pinecone/core-2020-05-10-deduplication huggingface dataset:
Modelling stress recognition in conflict resolution scenarios

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
the context information that was previously available in traditional Alternative
Dispute Resolution processes. The main weakness of this approach is that conflict
resolution may become focused solely on objective issues. In order to overcome
this inconvenience, we move forward to incorporate context and behavioural information
in an Online Dispute Resolution platform. In particular, we consider
the estimation of the level of stress and the prediction of the stress state evolution.
As a result, the conflict resolution platform or the mediator may predict to
what extent a party is affected by a particular matter, allowing one to adapt the
conflict resolution strategy to a specific scenario in real time
Modeling Internet as a User-Adapted Speech Service

Proceedings of: 7th International Conference, HAIS 2012, Salamanca, Spain, March 28-30th, 2012.The web has become the largest repository of multimedia information and its convergence with telecommunications is now bringing the benefits of web technology and hybrid artificial intelligence systems to hand-held devices. However, maximizing accessibility is not always the main objective in the design of web applications, specially if it is concerned with facilitating access for disabled people. This way, natural spoken conversation and multimodal conversational agents have been proposed as a solution to facilitate a more natural interaction with these kind of devices. In this paper, we describe a proposal to provide spoken access to Internet information that is valid not only to generate basic applications (e.g., web search engines), but also to develop dialog-based speech interfaces that facilitate a user-adapted access that enhances web services. We describe our proposal and detail several applications developed to provide evidences about the benefits of introducing speech to make the enormous web content accessible to all mobile phone users.Research funded by projects CICYT TIN2011-28620-
C02-01, CICYT TEC2011-28626-C02-02,CAM CONTEXTS (S2009/TIC-1485),
and DPS2008-07029-C02-02.Publicad
Novel membrane emulsification method of producing highly uniform silica particles using inexpensive silica sources

The original publication is available at www.springerlink.comA membrane emulsification method for production of monodispersed silica-based ion exchange particles through water-in-oil emulsion route is developed. A hydrophobic microsieve membrane with 15 :m pore size and 200 :m pore spacing was used to produce droplets, with a mean size between 65 and 240 :m containing acidified sodium silicate solution (with 4 and 6% wt. SiO2) in kerosene. After drying, the final silica particles had a mean size in the range between 30 and 70 :m. Coefficient of variation for both the droplets and particles did not exceed 35%. The most uniform particles had a mean diameter of 40 :m and coefficient of variation of 17%. The particles were functionalised with 3-aminopropyltrimethoxysilane and used for chemisorption of Cu(II) from an aqueous solution of CuSO4 in a continuous flow stirred cell with slotted pore microfiltration membrane. Functionalised silica particles showed a higher binding affinity toward Cu(II) than non-treated silica particles
Control over the shell thickness of core/shell drops in three-phase glass capillary devices

The original publication is available at www.springerlink.comMonodisperse core/shell drops with aqueous core and poly(dimethylsiloxane) (PDMS) shell of controllable thickness have been produced using a glass microcapillary device that combines co-flow and flow-focusing geometries. The throughput of the droplet generation was high, with droplet generation frequency in the range from 1,000 to 10,000 Hz. The size of the droplets can be tuned by changing the flow rate of the continuous phase. The technique enables control over the shell thickness through adjusting the flow rate ratio of the middle to inner phase. As the flow rate of the middle and inner phase increases, the droplet breakup occurs in the dripping-to-jetting transition regime, with each double emulsion droplet containing two monodisperse internal aqueous droplets. The resultant drops can be used subsequently as templates for monodisperse polymer capsules with a single or multiple inner compartments, as well as functional vesicles such as liposomes, polymersomes and colloidosomes
Pretreatment of used cooking oil for the preparation of biodiesel using heterogeneous catalysis

The original publication is available at www.springerlink.comUsed cooking oil (UCO) offers a number of benefits for the production of biodiesel because it is a waste material and relatively cheap; however UCOs contain free fatty acids (FFAs) which need to be removed. Esterification can be used to convert the FFAs to biodiesel, and this work has compared two types of heterogeneous catalyst for esterification. An immobilized enzyme, Novozyme 435, was investigated because it has been shown to give a high conversion of FFAs and it has been compared to an ion-exchange resin, Purolite D5081, which was developed for the esterification of UCO for the production of biodiesel. It was found that a conversion of 94% was achieved using Purolite D5081 compared to 90% conversion with Novozyme 435
Top-k retrieval using facility location analysis

The top-k retrieval problem aims to find the optimal set of k documents from a number of relevant documents given the user’s query. The key issue is to balance the relevance and diversity of the top-k search results. In this paper, we address this problem using Facility Location Analysis taken from Operations Research, where the locations of facilities are optimally chosen according to some criteria. We show how this analysis technique is a generalization of state-of-the-art retrieval models for diversification (such as the Modern Portfolio Theory for Information Retrieval), which treat the top-k search results like “obnoxious facilities” that should be dispersed as far as possible from each other. However, Facility Location Analysis suggests that the top-k search results could be treated like “desirable facilities” to be placed as close as possible to their customers. This leads to a new top-k retrieval model where the best representatives of the relevant documents are selected. In a series of experiments conducted on two TREC diversity collections, we show that significant improvements can be made over the current state-of-the-art through this alternative treatment of the top-k retrieval problem
A latent variable ranking model for content-based retrieval

Since their introduction, ranking SVM models [11] have become a powerful tool for training content-based retrieval systems. All we need for training a model are retrieval examples in the form of triplet constraints, i.e. examples specifying that relative to some query, a database item a should be ranked higher than database item b. These types of constraints could be obtained from feedback of users of the retrieval system. Most previous ranking models learn either a global combination of elementary similarity functions or a combination defined with respect to a single database item. Instead, we propose a “coarse to fine” ranking model where given a query we first compute a distribution over “coarse” classes and then use the linear combination that has been optimized for queries of that class. These coarse classes are hidden and need to be induced by the training algorithm. We propose a latent variable ranking model that induces both the latent classes and the weights of the linear combination for each class from ranking triplets. Our experiments over two large image datasets and a text retrieval dataset show the advantages of our model over learning a global combination as well as a combination for each test point (i.e. transductive setting). Furthermore, compared to the transductive approach our model has a clear computational advantages since it does not need to be retrained for each test query.34th European Conference on IR Research, ECIR 2012, Barcelona, Spain, April 1-5, 2012. ProceedingsSpanish Ministry of Science and Innovation (JCI-2009-04240)EU PASCAL2 Network of Excellence (FP7-ICT-216886
Combining Probabilistic Language Models for Aspect-Based Sentiment Retrieval

In this paper, we present a new methodology aimed at retrieving relevant product aspects from a collection of customer reviews, as well as the most salient sentiments expressed about them. Our proposal is both unsupervised and domain independent, and does not relies on NLP techniques such as parsing or dependence analysis. In our experiments, the proposed method achieves good values of precision. It is also shown that our approach is capable of properly retrieving the relevant aspects and their sentiments even from individual reviews
Robust Coin Flipping

Alice seeks an information-theoretically secure source of private random
data. Unfortunately, she lacks a personal source and must use remote sources
controlled by other parties. Alice wants to simulate a coin flip of specified
bias $\alpha$, as a function of data she receives from $p$ sources; she seeks
privacy from any coalition of $r$ of them. We show: If $p/2 \leq r < p$, the
bias can be any rational number and nothing else; if $0 < r < p/2$, the bias
can be any algebraic number and nothing else. The proof uses projective
varieties, convex geometry, and the probabilistic method. Our results improve
on those laid out by Yao, who asserts one direction of the $r=1$ case in his
seminal paper [Yao82]. We also provide an application to secure multiparty
computation.Comment: 22 pages, 1 figur
Faster Algorithms for Approximate Common Divisors: Breaking Fully-Homomorphic-Encryption Challenges over the Integers

International audienceAt EUROCRYPT '10, van Dijk et al. presented simple fully- homomorphic encryption (FHE) schemes based on the hardness of approximate integer common divisors problems, which were introduced in 2001 by Howgrave-Graham. There are two versions for these problems: the partial version (PACD) and the general version (GACD). The seemingly easier problem PACD was recently used by Coron et al. at CRYPTO '11 to build a more efficient variant of the FHE scheme by van Dijk et al.. We present a new PACD algorithm whose running time is essentially the "square root" of that of exhaustive search, which was the best attack in practice. This allows us to experimentally break the FHE challenges proposed by Coron et al. Our PACD algorithm directly gives rise to a new GACD algorithm, which is exponentially faster than exhaustive search. Interestingly, our main technique can also be applied to other settings, such as noisy factoring and attacking low-exponent RSA
Tightly-Secure Signatures from Lossy Identification Schemes

International audienceIn this paper we present three digital signature schemes with tight security reductions. Our first signature scheme is a particularly efficient version of the short exponent discrete log based scheme of Girault et al. (J. of Cryptology 2006). Our scheme has a tight reduction to the decisional Short Discrete Logarithm problem, while still maintaining the non-tight reduction to the computational version of the problem upon which the original scheme of Girault et al. is based. The second signature scheme we construct is a modification of the scheme of Lyubashevsky (Asiacrypt 2009) that is based on the worst-case hardness of the shortest vector problem in ideal lattices. And the third scheme is a very simple signature scheme that is based directly on the hardness of the Subset Sum problem. We also present a general transformation that converts what we term lossy identification schemes into signature schemes with tight security reductions. We believe that this greatly simplifies the task of constructing and proving the security of such signature schemes
The Contribution of Bayesian Networks to Manage Risks of Maritime Piracy against Oil Offshore Fields

International audienceIn recent years pirate attacks against shipping and oil fields have continued to increase in quantity and severity. For example, the attack against the Exxon Mobil oil rig in 2010 off the coast of Nigeria ended in the kidnap of 19 crew members and a reduction in daily oil production of 45,000 barrels, which resulted in an international rise in the price of oil. This example is a perfect illustration of current weaknesses in existing anti-piracy systems. The SARGOS project proposes an innovative system to address this problem. It takes into account the entire threat treatment process; from the detection of a potential threat to implementation of the response. The response to an attack must take into account all of the many parameters related to the threat, the potential target, the available protection resources, environmental constraints, etc. To manage these parameters, the power of Bayesian networks is harnessed to identify potential countermeasures and the means to manage them
Trends Prediction Using Social Diffusion Models

The importance of the ability to predict trends in social media has been growing rapidly in the past few years with the growing dominance of social media in our everyday’s life. Whereas many works focus on the detection of anomalies in networks, there exist little theoretical work on the prediction of the likelihood of anomalous network pattern to globally spread and become “trends”. In this work we present an analytic model for the social diffusion dynamics of spreading network patterns. Our proposed method is based on information diffusion models, and is capable of predicting future trends based on the analysis of past social interactions between the community’s members. We present an analytic lower bound for the probability that emerging trends would successfully spread through the network. We demonstrate our model using two comprehensive social datasets — the Friends and Family experiment that was held in MIT for over a year, where the complete activity of 140 users was analyzed, and a financial dataset containing the complete activities of over 1.5 million members of the eToro social trading community
Modeling Infection with Multi-agent Dynamics

Developing the ability to comprehensively study infections in small populations enables us to improve epidemic models and better advise individuals about potential risks to their health. We currently have a limited understanding of how infections spread within a small population because it has been difficult to closely track and infection within a complete community. This paper presents data closely tracking the spread of an infection centered on a student dormitory, collected by leveraging the residents’ use of cellular phones. This data is based on daily symptom surveys taken over a period of four months and proximity tracking through cellular phones. We demonstrate that using a Bayesian, discrete-time multi-agent model of infection to model the real-world symptom report and proximity tracking records can give us important insights about infections in small population
How Many Makes a Crowd? On the Evolution of Learning as a Factor of Community Coverage

As truly ubiquitous wearable computers, mobile phones are quickly becoming the primary source for social, behavioral and environmental sensing and data collection. Today’s smartphones are equipped with increasingly more sensors and accessible data types that enable the collection of literally dozens of signals related to the phone, its user, and its environment. A great deal of research effort in academia and industry is put into mining this raw data for higher level sense-making, such as understanding user context, inferring social networks, learning individual features, and so on. In many cases, this analysis work is the result of exploratory forays and trial-and-error. In this work we investigate the properties of learning and inferences of real world data collected via mobile phones for different sizes of analyzed networks. In particular, we examine how the ability to predict individual features and social links is incrementally enhanced with the accumulation of additional data. To accomplish this, we use the Friends and Family dataset, which contains rich data signals gathered from the smartphones of 130 adult members of a young-family residential community over the course of a year and consequently has become one of the most comprehensive mobile phone datasets gathered in academia to date. Our results show that features such as ethnicity, age and marital status can be detected by analyzing social and behavioral signals. We then investigate how the prediction accuracy is increased when the users sample set grows. Finally, we propose a method for advanced prediction of the maximal learning accuracy possible for the learning task at hand, based on an initial set of measurements. These predictions have practical implications, such as influencing the design of mobile data collection campaigns or evaluating analysis strategies
Accounting for Patterns of Collective Behavior in Crowd Locomotor Dynamics for Realistic Simulations

International audienceDo people in a crowd behave like a set of isolated individuals or like a cohesive group? Studies of crowd modeling usually consider pedestrian behavior either from the point of view of an isolated individual or from that of large swarms. We introduce here a study of small crowds walking towards a common goal and propose to make the link between individual behavior and crowd dynamics. Data show that participants, even though not instructed to behave collectively, do form a cohesive group and do not merely treat one another as obstacles. We present qualitative and quantitative measurements of this collective behavior, and propose a first set of patterns characterizing such behavior. This work is part of a wider effort to test crowd models against observed data
Do expressive geographic queries lead to improvement in retrieval effectiveness?

In an information retrieval (IR) context, users usually issue queries with few terms and no operators (e.g., and, or, +). However, most of users’ information needs involve more expressiveness (e.g., ‘Potato famine in Ireland, but not in Cork’). Our work deals with this category of queries that may be processed by geographic IR (GIR) systems to parse digital libraries according to spatial, temporal, and topical criteria. We propose a GIR framework that supports expressive queries and aggregates results of a multi-criteria search. We also conduct experiments to verify that this approach improves the effectiveness of such search engines (improvement of 27% for topical criteria only, and of 54% for spatial and temporal criteria)
On The Recovery Performance of Single- and Multipath OLSR in Wireless Multi-Hop Networks

International audienceIn this paper, we study and improve the recovery properties of single and multipath routing strategies when facing network failure situations. In particular, we focus our study on two MANET routing protocols: OLSR and its multipath extension MP-OLSR. In various wireless multi-hop network environments, especially in multiple chain topologies, we define and seek to evaluate the latency introduced by these protocols to find a new path after a link failure. Theoretical estimations and simulation results show that, under dual chain-topologies, this latency can be too long and incompatible with the needs of loss and delay constrained applications. As the source nodes cannot detect link failures immediately because of the delay incurred by the well-known nature of link state protocols in general, and of OLSR Topology Control (TC) messages in particular, these nodes keep sending packets along broken paths. We thus study the inconsistencies between the actual network topology and the nodes' own representation. After analyzing the consequences of this long latency, we seek to alleviate these problems with the introduction of adapted mechanisms. We propose three new different schemes and accordingly extend the original OLSR and MP-OLSR protocols in order to decrease the expected latency and improve the protocol performance. Simulation results show a steep decrease of the latency when using these new schemes in dual chain-topologies. We also discuss these results in terms of packet loss, end-to-end delay and overhead
Improving Data Dissemination in Multi-Hop Cognitive Radio Ad-Hoc Networks

International audienceIn this paper, we present SURF, a distributed channel selection strategy for efficient data dissemination in multi-hop cognitive radio ad-hoc networks (CRNs). SURF classifies the available channels on the basis of primary radio unoccupancy and the number of cognitive radio neighbors using the channels. Through extensive NS-2 simulations, we compare the performance of SURF with three related approaches. Simulation results confirm that SURF is effective in selecting the best channels for efficient communication and for highest dissemination reachability in multi-hop CRNs
A framework for security analysis of key derivation functions

This paper presents a comprehensive formal security framework for key derivation functions (KDF). The major security goal for a KDF is to produce cryptographic keys from a private seed value where the derived cryptographic keys are indistinguishable from random binary strings. We form a framework of five security models for KDFs. This consists of four security models that we propose: Known Public Inputs Attack (KPM, KPS), Adaptive Chosen Context Information Attack (CCM) and Adaptive Chosen Public Inputs Attack(CPM); and another security model, previously defined by Krawczyk [6], which we refer to as Adaptive Chosen Context Information Attack(CCS). These security models are simulated using an indistinguisibility game. In addition we prove the relationships between these five security models and analyse KDFs using the framework (in the random oracle model)
Iterated greedy algorithms for the maximal covering location problem

The problem of allocating a set of facilities in order to maximise\ud
the sum of the demands of the covered clients is known as the\ud
maximal covering location problem. In this work we tackle this problem\ud
by means of iterated greedy algorithms. These algorithms iteratively refine\ud
a solution by partial destruction and reconstruction, using a greedy\ud
constructive procedure. Iterated greedy algorithms have been applied\ud
successfully to solve a considerable number of problems. With the aim of\ud
providing additional results and insights along this line of research, this\ud
paper proposes two new iterated greedy algorithms that incorporate two\ud
innovative components: a population of solutions optimised in parallel\ud
by the iterated greedy algorithm, and an improvement procedure that\ud
explores a large neighbourhood by means of an exact solver. The benefits\ud
of the proposal in comparison to a recently proposed decomposition\ud
heuristic and a standalone exact solver are experimentally shown.Peer ReviewedPostprint (published version
Fitness in Evolutionary Art and Music: What Has Been Used and What Could Be Used?

This paper considers the notion of fitness in evolutionary art and music. A taxonomy is presented of the ways in which fitness is used in such systems, with two dimensions: what the fitness function is applied to, and the basis by which the function is constructed. Papers from a large collection are classified using this taxonomy. The paper then discusses a number of ideas that have not be used for fitness evaluation in evolutionary art and which might be valuable in future developments: memory, scaffolding, connotation and web search