pwc-archive/papers-with-abstracts huggingface dataset:
Machine Learning Based Routing Congestion Prediction in FPGA High-Level Synthesis
Authors: Jieru Zhao, Tingyuan Liang, Sharad Sinha, Wei zhang
Date: 2019-05-06 00:00:00
Tasks: BIG-bench Machine Learning, Face Detection, High-Level Synthesis, Vocal Bursts Intensity Prediction

High-level synthesis (HLS) shortens the development time of hardware designs and enables faster design space exploration at a higher abstraction level. Optimization of complex applications in HLS is challenging due to the effects of implementation issues such as routing congestion. Routing congestion estimation is absent or inaccurate in existing HLS design methods and tools. Early and accurate congestion estimation is of great benefit to guide the optimization in HLS and improve the efficiency of implementation. However, routability, a serious concern in FPGA designs, has been difficult to evaluate in HLS without analyzing post-implementation details after Place and Route. To this end, we propose a novel method to predict routing congestion in HLS using machine learning and map the expected congested regions in the design to the relevant high-level source code. This is greatly beneficial in early identification of routability oriented bottlenecks in the high-level source code without running time-consuming register-transfer level (RTL) implementation flow. Experiments demonstrate that our approach accurately estimates vertical and horizontal routing congestion with errors of 6.71% and 10.05% respectively. By presenting Face Detection application as a case study, we show that by discovering the bottlenecks in high-level source code, routing congestion can be easily and quickly resolved compared to the efforts involved in RTL implementation and design feedback.
Genuinely Distributed Byzantine Machine Learning
Authors: El-Mahdi El-Mhamdi, Rachid Guerraoui, Arsany Guirguis, Lê Nguyên Hoang, Sébastien Rouault
Date: 2019-05-05 00:00:00
Tasks: BIG-bench Machine Learning

Machine Learning (ML) solutions are nowadays distributed, according to the so-called server/worker architecture. One server holds the model parameters while several workers train the model. Clearly, such architecture is prone to various types of component failures, which can be all encompassed within the spectrum of a Byzantine behavior. Several approaches have been proposed recently to tolerate Byzantine workers. Yet all require trusting a central parameter server. We initiate in this paper the study of the ``general'' Byzantine-resilient distributed machine learning problem where no individual component is trusted. We show that this problem can be solved in an asynchronous system, despite the presence of $\frac{1}{3}$ Byzantine parameter servers and $\frac{1}{3}$ Byzantine workers (which is optimal). We present a new algorithm, ByzSGD, which solves the general Byzantine-resilient distributed machine learning problem by relying on three major schemes. The first, Scatter/Gather, is a communication scheme whose goal is to bound the maximum drift among models on correct servers. The second, Distributed Median Contraction (DMC), leverages the geometric properties of the median in high dimensional spaces to bring parameters within the correct servers back close to each other, ensuring learning convergence. The third, Minimum-Diameter Averaging (MDA), is a statistically-robust gradient aggregation rule whose goal is to tolerate Byzantine workers. MDA requires loose bound on the variance of non-Byzantine gradient estimates, compared to existing alternatives (e.g., Krum). Interestingly, ByzSGD ensures Byzantine resilience without adding communication rounds (on a normal path), compared to vanilla non-Byzantine alternatives. ByzSGD requires, however, a larger number of messages which, we show, can be reduced if we assume synchrony.
Zygarde: Time-Sensitive On-Device Deep Inference and Adaptation on Intermittently-Powered Systems
Authors: Bashima Islam, Shahriar Nirjon
Date: 2019-05-05 00:00:00
Tasks: Scheduling

We propose Zygarde -- which is an energy -- and accuracy-aware soft real-time task scheduling framework for batteryless systems that flexibly execute deep learning tasks1 that are suitable for running on microcontrollers. The sporadic nature of harvested energy, resource constraints of the embedded platform, and the computational demand of deep neural networks (DNNs) pose a unique and challenging real-time scheduling problem for which no solutions have been proposed in the literature. We empirically study the problem and model the energy harvesting pattern as well as the trade-off between the accuracy and execution of a DNN. We develop an imprecise computing-based scheduling algorithm that improves the timeliness of DNN tasks on intermittently powered systems. We evaluate Zygarde using four standard datasets as well as by deploying it in six real-life applications involving audio and camera sensor systems. Results show that Zygarde decreases the execution time by up to 26% and schedules 9%-34% more tasks with up to 21% higher inference accuracy, compared to traditional schedulers such as the earliest deadline first (EDF).
Biologically plausible deep learning -- but how far can we go with shallow networks?
Authors: Bernd Illing, Wulfram Gerstner, Johanni Brea
Date: 2019-02-27 00:00:00

Training deep neural networks with the error backpropagation algorithm is considered implausible from a biological perspective. Numerous recent publications suggest elaborate models for biologically plausible variants of deep learning, typically defining success as reaching around 98% test accuracy on the MNIST data set. Here, we investigate how far we can go on digit (MNIST) and object (CIFAR10) classification with biologically plausible, local learning rules in a network with one hidden layer and a single readout layer. The hidden layer weights are either fixed (random or random Gabor filters) or trained with unsupervised methods (PCA, ICA or Sparse Coding) that can be implemented by local learning rules. The readout layer is trained with a supervised, local learning rule. We first implement these models with rate neurons. This comparison reveals, first, that unsupervised learning does not lead to better performance than fixed random projections or Gabor filters for large hidden layers. Second, networks with localized receptive fields perform significantly better than networks with all-to-all connectivity and can reach backpropagation performance on MNIST. We then implement two of the networks - fixed, localized, random & random Gabor filters in the hidden layer - with spiking leaky integrate-and-fire neurons and spike timing dependent plasticity to train the readout layer. These spiking models achieve > 98.2% test accuracy on MNIST, which is close to the performance of rate networks with one hidden layer trained with backpropagation. The performance of our shallow network models is comparable to most current biologically plausible models of deep learning. Furthermore, our results with a shallow spiking network provide an important reference and suggest the use of datasets other than MNIST for testing the performance of future models of biologically plausible deep learning.
On the Behaviour of Differential Evolution for Problems with Dynamic Linear Constraints
Authors: Maryam Hasani-Shoreh, María-Yaneli Ameca-Alducin, Wilson Blaikie, Frank Neumann, Marc Schoenauer
Date: 2019-02-27 00:00:00
Tasks: Evolutionary Algorithms, Translation

Evolutionary algorithms have been widely applied for solving dynamic constrained optimization problems (DCOPs) as a common area of research in evolutionary optimization. Current benchmarks proposed for testing these problems in the continuous spaces are either not scalable in problem dimension or the settings for the environmental changes are not flexible. Moreover, they mainly focus on non-linear environmental changes on the objective function. While the dynamism in some real-world problems exists in the constraints and can be emulated with linear constraint changes. The purpose of this paper is to introduce a framework which produces benchmarks in which a dynamic environment is created with simple changes in linear constraints (rotation and translation of constraint's hyperplane). Our proposed framework creates dynamic benchmarks that are flexible in terms of number of changes, dimension of the problem and can be applied to test any objective function. Different constraint handling techniques will then be used to compare with our benchmark. The results reveal that with these changes set, there was an observable effect on the performance of the constraint handling techniques.
Deep Reinforcement Learning using Genetic Algorithm for Parameter Optimization
Authors: Adarsh Sehgal, Hung Manh La, Sushil J. Louis, Hai Nguyen
Date: 2019-02-19 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning (RL) enables agents to take decision based on a reward
function. However, in the process of learning, the choice of values for
learning algorithm parameters can significantly impact the overall learning
process. In this paper, we use a genetic algorithm (GA) to find the values of
parameters used in Deep Deterministic Policy Gradient (DDPG) combined with
Hindsight Experience Replay (HER), to help speed up the learning agent. We used
this method on fetch-reach, slide, push, pick and place, and door opening in
robotic manipulation tasks. Our experimental evaluation shows that our method
leads to better performance, faster than the original algorithm.
Significantly Fast and Robust Fuzzy C-MeansClustering Algorithm Based on MorphologicalReconstruction and Membership Filtering
Authors: Tao Lei, Xiaohong Jia, Yanning Zhang, Lifeng He, Hongy-ing Meng, Senior Member, and Asoke K. Nandi, Fellow, IEEE
Date: 2018-01-23 00:00:00
Proceeding: IEEE 2018 1
Tasks: Clustering, Image Segmentation, Semantic Segmentation

As  fuzzy  c-means  clustering  (FCM)  algorithm  issensitive  to  noise,  local  spatial  information  is  often  introducedto  an  objective  function  to  improve  the  robustness  of  the  FCMalgorithm for image segmentation. However, the introduction oflocal  spatial  information  often  leads  to  a  high  computationalcomplexity, arising out of an iterative calculation of the distancebetween   pixels   within   local   spatial   neighbors   and   clusteringcenters.  To  address  this  issue,  an  improved  FCM  algorithmbased on morphological reconstruction and membership filtering(FRFCM) that is significantly faster and more robust than FCM,is  proposed  in  this  paper.  Firstly,  the  local  spatial  informationof  images  is  incorporated  into  FRFCM  by  introducing  mor-phological reconstruction operation to guarantee noise-immunityand   image   detail-preservation.   Secondly,   the   modification   ofmembership  partition,  based  on  the  distance  between  pixelswithin local spatial neighbors and clustering centers, is replacedby  local  membership  filtering  that  depends  only  on  the  spatialneighbors  of  membership  partition.  Compared  to  state-of-the-art  algorithms,  the  proposed  FRFCM  algorithm  is  simpler  andsignificantly faster, since it is unnecessary to compute the distancebetween   pixels   within   local   spatial   neighbors   and   clusteringcenters.  In  addition,  it  is  efficient  for  noisy  image  segmentationbecause  membership  filtering  are  able  to  improve  membershippartition matrix efficiently. Experiments performed on syntheticand real-world images demonstrate that the proposed algorithmnot only achieves better results, but also requires less time thanstate-of-the-art algorithms for image segmentation.
PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization
Authors: Shunsuke Saito, Zeng Huang, Ryota Natsume, Shigeo Morishima, Angjoo Kanazawa, Hao Li
Date: 2019-05-13 00:00:00
Proceeding: ICCV 2019 10
Tasks: 3D Human Pose Estimation, 3D Human Reconstruction, 3D Object Reconstruction, 3D Object Reconstruction From A Single Image, 3D Shape Reconstruction, 3D Shape Reconstruction From A Single 2D Image, Lifelike 3D Human Generation, Vocal Bursts Intensity Prediction

We introduce Pixel-aligned Implicit Function (PIFu), a highly effective implicit representation that locally aligns pixels of 2D images with the global context of their corresponding 3D object. Using PIFu, we propose an end-to-end deep learning method for digitizing highly detailed clothed humans that can infer both 3D surface and texture from a single image, and optionally, multiple input images. Highly intricate shapes, such as hairstyles, clothing, as well as their variations and deformations can be digitized in a unified way. Compared to existing representations used for 3D deep learning, PIFu can produce high-resolution surfaces including largely unseen regions such as the back of a person. In particular, it is memory efficient unlike the voxel representation, can handle arbitrary topology, and the resulting surface is spatially aligned with the input image. Furthermore, while previous techniques are designed to process either a single image or multiple views, PIFu extends naturally to arbitrary number of views. We demonstrate high-resolution and robust reconstructions on real world images from the DeepFashion dataset, which contains a variety of challenging clothing types. Our method achieves state-of-the-art performance on a public benchmark and outperforms the prior work for clothed human digitization from a single image.
Zoom To Learn, Learn To Zoom
Authors: Xuaner Cecilia Zhang, Qifeng Chen, Ren Ng, Vladlen Koltun
Date: 2019-05-13 00:00:00
Tasks: Super-Resolution

This paper shows that when applying machine learning to digital zoom for photography, it is beneficial to use real, RAW sensor data for training. Existing learning-based super-resolution methods do not use real sensor data, instead operating on RGB images. In practice, these approaches result in loss of detail and accuracy in their digitally zoomed output when zooming in on distant image regions. We also show that synthesizing sensor data by resampling high-resolution RGB images is an oversimplified approximation of real sensor data and noise, resulting in worse image quality. The key barrier to using real sensor data for training is that ground truth high-resolution imagery is missing. We show how to obtain the ground-truth data with optically zoomed images and contribute a dataset, SR-RAW, for real-world computational zoom. We use SR-RAW to train a deep network with a novel contextual bilateral loss (CoBi) that delivers critical robustness to mild misalignment in input-output image pairs. The trained network achieves state-of-the-art performance in 4X and 8X computational zoom.
Adversarial Examples for Electrocardiograms
Authors: Xintian Han, Yuxuan Hu, Luca Foschini, Larry Chinitz, Lior Jankelson, Rajesh Ranganath
Date: 2019-05-13 00:00:00
Tasks: Adversarial Defense, Arrhythmia Detection, ECG Classification, Electrocardiography (ECG), Rhythm

In recent years, the electrocardiogram (ECG) has seen a large diffusion in both medical and commercial applications, fueled by the rise of single-lead versions. Single-lead ECG can be embedded in medical devices and wearable products such as the injectable Medtronic Linq monitor, the iRhythm Ziopatch wearable monitor, and the Apple Watch Series 4. Recently, deep neural networks have been used to automatically analyze ECG tracings, outperforming even physicians specialized in cardiac electrophysiology in detecting certain rhythm irregularities. However, deep learning classifiers have been shown to be brittle to adversarial examples, which are examples created to look incontrovertibly belonging to a certain class to a human eye but contain subtle features that fool the classifier into misclassifying them into the wrong class. Very recently, adversarial examples have also been created for medical-related tasks. Yet, traditional attack methods to create adversarial examples, such as projected gradient descent (PGD) do not extend directly to ECG signals, as they generate examples that introduce square wave artifacts that are not physiologically plausible. Here, we developed a method to construct smoothed adversarial examples for single-lead ECG. First, we implemented a neural network model achieving state-of-the-art performance on the data from the 2017 PhysioNet/Computing-in-Cardiology Challenge for arrhythmia detection from single lead ECG classification. For this model, we utilized a new technique to generate smoothed examples to produce signals that are 1) indistinguishable to cardiologists from the original examples and 2) incorrectly classified by the neural network. Finally, we show that adversarial examples are not unique and provide a general technique to collate and perturb known adversarial examples to create new ones.
Locally Weighted Regression Pseudo-Rehearsal for Online Learning of Vehicle Dynamics
Authors: Grady Williams, Brian Goldfain, James M. Rehg, Evangelos A. Theodorou
Date: 2019-05-13 00:00:00
Tasks: regression

We consider the problem of online adaptation of a neural network designed to represent vehicle dynamics. The neural network model is intended to be used by an MPC control law to autonomously control the vehicle. This problem is challenging because both the input and target distributions are non-stationary, and naive approaches to online adaptation result in catastrophic forgetting, which can in turn lead to controller failures. We present a novel online learning method, which combines the pseudo-rehearsal method with locally weighted projection regression. We demonstrate the effectiveness of the resulting Locally Weighted Projection Regression Pseudo-Rehearsal (LW-PR$^2$) method in simulation and on a large real world dataset collected with a 1/5 scale autonomous vehicle.
VideoGraph: Recognizing Minutes-Long Human Activities in Videos
Authors: Noureldien Hussein, Efstratios Gavves, Arnold W. M. Smeulders
Date: 2019-05-13 00:00:00
Tasks: Long-video Activity Recognition, Video Classification

Many human activities take minutes to unfold. To represent them, related works opt for statistical pooling, which neglects the temporal structure. Others opt for convolutional methods, as CNN and Non-Local. While successful in learning temporal concepts, they are short of modeling minutes-long temporal dependencies. We propose VideoGraph, a method to achieve the best of two worlds: represent minutes-long human activities and learn their underlying temporal structure. VideoGraph learns a graph-based representation for human activities. The graph, its nodes and edges are learned entirely from video datasets, making VideoGraph applicable to problems without node-level annotation. The result is improvements over related works on benchmarks: Epic-Kitchen and Breakfast. Besides, we demonstrate that VideoGraph is able to learn the temporal structure of human activities in minutes-long videos.
Federated Multi-task Hierarchical Attention Model for Sensor Analytics
Authors: Yujing Chen, Yue Ning, Zheng Chai, Huzefa Rangwala
Date: 2019-05-13 00:00:00
Tasks: Activity Recognition, General Classification, regression

Sensors are an integral part of modern Internet of Things (IoT) applications. There is a critical need for the analysis of heterogeneous multivariate temporal data obtained from the individual sensors of these systems. In this paper we particularly focus on the problem of the scarce amount of training data available per sensor. We propose a novel federated multi-task hierarchical attention model (FATHOM) that jointly trains classification/regression models from multiple sensors. The attention mechanism of the proposed model seeks to extract feature representations from the input and learn a shared representation focused on time dimensions across multiple sensors. The underlying temporal and non-linear relationships are modeled using a combination of attention mechanism and long-short term memory (LSTM) networks. We find that our proposed method outperforms a wide range of competitive baselines in both classification and regression settings on activity recognition and environment monitoring datasets. We further provide visualization of feature representations learned by our model at the input sensor level and central time level.
Let's Push Things Forward: A Survey on Robot Pushing
Authors: Jochen Stüber, Claudio Zito, Rustam Stolkin
Date: 2019-05-13 00:00:00
Tasks: Survey

As robot make their way out of factories into human environments, outer space, and beyond, they require the skill to manipulate their environment in multifarious, unforeseeable circumstances. With this regard, pushing is an essential motion primitive that dramatically extends a robot's manipulation repertoire. In this work, we review the robotic pushing literature. While focusing on work concerned with predicting the motion of pushed objects, we also cover relevant applications of pushing for planning and control. Beginning with analytical approaches, under which we also subsume physics engines, we then proceed to discuss work on learning models from data. In doing so, we dedicate a separate section to deep learning approaches which have seen a recent upsurge in the literature. Concluding remarks and further research perspectives are given at the end of the paper.
Analyzing Adversarial Attacks Against Deep Learning for Intrusion Detection in IoT Networks
Authors: Olakunle Ibitoye, Omair Shafiq, Ashraf Matrawy
Date: 2019-05-13 00:00:00
Tasks: Adversarial Robustness, Intrusion Detection

Adversarial attacks have been widely studied in the field of computer vision but their impact on network security applications remains an area of open research. As IoT, 5G and AI continue to converge to realize the promise of the fourth industrial revolution (Industry 4.0), security incidents and events on IoT networks have increased. Deep learning techniques are being applied to detect and mitigate many of such security threats against IoT networks. Feedforward Neural Networks (FNN) have been widely used for classifying intrusion attacks in IoT networks. In this paper, we consider a variant of the FNN known as the Self-normalizing Neural Network (SNN) and compare its performance with the FNN for classifying intrusion attacks in an IoT network. Our analysis is performed using the BoT-IoT dataset from the Cyber Range Lab of the center of UNSW Canberra Cyber. In our experimental results, the FNN outperforms the SNN for intrusion detection in IoT networks based on multiple performance metrics such as accuracy, precision, and recall as well as multi-classification metrics such as Cohen's Kappa score. However, when tested for adversarial robustness, the SNN demonstrates better resilience against the adversarial samples from the IoT dataset, presenting a promising future in the quest for safer and more secure deep learning in IoT networks.