Human Versus Machine: How Do We Know Who Is Winning? ROC Analysis for Comparing Human and Machine Performance under Varying Cost-Prevalence Assumptions

Background Receiver operating characteristic (ROC) analysis is commonly used for comparing models and humans; however, the exact analytical techniques vary and some are flawed. Objectives The aim of the study is to identify common flaws in ROC analysis for human versus model performance, and address them. Methods We review current use and identify common errors. We also review the ROC analysis literature for more appropriate techniques. Results We identify concerns in three techniques: (1) using mean human sensitivity and specificity; (2) assuming humans can be approximated by ROCs; and (3) matching sensitivity and specificity. We identify a technique from Provost et al using dominance tables and cost-prevalence gradients that can be adapted to address these concerns. Conclusion Dominance tables and cost-prevalence gradients provide far greater detail when comparing performances of models and humans, and address common failings in other approaches. This should be the standard method for such analyses moving forward.

Pruning harvesting with modular towed chipper: Little effect of the machine setting and configuration on performance despite strong impact on wood chip quality

Pruning residues can have a high quality as feedstock for energy purposes and are largely available in Europe. However, it is still an untapped resource. Such scarce use is due to the need to optimize their supply chain in term of collection machines and the associate cost of collection. A modular chipper (prototype PC50) for pruning harvest was developed. Such prototype is adaptable to various harvesting logistics and may produce a higher quality woodchip compared with the one produced by shredders available in the market. In this work, we tested the performance and quality of the product delivered by the prototype PC50 in various conditions and plant species, after a modulation of the machine settings (counter-rotating toothed rollers [CRR] speed), loading systems ([LS], either big bag or container), and knife types ([KT], either discontinuous hoe shaped knives or continuous helicoidal knives). To take into account of the covariates in the experiment (Cropping season and plant species), LSmeans were computed to have an unbiased estimate of the treatments means. The modulation of LS and KT scarcely affected the performance of the machine. In particular, the choice of the KT affected the field efficiency when the LS was a Tilting box but not a Big Bag. Whereas the continuous knife resulted in a 97% higher material capacity compared to hoe shape knives, the last of which the amount of short sized (<16 mm) fractions compared to helicoidal knives. No role of the CCR was found on the machine performance, but increasing CRR speed reduced the chip apparent bulk density and the fraction chips with a size <8 mm.

Deepfake detection by human crowds, machines, and machine-informed crowds

The recent emergence of machine-manipulated media raises an important societal question: How can we know whether a video that we watch is real or fake? In two online studies with 15,016 participants, we present authentic videos and deepfakes and ask participants to identify which is which. We compare the performance of ordinary human observers with the leading computer vision deepfake detection model and find them similarly accurate, while making different kinds of mistakes. Together, participants with access to the model’s prediction are more accurate than either alone, but inaccurate model predictions often decrease participants’ accuracy. To probe the relative strengths and weaknesses of humans and machines as detectors of deepfakes, we examine human and machine performance across video-level features, and we evaluate the impact of preregistered randomized interventions on deepfake detection. We find that manipulations designed to disrupt visual processing of faces hinder human participants’ performance while mostly not affecting the model’s performance, suggesting a role for specialized cognitive capacities in explaining human deepfake detection performance.

General mitigation techniques for coherent beam instabilities in particle accelerators

An important number of coherent beam instability mechanisms can be observed in a particle accelerator, depending if the latter is linear or circular, operated at low, medium or high energy, with a small or a huge amount of turns (for circular machines), close to transition energy or not (below or above), with only one bunch or many bunches, with counter-rotating beams (such as in colliders) or not, if the beam is positively or negatively charged, if one is interested in the longitudinal plane or in the transverse plane, in the presence of linear coupling between the transverse planes or not, in the presence of nonlinearities or not, in the presence of noise or not, etc. Building a realistic impedance model of a machine is a necessary step to be able to evaluate the machine performance limitations, identify the main contributors in case an impedance reduction is required, and study the interaction with other mechanisms such as optics (linear and nonlinear), RF gymnastics, transverse damper, noise, space charge, electron cloud, and beam–beam (in a collider). Better characterising an instability is the first step before trying to find appropriate mitigation measures and push the performance of a particle accelerator, as some mitigation methods are beneficial for some effects and detrimental for some others. For this, an excellent instrumentation is of paramount importance to be able to diagnose if the instability is longitudinal or transverse, single bunch, or coupled bunch, involving only one mode of oscillation or several, and the evolution of the intrabunch motion with intensity is a fundamental observable with high-intensity high-brightness beams. Finally, among the possible mitigation methods of coherent beam instabilities, the ones perturbing the least the single-particle motion (leading to the largest necessary dynamic aperture and beam lifetime) and easiest to implement for day-to-day operation in the machine control room should be preferred.

Evaluating predictive capabilities in Industry 4.0 framework using Regression Models

Observing decline in machine performance using a Linear Regression model<br>

Evaluating predictive capabilities in Industry 4.0 framework using Regression Models

Observing decline in machine performance using a Linear Regression model<br>

IoT Assisted Machine Learning Model for Warehouse Management

In the digital world of today, any enterprise that deals with the amounts of data in Warehouse Management Systems (WMS) are an important component. Furthermore, the amount of data being raisedand its complexity have become more challenging to maintain the WMS efficiency. Therefore, a device is required, which can manage such complexities autonomously with no human intervention. In this paper, Hybrid Machine Learning with the Internet of Things (HML-IoT) improves isolated doors. Furthermore, operating machine performance in the factory of hazardous goods. Decision-Making Algorithm (DMA) Data from the customer’s holding space’s dangerous goods warehouses shall be checked using separated doors. Thispaper’s significant aspect is that inventory and inventory operation’s organizational performance can be increased, further logistics costs minimized utilizing the fair use of isolated doors. Finally, the HML-IoT model integrated hazardous goods warehouse with isolated doors has been contrasted with the current one, demonstrating that the previous one has greater efficacy.