January 26, 2020

3496 words 17 mins read

Paper Group ANR 1380

Based on Graph-VAE Model to Predict Student’s Score. SIGMA : Strengthening IDS with GAN and Metaheuristics Attacks. Adversarial Seeded Sequence Growing for Weakly-Supervised Temporal Action Localization. Introducing Hann windows for reducing edge-effects in patch-based image segmentation. GraphDefense: Towards Robust Graph Convolutional Networks. M …

Based on Graph-VAE Model to Predict Student’s Score


Title	Based on Graph-VAE Model to Predict Student’s Score
Authors	Yang Zhang, Mingming Lu
Abstract	The OECD pointed out that the best way to keep students up to school is to intervene as early as possible [1]. Using education big data and deep learning to predict student’s score provides new resources and perspectives for early intervention. Previous forecasting schemes often requires manual filter of features , a large amount of prior knowledge and expert knowledge. Deep learning can automatically extract features without manual intervention to achieve better predictive performance. In this paper, the graph neural network matrix filling model (Graph-VAE) based on deep learning can automatically extract features without a large amount of prior knowledge. The experiment proves that our model is better than the traditional solution in the student’s score dataset, and it better describes the correlation and difference between the students and the curriculum, and dimensionality reducing the vector of coding result is visualized, the clustering effect is consistent with the real data distribution clustering. In addition, we use gradient-based attribution methods to analyze the key factors that influence performance prediction.
Tasks
Published	2019-03-08
URL	http://arxiv.org/abs/1903.03609v1
PDF	http://arxiv.org/pdf/1903.03609v1.pdf
PWC	https://paperswithcode.com/paper/based-on-graph-vae-model-to-predict-students
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SIGMA : Strengthening IDS with GAN and Metaheuristics Attacks


Title	SIGMA : Strengthening IDS with GAN and Metaheuristics Attacks
Authors	Simon Msika, Alejandro Quintero, Foutse Khomh
Abstract	An Intrusion Detection System (IDS) is a key cybersecurity tool for network administrators as it identifies malicious traffic and cyberattacks. With the recent successes of machine learning techniques such as deep learning, more and more IDS are now using machine learning algorithms to detect attacks faster. However, these systems lack robustness when facing previously unseen types of attacks. With the increasing number of new attacks, especially against Internet of Things devices, having a robust IDS able to spot unusual and new attacks becomes necessary. This work explores the possibility of leveraging generative adversarial models to improve the robustness of machine learning based IDS. More specifically, we propose a new method named SIGMA, that leverages adversarial examples to strengthen IDS against new types of attacks. Using Generative Adversarial Networks (GAN) and metaheuristics, SIGMA %Our method consists in generates adversarial examples, iteratively, and uses it to retrain a machine learning-based IDS, until a convergence of the detection rate (i.e. until the detection system is not improving anymore). A round of improvement consists of a generative phase, in which we use GANs and metaheuristics to generate instances ; an evaluation phase in which we calculate the detection rate of those newly generated attacks ; and a training phase, in which we train the IDS with those attacks. We have evaluated the SIGMA method for four standard machine learning classification algorithms acting as IDS, with a combination of GAN and a hybrid local-search and genetic algorithm, to generate new datasets of attacks. Our results show that SIGMA can successfully generate adversarial attacks against different machine learning based IDS. Also, using SIGMA, we can improve the performance of an IDS to up to 100% after as little as two rounds of improvement.
Tasks	Intrusion Detection
Published	2019-12-18
URL	https://arxiv.org/abs/1912.09303v1
PDF	https://arxiv.org/pdf/1912.09303v1.pdf
PWC	https://paperswithcode.com/paper/sigma-strengthening-ids-with-gan-and
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Adversarial Seeded Sequence Growing for Weakly-Supervised Temporal Action Localization


Title	Adversarial Seeded Sequence Growing for Weakly-Supervised Temporal Action Localization
Authors	Chengwei Zhang, Yunlu Xu, Zhanzhan Cheng, Yi Niu, Shiliang Pu, Fei Wu, Futai Zou
Abstract	Temporal action localization is an important yet challenging research topic due to its various applications. Since the frame-level or segment-level annotations of untrimmed videos require amounts of labor expenditure, studies on the weakly-supervised action detection have been springing up. However, most of existing frameworks rely on Class Activation Sequence (CAS) to localize actions by minimizing the video-level classification loss, which exploits the most discriminative parts of actions but ignores the minor regions. In this paper, we propose a novel weakly-supervised framework by adversarial learning of two modules for eliminating such demerits. Specifically, the first module is designed as a well-designed Seeded Sequence Growing (SSG) Network for progressively extending seed regions (namely the highly reliable regions initialized by a CAS-based framework) to their expected boundaries. The second module is a specific classifier for mining trivial or incomplete action regions, which is trained on the shared features after erasing the seeded regions activated by SSG. In this way, a whole network composed of these two modules can be trained in an adversarial manner. The goal of the adversary is to mine features that are difficult for the action classifier. That is, erasion from SSG will force the classifier to discover minor or even new action regions on the input feature sequence, and the classifier will drive the seeds to grow, alternately. At last, we could obtain the action locations and categories from the well-trained SSG and the classifier. Extensive experiments on two public benchmarks THUMOS’14 and ActivityNet1.3 demonstrate the impressive performance of our proposed method compared with the state-of-the-arts.
Tasks	Action Detection, Action Localization, Temporal Action Localization, Weakly-supervised Temporal Action Localization
Published	2019-08-07
URL	https://arxiv.org/abs/1908.02422v1
PDF	https://arxiv.org/pdf/1908.02422v1.pdf
PWC	https://paperswithcode.com/paper/adversarial-seeded-sequence-growing-for
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Introducing Hann windows for reducing edge-effects in patch-based image segmentation


Title	Introducing Hann windows for reducing edge-effects in patch-based image segmentation
Authors	Nicolas Pielawski, Carolina Wählby
Abstract	There is a limitation in the size of an image that can be processed using computationally demanding methods such as e.g. Convolutional Neural Networks (CNNs). Some imaging modalities - notably biological and medical - can result in images up to a few gigapixels in size, meaning that they have to be divided into smaller parts, or patches, for processing. However, when performing image segmentation, this may lead to undesirable artefacts, such as edge effects in the final re-combined image. We introduce windowing methods from signal processing to effectively reduce such edge effects. With the assumption that the central part of an image patch often holds richer contextual information than its sides and corners, we reconstruct the prediction by overlapping patches that are being weighted depending on 2-dimensional windows. We compare the results of four different windows: Hann, Bartlett-Hann, Triangular and a recently proposed window by Cui et al., and show that the cosine-based Hann window achieves the best improvement as measured by the Structural Similarity Index (SSIM). The proposed windowing method can be used together with any CNN model for segmentation without any modification and significantly improves network predictions.
Tasks	Semantic Segmentation
Published	2019-10-17
URL	https://arxiv.org/abs/1910.07831v1
PDF	https://arxiv.org/pdf/1910.07831v1.pdf
PWC	https://paperswithcode.com/paper/introducing-hann-windows-for-reducing-edge
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GraphDefense: Towards Robust Graph Convolutional Networks


Title	GraphDefense: Towards Robust Graph Convolutional Networks
Authors	Xiaoyun Wang, Xuanqing Liu, Cho-Jui Hsieh
Abstract	In this paper, we study the robustness of graph convolutional networks (GCNs). Despite the good performance of GCNs on graph semi-supervised learning tasks, previous works have shown that the original GCNs are very unstable to adversarial perturbations. In particular, we can observe a severe performance degradation by slightly changing the graph adjacency matrix or the features of a few nodes, making it unsuitable for security-critical applications. Inspired by the previous works on adversarial defense for deep neural networks, and especially adversarial training algorithm, we propose a method called GraphDefense to defend against the adversarial perturbations. In addition, for our defense method, we could still maintain semi-supervised learning settings, without a large label rate. We also show that adversarial training in features is equivalent to adversarial training for edges with a small perturbation. Our experiments show that the proposed defense methods successfully increase the robustness of Graph Convolutional Networks. Furthermore, we show that with careful design, our proposed algorithm can scale to large graphs, such as Reddit dataset.
Tasks	Adversarial Defense
Published	2019-11-11
URL	https://arxiv.org/abs/1911.04429v1
PDF	https://arxiv.org/pdf/1911.04429v1.pdf
PWC	https://paperswithcode.com/paper/graphdefense-towards-robust-graph
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Mapping (Dis-)Information Flow about the MH17 Plane Crash


Title	Mapping (Dis-)Information Flow about the MH17 Plane Crash
Authors	Mareike Hartmann, Yevgeniy Golovchenko, Isabelle Augenstein
Abstract	Digital media enables not only fast sharing of information, but also disinformation. One prominent case of an event leading to circulation of disinformation on social media is the MH17 plane crash. Studies analysing the spread of information about this event on Twitter have focused on small, manually annotated datasets, or used proxys for data annotation. In this work, we examine to what extent text classifiers can be used to label data for subsequent content analysis, in particular we focus on predicting pro-Russian and pro-Ukrainian Twitter content related to the MH17 plane crash. Even though we find that a neural classifier improves over a hashtag based baseline, labeling pro-Russian and pro-Ukrainian content with high precision remains a challenging problem. We provide an error analysis underlining the difficulty of the task and identify factors that might help improve classification in future work. Finally, we show how the classifier can facilitate the annotation task for human annotators.
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Published	2019-10-03
URL	https://arxiv.org/abs/1910.01363v1
PDF	https://arxiv.org/pdf/1910.01363v1.pdf
PWC	https://paperswithcode.com/paper/mapping-dis-information-flow-about-the-mh17
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2D Linear Time-Variant Controller for Human’s Intention Detection for Reach-to-Grasp Trajectories in Novel Scenes


Title	2D Linear Time-Variant Controller for Human’s Intention Detection for Reach-to-Grasp Trajectories in Novel Scenes
Authors	Claudio Zito, Tomasz Deregowski, Rustam Stolkin
Abstract	Designing robotic assistance devices for manipulation tasks is challenging. This work is concerned with improving accuracy and usability of semi-autonomous robots, such as human operated manipulators or exoskeletons. The key insight is to develop a system that takes into account context- and user-awareness to take better decisions in how to assist the user. The context-awareness is implemented by enabling the system to automatically generate a set of candidate grasps and reach-to-grasp trajectories in novel, cluttered scenes. The user-awareness is implemented as a linear time-variant feedback controller to facilitate the motion towards the most promising grasp. Our approach is demonstrated in a simple 2D example in which participants are asked to grasp a specific object in a clutter scene. Our approach also reduce the number of controllable dimensions for the user by providing only control on x- and y-axis, while orientation of the end-effector and the pose of its fingers are inferred by the system. The experimental results show the benefits of our approach in terms of accuracy and execution time with respect to a pure manual control.
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Published	2019-06-19
URL	https://arxiv.org/abs/1906.08380v2
PDF	https://arxiv.org/pdf/1906.08380v2.pdf
PWC	https://paperswithcode.com/paper/2d-linear-time-variant-controller-for-humans
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Improving Generalization and Robustness with Noisy Collaboration in Knowledge Distillation


Title	Improving Generalization and Robustness with Noisy Collaboration in Knowledge Distillation
Authors	Elahe Arani, Fahad Sarfraz, Bahram Zonooz
Abstract	Inspired by trial-to-trial variability in the brain that can result from multiple noise sources, we introduce variability through noise at different levels in a knowledge distillation framework. We introduce “Fickle Teacher” which provides variable supervision signals to the student for the same input. We observe that the response variability from the teacher results in a significant generalization improvement in the student. We further propose “Soft-Randomization” as a novel technique for improving robustness to input variability in the student. This minimizes the dissimilarity between the student’s distribution on noisy data with teacher’s distribution on clean data. We show that soft-randomization, even with low noise intensity, improves the robustness significantly with minimal drop in generalization. Lastly, we propose a new technique, “Messy-collaboration”, which introduces target variability, whereby student and/or teacher are trained with randomly corrupted labels. We find that supervision from a corrupted teacher improves the adversarial robustness of student significantly while preserving its generalization and natural robustness. Our extensive empirical results verify the effectiveness of adding constructive noise in the knowledge distillation framework for improving the generalization and robustness of the model.
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Published	2019-10-11
URL	https://arxiv.org/abs/1910.05057v1
PDF	https://arxiv.org/pdf/1910.05057v1.pdf
PWC	https://paperswithcode.com/paper/improving-generalization-and-robustness-with
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Walking on the Edge: Fast, Low-Distortion Adversarial Examples


Title	Walking on the Edge: Fast, Low-Distortion Adversarial Examples
Authors	Hanwei Zhang, Yannis Avrithis, Teddy Furon, Laurent Amsaleg
Abstract	Adversarial examples of deep neural networks are receiving ever increasing attention because they help in understanding and reducing the sensitivity to their input. This is natural given the increasing applications of deep neural networks in our everyday lives. When white-box attacks are almost always successful, it is typically only the distortion of the perturbations that matters in their evaluation. In this work, we argue that speed is important as well, especially when considering that fast attacks are required by adversarial training. Given more time, iterative methods can always find better solutions. We investigate this speed-distortion trade-off in some depth and introduce a new attack called boundary projection (BP) that improves upon existing methods by a large margin. Our key idea is that the classification boundary is a manifold in the image space: we therefore quickly reach the boundary and then optimize distortion on this manifold.
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Published	2019-12-04
URL	https://arxiv.org/abs/1912.02153v2
PDF	https://arxiv.org/pdf/1912.02153v2.pdf
PWC	https://paperswithcode.com/paper/walking-on-the-edge-fast-low-distortion-1
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Title	Transferring Adaptive Theory of Mind to social robots: insights from developmental psychology to robotics
Authors	Francesca Bianco, Dimitri Ognibene
Abstract	Despite the recent advancement in the social robotic field, important limitations restrain its progress and delay the application of robots in everyday scenarios. In the present paper, we propose to develop computational models inspired by our knowledge of human infants’ social adaptive abilities. We believe this may provide solutions at an architectural level to overcome the limits of current systems. Specifically, we present the functional advantages that adaptive Theory of Mind (ToM) systems would support in robotics (i.e., mentalizing for belief understanding, proactivity and preparation, active perception and learning) and contextualize them in practical applications. We review current computational models mainly based on the simulation and teleological theories, and robotic implementations to identify the limitations of ToM functions in current robotic architectures and suggest a possible future developmental pathway. Finally, we propose future studies to create innovative computational models integrating the properties of the simulation and teleological approaches for an improved adaptive ToM ability in robots with the aim of enhancing human-robot interactions and permitting the application of robots in unexplored environments, such as disasters and construction sites. To achieve this goal, we suggest directing future research towards the modern cross-talk between the fields of robotics and developmental psychology.
Tasks
Published	2019-08-31
URL	https://arxiv.org/abs/1909.00197v1
PDF	https://arxiv.org/pdf/1909.00197v1.pdf
PWC	https://paperswithcode.com/paper/transferring-adaptive-theory-of-mind-to
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StyleNAS: An Empirical Study of Neural Architecture Search to Uncover Surprisingly Fast End-to-End Universal Style Transfer Networks


Title	StyleNAS: An Empirical Study of Neural Architecture Search to Uncover Surprisingly Fast End-to-End Universal Style Transfer Networks
Authors	Jie An, Haoyi Xiong, Jinwen Ma, Jiebo Luo, Jun Huan
Abstract	Neural Architecture Search (NAS) has been widely studied for designing discriminative deep learning models such as image classification, object detection, and semantic segmentation. As a large number of priors have been obtained through the manual design of architectures in the fields, NAS is usually considered as a supplement approach. In this paper, we have significantly expanded the application areas of NAS by performing an empirical study of NAS to search generative models, or specifically, auto-encoder based universal style transfer, which lacks systematic exploration, if any, from the architecture search aspect. In our work, we first designed a search space where common operators for image style transfer such as VGG-based encoders, whitening and coloring transforms (WCT), convolution kernels, instance normalization operators, and skip connections were searched in a combinatorial approach. With a simple yet effective parallel evolutionary NAS algorithm with multiple objectives, we derived the first group of end-to-end deep networks for universal photorealistic style transfer. Comparing to random search, a NAS method that is gaining popularity recently, we demonstrated that carefully designed search strategy leads to much better architecture design. Finally compared to existing universal style transfer networks for photorealistic rendering such as PhotoWCT that stacks multiple well-trained auto-encoders and WCT transforms in a non-end-to-end manner, the architectures designed by StyleNAS produce better style-transferred images with details preserving, using a tiny number of operators/parameters, and enjoying around 500x inference time speed-up.
Tasks	Image Classification, Neural Architecture Search, Object Detection, Semantic Segmentation, Style Transfer
Published	2019-06-06
URL	https://arxiv.org/abs/1906.02470v1
PDF	https://arxiv.org/pdf/1906.02470v1.pdf
PWC	https://paperswithcode.com/paper/stylenas-an-empirical-study-of-neural
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PREMA: Principled Tensor Data Recovery from Multiple Aggregated Views


Title	PREMA: Principled Tensor Data Recovery from Multiple Aggregated Views
Authors	Faisal M. Almutairi, Charilaos I. Kanatsoulis, Nicholas D. Sidiropoulos
Abstract	Multidimensional data have become ubiquitous and are frequently involved in situations where the information is aggregated over multiple data atoms. The aggregation can be over time or other features, such as geographical location or group affiliation. We often have access to multiple aggregated views of the same data, each aggregated in one or more dimensions, especially when data are collected or measured by different agencies. However, data mining and machine learning models require detailed data for personalized analysis and prediction. Thus, data disaggregation algorithms are becoming increasingly important in various domains. The goal of this paper is to reconstruct finer-scale data from multiple coarse views, aggregated over different (subsets of) dimensions. The proposed method, called PREMA, leverages low-rank tensor factorization tools to provide recovery guarantees under certain conditions. PREMA is flexible in the sense that it can perform disaggregation on data that have missing entries, i.e., partially observed. The proposed method considers challenging scenarios: i) the available views of the data are aggregated in two dimensions, i.e., double aggregation, and ii) the aggregation patterns are unknown. Experiments on real data from different domains, i.e., sales data from retail companies, crime counts, and weather observations, are presented to showcase the effectiveness of PREMA.
Tasks
Published	2019-10-26
URL	https://arxiv.org/abs/1910.12001v1
PDF	https://arxiv.org/pdf/1910.12001v1.pdf
PWC	https://paperswithcode.com/paper/prema-principled-tensor-data-recovery-from
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Straight-Through Estimator as Projected Wasserstein Gradient Flow


Title	Straight-Through Estimator as Projected Wasserstein Gradient Flow
Authors	Pengyu Cheng, Chang Liu, Chunyuan Li, Dinghan Shen, Ricardo Henao, Lawrence Carin
Abstract	The Straight-Through (ST) estimator is a widely used technique for back-propagating gradients through discrete random variables. However, this effective method lacks theoretical justification. In this paper, we show that ST can be interpreted as the simulation of the projected Wasserstein gradient flow (pWGF). Based on this understanding, a theoretical foundation is established to justify the convergence properties of ST. Further, another pWGF estimator variant is proposed, which exhibits superior performance on distributions with infinite support,e.g., Poisson distributions. Empirically, we show that ST and our proposed estimator, while applied to different types of discrete structures (including both Bernoulli and Poisson latent variables), exhibit comparable or even better performances relative to other state-of-the-art methods. Our results uncover the origin of the widespread adoption of the ST estimator and represent a helpful step towards exploring alternative gradient estimators for discrete variables.
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Published	2019-10-05
URL	https://arxiv.org/abs/1910.02176v1
PDF	https://arxiv.org/pdf/1910.02176v1.pdf
PWC	https://paperswithcode.com/paper/straight-through-estimator-as-projected
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End-to-End Cascaded U-Nets with a Localization Network for Kidney Tumor Segmentation


Title	End-to-End Cascaded U-Nets with a Localization Network for Kidney Tumor Segmentation
Authors	Minh H. Vu, Guus Grimbergen, Attila Simkó, Tufve Nyholm, Tommy Löfstedt
Abstract	Kidney tumor segmentation emerges as a new frontier of computer vision in medical imaging. This is partly due to its challenging manual annotation and great medical impact. Within the scope of the Kidney Tumor Segmentation Challenge 2019, that is aiming at combined kidney and tumor segmentation, this work proposes a novel combination of 3D U-Nets—collectively denoted TuNet—utilizing the resulting kidney masks for the consecutive tumor segmentation. The proposed method achieves a S{\o}rensen-Dice coefficient score of 0.902 for the kidney, and 0.408 for the tumor segmentation, computed from a five-fold cross-validation on the 210 patients available in the data.
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Published	2019-10-16
URL	https://arxiv.org/abs/1910.07521v1
PDF	https://arxiv.org/pdf/1910.07521v1.pdf
PWC	https://paperswithcode.com/paper/end-to-end-cascaded-u-nets-with-a
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Toeplitz Inverse Covariance based Robust Speaker Clustering for Naturalistic Audio Streams


Title	Toeplitz Inverse Covariance based Robust Speaker Clustering for Naturalistic Audio Streams
Authors	Harishchandra Dubey, Abhijeet Sangwan, John Hansen
Abstract	Speaker diarization determines who spoke and when? in an audio stream. In this study, we propose a model-based approach for robust speaker clustering using i-vectors. The ivectors extracted from different segments of same speaker are correlated. We model this correlation with a Markov Random Field (MRF) network. Leveraging the advancements in MRF modeling, we used Toeplitz Inverse Covariance (TIC) matrix to represent the MRF correlation network for each speaker. This approaches captures the sequential structure of i-vectors (or equivalent speaker turns) belonging to same speaker in an audio stream. A variant of standard Expectation Maximization (EM) algorithm is adopted for deriving closed-form solution using dynamic programming (DP) and the alternating direction method of multiplier (ADMM). Our diarization system has four steps: (1) ground-truth segmentation; (2) i-vector extraction; (3) post-processing (mean subtraction, principal component analysis, and length-normalization) ; and (4) proposed speaker clustering. We employ cosine K-means and movMF speaker clustering as baseline approaches. Our evaluation data is derived from: (i) CRSS-PLTL corpus, and (ii) two meetings subset of the AMI corpus. Relative reduction in diarization error rate (DER) for CRSS-PLTL corpus is 43.22% using the proposed advancements as compared to baseline. For AMI meetings IS1000a and IS1003b, relative DER reduction is 29.37% and 9.21%, respectively.
Tasks	Speaker Diarization
Published	2019-07-12
URL	https://arxiv.org/abs/1907.05584v1
PDF	https://arxiv.org/pdf/1907.05584v1.pdf
PWC	https://paperswithcode.com/paper/toeplitz-inverse-covariance-based-robust
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