Commonly attributed smart home challenges for both primary and secondary users have been researched extensively, yet there is a gap in understanding mental models and their influence on how both types of users internalize and domesticate smart home lighting systems including bulbs, lamps, smart plugs, switches, and presence sensors. Our study targets the perceptual distinction between both users from a long-term domestication perspective. We conducted an in-situ ethnographic study with 12 households in the Netherlands and a subsequent online survey with 93 participants. Our research offers three contributions. By analyzing guided diaries, interviews, and surveys, we characterize the intricate mental models (spatial, contextual, functional, and metaphorical mapping) within and across households. Second, we addressed the distinctiveness and importance of the post-domestication phase in smart home lighting systems. Lastly, we present design recommendations to improve future smart home system designs aimed at long-term, evolving use.

Conceptual data modeling is a central activity in data work, yet how such models are created remains understudied. While data attributes play a key role, modeling is also shaped by tasks, tools, developers’ prior experiences, and often unfolds collaboratively between diverse stakeholders. In this study, we invited 22 participants with varying expertise in pairs to collaboratively sketch conceptual data models. We captured screen recordings, their evolving sketches, and conversations. Through a mixed-methods approach combining thematic analysis of dialogue with an examination of model artifacts, we identify how communication and collaboration patterns influenced the process. Our findings reveal a range of collaborative strategies and representations, as well as distinct ways dialogue shaped the emergence and expression of shared conceptual models. These insights deepen understanding of Human-Data Interaction in collaborative data work and point to design opportunities for tools that better support communication, negotiation, and sensemaking of data.

People's identities change during life transitions, e.g., studying abroad. They bring everyday objects that embody memories and reflect their identities during such moves. To assist in these transitions, we ask how people's human identities could be influenced by their objects through an artificial agent. This paper presents an exploratory research-through-design study around how people undergoing life transitions experience conversing with their everyday objects through a chatbot. Drawing on a two-week field deployment and interviews with 12 participants, we contribute (1) a conceptualization of 'trans-embodiment' describing the asynchronous imagination of object and human identities on the chatbot, (2) empirical evidence of the resulting emotional and reflective experiences, and (3) three types of object identities for designing conversational agents that role-play objects. Our contributions sum up to triangulating human-agent-object identity as trans-embodiment in supporting life transitions.

Sound offers unique affordances for interaction designers, enabling immediate, embodied, and expressive experiences. Yet sound is often treated as peripheral, limited to simple notifications rather than being explored as a central design material. Designing meaningful auditory feedback typically requires low-level programming, audio-specific expertise, and fragmented toolchains that hinder early-stage experimentation. We present TESI (Tangible and Embodied Sound Interaction), a modular toolkit that reduces these barriers by combining sensor-integrated hardware, open-source middleware, and a visual programming environment for real-time sensor-to-sound mapping. TESI enables interaction designers to prototype adaptive soundscapes across networked tangible interfaces with minimal setup. We describe the toolkit’s architecture, illustrate its use through examples, and discuss future directions for embodied sound design in distributed systems.

This studio invites participants to explore tangible and embodied interaction beyond standalone interfaces into interconnected ecologies of smart objects, focusing on sound as a primary design element. Through a series of hands-on experimentations, participants will prototype with a modular toolkit, exploring how sound can move beyond simple notification functions to create rich and adaptive soundscapes. Drawing from the practices of digital lutherie and sound design, in particular mapping and interaction design strategies, participants will learn how to map sensor data to sound using interactive machine learning techniques based on artificial neural networks. This studio explores sustainable practices by adopting a modular approach to create adaptable systems, while promoting community engagement, open source contributions, and wider accessibility.

Federated Active Learning (FAL) has emerged as a promising framework to leverage large quantities of unlabeled data across distributed clients while preserving data privacy. However, real-world deployments remain limited by high annotation costs and communication-intensive sampling processes, particularly in a cross-silo setting, when clients possess substantial local datasets. This paper addresses the crucial question: What is the best practice to reduce communication costs in human-in-the-loop learning with minimal annotator effort? Existing FAL methods typically rely on iterative annotation processes that separate active sampling from federated updates, leading to multiple rounds of expensive communication and annotation. In response, we introduce FAST, a two-pass FAL framework that harnesses foundation models for weak labeling in a preliminary pass, followed by a refinement pass focused exclusively on the most uncertain samples. By leveraging representation knowledge from foundation models and integrating refinement steps into a streamlined workflow, FAST substantially reduces the overhead incurred by iterative active sampling. Extensive experiments on diverse medical and natural image benchmarks demonstrate that FAST outperforms existing FAL methods by an average of 4.36% while reducing communication rounds eightfold under a limited 5% labeling budget.

Federated learning (FL) has emerged as a prominent method for collaboratively training machine learning models using local data from edge devices, all while keeping data decentralized. However, accounting for the quality of data contributed by local clients remains a critical challenge in FL, as local data are often susceptible to corruption by various forms of noise and perturbations, which compromise the aggregation process and lead to a subpar global model. In this work, we focus on addressing the problem of noisy data in the input space, an under-explored area compared to the label noise. We propose a comprehensive assessment of client input in the gradient space, inspired by the distinct disparity observed between the density of gradient norm distributions of models trained on noisy and clean input data. Based on this observation, we introduce a straightforward yet effective approach to identify clients with low-quality data at the initial stage of FL. Furthermore, we propose a noise-aware FL aggregation method, namely Federated Noise-Sifting (FedNS), which can be used as a plug-in approach in conjunction with widely used FL strategies. Our extensive evaluation on diverse benchmark datasets under different federated settings demonstrates the efficacy of FedNS. Our method effortlessly integrates with existing FL strategies, enhancing the global model’s performance by up to 13.68% in IID and 15.85% in non-IID settings when learning from noisy decentralized data.

New digital tools offer biodesign with unprecedented opportunities for monitoring, fabricating, iterating, and scaling designs. Serving aligned purposes and beyond, the Digital Twin (DT) is an emerging concept in bioindustries including bioprocessing and agri-food. In these fields, DTs enable comprehensive digital representations of a living system or process with continuous bidirectional connection to the physical world. Despite the concept’s potential to address certain challenges of uncertainty in designing (with) living systems, its applications and implications in biodesign and research remain largely underexplored. To bridge this gap, we propose a conceptual framework that synthesizes existing instantiations of DTs within bioindustries through a dataflow lens. We mobilize the framework through a proof-of-concept DT for biofabrication with mycelium, by unpacking the dataflows within the identified design and operation cycles. We conclude with a discussion on how DT capabilities respond to uncertainties in designing with living systems, limitations, and future work.

Increasing amounts of sensor-augmented research objects have been used in design research. We call these objects Data-Enabled Objects, which can be integrated into daily activities capturing data about people’s detailed whereabouts, behaviours, and routines. These objects provide data perspectives on everyday life for contextual design research. However, data-enabled objects are still computational devices with limited privacy awareness and nuanced data sharing. To better design data-enabled objects, we explore privacy design spaces by inviting 18 teams of undergraduate design students to re-design the same type of sensor-enabled home research camera. We developed the Connected Peekaboo Toolkit (CPT) to support the design teams in designing, building, and directly deploying their prototypes in real home studies. We conducted Thematic Analysis to analyze their outcomes which led us to interpret that privacy is not just an obstacle but can be a driver by unfolding an exploration of possible design spaces for data-enabled objects.

This studio revolves around designing with systemic openness and introduces the IoT Sandbox as a tool to design with systemic openness through multi-activity and multi-person scenarios. The studio knows three learning goals: (1) how to characterize and facilitate a design challenge with systemic openness, (2) how to design with multi-activity and multi-person scenarios, and (3) how to design for diverse interaction styles in a systems context. In a hands-on manner the fundamental concepts and challenges are introduced and experienced through a series of short design sprints. The studio ends with a reflective discussion on the propositions created by the participants, in relation to the learning goals as well as the studio and the IoT Sandbox platform.

Supervised machine learning approaches commonly require good availability and quality of training data. In applications that depend on human-labeled data, especially from experts, or that depend on contextual knowledge for training data sets, the human-in-the-loop presents a serious bottleneck to the scalability of training efforts. Even if human labeling is generally feasible, sustained human performance and high-quality labels in larger quantities are challenging. Interactive Machine Learning can help solve usability problems in traditional machine learning by giving users agency in deciding how systems learn from data. Yet, the field lacks clear design guidelines for such interfaces, specifically regarding the scaling of training processes. In this paper, we present results from a pilot study in which participants interacted with several interface variants of a recommender engine and evaluated them on interaction and efficiency parameters. Based on the performance of these different learning system implementations we propose design guidelines for the design of such systems and a score for comparative evaluation, in which we combine interaction experience and system learning efficiency into one relative scoring unit.

This issue of the Proceedings of the ACM on Human-Computer Interaction features contributions in the intersection of human-computer interaction and software engineering, with further disciplines blending into a rich set of scientific works. 2021 is the first time the annual conference on Engineering Interactive Computing Systems (EICS) is hosted in the Netherlands and in the context of an Industrial Design department. We take this opportunity to focus on the relations and influence of the design discipline on the work of the EICS community. This resulted in a new set of topics for EICS, which were already partly reflected in the many submissions we received in three extensive review rounds throughout 2020 and the beginning of 2021. In this editorial we offer a perspective on what EICS is not yet, looking at the inclusion of and interplay with design as a related discipline.

The COVID-19 pandemic is a global public health crisis. Alongside the rapid geographical development and constantly rising number of confirmed cases, mixed spread of information and disinformation also plagued the public with distress, fear, and anxiety, transmitting a negative sentiment across many groups. Facing this, we experiment with the aesthetics of visualizations in educating laymen about positive facts during the pandemic. By blending artistic expressions into the visual encoding, we intend to tell a compelling data story describing the international scientific dedication in fighting the disease collaboratively. We present the visualization to collect feedback and evaluate the expected functions from general audience instead of traditional expert user groups.

The home is a rich context for design research to study things and the Everyday. However, home is also a place of utmost privacy for most people. To better understand this context through an observational artifact without impacting privacy, we designed the Peekaboo cam that enables inhabitants to control their data release actively or passively. The Peekaboo cam is an observational research camera with a coverable lens. We validate in a field study in two homes for 14 days. The resulting photo streams provide qualitative insights on Everyday things in transition. We suggest four design guidelines for observational artifacts for home ecologies.

This pictorial describes the design and design process of the IoT Sandbox: a (scale model of a) smart home equipped with actuators and a modular interface that controls the interactive aspects of the smart home. This modular interface can 'grow" together with the house it controls whereby it offers unique possibilities for designers to explore different avenues of physically rich interaction in domestic personal spaces. We demonstrate that also physically rich interfaces can respond adequately to the dynamics of home IoT systems. We conclude with insight from the design process and a concise discussion and outlook to future work.

Unwind is a musical biofeedback interface which combines nature sounds and sedative music into a form of New-Age music for relaxation exercises. The nature sounds respond to the user’s physiological data, functioning as an informative layer for biofeedback display. The sedative music aims to induce calmness and evoke positive emotions. UnWind incorporates the benefits of biofeedback and sedative music to facilitate deep breathing, moderate arousal, and promote mental relaxation. We evaluated Unwind in a 2 × 2 factorial experiment with music and biofeedback as independent factors. Forty young adults performed the relaxation exercise under one of the following conditions after experiencing a stressful task: Nature sounds only (NS), Nature sounds with music (NM), and Auditory biofeedback with nature sounds (NSBFB), and UnWind musical biofeedback (NMBFB). The results revealed a significant interaction effect between music and biofeedback on the improvement of heart rate variability. The combination of music and nature sounds also showed benefits in lowering arousal and reducing self-report anxiety. We conclude with a discussion of UnWind for biofeedback and the wider potential of blending nature sounds with music as a musical interface.

Light is a common ambient medium to express additional information in a peripheral and calm way, but it is also an environmental stimulant to create atmosphere, evoke moods, and provide immersive experiences. Through the design of the DeLight system, we aim to establish a biofeedback-driven lighting environment that informs users about their stress level for intervention and assists them in biofeedback relaxation training. In this study, DeLight is interfaced with a heart rate variability biofeedback system with two modes for different purposes: stress intervention and relaxation assistance. We evaluated the prototype of DeLight in two user studies. The results of the first study show that DeLight has the potential for stress intervention; the HRV biofeedback through the changes of ambient light could improve a user’s awareness of stress and trigger behavioral conditioning, such as deep breathing. The results of the second study confirm that DeLight has potential as a new biofeedback interface for relaxation assistance; biofeedback through an immersive lighting environment can support physiological regulation as effectively as graphic biofeedback; it offers enhanced relaxation effects regarding both subjective experience and physiological arousal. These findings suggest that the biofeedback-driven ambient light can perform as persuasive technology in the domain of health self-management. The combination of decorative and informative aspects enables the lighting interface to offer the users a comfortable and relaxing condition for biofeedback-assisted relaxation training.

In today;s highly competitive environment, chronic stress is one of the main reasons for a health problem. To address this, biofeedback techniques have been used to assist in relaxation training and stress management. In this paper, we present "StressTree," a metaphorical visualization of heart rate variability (HRV) biofeedback system. StressTree aims to present HRV data in a more evocative, meaningful way in the context of stress management. The HRV biofeedback system consists of a heartbeat data acquisition unit, a data analysis unit, and a visualization unit. The feedback from interviews in the evaluation shows that StressTree could display different growth pattern during a stressful work or a relaxation training. The participants suggested that the biofeedback interaction through StressTree is explicit and engaging, and brings them a strong motivation to regulate their breathing pattern for a "healthy-looking" tree.

Calm technology has been stressed in designing the interaction with information, especially in ubiquitous computing, peripheral interaction and ambient display. Inspired by the research on calm technology and model-based sonification, we aim to build a model of nature soundscape for supporting calm information display. A three-layer structure is proposed for construction of the nature soundscape. The structure includes seven acoustic parameters. By setting each of seven acoustic parameters into three levels, seven groups of soundscape samples were created and evaluated in an experiment with 20 participants. Each participant was exposed to 21 soundscape samples to assess each sample regarding seven perceptual attributes through a rating scale. Based on the results, a perceptual model is proposed to link the acoustic parameters of individual nature sounds and the perceptual attributes of the nature soundscape. The developed model offers the designers and practitioners a new tool to utilize nature sounds in the design of the auditory display which could support the calm technology.

Although seemingly simple and easy to design for, the moment of waking up and deciding on how long to snooze poses interesting design challenges. While traditional alarm clock interfaces feature large buttons or dropdown menus to communicate (fixed) snooze time, embodied interaction with the entire alarm clock can help to express how much snooze time is desired in a more nuanced way. This paper presents the design of Tumble Clock, a buttonless "balance-object" that exposes its entire surface as interaction area, allowing for snoozing its alarm simply by tilting and releasing or pushing the entire artefact. The Tumble Clock's interaction possibilities and the appreciation of this tangible approach to interaction with time have been evaluated in a user study. Our study indicates that, when enriched with feedback on action, the Tumble Clock's balance behavior can successfully facilitate in tangibly communicating variable intangible information such as snooze time.

Designers adopt a large amount of generalpurpose tools for supporting their remote collaborative tasks.Each tool provides very diverse functionalities: from file sharing to instant communication and video collaboration.The designer struggles when filtering and combining the right information spread across the multitude of tools.This research extends McGrath's framework of task circumflex to map the collaborative demands of the design practitioner and proposes Peepdeck, a design exploration to support them.Peepdeck is a dashboard that assembles information scattered across multiple tools in a personalized and organized way.Through two design iterations followed by evaluations of the user interface, several requirements were identified for supporting collaboration awareness in design teams.Insights confirmed the relevance of combining information from different but already familiar tools, rather than attempting to replace them.It was identified the importance of optimizing for visual scanning, supporting search of content and allowing users to customize the tool.I. INTRODUCTION esign studios operate in a much more distributed fashion nowadays than in the past.Designers may be distributed geographically, potentially even in different time zones.This paper discusses the design of an application for supporting distributed collaborative design work.There is a myriad of new tools and online services available as shown by Fig 1. Tools like Dropbox, GoogleApps, Basecamp, Atlassian, Slack, Skype and Trello are just some examples of the diversity of commercial applications available on the Internet that support teamwork in the design studio.These technologies support very diverse functionalities and services, from file sharing, online edition of documents to instant communication and video collaboration.Previous research [13] revealed a collection of patterns of behavior that the designers conduct in the context of collaboration tools.A surprising finding was that designers adopt a large amount of generic tools that they appropriate for supporting collaborative design tasks, choosing different tools for different parts of the design process.Tools like

Heart Calligraphy is a biofeedback installation that creates abstract portraits of participants with their heartbeat data using a pen plotter. The real-time heart rate is mapped to the basic parameters of the pen's behaviors, namely speed, position, pressure and pen-down time. Due to the natural variability in heart rate, every portrait becomes personal and unique graphic, which reflects the natural biorhythm inside human body. The installation explores the role of the body as a channel through which physiology manifests itself in a form of beauty.

In this paper we describe the concept, design and implementation of LivingSurface, an interactive wall-like surface as a shape-changing display of biofeedback. The surface changes its shape responding to an individual's physiological data, reflecting the internal bodily processes. The surface design basically consists of two layers: the pattern layer (front layer) and the actuating layer (back layer). The first is a complex paper-based structure with repetitive incisions created by laser cutting. The actuating layer serves as a medium transforming the force from servomotors, vibration motors or fans into an action on the pattern layer. The cutout patterns are stimulated to vibrate, swing, bulge, or rotate which is used to display physiological information in dynamic physical form. This work has been exhibited on Milan Design Week 2015; we collected and analyzed the feedback from the visitors during the exhibition and discuss the possibilities of the proposed surfaces as a shape-changing interface of biofeedback or an ambient display of information.

This paper presents a Learning Analytics approach for understanding the learning behavior of students while interacting with Technology Enhanced Learning tools. In this work we show that it is possible to gain insight into the learning processes of students from their interaction data. We base our study on data collected through six laboratory sessions where first-year students of Computer Engineering at the University of Genoa were using a digital electronics simulator. We exploit Process Mining methods to investigate and compare the learning processes of students. For this purpose, we measure the understandability of their process models through a complexity metric. Then we compare the various clusters of students based on their academic achievements. The results show that the measured complexity has positive correlation with the final grades of students and negative correlation with the difficulty of the laboratory sessions. Consequently, complexity of process models can be used as an indicator of variations of student learning paths.

In today's continuously changing markets newly developed products often do not meet the demands and expectations of customers. Research on this problem identified a large gap between developer and user expectations. Approaches to bridge this gap are to provide the developers with better information on product usage and to create a fast feedback cycle that helps tackling usage problems. Therefore, the user interface of the product, the central point of human-computer interaction, has to be instrumented to collect accurate usage data which serves as basis for further improvement steps. This paper presents a novel engineering approach that combines model-driven user interface development and flexible instrumentation with runtime monitoring. In its application, it enables observation integration into products which provides comprehensive data about usage and thus allows for fast feedback cycles and consequently increased software quality. A case-study demonstrates the applicability of this approach.

The complicated user interfaces and complex functionality of nowadays interactive products lead to a new class of failures: People do not understand their products and thus fail to use them successfully; many products are returned for which no detectable errors can be found. These field problems of interactive products cannot be found by traditional testing methods. Industry needs reliable and structured information about the users' behavior to get understanding about the root cause of so called soft product failures. In this paper we present a framework that helps usability and quality experts to derive user models from product observation. This is supported by a novel visual language for specification what should be observed and how collected data is represented, and a system architecture for distributed self-observing systems. This approach separates the concern definition of observation from the implementation of observation facilities.

Nowadays interactive electronics products offer a huge functionality to prospective customers, but often it is too huge and complex to be grasped and used successfully. In this case, customers obviate the struggle and return the products to the shop. Also the variability in scope and features of a product is so large that an up-front specification becomes hard if not impossible. To avoid the problem of an inadequate match between customer expectations and designer assumptions, new sources of product usage information have to be developed. One possibility is to integrate observation functionality into the products, continuously involving real users in the product development process. The integration of such functionality is an often overlooked challenge that should be tackled with an appropriate engineering methodology. This paper presents on-going work about a novel design for observation approach that supports early observation integrations and enables the cooperation with various information stakeholders. We show how observation can be embedded seamlessly in a model-driven development process using UML. An industrial case-study shows the feasibility of the approach.