pwc-archive/papers-with-abstracts huggingface dataset:
Scalable Realistic Recommendation Datasets through Fractal Expansions
Authors: Francois Belletti, Karthik Lakshmanan, Walid Krichene, Yi-fan Chen, John Anderson
Date: 2019-01-23 00:00:00
Tasks: Recommendation Systems

Recommender System research suffers currently from a disconnect between the
size of academic data sets and the scale of industrial production systems. In
order to bridge that gap we propose to generate more massive user/item
interaction data sets by expanding pre-existing public data sets. User/item
incidence matrices record interactions between users and items on a given
platform as a large sparse matrix whose rows correspond to users and whose
columns correspond to items. Our technique expands such matrices to larger
numbers of rows (users), columns (items) and non zero values (interactions)
while preserving key higher order statistical properties. We adapt the
Kronecker Graph Theory to user/item incidence matrices and show that the
corresponding fractal expansions preserve the fat-tailed distributions of user
engagements, item popularity and singular value spectra of user/item
interaction matrices. Preserving such properties is key to building large
realistic synthetic data sets which in turn can be employed reliably to
benchmark Recommender Systems and the systems employed to train them. We
provide algorithms to produce such expansions and apply them to the MovieLens
20 million data set comprising 20 million ratings of 27K movies by 138K users.
The resulting expanded data set has 10 billion ratings, 864K items and 2
million users in its smaller version and can be scaled up or down. A larger
version features 655 billion ratings, 7 million items and 17 million users.
AlteregoNets: a way to human augmentation
Authors: Dr. David Kupeev
Date: 2019-01-23 00:00:00
Tasks: Object

A person dependent network, called an AlterEgo net, is proposed for
development. The networks are created per person. It receives at input an
object descriptions and outputs a simulation of the internal person's
representation of the objects. The network generates a textual stream
resembling the narrative stream of consciousness depicting multitudinous
thoughts and feelings related to a perceived object. In this way, the object is
described not by a 'static' set of its properties, like a dictionary, but by
the stream of words and word combinations referring to the object. The network
simulates a person's dialogue with a representation of the object. It is based
on an introduced algorithmic scheme, where perception is modeled by two
interacting iterative cycles, reminding one respectively the forward and
backward propagation executed at training convolution neural networks. The
'forward' iterations generate a stream representing the 'internal world' of a
human. The 'backward' iterations generate a stream representing an internal
representation of the object. People perceive the world differently. Tuning
AlterEgo nets to a specific person or group of persons, will allow simulation
of their thoughts and feelings. Thereby these nets is potentially a new human
augmentation technology for various applications.
Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation
Authors: Hongwei Wang, Fuzheng Zhang, Miao Zhao, Wenjie Li, Xing Xie, Minyi Guo
Date: 2019-01-23 00:00:00
Tasks: Collaborative Filtering, Graph Embedding, Knowledge Graph Embedding, Knowledge Graphs, Multi-Task Learning, News Recommendation, Recommendation Systems

Collaborative filtering often suffers from sparsity and cold start problems
in real recommendation scenarios, therefore, researchers and engineers usually
use side information to address the issues and improve the performance of
recommender systems. In this paper, we consider knowledge graphs as the source
of side information. We propose MKR, a Multi-task feature learning approach for
Knowledge graph enhanced Recommendation. MKR is a deep end-to-end framework
that utilizes knowledge graph embedding task to assist recommendation task. The
two tasks are associated by cross&compress units, which automatically share
latent features and learn high-order interactions between items in recommender
systems and entities in the knowledge graph. We prove that cross&compress units
have sufficient capability of polynomial approximation, and show that MKR is a
generalized framework over several representative methods of recommender
systems and multi-task learning. Through extensive experiments on real-world
datasets, we demonstrate that MKR achieves substantial gains in movie, book,
music, and news recommendation, over state-of-the-art baselines. MKR is also
shown to be able to maintain a decent performance even if user-item
interactions are sparse.
New nonasymptotic convergence rates of stochastic proximal pointalgorithm for convex optimization problems
Authors: Andrei Patrascu
Date: 2019-01-22 00:00:00
Tasks: Stochastic Optimization

Large sectors of the recent optimization literature focused in the last decade on the development of optimal stochastic first order schemes for constrained convex models under progressively relaxed assumptions. Stochastic proximal point is an iterative scheme born from the adaptation of proximal point algorithm to noisy stochastic optimization, with a resulting iteration related to stochastic alternating projections. Inspired by the scalability of alternating projection methods, we start from the (linear) regularity assumption, typically used in convex feasiblity problems to guarantee the linear convergence of stochastic alternating projection methods, and analyze a general weak linear regularity condition which facilitates convergence rate boosts in stochastic proximal point schemes. Our applications include many non-strongly convex functions classes often used in machine learning and statistics. Moreover, under weak linear regularity assumption we guarantee $\mathcal{O}\left(\frac{1}{k}\right)$ convergence rate for SPP, in terms of the distance to the optimal set, using only projections onto a simple component set. Linear convergence is obtained for interpolation setting, when the optimal set of the expected cost is included into the optimal sets of each functional component.
Driver Distraction Identification with an Ensemble of Convolutional Neural Networks
Authors: Hesham M. Eraqi, Yehya Abouelnaga, Mohamed H. Saad, Mohamed N. Moustafa
Date: 2019-01-22 00:00:00
Tasks: General Classification

The World Health Organization (WHO) reported 1.25 million deaths yearly due
to road traffic accidents worldwide and the number has been continuously
increasing over the last few years. Nearly fifth of these accidents are caused
by distracted drivers. Existing work of distracted driver detection is
concerned with a small set of distractions (mostly, cell phone usage).
Unreliable ad-hoc methods are often used.In this paper, we present the first
publicly available dataset for driver distraction identification with more
distraction postures than existing alternatives. In addition, we propose a
reliable deep learning-based solution that achieves a 90% accuracy. The system
consists of a genetically-weighted ensemble of convolutional neural networks,
we show that a weighted ensemble of classifiers using a genetic algorithm
yields in a better classification confidence. We also study the effect of
different visual elements in distraction detection by means of face and hand
localizations, and skin segmentation. Finally, we present a thinned version of
our ensemble that could achieve 84.64% classification accuracy and operate in a
real-time environment.
Anomaly detection in the dynamics of web and social networks
Authors: Volodymyr Miz, Benjamin Ricaud, Kirell Benzi, Pierre Vandergheynst
Date: 2019-01-22 00:00:00
Tasks: Anomaly Detection, Time Series, Time Series Analysis

In this work, we propose a new, fast and scalable method for anomaly
detection in large time-evolving graphs. It may be a static graph with dynamic
node attributes (e.g. time-series), or a graph evolving in time, such as a
temporal network. We define an anomaly as a localized increase in temporal
activity in a cluster of nodes. The algorithm is unsupervised. It is able to
detect and track anomalous activity in a dynamic network despite the noise from
multiple interfering sources. We use the Hopfield network model of memory to
combine the graph and time information. We show that anomalies can be spotted
with a good precision using a memory network. The presented approach is
scalable and we provide a distributed implementation of the algorithm. To
demonstrate its efficiency, we apply it to two datasets: Enron Email dataset
and Wikipedia page views. We show that the anomalous spikes are triggered by
the real-world events that impact the network dynamics. Besides, the structure
of the clusters and the analysis of the time evolution associated with the
detected events reveals interesting facts on how humans interact, exchange and
search for information, opening the door to new quantitative studies on
collective and social behavior on large and dynamic datasets.
Visualizing Semantic Structures of Sequential Data by Learning Temporal Dependencies
Authors: Kyoung-Woon On, Eun-Sol Kim, Yu-Jung Heo, Byoung-Tak Zhang
Date: 2019-01-20 00:00:00

While conventional methods for sequential learning focus on interaction
between consecutive inputs, we suggest a new method which captures composite
semantic flows with variable-length dependencies. In addition, the semantic
structures within given sequential data can be interpreted by visualizing
temporal dependencies learned from the method. The proposed method, called
Temporal Dependency Network (TDN), represents a video as a temporal graph whose
node represents a frame of the video and whose edge represents the temporal
dependency between two frames of a variable distance. The temporal dependency
structure of semantic is discovered by learning parameterized kernels of graph
convolutional methods. We evaluate the proposed method on the large-scale video
dataset, Youtube-8M. By visualizing the temporal dependency structures as
experimental results, we show that the suggested method can find the temporal
dependency structures of video semantic.
EXIT Analysis for Community Detection
Authors: Hussein Saad, Aria Nosratinia
Date: 2019-01-08 00:00:00
Tasks: Community Detection

This paper employs the extrinsic information transfer (EXIT) method, a
technique imported from the analysis of the iterative decoding of error control
codes, to study the performance of belief propagation in community detection in
the presence of side information. We consider both the detection of a single
(hidden) community, as well as the problem of identifying two symmetric
communities. For single community detection, this paper demonstrates the
suitability of EXIT to predict the asymptotic phase transition for weak
recovery. More importantly, EXIT analysis is leveraged to produce useful
insights such as the performance of belief propagation near the threshold. For
two symmetric communities, the asymptotic residual error for belief propagation
is calculated under finite-alphabet side information, generalizing a previous
result with noisy labels. EXIT analysis is used to illuminate the effect of
side information on community detection, its relative importance depending on
the correlation of the graphical information with node labels, as well as the
effect of side information on residual errors.
Fake News Detection via NLP is Vulnerable to Adversarial Attacks
Authors: Zhixuan Zhou, Huankang Guan, Meghana Moorthy Bhat, Justin Hsu
Date: 2019-01-05 00:00:00
Tasks: Fact Checking, Fake News Detection

News plays a significant role in shaping people's beliefs and opinions. Fake
news has always been a problem, which wasn't exposed to the mass public until
the past election cycle for the 45th President of the United States. While
quite a few detection methods have been proposed to combat fake news since
2015, they focus mainly on linguistic aspects of an article without any fact
checking. In this paper, we argue that these models have the potential to
misclassify fact-tampering fake news as well as under-written real news.
Through experiments on Fakebox, a state-of-the-art fake news detector, we show
that fact tampering attacks can be effective. To address these weaknesses, we
argue that fact checking should be adopted in conjunction with linguistic
characteristics analysis, so as to truly separate fake news from real news. A
crowdsourced knowledge graph is proposed as a straw man solution to collecting
timely facts about news events.
Deep Generative Graph Distribution Learning for Synthetic Power Grids
Authors: Mahdi Khodayar, Jianhui Wang, Zhaoyu Wang
Date: 2019-01-17 00:00:00

Power system studies require the topological structures of real-world power
networks; however, such data is confidential due to important security
concerns. Thus, power grid synthesis (PGS), i.e., creating realistic power
grids that imitate actual power networks, has gained significant attention. In
this letter, we cast PGS into a graph distribution learning (GDL) problem where
the probability distribution functions (PDFs) of the nodes (buses) and edges
(lines) are captured. A novel deep GDL (DeepGDL) model is proposed to learn the
topological patterns of buses/lines with their physical features (e.g., power
injection and line impedance). Having a deep nonlinear recurrent structure,
DeepGDL understands complex nonlinear topological properties and captures the
graph PDF. Sampling from the obtained PDF, we are able to create a large set of
realistic networks that all resemble the original power grid. Simulation
results show the significant accuracy of our created synthetic power grids in
terms of various topological metrics and power flow measurements.
Learning Vertex Representations for Bipartite Networks
Authors: Ming Gao, Xiangnan He, Leihui Chen, Tingting Liu, Jinglin Zhang, Aoying Zhou
Date: 2019-01-16 00:00:00
Tasks: Collaborative Filtering, Knowledge Graphs, Network Embedding, Recommendation Systems, Representation Learning

Recent years have witnessed a widespread increase of interest in network representation learning (NRL). By far most research efforts have focused on NRL for homogeneous networks like social networks where vertices are of the same type, or heterogeneous networks like knowledge graphs where vertices (and/or edges) are of different types. There has been relatively little research dedicated to NRL for bipartite networks. Arguably, generic network embedding methods like node2vec and LINE can also be applied to learn vertex embeddings for bipartite networks by ignoring the vertex type information. However, these methods are suboptimal in doing so, since real-world bipartite networks concern the relationship between two types of entities, which usually exhibit different properties and patterns from other types of network data. For example, E-Commerce recommender systems need to capture the collaborative filtering patterns between customers and products, and search engines need to consider the matching signals between queries and webpages.
The Intrinsic Scale of Networks is Small
Authors: Malik Magdon-Ismail, Kshiteesh Hegde
Date: 2019-01-15 00:00:00

We define the intrinsic scale at which a network begins to reveal its
identity as the scale at which subgraphs in the network (created by a random
walk) are distinguishable from similar sized subgraphs in a perturbed copy of
the network. We conduct an extensive study of intrinsic scale for several
networks, ranging from structured (e.g. road networks) to ad-hoc and
unstructured (e.g. crowd sourced information networks), to biological. We find:
(a) The intrinsic scale is surprisingly small (7-20 vertices), even though the
networks are many orders of magnitude larger. (b) The intrinsic scale
quantifies ``structure'' in a network -- networks which are explicitly
constructed for specific tasks have smaller intrinsic scale. (c) The structure
at different scales can be fragile (easy to disrupt) or robust.
Network Lens: Node Classification in Topologically Heterogeneous Networks
Authors: Kshiteesh Hegde, Malik Magdon-Ismail
Date: 2019-01-15 00:00:00
Tasks: Classification, General Classification, Node Classification

We study the problem of identifying different behaviors occurring in
different parts of a large heterogenous network. We zoom in to the network
using lenses of different sizes to capture the local structure of the network.
These network signatures are then weighted to provide a set of predicted labels
for every node. We achieve a peak accuracy of $\sim42\%$ (random=$11\%$) on two
networks with $\sim100,000$ and $\sim1,000,000$ nodes each. Further, we perform
better than random even when the given node is connected to up to 5 different
types of networks. Finally, we perform this analysis on homogeneous networks
and show that highly structured networks have high homogeneity.
Sensorimotor learning for artificial body perception
Authors: German Diez-Valencia, Takuya Ohashi, Pablo Lanillos, Gordon Cheng
Date: 2019-01-15 00:00:00

Artificial self-perception is the machine ability to perceive its own body,
i.e., the mastery of modal and intermodal contingencies of performing an action
with a specific sensors/actuators body configuration. In other words, the
spatio-temporal patterns that relate its sensors (e.g. visual, proprioceptive,
tactile, etc.), its actions and its body latent variables are responsible of
the distinction between its own body and the rest of the world. This paper
describes some of the latest approaches for modelling artificial body
self-perception: from Bayesian estimation to deep learning. Results show the
potential of these free-model unsupervised or semi-supervised
crossmodal/intermodal learning approaches. However, there are still challenges
that should be overcome before we achieve artificial multisensory body
perception.
The Music Streaming Sessions Dataset
Authors: Brian Brost, Rishabh Mehrotra, Tristan Jehan
Date: 2018-12-31 00:00:00
Tasks: counterfactual, Information Retrieval, Music Information Retrieval, Retrieval

At the core of many important machine learning problems faced by online streaming services is a need to model how users interact with the content they are served. Unfortunately, there are no public datasets currently available that enable researchers to explore this topic. In order to spur that research, we release the Music Streaming Sessions Dataset (MSSD), which consists of 160 million listening sessions and associated user actions. Furthermore, we provide audio features and metadata for the approximately 3.7 million unique tracks referred to in the logs. This is the largest collection of such track metadata currently available to the public. This dataset enables research on important problems including how to model user listening and interaction behaviour in streaming, as well as Music Information Retrieval (MIR), and session-based sequential recommendations. Additionally, a subset of sessions were collected using a uniformly random recommendation setting, enabling their use for counterfactual evaluation of such sequential recommendations. Finally, we provide an analysis of user behavior and suggest further research problems which can be addressed using the dataset.
Measuring the Robustness of Graph Properties
Authors: Yali Wan, Marina Meila
Date: 2018-12-03 00:00:00

In this paper, we propose a perturbation framework to measure the robustness
of graph properties. Although there are already perturbation methods proposed
to tackle this problem, they are limited by the fact that the strength of the
perturbation cannot be well controlled. We firstly provide a perturbation
framework on graphs by introducing weights on the nodes, of which the magnitude
of perturbation can be easily controlled through the variance of the weights.
Meanwhile, the topology of the graphs are also preserved to avoid
uncontrollable strength in the perturbation. We then extend the measure of
robustness in the robust statistics literature to the graph properties.
A model for prohibition and obligation dilemmas generation in virtual environments
Authors: Azzeddine Benabbou, Domitile Lourdeaux, Dominique Lenne
Date: 2019-01-17 00:00:00

Under the project Maccoy Critical, we would like to train individuals, in
virtual environments, to handle critical situations such as dilemmas. These
latter refer to situations where there is no ``good'' solution. In other words,
situations that lead to negative consequences whichever choice is made. Our
objective is to use Knowledge Models to extract necessary properties for
dilemmas to emerge. To do so, our approach consists in developing a Scenario
Orchestration System that generates dilemma situations dynamically without
having to write them beforehand. In this paper we present this approach and
expose a proof of concept of the generation process.
Practical Algorithms for Multi-Stage Voting Rules with Parallel Universes Tiebreaking
Authors: Jun Wang, Sujoy Sikdar, Tyler Shepherd, Zhibing Zhao, Chunheng Jiang, Lirong Xia
Date: 2019-01-16 00:00:00
Tasks: Decision Making

STV and ranked pairs (RP) are two well-studied voting rules for group
decision-making. They proceed in multiple rounds, and are affected by how ties
are broken in each round. However, the literature is surprisingly vague about
how ties should be broken. We propose the first algorithms for computing the
set of alternatives that are winners under some tiebreaking mechanism under STV
and RP, which is also known as parallel-universes tiebreaking (PUT).
Unfortunately, PUT-winners are NP-complete to compute under STV and RP, and
standard search algorithms from AI do not apply. We propose multiple DFS-based
algorithms along with pruning strategies, heuristics, sampling and machine
learning to prioritize search direction to significantly improve the
performance. We also propose novel ILP formulations for PUT-winners under STV
and RP, respectively. Experiments on synthetic and real-world data show that
our algorithms are overall faster than ILP.
Synthesising a Database of Parameterised Linear and Non-Linear Invariants for Time-Series Constraints
Authors: Ekaterina Arafailova, Nicolas Beldiceanu, Helmut Simonis
Date: 2019-01-15 00:00:00
Tasks: Time Series, Time Series Analysis

Many constraints restricting the result of some computations over an integer
sequence can be compactly represented by register automata. We improve the
propagation of the conjunction of such constraints on the same sequence by
synthesising a database of linear and non-linear invariants using their
register-automaton representation. The obtained invariants are formulae
parameterised by a function of the sequence length and proven to be true for
any long enough sequence. To assess the quality of such linear invariants, we
developed a method to verify whether a generated linear invariant is a facet of
the convex hull of the feasible points. This method, as well as the proof of
non-linear invariants, are based on the systematic generation of constant-size
deterministic finite automata that accept all integer sequences whose result
verifies some simple condition. We apply such methodology to a set of 44
time-series constraints and obtain 1400 linear invariants from which 70% are
facet defining, and 600 non-linear invariants, which were tested on short-term
electricity production problems.