Reviewerly/exHarmony huggingface dataset:
Research on application of helmet wearing detection improved by YOLOv4 algorithm

Aiming at the problem that the model of YOLOv4 algorithm has too many parameters and the detection effect of small targets is poor, this paper proposes an improved helmet fitting detection model based on YOLOv4 algorithm. Firstly, this model improves the detection accuracy of small targets by adding multi-scale prediction and improving the structure of PANet network. Then, the improved depth-separable convolution was used to replace the standard 3 × 3 convolution, which greatly reduced the model parameters without reducing the detection ability of the model. Finally, the k_means clustering algorithm is used to optimize the prior box. The model was tested on the self-made helmet dataset helmet_dataset. Experimental results show that compared with the safety helmet detection model based on Faster RCNN algorithm, the improved YOLOv4 algorithm has faster detection speed, higher detection accuracy and smaller number of model parameters. Compared with the original YOLOv4 model, the mAP of the improved YOLOv4 algorithm is increased by 0.49%, reaching 93.05%. The number of model parameters was reduced by about 58%, to about 105 MB. The model reasoning speed is 35 FPS. The improved YOLOv4 algorithm can meet the requirements of helmet wearing detection in multiple scenarios.
Building Community Consensus for Scientific Metadata with YAMZ

ABSTRACT This paper reports on a demonstration of YAMZ (Yet Another Metadata Zoo) as a mechanism for building community consensus around metadata terms. The demonstration is motivated by the complexity of the metadata standards environment and the need for more user-friendly approaches for researchers to achieve vocabulary consensus. The paper reviews a series of metadata standardization challenges, explores crowdsourcing factors that offer possible solutions, and introduces the YAMZ system. A YAMZ demonstration is presented with members of the Toberer materials science laboratory at the Colorado School of Mines, where there is a need to confirm and maintain a shared understanding for the vocabulary supporting research documentation, data management, and their larger metadata infrastructure. The demonstration involves three key steps: 1) Sampling terms for the demonstration, 2) Engaging graduate student researchers in the demonstration, and 3) Reflecting on the demonstration. The results of these steps, including examples of the dialog provenance among lab members and voting, show the ease with YAMZ can facilitate building metadata vocabulary consensus. The conclusion discusses implications and highlights next steps.
Texting for life: a mobile phone application to connect pregnant women with emergency transport and obstetric care in rural Nigeria

Difficulty in transportation to access skilled providers has been cited repeatedly as a major barrier to utilization of emergency obstetric care in Nigeria.The objective of this paper is to describe the design, implementation, and outcomes of a mobile phone technology aimed at rapidly reaching rural Nigerian women who experience pregnancy complications with emergency transportation and access to providers.The project was implemented in 20 communities in two predominantly rural Local Government Areas (LGAs) of Edo State, in southern Nigeria, as part of a larger implementation project aimed at improving the access of rural women to skilled pregnancy care. The digital health innovation named Text4Life, allowed women to send a brief message from their mobile phone to a server linked to Primary Health Care (PHC) facilities and to access pre-registered transport owners. Pregnant women were registered and taught to text short messages to a server from their mobile phones or those of a friend or relative when they experience complications.Over 18 months, 56 women out of 1620 registered women (3.5%) texted the server requesting emergency transportation. Of this number, 51 were successfully transported to the PHC facilities, 46 were successfully treated at the PHC, and five were referred to higher-level care facilities. No maternal deaths occurred during the period, while four perinatal deaths were recorded.We conclude that a rapid short message sent from a mobile phone to a central server and connected to transport providers and health facility managers is effective in increasing the access of pregnant women to skilled emergency obstetric services in rural Nigeria.
"Who's that?"

Robotic Telepresence (RT) is a promising medium for students who are unable to attend in-person classes. It enables remote students to be present in the classroom and interact with their classmates and instructors. However, it can be limiting to their identity self-perception and projection, which may have repercussions on the social dynamics and inclusion within the classroom. We present preliminary findings of a qualitative analysis of 12 observations and interviews with RT attendees. We examine RT design and use aspects that either supported identity self-perception and projection or limited it. Finally, we present telepresence robots design and use recommendations for the classroom context.
Multisensory Evaluation of Human-Robot Interaction in Retail Stores - The Effect of Mobile Cobots on Individuals' Physical and Neurophysiological Responses

As more mobile collaborative robots (cobots) are being deployed in domestic environments, it is necessary to ensure safety while interacting with humans. To this end, a better understanding of individuals' physical and neurophysiological responses (i.e., short term adaptation) during those interactions becomes crucial to frame the cobot's behavioral and control algorithms. The primary objective of this study was to assess individuals' physical and neurophysiological responses to the mobile cobot in a retail environment. Eight participants were recruited to complete typical grocery shopping tasks (i.e., cart pushing, item picking, and item sorting) with and without a mobile robot running in the same space. Results showed the co-existence of mobile cobot in the retail environment stimulated individuals' physical responses, by significantly changing their upper-limb kinematics, i.e., reducing the average flexion angles of L5/S1, T12/L1, and right shoulder in the sagittal plane. However, no significant differences were observed in the neurophysiological adaptation based on the measures of muscle activity of the latissimus dorsi, anterior deltoid, and bicep brachii, nor the pupil diameter.
Designing and Prototyping Drones for Emotional Support

Recent work in the interaction community has revealed that drones can potentially become social entities with emotional capabilities beyond the traditional ontological status of drones as mechanical objects. We build upon this work to envision drones as an emotional support technology. To explore this notion, we ran a series of exploratory design workshops with lay users (N=18) to create designs for a companion drone. We used their inputs to analyze the drone's concept for prototyping and future work.
TEAM3 Challenge

Intuitive human-robot collaboration requires adaptive modalities for humans and robots to communicate and learn from each other. For diverse teams of humans and robots to naturally collaborate on novel tasks, robots must be able to model roles for themselves and other team members, anticipate how team members may perceive their actions, and communicate back to team members to continuously promote inclusive team cohesion toward achieving a shared goal. Here, we describe a set of tasks for studying mixed multi-human and multi-robot teams with heterogenous roles to achieve joint goals through both voice and gestural interactions. Based around the cooperative game TEAM3, we specify a series of dyadic and triadic human-robot collaboration tasks that require both verbal and nonverbal communication to effectively accomplish. Task materials are inexpensive and provide methods for studying a diverse set of challenges associated with human-robot communication, learning, and perspective-taking.
Workshop YOUR Study Design 2023!

A well-designed and evaluated study plays an essential role in highlighting the impact and contribution of a research idea. However, novice Human-Robot Interaction (HRI) researchers often lack the experience and know-how to devise an effective study. This workshop aims to provide a platform for those doing research in HRI, and related fields to obtain expert feedback on their study design before running a user study. The workshop invites a 2-4 page long contribution from participants outlining an upcoming user study focusing on the methods section and planned analyses. Participants will take part in two separate mentoring sessions led by different mentors. The workshop is interactive in nature and will also include mentor-led discussion sessions on topics relevant to study design such as hypothesis design and analysis, and human-centered study design.
Robot-Assisted First Language Learning in a New Latin Alphabet

This works addresses the lately initiated Cyrillic-to-Latin alphabet shift in Kazakhstan that may bring challenges for early literacy development and acquisition; both public and scientific communities agree on possible resistance to acquiring and using a new alphabet. To support the acquisition of the new Kazakh Latin alphabet and its handwriting, this study proposes a reinforcement-learning (RL) system named QWriter. It comprises a humanoid robot NAO, a tablet with a stylus, and an RL agent that learns from a child's mistakes and progresses to maximize alphabet learning in the shortest amount of time by altering the order of practice words in response to a child's mistakes. We conducted a five-sessions experiment using a between-subject design with 69 Kazakh children ages 7 to 10 and compared their learning performance with a human tutor to assess the effectiveness of the QWriter system. Overall results show no significant differences in learning gains between the two conditions. Our study foregrounds the promising potential of the RL-based social robot in teaching foundational letter acquisition and writing over time.
Language Learning using Caption Generation within Reciprocal Multi-Party Child-Tutor-Tutee Interaction

Reciprocal Peer Tutoring (RPT) is a learning paradigm characteristic of collaborative interaction between learners with alternating tutor-tutee roles. In recent years, robot-assisted language learning (RALL) has gained traction by its wide application for learning language skills, such as speaking or writing, using social robots. Our work aims at exploring the effectiveness of RPT in learning Kazakh as a second language with the help of two robots acting either as a tutee or a tutor. To this end, we piloted a within-subject experiment with 21 children aged 8 and 9 years old from a primary school with Kazakh and Russian languages of instruction. The results show that the tutor robot was more effective in terms of learning gains, while the tutee robot brought positive emotional experiences.
Resolving References in Natural Language Explanation Requests about Robot Behavior in HRI

In HRI, users have been shown to request explanations when their interpretation of autonomous robot behavior fails. These requests can refer to the behavior either by open questions or with attributes of the behavior. The presented work aims to resolve these references in explanation requests by developing an episodic memory with a graph database that stores and queries representations of the internal execution. The reference resolution is done by the detection of temporal adverb and verb constraints in the syntactical dependency tree of utterances, the execution of a query in the episodic memory, and the scoring of the resulting entries to find the referred behavior. The explanation generation process of the original model is adapted to the new approach and can contain additional information such as detected constraints, a failed execution state, and the distinction between running and completed executions.
Investigating Learning from Demonstration in Imperfect and Real World Scenarios

As the world's population is aging and there are growing shortages of caregivers, research into assistive robots is increasingly important. Due to differing needs and preferences, which may change over time, end-users will need to be able to communicate their preferences to a robot. Learning from Demonstration (LfD) is one method that enables non-expert users to program robots. While a powerful tool, prior research in LfD has made assumptions that break down in real-world scenarios. In this work, we investigate how to learn from suboptimal and heterogeneous demonstrators, how users react to failure with LfD, and the feasibility of LfD with a target population of older adults.
Borrowing, Poking and Entangling. In Search of Shared Spaces Between Science and Technology Studies and Human-Robot Interaction

In this paper, we reflect on the disciplinary foundations and dominant practices in the field of Human-Robot Interaction (HRI) from the perspective of our own experience of working interdisciplinarily and drawing on colleagues' ongoing work that transcends disciplinary boundaries. As a part of this reflection, we explore possibilities for the field's theoretical and methodological expansion, which we contend is needed, given the rapid expansion of robotic technologies in the real world settings. We argue the field of science and technology studies (STS) can be a valuable collaborator and contributor in the process of negotiating disciplinary boundaries of HRI and advancing the field beyond common narratives of technological solutionism and determinism. We frame STS as a field with a strong tradition of studying social and political embeddedness of science and technology, and how these are co-constitutive and co-emergent. STS also investigates the roles and responsibility different actors share in this process. To further explore how the interfacing between STS and HRI can be enacted, we sketch out three modes of interdisciplinary collaboration we call i) Borrowing, ii) Poking and iii) Entangling. We argue that each of these modes comes with advantages, disadvantages and challenges. In the conclusion, we engage the notions of "thinking with care'' and disciplinary reflexivity, as an invitation to fellow scholars to consider which disciplinary assumptions are brought to the table when enacting different modes of interfacing between HRI and STS, and how these are entangled with the goals and (desired) outcomes of research practices.
A quantum key distribution network in the metropolitan area of Padova

One of the most advanced technologies within the field of quantum mechanics is quantum key distribution (QKD), which allows the secure generation of secret keys among remote users. In order for QKD to be more widely adopted, it must be integrated into existing classical communication systems. However, this can be difficult due to the use of various technologies and channels in deployed networks. Recently, we developed a QKD network in the metropolitan area of Padova, which connects various nodes across the city through a combination of fiber and free-space links. By utilizing a modular design based on the iPOGNAC encoder and the Qubit4Sync method, we have realized portable and adaptable systems that operate in the C and O bands. This allowed us to deploy and test the compatibility of both research and commercial QKD systems by ThinkQuantum with classical communication over a variety of links, as well as their ability to switch between free-space and fiber connections. Finally, we developed and experimentally implemented complex network configurations such as star networks, where a fiber-based transmitter and free-space transmitter could operate with a single receiver.
Progress towards a three-node ion-trap quantum network

Trapped ions are one of the leading platforms for quantum computation and networking, with long coherence times and high fidelity qubit operation. We exploit these attributes to build a quantum network with the use of three trapped ion modules connected via photonic links. In each of these systems, we have ions of different species for use as memory or communication qubits. Our newest module contains two high NA=0.8 objectives, each covering 20% of the solid angle of photons emitted from the ion, making it suitable for use as the middle node of the network. Photons are collected from each chamber, and we generate remote entanglement between each pair of nodes by interfering the photons on a beamsplitter. This heralds entanglement of the communication ions in the pair of network nodes, which we then swap onto the memory qubits. I will discuss our progress towards creating a Greenberger–Horne–Zeilinger (GHZ) state between the memory ions in the three nodes.
Drone-based quantum communication links

Quantum networks between mobile platforms enable secure communication, distributed quantum sensors, and distributed quantum computing. As progress towards a future quantum internet continues, connecting mobile platforms (e.g., unmanned drones, smart vehicles, ships, and planes) to quantum networks remains a challenge. For instance, engineering constraints for real-world mobile platforms require low size, weight, and power (SWaP) for quantum systems. Additionally, single photons must be routed to platforms that are in motion and experience vibrations. In this effort, we discuss progress toward developing and demonstrating quantum communication links, including decoy-state quantum key distribution (QKD), between mobile drone and vehicle platforms in several configurations (drone-to-drone, drone-to-moving vehicle, and vehicle-to-vehicle). We will discuss and analyze critical subsystems including our decoy-state QKD source based on resonant cavity light emitting diodes (LED), compact optical system design, pointing, acquisition, and tracking (PAT) subsystem, single-photon detectors, field-programmable gate array-based time-tagger, and a novel time-synchronization algorithm. In addition, we present system performance including tracking performance under multiple conditions and mobile platform configurations.
Satellite quantum key distribution performance analysis and optimization with finite key size constraints

Quantum Key Distribution (QKD) is a potential mitigation against quantum computation advances that threaten currently deployed public key cryptosystems. QKD is also a precursor to large-scale quantum communications and the quantum internet. Terrestrial direct transmission of quantum photonic signals is restricted by optical fibre exponential absorption, hence space-based systems have been proposed for intercontinental distribution of quantum keys with lower-loss free-space transmission through vacuum. However, challenges remain for the practical deployment of satellite QKD (SatQKD) such as weather, background light, and terminal deployment. In particular, the short transmission time inherent in low Earth orbit satellite QKD and finite statistical uncertainty can limit secure key generation. Here we illustrate the analysis of these effects and some practical engineering design and operational constraints on key length using the Satellite Quantum Modelling and Analysis (SatQuMA) software package being developed for satellite quantum communications research.
An Empirical Study on Code Smell Introduction and Removal in Deep Learning Software Projects

With increasing popularity of Deep Learning (DL) software development, code quality issues arise in DL software development. Code smell is one of the factors which reduce the quality of source code. Several previous studies investigated the prevalence of code smell in DL software systems to evaluate the quality of DL source code. However, there is still a lack of understanding of the awareness of individual DL developers in code smell. To more deeply understand the code smell risk in DL software development, it is needed to investigate the code smell awareness of DL developers. In this paper, we present an empirical study on code smell practices of DL developers. Specifically, we performed a quantitative analysis on code smell introduction and removal practices of DL developers. We collected a dataset of code smell introduction and removal history of DL developers from several open source DL software GitHub repositories. We then quantitatively analyzed the collected dataset. As a result of the quantitative analysis, we observed the following three findings on code smell introduction and removal practices of DL developers. First, DL developers tend to perform code smell introduction practice more than code smell removal practice. Second, DL developers have slightly broader code smell introduction scope than code smell removal scope. Third, regular and irregular DL developers have less difference in both code smell introduction and removal practices. The results indicate that DL developers have very poor awareness on code smell risk. Our findings suggest that DL software development project managers should provide a helpful guideline that makes DL developers actively participate code smell removal tasks.
Extending two families of bivariate APN functions

In this work, we present two families of quadratic APN functions. The first one (F1) is constructed via biprojective polynomials. This family extends one of the two APN families introduced by Göloǧlu in 2022. Then, following a similar approach as in Li et al. (2022) [28], we give another family (F2) obtained by adding certain terms to F1. As a byproduct, this second family extends one of the two families introduced by Li et al. (2022) [28]. Moreover, we show that for n=12, from our constructions, we can obtain APN functions which are CCZ-inequivalent to any other known APN function over F212.
Background subtraction via regional multi-feature-frequency model in complex scenes

Although a wide variety of background subtraction methods has been proposed in recent years, none has been able to fully address multi-scale moving objects and dynamic background in real surveillance tasks. In this paper, a novel and effective background subtraction method, named regional multi-feature-frequency (RMFF), is proposed to detect multi-scale moving objects under dynamic background. Unlike many existing methods construct background model using simple multi-feature combinations, RMFF exploits the spatiotemporal cues of multi-feature as well as superpixels at each scale, thus allowing for more robust information to be exploited for background modeling. Specifically, the spatial relationship between pixels in a neighborhood and the frequencies of features over time are first exploited, enabling accurate detection of moving objects while ignoring most dynamic background changes. Then, the use of multi-scale superpixels for exploiting the structural information existing in real-world scenes further enhances robustness to multi-scale objects and environmental variations. Finally, an adaptive strategy is employed to dynamically adjust the foreground/background segmentation threshold for each region without user intervention. This adaptive threshold is defined for each region separately, and can adjust dynamically based on continuous monitoring of the background changes, thereby effectively reducing potential segmentation noise. Experiments on the 2014 version of the ChangeDetection.net dataset demonstrate that the proposed method outperforms the 12 state-of-the-art algorithms in terms of overall F-Measure and performs effectively in many complex scenes. Consequently, it is verified that the developed approach is feasible and useful for robust application in practical video surveillance.