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

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

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

Statistics of low-energy levels of a one-dimensional weakly localized Frenkel exciton: A numerical study

Numerical study of the one-dimensional Frenkel Hamiltonian with on-site randomness is carried out. We focus on the statistics of the energy levels near the lower exciton band edge, i. e. those determining optical response. We found that the distribution of the energy spacing between the states that are well localized at the same segment is characterized by non-zero mean, i.e. these states undergo repulsion. This repulsion results in a local discrete energy structure of a localized Frenkel exciton. On the contrary, the energy spacing distribution for weakly overlapping local ground states (the states with no nodes within their localization segments) that are localized at different segments has zero mean and shows almost no repulsion. The typical width of the latter distribution is of the same order as the typical spacing in the local discrete energy structure, so that this local structure is hidden; it does not reveal itself neither in the density of states nor in the linear absorption spectra. However, this structure affects the two-exciton transitions involving the states of the same segment and can be observed by the pump-probe spectroscopy. We analyze also the disorder degree scaling of the first and second momenta of the distributions.Comment: 10 pages, 6 figure

Third-order nonlinear optical properties of stacked bacteriochlorophylls in bacterial photosynthetic light-harvesting proteins

Enhancement of the nonresonant second order molecular hyperpolarizabilities {gamma} were observed in stacked macrocyclic molecular systems, previously in a {micro}-oxo silicon phthalocyanine (SiPcO) monomer, dimer and trimer series, and now in bacteriochlorophyll a (BChla) arrays of light harvesting (LH) proteins. Compared to monomeric BChla in a tetrahydrofuran (THF) solution, the <{gamma}> for each macrocycle was enhanced in naturally occurring stacked macrocyclic molecular systems in the bacterial photosynthetic LH proteins where BChla`s are arranged in tilted face-to-face arrays. In addition, the {gamma} enhancement is more significant in B875 of LH1 than in B850 in LH2. Theoretical modeling of the nonresonant {gamma} enhancement using simplified molecular orbitals for model SiPcO indicated that the energy level of the two photon state is crucial to the {gamma} enhancement when a two photon process is involved, whereas the charge transfer between the monomers is largely responsible when one photon near resonant process is involved. The calculated results can be extended to {gamma} enhancement in B875 and B850 arrays, suggesting that BChla in B875 are more strongly coupled than in B850. In addition, a 50--160 fold increase in <{gamma}> for the S{sub 1} excited state of relative to S{sub 0} of bacteriochlorophyll in vivo was observed which provides an alternative method for probing excited state dynamics and a potential application for molecular switching

Ab initio calculations of structural and electronic properties of CdTe clusters

We present results of a study of small stoichiometric $Cd_{n}Te_{n}$ ($1{\leq}n{\leq}6$) clusters and few medium sized non-stoichiometric $Cd_{m}Te_{n}$ [($m,n= 13, 16, 19$); ($m{\neq}n$)] clusters using the Density Functional formalism and projector augmented wave method within the generalized gradient approximation. Structural properties {\it viz.} geometry, bond length, symmetry and electronic properties like HOMO-LUMO gap, binding energy, ionization potential and nature of bonding {\it etc.} have been analyzed. Medium sized non-stoichiometric clusters were considered as fragments of the bulk with T{$_{d}$} symmetry. It was observed that upon relaxation, the symmetry changes for the Cd rich clusters whereas the Te rich clusters retain their symmetry. The Cd rich clusters develop a HOMO-LUMO gap due to relaxation whereas there is no change in the HOMO-LUMO gap of the Te rich clusters. Thus, the symmetry of a cluster seems to be an important factor in determining the HOMO-LUMO gap.Comment: 8 pages 16 figure