pinecone/core-2020-05-10-deduplication huggingface dataset:
Applying ACO To Large Scale TSP Instances

Ant Colony Optimisation (ACO) is a well known metaheuristic that has proven
successful at solving Travelling Salesman Problems (TSP). However, ACO suffers
from two issues; the first is that the technique has significant memory
requirements for storing pheromone levels on edges between cities and second,
the iterative probabilistic nature of choosing which city to visit next at
every step is computationally expensive. This restricts ACO from solving larger
TSP instances. This paper will present a methodology for deploying ACO on
larger TSP instances by removing the high memory requirements, exploiting
parallel CPU hardware and introducing a significant efficiency saving measure.
The approach results in greater accuracy and speed. This enables the proposed
ACO approach to tackle TSP instances of up to 200K cities within reasonable
timescales using a single CPU. Speedups of as much as 1200 fold are achieved by
the technique
Applying LCA to Estimate Development Energy Needs: The Cases of India and Brazil

This paper illustrates the use of life cycle assessment (LCA) methods to link human wellbeing to resource consumption. Based on a previously developed framework of the material requirements for human well-being, we use LCA and Input-Output (I/O) analysis, as appropriate, to estimate the life-cycle energy needed to meet the gap in living standards in two emerging economies, India and Brazil. We illustrate the relative contribution of different living standards components to energy requirements, as well as the uncertainty and trade-offs between upfront and long-term operating energy costs, and how these factors differ in the two countries. This analysis provides insights on how LCA analysis can be used to inform energy planning and its links to development goals
Text segmentation techniques: A critical review

Text segmentation is widely used for processing text. It is a method of splitting a document into smaller parts, which is usually called segments. Each segment has its relevant meaning. Those segments categorized as word, sentence, topic, phrase or any information unit depending on the task of the text analysis. This study presents various reasons of usage of text segmentation for different analyzing approaches. We categorized the types of documents and languages used. The main contribution of this study includes a summarization of 50 research papers and an illustration of past decade (January 2007- January 2017)’s of research that applied text segmentation as their main approach for analysing text. Results revealed the popularity of using text segmentation in different languages. Besides that, the “word” seems to be the most practical and usable segment, as it is the smaller unit than the phrase, sentence or line
Preserving Bibliographic Relationships in Mappings from FRBR to BIBFRAME 2.0

In the environment of the World Wide Web large volumes of library data have been pub-lished following different conceptual models. The navigation through these volumes and the data interlinking require the development of mappings between the conceptual models. Library conceptual models provide constructs for the representation of bibliographic families and the relationships between Works. A key requirement for successful map-pings between different conceptual models is to preserve such content relationships. This paper studies a set of cases (Work with single Expression, Work with multiple Expres-sions, translation, adaptation) to examine if and how bibliographic content relationships and families could be preserved in mappings from FRBR to BIBFRAME 2.0. Even though, relationships between Works of the same bibliographic family may be preserved, the progenitor Work is not always represented in BIBFRAME after mappings
A Complete Year of User Retrieval Sessions in a Social Sciences Academic
  Search Engine

In this paper, we present an open data set extracted from the transaction log
of the social sciences academic search engine sowiport. The data set includes a
filtered set of 484,449 retrieval sessions which have been carried out by
sowiport users in the period from April 2014 to April 2015. We propose a
description of interactions performed by the academic search engine users that
can be used in different applications such as result ranking improvement, user
modeling, query reformulation analysis, search pattern recognition.Comment: 6 pages, 2 figures, accepted short paper at the 21st International
  Conference on Theory and Practice of Digital Libraries (TPDL 2017
Towards a Semantic Search Engine for Scientific Articles

Because of the data deluge in scientific publication, finding relevant
information is getting harder and harder for researchers and readers. Building
an enhanced scientific search engine by taking semantic relations into account
poses a great challenge. As a starting point, semantic relations between
keywords from scientific articles could be extracted in order to classify
articles. This might help later in the process of browsing and searching for
content in a meaningful scientific way. Indeed, by connecting keywords, the
context of the article can be extracted. This paper aims to provide ideas to
build such a smart search engine and describes the initial contributions
towards achieving such an ambitious goal
On the Solution of Linear Programming Problems in the Age of Big Data

The Big Data phenomenon has spawned large-scale linear programming problems.
In many cases, these problems are non-stationary. In this paper, we describe a
new scalable algorithm called NSLP for solving high-dimensional, non-stationary
linear programming problems on modern cluster computing systems. The algorithm
consists of two phases: Quest and Targeting. The Quest phase calculates a
solution of the system of inequalities defining the constraint system of the
linear programming problem under the condition of dynamic changes in input
data. To this end, the apparatus of Fejer mappings is used. The Targeting phase
forms a special system of points having the shape of an n-dimensional
axisymmetric cross. The cross moves in the n-dimensional space in such a way
that the solution of the linear programming problem is located all the time in
an "-vicinity of the central point of the cross.Comment: Parallel Computational Technologies - 11th International Conference,
  PCT 2017, Kazan, Russia, April 3-7, 2017, Proceedings (to be published in
  Communications in Computer and Information Science, vol. 753
T${}^2$K${}^2$: The Twitter Top-K Keywords Benchmark

Information retrieval from textual data focuses on the construction of
vocabularies that contain weighted term tuples. Such vocabularies can then be
exploited by various text analysis algorithms to extract new knowledge, e.g.,
top-k keywords, top-k documents, etc. Top-k keywords are casually used for
various purposes, are often computed on-the-fly, and thus must be efficiently
computed. To compare competing weighting schemes and database implementations,
benchmarking is customary. To the best of our knowledge, no benchmark currently
addresses these problems. Hence, in this paper, we present a top-k keywords
benchmark, T${}^2$K${}^2$, which features a real tweet dataset and queries with
various complexities and selectivities. T${}^2$K${}^2$ helps evaluate weighting
schemes and database implementations in terms of computing performance. To
illustrate T${}^2$K${}^2$'s relevance and genericity, we successfully performed
tests on the TF-IDF and Okapi BM25 weighting schemes, on one hand, and on
different relational (Oracle, PostgreSQL) and document-oriented (MongoDB)
database implementations, on the other hand
Predicting access to persistent objects through static code analysis

In this paper, we present a fully-automatic, high-accuracy approach to predict access to persistent objects through static code analysis of object-oriented applications. The most widely-used previous technique uses a simple heuristic to make the predictions while approaches that offer higher accuracy are based on monitoring application execution. These approaches add a non-negligible overhead to the application’s execution time and/or consume a considerable amount of memory. By contrast, we demonstrate in our experimental study that our proposed approach offers better accuracy than the most common technique used to predict access to persistent objects, and makes the predictions farther in advance, without performing any analysis during application executionThis work has been supported by the European Union’s Horizon 2020 research and\ud
innovation program (grant H2020-MSCA-ITN-2014-642963), the Spanish Government\ud
(grant SEV2015-0493 of the Severo Ochoa Program), the Spanish Ministry of Science and Innovation (contract TIN2015-65316) and Generalitat de Catalunya (contract 2014-SGR-1051). The authors would also like to thank Alex Barceló for his feedback on the formalization included in this paper.Peer ReviewedPostprint (author's final draft
A Thorough Formalization of Conceptual Spaces

The highly influential framework of conceptual spaces provides a geometric
way of representing knowledge. Instances are represented by points in a
high-dimensional space and concepts are represented by convex regions in this
space. After pointing out a problem with the convexity requirement, we propose
a formalization of conceptual spaces based on fuzzy star-shaped sets. Our
formalization uses a parametric definition of concepts and extends the original
framework by adding means to represent correlations between different domains
in a geometric way. Moreover, we define computationally efficient operations on
concepts (intersection, union, and projection onto a subspace) and show that
these operations can support both learning and reasoning processes.Comment: accepted at KI 2017 (http://ki2017.tu-dortmund.de/), final
  publication is available at Springer via
  http://dx.doi.org/10.1007/978-3-319-67190-1_
Glucanase Inhibitor Protein (GIP)

Several key cellular events, such as adhesion to the host surface, penetration, and colonization of host tissue, take place during plant infection by oomycetes that can also manipulate biochemical and physiological processes in their host plants through a diverse array of virulence or avirulence molecules, known as effectors (Birch et al. 2006; Ellis et al. 2006; Kamoun 2007; Schornack et al. 2009). In susceptible plants, these effectors promote infection by suppressing defense responses, enhancing susceptibility, or inducing disease symptoms. In resistant plants, the products of the resistance genes are able to recognize the effectors, promoting an efective defense response known as hypersensitive response (HR) which restricts the pathogen to an area of scorched earth besides host cell death (Kamoun 2003; Kamoun 2007; Schornack et al. 2009). Phytophthora effectors that suppress host defense responses have be ...info:eu-repo/semantics/publishedVersio
Understanding online political networks: The case of the far-right and far-left in Greece

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record.This paper examines the connectivity among political networks on Twitter. We explore dynamics inside and between the far right and the far left, as well as the relation between the structure of the network and sentiment. The 2015 Greek political context offers a unique opportunity to investigate political communication in times of political intensity and crisis. We explore interactions inside and between political networks on Twitter in the run up to the elections of three different ballots: the parliamentary election of 25 January, the bailout referendum of 5 July, the snap election of 20 September; we, then, compare political action during campaigns with that during routinized politics.This work received funding from the European Union Horizon 2020 Programme (Horizon2020/2014–2020), under grant agreement 688380
How Polarized Have We Become? A Multimodal Classification of Trump
  Followers and Clinton Followers

Polarization in American politics has been extensively documented and
analyzed for decades, and the phenomenon became all the more apparent during
the 2016 presidential election, where Trump and Clinton depicted two radically
different pictures of America. Inspired by this gaping polarization and the
extensive utilization of Twitter during the 2016 presidential campaign, in this
paper we take the first step in measuring polarization in social media and we
attempt to predict individuals' Twitter following behavior through analyzing
ones' everyday tweets, profile images and posted pictures. As such, we treat
polarization as a classification problem and study to what extent Trump
followers and Clinton followers on Twitter can be distinguished, which in turn
serves as a metric of polarization in general. We apply LSTM to processing
tweet features and we extract visual features using the VGG neural network.
Integrating these two sets of features boosts the overall performance. We are
able to achieve an accuracy of 69%, suggesting that the high degree of
polarization recorded in the literature has started to manifest itself in
social media as well.Comment: 16 pages, SocInfo 2017, 9th International Conference on Social
  Informatic
The Message or the Messenger? Inferring Virality and Diffusion Structure
  from Online Petition Signature Data

Goel et al. (2016) examined diffusion data from Twitter to conclude that
online petitions are shared more virally than other types of content. Their
definition of structural virality, which measures the extent to which diffusion
follows a broadcast model or is spread person to person (virally), depends on
knowing the topology of the diffusion cascade. But often the diffusion
structure cannot be observed directly. We examined time-stamped signature data
from the Obama White House's We the People petition platform. We developed
measures based on temporal dynamics that, we argue, can be used to infer
diffusion structure as well as the more intrinsic notion of virality sometimes
known as infectiousness. These measures indicate that successful petitions are
likely to be higher in both intrinsic and structural virality than unsuccessful
petitions are. We also investigate threshold effects on petition signing that
challenge simple contagion models, and report simulations for a theoretical
model that are consistent with our data.Comment: 19 pages, 6 figures, 4 tables, to appear in Giovanni Luca Ciampaglia,
  Afra J. Mashhadi, and Taha Yasseri (Editors), Social Informatics: Proceedings
  of the 9th International Conference, SocInfo 2017 (Oxford, UK, September
  13-15), Springer LNCS, 201
Speaker Diarization using Deep Recurrent Convolutional Neural Networks
  for Speaker Embeddings

In this paper we propose a new method of speaker diarization that employs a
deep learning architecture to learn speaker embeddings. In contrast to the
traditional approaches that build their speaker embeddings using manually
hand-crafted spectral features, we propose to train for this purpose a
recurrent convolutional neural network applied directly on magnitude
spectrograms. To compare our approach with the state of the art, we collect and
release for the public an additional dataset of over 6 hours of fully annotated
broadcast material. The results of our evaluation on the new dataset and three
other benchmark datasets show that our proposed method significantly
outperforms the competitors and reduces diarization error rate by a large
margin of over 30% with respect to the baseline
From Relational Data to Graphs: Inferring Significant Links using
  Generalized Hypergeometric Ensembles

The inference of network topologies from relational data is an important
problem in data analysis. Exemplary applications include the reconstruction of
social ties from data on human interactions, the inference of gene
co-expression networks from DNA microarray data, or the learning of semantic
relationships based on co-occurrences of words in documents. Solving these
problems requires techniques to infer significant links in noisy relational
data. In this short paper, we propose a new statistical modeling framework to
address this challenge. It builds on generalized hypergeometric ensembles, a
class of generative stochastic models that give rise to analytically tractable
probability spaces of directed, multi-edge graphs. We show how this framework
can be used to assess the significance of links in noisy relational data. We
illustrate our method in two data sets capturing spatio-temporal proximity
relations between actors in a social system. The results show that our
analytical framework provides a new approach to infer significant links from
relational data, with interesting perspectives for the mining of data on social
systems.Comment: 10 pages, 8 figures, accepted at SocInfo201
The Digital Flynn Effect: Complexity of Posts on Social Media Increases
  over Time

Parents and teachers often express concern about the extensive use of social
media by youngsters. Some of them see emoticons, undecipherable initialisms and
loose grammar typical for social media as evidence of language degradation. In
this paper, we use a simple measure of text complexity to investigate how the
complexity of public posts on a popular social networking site changes over
time. We analyze a unique dataset that contains texts posted by 942, 336 users
from a large European city across nine years. We show that the chosen
complexity measure is correlated with the academic performance of users: users
from high-performing schools produce more complex texts than users from
low-performing schools. We also find that complexity of posts increases with
age. Finally, we demonstrate that overall language complexity of posts on the
social networking site is constantly increasing. We call this phenomenon the
digital Flynn effect. Our results may suggest that the worries about language
degradation are not warranted
Like trainer, like bot? Inheritance of bias in algorithmic content
  moderation

The internet has become a central medium through which `networked publics'
express their opinions and engage in debate. Offensive comments and personal
attacks can inhibit participation in these spaces. Automated content moderation
aims to overcome this problem using machine learning classifiers trained on
large corpora of texts manually annotated for offence. While such systems could
help encourage more civil debate, they must navigate inherently normatively
contestable boundaries, and are subject to the idiosyncratic norms of the human
raters who provide the training data. An important objective for platforms
implementing such measures might be to ensure that they are not unduly biased
towards or against particular norms of offence. This paper provides some
exploratory methods by which the normative biases of algorithmic content
moderation systems can be measured, by way of a case study using an existing
dataset of comments labelled for offence. We train classifiers on comments
labelled by different demographic subsets (men and women) to understand how
differences in conceptions of offence between these groups might affect the
performance of the resulting models on various test sets. We conclude by
discussing some of the ethical choices facing the implementers of algorithmic
moderation systems, given various desired levels of diversity of viewpoints
amongst discussion participants.Comment: 12 pages, 3 figures, 9th International Conference on Social
  Informatics (SocInfo 2017), Oxford, UK, 13--15 September 2017 (forthcoming in
  Springer Lecture Notes in Computer Science
PrivacyScore: Improving Privacy and Security via Crowd-Sourced
  Benchmarks of Websites

Website owners make conscious and unconscious decisions that affect their
users, potentially exposing them to privacy and security risks in the process.
In this paper we introduce PrivacyScore, an automated website scanning portal
that allows anyone to benchmark security and privacy features of multiple
websites. In contrast to existing projects, the checks implemented in
PrivacyScore cover a wider range of potential privacy and security issues.
Furthermore, users can control the ranking and analysis methodology. Therefore,
PrivacyScore can also be used by data protection authorities to perform
regularly scheduled compliance checks. In the long term we hope that the
transparency resulting from the published benchmarks creates an incentive for
website owners to improve their sites. The public availability of a first
version of PrivacyScore was announced at the ENISA Annual Privacy Forum in June
2017.Comment: 14 pages, 4 figures. A german version of this paper discussing the
  legal aspects of this system is available at arXiv:1705.0888
Privacy Data Management and Awareness for Public Administrations: a Case Study from the Healthcare Domain

Development of Information Systems that ensure privacy is a challenging task that spans various fields such as technology, law and policy. Reports of recent privacy infringements indicate that we are far from not only achieving privacy but also from applying Privacy by Design principles. This is due to lack of holistic methods and tools which should enable to understand privacy issues, incorporate appropriate privacy controls during design-time and create and enforce a privacy policy during run-time. To address these issues, we present VisiOn Privacy Platform which provides holistic privacy management throughout the whole information system lifecycle. It contains a privacy aware process that is supported by a software platform and enables Data Controllers to ensure privacy and Data Subjects to gain control of their data, by participating in the privacy policy formulation. A case study from the healthcare domain is used to demonstrate the platform's benefits
An axiom based metamodel for software process formalisation: An ontology approach

Software development usually follows well known process models and standards for development processes. However, these are usually diverse and described in natural language which complicates their automation, adaptivity and verification. The need for process formalisation has long been highlighted, and we have provided a formalisation and translation algorithm to that effect in earlier work. However, to systematically and faithfully formalise heterogeneous processes from different standards and process models, there is a need to utilise uniform concepts to underpin the formalisation process. Metamodels and ontologies have been explored recently to lay a foundation for structuring and expressing additional rigour to process formalisation. In this study, we develop an axiom based metamodel utilising powertype patterns as a conceptual framework to underpin homogeneous process formalisation. The advantage of an axiomatic and powertype based metamodel approach lies in its potential to determine the metamodel basic constituents and formalism as well as its extensibility and adaptability. We formalise the metamodel using ontologies while adopting use cases from ISO/IEC 29110 and ISO/IEC 24744 standards for metamodel illustrations. Ontology based process descriptions enable process automated verification and adaptivity capability through the use of ontology reasoning support engines.Griffith Sciences, School of Information and Communication TechnologyFull Tex