pwc-archive/papers-with-abstracts huggingface dataset:
VRKitchen: an Interactive 3D Virtual Environment for Task-oriented Learning
Authors: Xiaofeng Gao, Ran Gong, Tianmin Shu, Xu Xie, Shu Wang, Song-Chun Zhu
Date: 2019-03-13 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Task Planning

One of the main challenges of advancing task-oriented learning such as visual
task planning and reinforcement learning is the lack of realistic and
standardized environments for training and testing AI agents. Previously,
researchers often relied on ad-hoc lab environments. There have been recent
advances in virtual systems built with 3D physics engines and photo-realistic
rendering for indoor and outdoor environments, but the embodied agents in those
systems can only conduct simple interactions with the world (e.g., walking
around, moving objects, etc.). Most of the existing systems also do not allow
human participation in their simulated environments. In this work, we design
and implement a virtual reality (VR) system, VRKitchen, with integrated
functions which i) enable embodied agents powered by modern AI methods (e.g.,
planning, reinforcement learning, etc.) to perform complex tasks involving a
wide range of fine-grained object manipulations in a realistic environment, and
ii) allow human teachers to perform demonstrations to train agents (i.e.,
learning from demonstration). We also provide standardized evaluation
benchmarks and data collection tools to facilitate a broad use in research on
task-oriented learning and beyond.
Detection and Tracking of Small Objects in Sparse 3D Laser Range Data
Authors: Jan Razlaw, Jan Quenzel, Sven Behnke
Date: 2019-03-14 00:00:00
Tasks: Multi-Object Tracking, Object Tracking

Detection and tracking of dynamic objects is a key feature for autonomous
behavior in a continuously changing environment. With the increasing popularity
and capability of micro aerial vehicles (MAVs) efficient algorithms have to be
utilized to enable multi object tracking on limited hardware and data provided
by lightweight sensors. We present a novel segmentation approach based on a
combination of median filters and an efficient pipeline for detection and
tracking of small objects within sparse point clouds generated by a Velodyne
VLP-16 sensor. We achieve real-time performance on a single core of our MAV
hardware by exploiting the inherent structure of the data. Our approach is
evaluated on simulated and real scans of in- and outdoor environments,
obtaining results comparable to the state of the art. Additionally, we provide
an application for filtering the dynamic and mapping the static part of the
data, generating further insights into the performance of the pipeline on
unlabeled data.
Water Distribution System Design Using Multi-Objective Particle Swarm Optimisation
Authors: Mahesh B. Patil, M. Naveen Naidu, A. Vasan, Murari R. R. Varma
Date: 2019-03-14 00:00:00

Application of the multi-objective particle swarm optimisation (MOPSO)
algorithm to design of water distribution systems is described. An earlier
MOPSO algorithm is augmented with (a) local search, (b) a modified strategy for
assigning the leader, and (c) a modified mutation scheme. For one of the
benchmark problems described in the literature, the effect of each of the above
features on the algorithm performance is demonstrated. The augmented MOPSO
algorithm (called MOPSO+) is applied to five benchmark problems, and in each
case, it finds non-dominated solutions not reported earlier. In addition, for
the purpose of comparing Pareto fronts (sets of non-dominated solutions)
obtained by different algorithms, a new criterion is suggested, and its
usefulness is pointed out with an example. Finally, some suggestions regarding
future research directions are made.
Low-Resource Syntactic Transfer with Unsupervised Source Reordering
Authors: Mohammad Sadegh Rasooli, Michael Collins
Date: 2019-03-13 00:00:00
Proceeding: NAACL 2019 6
Tasks: Cross-Lingual Transfer, Dependency Parsing

We describe a cross-lingual transfer method for dependency parsing that takes
into account the problem of word order differences between source and target
languages. Our model only relies on the Bible, a considerably smaller parallel
data than the commonly used parallel data in transfer methods. We use the
concatenation of projected trees from the Bible corpus, and the gold-standard
treebanks in multiple source languages along with cross-lingual word
representations. We demonstrate that reordering the source treebanks before
training on them for a target language improves the accuracy of languages
outside the European language family. Our experiments on 68 treebanks (38
languages) in the Universal Dependencies corpus achieve a high accuracy for all
languages. Among them, our experiments on 16 treebanks of 12 non-European
languages achieve an average UAS absolute improvement of 3.3% over a
state-of-the-art method.
Consistent Dialogue Generation with Self-supervised Feature Learning
Authors: Yizhe Zhang, Xiang Gao, Sungjin Lee, Chris Brockett, Michel Galley, Jianfeng Gao, Bill Dolan
Date: 2019-03-13 00:00:00
Tasks: Dialogue Generation, Response Generation

Generating responses that are consistent with the dialogue context is one of the central challenges in building engaging conversational agents. We demonstrate that neural conversation models can be geared towards generating consistent responses by maintaining certain features related to topics and personas throughout the conversation. Past work has required external supervision that exploits features such as user identities that are often unavailable. In our approach, topic and persona feature extractors are trained using a contrastive training scheme that utilizes the natural structure of dialogue data. We further adopt a feature disentangling loss which, paired with controllable response generation techniques, allows us to promote or demote certain learned topics and persona features. Evaluation results demonstrate the model's ability to capture meaningful topics and persona features. The incorporation of the learned features brings significant improvement in terms of the quality of generated responses on two dialogue datasets.
Survey of Text-based Epidemic Intelligence: A Computational Linguistic Perspective
Authors: Aditya Joshi, Sarvnaz Karimi, Ross Sparks, Cecile Paris, C Raina MacIntyre
Date: 2019-03-14 00:00:00
Tasks: Event Detection, General Classification, Survey

Epidemic intelligence deals with the detection of disease outbreaks using
formal (such as hospital records) and informal sources (such as user-generated
text on the web) of information. In this survey, we discuss approaches for
epidemic intelligence that use textual datasets, referring to it as `text-based
epidemic intelligence'. We view past work in terms of two broad categories:
health mention classification (selecting relevant text from a large volume) and
health event detection (predicting epidemic events from a collection of
relevant text). The focus of our discussion is the underlying computational
linguistic techniques in the two categories. The survey also provides details
of the state-of-the-art in annotation techniques, resources and evaluation
strategies for epidemic intelligence.
Deep Patent Landscaping Model Using Transformer and Graph Embedding
Authors: Seokkyu Choi, Hyeonju Lee, Eunjeong Lucy Park, Sungchul Choi
Date: 2019-03-14 00:00:00
Tasks: Benchmarking, Graph Embedding

Patent landscaping is a method used for searching related patents during a research and development (R&D) project. To avoid the risk of patent infringement and to follow current trends in technology, patent landscaping is a crucial task required during the early stages of an R&D project. As the process of patent landscaping requires advanced resources and can be tedious, the demand for automated patent landscaping has been gradually increasing. However, a shortage of well-defined benchmark datasets and comparable models makes it difficult to find related research studies. In this paper, we propose an automated patent landscaping model based on deep learning. To analyze the text of patents, the proposed model uses a modified transformer structure. To analyze the metadata of patents, we propose a graph embedding method that uses a diffusion graph called Diff2Vec. Furthermore, we introduce four benchmark datasets for comparing related research studies in patent landscaping. The datasets are produced by querying Google BigQuery, based on a search formula from a Korean patent attorney. The obtained results indicate that the proposed model and datasets can attain state-of-the-art performance, as compared with current patent landscaping models.
Interactive Concept Mining on Personal Data -- Bootstrapping Semantic Services
Authors: Markus Schröder, Christian Jilek, Andreas Dengel
Date: 2019-03-14 00:00:00
Tasks: Management

Semantic services (e.g. Semantic Desktops) are still afflicted by a cold
start problem: in the beginning, the user's personal information sphere, i.e.
files, mails, bookmarks, etc., is not represented by the system. Information
extraction tools used to kick-start the system typically create 1:1
representations of the different information items. Higher level concepts, for
example found in file names, mail subjects or in the content body of these
items, are not extracted. Leaving these concepts out may lead to
underperformance, having to many of them (e.g. by making every found term a
concept) will clutter the arising knowledge graph with non-helpful relations.
In this paper, we present an interactive concept mining approach proposing
concept candidates gathered by exploiting given schemata of usual personal
information management applications and analysing the personal information
sphere using various metrics. To heed the subjective view of the user, a
graphical user interface allows to easily rank and give feedback on proposed
concept candidates, thus keeping only those actually considered relevant. A
prototypical implementation demonstrates major steps of our approach.
Absit invidia verbo: Comparing Deep Learning methods for offensive language
Authors: Silvia Sapora, Bogdan Lazarescu, Christo Lolov
Date: 2019-03-14 00:00:00
Tasks: Deep Learning, Language Identification

This document describes our approach to building an Offensive Language
Classifier. More specifically, the OffensEval 2019 competition required us to
build three classifiers with slightly different goals:
  - Offensive language identification: would classify a tweet as offensive or
not.
  - Automatic categorization of offense types: would recognize if the target of
the offense was an individual or not.
  - Offense target identification: would identify the target of the offense
between an individual, group or other.
  In this report, we will discuss the different architectures, algorithms and
pre-processing strategies we tried, together with a detailed description of the
designs of our final classifiers and the reasons we choose them over others.
  We evaluated our classifiers on the official test set provided for the
OffenseEval 2019 competition, obtaining a macro-averaged F1-score of 0.7189 for
Task A, 0.6708 on Task B and 0.5442 on Task C.
A Multi-armed Bandit MCMC, with applications in sampling from doubly intractable posterior
Authors: Guanyang Wang
Date: 2019-03-13 00:00:00
Tasks: Bayesian Inference

Markov chain Monte Carlo (MCMC) algorithms are widely used to sample from
complicated distributions, especially to sample from the posterior distribution
in Bayesian inference. However, MCMC is not directly applicable when facing the
doubly intractable problem. In this paper, we discussed and compared two
existing solutions -- Pseudo-marginal Monte Carlo and Exchange Algorithm. This
paper also proposes a novel algorithm: Multi-armed Bandit MCMC (MABMC), which
chooses between two (or more) randomized acceptance ratios in each step. MABMC
could be applied directly to incorporate Pseudo-marginal Monte Carlo and
Exchange algorithm, with higher average acceptance probability.
Natural Language Interaction with Explainable AI Models
Authors: Arjun R. Akula, Sinisa Todorovic, Joyce Y. Chai, Song-Chun Zhu
Date: 2019-03-13 00:00:00
Tasks: Explainable Artificial Intelligence (XAI)

This paper presents an explainable AI (XAI) system that provides explanations for its predictions. The system consists of two key components -- namely, the prediction And-Or graph (AOG) model for recognizing and localizing concepts of interest in input data, and the XAI model for providing explanations to the user about the AOG's predictions. In this work, we focus on the XAI model specified to interact with the user in natural language, whereas the AOG's predictions are considered given and represented by the corresponding parse graphs (pg's) of the AOG. Our XAI model takes pg's as input and provides answers to the user's questions using the following types of reasoning: direct evidence (e.g., detection scores), part-based inference (e.g., detected parts provide evidence for the concept asked), and other evidences from spatio-temporal context (e.g., constraints from the spatio-temporal surround). We identify several correlations between user's questions and the XAI answers using Youtube Action dataset.
Incremental Learning of Discrete Planning Domains from Continuous Perceptions
Authors: Luciano Serafini, Paolo Traverso
Date: 2019-03-14 00:00:00
Tasks: Incremental Learning

We propose a framework for learning discrete deterministic planning domains.
In this framework, an agent learns the domain by observing the action effects
through continuous features that describe the state of the environment after
the execution of each action. Besides, the agent learns its perception
function, i.e., a probabilistic mapping between state variables and sensor data
represented as a vector of continuous random variables called perception
variables. We define an algorithm that updates the planning domain and the
perception function by (i) introducing new states, either by extending the
possible values of state variables, or by weakening their constraints; (ii)
adapts the perception function to fit the observed data (iii) adapts the
transition function on the basis of the executed actions and the effects
observed via the perception function. The framework is able to deal with
exogenous events that happen in the environment.
Computing the Scope of Applicability for Acquired Task Knowledge in Experience-Based Planning Domains
Authors: Vahid Mokhtari, Luis Seabra Lopes, Armando Pinho, Roman Manevich
Date: 2019-03-13 00:00:00

Experience-based planning domains have been proposed to improve problem
solving by learning from experience. They rely on acquiring and using task
knowledge, i.e., activity schemata, for generating solutions to problem
instances in a class of tasks. Using Three-Valued Logic Analysis (TVLA), we
extend previous work to generate a set of conditions that determine the scope
of applicability of an activity schema. The inferred scope is a bounded
representation of a set of problems of potentially unbounded size, in the form
of a 3-valued logical structure, which is used to automatically find an
applicable activity schema for solving task problems. We validate this work in
two classical planning domains.
The Skipping Behavior of Users of Music Streaming Services and its Relation to Musical Structure
Authors: Nicola Montecchio, Pierre Roy, François Pachet
Date: 2019-03-12 00:00:00
Tasks: Relation

The behavior of users of music streaming services is investigated from the
point of view of the temporal dimension of individual songs; specifically, the
main object of the analysis is the point in time within a song at which users
stop listening and start streaming another song ("skip"). The main contribution
of this study is the ascertainment of a correlation between the distribution in
time of skipping events and the musical structure of songs. It is also shown
that such distribution is not only specific to the individual songs, but also
independent of the cohort of users and, under stationary conditions, date of
observation. Finally, user behavioral data is used to train a predictor of the
musical structure of a song solely from its acoustic content; it is shown that
the use of such data, available in large quantities to music streaming
services, yields significant improvements in accuracy over the customary
fashion of training this class of algorithms, in which only smaller amounts of
hand-labeled data are available.
Regular Expressions with Backreferences: Polynomial-Time Matching Techniques
Authors: Markus L. Schmid
Date: 2019-03-14 00:00:00

Regular expressions with backreferences (regex, for short), as supported by most modern libraries for regular expression matching, have an NP-complete matching problem. We define a complexity parameter of regex, called active variable degree, such that regex with this parameter bounded by a constant can be matched in polynomial-time. Moreover, we formulate a novel type of determinism for regex (on an automaton-theoretic level), which yields the class of memory-deterministic regex that can be matched in time O(|w|p(|r|)) for a polynomial p (where r is the regex and w the word). Natural extensions of these concepts lead to properties of regex that are intractable to check.
Investigating Order Effects in Multidimensional Relevance Judgment using Query Logs
Authors: Sagar Uprety, Dawei Song
Date: 2018-07-14 00:00:00
Tasks: Decision Making, Sequential Decision Making

There is a growing body of research which has investigated relevance judgment
in IR being influenced by multiple factors or dimensions. At the same time, the
Order Effects in sequential decision making have been quantitatively detected
and studied in Mathematical Psychology. Combining the two phenomena, there have
been some user studies carried out which investigate the Order Effects and thus
incompatibility in different dimensions of relevance. In this work, we propose
a methodology for carrying out such an investigation in large scale and real
world data using query logs of a web search engine, and device a test to detect
the presence of an irrational user behavior in relevance judgment of documents.
We further validate this behavior through a Quantum Cognitive explanation of
the Order and Context effects.
Investigating Bell Inequalities for Multidimensional Relevance Judgments in Information Retrieval
Authors: Sagar Uprety, Dimitris Gkoumas, Dawei Song
Date: 2018-11-16 00:00:00
Tasks: Information Retrieval, Retrieval

Relevance judgment in Information Retrieval is influenced by multiple
factors. These include not only the topicality of the documents but also other
user oriented factors like trust, user interest, etc. Recent works have
identified these various factors into seven dimensions of relevance. In a
previous work, these relevance dimensions were quantified and user's cognitive
state with respect to a document was represented as a state vector in a Hilbert
Space, with each relevance dimension representing a basis. It was observed that
relevance dimensions are incompatible in some documents, when making a
judgment. Incompatibility being a fundamental feature of Quantum Theory, this
motivated us to test the Quantum nature of relevance judgments using Bell type
inequalities. However, none of the Bell-type inequalities tested have shown any
violation. We discuss our methodology to construct incompatible basis for
documents from real world query log data, the experiments to test Bell
inequalities on this dataset and possible reasons for the lack of violation.
AutoRec: Autoencoders Meet Collaborative Filtering
Authors: Suvash Sedhain, Aditya Krishna Menon, Scott Sanner, Lexing Xie
Date: 2015-05-18 00:00:00
Proceeding: Proceedings of the 24th International Conference on World Wide Web 2015 5
Tasks: Collaborative Filtering, Recommendation Systems

This paper proposes AutoRec, a novel autoencoder framework for collaborative filtering (CF). Empirically, AutoRec’s compact and efficiently trainable model outperforms stateof-the-art CF techniques (biased matrix factorization, RBMCF and LLORMA) on the Movielens and Netflix datasets.
Forecasting Spatio-Temporal Renewable Scenarios: a Deep Generative Approach
Authors: Congmei Jiang, Yize Chen, Yongfang Mao, Yi Chai, Mingbiao Yu
Date: 2019-03-13 00:00:00
Tasks: Time Series Analysis

The operation and planning of large-scale power systems are becoming more
challenging with the increasing penetration of stochastic renewable generation.
In order to minimize the decision risks in power systems with large amount of
renewable resources, there is a growing need to model the short-term generation
uncertainty. By producing a group of possible future realizations for certain
set of renewable generation plants, scenario approach has become one popular
way for renewables uncertainty modeling. However, due to the complex spatial
and temporal correlations underlying in renewable generations, traditional
model-based approaches for forecasting future scenarios often require extensive
knowledge, while fitted models are often hard to scale. To address such
modeling burdens, we propose a learning-based, data-driven scenario forecasts
method based on generative adversarial networks (GANs), which is a class of
deep-learning generative algorithms used for modeling unknown distributions. We
firstly utilize an improved GANs with convergence guarantees to learn the
intrinsic patterns and model the unknown distributions of (multiple-site)
renewable generation time-series. Then by solving an optimization problem, we
are able to generate forecasted scenarios without any scenario number and
forecasting horizon restrictions. Our method is totally model-free, and could
forecast scenarios under different level of forecast uncertainties. Extensive
numerical simulations using real-world data from NREL wind and solar
integration datasets validate the performance of proposed method in forecasting
both wind and solar power scenarios.
Global-to-Local Generative Model for 3D Shapes
Authors: Hao Wang, Nadav Schor, Ruizhen Hu, Haibin Huang, Daniel Cohen-Or, Hui Huang
Date: 2019-01-10 00:00:00
Proceeding: ACM Transactions on Graphics (Proc. SIGGRAPH ASIA) 2019 1

We introduce a generative model for 3D man-made shapes. The presented method takes a global-to-local (G2L) approach. An adversarial network (GAN) is built first to construct the overall structure of the shape, segmented and labeled into parts. A novel conditional auto-encoder (AE) is then augmented to act as a part-level refiner. The GAN, associated with additional local discriminators and quality losses, synthesizes a voxel-based model, and assigns the voxels with part labels that are represented in separate channels. The AE is trained to amend the initial synthesis of the parts, yielding more plausible part geometries. We also introduce new means to measure and evaluate the performance of an adversarial generative model. We demonstrate that our global-to-local generative model produces significantly better results than a plain three-dimensional GAN, in terms of both their shape variety and the distribution with respect to the training data.
Image Authentication Using Local Binary Pattern on the Low Frequency Components
Authors: S.B.G. Tilak Babu, Ch. Srinivasa Rao
Date: 2016-12-25 00:00:00
Proceeding: journal 2016 12

Detection of copy move forgery in images is helpful in legal evidence, in forensic investigation and many other fields. Many Copy Move Forgery Detection (CMFD) schemes are existing in the literature. However, most of them fail to withstand post-processing operations viz., JPEG Compression, noise contamination, rotation. Even if able to identify, they consumes much time to detect and
locate. In this paper, a technique is proposed which uses Discrete Wavelet Transform (DWT) and Local Binary Pattern (LBP) to identify copy-move forgery. Features are extracted by using LBP on the LL band obtained by applying DWT on the input image. Proper selection of similarity and distance thresholds can localize the forged region correctly.