. In this paper, we propose an approach for identifying and classifying spam SMS based on the Syntactical features and patterns of the message. The proposed approach consists of three main parts, namely Data Pre-processing, Features Extraction, and Classification. Experimental results show that the proposed approach achieves a good level of accuracy. In addition, to show the effectiveness of handling class imbalance on the classification performance, two additional experiments were conducted using the implementation of the SMOTE algorithm. There, the results depicted that handling class imbalance help in improving identification and classification accuracy. Furthermore, based on the above, a risk model has been proposed that addresses the risk probability and the impact of spam SMS.

Though sharing a similar practice form, the emission trading scheme is distinguished from traditional financial markets: firms coordinate three abatement options at the micro level, including allowance trading, output adjustment, and low-carbon technology adoption. Then, at the macro level, this leads to dynamic interactions among allowance market, output market, and low-carbon technology diffusion. This is the fundamental characteristic of the emission trading scheme, and modeling the dynamics behind is a major difficulty for relevant studies, especially when following complexities are considered: (1) different planning horizons of the three abatement options, (2) heterogeneity among sectors and firms, and (3) details of firms' production and optional low-carbon technologies. Aiming at this difficulty, we establish an agent-based model for the emission trading scheme, and within a novel multi-level time frame, the fundamental characteristic is reflected and the complexities are considered. Firms' production and low-carbon technologies are discretely modeled at a process level from a bottom-up perspective, and based on European data, our model is calibrated to cover 5 industrial sectors, 11 emission-intensive products, 25 production processes, and 52 low-carbon technologies. With this model, the emergence properties and uncertainty of the system are captured, and the non-linear impact of the abatement target is reflected and discussed. We find that, after a certain level, higher target leads to lower allowance price uncertainty but stronger output impact, which is a trade-off for setting the abatement target.

This paper describes a novel Deep Learning architecture to assist with steering a powered wheelchair. A rule-based approach is utilized to train and test a Long Short Term Memory (LSTM) Neural Network. It is the first time a LSTM has been used for steering a powered wheelchair. A disabled driver uses a joystick to provide desired speed and direction, and the Neural Network provides a safe direction for the wheelchair. Results from the Neural Network are mixed with desired speed and direction to avoid obstacles. Inputs originate from a joystick and from three ultrasonic transducers attached to the chair. The resultant course is a blend of desired directions and directions that steer the chair to avoid collision. A rule-based approach is used to create a training and test set for the Neural Network system and applies deep learning to predict a safe route for a wheelchair. The user can over-ride the new system if necessary.

The purpose of this paper is to develop a data-driven matheuristic for the Commodity-Split Multi-Compartment Capacitated Arc Routing Problem (CSMC-CARP). This problem arises in curbside waste collection, where there are different recyclable waste types called fractions. The CSMC-CARP is defined on an undirected graph with a limited heterogeneous fleet of multi-compartment vehicle types based at a depot, where each compartment’s capacity can vary depending on the waste fraction assigned to it and on the compression factor of that fraction in that vehicle type. The aim is to determine a set of least-cost routes starting and ending at the depot, such that the demand of each edge for each waste fraction is collected exactly once by one vehicle, without violating the capacity of any compartment. The CSMC-CARP consists of three decision levels: selecting the number of vehicles of each type, assigning waste fractions to the compartments of each selected vehicle, and routing the vehicles. Our three-phase algorithm decomposes the problem into incomplete solution representations and heuristically solves one or more decision levels at a time. The first phase selects a subset of attractive compartment assignments from all assignments of all vehicle types. The second phase solves the CSMC-CARP with an unlimited fleet of the selected assignments. This is done by our C-split tour splitting algorithm, which can simultaneously split a giant tour of required edges into feasible routes while making decisions on the fractions that are collected by each route. The third phase selects the set of best routes servicing all fractions of all required edges without exceeding the number of vehicles available of each type. The algorithm is applied to real-life instances arising from recyclable waste collection operations in Denmark, with graph sizes up to 6,149 nodes and 3,797 required edges, the waste sorted in three to six fractions, and four to six vehicle types with one to four compartments. Computational results show that the generated solutions favor combining different fractions together in vehicles with higher numbers of compartments, and that the algorithm adapts well to the characteristics of the data, in terms of the graph, vehicle types, degree of sorting, and to skewness in demand among waste fractions.

Collecting complex information on the status of machinery is the enabler for advanced maintenance activities, and one of the main players in this process is the sensor. This paper describes modern maintenance strategies that lead to Condition-Based Maintenance. This paper discusses the sensors that can be used to support maintenance, as of different categories, spanning from common off-the-shelf sensors, to specialized sensors monitoring very specific characteristics, and to virtual sensors. This paper also presents four different real-world examples of project pilots that make use of the described sensors and draws a comparison between them. In particular, each scenario has unique characteristics requiring different families of sensors, but on the other hand provides similar characteristics on other aspects., This work was partially supported by National Funds through FCT/MEC (Portuguese Foundation for Science and Technology), Slovenian Research Agency (research core funding No. P2-0098), Finnish Funding Agency for Innovation Tekes, and co-financed by ERDF (European Regional Development Fund) under the PT2020 Partnership, within the CISTER Research Unit (PCEC/04234); also by the EU ECSELJU under the H2020 Framework Programme, within project ECSEL/0004/2014, JU grant no. 662189 (MANTIS); also by EU ECSEL JU under the H2020 Framework Programme, JU grant no. 737459 (Productive4.0 project).

Castelli, M., Dondi, R., & Hosseinzadeh, M. M. (2020). Genetic algorithms for finding episodes in temporal networks. Procedia Computer Science, 176, 215-224. https://doi.org/10.1016/j.procs.2020.08.023 The evolution of networks is a fundamental topic in network analysis and mining. One of the approaches that has been recently considered in this field is the analysis of temporal networks, where relations between elements can change over time. A relevant problem in the analysis of temporal networks is the identification of cohesive or dense subgraphs since they are related to communities. In this contribution, we present a method based on genetic algorithms and on a greedy heuristic to identify dense subgraphs in a temporal network. We present experimental results considering both synthetic and real-networks, and we analyze the performance of the proposed method when varying the size of the population and the number of generations. The experimental results show that our heuristic generally performs better in terms of quality of the solutions than the state-of-art method for this problem. On the other hand, the state-of-art method is faster, although comparable with our method, when the size of the population and the number of generations are limited to small values. publishersversion published

With FPGAs now being deployed in the cloud and at the edge, there is a need for scalable design methods that can incorporate the heterogeneity present in the hardware and software components of FPGA systems. Moreover, these FPGA systems need to be maintainable and adaptable to changing workloads while improving accessibility for the application developers. However, current FPGA systems fail to achieve modularity and support for multi-tenancy due to dependencies between system components and the lack of standardised abstraction layers. To solve this, we introduce a modular FPGA operating system – FOS, which adopts a modular FPGA development flow to allow each system component to be changed and be agnostic to the heterogeneity of EDA tool versions, hardware and software layers. Further, to dynamically maximise the utilisation transparently from the users, FOS employs resource-elastic scheduling to arbitrate the FPGA resources in both time and spatial domain for any type of accelerators. Our evaluation on different FPGA boards shows that FOS can provide performance improvements in both single-tenant and multi-tenant environments while substantially reducing the development time and, at the same time, improving flexibility.

Group decision making (GDM) approaches have commonly been used in order to choose the most appropriate choice from an alternative set. It has been applied in environments, where the group of people is involved to make a decision on cases. In online social networks (OSNs), GDM is a need especially on a content of data sharing process where the content of the data has more than one user's information on. In OSNs, social network group decision making (SNGDM) is needed in order to prevent privacy leakage and keep users in OSNs platforms. This is because some OSNs' users might get quit from an OSN platform because of the privacy issues, or they might become unfriend with the user who causes the privacy problem.Application of having group decision making based OSNs' platforms has a challenge. The challenging part of SNGDM process is to weight users' opinions on their choices on the alternative. One way to weight group members' opinion is to use the trust values. In this paper, we propose an approach where the trust values are used to weight decision-makers' opinions on a co-owned data sharing process. The trust values are used as weight vectors in the Extended Induced Ordered Weighted Average (EIOWA) group decision making technique.

UIDB/00066/2020. This article presents a 10-year experience of soft and transferable skills acquisition through the involvement of PhD students in the organization of an international conference. Soft and transferable skills acquisition is currently perceived as a core component of doctoral studies. Examples include writing and communication, teamwork, time management, leadership, resource management, negotiation, problem solving, listening, planning, entrepreneurial spirit, mastering ethics awareness, etc. The need for such skills is due to the leading role that doctoral students are expected to play in society. As such, various organizations have issued recommendations for doctoral programs to include a formal component of soft skills training. In this article, an effective way of introducing soft and transferable skills acquisition in doctoral engineering education is introduced. Namely, a form of collaborative project-based learning is designed as a compulsory course. This includes a set of base lectures, a long period of parallel working groups focusing on the various aspects of organizing an international conference, running the actual conference, and performing a post-conference assessment. Results and lessons learned demonstrate the validity and effectiveness of the proposed approach. publishersversion published

The rise of social media has led to an abundance of textual data, as well as the rise of unhealthy behaviours targeted at others (e.g. bullying, hate speech) or at oneself (e.g. suicide). In recent years, machine learning approaches have been employed to detect such behaviours, which tend to constitute a small portion of the social media content and need to be distinguished from other discourse on social media that may discuss such behaviours without displaying that behaviour, e.g. social media posts about helping people who may be at risk of suicide, thus, making this a very challenging task. In the context of machine learning, such behaviours are referred to as target classes, i.e. the main behaviours to be detected. In this paper we proposed an approach for disambiguation of features in relation to their membership to the target class vs. non-target class(es). We validate our approach with a case study on suicide detection and our results show that the proposed disambiguation approach leads to a better detection rate of suicide.

Pinto, J., & dos Santos, V. D. (2020). Assessing the relationship between bpm maturity and the success of organizations. In R. Silhavy (Ed.), Applied Informatics and Cybernetics in Intelligent Systems: Proceedings of the 9th Computer Science On-line Conference, CSOC 2020 (pp. 108-126). (Advances in Intelligent Systems and Computing; Vol. 1226 AISC). Springer. https://doi.org/10.1007/978-3-030-51974-2_10 For the past decades, organizations have been investing heavily in BPM projects in the hope of improving their competitive advantage in an increasingly complex environment. However, although it is believed that the higher the level of BPM maturity the greater the success of the organization, experience shows that this relationship is not always possible to prove. The purpose of this study is to help clarify the relationship between the level of BPM maturity and the success of an organization. This was done through the implementation of a case study-based research within a global company, focusing on the shared services organization. An analysis of the existing BPM maturity models and its level of coverage of BPM core areas was conducted to select the most suitable BPM maturity model to conduct the assessment of the current BPM maturity level. It was also established a framework to characterize the success of an organization. These two inputs, along with information gathered to understand implemented process improvements, were the basis for conducting the research. Results show a successful organization, with a high maturity level according to the BPM OMG maturity model, that has been investing in continually improving its processes with a strong focus on digital transformation. The identified benefits from a high level of BPM maturity, namely improved productivity, cost reduction, error & risk prevention, higher agility, employee upskilling and knowledge retention, were shown to have a positive influence in the majority of the dimensions used to characterize the success of the organization. authorsversion published

Background Delay in identifying deterioration in hospitalised patients is associated with delayed admission to an intensive care unit (ICU) and poor outcomes. For the HAVEN project (HICF ref.: HICF-R9–524), we have developed a mathematical model that identifies deterioration in hospitalised patients in real time and facilitates the intervention of an ICU outreach team. This paper describes the system that has been designed to implement the model. We have used innovative technologies such as Portable Format for Analytics (PFA) and Open Services Gateway initiative (OSGi) to define the predictive statistical model and implement the system respectively for greater configurability, reliability, and availability. Results The HAVEN system has been deployed as part of a research project in the Oxford University Hospitals NHS Foundation Trust. The system has so far processed > 164,000 vital signs observations and > 68,000 laboratory results for > 12,500 patients and the algorithm generated score is being evaluated to review patients who are under consideration for transfer to ICU. No clinical decisions are being made based on output from the system. The HAVEN score has been computed using a PFA model for all these patients. The intent is that this score will be displayed on a graphical user interface for clinician review and response. Conclusions The system uses a configurable PFA model to compute the HAVEN score which makes the system easily upgradable in terms of enhancing systems’ predictive capability. Further system enhancements are planned to handle new data sources and additional management screens.

Phishing is currently defined as a criminal mechanism employing both social engineering and technical subterfuge to gather any useful information such as user personal data or financial account credentials. Many users are sensible about this kind of attack from suspicious URL addresses or obvious warning information from browsers, but phishing still accounts for a larger proportion of all of malicious attacks. Moreover, these warning features will be eliminated if the victim is under a DNS hijacking attack. There is much research about the prevention and evaluation of phishing, in both PC platforms and mobile platforms, but there are still technical challenges to reducing the risk from phishing, especially in mobile platforms. We presented a novel method to prevent phishing attacks by using an Optical Character Recognition (OCR) technology in a previous paper. This method not only overcomes the limitation of current preventions, but also provides a high detection accuracy rate. However, whether this method can be implemented ideally in mobile devices needed to be further examined, especially in relation to the challenges of limited resources (power and bandwidth). In this paper, we apply the OCR method in a mobile platform and provide a prototype implementation scheme to determine applicability. Experiments are performed to test the technique under DNS hijacking attacks. Postprint

Terras Marques, I., Santos, C., & Santos, V. (2020). Process modelling (bpm) in healthcare : breast cancer screening. In M. Antona, & C. Stephanidis (Eds.), Universal Access in Human-Computer Interaction: Design Approaches and Supporting Technologies. 14th International Conference, UAHCI 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Copenhagen, Denmark, July 19–24, 2020, Proceedings, Part I (pp. 98-109). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12188 LNCS). Springer. https://doi.org/10.1007/978-3-030-49282-3_7 Breast cancer is a malignant epithelial neoplasm and it is a public health problem that has high incidence and mortality in women. Focusing the clinical performance on processes is proving to be the way to improve morbidity and mortality statistics. Business process management (BPM) is a management field that improves and analyzes business processes according to organizations’ strategies. The early diagnosis of breast cancer is of great importance since it will enable more conservative treatments and a longer disease-free survival. Organized oncology screening programs, with all elements properly prepared, revealed to be more efficient than the opportunistic screenings. BPM usage will enable optimize and manage all processes from the screening until the diagnosis and treatment. The aim of this study is identification and modelling of BPM processes for the healthcare sector, namely, for Portuguese organized breast cancer screening. To achieve this goal, it was required the identification of the main processes by an interview to the employees and the development of “As-Is” diagrams. Some of the problems in a macroscopic way were detected and improvement suggestions were made. authorsversion published

The paper presents Intextbooks - the system for automated conversion of PDF-based textbooks into intelligent educational Web resources. The papers focuses on the new component of Intextbooks responsible for transformation of PDF-based content into semantically-annotated HTML/CSS. The architecture of the system, the design of the client application rendering resulting textbooks and a short validation experiment demonstrating the quality of the transformation work-flow are presented.

The paper presents Intextbooks - the system for automated conversion of PDF-based textbooks into intelligent educational Web resources. The papers focuses on the new component of Intextbooks responsible for transformation of PDF-based content into semantically-annotated HTML/CSS. The architecture of the system, the design of the client application rendering resulting textbooks and a short validation experiment demonstrating the quality of the transformation work-flow are presented.

In the real world, much of the information on which decisions are based is vague, imprecise and incomplete. Artificial intelligence techniques can deal with extensive uncertainties. Currently, various types of artificial intelligence technologies, like fuzzy logic and artificial neural network are broadly utilized in the engineering field. In this paper, the combined Z-number and neural network techniques are studied. Furthermore, the applications of Z-numbers and neural networks in engineering are introduced.