Human-animal communication in captive species: Dogs, horses, and whales

My hopes for this project are to collect and analyze the current research in the field of animal communication. In the first part, my goal is to define animal communication, specifically within human contexts. I will look at how the history of humans and certain species have intertwined to result in their modern day relationships. I will also explain why we should care about animal communication. In the second part, I will look at three specific species I have chosen to study: dogs, horses, and cetaceans. I will provide a brief history of our roles as humans in the evolution of their contemporary domestic counterparts, as well as make a few comparisons to their wild counterparts. Mainly, I want to compare the wild and the domesticated in order to illustrate the immense effect human communication has had on their development. Finally, I hope to make some of my own conclusions on what the future could hold for whale and dolphin species, as well as any other species held in human captivity

Sound use, sequential behavior and ecology of foraging bottlenose dolphins, Tursiops truncatus

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution September 1999Odontocetes are assumed to use echolocation for navigation and foraging, but neither of these uses of biosonar has been conclusively demonstrated in free-ranging animals. Many bats are known to use echolocation throughout foraging sequences, changing the structure and timing of clicks as they progress towards prey capture. For odontocetes, however, we do not know enough about their foraging behavior to describe such sequences. To conduct detailed behavioral observations of any subject animal, the observer must be able to maintain continuous visual contact with the subject for a period commensurate with the duration of the behavior(s) of interest. Behavioral studies of cetaceans, which spend approximately 95% of their time below the water's surface, have been limited to sampling surface behavior except in special circumstances, e.g. clear-water environments, or with the use of technological tools. I addressed this limitation through development of an observation platform consisting of a remote controlled video camera suspended from a tethered airship with boat-based monitoring, adjustment, and recording of video. The system was used successfully to conduct continuous behavioral observations of bottlenose dolphins in the Sarasota Bay, FL area. This system allowed me to describe previously unreported foraging behaviors and elucidate functions for behaviors already defined but poorly understood. Dolphin foraging was modeled as a stage-structured sequence of behaviors, with the goal-directed feeding event occurring at the end of a series of search, encounter, and pursuit behaviors. The behaviors preceding a feeding event do not occur in a deterministic sequence, but are adaptive and plastic. A single-step transition analysis beginning with prey capture and receding in time has identified significant links between observed behaviors and demonstrated the stage-structured nature of dolphin foraging. Factors affecting the occurrence of specific behaviors and behavioral transitions include mesoscale habitat variation and individual preferences. The role of sound in foraging, especially echolocation, is less well understood than the behavioral component. Recent studies have explored the use of echolocation in captive odontocete foraging and presumed feeding in wild animals, but simultaneous, detailed behavioral and acoustic observations have eluded researchers. The current study used two methods to obtain acoustic data. The overhead video system includes two towed hydrophones used to record 'ambient' sounds of dolphin foraging. The recordings are of the 'ambient' sounds because the source of the sounds, i.e. animal, could not be localized. Many focal follows, however, were conducted with single animals, and from these records the timing of echolocation and other sounds relative to the foraging sequence could be examined. The 'ambient' recordings revealed that single animals are much more vocal than animals in groups, both overall and during foraging. When not foraging, single animals vocalized at a rate similar to the per animal rate in groups of ≥2 animals. For single foraging animals, the use of different sound types varies significantly by the habitat in which the animal is foraging. These patterns of use coupled with the characteristics of the different sound types suggest specific functions for each. The presence of multiple animals in a foraging group apparently reduces the need to vocalize, and potential reasons for this pattern are discussed. In addition, the increased vocal activity of single foraging animals lends support to specific hypotheses of sound use in bottlenose dolphins and odontocetes in general. The second acoustic data collection method records sounds known to be from a specific animal. An acoustic recording tag was developed that records all sounds produced by an animal including every echolocation click. The tag also includes an acoustic sampling interval controller and a sensor suite that measures pitch, roll, heading, and surfacing events. While no foraging events occurred while an animal was wearing an acoustic data logger, the rates of echolocation and whistling during different activities, e.g. traveling, were measured.This work was supported by the Education Office of the Woods Hole Oceanographic Institution, two grants from the Rinehart Coastal Research Center, the Ocean Ventures Fund; WHOI Sea Grant, ONR Grant #N00014-94-1-0692 to P. Tyack, and a Graduate Fellowship from the Office of Naval Research

A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics

The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at an accelerated pace. Because of that, in spite of the fact that there are a lot of surveys and reviews in the field, they quickly become dated. Thus, it is of importance to keep pace with the current developments. In this review, we first consider a possible classification of bio-inspired multiparameter optimization methods because papers dedicated to that area are relatively scarce and often contradictory. We proceed by describing in some detail some more prominent approaches, as well as those most recently published. Finally, we consider the use of biomimetic algorithms in two related wide fields, namely microelectronics (including circuit design optimization) and nanophotonics (including inverse design of structures such as photonic crystals, nanoplasmonic configurations and metamaterials). We attempted to keep this broad survey self-contained so it can be of use not only to scholars in the related fields, but also to all those interested in the latest developments in this attractive area

2016 Undergraduate Research Symposium Abstract Book

Abstract book from the 2016 Sixteenth Annual UMM Undergraduate Research Symposium (URS) which celebrates student scholarly achievement and creative activities

Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration Versus Algorithmic Behavior, Critical Analysis Recommendations

In recent algorithmic family simulates different biological processes observed in Nature in order to efficiently address complex optimization problems. In the last years the number of bio-inspired optimization approaches in literature has grown considerably, reaching unprecedented levels that dark the future prospects of this field of research. This paper addresses this problem by proposing two comprehensive, principle-based taxonomies that allow researchers to organize existing and future algorithmic developments into well-defined categories, considering two different criteria: the source of inspiration and the behavior of each algorithm. Using these taxonomies we review more than three hundred publications dealing with nature- inspired and bio-inspired algorithms, and proposals falling within each of these categories are examined, leading to a critical summary of design trends and similarities between them, and the identification of the most similar classical algorithm for each reviewed paper. From our analysis we conclude that a poor relationship is often found between the natural inspiration of an algorithm and its behavior. Furthermore, similarities in terms of behavior between different algorithms are greater than what is claimed in their public disclosure: specifically, we show that more than one-third of the reviewed bio-inspired solvers are versions of classical algorithms. Grounded on the conclusions of our critical analysis, we give several recommendations and points of improvement for better methodological practices in this active and growing research field

Ransomware Detection and Classification Strategies

Ransomware uses encryption methods to make data inaccessible to legitimate users. To date a wide range of ransomware families have been developed and deployed, causing immense damage to governments, corporations, and private users. As these cyberthreats multiply, researchers have proposed a range of ransomware detection and classification schemes. Most of these methods use advanced machine learning techniques to process and analyze real-world ransomware binaries and action sequences. Hence this paper presents a survey of this critical space and classifies existing solutions into several categories, i.e., including network-based, host-based, forensic characterization, and authorship attribution. Key facilities and tools for ransomware analysis are also presented along with open challenges.Comment: 9 pages, 2 figure

Artificial Intelligence and National Security

This report discuses research and development of artificial intelligence (AI) applications for the military, their potential uses, and the programs of competing nations such as China and Russia

FrameShift: Shift Your Attention, Shift the Story

Attention is a limited resource that intrinsically dictates our perceptions, memories, and behaviors. Further, visuospatial attention correlates highly with user engagement, heart rate, and arousal. Artists and interactive game designers strive to capture and direct attention, yet even in the most carefully crafted graphic narratives viewer eye paths -- a proxy for attention -- vary up to 20 percent. Our aim is to use attentional measures to enrich graphic novel narratives.FrameShift uses eye tracking to measure reader attention and changes text and visual elements later on in the story accordingly. We have built an extensible framework for using attention to introduce perceptual changes in narratives. We use attention as an indirect method for interactions and introduce shiftable frame nodes that change readers\u27 belief states over time

and Cost/Benefits Opportunities

Acquisition Research Program Sponsored Report SeriesSponsored Acquisition Research & Technical ReportsThe acquisition of artificial intelligence (AI) systems is a relatively new challenge for the U.S. Department of Defense (DoD). Given the potential for high-risk failures of AI system acquisitions, it is critical for the acquisition community to examine new analytical and decision-making approaches to managing the acquisition of these systems in addition to the existing approaches (i.e., Earned Value Management, or EVM). In addition, many of these systems reside in small start-up or relatively immature system development companies, further clouding the acquisition process due to their unique business processes when compared to the large defense contractors. This can lead to limited access to data, information, and processes that are required in the standard DoD acquisition approach (i.e., the 5000 series). The well-known recurring problems in acquiring information technology automation within the DoD will likely be exacerbated in acquiring complex and risky AI systems. Therefore, more robust, agile, and analytically driven acquisition methodologies will be required to help avoid costly disasters in acquiring these kinds of systems. This research provides a set of analytical tools for acquiring organically developed AI systems through a comparison and contrast of the proposed methodologies that will demonstrate when and how each method can be applied to improve the acquisitions lifecycle for AI systems, as well as provide additional insights and examples of how some of these methods can be applied. This research identifies, reviews, and proposes advanced quantitative, analytically based methods within the integrated risk management (IRM)) and knowledge value added (KVA) methodologies to complement the current EVM approach. This research examines whether the various methodologies—EVM, KVA, and IRM—could be used within the Defense Acquisition System (DAS) to improve the acquisition of AI. While this paper does not recommend one of these methodologies over the other, certain methodologies, specifically IRM, may be more beneficial when used throughout the entire acquisition process instead of within a portion of the system. Due to this complexity of AI system, this research looks at AI as a whole and not specific types of AI.Approved for public release; distribution is unlimited.Approved for public release; distribution is unlimited

Semantic Processing of Nominal Metaphor: Figurative Abstraction and Embodied Simulation

In a metaphor such as that lawyer is a shark, the concept lawyer, which is the metaphor topic, and the concept shark, which is the metaphor vehicle, interact to produce a figurative meaning such that lawyers are predatory. Some theorists argue that sensorimotor properties of the vehicle are the basis of metaphor comprehension (Gibbs & Matlock, 2008; Paivio, 1979; Wilson & Gibbs, 2007). As such, that lawyer is a shark is processed by an embodied simulation where sensorimotor imagery associated with sharks is simulated (e.g., sharks hunting in deep water). However, the long-standing assumption is that metaphors are processed abstractly and sensorimotor representations play no role (e.g., Gentner & Bowdle, 2008; Glucksberg, 2008). This thesis examines the role of sensorimotor simulation in processing metaphor. In Studies 1 – 2, participants rated metaphors on comprehensibility. The metaphors contained vehicles that varied on a semantic richness variable known as body-object interaction (BOI), which characterizes the degree to which a concept is easy to interact with (Siakaluk et al., 2008). A high-BOI metaphor contains a vehicle concept that is easy-to-interact with (e.g., life is a bicycle) whereas a low-BOI metaphor contains a concept that is difficult-to-interact with (e.g., life is a rainbow). Participants rated low-BOI metaphors to be more comprehensible than high-BOI metaphors, a finding that suggests sensorimotor properties are not heavily involved in metaphor processing. In Study 3 participants created novel metaphors by pairing abstract topics with words that varied on BOI to serve as vehicles. In creating metaphors, participants chose more low-BOI words to serve as vehicles than high-BOI words. However, to interpret their created metaphors, participants used language reflective of an embodied simulation for both high and low-BOI metaphors, indicating that nominal metaphors do indeed involve sensorimotor imagery. In Studies 4 – 7, a priming paradigm showed that processing novel metaphors (e.g., highways are snakes) immediately activates sensorimotor properties (e.g., slither) whereas familiar metaphors (e.g., lawyers are sharks) do not activate sensorimotor properties (e.g., bite) but rather, activate abstract associations (e.g., killer). In sum, the experiments in this dissertation are the first to demonstrate novel metaphors are processed by sensorimotor simulations