pwc-archive/papers-with-abstracts huggingface dataset:
AutoEmbedder: A semi-supervised DNN embedding system for clustering
Authors: Abu Quwsar Ohi, M. F. Mridha, Farisa Benta Safir, Md. Abdul Hamid, Muhammad Mostafa Monowar
Date: 2020-07-11 00:00:00
Proceeding: Knowledge-Based Systems 2020 7
Tasks: Clustering, Deep Clustering

Clustering is widely used in unsupervised learning method that deals with unlabeled data. Deep clustering has become a popular study area that relates clustering with Deep Neural Network (DNN) architecture. Deep clustering method downsamples high dimensional data, which may also relate clustering loss. Deep clustering is also introduced in semi-supervised learning (SSL). Most SSL methods depend on pairwise constraint information, which is a matrix containing knowledge if data pairs can be in the same cluster or not. This paper introduces a novel embedding system named AutoEmbedder, that downsamples higher dimensional data to clusterable embedding points. To the best of our knowledge, this is the first research endeavor that relates to traditional classifier DNN architecture with a pairwise loss reduction technique. The training process is semi-supervised and uses Siamese network architecture to compute pairwise constraint loss in the feature learning phase. The AutoEmbedder outperforms most of the existing DNN based semi-supervised methods tested on famous datasets.
Understanding Object Detection Through An Adversarial Lens
Authors: Ka-Ho Chow, Ling Liu, Mehmet Emre Gursoy, Stacey Truex, Wenqi Wei, Yanzhao Wu
Date: 2020-07-11 00:00:00
Tasks: Adversarial Robustness, Autonomous Vehicles, Object, object-detection, Object Detection, Real-Time Object Detection

Deep neural networks based object detection models have revolutionized computer vision and fueled the development of a wide range of visual recognition applications. However, recent studies have revealed that deep object detectors can be compromised under adversarial attacks, causing a victim detector to detect no object, fake objects, or mislabeled objects. With object detection being used pervasively in many security-critical applications, such as autonomous vehicles and smart cities, we argue that a holistic approach for an in-depth understanding of adversarial attacks and vulnerabilities of deep object detection systems is of utmost importance for the research community to develop robust defense mechanisms. This paper presents a framework for analyzing and evaluating vulnerabilities of the state-of-the-art object detectors under an adversarial lens, aiming to analyze and demystify the attack strategies, adverse effects, and costs, as well as the cross-model and cross-resolution transferability of attacks. Using a set of quantitative metrics, extensive experiments are performed on six representative deep object detectors from three popular families (YOLOv3, SSD, and Faster R-CNN) with two benchmark datasets (PASCAL VOC and MS COCO). We demonstrate that the proposed framework can serve as a methodical benchmark for analyzing adversarial behaviors and risks in real-time object detection systems. We conjecture that this framework can also serve as a tool to assess the security risks and the adversarial robustness of deep object detectors to be deployed in real-world applications.
Generalization bound of globally optimal non-convex neural network training: Transportation map estimation by infinite dimensional Langevin dynamics
Authors: Taiji Suzuki
Date: 2020-07-11 00:00:00
Proceeding: NeurIPS 2020 12

We introduce a new theoretical framework to analyze deep learning optimization with connection to its generalization error. Existing frameworks such as mean field theory and neural tangent kernel theory for neural network optimization analysis typically require taking limit of infinite width of the network to show its global convergence. This potentially makes it difficult to directly deal with finite width network; especially in the neural tangent kernel regime, we cannot reveal favorable properties of neural networks beyond kernel methods. To realize more natural analysis, we consider a completely different approach in which we formulate the parameter training as a transportation map estimation and show its global convergence via the theory of the infinite dimensional Langevin dynamics. This enables us to analyze narrow and wide networks in a unifying manner. Moreover, we give generalization gap and excess risk bounds for the solution obtained by the dynamics. The excess risk bound achieves the so-called fast learning rate. In particular, we show an exponential convergence for a classification problem and a minimax optimal rate for a regression problem.
ManiGen: A Manifold Aided Black-box Generator of Adversarial Examples
Authors: Guanxiong Liu, Issa Khalil, Abdallah Khreishah, Abdulelah Algosaibi, Adel Aldalbahi, Mohammed Alaneem, Abdulaziz Alhumam, Mohammed Anan
Date: 2020-07-11 00:00:00

Machine learning models, especially neural network (NN) classifiers, have acceptable performance and accuracy that leads to their wide adoption in different aspects of our daily lives. The underlying assumption is that these models are generated and used in attack free scenarios. However, it has been shown that neural network based classifiers are vulnerable to adversarial examples. Adversarial examples are inputs with special perturbations that are ignored by human eyes while can mislead NN classifiers. Most of the existing methods for generating such perturbations require a certain level of knowledge about the target classifier, which makes them not very practical. For example, some generators require knowledge of pre-softmax logits while others utilize prediction scores. In this paper, we design a practical black-box adversarial example generator, dubbed ManiGen. ManiGen does not require any knowledge of the inner state of the target classifier. It generates adversarial examples by searching along the manifold, which is a concise representation of input data. Through extensive set of experiments on different datasets, we show that (1) adversarial examples generated by ManiGen can mislead standalone classifiers by being as successful as the state-of-the-art white-box generator, Carlini, and (2) adversarial examples generated by ManiGen can more effectively attack classifiers with state-of-the-art defenses.
A Tutorial on Graph Theory for Brain Signal Analysis
Authors: Nikolaos Laskaris, Dimitrios A. Adamos, Anastasios Bezerianos
Date: 2020-07-11 00:00:00
Tasks: ERP

This tutorial paper refers to the use of graph-theoretic concepts for analyzing brain signals. For didactic purposes it splits into two parts: theory and application. In the first part, we commence by introducing some basic elements from graph theory and stemming algorithmic tools, which can be employed for data-analytic purposes. Next, we describe how these concepts are adapted for handling evolving connectivity and gaining insights into network reorganization. Finally, the notion of signals residing on a given graph is introduced and elements from the emerging field of graph signal processing (GSP) are provided. The second part serves as a pragmatic demonstration of the tools and techniques described earlier. It is based on analyzing a multi-trial dataset containing single-trial responses from a visual ERP paradigm. The paper ends with a brief outline of the most recent trends in graph theory that are about to shape brain signal processing in the near future and a more general discussion on the relevance of graph-theoretic methodologies for analyzing continuous-mode neural recordings.
Planning on the fast lane: Learning to interact using attention mechanisms in path integral inverse reinforcement learning
Authors: Sascha Rosbach, Xing Li, Simon Großjohann, Silviu Homoceanu, Stefan Roth
Date: 2020-07-11 00:00:00
Tasks: Trajectory Planning

General-purpose trajectory planning algorithms for automated driving utilize complex reward functions to perform a combined optimization of strategic, behavioral, and kinematic features. The specification and tuning of a single reward function is a tedious task and does not generalize over a large set of traffic situations. Deep learning approaches based on path integral inverse reinforcement learning have been successfully applied to predict local situation-dependent reward functions using features of a set of sampled driving policies. Sample-based trajectory planning algorithms are able to approximate a spatio-temporal subspace of feasible driving policies that can be used to encode the context of a situation. However, the interaction with dynamic objects requires an extended planning horizon, which depends on sequential context modeling. In this work, we are concerned with the sequential reward prediction over an extended time horizon. We present a neural network architecture that uses a policy attention mechanism to generate a low-dimensional context vector by concentrating on trajectories with a human-like driving style. Apart from this, we propose a temporal attention mechanism to identify context switches and allow for stable adaptation of rewards. We evaluate our results on complex simulated driving situations, including other moving vehicles. Our evaluation shows that our policy attention mechanism learns to focus on collision-free policies in the configuration space. Furthermore, the temporal attention mechanism learns persistent interaction with other vehicles over an extended planning horizon.
Decoupling Inherent Risk and Early Cancer Signs in Image-based Breast Cancer Risk Models
Authors: Yue Liu, Hossein Azizpour, Fredrik Strand, Kevin Smith
Date: 2020-07-11 00:00:00
Tasks: Decision Making

The ability to accurately estimate risk of developing breast cancer would be invaluable for clinical decision-making. One promising new approach is to integrate image-based risk models based on deep neural networks. However, one must take care when using such models, as selection of training data influences the patterns the network will learn to identify. With this in mind, we trained networks using three different criteria to select the positive training data (i.e. images from patients that will develop cancer): an inherent risk model trained on images with no visible signs of cancer, a cancer signs model trained on images containing cancer or early signs of cancer, and a conflated model trained on all images from patients with a cancer diagnosis. We find that these three models learn distinctive features that focus on different patterns, which translates to contrasts in performance. Short-term risk is best estimated by the cancer signs model, whilst long-term risk is best estimated by the inherent risk model. Carelessly training with all images conflates inherent risk with early cancer signs, and yields sub-optimal estimates in both regimes. As a consequence, conflated models may lead physicians to recommend preventative action when early cancer signs are already visible.
Generalization of Deep Convolutional Neural Networks -- A Case-study on Open-source Chest Radiographs
Authors: Nazanin Mashhaditafreshi, Amara Tariq, Judy Wawira Gichoya, Imon Banerjee
Date: 2020-07-11 00:00:00
Tasks: Medical Image Analysis

Deep Convolutional Neural Networks (DCNNs) have attracted extensive attention and been applied in many areas, including medical image analysis and clinical diagnosis. One major challenge is to conceive a DCNN model with remarkable performance on both internal and external data. We demonstrate that DCNNs may not generalize to new data, but increasing the quality and heterogeneity of the training data helps to improve the generalizibility factor. We use InceptionResNetV2 and DenseNet121 architectures to predict the risk of 5 common chest pathologies. The experiments were conducted on three publicly available databases: CheXpert, ChestX-ray14, and MIMIC Chest Xray JPG. The results show the internal performance of each of the 5 pathologies outperformed external performance on both of the models. Moreover, our strategy of exposing the models to a mix of different datasets during the training phase helps to improve model performance on the external dataset.
Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks
Authors: Wei Fang, Zhaofei Yu, Yanqi Chen, Timothee Masquelier, Tiejun Huang, Yonghong Tian
Date: 2020-07-11 00:00:00
Proceeding: ICCV 2021 10
Tasks: image-classification, Image Classification

Spiking Neural Networks (SNNs) have attracted enormous research interest due to temporal information processing capability, low power consumption, and high biological plausibility. However, the formulation of efficient and high-performance learning algorithms for SNNs is still challenging. Most existing learning methods learn weights only, and require manual tuning of the membrane-related parameters that determine the dynamics of a single spiking neuron. These parameters are typically chosen to be the same for all neurons, which limits the diversity of neurons and thus the expressiveness of the resulting SNNs. In this paper, we take inspiration from the observation that membrane-related parameters are different across brain regions, and propose a training algorithm that is capable of learning not only the synaptic weights but also the membrane time constants of SNNs. We show that incorporating learnable membrane time constants can make the network less sensitive to initial values and can speed up learning. In addition, we reevaluate the pooling methods in SNNs and find that max-pooling will not lead to significant information loss and have the advantage of low computation cost and binary compatibility. We evaluate the proposed method for image classification tasks on both traditional static MNIST, Fashion-MNIST, CIFAR-10 datasets, and neuromorphic N-MNIST, CIFAR10-DVS, DVS128 Gesture datasets. The experiment results show that the proposed method outperforms the state-of-the-art accuracy on nearly all datasets, using fewer time-steps. Our codes are available at https://github.com/fangwei123456/Parametric-Leaky-Integrate-and-Fire-Spiking-Neuron.
Simulating multi-exit evacuation using deep reinforcement learning
Authors: Dong Xu, Xiao Huang, Joseph Mango, Xiang Li, Zhenlong Li
Date: 2020-07-11 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Conventional simulations on multi-exit indoor evacuation focus primarily on how to determine a reasonable exit based on numerous factors in a changing environment. Results commonly include some congested and other under-utilized exits, especially with massive pedestrians. We propose a multi-exit evacuation simulation based on Deep Reinforcement Learning (DRL), referred to as the MultiExit-DRL, which involves in a Deep Neural Network (DNN) framework to facilitate state-to-action mapping. The DNN framework applies Rainbow Deep Q-Network (DQN), a DRL algorithm that integrates several advanced DQN methods, to improve data utilization and algorithm stability, and further divides the action space into eight isometric directions for possible pedestrian choices. We compare MultiExit-DRL with two conventional multi-exit evacuation simulation models in three separate scenarios: 1) varying pedestrian distribution ratios, 2) varying exit width ratios, and 3) varying open schedules for an exit. The results show that MultiExit-DRL presents great learning efficiency while reducing the total number of evacuation frames in all designed experiments. In addition, the integration of DRL allows pedestrians to explore other potential exits and helps determine optimal directions, leading to the high efficiency of exit utilization.
A Hybrid Multi-Objective Carpool Route Optimization Technique using Genetic Algorithm and A* Algorithm
Authors: Romit S Beed, Sunita Sarkar, Arindam Roy, Suvranil D Biswas, Suhana Biswas
Date: 2020-07-11 00:00:00

Carpooling has gained considerable importance in developed as well as in developing countries as an effective solution for controlling vehicular pollution, both sound and air. As carpooling decreases the number of vehicles used by commuters, it results in multiple benefits like mitigation of traffic and congestion on the roads, reduced demand for parking facilities, lesser energy or fuel consumption and most importantly, reduction in carbon emission, thus improving the quality of life in cities. This work presents a hybrid GA-A* algorithm to obtain optimal routes for the carpooling problem in the domain of multi-objective optimization having multiple conflicting objectives. Though Genetic algorithm provides optimal solutions, A* algorithm because of its efficiency in providing the shortest route between any two points based on heuristics, enhances the optimal routes obtained using Genetic algorithm. The refined routes, obtained using the GA-A* algorithm, are further subjected to dominance test to obtain non-dominating solutions based on Pareto-Optimality. The routes obtained maximize the profit of the service provider by minimizing the travel and detour distance as well as pick-up/drop costs while maximizing the utilization of the car. The proposed algorithm has been implemented over the Salt Lake area of Kolkata. Route distance and detour distance for the optimal routes obtained using the proposed algorithm are consistently lesser for the same number of passengers when compared with the corresponding data obtained using the existing algorithm. Various statistical analyses like boxplots have also confirmed that the proposed algorithm regularly performed better than the existing algorithm using only Genetic Algorithm.
Exploit the potential of Multi-column architecture for Crowd Counting
Authors: Junhao Cheng, Zhuojun Chen, Xin-Yu Zhang, Yizhou Li, Xiaoyuan Jing
Date: 2020-07-11 00:00:00
Tasks: Crowd Counting

Crowd counting is an important yet challenging task in computer vision due to serious occlusions, complex background and large scale variations, etc. Multi-column architecture is widely adopted to overcome these challenges, yielding state-of-the-art performance in many public benchmarks. However, there still are two issues in such design: scale limitation and feature similarity. Further performance improvements are thus restricted. In this paper, we propose a novel crowd counting framework called Pyramid Scale Network (PSNet) to explicitly address these issues. Specifically, for scale limitation, we adopt three Pyramid Scale Modules (PSM) to efficiently capture multi-scale features, which integrate a message passing mechanism and an attention mechanism into multi-column architecture. Moreover, for feature similarity, a novel loss function named Multi-column variance loss is introduced to make the features learned by each column in PSM appropriately different from each other. To the best of our knowledge, PSNet is the first work to explicitly address scale limitation and feature similarity in multi-column design. Extensive experiments on five benchmark datasets demonstrate the effectiveness of the proposed innovations as well as the superior performance over the state-of-the-art. Our code is publicly available at: https://github.com/oahunc/Pyramid_Scale_Network
I3rab: A New Arabic Dependency Treebank Based on Arabic Grammatical Theory
Authors: Dana Halabi, Ebaa Fayyoumi, Arafat Awajan
Date: 2020-07-11 00:00:00
Tasks: POS, Sentence

Treebanks are valuable linguistic resources that include the syntactic structure of a language sentence in addition to POS-tags and morphological features. They are mainly utilized in modeling statistical parsers. Although the statistical natural language parser has recently become more accurate for languages such as English, those for the Arabic language still have low accuracy. The purpose of this paper is to construct a new Arabic dependency treebank based on the traditional Arabic grammatical theory and the characteristics of the Arabic language, to investigate their effects on the accuracy of statistical parsers. The proposed Arabic dependency treebank, called I3rab, contrasts with existing Arabic dependency treebanks in two main concepts. The first concept is the approach of determining the main word of the sentence, and the second concept is the representation of the joined and covert pronouns. To evaluate I3rab, we compared its performance against a subset of Prague Arabic Dependency Treebank that shares a comparable level of details. The conducted experiments show that the percentage improvement reached up to 7.5% in UAS and 18.8% in LAS.
Feature Interactions in XGBoost
Authors: Kshitij Goyal, Sebastijan Dumancic, Hendrik Blockeel
Date: 2020-07-11 00:00:00

In this paper, we investigate how feature interactions can be identified to be used as constraints in the gradient boosting tree models using XGBoost's implementation. Our results show that accurate identification of these constraints can help improve the performance of baseline XGBoost model significantly. Further, the improvement in the model structure can also lead to better interpretability.
Generative Compositional Augmentations for Scene Graph Prediction
Authors: Boris Knyazev, Harm de Vries, Cătălina Cangea, Graham W. Taylor, Aaron Courville, Eugene Belilovsky
Date: 2020-07-11 00:00:00
Proceeding: ICCV 2021 10
Tasks: Graph Generation, Language Modelling, Prediction, Scene Graph Generation

Inferring objects and their relationships from an image in the form of a scene graph is useful in many applications at the intersection of vision and language. We consider a challenging problem of compositional generalization that emerges in this task due to a long tail data distribution. Current scene graph generation models are trained on a tiny fraction of the distribution corresponding to the most frequent compositions, e.g. <cup, on, table>. However, test images might contain zero- and few-shot compositions of objects and relationships, e.g. <cup, on, surfboard>. Despite each of the object categories and the predicate (e.g. 'on') being frequent in the training data, the models often fail to properly understand such unseen or rare compositions. To improve generalization, it is natural to attempt increasing the diversity of the training distribution. However, in the graph domain this is non-trivial. To that end, we propose a method to synthesize rare yet plausible scene graphs by perturbing real ones. We then propose and empirically study a model based on conditional generative adversarial networks (GANs) that allows us to generate visual features of perturbed scene graphs and learn from them in a joint fashion. When evaluated on the Visual Genome dataset, our approach yields marginal, but consistent improvements in zero- and few-shot metrics. We analyze the limitations of our approach indicating promising directions for future research.
Driver Behavior Modelling at the Urban Intersection via Canonical Correlation Analysis
Authors: Zirui Li, Chao Lu, Cheng Gong, Jinghang Li, Lianzhen Wei
Date: 2020-07-11 00:00:00
Tasks: feature selection, regression

The urban intersection is a typically dynamic and complex scenario for intelligent vehicles, which exists a variety of driving behaviors and traffic participants. Accurately modelling the driver behavior at the intersection is essential for intelligent transportation systems (ITS). Previous researches mainly focus on using attention mechanism to model the degree of correlation. In this research, a canonical correlation analysis (CCA)-based framework is proposed. The value of canonical correlation is used for feature selection. Gaussian mixture model and Gaussian process regression are applied for driver behavior modelling. Two experiments using simulated and naturalistic driving data are designed for verification. Experimental results are consistent with the driver's judgment. Comparative studies show that the proposed framework can obtain a better performance.