Discovery Learning Experiments in a New Machine Design Laboratory

A new Machine Design Laboratory at Marquette University has been created to foster student exploration with hardware and real-world systems. The Laboratory incorporates areas for teaching and training, and has been designed to promote “hands-on” and “minds-on” learning. It reflects the spirit of transformational learning that is a theme in the College of Engineering. The goal was to create discovery learning oriented experiments for a required junior-level “Design of Machine Elements” course in mechanical engineering that would give students practical experiences and expose them to physical hardware, actual tools, and real-world design challenges. In the experiments students face a range of real-world tasks: identify and select components, measure parameters (dimensions, speed, force), distinguish between normal and used (worn) components and between proper and abnormal behavior, reverse engineer systems, and justify design choices. The experiments serve to motivate the theory and spark interest in the subject of machine design. This paper presents details of the experiments and summarizes student reactions and our experiences in the Machine Design Laboratory. In addition, the paper provides some insights for others who may wish to develop similar types of experiments

Cryptanalysis of SIGABA

SIGABA is a World War II cipher machine used by the United States. Both the United States Army and the United States Navy used it for tactical communication. In this paper, we consider an attack on SIGABA using the largest practical keyspace for the machine. This attack will highlight the strengths and weaknesses of the machine, as well as provide an insight into the strength of the security provided by the cipher

Enabling stream processing for people-centric IoT based on the fog computing paradigm

The world of machine-to-machine (M2M) communication is gradually moving from vertical single purpose solutions to multi-purpose and collaborative applications interacting across industry verticals, organizations and people - A world of Internet of Things (IoT). The dominant approach for delivering IoT applications relies on the development of cloud-based IoT platforms that collect all the data generated by the sensing elements and centrally process the information to create real business value. In this paper, we present a system that follows the Fog Computing paradigm where the sensor resources, as well as the intermediate layers between embedded devices and cloud computing datacenters, participate by providing computational, storage, and control. We discuss the design aspects of our system and present a pilot deployment for the evaluating the performance in a real-world environment. Our findings indicate that Fog Computing can address the ever-increasing amount of data that is inherent in an IoT world by effective communication among all elements of the architecture

Algorithmic Fairness from a Non-ideal Perspective

Inspired by recent breakthroughs in predictive modeling, practitioners in both industry and government have turned to machine learning with hopes of operationalizing predictions to drive automated decisions. Unfortunately, many social desiderata concerning consequential decisions, such as justice or fairness, have no natural formulation within a purely predictive framework. In efforts to mitigate these problems, researchers have proposed a variety of metrics for quantifying deviations from various statistical parities that we might expect to observe in a fair world and offered a variety of algorithms in attempts to satisfy subsets of these parities or to trade o the degree to which they are satised against utility. In this paper, we connect this approach to fair machine learning to the literature on ideal and non-ideal methodological approaches in political philosophy. The ideal approach requires positing the principles according to which a just world would operate. In the most straightforward application of ideal theory, one supports a proposed policy by arguing that it closes a discrepancy between the real and the perfectly just world. However, by failing to account for the mechanisms by which our non-ideal world arose, the responsibilities of various decision-makers, and the impacts of proposed policies, naive applications of ideal thinking can lead to misguided interventions. In this paper, we demonstrate a connection between the fair machine learning literature and the ideal approach in political philosophy, and argue that the increasingly apparent shortcomings of proposed fair machine learning algorithms reflect broader troubles faced by the ideal approach. We conclude with a critical discussion of the harms of misguided solutions, a reinterpretation of impossibility results, and directions for future researc