Data Driven Characterization and Detection of COVID 19 Themed Malicious Websites

COVID-19 has hit hard on the global community, and organizations are working to cope with the new norm of “work from home” The volume of remote work is unprecedented and creates opportunities for cyber attackersto penetrate home computers . Attackers are agile and are deceptively crafty in designing geolocationtargeted websites, often leveraging popular domain registrars and top-leveldomains .…

Leveraged Trading on Blockchain Technology

We document an ongoing research process towards the implementation andintegration of a digital artefact, executing the lifecycle of a leveraged tradewith permissionless blockchain technology . By employing core functions of the’Dai Stablecoin system’ deployed on the . Ethereum blockchain, we produce theequivalent exposure of a .…

Multi Domain Learning by Meta Learning Taking Optimal Steps in Multi Domain Loss Landscapes by Inner Loop Learning

We consider a model-agnostic solution to the problem of Multi-Domain Learning(MDL) for multi-modal applications . We aim to enable MDL purely algorithmically so that neural networks can trivially achieve MDL in a model independent manner . We demonstrate our solution to a fitting problem in medical imaging, specifically in the automatic segmentation of white matter hyperintensity(WMH) We look at two neuroimaging modalities (T1-MR and FLAIR) withcomplementary information fitting for our problem.…

Robust Pollen Imagery Classification with Generative Modeling and Mixup Training

Deep learning approaches have shown great success in image classification tasks and can aid greatly towards the fast and reliable classification of pollen grain aerial imagery . The proposed approachearned a fourth-place in the final rankings in the ICPR-2020 Pollen Grain Classification Challenge; with a 0.972578 weighted F1 score, 0.950828 macroaverage F1 scores, and .972877 recognition accuracy .…

DeepSZ Identification of Sunyaev Zel dovich Galaxy Clusters using Deep Learning

Galaxy clusters identified from the Sunyaev Zel’dovich (SZ) effect are a keyingredient in multi-wavelength cluster-based cosmology . We present a comparisonbetween two methods of cluster identification: the standard Matched Filter (MF)method in SZ cluster finding and a method using Convolutional Neural Networks(CNN) The CNN method requires very littlepre-processing of images, while the MF method requires little pre-processing .…

Images Emotions and Credibility Effect of Emotional Facial Images on Perceptions of News Content Bias and Source Credibility in Social Media

Emotional images from sources of misinformation can greatly influence ourjudgements . Users are more likely to find sources as less credible and their content as biased . When sources portray specific politicians as angry, users find them less credible . These results highlight how implicit visual propositions manifested by emotions infacial expressions might have a substantial effect on our trust of news content and sources.…

VPIC 2 0 Next Generation Particle in Cell Simulations

VPIC is a general purpose Particle-in-Cell simulation code for modeling plasma phenomena such as magnetic reconnection, fusion, solar weather, and laser-plasma interaction in three dimensions . VPIC’s capacity in both fidelity and scale makes it particularly well-suited for plasma research on pre-exascale and exascale platforms .…

MEDAL An AI driven Data Fabric Concept for Elastic Cloud to Edge Intelligence

Current Cloud solutions for Edge Computing are inefficient for data-centric applications, as they focus on the IaaS/PaaS level and miss the datamodeling and operations perspective . MEDAL is an intelligent Cloud-to-Edge Data Fabric to support Data Operations(DataOps)across the continuum and to automate management and orchestrationoperations over a combined view of the data and the resource layer .…

Graph Community Detection from Coarse Measurements Recovery Conditions for the Coarsened Weighted Stochastic Block Model

We study the problem of community recovery from coarse measurements of agraph . We build on thestochastic block model by mathematically formalizing the coarsening process . We characterize an error bound for communityrecovery . The error bound yields simple and closed-form asymptotic conditionsto achieve the perfect recovery of the coarse graph communities, the authors say .…

Recovery of regular ridge functions on the ball

We consider the problem of the uniform (in $L_\infty) recovery of ridgefunctions $f(x) The problem suffers from the curse of dimensionality: in order to provide good accuracy for the recovery it is necessary to make exponential number of evaluations . We prove that if$\varphi$ is analytic in a neighborhood of $[-1,1]$ and the noise is small, then there is an efficient algorithm that recovers $f$ with good accuracy using $asymp n\log^2n function evaluations .…

Contrast independent partially explicit time discretizations for multiscale wave problems

In this work, we design and investigate contrast-independent partiallyexplicit time discretizations for wave equations in heterogeneous high-contrast media . We consider multiscale problems, where the spatial heterogeneities are not resolved . The splitting requires a careful design. We prove that the proposed splitting isunconditionally stable under some suitable conditions formulated for the secondspace (slow) We present numerical results and show that proposed methods provide results similar to implicit methods with the time step that is independent of the contrast.…

Exact and heuristic approaches for multi objective garbage accumulation points location in real scenarios

Municipal solid waste management is a major challenge for nowadays urbansocieties, because it accounts for a large proportion of public budget and,when mishandled, it can lead to environmental and social problems . This article contributes with an exact multiobjective approach to solve the waste bin location in which the optimization criteria that are considered are: the accessibility to the system (as quality of servicemeasure), the investment cost, and the required frequency of waste removal from the bins .…

Directional Bias Amplification

Mitigating bias in machine learning systems requires refining our understanding of bias propagation pathways . A metric formeasuring bias amplification was introduced in the seminal work by Zhao et al. We introduceand analyze a new, decoupled metric for measuring bias amplification,$\text{BiasAmp}_{\rightarrow}$ (Directional Bias Amplification) We provide suggestions about its measurement by cautioning against predicting sensitive attributes, encouraging the use ofconfidence intervals due to fluctuations in the fairness of models across runs,and discussing the limitations of what this metric captures .…

The Logical Options Framework

Logical Options Framework (LOF) learns policies that are satisfying, optimal, and composable . LOF efficiently learns policies thatsatisfy tasks by representing the task as an automaton and integrating it into learning and planning . We evaluate LOF on four tasks in discrete and continuous domains, including a 3D pick-and-place environment .…

PsiPhi Learning Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning

We propose amulti-task inverse reinforcement learning (IRL) algorithm, called \emph{inversetemporal difference learning} (ITD) that learns shared state features and per-agent successor features . We further show how to seamlesslyintegrate ITD with learning from online environment interactions, arriving at anovel algorithm for reinforcement learning with demonstrations, called $\Psi\Phi$-learning (pronounced `Sci-Fi’) We provide empirical evidence for the effectiveness of this method for improving RL, IRL,imitation, and few-shot transfer, and we derive worst-case bounds for its performance in zero-shot transfers to new tasks .…

Learning Emergent Discrete Message Communication for Cooperative Reinforcement Learning

Communication is a important factor that enables agents to work cooperatively in multi-agent reinforcement learning (MARL) Most previous work uses continuous communication whose high representational capacity comes at the expense of interpretability . Allowing agents to learn their own discrete message protocol emerged from a variety of domains can increase theinterpretability for human designers and other agents .…

Deep Reinforcement Learning for Safe Landing Site Selection with Concurrent Consideration of Divert Maneuvers

This research proposes a new integrated framework for identifying safelanding locations and planning in-flight divert maneuvers . The proposed framework wasable to achieve 94.8% of successful landing in highly challenging landingsites where over 80$\%$ of the area around the initial target lading point ishazardous, by effectively updating the target landing site and feedback controlgain during descent .…

Designing Explanations for Group Recommender Systems

Explanations are used in recommender systems for various reasons . Users have to be supported in making (high-quality) decisions more quickly . Explanation is designed in order to achieve specific goals such as increasing transparency of areendation or increasing a user’s trust in the recommender system .…

AutoAI TS AutoAI for Time Series Forecasting

AutoAI for Time Series Forecasting (AutoAI-TS) provides users with a zeroconfiguration (zero-conf ) system to efficiently train, optimize and choose best forecasting model among various classes of models for the given dataset . Automatically providing a good set of models to users for a givendataset saves both time and effort from using trial-and-error approaches .…

Memory based Deep Reinforcement Learning for POMDP

A promising characteristic of Deep Reinforcement Learning (DRL) is itsability to learn optimal policy in an end-to-end manner without relying on feature engineering . Most approaches assume a fully observable statespace, i.e. fully observable Markov Decision Process (MDP) In real-worldrobotics, this assumption is unpractical, because of sensor issues such assensors’ capacity limitation and sensor noise .…

AGENT A Benchmark for Core Psychological Reasoning

For machine agents to successfully interact with humans in real-world settings, they will need to develop an understanding of human mental life . We present a benchmark consisting of a large dataset of procedurally generated 3D animations, AGENT (Action,Goal, Efficiency, coNstraint, uTility) We validateAGENT with human-ratings, propose an evaluation protocol emphasizing generalization, and compare two strong baselines built on Bayesian inverseplanning and Theory of Mind neural network .…

Image Augmentation for Multitask Few Shot Learning Agricultural Domain Use Case

Large datasets’ availability is catalyzing a rapid expansion of deep learning in general and computer vision in particular . In many domains, lack of training data may become an obstacle to the practical application of computer vision techniques . We introduce an image augmentation framework, which enablesus to enlarge the number of training samples while providing the data for such tasks as object detection, semantic segmentation, instancesegmentation, object counting, image denoising, and classification .…

Credit Assignment with Meta Policy Gradient for Multi Agent Reinforcement Learning

Reward decomposition is a critical problem in centralized training withdecentralized execution~(CTDE) paradigm for multi-agent reinforcement learning . We propose a general meta-learning-based Mixing Network with MetaPolicy Gradient~(MNMPG) framework to distill the global hierarchy for delicatereward decomposition . Our method is generally applicable to theCTDE method using a monotonic mixing network .…

A CP Net based Qualitative Composition Approach for an IaaS Provider

We propose a novel CP-Net based composition approach to qualitatively select an optimal set of consumers for an IaaS provider . The provider’s and consumers’ qualitative preferences are captured using CP-Nets . A greedy-based and a heuristic-based consumer selection approaches are proposed that effectively reduce the search space of candidates in the composition .…