The vulnerability of smartphones to cyberattacks has been a severe concern to users arising from the integrity of installed applications (\textit{apps}). Although applications are to provide legitimate and diversified on-the-go services, harmful and dangerous ones have also uncovered the feasible way to penetrate smartphones for malicious behaviors. Thorough application analysis is key to revealing malicious intent and providing more insights into the application behavior for security risk assessments. Such in-depth analysis motivates employing deep neural networks (DNNs) for a set of features and patterns extracted from applications to facilitate detecting potentially dangerous applications independently. This paper presents an Analytic-based deep neural network, Android Malware detection (ADAM), that employs a fine-grained set of features to train feature-specific DNNs to have consensus on the application labels when their ground truth is unknown. In addition, ADAM leverages the transfer learning technique to obtain its adjustability to new applications across smartphones for recycling the pre-trained model(s) and making them more adaptable by model personalization and federated learning techniques. This adjustability is also assisted by federated learning guards, which protect ADAM against poisoning attacks through model analysis. ADAM relies on a diverse dataset containing more than 153000 applications with over 41000 extracted features for DNNs training. The ADAM's feature-specific DNNs, on average, achieved more than 98% accuracy, resulting in an outstanding performance against data manipulation attacks.

Mutual Information (MI) and Conditional Mutual Information (CMI) are multi-purpose tools from information theory that are able to naturally measure the statistical dependencies between random variables, thus they are usually of central interest in several statistical and machine learning tasks, such as conditional independence testing and representation learning. However, estimating CMI, or even MI, is infamously challenging due the intractable formulation. In this study, we introduce DINE (Diffeomorphic Information Neural Estimator)-a novel approach for estimating CMI of continuous random variables, inspired by the invariance of CMI over diffeomorphic maps. We show that the variables of interest can be replaced with appropriate surrogates that follow simpler distributions, allowing the CMI to be efficiently evaluated via analytical solutions. Additionally, we demonstrate the quality of the proposed estimator in comparison with state-of-the-arts in three important tasks, including estimating MI, CMI, as well as its application in conditional independence testing. The empirical evaluations show that DINE consistently outperforms competitors in all tasks and is able to adapt very well to complex and high-dimensional relationships.

Remote state estimation of large-scale distributed dynamic processes plays an important role in Industry 4.0 applications. In this paper, by leveraging the theoretical results of structural properties of optimal scheduling policies, we develop a structure-enhanced deep reinforcement learning (DRL) framework for optimal scheduling of a multi-sensor remote estimation system to achieve the minimum overall estimation mean-square error (MSE). In particular, we propose a structure-enhanced action selection method, which tends to select actions that obey the policy structure. This explores the action space more effectively and enhances the learning efficiency of DRL agents. Furthermore, we introduce a structure-enhanced loss function to add penalty to actions that do not follow the policy structure. The new loss function guides the DRL to converge to the optimal policy structure quickly. Our numerical results show that the proposed structure-enhanced DRL algorithms can save the training time by 50% and reduce the remote estimation MSE by 10% to 25%, when compared to benchmark DRL algorithms.

"Can't live this lifeless life anymore. Screens, lectures, messages, mails, marks, deadlines, expectations, this room, that laptop, religion, restrictions, health, family, feelings, theories, equations, numbers…………and me, reasons are many. These were the last words of a young student from a premier institution in India before he took his life. He was a young man in his prime who should have been happy and enjoying life. Was this a one-off incident?

"Waiting in front of the lecture hall for my next class to start, and beside me two students are discussing which AI program works best for writing their essays. Is this what I'm marking? The tweet by historian Carla Ionescu late last month captures growing unease about what artificial intelligence portends for traditional university assessment. "Tell me we're not there yet." But AI has been banging on the university's gate for some time now. In 2012, computer theorist Ben Goertzel proposed what he called the "robot university student test", arguing that an AI capable of obtaining a degree in a same ways as a human should be considered conscious. Goertzel's idea – an alternative to the more famous "Turing test" – might have remained a thought experiment were it not for the successes of AIs employing natural language processing (NLP): most famously, GPT-3, the language model created by the OpenAi research laboratory. Two years ago, computer scientist Nassim Dehouche published a piece demonstrating that GPT-3 could produce credible academic writing undetectable by the usual anti-plagiarism software. "[I] found the output," Dehouche told Guardian Australia, "to be indistinguishable from an excellent undergraduate essay, both in terms of soundness and originality.

Just three days before England kick off their FIFA World Cup campaign against Iran, scientists have a rather pessimistic forecast. The experts, based at London's Alan Turing Institute, say Gareth Southgate's men have only a seven per cent chance of winning the World Cup for the first time since 1966. However, out of the 32 participating teams, England is the fifth mostly likely team to bring home the trophy, just behind the likes of France, Belgium and Brazil. Meanwhile, Wales, which is playing in its first World Cup since 1958, has only a 0.5 per cent chance of winning the tournament – and only a 46 per cent chance of making it out of the group stage. Brazil is most likely to win the World Cup this year, according to the team's research.

This quarter marked demonstrable progress toward sector-specific approaches to the regulation of artificial intelligence and machine learning ("AI"). As the EU continues to inch toward finalizing its draft Artificial Intelligence Act--the landmark, cross-sector regulatory framework for AI/ML technologies--the White House published a "Blueprint for an AI Bill of Rights," a non-binding set of principles memorializing the Biden administration's approach to algorithmic regulation. The AI Bill of Rights joins a number of recent U.S. legislative proposals, both at the federal and state levels,[1] and the Federal Trade Commission's ("FTC") Advanced Notice of Proposed Rulemaking to solicit input on questions related to potentially harmful data privacy and security practices, including automated decision-making systems. Our 3Q22 Artificial Intelligence and Automated Systems Legal Update focuses on these regulatory efforts and also examines other policy developments within the U.S. and Europe. The past several years have seen a number of new algorithmic governance initiatives take shape at the federal level, building on the December 2020 Trustworthy AI Executive Order that outlined nine distinct principles to ensure agencies "design, develop, acquire and use AI in a manner that fosters public trust and confidence while protecting privacy."[2]

We propose a sim-to-real framework for dexterous manipulation which can generalize to new objects of the same category in the real world. The key of our framework is to train the manipulation policy with point cloud inputs and dexterous hands. We propose two new techniques to enable joint learning on multiple objects and sim-to-real generalization: (i) using imagined hand point clouds as augmented inputs; and (ii) designing novel contact-based rewards. We empirically evaluate our method using an Allegro Hand to grasp novel objects in both simulation and real world. To the best of our knowledge, this is the first policy learning-based framework that achieves such generalization results with dexterous hands. Our project page is available at https://yzqin.github.io/dexpoint

(1) Background: The success of physiotherapy depends on the regular and correct performance of movement exercises. A system that automatically evaluates these could support the therapy. Previous approaches in this area rarely rely on Deep Learning methods and do not yet fully use their potential. (2) Methods: Using a measurement system consisting of 17 IMUs, a dataset of four Functional Movement Screening (FMS) exercises is recorded. Exercise execution is evaluated by physiotherapists using the FMS criteria. This dataset is used to train a neural network that assigns the correct FMS score to an exercise repetition. We use an architecture consisting of CNN, LSTM and Dense layers. Based on this framework, we apply various methods to optimize the performance of the network. For the optimization, we perform a extensive hyperparameter optimization. In addition, we are comparing different CNN structures that have been specifically adapted for use with IMU data. Finally, the developed network is trained with the data of different FMS exercises and the performance is compared. (3) Results: The evaluation shows that the presented approach achieves a convincing performance in the classification of unknown repetitions of already known subjects. However, the trained network is yet unable to achieve consistent performance on the data of a previously unknown subjects. Additionally, it can be seen that the performance of the network differs significantly depending on the exercise it is trained for.

A first causal discovery analysis from observational data of pyroCb (storm clouds generated from extreme wildfires) is presented. Invariant Causal Prediction was used to develop tools to understand the causal drivers of pyroCb formation. This includes a conditional independence test for testing $Y$ conditionally independent of $E$ given $X$ for binary variable $Y$ and multivariate, continuous variables $X$ and $E$, and a greedy-ICP search algorithm that relies on fewer conditional independence tests to obtain a smaller more manageable set of causal predictors. With these tools, we identified a subset of seven causal predictors which are plausible when contrasted with domain knowledge: surface sensible heat flux, relative humidity at $850$ hPa, a component of wind at $250$ hPa, $13.3$ micro-meters, thermal emissions, convective available potential energy, and altitude.