pwc-archive/papers-with-abstracts huggingface dataset:
Telepath: Understanding Users from a Human Vision Perspective in Large-Scale Recommender Systems
Authors: Yu Wang, Jixing Xu, Aohan Wu, Mantian Li, Yang He, Jinghe Hu, Weipeng P. Yan
Date: 2017-09-01 00:00:00
Tasks: Recommendation Systems

Designing an e-commerce recommender system that serves hundreds of millions
of active users is a daunting challenge. From a human vision perspective,
there're two key factors that affect users' behaviors: items' attractiveness
and their matching degree with users' interests. This paper proposes Telepath,
a vision-based bionic recommender system model, which understands users from
such perspective. Telepath is a combination of a convolutional neural network
(CNN), a recurrent neural network (RNN) and deep neural networks (DNNs). Its
CNN subnetwork simulates the human vision system to extract key visual signals
of items' attractiveness and generate corresponding activations. Its RNN and
DNN subnetworks simulate cerebral cortex to understand users' interest based on
the activations generated from browsed items. In practice, the Telepath model
has been launched to JD's recommender system and advertising system. For one of
the major item recommendation blocks on the JD app, click-through rate (CTR),
gross merchandise value (GMV) and orders have increased 1.59%, 8.16% and 8.71%
respectively. For several major ads publishers of JD demand-side platform, CTR,
GMV and return on investment have increased 6.58%, 61.72% and 65.57%
respectively by the first launch, and further increased 2.95%, 41.75% and
41.37% respectively by the second launch.
Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data
Authors: Daniele Ramazzotti, Alex Graudenzi, Luca De Sano, Marco Antoniotti, Giulio Caravagna
Date: 2017-09-04 00:00:00

Background. A large number of algorithms is being developed to reconstruct
evolutionary models of individual tumours from genome sequencing data. Most
methods can analyze multiple samples collected either through bulk multi-region
sequencing experiments or the sequencing of individual cancer cells. However,
rarely the same method can support both data types.
  Results. We introduce TRaIT, a computational framework to infer mutational
graphs that model the accumulation of multiple types of somatic alterations
driving tumour evolution. Compared to other tools, TRaIT supports multi-region
and single-cell sequencing data within the same statistical framework, and
delivers expressive models that capture many complex evolutionary phenomena.
TRaIT improves accuracy, robustness to data-specific errors and computational
complexity compared to competing methods.
  Conclusions. We show that the application of TRaIT to single-cell and
multi-region cancer datasets can produce accurate and reliable models of
single-tumour evolution, quantify the extent of intra-tumour heterogeneity and
generate new testable experimental hypotheses.
Learning Implicit Generative Models Using Differentiable Graph Tests
Authors: Josip Djolonga, Andreas Krause
Date: 2017-09-04 00:00:00
Tasks: Stochastic Optimization

Recently, there has been a growing interest in the problem of learning rich
implicit models - those from which we can sample, but can not evaluate their
density. These models apply some parametric function, such as a deep network,
to a base measure, and are learned end-to-end using stochastic optimization.
One strategy of devising a loss function is through the statistics of two
sample tests - if we can fool a statistical test, the learned distribution
should be a good model of the true data. However, not all tests can easily fit
into this framework, as they might not be differentiable with respect to the
data points, and hence with respect to the parameters of the implicit model.
Motivated by this problem, in this paper we show how two such classical tests,
the Friedman-Rafsky and k-nearest neighbour tests, can be effectively smoothed
using ideas from undirected graphical models - the matrix tree theorem and
cardinality potentials. Moreover, as we show experimentally, smoothing can
significantly increase the power of the test, which might of of independent
interest. Finally, we apply our method to learn implicit models.
A Geometric Approach to Harmonic Color Palette Design
Authors: Carlos Lara-Alvarez, Tania Reyes
Date: 2017-09-04 00:00:00

We address the problem of finding harmonic colors, this problem has many
applications, from fashion to industrial design. In order to solve this problem
we consider that colors follow normal distributions in tone (chroma and
lightness) and hue. The proposed approach relies in the CIE standard for
representing colors and evaluate proximity. Other approaches to this problem
use a set of rules. Experimental results show that lines with specific
parameters angles of inclination, and distance from the reference point are
preferred over others, and that uncertain line patterns outperform non-linear
patterns.
Towards Around-Device Interaction using Corneal Imaging
Authors: Daniel Schneider, Jens Grubert
Date: 2017-09-04 00:00:00

Around-device interaction techniques aim at extending the input space using
various sensing modalities on mobile and wearable devices. In this paper, we
present our work towards extending the input area of mobile devices using
front-facing device-centered cameras that capture reflections in the human eye.
As current generation mobile devices lack high resolution front-facing cameras
we study the feasibility of around-device interaction using corneal reflective
imaging based on a high resolution camera. We present a workflow, a technical
prototype and an evaluation, including a migration path from high resolution to
low resolution imagers. Our study indicates, that under optimal conditions a
spatial sensing resolution of 5 cm in the vicinity of a mobile phone is
possible.
Feasibility of Corneal Imaging for Handheld Augmented Reality
Authors: Daniel Schneider, Jens Grubert
Date: 2017-09-04 00:00:00

Smartphones are a popular device class for mobile Augmented Reality but
suffer from a limited input space. Around-device interaction techniques aim at
extending this input space using various sensing modalities. In this paper we
present our work towards extending the input area of mobile devices using
front-facing device-centered cameras that capture reflections in the cornea. As
current generation mobile devices lack high resolution front-facing cameras, we
study the feasibility of around-device interaction using corneal reflective
imaging based on a high resolution camera. We present a workflow, a technical
prototype and a feasibility evaluation.
A Reproducible Study on Remote Heart Rate Measurement
Authors: Guillaume Heusch, André Anjos, Sébastien Marcel
Date: 2017-09-04 00:00:00

This paper studies the problem of reproducible research in remote
photoplethysmography (rPPG). Most of the work published in this domain is
assessed on privately-owned databases, making it difficult to evaluate proposed
algorithms in a standard and principled manner. As a consequence, we present a
new, publicly available database containing a relatively large number of
subjects recorded under two different lighting conditions. Also, three
state-of-the-art rPPG algorithms from the literature were selected, implemented
and released as open source free software. After a thorough, unbiased
experimental evaluation in various settings, it is shown that none of the
selected algorithms is precise enough to be used in a real-world scenario.
Learning Word Embeddings from the Portuguese Twitter Stream: A Study of some Practical Aspects
Authors: Pedro Saleiro, Luís Sarmento, Eduarda Mendes Rodrigues, Carlos Soares, Eugénio Oliveira
Date: 2017-09-04 00:00:00
Tasks: GPU, Learning Word Embeddings, Playing the Game of 2048, Word Embeddings

This paper describes a preliminary study for producing and distributing a
large-scale database of embeddings from the Portuguese Twitter stream. We start
by experimenting with a relatively small sample and focusing on three
challenges: volume of training data, vocabulary size and intrinsic evaluation
metrics. Using a single GPU, we were able to scale up vocabulary size from 2048
words embedded and 500K training examples to 32768 words over 10M training
examples while keeping a stable validation loss and approximately linear trend
on training time per epoch. We also observed that using less than 50\% of the
available training examples for each vocabulary size might result in
overfitting. Results on intrinsic evaluation show promising performance for a
vocabulary size of 32768 words. Nevertheless, intrinsic evaluation metrics
suffer from over-sensitivity to their corresponding cosine similarity
thresholds, indicating that a wider range of metrics need to be developed to
track progress.
ARIGAN: Synthetic Arabidopsis Plants using Generative Adversarial Network
Authors: Mario Valerio Giuffrida, Hanno Scharr, Sotirios A. Tsaftaris
Date: 2017-09-04 00:00:00
Tasks: Generative Adversarial Network, Plant Phenotyping

In recent years, there has been an increasing interest in image-based plant
phenotyping, applying state-of-the-art machine learning approaches to tackle
challenging problems, such as leaf segmentation (a multi-instance problem) and
counting. Most of these algorithms need labelled data to learn a model for the
task at hand. Despite the recent release of a few plant phenotyping datasets,
large annotated plant image datasets for the purpose of training deep learning
algorithms are lacking. One common approach to alleviate the lack of training
data is dataset augmentation. Herein, we propose an alternative solution to
dataset augmentation for plant phenotyping, creating artificial images of
plants using generative neural networks. We propose the Arabidopsis Rosette
Image Generator (through) Adversarial Network: a deep convolutional network
that is able to generate synthetic rosette-shaped plants, inspired by DCGAN (a
recent adversarial network model using convolutional layers). Specifically, we
trained the network using A1, A2, and A4 of the CVPPP 2017 LCC dataset,
containing Arabidopsis Thaliana plants. We show that our model is able to
generate realistic 128x128 colour images of plants. We train our network
conditioning on leaf count, such that it is possible to generate plants with a
given number of leaves suitable, among others, for training regression based
models. We propose a new Ax dataset of artificial plants images, obtained by
our ARIGAN. We evaluate this new dataset using a state-of-the-art leaf counting
algorithm, showing that the testing error is reduced when Ax is used as part of
the training data.
A Computer Composes A Fabled Problem: Four Knights vs. Queen
Authors: Azlan Iqbal
Date: 2017-09-04 00:00:00

We explain how the prototype automatic chess problem composer, Chesthetica,
successfully composed a rare and interesting chess problem using the new
Digital Synaptic Neural Substrate (DSNS) computational creativity approach.
This problem represents a greater challenge from a creative standpoint because
the checkmate is not always clear and the method of winning even less so.
Creating a decisive chess problem of this type without the aid of an omniscient
7-piece endgame tablebase (and one that also abides by several chess
composition conventions) would therefore be a challenge for most human players
and composers working on their own. The fact that a small computer with
relatively low processing power and memory was sufficient to compose such a
problem using the DSNS approach in just 10 days is therefore noteworthy. In
this report we document the event and result in some detail. It lends
additional credence to the DSNS as a viable new approach in the field of
computational creativity. In particular, in areas where human-like creativity
is required for targeted or specific problems with no clear path to the
solution.
Self-Supervised Learning for Stereo Matching with Self-Improving Ability
Authors: Yiran Zhong, Yuchao Dai, Hongdong Li
Date: 2017-09-04 00:00:00
Tasks: Self-Supervised Learning, Stereo Matching, Stereo Matching Hand

Exiting deep-learning based dense stereo matching methods often rely on
ground-truth disparity maps as the training signals, which are however not
always available in many situations. In this paper, we design a simple
convolutional neural network architecture that is able to learn to compute
dense disparity maps directly from the stereo inputs. Training is performed in
an end-to-end fashion without the need of ground-truth disparity maps. The idea
is to use image warping error (instead of disparity-map residuals) as the loss
function to drive the learning process, aiming to find a depth-map that
minimizes the warping error. While this is a simple concept well-known in
stereo matching, to make it work in a deep-learning framework, many non-trivial
challenges must be overcome, and in this work we provide effective solutions.
Our network is self-adaptive to different unseen imageries as well as to
different camera settings. Experiments on KITTI and Middlebury stereo benchmark
datasets show that our method outperforms many state-of-the-art stereo matching
methods with a margin, and at the same time significantly faster.
Automation of Android Applications Testing Using Machine Learning Activities Classification
Authors: Ariel Rosenfeld, Odaya Kardashov, Orel Zang
Date: 2017-09-04 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification

Mobile applications are being used every day by more than half of the world's
population to perform a great variety of tasks. With the increasingly
widespread usage of these applications, the need arises for efficient
techniques to test them. Many frameworks allow automating the process of
application testing, however existing frameworks mainly rely on the application
developer for providing testing scripts for each developed application, thus
preventing reuse of these tests for similar applications. In this paper, we
present a novel approach for the automation of testing Android applications by
leveraging machine learning techniques and reusing popular test scenarios. We
discuss and demonstrate the potential benefits of our approach in an empirical
study where we show that our developed testing tool, based on the proposed
approach, outperforms standard methods in realistic settings.
Using Optimal Ratio Mask as Training Target for Supervised Speech Separation
Authors: Shasha Xia, Hao Li, Xueliang Zhang
Date: 2017-09-04 00:00:00
Tasks: Speech Separation

Supervised speech separation uses supervised learning algorithms to learn a
mapping from an input noisy signal to an output target. With the fast
development of deep learning, supervised separation has become the most
important direction in speech separation area in recent years. For the
supervised algorithm, training target has a significant impact on the
performance. Ideal ratio mask is a commonly used training target, which can
improve the speech intelligibility and quality of the separated speech.
However, it does not take into account the correlation between noise and clean
speech. In this paper, we use the optimal ratio mask as the training target of
the deep neural network (DNN) for speech separation. The experiments are
carried out under various noise environments and signal to noise ratio (SNR)
conditions. The results show that the optimal ratio mask outperforms other
training targets in general.
Neural Networks for Safety-Critical Applications - Challenges, Experiments and Perspectives
Authors: Chih-Hong Cheng, Frederik Diehl, Yassine Hamza, Gereon Hinz, Georg Nührenberg, Markus Rickert, Harald Ruess, Michael Troung-Le
Date: 2017-09-04 00:00:00

We propose a methodology for designing dependable Artificial Neural Networks
(ANN) by extending the concepts of understandability, correctness, and validity
that are crucial ingredients in existing certification standards. We apply the
concept in a concrete case study in designing a high-way ANN-based motion
predictor to guarantee safety properties such as impossibility for the ego
vehicle to suggest moving to the right lane if there exists another vehicle on
its right.
Multi-modal Conditional Attention Fusion for Dimensional Emotion Prediction
Authors: Shizhe Chen, Qin Jin
Date: 2017-09-04 00:00:00

Continuous dimensional emotion prediction is a challenging task where the
fusion of various modalities usually achieves state-of-the-art performance such
as early fusion or late fusion. In this paper, we propose a novel multi-modal
fusion strategy named conditional attention fusion, which can dynamically pay
attention to different modalities at each time step. Long-short term memory
recurrent neural networks (LSTM-RNN) is applied as the basic uni-modality model
to capture long time dependencies. The weights assigned to different modalities
are automatically decided by the current input features and recent history
information rather than being fixed at any kinds of situation. Our experimental
results on a benchmark dataset AVEC2015 show the effectiveness of our method
which outperforms several common fusion strategies for valence prediction.
CSSTag: Optical Nanoscale Radar and Particle Tracking for In-Body and Microfluidic Systems with Vibrating Graphene and Resonance Energy Transfer
Authors: Burhan Gulbahar, Gorkem Memisoglu
Date: 2017-09-04 00:00:00
Tasks: TAG

Single particle tracking systems monitor cellular processes with great
accuracy in nano-biological systems. The emissions of the fluorescent molecules
are detected with cameras or photodetectors. However, state-of-the-art imaging
systems have challenges in the detection capability, collection and analysis of
imaging data, penetration depth and complicated set-ups. In this article, a
\textit{signaling based nanoscale acousto-optic radar and microfluidic particle
tracking system} is proposed based on the theoretical design providing
nanoscale optical modulator with vibrating F{\"{o}}rster resonance energy
transfer (VFRET) and vibrating CdSe/ZnS quantum dots (QDs) on graphene
resonators. The modulator structure combines the significant advantages of
graphene membranes having wideband resonance frequencies with QDs having broad
absorption spectrum and tunable properties. The solution denoted by chirp
spread spectrum (CSS) Tag (\textit{CSSTag}) utilizes classical radar target
tracking approaches in nanoscale environments based on the capability to
generate CSS sequences to identify different bio-particles. Numerical and
Monte-Carlo simulations are realized showing the significant performance for
multiple particle tracking (MPT) with a modulator of $10 \, \mu$m $\times$ $10
\, \mu$m $\times$ $10 \, \mu$m dimension and several picograms of weight,
signal to noise ratio (SNR) in the range $-7$ dB to $10$ dB and high speed
tracking capability for microfluidic and in-body environments.
Generating Video Descriptions with Topic Guidance
Authors: Shizhe Chen, Jia Chen, Qin Jin
Date: 2017-08-31 00:00:00
Tasks: Decoder, Image Captioning, Video Captioning

Generating video descriptions in natural language (a.k.a. video captioning)
is a more challenging task than image captioning as the videos are
intrinsically more complicated than images in two aspects. First, videos cover
a broader range of topics, such as news, music, sports and so on. Second,
multiple topics could coexist in the same video. In this paper, we propose a
novel caption model, topic-guided model (TGM), to generate topic-oriented
descriptions for videos in the wild via exploiting topic information. In
addition to predefined topics, i.e., category tags crawled from the web, we
also mine topics in a data-driven way based on training captions by an
unsupervised topic mining model. We show that data-driven topics reflect a
better topic schema than the predefined topics. As for testing video topic
prediction, we treat the topic mining model as teacher to train the student,
the topic prediction model, by utilizing the full multi-modalities in the video
especially the speech modality. We propose a series of caption models to
exploit topic guidance, including implicitly using the topics as input features
to generate words related to the topic and explicitly modifying the weights in
the decoder with topics to function as an ensemble of topic-aware language
decoders. Our comprehensive experimental results on the current largest video
caption dataset MSR-VTT prove the effectiveness of our topic-guided model,
which significantly surpasses the winning performance in the 2016 MSR video to
language challenge.
Enriching Linked Datasets with New Object Properties
Authors: Subhashree S, P. Sreenivasa Kumar
Date: 2016-06-24 00:00:00
Tasks: Object

Although several RDF knowledge bases are available through the LOD
initiative, the ontology schema of such linked datasets is not very rich. In
particular, they lack object properties. The problem of finding new object
properties (and their instances) between any two given classes has not been
investigated in detail in the context of Linked Data. In this paper, we present
DART (Detecting Arbitrary Relations for enriching T-Boxes of Linked Data) - an
unsupervised solution to enrich the LOD cloud with new object properties
between two given classes. DART exploits contextual similarity to identify text
patterns from the web corpus that can potentially represent relations between
individuals. These text patterns are then clustered by means of paraphrase
detection to capture the object properties between the two given LOD classes.
DART also performs fully automated mapping of the discovered relations to the
properties in the linked dataset. This serves many purposes such as
identification of completely new relations, elimination of irrelevant
relations, and generation of prospective property axioms. We have empirically
evaluated our approach on several pairs of classes and found that the system
can indeed be used for enriching the linked datasets with new object properties
and their instances. We compared DART with newOntExt system which is an
offshoot of the NELL (Never-Ending Language Learning) effort. Our experiments
reveal that DART gives better results than newOntExt with respect to both the
correctness, as well as the number of relations.
Interactive Attention Networks for Aspect-Level Sentiment Classification
Authors: Dehong Ma, Sujian Li, Xiaodong Zhang, Houfeng Wang
Date: 2017-09-04 00:00:00
Tasks: Aspect-Based Sentiment Analysis (ABSA), Classification, General Classification, Sentiment Classification

Aspect-level sentiment classification aims at identifying the sentiment
polarity of specific target in its context. Previous approaches have realized
the importance of targets in sentiment classification and developed various
methods with the goal of precisely modeling their contexts via generating
target-specific representations. However, these studies always ignore the
separate modeling of targets. In this paper, we argue that both targets and
contexts deserve special treatment and need to be learned their own
representations via interactive learning. Then, we propose the interactive
attention networks (IAN) to interactively learn attentions in the contexts and
targets, and generate the representations for targets and contexts separately.
With this design, the IAN model can well represent a target and its collocative
context, which is helpful to sentiment classification. Experimental results on
SemEval 2014 Datasets demonstrate the effectiveness of our model.