pwc-archive/papers-with-abstracts huggingface dataset:
Expert and Non-Expert Opinion about Technological Unemployment
Authors: Toby Walsh
Date: 2017-06-21 00:00:00

There is significant concern that technological advances, especially in
Robotics and Artificial Intelligence (AI), could lead to high levels of
unemployment in the coming decades. Studies have estimated that around half of
all current jobs are at risk of automation. To look into this issue in more
depth, we surveyed experts in Robotics and AI about the risk, and compared
their views with those of non-experts. Whilst the experts predicted a
significant number of occupations were at risk of automation in the next two
decades, they were more cautious than people outside the field in predicting
occupations at risk. Their predictions were consistent with their estimates for
when computers might be expected to reach human level performance across a wide
range of skills. These estimates were typically decades later than those of the
non-experts. Technological barriers may therefore provide society with more
time to prepare for an automated future than the public fear. In addition,
public expectations may need to be dampened about the speed of progress to be
expected in Robotics and AI.
Stance Detection in Turkish Tweets
Authors: Dilek Küçük
Date: 2017-06-21 00:00:00
Tasks: Sentiment Analysis, Stance Detection

Stance detection is a classification problem in natural language processing
where for a text and target pair, a class result from the set {Favor, Against,
Neither} is expected. It is similar to the sentiment analysis problem but
instead of the sentiment of the text author, the stance expressed for a
particular target is investigated in stance detection. In this paper, we
present a stance detection tweet data set for Turkish comprising stance
annotations of these tweets for two popular sports clubs as targets.
Additionally, we provide the evaluation results of SVM classifiers for each
target on this data set, where the classifiers use unigram, bigram, and hashtag
features. This study is significant as it presents one of the initial stance
detection data sets proposed so far and the first one for Turkish language, to
the best of our knowledge. The data set and the evaluation results of the
corresponding SVM-based approaches will form plausible baselines for the
comparison of future studies on stance detection.
Endoscopic Depth Measurement and Super-Spectral-Resolution Imaging
Authors: Jianyu Lin, Neil T. Clancy, Yang Hu, Ji Qi, Taran Tatla, Danail Stoyanov, Lena Maier-Hein, Daniel S. Elson
Date: 2017-06-19 00:00:00
Tasks: Decision Making, Optical Flow Estimation

Intra-operative measurements of tissue shape and multi/ hyperspectral
information have the potential to provide surgical guidance and decision making
support. We report an optical probe based system to combine sparse
hyperspectral measurements and spectrally-encoded structured lighting (SL) for
surface measurements. The system provides informative signals for navigation
with a surgical interface. By rapidly switching between SL and white light (WL)
modes, SL information is combined with structure-from-motion (SfM) from white
light images, based on SURF feature detection and Lucas-Kanade (LK) optical
flow to provide quasi-dense surface shape reconstruction with known scale in
real-time. Furthermore, "super-spectral-resolution" was realized, whereby the
RGB images and sparse hyperspectral data were integrated to recover dense
pixel-level hyperspectral stacks, by using convolutional neural networks to
upscale the wavelength dimension. Validation and demonstration of this system
is reported on ex vivo/in vivo animal/ human experiments.
MEC: Memory-efficient Convolution for Deep Neural Network
Authors: Minsik Cho, Daniel Brand
Date: 2017-06-21 00:00:00
Proceeding: ICML 2017 8

Convolution is a critical component in modern deep neural networks, thus
several algorithms for convolution have been developed. Direct convolution is
simple but suffers from poor performance. As an alternative, multiple indirect
methods have been proposed including im2col-based convolution, FFT-based
convolution, or Winograd-based algorithm. However, all these indirect methods
have high memory-overhead, which creates performance degradation and offers a
poor trade-off between performance and memory consumption. In this work, we
propose a memory-efficient convolution or MEC with compact lowering, which
reduces memory-overhead substantially and accelerates convolution process. MEC
lowers the input matrix in a simple yet efficient/compact way (i.e., much less
memory-overhead), and then executes multiple small matrix multiplications in
parallel to get convolution completed. Additionally, the reduced memory
footprint improves memory sub-system efficiency, improving performance. Our
experimental results show that MEC reduces memory consumption significantly
with good speedup on both mobile and server platforms, compared with other
indirect convolution algorithms.
Visual Decoding of Targets During Visual Search From Human Eye Fixations
Authors: Hosnieh Sattar, Mario Fritz, Andreas Bulling
Date: 2017-06-19 00:00:00

What does human gaze reveal about a users' intents and to which extend can
these intents be inferred or even visualized? Gaze was proposed as an implicit
source of information to predict the target of visual search and, more
recently, to predict the object class and attributes of the search target. In
this work, we go one step further and investigate the feasibility of combining
recent advances in encoding human gaze information using deep convolutional
neural networks with the power of generative image models to visually decode,
i.e. create a visual representation of, the search target. Such visual decoding
is challenging for two reasons: 1) the search target only resides in the user's
mind as a subjective visual pattern, and can most often not even be described
verbally by the person, and 2) it is, as of yet, unclear if gaze fixations
contain sufficient information for this task at all. We show, for the first
time, that visual representations of search targets can indeed be decoded only
from human gaze fixations. We propose to first encode fixations into a semantic
representation and then decode this representation into an image. We evaluate
our method on a recent gaze dataset of 14 participants searching for clothing
in image collages and validate the model's predictions using two human studies.
Our results show that 62% (Chance level = 10%) of the time users were able to
select the categories of the decoded image right. In our second studies we show
the importance of a local gaze encoding for decoding visual search targets of
user
Embedding Words and Senses Together via Joint Knowledge-Enhanced Training
Authors: Massimiliano Mancini, Jose Camacho-Collados, Ignacio Iacobacci, Roberto Navigli
Date: 2016-12-08 00:00:00
Proceeding: CONLL 2017 8
Tasks: Word Embeddings

Word embeddings are widely used in Natural Language Processing, mainly due to
their success in capturing semantic information from massive corpora. However,
their creation process does not allow the different meanings of a word to be
automatically separated, as it conflates them into a single vector. We address
this issue by proposing a new model which learns word and sense embeddings
jointly. Our model exploits large corpora and knowledge from semantic networks
in order to produce a unified vector space of word and sense embeddings. We
evaluate the main features of our approach both qualitatively and
quantitatively in a variety of tasks, highlighting the advantages of the
proposed method in comparison to state-of-the-art word- and sense-based models.
Multi-Level and Multi-Scale Feature Aggregation Using Sample-level Deep Convolutional Neural Networks for Music Classification
Authors: Jongpil Lee, Juhan Nam
Date: 2017-06-21 00:00:00
Tasks: Classification, General Classification, Music Classification, TAG

Music tag words that describe music audio by text have different levels of
abstraction. Taking this issue into account, we propose a music classification
approach that aggregates multi-level and multi-scale features using pre-trained
feature extractors. In particular, the feature extractors are trained in
sample-level deep convolutional neural networks using raw waveforms. We show
that this approach achieves state-of-the-art results on several music
classification datasets.
CBinfer: Change-Based Inference for Convolutional Neural Networks on Video Data
Authors: Lukas Cavigelli, Philippe Degen, Luca Benini
Date: 2017-04-14 00:00:00
Tasks: GPU

Extracting per-frame features using convolutional neural networks for
real-time processing of video data is currently mainly performed on powerful
GPU-accelerated workstations and compute clusters. However, there are many
applications such as smart surveillance cameras that require or would benefit
from on-site processing. To this end, we propose and evaluate a novel algorithm
for change-based evaluation of CNNs for video data recorded with a static
camera setting, exploiting the spatio-temporal sparsity of pixel changes. We
achieve an average speed-up of 8.6x over a cuDNN baseline on a realistic
benchmark with a negligible accuracy loss of less than 0.1% and no retraining
of the network. The resulting energy efficiency is 10x higher than that of
per-frame evaluation and reaches an equivalent of 328 GOp/s/W on the Tegra X1
platform.
JaTeCS an open-source JAva TExt Categorization System
Authors: Andrea Esuli, Tiziano Fagni, Alejandro Moreo Fernandez
Date: 2017-06-21 00:00:00
Tasks: feature selection, Opinion Mining, Text Categorization

JaTeCS is an open source Java library that supports research on automatic
text categorization and other related problems, such as ordinal regression and
quantification, which are of special interest in opinion mining applications.
It covers all the steps of an experimental activity, from reading the corpus to
the evaluation of the experimental results. As JaTeCS is focused on text as the
main input data, it provides the user with many text-dedicated tools, e.g.:
data readers for many formats, including the most commonly used text corpora
and lexical resources, natural language processing tools, multi-language
support, methods for feature selection and weighting, the implementation of
many machine learning algorithms as well as wrappers for well-known external
software (e.g., SVM_light) which enable their full control from code. JaTeCS
support its expansion by abstracting through interfaces many of the typical
tools and procedures used in text processing tasks. The library also provides a
number of "template" implementations of typical experimental setups (e.g.,
train-test, k-fold validation, grid-search optimization, randomized runs) which
enable fast realization of experiments just by connecting the templates with
data readers, learning algorithms and evaluation measures.
Saliency Detection by Forward and Backward Cues in Deep-CNNs
Authors: Nevrez Imamoglu, Chi Zhang, Wataru Shimoda, Yuming Fang, Boxin Shi
Date: 2017-03-01 00:00:00
Tasks: Object, Saliency Detection

As prior knowledge of objects or object features helps us make relations for
similar objects on attentional tasks, pre-trained deep convolutional neural
networks (CNNs) can be used to detect salient objects on images regardless of
the object class is in the network knowledge or not. In this paper, we propose
a top-down saliency model using CNN, a weakly supervised CNN model trained for
1000 object labelling task from RGB images. The model detects attentive regions
based on their objectness scores predicted by selected features from CNNs. To
estimate the salient objects effectively, we combine both forward and backward
features, while demonstrating that partially-guided backpropagation will
provide sufficient information for selecting the features from forward run of
CNN model. Finally, these top-down cues are enhanced with a state-of-the-art
bottom-up model as complementing the overall saliency. As the proposed model is
an effective integration of forward and backward cues through objectness
without any supervision or regression to ground truth data, it gives promising
results compared to state-of-the-art models in two different datasets.
GM-Net: Learning Features with More Efficiency
Authors: Yujia Chen, Ce Li
Date: 2017-06-21 00:00:00
Tasks: General Classification, image-classification, Image Classification

Deep Convolutional Neural Networks (CNNs) are capable of learning
unprecedentedly effective features from images. Some researchers have struggled
to enhance the parameters' efficiency using grouped convolution. However, the
relation between the optimal number of convolutional groups and the recognition
performance remains an open problem. In this paper, we propose a series of
Basic Units (BUs) and a two-level merging strategy to construct deep CNNs,
referred to as a joint Grouped Merging Net (GM-Net), which can produce joint
grouped and reused deep features while maintaining the feature discriminability
for classification tasks. Our GM-Net architectures with the proposed BU_A
(dense connection) and BU_B (straight mapping) lead to significant reduction in
the number of network parameters and obtain performance improvement in image
classification tasks. Extensive experiments are conducted to validate the
superior performance of the GM-Net than the state-of-the-arts on the benchmark
datasets, e.g., MNIST, CIFAR-10, CIFAR-100 and SVHN.
NPGLM: A Non-Parametric Method for Temporal Link Prediction
Authors: Sina Sajadmanesh, Jiawei Zhang, Hamid R. Rabiee
Date: 2017-06-21 00:00:00
Tasks: Link Prediction, Prediction

In this paper, we try to solve the problem of temporal link prediction in
information networks. This implies predicting the time it takes for a link to
appear in the future, given its features that have been extracted at the
current network snapshot. To this end, we introduce a probabilistic
non-parametric approach, called "Non-Parametric Generalized Linear Model"
(NP-GLM), which infers the hidden underlying probability distribution of the
link advent time given its features. We then present a learning algorithm for
NP-GLM and an inference method to answer time-related queries. Extensive
experiments conducted on both synthetic data and real-world Sina Weibo social
network demonstrate the effectiveness of NP-GLM in solving temporal link
prediction problem vis-a-vis competitive baselines.
Assigning personality/identity to a chatting machine for coherent conversation generation
Authors: Qiao Qian, Minlie Huang, Haizhou Zhao, Jingfang Xu, Xiaoyan Zhu
Date: 2017-06-09 00:00:00
Tasks: Chatbot, Decoder, Position

Endowing a chatbot with personality or an identity is quite challenging but
critical to deliver more realistic and natural conversations. In this paper, we
address the issue of generating responses that are coherent to a pre-specified
agent profile. We design a model consisting of three modules: a profile
detector to decide whether a post should be responded using the profile and
which key should be addressed, a bidirectional decoder to generate responses
forward and backward starting from a selected profile value, and a position
detector that predicts a word position from which decoding should start given a
selected profile value. We show that general conversation data from social
media can be used to generate profile-coherent responses. Manual and automatic
evaluation shows that our model can deliver more coherent, natural, and
diversified responses.
Saliency Guided End-to-End Learning for Weakly Supervised Object Detection
Authors: Baisheng Lai, Xiaojin Gong
Date: 2017-06-21 00:00:00
Tasks: object-detection, Object Detection, Saliency Prediction, Weakly Supervised Object Detection

Weakly supervised object detection (WSOD), which is the problem of learning
detectors using only image-level labels, has been attracting more and more
interest. However, this problem is quite challenging due to the lack of
location supervision. To address this issue, this paper integrates saliency
into a deep architecture, in which the location in- formation is explored both
explicitly and implicitly. Specifically, we select highly confident object pro-
posals under the guidance of class-specific saliency maps. The location
information, together with semantic and saliency information, of the selected
proposals are then used to explicitly supervise the network by imposing two
additional losses. Meanwhile, a saliency prediction sub-network is built in the
architecture. The prediction results are used to implicitly guide the
localization procedure. The entire network is trained end-to-end. Experiments
on PASCAL VOC demonstrate that our approach outperforms all state-of-the-arts.
Interpretation of Semantic Tweet Representations
Authors: J Ganesh, Manish Gupta, Vasudeva Varma
Date: 2017-04-04 00:00:00
Tasks: Feature Engineering, Property Prediction, Representation Learning, Semantic Textual Similarity, Sentiment Analysis

Research in analysis of microblogging platforms is experiencing a renewed
surge with a large number of works applying representation learning models for
applications like sentiment analysis, semantic textual similarity computation,
hashtag prediction, etc. Although the performance of the representation
learning models has been better than the traditional baselines for such tasks,
little is known about the elementary properties of a tweet encoded within these
representations, or why particular representations work better for certain
tasks. Our work presented here constitutes the first step in opening the
black-box of vector embeddings for tweets. Traditional feature engineering
methods for high-level applications have exploited various elementary
properties of tweets. We believe that a tweet representation is effective for
an application because it meticulously encodes the application-specific
elementary properties of tweets. To understand the elementary properties
encoded in a tweet representation, we evaluate the representations on the
accuracy to which they can model each of those properties such as tweet length,
presence of particular words, hashtags, mentions, capitalization, etc. Our
systematic extensive study of nine supervised and four unsupervised tweet
representations against most popular eight textual and five social elementary
properties reveal that Bi-directional LSTMs (BLSTMs) and Skip-Thought Vectors
(STV) best encode the textual and social properties of tweets respectively.
FastText is the best model for low resource settings, providing very little
degradation with reduction in embedding size. Finally, we draw interesting
insights by correlating the model performance obtained for elementary property
prediction tasks with the highlevel downstream applications.
Solar Power Plant Detection on Multi-Spectral Satellite Imagery using Weakly-Supervised CNN with Feedback Features and m-PCNN Fusion
Authors: Nevrez Imamoglu, Motoki Kimura, Hiroki Miyamoto, Aito Fujita, Ryosuke Nakamura
Date: 2017-04-21 00:00:00
Tasks: General Classification, image-classification, Image Classification

Most of the traditional convolutional neural networks (CNNs) implements
bottom-up approach (feed-forward) for image classifications. However, many
scientific studies demonstrate that visual perception in primates rely on both
bottom-up and top-down connections. Therefore, in this work, we propose a CNN
network with feedback structure for Solar power plant detection on
middle-resolution satellite images. To express the strength of the top-down
connections, we introduce feedback CNN network (FB-Net) to a baseline CNN model
used for solar power plant classification on multi-spectral satellite data.
Moreover, we introduce a method to improve class activation mapping (CAM) to
our FB-Net, which takes advantage of multi-channel pulse coupled neural network
(m-PCNN) for weakly-supervised localization of the solar power plants from the
features of proposed FB-Net. For the proposed FB-Net CAM with m-PCNN,
experimental results demonstrated promising results on both solar-power plant
image classification and detection task.
Jointly Learning Word Embeddings and Latent Topics
Authors: Bei Shi, Wai Lam, Shoaib Jameel, Steven Schockaert, Kwun Ping Lai
Date: 2017-06-21 00:00:00
Tasks: Learning Word Embeddings, Topic Models, Word Embeddings

Word embedding models such as Skip-gram learn a vector-space representation
for each word, based on the local word collocation patterns that are observed
in a text corpus. Latent topic models, on the other hand, take a more global
view, looking at the word distributions across the corpus to assign a topic to
each word occurrence. These two paradigms are complementary in how they
represent the meaning of word occurrences. While some previous works have
already looked at using word embeddings for improving the quality of latent
topics, and conversely, at using latent topics for improving word embeddings,
such "two-step" methods cannot capture the mutual interaction between the two
paradigms. In this paper, we propose STE, a framework which can learn word
embeddings and latent topics in a unified manner. STE naturally obtains
topic-specific word embeddings, and thus addresses the issue of polysemy. At
the same time, it also learns the term distributions of the topics, and the
topic distributions of the documents. Our experimental results demonstrate that
the STE model can indeed generate useful topic-specific word embeddings and
coherent latent topics in an effective and efficient way.
GPGPU Acceleration of the KAZE Image Feature Extraction Algorithm
Authors: Ramkumar B, R. S. Hegde, Rob Laber, Hristo Bojinov
Date: 2017-06-21 00:00:00
Tasks: CPU, GPU

The recently proposed open-source KAZE image feature detection and
description algorithm offers unprecedented performance in comparison to
conventional ones like SIFT and SURF as it relies on nonlinear scale spaces
instead of Gaussian linear scale spaces. The improved performance, however,
comes with a significant computational cost limiting its use for many
applications. We report a GPGPU implementation of the KAZE algorithm without
resorting to binary descriptors for gaining speedup. For a 1920 by 1200 sized
image our Compute Unified Device Architecture (CUDA) C based GPU version took
around 300 milliseconds on a NVIDIA GeForce GTX Titan X (Maxwell
Architecture-GM200) card in comparison to nearly 2400 milliseconds for a
multithreaded CPU version (16 threaded Intel(R) Xeon(R) CPU E5-2650
processsor). The CUDA based parallel implementation is described in detail with
fine-grained comparison between the GPU and CPU implementations. By achieving
nearly 8 fold speedup without performance degradation our work expands the
applicability of the KAZE algorithm. Additionally, the strategies described
here can prove useful for the GPU implementation of other nonlinear scale space
based methods.
Cross-language Learning with Adversarial Neural Networks: Application to Community Question Answering
Authors: Shafiq Joty, Preslav Nakov, Lluís Màrquez, Israa Jaradat
Date: 2017-06-21 00:00:00
Tasks: Community Question Answering, Question Answering, Question Similarity, Reranking

We address the problem of cross-language adaptation for question-question
similarity reranking in community question answering, with the objective to
port a system trained on one input language to another input language given
labeled training data for the first language and only unlabeled data for the
second language. In particular, we propose to use adversarial training of
neural networks to learn high-level features that are discriminative for the
main learning task, and at the same time are invariant across the input
languages. The evaluation results show sizable improvements for our
cross-language adversarial neural network (CLANN) model over a strong
non-adversarial system.