pwc-archive/papers-with-abstracts huggingface dataset:
Evaluation of Machine Learning Algorithms for Intrusion Detection System
Authors: Mohammad Almseidin, Maen Alzubi, Szilveszter Kovacs, Mouhammd Al-kasassbeh
Date: 2018-01-08 00:00:00
Tasks: BIG-bench Machine Learning, Intrusion Detection

Intrusion detection system (IDS) is one of the implemented solutions against
harmful attacks. Furthermore, attackers always keep changing their tools and
techniques. However, implementing an accepted IDS system is also a challenging
task. In this paper, several experiments have been performed and evaluated to
assess various machine learning classifiers based on KDD intrusion dataset. It
succeeded to compute several performance metrics in order to evaluate the
selected classifiers. The focus was on false negative and false positive
performance metrics in order to enhance the detection rate of the intrusion
detection system. The implemented experiments demonstrated that the decision
table classifier achieved the lowest value of false negative while the random
forest classifier has achieved the highest average accuracy rate.
Deep Nearest Class Mean Model for Incremental Odor Classification
Authors: Yu Cheng, Angus Wong, Kevin Hung, Zhizhong Li, Weitong Li, Jun Zhang
Date: 2018-01-08 00:00:00
Tasks: Classification, General Classification

In recent years, more machine learning algorithms have been applied to odor
classification. These odor classification algorithms usually assume that the
training datasets are static. However, for some odor recognition tasks, new
odor classes continually emerge. That is, the odor datasets are dynamically
growing while both training samples and number of classes are increasing over
time. Motivated by this concern, this paper proposes a Deep Nearest Class Mean
(DNCM) model based on the deep learning framework and nearest class mean
method. The proposed model not only leverages deep neural network to extract
deep features, but is also able to dynamically integrate new classes over time.
In our experiments, the DNCM model was initially trained with 10 classes, then
25 new classes are integrated. Experiment results demonstrate that the proposed
model is very efficient for incremental odor classification, especially for new
classes with only a small number of training examples.
Sanskrit Sandhi Splitting using seq2(seq)^2
Authors: Rahul Aralikatte, Neelamadhav Gantayat, Naveen Panwar, Anush Sankaran, Senthil Mani
Date: 2018-01-01 00:00:00
Tasks: Chinese Word Segmentation, Decoder

In Sanskrit, small words (morphemes) are combined to form compound words through a process known as Sandhi. Sandhi splitting is the process of splitting a given compound word into its constituent morphemes. Although rules governing word splitting exists in the language, it is highly challenging to identify the location of the splits in a compound word. Though existing Sandhi splitting systems incorporate these pre-defined splitting rules, they have a low accuracy as the same compound word might be broken down in multiple ways to provide syntactically correct splits. In this research, we propose a novel deep learning architecture called Double Decoder RNN (DD-RNN), which (i) predicts the location of the split(s) with 95% accuracy, and (ii) predicts the constituent words (learning the Sandhi splitting rules) with 79.5% accuracy, outperforming the state-of-art by 20%. Additionally, we show the generalization capability of our deep learning model, by showing competitive results in the problem of Chinese word segmentation, as well.
Long-term Multi-granularity Deep Framework for Driver Drowsiness Detection
Authors: Jie Lyu, Zejian yuan, Dapeng Chen
Date: 2018-01-08 00:00:00

For real-world driver drowsiness detection from videos, the variation of head
pose is so large that the existing methods on global face is not capable of
extracting effective features, such as looking aside and lowering head.
Temporal dependencies with variable length are also rarely considered by the
previous approaches, e.g., yawning and speaking. In this paper, we propose a
Long-term Multi-granularity Deep Framework to detect driver drowsiness in
driving videos containing the frontal faces. The framework includes two key
components: (1) Multi-granularity Convolutional Neural Network (MCNN), a novel
network utilizes a group of parallel CNN extractors on well-aligned facial
patches of different granularities, and extracts facial representations
effectively for large variation of head pose, furthermore, it can flexibly fuse
both detailed appearance clues of the main parts and local to global spatial
constraints; (2) a deep Long Short Term Memory network is applied on facial
representations to explore long-term relationships with variable length over
sequential frames, which is capable to distinguish the states with temporal
dependencies, such as blinking and closing eyes. Our approach achieves 90.05%
accuracy and about 37 fps speed on the evaluation set of the public NTHU-DDD
dataset, which is the state-of-the-art method on driver drowsiness detection.
Moreover, we build a new dataset named FI-DDD, which is of higher precision of
drowsy locations in temporal dimension.
Revisiting Simple Neural Networks for Learning Representations of Knowledge Graphs
Authors: Srinivas Ravishankar, Chandrahas, Partha Pratim Talukdar
Date: 2017-11-15 00:00:00
Tasks: Knowledge Base Completion, Knowledge Graphs

We address the problem of learning vector representations for entities and
relations in Knowledge Graphs (KGs) for Knowledge Base Completion (KBC). This
problem has received significant attention in the past few years and multiple
methods have been proposed. Most of the existing methods in the literature use
a predefined characteristic scoring function for evaluating the correctness of
KG triples. These scoring functions distinguish correct triples (high score)
from incorrect ones (low score). However, their performance vary across
different datasets. In this work, we demonstrate that a simple neural network
based score function can consistently achieve near start-of-the-art performance
on multiple datasets. We also quantitatively demonstrate biases in standard
benchmark datasets, and highlight the need to perform evaluation spanning
various datasets.
Log-Scale Shrinkage Priors and Adaptive Bayesian Global-Local Shrinkage Estimation
Authors: Daniel F. Schmidt, Enes Makalic
Date: 2018-01-08 00:00:00

Global-local shrinkage hierarchies are an important innovation in Bayesian estimation. We propose the use of log-scale distributions as a novel basis for generating familes of prior distributions for local shrinkage hyperparameters. By varying the scale parameter one may vary the degree to which the prior distribution promotes sparsity in the coefficient estimates. By examining the class of distributions over the logarithm of the local shrinkage parameter that have log-linear, or sub-log-linear tails, we show that many standard prior distributions for local shrinkage parameters can be unified in terms of the tail behaviour and concentration properties of their corresponding marginal distributions over the coefficients $\beta_j$. We derive upper bounds on the rate of concentration around $|\beta_j|=0$, and the tail decay as $|\beta_j| \to \infty$, achievable by this wide class of prior distributions. We then propose a new type of ultra-heavy tailed prior, called the log-$t$ prior with the property that, irrespective of the choice of associated scale parameter, the marginal distribution always diverges at $\beta_j = 0$, and always possesses super-Cauchy tails. We develop results demonstrating when prior distributions with (sub)-log-linear tails attain Kullback--Leibler super-efficiency and prove that the log-$t$ prior distribution is always super-efficient. We show that the log-$t$ prior is less sensitive to misspecification of the global shrinkage parameter than the horseshoe or lasso priors. By incorporating the scale parameter of the log-scale prior distributions into the Bayesian hierarchy we derive novel adaptive shrinkage procedures. Simulations show that the adaptive log-$t$ procedure appears to always perform well, irrespective of the level of sparsity or signal-to-noise ratio of the underlying model.
HeNet: A Deep Learning Approach on Intel$^\circledR$ Processor Trace for Effective Exploit Detection
Authors: Li Chen, Salmin Sultana, Ravi Sahita
Date: 2018-01-08 00:00:00
Tasks: Deep Learning, Feature Engineering, General Classification, Malware Detection, Time Series Analysis, Transfer Learning

This paper presents HeNet, a hierarchical ensemble neural network, applied to
classify hardware-generated control flow traces for malware detection. Deep
learning-based malware detection has so far focused on analyzing executable
files and runtime API calls. Static code analysis approaches face challenges
due to obfuscated code and adversarial perturbations. Behavioral data collected
during execution is more difficult to obfuscate but recent research has shown
successful attacks against API call based malware classifiers. We investigate
control flow based characterization of a program execution to build robust deep
learning malware classifiers. HeNet consists of a low-level behavior model and
a top-level ensemble model. The low-level model is a per-application behavior
model, trained via transfer learning on a time-series of images generated from
control flow trace of an execution. We use Intel$^\circledR$ Processor Trace
enabled processor for low overhead execution tracing and design a lightweight
image conversion and segmentation of the control flow trace. The top-level
ensemble model aggregates the behavior classification of all the trace segments
and detects an attack. The use of hardware trace adds portability to our system
and the use of deep learning eliminates the manual effort of feature
engineering. We evaluate HeNet against real-world exploitations of PDF readers.
HeNet achieves 100\% accuracy and 0\% false positive on test set, and higher
classification accuracy compared to classical machine learning algorithms.
Log-concave sampling: Metropolis-Hastings algorithms are fast
Authors: Raaz Dwivedi, Yuansi Chen, Martin J. Wainwright, Bin Yu
Date: 2018-01-08 00:00:00

We consider the problem of sampling from a strongly log-concave density in $\mathbb{R}^d$, and prove a non-asymptotic upper bound on the mixing time of the Metropolis-adjusted Langevin algorithm (MALA). The method draws samples by simulating a Markov chain obtained from the discretization of an appropriate Langevin diffusion, combined with an accept-reject step. Relative to known guarantees for the unadjusted Langevin algorithm (ULA), our bounds show that the use of an accept-reject step in MALA leads to an exponentially improved dependence on the error-tolerance. Concretely, in order to obtain samples with TV error at most $\delta$ for a density with condition number $\kappa$, we show that MALA requires $\mathcal{O} \big(\kappa d \log(1/\delta) \big)$ steps, as compared to the $\mathcal{O} \big(\kappa^2 d/\delta^2 \big)$ steps established in past work on ULA. We also demonstrate the gains of MALA over ULA for weakly log-concave densities. Furthermore, we derive mixing time bounds for the Metropolized random walk (MRW) and obtain $\mathcal{O}(\kappa)$ mixing time slower than MALA. We provide numerical examples that support our theoretical findings, and demonstrate the benefits of Metropolis-Hastings adjustment for Langevin-type sampling algorithms.
Cross-domain Human Parsing via Adversarial Feature and Label Adaptation
Authors: Si Liu, Yao Sun, Defa Zhu, Guanghui Ren, Yu Chen, Jiashi Feng, Jizhong Han
Date: 2018-01-04 00:00:00
Tasks: Human Parsing

Human parsing has been extensively studied recently due to its wide
applications in many important scenarios. Mainstream fashion parsing models
focus on parsing the high-resolution and clean images. However, directly
applying the parsers trained on benchmarks to a particular application scenario
in the wild, e.g., a canteen, airport or workplace, often gives
non-satisfactory performance due to domain shift. In this paper, we explore a
new and challenging cross-domain human parsing problem: taking the benchmark
dataset with extensive pixel-wise labeling as the source domain, how to obtain
a satisfactory parser on a new target domain without requiring any additional
manual labeling? To this end, we propose a novel and efficient cross-domain
human parsing model to bridge the cross-domain differences in terms of visual
appearance and environment conditions and fully exploit commonalities across
domains. Our proposed model explicitly learns a feature compensation network,
which is specialized for mitigating the cross-domain differences. A
discriminative feature adversarial network is introduced to supervise the
feature compensation to effectively reduce the discrepancy between feature
distributions of two domains. Besides, our model also introduces a structured
label adversarial network to guide the parsing results of the target domain to
follow the high-order relationships of the structured labels shared across
domains. The proposed framework is end-to-end trainable, practical and scalable
in real applications. Extensive experiments are conducted where LIP dataset is
the source domain and 4 different datasets including surveillance videos,
movies and runway shows are evaluated as target domains. The results
consistently confirm data efficiency and performance advantages of the proposed
method for the cross-domain human parsing problem.
Learning to Generate Long-term Future via Hierarchical Prediction
Authors: Ruben Villegas, Jimei Yang, Yuliang Zou, Sungryull Sohn, Xunyu Lin, Honglak Lee
Date: 2017-04-19 00:00:00
Proceeding: ICML 2017 8
Tasks: Decoder, Prediction, Video Prediction

We propose a hierarchical approach for making long-term predictions of future
frames. To avoid inherent compounding errors in recursive pixel-level
prediction, we propose to first estimate high-level structure in the input
frames, then predict how that structure evolves in the future, and finally by
observing a single frame from the past and the predicted high-level structure,
we construct the future frames without having to observe any of the pixel-level
predictions. Long-term video prediction is difficult to perform by recurrently
observing the predicted frames because the small errors in pixel space
exponentially amplify as predictions are made deeper into the future. Our
approach prevents pixel-level error propagation from happening by removing the
need to observe the predicted frames. Our model is built with a combination of
LSTM and analogy based encoder-decoder convolutional neural networks, which
independently predict the video structure and generate the future frames,
respectively. In experiments, our model is evaluated on the Human3.6M and Penn
Action datasets on the task of long-term pixel-level video prediction of humans
performing actions and demonstrate significantly better results than the
state-of-the-art.
Decomposing Motion and Content for Natural Video Sequence Prediction
Authors: Ruben Villegas, Jimei Yang, Seunghoon Hong, Xunyu Lin, Honglak Lee
Date: 2017-06-25 00:00:00
Tasks: Decoder, Future prediction, Prediction, Video Prediction

We propose a deep neural network for the prediction of future frames in
natural video sequences. To effectively handle complex evolution of pixels in
videos, we propose to decompose the motion and content, two key components
generating dynamics in videos. Our model is built upon the Encoder-Decoder
Convolutional Neural Network and Convolutional LSTM for pixel-level prediction,
which independently capture the spatial layout of an image and the
corresponding temporal dynamics. By independently modeling motion and content,
predicting the next frame reduces to converting the extracted content features
into the next frame content by the identified motion features, which simplifies
the task of prediction. Our model is end-to-end trainable over multiple time
steps, and naturally learns to decompose motion and content without separate
training. We evaluate the proposed network architecture on human activity
videos using KTH, Weizmann action, and UCF-101 datasets. We show
state-of-the-art performance in comparison to recent approaches. To the best of
our knowledge, this is the first end-to-end trainable network architecture with
motion and content separation to model the spatiotemporal dynamics for
pixel-level future prediction in natural videos.
Winograd Schema - Knowledge Extraction Using Narrative Chains
Authors: Vatsal Mahajan
Date: 2018-01-08 00:00:00
Tasks: Common Sense Reasoning, Sentence

The Winograd Schema Challenge (WSC) is a test of machine intelligence,
designed to be an improvement on the Turing test. A Winograd Schema consists of
a sentence and a corresponding question. To successfully answer these
questions, one requires the use of commonsense knowledge and reasoning. This
work focuses on extracting common sense knowledge which can be used to generate
answers for the Winograd schema challenge. Common sense knowledge is extracted
based on events (or actions) and their participants; called Event-Based
Conditional Commonsense (ECC). I propose an approach using Narrative Event
Chains [Chambers et al., 2008] to extract ECC knowledge. These are stored in
templates, to be later used for answering the WSC questions. This approach
works well with respect to a subset of WSC tasks.
An Approximate Dynamic Programming Approach to Adversarial Online Learning
Authors: Vijay Kamble, Patrick Loiseau, Jean Walrand
Date: 2016-03-16 00:00:00
Tasks: Decision Making

We describe an approximate dynamic programming (ADP) approach to compute approximations of the optimal strategies and of the minimal losses that can be guaranteed in discounted repeated games with vector-valued losses. Such games prominently arise in the analysis of regret in repeated decision-making in adversarial environments, also known as adversarial online learning. At the core of our approach is a characterization of the lower Pareto frontier of the set of expected losses that a player can guarantee in these games as the unique fixed point of a set-valued dynamic programming operator. When applied to the problem of regret minimization with discounted losses, our approach yields algorithms that achieve markedly improved performance bounds compared to off-the-shelf online learning algorithms like Hedge. These results thus suggest the significant potential of ADP-based approaches in adversarial online learning.
Perceptual Context in Cognitive Hierarchies
Authors: Bernhard Hengst, Maurice Pagnucco, David Rajaratnam, Claude Sammut, Michael Thielscher
Date: 2018-01-07 00:00:00

Cognition does not only depend on bottom-up sensor feature abstraction, but
also relies on contextual information being passed top-down. Context is higher
level information that helps to predict belief states at lower levels. The main
contribution of this paper is to provide a formalisation of perceptual context
and its integration into a new process model for cognitive hierarchies. Several
simple instantiations of a cognitive hierarchy are used to illustrate the role
of context. Notably, we demonstrate the use context in a novel approach to
visually track the pose of rigid objects with just a 2D camera.
Sample-Efficient Reinforcement Learning through Transfer and Architectural Priors
Authors: Benjamin Spector, Serge Belongie
Date: 2018-01-07 00:00:00
Tasks: Atari Games, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning

Recent work in deep reinforcement learning has allowed algorithms to learn
complex tasks such as Atari 2600 games just from the reward provided by the
game, but these algorithms presently require millions of training steps in
order to learn, making them approximately five orders of magnitude slower than
humans. One reason for this is that humans build robust shared representations
that are applicable to collections of problems, making it much easier to
assimilate new variants. This paper first introduces the idea of
automatically-generated game sets to aid in transfer learning research, and
then demonstrates the utility of shared representations by showing that models
can substantially benefit from the incorporation of relevant architectural
priors. This technique affords a remarkable 50x positive transfer on a toy
problem-set.
Anatomical Data Augmentation For CNN based Pixel-wise Classification
Authors: Avi Ben-Cohen, Eyal Klang, Michal Marianne Amitai, Jacob Goldberger, Hayit Greenspan
Date: 2018-01-07 00:00:00
Tasks: Classification, Computed Tomography (CT), Data Augmentation, General Classification

In this work we propose a method for anatomical data augmentation that is
based on using slices of computed tomography (CT) examinations that are
adjacent to labeled slices as another resource of labeled data for training the
network. The extended labeled data is used to train a U-net network for a
pixel-wise classification into different hepatic lesions and normal liver
tissues. Our dataset contains CT examinations from 140 patients with 333 CT
images annotated by an expert radiologist. We tested our approach and compared
it to the conventional training process. Results indicate superiority of our
method. Using the anatomical data augmentation we achieved an improvement of 3%
in the success rate, 5% in the classification accuracy, and 4% in Dice.
Denoising Dictionary Learning Against Adversarial Perturbations
Authors: John Mitro, Derek Bridge, Steven Prestwich
Date: 2018-01-07 00:00:00
Tasks: Denoising, Dictionary Learning

We propose denoising dictionary learning (DDL), a simple yet effective
technique as a protection measure against adversarial perturbations. We
examined denoising dictionary learning on MNIST and CIFAR10 perturbed under two
different perturbation techniques, fast gradient sign (FGSM) and jacobian
saliency maps (JSMA). We evaluated it against five different deep neural
networks (DNN) representing the building blocks of most recent architectures
indicating a successive progression of model complexity of each other. We show
that each model tends to capture different representations based on their
architecture. For each model we recorded its accuracy both on the perturbed
test data previously misclassified with high confidence and on the denoised one
after the reconstruction using dictionary learning. The reconstruction quality
of each data point is assessed by means of PSNR (Peak Signal to Noise Ratio)
and Structure Similarity Index (SSI). We show that after applying (DDL) the
reconstruction of the original data point from a noisy
Theory of Deep Learning IIb: Optimization Properties of SGD
Authors: Chiyuan Zhang, Qianli Liao, Alexander Rakhlin, Brando Miranda, Noah Golowich, Tomaso Poggio
Date: 2018-01-07 00:00:00
Tasks: Deep Learning

In Theory IIb we characterize with a mix of theory and experiments the
optimization of deep convolutional networks by Stochastic Gradient Descent. The
main new result in this paper is theoretical and experimental evidence for the
following conjecture about SGD: SGD concentrates in probability -- like the
classical Langevin equation -- on large volume, "flat" minima, selecting flat
minimizers which are with very high probability also global minimizers