pinecone/core-2020-05-10-deduplication huggingface dataset:
New Initiatives for the Empowerment of People with Activity Limitations

The paper exploits an extensive quantitative case mapping to blend three strands of research: First, social innovation will be considered as an approach to improve and/or guarantee social inclusion for people with activity limitations. Secondly, ICT will be understood as a means to empower people with activity limitations; and thirdly, the needs of vulnerable people will be regarded as a lens for scrutinizing those two approaches: How can ICT based social innovation/digital social innovation (DSI) empower people? The analysed cases shed light on the phenomenon of digital social innovations and allow first insights into their practice. The paper illustrates a quantitative overview of the actors behind these initiatives, their funding structures and drivers and barriers. In a conclusion, the new phenomenon of digital social innovation is described in contours
Effect of heuristics on serendipity in path-based storytelling with linked data

Path-based storytelling with Linked Data on the Web provides users the ability to discover concepts in an entertaining and educational way. Given a query context, many state-of-the-art pathfinding approaches aim at telling a story that coincides with the user's expectations by investigating paths over Linked Data on the Web. By taking into account serendipity in storytelling, we aim at improving and tailoring existing approaches towards better fitting user expectations so that users are able to discover interesting knowledge without feeling unsure or even lost in the story facts. To this end, we propose to optimize the link estimation between - and the selection of facts in a story by increasing the consistency and relevancy of links between facts through additional domain delineation and refinement steps. In order to address multiple aspects of serendipity, we propose and investigate combinations of weights and heuristics in paths forming the essential building blocks for each story. Our experimental findings with stories based on DBpedia indicate the improvements when applying the optimized algorithm
Invisible hands behind 3.5GHz spectrum sharing

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2016. There has been considerable discussion surrounding the barriers to spectrum sharing in the literature. Among those is the ‘trust gap’ that exists, according to the PCAST report. Trust is a complex human construct that significantly includes risk. In this paper, we examine the risks faced by the different user classes proposed by the FCC for sharing in the 3.5 GHz band. We argue that the “invisible hands” of spectrum sharing in this band is the balance between spectrum sharing gain and associated risks. We find that both gains and risks can be linked to the distance between incumbents’ systems and Citizen Broadband Radio Services (CBRS)’ systems. The risk portfolio is linked to spectrum rights that each tier has, since the rights they have determine risks and risk mitigation strategies. We further propose a model to calculate spectrum sharing utilities for different tiers. The optimized utility determines the distance between incumbents and CBRS systems
Design of a gamified interface to improve fuel efficiency and safe driving

The increasing reliance on motor vehicles has negative effects on both human health and the environment. Improving driving style has been shown to be a particularly crucial and relatively quick step to reducing fuel consumption and vehicle emissions. In this paper, a series of conceptual designs of in-vehicle gamified interfaces were evaluated, with a particular focus on the ability to use such systems to increase driver acceptance of feedback from such interfaces in order to promote eco-safe driving. Self-determination theory (SDT) was used to inform the design of the gamified interface concepts, with a particular focus on competence, autonomy and relatedness, as well as intrinsic versus extrinsic incentives and social persuasion feedback. The study adopts a user-centered design approach, utilizing focus groups to establish user needs and motivations to aid the design of a prototype system
Solving contact mechanics problems with PERMON

PERMON makes use of theoretical results in quadratic programming algorithms and domain decomposition methods. It is built on top of the PETSc framework for numerical computations. This paper describes its fundamental packages and shows their applications. We focus here on contact problems of mechanics decomposed by means of a FETI-type non-overlapping domain decomposition method. These problems lead to inequality constrained quadratic programming problems that can be solved by our PermonQP package.11510
Category theoretic semantics for theorem proving in logic programming: embracing the laxness

A propositional logic program P may be identified with a P f P f -coalgebra on the set of atomic propositions in the program. The corresponding C(P f P f )-coalgebra, where C(P f P f ) is the cofree comonad on P f P f , describes derivations by resolution. Using lax semantics, that correspondence may be extended to a class of first-order logic programs without existential variables. The resulting extension captures the proofs by term-matching resolution in logic programming. Refining the lax ap- proach, we further extend it to arbitrary logic programs. We also exhibit a refinement of Bonchi and Zanasi’s saturation semantics for logic pro- gramming that complements lax semantics
Backend infrastructure supporting audio augmented reality and storytelling

Today, museums are looking for new ways to attract and engage audience. These include virtual exhibitions, augmented reality and 3D modelling based applications, and interactive digital storytelling. The target of all these activities is to provide better experiences for audiences that are very familiar with the digital world. In augmented reality (AR) and interactive digital storytelling (IDS) systems, visual presentation has been dominant. In contrast to this trend, we have chosen to concentrate on auditory presentation. A key element for this is a backend service supporting different client applications. This paper discusses our experiences from designing a portable open source based audio digital asset management system (ADAM), which supports interaction with smart phones and tablets containing audio augmented reality and audio story applications.We have successfully implemented ADAM system and evaluated it in the Museum of Technology in Helsinki, Finlan
To mesh or not to mesh: flexible wireless indoor communication among mobile robots in industrial environments

Mobile robots such as automated guided vehicles become increasingly important in industry as they can greatly increase efficiency. For their operation such robots must rely on wireless communication, typically realized by connecting them to an existing enterprise network. In this paper we motivate that such an approach is not always economically viable or might result in performance issues. Therefore we propose a flexible and configurable mixed architecture that leverages on mesh capabilities whenever appropriate. Through experiments on a wireless testbed for a variety of scenarios, we analyse the impact of roaming, mobility and traffic separation and demonstrate the potential of our approach
An Original Correction Method for Indoor Ultra Wide Band Ranging-based Localisation System

During this decade, Wireless Sensor Networks (WSNs) brought an  increasing interest in the industrial and research world. One of their applications  is the indoor localization. The ranging, i.e. the distance evaluation mechanism  between nodes, is required to determine the position of the nodes. The research  work presented in this article aims to use Ultra Wide Band (UWB) radio links  to achieve an efficient ranging, based on Time of Flight (ToF) measurement. A  good solution consists in integrating ranging traffic into the usual network messages.  However, the ToF ranging process is based on information exchanges  which are temporally constrained. Once this information is encapsulated into  the usual messages, the temporal constraint cannot be honoured, resulting in  important ranging errors due to clock drifts. To mitigate these errors, we have  introduced an original dynamic correction technique which enables a precision  of twenty centimetres allowing the inclusion of ranging traffic in usual traffic
Performance Engineering and Energy Efficiency of Building Blocks for Large, Sparse Eigenvalue Computations on Heterogeneous Supercomputers

Numerous challenges have to be mastered as applications in scientific computing are being developed for post-petascale parallel systems. While ample parallelism is usually available in the numerical problems at hand, the efficient use of supercomputer resources requires not only good scalability but also a verifiably effective use of resources on the core, the processor, and the accelerator level. Furthermore, power dissipation and energy consumption are becoming further optimization targets besides time to solution. Performance Engineering (PE) is the pivotal strategy for developing effective parallel code on all levels of modern architectures. In this paper we report on the development and use of low-level parallel building blocks in the GHOST library ("General, Hybrid, and Optimized Sparse Toolkit"). We demonstrate the use of PE in optimizing a density of states computation using the Kernel Polynomial Method, and show that reduction of runtime  and reduction of energy are literally the same goal in this case.  We also give a brief overview of the capabilities of GHOST and the applications in which it is being used successfully
Relational kernel-based grasping with numerical features

Object grasping is a key task in robot manipulation. Performing a grasp largely depends on the object properties and grasp constraints. This paper proposes a new statistical relational learning approach to recognize graspable points in object point clouds. We characterize each point with numerical shape features and represent each cloud as a (hyper-) graph by considering qualitative spatial relations between neighboring points. Further, we use kernels on graphs to exploit extended contextual shape information and compute discriminative features which show improvement upon local shape features. Our work for robot grasping highlights the importance of moving towards integrating relational representations with low-level descriptors for robot vision. We evaluate our relational kernel-based approach on a realistic dataset with 8 objects.status: publishe
kProlog: An algebraic Prolog for kernel programming

kProlog is a simple algebraic extension of Prolog with facts
and rules annotated with semiring labels. We propose kProlog as a language
for learning with kernels. kProlog allows to elegantly specify systems
of algebraic expressions on databases. We propose some code examples
of gradually increasing complexity, we give a declarative specification
of some matrix operations and an algorithm to solve linear systems.
Finally we show the encodings of state-of-the-art graph kernels such as
Weisfeiler-Lehman graph kernels, propagation kernels and an instance of
Graph Invariant Kernels (GIKs), a recent framework for graph kernels
with continuous attributes. The number of feature extraction schemas,
that we can compactly specify in kProlog, illustrates its potential for
machine learning applications.status: publishe
An exercise in declarative modeling for relational query mining

Motivated by the declarative modeling paradigm for data mining, we report on our experience in modeling and solving relational query and graph mining problems with the IDP system, a variation of the answer set programming paradigm. Using IDP or other ASP languages for modeling appears to be natural given that they provide rich logical languages for modeling and solving many search problems and that relational query mining (and ILP) employs the same type of representation.

Nevertheless, our results indicate that second order extensions to these languages are necessary for expressing the model as well as for efficient solving, especially for what concerns subsumption testing. We propose such second order extensions and evaluate their potential effectiveness with a number of experiments in subsumption as well as in query mining.status: publishe
Statistical relational learning with soft quantifiers

Quantification in statistical relational learning (SRL) is either existential or universal, however humans might be more inclined to express knowledge using soft quantifiers, such as ``most'' and ``a few''. In this paper, we define the syntax and semantics of PSL^Q, a new SRL framework that supports reasoning with soft quantifiers, and present its most probable explanation (MPE) inference algorithm. To the best of our knowledge, PSL^Q is the first SRL framework that combines soft quantifiers with first-order logic rules for modelling uncertain relational data. Our experimental results for link prediction in social trust networks demonstrate that the use of soft quantifiers not only allows for a natural and intuitive formulation of domain knowledge, but also improves the accuracy of inferred results
Mine 'em all: A note on mining all graphs

We study the complexity of the problem of enumerating all graphs with frequency at least 1 and computing their support. We show that there are hereditary classes of graphs for which the complexity of this problem depends on the order in which the graphs should be enumerated (e.g. from frequent to infrequent or from small to large). For instance, the problem can be solved with polynomial delay for databases of planar graphs when the enumerated graphs should be output from large to small but it cannot be solved even in incremental-polynomial time when the enumerated graphs should be output from most frequent to least frequent (unless P=NP).status: publishe
Static VS Dynamic Reversibility in CCS

The notion of reversible computing is attracting interest because of its applications in diverse fields, in particular the study of programming abstractions for fault tolerant systems. Reversible CCS (RCCS), proposed by Danos and Krivine, enacts reversibility by means of memory stacks. Ulidowski and Phillips proposed a general method to reverse a process calculus given in a particular SOS format, by exploiting the idea of making all the operators of a calculus static. CCSK is then derived from CCS with this method. In this paper we show that RCCS is at least as expressive as CCSK
Application of Permutation Group Theory in Reversible Logic Synthesis

The paper discusses various applications of permutation group theory in the
synthesis of reversible logic circuits consisting of Toffoli gates with
negative control lines. An asymptotically optimal synthesis algorithm for
circuits consisting of gates from the NCT library is described. An algorithm
for gate complexity reduction, based on equivalent replacements of gates
compositions, is introduced. A new approach for combining a group-theory-based
synthesis algorithm with a Reed-Muller-spectra-based synthesis algorithm is
described. Experimental results are presented to show that the proposed
synthesis techniques allow a reduction in input lines count, gate complexity or
quantum cost of reversible circuits for various benchmark functions.Comment: In English, 15 pages, 2 figures, 7 tables. Proceeding of the RC 2016
  conferenc
A calculus for local reversibility

The file associated with this record is under a 12-month embargo from publication in accordance with the publisher's self-archiving policy. The full text may be available through the publisher links provided above.We introduce a process calculus with a new prefixing operator that allows us to model locally controlled reversibility. Actions can be undone spontaneously, as in other reversible process calculi, or as pairs of concerted actions, where performing a weak action forces undoing of another action. The new operator in its full generality allows us to model out-of-causal order computation, where effects are undone before their causes are undone, which goes beyond what typical reversible calculi can express. However, the core calculus, with a restricted form of the new operator, is well behaved as it satisfied causal consistency. We demonstrate the usefulness of the calculus by modelling the hydration of formaldehyde in water into methanediol, an industrially important reaction, where the creation and breaking of some bonds are examples of locally controlled out-of-causal order computation.Peer-reviewedPost-print8th Conference on Reversible Computation, July 7th-8th, 2016, Bologna, Ital
Reversing Single Sessions

© Springer International Publishing Switzerland 2016.Session-based communication has gained a widespread acceptance in practice as a means for developing safe communicating systems via structured interactions. In this paper, we investigate how these structured interactions are affected by reversibility, which provides a computational model allowing executed interactions to be undone. In particular, we provide a systematic study of the integration of different notions of reversibility in both binary and multiparty single sessions. The considered forms of reversibility are: one for completely reversing a given session with one backward step, and another for also restoring any intermediate state of the session with either one backward step or multiple ones. We analyse the costs of reversing a session in all these different settings. Our results show that extending binary single sessions to multiparty ones does not affect the reversibility machinery and its costs
Reversible Causal Graph Dynamics

Causal Graph Dynamics extend Cellular Automata to arbitrary, bounded-degree,
time-varying graphs. The whole graph evolves in discrete time steps, and this
global evolution is required to have a number of physics-like symmetries:
shift-invariance (it acts everywhere the same) and causality (information has a
bounded speed of propagation). We add a further physics-like symmetry, namely
reversibility. KEYWORDS: Bijective, invertible, injective, surjective,
one-to-one, onto, Cayley graphs, Hedlund, Block representation, Lattice-gas
automaton, Reversible Cellular Automata.Comment: 33 pages, 10 figure
Belief Revision and the EM Algorithm

This paper provides a natural interpretation of the EM algorithm as a succession of revision steps that try to find a probability distribution in a parametric family of models in agreement with frequentist observations over a partition of a domain. Each step of the algorithm corresponds to a revision operation that respects a form of minimal change. In particular, the so-called expectation step actually applies Jeffrey’s revision rule to the current best parametric model so as to respect the frequencies in the available data. We also indicate that in the presence of incomplete data, one must be careful in the definition of the likelihood function in the maximization step, which may differ according to whether one is interested by the precise modeling of the underlying random phenomenon together with the imperfect observation process, or by the modeling of the underlying random phenomenon alone, despite imprecision
Reconstructing human-generated provenance through similarity-based clustering

In this paper, we revisit our method for reconstructing the primary sources of documents, which make up an important part of their provenance. Our method is based on the assumption that if two documents are semantically similar, there is a high chance that they also share a common source. We previously evaluated this assumption on an excerpt from a news archive, achieving 68.2% precision and 73% recall when reconstructing the primary sources of all articles. However, since we could not release this dataset to the public, it made our results hard to compare to others. In this work, we extend the flexibility of our method by adding a new parameter, and re-evaluate it on the human-generated dataset created for the 2014 Provenance Reconstruction Challenge. The extended method achieves up to 86% precision and 59% recall, and is now directly comparable to any approach that uses the same dataset
Sets of Priors Reflecting Prior-Data Conflict and Agreement

In Bayesian statistics, the choice of prior distribution is often debatable,
especially if prior knowledge is limited or data are scarce. In imprecise
probability, sets of priors are used to accurately model and reflect prior
knowledge. This has the advantage that prior-data conflict sensitivity can be
modelled: Ranges of posterior inferences should be larger when prior and data
are in conflict. We propose a new method for generating prior sets which, in
addition to prior-data conflict sensitivity, allows to reflect strong
prior-data agreement by decreased posterior imprecision.Comment: 12 pages, 6 figures, In: Paulo Joao Carvalho et al. (eds.), IPMU
  2016: Proceedings of the 16th International Conference on Information
  Processing and Management of Uncertainty in Knowledge-Based Systems,
  Eindhoven, The Netherland
Finding the set of k-additive dominating measures viewed as a flow problem

n this paper we deal with the problem of obtaining the set of k-additive measures dominating a fuzzy measure. This problem extends the problem of deriving the set of probabilities dominating a fuzzy measure, an important problem appearing in Decision Making and Game Theory. The solution proposed in the paper follows the line developed by Chateauneuf and Jaffray for dominating probabilities and continued by Miranda et al. for dominating k-additive belief functions. Here, we address the general case transforming the problem into a similar one such that the involved set functions have non-negative Möbius transform; this simplifies the problem and allows a result similar to the one developed for belief functions. Although the set obtained is very large, we show that the conditions cannot be sharpened. On the other hand, we also show that it is possible to define a more restrictive subset, providing a more natural extension of the result for probabilities, such that it is possible to derive any k-additive dominating measure from it
An Evidential Filter for Indoor Navigation of a Mobile Robot in Dynamic Environment

International audienceRobots are destined to live with humans and perform tasks for them. In order to do that, an adapted representation of the world including human detection is required. Evidential grids enable the robot to handle partial information and ignorance, which can be useful in various situations. This paper deals with an audiovisual perception scheme of a robot in indoor environment (apartment, house..). As the robot moves, it must take into account its environment and the humans in presence. This article presents the key-stages of the multimodal fusion: an evidential grid is built from each modality using a modified Dempster combination, and a temporal fusion is made using an evidential filter based on an adapted version of the generalized bayesian theorem. This enables the robot to keep track of the state of its environment. A decision can then be made on the next move of the robot depending on the robot's mission and the extracted information. The system is tested on a simulated environment under realistic conditions
Generalized Sugeno Integrals

Sugeno integrals are aggregation functions defined on a qualitative scale where only minimum, maximum and order-reversing maps are allowed. Recently, variants of Sugeno integrals based on Gödel implication and its contraposition were defined and axiomatized in the setting of bounded chain with an involutive negation. This paper proposes a more general approach. We consider totally ordered scales, multivalued conjunction operations not necessarily commutative, and implication operations induced from them by means of an involutive negation. In such a context, different Sugeno-like integrals are defined and axiomatized