pwc-archive/papers-with-abstracts huggingface dataset:
Current Challenges and Visions in Music Recommender Systems Research
Authors: Schedl Markus, Zamani Hamed, Chen Ching-Wei, Deldjoo Yashar, Elahi Mehdi
Date: 2018-03-21 00:00:00
Tasks: Recommendation Systems

Music recommender systems (MRS) have experienced a boom in recent years,
thanks to the emergence and success of online streaming services, which
nowadays make available almost all music in the world at the user's fingertip.
While today's MRS considerably help users to find interesting music in these
huge catalogs, MRS research is still facing substantial challenges. In
particular when it comes to build, incorporate, and evaluate recommendation
strategies that integrate information beyond simple user--item interactions or
content-based descriptors, but dig deep into the very essence of listener
needs, preferences, and intentions, MRS research becomes a big endeavor and
related publications quite sparse.
  The purpose of this trends and survey article is twofold. We first identify
and shed light on what we believe are the most pressing challenges MRS research
is facing, from both academic and industry perspectives. We review the state of
the art towards solving these challenges and discuss its limitations. Second,
we detail possible future directions and visions we contemplate for the further
evolution of the field. The article should therefore serve two purposes: giving
the interested reader an overview of current challenges in MRS research and
providing guidance for young researchers by identifying interesting, yet
under-researched, directions in the field.
Privacy-preserving Targeted Advertising
Authors: Tulabandhula Theja, Vaya Shailesh, Dhar Aritra
Date: 2018-06-18 00:00:00
Tasks: Collaborative Filtering, Privacy Preserving, Recommendation Systems

Recommendation systems form the center piece of a rapidly growing trillion
dollar online advertisement industry. Even with numerous optimizations and
approximations, collaborative filtering (CF) based approaches require real-time
computations involving very large vectors. Curating and storing such related
profile information vectors on web portals seriously breaches the user's
privacy. Modifying such systems to achieve private recommendations further
requires communication of long encrypted vectors, making the whole process
inefficient. We present a more efficient recommendation system alternative, in
which user profiles are maintained entirely on their device, and appropriate
recommendations are fetched from web portals in an efficient privacy preserving
manner. We base this approach on association rules.
Adapting a Formal Model Theory to Applications in Augmented Personalized Medicine
Authors: Plamen L. Simeonov, Andrée C. Ehresmann
Date: 2017-10-01 00:00:00

The goal of this paper is to advance an extensible theory of living systems using an approach to biomathematics and biocomputation that suitably addresses self-organized, self-referential and anticipatory systems with multi-temporal multi-agents. Our first step is to provide foundations for modelling of emergent and evolving dynamic multi-level organic complexes and their sustentative processes in artificial and natural life systems. Main applications are in life sciences, medicine, ecology and astrobiology, as well as robotics, industrial automation and man-machine interface. Since 2011 over 100 scientists from a number of disciplines have been exploring a substantial set of theoretical frameworks for a comprehensive theory of life known as Integral Biomathics. That effort identified the need for a robust core model of organisms as dynamic wholes, using advanced and adequately computable mathematics. The work described here for that core combines the advantages of a situation and context aware multivalent computational logic for active self-organizing networks, Wandering Logic Intelligence (WLI), and a multi-scale dynamic category theory, Memory Evolutive Systems (MES), hence WLIMES. This is presented to the modeller via a formal augmented reality language as a first step towards practical modelling and simulation of multi-level living systems. Initial work focuses on the design and implementation of this visual language and calculus (VLC) and its graphical user interface. The results will be integrated within the current methodology and practices of theoretical biology and (personalized) medicine to deepen and to enhance the holistic understanding of life.
The number of linked references of publications in Microsoft Academic in comparison with the Web of Science
Authors: Haunschild Robin, Hug Sven E., Brändle Martin P., Bornmann Lutz
Date: 2017-10-11 00:00:00

In the context of a comprehensive Microsoft Academic (MA) study, we explored
in an initial step the quality of linked references data in MA in comparison
with Web of Science (WoS). Linked references are the backbone of bibliometrics,
because they are the basis of the times cited information in citation indexes.
We found that the concordance of linked references between MA and WoS ranges
from weak to non-existent for the full sample (publications of the University
of Zurich with less than 50 linked references in MA). An analysis with a sample
restricted to less than 50 linked references in WoS showed a strong agreement
between linked references in MA and WoS.
Which is better? A Modularized Evaluation for Topic Popularity Prediction
Authors: Zhang Yiming, Luo Jiacheng, Gao Xiaofeng, Chen Guihai
Date: 2017-10-16 00:00:00

Topic popularity prediction in social networks has drawn much attention
recently. Various elegant models have been proposed for this issue. However,
different datasets and evaluation metrics they use lead to low comparability.
So far there is no unified scheme to evaluate them, making it difficult to
select and compare models. We conduct a comprehensible survey, propose an
evaluation scheme and apply it to existing methods. Our scheme consists of four
modules: classification; qualitative evaluation on several metrics;
quantitative experiment on real world data; final ranking with risk matrix and
$\textit{MinDis}$ to reflect performances under different scenarios.
Furthermore, we analyze the efficiency and contribution of features used in
feature oriented methods. The results show that feature oriented methods are
more suitable for scenarios requiring high accuracy, while relation based
methods have better consistency. Our work helps researchers compare and choose
methods appropriately, and provides insights for further improvements.
Classifying Web Exploits with Topic Modeling
Authors: Ruohonen Jukka
Date: 2017-10-16 00:00:00
Tasks: Classification, General Classification

This short empirical paper investigates how well topic modeling and database
meta-data characteristics can classify web and other proof-of-concept (PoC)
exploits for publicly disclosed software vulnerabilities. By using a dataset
comprised of over 36 thousand PoC exploits, near a 0.9 accuracy rate is
obtained in the empirical experiment. Text mining and topic modeling are a
significant boost factor behind this classification performance. In addition to
these empirical results, the paper contributes to the research tradition of
enhancing software vulnerability information with text mining, providing also a
few scholarly observations about the potential for semi-automatic
classification of exploits in the existing tracking infrastructures.
CancerLinker: Explorations of Cancer Study Network
Authors: Nguyen Vinh, Kabir Md Yasin, Dang Tommy
Date: 2017-10-19 00:00:00

Interactive visualization tools are highly desirable to biologist and cancer
researchers to explore the complex structures, detect patterns and find out the
relationships among bio-molecules responsible for a cancer type. A pathway
contains various bio-molecules in different layers of the cell which is
responsible for specific cancer type. Researchers are highly interested in
understanding the relationships among the proteins of different pathways and
furthermore want to know how those proteins are interacting in different
pathways for various cancer types. Biologists find it useful to merge the data
of different cancer studies in a single network and see the relationships among
the different proteins which can help them detect the common proteins in cancer
studies and hence reveal the pattern of interactions of those proteins. We
introduce the CancerLinker, a visual analytic tool that helps researchers
explore cancer study interaction network. Twenty-six cancer studies are merged
to explore pathway data and bio-molecules relationships that can provide the
answers to some significant questions which are helpful in cancer research. The
CancerLinker also helps biologists explore the critical mutated proteins in
multiple cancer studies. A bubble graph is constructed to visualize common
protein based on its frequency and biological assemblies. Parallel coordinates
highlight patterns of patient profiles (obtained from cBioportal by WebAPI
services) on different attributes for a specified cancer study
Learning Visual Features from Snapshots for Web Search
Authors: Fan Yixing, Guo Jiafeng, Lan Yanyan, Xu Jun, Pang Liang, Cheng Xueqi
Date: 2017-10-19 00:00:00
Tasks: Learning-To-Rank, Retrieval

When applying learning to rank algorithms to Web search, a large number of
features are usually designed to capture the relevance signals. Most of these
features are computed based on the extracted textual elements, link analysis,
and user logs. However, Web pages are not solely linked texts, but have
structured layout organizing a large variety of elements in different styles.
Such layout itself can convey useful visual information, indicating the
relevance of a Web page. For example, the query-independent layout (i.e., raw
page layout) can help identify the page quality, while the query-dependent
layout (i.e., page rendered with matched query words) can further tell rich
structural information (e.g., size, position and proximity) of the matching
signals. However, such visual information of layout has been seldom utilized in
Web search in the past. In this work, we propose to learn rich visual features
automatically from the layout of Web pages (i.e., Web page snapshots) for
relevance ranking. Both query-independent and query-dependent snapshots are
considered as the new inputs. We then propose a novel visual perception model
inspired by human's visual search behaviors on page viewing to extract the
visual features. This model can be learned end-to-end together with traditional
human-crafted features. We also show that such visual features can be
efficiently acquired in the online setting with an extended inverted indexing
scheme. Experiments on benchmark collections demonstrate that learning visual
features from Web page snapshots can significantly improve the performance of
relevance ranking in ad-hoc Web retrieval tasks.
Preference Modeling by Exploiting Latent Components of Ratings
Authors: Chen Junhua, Zeng Wei, Shao Junming, Fan Ge
Date: 2017-10-19 00:00:00
Tasks: Recommendation Systems

Understanding user preference is essential to the optimization of recommender
systems. As a feedback of user's taste, rating scores can directly reflect the
preference of a given user to a given product. Uncovering the latent components
of user ratings is thus of significant importance for learning user interests.
In this paper, a new recommendation approach, called LCR, was proposed by
investigating the latent components of user ratings. The basic idea is to
decompose an existing rating into several components via a cost-sensitive
learning strategy. Specifically, each rating is assigned to several latent
factor models and each model is updated according to its predictive errors.
Afterwards, these accumulated predictive errors of models are utilized to
decompose a rating into several components, each of which is treated as an
independent part to retrain the latent factor models. Finally, all latent
factor models are combined linearly to estimate predictive ratings for users.
In contrast to existing methods, LCR provides an intuitive preference modeling
strategy via multiple component analysis at an individual perspective.
Meanwhile, it is verified by the experimental results on several benchmark
datasets that the proposed method is superior to the state-of-art methods in
terms of recommendation accuracy.
UniWalk: Explainable and Accurate Recommendation for Rating and Network Data
Authors: Park Haekyu, Jeon Hyunsik, Kim Junghwan, Ahn Beunguk, Kang U
Date: 2017-10-18 00:00:00
Tasks: Recommendation Systems

How can we leverage social network data and observed ratings to correctly
recommend proper items and provide a persuasive explanation for the
recommendations? Many online services provide social networks among users, and
it is crucial to utilize social information since recommendation by a friend is
more likely to grab attention than the one from a random user. Also, explaining
why items are recommended is very important in encouraging the users' actions
such as actual purchases. Exploiting both ratings and social graph for
recommendation, however, is not trivial because of the heterogeneity of the
data.
  In this paper, we propose UniWalk, an explainable and accurate recommender
system that exploits both social network and rating data. UniWalk combines both
data into a unified graph, learns latent features of users and items, and
recommends items to each user through the features. Importantly, it explains
why items are recommended together with the recommendation results. Extensive
experiments show that UniWalk provides the best explainability and achieves the
state-of-the-art-accuracy.
An extension of the Moran process using type-specific connection graphs
Authors: Themistoklis Melissourgos, Sotiris Nikoletseas, Christoforos Raptopoulos, Paul Spirakis
Date: 2017-10-19 00:00:00
Tasks: Vocal Bursts Type Prediction

The Moran process, as studied by [Lieberman, E., Hauert, C. and Nowak, M. Evolutionary dynamics on graphs. Nature 433, pp. 312-316 (2005)], is a stochastic process modeling the spread of genetic mutations in populations. In this process, agents of a two-type population (i.e. mutants and residents) are associated with the vertices of a graph. Initially, only one vertex chosen uniformly at random is a mutant, with fitness $r > 0$, while all other individuals are residents, with fitness $1$. In every step, an individual is chosen with probability proportional to its fitness, and its state (mutant or resident) is passed on to a neighbor which is chosen uniformly at random. In this paper, we introduce and study a generalization of the model of Lieberman et al. by assuming that different types of individuals perceive the population through different graphs, namely $G_R(V,E_R)$ for residents and $G_M(V,E_M)$ for mutants. In this model, we study the fixation probability, i.e. the probability that eventually only mutants remain in the population, for various pairs of graphs. First, we transfer known results from the original single-graph model of Lieberman et al. to our 2-graph model. Among them, we provide a generalization of the Isothermal Theorem of Lieberman et al., that gives sufficient conditions for a pair of graphs to have the same fixation probability as a pair of cliques. Next, we give a 2-player strategic game view of the process where player payoffs correspond to fixation and/or extinction probabilities. In this setting, we attempt to identify best responses for each player and give evidence that the clique is the most beneficial graph for both players. Finally, we examine the possibility of efficient approximation of the fixation probability and provide a FPRAS for the special case where the mutant graph is complete.
An Empirical Investigation On Search Engine Ad Disclosure
Authors: Lewandowski Dirk, Kerkmann Friederike, Ruemmele Sandra, Suenkler Sebastian
Date: 2017-10-23 00:00:00

This representative study of German search engine users (N=1,000) focuses on
the ability of users to distinguish between organic results and advertisements
on Google results pages. We combine questions about Google's business with
task-based studies in which users were asked to distinguish between ads and
organic results in screenshots of results pages. We find that only a small
percentage of users is able to reliably distinguish between ads and organic
results, and that user knowledge of Google's business model is very limited. We
conclude that ads are insufficiently labelled as such, and that many users may
click on ads assuming that they are selecting organic results.
Interpreting Contextual Effects By Contextual Modeling In Recommender Systems
Authors: Zheng Yong
Date: 2017-10-23 00:00:00
Tasks: Decision Making, Recommendation Systems

Recommender systems have been widely applied to assist user's decision making
by providing a list of personalized item recommendations. Context-aware
recommender systems (CARS) additionally take context information into
considering in the recommendation process, since user's tastes on the items may
vary from contexts to contexts. Several context-aware recommendation algorithms
have been proposed and developed to improve the quality of recommendations.
However, there are limited research which explore and discuss the capability of
interpreting the contextual effects by the recommendation models. In this
paper, we specifically focus on different contextual modeling approaches,
reshape the structure of the models, and exploit how to utilize the existing
contextual modeling to interpret the contextual effects in the recommender
systems. We compare the explanations of contextual effects, as well as the
recommendation performance over two-real world data sets in order to examine
the quality of interpretations.
Early identification of important patents through network centrality
Authors: Mariani Manuel Sebastian, Medo Matus, Lafond François
Date: 2017-10-25 00:00:00

One of the most challenging problems in technological forecasting is to
identify as early as possible those technologies that have the potential to
lead to radical changes in our society. In this paper, we use the US patent
citation network (1926-2010) to test our ability to early identify a list of
historically significant patents through citation network analysis. We show
that in order to effectively uncover these patents shortly after they are
issued, we need to go beyond raw citation counts and take into account both the
citation network topology and temporal information. In particular, an
age-normalized measure of patent centrality, called rescaled PageRank, allows
us to identify the significant patents earlier than citation count and PageRank
score. In addition, we find that while high-impact patents tend to rely on
other high-impact patents in a similar way as scientific papers, the patents'
citation dynamics is significantly slower than that of papers, which makes the
early identification of significant patents more challenging than that of
significant papers.
Efficient analysis of stochastic gene dynamics in the non-adiabatic regime using piecewise deterministic Markov processes
Date: 2017-10-25 00:00:00

Single-cell experiments show that gene expression is stochastic and bursty, a
feature that can emerge from slow switching between promoter states with
different activities. One source of long-lived promoter states is the slow
binding and unbinding kinetics of transcription factors to promoters, i.e. the
non-adiabatic binding regime. Here, we introduce a simple analytical framework,
known as a piecewise deterministic Markov process (PDMP), that accurately
describes the stochastic dynamics of gene expression in the non-adiabatic
regime. We illustrate the utility of the PDMP on a non-trivial dynamical system
by analyzing the properties of a titration-based oscillator in the
non-adiabatic limit. We first show how to transform the underlying Chemical
Master Equation into a PDMP where the slow transitions between promoter states
are stochastic, but whose rates depend upon the faster deterministic dynamics
of the transcription factors regulated by these promoters. We show that the
PDMP accurately describes the observed periods of stochastic cycles in
activator and repressor-based titration oscillators. We then generalize our
PDMP analysis to more complicated versions of titration-based oscillators to
explain how multiple binding sites lengthen the period and improve coherence.
Last, we show how noise-induced oscillation previously observed in a
titration-based oscillator arises from non-adiabatic and discrete binding
events at the promoter site.
Chirality provides a direct fitness advantage and facilitates intermixing in cellular aggregates
Date: 2020-07-10 00:00:00

Chirality in shape and motility can evolve rapidly in microbes and cancer
cells. To determine how chirality affects cell fitness, we developed a model of
chiral growth in compact aggregates such as microbial colonies and solid
tumors. Our model recapitulates previous experimental findings and shows that
mutant cells can invade by increasing their chirality or switching their
handedness. The invasion results either in a takeover or stable coexistence
between the mutant and the ancestor depending on their relative chirality. For
large chiralities, the coexistence is accompanied by strong intermixing between
the cells, while spatial segregation occurs otherwise. We show that the
competition within the aggregate is mediated by bulges in regions where the
cells with different chiralities meet. The two-way coupling between aggregate
shape and natural selection is described by the chiral Kardar-Parisi-Zhang
equation coupled to the Burgers' equation with multiplicative noise. We solve
for the key features of this theory to explain the origin of selection on
chirality. Overall, our work suggests that chirality could be an important
ecological trait that mediates competition, invasion, and spatial structure in
cellular populations.
A Mathematical Analysis of Technical Analysis
Date: 2019-02-22 00:00:00

In this paper, we investigate trading strategies based on exponential moving
averages (ExpMAs) of an underlying risky asset. We study both logarithmic
utility maximization and long-term growth rate maximization problems and find
closed-form solutions when the drift of the underlying is modeled by either an
Ornstein-Uhlenbeck process or a two-state continuous-time Markov chain. For the
case of an Ornstein-Uhlenbeck drift, we carry out several Monte Carlo
experiments in order to investigate how the performance of optimal ExpMA
strategies is affected by variations in model parameters and by transaction
costs.
A Link Quality Model for Generalised Frequency Division Multiplexing
Date: 2017-10-25 00:00:00

5G systems aim to achieve extremely high data rates, low end-to-end latency
and ultra-low power consumption. Recently, there has been considerable interest
in the design of 5G physical layer waveforms. One important candidate is
Generalised Frequency Division Multiplexing (GFDM). In order to evaluate its
performance and features, system-level studies should be undertaken in a range
of scenarios. These studies, however, require highly complex computations if
they are performed using bit-level simulators. In this paper, the Mutual
Information (MI) based link quality model (PHY abstraction), which has been
regularly used to implement system-level studies for Orthogonal Frequency
Division Multiplexing (OFDM), is applied to GFDM. The performance of the GFDM
waveform using this model and the bit-level simulation performance is measured
using different channel types. Moreover, a system-level study for a GFDM based
LTE-A system in a realistic scenario, using both a bit-level simulator and this
abstraction model, has been studied and compared. The results reveal the
accuracy of this model using realistic channel data. Based on these results,
the PHY abstraction technique can be applied to evaluate the performance of
GFDM based systems in an effective manner with low complexity. The maximum
difference in the Packet Error Rate (PER) and throughput results in the
abstraction case compared to bit-level simulation does not exceed 4% whilst
offering a simulation time saving reduction of around 62,000 times.