pinecone/core-2020-05-10-deduplication huggingface dataset:
The heterogeneous cluster ensemble method using hubness for clustering text documents

We propose a cluster ensemble method to map the corpus documents into the semantic space embedded in Wikipedia and group them using multiple types of feature space. A heterogeneous cluster ensemble is constructed with multiple types of relations i.e. document-term, document-concept and document-category. A final clustering solution is obtained by exploiting associations between document pairs and hubness of the documents. Empirical analysis with various real data sets reveals that the proposed meth-od outperforms state-of-the-art text clustering approaches
Exploratory search on the top of DBpedia chapters with the Discovery Hub application

International audienceDiscovery Hub is an exploratory search engine that helps users explore topics of interests for learning and leisure purposes.It makes use of a semantic spreading activation algorithm coupled with a sampling technique so that it does not require a preprocessing step. 1 Linked data based exploratory search and recommendation Exploratory search [2] systems are designed to assist users during expensive cognitive consuming search tasks such as learning or topic investigation. Theyprovide a high level of assistance during the navigation in the results space and advanced results explanations. In the past few years several works showed the interest of using linked datadatasets, and especially DBpedia 1 , for resources discovery in recommender and exploratory search systems. For instance Seevl 2 is a band recommender helping the discovery of musical content and artists on Youtube thanks to a recommendation algorithm and a faceted browsing functionality. MORE 3 is a movie recommender in the form of a Facebook application that performs film recommendations thanks to DBpedia.Aemoo 4 is an exploratory search system that offers a filtered view on the DBpediagraph and gives explanations on the relations between the resources shown to the user.Yovisto 5 is a video platform offeringan exploratory search featurethat proposes a ranked list of related topic besides search results
Reenacting sensorimotor features of drawing movements from friction sounds

International audienceEven though we generally don't pay attention to the friction sounds produced when we are writing or drawing, these sounds are recordable, and can even evoke the underlying gesture. In this paper, auditory perception of such sounds, and the internal representations they evoke when we listen to them, is considered from the sensorimotor learning point of view. The use of synthesis processes of friction sounds makes it possible to investigate the perceptual influence of each gestures parameter separately. Here, the influence of the velocity profile on the mental representation of the gesture induced by a friction sound was investigated through 3 experiments. The results reveal the perceptual relevance of this parameter, and particularly a specific morphology corresponding to biological movements, the so-called 1/3-power law. The experiments are discussed according to the sensorimotor theory and the invariant taxonomy of the ecological approach
Multi-level Service Approach for Flexible Support of Design Processes

Part III: Sustainable ServicesInternational audienceThe need to answer quickly to new market opportunities and the high variability of consumer demands tend industrial companies to review their adopted organisation, so to improve their reactivity and to facilitate the coupling with the business enactment. Therefore, these companies require agility in their information systems to allow business needs scalability and design process flexibility. We propose in this paper, the business activities as a service based on the service paradigm and whereas a design process is made of agile services orchestrations. We discuss the interest to use a service-oriented approach and propose a layered architecture for design process enactment
Metamodel assisted mixed-integer evolution strategies based on Kendall rank correlation coefficient

Although mixed-integer evolution strategies (MIES) have been successfully applied to optimization of mixed-integer problems, they may encounter challenges when fitness evaluations are time consuming. In this paper, we propose to use a radial-basis-function network (RBFN) trained based on the rank correlation coefficient distance metric to assist MIES. For the distance metric of the RBFN, we modified a heterogeneous metric (HEOM) by multiplying the weight for each dimension. Whilst the standard RBFN aims to approximate the fitness accurately, the proposed RBFN tries to rank the individuals (according to their fitness) correctly. Kendall rank correlation Coefficient (RCC) is adopted to measure the degree of rank correlation between the fitness and each variable. The higher the rank similarity with fitness, the greater the weight one variable will be given. Experimental results show the efficacy of the MIES assisted by the RBFN trained by maximizing the RCC performs. © 2013 Springer-Verlag
Systematic Analysis of Defenses against Return-Oriented Programming

Since the introduction of return-oriented programming, increasingly complex defenses and subtle attacks that bypass them have been proposed. Unfortunately the lack of a unifying threat model among code reuse security papers makes it difficult to evaluate the effectiveness of defenses, and answer critical questions about the interoperability, composability, and efficacy of existing defensive techniques. For example, what combination of defenses protect against every known avenue of code reuse? What is the smallest set of such defenses? In this work, we study the space of code reuse attacks by building a formal model of attacks and their requirements, and defenses and their assumptions. We use a SAT solver to perform scenario analysis on our model in two ways. First, we analyze the defense configurations of a real-world system. Second, we reason about hypothetical defense bypasses. We prove by construction that attack extensions implementing the hypothesized functionality are possible even if a ‘perfect’ version of the defense is implemented. Our approach can be used to formalize the process of threat model definition, analyze defense configurations, reason about composability and efficacy, and hypothesize about new attacks and defenses.United States. Air Force (Assistant Secretary of Defense for Research & Engineering Contract #FA8721-05-C-0002
Usability and security of gaze-based graphical grid passwords

We present and analyze several gaze-based graphical password schemes based on recall and cued-recall of grid points; eye-trackers are used to record user's gazes, which can prevent shoulder-surfing and may be suitable for users with disabilities. Our 22-subject study observes that success rate and entry time for the grid-based schemes we consider are comparable to other gaze-based graphical password schemes.  We propose the first password security metrics suitable for analysis of graphical grid passwords and provide an in-depth security analysis of user-generated passwords from our study, observing that, on several metrics, user-generated graphical grid passwords are substantially weaker than uniformly random passwords, despite our attempts at designing schemes to improve quality of user-generated passwords
Ontology-based data access: ontop of databases

We present the architecture and technologies underpinning the OBDA system Ontop and taking full advantage of storing data in relational databases. We discuss the theoretical foundations of Ontop: the tree-witness query rewriting, T-mappings and optimisations based on database integrity constraints and SQL features. We analyse the performance of Ontop in a series of experiments and demonstrate that, for standard ontologies, queries and data stored in relational databases, Ontop is fast, efficient and produces SQL rewritings of high quality
Introducing Statistical Design of Experiments to SPARQL Endpoint Evaluation

This paper argues that the common practice of benchmarking is inadequate as a scientific evaluation methodology. It further attempts to introduce the empirical tradition of the physical sciences by using techniques from Statistical Design of Experiments applied to the example of SPARQL endpoint performance evaluation. It does so by studying full as well as fractional factorial experiments designed to evaluate an assertion that some change introduced in a system has improved performance. This paper does not present a finished experimental design, rather its main focus is didactical, to shift the focus of the community away from benchmarking towards higher scientific rigor.

The Semantic Web – ISWC 2013. Lecture Notes in Computer Science Volume 8219, 2013, pp 360-375. The final publication is available at Springe
Evaluating and benchmarking SPARQL query containment solvers

wudagechekol2013aInternational audienceQuery containment is the problem of deciding if the answers to a query are included in those of another query for any queried database. This problem is very important for query optimization purposes. In the SPARQL context, it can be equally useful. This problem has recently been investigated theoretically and some query containment solvers are available. Yet, there were no benchmarks to compare theses systems and foster their improvement. In order to experimentally assess implementation strengths and limitations, we provide a first SPARQL containment test benchmark. It has been designed with respect to both the capabilities of existing solvers and the study of typical queries. Some solvers support optional constructs and cycles, while other solvers support projection, union of conjunctive queries and RDF Schemas. No solver currently supports all these features or OWL entailment regimes. The study of query demographics on DBPedia logs shows that the vast majority of queries are acyclic and a significant part of them uses UNION or projection. We thus test available solvers on their domain of applicability on three different benchmark suites. These experiments show that (i) tested solutions are overall functionally correct, (ii) in spite of its complexity, SPARQL query containment is practicable for acyclic queries, (iii) state-of-the-art solvers are at an early stage both in ter
Integrating formal and informal learning through a FLOSS-based innovative approach

It is said that due to the peculiar dynamics of FLOSS communities and their projects, effective participation in them is a privileged way to acquire the relevant skills and expertise in FLOSS development. Such is probably the reason for a number of higher education institutions to include in their curricula in Software Engineering some form of contact with the FLOSS reality. This paper explores such a perspective through an on-going case study on university students collaboration in FLOSS projects. The aim of this research is to 1) identify what should be learnt about software development through regular participation in a FLOSS project/community, and 2) assess the didactic potential of this kind of non-standard learning experiences. To this aim we resorted to a participatory research action approach and qualitative methods, namely case studies combining direct observation and interviews.This work is supported by Macao Science and Technology Development Fund (MSTDF), File No. 019 / 2011 / A1, as well as the ERDF - European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness), by National Funds through FCT - the Portuguese Foundation for Science and Technology, within project FCOMP-01-0124-FEDER-010047
Ontology-Based Resource Discovery in Pervasive Collaborative Environments

International audienceMost of the working environments offer multiple hardware and software that could be shared among the members of staff. However, it could be particularly difficult to take advantages of all these resources without a proper software support capable of discovering the ones that fulfill both a user's requirements and each resource owner's sharing preferences. To try to overcome this problem, several service discovery protocols have been developed, aiming to promote the use of network resources and to reduce configuration tasks. Unfortunately, these protocols are mainly focused on finding resources based just on their type or some minimal features, lacking information about: user preferences, restrictions and contextual variables. To outstrip this deficiency, we propose to exploit the power of semantic description, by creating a knowledge base integrated by a set of ontologies generically designed to be adopted by any type of organization. To validate this proposal, we have customized the ontologies for our case of study, which is a research center
Analysis of Twitter Data Using a Multiple-level Clustering Strategy

Twitter, currently the leading microblogging social network, has attracted a great body of research works. This paper proposes a data analysis framework to discover groups of similar twitter messages posted on a given event. By analyzing these groups, user emotions or thoughts that seem to be associated with specific events can be extracted, as well as aspects characterizing events according to user perception. To deal with the inherent sparseness of micro-messages, the proposed approach relies on a multiple-level strategy that allows clustering text data with a variable distribution. Clusters are then characterized through the most representative words appearing in their messages, and association rules are used to highlight correlations among these words. To measure the relevance of specific words for a given event, text data has been represented in the Vector Space Model using the TF-IDF weighting score. As a case study, two real Twitter datasets have been analyse
Unconditionally secure oblivious transfer from real network behavior

Secure multi-party computation (MPC) deals with the problem of shared computation between parties that do not trust each other: they are interested in performing a joint task, but they also want to keep their respective inputs private. In a world where an ever-increasing amount of computation is outsourced, for example to the cloud, MPC is a subject of crucial importance. However, unconditionally secure MPC protocols have never found practical application: the lack of realistic noisy channel models, that are required to achieve security against computationally unbounded adversaries, prevents implementation over real-world, standard communication protocols. In this paper we show for the first time that the inherent noise of wireless communication can be used to build multi-party protocols that are secure in the information-theoretic setting. In order to do so, we propose a new noisy channel, the Delaying-Erasing Channel (DEC), that models network communication in both wired and wireless contexts. This channel integrates erasures and delays as sources of noise, and models reordered, lost and corrupt packets. We provide a protocol that uses the properties of the DEC to achieve Oblivious Transfer (OT), a fundamental primitive in cryptography that implies any secure computation. In order to show that the DEC reflects the behavior of wireless communication, we run an experiment over a 802.11n wireless link, and gather extensive experimental evidence supporting our claim. We also analyze the collected data in order to estimate the level of security that such a network can provide in our model. We show the flexibility of our construction by choosing for our implementation of OT a standard communication protocol, the Real-time Transport Protocol (RTP). Since the RTP is used in a number of multimedia streaming and teleconference applications, we can imagine a wide variety of practical uses and application settings for our construction
Estimating prediction certainty in decision trees

Decision trees estimate prediction certainty using the class distribution in
the leaf responsible for the prediction. We introduce an alternative method
that yields better estimates. For each instance to be predicted, our method
inserts the instance to be classified in the training set with one of the
possible labels for the target attribute; this procedure is repeated for each
one of the labels. Then, by comparing the outcome of the different trees, the
method can identify instances that might present some difficulties to be
correctly classified, and attribute some uncertainty to their prediction. We
perform an extensive evaluation of the proposed method, and show that it is
particularly suitable for ranking and reliability estimations. The ideas
investigated in this paper may also be applied to other machine learning
techniques, as well as combined with other methods for prediction certainty
estimation.status: publishe
Interactive discovery of interesting subgroup sets

Although subgroup discovery aims to be a practical tool for exploratory data mining, its wider adoption is hampered by redundancy and the re-discovery of common knowledge. This can be remedied by parameter tuning and manual result filtering, but this requires consid- erable effort from the data analyst. In this paper we argue that it is essential to involve the user in the discovery process to solve these is- sues. To this end, we propose an interactive algorithm that allows a user to provide feedback during search, so that it is steered towards more in- teresting subgroups. Specifically, the algorithm exploits user feedback to guide a diverse beam search. The empirical evaluation and a case study demonstrate that uninteresting subgroups can be effectively eliminated from the results, and that the overall effort required to obtain interesting and diverse subgroup sets is reduced. This confirms that within-search interactivity can be useful for data analysis.status: publishe
On the importance of nonlinear modeling in computer performance
  prediction

Computers are nonlinear dynamical systems that exhibit complex and sometimes
even chaotic behavior. The models used in the computer systems community,
however, are linear. This paper is an exploration of that disconnect: when
linear models are adequate for predicting computer performance and when they
are not. Specifically, we build linear and nonlinear models of the processor
load of an Intel i7-based computer as it executes a range of different
programs. We then use those models to predict the processor loads forward in
time and compare those forecasts to the true continuations of the time seriesComment: Appeared in "Proceedings of the 12th International Symposium on
  Intelligent Data Analysis
1d-SAX: A Novel Symbolic Representation for Time Series

International audienceSAX (Symbolic Aggregate approXimation) is one of the main symbolization techniques for time series. A well-known limitation of SAX is that trends are not taken into account in the symbolization. This paper proposes 1d-SAX a method to represent a time series as a sequence of symbols that each contain information about the average and the trend of the series on a segment. We compare the efficiency of SAX and 1d-SAX in terms of goodness-of-fit, retrieval and classification performance for querying a time series database with an asymmetric scheme. The results show that 1d-SAX improves performance using equal quantity of information, especially when the compression rate increases
Domain-Based Storage Protection (DBSP) in Public\ud
Infrastructure Clouds\ud

Confidentiality and integrity of data in Infrastructure-as-a-Service\ud
(IaaS) environments increase in relevance as adoption of IaaS advances towards\ud
maturity. While current solutions assume a high degree of trust in IaaS provider\ud
staff and infrastructure management processes, earlier incidents have demon-\ud
strated that neither are impeccable.\ud
In this paper we introduce Domain-Based Storage Protection (DBSP) a data\ud
confidentiality and integrity protection mechanism for IaaS environments, which\ud
relies on trusted computing principles to provide transparent storage isolation\ud
between IaaS clients.\ud
We describe the building blocks of this mechanism and provide a set of detailed\ud
protocols for generation and handling of keys for confidentiality and integrity pro-\ud
tection of data stored by guest VM instances. The protocols assume an untrusted\ud
IaaS provider and aim to prevent both malicious and accidental faulty config-\ud
urations that could lead to breach of data confidentiality and integrity in IaaS\ud
deployments.\u
Inferring Required Permissions for Statically Composed Programs

Permission-based security models are common in smartphone
operating systems. Such models implement access control for sensitive
APIs, introducing an additional concern for application developers. It is
important for the correct set of permissions to be declared for an application,
as too small a set is likely to result in runtime errors, whereas
too large a set may needlessly worry users. Unfortunately, not all platform
vendors provide tools support to assist in determining the set of
permissions that an application requires.
We present a language-based solution for permission management. It
entails the specification of permission information within a collection of
source code, and allows for the inference of permission requirements for a
chosen program composition. Our implementation is based on Magnolia,
a programming language demonstrating characteristics that are favorable
for this use case. A language with a suitable component system supports
permission management also in a cross-platform codebase, allowing abstraction
over different platform-specific implementations and concrete
permission requirements. When the language also requires any “wiring”
of components to be known at compile time, and otherwise makes design
tradeoffs that favor ease of static analysis, then accurate inference
of permission requirements becomes possible
Unified Taxonomy for Reference Ontology of Shape Features in Product Model

This paper presents a unified taxonomy of shape features. Such taxonomy is required to construct ontologies to address heterogeneity in product/shape models. Literature provides separate classifications for volumetric, deformation and free-form surface features. The unified taxonomy proposed allows classification, representation and extraction of shape features in a product model. The novelty of the taxonomy is that the classification is based purely on shape entities and therefore it is possible to automatically extract the features from any shape model. This enables the use of this taxonomy to build reference ontology
Digital Factory Assistant: Conceptual Framework and Research Propositions

Part 9: PLM and Collaborative Product DevelopmentInternational audienceNowadays, knowledge has a major role in factory efficiency. The integration of knowledge and decision support systems in the whole factory process appears as a key for the factory of the future. The basic concept of our research work is facilitating interactions between the human being in critical working situations and information set in the factory. Our proposition is to develop a "digital factory assistant" to provide factory actors with the right information, in the right time and place. Our purpose is to build a generic factory model based on context awareness and decision support models and then create relevant information and knowledge extraction mechanisms. In this paper we discuss the conceptual architecture of the desired information system