Miracle-dz/UniFAR-FTUs huggingface dataset:
Knowledge Gaps and Research Priorities in Living Shorelines Science: Insights from Stakeholder Interviews Throughout the U.S. Pacific Coast

Abstract. Living shorelines provide a nature-based strategy for coastal restoration with ample opportunity for community engagement and collaboration with interdisciplinary stakeholders. While their implementation has increased over the past few decades, restoration via this technique is limited by several factors including a lack of data sharing among projects and geographical regions, a shortage of long-term monitoring to demonstrate efficacy at meeting project goals, and a need for greater interdisciplinary communication moving forward. In this study, we reviewed recent literature from a range of living shorelines studies throughout the United States and conducted interviews with nature-based coastal restoration practitioners primarily from the U.S. west coast. The insight from these stakeholder interviews allowed us to identify major knowledge gaps about living shorelines and establish priorities for future research and funding, including: (1) funding demonstration projects in their early research stages, (2) supporting projects and trainings for engineers utilizing nature-based infrastructure, (3) conducting long-term monitoring of both ecological and structural properties, (4) communicating findings, importance, and project visualizations to stakeholders within and between communities, and (5) advancing the causes of environmental justice and equity. By reviewing recent literature and engaging with living shoreline practitioners to gather their experiences and suggestions, we have increased understanding of how living shoreline restoration can be more effectively planned, constructed, and monitored at scale, in varied locations and using a range of techniques.
Impacts of Robot Learning on User Attitude and Behavior

With an aging population and a growing shortage of caregivers, the need for in-home robots is increasing. However, it is intractable for robots to have all functionalities pre-programmed prior to deployment. Instead, it is more realistic for robots to engage in supplemental, on-site learning about the user's needs and preferences. Such learning may occur in the presence of or involve the user. We investigate the impacts on end-users of in situ robot learning through a series of human-subjects experiments. We examine how different learning methods influence both in-person and remote participants' perceptions of the robot. While we find that the degree of user involvement in the robot's learning method impacts perceived anthropomorphism (p=.001), we find that it is the participants' perceived success of the robot that impacts the participants' trust in (p<.001) and perceived usability of the robot (p<.001) rather than the robot's learning method. Therefore, when presenting robot learning, the performance of the learning method appears more important than the degree of user involvement in the learning. Furthermore, we find that the physical presence of the robot impacts perceived safety (p<.001), trust (p<.001), and usability (p<.014). Thus, for tabletop manipulation tasks, researchers should consider the impact of physical presence on experiment participants.
A Social Robot Reading Partner for Explorative Guidance

Pedagogical agent research has yielded fruitful results in both academic skill learning and meta-cognitive skill acquisition, often studied in instructional or peer-to-peer paradigms. In the past decades, child-centric pedagogical research, which emphasizes the learner's active participation in learning with self-motivation, curiosity, and exploration, has attracted scholarly attention. Studies show that combining child-driven pedagogy with appropriate adult guidance leads to efficient learning and a strengthened feeling of self-efficacy. However, research on using social robots for guidance in child-driven learning still remains open and under-explored. In our study, we focus on children's exploration as the vehicle in literacy learning and develop a social robot companion that provides guidance to encourage and motivate children to explore during a storybook reading interaction. To investigate the effect of the robot's explorative guidance, we compare it against a control condition in which children have full autonomy to explore and read the storybooks. We conduct a between-subjects study with 31 children aged 4 to 6, and the result shows that children who receive explorative guidance from the social robot exhibit a growing trend of self-exploration. Further, children's self-exploration in the explorative guidance condition is found correlated to their learning outcome. We conclude the study with recommendations for designing social agents to guide children's exploration and future research directions in child-centric AI-assisted pedagogy.
Transfer Learning of Human Preferences for Proactive Robot Assistance in Assembly Tasks

We focus on enabling robots to proactively assist humans in assembly tasks by adapting to their preferred sequence of actions. Much work on robot adaptation requires human demonstrations of the task. However, human demonstrations of real-world assemblies can be tedious and time-consuming. Thus, we propose learning human preferences from demonstrations in a shorter, canonical task to predict user actions in the actual assembly task. The proposed system uses the preference model learned from the canonical task as a prior and updates the model through interaction when predictions are inaccurate. We evaluate the proposed system in simulated assembly tasks and in a real-world human-robot assembly study and we show that both transferring the preference model from the canonical task, as well as updating the model online, contribute to improved accuracy in human action prediction. This enables the robot to proactively assist users, significantly reduce their idle time, and improve their experience working with the robot, compared to a reactive robot.
The Effect of Robot Skill Level and Communication in Rapid, Proximate Human-Robot Collaboration

As high-speed, agile robots become more commonplace, these robots will have the potential to better aid and collaborate with humans. However, due to the increased agility and functionality of these robots, close collaboration with humans can create safety concerns that alter team dynamics and degrade task performance. In this work, we aim to enable the deployment of safe and trustworthy agile robots that operate in proximity with humans. We do so by 1) Proposing a novel human-robot doubles table tennis scenario to serve as a testbed for studying agile, proximate human-robot collaboration and 2) Conducting a user-study to understand how attributes of the robot (e.g., robot competency or capacity to communicate) impact team dynamics, perceived safety, and perceived trust, and how these latent factors affect human-robot collaboration (HRC) performance. We find that robot competency significantly increases perceived trust (p < .001), extending skill-to-trust assessments in prior studies to agile, proximate HRC. Furthermore, interestingly, we find that when the robot vocalizes its intention to perform a task, it results in a significant decrease in team performance (p = .037) and perceived safety of the system (p = .009).
Effects of Human-Swarm Interaction on Subjective Time Perception: Swarm Size and Speed

Many large-scale multi-robot systems require human input during operation in different applications. To still minimize the human effort, interaction is intermittent or restricted to a subset of robots. Despite this reduced demand for human interaction, the mental load and stress can be challenging for the human operator. A specific effect of human-swarm interaction may be a hypothesized change of subjective time perception in the human operator. In a series of simple human-swarm interaction experiments with robot swarms of up to 15 physical robots, we study whether human operators have altered time perception due to the number of controlled robots or robot speeds. Using data gathered by questionnaires, we found that increased swarm size shrinks perceived time and decreased robot speeds expand the perceived time. We introduce the concept of subjective time perception to human-swarm interaction. Future research will enable swarm systems to autonomously modulate subjective timing to ease the job of human operators.
"What If It Is Wrong": Effects of Power Dynamics and Trust Repair Strategy on Trust and Compliance in HRI

Robotic systems designed to work alongside people are susceptible to technical and unexpected errors. Prior work has investigated a variety of strategies aimed at repairing people's trust in the robot after its erroneous operations. In this work, we explore the effect of post-error trust repair strategies (promise and explanation) on people's trust in the robot under varying power dynamics (supervisor and subordinate robot). Our results show that, regardless of the power dynamics, promise is more effective at repairing user trust than explanation. Moreover, people found a supervisor robot with verbal trust repair to be more trustworthy than a subordinate robot with verbal trust repair. Our results further reveal that people are prone to complying with the supervisor robot even if it is wrong. We discuss the ethical concerns in the use of supervisor robot and potential interventions to prevent improper compliance in users for more productive human-robot collaboration.
Hey?: ! What did you think about that Robot? Groups Polarize Users' Acceptance and Trust of Food Delivery Robots

As food delivery robots are spreading onto streets and college campuses worldwide, users' views of these robots will depend on their direct and indirect interactions with the robots and their conversations with other people, such as those with whom they are ordering food via a robot. We examined if being in a group of 2 to 3 people affects the acceptance and trust of the robot compared to being an individual user. First-time users of the food delivery robot service (N = 60) ordered food either as an Individual or in a Group. We measured the acceptance and trust of the robots after three Exposures (pre-exposure, after ordering food on the app, and after the robots delivered the food). Results indicated that Individual users had more acceptance and trust compared to Group users. Further, as hypothesized, groups had more variation in acceptance and trust compared to individual users, consistent with patterns of group polarization i.e., group members influencing each other's perceptions to become more positive or negative. Further analysis demonstrated that group members were highly influenced by their groupmates. Designers and restaurant operators should consider how to enhance group members' experience of delivery robots.
Lively: Enabling Multimodal, Lifelike, and Extensible Real-time Robot Motion

Robots designed to interact with people in collaborative or social scenarios must move in ways that are consistent with the robot's task and communication goals. However, combining these goals in a naïve manner can result in mutually exclusive solutions, or infeasible or problematic states and actions. In this paper, we present Lively, a framework which supports configurable, real-time, task-based and communicative or socially-expressive motion for collaborative and social robotics across multiple levels of programmatic accessibility. Lively supports a wide range of control methods (i.e. position, orientation, and joint-space goals), and balances them with complex procedural behaviors for natural, lifelike motion that are effective in collaborative and social contexts. We discuss the design of three levels of programmatic accessibility of Lively, including a graphical user interface for visual design called LivelyStudio, the core library Lively for full access to its capabilities for developers, and an extensible architecture for greater customizability and capability.
Self-Annotation Methods for Aligning Implicit and Explicit Human Feedback in Human-Robot Interaction

Recent research in robot learning suggests that implicit human feedback is a low-cost approach to improving robot behavior without the typical teaching burden on users. Because implicit feedback can be difficult to interpret, though, we study different methods to collect fine-grained labels from users about robot performance across multiple dimensions, which can then serve to map implicit human feedback to performance values. In particular, we focused on understanding the effects of annotation order and frequency on human perceptions of the self-annotation process and the usefulness of the labels for creating data-driven models to reason about implicit feedback. Our results demonstrate that different annotation methods can influence perceived memory burden, annotation difficulty, and overall annotation time. Based on our findings, we conclude with recommendations to create future implicit feedback datasets in Human-Robot Interaction.
Fast One-Sided RDMA-Based State Machine Replication for Disaggregated Memory

Disaggregated memory architecture has risen in popularity for large datacenters with the advantage of improved resource utilization, failure isolation, and elasticity. Replicated state machines (RSMs) have been extensively used for reliability and consistency. In traditional RSM protocols, each replica stores replicated data and has the computing power to participate in some part of the protocols. However, traditional RSM protocols fail to work in the disaggregated memory architecture due to asymmetric resources on CPU nodes and memory nodes. This article proposes ECHO, a fast one-sided RDMA-based RSM protocol with lightweight log replication and remote applying, efficient linearizability guarantee, and fast coordinator failure recovery. ECHO enables all operations in the protocol to be efficiently executed using only one-sided RDMA, without the participation of any computing resource in the memory pool. To provide lightweight log replication and remote applying, ECHO couples the replicated log and the state machine to avoid dual-copy and performs remote applying by updating pointers. To enable efficient remote log state management, ECHO leverages a hitchhiked log state updating scheme to eliminate extra network round trips. To provide efficient linearizability guarantee, ECHO performs immediate remote applying after log replication and leverages the local locks at the coordinator to ensure linear consistency. Moreover, ECHO adopts a commit-aware log cache to make data visible immediately after being committed. To achieve fast failure recovery, ECHO leverages a commit point identification scheme to reduce the overhead of log consistency recovery. Experimental results demonstrate that ECHO outperforms the state-of-the-art RSM protocol (namely Sift) in multiple scenarios. For example, ECHO achieves 27%–52% higher throughput on typical write-intensive workloads. Moreover, ECHO reduces the consistency recovery time by three orders of magnitude for coordinator failure.
Grounding Metaphors and Inscriptional Resonance: Children's Emerging Understanding of Mathematical Similarity

The goal of this classroom study of third-grade students was to support and document the emergence of multiple senses of mathematical similarity. Beginning with grounding metaphors of scale, magnification, and classification, the classroom teacher helped students redescribe their perceptions of these everyday experiences with the mathematics of similarity. Each sense of similarity was mediated by a distinct form of mathematical inscription: a ratio, an algebraic "rule," and a line in a Cartesian system. These forms of inscription were tools for externalizing and structuring children's perceptions of magnification and scale ("growing") and of classification ("same shape"). Children's interpretations of the mathematical meanings of the notational systems employed were supported by successive forms of signification, which Peirce (1898/1992) described as iconic, indexical, and symbolic. We tracked student sense making through 2 sequences of lessons, first involving 2- and then 3-dimensional forms. The shift in dimension supported students' integration of multiple senses of similarity as ratio and as scale. The process of integration was assisted by resonances among diverse forms of inscription. Students' explorations of similarity served later in the year as a resource for modeling nature (e.g., by conducting explorations of density and growth). This shift toward modeling introduced a troublesome, yet ultimately rewarding, epistemological dissonance between mathematics and science. Postinstructional interviews suggested that most children came to appreciate the mathematical generalizations afforded by the algebraic and graphical forms of notation used to inscribe similar forms. A follow-up design experiment conducted in the fifth grade included some revisions to instruction that proved fruitful. Our concluding comments about design experiments and developmental corridors are motivated by a need to rethink these ideas in light of the contingent and historical nature of student thinking as it unfolded in these classrooms.
Resolving Conflicts During Human-Robot Co-Manipulation

This paper proposes a machine learning (ML) approach to detect and resolve motion conflicts that occur between a human and a proactive robot during the execution of a physically collaborative task. We train a random forest classifier to distinguish between harmonious and conflicting human-robot interaction behaviors during object co-manipulation. Kinesthetic information generated through the teamwork is used to describe the interactive quality of collaboration. As such, we demonstrate that features derived from haptic (force/torque) data are sufficient to classify if the human and the robot harmoniously manipulate the object or they face a conflict. A conflict resolution strategy is implemented to get the robotic partner to proactively contribute to the task via online trajectory planning whenever interactive motion patterns are harmonious, and to follow the human lead when a conflict is detected. An admittance controller regulates the physical interaction between the human and the robot during the task. This enables the robot to follow the human passively when there is a conflict. An artificial potential field is used to proactively control the robot motion when partners work in harmony. An experimental study is designed to create scenarios involving harmonious and conflicting interactions during collaborative manipulation of an object, and to create a dataset to train and test the random forest classifier. The results of the study show that ML can successfully detect conflicts and the proposed conflict resolution mechanism reduces human force and effort significantly compared to the case of a passive robot that always follows the human partner and a proactive robot that cannot resolve conflicts.
"Being in on the Action" in Mobile Robotic Telepresence: Rethinking Presence in Hybrid Participation

Mobile Robotic Telepresence (MRP) systems afford remote communication with an embodied physicality and autonomous mobility, which is thought to be useful for creating a sense of presence in hybrid activities. In this paper, drawing on phenomenology, we interviewed seven long term users of MRP to understand the lived experience of participating in hybrid spaces through a telepresence robot. The users' accounts show how the capabilities of the robot impact interactions, and how telepresence differs from in-person presence. Whilst not feeling as if they were really there, users felt present when they were being able to participate in local action and be treated as present. They also report standing out and being subject to behaviour amounting to 'othering'. We argue that these experiences point to a need for future work on telepresence to focus on giving remote users the means to exercise autonomy in ways that enable them to participate --- to be 'in on the action' --- rather than in ways that simply simulate being in-person.
A2SC: Adversarial Attacks on Subspace Clustering

Many studies demonstrate that supervised learning techniques are vulnerable to adversarial examples. However, adversarial threats in unsupervised learning have not drawn sufficient scholarly attention. In this article, we formally address the unexplored adversarial attacks in the equally important unsupervised clustering field and propose the concept of the adversarial set and adversarial set attack for clustering. To illustrate the basic idea, we design a novel adversarial space-mapping attack algorithm to confuse subspace clustering, one of the mainstream branches of unsupervised clustering. It maps a sample into one wrong class by moving it towards the closest point on the linear subspace of the target class, that is, along the normal of the closest point. This simple single-step algorithm has the power to craft the adversarial set where the image samples can be wrongly clustered, even into the targeted labels. Empirical results on different image datasets verify the effectiveness and superiority of our algorithm. We further show that deep supervised learning algorithms (such as VGG and ResNet) are also vulnerable to our crafted adversarial set, which illustrates the good cross-task transferability of the adversarial set.
DiaFocus: A Personal Health Technology for Adaptive Assessment in Long-Term Management of Type 2 Diabetes

Type 2 diabetes (T2D) is a large disease burden worldwide and represents an increasing and complex challenge for all societies. For the individual, T2D is a complex, multi-dimensional, and long-term challenge to manage, and it is challenging to establish and maintain good communication between the patient and healthcare professionals. This article presents DiaFocus, which is a mobile health sensing application for long-term ambulatory management of T2D. DiaFocus supports an adaptive collection of physiological, behavioral, and contextual data in combination with ecological assessments of psycho-social factors. This data is used for improving patient-clinician communication during consultations. DiaFocus is built using a generic data collection framework for mobile and wearable sensing and is highly extensible and customizable. We deployed DiaFocus in a 6-week feasibility study involving 12 patients with T2D. The patients found the DiaFocus approach and system useful and usable for diabetes management. Most patients would use such a system, if available as part of their treatment. Analysis of the collected data shows that mobile sensing is feasible for longitudinal ambulatory assessment of T2D, and helped identify the most appropriate target users being early diagnosed and technically literate T2D patients.