Convolutional neural networks (CNNs) are fragile to small perturbations in the input images. These networks are thus prone to malicious attacks that perturb the inputs to force a misclassification. Such slightly manipulated images aimed at deceiving the classifier are known as adversarial images. In this work, we investigate statistical differences between natural images and adversarial ones. More precisely, we show that employing a proper image transformation and for a class of adversarial attacks, the distribution of the leading digit of the pixels in adversarial images deviates from Benford's law. The stronger the attack, the more distant the resulting distribution is from Benford's law. Our analysis provides a detailed investigation of this new approach that can serve as a basis for alternative adversarial example detection methods that do not need to modify the original CNN classifier neither work on the raw high-dimensional pixels as features to defend against attacks.

Microservices are becoming the defacto design choice for software architecture. It involves partitioning the software components into finer modules such that the development can happen independently. It also provides natural benefits when deployed on the cloud since resources can be allocated dynamically to necessary components based on demand. Therefore, enterprises as part of their journey to cloud, are increasingly looking to refactor their monolith application into one or more candidate microservices; wherein each service contains a group of software entities (e.g., classes) that are responsible for a common functionality. Graphs are a natural choice to represent a software system. Each software entity can be represented as nodes and its dependencies with other entities as links. Therefore, this problem of refactoring can be viewed as a graph based clustering task. In this work, we propose a novel method to adapt the recent advancements in graph neural networks in the context of code to better understand the software and apply them in the clustering task. In that process, we also identify the outliers in the graph which can be directly mapped to top refactor candidates in the software. Our solution is able to improve state-of-the-art performance compared to works from both software engineering and existing graph representation based techniques.

WhatsApp is the most popular messaging app in the world. Due to its popularity, WhatsApp has become a powerful and cheap tool for political campaigning being widely used during the 2019 Indian general election, where it was used to connect to the voters on a large scale. Along with the campaigning, there have been reports that WhatsApp has also become a breeding ground for harmful speech against various protected groups and religious minorities. Many such messages attempt to instil fear among the population about a specific (minority) community. According to research on inter-group conflict, such `fear speech' messages could have a lasting impact and might lead to real offline violence. In this paper, we perform the first large scale study on fear speech across thousands of public WhatsApp groups discussing politics in India. We curate a new dataset and try to characterize fear speech from this dataset. We observe that users writing fear speech messages use various events and symbols to create the illusion of fear among the reader about a target community. We build models to classify fear speech and observe that current state-of-the-art NLP models do not perform well at this task. Fear speech messages tend to spread faster and could potentially go undetected by classifiers built to detect traditional toxic speech due to their low toxic nature. Finally, using a novel methodology to target users with Facebook ads, we conduct a survey among the users of these WhatsApp groups to understand the types of users who consume and share fear speech. We believe that this work opens up new research questions that are very different from tackling hate speech which the research community has been traditionally involved in.

We study neural-linear bandits for solving problems where both exploration and representation learning play an important role. Neural-linear bandits leverage the representation power of Deep Neural Networks (DNNs) and combine it with efficient exploration mechanisms designed for linear contextual bandits on top of the last hidden layer. A recent analysis of DNNs in the "infinite-width" regime suggests that when these models are trained with gradient descent the optimal solution is close to the initialization point and the DNN can be viewed as a kernel machine. As a result, it is possible to exploit linear exploration algorithms on top of a DNN via the kernel construction. The problem is that in practice the kernel changes during the learning process and the agent's performance degrades. This can be resolved by recomputing new uncertainty estimations with stored data. Nevertheless, when the buffer's size is limited, a phenomenon called catastrophic forgetting emerges. Instead, we propose a likelihood matching algorithm that is resilient to catastrophic forgetting and is completely online. We perform simulations on a variety of datasets and observe that our algorithm achieves comparable performance to the unlimited memory approach while exhibits resilience to catastrophic forgetting.

LABoral Centro de Arte y Creación Industrial, a multidisciplinary institution that promotes access to transversal forms of knowledge and creative use of new technologies, presents When the butterflies of the soul flutter their wings with the idea of providing space to one of the most explored fields of research in the current cultural scenario. Opened on December 11th, 2020 and running until April 24th, 2021, the exhibition curated by Karin Ohlenschläger investigates the world of neuroscience and artificial intelligence, converging upon the possibilities these two domains can bring to the surface through a mutual feeding. The show revolves around the brain's interest and functioning, aiming to map human cognitive processes to support the latest technological tools and developments. Which potential lies within artworks based on neuro-technology and robotics, what art can suggest about the perceptual sphere shaping the mechanisms of our mind, and how artistic creation with AI can contribute to a more in-depth discernment of our psychic, emotional and intellectual dimensions, are the main queries enlivening the exhibition. Specifically, at the heart of the project is a reflection on neurons, poetically defined by the neuroscientist Santiago Ramón y Cajal as the soul's mysterious butterflies.

Recently, distributed controller architectures have been quickly gaining popularity in Software-Defined Networking (SDN). However, the use of distributed controllers introduces a new and important Request Dispatching (RD) problem with the goal for every SDN switch to properly dispatch their requests among all controllers so as to optimize network performance. This goal can be fulfilled by designing an RD policy to guide distribution of requests at each switch. In this paper, we propose a Multi-Agent Deep Reinforcement Learning (MA-DRL) approach to automatically design RD policies with high adaptability and performance. This is achieved through a new problem formulation in the form of a Multi-Agent Markov Decision Process (MA-MDP), a new adaptive RD policy design and a new MA-DRL algorithm called MA-PPO. Extensive simulation studies show that our MA-DRL technique can effectively train RD policies to significantly outperform man-made policies, model-based policies, as well as RD policies learned via single-agent DRL algorithms.

In this paper I explore the scaffolding of normative assumptions that supports Sabina Leonelli's implicit appeal to the values of epistemic integrity and the global public good that conjointly animate the ethos of responsible and sustainable data work in the context of COVID-19. Drawing primarily on the writings of sociologist Robert K. Merton, the thinkers of the Vienna Circle, and Charles Sanders Peirce, I make some of these assumptions explicit by telling a longer story about the evolution of social thinking about the normative structure of science from Merton's articulation of his well-known norms (those of universalism, communism, organized skepticism, and disinterestedness) to the present. I show that while Merton's norms and his intertwinement of these with the underlying mechanisms of democratic order provide us with an especially good starting point to explore and clarify the commitments and values of science, Leonelli's broader, more context-responsive, and more holistic vision of the epistemic integrity of data scientific understanding, and her discernment of the global and biospheric scope of its moral-practical reach, move beyond Merton's schema in ways that effectively draw upon important critiques. Stepping past Merton, I argue that a combination of situated universalism, methodological pluralism, strong objectivity, and unbounded communalism must guide the responsible and sustainable data work of the future.

Vampire has been for a long time the strongest first-order automated theorem prover, widely used for hammer-style proof automation in ITPs such as Mizar, Isabelle, HOL and Coq. In this work, we considerably improve the performance of Vampire in hammering over the full Mizar library by enhancing its saturation procedure with efficient neural guidance. In particular, we employ a recursive neural network classifying the generated clauses based only on their derivation history. Compared to previous neural methods based on considering the logical content of the clauses, this leads to large real-time speedup of the neural guidance. The resulting system shows good learning capability and achieves state-of-the-art performance on the Mizar library, while proving many theorems that the related ENIGMA system could not prove in a similar hammering evaluation.

While the complexity of computing Bayes-Nash equilibria Applications of combinatorial auctions (CA) as market mechanisms (BNE) is not well understood, Cai and Papadimitriou [14] show that are prevalent in practice, yet their Bayesian Nash equilibria (BNE) BNE computation for a specific combinatorial auction is already (at remain poorly understood. Analytical solutions are known only for least) PP-hard. Furthermore, finding an -approximation to a BNE is a few cases where the problem can be reformulated as a tractable still NP-hard. Explicit solutions exist for very few specific environments, partial differential equation (PDE). In the general case, finding BNE but in general, we neither know whether a BNE exists nor is known to be computationally hard. Previous work on numerical do we have a solution theory. Combinatorial auctions have become computation of BNE in auctions has relied either on solving such a pivotal research problem in algorithmic game theory [29] and PDEs explicitly, calculating pointwise best-responses in strategy they are widely used in the field [8, 15]. Thus, understanding their space, or iteratively solving restricted subgames. In this study, we equilibria is paramount, and access to scalable numerical methods present a generic yet scalable alternative multi-agent equilibrium for computing or approximating BNE can have a significant impact.

We propose to build directly upon our longstanding, prior r&d in AI/machine ethics in order to attempt to make real the blue-sky idea of AI that can thwart mass shootings, by bringing to bear its ethical reasoning. The r&d in question is overtly and avowedly logicist in form, and since we are hardly the only ones who have established a firm foundation in the attempt to imbue AI's with their own ethical sensibility, the pursuit of our proposal by those in different methodological camps should, we believe, be considered as well. We seek herein to make our vision at least somewhat concrete by anchoring our exposition to two simulations, one in which the AI saves the lives of innocents by locking out a malevolent human's gun, and a second in which this malevolent agent is allowed by the AI to be neutralized by law enforcement. Along the way, some objections are anticipated, and rebutted.