Delphi Towards Machine Ethics and Norms

What would it take to teach a machine to behave ethically? While broadethical rules may seem straightforward to state (“thou shalt not kill”), applying such rules to real-world situations is far more complex . We present Commonsense NormBank, a moral textbook customized for machines, which compiles 1.7M examples of people’s ethical judgments on a broad spectrum of everyday situations .…

DeepOrder Deep Learning for Test Case Prioritization in Continuous Integration Testing

DeepOrder is a deep learning-based model that works on thebasis of regression machine learning . DeepOrder learns failed test cases based on multiple factors including theduration and execution status of test cases . The results show that DeepOrder outperforms the industry practice and state-of-the-art test prioritization approaches in terms of time-effectiveness and fault detection effectiveness of the DeepOrder approach .…

A Dual Attention Neural Network for Pun Location and Using Pun Gloss Pairs for Interpretation

Pun location is to identify the punning word (usually a word or phrase that makes the text ambiguous) in a given short text . Pun interpretation is to find out two different meanings of the word . DANN (Dual-Attentive Neural Network) is proposed for pun location, effectively integrates word senses and pronunciation with context information to address two kinds of pun at the same time .…

Retrieval guided Counterfactual Generation for QA

Deep NLP models have been shown to learn spurious correlations, leaving them brittle to input perturbations . We develop a Retrieve-Generate-Filter-Filter technique to create counterfactualevaluation and training data with minimal human supervision . Using anopen-domain QA framework and question generation model trained on original task data, we create counterfactsuals that are fluent, semantically diverse, andautomatically labeled.…

A Dual Attention Neural Network for Pun Location and Using Pun Gloss Pairs for Interpretation

Pun location is to identify the punning word (usually a word or phrase that makes the text ambiguous) in a given short text . Pun interpretation is to find out two different meanings of the word . DANN (Dual-Attentive Neural Network) is proposed for pun location, effectively integrates word senses and pronunciation with context information to address two kinds of pun at the same time .…

Physics informed neural networks for continuum micromechanics

Recently, physics informed neural networks have successfully been applied to a broad variety of problems in applied mathematics and engineering . The principle idea is to use a neural network as a global ansatz function topartial differential equations . Due to the global approximation, physicsinformed neural networks struggle to display localized effects and strong non-linear solutions by optimization .…

Conformer Based Self Supervised Learning for Non Speech Audio Tasks

In this paper, we propose a self-supervised audio representationlearning method and apply it to a variety of downstream non-speech audio tasks . We achieve a mean average precision(mAP) score of 0.415, which is a new state-of-the-art on this dataset . Our fine-tuned conformers also surpass ormatch the performance of previous systems pre-trained in a supervised way on several downstream tasks, we say .…

Bugs in our Pockets The Risks of Client Side Scanning

Some in industry and government now advocate a new technology to access targeted data: client-side scanning . CSS would enable on-device analysis of data in the clear . CSS by its nature createsserious security and privacy risks for all society while it can provide assistance for law enforcement is at best problematic, authors say .…

Diffusion Normalizing Flow

We present a novel generative modeling method called diffusion normalizingflow based on stochastic differential equations . The algorithm consistsof two neural SDEs: a forward SDE that gradually adds noise to the data totransform the data into Gaussian random noise . The backward SDE converges to a diffusion process the starts with a Gaussian distribution and ends with the desired datadistribution .…

Domain Adaptation on Semantic Segmentation with Separate Affine Transformation in Batch Normalization

In recent years, unsupervised domain adaptation (UDA) for semanticsegmentation has brought many researchers’attention . The proposed SEAT is simple, easily implemented and easy to integrate into existing adversarial learning based UDA methods . We introduce multi level adaptation by adding thelower-level features to the higher-level ones before feeding them to the discriminator, without adding extra discriminator like others.…

Brittle interpretations The Vulnerability of TCAV and Other Concept based Explainability Tools to Adversarial Attack

Methods for model explainability have become increasingly critical fortesting the fairness and soundness of deep learning . In safety-critical applications, there is need for security around not only the machine learning pipeline but also the modelinterpretation process . We show that by perturbing the examples of the concept that is being investigated, we can radically change the output of the interpretability method, e.g.…

On Adversarial Vulnerability of PHM algorithms An Initial Study

With proliferation of deep learning (DL) applications in diverse domains, vulnerability of those PHM algorithms to adversarial attacks has become an increasingly important topic in the domains of Computer Vision (CV) and NaturalLanguage Processing (NLP) We use two real-world PHM applications as examples tovalidate our attack strategies and to demonstrate that PHM .…

The Neural MMO Platform for Massively Multiagent Research

Neural MMO is a computationally accessible research platform that combines large agent populations, long time horizons, open-ended tasks, and modular gamesystems . We present Neural MMO as free and opensource software with active support, ongoing development, documentation, and training, logging, and visualization tools to help users adapt to the new setting .…

Secure Precoding in MIMO NOMA A Deep Learning Approach

A novel signaling design for secure transmission over two-user multiple-inputmultiple-output non-orthogonal multiple access channel using deep neuralnetworks (DNNs) is proposed . The goal of the DNN is to form the covariancematrix of users’ signals such that the message of each user is transmitted reliably while being confidential from its counterpart .…

Learning Temporal 3D Human Pose Estimation with Pseudo Labels

We present a simple, yet effective, approach for self-supervised 3D humanpose estimation . During training, we rely ontriangulating 2D body pose estimates of a multiple-view camera system . Atemporal convolutional neural network is trained with the generated 3Dground-truth and the geometric multi-view consistency loss, imposinggeometrical constraints on the predicted 3D body skeleton .…

Spoken ObjectNet A Bias Controlled Spoken Caption Dataset

Modern audio-visual datasets contain biases that undermine the real-world performance of models trained on that data . We introduce Spoken ObjectNet to remove some of these biases . This dataset expands upon ObjectNet, which is a bias-controlled image dataset . We detail our datacollection pipeline, which features several methods to improve caption quality, including automated language model checks .…

Omni Training for Data Efficient Deep Learning

A properlypre-trained model endows an important property: transferability . A highertransferability of the learned representations indicates a bettergeneralizability across domains of different distributions . Transferability has become the key to enable data-efficient deep learning, however, existing pre-training methods focus only on the domaintransferability .…

Playing for 3D Human Recovery

Image- and video-based 3D human recovery (i.e. pose and shape estimation)have achieved substantial progress . However, due to the prohibitive cost ofmotion capture, existing datasets are often limited in scale and diversity . In this work, we obtain massive human sequences as well as their 3D ground truths by playing video games .…

Retrieval guided Counterfactual Generation for QA

Deep NLP models have been shown to learn spurious correlations, leaving them brittle to input perturbations . We develop a Retrieve-Generate-Filter-Filter technique to create counterfactualevaluation and training data with minimal human supervision . Using anopen-domain QA framework and question generation model trained on original task data, we create counterfactsuals that are fluent, semantically diverse, andautomatically labeled.…

Hybrid Quantum Classical Neural Network for Cloud supported In Vehicle Cyberattack Detection

A classical computer works with ones and zeros, whereas a quantum computer uses ones, zeros and superpositions of ones and zero . In a cloud-supported cyber-physical system environment, running a machine learning application in quantum computers is often difficult . With the combination of quantum-classical neural networks (NN), complex and high-dimensional features can be extracted by the classical NN to a reduced butmore informative feature space to be processed by the existing quantumcomputers .…

Solving Aspect Category Sentiment Analysis as a Text Generation Task

We consider casting the ACSAtasks into natural language generation tasks, using natural language sentencesto represent the output . Our method allows more direct use of pre-trained knowledge in seq2seq language models by directly following the task setting during pre-training . Experiments on several benchmarks show that our methodgives the best reported results, having large advantages in few-shot and zero-shot settings .…

CNN DST ensemble deep learning based on Dempster Shafer theory for vibration based fault recognition

Using vibration data in conjunction with pattern recognition methods is one of the most common fault detection strategies for structures . Deep learning facilitates the fault detectionprocedure by automating the feature extraction and selection, and classification procedure . This study proposes an ensemble deeplearning framework based on a convolutional neural network (CNN) andDempster-Shafer theory (DST) Called CNN-DST, it classifies turbine blades with an average prediction accuracy of 97.19% .…

Solving Large Break Minimization Problems in a Mirrored Double Round robin Tournament Using Quantum Annealing

Quantum annealing (QA) has gained considerable attention because it can be applied to combinatorial optimization problems . In recent years, research on solving practical combinatorials optimization problems using them hasaccelerated . In our study, wedetermine that QA demonstrates better performance than the solvers in the breakminimization problem in a mirrored double round-robin tournament (MDRRT) We also explain the desirable performance of QA for the sparse interaction betweenvariables and a problem without constraints .…

VABO Violation Aware Bayesian Optimization for Closed Loop Control Performance Optimization with Unmodeled Constraints

Bayesian optimization (BO) has been effective for improving closed-loop performance by automatically tuning controller gains or reference setpoints in a model-free manner . However,BO methods have rarely been tested on dynamical systems with unmodeled dynamics . In this paper, we propose a violation-aware BO algorithm (VABO) that optimizes performance while simultaneously learningconstraint-feasible solutions .…