How Should Congress Respond to McDonnell?

Discussion of question of whether McDonnell was essentially right or wrong. Should Congress act to change the McDonnell rule? Should the Supreme Court reconsider it? What would be an alternative or a better way, if there is one, to approach the question of public corruption prosecution

Artificial Consciousness: What is it?

The advancements we have made in robotics thus far is extraordinary. As extraordinary as those advancements have been, robots are simply used as tools. They can only perform tasks that they have been programmed to do. But, what if they somehow become self-aware, and now are able to make decisions based on their own reasoning and judgement? What would that mean for the human race, and would Asimov’s three laws of robotics be enough? The only way to answer these questions and to gain a complete understanding, would be to first understand what it means to be a robot with artificial consciousness. Through our research, we hope to provide an understanding of what classifies as a robot with artificial consciousness, the implications those robots will have on the human race, as well as the need for laws concerning the programming and legal rights of said robots, and whether those laws will be enough to protect the human race. To support our research we will look at the various definitions of scholars, and fictional works of literature concerning artificial consciousness, such as, Artificial Beings: The Conscience of a Conscious Machine, by Jacques Pitrat, Age of Spiritual Machines: When Computers exceed Human Intelligence-Kay Kurzwell, and Artificial Consciousness, by Antonio Chello and Riccardo Manzottti

Exploring the reasons why people home educate in Hertfordshire

Final Published versio

Do laypersons conflate poverty and neglect?

ObjectiveChild neglect is often initially identified via adults who come into contact with children and report their suspicions to the authorities. Little is known about what behaviors laypersons view as constituting neglect and hence worth reporting. We examined laypersons' perceptions of neglect and poverty, particularly how these factors independently and jointly shaped laypersons' decisions about what warrants official reporting of neglect, and how laypersons' socioeconomic background related to their decisions.HypothesesWe anticipated that neglect would be correctly perceived as such, but that extreme poverty would also be perceived as neglect, with these latter perceptions being most pronounced among laypersons of higher socioeconomic background.MethodIn 2 studies, adults read vignettes about a mother's care of her daughter and rendered decisions about whether the mother's behavior met the legal standard of neglect and should be reported. In Study 1 (N = 365, 55% female, mean age = 37.12 years), indicators of poverty and neglect were manipulated. In Study 2 (N = 474, 53% female, mean age = 38.25 years), only poverty (housing instability: homelessness vs. not) was manipulated.ResultsLaypersons often conflated poverty and neglect, especially in circumstances of homelessness. Laypersons of lower socioeconomic background were less likely to perceive neglect in general and to report an obligation to make a referral (R2s ranged from 17-26%, odds ratios ranged from 2.24-3.08).ConclusionsLaypersons may overreport neglect in circumstances of poverty. Increasing public awareness of how to recognize and separate neglect from poverty may enhance identification of vulnerable children and families. (PsycInfo Database Record (c) 2020 APA, all rights reserved)

Networked engineering notebooks for smart manufacturing

A goal of the industrial internet is to make information about manufacturing processes and resources available wherever decision making may be required. Agile use of information is a cornerstone of data analytics, but analytical methods more generally, including model-based investigations of manufacturability and operations, do not so easily benefit from this data. Rather than relating anonymous patterns of data to outcomes, these latter analytical methods are distinguished as relying on conceptual or physics-based models of the real world. Such models require careful consideration of the fitness of the data to the purpose of the analysis. Verification of these analyses, then, is a significant bottleneck. A related problem, that of ascertaining reproducible results in scientific claims, is being addressed through executable notebook technology. This paper proposes to use notebook technologies to address that bottleneck. It describes how this notebook technology, linked to internet-addressable ontologies and analytical metamodels, can be used to make model-based analytical methods more verifiable, and thus more effective for manufacturers

Dynamic production system identification for smart manufacturing systems

This paper presents a methodology, called production system identification, to produce a model of a manufacturing system from logs of the system's operation. The model produced is intended to aid in making production scheduling decisions. Production system identification is similar to machine-learning methods of process mining in that they both use logs of operations. However, process mining falls short of addressing important requirements; process mining does not (1) account for infrequent exceptional events that may provide insight into system capabilities and reliability, (2) offer means to validate the model relative to an understanding of causes, and (3) updated the model as the situation on the production floor changes. The paper describes a genetic programming (GP) methodology that uses Petri nets, probabilistic neural nets, and a causal model of production system dynamics to address these shortcomings. A coloured Petri net formalism appropriate to GP is developed and used to interpret the log. Interpreted logs provide a relation between Petri net states and exceptional system states that can be learned by means of novel formulation of probabilistic neural nets (PNNs). A generalized stochastic Petri net and the PNNs are used to validate the GP-generated solutions. The methodology is evaluated with an example based on an automotive assembly system

Production system identification with genetic programming

Modern system-identification methodologies use artificial neural nets, integer linear programming, genetic algorithms, and swarm intelligence to discover system models. Pairing genetic programming, a variation of genetic algorithms, with Petri nets seems to offer an attractive, alternative means to discover system behaviour and structure. Yet to date, very little work has examined this pairing of technologies. Petri nets provide a grey-box model of the system, which is useful for verifying system behaviour and interpreting the meaning of operational data. Genetic programming promises a simple yet robust tool to search the space of candidate systems. Genetic programming is inherently highly parallel. This paper describes early experiences with genetic programming of Petri nets to discover the best interpretation of operational data. The systems studied are serial production lines with buffers