pwc-archive/papers-with-abstracts huggingface dataset:
Predicting Triple Scoring with Crowdsourcing-specific Features - The fiddlehead Triple Scorer at WSDM Cup 2017
Authors: Sato Masahiro  Fuji Xerox Co., Ltd.
Date: 2017-12-22 00:00:00
Tasks: Prediction

The Triple Scoring Task at the WSDM Cup 2017 involves the prediction of the
relevance scores between persons and professions/nationalities. The ground
truth of the relevance scores was obtained by counting the vote of seven
crowdworkers. I confirmed that features related to task difficulty correlate
with the discrepancy among crowdworkers' judgement. This means such features
are useful for predicting whether a score is in the middle or not. Hence, the
features were incorporated into the prediction model of the crowdsourced
relevance scores. The introduced features improve the average score difference
of the prediction. The final ranking of my prediction was 4th for average score
difference and 12th for both accuracy and Kendall's tau.
Triple Scoring Using a Hybrid Fact Validation Approach - The Catsear Triple Scorer at WSDM Cup 2017
Authors: Marx Edgard  1 and 2, Soru Tommaso, Valdestilhas André
Date: 2017-12-22 00:00:00
Tasks: regression

With the continuous increase of data daily published in knowledge bases
across the Web, one of the main issues is regarding information relevance. In
most knowledge bases, a triple (i.e., a statement composed by subject,
predicate, and object) can be only true or false. However, triples can be
assigned a score to have information sorted by relevance. In this work, we
describe the participation of the Catsear team in the Triple Scoring Challenge
at the WSDM Cup 2017. The Catsear approach scores triples by combining the
answers coming from three different sources using a linear regression
classifier. We show how our approach achieved an Accuracy2 value of 79.58% and
the overall 4th place.
Relevance Score of Triplets Using Knowledge Graph Embedding - The Pigweed Triple Scorer at WSDM Cup 2017
Authors: Kanojia Vibhor  Yahoo! Japan, Togashi Riku  Yahoo! Japan, Maeda Hideyuki  Yahoo! Japan
Date: 2017-12-22 00:00:00
Tasks: Graph Embedding, Knowledge Graph Embedding, Retrieval, Triplet

Collaborative Knowledge Bases such as Freebase and Wikidata mention multiple
professions and nationalities for a particular entity. The goal of the WSDM Cup
2017 Triplet Scoring Challenge was to calculate relevance scores between an
entity and its professions/nationalities. Such scores are a fundamental
ingredient when ranking results in entity search. This paper proposes a novel
approach to ensemble an advanced Knowledge Graph Embedding Model with a simple
bag-of-words model. The former deals with hidden pragmatics and deep semantics
whereas the latter handles text-based retrieval and low-level semantics.
Supervised Ranking of Triples for Type-Like Relations - The Cress Triple Scorer at the WSDM Cup 2017
Authors: Hasibi Faegheh  NTNU Trondheim, Garigliotti Darío  University of Stavanger, Zhang Shuo  University of Stavanger, Balog Krisztian  University of Stavanger
Date: 2017-12-22 00:00:00

This paper describes our participation in the Triple Scoring task of WSDM Cup
2017, which aims at ranking triples from a knowledge base for two type-like
relations: profession and nationality. We introduce a supervised ranking method
along with the features we designed for this task. Our system has been top
ranked with respect to average score difference and 2nd best in terms of
Kendall's tau.
Ranking Triples using Entity Links in a Large Web Crawl - The Chicory Triple Scorer at WSDM Cup 2017
Authors: Dorssers Frank  Radboud University, de Vries Arjen P.  Radboud University, Alink Wouter  Spinque, Cornacchia Roberto  Spinque
Date: 2017-12-22 00:00:00

This paper describes the participation of team Chicory in the Triple Ranking
Challenge of the WSDM Cup 2017. Our approach deploys a large collection of
entity tagged web data to estimate the correctness of the relevance relation
expressed by the triples, in combination with a baseline approach using
Wikipedia abstracts following [1]. Relevance estimations are drawn from
ClueWeb12 annotated by Google's entity linker, available publicly as the FACC1
dataset. Our implementation is automatically generated from a so-called 'search
strategy' that specifies declaratively how the input data are combined into a
final ranking of triples.
Leveraging Text and Knowledge Bases for Triple Scoring: An Ensemble Approach - The BOKCHOY Triple Scorer at WSDM Cup 2017
Authors: Ding Boyang  Chinese Academy of Sciences, Wang Quan  Chinese Academy of Sciences, Wang Bin  Chinese Academy of Sciences
Date: 2017-12-22 00:00:00

We present our winning solution for the WSDM Cup 2017 triple scoring task. We
devise an ensemble of four base scorers, so as to leverage the power of both
text and knowledge bases for that task. Then we further refine the outputs of
the ensemble by trigger word detection, achieving even better predictive
accuracy. The code is available at https://github.com/wsdm-cup-2017/bokchoy.
Integrating Knowledge from Latent and Explicit Features for Triple Scoring - Team Radicchio's Triple Scorer at WSDM Cup 2017
Authors: Chen Liang-Wei  University of Illinois at
  Urbana-Champaign, Mangipudi Bhargav  University of Illinois at
  Urbana-Champaign, Bandlamudi Jayachandu  University of Illinois at
  Urbana-Champaign, Sehgal Richa  University of Illinois at
  Urbana-Champaign, Hao Yun  University of Illinois at
  Urbana-Champaign, Jiang Meng  University of Illinois at
  Urbana-Champaign, Gui Huan  University of Illinois at
  Urbana-Champaign
Date: 2017-12-22 00:00:00
Tasks: Information Retrieval, Retrieval

The objective of the triple scoring task in WSDM Cup 2017 is to compute
relevance scores for knowledge-base triples of type-like relations. For
example, consider Julius Caesar who has had various professions, including
Politician and Author. For two given triples (Julius Caesar, profession,
Politician) and (Julius Caesar, profession, Author), the former triple is
likely to have a higher relevance score (also called "triple score") because
Julius Caesar was well-known as a politician and not as an author. Accurate
prediction of such triple scores greatly benefits real-world applications, such
as information retrieval or knowledge base query. In these scenarios, being
able to rank all relations (Profession/Nationality) can help improve the user
experience. We propose a triple scoring model which integrates knowledge from
both latent features and explicit features via an ensemble approach. The latent
features consist of representations for a person learned by using a word2vec
model and representations for profession/nationality values extracted from a
pre-trained GloVe embedding model. In addition, we extract explicit features
for person entities from the Freebase knowledge base. Experimental results show
that the proposed method performs competitively at WSDM Cup 2017, ranking at
the third place with an accuracy of 79.72% for predicting within two places of
the ground truth score.
Predicting Relevance Scores for Triples from Type-Like Relations using Neural Embedding - The Cabbage Triple Scorer at WSDM Cup 2017
Authors: Brumer Yael  Ben-Gurion University of the Negev, Shapira Bracha  Ben-Gurion University of the Negev, Rokach Lior  Ben-Gurion University of the Negev, Barkan Oren  Tel Aviv University
Date: 2017-12-22 00:00:00

The WSDM Cup 2017 Triple scoring challenge is aimed at calculating and
assigning relevance scores for triples from type-like relations. Such scores
are a fundamental ingredient for ranking results in entity search. In this
paper, we propose a method that uses neural embedding techniques to accurately
calculate an entity score for a triple based on its nearest neighbor. We strive
to develop a new latent semantic model with a deep structure that captures the
semantic and syntactic relations between words. Our method has been ranked
among the top performers with accuracy - 0.74, average score difference - 1.74,
and average Kendall's Tau - 0.35.
Triple Scoring Using Paragraph Vector - The Gailan Triple Scorer at WSDM Cup 2017
Authors: Ali Esraa  Trinity College Dublin, Caputo Annalina  Trinity College Dublin, Lawless Séamus  Trinity College Dublin
Date: 2017-12-22 00:00:00

In this paper we describe our solution to the WSDM Cup 2017 Triple Scoring
task. Our approach generates a relevance score based on the textual description
of the triple's subject and value (Object). It measures how similar (related)
the text description of the subject is to the text description of its values.
The generated similarity score can then be used to rank the multiple values
associated with this subject. We utilize the Paragraph Vector algorithm to
represent the unstructured text into fixed length vectors. The fixed length
representation is then employed to calculate the similarity (relevance) score
between the subject and its multiple values. Our experimental results have
shown that the suggested approach is promising and suitable to solve this
problem.
A Model of Optimal Network Structure for Decentralized Nearest Neighbor Search
Authors: Ponomarenko Alexander, Utkina Irina, Batsyn Mikhail
Date: 2017-12-22 00:00:00

One of the approaches for the nearest neighbor search problem is to build a
network which nodes correspond to the given set of indexed objects. In this
case the search of the closest object can be thought as a search of a node in a
network. A procedure in a network is called decentralized if it uses only local
information about visited nodes and its neighbors. Networks, which structure
allows efficient performing the nearest neighbour search by a decentralised
search procedure started from any node, are of particular interest especially
for pure distributed systems. Several algorithms that construct such networks
have been proposed in literature. However, the following questions arise: "Are
there network models in which decentralised search can be performed faster?";
"What are the optimal networks for the decentralised search?"; "What are their
properties?". In this paper we partially give answers to these questions. We
propose a mathematical programming model for the problem of determining an
optimal network structure for decentralized nearest neighbor search. We have
found an exact solution for a regular lattice of size 4x4 and heuristic
solutions for sizes from 5x5 to 7x7. As a distance function we use L1 , L2 and
L_inf metrics. We hope that our results and the proposed model will initiate
study of optimal network structures for decentralised nearest neighbour search.
Analysis of a model for hepatitis C virus transmission that includes the effects of vaccination with waning immunity
Date: 2017-12-22 00:00:00
Tasks: Unity

This paper considers a mathematical model based on the transmission dynamics
of hepatitis C virus (HCV) infection. In addition to the usual compartments for
susceptible, exposed, and infected individuals, this model includes
compartments for individuals who are under treatment and those who have had
vaccination against HCV infection. It is assumed that the immunity provided by
the vaccine fades with time. The basic reproduction number, $R_0$, and the
equilibrium solutions of the model are determined. The model exhibits the
phenomenon of backward bifurcation where a stable disease-free equilibrium
co-exists with a stable endemic equilibrium whenever $R_0$ is less than unity.
It is shown that the use of only a perfect vaccine can eliminate backward
bifurcation completely. Furthermore, a unique endemic equilibrium of the model
is proved to be globally asymptotically stable under certain restrictions on
the parameter values. Numerical simulation results are given to support the
theoretical predictions. [epidemiological model; equilibrium solutions;
backward bifurcation; global asymptotic stability; Lyapunov function.]
DancingLines: An Analytical Scheme to Depict Cross-Platform Event Popularity
Authors: Gao Tianxiang, Bao Weiming, Li Jinning, Gao Xiaofeng, Kong Boyuan, Tang Yan, Chen Guihai, Li Xuan
Date: 2017-12-22 00:00:00
Tasks: Diversity, Dynamic Time Warping, Time Series, Time Series Alignment, Time Series Analysis

Nowadays, events usually burst and are propagated online through multiple
modern media like social networks and search engines. There exists various
research discussing the event dissemination trends on individual medium, while
few studies focus on event popularity analysis from a cross-platform
perspective. Challenges come from the vast diversity of events and media,
limited access to aligned datasets across different media and a great deal of
noise in the datasets. In this paper, we design DancingLines, an innovative
scheme that captures and quantitatively analyzes event popularity between
pairwise text media. It contains two models: TF-SW, a semantic-aware popularity
quantification model, based on an integrated weight coefficient leveraging
Word2Vec and TextRank; and wDTW-CD, a pairwise event popularity time series
alignment model matching different event phases adapted from Dynamic Time
Warping. We also propose three metrics to interpret event popularity trends
between pairwise social platforms. Experimental results on eighteen real-world
event datasets from an influential social network and a popular search engine
validate the effectiveness and applicability of our scheme. DancingLines is
demonstrated to possess broad application potentials for discovering the
knowledge of various aspects related to events and different media.
GSM-CommSense-based through-the-wall sensing
Date: 2017-12-20 00:00:00

We have recently proposed a scheme to use the channel equalization blocks of
telecommunication systems to sense changes in an environment. We call this
communication-sensing, CommSense for short. After some initial positive results
we tried to use our global system for mobile communication (GSM) based
CommSense system for a through-the-wall sensing application. As the system was
inherently highly under-determined we used statistical machine learning
techniques to help us sense environmental changes in the behind-the-wall
experiments. We observed that with limited amount of data per GSM frame of 577
{\mu}s a person can be detected across a wall to an accuracy of 77.458% and a
person carrying a weapon can be detected to an accuracy of 95.208%. We present
details of the experiments and the encouraging results that we have obtained in
this article.
Lipid bilayer mediates ion-channel cooperativity in a model of hair-cell mechanotransduction
Date: 2017-12-22 00:00:00

Mechanoelectrical transduction in the inner ear is a biophysical process
underlying the senses of hearing and balance. The key players involved in this
process are mechanosensitive ion channels. They are located in the stereocilia
of hair cells and opened by the tension in specialized molecular springs, the
tip links, connecting adjacent stereocilia. When channels open, the tip links
relax, reducing the hair-bundle stiffness. This gating compliance makes hair
cells especially sensitive to small stimuli. The classical explanation for the
gating compliance is that the conformational rearrangement of a single channel
directly shortens the tip link. However, to reconcile theoretical models based
on this mechanism with experimental data, an unrealistically large structural
change of the channel is required. Experimental evidence indicates that each
tip link is a dimeric molecule, associated on average with two channels at its
lower end. It also indicates that the lipid bilayer modulates channel gating,
although it is not clear how. Here, we design and analyze a model of
mechanotransduction where each tip link attaches to two channels, mobile within
the membrane. Their states and positions are coupled by membrane-mediated
elastic forces arising from the interaction between the channels' hydrophobic
cores and that of the lipid bilayer. This coupling induces cooperative opening
and closing of the channels. The model reproduces the main properties of
hair-cell mechanotransduction using only realistic parameters constrained by
experimental evidence. This work provides an insight into the fundamental role
that membrane-mediated ion-channel cooperativity can play in sensory
physiology.
Quickest Attack Detection in Smart Grid Based on Sequential Monte Carlo Filtering
Date: 2017-12-22 00:00:00
Tasks: State Estimation

Quick and accurate detection of cyber attack is key to the normal operation
of the smart grid system. In this paper, a joint state estimation and
sequential attack detection method for a given bus with grid frequency drift is
proposed that utilizes the commonly monitored output voltage. In particular,
based on a non-linear state-space model derived from the three-phase sinusoidal
voltage equations, we employ the sequential Monte Carlo (SMC) filtering to
estimate the system state. The output of the SMC filter is fed into a CUSUM
test to detect the attack in a fastest way. Moreover, an adaptive sampling
strategy is proposed to reduce the rate of taking measurements and
communicating with the controller. Extensive simulation results demonstrate
that the proposed method achieves high adaptivity and efficient detection of
various types of attacks in power systems.
Relevance Scoring of Triples Using Ordinal Logistic Classification - The Celosia Triple Scorer at WSDM Cup 2017
Authors: Fatma Nausheen  IIIT Hyderabad, Chinnakotla Manoj K.  Microsoft, Shrivastava Manish  IIIT Hyderabad
Date: 2017-12-22 00:00:00
Tasks: Task 2

In this paper, we report our participation in the Task 2: Triple Scoring of
WSDM Cup challenge 2017. In this task, we were provided with triples of
"type-like" relations which were given human-annotated relevance scores ranging
from 0 to 7, with 7 being the "most relevant" and 0 being the "least relevant".
The task focuses on two such relations: profession and nationality. We built a
system which could automatically predict the relevance scores for unseen
triples. Our model is primarily a supervised machine learning based one in
which we use well-designed features which are used to a make a Logistic Ordinal
Regression based classification model. The proposed system achieves an overall
accuracy score of 0.73 and Kendall's tau score of 0.36.
RelSifter: Scoring Triples from Type-like Relations - The Samphire Triple Scorer at WSDM Cup 2017
Authors: Shiralkar Prashant  Indiana University Bloomington, Avram Mihai  Indiana University Bloomington, Ciampaglia Giovanni Luca  Indiana University Bloomington, Menczer Filippo  Indiana University Bloomington, Flammini Alessandro  Indiana University Bloomington
Date: 2017-12-22 00:00:00
Tasks: Knowledge Graphs, Relation

We present RelSifter, a supervised learning approach to the problem of
assigning relevance scores to triples expressing type-like relations such as
'profession' and 'nationality.' To provide additional contextual information
about individuals and relations we supplement the data provided as part of the
WSDM 2017 Triple Score contest with Wikidata and DBpedia, two large-scale
knowledge graphs (KG). Our hypothesis is that any type relation, i.e., a
specific profession like 'actor' or 'scientist,' can be described by the set of
typical "activities" of people known to have that type relation. For example,
actors are known to star in movies, and scientists are known for their academic
affiliations. In a KG, this information is to be found on a properly defined
subset of the second-degree neighbors of the type relation. This form of local
information can be used as part of a learning algorithm to predict relevance
scores for new, unseen triples. When scoring 'profession' and 'nationality'
triples our experiments based on this approach result in an accuracy equal to
73% and 78%, respectively. These performance metrics are roughly equivalent or
only slightly below the state of the art prior to the present contest. This
suggests that our approach can be effective for evaluating facts, despite the
skewness in the number of facts per individual mined from KGs.
Rare-Event Sampling of Epigenetic Landscapes and Phenotype Transitions
Date: 2017-12-29 00:00:00
Tasks: Protein Folding

Stochastic simulation has been a powerful tool for studying the dynamics of
gene regulatory networks, particularly in terms of understanding how
cell-phenotype stability and fate-transitions are impacted by noisy gene
expression. However, gene networks often have dynamics characterized by
multiple attractors. Stochastic simulation is often inefficient for such
systems, because most of the simulation time is spent waiting for rare,
barrier-crossing events to occur. We present a rare-event simulation-based
method for computing epigenetic landscapes and phenotype-transitions in
metastable gene networks. Our computational pipeline was inspired by studies of
metastability and barrier-crossing in protein folding, and provides an
automated means of computing and visualizing essential stationary and dynamic
information that is generally inaccessible to conventional simulation. Applied
to a network model of pluripotency in Embryonic Stem Cells, our simulations
revealed rare phenotypes and approximately Markovian transitions among
phenotype-states, occurring with a broad range of timescales. The relative
probabilities of phenotypes and the transition paths linking pluripotency and
differentiation are sensitive to global kinetic parameters governing
transcription factor-DNA binding kinetics. Our approach significantly expands
the capability of stochastic simulation to investigate gene regulatory network
dynamics, which may help guide rational cell reprogramming strategies. Our
approach is also generalizable to other types of molecular networks and
stochastic dynamics frameworks.
Gene expression for simulation of biological tissue
Date: 2018-03-12 00:00:00

BioDynaMo is a biological processes simulator developed by an international
community of researchers and software engineers working closely with
neuroscientists. The authors have been working on gene expression, i.e. the
process by which the heritable information in a gene - the sequence of DNA base
pairs - is made into a functional gene product, such as protein or RNA.
Typically, gene regulatory models employ either statistical or analytical
approaches, being the former already well understood and broadly used. In this
paper, we utilize analytical approaches representing the regulatory networks by
means of differential equations, such as Euler and Runge-Kutta methods. The two
solutions are implemented and have been submitted for inclusion in the
BioDynaMo project and are compared for accuracy and performance.
Biological Systems as Heterogeneous Information Networks: A Mini-review and Perspectives
Authors: Tsuyuzaki Koki, Nikaido Itoshi
Date: 2017-12-24 00:00:00

In the real world, most objects and data have multiple types of attributes
and inter-connections. Such data structures are named "Heterogeneous
Information Networks" (HIN) and have been widely researched. Biological systems
are also considered to be highly complicated HIN. In this work, we review
various applications of HIN methods to biological and chemical data, discuss
some advanced topics, and describe some future research directions.
Player-Compatible Learning and Player-Compatible Equilibrium
Date: 2020-05-27 00:00:00

Player-Compatible Equilibrium (PCE) imposes cross-player restrictions on the
magnitudes of the players' "trembles" onto different strategies. These
restrictions capture the idea that trembles correspond to deliberate
experiments by agents who are unsure of the prevailing distribution of play.
PCE selects intuitive equilibria in a number of examples where trembling-hand
perfect equilibrium (Selten, 1975) and proper equilibrium (Myerson, 1978) have
no bite. We show that rational learning and weighted fictitious play imply our
compatibility restrictions in a steady-state setting.