Military Intelligence Applications for Blockchain Technology

In this paper, the authors review documented problems in military intelligence that appear well suited for improvement via blockchain technology. We review guidance from the literature related to determining blockchain technology applicability and propose a decision aid tailored to military intelligence perspectives. We also propose applying batch queueing theory to enable initial feasibility studies and present analysis toward the first known case study of military intelligence incorporation of blockchain technology, a project reviewing blockchain applicability to an intelligence database that stores geographic locations of units of interest

Bayesian Filtering with Multiple Internal Models: Toward a Theory of Social Intelligence

To exhibit social intelligence, animals have to recognize whom they are communicating with. One way to make this inference is to select among internal generative models of each conspecific who may be encountered. However, these models also have to be learned via some form of Bayesian belief updating. This induces an interesting problem: When receiving sensory input generated by a particular conspecific, how does an animal know which internal model to update? We consider a theoretical and neurobiologically plausible solution that enables inference and learning of the processes that generate sensory inputs (e.g., listening and understanding) and reproduction of those inputs (e.g., talking or singing), under multiple generative models. This is based on recent advances in theoretical neurobiology—namely, active inference and post hoc (online) Bayesian model selection. In brief, this scheme fits sensory inputs under each generative model. Model parameters are then updated in proportion to the probability that each model could have generated the input (i.e., model evidence). The proposed scheme is demonstrated using a series of (real zebra finch) birdsongs, where each song is generated by several different birds. The scheme is implemented using physiologically plausible models of birdsong production. We show that generalized Bayesian filtering, combined with model selection, leads to successful learning across generative models, each possessing different parameters. These results highlight the utility of having multiple internal models when making inferences in social environments with multiple sources of sensory information

The Image of Future Human Represented in Chappie (2015)

This research discussed the image of the future human which is represented in Chappie (2015) movie by using representation theory. The researcher focused on the way the movie director created the figure of the future humans in his movie. The implementation of representation theory is used to analyze the image of the future humans created by the movie maker in Chappie (2015) movie. Chappie is a robot that has artificial intelligence to support itself in making decisions toward its acts. Furthermore, Chappie which is an Artificial Intelligence human-robot occurs as the most suitable figure to represent the human in the future era

Implications of Training in Incremental Theories of Intelligence for Undergraduate Statistics Students

This chapter documents the effects of training in incremental theories of intelligence on students in introductory statistics courses at a liberal arts university in the US. Incremental theories of intelligence examine the beliefs individuals hold of knowledge and how it is attained. An individual with an incremental theory of intelligence believes that intelligence can be developed. The research examined differences by gender in mastery of statistics and attitudes toward statistics for students who received growth mind-set training. A pre-test, post-test design utilised the Students’ Attitudes Toward Statistics© instrument and the Comprehensive Assessment of Outcomes in a first Statistics course. An ANCOVA revealed that females gained more than males on their value of statistics (F(1, 63) 9.40, MSE 3.79, p .003, η2 P 0.134) and decreased less for effort expended to learn statistics (F(1, 63) 4.41, MSE 4.07, p .040, η2 P 0.067). Females also gained mastery of statistical concepts at a greater rate (F(1, 63) 5.30, MSE 0.06, p .025, η2 P 0.080) indicating a possible path to alleviate the under-representation of females in STEM

Quantum Interaction Approach in Cognition, Artificial Intelligence and Robotics

The mathematical formalism of quantum mechanics has been successfully employed in the last years to model situations in which the use of classical structures gives rise to problematical situations, and where typically quantum effects, such as 'contextuality' and 'entanglement', have been recognized. This 'Quantum Interaction Approach' is briefly reviewed in this paper focusing, in particular, on the quantum models that have been elaborated to describe how concepts combine in cognitive science, and on the ensuing identification of a quantum structure in human thought. We point out that these results provide interesting insights toward the development of a unified theory for meaning and knowledge formalization and representation. Then, we analyze the technological aspects and implications of our approach, and a particular attention is devoted to the connections with symbolic artificial intelligence, quantum computation and robotics.Comment: 10 page

A weight-related growth mindset increases negative attitudes toward obese people

© 2018 Wiley Periodicals, Inc. In implicit personality theory, people with entity views or a fixed mindset perceive characteristics (e.g., intelligence) as uncontrollable, whereas people with incremental views or a growth mindset perceive characteristics as controllable. In addition to other benefits, the literature sometimes suggests that having a growth mindset will protect against prejudice, which the current two studies examine in terms of negative attitudes toward obese people. Participants (total N = 501) were randomly assigned to complete a questionnaire assessing attitudes toward an obese or nonobese person and a self-theory questionnaire also assessed ideas about body weight. People with a growth mindset, and not fixed mindset, were more likely to have negative attitudes toward obese individuals, pointing to a potential downside of growth mindset in the obesity domain

Generating Functions For Kernels of Digraphs (Enumeration & Asymptotics for Nim Games)

In this article, we study directed graphs (digraphs) with a coloring constraint due to Von Neumann and related to Nim-type games. This is equivalent to the notion of kernels of digraphs, which appears in numerous fields of research such as game theory, complexity theory, artificial intelligence (default logic, argumentation in multi-agent systems), 0-1 laws in monadic second order logic, combinatorics (perfect graphs)... Kernels of digraphs lead to numerous difficult questions (in the sense of NP-completeness, #P-completeness). However, we show here that it is possible to use a generating function approach to get new informations: we use technique of symbolic and analytic combinatorics (generating functions and their singularities) in order to get exact and asymptotic results, e.g. for the existence of a kernel in a circuit or in a unicircuit digraph. This is a first step toward a generatingfunctionology treatment of kernels, while using, e.g., an approach "a la Wright". Our method could be applied to more general "local coloring constraints" in decomposable combinatorial structures.Comment: Presented (as a poster) to the conference Formal Power Series and Algebraic Combinatorics (Vancouver, 2004), electronic proceeding

An Exploratory Comparative Study of Students\u27 Thinking in Arts Classrooms

To be successfully intelligent in the 21st century, students must be able to think well in at least three ways: creatively, critically, and practically, with complexity and wisdom. The purpose of this research was to explore the differences in middle school students\u27 quality of thinking in arts classrooms that are designed to be learner centered to a greater or lesser degree. Classroom environments which foster balanced intelligence in analytical, creative, and practical ways toward depth of understanding were the focus of this study. A better understanding of the impact of learner-centered environments on students\u27 perceptions of their learning and understanding in these classrooms was also sought. This research study supported theory in the area of balanced intelligence, toward the realization of students\u27 increased capacity to learn and achieve. Results of this mixed model comparative study indicated that classrooms designed to be more learner-centered (utilizing inquiry, connection-making, and self-direction to a greater degree) had a positive effect on students\u27 overall quality of thinking as demonstrated in a balanced way. Results also indicated that more learner-centered classrooms also had a positive effect on students\u27 self-beliefs regarding their intelligence and understanding in the context of visual art. This study suggests that infusion of best practice research toward the development of balanced thinking and overall cognitive development in the arts is beneficial to students and provided insight into the ways in which personal belief systems about capabilities and intelligence drive motivation, which may in turn drive learning goals and overall achievement. The mixed model exploratory design led to an emerging theory regarding a systems approach to the development quality thinking, as driven by the learning and thinking culture, belief systems, and dynamic classroom environments. This study provides insight into how dynamic learning systems may better nurture the kind of flexible, adaptive thinkers--at all levels of the learning organization--needed in a complex world

Analytical learning and term-rewriting systems

Analytical learning is a set of machine learning techniques for revising the representation of a theory based on a small set of examples of that theory. When the representation of the theory is correct and complete but perhaps inefficient, an important objective of such analysis is to improve the computational efficiency of the representation. Several algorithms with this purpose have been suggested, most of which are closely tied to a first order logical language and are variants of goal regression, such as the familiar explanation based generalization (EBG) procedure. But because predicate calculus is a poor representation for some domains, these learning algorithms are extended to apply to other computational models. It is shown that the goal regression technique applies to a large family of programming languages, all based on a kind of term rewriting system. Included in this family are three language families of importance to artificial intelligence: logic programming, such as Prolog; lambda calculus, such as LISP; and combinatorial based languages, such as FP. A new analytical learning algorithm, AL-2, is exhibited that learns from success but is otherwise quite different from EBG. These results suggest that term rewriting systems are a good framework for analytical learning research in general, and that further research should be directed toward developing new techniques

K-12 online teacher beliefs: relationships among intelligence, confidence, teacher-student interactions, and student outcomes

The vigorous expansion of online learning in K-12 education is a recent change to the conceptualization of schooling that has been occurring for more than 10 years. However, methods used for recruiting, hiring, and preparing online teachers have not been altered beyond the current federal standard defined by No Child Left Behind of Highly Qualified Teachers in order to provide students with teachers demonstrating an orientation toward learning. Historically, educational theory and research suggest that teachers who are learners make a difference for student learning. Recently, social cognitive psychology and neuroscience research has demonstrated a key finding that beliefs about intelligence influence learning success. The purpose of this empirical inferential study was to examine teacher belief about intelligence, teacher confidence in one\u27s intelligence, and the relationship with teacher-student interactions and student outcomes through the administration of a 9-item online questionnaire. The study used the Theory of Intelligence Scale and Confidence in One\u27s Intelligence Scale created by Carol Dweck combined with student academic gains from the 2010-2011 Fall and Spring Scantron Performance Series assessments and archived documentation from the internal communication system. Data from 298 randomly selected K-12 online teachers serving as a primary teacher of record for 1 of 18 cyber charters, managed by the same education management organization, were used to address 6 null hypotheses and 4 research questions. Findings suggest teacher belief in the malleability of intelligence positively affects student learning in literacy, which subsequently impacts math achievement. This affirming belief of intelligence shapes teacher behavior evidenced through greater interaction with students in a virtual classroom using a diverse set of interaction strategies. Teachers\u27 confidence in one\u27s intelligence alone was not an effective predictor of class achievement gains. However, once teacher\u27s confidence was combined with his or her framework for intelligence, it served to identify the population that resorted to using known strategies as the primary means for interacting with students and the population of online teachers that seemingly disengaged through their limited teacher-student interaction