BeamTransformer Microphone Array based Overlapping Speech Detection

BeamTransformer seeks to optimize modeling of sequential sequentialrelationship among signals from different spatial direction . The results indicate that a successful incorporation of microphonearray signals can lead to remarkable gains . Beam transformer takes onestep further, as speech from overlapped speakers have been internally separated internally separated into different beams.…

ACP Action Co occurrence Priors for Human Object Interaction Detection

A common problem in the task of human-object interaction (HOI) detection is that numerous HOI classes have only a small number of labeled examples . The lack ofpositive labels can lead to low classification accuracy for these classes . In this paper, we model the correlations as action co-occurrence matrices and present techniques to learn these priors and leverage them for more effective training, especially onrare classes .…

Risk Averse Decision Making Under Uncertainty

A large class of decision making under uncertainty problems can be describedvia Markov decision processes (MDPs) or partially observable MDPs . Traditionally, policy synthesis techniques are proposed such that atotal expected cost or reward is minimized or maximized . However, optimality inthe total expected cost sense is only reasonable if system behavior in thelarge number of runs is of interest, which has limited the use of such policies in practical mission-critical scenarios .…

Taming Self Supervised Learning for Presentation Attack Detection In Image De Folding and Out of Image De Mixing

Biometric systems are vulnerable to Presentation Attacks (PA) performedusing various Presentation Attack Instruments (PAIs) There arenumerous PAD techniques based on both deeplearning and hand-crafted features . The proposed method, denoted as IF-OM, is based on a global-local view coupled with De-Folding and De-Mixing to derive the task-specific representation for PAD .…

Worbel Aggregating Point Labels into Word Clouds

In this paper, we study a hybrid visualization, which combines aspects ofword clouds and point labeling . Point feature labeling is a classical problem in cartography and GIS that has been extensively studied for geospatial point data . We show that computing a minimum set of such rectangles is NP-hard .…

DeepZensols Deep Natural Language Processing Framework

Given the difficulty of reproducing machinelearning (ML) experiments, there have been significant efforts in reducing thevariance of these results . As in any science, the ability to consistentlyreproduce results effectively strengthens the underlying hypothesis of thework . The contribution of this work is a framework that is able tore tore toreproduce consistent results and provides a means of easily creating, training,and evaluating natural language processing (NLP) deep learning (DL) models .…

Do What Nature Did To Us Evolving Plastic Recurrent Neural Networks For Task Generalization

Evolutionary Plastic Recurrent Neural Networks (EPRNN) incorporates with nested loops for metalearning . An outer loop searches for optimal initial parameters of the neuralnetwork and learning rules; an inner loop adapts to specific tasks . In theinner loop of EPRNN, we effectively attain both long term memory and short termmemory by forging plasticity with recursion-based learning mechanisms, both of which are believed to be responsible for memristance in BNNs .…

Malware Squid A Novel IoT Malware Traffic Analysis Framework using Convolutional Neural Network and Binary Visualisation

Internet of Things devices have seen a rapid growth and popularity in recent years . Traditional security systems are not able to detect unknown malware as they use signature-based methods . The proposed approach is to faster detect and classify new malware (zero-daymalware) The experiment results show that our method can satisfy the accuracyrequirement of practical application.…

Attributing Fair Decisions with Attention Interventions

The widespread use of Artificial Intelligence in consequential domains, such as healthcare and parole decision-making systems, has drawn intensescrutiny on the fairness of these methods . However, ensuring fairness is often insufficient as the rationale for a contentious decision needs to be audited,understood, and defended .…

Self supervised Contrastive Cross Modality Representation Learning for Spoken Question Answering

Spoken question answering (SQA) requires fine-grained understanding of both documents and questions for the optimal answer prediction . Wethen propose to learn noise-invariant utterance representations in acontrastive objective by adopting multiple augmentation strategies, includingspan deletion and span substitution . Temporal-Alignmentattention to semantically align the speech-text clues in the learned commonspace and benefit the SQA tasks .…

On Event Driven Knowledge Graph Completion in Digital Factories

Smart factories are equipped with machines that can sense their manufacturing environments, interact with each other, and control production processes . We show how machine learning that is specificallytailored towards industrial applications can help in knowledge graphcompletion . In particular, we show how knowledge completion can benefit from event logs that are common in smart factories .…

RoadAtlas Intelligent Platform for Automated Road Defect Detection and Asset Management

RoadAtlas is a novel end-to-endintegrated system that can support 1) road defect detection, 2) road markingparsing, 3) a web-based dashboard for presenting and inputting data by users,and 4) a backend containing a well-structured database and developed APIs . This can effectively address the issue of an expensiveand time-consuming process for professional inspectors to review the street manually .…

Convergence of Batch Asynchronous Stochastic Approximation With Applications to Reinforcement Learning

The stochastic approximation (SA) algorithm is a widely used probabilisticmethod for finding a solution to an equation of the form$\mathbf{f}(\boldsymbol{\theta) = \mathbf {R}$ The algorithm is widely used in Reinforcement Learning (RL) In this note, we provide a general theory of convergence for batchasymchronous stochastastic approximation, that works whether the updates use a local clock or a global clock, for the case where the measurement noises form a martingale difference sequence .…

Cost Problems for Parametric Time Petri Nets

We investigate the problem of parameter synthesis for time Petri nets with acost variable that evolves both continuously with time, and discretely when firing transitions . We provide symbolicsemi-algorithms for the two synthesis problems . We also show how to modify them for the case whenparameter values are integers .…

Good Enough Synthesis

In good-enoughsynthesis, the system is required to generate a satisfying computation only if one exists . Formally, an input sequence x is hopeful if there exists some output sequence y such that the computation x \otimes ysatisfies \psi . In practice, the requirement to satisfy the specification in all environments is often too strong, and it is common to add assumptions on the environment .…

Fixed Support Tree Sliced Wasserstein Barycenter

The Wasserstein barycenter has been widely studied in various fields, including natural language processing, and computer vision . It requires a quadratic timewith respect to the number of supports . By contrast, the tree-Wasserstein distance can be computed in linear time and allows for the fast comparison of a large number of distributions .…