pinecone/core-2020-05-10-deduplication huggingface dataset:
Non-invasive estimation of stress in conflict resolution environments

The current trend in Online Dispute Resolution focuses mostly on the
development of technological tools that allow parties to solve conflicts through
telecommunication means. However, this tendency leaves aside key issues, namely our
concern with respect to context information that was previously available in traditional
Alternative Dispute Resolution processes. The main weakness of this approach is that
conflict resolution may become a cold process, focused solely on objective questions. In
order to overcome this inconvenience, we move forward to incorporate context information
in an Online Dispute Resolution platform. In particular, we consider the estimation of the
level of stress of the users by analyzing their interaction patterns. As a result, the conflict
resolution platform or the mediator may weight to what extent a party is affected by a
particular matter, allowing one to adapt the conflict resolution strategy to a specific
problem in real time
Incorporating stress estimation into user-centred agent-based platforms

Current virtual environments for communication, cooperation and problem solving lack the rich context information that is present in face-to-face interactions. People rely on this context information, that includes body language or level of stress just to name a few, to take decisions. In this paper we present an approach whose objective is to be able to acquire context information about the users of such technological tools, making that information available for the tool and eventually to other people. We present a prototype being developed in the context of an agent-based conflict resolution platform. As a result, we achieve communication and problem solving virtual environments that are richer and closer to traditional environments, allowing people and software agents to take better and more rational decisions
Learning Methods for Variable Selection and Time Series Prediction

In the recent years, machine learning methods have become increasingly popular for modelling many different phenomena: financial markets, spatio-temporal data sets, pattern recognition, speech and image processing, recommender systems and many others. This huge interest in machine learning comes from the great success of their application and the increasingly easier acquisition, storage and access of data.
In this thesis, two general problems in machine learning are discussed and several solutions are offered. The first problem is variable selection, an approach to automatically select the most relevant features in the data. Two key phases of variable selection are the search criterion and the search algorithm. The thesis focuses on the Delta test as a search criterion, while several solutions are offered for the search algorithm, such as the Genetic Algorithm and Tabu Search. Furthermore, the selection procedure is extended for more general cases of scaling and projection, as well as their combination. Finally, some of the above proposed solutions have been developed for parallel architectures which enable the whole variable selection procedure to be used for data sets with a high number of features. 
The second problem tackled in the thesis is time series prediction that arises in many fields of science and industry. In simple words: time series prediction involves the estimation of future values for a series of measurements of a/the phenomenon of interest. The number of these estimations can be small, leading to short-term prediction, or several hundreds which constitute long-term prediction. Two models have been developed for this particular task. One is based on a recently popular neural network type called Extreme Learning Machine, while the other is a juxtaposition of Generative Topographic Mapping and Relevance Learning modified for regression tasks.
Finally, the above problems are tackled together for real-world time series coming from a biological domain. The difficulty of making any kind of inference in biological time series is due to really small amount of available samples, irregular sampling frequency and spatial coverage of areas of interest. Nevertheless, more stable model parameter estimation is possible with the combined use of global climate indicators and regional measurements in the form of a multifactor approach.Peer reviewe
Algebraic analysis of the computation in the Belousov-Zhabotinksy reaction

We analyse two very simple Petri nets inspired by the Oregonator model of the Belousov-Zhabotinsky reaction using our stochastic Petri net simulator. We then perform the Krohn-Rhodes holonomy decomposition of the automata derived from the Petri nets. The simplest case shows that the automaton can be expressed as a cascade of permutation-reset cyclic groups, with only 2 out of the 12 levels having only trivial permutations. The second case leads to a 35-level decomposition with 5 different simple non-abelian groups (SNAGs), the largest of which is A 9. Although the precise computational significance of these algebraic structures is not clear, the results suggest a correspondence between simple oscillations and cyclic groups, and the presence of SNAGs indicates that even extremely simple chemical systems may contain functionally complete algebras
Lost in translation: data integration tools meet the Semantic Web
  (experiences from the Ondex project)

More information is now being published in machine processable form on the
web and, as de-facto distributed knowledge bases are materializing, partly
encouraged by the vision of the Semantic Web, the focus is shifting from the
publication of this information to its consumption. Platforms for data
integration, visualization and analysis that are based on a graph
representation of information appear first candidates to be consumers of
web-based information that is readily expressible as graphs. The question is
whether the adoption of these platforms to information available on the
Semantic Web requires some adaptation of their data structures and semantics.
Ondex is a network-based data integration, analysis and visualization platform
which has been developed in a Life Sciences context. A number of features,
including semantic annotation via ontologies and an attention to provenance and
evidence, make this an ideal candidate to consume Semantic Web information, as
well as a prototype for the application of network analysis tools in this
context. By analyzing the Ondex data structure and its usage, we have found a
set of discrepancies and errors arising from the semantic mismatch between a
procedural approach to network analysis and the implications of a web-based
representation of information. We report in the paper on the simple methodology
that we have adopted to conduct such analysis, and on issues that we have found
which may be relevant for a range of similar platformsComment: Presented at DEIT, Data Engineering and Internet Technology, 2011
  IEEE: CFP1113L-CD
Using Virtual Worlds and Sloodle to Develop Educative Applications

Proceedings of: International Workshop on evidenced-based Technology Enhanced Learning (ebTEL'12). Salamanca (Spain), 28-30 March 2012Education is one of the most interesting applications of virtual worlds, as they can create opportunities to offer educative contents with the advantages of online courses, with the feel of "presence" that this immersive environments can provide. While most of social networking resources are mainly focused on sharing contents using a traditional web interface, virtual worlds facilitate the creation of social networks that enhance the perception and communication among its users through the use of additional modalities. In this paper we analyze the main resources provided by the Second Life virtual world and Sloodle to develop educational environments and describe their application in a educative project at the Universidad Carlos III de MadridResearch funded by projects CICYT TIN2011-28620-C02-01, CICYT TEC 2011-28626-C02-02, CAM CONTEXTS (S2009/TIC-1485), and DPS2008-07029-C02-02.Publicad
Origin of Thermal Waters in Budapest Based on Chemical and Isotope Investigations Including Chlorine-36

Budapest is a major spa center with numerous thermal baths that are open to the public. Thermal spas in Budapest were first developed by the Romans and followed by the Turks, present spas were built mainly in the 19th and 20th centuries. At the city of Budapest the Danube River flows along a geological fault which separates the Buda Hills from the Great Plain. Within this fault zone in the vicinity of the Danube more than 100 thermal springs are arising yielding totally about 40,000 m3 per day of warm mineral water. In this study the results of thermal, chemical and isotope analyses (including tritium and chlorine-36) of 12 thermal springs and wells are presented. These results are interpreted with respect to the origin and recharge conditions of the investigated thermal waters
A systematic approach to the interrogation and sharing of standardised biofilm signatures

Publicado em "6th International Conference on Practical Applications of Computational Biology & Bioinformatics", ISBN 978-3-642-28838-8The study of microorganism consortia, also known as biofilms, is associated to a number of applications in biotechnology, ecotechnology and clinical domains. A public repository on existing biofilm studies would aid in the design of new studies as well as promote collaborative and incremental work. However, bioinformatics approaches are hampered by the limited access to existing data. Scientific publications summarise the studies whilst results are kept in researchers’ private ad hoc files.

Since the collection and ability to compare existing data is imperative to move forward in biofilm analysis, the present work has addressed the development of a systematic computer-amenable approach to biofilm data organisation and standardisation. A set of in-house studies involving pathogens and employing different state-of-the-art devices and methods of analysis was used to validate the approach. The approach is now supporting the activities of BiofOmics, a public repository on biofilm signatures (http://biofomics.org).The authors thank, among others, Rosario Oliveira, Maria Joao Vieira, Idalina Machado, Nuno Cerca, Mariana Henriques, Pilar Teixeira, Douglas Monteiro, Melissa Negri, Susana Lopes, Carina Almeida and Helder Lopes, for submitting their data. The financial support from IBB-CEB, Fundacao para a Ciencia e Tecnologia (FCT) and European Community fund FEDER (Program COMPETE), project PTDC/SAU-ESA/646091/2006/FCOMP-01-0124-FEDER-007480, are also gratefully acknowledged
SAD_BaSe: a blood bank data analysis software

Publicado em "6th International Conference on Practical Applications of Computational Biology & Bioinformatics (ISBN : 978-3-642-28838-8)"The main goal of this project was to build a Web-based information system – SAD_BaSe – that monitors blood donations and the blood production chain in a user-friendly way. In particular, the system keeps track of several data indicators and supports their analysis, enabling the definition of collection and production strategies and, the measurement of quality indicators required by the Quality Management System of blood establishments. Data mining supports the analysis of donor eligibility criteria
MorphoCol: a powerful tool for the clinical profiling of pathogenic bacteria

Pathogenicity, virulence and resistance of infection-causing bacteria are
noteworthy problems in clinical settings, even after disinfection practices and antibiotic
courses. Although it is common knowledge that these traits are associated
to phenotypic and genetic variations, recent studies indicate that colony morphology
variations are a sign of increased bacterial resistance to antimicrobial agents
(i.e. antibiotics and disinfectants) and altered virulence and persistence.
The ability to search for and compare similar phenotypic appearances within
and across species is believed to have vast potential in medical diagnose and clinical
decision making. Therefore, we are developing a novel phenotypic ontology,
the Colony Morphology Ontology (CMO), to share knowledge on the colony
morphology variations of infection-causing bacteria. A study on the morphological
variations of Pseudomonas aeruginosa and Staphylococcus aureus strains, two
pathogenic bacteria associated with nosocomial infections, supported the development
of CMO. We are also developing a new Web-based framework for the
modelling and analysis of biofilm phenotypic signatures, supported by the CMO.
This framework, named MorphoCol, will enable data integration and interoperability
across research groups and other biological databases
Computational tools for strain optimization by adding reactions

This paper introduces a new plug-in for the OptFlux Metabolic Engineering
platform, aimed at finding suitable sets of reactions to add to the genomes of microbes
(wild type strain), as well as finding complementary sets of deletions, so that
the mutant becomes able to overproduce compounds with industrial interest, while
preserving their viability. The optimization methods used are Evolutionary Algorithms
and Simulated Annealing. The usefulness of this plug-in is demonstrated by
a case study, regarding the production of vanillin by the bacterium E. coli
Computational tools for strain optimization by tuning the optimal level of gene expression

In this work, a plug-in for the OptFlux Metabolic Engineering platform is presented, implementing methods that allow the identification of sets of genes to over/under express, relatively to their wild type levels. The optimization methods used are Simulated Annealing and Evolutionary Algorithms, working with a novel representation and operators. This overcomes the limitations of previous approaches based solely on gene knockouts, bringing new avenues for Biotechnology, fostering the discovery of genetic manipulations able to increase the production of certain compounds using a host microbe. The plug-in is made freely available together with appropriate documentation.Support of FCT and Programa COMPETE (ref. PTDC/EIA-EIA/ 115176/2009)
Model-Based Filtering of Combinatorial Test Suites

Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2012International audienceTobias is a combinatorial test generation tool which can efficiently generate a large number of test cases by unfolding a test pattern and computing all combinations of parameters. In this paper, we first propose a model-based testing approach where Tobias test cases are first run on an executable UML/OCL specification. This animation of test cases on a model allows to filter out invalid test sequences produced by blind enumeration, typically the ones which violate the pre-conditions of operations, and to provide an oracle for the valid ones. We then introduce recent extensions of the Tobias tool which support an incremental unfolding and filtering process, and its associated toolset. This allows to address explosive test patterns featuring a large number of invalid test cases, and only a small number of valid ones. For instance, these new constructs could mandate test cases to satisfy a given predicate at some point or to follow a given behavior. The early detection of invalid test cases improves the calculation time of the whole generation and execution process, and helps fighting combinatorial explosion
Consistency of service composition

We address the problem of ensuring that, when an application executing a service binds to a service that matches required functional properties, both the application and the service can work together, i.e., their composition is consistent. Our approach is based on a component algebra for service-oriented computing in which the configurations of applications and of services are modelled as asynchronous relational nets typed with logical interfaces. The techniques that we propose allow for the consistency of composition to be guaranteed based on properties of service orchestrations (implementations) and interfaces that can be checked at design time, which is essential for supporting the levels of dynamicity required by run-time service binding. © 2012 Springer-Verlag Berlin Heidelberg
A privacy-protecting architecture for collaborative filtering via forgery and suppression of ratings

Recommendation systems are information-filtering systems that help users deal with information overload. Unfortunately, current recommendation systems prompt serious privacy concerns. In this work, we propose an architecture that protects user privacy in such collaborative-filtering systems, in which users are profiled on the basis of their ratings. Our approach capitalizes on the combination of two perturbative techniques, namely the forgery and the suppression of ratings. In our scenario, users rate those items they have an opinion on. However, in order to avoid privacy risks, they may want to refrain from rating some of those items, and/or rate some items that do not reflect their actual preferences. On the other hand, forgery and suppression may degrade the quality of the recommendation system. Motivated by this, we describe the implementation details of the proposed architecture and present a formulation of the optimal trade-off among privacy, forgery rate and suppression rate. Finally, we provide a numerical example that illustrates our formulation.Peer ReviewedPostprint (published version
The Markov network fitness model

This book chapter was accepted for publication in Markov Networks in Evolutionary Computation [© Springer]: http://www.springer.com/engineering/computational+intelligence+and+complexity/book/978-3-642-28899-9Fitness modelling is an area of research which has recently received much\ud
interest among the evolutionary computing community. Fitness models can improve\ud
the efficiency of optimisation through direct sampling to generate new solutions,\ud
guiding of traditional genetic operators or as surrogates for a noisy or long-running\ud
fitness functions. In this chapter we discuss the application of Markov networks to\ud
fitness modelling of black-box functions within evolutionary computation, accompanied\ud
by discussion on the relationship betweenMarkov networks andWalsh analysis\ud
of fitness functions.We review alternative fitness modelling and approximation\ud
techniques and draw comparisons with the Markov network approach. We discuss\ud
the applicability of Markov networks as fitness surrogates which may be used for\ud
constructing guided operators or more general hybrid algorithms.We conclude with\ud
some observations and issues which arise from work conducted in this area so far
Bounded-Collusion IBE from Key Homomorphism

In this work, we show how to construct IBE schemes that are secure against a bounded number of collusions, starting with underlying PKE schemes which possess linear homomorphisms over their keys. In particular, this enables us to exhibit a new (bounded-collusion) IBE construction based on the quadratic residuosity assumption, without any need to assume the existence of random oracles. The new IBE’s public parameters are of size O(tλlogI) where I is the total number of identities which can be supported by the system, t is the number of collusions which the system is secure against, and λ is a security parameter. While the number of collusions is bounded, we note that an exponential number of total identities can be supported.
More generally, we give a transformation that takes any PKE satisfying Linear Key Homomorphism, Identity Map Compatibility, and the Linear Hash Proof Property and translates it into an IBE secure against bounded collusions. We demonstrate that these properties are more general than our quadratic residuosity-based scheme by showing how a simple PKE based on the DDH assumption also satisfies these properties.National Science Foundation (U.S.) (NSF CCF-0729011)National Science Foundation (U.S.) (NSF CCF-1018064)United States. Defense Advanced Research Projects Agency (DARPA FA8750-11-2-0225
Randomness Condensers for Efficiently Samplable, Seed-Dependent Sources

We initiate a study of randomness condensers for sources that are efficiently samplable but may depend on the seed of the con- denser. That is, we seek functions Cond : {0, 1}n ×{0, 1}d → {0, 1}m such that if we choose a random seed S ← {0,1}d, and a source X = A(S) is generated by a randomized circuit A of size t such that X has min- entropy at least k given S, then Cond(X;S) should have min-entropy at least some k′ given S. The distinction from the standard notion of ran- domness condensers is that the source X may be correlated with the seed S (but is restricted to be efficiently samplable). Randomness extractors of this type (corresponding to the special case where k′ = m) have been implicitly studied in the past (by Trevisan and Vadhan, FOCS ‘00). We show that:
– Unlike extractors, we can have randomness condensers for samplable, seed-dependent sources whose computational complexity is smaller than the size t of the adversarial sampling algorithm A. Indeed, we show that sufficiently strong collision-resistant hash functions are seed-dependent condensers that produce outputs with min-entropy k′ = m − O(log t), i.e. logarithmic entropy deficiency.
– Randomness condensers suffice for key derivation in many crypto- graphic applications: when an adversary has negligible success proba- bility (or negligible “squared advantage” [3]) for a uniformly random key, we can use instead a key generated by a condenser whose output has logarithmic entropy deficiency.
– Randomness condensers for seed-dependent samplable sources that are robust to side information generated by the sampling algorithm imply soundness of the Fiat-Shamir Heuristic when applied to any constant-round, public-coin interactive proof system.Engineering and Applied Science
An evolutionary approach to generate solutions for conflict scenarios

Conflict resolution is nowadays an important topic. Online Dispute
Resolution in particular is nowadays a major research topic, focusing on the
development of technology-based tools to assist parties involved in conflict
resolution processes. In this paper we present such a tool aimed at the
generation of solutions. It is based on Genetic Algorithms that evolve a
population of solutions through successive iterations, generating more
specialized ones. The result is a tree of solutions that the conflict resolution
platform can use to guide the conflict resolution process. This approach is
especially suited for parties which have no ability or are unwilling to generate
realistic proposals for the resolution of the conflict
Initialization Procedures for Multiobjective Evolutionary Approaches to the Segmentation Issue

Proceedings of: 7th International Conference, HAIS 2012, Salamanca, Spain, March 28-30, 2012.Evolutionary algorithms have been applied to a wide variety of domains with successful results, supported by the increase of computational resources. One of such domains is segmentation, the representation of a given curve by means of a series of linear models minimizing the representation error. This work analyzes the impact of the initialization method on the performance of a multiobjective evolutionary algorithm for this segmentation domain, comparing a random initialization with two different approaches introducing domain knowledge: a hybrid approach
based on the application of a local search method and a novel method based on the analysis of the Pareto Front structure.This work was supported in part by Projects CICYT TIN2011-28620-C02-01, CICYT TEC2011-28626-C02-02, CAM CONTEXTS
(S2009/TIC-1485) and DPS2008-07029-C02-02.Publicad