pwc-archive/papers-with-abstracts huggingface dataset:
Tightness-aware Evaluation Protocol for Scene Text Detection
Authors: Yuliang Liu, Lianwen Jin, Zecheng Xie, Canjie Luo, Shuaitao Zhang, Lele Xie
Date: 2019-03-27 00:00:00
Proceeding: CVPR 2019 6
Tasks: object-detection, Object Detection, Scene Text Detection, Text Detection

Evaluation protocols play key role in the developmental progress of text
detection methods. There are strict requirements to ensure that the evaluation
methods are fair, objective and reasonable. However, existing metrics exhibit
some obvious drawbacks: 1) They are not goal-oriented; 2) they cannot recognize
the tightness of detection methods; 3) existing one-to-many and many-to-one
solutions involve inherent loopholes and deficiencies. Therefore, this paper
proposes a novel evaluation protocol called Tightness-aware
Intersect-over-Union (TIoU) metric that could quantify completeness of ground
truth, compactness of detection, and tightness of matching degree.
Specifically, instead of merely using the IoU value, two common detection
behaviors are properly considered; meanwhile, directly using the score of TIoU
to recognize the tightness. In addition, we further propose a straightforward
method to address the annotation granularity issue, which can fairly evaluate
word and text-line detections simultaneously. By adopting the detection results
from published methods and general object detection frameworks, comprehensive
experiments on ICDAR 2013 and ICDAR 2015 datasets are conducted to compare
recent metrics and the proposed TIoU metric. The comparison demonstrated some
promising new prospects, e.g., determining the methods and frameworks for which
the detection is tighter and more beneficial to recognize. Our method is
extremely simple; however, the novelty is none other than the proposed metric
can utilize simplest but reasonable improvements to lead to many interesting
and insightful prospects and solving most the issues of the previous metrics.
The code is publicly available at https://github.com/Yuliang-Liu/TIoU-metric .
Precise Detection in Densely Packed Scenes
Authors: Eran Goldman, Roei Herzig, Aviv Eisenschtat, Oria Ratzon, Itsik Levi, Jacob Goldberger, Tal Hassner
Date: 2019-04-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Dense Object Detection, Object, object-detection, Object Detection

Man-made scenes can be densely packed, containing numerous objects, often
identical, positioned in close proximity. We show that precise object detection
in such scenes remains a challenging frontier even for state-of-the-art object
detectors. We propose a novel, deep-learning based method for precise object
detection, designed for such challenging settings. Our contributions include:
(1) A layer for estimating the Jaccard index as a detection quality score; (2)
a novel EM merging unit, which uses our quality scores to resolve detection
overlap ambiguities; finally, (3) an extensive, annotated data set, SKU-110K,
representing packed retail environments, released for training and testing
under such extreme settings. Detection tests on SKU-110K and counting tests on
the CARPK and PUCPR+ show our method to outperform existing state-of-the-art
with substantial margins. The code and data will be made available on
\url{www.github.com/eg4000/SKU110K_CVPR19}.
EventNet: Asynchronous Recursive Event Processing
Authors: Yusuke Sekikawa, Kosuke Hara, Hideo Saito
Date: 2018-12-07 00:00:00
Proceeding: CVPR 2019 6
Tasks: CPU

Event cameras are bio-inspired vision sensors that mimic retinas to
asynchronously report per-pixel intensity changes rather than outputting an
actual intensity image at regular intervals. This new paradigm of image sensor
offers significant potential advantages; namely, sparse and non-redundant data
representation. Unfortunately, however, most of the existing artificial neural
network architectures, such as a CNN, require dense synchronous input data, and
therefore, cannot make use of the sparseness of the data. We propose EventNet,
a neural network designed for real-time processing of asynchronous event
streams in a recursive and event-wise manner. EventNet models dependence of the
output on tens of thousands of causal events recursively using a novel temporal
coding scheme. As a result, at inference time, our network operates in an
event-wise manner that is realized with very few sum-of-the-product
operations---look-up table and temporal feature aggregation---which enables
processing of 1 mega or more events per second on standard CPU. In experiments
using real data, we demonstrated the real-time performance and robustness of
our framework.
On the Estimation and Use of Statistical Modelling in Information Retrieval
Authors: Casper Petersen
Date: 2019-03-30 00:00:00
Tasks: Information Retrieval, Retrieval

Several tasks in information retrieval (IR) rely on assumptions regarding the
distribution of some property (such as term frequency) in the data being
processed. This thesis argues that such distributional assumptions can lead to
incorrect conclusions and proposes a statistically principled method for
determining the "true" distribution. This thesis further applies this method to
derive a new family of ranking models that adapt their computations to the
statistics of the data being processed. Experimental evaluation shows results
on par or better than multiple strong baselines on several TREC collections.
Overall, this thesis concludes that distributional assumptions can be replaced
with an effective, efficient and principled method for determining the "true"
distribution and that using the "true" distribution can lead to improved
retrieval performance.
Multi-Task Ordinal Regression for Jointly Predicting the Trustworthiness and the Leading Political Ideology of News Media
Authors: Ramy Baly, Georgi Karadzhov, Abdelrhman Saleh, James Glass, Preslav Nakov
Date: 2019-04-01 00:00:00
Proceeding: NAACL 2019 6
Tasks: Articles

In the context of fake news, bias, and propaganda, we study two important but
relatively under-explored problems: (i) trustworthiness estimation (on a
3-point scale) and (ii) political ideology detection (left/right bias on a
7-point scale) of entire news outlets, as opposed to evaluating individual
articles. In particular, we propose a multi-task ordinal regression framework
that models the two problems jointly. This is motivated by the observation that
hyper-partisanship is often linked to low trustworthiness, e.g., appealing to
emotions rather than sticking to the facts, while center media tend to be
generally more impartial and trustworthy. We further use several auxiliary
tasks, modeling centrality, hyperpartisanship, as well as left-vs.-right bias
on a coarse-grained scale. The evaluation results show sizable performance
gains by the joint models over models that target the problems in isolation.
Combining Crowd and Machines for Multi-predicate Item Screening
Authors: Evgeny Krivosheev, Fabio Casati, Marcos Baez, Boualem Benatallah
Date: 2019-04-01 00:00:00
Tasks: Classification, General Classification

This paper discusses how crowd and machine classifiers can be efficiently
combined to screen items that satisfy a set of predicates. We show that this is
a recurring problem in many domains, present machine-human (hybrid) algorithms
that screen items efficiently and estimate the gain over human-only or
machine-only screening in terms of performance and cost. We further show how,
given a new classification problem and a set of classifiers of unknown accuracy
for the problem at hand, we can identify how to manage the cost-accuracy trade
off by progressively determining if we should spend budget to obtain test data
(to assess the accuracy of the given classifiers), or to train an ensemble of
classifiers, or whether we should leverage the existing machine classifiers
with the crowd, and in this case how to efficiently combine them based on their
estimated characteristics to obtain the classification. We demonstrate that the
techniques we propose obtain significant cost/accuracy improvements with
respect to the leading classification algorithms.
Affect in Tweets Using Experts Model
Authors: Subba Reddy Oota, Adithya Avvaru, Mounika Marreddy, Radhika Mamidi
Date: 2019-03-20 00:00:00
Proceeding: PACLIC 2018 12
Tasks: Mixture-of-Experts, model, Sentiment Analysis

Estimating the intensity of emotion has gained significance as modern textual
inputs in potential applications like social media, e-retail markets,
psychology, advertisements etc., carry a lot of emotions, feelings, expressions
along with its meaning. However, the approaches of traditional sentiment
analysis primarily focuses on classifying the sentiment in general (positive or
negative) or at an aspect level(very positive, low negative, etc.) and cannot
exploit the intensity information. Moreover, automatically identifying emotions
like anger, fear, joy, sadness, disgust etc., from text introduces challenging
scenarios where single tweet may contain multiple emotions with different
intensities and some emotions may even co-occur in some of the tweets. In this
paper, we propose an architecture, Experts Model, inspired from the standard
Mixture of Experts (MoE) model. The key idea here is each expert learns
different sets of features from the feature vector which helps in better
emotion detection from the tweet. We compared the results of our Experts Model
with both baseline results and top five performers of SemEval-2018 Task-1,
Affect in Tweets (AIT). The experimental results show that our proposed
approach deals with the emotion detection problem and stands at top-5 results.
Keyphrase Generation: A Text Summarization Struggle
Authors: Erion Çano, Ondřej Bojar
Date: 2019-03-29 00:00:00
Tasks: Articles, Keyphrase Generation, Text Summarization

Authors' keyphrases assigned to scientific articles are essential for
recognizing content and topic aspects. Most of the proposed supervised and
unsupervised methods for keyphrase generation are unable to produce terms that
are valuable but do not appear in the text. In this paper, we explore the
possibility of considering the keyphrase string as an abstractive summary of
the title and the abstract. First, we collect, process and release a large
dataset of scientific paper metadata that contains 2.2 million records. Then we
experiment with popular text summarization neural architectures. Despite using
advanced deep learning models, large quantities of data and many days of
computation, our systematic evaluation on four test datasets reveals that the
explored text summarization methods could not produce better keyphrases than
the simpler unsupervised methods, or the existing supervised ones.
Enhancing the long-term performance of recommender system
Authors: Leyang Xue, Peng Zhang, An Zeng
Date: 2019-04-01 00:00:00
Tasks: Diversity, Recommendation Systems

Recommender system is a critically important tool in online commercial system
and provide users with personalized recommendation on items. So far, numerous
recommendation algorithms have been made to further improve the recommendation
performance in a single-step recommendation, while the long-term recommendation
performance is neglected. In this paper, we proposed an approach called
Adjustment of Recommendation List (ARL) to enhance the long-term recommendation
accuracy. In order to observe the long-term accuracy, we developed an evolution
model of network to simulate the interaction between the recommender system and
user's behaviour. The result shows that not only long-term recommendation
accuracy can be enhanced significantly but the diversity of item in online
system maintains healthy. Notably, an optimal parameter n* of ARL existed in
long-term recommendation, indicating that there is a trade-off between keeping
diversity of item and user's preference to maximize the long-term
recommendation accuracy. Finally, we confirmed that the optimal parameter n* is
stable during evolving network, which reveals the robustness of ARL method.
CoaCor: Code Annotation for Code Retrieval with Reinforcement Learning
Authors: Ziyu Yao, Jayavardhan Reddy Peddamail, Huan Sun
Date: 2019-03-13 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Retrieval

To accelerate software development, much research has been performed to help
people understand and reuse the huge amount of available code resources. Two
important tasks have been widely studied: code retrieval, which aims to
retrieve code snippets relevant to a given natural language query from a code
base, and code annotation, where the goal is to annotate a code snippet with a
natural language description. Despite their advancement in recent years, the
two tasks are mostly explored separately. In this work, we investigate a novel
perspective of Code annotation for Code retrieval (hence called `CoaCor'),
where a code annotation model is trained to generate a natural language
annotation that can represent the semantic meaning of a given code snippet and
can be leveraged by a code retrieval model to better distinguish relevant code
snippets from others. To this end, we propose an effective framework based on
reinforcement learning, which explicitly encourages the code annotation model
to generate annotations that can be used for the retrieval task. Through
extensive experiments, we show that code annotations generated by our framework
are much more detailed and more useful for code retrieval, and they can further
improve the performance of existing code retrieval models significantly.
How to Estimate the Ability of a Metaheuristic Algorithm to Guide Heuristics During Optimization
Authors: Miloš Simić
Date: 2019-03-29 00:00:00

Metaheuristics are general methods that guide application of concrete
heuristic(s) to problems that are too hard to solve using exact algorithms.
However, even though a growing body of literature has been devoted to their
statistical evaluation, the approaches proposed so far are able to assess only
coupled effects of metaheuristics and heuristics. They do not reveal us
anything about how efficient the examined metaheuristic is at guiding its
subordinate heuristic(s), nor do they provide us information about how much the
heuristic component of the combined algorithm contributes to the overall
performance. In this paper, we propose a simple yet effective methodology of
doing so by deriving a naive, placebo metaheuristic from the one being studied
and comparing the distributions of chosen performance metrics for the two
methods. We propose three measures of difference between the two distributions.
Those measures, which we call BER values (benefit, equivalence, risk) are based
on a preselected threshold of practical significance which represents the
minimal difference between two performance scores required for them to be
considered practically different. We illustrate usefulness of our methodology
on the example of Simulated Annealing, Boolean Satisfiability Problem, and the
Flip heuristic.
Using Scratch to Teach Undergraduate Students' Skills on Artificial Intelligence
Authors: Julian Estevez, Gorka Garate, JM Lopez Guede, Manuel Graña
Date: 2019-03-30 00:00:00
Tasks: Clustering

This paper presents a educational workshop in Scratch that is proposed for
the active participation of undergraduate students in contexts of Artificial
Intelligence. The main objective of the activity is to demystify the complexity
of Artificial Intelligence and its algorithms. For this purpose, students must
realize simple exercises of clustering and two neural networks, in Scratch. The
detailed methodology to get that is presented in the article.
A New Expert Questioning Approach to More Efficient Fault Localization in Ontologies
Authors: Patrick Rodler, Michael Eichholzer
Date: 2019-03-31 00:00:00
Tasks: Fault localization

When ontologies reach a certain size and complexity, faults such as inconsistencies, unsatisfiable classes or wrong entailments are hardly avoidable. Locating the incorrect axioms that cause these faults is a hard and time-consuming task. Addressing this issue, several techniques for semi-automatic fault localization in ontologies have been proposed. Often, these approaches involve a human expert who provides answers to system-generated questions about the intended (correct) ontology in order to reduce the possible fault locations. To suggest as informative questions as possible, existing methods draw on various algorithmic optimizations as well as heuristics. However, these computations are often based on certain assumptions about the interacting user. In this work, we characterize and discuss different user types and show that existing approaches do not achieve optimal efficiency for all of them. As a remedy, we suggest a new type of expert question which aims at fitting the answering behavior of all analyzed experts. Moreover, we present an algorithm to optimize this new query type which is fully compatible with the (tried and tested) heuristics used in the field. Experiments on faulty real-world ontologies show the potential of the new querying method for minimizing the expert consultation time, independent of the expert type. Besides, the gained insights can inform the design of interactive debugging tools towards better meeting their users' needs.
Cooperative Multi-Agent Reinforcement Learning Framework for Scalping Trading
Authors: Uk Jo, Taehyun Jo, Wanjun Kim, Iljoo Yoon, Dongseok Lee, Seungho Lee
Date: 2019-03-31 00:00:00
Tasks: Deep Reinforcement Learning, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We explore deep Reinforcement Learning(RL) algorithms for scalping trading
and knew that there is no appropriate trading gym and agent examples. Thus we
propose gym and agent like Open AI gym in finance. Not only that, we introduce
new RL framework based on our hybrid algorithm which leverages between
supervised learning and RL algorithm and uses meaningful observations such
order book and settlement data from experience watching scalpers trading. That
is very crucial information for traders behavior to be decided. To feed these
data into our model, we use spatio-temporal convolution layer, called Conv3D
for order book data and temporal CNN, called Conv1D for settlement data. Those
are preprocessed by episode filter we developed. Agent consists of four sub
agents divided to clarify their own goal to make best decision. Also, we
adopted value and policy based algorithm to our framework. With these features,
we could make agent mimic scalpers as much as possible. In many fields, RL
algorithm has already begun to transcend human capabilities in many domains.
This approach could be a starting point to beat human in the financial stock
market, too and be a good reference for anyone who wants to design RL algorithm
in real world domain. Finally, weexperiment our framework and gave you
experiment progress.
Elaboration Tolerant Representation of Markov Decision Process via Decision-Theoretic Extension of Probabilistic Action Language pBC+
Authors: Yi Wang, Joohyung Lee
Date: 2019-04-01 00:00:00

We extend probabilistic action language pBC+ with the notion of utility as in decision theory. The semantics of the extended pBC+ can be defined as a shorthand notation for a decision-theoretic extension of the probabilistic answer set programming language LPMLN. Alternatively, the semantics of pBC+ can also be defined in terms of Markov Decision Process (MDP), which in turn allows for representing MDP in a succinct and elaboration tolerant way as well as to leverage an MDP solver to compute pBC+. The idea led to the design of the system pbcplus2mdp, which can find an optimal policy of a pBC+ action description using an MDP solver. This paper is under consideration in Theory and Practice of Logic Programming (TPLP).
Constructing Hierarchical Q&A Datasets for Video Story Understanding
Authors: Yu-Jung Heo, Kyoung-Woon On, SeongHo Choi, Jaeseo Lim, Jinah Kim, Jeh-Kwang Ryu, Byung-Chull Bae, Byoung-Tak Zhang
Date: 2019-04-01 00:00:00
Tasks: Video Understanding

Video understanding is emerging as a new paradigm for studying human-like AI.
Question-and-Answering (Q&A) is used as a general benchmark to measure the
level of intelligence for video understanding. While several previous studies
have suggested datasets for video Q&A tasks, they did not really incorporate
story-level understanding, resulting in highly-biased and lack of variance in
degree of question difficulty. In this paper, we propose a hierarchical method
for building Q&A datasets, i.e. hierarchical difficulty levels. We introduce
three criteria for video story understanding, i.e. memory capacity, logical
complexity, and DIKW (Data-Information-Knowledge-Wisdom) pyramid. We discuss
how three-dimensional map constructed from these criteria can be used as a
metric for evaluating the levels of intelligence relating to video story
understanding.
Distant Supervision Relation Extraction with Intra-Bag and Inter-Bag Attentions
Authors: Zhi-Xiu Ye, Zhen-Hua Ling
Date: 2019-03-30 00:00:00
Proceeding: NAACL 2019 6
Tasks: Relation, Relation Extraction, Sentence, Sentence Embeddings

This paper presents a neural relation extraction method to deal with the
noisy training data generated by distant supervision. Previous studies mainly
focus on sentence-level de-noising by designing neural networks with intra-bag
attentions. In this paper, both intra-bag and inter-bag attentions are
considered in order to deal with the noise at sentence-level and bag-level
respectively. First, relation-aware bag representations are calculated by
weighting sentence embeddings using intra-bag attentions. Here, each possible
relation is utilized as the query for attention calculation instead of only
using the target relation in conventional methods. Furthermore, the
representation of a group of bags in the training set which share the same
relation label is calculated by weighting bag representations using a
similarity-based inter-bag attention module. Finally, a bag group is utilized
as a training sample when building our relation extractor. Experimental results
on the New York Times dataset demonstrate the effectiveness of our proposed
intra-bag and inter-bag attention modules. Our method also achieves better
relation extraction accuracy than state-of-the-art methods on this dataset.
Lane Change Decision-making through Deep Reinforcement Learning with Rule-based Constraints
Authors: Jun-Jie Wang, Qichao Zhang, Dongbin Zhao, Yaran Chen
Date: 2019-03-30 00:00:00
Tasks: Autonomous Driving, Decision Making, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Autonomous driving decision-making is a great challenge due to the complexity
and uncertainty of the traffic environment. Combined with the rule-based
constraints, a Deep Q-Network (DQN) based method is applied for autonomous
driving lane change decision-making task in this study. Through the combination
of high-level lateral decision-making and low-level rule-based trajectory
modification, a safe and efficient lane change behavior can be achieved. With
the setting of our state representation and reward function, the trained agent
is able to take appropriate actions in a real-world-like simulator. The
generated policy is evaluated on the simulator for 10 times, and the results
demonstrate that the proposed rule-based DQN method outperforms the rule-based
approach and the DQN method.