pinecone/core-2020-05-10-deduplication huggingface dataset:
Persistence in voting behavior: stronghold dynamics in elections

Influence among individuals is at the core of collective social phenomena
such as the dissemination of ideas, beliefs or behaviors, social learning and
the diffusion of innovations. Different mechanisms have been proposed to
implement inter-agent influence in social models from the voter model, to
majority rules, to the Granoveter model. Here we advance in this direction by
confronting the recently introduced Social Influence and Recurrent Mobility
(SIRM) model, that reproduces generic features of vote-shares at different
geographical levels, with data in the US presidential elections. Our approach
incorporates spatial and population diversity as inputs for the opinion
dynamics while individuals' mobility provides a proxy for social context, and
peer imitation accounts for social influence. The model captures the observed
stationary background fluctuations in the vote-shares across counties. We study
the so-called political strongholds, i.e., locations where the votes-shares for
a party are systematically higher than average. A quantitative definition of a
stronghold by means of persistence in time of fluctuations in the voting
spatial distribution is introduced, and results from the US Presidential
Elections during the period 1980-2012 are analyzed within this framework. We
compare electoral results with simulations obtained with the SIRM model finding
a good agreement both in terms of the number and the location of strongholds.
The strongholds duration is also systematically characterized in the SIRM
model. The results compare well with the electoral results data revealing an
exponential decay in the persistence of the strongholds with time.Comment: Lecture Notes in Computer Science: Social Computing,
  Behavioral-Cultural Modeling, and Prediction (2015
Translation of Requirements Engineering Models

The globalization and the rapid development of information and communication technologies encourage organizations to work together. In software development, many works have emerged to support this cooperation using different tools and methodologies. Most of them focus on the design-stage concerns. However, very little works have dealt with cooperation during the early stage of software projects, namely Requirements Engineering (RE), despite the importance of this stage for the failure or the success of software projects. There exist different kinds of approaches to support the RE process in different contexts, based on models such as goal, viewpoint and scenario oriented. Each of these models relies on concepts which differ from one model to another. One of the difficulties for organizations that intend to work together in the upstream phases of software projects is summarized by the following question: What is the most appropriate approach every partner have to adopt? In this paper, we propose a translation process between RE models in order to ensure that organizations with different types of RE backgrounds and methodologies can work together to achieve their objectives while still using their own approach. The translation is performed using a unified meta-model issued from a semantic process of computing similarities between concepts of RE models
A traffic aware energy saving scheme for multicarrier HSPA+

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-16292-8_14In the near future, an increase in cellular network density is expected to be one of the main enablers for the newly introduced challenging capacity goals. This development will lead to an increase in the network energy consumption. In this context, we propose an energy efficient dynamic scheme for HSDPA+ (High Speed Downlink Packet Access-Advanced) systems aggregating several carriers. In the proposed scheme the network adapts dynamically to the network traffic. The scheme evaluates whether node-B deactivation is feasible without compromising the user flow throughput. Furthermore, instead of progressive de-activation of carriers and/or node-B switch-off, we evaluate the approach where feasible combination of inter-site distance and number of carriers is searched to obtain best savings. The solution exploits the fact that re-activation of carriers might permit turning off the BSs earlier at relatively higher load than existing policies, which provides the highest energy saving. Remote electrical downtilt is also considered as a means to reduce the utilization of lower modulation and coding schemes (MCS) in the new extended cells. This approach promises significant energy savings when compared with existing policies - not only for low traffic hours but also for medium load scenarios.Peer ReviewedPostprint (author's final draft
Methodological Guidelines for Engineering Self-organization and Emergence

The ASCENS project deals with the design and development of complex self-adaptive systems, where self-organization is one of the possible means by which to achieve self-adaptation. However, to support the development of self-organising systems, one has to extensively re-situate their engineering from a software architectures and requirements point of view. In particular, in this chapter, we highlight the importance of the decomposition in components to go from the problem to the engineered solution. This leads us to explain and rationalise the following architectural strategy: designing by following the problem organisation. We discuss architectural advantages for development and documentation, and its coherence with existing methodological approaches to self-organisation, and we illustrate the approach with an example on the area of swarm robotics
Limiting distributions for explosive PAR(1) time series with strongly mixing innovation

International audienceThis work deals with the limiting distribution of the least squares estimators of the coefficients a r of an explosive periodic autoregressive of order 1 (PAR(1)) time series X r = a r X r−1 +u r when the innovation {u k } is strongly mixing. More precisely {a r } is a periodic sequence of real numbers with period P > 0 and such that P r=1 |a r | > 1. The time series {u r } is periodically distributed with the same period P and satisfies the strong mixing property, so the random variables u r can be correlated
Knowledge-Based Representation for Transductive Multilingual Document Classification

International audienceMultilingual document classification is often addressed by approaches that rely on language-specific resources (e.g., bilingual dictionaries and machine translation tools) to evaluate cross-lingual document similarities. However, the required transformations may alter the original document semantics, raising additional issues to the known difficulty of obtaining high-quality labeled datasets. To overcome such issues we propose a new framework for multilingual document classification under a transductive learning setting. We exploit a large-scale multilingual knowledge base, BabelNet, to support the modeling of different language-written documents into a common conceptual space, without requiring any language translation process. We resort to a state-of-the-art transductive learner to produce the document classification. Results on two real-world multilingual corpora have highlighted the effectiveness of the proposed document model w.r.t. document representations usually involved in multilingual and cross-lingual analysis, and the robustness of the transductive setting for multilingual document classification
Diagnose this if you can: On the effectiveness of search engines in finding medical self-diagnosis information

An increasing amount of people seek health advice on the web using search engines; this poses challenging problems for current search technologies. In this paper we report an initial study of the effectiveness of current search engines in retrieving relevant information for diagnostic medical circumlocutory queries, i.e., queries that are issued by people seeking information about their health condition using a description of the symptoms they observes (e.g. hives all over body) rather than the medical term (e.g. urticaria). This type of queries frequently happens when people are unfamiliar with a domain or language and they are common among health information seekers attempting to self-diagnose or self-treat themselves. Our analysis reveals that current search engines are not equipped to effectively satisfy such information needs; this can have potential harmful outcomes on people’s health. Our results advocate for more research in developing information retrieval methods to support such complex information needs
Temporal Multinomial Mixture for Instance-Oriented Evolutionary
  Clustering

Evolutionary clustering aims at capturing the temporal evolution of clusters.
This issue is particularly important in the context of social media data that
are naturally temporally driven. In this paper, we propose a new probabilistic
model-based evolutionary clustering technique. The Temporal Multinomial Mixture
(TMM) is an extension of classical mixture model that optimizes feature
co-occurrences in the trade-off with temporal smoothness. Our model is
evaluated for two recent case studies on opinion aggregation over time. We
compare four different probabilistic clustering models and we show the
superiority of our proposal in the task of instance-oriented clustering
Document Priors Based On Time-Sensitive Social Signals

Relevance estimation of a Web resource (document) can benefit from using social signals. In this paper, we propose a language model document prior exploiting temporal characteristics of social signals. We assume that a priori significance of a document depends on the date of users actions (social signals) and on the publication date (first occurrence) of the document. Particularly, rather than estimating the priors by simply counting signals related to the document, we bias this counting by taking into account the dates of the resource and the action. We evaluate our approach on IMDb dataset containing 167438 resources and their social data collected from several social networks. The experiments show the interest of temporally-aware signals at capturing relevant resources
The iCrawl Wizard -- Supporting Interactive Focused Crawl Specification

Collections of Web documents about specific topics are needed for many areas
of current research. Focused crawling enables the creation of such collections
on demand. Current focused crawlers require the user to manually specify
starting points for the crawl (seed URLs). These are also used to describe the
expected topic of the collection. The choice of seed URLs influences the
quality of the resulting collection and requires a lot of expertise. In this
demonstration we present the iCrawl Wizard, a tool that assists users in
defining focused crawls efficiently and semi-automatically. Our tool uses major
search engines and Social Media APIs as well as information extraction
techniques to find seed URLs and a semantic description of the crawl intent.
Using the iCrawl Wizard even non-expert users can create semantic
specifications for focused crawlers interactively and efficiently.Comment: Published in the Proceedings of the European Conference on
  Information Retrieval (ECIR) 201
The Role of Women in the Sustainability of the Wine Industry: Two Case Studies in Italy

The chapter aims at analyzing whether female entrepreneurs have some distinctive capacities than men in managing business within the Italian wine industry and if these skills are useful for the sustainability of their farms and the whole sector. After the sketch of the women situation in the Italian agriculture and in the wine sector, we have analyzed two case studies located in two wine regions, Tuscany and Sardinia, by having meetings and interviewing two wine women. The two regions are quite different for the general condition of winemaking and for the internationally perceived image. Furthermore, the two informants have different family histories and manage different dimensioned farms. Our findings are that these two female entrepreneurs, even starting from different general conditions, have been able to use their own leading capabilities in the improvement of business and in collective regional and national initiative of wine promotion. Basing eco- nomic decisions on formal and/or contextual training, they have introduced inno- vations in farming, winemaking, and marketing in a specific way. We can say they have been someway slower, less hasty, and even more effective than men in involving collaborators with a charismatic heading and attracting other entrepre- neurs in doing business. The role of women in farming has been too long underrated by society and the policymakers, who, on the contrary, should provide more specific attentions to the female capacity to foster the sustainability of their own farms and the wine Italian industr
Analysis of Solution Quality of a Multiobjective Optimization-based Evolutionary Algorithm for Knapsack Problem

Multi-objective optimisation is regarded as one of the most promising ways for dealing with constrained optimisation problems in evolutionary optimisation. This paper presents a theoretical investigation of a multi-objective optimisation evolutionary algorithm for solving the 0-1 knapsack problem. Two initialisation methods are considered in the algorithm: local search initialisation and greedy search initialisation. Then the solution quality of the algorithm is analysed in terms of the approximation ratio.preprin
Computational inference in systems biology

Parameter inference in mathematical models of biological pathways, expressed as coupled ordinary differential equations (ODEs), is a challenging problem. The computational costs associated with repeatedly solving the ODEs are often high. Aimed at reducing this cost, new concepts using gradient matching have been proposed. This paper combines current adaptive gradient matching approaches, using Gaussian processes, with a parallel tempering scheme, and conducts a comparative evaluation with current methods used for parameter inference in ODEs
Externalising moods and psychological states to smooth pet-robot/child interaction through bluetooth communication

Nowadays have already passed more than two years since PATRICIA research project about using pet robots to reduce pain and anxiety in hospitalized children was started and the advances made are more than significant. Patients, parents, nurses, psychologists, engineers... all of them have adopted with illusion Pleo robot, a kind of robotic pet, working hard looking for new procedures and new solutions to the current pediatrics diseases. From this work, a technological contribution is provided going one step beyond to what seems a long path. Concretely, it is wanted to develop a system to wirelessly communicate with Pleo in order to help the coordinator who leads the therapy with the kid, to understand and control Pleo’s behaviour at any moment. This article explains how this technological part is being developed and obtained technical results.Peer ReviewedPostprint (author’s final draft
A weighted Cramer's V index for the assessment of stability in the fuzzy clustering of class C G protein-coupled receptors

After decades of intensive use, K-Means is still a common choice for crisp data clustering in real-world applications, particularly in biomedicine and bioinformatics. It is well-known that different initializations of the algorithm can lead to different solutions, precluding replicability. It has also been reported that even solutions with very similar errors may widely differ. A criterion for the choice of clustering solutions according to a combination of error and stability measures has recently been suggested. It is based on the use of Cramér’s V index, calculated from contingency tables, which is valid only for crisp clustering. Here, this criterion is extended to fuzzy and probabilistic clustering by first defining weighted contingency tables and a corresponding weighted Cramér’s V index. The proposed method is illustrated using Fuzzy C-Means in a proteomics problem.Peer ReviewedPostprint (author's final draft
A 3D voxel neighborhood classification approach within a multiparametric MRI classifier for prostate cancer detection

Prostate Magnetic Resonance Imaging (MRI) is one of the most promising approaches to facilitate prostate cancer diagnosis. The effort of research community is focused on classification techniques of MR images in order to predict the cancer position and its aggressiveness. The reduction of False Negatives (FNs) is a key aspect to reduce mispredictions and to increase sensitivity. In order to deal with this issue, the most common approaches add extra filtering algorithms after the classification step; unfortunately, this solution increases the prediction time and it may introduce errors. The aim of this study is to present a methodology implementing a 3D voxel-wise neighborhood features evaluation within a Support Vector Machine (SVM) classification model. When compared with a common single-voxel-wise classification, the presented technique increases both specificity and sensitivity of the classifier, without impacting on its performances. Different neighborhood sizes have been tested to prove the overall good performance of the classificatio
Improved Core Genes Prediction for Constructing well-supported
  Phylogenetic Trees in large sets of Plant Species

The way to infer well-supported phylogenetic trees that precisely reflect the
evolutionary process is a challenging task that completely depends on the way
the related core genes have been found. In previous computational biology
studies, many similarity based algorithms, mainly dependent on calculating
sequence alignment matrices, have been proposed to find them. In these kinds of
approaches, a significantly high similarity score between two coding sequences
extracted from a given annotation tool means that one has the same genes. In a
previous work article, we presented a quality test approach (QTA) that improves
the core genes quality by combining two annotation tools (namely NCBI, a
partially human-curated database, and DOGMA, an efficient annotation algorithm
for chloroplasts). This method takes the advantages from both sequence
similarity and gene features to guarantee that the core genome contains correct
and well-clustered coding sequences (\emph{i.e.}, genes). We then show in this
article how useful are such well-defined core genes for biomolecular
phylogenetic reconstructions, by investigating various subsets of core genes at
various family or genus levels, leading to subtrees with strong bootstraps that
are finally merged in a well-supported supertree.Comment: 12 pages, 7 figures, IWBBIO 2015 (3rd International Work-Conference
  on Bioinformatics and Biomedical Engineering
A Computational Method for the Rate Estimation of Evolutionary
  Transpositions

Genome rearrangements are evolutionary events that shuffle genomic
architectures. Most frequent genome rearrangements are reversals,
translocations, fusions, and fissions. While there are some more complex genome
rearrangements such as transpositions, they are rarely observed and believed to
constitute only a small fraction of genome rearrangements happening in the
course of evolution. The analysis of transpositions is further obfuscated by
intractability of the underlying computational problems.
  We propose a computational method for estimating the rate of transpositions
in evolutionary scenarios between genomes. We applied our method to a set of
mammalian genomes and estimated the transpositions rate in mammalian evolution
to be around 0.26.Comment: Proceedings of the 3rd International Work-Conference on
  Bioinformatics and Biomedical Engineering (IWBBIO), 2015. (to appear
A weighted Cramér’s V Index for the assessment of stability in the fuzzy clustering of class C G protein-coupled receptors

After decades of intensive use, K-Means is still a common choice for crisp data clustering in real-world applications, particularly in biomedicine and bioinformatics. It is well-known that different initializations of the algorithm can lead to different solutions, precluding replicability. It has also been reported that even solutions with very similar errors may widely differ. A criterion for the choice of clustering solutions according to a combination of error and stability measures has recently been suggested. It is based on the use of Cramér’s V index, calculated from contingency tables, which is valid only for crisp clustering. Here, this criterion is extended to fuzzy and probabilistic clustering by first defining weighted contingency tables and a corresponding weighted Cramér’s V index. The proposed method is illustrated using Fuzzy C-Means in a proteomics problem.Peer ReviewedPostprint (author's final draft
Gene Expression vs. Network Attractors

Microarrays, RNA-Seq, and Gene Regulatory Networks (GRNs) are common tools used to study the regulatory mechanisms mediating the expression of the genes involved in the biological processes of a cell. Whereas microarrays and RNA-Seq provide a snapshot of the average expression of a set of genes of a population of cells, GRNs are used to model the dynamics of the regulatory dependencies among a subset of genes believed to be the main actors in a biological process. In this paper we discuss the possibility of correlating a GRN dynamics with a gene expression profile extracted from one or more wet-lab expression experiments. This is more a position paper to promote discussion than a research paper with final result
Interacting and making personalized recommendations of places of interest to tourists

Advances in Intelligent Systems and Computing, 353Nowadays, applications that are developed to support tourists should go much further than simply providing information about places or recommending places or routes based on the user location. They should be able to provide users with simple mechanisms to interact with places of interest and provide them with relevant information and recommendations about new relevant places of interest or tours according to their preferences and the preferences of other tourists with similar interests. In this work we describe a system that explores information about tourists’ interactions with places of interest and their opinions about each place, to recommend new places of interest, pedestrian tours and to promote products and services which are in accordance with their expectations. First experiments show that the system can help the tourists to interact with places of interest, helping them in their visits and also to promote shops and services
Concurrency detection on finish-to-start activity precedence networks

We explore the finish-to-start precedence relations of project activities used in scheduling problems. From these relations, we devise a method to identify groups of activities that could execute concurrently, i.e. activities in the same group can all execute in parallel.
The method derives a new set of relations to describe the concurrency. Then, it is represented by an undirected graph and the maximal cliques problem identifies the groups.
We provide a running example with a project from our previous studies in resource constrained project cost minimization together with an example application on the concurrency detection method: the evaluation of the resource stress