Miracle-dz/UniFAR-FTUs huggingface dataset:
A Database of Multimodal Data to Construct a Simulated Dialogue Partner with Varying Degrees of Cognitive Health

An assistive robot that could communicate with dementia patients would have great social benefit. An assistive robot Pepper has been designed to administer Referential Communication Tasks (RCTs) to human subjects without dementia as a step towards an agent to administer RCTs to dementia patients, potentially for earlier diagnosis. Currently, Pepper follows a rigid RCT script, which affects the user experience. We aim to replace Pepper’s RCT script with a dialogue management approach, to generate more natural interactions with RCT subjects. A Partially Observable Markov Decision Process (POMDP) dialogue policy will be trained using reinforcement learning, using simulated dialogue partners. This paper describes two RCT datasets and a methodology for their use in creating a database that the simulators can access for training the POMDP policies.
Fearless Steps APOLLO: Advanced Naturalistic Corpora Development

In this study, we present the Fearless Steps APOLLO Community Resource, a collection of audio and corresponding meta-data diarized from the NASA Apollo Missions. Massive naturalistic speech data which is time-synchronized, without any human subject privacy constraints is very rare and difficult to organize, collect, and deploy. The Apollo Missions Audio is the largest collection of multi-speaker multi-channel data, where over 600 personnel are communicating over multiple missions to achieve strategic space exploration goals. A total of 12 manned missions over a six-year period produced extensive 30-track 1-inch analog tapes containing over 150,000 hours of audio. This presents the wider research community a unique opportunity to extract multi-modal knowledge in speech science, team cohesion and group dynamics, and historical archive preservation. We aim to make this entire resource and supporting speech technology meta-data creation publicly available as a Community Resource for the development of speech and behavioral science. Here we present the development of this community resource, our outreach efforts, and technological developments resulting from this data. We finally discuss the planned future directions for this community resource.
Change My Mind: How Syntax-based Hate Speech Recognizer Can Uncover Hidden Motivations Based on Different Viewpoints

Hate speech recognizers may mislabel sentences by not considering the different opinions that society has on selected topics. In this paper, we show how explainable machine learning models based on syntax can help to understand the motivations that induce a sentence to be offensive to a certain demographic group. By comparing and contrasting the results, we show the key points that make a sentence labeled as hate speech and how this varies across different ethnic groups.
Signed Language Transcription and the Creation of a Cross-linguistic Comparative Database

As the availability of signed language data has rapidly increased, sign scholars have been confronted with the challenge of creating a common framework for the cross-linguistic comparison of the phonological forms of signs. While transcription techniques have played a fundamental role in the creation of cross-linguistic comparative databases for spoken languages, transcription has featured much less prominently in sign research and lexicography. Here we report the experiences of the Sign Change project in using the signed language transcription system HamNoSys to create a comparative database of basic vocabulary for thirteen signed languages. We report the results of a small-scale study, in which we measured (i) the average time required for two trained transcribers to complete a transcription and (ii) the similarity of their independently produced transcriptions. We find that, across the two transcribers, the transcription of one sign required, on average, one minute and a half. We also find that the similarity of transcriptions differed across phonological parameters. We consider the implications of our findings about transcription time and transcription similarity for other projects that plan to incorporate transcription techniques.
Automatic Approach for Building Dataset of Citation Functions for COVID-19 Academic Papers

This paper develops a new dataset of citation functions of COVID-19-related academic papers. Because the preparation of new labels of citation functions and building a new dataset requires much human effort and is time-consuming, this paper uses our previous citation functions that were built for the Computer Science (CS) domain, which consists of five coarse-grained labels and 21 fine-grained labels. This paper uses the COVID-19 Open Research Dataset (CORD-19) and extracts 99.6k random citing sentences from 10.1k papers. These citing sentences are categorized using the classification models built from the CS domain. The manually check on 475 random samples resulted accuracies of 76.6% and 70.2% on coarse-grained labels and fine-grained labels, respectively. The evaluation reveals three findings. First, two fine-grained labels experienced meaning shift while retaining the same idea. Second, the COVID-19 domain is dominated by statements highlighting the importance, cruciality, usefulness, benefit, consideration, etc. of certain topics for making sensible argumentation. Third, discussing State of The Arts (SOTA) in terms of their outperforming previous works in the COVID-19 domain is less popular compared to the CS domain. Our results will be used for further dataset development by classifying citing sentences in all papers from CORD-19.
NewYeS: A Corpus of New Year’s Speeches with a Comparative Analysis

This paper introduces the NewYeS corpus, which contains the Christmas messages and New Year’s speeches held at the end of the year by the heads of state of different European countries (namely Denmark, France, Italy, Norway, Spain and the United Kingdom). The corpus was collected via web scraping of the speech transcripts available online. A comparative analysis was conducted to examine some of the cultural differences showing through the texts, namely a frequency distribution analysis of the term “God” and the identification of the three most frequent content words per year, with a focus on years in which significant historical events happened. An analysis of positive and negative emotion scores, examined along with the frequency of religious references, was carried out for those countries whose languages are supported by LIWC, a tool for sentiment analysis. The corpus is available for further analyses, both comparative (across countries) and diachronic (over the years).
Dialogue Act and Slot Recognition in Italian Complex Dialogues

Since the advent of Transformer-based, pretrained language models (LM) such as BERT, Natural Language Understanding (NLU) components in the form of Dialogue Act Recognition (DAR) and Slot Recognition (SR) for dialogue systems have become both more accurate and easier to create for specific application domains. Unsurprisingly however, much of this progress has been limited to the English language, due to the existence of very large datasets in both dialogue and written form, while only few corpora are available for lower resourced languages like Italian. In this paper, we present JILDA 2.0, an enhanced version of a Italian task-oriented dialogue dataset, using it to realise a Italian NLU baseline by evaluating three of the most recent pretrained LMs: Italian BERT, Multilingual BERT, and AlBERTo for the DAR and SR tasks. Thus, this paper not only presents an updated version of a dataset characterised by complex dialogues, but it also highlights the challenges that still remain in creating effective NLU components for lower resourced languages, constituting a first step in improving NLU for Italian dialogue.
GYNECOLOGICAL AND OBSTETRIC PROFILE, URINARY LOSS AND QUALITY OF LIFE OF ELDERLY WOMEN WITH URINARY INCONTINENCE PERFIL GINECOLÓGICO E OBSTÉTRICO, PERDA URINÁRIA E QUALIDADE DE VIDA DE IDOSAS COM INCONTINÊNCIA URINÁRIA

: Introduction: The prevalence of urinary incontinence (UI) in postmenopausal women can reach 30% to 70%, being considered a global public health problem. Objective: To describe the gynecological and obstetrical profile of elderly women with UI, quantify urine loss and assess the impact on quality of life. Methodology: This is a quantitative, cross-sectional study performed with elderly women linked to social groups in Santo Antônio da Patrulha/RS. Data collection was performed through the International Consultation on Incontinence Questionnaire - Short Form (ICIQ-SF); an initial interview; the King's Health Questionnaire (KHQ) and the 1-hour Pad Test. Results: The sample consisted of 25 elderly women, 60% had a vaginal birth and 32% had pelvic organ prolapse. Mixed urinary incontinence was the most prevalent (64%), with a mean of 4.5 ± 8.2 g of urine lost in the Pad Test, and the most affected KHQ domains were UI impact (49.6 ± 24) and measurements of gravity (42.3 ± 22.4). Correlation was found between the Pad Test and the domains of general health perception (r=0.51; p=0.041), sleep and mood (r=0.59; p=0.016) of the KHQ and frequency (r= -0.48; p= 0.047) of the ICIQ-SF. Conclusion: The elderly women had mild UI, and the associations suggest that the greater the urinary loss, the worse the sleep quality and the general perception of health, and the lower the frequency of occurrence of losses.
TURBULENT AMPLIFICATION OF A MAGNETIC FIELD DRIVEN BY THE DYNAMO EFFECT AT RIPPLED SHOCKS

We analytically derive the vorticity generated downstream of a two-dimensional (2D) rippled hydromagnetic shock neglecting fluid viscosity and resistivity. The growth of the turbulent component of the downstream magnetic field is driven by the motion of vortical eddies. We determine an analytic time evolution of the magnetic field amplification at shocks, so far described only numerically, until saturation occurs due to seed-field reaction to the whirling of field lines. The explicit expression of the amplification growth rate and of the nonlinear field back-reaction in terms of the parameters of shock and interstellar density fluctuation is derived from MHD jump conditions at rippled shocks. A magnetic field saturation up to the order of milli-Gauss and a short-term variability in the X-ray observations of supernova remnants can be obtained by using reasonable parameters for the interstellar turbulence.
Effect of Mirena Intrauterine Device on Endometrial Thickness, Quality of Life Score, and Curative Effect in Patients with Perimenopausal Abnormal Uterine Bleeding

Objective To study the effect of Mirena intrauterine device (IUD) on endometrial thickness, life quality score, and curative effect in patients with perimenopausal abnormal uterine bleeding. Methods Eighty patients with perimenopausal abnormal uterine bleeding cured from January 2020 to December 2021 were enrolled as the object of study. According to random number table, the patients were classified into the study (n = 40) and control (n = 40) groups. The control cases were cured with medroxyprogesterone. The study cases were cured with Mirena IUD. The effective rate of clinical therapies was evaluated after 3 months of treatment. The endometrial thickness, menstrual volume score, and life quality score (WHOQOL-BREF) was measured after 1 month, 2 months, and 3 months of treatment. Results The effective rate of patients with Mirena IUD for 3 months was higher compared to the control group (P < 0.05). The endometrial thickness and menstrual volume scores of study cohort after 1 month, 2 months, and 3 months following treatment were remarkably lower than those before treatment (P < 0.05) and were considerably lower than those of control cohort (P < 0.05). The hemoglobin level of the studied cases after 1 month, 2 months, and 3 months after therapy was remarkably upregulated (P < 0.05) and was greatly higher compared to the controlled cases (P < 0.05). After 3-month treatment, the WHOQOL-BREF score of the study group was higher compared to the control group (P < 0.05). Conclusion The Mirena IUD is far more effective in the treatment of perimenopausal abnormal uterine bleeding and is helpful in reducing the thickness of the endometrium. Patients' menstrual flow can be controlled, and anemia can be corrected; thus, patients improve their quality of life and health status and can be considered for further promotion.
Dynamics between Direct Industrial Real Estate and the Macroeconomy: An Empirical Study of Hong Kong

Pricing of direct industrial real estate (DIRE) has long been under-researched due to the paucity of analysable data. Compared to other types of real estate, DIRE has often been regarded as more inefficient because of information asymmetry amongst market players stemming from a lack of market transparency. Therefore, pricing of DIRE usually does not follow a random walk and should be more predictable than other types of real estate. Along this line of reasoning, this study empirically investigates the causal relationships between the price-to-rent ratio of DIRE and macroeconomic attributes using cointegration and causality techniques. More specifically, we employ data on the market of Hong Kong to investigate the lead-lag relationships between the price-to-rent ratio of DIRE and a wide spectrum of macroeconomic and financial indicators, including inflation, money supply, national income, exchange rates, performance of housing market and other economic indicators specific to the industrial sector. The results of our statistical tests reveal significant evidence that DIRE is generally moving in syncs with other segments of the economy over time in terms of long-term cointegration. Further, DIRE tends to lag behind the overall macroeconomy in terms of Granger causation with the price-to-rent ratio exhibiting varying lengths of time lag with the macroeconomic determinants. The findings of the study carry important implications for informing property valuation practices and industrial land policy, particularly in designing urban revitalization programmes aimed at optimising industrial land use.
Button Antenna for Wireless Body Area Network Applications

This document designs and studies a button-shaped patch antenna for four different patch shapes. The coaxial feed is used for high impedance matching and better radiation patterns. The button antenna is designed with Fr4 epoxy substrate material. The Model 1 button antenna resonates at a frequency of 6.2174 GHz, has a reflection coefficient of 20.1970 dB, provides a bandwidth of 261 MHz (6.1307 GHz - 6.3920 GHz), and the maximum observed gain is 5.023dBi. The Model 2 button antenna resonates at 6.4523 GHz with a reflection coefficient of 35 dB and provides a bandwidth of 120 MHz (6.211 GHz - 6.3317 GHz) and the maximum observed gain is 4.7689dBi. The model 3 button antennas resonates at a frequency of 6.4523 GHz, has a reflection coefficient of 49 dB, and provides a bandwidth of 120 MHz (6.2111 GHz - 6.3317 GHz) and a maximum observed gain of 4.7925dBi. The Model 4 button antenna resonates at 5.8844 GHz frequency respectively with a reflection coefficient of -26.5 dB and offers the Bandwidth of 226 MHz (5.7487 GHz – 5.9749 GHz) and the maximum gain observed as 4.7957dBi. The compact design proves the antenna suitable for WBAN applications.
The Sensitivity of Annotator Bias to Task Deﬁnitions

NLP models are biased by the data they are 001 trained on, including how it is annotated, but 002 NLP research increasingly examines the social 003 biases of models, often in the light of their 004 training data. This paper is ﬁrst to examine to 005 what extent social bias is sensitive to how data 006 is annotated . We do so by collecting annota-007 tions of arguments in the same documents fol-008 lowing four different guidelines and from four 009 different demographic annotator backgrounds . 010 We show that annotations exhibit widely dif-011 ferent levels of group disparity depending on 012 which guidelines annotators follow. The dif-013 ferences are not explained by task complexity, 014 but rather by characteristics of these groups, as 015 previously identiﬁed by sociological studies
Quercetin-Embedded Gelastin Injectable Hydrogel as Provisional Biotemplate for Future Cutaneous Application: Optimization and In Vitro Evaluation

Chronic wounds have become an epidemic in millions of patients and result in amputations. In order to overcome this, immediate treatment is a realistic strategy to minimize the risk of complications and aid in the healing rate of the cutaneous wound. Functionalized engineered biomaterials are proven to be a potential approach to embarking on skin wound management. Thus, this study aimed to evaluate the efficacy of a quercetin-embedded gelatin–elastin (Gelastin) injectable hydrogel to act as a provisional biotemplate with excellent physicochemical properties, to be utilized for future cutaneous application. Briefly, the hydrogel was homogenously pre-mixed with genipin (GNP), followed by the incorporation of quercetin (QC). The physicochemical properties comprised the contact angle, swelling ratio, crosslinking degree, enzymatic biodegradation, and water vapor transmission rate (WVTR), as well as chemical characterization. Energy-dispersive X-ray (EDX), XRD, and Fourier transform infra-red (FTIR) analyses were conducted. Briefly, the findings demonstrated that the crosslinked hybrid biomatrix demonstrated better resilience at >100%, a contact angle of >20°, a swelling ratio average of 500 ± 10%, a degradation rate of <0.05 mg/hour, and a successful crosslinking degree (<70%free amine group), compared to the non-crosslinked hybrid biomatrix. In addition, the WVTR was >1500 g/m2 h, an optimal moisture content designed to attain regular cell function and proliferation. The outcomes convey that Gelastin-QC hydrogels deliver the optimum features to be used as a provisional biotemplate for skin tissue engineering purposes.
Spatio-Temporal Action Order Representation for Mobile Manipulation Planning*

Social robots are used to perform mobile manipulation tasks, such as tidying up and carrying, based on instructions provided by humans. A mobile manipulation planner, which is used to exploit the robot’s functions, requires a better understanding of the feasible actions in real space based on the robot’s subsystem configuration and the object placement in the environment. This study aims to realize a mobile manipulation planner considering the world state, which consists of the robot state (subsystem configuration and their state) required to exploit the robot’s functions. In this paper, this study proposes a novel environmental representation called a world state-dependent action graph (WDAG). The WDAG represents the spatial and temporal order of feasible actions based on the world state by adopting the knowledge representation with scene graphs and a recursive multilayered graph structure. The study also proposes a mobile manipulation planning method using the WDAG. The planner enables the derivation of many effective action sequences to accomplish the given tasks based on an exhaustive understanding of the spatial and temporal connections of actions. The effectiveness of the proposed method is evaluated through practical machine experiments performed. The experimental result demonstrates that the proposed method facilitates the effective utilization of the robot’s functions.
AugRE: Augmented Robot Environment to Facilitate Human-Robot Teaming and Communication *

Augmented Reality (AR) provides a method to superimpose real-time information on the physical world. AR is well-suited for complex robotic systems to help users understand robot behavior, status, and intent. This paper presents an AR system, Augmented Robot Environment (AugRE), that combines ROS-based robotic systems with Microsoft HoloLens 2 AR headsets to form a scalable multi-agent human-robot teaming system for indoor and outdoor exploration. The system allows multiple users to simultaneously localize, supervise, and receive labeled images from robotic clients. An overview of AugRE and details of the novel system architecture that allows for large-scale human-robot teaming is presented below. Studies showcasing system performance with multiple robotic clients are presented. Results show that AugRE can scale to 50 robotic clients with minimal performance degradation, due in part to key components that leverage a recent advancement in robotic client-to-client communication called Robofleet. Finally we discuss new capabilities enabled by AugRE.
The Frenet Vector Fields and the Curvatures of the Natural Lift Curve

In this paper, Frenet vector fields, curvature and torsion of the natural lift curve of a given curve is calculated by using the angle between Darboux vector field and the binormal vector field of the given curve in . Also, a similar calculation is made in considering timelike or spacelike Darboux vector field.
Uncertainty prediction and calibration using multi-expert gating mechanism

Time series prediction is applied in many fields as a fundamental task. Although existing methods have achieved satisfactory accuracy, making their prediction more credible is still a significant challenge. In the existing research, the uncertainty estimate method is relatively mature. However, the uncertainty is generally calibrated with post-processing so that the prediction probability of the model matches the actual likelihood. In this paper, we present a novel approach to predict and calibrate the uncertainty of existing state-of-the-art time series models. We regularize the model through multi-expert gating mechanisms while taking advantage of the differences between experts to build new uncertainty estimation methods and propose the AU-Loss function, which combines accuracy and uncertainty terms. Experiments across different real-world datasets with various state-of-the-art time series models show that our method can effectively improve the calibration effect and credibility for regression problems.
Task-Oriented Robot-to-Human Handovers in Collaborative Tool-Use Tasks

Robot-to-Human handovers are common exercises in many robotics application domains. The requirements of handovers may vary across these different domains. In this paper, we first devised a taxonomy to organize the diverse and sometimes contradictory requirements. Among these, task- oriented handovers were not well-studied but important because the purpose of the handovers in human–robot collaboration (HRC) is not merely to pass an object from a robot to a human receiver, but to enable the human receiver to use it in a subsequent tool-use task. A successful task-oriented handover should incorporate task-related information – orienting the tool such that the human can grasp it in a way that is suitable for the task. We identified multiple difficulty levels of task-oriented handovers, and implemented a system to generate task-oriented handovers with novel tools on a physical robot. Unlike previous studies on task-oriented handovers, we trained the robot with tool-use demonstrations rather than handover demonstrations, since task-oriented handovers are dependent on the tool usages in the subsequent task. We demonstrated that our method can adapt to all difficulty levels of task-oriented handovers, including tasks that matched the typical usage of the tool (level I), tasks that required an improvised and unusual usage of the tool (level II), and tasks where the handover was adapted to the pose of a manipulandum (level III). We evaluated the generated handovers with online surveys. Participants rated our handovers to appear more comfortable for the human receiver and more appropriate for subsequent tasks when compared with typical handovers from prior work.