pwc-archive/papers-with-abstracts huggingface dataset:
An Argumentation-Based Reasoner to Assist Digital Investigation and Attribution of Cyber-Attacks
Authors: Erisa Karafili, Linna Wang, Emil C. Lupu
Date: 2019-04-30 00:00:00

We expect an increase in the frequency and severity of cyber-attacks that comes along with the need for efficient security countermeasures. The process of attributing a cyber-attack helps to construct efficient and targeted mitigating and preventive security measures. In this work, we propose an argumentation-based reasoner (ABR) as a proof-of-concept tool that can help a forensics analyst during the analysis of forensic evidence and the attribution process. Given the evidence collected from a cyber-attack, our reasoner can assist the analyst during the investigation process, by helping him/her to analyze the evidence and identify who performed the attack. Furthermore, it suggests to the analyst where to focus further analyses by giving hints of the missing evidence or new investigation paths to follow. ABR is the first automatic reasoner that can combine both technical and social evidence in the analysis of a cyber-attack, and that can also cope with incomplete and conflicting information. To illustrate how ABR can assist in the analysis and attribution of cyber-attacks we have used examples of cyber-attacks and their analyses as reported in publicly available reports and online literature. We do not mean to either agree or disagree with the analyses presented therein or reach attribution conclusions.
Learning Restricted Regular Expressions with Interleaving
Authors: Chunmei Dong, Yeting Li, Haiming Chen
Date: 2019-04-30 00:00:00
Tasks: valid

The advantages for the presence of an XML schema for XML documents are
numerous. However, many XML documents in practice are not accompanied by a
schema or by a valid schema. Relax NG is a popular and powerful schema
language, which supports the unconstrained interleaving operator. Focusing on
the inference of Relax NG, we propose a new subclass of regular expressions
with interleaving and design a polynomial inference algorithm. Then we
conducted a series of experiments based on large-scale real data and on three
XML data corpora, and experimental results show that our subclass has a better
practicality than previous ones, and the regular expressions inferred by our
algorithm are more precise.
Facial Expressions Analysis Under Occlusions Based on Specificities of Facial Motion Propagation
Authors: Delphine Poux, Benjamin Allaert, Jose Mennesson, Nacim Ihaddadene, Ioan Marius Bilasco, Chaabane Djeraba
Date: 2019-04-30 00:00:00

Although much progress has been made in the facial expression analysis field,
facial occlusions are still challenging. The main innovation brought by this
contribution consists in exploiting the specificities of facial movement
propagation for recognizing expressions in presence of important occlusions.
The movement induced by an expression extends beyond the movement epicenter.
Thus, the movement occurring in an occluded region propagates towards
neighboring visible regions. In presence of occlusions, per expression, we
compute the importance of each unoccluded facial region and we construct
adapted facial frameworks that boost the performance of per expression binary
classifier. The output of each expression-dependant binary classifier is then
aggregated and fed into a fusion process that aims constructing, per occlusion,
a unique model that recognizes all the facial expressions considered. The
evaluations highlight the robustness of this approach in presence of
significant facial occlusions.
PR Product: A Substitute for Inner Product in Neural Networks
Authors: Zhennan Wang, Wenbin Zou, Chen Xu
Date: 2019-04-30 00:00:00
Proceeding: ICCV 2019 10
Tasks: General Classification, Image Captioning, image-classification, Image Classification

In this paper, we analyze the inner product of weight vector w and data vector x in neural networks from the perspective of vector orthogonal decomposition and prove that the direction gradient of w decreases with the angle between them close to 0 or {\pi}. We propose the Projection and Rejection Product (PR Product) to make the direction gradient of w independent of the angle and consistently larger than the one in standard inner product while keeping the forward propagation identical. As a reliable substitute for standard inner product, the PR Product can be applied into many existing deep learning modules, so we develop the PR Product version of fully connected layer, convolutional layer and LSTM layer. In static image classification, the experiments on CIFAR10 and CIFAR100 datasets demonstrate that the PR Product can robustly enhance the ability of various state-of-the-art classification networks. On the task of image captioning, even without any bells and whistles, our PR Product version of captioning model can compete or outperform the state-of-the-art models on MS COCO dataset. Code has been made available at:https://github.com/wzn0828/PR_Product.
Incorporating Symbolic Sequential Modeling for Speech Enhancement
Authors: Chien-Feng Liao, Yu Tsao, Xugang Lu, Hisashi Kawai
Date: 2019-04-30 00:00:00
Tasks: Language Modeling, Language Modelling, Speech Enhancement

In a noisy environment, a lossy speech signal can be automatically restored by a listener if he/she knows the language well. That is, with the built-in knowledge of a "language model", a listener may effectively suppress noise interference and retrieve the target speech signals. Accordingly, we argue that familiarity with the underlying linguistic content of spoken utterances benefits speech enhancement (SE) in noisy environments. In this study, in addition to the conventional modeling for learning the acoustic noisy-clean speech mapping, an abstract symbolic sequential modeling is incorporated into the SE framework. This symbolic sequential modeling can be regarded as a "linguistic constraint" in learning the acoustic noisy-clean speech mapping function. In this study, the symbolic sequences for acoustic signals are obtained as discrete representations with a Vector Quantized Variational Autoencoder algorithm. The obtained symbols are able to capture high-level phoneme-like content from speech signals. The experimental results demonstrate that the proposed framework can obtain notable performance improvement in terms of perceptual evaluation of speech quality (PESQ) and short-time objective intelligibility (STOI) on the TIMIT dataset.
A critical analysis of self-supervision, or what we can learn from a single image
Authors: Yuki M. Asano, Christian Rupprecht, Andrea Vedaldi
Date: 2019-04-30 00:00:00
Proceeding: ICLR 2020 1
Tasks: Data Augmentation, Representation Learning

We look critically at popular self-supervision techniques for learning deep convolutional neural networks without manual labels. We show that three different and representative methods, BiGAN, RotNet and DeepCluster, can learn the first few layers of a convolutional network from a single image as well as using millions of images and manual labels, provided that strong data augmentation is used. However, for deeper layers the gap with manual supervision cannot be closed even if millions of unlabelled images are used for training. We conclude that: (1) the weights of the early layers of deep networks contain limited information about the statistics of natural images, that (2) such low-level statistics can be learned through self-supervision just as well as through strong supervision, and that (3) the low-level statistics can be captured via synthetic transformations instead of using a large image dataset.
A scalable saliency-based Feature selection method with instance level information
Authors: Brais Cancela, Verónica Bolón-Canedo, Amparo Alonso-Betanzos, João Gama
Date: 2019-04-30 00:00:00
Tasks: feature selection

Classic feature selection techniques remove those features that are either
irrelevant or redundant, achieving a subset of relevant features that help to
provide a better knowledge extraction. This allows the creation of compact
models that are easier to interpret. Most of these techniques work over the
whole dataset, but they are unable to provide the user with successful
information when only instance information is needed. In short, given any
example, classic feature selection algorithms do not give any information about
which the most relevant information is, regarding this sample. This work aims
to overcome this handicap by developing a novel feature selection method,
called Saliency-based Feature Selection (SFS), based in deep-learning saliency
techniques. Our experimental results will prove that this algorithm can be
successfully used not only in Neural Networks, but also under any given
architecture trained by using Gradient Descent techniques.
Deep Spectral Clustering using Dual Autoencoder Network
Authors: Xu Yang, Cheng Deng, Feng Zheng, Junchi Yan, Wei Liu
Date: 2019-04-30 00:00:00
Proceeding: CVPR 2019 6
Tasks: Clustering, Deep Clustering, Mutual Information Estimation

The clustering methods have recently absorbed even-increasing attention in
learning and vision. Deep clustering combines embedding and clustering together
to obtain optimal embedding subspace for clustering, which can be more
effective compared with conventional clustering methods. In this paper, we
propose a joint learning framework for discriminative embedding and spectral
clustering. We first devise a dual autoencoder network, which enforces the
reconstruction constraint for the latent representations and their noisy
versions, to embed the inputs into a latent space for clustering. As such the
learned latent representations can be more robust to noise. Then the mutual
information estimation is utilized to provide more discriminative information
from the inputs. Furthermore, a deep spectral clustering method is applied to
embed the latent representations into the eigenspace and subsequently clusters
them, which can fully exploit the relationship between inputs to achieve
optimal clustering results. Experimental results on benchmark datasets show
that our method can significantly outperform state-of-the-art clustering
approaches.
Restricted Boltzmann Machine Assignment Algorithm: Application to solve many-to-one matching problems on weighted bipartite graph
Authors: Francesco Curia
Date: 2019-04-30 00:00:00

In this work an iterative algorithm based on unsupervised learning is presented, specifically on a Restricted Boltzmann Machine (RBM) to solve a perfect matching problem on a bipartite weighted graph. Iteratively is calculated the weights $w_{ij}$ and the bias parameters $\theta = ( a_i, b_j) $ that maximize the energy function and assignment element $i$ to element $j$. An application of real problem is presented to show the potentiality of this algorithm.
Graph Convolutional Networks with EigenPooling
Authors: Yao Ma, Suhang Wang, Charu C. Aggarwal, Jiliang Tang
Date: 2019-04-30 00:00:00
Tasks: Classification, General Classification, Graph Classification, Graph Neural Network, Graph Representation Learning, Link Prediction, Node Classification, Representation Learning

Graph neural networks, which generalize deep neural network models to graph structured data, have attracted increasing attention in recent years. They usually learn node representations by transforming, propagating and aggregating node features and have been proven to improve the performance of many graph related tasks such as node classification and link prediction. To apply graph neural networks for the graph classification task, approaches to generate the \textit{graph representation} from node representations are demanded. A common way is to globally combine the node representations. However, rich structural information is overlooked. Thus a hierarchical pooling procedure is desired to preserve the graph structure during the graph representation learning. There are some recent works on hierarchically learning graph representation analogous to the pooling step in conventional convolutional neural (CNN) networks. However, the local structural information is still largely neglected during the pooling process. In this paper, we introduce a pooling operator $\pooling$ based on graph Fourier transform, which can utilize the node features and local structures during the pooling process. We then design pooling layers based on the pooling operator, which are further combined with traditional GCN convolutional layers to form a graph neural network framework $\m$ for graph classification. Theoretical analysis is provided to understand $\pooling$ from both local and global perspectives. Experimental results of the graph classification task on $6$ commonly used benchmarks demonstrate the effectiveness of the proposed framework.
Facial Pose Estimation by Deep Learning from Label Distributions
Authors: Zhaoxiang Liu, Zezhou Chen, Jinqiang Bai, Shaohua Li, Shiguo Lian
Date: 2019-04-30 00:00:00
Tasks: Deep Learning, Gaze Estimation, Pose Estimation

Facial pose estimation has gained a lot of attentions in many practical applications, such as human-robot interaction, gaze estimation and driver monitoring. Meanwhile, end-to-end deep learning-based facial pose estimation is becoming more and more popular. However, facial pose estimation suffers from a key challenge: the lack of sufficient training data for many poses, especially for large poses. Inspired by the observation that the faces under close poses look similar, we reformulate the facial pose estimation as a label distribution learning problem, considering each face image as an example associated with a Gaussian label distribution rather than a single label, and construct a convolutional neural network which is trained with a multi-loss function on AFLW dataset and 300W-LP dataset to predict the facial poses directly from color image. Extensive experiments are conducted on several popular benchmarks, including AFLW2000, BIWI, AFLW and AFW, where our approach shows a significant advantage over other state-of-the-art methods.
Efficiently Checking Actual Causality with SAT Solving
Authors: Amjad Ibrahim, Simon Rehwald, Alexander Pretschner
Date: 2019-04-30 00:00:00

Recent formal approaches towards causality have made the concept ready for
incorporation into the technical world. However, causality reasoning is
computationally hard; and no general algorithmic approach exists that
efficiently infers the causes for effects. Thus, checking causality in the
context of complex, multi-agent, and distributed socio-technical systems is a
significant challenge. Therefore, we conceptualize an intelligent and novel
algorithmic approach towards checking causality in acyclic causal models with
binary variables, utilizing the optimization power in the solvers of the
Boolean Satisfiability Problem (SAT). We present two SAT encodings, and an
empirical evaluation of their efficiency and scalability. We show that
causality is computed efficiently in less than 5 seconds for models that
consist of more than 4000 variables.
Detecting Adversarial Examples through Nonlinear Dimensionality Reduction
Authors: Francesco Crecchi, Davide Bacciu, Battista Biggio
Date: 2019-04-30 00:00:00
Tasks: Density Estimation, Dimensionality Reduction, General Classification

Deep neural networks are vulnerable to adversarial examples, i.e.,
carefully-perturbed inputs aimed to mislead classification. This work proposes
a detection method based on combining non-linear dimensionality reduction and
density estimation techniques. Our empirical findings show that the proposed
approach is able to effectively detect adversarial examples crafted by
non-adaptive attackers, i.e., not specifically tuned to bypass the detection
method. Given our promising results, we plan to extend our analysis to adaptive
attackers in future work.
Early Action Prediction with Generative Adversarial Networks
Authors: Dong Wang, Yuan Yuan, Qi. Wang
Date: 2019-04-30 00:00:00
Tasks: Early Action Prediction, Generative Adversarial Network, Prediction

Action Prediction is aimed to determine what action is occurring in a video
as early as possible, which is crucial to many online applications, such as
predicting a traffic accident before it happens and detecting malicious actions
in the monitoring system. In this work, we address this problem by developing
an end-to-end architecture that improves the discriminability of features of
partially observed videos by assimilating them to features from complete
videos. For this purpose, the generative adversarial network is introduced for
tackling action prediction problem, which improves the recognition accuracy of
partially observed videos though narrowing the feature difference of partially
observed videos from complete ones. Specifically, its generator comprises of
two networks: a CNN for feature extraction and an LSTM for estimating residual
error between features of the partially observed videos and complete ones, and
then the features from CNN adds the residual error from LSTM, which is regarded
as the enhanced feature to fool a competing discriminator. Meanwhile, the
generator is trained with an additional perceptual objective, which forces the
enhanced features of partially observed videos are discriminative enough for
action prediction. Extensive experimental results on UCF101, BIT and
UT-Interaction datasets demonstrate that our approach outperforms the
state-of-the-art methods, especially for videos that less than 50% portion of
frames is observed.
Sequence to sequence deep learning models for solar irradiation forecasting
Authors: Bhaskar Pratim Mukhoty, Vikas Maurya, Sandeep Kumar Shukla
Date: 2019-04-30 00:00:00
Tasks: Decoder, Deep Learning, Prediction, Time Series, Time Series Analysis, Time Series Forecasting

The energy output a photo voltaic(PV) panel is a function of solar
irradiation and weather parameters like temperature and wind speed etc. A
general measure for solar irradiation called Global Horizontal Irradiance
(GHI), customarily reported in Watt/meter$^2$, is a generic indicator for this
intermittent energy resource. An accurate prediction of GHI is necessary for
reliable grid integration of the renewable as well as for power market trading.
While some machine learning techniques are well introduced along with the
traditional time-series forecasting techniques, deep-learning techniques
remains less explored for the task at hand. In this paper we give deep learning
models suitable for sequence to sequence prediction of GHI. The deep learning
models are reported for short-term forecasting $\{1-24\}$ hour along with the
state-of-the art techniques like Gradient Boosted Regression Trees(GBRT) and
Feed Forward Neural Networks(FFNN).
  We have checked that spatio-temporal features like wind direction, wind speed
and GHI of neighboring location improves the prediction accuracy of the deep
learning models significantly. Among the various sequence-to-sequence
encoder-decoder models LSTM performed superior, handling short-comings of the
state-of-the-art techniques.
Memory-Augmented Temporal Dynamic Learning for Action Recognition
Authors: Yuan Yuan, Dong Wang, Qi. Wang
Date: 2019-04-30 00:00:00
Tasks: Action Recognition, Temporal Action Localization

Human actions captured in video sequences contain two crucial factors for
action recognition, i.e., visual appearance and motion dynamics. To model these
two aspects, Convolutional and Recurrent Neural Networks (CNNs and RNNs) are
adopted in most existing successful methods for recognizing actions. However,
CNN based methods are limited in modeling long-term motion dynamics. RNNs are
able to learn temporal motion dynamics but lack effective ways to tackle
unsteady dynamics in long-duration motion. In this work, we propose a
memory-augmented temporal dynamic learning network, which learns to write the
most evident information into an external memory module and ignore irrelevant
ones. In particular, we present a differential memory controller to make a
discrete decision on whether the external memory module should be updated with
current feature. The discrete memory controller takes in the memory history,
context embedding and current feature as inputs and controls information flow
into the external memory module. Additionally, we train this discrete memory
controller using straight-through estimator. We evaluate this end-to-end system
on benchmark datasets (UCF101 and HMDB51) of human action recognition. The
experimental results show consistent improvements on both datasets over prior
works and our baselines.
Anomaly Detection in Traffic Scenes via Spatial-aware Motion Reconstruction
Authors: Yuan Yuan, Dong Wang, Qi. Wang
Date: 2019-04-30 00:00:00
Tasks: Anomaly Detection, Autonomous Vehicles

Anomaly detection from a driver's perspective when driving is important to
autonomous vehicles. As a part of Advanced Driver Assistance Systems (ADAS), it
can remind the driver about dangers timely. Compared with traditional studied
scenes such as the university campus and market surveillance videos, it is
difficult to detect abnormal event from a driver's perspective due to camera
waggle, abidingly moving background, drastic change of vehicle velocity, etc.
To tackle these specific problems, this paper proposes a spatial localization
constrained sparse coding approach for anomaly detection in traffic scenes,
which firstly measures the abnormality of motion orientation and magnitude
respectively and then fuses these two aspects to obtain a robust detection
result. The main contributions are threefold: 1) This work describes the motion
orientation and magnitude of the object respectively in a new way, which is
demonstrated to be better than the traditional motion descriptors. 2) The
spatial localization of object is taken into account of the sparse
reconstruction framework, which utilizes the scene's structural information and
outperforms the conventional sparse coding methods. 3) Results of motion
orientation and magnitude are adaptively weighted and fused by a Bayesian
model, which makes the proposed method more robust and handle more kinds of
abnormal events. The efficiency and effectiveness of the proposed method are
validated by testing on nine difficult video sequences captured by ourselves.
Observed from the experimental results, the proposed method is more effective
and efficient than the popular competitors, and yields a higher performance.
Interpretation of Feature Space using Multi-Channel Attentional Sub-Networks
Authors: Masanari Kimura, Masayuki Tanaka
Date: 2019-04-30 00:00:00
Tasks: Attribute

Convolutional Neural Networks have achieved impressive results in various
tasks, but interpreting the internal mechanism is a challenging problem. To
tackle this problem, we exploit a multi-channel attention mechanism in feature
space. Our network architecture allows us to obtain an attention mask for each
feature while existing CNN visualization methods provide only a common
attention mask for all features. We apply the proposed multi-channel attention
mechanism to multi-attribute recognition task. We can obtain different
attention mask for each feature and for each attribute. Those analyses give us
deeper insight into the feature space of CNNs. The experimental results for the
benchmark dataset show that the proposed method gives high interpretability to
humans while accurately grasping the attributes of the data.