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Access to complete data in large-scale networks is often infeasible. Therefore, the problem of missing data is a crucial and unavoidable issue in the analysis and modeling of real-world social networks. However, most of the research on different aspects of social networks does not consider this limitation. One effective way to solve this problem is to recover the missing data as a pre-processing step. In this paper, a model is learned from partially observed data to infer unobserved diffusion and structure networks. To jointly discover omitted diffusion activities and hidden network structures, we develop a probabilistic generative model called "DiffStru." The interrelations among links of nodes and cascade processes are utilized in the proposed method via learning coupled with low-dimensional latent factors. Besides inferring unseen data, latent factors such as community detection may also aid in network classification problems. We tested different missing data scenarios on simulated independent cascades over LFR networks and real datasets, including Twitter and Memetracker. Experiments on these synthetic and real-world datasets show that the proposed method successfully detects invisible social behaviors, predicts links, and identifies latent features. [ABSTRACT FROM AUTHOR]

Research on human mobility drives the development of economy and society. How to predict when and where one will go accurately is one of the core research questions. Existing work is mainly concerned with performance of mobility prediction models. Since accuracy of predict models does not indicate whether or not one's mobility is inherently easy to predict, there has not been a definite conclusion about that to what extent can our predictions of human mobility be accurate. To help solve this problem, we describe the formalized definition of predictability of human mobility, propose a model based on additive Markov chain to measure the probability of exploration, and further develop an information theory based method for quantifying the predictability considering exploration of human mobility. Then, we extend our method by using mutual information in order to measure the predictability considering external influencing factors, which has not been studied before. Experiments on simulation data and three real-world datasets show that our method yields a tighter upper bound on predictability of human mobility than previous work, and that predictability increased slightly when considering external factors such as weather and temperature. [ABSTRACT FROM AUTHOR]

In this paper, we report a method of implementing a universal volumetric haptic actuation platform which can be adapted to fit a wide variety of visual displays with flat surfaces. This platform aims to enable the simulation of the 3D features of input interfaces. This goal is achieved using four readily available stepper motors in a diagonal cross configuration with which we can quickly change the position of a surface in a manner that can render these volumetric features. In our research, we use a Microsoft Surface Go tablet placed on the haptic enhancement actuation platform to replicate the exploratory features of virtual keyboard keycaps displayed on the touchscreen. We ask seven participants to explore the surface of a virtual keypad comprised of 12 keycaps. As a second task, random key positions are announced one at a time, which the participant is expected to locate. These experiments are used to understand how and with what fidelity the volumetric feedback could improve performance (detection time, track length, and error rate) of detecting the specific keycaps location with haptic feedback and in the absence of visual feedback. Participants complete the tasks with great success (p < 0.05). In addition, their ability to feel convex keycaps is confirmed within the subjective comments. [ABSTRACT FROM AUTHOR]

There has been a resurgence of applications focused on human activity recognition (HAR) in smart homes, especially in the field of ambient intelligence and assisted-living technologies. However, such applications present numerous significant challenges to any automated analysis system operating in the real world, such as variability, sparsity, and noise in sensor measurements. Although state-of-the-art HAR systems have made considerable strides in addressing some of these challenges, they suffer from a practical limitation: they require successful pre-segmentation of continuous sensor data streams prior to automated recognition, i.e., they assume that an oracle is present during deployment, and that it is capable of identifying time windows of interest across discrete sensor events. To overcome this limitation, we propose a novel graph-guided neural network approach that performs activity recognition by learning explicit co-firing relationships between sensors. We accomplish this by learning a more expressive graph structure representing the sensor network in a smart home in a data-driven manner. Our approach maps discrete input sensor measurements to a feature space through the application of attention mechanisms and hierarchical pooling of node embeddings. We demonstrate the effectiveness of our proposed approach by conducting several experiments on CASAS datasets, showing that the resulting graph-guided neural network outperforms the state-of-the-art method for HAR in smart homes across multiple datasets and by large margins. These results are promising because they push HAR for smart homes closer to real-world applications.

Physical distancing is a key measure to slow the spread of many highly infectious diseases, e.g. COVID-19. Streetscape interventions such as pedestrian signage can contribute to ensuring distances are kept, but it is unclear to what extent people comply with these in practice. This paper tackles this question using an immersive video environment to realistically simulate real-life streetscapes in the lab. In a controlled user study, we augmented panoramic video footage with pedestrian one-way street signage and recorded route decisions to assess compliance with distance keeping measures. Our results indicate that such signage affects routing decisions and can thus help pedestrians to avoid crowded situations where distance keeping is difficult. We also identified further factors affecting decisions and a correlation between intention to comply and actual compliance. The experimental method we used enabled us to effectively and safely carry out a study of a phenomenon that in the real world depends on interaction with the physical environment. This method may have applications in other areas in which simulations of physical environments are important. [ABSTRACT FROM AUTHOR]

Since the advent of smartphones, work tasks shift progressively towards mobile phones. The main hurdle of this progression is the substantially slower input speed of smartphones in comparison with physical keyboards. This input speed could be improved by using keyboard layouts optimized for use with smartphones. As these keyboard layouts are not commonly used, switching to them results in a poor novice experience with an initially decreased text input speed. Previous investigations confirmed that this poor novice experience can be attenuated by using visual clues accentuating the most probable keys. This gives rise to the question of which visual impression leads to the best performance improvement. Therefore, this article evaluates the performance of six visual clues with different visual impressions via a user study conducted with 28 participants. The results showed that the visual clue with a pop-up animation and the visual clue with an increased font size performed the best with a 4% and 3%, respectively, typing performance improvement. Nonetheless, the questionnaire and personal preferences part of the user study showed that a 4% performance gain is not enough to crown one individual visual clue as the best visual clue to be used with every individual. This leads to the conclusion that functionality to choose one personal preference is more important than focusing on one specific best clue. [ABSTRACT FROM AUTHOR]

In robot teaching, the grasping strategies taught to robots by users are critical information, because these strategies contain the implicit knowledge necessary to successfully perform a series of manipulations; however, limited practical knowledge exists on how to utilize linguistic information for supporting grasp-type recognition in multimodal teaching. This study focused on the effects of text-driven object affordance—a prior distribution of grasp types for each object—on image-based grasp-type recognition. To this end, we created the datasets of first-person grasping-hand images labeled with grasp types and object names and tested if the object affordance enhanced the performance of image-based recognition. We evaluated two scenarios with real and illusory objects to be grasped, considering a teaching condition in mixed reality, where the lack of visual object information can make image-based recognition challenging. The results show that object affordance guided the image-based recognition in two scenarios, that is, increasing the recognition accuracy by (1) excluding the unlikely grasp types from the candidates and (2) enhancing the likely grasp types. Additionally, the “enhancing effect” was more pronounced with greater grasp-type bias for each object in a test dataset. These results indicate the effectiveness of object affordance for guiding grasp-type recognition in multimodal robot teaching applications. The contributions of this study are (1) demonstrating the effectiveness of object affordance in guiding grasp-type recognition both with and without the real objects in images, (2) demonstrating the conditions under which the merits of object affordance are pronounced, and (3) providing a dataset of first-person grasping images labeled with possible grasp types for each object. [ABSTRACT FROM AUTHOR]

According to the U.S. Department of Health and Human Services, Applied Behavior Analysis (ABA) is regarded as the gold standard for treating autism. However, it requires a significant time commitment of 20 to 40 hours per week, which can be expensive for families who may not be able to afford the treatment. ABA therapists emphasize the importance of practicing therapeutic techniques in the child’s natural environment to maintain their effectiveness. Therefore, there is a need to supplement therapy in the home environment where the children spend most of their time. If a parent or caregiver assumes the role of co-therapist and practices with the child, it can result in faster skills improvement. However, many parents lack knowledge about therapeutic protocols. To address this gap, we conducted research and developed educational software for therapeutic interventions for Romanian children diagnosed with Autism Spectrum Disorder (ASD) in a home-based setting. We followed the Double Diamond Model, emphasizing the core principle of Human-Centered Design (HCD) methodology, to design the software with a focus on the special needs of end-users. This involved developing proto-personas, wireframes, and interactive prototypes, exploring the latest technologies, and incorporating feedback from experienced ABA therapists. Based on these inputs, we developed the Autism Assistant platform, which includes a mobile application built with Unity and a web application developed with React and Ruby on Rails. The platform underwent evaluation using the Quantitative Evaluation Framework (QEF) based on ISO 9126, and it was considered suitable as educational software for special education.

Recently, the use of Electroencephalography (EEG) in scientific research on User Authentication (UA) has led to cutting-edge experiments that seek to identify and authenticate individuals based on their brain activity in particular usage scenarios. Utilizing EEG signals, derived from brain activity, might provide innovative solutions to contemporary security issues in traditional knowledge-based user authentication, including the threat of shoulder surfing. In this review paper, we analyze 108 different EEG-based user authentication experiments based on the following perspectives: a) the user experimental setup, with an emphasis on the applied EEG- protocols; b) the artificial intelligence techniques employed and finally c) the security and privacy preservation aspects. The reviewed papers cover a broad time frame from 1998 to 2022 and include various experimental protocols and algorithms used for classifying EEG signals. Additionally, the majority of the referenced works report findings from multiple experiments that incorporate distinct approaches and configurations. This leads to a discussion on best practices for EEG-based User Authentication and conclusions suggesting future research directions that consists, among others, of considering homomorphically encrypted biometric templates for information leakage prevention through federated learning approaches in decentralized architectures. We anticipate that the present literature review will provide a roadmap for future research by considering efficiently and effective EEG-based User Authentication methods while at the same time preserving privacy.

The way Complex Machine Learning (ML) models generate their results is not fully understood, including by very knowledgeable users. If users cannot interpret or trust the predictions generated by the model, they will not use them. Furthermore, the human role is often not properly considered in the development of ML systems. In this article, we present the design, implementation and evaluation of Explain-ML, an Interactive Machine Learning (IML) system for Explainable Machine Learning that follows the principles of Human-Centered Machine Learning (HCML). We assess the user experience with the Explain-ML interpretability strategies, contrasting them with the analysis of how other IML tools address the IML principles. To do so, we have conducted an analysis of the results of the evaluation of Explain-ML with potential users in light of principles for IML systems design and a systematic inspection of three other tools – Rulematrix, Explanation Explorer and ATMSeer – using the Semiotic Inspection Method (SIM). Our results generated positive indicators regarding Explain-ML and the process that guided its development. Our analyses also highlighted aspects of the IML principles that are relevant from the users’ perspective. By contrasting the results with Explain-ML and SIM inspections of the other tools we were able to identify common interpretability strategies. We believe that the results reported in this work contribute to the understanding and consolidation of the IML principles, ultimately advancing the knowledge in HCML.

Efficient locomotion methods have been proposed to compensate for the limited space in real-world environments, and such methods offer users more immersive and natural experiences in relatively large virtual environments. The foot-based locomotion method is one of the best options for implementing natural locomotion using foot movement as an input. However, existing foot-based locomotion methods force users to wear equipment or take a video of the user's body. These actions can cause discomfort, unnatural feelings, or privacy problems. Thus, we propose a Seamless-walk system that can seamlessly translate a real-world gait action to locomotion signals using a high-resolution tactile carpet sensor without requiring wearable equipment. The proposed method captures and analyzes high-resolution footprint information using a machine learning technique and calculates the user's movement direction and speed in a real-time manner. In addition, the modular structure of Seamless-walk enables scalable installation of a tactile sensing platform at reasonable cost. Human tests (n = 80) confirmed that the proposed Seamless-walk system's technical advantage increases usability. A 3D virtual world exploration game experiment revealed that the proposed method significantly increases comfort and overall naturalness. Additionally, The proposed method has no negative effects on exploration suitability, task load, simulator sickness, or the game experience. [ABSTRACT FROM AUTHOR]

Human cyber-physical systems present an appealing platform for facilitating human-centered computing, particularly in environments where humans are required to interact with systems comprised of cyber and physical layers. Universally accepted elements of a human cyber-physical system, and interactions among these elements, do not appear to emerge in literature. To address this, a human cyber-physical system architecture is offered, led by the seafaring focus of this study. A human cyber-physical system implementation, called Mariner 4.0, for monitoring motion sickness of seafarers is presented to demonstrate the operation of a human cyber-physical system as a platform for human-centered computing. The human cyber-physical system implementation enabled the digital representation of the state of motion sickness and location of a seafarer, and conveyance of related information through a state monitor display. Using data captured on board South Africa's polar supply and research vessel, the SA Agulhas II, results showed that the human cyber-physical system incorporated location-specific variation in motion exposure experienced between two seafarers in motion sickness computations through facilitating individualized data acquisition and integration. [ABSTRACT FROM AUTHOR]

To improve the perception of haptic feedback, materials and sense-modifier effects should be examined. Teflon, Nylon mesh, and Silicone overlays were tested in combination with lateral vibrations to study their impact on the tactile sense. A feelable point moving along a line was implemented through the use of a dynamically moving interference maximum generated via the offset actuation of four haptic exciters affixed to corners of a Gorilla Glass surface. This feedback was presented to eight participants in a series of randomized experiments. Both the Nylon mesh and Teflon covering revealed a statistically significant (p < 0.05) impact of improvement to the user performance in the task of dynamic haptic virtual straight lines localization. While Silicone covering, having three times greater friction than Gorilla Glass, has less or no impact on both decision time, the number of task repetitions, and error rate (p > 0.05). The lateral vibration modifier (60 Hz) can also successfully be used with an increase in performance by about twofold, at least that was demonstrated for both the Nylon mesh and Teflon covering. [ABSTRACT FROM AUTHOR]

Summary: This study examined the influence of set-interval and repetition-interval rest time of virtual reality (VR) boxing game in supine-lying posture. Fifty healthy middle-aged adults were randomly assigned into VR and non-VR groups to perform six different exercise protocols with varying set-interval and repetition-interval rest times (S0R0, S0R1/3, S0R2/3, S40R0, S40R1/3, and S40R2/3). Analysis on the non-VR group showed significant differences between exercise protocols for average heart rate (p

Immersive Learning (iL) is known as a recent area of research that uses three-dimensional virtual environments and multi-sensory devices, also known as immersive technologies, to support the improvement of learning outcomes. This work aims to obtain evidence of theoretical and technological aspects of iL from the Symposium on Virtual and Augmented Reality (SVR) publications. A Systematic Literature Mapping protocol was developed and executed in order to select the primary studies to perform the analysis and data extraction. 76 primary studies helped to answer the research questions. A large part of the contributions by the SVR community are virtual environments that support education in the health area. In addition, some gaps and research opportunities were identified: virtual environments that serve audiences with special needs; development frameworks that consider pedagogical aspects; the use of biometric measures to support the validation of improved learning outcomes and more flexible tools that support educators in the development of immersive virtual environments that do not require specific knowledge in coding or 3D modeling.

Visualization literacy, the ability to interpret and comprehend visual designs, is recognized as an essential skill by the visualization community. We identify and investigate barriers to comprehending parallel coordinates plots (PCPs), one of the advanced graphical representations for the display of multivariate and high-dimensional data. We develop a parallel coordinates literacy test with diverse images generated using popular PCP software tools. The test improves PCP literacy and evaluates the user’s literacy skills. We introduce an interactive educational tool that assists the teaching and learning of parallel coordinates by offering a more active learning experience. Using this pedagogical tool, we aim to advance novice users’ parallel coordinates literacy skills. Based on the hypothesis that an interactive tool that links traditional Cartesian Coordinates with PCPs interactively will enhance PCP literacy further than static slides, we compare the learning experience using traditional slides with our novel software tool and investigate the efficiency of the educational software with an online, crowdsourced user-study. User-study results show that our pedagogical tool positively impacts a user’s PCP comprehension.

A recently developed model of general collective intelligence defines a method for organizing humans or artificially intelligent agents that is believed to create the potential to exponentially increase the general problem-solving ability of groups of such entities over that of any individual entity. An analysis based on this model suggests that many and perhaps all "wicked problems" are collective optimization problems that cannot reliably be addressed without a system of collective optimization, but that might be reliably addressed through such a system. This paper briefly introduces the concept of GCI as well as why it is a system of collective optimization, and explores why "wicked problems" from the perspective of GCI are collective optimization problems, and therefore why such problems might require GCI to be reliably solvable. [ABSTRACT FROM AUTHOR]

Setup and management of smart home systems is a complex task, and thus challenging for technically inexperienced users. We conducted a qualitative user study to evaluate whether an assistance system could empower users to make better and informed decisions regarding the selection of devices, their interoperability, the resulting set of features and their price. A group of 20 participants used our assistance app on a smartphone to configure a smart home while optimizing for features, interoperability, and the price-performance ratio. The results of our user study show that our assistance app can ease the problem of selecting useful devices and at the same time users become aware of new features resulting from the interoperation of selected devices. Furthermore, the assistance app can counteract the inherent interoperability problem between devices of different vendors or platforms. Finally, users are not only interested in individual device prices. They want to learn the cost of a certain feature set, including the cost of all devices necessary to realize this feature. Interestingly, none of the current smart home systems on the market offer a comparable assistance mechanism. Third-party solutions are not available either, because an assistance app requires meta data about features, interoperability, and usage of devices. This meta data is currently not available via APIs in state-of-the-art smart home systems and marketplaces. Therefore, we present a smart home architecture resulting from our research that can, among other benefits, provide the necessary meta data. Our research indicates that commercial smart home systems should invest more effort in user assistance to gain widespread adoption among technically inexperienced users. This in turn requires substantial changes to the meta data management in smart homes, because otherwise these assistance systems cannot be realized. [ABSTRACT FROM AUTHOR]

To the aim of constructing effective human-AI teams, that can be useful for improving caregiving in medicine and enhancing human performance also in other sectors (i.e., teaching), agents which interact with humans should be endowed with an emotion recognition and management module, capable of empathy, and of modeling aspects of the Theory of Mind, in the sense of being able to reconstruct what someone is thinking or feeling. In this paper, we propose an architecture for such a module, based upon an enhanced notion of Behavior Trees. We illustrate the effectiveness of the proposed architecture on a significant example and on a wider case study. [ABSTRACT FROM AUTHOR]

Emotion is important for the conversational user interface. In prior research, conversational agents (CAs) employ natural language process techniques to create affective interaction based on text. However, the use of acoustic features of speech for voice-based CAs is under exploration. This work presents an acoustically emotion-aware CA that enables speech emotion recognition and stylizes responses with empathetic feedback and interjections. We conducted an experiment with 75 participants to evaluate their perceived emotional intelligence (PEI) after interacting with the CA. Our results show that the acoustical emotion-awareness increased the participants’ PEI of the CA, and the empathetic responses from the CA helped alleviate some participants’ negative emotions. Our work provides implications for designing future CAs with better PEI. [ABSTRACT FROM AUTHOR]

Background: The personality and feedback of an animated pedagogical agent (APA) are vital social-emotional features that render the agent perceptually believable. The effect of them on learning in virtual training remains to be examined. Methods: In this paper, an explanation model was proposed to clarify the underlying mechanism of how these two features affect learners. Two studies were conducted to investigate the model. Study 1 reexamined the effect of APA’s personality type and feedback strategy on flow experience and performance, revealing significant effects of feedback strategy on flow and performance, as well as a marginal significant effect of personality type on performance. To explore the mechanism behind these effects, a theoretical model was proposed by distinguishing between intrinsic and extrinsic motivation effect. Study 2 tested the model and round that the APA’s personality type significantly influences factors in the path of extrinsic motivation effect rather than those in the path of intrinsic motivation effect. Results: By contrast, feedback strategy significantly affected factors in the path of intrinsic motivation effect. Conclusions: The proposed model was supported by these results; further distinguishing the two motivation effects is necessary to understand the respective effects of an APA’s personality and feedback features on learning experiences and outcomes.

In this paper, we report a method of implementing a universal volumetric haptic actuation platform which can be adapted to fit a wide variety of visual displays with flat surfaces. This platform aims to enable the simulation of the 3D features of input interfaces. This goal is achieved using four readily available stepper motors in a diagonal cross configuration with which we can quickly change the position of a surface in a manner that can render these volumetric features. In our research, we use a Microsoft Surface Go tablet placed on the haptic enhancement actuation platform to replicate the exploratory features of virtual keyboard keycaps displayed on the touchscreen. We ask seven participants to explore the surface of a virtual keypad comprised of 12 keycaps. As a second task, random key positions are announced one at a time, which the participant is expected to locate. These experiments are used to understand how and with what fidelity the volumetric feedback could improve performance (detection time, track length, and error rate) of detecting the specific keycaps location with haptic feedback and in the absence of visual feedback. Participants complete the tasks with great success (p < 0.05). In addition, their ability to feel convex keycaps is confirmed within the subjective comments.

Next app prediction can help enhance user interface design, pre-loading of apps, and network optimizations. Prior work has explored this topic, utilizing multiple different approaches but challenges like the user cold-start problem, data sparsity, and privacy concerns related to contextual data like location histories, persist. The user cold-start problem occurs when a user has recently registered to the smartphone app system and there is not enough information about his/her preferences and his/her history of smartphone usage. In this work, we try to address the above issues. We introduce WhatsNextApp, an approach based on LSTM (Long Short-Term Memory) networks using sequences of app usage logs. Our approach is inspired by Word Embeddings and treats sequences of app usage logs as sequences of words. We collect a real-life data set consisting of 975 Android users with over 22 million app usage events. We build a generic (user-independent) WhatsNextApp model and the evaluation with our data set shows that it outperforms related studies for existing users where we achieve a recall@8 (recall for the top 8 apps) of 92%. For the user cold-start problem with the 500 most frequent apps, we achieve a recall@8 of 82.7%.

Increasingly, laws are being proposed and passed by governments around the world to regulate artificial intelligence (AI) systems implemented into the public and private sectors. Many of these regulations address the transparency of AI systems, and related citizen-aware issues like allowing individuals to have the right to an explanation about how an AI system makes a decision that impacts them. Yet, almost all AI governance documents to date have a significant drawback: they have focused on what to do (or what not to do) with respect to making AI systems transparent, but have left the brunt of the work to technologists to figure out how to build transparent systems. We fill this gap by proposing a stakeholder-first approach that assists technologists in designing transparent, regulatory-compliant systems. We also describe a real-world case study that illustrates how this approach can be used in practice.

Various web-based image-editing tools and web-based collaborative tools exist in isolation. Research focusing to bridge the gap between these two domains is sparse. We respond to the above and develop prototype groupware for real-time collaborative editing of raster and vector images in a web browser. To better understand the requirements, we conduct a preliminary user study and establish communication and synchronization as key elements. The existing groupware for text documents or presentations handles the above through well-established techniques. However, those cannot be extended as it is for raster or vector graphics manipulation. To this end, we develop a document model that is maintained by a server and is delivered and synchronized to multiple clients. Our prototypical implementation is based on a scalable client–server architecture: using WebGL for interactive browser-based rendering and WebSocket connections to maintain synchronization. We evaluate our work qualitatively through a post-deployment user study for three different scenarios. For quantitative evaluation, we perform a thorough performance measure on both client and server side, thereby identifying design recommendations for future concurrent image-editing software(s). [ABSTRACT FROM AUTHOR]

Genomic research emerges from collaborative work within and across different scientific disciplines. A diverse range of visualisation techniques has been employed to aid this research, yet relatively little is known as to how these techniques facilitate collaboration. We conducted a case study of collaborative research within a biomedical institute to learn more about the role visualisation plays in genomic mapping. Interviews were conducted with molecular biologists (N = 5) and bioinformaticians (N = 6). We found that genomic research comprises a variety of distinct disciplines engaged in complex analytic tasks that each resist simplification, and their complexity influences how visualisations were used. Visualisation use was impacted by group-specific interactions and temporal work patterns. Visualisations were also crucial to the scientific workflow, used for both question formation and confirmation of hypotheses, and acted as an anchor for the communication of ideas and discussion. In the latter case, two approaches were taken: providing collaborators with either interactive or static imagery representing a viewpoint. The use of generic software for simplified visualisations, and quick production and curation was also noted. We discuss these findings with reference to group-specific interactions and present recommendations for improving collaborative practices through visual analytics. [ABSTRACT FROM AUTHOR]

Participatory design includes stakeholders in the development of products intended to solve real-life challenges. Involving end users in the design of robots is vital for developing effective, useful, acceptable and user-friendly products that meet expectations, needs, and preferences. This four-year international project developed and evaluated a home-based robot for mood stabilization and cognitive improvement in older adults with mild cognitive impairment and age-related health needs. The daily-care robot was developed in collaboration with experts, carers, relatives, and older adults, through six phases. Two phases were dedicated to cognitive stimulation games. This paper provides a summary of the participatory design and mixed-methods evaluation processes undertaken to develop, refine, and test the robot. The final robot and games were acceptable to older adults, and useful for delivering stimulating activities and providing reminders for medication, health and wellbeing checks. Personalization is required to optimize human-robot interaction, and imagery and speech should be consistent with local users. Functions should be personalizable to accommodate individual health needs and preferences. This project highlights the importance of participatory design and testing robotics in end-user environments, as technical issues associated with long-term use were uncovered. Recommendations for future development and the design of assistive health robots are made. [ABSTRACT FROM AUTHOR]

In Greek art, the phase from 900 to 700 BCE is referred to as the Geometric period due to the characteristically simple geometry-like ornamentations appearing on painted pottery surfaces during this era. Distinctive geometric patterns are typical for specific periods, regions, workshops as well as painters and are an important cue for archaeological tasks, such as dating and attribution. To date, these analyses are mostly conducted with the support of information technology. The primitives of an artefact's ornamentation can be generally classified into a set of distinguishable pattern classes, which also appear in a similar fashion on other objects. Although a taxonomy of known pattern classes is given in subject-specific publications, the automatic detection and classification of surface patterns from object depictions poses a non-trivial challenge. Our long-term goal is to provide this classification functionality using a specifically designed and trained neural network. This, however, requires a large amount of labelled training data, which at this point does not exist for this domain context. In this work, we propose an effective annotation system, which allows a domain expert to interactively segment and label parts of digitized vessel surfaces. These user inputs are constantly fed back to a Convolutional Neural Network (CNN), enabling the prediction of pattern classes for a given surface area with ever increasing precision. Our work paves the way for a fully automatic classification and analysis of large surface pattern collections, which, with the help of suitable visual analysis techniques, can answer research questions like pattern variability or change over time. While the capability of our proposed annotation pipeline is demonstrated at the example of two characteristic Greek pottery artefacts from the Geometric period, the proposed methods can be readily adopted for the patternation in any other chronological periods as well as for stamped motifs. [ABSTRACT FROM AUTHOR]