Although eavesdropping and fingerprinting Connected robots play a key role in Industry 4.0, providing automation is a more passive opportunity for attackers, the resulting attacks and higher efficiency for many industrial workflows. Unfortunately, on robotic operations are still of importance. Even if strict security these robots can leak sensitive information regarding these requirements are conformed to, such as using cryptographic measures operational workflows to remote adversaries. While there exists to ensure the confidentiality of transmitted data, there are mandates for the use of end-to-end encryption for data transmission still arising threats that cannot be ignored. The question we aim in such settings, it is entirely possible for passive adversaries to address is whether it is still possible for an adversary to infer to fingerprint and reconstruct entire workflows being carried out information about what operations a robot is carrying out? Further, - establishing an understanding of how facilities operate. In this can this be successful even when the communication is protected paper, we investigate whether a remote attacker can accurately fingerprint by appropriate channel security technology (e.g. TLS)? robot movements and ultimately reconstruct operational workflows.

There is a substantial and ever-growing corpus of evidence and literature exploring the impacts of Artificial intelligence (AI) technologies on society, politics, and humanity as a whole. A separate, parallel body of work has explored existential risks to humanity, including but not limited to that stemming from unaligned Artificial General Intelligence (AGI). In this paper, we problematise the notion that current and near-term artificial intelligence technologies have the potential to contribute to existential risk by acting as intermediate risk factors, and that this potential is not limited to the unaligned AGI scenario. We propose the hypothesis that certain already-documented effects of AI can act as existential risk factors, magnifying the likelihood of previously identified sources of existential risk. Moreover, future developments in the coming decade hold the potential to significantly exacerbate these risk factors, even in the absence of artificial general intelligence. Our main contribution is a (non-exhaustive) exposition of potential AI risk factors and the causal relationships between them, focusing on how AI can affect power dynamics and information security. This exposition demonstrates that there exist causal pathways from AI systems to existential risks that do not presuppose hypothetical future AI capabilities.

Representation disentanglement is an important goal of the representation learning that benefits various of downstream tasks. To achieve this goal, many unsupervised learning representation disentanglement approaches have been developed. However, the training process without utilizing any supervision signal have been proved to be inadequate for disentanglement representation learning. Therefore, we propose a novel weakly-supervised training approach, named as SW-VAE, which incorporates pairs of input observations as supervision signal by using the generative factors of datasets. Furthermore, we introduce strategies to gradually increase the learning difficulty during training to smooth the training process. As shown on several datasets, our model shows significant improvement over state-of-the-art (SOTA) methods on representation disentanglement tasks.

As one of the most successful AI-powered applications, recommender systems aim to help people make appropriate decisions in an effective and efficient way, by providing personalized suggestions in many aspects of our lives, especially for various human-oriented online services such as e-commerce platforms and social media sites. In the past few decades, the rapid developments of recommender systems have significantly benefited human by creating economic value, saving time and effort, and promoting social good. However, recent studies have found that data-driven recommender systems can pose serious threats to users and society, such as spreading fake news to manipulate public opinion in social media sites, amplifying unfairness toward under-represented groups or individuals in job matching services, or inferring privacy information from recommendation results. Therefore, systems' trustworthiness has been attracting increasing attention from various aspects for mitigating negative impacts caused by recommender systems, so as to enhance the public's trust towards recommender systems techniques. In this survey, we provide a comprehensive overview of Trustworthy Recommender systems (TRec) with a specific focus on six of the most important aspects; namely, Safety & Robustness, Nondiscrimination & Fairness, Explainability, Privacy, Environmental Well-being, and Accountability & Auditability. For each aspect, we summarize the recent related technologies and discuss potential research directions to help achieve trustworthy recommender systems in the future.

We apply the technique of implicit differentiation to boost performance, reduce numerical error, and remove required user-tuning in the Marginal Unbiased Score Expansion (MUSE) algorithm for hierarchical Bayesian inference. We demonstrate these improvements on three representative inference problems: 1) an extended Neal's funnel 2) Bayesian neural networks, and 3) probabilistic principal component analysis. On our particular test cases, MUSE with implicit differentiation is faster than Hamiltonian Monte Carlo by factors of 155, 397, and 5, respectively, or factors of 65, 278, and 1 without implicit differentiation, and yields good approximate marginal posteriors. The Julia and Python MUSE packages have been updated to use implicit differentiation, and can solve problems defined by hand or with any of a number of popular probabilistic programming languages and automatic differentiation backends.

While active attacks can be deadly to the Connected teleoperated robotic systems play a key role in ensuring operating environment and subject(s) involved, passive attacks can operational workflows are carried out with high levels of accuracy result in huge losses that stem from stealthy, unintentional information and low margins of error. In recent years, a variety of attacks have leakage. For example, if an attacker is able to identify what been proposed that actively target the robot itself from the cyber workflows a robot is carrying out, such as the movement of packages domain. However, little attention has been paid to the capabilities of in a warehouse between belts, they could use this information a passive attacker. In this work, we investigate whether an insider to sell on to competitors that can understand how competing warehousing adversary can accurately fingerprint robot movements and operational facilities operate and use this information to a malicious warehousing workflows via the radio frequency side channel advantage [21, 27]. in a stealthy manner. Using an SVM for classification, we found In this work we seek to explore other mechanisms to passively that an adversary can fingerprint individual robot movements with learn about robotic workflows. Side channels have previously been at least 96% accuracy, increasing to near perfect accuracy when used in different technological domains as a means to learn sensitive reconstructing entire warehousing workflows.

The spread of COVID-19 revealed that transmission risk patterns are not homogenous across different cities and communities, and various heterogeneous features can influence the spread trajectories. Hence, for predictive pandemic monitoring, it is essential to explore latent heterogeneous features in cities and communities that distinguish their specific pandemic spread trajectories. To this end, this study creates a network embedding model capturing cross-county visitation networks, as well as heterogeneous features to uncover clusters of counties in the United States based on their pandemic spread transmission trajectories. We collected and computed location intelligence features from 2,787 counties from March 3 to June 29, 2020 (initial wave). Second, we constructed a human visitation network, which incorporated county features as node attributes, and visits between counties as network edges. Our attributed network embeddings approach integrates both typological characteristics of the cross-county visitation network, as well as heterogeneous features. We conducted clustering analysis on the attributed network embeddings to reveal four archetypes of spread risk trajectories corresponding to four clusters of counties. Subsequently, we identified four features as important features underlying the distinctive transmission risk patterns among the archetypes. The attributed network embedding approach and the findings identify and explain the non-homogenous pandemic risk trajectories across counties for predictive pandemic monitoring. The study also contributes to data-driven and deep learning-based approaches for pandemic analytics to complement the standard epidemiological models for policy analysis in pandemics.

This paper develops a Deep Graph Operator Network (DeepGraphONet) framework that learns to approximate the dynamics of a complex system (e.g. the power grid or traffic) with an underlying sub-graph structure. We build our DeepGraphONet by fusing the ability of (i) Graph Neural Networks (GNN) to exploit spatially correlated graph information and (ii) Deep Operator Networks~(DeepONet) to approximate the solution operator of dynamical systems. The resulting DeepGraphONet can then predict the dynamics within a given short/medium-term time horizon by observing a finite history of the graph state information. Furthermore, we design our DeepGraphONet to be resolution-independent. That is, we do not require the finite history to be collected at the exact/same resolution. In addition, to disseminate the results from a trained DeepGraphONet, we design a zero-shot learning strategy that enables using it on a different sub-graph. Finally, empirical results on the (i) transient stability prediction problem of power grids and (ii) traffic flow forecasting problem of a vehicular system illustrate the effectiveness of the proposed DeepGraphONet.

"Two guys in a garage is great," said Edlyn Levine, co-founder and chief science officer at America's Frontier Fund, a nonprofit venture-capital fund aimed at investing in chip makers. But taking that idea and scaling into a company requires access to multibillion-dollar facilities, she said. The Morning Download delivers daily insights and news on business technology from the CIO Journal team. Ms. Levine, speaking Tuesday at The Wall Street Journal's CIO Network online summit, said a robust domestic semiconductor industry can help unlock downstream innovation. Chips are a key component of technologies including 5G wireless devices, artificial-intelligence software, autonomous vehicles and cryptocurrencies, she noted.

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