## PharmKE Knowledge Extraction Platform for Pharmaceutical Texts using Transfer Learning

PharmKE is a text analysis platform focused on thepharmaceutical domain . It performs text classification using state-of-the-art transfer learning models . The recognized entities are alsoused to expand the knowledge graph generated by DBpedia Spotlight for a given pharmaceutical text .…

## Automated essay scoring using efficient transformer based language models

Automated Essay Scoring (AES) is a cross-disciplinary effort involving education, Linguistics, and Natural Language Processing (NLP) The efficacy of an NLP model in AES tests it ability to evaluate long-term dependencies and extract meaning even when text is poorly written .…

## ANEA Distant Supervision for Low Resource Named Entity Recognition

ANEA is a tool to automatically annotate named entities in text based on entity lists . It spans the whole pipeline from obtaining the lists to analyzing the errors of the distant supervision . A tuning step allows the userto improve the automatic annotation with their linguistic insights withouthaving to manually label or check all tokens .…

## 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 .…

## Characterizing the Landscape of COVID 19 Themed Cyberattacks and Defenses

COVID-19 (Coronavirus) hit the global society and economy with a big surprise . In particular, work-from-home has become a new norm for employees . It is ironic to see surges in cyberattacks leveraging the theme of cyberattacks . These attacks represent a new phenomenon that has yet to be systematically understood .…

## 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 .…

## Nonlinear Projection Based Gradient Estimation for Query Efficient Blackbox Attacks

We propose anovel query-efficient NonLinear Gradient Projection-based Boundary BlackboxAttack (NonLinear-BA) The code is publiclyavailable at https://://://github.com/AI-secure/NonLinearly-BA . We show that the projection-based boundary blackbox attacks are able to achieve muchsmaller magnitude of perturbations with 100% attack success rate based one efficient queries .…

## 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.…

## Checkpointing with cp the POSIX Shared Memory System

Abacus is an $N$-body simulation codethat allocates all persistent state in POSIX shared memory, or ramdisk . The main simulation executable is invoked once per time step, memorymapping the input state, computing the output state directly into ramdisk, and unmapping it.…

## 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 .…

## 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.…

## Using Inverse Optimization to Learn Cost Functions in Generalized Nash Games

In generalized Nash equilibrium problems (GNEPs), a player’s set of feasibleactions is also impacted by the actions taken by other players in the game . We extend the framework of Ratliff et al. (2014) to find inverse optimization solutions for the class of GNEPs with joint constraints .…

## 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 .…

## An Overview of Direct Diagnosis and Repair Techniques in the WeeVis Recommendation Environment

Constraint-based recommenders support users in the identification of items fitting their wishes and needs . Example domains are financialservices and electronic equipment . In this paper we show how divide-and-conquerbased (direct) diagnosis algorithms (no conflict detection is needed) can be used in constraint-based recommendation scenarios .…

## 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 .…

## Train one Classify one Teach one Cross surgery transfer learning for surgical step recognition

In machine learning, this approach is often referred to as transferlearning . In this work, we analyze surgical step recognition on four different laparoscopic surgeries: Cholecystectomy, Right Hemicolectomy, Sleeve Gastrectomy, and Appendectomy . We introduce a new architecture, the Time-Series Adaptation Network (TSAN), an architectureoptimized for transfer learning .…

## Balancing Rational and Other Regarding Preferences in Cooperative Competitive Environments

Mixed environments are notorious for the conflicts of selfish and social interests . Wepropose BAROCCO is an extension of these algorithms capable to balance individual and social incentives . The mechanism behind BAROccO is to train two distinctbut interwoven components that jointly affect each agent’s decisions .…

## 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 .…

## Multi Task Attentive Residual Networks for Argument Mining

We explore the use of residual networks and neural attention for argumentmining and in particular link prediction . The method we propose makes noassumptions on document or argument structure . We propose a residualarchitecture that exploits attention, multi-task learning, and makes use ofensemble .…

## 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 .…

## PADA A Prompt based Autoregressive Approach for Adaptation to Unseen Domains

We present PADA: A Prompt-based AutoregressiveDomain Adaptation algorithm, based on the T5 model . Given a test example, PADA generates a unique prompt and then, conditioned on this prompt, labelsthe example with respect to the task . PADA strongly outperforms state-of-the-art approaches and additional strong baselines .…

## Combining Off and On Policy Training in Model Based Reinforcement Learning

Deep learning and Monte Carlo Tree Search (MCTS) has shownto be effective in various domains, such as board and video games . MuZero demonstrated that it is possible to masterboth Atari games and board games by directly learning a model of theenvironment .…