ABSTRACT In light of the Industry 4.0 era, the global pandemic, and wars, interest in deploying digital technologies to increase supply chain resilience (SCRes) is rising. The utilization of recommender systems as a supply chain (SC) resilience measure is neglected, although these systems can enhance SC resilience. To address this problem, this research proposed a data-driven supply chain disruption response framework based on intelligent recommender system techniques. A prototype implementation was conducted to validate the developed framework through a practical use case. Results show that the proposed framework can be implemented as an effective SC disruption mitigation measure in the SCRes response phase and help SC participants better react after the SC disruption. Keywords: Supply chain resilience, Disruption risk, Recommender System, Supply chain risk management, Decision Support System 1 INTRODUCTION Supply chains (SC) are becoming more sophisticated and complex with globalization, as well as more risks and uncertainty (Manners-Bell 2017).

Uncertainty quantification and robustness to distribution shifts are important goals in machine learning and artificial intelligence. Although Bayesian Neural Networks (BNNs) allow for uncertainty in the predictions to be assessed, different sources of uncertainty are indistinguishable. We present Imprecise Bayesian Neural Networks (IBNNs); they generalize and overcome some of the drawbacks of standard BNNs. These latter are trained using a single prior and likelihood distributions, whereas IBNNs are trained using credal prior and likelihood sets. They allow to distinguish between aleatoric and epistemic uncertainties, and to quantify them. In addition, IBNNs are more robust than BNNs to prior and likelihood misspecification, and to distribution shift. They can also be used to compute sets of outcomes that enjoy probabilistic guarantees. We apply IBNNs to two case studies. One, for motion prediction in autonomous driving scenarios, and two, to model blood glucose and insulin dynamics for artificial pancreas control. We show that IBNNs performs better when compared to an ensemble of BNNs benchmark.

Zoom has made changes to its terms of service after online blowback over recent updates to the company's fine print allowing AI training on customer data. A report from StackDiary over the weekend highlighted how the changes, which rolled out in March without fanfare, appeared to grant the company sweeping control over customer data for AI training purposes. In response, Zoom published a blog post today claiming it wouldn't do what its terms said it could do; the company then updated its terms in response to the continued blowback. It now says it doesn't train AI models on consumer video, audio or chats "without customer consent." At least part of the issue stemmed from Zoom's experimental AI tools, including IQ Meeting Summary (ML-powered summarizations) and IQ Team Chat Compose (AI-powered message drafting).

On Tuesday morning, at least eight attack drones entered Moscow's airspace before being shot down by the city's air defences, a few hitting residential buildings on the way down. The Russian government accused Ukraine of a "terrorist attack", which Kyiv officials wryly denied. "You know, we are being drawn into the era of artificial intelligence. Perhaps not all drones are ready to attack Ukraine and want to return to their creators and ask them questions like: 'Why are you sending us [to hit] the children of Ukraine? In Kyiv?'" Ukrainian presidential adviser Mykhailo Podolyak said on the YouTube breakfast show of exiled Russian journalist Alexander Plushev.

Our work is the first attempt to apply Natural Language Processing to automate the development of simulation models of systems vitally important for logistics. We demonstrated that the framework built on top of the fine-tuned GPT-3 Codex, a Transformer-based language model, could produce functionally valid simulations of queuing and inventory control systems given the verbal description. In conducted experiments, GPT-3 Codex demonstrated convincing expertise in Python as well as an understanding of the domain-specific vocabulary. As a result, the language model could produce simulations of a single-product inventory-control system and single-server queuing system given the domain-specific context, a detailed description of the process, and a list of variables with the corresponding values. The demonstrated results, along with the rapid improvement of language models, open the door for significant simplification of the workflow behind the simulation model development, which will allow experts to focus on the high-level consideration of the problem and holistic thinking.

We describe an assonance extraction algorithm, and consider results from an extensive set of machine learning experiments, based on a historical corpus of 18th century American and British texts. The results are compared with those obtained from the use of other prosodic and traditional stylistic features.

We present an early prototype of a virtual reality environment, intended to teach pre-teenage children how to deal with bullying situations. The environment consists of several scenes where the participant needs to resolve a bullying conflict. The simulation is based on short dialogs between the bully and the participant. We employ natural language processing to accept the participant's verbal responses to the bully's taunts and use these responses to drive the simulation. The participant is expected to determine on his/her own the correct approach to resolving the bullying situation.

This paper presents an automatic classification model for the identification of various types/makes of guitars and other string instruments. The classification is carried out by machine learning classifiers trained on mel frequency cepstral coefficients (MFCCs) features, extracted from audio recordings of different guitar models. The classification results are analyzed and insights from the experiments are shared.

In the 1920s, the Soviet scientist Ilya Ivanovich Ivanov used artificial insemination to breed a'humanzee' – a cross between a human and our closest relative species, the chimpanzee. Given the moral quandaries a humanzee might create, we can be thankful that Ivanov failed: when the winds of Soviet scientific preferences changed, he was arrested and exiled. But Ivanov's endeavour points to the persistent, post-Darwinian fear and fascination with the question of whether humans are a creature apart, above all other life, or whether we're just one more animal in a mad scientist's menagerie. Humans have searched and repeatedly failed to rescue ourselves from this disquieting commonality. Numerous dividers between humans and beasts have been proposed: thought and language, tools and rules, culture, imitation, empathy, morality, hate, even a grasp of'folk' physics. But they've all failed, in one way or another. I'd like to put forward a new contender – strangely, the very same tendency that elicits the most dread and excitement among political and economic commentators today.

It is a bit of a stretch, but by no means impossible or even unlikely that a hybrid or a chimera combining a human being and a chimpanzee could be produced in a laboratory. Granted this 1 percent difference presumably involves some key alleles, the new gene-editing tool CRISPR offers the prospect (for some, the nightmare) of adding and deleting targeted genes as desired. As a result, it is not unreasonable to foresee the possibility--eventually, perhaps, the likelihood--of producing "humanzees" or "chimphumans." Such an individual would not be an exact equal-parts-of-each combination, but would be neither human nor chimp: rather, something in between. If that prospect isn't shocking enough, here is an even more controversial suggestion: Doing so would be a terrific idea. The year 2018 is the bicentennial of Mary Shelley's Frankenstein, subtitled the modern Prometheus.