MASKS: A Multi-Classifier's verification approach

Classifiers are one of the most widely applied approaches in Artificial Intelligence (AI). However, the employment of classifiers in critical applications would render any errors in these systems more consequential; particularly due to the lack of formal verification methods in these systems. This study aims to develop a verification method that eliminates errors through the integration of multiple classifiers. In order to do this, primarily, we have defined a special property for the classifiers which extracts the knowledge of these classifiers. Secondly, we have designed a multi-agent system, comprised of multiple classifiers, in order to check the satisfaction of the aforementioned special property. Also, in order to help examine the reasoning concerning the aggregation of the distributed knowledge, itself gained through the combined effort of separate classifiers and acquired external information sources, a dynamic epistemic logic-based method has been proposed. Our proposed model is capable of verifying itself given specific inputs if the cumulative knowledge of the entire system proves their correctness, which results in self-awareness of this system. Finally, we applied this model to the MNIST dataset, and it successfully reduced the error rate to approximately one-tenth of the individual classifiers. In conclusion, we have formulated and developed a Multi-Agent Systems' Knowledge-Sharing algorithm (MASKS) and verified its utility compared to individual classifiers using the MNIST dataset.Comment: 17 pages, 8 figure

How Does Science Come to Speak in the Courts? Citations Intertexts, Expert Witnesses, Consequential Facts, and Reasoning

Citations, in their highly conventionalized forms, visibly indicate each texts explicit use of the prior literature that embodies the knowledge and contentions of its field. This relation to prior texts has been called intertextuality in literary and literacy studies. Here, Bazerman discusses the citation practices and intertextuality in science and the law in theoretical and historical perspective, and considers the intersection of science and law by identifying the judicial rules that limit and shape the role of scientific literature in court proceedings. He emphasizes that from the historical and theoretical analysis, it is clear that, in the US, judicial reasoning is an intertextually tight and self-referring system that pays only limited attention to documents outside the laws, precedents, and judicial rules. The window for scientific literature to enter the courts is narrow, focused, and highly filtered. It serves as a warrant for the expert witnesses\u27 expertise, which in turn makes opinion admissible in a way not available to ordinary witnesses

Leveraging a Relationship with Biology to Expand a Relationship with Physics

This work examines how experiences in one disciplinary domain (biology) can impact the relationship a student builds with another domain (physics). We present a model for disciplinary relationships using the constructs of identity, affect, and epistemology. With these constructs we examine an ethnographic case study of a student who experienced a significant shift in her relationship with physics. We describe how this shift demonstrates (1) a stronger identification with physics, (2) a more mixed affective stance towards physics, and (3) more expert-like ways of knowing in physics. We argue that recruiting the students relationship with biology into experiences of learning physics impacted her relationship with physics as well as her sense of how physics and biology are linked

A Philosophical Basis for Judicial Restraint

The purpose of this article is to establish a principled basis for restraint of judicial lawmaking. The principle is that all findings of fact, whether of legislative or adjudicative facts, must be based on evidence in the record of a case. This principle is grounded in moral philosophy. I will begin with a discussion of the relevant aspect of moral philosophy, then state and defend the principle, and finally apply it to a line of cases

Personality attributes that predict cadet performance at West Point

Using data from the United States Military Academy at West Point (N = 1102 and N = 1049) from two successive years, we examined psychological measures of cadets and the correlations of those measures with consequential outcomes such as cadet performance and leadership potential. We examined four broad intelligences, two of which were thing-focused (spatial and mathematical) and two people-focused (verbal and personal intelligences) and their predictions to thing- and people-centered courses (e.g., chemistry versus psychology). We found support for a thing-people differential in reasoning. The broad intelligences and the Big Five personality traits also predicted academic and other performance criteria at consequential levels