pinecone/core-2020-05-10-deduplication huggingface dataset:
Broadening the Scope of Differential Privacy Using Metrics

International audienceDifferential Privacy is one of the most prominent frameworks used to deal with disclosure prevention in statistical databases. It provides a formal privacy guarantee, ensuring that sensitive information relative to individuals cannot be easily inferred by disclosing answers to aggregate queries. If two databases are adjacent, i.e. differ only for an individual, then the query should not allow to tell them apart by more than a certain factor. This induces a bound also on the distinguishability of two generic databases, which is determined by their distance on the Hamming graph of the adjacency relation. In this paper we explore the implications of differential privacy when the indistinguishability requirement depends on an arbitrary notion of distance. We show that we can naturally express, in this way, (protection against) privacy threats that cannot be represented with the standard notion, leading to new applications of the differential privacy framework. We give intuitive characterizations of these threats in terms of Bayesian adversaries, which generalize two interpretations of (standard) differential privacy from the literature. We revisit the well-known results stating that universally optimal mechanisms exist only for counting queries: We show that, in our extended setting, universally optimal mechanisms exist for other queries too, notably sum, average, and percentile queries. We explore various applications of the generalized definition, for statistical databases as well as for other areas, such that geolocation and smart metering
A formal concept view of argumentation

The paper presents a parallel between two important theories for the treatment of information which address questions that are apparently unrelated and that are studied by different research communities: an enriched view of formal concept analysis and abstract argumentation. Both theories exploit a binary relation (expressing object-property links, attacks between arguments). We show that when an argumentation framework rather considers the complementary relation does not attack, then its stable extensions can be seen as the exact counterparts of formal concepts. This leads to a cube of oppositions, a generalization of the well-known square of oppositions, between eight remarkable sets of arguments. This provides a richer view for argumentation in cases of bi-valued attack relations and fuzzy ones
Qualitative Capacities as Imprecise Possibilities

This paper studies the structure of qualitative capacities, that is, monotonic set-functions, when they range on a finite totally ordered scale equipped with an order-reversing map. These set-functions correspond to general representations of uncertainty, as well as importance levels of groups of criteria in multicriteria decision-making. More specifically, we investigate the question whether these qualitative set-functions can be viewed as classes of simpler set-functions, typically possibility measures, paralleling the situation of quantitative capacities with respect to imprecise probability theory. We show that any capacity is characterized by a non-empty class of possibility measures having the structure of an upper semi-lattice. The lower bounds of this class are enough to reconstruct the capacity, and their number is characteristic of its complexity. We introduce a sequence of axioms generalizing the maxitivity property of possibility measures, and related to the number of possibility measures needed for this reconstruction. In the Boolean case, capacities are closely related to non-regular multi-source modal logics and their neighborhood semantics can be described in terms of qualitative Moebius transforms
Conditional Preference Nets and Possibilistic Logic

CP-nets (Conditional preference networks) are a well-known compact graphical representation of preferences in Artificial Intelligence, that can be viewed as a qualitative counterpart to Bayesian nets. In case of binary attributes it captures specific partial orderings over Boolean interpretations where strict preference statements are defined between interpretations which differ by a single flip of an attribute value. It respects preferential independence encoded by the ceteris paribus property. The popularity of this approach has motivated some comparison with other preference representation setting such as possibilistic logic. In this paper, we focus our discussion on the possibilistic representation of CP-nets, and the question whether it is possible to capture the CP-net partial order over interpretations by means of a possibilistic knowledge base and a suitable semantics. We show that several results in the literature on the alleged faithful representation of CP-nets by possibilistic bases are questionable. To this aim we discuss some canonical examples of CP-net topologies where the considered possibilistic approach fails to exactly capture the partial order induced by CP-nets, thus shedding light on the difficulties encountered when trying to reconcile the two frameworks
Sorted-pareto dominance and qualitative notions of optimality

Pareto dominance is often used in decision making to compare decisions that have multiple preference values – however it can produce an unmanageably large number of Pareto optimal decisions. When preference value scales can be made commensurate, then the Sorted-Pareto relation produces a smaller, more manageable set of decisions that are still Pareto optimal. Sorted-Pareto relies only on qualitative or ordinal preference information, which can be easier to obtain than quantitative information. This leads to a partial order on the decisions, and in such partially-ordered settings, there can be many different natural notions of optimality. In this paper, we look at these natural notions of optimality, applied to the Sorted-Pareto and min-sum of weights case; the Sorted-Pareto ordering has a semantics in decision making under uncertainty, being consistent with any possible order-preserving function that maps an ordinal scale to a numerical one. We show that these optimality classes and the relationships between them provide a meaningful way to categorise optimal decisions for presenting to a decision maker
Analogical Proportions and Multiple-Valued Logics

Recently, a propositional logic modeling of analogical proportions, i.e., statements of the form “A is to B as C is to D”, has been proposed, and has then led to introduce new related proportions in a general setting. This framework is well-suited for analogical reasoning and classification tasks about situations described by means of Boolean properties. There is a clear need for extending this approach to deal with the cases where i) properties are gradual ; ii) properties may not apply to some situations ; iii) the truth status of a property is unknown. The paper investigates the appropriate extension in each of these three cases
Predicting aquaplaning performance from tyre profile images with machine learning

The tread of a tyre consists of a profile (pattern of grooves, sipes, and blocks) mainly designed to improve wet performance and inhibit aquaplaning by providing a conduit for water to be expelled underneath the tyre as it makes contact with the road surface. Testing different tread profile designs is time consuming, as it requires fabrication and physical measurement of tyres. We propose a supervised machine learning method to predict tyres’ aquaplaning performance based on the tread profile described in geometry and rubber stiffness. Our method provides a regressor from the space of profile geometry, reduced to images, to aquaplaning performance. Experimental results demonstrate that image analysis and machine learning combined with other methods can yield improved prediction of aquaplaning performance, even using non-normalised data. Therefore this method has can potentially save substantial cost and time in tyre development. This investigation is based on data provided by Continental Reifen Deutschland GmbH
A Novel Method for Barcode Localization in Image Domain

Barcode localization is an essential step of the barcode reading
process. For industrial environments, having high-resolution 
cameras and eventful scenarios, fast and reliable localization 
is crucial. Images acquired in those setups have limited 
parameters, however, they vary at each application. In earlier 
works we have already presented various barcode features to 
track for localization process. In this paper, we present a 
novel approach for fast barcode localization using a limited set 
of pixels in image domain
Automatic segmentation of adipose tissue from thigh magnetic resonance images

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-39094-4_51Automatic segmentation of adipose tissue in thigh magnetic resonance imaging (MRI) scans is challenging and rarely reported in the literature. To address this problem, we propose a fully automated unsupervised segmentation method involving the use of spatial intensity constraints to guide the segmentation process. The novelty of this method lies in two aspects: firstly, an adaptive distance classifier, incorporating intra-slice spatial continuity, is used for robust region growing and segmentation estimation; secondly, polynomial based intensity inhomogeneity maps are generated to model inter- and intra-slice intensity variation of each pixel class and thus refine the initial classification. Our experimental results have demonstrated the effectiveness of imposing 3D intensity constraints to successfully classify the adipose tissue from muscles in the presence of image noise and considerable amounts of non-uniform MRI intensity. © 2013 Springer-Verlag
Image analysis for automatic characterization of polyhydroxyalcanoates granules

A new monitoring approach for polyhydroxyalcanoates (PHA) granules
identification and characterization based on image analysis procedures is
proposed. PHA granules were analyzed by Sudan Black B (SBB) staining in an
enhanced biological phosphorus removal (EBPR) system. Color images
captured on an optical microscope were analyzed through quantitative image
analysis. The distribution of PHA granules was estimated by determination of
the proportion of blue-black pixels. A relationship was found between image
analysis parameters and PHA concentration. In conclusion, it may be inferred
that the present image analysis procedure is suitable to quantify PHA granules
in SBB staining images and a promising alternative to standard analysis
Integrating internationalization in the user-centered software development process

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-39143-9_27Proceedings of 5th International Conference, CCD 2013, Held as Part of HCI International 2013, Las Vegas, NV, USA, July 21-26, 2013Internationalization is a common practice today in software development. In the most basic sense, internationalization is carried out by applying localization design guidelines to face language translation, icon representation, character sets and so on. However, this practice is mostly intended for design purposes, which results insufficient when applying internationalization in huge projects and, specifically, through a concrete development process. In this paper, a broader framework is provided in order to ensure internationalization through a software development process. To this end, a set of activities and sub-activities will be presented involving not only design but pre-development, analysis, implementation and evaluation issues that need to be considered for a right internationalization assurance in international software development. The idea behind is to bridge the gap between simple and usual localization activities and the user-centered software development process as internationalization assurance also helps increase the quality and usability of the software overall.This work has been supported by the founded projects TIN2011-24139, S2009/TIC-1650 and TIN2011-15009-
Hypothesis generation by interactive visual exploration of heterogeneous medical data

High dimensional, heterogeneous datasets are challenging for domain experts to analyze. A very large number of dimensions often pose problems when visual and computational analysis tools are considered. Analysts tend to limit their attention to subsets of the data and lose potential insight in relation to the rest of the data. Generating new hypotheses is becoming problematic due to these limitations. In this paper, we discuss how interactive analysis methods can help analysts to cope with these challenges and aid them in building new hypotheses. Here, we report on the details of an analysis of data recorded in a comprehensive study of cognitive aging. We performed the analysis as a team of visualization researchers and domain experts. We discuss a number of lessons learned related to the usefulness of interactive methods in generating hypotheses
Combining Protein Fragment Feature-Based Resampling and Local Optimisation

Protein structure prediction (PSP) suites can predict 'nearnative'  protein models. However, not always these predicted models are  close to the native structure with enough precision to be useful for biologists.  The literature to date demonstrates that one of the best techniques  to predict 'near-native' protein models is to use a fragment-based search  strategy. Another technique that can help refine protein models is local  optimisation. Local optimisation algorithms use the gradient of the function  being optimised to suggest which move will bring the function value  closer to its local minimum. In this work we combine the concepts of  structural refinement through feature-based resampling, fragment-based  PSP, and local optimisation to create an algorithm that can create protein  models that are closer to their native states. In experiments we  demonstrated that our new method generates models that are close to  their native conformations. For structures in the test set, it obtained an  average RMSD of 5.09`and an average best TM-Score of 0.47 when no  local optimisation was applied. However, by applying local optimisation  to our algorithm, additional improvements were achieved.Griffith Sciences, School of Information and Communication TechnologyFull Tex
On the Induction of New Fuzzy Relations, New Fuzzy Operators and Their Aggregation

In this paper we generate fuzzy relations and fuzzy operators using different kind of generators and we study the relationship between them. Firstly, we introduce a new fuzzy preorder induced by a fuzzy operator. We generalize this preorder to a fuzzy relation generated by two fuzzy operators and we analyze its properties. Secondly, we introduce and explore two ways of inducing a fuzzy operator, one from a fuzzy operator and a fuzzy relation and the other one from two fuzzy operators. The first one is an extension of the well-known fuzzy operator induced by a fuzzy relation through Zadeh’s compositional rule. Finally, we aggregate these operators using the quasi-arithmetic mean associated to a continuous Archimedean t-norm. The aim is to compare the operator induced by the quasi-arithmetic mean of the generators with the quasi-arithmetic mean of the generated operators.Peer ReviewedPostprint (author’s final draft
Consistency and stability in aggregation operators: An application to missing data problems

In this work we analyze the key issue of the relationship that should hold between the operators in a family {An} of aggregation operators in order to understand they properly define a consistent whole. Here we extend some of the ideas about stability of a family of aggregation operators into a more general framework, formally defining the notions of i – L and j – R strict stability for families of aggregation operators. The notion of strict stability of order k is introduced as well. Finally, we also present an application of the strict stability conditions to deal with missing data problems in an information aggregation process. For this analysis, we have focused in the weighted mean family and the quasi-arithmetic weighted means families
A Methodology for Software Process Improvement Roadmaps for Regulated Domains – Example with ISO 62366

Software process improvement initiatives offer many benefits in terms of productivity, cost savings and quality. As part of these initiatives organisations undergo an assessment and then embark on a software process improvement program to improve their existing processes to meet a desired target. These programs can be improved by the use of process improvement roadmaps that are tailored to the organisation and are usually non-transferrable. Within regulated domains, such as the medical device industry, adherence to international standards must be achieved before products can be placed on the market. This work proposes the use of software process improvement roadmaps to assist organisations achieve compliance with medical device standards. These proposed roadmaps will be generic in nature to meet the requirements of the standard, but will be subsequently tailored to meet the specific requirements of an individual organisation. In this paper we introduce the concept of the software process improvement roadmaps for the implementation of standards and detail a methodology for developing these roadmaps
A biological and real-time framework for hand gestures and head poses

Human-robot interaction is an interdisciplinary research area that aims at the development of social robots. Since social robots are expected to interact with humans and understand their behavior through gestures and body movements, cognitive psychology and robot technology must be integrated. In this paper we present a biological and real-time framework for detecting and tracking hands and heads. This framework is based on keypoints extracted by means of cortical V1 end-stopped cells. Detected keypoints and the cells’ responses are used to classify the junction type. Through the combination of annotated keypoints in a hierarchical, multi-scale tree structure, moving and deformable hands can be segregated and tracked over time. By using hand templates with lines and edges at only a few scales, a hand’s gestures can be recognized. Head tracking and pose detection are also implemented, which can be integrated with detection of facial expressions in the future. Through the combinations of head poses and hand gestures a large number of commands can be given to a robot
Using human factors standards to support user experience and agile design

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-39188-0_20.The ISO 9241-210 standard provides a framework for human-centred design (HCD) activities comprising the four stages: context of use, specification of user and organisational requirements, design solutions, and evaluation against requirements. Other parts of the 9241 standard cover user interface design and usability. This paper uses the HCD framework to emphasise user experience (UX) design and methods used to help create good user experiences. It also relates the framework to an agile software development environment. It is concluded that the flexible and iterative nature of ISO 9241-210 makes it a good basis for both user experience design and an agile development process
A Domain Specific Language for Orchestrating User Tasks Whilst Navigation Web Sites

In this paper we claim that there are a lot of processes over Web applications that require a high level of coordination between individuals and tasks featuring procedures. We propose hereafter a Domain Specific Language (DSL) for describing the asynchronous orchestration users’ tasks including manual users’ tasks (i.e. simple instructions that tell users what to do during the navigation) and automated tasks (i.e. tasks that can be partially or completely automated by client-side scripts). The approach is illustrated by examples and a case study showing the tools, for which an empiric evaluation is presented
Summaries on the fly: Query-based extraction of structured knowledge from web documents

A large part of Web resources consists of unstructured textual content. Processing and retrieving relevant content for a particular information need is challenging for both machines and humans. While information retrieval techniques provide methods for detecting suitable resources for a particular query, information extraction techniques enable the extraction of structured data and text summarization allows the detection of important sentences. However, these techniques usually do not consider particular user interests and information needs. In this paper, we present a novel method to automatically generate structured summaries from user queries that uses POS patterns to identify relevant statements and entities in a certain context. Finally, we evaluate our work using the publicly available New York Times corpus, which shows the applicability of our method and the advantages over previous works. The final publication is available at Springer via https://doi.org/10.1007/978-3-642-39200-9_2
Identifying candidate datasets for data interlinking

One of the design principles that can stimulate the growth and increase the usefulness of the Web of data is URIs linkage. However, the related URIs are typically in different datasets managed by different publishers. Hence, the designer of a new dataset must be aware of the existing datasets and inspect their content to define sameAs links. This paper proposes a technique based on probabilistic classifiers that, given a datasets S to be published and a set T of known published datasets, ranks each Ti ∈ T according to the probability that links between S and Ti can be found by inspecting the most relevant datasets. Results from our technique show that the search space can be reduced up to 85%, thereby greatly decreasing the computational effort.
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-39200-9_29
Tight Lower Bound for Linear Sketches of Moments

The problem of estimating frequency moments of a data stream has attracted a lot of attention since the onset of streaming algorithms [AMS99]. While the space complexity for approximately computing the p [superscript th] moment, for p ∈ (0,2] has been settled [KNW10], for p > 2 the exact complexity remains open. For p > 2 the current best algorithm uses O(n [superscript 1 − 2/p] logn) words of space [AKO11,BO10], whereas the lower bound is of Ω(n [superscript 1 − 2/p]) [BJKS04].
In this paper, we show a tight lower bound of Ω(n [superscript 1 − 2/p] logn) words for the class of algorithms based on linear sketches, which store only a sketch Ax of input vector x and some (possibly randomized) matrix A. We note that all known algorithms for this problem are linear sketches.National Science Foundation (U.S.). Center for Science of Information (Grant Agreement CCF-0939370
Combining Binary Search Trees

We present a general transformation for combining a constant number of binary search tree data structures (BSTs) into a single BST whose running time is within a constant factor of the minimum of any “well-behaved” bound on the running time of the given BSTs, for any online access sequence. (A BST has a well-behaved bound with f(n) overhead if it spends at most O(f(n)) time per access and its bound satisfies a weak sense of closure under subsequences.) In particular, we obtain a BST data structure that is O(loglogn) competitive, satisfies the working set bound (and thus satisfies the static finger bound and the static optimality bound), satisfies the dynamic finger bound, satisfies the unified bound with an additive O(loglogn) factor, and performs each access in worst-case O(logn) time