Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterisation of extrasolar planetary transit candidates

The detection of transits is an efficient technique to uncover faint companions around stars. The full characterisation of the companions (M-type stars, brown dwarfs or exoplanets) requires high-resolution spectroscopy to measure properly masses and radii. With the advent of massive variability surveys over wide fields, the large number of possible candidates makes such a full characterisation for all of them impractical. We explore here a fast technique to pre-select the most promising candidates using either near-IR photometry or low resolution spectroscopy. We develop a new method based on the well-calibrated surface brightness relation along with the correlation between mass and luminosity for main sequence stars, so that not only can giant stars be excluded but also accurate effective temperatures and radii measured. The main source of uncertainty arises from the unknown dispersion of extinction at a given distance. We apply this technique to our observations of a sample of 34 stars extracted from the 62 low-depth transits identified by OGLE during their survey of some 10 5 stars in the Carina fields of the Galactic disc. We infer that at least 78% of the companions of the stars which are well characterised in this sample are not exoplanets. Stars OGLE-TR-105, OGLE-TR-109 and OGLE-TR-111 are the likeliest to host exoplanets and deserve high-resolution follow-up studies. Most recently, OGLE-TR-111 was confirmed as an exoplanet with $M_{\rm planet} \cong 0.53 \pm 0.11 ~M_{\rm Jup}$ (Pont et al. [CITE]), confirming the efficiency of our method in pre-selecting reliable planetary transit candidates.
Interrater Reliability of the Simulation Fidelity (SiFi) Scale

A limitation of the extant research on the training effectiveness of high versus low fidelity simulation is the lack of a standardized methodology to define high and low fidelity in way that is generalizable across studies. This paper presents a novel approach to standardizing the definition of fidelity using The Simulation Fidelity (SiFi) scale. The SiFi scale is a 6-point scale used to rate the fidelity of a simulation. The present study examines the inter-rater reliability of the SiFi scale to define the fidelity of simulated task elements with respect to their real-world referent. Two subject matter experts (SMEs) completed a series of aviation maneuvers in a virtual reality (VR) simulator. Following the maneuvers, each SME provided 117 ratings of simulated elements using the SiFi scale. Cohen’s k was calculated to determine interrater reliability. Results of this analysis indicate strong reliability of the SiFi scale.
Modeling Driver Takeover Intention in Automated Vehicles With Attention-Based CNN Algorithm

In highly and fully automated vehicles (AV), drivers could divert their attention to non-driving-related activities. Drivers may also take over AVs if they do not trust the way AVs drive in specific driving scenarios. Existing models have been developed to predict drivers’ takeover performance in responding to takeover requests initiated by AVs in semi-AVs. However, few models predicted driver-initiated takeover behavior in highly and fully AVs. The present study develops an attention-based multiple-input Convolutional Neural Network (CNN) to predict drivers’ takeover intention in fully AVs. The results indicated that the developed model successfully predicted takeover intentions of drivers with a precision of 0.982 and an F1-Score of.989, which were found to be substantially higher than other machine learning algorithms. The developed CNN model could be applied in improving the driving algorithms of the AV by considering drivers’ driving styles to reduce drivers’ unnecessary takeover behaviors.
Moderation Analysis of Gender in Social Robot Interactions

The current study aimed to further analyze Schadenberg et al.’s (2021) dataset examining perceived social attributions after observing human-robot interactions. In their study, visibility of an external cause was found to significantly predict perceived competence of the Robot Social Attributes Scale (RoSAS) only. However, no demographic variable was considered in the analyzes. Therefore, gender was analyzed as a moderator between visibility and participants’ perceived competence of the robot. Additionally, a Confirmatory Factor Analysis was conducted on the RoSAS measure to examine its validity in the current context. Results indicated that gender did not significantly moderate the relationship between visibility and perceived competence. This finding could be explained by the RoSAS demonstrating poor fit with the data even though the measure indicated high internal reliability. In light of these results, the current study advocates for further psychometric validation of newer scales across varying conditions, especially within the social robotics field.
Wormhole Shadows

We propose a new method of detecting Ellis wormholes by use of the images of wormholes surrounded by optically thin dust. First, we derive steady solutions of dust and more general medium surrounding the wormhole by solving relativistic Euler equations. We find two types of dust solutions: one is a static solution with arbitrary density profile, and the other is a solution of dust which passes into the wormhole and escapes into the other side with constant velocity. Next, solving null geodesic equations and radiation transfer equations, we investigate the images of the wormhole surrounded by dust for the above steady solutions. Because the wormhole spacetime possesses unstable circular orbits of photons, a bright ring appears in the image, just as in Schwarzschild spacetime. This indicates that the appearance of a bright ring solely confirms neither a black hole nor a wormhole. However, we find that the intensity contrast between the inside and the outside of the ring are quite different. Therefore, we could tell the difference between an Ellis wormhole and a black hole with high-resolution very-long-baseline-interferometry observations in the near future.
Quantum entropy of supersymmetric black holes

Dans cette these, nous presentons des resultats exacts pour l'entropie quantique d'une large classe de trous noirs supersymetriques dans le contexte de la theorie des cordes, soit du point de vue de la theorie microscopique soit du cote de la gravite (macroscopique). Comme exemple, nous montrons, pour les petits trous noirs dans la theorie des cordes N = 4, que l'entropie macroscopique est en accord parfait avec la reponse microscopique pour toute valeur des charges. En generalisant des travaux anterieurs, ces corrections a l'entropie de Bekenstein-Hawking peuvent aussi etre considerees comme un exemple d'holographie exacte dans le contexte de la correspondance AdS2/CFT1. Nos resultats sont une nouvelle application des techniques de localisation en supergravite dans le contexte de la fonction d'entropie quantique. Cette technique permet de simplifier grandement une integrale de chemin de la theorie des cordes dans AdS2 en une integrale finie. Les solutions de localisation dependent des nv parametres, avec nv le nombre de multiplets vectoriels dans la theorie de supergravite N = 2. Dans l'exemple des petits trous noirs, nous pouvons reecrire la reponse microscopique comme une somme exacte de fonctions de Bessel dans un developpement de Rademacher. Ceci permet une comparaison plus facile avec le calcul macroscopique. L'application de la technique de localisation en supergravite sur AdS2xS2 permet de reproduire exactement la fonction de Bessel. L'introduction d'orbifolds supplementaires de AdS2xS2 permet de reproduire exactement toutes les autres contributions non-perturbatives, y compris les sommes de Kloosterman
Visual feedback and control configuration influences in a head tracking interface

Methods for enhancing pilot interaction with large, information dense, cockpit displays were explored. Specifically, the effects of visual feedback and selection button configuration were manipulated when augmenting cursor control with head tracking technology. Previous studies demonstrated that head tracking can be combined with traditional cursor control to decrease selection times but can increase pilot mental and physical workload. A human subject experiment was performed to evaluate two control button configurations and three visual feedback conditions. A repeated measures analysis of variance revealed that removing visual feedback reduced accuracy and selection time, control configuration did not affect performance. However, all head tracking configurations were faster than the current cursor control system. Fitts’ Law models provided a poor fit to the observed data. It is proposed that conditions without visual feedback may impose lower physical workload than the other configurations.
Evaluating Team Metrics in Cooperative Video Games

This research aims to understand how cooperative game features lead to teamwork behaviors as a step in developing gamified testbeds where human-human and human-autonomy teaming can be evaluated. Multiplayer video games incorporate features that encourage cooperation and interdependency, making them potential task environments for evaluating team metrics. This study analyzed three cooperative video games through annotating play sessions by tracking cooperative features and teamwork behavioral markers. The results show how cooperative features lead to a high frequency of teamwork behaviors, including coordination, planning, backup behavior, and mutual monitoring. Additionally, game features such as complementary and shared obstacles and puzzles, community survival, and team spirit led to the highest frequencies of behavioral markers. This study can serve as an initial assessment to develop a list of requirements for the gamification of teaming testbeds that can support objective quantification of team measurement.
Landscape of User-Centered Design Practices for Fostering Trustworthy Human-AI Interactions

In the advent of the fourth industrial revolution, leaders in the commercial artificial intelligence (AI) market have shaped parameters of trustworthy AI design. Resulting directives, however, typically approach this issue from a technical perspective, while largely ignoring its human factors counterpart. As such, we conducted an information synthesis to capture the current landscape of user-centered design for developing trustworthy human-AI interactions. As part of our review, we analyzed resources from 50+ publications and summarized their respective protocols into three major categories: (1) design standards and guidelines based on ethical principles, 2) best practices for designing the nature of human-AI relationships across the user experience, and 3) best practices for designing redress mechanisms when trust is at risk, low, or broken. Part and parcel to this review, we provide concrete prescriptions for designing trustworthy human-AI interactions, identify limitations of existing protocols, and suggest areas requiring further exploration.
It’s risk, Jim, but not as we know it: identifying the risks associated with future Artificial General Intelligence-based Unmanned Combat Aerial Vehicle systems

The next generation of artificial intelligence, known as Artificial General Intelligence (AGI), could either revolutionise or destroy humanity. Human Factors and Ergonomics (HFE) has a critical role to play in the design of safe and ethical AGI; however, there is little evidence that HFE is contributing to development programs. This paper presents the findings from a study which involved the use of the Work Domain Analysis-Broken Nodes approach to identify the risks that could emerge in a future ‘envisioned world’ AGI-based unmanned combat aerial vehicle system. The findings demonstrate that there are various potential risks, but that the most critical arise not due to poor performance, but rather when the AGI attempts to achieve goals at the expense of other system values, or when the AGI becomes ‘super-intelligent’, and humans can no longer manage it. The urgent need for further work exploring the design of AGI controls is emphasised.
A Discussion on Accessibility, Inclusivity, Cultural Design Considerations, and Health & Safety Advisories for Virtual Reality Head Mounted Displays

With the exponential growth in availability of Virtual Reality (VR) head-mounted displays (HMDs), this study aims to discuss Health & Safety advisory information provided by VR products and observe the extent to which each platform considers accessibility and inclusive and culturally informed design. To this end, five commercially available PC-based VR HMDs, and their corresponding setup applications were assessed in this study - the Oculus Quest 2, Oculus Rift S, Samsung Odyssey, HTC Vive Pro, and Varjo XR-3. Three proto personas were created to evaluate these platforms from various perspectives. VR HMDs are moving in the right direction to consider diverse populations in animation design, but there is room for improvement regarding individuals with visual, auditory, or mobility restrictions. Advancements in multimodal sensory cues and inclusive design considerations may increase adoption of VR technologies as well as compliance with Health & Safety advisory information
The Phase II Murchison Widefield Array: Design overview

Abstract We describe the motivation and design details of the ‘Phase II’ upgrade of the Murchison Widefield Array radio telescope. The expansion doubles to 256 the number of antenna tiles deployed in the array. The new antenna tiles enhance the capabilities of the Murchison Widefield Array in several key science areas. Seventy-two of the new tiles are deployed in a regular configuration near the existing array core. These new tiles enhance the surface brightness sensitivity of the array and will improve the ability of the Murchison Widefield Array to estimate the slope of the Epoch of Reionisation power spectrum by a factor of ∼3.5. The remaining 56 tiles are deployed on long baselines, doubling the maximum baseline of the array and improving the array u, v coverage. The improved imaging capabilities will provide an order of magnitude improvement in the noise floor of Murchison Widefield Array continuum images. The upgrade retains all of the features that have underpinned the Murchison Widefield Array’s success (large field of view, snapshot image quality, and pointing agility) and boosts the scientific potential with enhanced imaging capabilities and by enabling new calibration strategies.
Boundedness of fractional integral operators on non-homogeneous metric measure spaces

In this paper, the fractional integral operator on non-homogeneous metric measure spaces is introduced, which contains the classic fractional integral operator, fractional integral with non-doubling measures and fractional integral with fractional kernel of order $\alpha$ and regularity $\epsilon$ introduced by Garc\'{i}a-Cuerva and Gatto as special cases. And the $(L^{p}(\mu),L^{q}(\mu))$-boundedness for fractional integral operators on non-homogeneous metric measure spaces is established. From this, the $(L^{p}(\mu),L^{q}(\mu))$-boundedness for commutators and multilinear commutators generated by fractional integral operators with $RBMO(\mu)$ function are further obtained. These results in this paper includes the corresponding results on both the homogeneous spaces and non-doubling measure spaces.
A User Study on the Feasibility of Topic-aware Misinformation Warning on Social Media

Misinformation is one of the most fundamental problems in social media with increasing cases and underlying harmful effects on users. To mitigate such problem, misinformation warnings have been developed, including alerting with warning messages and hiding the contents. Previous studies mainly explored the most effective, one-size-fits-all design. Therefore, little has been known about customizable and flexible warning designs. In this study, we propose a “topic-aware misinformation warning” where users’ preferences for warning designs can vary on topics. To illustrate our ideas, we developed Twitter-like pages using three topics (i.e., politics, gossip, and Covid-19) and three designs (i.e., interstitial, contextual, and highlight). We conducted semi-structured interviews with 18 participants to explore their preferences and opinions on the designs. Our results show that users’ preferences for misinformation warnings are diverse in topics. Thus, topic-aware misinformation warning is promising to alleviate misinformation problems on Twitter.
Screen Reader Voices: Effects of Pauses and Voice Changes on Comprehension

This study seeks to investigate the effects of manipulating aspects of a text-to-speech (TTS) voice on the learning and comprehension of a short passage, as well as detection of aspects of the passage such as its organization and key information. Pauses and pitch changes were used to demarcate this type of information. Participants listened to the passages and answered a series of cued and uncued recall questions to measure comprehension and learning, followed by a task to identify header structure. Preliminary results show trends that adding pauses might be beneficial, but more participants are needed to provide conclusive evidence. This study will contribute to the body of research surrounding technology adoption, assistive technology, and how to improve AI voices for the purposes of learning, as well as our understanding of how we process auditory information.
An Observational Study Investigating Verbal Communication Differences in Online vs. In-person Group Exercise Training

Regular physical activity improves both physical and mental well-being as well as quality of life (Bouchard, Blair, & Haskell, 2012). Group based exercise program can provide effective outcome in terms of adherence and motivation while participating in exercise in-person, as exercising alone does not lead to a sustainable habit (Stødle, Debesay, Pajalic, Lid, & Bergland, 2019). However, limitations, i.e., lack of transportation (Melillo et al., 1996), absence of such facilities in the vicinity of a community (Eng et al., 2003) along with the COVID-19 pandemic in recent times (Fallon, 2020) has reduced the effectiveness of in-person group-based programs and increased the usage of video conferencing platforms for online group exercise program (Taylor, Raisborough, Harrison, & Dulson, 2021). Despite the increasing use of online video conferencing platforms, both instructors and participants experience a variety of challenges, as existing video conferencing platforms are not specifically designed to facilitate groupbased physical exercise. The aim of this study was to identify the challenges that both instructors and participants face during remote groupbased physical exercise via video conferencing platforms by assessing the verbal communication and interaction in recorded group yoga sessions. Verbal communication events were sorted into four categories, i.e., guidance, group feedback, individual feedback, and social interaction, for comparison.
Cue Use is Unaffected by Covertly Performing a Task

Humans learn by watching others. One aspect of these observations are our Judgments of Difficulty (JODs) about a task. Research has revealed discrepancies in the judgments we make while performing and observing; these discrepancies are alternatively explained by the Simulation and Theory Models of metacognition. This study tested these models by capitalizing on a behavior that naturally occurs during observation: covert performance. We compared the cues to difficulty used by pure observers and covert performers as they watched an automated system (AS) perform a visual search task. Students used peripheral and central cues to difficulty similarly, regardless of whether they purely observed or covertly performed the task, lending support to the Theory Model of metacognition. The study offers an explanation for peoples’ inflated sense of ability while watching others perform and suggests that providing people with experience – not just observation– is a critical part of correcting these faulty judgments.
Identifying Individual-, Team-, and Organizational-Level Factors that Affect Team Performance in Complex Domains Based on Recent Literature

Working in teams provides several advantages to dynamic, data-driven domains, but can also add a layer of complexity to operations. There have been several reviews on teams and team performance analysis; however, there has been limited work in the last five years that has examined micro- and macro-level factors that affect overall team performance. Previous research has proposed a framework within healthcare characterizing team characteristics into three categories: individual contributions, team processes, and organizational structures. However, it is still unclear how new emerging topics in the team literature fit within this framework. Here we provide more specific definitions of the three categories proposed and conduct a review that builds on this framework by adding topics identified from the current literature. To this end, we carried out a systematic search of the human factors literature to examine the research on team performance across various domains from the past five years centered. We then propose ideas for future research on team performance.
“Paying with my face” – Understanding users’ adoption and privacy concerns of facial recognition payment

Facial recognition payment (FRP) is a widely adopted technology in countries outside of the United States (US), particularly in east Asian countries. However, FRP is still not widely accepted and implemented within the US, partly due to users' privacy concerns of FRP. In our survey study of 164 US participants, we adopted the extended Technology Acceptance Model (TAM) to investigate the relationship between users' privacy concerns, power usage, attitudes, and their intentions of adopting FRP. Our findings showed significant relationships among these assessed variables and indicated that privacy concerns was indeed an important factor in predicting individuals' intentions of adopting FRP, but power users might become early adopters that could spearhead widespread acceptance of FRP within the US due to their lessened privacy concerns.
The Impact of Role Authority and Communication Initiative on HAT Outcomes

Theobjective ofthisstudy wasto understand howrole authority and communication initiative of autonomous agents impact Human-Autonomy Team (HAT) performance, trust, workload, and cohesion. The study employed a 2 (role authority: peer vs.subordinate) x 2 (communication initiative: proactive vs.reactive robot communication) mixed factorial design.Participantsworkedcollaboratively with Boston Dynamic’s Spot robotto conductanin-person search and rescue taskin afield environment.The study revealedthe subordinate role authority condition, where therobot-is-subordinate tothehuman, imposed a higher workload thanthe peer condition, where the robot-is-peertothe human.
Towards Real-Time Minimum-Input Prediction of Lumbar Moment Based on Flexible Sensors and Machine Learning

Knowledge of low-back loading is essential for understanding and mitigating the risk of low-back overexertion injuries. Conventional data acquisition methods for estimating joint loading are limited to laboratory settings, whereas wearable sensors can provide a mobile and cost-effective alternative. This study investigated the feasibility of learning prediction of L5S1 flexion moment based on kinematics and electromyography (EMG) measurements from flexible sensors. Four machine learning methods were compared, and different subsets of sensor inputs were explored. Results indicated that the support vector machine (SVM) method outperformed others, and a subset of four out of seven sensor locations, namely sacrum, thigh, shank, and thoracic erector spinae, yielded the best predictive accuracy. The study demonstrates that machine learning can unlock the potential of mobile miniaturized flexible sensors in field biomechanics or ergonomics studies.