Miracle-dz/UniFAR-FTUs huggingface dataset:
How do People Type on Mobile Devices?: Observations from a Study with 37,000 Volunteers

This paper presents a large-scale dataset on mobile text entry collected via a web-based transcription task performed by 37,370 volunteers. The average typing speed was 36.2 WPM with 2.3% uncorrected errors. The scale of the data enables powerful statistical analyses on the correlation between typing performance and various factors, such as demographics, finger usage, and use of intelligent text entry techniques. We report effects of age and finger usage on performance that correspond to previous studies. We also find evidence of relationships between performance and use of intelligent text entry techniques: auto-correct usage correlates positively with entry rates, whereas word prediction usage has a negative correlation. To aid further work on modeling, machine learning and design improvements in mobile text entry, we make the code and dataset openly available.
How to Enhance Social Robots' Heartwarming Interaction in Service Encounters

As social robots are being increasingly employed in service encounters, effective interactive design should be considered. This paper focused on heartwarming interaction for hospitality, and explored an impact of a kind of robot's behavior, perceived cuteness of a robot, and users' personality on the interactions. The experimental results identified key factors which affects impression of heartwarming interaction.
Bayesian Model Selection Approach to Multiple Change-Points Detection with Non-Local Prior Distributions

We propose a Bayesian model selection (BMS) boundary detection procedure using non-local prior distributions for a sequence of data with multiple systematic mean changes. By using the non-local priors in the BMS framework, the BMS method can effectively suppress the non-boundary spike points with large instantaneous changes. Further, we speedup the algorithm by reducing the multiple change points to a series of single change point detection problems. We establish the consistency of the estimated number and locations of the change points under various prior distributions. From both theoretical and numerical perspectives, we show that the non-local inverse moment prior leads to the fastest convergence rate in identifying the true change points on the boundaries. Extensive simulation studies are conducted to compare the BMS with existing methods, and our method is illustrated with application to the magnetic resonance imaging guided radiation therapy data.
Performance Evaluation of HEVC RCL Applications Mapped onto NoC-Based Embedded Platforms

Today, several applications running into embedded systems have to fulfill soft or hard timing constraints. Video applications, like the modern High Efficiency Video Coding (HEVC), e.g., most often have soft real-time constraints. However, in specific scenarios, such as in robotic surgeries, the coupling of satellites and so on, harder timing constraints arise, becoming a huge challenge. Although the implementation of such applications in Networks-on-Chip (NoCs) being an alternative to reduce their algorithmic complexity and meet real-time constraints, a performance evaluation of the mapped NoC and the schedulability analysis for a given application are mandatory. In this work we make a performance evaluation of HEVC Residual Coding Loop (RCL) mapped onto a NoC-based embedded platform, considering the encoding of a single $1920\mathrm{x}1080$ pixels frame. A set of analysis exploring the combination of different NoC sizes and task mapping strategies were performed, showing for the typical and upper-bound workload cases scenarios when the application is schedulable and meets the real-time constraints.
Should Speech Dialogue System Use Honorific Expression?

As voice recognition technology develops, we talk to smart speakers and other voice assistants more and more frequently in everyday life. Many such speech dialog systems are designed to use honorifics. However, humans do not always use honorifics in everyday conversation. Thus, we consider that voice assistants need to use other forms of expression in addition to so-called polite language. In this study, we conducted a comparative experiment to investigate how the speaking styles of smart speakers affect human listeners. In this experiment, a participant talked to a smart speaker to make travel plans. We analyzed the results of questionnaires. We found that a smart speaker's friendly speaking style influenced the participants' impressions and thus shaped the development of good relationships between humans and agents. Our findings indicate that people regard agents as friends if they speak in a friendly manner.
Cognitive Modeling of Intrinsic Motivation for Long-Term Interaction

In the field of Human-Agent Interaction (HAI), continuation of interaction is one of the main areas of research. If the behavior of the agent is too predictable, humans stop interacting with it when they get bored. In this study, we aim to build agents that people want to keep interacting with, by employing the Adaptive Control of Thought-Rational (ACT-R) cognitive architecture. As a preliminary step, we attempt to clarify the conditions required to maintain interaction between humans and agents, by modeling the experiences of fun and boredom based on intrinsic motivation.
Optimal Task Assignment Based on Incentive Design in the Virtual Enterprise: a Principal-agent Perspective

Based on incentive design, this paper studies a type of task assignment problem in the context of virtual enterprise. We propose a multi-type task assignment model with the optimization objectives of total profit, task execution cost and time, which considers the effort level of partner enterprises. Next, an incentive contract for the task assignment considering monitoring is designed based on the principal-agent theory to motivate all partners to improve their effort levels. Further, we analyze the relationships among the intensity of incentives and monitoring, the effort level and the task assignment through numerical examples. The results demonstrate that appropriate incentive contracts are beneficial to improve the efficiency of task assignment in virtual enterprise.
A Markovian Method for Predicting Trust Behavior in Human-Agent Interaction

Trust calibration is critical to the success of human-agent interaction (HAI). However, individual differences are ubiquitous in people's trust relationships with autonomous systems. To assist its heterogeneous human teammates calibrate their trust in it, an agent must first dynamically model them as individuals, rather than communicating with them all in the same manner. It can then generate expectations of its teammates' behavior and optimize its own communication based on the current state of the trust relationship it has with them. In this work, we examine how an agent can generate accurate expectations given observations of only the teammate's trust-related behaviors (e.g., did the person follow or ignore its advice? ). In addition to this limited input, we also seek a specific output: accurately predicting its human teammate's future trust behavior (e.g., will the person follow or ignore my next suggestion? ). In this investigation, we construct a model capable of generating such expectations using data gathered in a human-subject study of behavior in a simulated human-robot interaction (HRI) scenario. We first analyze the ability of measures from a pre-survey on trust-related traits to accurately predict subsequent trust behaviors. However, as the interaction progresses, this effect is dwarfed by the direct experience. We therefore analyze the ability of sequences of prior behavior by the teammate to accurately predict subsequent trust behaviors. Such behavioral sequences have shown to be indicative of the subjective beliefs of other teammates, and we show here that they have a predictive power as well.
App-LSTM: Data-driven Generation of Socially Acceptable Trajectories for Approaching Small Groups of Agents

While many works involving human-agent interactions have focused on individuals or crowds, modelling interactions on the group scale has not been considered in depth. Simulation of interactions with groups of agents is vital in many applications, enabling more comprehensive and realistic behavior encompassing all possibilities between crowd and individual levels. In this paper, we propose a novel neural network App-LSTM to generate the approach trajectory of an agent towards a small free-standing conversational group of agents. The App-LSTM model is trained on a dataset of approach behaviors towards the group. Since current publicly available datasets for these encounters are limited, we develop a social-aware navigation method as a basis for creating a semi-synthetic dataset composed of a mixture of real and simulated data representing safe and socially-acceptable approach trajectories. Via a group interaction module, App-LSTM then captures the position and orientation features of the group and refines the current state of the approaching agent iteratively to better focus on the current intention of group members. We show our App-LSTM outperforms baseline methods in generating approaching group trajectories.
Virtual Humans in Augmented Reality: A First Step towards Real-World Embedded Virtual Roleplayers

We present one of the first applications of virtual humans in Augmented Reality (AR), which allows young adults with Autism Spectrum Disorder (ASD) the opportunity to practice job interviews. It uses the Magic Leap's AR hardware sensors to provide users with immediate feedback on six different metrics, including eye gaze, blink rate and head orientation. The system provides two characters, with three conversational modes each. Ported from an existing desktop application, the main development lessons learned were: 1) provide users with navigation instructions in the user interface, 2) avoid dark colors as they are rendered transparently, 3) use dynamic gaze so characters maintain eye contact with the user, 4) use hardware sensors like eye gaze to provide user feedback, and 5) use surface detection to place characters dynamically in the world.
A Visual Sensing Platform for Robot Teachers

This paper describes our ongoing work to develop a visual sensing platform that can inform a robot teacher about the behaviour and affective state of its student audience. We have developed a multi-student behaviour recognition system, which can detect behaviours such as "listening" to the lecturer, "raising hand", or "sleeping". We have also developed a multi-student affect recognition system which, starting from eight basic emotions detected from facial expressions, can infer higher emotional states relevant to a learning context, such as "interested", "distracted" and "confused". Both systems are being tested with the Softbank robot Pepper that can respond to various students' behaviours and emotional states with adapted movements, postures and speech.
Determining Optimal Coarse‐Grained Representation for Biomolecules Using Internal Cluster Validation Indexes

The development of ultracoarse‐grained models for large biomolecules needs to derive the optimal number of coarse‐grained (CG) sites to represent the targets. In this work, we propose to use the statistical internal cluster validation indexes to determine the optimal number of CG sites that are optimized based on the essential dynamics coarse‐graining method. The calculated curves of Calinski‐Harabasz and Silhouette Coefficient indexes exhibit the extrema corresponding to the similar CG numbers. The calculated ratios of the optimal CG numbers to the residue numbers of fine‐grained models are in the range from 4 to 2. The comparison of the stability of index results indicates that Calinski‐Harabasz index is the better choice to determine the optimal CG representation in coarse‐graining. © 2019 Wiley Periodicals, Inc.
Biomaterials Based on Marine Resources for 3D Bioprinting Applications

Three-dimensional (3D) bioprinting has become a flexible tool in regenerative medicine with potential for various applications. Further development of the new 3D bioprinting field lies in suitable bioink materials with satisfied printability, mechanical integrity, and biocompatibility. Natural polymers from marine resources have been attracting increasing attention in recent years, as they are biologically active and abundant when comparing to polymers from other resources. This review focuses on research and applications of marine biomaterials for 3D bioprinting. Special attention is paid to the mechanisms, material requirements, and applications of commonly used 3D bioprinting technologies based on marine-derived resources. Commonly used marine materials for 3D bioprinting including alginate, carrageenan, chitosan, hyaluronic acid, collagen, and gelatin are also discussed, especially in regards to their advantages and applications.
Utilizing a Hospital-based Setting to Increase Organ Donor Registrations

Background. Shortages of organs for transplantation are a concern for many countries. In Australia’s “opt-in” system, people register their donation decision on the Australian Organ Donor Register (AODR) in their own time, yet <30% of the population have done so. Consent registrations are honored by the next-of-kin in 90% of cases, so increasing registrations will increase donated organs for transplantation. This study investigated the efficacy of offering an immediate registration opportunity in 2 hospitals, and the role that beliefs about organ donation play in registration behavior. Methods. An immediate registration opportunity was offered at a public and a private hospital in New South Wales, Australia. Participants (N = 168) categorized as medical/healthcare (eg, doctor and nurse) and nonhealthcare (eg, teacher and chef) completed a measure of beliefs about organ donation, were encouraged to discuss their fears and concerns about organ donation, and given an immediate opportunity to register on the AODR. Results. A total of 81.5% of medical/healthcare participants who were eligible registered, and 71.5% of all eligible participants registered on the spot. Beliefs about the negative consequences of donation and concerns over the medical care given to potential donors predicted (non)registration. Medical/healthcare participants reported lower levels of fears and concerns than nonhealthcare participants. Although both groups reported strong positive beliefs about donation, these did not predict registration. Conclusions. Offering an immediate registration opportunity in 2 hospitals notably increased the number of registrations on the AODR, suggesting this is a strategy that could potentially increase registrations in opt-in donation systems.
Comparison of psychiatric disturbances in patients with multiple sclerosis and neuromyelitis optica

Abstract Although both multiple sclerosis (MS) and neuromyelitis optica (NMO) are demyelinating diseases, their psychiatric disturbances may differ given differences in the neurological manifestations. We used subjective and objective measurements to compare the psychiatric disturbances in patients with MS and NMO. Psychiatric disturbances were assessed in 24 MS and 35 NMO patients using the Beck Hopelessness Scale, Symptom Checklist-95 and the brief version of World Health Organization Quality of Life. Personality was assessed using the Big Five Inventory-10. Disease-related function was assessed using the Fatigue Severity Scale, Short-Form McGill Pain Questionnaire, and the Global Assessment of Function. Positivity offset (PO) and negativity bias (NB) and heart rate variability (HRV) were measured using a modified implicit affect test and photoplethysmograph, respectively. Data were analyzed using analysis of covariance with age and sex as covariates. MS patients had higher levels of depression, anxiety, panic attacks, obsessive–compulsiveness, aggression, paranoia, interpersonal sensitivity, self-regulation problems, stress vulnerability, and lower psychological quality of life (QOL) compared with NMO patients. The PO and NB and HRV values were not significantly different between groups. However, NMO patients had lower QOL, and higher levels of hopelessness, suicidality, and fatigue than the normal range. Disease duration was associated with hopelessness in NMO patients and with several psychiatric disturbances, but not hopelessness, in MS patients. Subjective psychiatric disturbances were more severe in patients with MS than in those with NMO, whereas PO and NB and HRV in patients with NMO were comparable with those of MS patients. Our findings highlight the need for different clinical approaches to assess and treat psychiatric disturbances in patients with MS and NMO.
What makes one respond to acupuncture for insomnia? Perspectives of cancer survivors

Abstract Objective Like any therapy, acupuncture is effective for some patients, while not helpful for others. Understanding from a patients' perspective what makes one respond or not to acupuncture can help guide further intervention development. This study aimed to identify factors that influence the perception of acupuncture's therapeutic effect among cancer survivors with insomnia. Method We conducted post-treatment semi-structured interviews with cancer survivors who were randomized to the acupuncture group in a clinical trial for the treatment of insomnia. Survivors were categorized into Responders and Non-Responders to acupuncture treatment based on the change in the Insomnia Severity Index with a reduction of eight points or greater as the cut-off for the response. An integrated approach to data analysis was utilized by merging an a priori set of codes derived from the key ideas and a set of codes that emerged from the data through a grounded theory approach. Codes were examined for themes and patterns. Results Among 28 cancer survivors interviewed, 18 (64%) were classified as Responders. Participants perceived the ability to respond to acupuncture as dependent on treatment that effectively: (1) alleviated co-morbidities contributing to insomnia, (2) supported sleep hygiene practices, and (3) provided a durable therapeutic effect. Acupuncture treatment that did not address one of these themes often detracted from positive treatment outcomes and diminished perceived benefit from acupuncture. Significance of results We identified patient-perceived contributors to response to acupuncture, such as co-morbid medical conditions, adequate support for sleep hygiene practices, and temporary therapeutic relief. Addressing these factors may improve the overall effectiveness of acupuncture for insomnia.
Polymorphic post-transplant lymphoproliferative disorder in a gilt

Giorgia Tura, Valeria Pellegrino, Giancarlo Avallone, Francesca Barone, Maria Laura Bacci, Riccardo Villa, Alessandro Spadari, Domenico Ventrella, Francesco Dondi, Valeria Corradetti, Gaetano La Manna and Giuseppe Sarli Department of Veterinary Medical Science, University of Bologna, Ozzano Emilia, BO, Italy; Cellular Substrates, The Lombardy and Emilia Romagna experimental Zootechnic Institute (IZSLER), Brescia, Italy; U.O. Nephrology, Dialysis and Transplantation Unit, Sant’Orsola-Malpighi University Hospital, Bologna, Italy
Effect of childbirth education on the perceptions of childbirth and breastfeeding self-efficacy and the obstetric outcomes of nulliparous women*,**,***

Abstract In this quasi-experimental and prospective study, we aimed to determine the effect of education about childbirth on the perceptions of nulliparous women regarding the experience of childbirth, obstetric outcomes (e.g., type of delivery, use of induction, and instrument-assisted delivery), and breastfeeding self-efficacy. The study population comprised 121 women, of whom 64 and 57 were classified into the education and control groups, respectively. Study data were collected using a participant identification form, the Perception of Birth Scale, Visual Analog Scale, and Breastfeeding Self-Efficacy Scale-Short Form. Compared to the control group, participants in the education group held significantly more positive birth-related perceptions (p = 0.000) and experienced a lower level of pain during delivery (p = 0.016). However, education did not affect the obstetric outcomes. During the first month postpartum, a higher level of breastfeeding self-efficacy was reported by mothers in the education group than by those in the control group. In conclusion, systematic childbirth education positively affected the mothers’ perceptions of the childbirth experience and their breastfeeding self-efficacy, but had no effect on the type of delivery or other birth-related obstetric interventions.
Measurement bias in estimates of system power during a vertical jump

ABSTRACT This investigation sought to quantify the level of measurement agreement in system force, velocity and power values derived across three commonly applied assessment techniques during a countermovement jump (CMJ). Twenty-five male national representative athletes completed three CMJs under unloaded (0%1RM) and loaded (40%1RM) jump conditions. Associated values of force, velocity and power were captured simultaneously from either a linear optical encoder (LOE) or force plate (FP) and then compared to the gold-standard reference values derived from a combined force plate and three-dimensional motion capture system (FPMC). The LOE significantly (p < 0.001) overestimated and failed to meet the minimum level of relatedness (<0.80) for measures of peak velocity, peak force, peak power and mean power across both conditions compared to the FPMC reference values. A reduction in measurement dispersion and bias was, however, evident during the loaded condition. The FP significantly (p < 0.05) underestimated mean and peak power across both conditions, yet measurement bias and dispersion remained consistent. These findings highlight a disparity in measurement agreement in force, velocity and power values across alternative assessment techniques and loads. Such variance in measurement agreement will uniquely alter derived force-velocity profiles, and thus the prescription of training loads to maximise system power during unrestricted CMJs.