Defining CARE Properties Through Temporal Input Models

In this paper we show how it is possible to represent the CARE properties (complementarity, assignment, redundancy, equivalence) modelling the temporal relationships among inputs provided through different modalities. For this purpose we extended GestIT, which provides a declarative and compositional model for gestures, in order to support other modalities. The generic models for the CARE properties can be used for the input model design, but also for an analysis of the relationships between the different modalities included into an existing input model

VMXR: a EUD Environment for Virtual Merchandizing in XR

This paper presents the current development state of VMXR, a Proof of Concept (PoC) environment allowing people without programming experience to create and configure product showcases in a Virtual and eXtended reality setting. The aim of the PoC is to identify proper metaphors and workflows for supporting showcase designers in creating interactions with the virtual product representation or enhancing the physical environment through additional information and media

Considerations for applying logical reasoning to explain neural network outputs

We discuss the impact of presenting explanations to people for Artificial Intelligence (AI) decisions powered by Neural Networks, according to three types of logical reasoning (inductive, deductive, and abductive). We start from examples in the existing literature on explaining artificial neural networks. We see that abductive reasoning is (unintentionally) the most commonly used as default in user testing for comparing the quality of explanation techniques. We discuss whether this may be because this reasoning type balances the technical challenges of generating the explanations, and the effectiveness of the explanations. Also, by illustrating how the original (abductive) explanation can be converted into the remaining two reasoning types we are able to identify considerations needed to support these kinds of transformations

XRSpotlight: Example-based Programming of XR Interactions using a Rule-based Approach

Research on enabling novice AR/VR developers has emphasized the need to lower the technical barriers to entry. This is often achieved by providing new authoring tools that provide simpler means to implement XR interactions through abstraction. However, novices are then bound by the ceiling of each tool and may not form the correct mental model of how interactions are implemented. We present XRSpotlight, a system that supports novices by curating a list of the XR interactions defined in a Unity scene and presenting them as rules in natural language. Our approach is based on a model abstraction that unifies existing XR toolkit implementations. Using our model, XRSpotlight can find incomplete specifications of interactions, suggest similar interactions, and copy-paste interactions from examples using different toolkits. We assess the validity of our model with professional VR developers and demonstrate that XRSpotlight helps novices understand how XR interactions are implemented in examples and apply this knowledge in their projects

Variation of endophytic cork oak‐associated fungal communities in relation to plant health and water stress

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86852/1/j.1439-0329.2010.00652.x.pd

Supporting High-Uncertainty Decisions through AI and Logic-Style Explanations

A common criteria for Explainable AI (XAI) is to support users in establishing appropriate trust in the AI - rejecting advice when it is incorrect, and accepting advice when it is correct. Previous findings suggest that explanations can cause an over-reliance on AI (overly accepting advice). Explanations that evoke appropriate trust are even more challenging for decision-making tasks that are difficult for humans and AI. For this reason, we study decision-making by non-experts in the high-uncertainty domain of stock trading. We compare the effectiveness of three different explanation styles (influenced by inductive, abductive, and deductive reasoning) and the role of AI confidence in terms of a) the users' reliance on the XAI interface elements (charts with indicators, AI prediction, explanation), b) the correctness of the decision (task performance), and c) the agreement with the AI's prediction. In contrast to previous work, we look at interactions between different aspects of decision-making, including AI correctness, and the combined effects of AI confidence and explanations styles. Our results show that specific explanation styles (abductive and deductive) improve the user's task performance in the case of high AI confidence compared to inductive explanations. In other words, these styles of explanations were able to invoke correct decisions (for both positive and negative decisions) when the system was certain. In such a condition, the agreement between the user's decision and the AI prediction confirms this finding, highlighting a significant agreement increase when the AI is correct. This suggests that both explanation styles are suitable for evoking appropriate trust in a confident AI. Our findings further indicate a need to consider AI confidence as a criterion for including or excluding explanations from AI interfaces. In addition, this paper highlights the importance of carefully selecting an explanation style according to the characteristics of the task and data

Qualification tests on the optical retro-reflectors of LARES satellite.

LARES Satellite has been successfully launched on February 13th 2012 with the first flight of the new European Launcher VEGA. The passive, laser ranged satellite carries 92 cube corner reflectors (CCR). Due to its high density LARES represents the known orbiting object with the highest mean density in the solar system. This property makes it an almost perfect proof particle in the gravitational field of Earth. LARES is now operational and it is tracked by the International Laser Ranging Service stations. It will be used to test General Relativity and in particular the fact that the rotating Earth drags spacetime. The satellite design is quite innovative in the use of tungsten alloy as a structural material; indeed, the satellite body has been machined from a single piece of high density sintered alloy. The sintered alloy is characterized by a porous surface that shall be carefully cleaned before the integration of the optical components, in order to avoid contamination of the back faces of the CCR from the metal. Two cleaning procedures have been identified, to be performed on LARES. One procedure consisted in chemical cleaning with different solvents and cleaning agents; the second procedure consisted in a chemical cleaning followed by degassing in a high vacuum oven. The cleanness procedures have been tested on breadboards reproducing the satellite materials. The breadboards were tungsten alloy cylinders, carrying a cube corner reflector. The test was performed on two different breadbords each one for one of the two cleaning procedure. To simulate the operative space conditions the Thermal Vacuum Facility of Sapienza University of Rome has been used. The breadboards were maintained in simulated space environment to allow degassing of possible contaminants from the metal and possible detachment of contaminants from the metal to the back faces of the CCR. Visual inspection and Far Field Diffraction Patter tests have been performed to verify the possible presence and effect of contaminants on the of the CCR back faces. In the paper some detail on the LARES mission and on the scientific objectives will be described along with all the details on this qualification process

Bioprospecting antimicrobials from lactiplantibacillus plantarum: Key factors underlying its probiotic action

Lactiplantibacillus plantarum (L. plantarum) is a well‐studied and versatile species of lactobacilli. It is found in several niches, including human mucosal surfaces, and it is largely employed in the food industry and boasts a millenary tradition of safe use, sharing a long‐lasting relationship with humans. L. plantarum is generally recognised as safe and exhibits a strong probiotic character, so that several strains are commercialised as health‐promoting supplements and functional food products. For these reasons, L. plantarum represents a valuable model to gain insight into the nature and mechanisms of antimicrobials as key factors underlying the probiotic action of health‐promoting microbes. Probiotic antimicrobials can inhibit the growth of pathogens in the gut ensuring the intestinal homeostasis and contributing to the host health. Furthermore, they may be attractive alternatives to conventional antibiotics, holding potential in several biomedical applications. The aim of this review is to investigate the most relevant papers published in the last ten years, bioprospecting the antimicrobial activity of characterised probiotic L. plantarum strains. Specifically, it focuses on the different chemical nature, the action spectra and the mechanisms underlying the bioactivity of their antibacterial and antiviral agents. Emerging trends in postbiotics, some in vivo applications of L. plantarum antimicrobials, including strengths and limitations of their therapeutic potential, are addressed and discussed

AR TutorialKit: an Augmented Reality Toolkit to Create Tutorials

Augmented Reality (AR) is a widely used technology in fields such as medicine, engineering, and architecture, and is also prevalent in social media platforms like Snapchat, Instagram, and TikTok. In recent years, the availability of AR applications and improvements in hardware have made it affordable for educational training in various disciplines. However, limited options are available for the general construction of AR tutorials in the literature. Most solutions are specific for particular contexts, such as medical procedures or industry-specific tasks. This paper proposes an AR toolkit that enables novice programmers to create tutorials without topic restrictions. Our aim is to keep improving TutorialKit in such a way that it can be used flexibly and effectively in a variety of different contexts, enabling it to meet the diverse needs and requirements of users