Enabling Explainable Fusion in Deep Learning with Fuzzy Integral Neural Networks

Information fusion is an essential part of numerous engineering systems and biological functions, e.g., human cognition. Fusion occurs at many levels, ranging from the low-level combination of signals to the high-level aggregation of heterogeneous decision-making processes. While the last decade has witnessed an explosion of research in deep learning, fusion in neural networks has not observed the same revolution. Specifically, most neural fusion approaches are ad hoc, are not understood, are distributed versus localized, and/or explainability is low (if present at all). Herein, we prove that the fuzzy Choquet integral (ChI), a powerful nonlinear aggregation function, can be represented as a multi-layer network, referred to hereafter as ChIMP. We also put forth an improved ChIMP (iChIMP) that leads to a stochastic gradient descent-based optimization in light of the exponential number of ChI inequality constraints. An additional benefit of ChIMP/iChIMP is that it enables eXplainable AI (XAI). Synthetic validation experiments are provided and iChIMP is applied to the fusion of a set of heterogeneous architecture deep models in remote sensing. We show an improvement in model accuracy and our previously established XAI indices shed light on the quality of our data, model, and its decisions.Comment: IEEE Transactions on Fuzzy System

Roach infestation optimization

Abstract only availableThere are many function optimization algorithms based on the collective behavior of natural systems — Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) are two of the most popular. This poster presents a new adaptation of the PSO algorithm, entitled Roach Infestation Optimization (RIO), which is inspired by recent discoveries in the social behavior of cockroaches. We present the development of the simple behaviors of the individual agents, which emulate some of the discovered cockroach social behaviors. We also describe a "hungry" version of the PSO and RIO, which we aptly call Hungry PSO and Hungry RIO. Comparisons with standard PSO show that Hungry PSO, RIO, and Hungry RIO are all more effective at finding the global optima of a suite of test functions.College of Engineering Undergraduate Research Optio

Traffic Control for Maintenance on High-Speed Highways

Observations were first made at lane closures on interstate highways where yellow warning signs were erected routinely in conjunction with contract work. Later data provided direct comparison between new yellow and new orange signs. One sign scheme was used throughout the study. Driver obedience improved when new signs of either color were used; this finding implies that signs should always be maintained in good condition. Orange signs were slightly more effective than yellow signs in reducing traffic conflicts and merges near the traffic cones. Results of the study tend to support the adoption of orange as the standard color for signing construction and maintenance sites. However, differences between the two colors were rather small. Driver preference polls supported the orange signs more strongly. A degree of driver insensitivity toward signing was shown. In general, variables such as short sight distances, high volumes, poor condition of signs, and driver insensitivity produced unsafe situations at lane closures. However, the scope of the study did not permit observations at sufficient sites and(or) at sufficient times to serve as a definitive exploration of such variables as weather, terrain, vertical and horizontal alignment, or level of service

From interval-valued data to general type-2 fuzzy sets

In this paper, a new approach is presented to model interval-based data using fuzzy sets (FSs). Specifically, we show how both crisp and uncertain intervals (where there is uncertainty about the endpoints of intervals) collected from individual or multiple survey participants over single or repeated surveys can be modeled using type-1, interval type-2, or general type-2 FSs based on zSlices. The proposed approach is designed to minimize any loss of information when transferring the interval-based data into FS models, and to avoid, as much as possible, assumptions about the distribution of the data. Furthermore, our approach does not rely on data preprocessing or outlier removal, which can lead to the elimination of important information. Different types of uncertainty contained within the data, namely intra- and inter-source uncertainty, are identified and modeled using the different degrees of freedom of type-2 FSs, thus providing a clear representation and separation of these individual types of uncertainty present in the data. We provide full details of the proposed approach, as well as a series of detailed examples based on both real-world and synthetic data. We perform comparisons with analogue techniques to derive FSs from intervals, namely the interval approach and the enhanced interval approach, and highlight the practical applicability of the proposed approach

Could humans recognize odor by phonon assisted tunneling?

Our sense of smell relies on sensitive, selective atomic-scale processes that are initiated when a scent molecule meets specific receptors in the nose. However, the physical mechanisms of detection are not clear. While odorant shape and size are important, experiment indicates these are insufficient. One novel proposal suggests inelastic electron tunneling from a donor to an acceptor mediated by the odorant actuates a receptor, and provides critical discrimination. We test the physical viability of this mechanism using a simple but general model. Using values of key parameters in line with those for other biomolecular systems, we find the proposed mechanism is consistent both with the underlying physics and with observed features of smell, provided the receptor has certain general properties. This mechanism suggests a distinct paradigm for selective molecular interactions at receptors (the swipe card model): recognition and actuation involve size and shape, but also exploit other processes.Comment: 10 pages, 1 figur

Extension of the fuzzy integral for general fuzzy set-valued information

The fuzzy integral (FI) is an extremely flexible aggregation operator. It is used in numerous applications, such as image processing, multicriteria decision making, skeletal age-at-death estimation, and multisource (e.g., feature, algorithm, sensor, and confidence) fusion. To date, a few works have appeared on the topic of generalizing Sugeno's original real-valued integrand and fuzzy measure (FM) for the case of higher order uncertain information (both integrand and measure). For the most part, these extensions are motivated by, and are consistent with, Zadeh's extension principle (EP). Namely, existing extensions focus on fuzzy number (FN), i.e., convex and normal fuzzy set- (FS) valued integrands. Herein, we put forth a new definition, called the generalized FI (gFI), and efficient algorithm for calculation for FS-valued integrands. In addition, we compare the gFI, numerically and theoretically, with our non-EP-based FI extension called the nondirect FI (NDFI). Examples are investigated in the areas of skeletal age-at-death estimation in forensic anthropology and multisource fusion. These applications help demonstrate the need and benefit of the proposed work. In particular, we show there is not one supreme technique. Instead, multiple extensions are of benefit in different contexts and applications