Miracle-dz/UniFAR-FTUs huggingface dataset:
Static aeroelastic optimization of composite wind turbine blades using variable stiffness laminates : Exploring twist coupled composite blades in stall control

There is a growth in the energy consumption of the world, leading to rapid depletion of natural resources, such as fossil fuels. Added to that, the environmental impact of fossil fuels (e.g. global warming) makes a renewable source of energy a better alternative for power generation. Among renewable energy sources, generating energy from wind is becoming more popular. Although the number of, installed, wind turbines is increasing rapidly, there are still many challenges ahead for making the cost of generating energy from offshore wind competitive with other energy sources. One method for making the Cost of Energy from wind competitive is to reduce the operational and maintenance cost of wind turbines. The operational and maintenance cost of wind turbines may be reduced by eliminating, as much as possible, rotating components of the turbine which are prone to wear and tear. An alternative way to regulate power is to use stall control scheme, thereby eliminating the need to use the pitch mechanism. With recent advances in composite technology for tailoring the structural response of composite structures, it is possible to apply the technique to the conventional passive stall control scheme. Particularly, the use of twist coupling for regulating (passively) the angle of attack, thus also the torque and power of the wind turbine, shows a promise to design adaptive blades for stall regulated wind turbines, with improved performance in terms of power and load control, as well as in terms of cost reduction. Most of the research conducted so far investigates the benefit of twist coupled blades for power and/or load regulation; either based on a parametric study using few design variables or using simplified models for analysing the aeroelastic response of adaptive blades. In this thesis, a detailed optimization study is performed using variable stiffness laminates, to evaluate the potential of twist coupled blades to enhance the aerodynamic performance of stall controlled wind turbines. Furthermore, detailed structural and aerodynamic constraints are included in the optimization study, while using an analysis tool with sufficient complexity to accurately capture the aeroelastic response of twist coupled blades.
Selective detection of glutathione based on the recovered fluorescence of BSAâ•’AuNCs/Cu<sup>2+</sup> system

A bovine serum albumin-stabilised gold nanoclusters/Cu (BSA-Au NCs/Cu) system was constructed as a fluorescent sensor for the ‘off–on’ detection of glutathione (GSH). The fluorescence of BSA-Au NCs could be effectively quenched by Cu due to the strong affinity between BSA-Au NCs and Cu. Then, coordination of Cu to GSH enabled the recovery of the fluorescence of BSA-Au NCs. To assess the selectivity of the BSA-Au NCs/Cu for GSH, K, Mg, Ca, glucose, cysteine, tryptophan, and phenylalanine were examined under identical conditions, and all of them did not induce noticeable responses to BSA-Au NCs/Cu except for Cys. There was a good linear dependence of fluorescence intensity on the concentration of GSH in the range of 0.4–20.0 μM with the detection limit of 0.36 μM. The proposed sensor showed excellent performance in the selective detection of GSH.
Memory Efficient Scheduling of Query Pipeline Execution

State-of-the-art query engines pursue a pipeline-based query execution model. Using such a model, a pipeline computes a query plan fragment up to a pipeline breaker resulting in an intermediate result, which will be consumed by subsequent pipelines. Interestingly, the ordering of execution of such pipelines poses an opportunity for memory savings. Within this paper, we tackle the challenge to compute an optimal schedule of the individual pipelines with respect to minimizing the memory consumption needed for a particular query execution plan. We therefore will precisely state the problem and show the potential of an optimal pipeline execution ordering. We will then provide a formal model to describe the search space and propose four different algorithms to identify optimal/near-optimal schedules. The paper also presents insights into our implementation within a prototypical query execution engine and reports results relying on the Join Order Benchmark scenarios. Specifically, the experimental evaluation focuses on the identified memory savings and the stability of the query runtime behavior. Furthermore, the evaluation reports on the small overhead of the proposed search algorithms during the planning time, thus emphasizing the practical applicability of our approach.
Differential effect of Phenoxy Substituted Herbicide 2, 4-D on Pigments, Metabolites, and Enzyme Activities of Three Species of Cyanobacteria

The effect of graded concentrations of common rice field herbicide (2,4-Dichlorophenoxy Acetic Acid Ethyl ester) on pigments, metabolic contents, enzyme activities and tolerance potential of cyanobacterial strains Anabaena fertilissima, Aulosira fertilissima and Westiellopsis prolifica was assessed and compared at periodic intervals of 4 and 16 day after treatment. Cyanobacterial strains showed progressive declination of pigments with increase in dosage of herbicides. This is evident from considerable diminution in pigments contents like chlorophyll-a (60-86%), carotenoids (64-80%), phycocyanin (65-93%), phycoerythrin (6795%) and allophycocyanin (63-94%), metabolites like carbohydrates (65-81%), proteins (50-63%) and aminoacids (57-75%) and suppression of enzymatic activity such as nitrate reductase (62-78%), glutamine synthetase (69-97%) and Succinate dehydrogenase (73-89%). However, an increase by 15-29% in phenol content was recorded in all the test species against to herbicide doses. W. prolifica showed the greater tolerance towards different doses of 2,4-D than Aulosira fertilissima and Anabaena fertilissima.
Instrumenting Virtual Reality for Priming Cultural Differences in Design Creativity

Many researchers focus on the relationship between culture and creativity, often analyzing the influence of culture on design creativity. However, most studies only draw conclusions about the positive or negative effects of culture on creativity, rather than providing solutions to mitigate the adverse impact or magnify the positive impact. Therefore, in the current study, we first developed a dedicated framework to explain the influence of culture on design creativity based on the 4Ps model of creativity (person, process, product, and press). Then, we analyzed the influence of culture on design creativity based on Hofstede’s dimensions of culture. Following this, we identified the methods and solutions that can be adopted to mitigate negative cultural influences and prime positive cultural influences on design creativity. Finally, we proposed possible solutions to mitigate the negative cultural influences and prime the positive cultural influences by applying innovative technology to design creativity. Our study will inspire researchers to mitigate the negative influences of culture, magnify the positive effects, and apply new technology to enhance creativity.
Towards Fair Unsupervised Learning

Bias-mitigating techniques are now well established in the supervised learning literature and have shown their ability to tackle fairness-accuracy, as well as fairness-fairness trade-offs. These are usually predicated on different conceptions of fairness, such as demographic parity or equal odds that depend on the available labels in the dataset. However, it is often the case in practice that unsupervised learning is used as part of a machine learning pipeline (for instance, to perform dimensionality reduction or representation learning via SVD) or as a standalone model (for example, to derive a customer segmentation via k-means). It is thus crucial to develop approaches towards fair unsupervised learning. This work investigates fair unsupervised learning within the broad framework of generalised low-rank models (GLRM). Importantly, we introduce the concept of fairness functional that encompasses both traditional unsupervised learning techniques and min-max algorithms (whereby one minimises the maximum group loss). To do so, we design straightforward alternate convex search or biconvex gradient descent algorithms that also provide partial debiasing techniques. Finally, we show on benchmark datasets that our fair generalised low-rank models (“fGLRM”) perform well and help reduce disparity amongst groups while only incurring small runtime overheads.
Characterizing Sponsored Content in Facebook and Instagram

In this work we present a comparative analysis of influencer marketing evolution on Facebook and Instagram, spanning the pre and post Covid-19 pandemic onset periods. We collected and characterized a large-scale cross-platform dataset, comprised of 9.5 million sponsored posts. We analyzed the relative growth rates of the number of ads and of user engagement within different topics of interest, such as sports, retail, travel, and politics. We discuss which topics have been most impacted by the onset of the pandemic, both in terms of sponsored content supply and demand. With this work we hope to expand the understanding of influence dynamics on social networks and provide support for the development of more contextualized and effective branding strategies.
Female, white, 27? Bias Evaluation on Data and Algorithms for Affect Recognition in Faces

Nowadays, Artificial Intelligence (AI) algorithms show a strong performance for many use cases, making them desirable for real-world scenarios where the algorithms provide high-impact decisions. However, one major drawback of AI algorithms is their susceptibility to bias and resulting unfairness. This has a huge influence for their application, as they have a higher failure rate for certain subgroups. In this paper, we focus on the field of affective computing and particularly on the detection of bias for facial expressions. Depending on the deployment scenario, bias in facial expression models can have a disadvantageous impact and it is therefore essential to evaluate the bias and limitations of the model. In order to analyze the metadata distribution in affective computing datasets, we annotate several benchmark training datasets, containing both Action Units and categorical emotions, with age, gender, ethnicity, glasses, and beards. We show that there is a significantly skewed distribution, particularly for ethnicity and age. Based on this metadata annotation, we evaluate two trained state-of-the-art affective computing algorithms. Our evaluation shows that the strongest bias is in age, with the best performance for persons under 34 and a sharp decrease for older persons. Furthermore, we see an ethnicity bias with varying direction depending on the algorithm, a slight gender bias and worse performance for facial parts occluded by glasses.
Creative and Motivational Strategies Used by Expert Creative Practitioners

Creative practice often requires persevering through moments of ambiguity, where the outcome of a process is unclear. Creative practitioners intentionally manage this process, for example by developing strategies to break out of creative ruts, or stay motivated through uncertainty. Understanding the way experts engage with and manage these creativity-relevant processes represents a rich source of foundational knowledge for designers of Creativity Support Tools. These strategies represent an opportunity for CST research: to create CSTs that embody emotional and process-focused strategies and techniques. Through interviews with expert practitioners in diverse domains including performance, craft, engineering, and design, we identify four strategies for managing process: Strategic Forgetting, Mode Switching, Embodying Process, and Aestheticizing. Understanding tool- and domain-agnostic creative strategies used by experts to manage their own creative process can inform the design of future CSTs that amplify the benefits of successful strategies and scaffold new techniques.
Adversarial Scrutiny of Evidentiary Statistical Software

The U.S. criminal legal system increasingly relies on software output to convict and incarcerate people. In a large number of cases each year, the government makes these consequential decisions based on evidence from statistical software—such as probabilistic genotyping, environmental audio detection and toolmark analysis tools—that the defense counsel cannot fully cross-examine or scrutinize. This undermines the commitments of the adversarial criminal legal system, which relies on the defense’s ability to probe and test the prosecution’s case to safeguard individual rights. Responding to this need to adversarially scrutinize output from such software, we propose robust adversarial testing as a framework to examine the validity of evidentiary statistical software. We define and operationalize this notion of robust adversarial testing for defense use by drawing on a large body of recent work in robust machine learning and algorithmic fairness. We demonstrate how this framework both standardizes the process for scrutinizing such tools and empowers defense lawyers to examine their validity for instances most relevant to the case at hand. We further discuss existing structural and institutional challenges within the U.S. criminal legal system which may create barriers for implementing this framework and close with a discussion on policy changes that could help address these concerns.
Zooming in on the Effect of Sociometric Signals on Different Stages of the Design Process

Collaborative creativity is an essential part of modern teamwork and is often supported by formal techniques, such as design thinking. Current support tools are often limited in scope as understanding the time-varying nature and structure of team communication is insufficient. We investigate how collaborative creative activities in new product development teams can be digitally supported while maintaining face-to-face communication. This work analyzes to what extent paralinguistic and proxemic features of team interaction relate to performance in new product development teams and if and how this relationship differs for different stages in the design process. This is investigated by applying multilevel modeling on data collected during a four-week new product development cycle. The cycle was completed by four teams, during which data were collected automatically using sociometric badges that capture social signals of team interactions. In addition, the data are combined with survey-based measurements on the team’s daily design process and periodic performance evaluations. The current paper provides evidence that social signals are related to team performance and that this relationship varies across the stages in the product design process. Certain social signals contribute positively in one stage but less in other stages, showing the importance of using multimodal signals when modeling high-level collaborative patterns. This research contributes to the literature by providing a better understanding of relevant factors when designing supporting tools or methods for collaborative creative problem solving.
Algorithms Off-limits?: If digital trade law restricts access to source code of software then accountability will suffer

Free trade agreements are increasingly used to construct an additional layer of protection for source code of software. This comes in the shape of a new prohibition for governments to require access to, or transfer of, source code of software, subject to certain exceptions. A clause on software source code is also part and parcel of an ambitious set of new rules on trade-related aspects of electronic commerce currently negotiated by 86 members of the World Trade Organization. Our understanding to date of how such a commitment inside trade law impacts on governments right to regulate digital technologies and the policy space that is allowed under trade law is limited. Access to software source code is for example necessary to meet regulatory and judicial needs in order to ensure that digital technologies are in conformity with individuals’ human rights and societal values. This article will unpack and analyze the implications of such a source code clause for current and future digital policies by governments that aim to ensure transparency, fairness and accountability of computer and machine learning algorithms.
Locality of Technical Objects and the Role of Structural Interventions for Systemic Change

Technical objects, like algorithms, exhibit causal capacities both in terms of their internal makeup and the position they occupy in relation to other objects and processes within a system. At the same time, systems encompassing technical objects interact with other systems themselves, producing a multi-scale structural composition. In the framework of fair artificial intelligence, typical causal inference interventions focus on the internal workings of technical objects (fairness constraints), and often forsake structural properties of the system. However, these interventions are often not sufficient to capture forms of discrimination and harm at a systemic level. To complement this approach we introduce the notion of locality and define structural interventions. We compare the effect of structural interventions on a system compared to local, structure-preserving interventions on technical objects. We focus on comparing interventions on generating mechanisms (representing social dynamics giving rise to discrimination) with constraining algorithms to satisfy some measure of fairness. This framework allows us to identify bias outside the algorithmic stage and propose joint interventions on social dynamics and algorithm design. We show how, for a model of financial lending, structural interventions can drive the system towards equality even when algorithmic interventions are unable to do so. This suggests that the responsibility of decision makers extends beyond ensuring that local fairness metrics are satisfied to an ecosystem that fosters equity for all.
Readymades & Repertoires: Artifact-Mediated Improvisation in Tabletop Role-Playing Games

Game masters (GMs) are creative practitioners who plan and orchestrate tabletop role-playing games. Through an interview study, we investigate how eight expert game masters adapt everyday technologies and materials as creativity support tools (CSTs) for improvisational and collaborative play. We integrate theories of improvisational and distributed creativity with the human-artifact model, which provides an activity-theoretical vocabulary for analyzing the mediating relationships between specialist practitioners and their tools. We show how GMs prepare and deploy readymade artifacts: analog and digital CSTs that flexibly mediate recurring creative tasks in their practice, such as improvising narrative elements, facilitating smooth play, and creating aesthetic effects. We find that GMs demonstrate designerly thinking as they create, share, and refine repertoires of readymade artifacts. We argue that our theoretical approach can inform future studies of IT-mediated creativity, and that readymade artifacts can be an analytical and generative concept for the design of novel creativity support tools.
Supporting Designers in the Sharing Economy Through a Generative Design Cards Toolkit

Non-profit approaches in the sharing economy are proliferating in everyday life (e.g., tool libraries, food sharing collectives). They focus on promoting social, economic, and ecological sustainability within local communities. However, prior research uncovered emergent challenges of these communities that prevent their endurance and growth. Despite growing academic interest in studying these communities, designers found it difficult to effectively adopt newly generated knowledge in practice. Addressing the research-practice gap, we developed a translational resource for design practitioners to conceptualize new value-added features for supporting technologies in the sharing economy context. Specifically, we synthesized emergent challenges in non-profit sharing economy communities and key social and environmental sustainability efforts in design research into a familiar format for designers – the card-based toolkit to support generative design activities. Beyond introducing the toolkit, we offer a “how-to” guide for its adoption in collaborative settings.
Goldwork Embroidery: Interviews with Practitioners on Working with Metal Threads and Opportunities for E-textile Hybrid Crafts

Within the emerging field of e-textiles, goldwork embroidery (also known as metalwork) which uses metal threads and materials is an underexplored area, despite being a centuries-old practice in traditional crafts of different cultures. In this paper, we explore the material culture of textile goldwork to better understand how e-textile researchers can leverage their material properties, palette, and practices. First, we provide a historical background of English goldwork to give HCI researchers context on this craft field including technological and cultural influences. Then, we interview 13 contemporary goldwork practitioners on their creative practice to better understand the tools, techniques, and skills they employ. Our study findings show how goldwork practitioners deal with the unique constraints of metal threads and materials, and how these materials need to be handled differently than regular non-metal threads and fibers. This paper contributes an analysis of goldwork practices for HCI audiences with suggestions on how we can leverage these practices for the future of e-textile hybrid crafts.
Identifying Engagement in Children’s Interaction whilst Composing Digital Music at Home

Identifying points of engagement from a person’s interaction with computers could be used to assess their experience and to adapt user interfaces in real-time. However, it is difficult to identify points of engagement unobtrusively; HCI studies typically use retrospective protocols or rely on cumbersome sensors for real-time analysis. We present a case study on how children compose digital music at home in which we remotely identify points of engagement from patterns of interaction with a musical interface. A mixed-methods approach is contributed in which video recordings of children’s interactions whilst composing are labelled for engagement and linked to i) interaction logs from the interface to identify indicators of engagement in interaction, and ii) interview data gathered using a remote video-cued recall technique to understand the experiential qualities of engaging interactions directly from users. We conclude by speculating on how the suggested indicators of engagement inform the design of adaptive music systems.
From Demo to Design in Teaching Machine Learning

The prevalence of artificial intelligence (AI) and machine learning (ML) technologies in digital ecosystems has led to a push for AI literacy, giving everybody, including K-12 students, the necessary knowledge and abilities to engage critically with these new technologies. While there is an increasing focus on designing tools and activities for teaching machine learning, most tools sidestep engaging with the complexity and trade-offs inherent in the design of ML models in favor of demonstrating the power and functionality of the technology. In this paper, we investigate how a design perspective can inform the design of educational tools and activities for teaching machine learning. Through a literature review, we identify 34 tools and activities for teaching ML, and using a design perspective on ML system development, we examine strengths and limitations in how they engage students in the complex design considerations linked to the different components of machine learners. Based on this work, we suggest directions for furthering AI literacy through adopting a design approach in teaching ML.