pwc-archive/papers-with-abstracts huggingface dataset:
Human-Centered Emotion Recognition in Animated GIFs
Authors: Zhengyuan Yang, Yixuan Zhang, Jiebo Luo
Date: 2019-04-27 00:00:00
Tasks: Emotion Recognition

As an intuitive way of expression emotion, the animated Graphical Interchange
Format (GIF) images have been widely used on social media. Most previous
studies on automated GIF emotion recognition fail to effectively utilize GIF's
unique properties, and this potentially limits the recognition performance. In
this study, we demonstrate the importance of human related information in GIFs
and conduct human-centered GIF emotion recognition with a proposed Keypoint
Attended Visual Attention Network (KAVAN). The framework consists of a facial
attention module and a hierarchical segment temporal module. The facial
attention module exploits the strong relationship between GIF contents and
human characters, and extracts frame-level visual feature with a focus on human
faces. The Hierarchical Segment LSTM (HS-LSTM) module is then proposed to
better learn global GIF representations. Our proposed framework outperforms the
state-of-the-art on the MIT GIFGIF dataset. Furthermore, the facial attention
module provides reliable facial region mask predictions, which improves the
model's interpretability.
Missing MRI Pulse Sequence Synthesis using Multi-Modal Generative Adversarial Network
Authors: Anmol Sharma, Ghassan Hamarneh
Date: 2019-04-27 00:00:00
Tasks: Generative Adversarial Network

Magnetic resonance imaging (MRI) is being increasingly utilized to assess, diagnose, and plan treatment for a variety of diseases. The ability to visualize tissue in varied contrasts in the form of MR pulse sequences in a single scan provides valuable insights to physicians, as well as enabling automated systems performing downstream analysis. However many issues like prohibitive scan time, image corruption, different acquisition protocols, or allergies to certain contrast materials may hinder the process of acquiring multiple sequences for a patient. This poses challenges to both physicians and automated systems since complementary information provided by the missing sequences is lost. In this paper, we propose a variant of generative adversarial network (GAN) capable of leveraging redundant information contained within multiple available sequences in order to generate one or more missing sequences for a patient scan. The proposed network is designed as a multi-input, multi-output network which combines information from all the available pulse sequences, implicitly infers which sequences are missing, and synthesizes the missing ones in a single forward pass. We demonstrate and validate our method on two brain MRI datasets each with four sequences, and show the applicability of the proposed method in simultaneously synthesizing all missing sequences in any possible scenario where either one, two, or three of the four sequences may be missing. We compare our approach with competing unimodal and multi-modal methods, and show that we outperform both quantitatively and qualitatively.
Towards Automation of Creativity: A Machine Intelligence Approach
Authors: Subodh Deolekar, Siby Abraham
Date: 2019-04-27 00:00:00

This paper demonstrates emergence of computational creativity in the field of
music. Different aspects of creativity such as producer, process, product and
press are studied and formulated. Different notions of computational creativity
such as novelty, quality and typicality of compositions as products are studied
and evaluated. We formulate an algorithmic perception on human creativity and
propose a prototype that is capable of demonstrating human-level creativity. We
then validate the proposed prototype by applying various creativity benchmarks
with the results obtained and compare the proposed prototype with the other
existing computational creative systems.
Linearized two-layers neural networks in high dimension
Authors: Behrooz Ghorbani, Song Mei, Theodor Misiakiewicz, Andrea Montanari
Date: 2019-04-27 00:00:00
Tasks: regression, Vocal Bursts Intensity Prediction, Vocal Bursts Valence Prediction

We consider the problem of learning an unknown function $f_{\star}$ on the $d$-dimensional sphere with respect to the square loss, given i.i.d. samples $\{(y_i,{\boldsymbol x}_i)\}_{i\le n}$ where ${\boldsymbol x}_i$ is a feature vector uniformly distributed on the sphere and $y_i=f_{\star}({\boldsymbol x}_i)+\varepsilon_i$. We study two popular classes of models that can be regarded as linearizations of two-layers neural networks around a random initialization: the random features model of Rahimi-Recht (RF); the neural tangent kernel model of Jacot-Gabriel-Hongler (NT). Both these approaches can also be regarded as randomized approximations of kernel ridge regression (with respect to different kernels), and enjoy universal approximation properties when the number of neurons $N$ diverges, for a fixed dimension $d$. We consider two specific regimes: the approximation-limited regime, in which $n=\infty$ while $d$ and $N$ are large but finite; and the sample size-limited regime in which $N=\infty$ while $d$ and $n$ are large but finite. In the first regime we prove that if $d^{\ell + \delta} \le N\le d^{\ell+1-\delta}$ for small $\delta > 0$, then \RF\, effectively fits a degree-$\ell$ polynomial in the raw features, and \NT\, fits a degree-$(\ell+1)$ polynomial. In the second regime, both RF and NT reduce to kernel methods with rotationally invariant kernels. We prove that, if the number of samples is $d^{\ell + \delta} \le n \le d^{\ell +1-\delta}$, then kernel methods can fit at most a a degree-$\ell$ polynomial in the raw features. This lower bound is achieved by kernel ridge regression. Optimal prediction error is achieved for vanishing ridge regularization.
Fuzzy Rule Interpolation Methods and Fri Toolbox
Authors: Maen Alzubi, Zsolt Csaba Johanyák, Szilveszter Kovács
Date: 2019-04-27 00:00:00

FRI methods are less popular in the practical application domain. One
possible reason is the missing common framework. There are many FRI methods
developed independently, having different interpolation concepts and features.
One trial for setting up a common FRI framework was the MATLAB FRI Toolbox,
developed by Johany\'ak et. al. in 2006. The goals of this paper are divided as
follows: firstly, to present a brief introduction of the FRI methods. Secondly,
to introduce a brief description of the refreshed and extended version of the
original FRI Toolbox. And thirdly, to use different unified numerical benchmark
examples to evaluate and analyze the Fuzzy Rule Interpolation Techniques (FRI)
(KH, KH Stabilized, MACI, IMUL, CRF, VKK, GM, FRIPOC, LESFRI, and SCALEMOVE),
that will be classified and compared based on different features by following
the abnormality and linearity conditions [15].
Unsupervised and Unregistered Hyperspectral Image Super-Resolution with Mutual Dirichlet-Net
Authors: Ying Qu, Hairong Qi, Chiman Kwan, Naoto Yokoya, Jocelyn Chanussot
Date: 2019-04-27 00:00:00
Tasks: Hyperspectral Image Super-Resolution, Image Super-Resolution, Super-Resolution

Hyperspectral images (HSI) provide rich spectral information that contributed to the successful performance improvement of numerous computer vision tasks. However, it can only be achieved at the expense of images' spatial resolution. Hyperspectral image super-resolution (HSI-SR) addresses this problem by fusing low resolution (LR) HSI with multispectral image (MSI) carrying much higher spatial resolution (HR). All existing HSI-SR approaches require the LR HSI and HR MSI to be well registered and the reconstruction accuracy of the HR HSI relies heavily on the registration accuracy of different modalities. This paper exploits the uncharted problem domain of HSI-SR without the requirement of multi-modality registration. Given the unregistered LR HSI and HR MSI with overlapped regions, we design a unique unsupervised learning structure linking the two unregistered modalities by projecting them into the same statistical space through the same encoder. The mutual information (MI) is further adopted to capture the non-linear statistical dependencies between the representations from two modalities (carrying spatial information) and their raw inputs. By maximizing the MI, spatial correlations between different modalities can be well characterized to further reduce the spectral distortion. A collaborative $l_{2,1}$ norm is employed as the reconstruction error instead of the more common $l_2$ norm, so that individual pixels can be recovered as accurately as possible. With this design, the network allows to extract correlated spectral and spatial information from unregistered images that better preserves the spectral information. The proposed method is referred to as unregistered and unsupervised mutual Dirichlet Net ($u^2$-MDN). Extensive experimental results using benchmark HSI datasets demonstrate the superior performance of $u^2$-MDN as compared to the state-of-the-art.
Prediction with Unpredictable Feature Evolution
Authors: Bo-Jian Hou, Lijun Zhang, Zhi-Hua Zhou
Date: 2019-04-27 00:00:00
Tasks: Matrix Completion, Prediction

Learning with feature evolution studies the scenario where the features of the data streams can evolve, i.e., old features vanish and new features emerge. Its goal is to keep the model always performing well even when the features happen to evolve. To tackle this problem, canonical methods assume that the old features will vanish simultaneously and the new features themselves will emerge simultaneously as well. They also assume there is an overlapping period where old and new features both exist when the feature space starts to change. However, in reality, the feature evolution could be unpredictable, which means the features can vanish or emerge arbitrarily, causing the overlapping period incomplete. In this paper, we propose a novel paradigm: Prediction with Unpredictable Feature Evolution (PUFE) where the feature evolution is unpredictable. To address this problem, we fill the incomplete overlapping period and formulate it as a new matrix completion problem. We give a theoretical bound on the least number of observed entries to make the overlapping period intact. With this intact overlapping period, we leverage an ensemble method to take the advantage of both the old and new feature spaces without manually deciding which base models should be incorporated. Theoretical and experimental results validate that our method can always follow the best base models and thus realize the goal of learning with feature evolution.
HELP: A Dataset for Identifying Shortcomings of Neural Models in Monotonicity Reasoning
Authors: Hitomi Yanaka, Koji Mineshima, Daisuke Bekki, Kentaro Inui, Satoshi Sekine, Lasha Abzianidze, Johan Bos
Date: 2019-04-27 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Data Augmentation, Natural Language Inference

Large crowdsourced datasets are widely used for training and evaluating
neural models on natural language inference (NLI). Despite these efforts,
neural models have a hard time capturing logical inferences, including those
licensed by phrase replacements, so-called monotonicity reasoning. Since no
large dataset has been developed for monotonicity reasoning, it is still
unclear whether the main obstacle is the size of datasets or the model
architectures themselves. To investigate this issue, we introduce a new
dataset, called HELP, for handling entailments with lexical and logical
phenomena. We add it to training data for the state-of-the-art neural models
and evaluate them on test sets for monotonicity phenomena. The results showed
that our data augmentation improved the overall accuracy. We also find that the
improvement is better on monotonicity inferences with lexical replacements than
on downward inferences with disjunction and modification. This suggests that
some types of inferences can be improved by our data augmentation while others
are immune to it.
Improved Conditional VRNNs for Video Prediction
Authors: Lluis Castrejon, Nicolas Ballas, Aaron Courville
Date: 2019-04-27 00:00:00
Proceeding: ICCV 2019 10
Tasks: Prediction, Video Generation, Video Prediction

Predicting future frames for a video sequence is a challenging generative
modeling task. Promising approaches include probabilistic latent variable
models such as the Variational Auto-Encoder. While VAEs can handle uncertainty
and model multiple possible future outcomes, they have a tendency to produce
blurry predictions. In this work we argue that this is a sign of underfitting.
To address this issue, we propose to increase the expressiveness of the latent
distributions and to use higher capacity likelihood models. Our approach relies
on a hierarchy of latent variables, which defines a family of flexible prior
and posterior distributions in order to better model the probability of future
sequences. We validate our proposal through a series of ablation experiments
and compare our approach to current state-of-the-art latent variable models.
Our method performs favorably under several metrics in three different
datasets.
IsMo-GAN: Adversarial Learning for Monocular Non-Rigid 3D Reconstruction
Authors: Soshi Shimada, Vladislav Golyanik, Christian Theobalt, Didier Stricker
Date: 2019-04-27 00:00:00
Tasks: 3D Reconstruction, Generative Adversarial Network

The majority of the existing methods for non-rigid 3D surface regression from monocular 2D images require an object template or point tracks over multiple frames as an input, and are still far from real-time processing rates. In this work, we present the Isometry-Aware Monocular Generative Adversarial Network (IsMo-GAN) - an approach for direct 3D reconstruction from a single image, trained for the deformation model in an adversarial manner on a light-weight synthetic dataset. IsMo-GAN reconstructs surfaces from real images under varying illumination, camera poses, textures and shading at over 250 Hz. In multiple experiments, it consistently outperforms several approaches in the reconstruction accuracy, runtime, generalisation to unknown surfaces and robustness to occlusions. In comparison to the state-of-the-art, we reduce the reconstruction error by 10-30% including the textureless case and our surfaces evince fewer artefacts qualitatively.
Exploring Information Centrality for Intrusion Detection in Large Networks
Authors: Nidhi Rastogi
Date: 2019-04-27 00:00:00
Tasks: Anomaly Detection, Intrusion Detection, Outlier Detection

Modern networked systems are constantly under threat from systemic attacks. There has been a massive upsurge in the number of devices connected to a network as well as the associated traffic volume. This has intensified the need to better understand all possible attack vectors during system design and implementation. Further, it has increased the need to mine large data sets, analyzing which has become a daunting task. It is critical to scale monitoring infrastructures to match this need, but a difficult goal for the small and medium organization. Hence, there is a need to propose novel approaches that address the big data problem in security. Information Centrality (IC) labels network nodes with better vantage points for detecting network-based anomalies as central nodes and uses them for detecting a category of attacks called systemic attacks. The main idea is that since these central nodes already see a lot of information flowing through the network, they are in a good position to detect anomalies before other nodes. This research first dives into the importance of using graphs in understanding the topology and information flow. We then introduce the usage of information centrality, a centrality-based index, to reduce data collection in existing communication networks. Using IC-identified central nodes can accelerate outlier detection when armed with a suitable anomaly detection technique. We also come up with a more efficient way to compute Information centrality for large networks. Finally, we demonstrate that central nodes detect anomalous behavior much faster than other non-central nodes, given the anomalous behavior is systemic in nature.
Regulating AI: do we need new tools?
Authors: Otello Ardovino, Jacopo Arpetti, Marco Delmastro
Date: 2019-04-27 00:00:00

The Artificial Intelligence paradigm (hereinafter referred to as "AI") builds
on the analysis of data able, among other things, to snap pictures of the
individuals' behaviors and preferences. Such data represent the most valuable
currency in the digital ecosystem, where their value derives from their being a
fundamental asset in order to train machines with a view to developing AI
applications. In this environment, online providers attract users by offering
them services for free and getting in exchange data generated right through the
usage of such services. This swap, characterized by an implicit nature,
constitutes the focus of the present paper, in the light of the disequilibria,
as well as market failures, that it may bring about. We use mobile apps and the
related permission system as an ideal environment to explore, via econometric
tools, those issues. The results, stemming from a dataset of over one million
observations, show that both buyers and sellers are aware that access to
digital services implicitly implies an exchange of data, although this does not
have a considerable impact neither on the level of downloads (demand), nor on
the level of the prices (supply). In other words, the implicit nature of this
exchange does not allow market indicators to work efficiently. We conclude that
current policies (e.g. transparency rules) may be inherently biased and we put
forward suggestions for a new approach.
Incremental personalized E-mail spam filter using novel TFDCR feature selection with dynamic feature update
Authors: Gopi Sanghani, Ketan Kotecha
Date: 2019-04-27 00:00:00
Tasks: feature selection, Incremental Learning

Communication through e-mails remains to be highly formalized, conventional
and indispensable method for the exchange of information over the Internet. An
ever-increasing ratio and adversary nature of spam e-mails have posed a great
many challenges such as uneven class distribution, unequal error cost, frequent
change of content and personalized context-sensitive discrimination. In this
research, we propose a novel and distinctive approach to develop an incremental
personalized e-mail spam filter. The proposed work is described using three
significant contributions. First, we applied a novel term frequency difference
and category ratio based feature selection function TFDCR to select the most
discriminating features irrespective of the number of samples in each class.
Second, an incremental learning model is used which enables the classifier to
update the discriminant function dynamically. Third, a heuristic function
called selectionRankWeight is introduced to upgrade the existing feature set
that determines new features carrying strong discriminating ability from an
incoming set of e-mails. Three public e-mail datasets possessing different
characteristics are used to evaluate the filter performance. Experiments are
conducted to compare the feature selection efficiency of TFDCR and to observe
the filter performance under both the batch and the incremental learning mode.
The results demonstrate the superiority of TFDCR as the most effective f eature
selection function. The incremental learning model incorporating dynamic
feature update function overcomes the problem of drifting concepts. The
proposed filter validates its efficiency and feasibility by substantially
improving the classification accuracy and reducing the false positive error of
misclassifying legitimate e-mail as spam.
A Novel Dual-Lidar Calibration Algorithm Using Planar Surfaces
Authors: Jianhao Jiao, Qinghai Liao, Yilong Zhu, Tianyu Liu, Yang Yu, Rui Fan, Lujia Wang, Ming Liu
Date: 2019-04-27 00:00:00
Tasks: Translation

Multiple lidars are prevalently used on mobile vehicles for rendering a broad
view to enhance the performance of localization and perception systems.
However, precise calibration of multiple lidars is challenging since the
feature correspondences in scan points cannot always provide enough
constraints. To address this problem, the existing methods require fixed
calibration targets in scenes or rely exclusively on additional sensors. In
this paper, we present a novel method that enables automatic lidar calibration
without these restrictions. Three linearly independent planar surfaces
appearing in surroundings is utilized to find correspondences. Two components
are developed to ensure the extrinsic parameters to be found: a closed-form
solver for initialization and an optimizer for refinement by minimizing a
nonlinear cost function. Simulation and experimental results demonstrate the
high accuracy of our calibration approach with the rotation and translation
errors smaller than 0.05rad and 0.1m respectively.
A Novel Fuzzy Search Approach over Encrypted Data with Improved Accuracy and Efficiency
Authors: Jinkun Cao, Jinhao Zhu, Liwei Lin, Zhengui Xue, Ruhui Ma, Haibing Guan
Date: 2019-04-27 00:00:00
Tasks: Cloud Computing, Diversity

As cloud computing becomes prevalent in recent years, more and more enterprises and individuals outsource their data to cloud servers. To avoid privacy leaks, outsourced data usually is encrypted before being sent to cloud servers, which disables traditional search schemes for plain text. To meet both end of security and searchability, search-supported encryption is proposed. However, many previous schemes suffer severe vulnerability when typos and semantic diversity exist in query requests. To overcome such flaw, higher error-tolerance is always expected for search-supported encryption design, sometimes defined as 'fuzzy search'. In this paper, we propose a new scheme of multi-keyword fuzzy search over encrypted and outsourced data. Our approach introduces a new mechanism to map a natural language expression into a word-vector space. Compared with previous approaches, our design shows higher robustness when multiple kinds of typos are involved. Besides, our approach is enhanced with novel data structures to improve search efficiency. These two innovations can work well for both accuracy and efficiency. Moreover, these designs will not hurt the fundamental security. Experiments on a real-world dataset demonstrate the effectiveness of our proposed approach, which outperforms currently popular approaches focusing on similar tasks.
Understanding Dataset Design Choices for Multi-hop Reasoning
Authors: Jifan Chen, Greg Durrett
Date: 2019-04-27 00:00:00
Proceeding: NAACL 2019 6
Tasks: Multi-hop Question Answering, Multiple-choice, Question Answering, Reading Comprehension, Sentence

Learning multi-hop reasoning has been a key challenge for reading
comprehension models, leading to the design of datasets that explicitly focus
on it. Ideally, a model should not be able to perform well on a multi-hop
question answering task without doing multi-hop reasoning. In this paper, we
investigate two recently proposed datasets, WikiHop and HotpotQA. First, we
explore sentence-factored models for these tasks; by design, these models
cannot do multi-hop reasoning, but they are still able to solve a large number
of examples in both datasets. Furthermore, we find spurious correlations in the
unmasked version of WikiHop, which make it easy to achieve high performance
considering only the questions and answers. Finally, we investigate one key
difference between these datasets, namely span-based vs. multiple-choice
formulations of the QA task. Multiple-choice versions of both datasets can be
easily gamed, and two models we examine only marginally exceed a baseline in
this setting. Overall, while these datasets are useful testbeds,
high-performing models may not be learning as much multi-hop reasoning as
previously thought.