FOSTERING PRO-ENVIRONMENTAL BEHAVIOR WITH GREEN CONSUMER IS: THE EFFECTS OF IS-INDUCED CONSTRUAL AND GENERAL IS USAGE MOTIVATIONS

In the context of the environmental challenges we are facing, technology is often seen as both a cause of and a potential remedy for humanity’s environmental impact. Green consumer information systems (IS) have shown to be powerful in promoting individuals’ pro-environmental behavior. Yet, there is little knowledge about the mechanisms of how information systems lead to a sustainable change in behavior for the good. To fill this gap, we propose an experiment on the basis of a research model that sheds light on two critical aspects of how green consumer IS affects pro-environmental behavior: First, green consumer IS may be used to induce higher-level construals that foster superordinate de-terminants of pro-environmental behavior by displaying rather abstract than concrete information. Second, we analyze the direct and indirect role of technology adoption as a means to motivate pro-environmental behavior. To test our hypotheses, we propose an online experiment on eco-driving feedback and present first drafts of stimuli. Implications for consumer IS theory as well as for practice regarding feedback design are discussed

AN IN-VEHICLE INFORMATION SYSTEM PROVIDING ACCIDENT HOTSPOT WARNINGS

Accident hotspots, locations where accidents are historically concentrated, contribute significantly to road traffic accidents being the leading cause of death by injury. A notable improvement in driver safety can be achieved through warnings of known upcoming hazardous features. However, as installing and maintaining traditional road sign infrastructure can be costly, warnings on accident hotspots are not typically available. This paper presents an in-vehicle information system prototype which provides warnings of upcoming accident hotspots based initially on historic data. Additionally, significant research has focused on the identification, analysis and treatment of these accident hotspots. However, a true picture of road safety can be hard to achieve as many traffic accidents go unreported. Information on near-miss events, such as heavy braking or taking evasive action to avoid an accident, could help identify and provide life saving insights into hazardous areas before an accident occurs. The prototype therefore additionally collects vehicle data in order to learn characteristics of accident hotspots and identify near-miss events, in order to improve the system and provide new insights

RFID Controlled Door Lock

This device is an automated door lock attachment for an existing deadbolt-locked door. The device takes an exterior RFID input and can both lock and unlock the door. The device also implements an exterior doorbell switch where a guest can turn on a buzzer, and an interior switch for the operator to unlock the door from the inside. The front interface of the device is pictured in Figure 1; the working mechanism to turn the lock is pictured in Figure 2, and the housing for the processing and wirings is pictured in Figure 3. The logic is implemented using a PIC16F88 and Arduino Nano. The code implemented on the PIC is located in Appendix A1.1; the code for the Arduino is in A1.2; and the wiring diagram that ties the entire design together is in A2

Information-Theoretic Trust Regions for Stochastic Gradient-Based Optimization

Stochastic gradient-based optimization is crucial to optimize neural networks. While popular approaches heuristically adapt the step size and direction by rescaling gradients, a more principled approach to improve optimizers requires second-order information. Such methods precondition the gradient using the objective's Hessian. Yet, computing the Hessian is usually expensive and effectively using second-order information in the stochastic gradient setting is non-trivial. We propose using Information-Theoretic Trust Region Optimization (arTuRO) for improved updates with uncertain second-order information. By modeling the network parameters as a Gaussian distribution and using a Kullback-Leibler divergence-based trust region, our approach takes bounded steps accounting for the objective's curvature and uncertainty in the parameters. Before each update, it solves the trust region problem for an optimal step size, resulting in a more stable and faster optimization process. We approximate the diagonal elements of the Hessian from stochastic gradients using a simple recursive least squares approach, constructing a model of the expected Hessian over time using only first-order information. We show that arTuRO combines the fast convergence of adaptive moment-based optimization with the generalization capabilities of SGD

Jumbo: Origin of the Word and History of the Elephant

JUMBO was an African elephant (Loxodonta africana) whose exact place of origin is unknown. He was collected as a calf in 1861, probably in the French Sudan, south of Lake Chad, and was transferred to the Jardin des Plantes in Paris, France. In 1865 he was taken to the London Zoological Gardens, England, where he was named Jumbo. The origin of the name is also unknown. Most likely it originated from Angola, West Africa ( onjamba = elephant). During 1880-1881 Jumbo showed signs of unreliable temper which paved the way for his sale to the American showman P.T. Barnum in 1882. In the United States, from 1882 to 1885, Jumbo was exhibited by the Barnum and London Circus and was heralded as the towering monarch of his race. Jumbo was indeed large for his age, but his size was certainly exaggerated in print. On September 15, 1885 Jumbo was killed by a locomotive at St. Thomas, Ontario, Canada. His mounted skin and skeleton were displayed on tours until 1890. The skeleton was given to the American Museum of Natural History (AMNH), New York, in 1900, and the skin donated to the Barnum collection at Tufts College, Medford, Massachusetts (later Tufts University) where it was destroyed in a fire in 1975. Jumbo Centennial was celebrated in 1985 at St. Thomas, Ontario and a statue was erected just outside of St. Thomas. Jumbo is the type specimen (AMNH 3283) of Elephas africanus rothschildi after Lydekker, 1907. Much mythos developed about Jumbo, most of which centered on his size (especially when P.T. Barnum and his partner, James A. Bailey, would not allow him to be measured); his name lives as a gift to the English language - a synonym for all things gigantic

Information-Theoretic Trust Regions for Stochastic Gradient-Based Optimization

Stochastic gradient-based optimization is crucial to optimize neural networks. While popular approaches heuristically adapt the step size and direction by rescaling gradients, a more principled approach to improve optimizers requires second-order information. Such methods precondition the gradient using the objective’s Hessian. Yet, computing the Hessian is usually expensive and effectively using second-order information in the stochastic gradient setting is non-trivial. We propose using Information-Theoretic Trust Region Optimization (arTuRO) for improved updates with uncertain second-order information. By modeling the network parameters as a Gaussian distribution and using a Kullback-Leibler divergence-based trust region, our approach takes bounded steps accounting for the objective’s curvature and uncertainty in the parameters. Before each update, it solves the trust region problem for an optimal step size, resulting in a more stable and faster optimization process. We approximate the diagonal elements of the Hessian from stochastic gradients using a simple recursive least squares approach, constructing a model of the expected Hessian over time using only first-order information. We show that arTuRO combines the fast convergence of adaptive moment-based optimization with the generalization capabilities of SGD

Swarm Reinforcement Learning For Adaptive Mesh Refinement

Adaptive Mesh Refinement (AMR) enhances the Finite Element Method, an important technique for simulating complex problems in engineering, by dynamically refining mesh regions, enabling a favorable trade-off between computational speed and simulation accuracy. Classical methods for AMR depend on heuristics or expensive error estimators, hindering their use for complex simulations. Recent learning-based AMR methods tackle these issues, but so far scale only to simple toy examples. We formulate AMR as a novel Adaptive Swarm Markov Decision Process in which a mesh is modeled as a system of simple collaborating agents that may split into multiple new agents. This framework allows for a spatial reward formulation that simplifies the credit assignment problem, which we combine with Message Passing Networks to propagate information between neighboring mesh elements. We experimentally validate our approach, Adaptive Swarm Mesh Refinement (ASMR), on challenging refinement tasks. Our approach learns reliable and efficient refinement strategies that can robustly generalize to different domains during inference. Additionally, it achieves a speedup of up to $2$ orders of magnitude compared to uniform refinements in more demanding simulations. We outperform learned baselines and heuristics, achieving a refinement quality that is on par with costly error-based oracle AMR strategies