Miracle-dz/UniFAR-FTUs huggingface dataset:
Embodiment Matters in Social HRI Research: Effectiveness of Anthropomorphism on Subjective and Objective Outcomes

Anthropomorphism, as a design feature of robots, is widely applied to enhance human-robot interaction in the social domain. Current knowledge of how anthropomorphism influences perception, attitudes, and actual behavior is gained via research with depicted and embodied robots. However, it remains unclear how comparable gained insights of both approaches are. Results of a current meta-analysis suggest that anthropomorphism positively influences subjective and objective measures in case of embodied robots, whereas in case of depicted robots predominantly effects on subjective measures seem to emerge. This follow-up analysis aims to further investigate this difference by using a recoded dataset including data of 41 studies, involving over 3,000 participants. The results illustrate that anthropomorphism investigated via embodied robots indeed facilitates both subjective and objective outcomes. Remarkably, studies concerning effects of anthropomorphism using depicted robots showed positive effects on a subjective level but failed to show any effects on an objective level. In conclusion, the results show that the consequences of anthropomorphism in human-robot interaction depend on how robots are presented to participants. Moreover, they reveal that the transfer of results gained via depicted robots to embodied human-robot interaction might lead to both overestimation on a subjective level and underestimation on an objective level regarding the consequences of anthropomorphism.
SEED: a SIM-based solution to 5G failures

Failures in 5G mobile networks are becoming the norm with the ongoing global rollout. If left unattended, they affect mobile user experiences and the proper functioning of applications. In this work, we describe SEED, which offers a novel SIM-based solution to 5G failure diagnosis and handling. SEED infers failure causes by exploiting current standardized 5G error codes and decision-tree/online learning algorithms. It further takes corresponding multi-tier reset/redo actions (reset protocol operations, refresh outdated configurations, reload profiles, etc.) once the failure cause is inferred. SEED takes the operator's perspective in its design for fast deployment. SEED design works within the 5G standard framework and does not require changes on the device firmware or infrastructure hardware. Our evaluation has confirmed the viability of SEED.
Identification of Black Fungus Diseases Using CNN and Transfer-Learning Approach

Black Fungus is a dangerous fungal illness, commonly referred to as ’mucormycosis’, usually infecting uncompromising people. Mucormycosis is generally rare, affecting less than two individuals per million each year, but it is currently 80 times more frequent in India. People of all ages, including prematurity infants, may be impacted. To minimize the time and effort required by medical experts to investigate black fungus disease and enhance the consistency of image identification of black fungus infections, we suggested a transfer learning based CNN model as the dataset for the black fungal disease is limited. The experimental outcomes of several different architectures, including VGG16, InceptionV3, and Xception, are compared and analyzed in this study, and it is observed that Xception has the best performance, with 97.92% training accuracy and 95.60% testing accuracy.
Decentralized management of ephemeral traffic incidents

Ephemeral traffic incidents, such as a fallen tree on a road, pose traffic safety hazards, and impact locally on traffic. While these incidents are neither predictable nor persistent, their existence is relevant for all vehicles planning to pass by while the impact continues. This article develops a novel communication strategy for vehicular ad hoc networks aiming to inform all the affected vehicles, while involving only the minimum number of non‐affected vehicles. This strategy exploits time geography as a spatial and temporal filter, ensuring also that the information broadcasting timely terminates when the incident is over. Agent‐based traffic simulations show that, when a road is temporarily blocked due to an ephemeral incident, the proposed decentralized information management model achieves significant improvement in travel efficiency and automatically updates outdated incident information in time.
Molecular mechanisms defining penetrance of LRRK2-associated Parkinson’s disease

Abstract Mutations in Leucine-rich repeat kinase 2 (LRRK2) are the most frequent cause of dominantly inherited Parkinson’s disease (PD). LRRK2 mutations, among which p.G2019S is the most frequent, are inherited with reduced penetrance. Interestingly, the disease risk associated with LRRK2 G2019S can vary dramatically depending on the ethnic background of the carrier. While this would suggest a genetic component in the definition of LRRK2-PD penetrance, only few variants have been shown to modify the age at onset of patients harbouring LRRK2 mutations, and the exact cellular pathways controlling the transition from a healthy to a diseased state currently remain elusive. In light of this knowledge gap, recent studies also explored environmental and lifestyle factors as potential modifiers of LRRK2-PD. In this article, we (i) describe the clinical characteristics of LRRK2 mutation carriers, (ii) review known genes linked to LRRK2-PD onset and (iii) summarize the cellular functions of LRRK2 with particular emphasis on potential penetrance-related molecular mechanisms. This section covers LRRK2’s involvement in Rab GTPase and immune signalling as well as in the regulation of mitochondrial homeostasis and dynamics. Additionally, we explored the literature with regard to (iv) lifestyle and (v) environmental factors that may influence the penetrance of LRRK2 mutations, with a view towards further exposomics studies. Finally, based on this comprehensive overview, we propose potential future in vivo, in vitro and in silico studies that could provide a better understanding of the processes triggering PD in individuals with LRRK2 mutations.
Identifying and Validating Selection Tools for Predicting Officer Performance and Retention

Abstract : The U.S. Army must commission officers who are likely to perform well as junior officers, fit into the Army's culture, demonstrate leadership potential for higher ranks, and be motivated to stay beyond their initial Active Duty Service Obligation (ADSO). To address this requirement, the U.S. Army Research Institute for the Behavioral and Social Sciences (ARI) has undertaken a program of research to enhance officer selection, assignment, and retention. The primary purpose of this effort was to evaluate a number of predictor measures against officer performance and career intentions. The core activity was a concurrent, criterion-related validation project in which several predictor and criterion measures were administered to over 800 early- and mid-career officers. Results showed that a number of measures were useful predictors of officer performance and retention. The Rational Biodata Inventory (RBI), the Leader Knowledge Test (LKT), the Objective-Format Consequences Test, and College GPA were good predictors of technical, managerial, leadership, and effort/discipline performance dimensions.
The Effect of Uncertainty Principle on the Thermodynamics of Early Universe

We discuss the concept of measurement in cosmology from the relativistic and quantum mechanical points of view. The uncertainty principle within the particle horizon, excludes the momentum of particles to be less than $\pi\hbar H/c$. This effect modifies the standard thermodynamics of early universe for the ultra-relativistic particles such that the equation of state as well as dependence of energy density and pressure to the temperature. We show that this modification to the thermodynamics of early universe is important for energies $E>10^{17} GeV$. During the inflation, the particle horizon inflates to a huge size and makes the uncertainty in the momentum to be negligible.
The Second NeurIPS Tournament of Reconnaissance Blind Chess

Reconnaissance Blind Chess is an imperfect-information variant of chess with significant private information that challenges state-of-the-art algorithms. The Johns Hopkins University Applied Physics Laboratory and several organizing partners held the second NeurIPS machine Reconnaissance Blind Chess competition in 2021. 18 bots competed in 9,180 games, revealing a dominant champion with 91% wins. The top four bots in the tournament matched or exceeded the performance of the inaugural tournament’s winner. However, none of the algorithms converge to an optimal, unexploitable strategy or appear to have addressed the core research challenges associated with Reconnaissance Blind Chess.
Interactive Corpora Visualization for 60 Years of AI Research

Research in artificial intelligence (AI) has been around for over six decades. In that time, it has experienced rich growth, with on and off interest, as researchers tackle this problem from different angles using inspiration from various fields. However, it is difficult to see an overview of the journey that research in AI has taken in its lifespan. We created a visualization we call “60 Years of AI” that explores a (biased) selection of the most influential publications that have shaped the field. Our visualization shows similar works clustered together throughout time and allows users to input abstracts of new ideas to see where their ideas position in the landscape of the ever-growing field of AI
Exploring Conceptual Soundness with TruLens

As machine learning has become increasingly ubiquitous, there has been a growing need to assess the trustworthiness of learned models. One important aspect to model trust is conceptual soundness, i.e., the extent to which a model uses features that are appropriate for its intended task. Deep networks have quickly become the face of modern machine learning, with unparalleled success at complex human tasks such as vision and natural language processing. However, deep networks are notoriously opaque, further emphasizing the need for transparency into their internal logic. A large body of work has arisen to address this problem, by providing explanations that distill aspects of a model’s behavior to be better understood by human practitioners. In this demonstration,1 we present TruLens, a new cross-platform library for explaining deep network behavior that implements a general class of gradient-based explanations captured by the “influence-directed” explanation framework of Leino et al. (2018). Throughout our presentation, we will take the unique perspective that to accurately assess the conceptual soundness of a model, an explanation must be faithful—i.e., the explanation must be causally related to the model’s behavior. By contrast, the literature has often attempted to justify explanations based on their appeal to human intuition. However, this begs the question, as it assumes the model captured human intuition in the first place. Instead, we argue that the utility of an explanation framework comes from its flexibility to faithfully answer a wide range of queries, but not from its tendency to produce reasonable, visually-appealing, or intuitive explanations. Our demonstration will show that faithful explanations can surface erroneous model behavior that may not be manifested in the validation set, and would therefore otherwise go unnoticed prior to model deployment. Thus, conversely, faithful explanations that align with our expectations of conceptually sound predictions, serve as evidence that the model is trustworthy. Finally, we observe that erroneous behavior caused by adversarial examples (Szegedy et al., 2014) is indicative of a lack of conceptual soundness. As adversarial examples are ubiquitous in standard deep networks, this observation suggests that robustness to adversarial examples is necessary for establishing conceptual soundness.
AIMEE: Interactive model maintenance with rule-based surrogates

In real-world applications, such as loan approvals or claims management, machine learning (ML) models need to be updated or retrained to adhere to new rules and regulations. But how can a new model be built and new decision boundaries be formed without having new training data available? We present the AI Model Explorer and Editor tool (AIMEE) for model exploration and model editing using human understandable rules. It addresses the problem of changing decision boundaries by leveraging user-specified feedback rules that are used to pre-process training data such that a retrained model will reflect user changes. The pre-processing step uses synthetic oversampling and relabeling and assumes black-box access to the algorithm that retrains the model. AIMEE provides interactive methods to edit rule sets, visualize changes to decision boundaries, and generate interpretable comparisons of model changes so that users see their feedback reflected in the updated model. The demo shows an end-to-end solution that supports the full update lifecycle of an ML model.
An Interactive Visual Demo of Bias Mitigation Techniques for Word Representations From a Geometric Perspective

Language representations are known to encode and propagate biases, i.e., stereotypical associations between words or groups of words that may cause representational harm. In this demo, we utilize interactive visualization to increase the interpretability of a number of state-of-the-art techniques that are designed to identify, mitigate, and attenuate these biases in word representations, in particular, from a geometric perspective. We provide an open source web-based visualization tool and offer hands-on experience in exploring the effects of these debiasing techniques on the geometry of high-dimensional word vectors. To help understand how various debiasing techniques change the underlying geometry, we decompose each technique into modular and interpretable sequences of primitive operations, and study their effect on the word vectors using dimensionality reduction and interactive visual exploration. This demo is primarily designed to aid natural language processing (NLP) practitioners and researchers working with fairness and ethics of machine learning systems. It can also be used to educate NLP novices in understanding the existence of and then mitigating biases in word embeddings.
Tungsten carbide material tribology and circular economy relationship in polymer and composites industries

The implementation of the circular economy concept in industries demands experimental and innovative investigations. The set of strategies and paradigms that express the theoretical base of the circular economy plays a vital role in the enhancement of the quality and performance of polymer and composite materials. In this article, tungsten carbide material is advanced for tribological investigations. The scanning electron microscope and an optical and mechanical profilometer were used for the analysis of cotton polymer and tungsten carbide ball surfaces. A newly developed tribometry technique was introduced to investigate the coefficient of friction, wear, and deformation. The cotton surface was found damaged and rough. The tungsten carbide balls showed low roughness and higher hardness. The average surface roughness parameters Ra, Rz, and Rp of tungsten carbide balls were 0.10, 0.15, and 0.20, respectively. The average friction constant values were found to be 0.12–0.15 in the perpendicular direction and 0.11–0.17 in the parallel direction. Reciprocation distance increment has been used for industrial optimization. The coefficient of friction remained constant and slightly deformed cotton polymer. Based on the friction values, deformation, wear, and morphology evaluations, tungsten carbide ceramic materials could be used operationally for surface alterations of industrial machinery parts. The results could also enhance the quality and performance of polymer and composite products.
A convenient method of manufacturing liquid-gated MoS2 field effect transistors

In this paper, we present a simple and convenient method of manufacturing liquid-gated MoS2 field effect transistors (FETs). A Si3N4 chip is firstly fabricated by the semiconductor manufacturing process, then the mechanical exfoliation MoS2 is transferred onto the Si3N4 chip and is connected with the gold electrodes by depositing platinum to construct the MoS2 FETs. The liquid-gated is formed by injecting 0.1 M NaCl solution into reservoir to contact the back side of the Si3N4. Our measured results show that the contact properties between MoS2 and electrodes are in well condition and the liquid-gated MoS2 FETs have a high mobility that can reach up to 109 cm2 V−1 s−1.
Unlocking Innovation in Healthcare: The Case of the Patient Innovation Platform

Multisided platforms are new organizing forms that can boost innovation in the healthcare sector by empowering patients as innovators and facilitating the commercialization of innovations by and for patients. However, applying the playbook script for the platform model from other sectors may prove challenging given the distinctive nature of the healthcare services. Drawing on the case of a leading healthcare innovation platform in Europe, this article examines the platform’s role in identifying and enabling the development and diffusion of patient innovations. This involves three main roles (community organizer, market matchmaker, and innovation manager) and corresponding orchestration activities.
Self-Reported Behaviors and Habits of Distracted College Pedestrians While Walking

Walking requires paying attention to the surrounding environment to ensure safe maneuvers. Walkers engaging in multitask activities such as texting, calling, listening to music, snacking, and reading while walking have detrimental effects on safety similar to those experienced with distracted drivers. Because this is an emerging problem, few data have been collected to assess the negative impact of distracted walking on safety. The present survey was conducted to collect information on distracted walking by college students. The objectives were to estimate the extent of distracting activities of college pedestrians and to assess the perception of the safety problem of college pedestrians while walking. The survey was sent to the potential student population of 5,000 by Survey Monkey at South Carolina State University in Orangeburg, and 297 surveys were completed. The results indicated that about 55% of respondents always checked their electronic devices while walking. Further, about 4% of the respondents reported that they or their immediate family members sustained injuries resulting from distracted walking. More important, approximately 53% of the respondents had seen near crashes resulting from pedestrians engaging in distracting activities while walking. In this survey, males engaged more in distracting activities than females. About 70% of females perceived that distracted walking had become a problem, compared with 30% of males. The most common proposed interventions by the respondents to curb distracted walking include education, outreach programs, citations, and legislation.
Data-driven Global Subseasonal Forecast Model (GSFM 1 v1.0) for intraseasonal oscillation components 2

As a challenge in the construction of a “seamless forecast” system, improving 11 the prediction skills of subseasonal forecasts is a key issue for meteorologists. In view 12 of the evolution characteristics of numerical models and recent deep learning models 13 for subseasonal forecasts, as forecast times increase, forecast results tend to become 14 intraseasonal low-frequency components, which are essential to the change in general 15 circulation on the subseasonal timescale as well as persistent extreme weather. In this 16 paper, the Global Subseasonal Forecast Model (GSFM v1.0) first extracted the 17 intraseasonal oscillation (ISO) components of atmospheric signals and used an 18 improved deep learning model (SE-ResNet) to train and predict the ISO components of 19 geopotential height at 500 hPa (Z500) and temperature at 850 hPa (T850). The results 20 show that the 10-30 day prediction performance of the model used in this paper is better 21 than that of the model trained directly with original data. Compared with other 22 models/methods, the SE-ResNet model has a good ability to depict the subseasonal 23 evolution of the ISO components of Z500 and T850. In particular, although the CFSv2 24 results have a better prediction performance through 10 days, the SE-ResNet model is 25 substantially superior to CFSv2 through 10-30 day, especially in the middle and high 26 latitudes. The SE-ResNet model also has a better effect in predicting 3-8 planetary 27 waves, which leads to the difference in model prediction performance in extratropical 28 areas. A case study shows that the SE-ResNet model depicted the phase change and
Black holes and the positive cosmological constant

We address some aspects of black hole spacetimes endowed with a positive cosmological constant, i.e. black holes located inside a cosmological event horizon. First we establish a general criterion for existence of cosmological event horizons. Using the geometrical set up built for this, we study classical black hole no hair theorems for both static and stationary axisymmetric spacetimes. We discuss cosmic Nielsen-Olesen strings as hair in Schwarzschild-de Sitter spacetime. We also give a general calculation for particle creation by a Killing horizon using complex path analysis and using this we study particle creation in Schwarzschild-de Sitter spacetime by both black hole and the cosmological event horizons.
XTOVERID: Stata module to calculate tests of overidentifying restrictions after xtreg, xtivreg, xtivreg2, xthtaylor

xtoverid computes versions of a test of overidentifying restrictions (orthogonality conditions) for a panel data estimation. For an instrumental variables estimation, this is a test of the null hypothesis that the excluded instruments are valid instruments, i.e., uncorrelated with the error term and correctly excluded from the estimated equation. The test statistic is distributed as chi-squared with degrees of freedom = L-K, where L is the number of excluded instruments and K is the number of regressors, and a rejection casts doubt on the validity of the instruments. xtoverid will report tests of overidentifying restrictions after IV estimation using fixed effects, first differences, random effects, and the Hausman-Taylor estimator. A test of fixed vs. random effects is also a test of overidentifying restrictions, and xtoverid will report this test after a standard panel data estimation with xtreg,re.
CONGRUENCE AMALGAMATION OF LATTICES

J. Tu uma proved an interesting "congruence amalgamation" result. We are generalizing and providing an alternate proof for it. We then provide applications of this result: (i) A.P. Huhn proved that every distributive algebraic lattice D with at most @1 compact elements can be represented as the congruence lattice of a lattice L. We show that L can be constructed as a locally finite relatively complemented lattice with zero. (ii) We find a large class of lattices, the ! -congruence-finite lattices, that contains all locally finite countable lattices, in which every lattice has a relatively complemented congruence-preserving extension.