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10.13039/100010661-EU H2020 STAMINA (Grant Number: 883441)

In an aging population, technology-based solutions such as Ambient Assisted Living (AAL) systems are indispensable for supporting senior citizens in retaining their independence. Senior citizens have different physical abilities, contexts of use, and attitudes towards technology than younger user groups. This calls for a different approach to assure their acceptance of technologies. In the STuDi research project, we constructed a set of interview guidelines based on literature on technology acceptance and requirements elicitation and validation with senior citizens, and conducted 33 validation interviews with senior citizens who used a prototypical AAL system for 5–12 months. The guidelines facilitated pleasant conversations and helped obtain a good understanding of factors contributing to acceptance versus avoidance and dismissal of the AAL system. We share the findings of our literature search and the resulting interview guidelines, which we consider a suitable instrument for researchers and practitioners for measuring technology acceptance and/or validating a (prototypical) system with senior citizens, and possibly also for requirements elicitation.

An overwhelming number of users access app repositories like App Store/Google Play and social media platforms like Twitter, where they provide feedback on digital experiences. This vast textual corpus comprising user feedback has the potential to unearth detailed insights regarding the users’ opinions on products and services. Various tools have been proposed that employ natural language processing (NLP) and traditional machine learning (ML) based models as an inexpensive mechanism to identify requirements in user feedback. However, they fall short on their classification accuracy over unseen data due to factors like the cost of generating voluminous de-biased labeled datasets and general inefficiency. Recently, Van Vliet et al. [1] achieved state-of-the-art results extracting and classifying requirements from user reviews through traditional crowdsourcing. Based on their reference classification tasks and outcomes, we successfully developed and validated a deep-learning-backed artificial intelligence pipeline to achieve a state-of-the-art averaged classification accuracy of ∼87% on standard tasks for user feedback analysis. This approach, which comprises a BERT-based sequence classifier, proved effective even in extremely low-volume dataset environments. Additionally, our approach drastically reduces the time and costs of evaluation, and improves on the accuracy measures achieved using traditional ML-/NLP-based techniques.

Welcome to the 5th International Workshop on Crowd-Based Requirements Engineering (CrowdRE’21), where scientists and representatives of industry meet to exchange ideas, experiences, and other contributions regarding the state of the art of Crowd-Based Requirements Engineering (CrowdRE). The discipline of CrowdRE seeks to address the challenges of traditional requirements engineering (RE) in scaling up to settings with thousands to millions of users of (software) products or (software-driven) services, who form a large and heterogeneous group that can be denoted as a ‘crowd’ [1, 3]. The user feedback generated by the crowd, such as texts or usage data, can be a valuable source of requirements, problems, wishes, and needs. Responding quickly, effectively, and iteratively to this feedback can greatly increase a product’s success. CrowdRE comprises any approach that provides RE with suitable means for this crowd paradigm, especially by involving the crowd and by collecting, harmonizing, analyzing, and interpreting their user feedback.

Most state-of-the-art local interpretation methods explain the behavior of deep learning classification models by assigning importance scores to image pixels based on how influential each pixel was towards the final decision. These interpretations are unable to provide further details to aid understanding of a complex concept in a domain such as medicine. We propose a novel Hierarchical Visual Concept (HVC) interpretation framework for CNN-based image classification models. As an explanation of the classification decision of a given image, HVC presents a concept hierarchy of most relevant visual concepts at multiple semantic levels. These concepts are automatically learned during training such that the lower-level concepts in the hierarchy support the corresponding higher-level concepts. Our quantitative and qualitative evaluation of the interpretation of VGG16 and ResNet50 classifiers on public and private colonoscopy image datasets shows very promising results.

With over 79 million people forcibly displaced, forced human migration becomes a common issue in the modern world and a serious challenge for the global community. The Flee is a validated agent-based social simulation framework for forecasting the population displacements in the armed conflict settings. In this paper, we present two schemes to parallelize Flee, analyze computational complexity of those schemes, and outline results for benchmarks of our parallel codes with the real-world and synthetic scenarios on four state-of-the-art systems including a new European pre-exascale system, Hawk. On all testbeds, we evidenced high scalability of our codes. It exceeds more than 16,384 cores in our largest benchmark with 100 million agents on Hawk. Parallelization schemes discussed in this work, can be extrapolated to a wide range of ABSS applications with frequent agent movement and lesser impact of direct communications between agents.

This paper contributes to a valuation framework for valuing data as an intangible asset. Especially those industrial manufacturers developing and delivering holistic digital solutions are limited in calculating the true business value of data initiatives. Since the value of data is strongly dependent on the respective use case, a completely objective valuation is not possible. This complicates decision-making on the internal side regarding investments in digital transformation, and on the external side to communicate existing benefits to third parties via financial reporting. Therefore, the target is to design a valuation framework that allows industrial manufacturers to determine an intersubjective, i.e., traceable and transparent, data value. In order to develop a framework that can be applied in practice, the approach is based on industrial case study research.

REPROOF analytically and experimentally quantifies a Jam Resistant BBC based Uncoordinated Frequency Division Multiplexing (FDM) system that does not require any shared secret.

Welcome to the 4th International Workshop on Crowd-Based Requirements Engineering (CrowdRE'20), where scientists and representatives of industry meet to exchange ideas, experiences, and other contributions regarding the state of the art of Crowd-Based Requirements Engineering (CrowdRE). The discipline of CrowdRE seeks to address the challenges of traditional requirements engineering (RE) in scaling up to settings with thousands to millions of users of (software) products or (softwaredriven) services, who form a large and heterogeneous group that can be denoted as a 'crowd' [1],[2]. The user feedback generated by the crowd, such as texts or usage data, can be a valuable source of requirements, problems, wishes, and needs. Responding quickly, effectively, and iteratively to this feedback can greatly increase a product's success. CrowdRE comprises any approach that provides RE with suitable means for this crowd paradigm, especially by involving the crowd and by collecting, harmonizing, analyzing, and interpreting their user feedback.

This paper describes a 4 × 112 Gb/s digital receiver targeting Long Reach (LR) channels. The discrete time front-end overcomes gain-BW limitations to provide 10+dB gain at 28GHz. A 56GS/s ADC then converts the signal to 6-b digital consuming only 195mW. The following DFE-FFE based digital equalizer is capable of compensating 36 dB loss achieving BER of 2e-5. Furthermore, TDC and ISI filter based low latency timing recovery meets jitter tolerance specs over a wide range of data rates (25Gb/s NRZ to 106.25Gb/s PAM-4). The overall receiver consumes 338mW with 3.18pJ/bit energy efficiency

One of the central tenants of information security is availability. One common form of attack against the availability of information in wireless networks is jamming. Currently, the most common techniques to provide jam-resistant communication, such as frequency-hopping spread spectrum (FHSS), are based on the use of a symmetric shared secret. However, there are theoretical approaches to jam resistance without a pre-shared secret. One theoretical approach using concurrent codes, called the BBC algorithm, was developed at the United States Air Force Academy.We developed and tested the effectiveness of REBUF, a Jam Resistant BBC based Uncoordinated Frequency Division Multiplexing (FDM) system. REBUF is the first known implementation of the BBC algorithm in a simultaneous frequency division multiplexing system. The contributions of this paper include: demonstrating the practical use of a BBC based FDM system, the ability of such a system to jam traditional orthogonal frequency division multiplexing (OFDM) systems, and the resilience of such a system to some common forms of jamming.

We present a modelling approach to investigate the evolution of violent events on the forced displacement of people in affected countries. This work is performed in the context of the EU-funded HiDALGO Centre of Excellence, where we seek to establish more scalable and accurate models for migration prediction. Such a hybrid simulation approach is necessary as we need to understand how conflicts may evolve if we are to forecast the escape of people from future conflicts. To accomplish this, we couple a new model for conflict propagation with existing forced migration models. We explore the implications of our setup by studying the effect of different conflict progressions on the forced displacement of people, using an established model of the Mali 2012 conflict. We conclude that accurately predicting conflict evolutions is a key determinant in the outcomes of forced migration simulations, particularly if such forecasts are made over longer periods.

Internet of things (IoT) devices such as self-driven cars and home appliances are creating massive amounts of data traffic. Obviously, such demand trickles down to every electrical interface in a network. Therefore, next-generation data centers need to evolve accordingly to accommodate the bandwidth demand of a rapidly changing world of technology. Since the channel loss is not improving at the same rate, most of the standards are adopting multilevel signaling to make more efficient use of the frequency spectrum. A 4×112Gb/s PAM-4 transmitter presented in this work is leading this trend to keep electrical signaling viable and relevant for future data centers. It introduces three techniques to enable flexibility over different protocols and achieves 1.56pJ/b (175mW at 112Gb/s data rate including clocking) energy efficiency. First, improving signal-to-noise ratio is key to achieve lower BER in multilevel signaling. This work introduces a ‘soft-switching’ H-bridge output driver to enable a 1.2V peak-to-peak differential output swing without exposing devices beyond breakdown voltage. Second, ‘flex clocking’ is achieved by combining a central LC-PLL with a per-lane sub-sampling ring PLL. This combination enables flexible clock generation to seamlessly support data rates between 10Gb/s NRZ and 112Gb/s PAM-4. This clocking architecture enables low-frequency clock distribution for power saving and is easily scalable to more lanes. Third, a DSP-DAC-based transmitter is used to support an arbitrary number of equalization taps and different modulation schemes [1]. A segmented lookup-table-based (LUP-based) implementation further reduces the DSP power.

Physical inactivity is a major risk factor for non-communicable disease and has a negative impact on quality of life in both high and low- and middle- income countries (LMICs). Increasing levels of physical activity is recognized as a strategic pathway to achieving the UN’s 2030 Sustainable Development Goals. Research can support policy makers in evaluating strategies for achieving this goal. Barriers limit the capacity of researchers in LMICs. We discuss how global research capacity might be developed in public health by supporting collaboration via Open Science approaches and technologies such as Science Gateways and Open Access Repositories. The paper reports on how we are contributing to research capacity building in Ghana using a Science Gateway for our PALMS (Physical Activity Lifelong Modelling & Simulation) agent-based micro-simulation that we developed in the UK, and how we use an Open Access Repository to share the outputs of the research.

600 mV magneto-electric switching in 30 nm La-doped BiFeO 3 multiferroic oxide and a proof of concept 7 μV spin-orbit signal output in Pt / CoFe local spin injection device with 100 μA supply current were experimentally demonstrated at room temperature for the 1st time. Also demonstrated was a path towards 70 mV spin orbit output using Bi 2 Se 3 in a local spin injection device. These are key accomplishments for WRITE and READ building blocks, respectively, toward realization of a magneto-electric spin-orbit (MESO) energy-efficient logic device. Moreover, the 1st generation of a MESO logic device with a functional READ unit is demonstrated.

In 2018, the General Data Protection Regulation (GDPR) came into force, imposing strict laws aimed to protect the privacy of natural persons in member states of the European Union. However, the implications of the GDPR with respect to gathering, storing, and analyzing online user feedback — which is an important source of information for Crowd-based Requirements Engineering (CrowdRE) — have not been assessed yet. User feedback has been found to contain personal data, so the GDPR applies. It may be used for CrowdRE if conditions regarding data storage and handling are met and if, when used commercially, the duty to inform is carried out and the data subjects' rights and freedoms are respected. This can be a burden on the application of CrowdRE and might even inhibit its adoption. We propose a heuristic-based solution to anonymize the most prevalent types of personal data while crawling user feedback so that the data processing is no longer subject to GDPR.

BiFeO3 is a multiferroic material with the perovskite structure which is promising for use in sensors and transducers. Single phase production of BiFeO3 remains a challenge, however. In this study, the optimal calcination temperature to obtain close to single phase powder was determined to be 750°C. The sintering temperature of 775°C was also found to obtain high density ceramics (≈ 95 % of theoretical density). It is shown that off-stoichiometry of bismuth oxide in precursors effects the content of secondary phase. Impedance spectroscopy indicates that the content of secondary phases has a large effect on the electrical conductivity BiFeO3.

Conductive polymer composites have been receiving increased interest both from the scientific community and industry with a special focus on electromagnetic interference (EMI) shielding applications. In this paper, we present the design, EM wave simulation, and validation through S-parameters measurements of an EMI shielding effectiveness (SE) tester based on the ASTM D4935 standard, to be used in the development of such materials. EM wave simulations and computer aided design were used in parallel to improve the SE test setup performance, which resulted in a unique low-loss coaxial-spherical-conical smooth transition design that ensured the best tradeoff between sample size and performance. The proposed SE tester has an insertion loss smaller than 1 dB, with good reproducibility and a setup-independent frequency response in the frequency range from a few kHz up to 3 GHz.

Safe and affordable surgery is not accessible for five billion people when they need it. Multiple surgical capacity studies have shown that hospitals in low-And-middle income countries do not have complete coverage of basic surgical equipment such as, theatre lights, anesthesia machines and electro surgical units. Currently, almost all equipment is designed and manufactured with a main focus on the context in high income countries. The context in low-And-middle income countries in which surgical equipment is used, differs from high income countries, especially in terms of financial resources and access to maintenance, spare parts and consumables. The aim of this study is to present a roadmap for design of surgical equipment for worldwide use. The roadmap consists of four phases: before the start of a design project a clear need for certain surgical equipment should be identified (Phase 0). During Phase 1 the context should be researched thoroughly by determining the barriers encountered by patients to surgical care, the structure of the health care system and if the aspects required for safe surgery are in place. In Phase 2 the implementation strategy and design requirements should be determined and in phase 3 prototyping starts in close interaction with local end-users. We believe that designers should strive for design that is of the same quality and complies with the same safety regulations as equipment designed for HICs. In this way user and patient safety can be assured in any setting worldwide. And we advocate for surgical equipment that fits the context optimally and that will be applicable in comparable settings globally.

We report on a method for determining the beam quality factor (M2) for all or selected lobes of coherently-coupled vertically-emitting laser arrays, such as on-wafer photonic crystal vertical-cavity surface-emitting laser (PhC VCSEL) arrays.

We would like to welcome you to the 5th International Workshop on Artificial Intelligence for Requirements Engineering (AIRE'18). This interdisciplinary workshop is intended to explore and extend the synergies between Artificial Intelligence and Requirements Engineering. Our objective is to discover Requirements Engineering areas that may benefit from the application of AI tools and techniques. We intend to inspire a new and broad community for interdisciplinary discussions concerning novel research directions for Requirements Engineering and Artificial Intelligence.

We are now living in the era of digital transformation: Innovative and digital business models are transforming the global business world and society. However, the authors of this paper have perceived barriers that prevent requirements engineers from contributing properly to the development of the software systems that underpin the digital transformation. We also realized that breaking down each of these barriers would contribute to requirements engineering (RE) becoming ubiquitous in certain dimensions: RE everywhere, with everyone, for everything, automated, accepting openness, and cross-domain. In this paper, we analyze each dimension of ubiquity in the scope of the interaction between requirements engineers and end users. In particular, we point out the transformation that is required to break down each barrier, present the perspective of the scientific community and our own practical perspective, and discuss our vision on how to achieve this dimension of ubiquity. Our goal is to raise the interest of the research community in providing approaches to address the barriers and move towards ubiquitous RE.

The industrialization of perovskite solar cells (PSC) require processes to efficiently deposit the constituent layers over large areas. In this work we optimized the use of slot die coating process for both sheet-to-sheet (S2S) and roll-to-roll (R2R) manufacturing. Particular focus have been put on the control of the uniformity of perovskite crystallization by tailoring the ink formulation and the drying process in order to avoid any efficiency losses over an area increase of 3 orders of magnitude (from 0.09 to 144 cm2). In this way we were able to fabricate 144 cm2 modules with 13.8% stabilized efficiency on aperture area on glass (14.5% on active area). At the same time, by developing the R2R slot die deposition process of the perovskite layer, we demonstrated 160 cm2 flexible module with 10.1 % stabilized efficiency on aperture area (11.0% on active area). In addition, fully R2R coated cells exhibit up to 16% efficiency. An outlook on how to upscale more stable perovskite cells (i.e. with pin structure) will be given. These results prove how to optimize the upscaling process towards large area manufacturing of perovskite solar modules on both rigid and flexible substrate.