pinecone/core-2020-05-10-deduplication huggingface dataset:
Exploring smart environments through human computation for enhancing blind navigation

In this paper the orchestration of wearable sensors with human computation is explored to provide map metadata for blind navigation. Technological navigation aids for blind must provide accurate information about the environment and select the best path to reach a chosen destination. Urban barriers represent dangers for the blind users. The dynamism of smart cities promotes a constant change of these dangers and therefore a potentially “dangerous territory” for these users. Previous work demonstrated that redundant solutions in smart environments complemented by human computation could provide a reliable and trustful data source for a new generation of blind navigation systems. We propose and discuss a modular architecture, which interacts with environmental sensors to gather information and process the acquired data with advanced algorithms empowered by human computation. The gathered metadata should enable the creation of “happy maps” that are delivered to blind users through a previously developed navigation system.International Workshop on Collaborative Agents Research and Development - CARE (6. 2015 Istanbul); International Workshop on Multiagent Foundations of Social Computing - MFSC (2. 2015 Istanbul
Digital Waste Sorting: A Goal-Based, Self-Learning Approach to Label Spam Email Campaigns

Fast analysis of correlated spam emails may be vital in the effort of finding and prosecuting spammers performing cybercrimes such as phishing and online frauds. This paper presents a self-learning framework to automatically divide and classify large amounts of spam emails in correlated labeled groups. Building on large datasets daily collected through honeypots, the emails are firstly divided into homogeneous groups of similar messages campaigns), which can be related to a specific spammer. Each campaign is then associated to a class which specifies the goal of the spammer, i.e. phishing, advertisement, etc. The proposed framework exploits a categorical clustering algorithm to group similar emails, and a classifier to subsequently label each email group. The main advantage of the proposed framework is that it can be used on large spam emails datasets, for which no prior knowledge is provided. The approach has been tested on more than 3200 real and recent spam emails, divided in more than 60 campaigns, reporting a classification accuracy of 97% on the classified data.pringer International Publishing Switzerland 2015 S. Foresti (Ed.): STM 2015, LNCS 9331, pp. 3?19, 2015. DOI: 10.1007/978-3-319-24858-5
Intrusion Detection System for Applications using Linux Containers

Linux containers are gaining increasing traction in both individual and
industrial use, and as these containers get integrated into mission-critical
systems, real-time detection of malicious cyber attacks becomes a critical
operational requirement. This paper introduces a real-time host-based intrusion
detection system that can be used to passively detect malfeasance against
applications within Linux containers running in a standalone or in a cloud
multi-tenancy environment. The demonstrated intrusion detection system uses
bags of system calls monitored from the host kernel for learning the behavior
of an application running within a Linux container and determining anomalous
container behavior. Performance of the approach using a database application
was measured and results are discussed.Comment: The final publication is available at
  http://link.springer.com/chapter/10.1007%2F978-3-319-24858-5_8. arXiv admin
  note: substantial text overlap with arXiv:1611.0305
Spontaneous symmetry breaking in nonlinear systems: An overview and a
  simple model

The paper combines two topics belonging to the general theme of the
spontaneous symmetry breaking (SSB) in systems including two basic competing
ingredients: the self-focusing cubic nonlinearity and a double-well-potential
(DWP) structure. Such systems find diverse physical realizations, chiefly in
optical waveguides, made of a nonlinear material and featuring a transverse DWP
structure, and in models of atomic BEC with attractive inter-atomic
interactions, loaded into a pair of symmetric potential wells coupled by
tunneling across the separating barrier. With the increase of the nonlinearity
strength, the SSB occurs at a critical value of the strength. The first part of
the paper offers a brief overview of the topic. The second part presents a
model which is designed as the simplest one capable to produce the SSB
phenomenology in the one-dimensional geometry. The model is based on the DWP
built as an infinitely deep potential box, which is split into two wells by a
delta-functional barrier at the central point. Approximate analytical
predictions for the SSB are produced for two limit cases: strong (deep) or weak
(shallow) splitting of the potential box by the central barrier. Critical
values of the strength of the nonlinearity at the SSB point, represented by the
norm of the stationary wave field, are found in both cases (the critical
strength is small in the former case, and large in the latter one). For the
intermediate case, a less accurate variational approximation (VA) is developed.Comment: In: Nonlinear Dynamics: Materials, Theory and Experiments (Springer
  Proceedings in Physics, vol. 173, 2016), ed. by M. Tlidi and M. Clerc, pp.
  97-11
Spreading, Nonergodicity, and Selftrapping: a puzzle of interacting
  disordered lattice waves

Localization of waves by disorder is a fundamental physical problem
encompassing a diverse spectrum of theoretical, experimental and numerical
studies in the context of metal-insulator transitions, the quantum Hall effect,
light propagation in photonic crystals, and dynamics of ultra-cold atoms in
optical arrays, to name just a few examples. Large intensity light can induce
nonlinear response, ultracold atomic gases can be tuned into an interacting
regime, which leads again to nonlinear wave equations on a mean field level.
The interplay between disorder and nonlinearity, their localizing and
delocalizing effects is currently an intriguing and challenging issue in the
field of lattice waves. In particular it leads to the prediction and
observation of two different regimes of destruction of Anderson localization -
asymptotic weak chaos, and intermediate strong chaos, separated by a crossover
condition on densities. On the other side approximate full quantum interacting
many body treatments were recently used to predict and obtain a novel many body
localization transition, and two distinct phases - a localization phase, and a
delocalization phase, both again separated by some typical density scale. We
will discuss selftrapping, nonergodicity and nonGibbsean phases which are
typical for such discrete models with particle number conservation and their
relation to the above crossover and transition physics. We will also discuss
potential connections to quantum many body theories.Comment: 13 pages in Springer International Publishing Switzerland 2016 1 M.
  Tlidi and M. G. Clerc (eds.), Nonlinear Dynamics: Materials, Theory and
  Experiment, Springer Proceedings in Physics 173. arXiv admin note: text
  overlap with arXiv:1405.112
Semi-automatic Liver Tumor Segmentation in Dynamic Contrast-Enhanced CT Scans Using Random Forests and Supervoxels

International audiencePre-operative locoregional treatments (PLT) delay the tumor progression by necrosis for patients with hepato-cellular carcinoma (HCC). Toward an efficient evaluation of PLT response, we address the estimation of liver tumor necrosis (TN) from CT scans. The TN rate could shortly supplant standard criteria (RECIST, mRECIST, EASL or WHO) since it has recently shown higher correlation to survival rates. To overcome the inter-expert variability induced by visual qualitative assessment, we propose a semi-automatic method that requires weak interaction efforts to segment parenchyma, tumoral active and necrotic tissues. By combining SLIC supervoxels and random decision forest, it involves discriminative multi-phase cluster-wise features extracted from registered dynamic contrast-enhanced CT scans. Quantitative assessment on expert groundtruth annotations confirms the benefits of exploiting multi-phase information from semantic regions to accurately segment HCC liver tumors
Human Computer Interaction Meets Psychophysiology: A Critical Perspective

Human computer interaction (HCI) groups are more and more often exploring the utility of new, lower cost electroencephalography (EEG) interfaces for assessing user engagement and experience as well as for directly controlling computers. While the potential benefits of using EEG are considerable, we argue that research is easily driven by what we term naïve neurorealism. That is, data obtained with psychophysiological devices have poor reliability and uncertain validity, making inferences on mental states difficult. This means that unless sufficient care is taken to address the inherent shortcomings, the contributions of psychophysiological human computer interaction are limited to their novelty value rather than bringing scientific advance. Here, we outline the nature and severity of the reliability and validity problems and give practical suggestions for HCI researchers and reviewers on the way forward, and which obstacles to avoid. We hope that this critical perspective helps to promote good practice in the emerging field of psychophysiology in HCI
A parametric spectral model for texture-based salience

We present a novel saliency mechanism based on texture. Local texture at each pixel is characterised by the 2D spectrum obtained from oriented Gabor filters. We then apply a parametric model and describe the texture at each pixel by a combination of two 1D Gaussian approximations. This results in a simple model which consists of only four parameters. These four parameters are then used as feature channels and standard Difference-of-Gaussian blob detection is applied in order to detect salient areas in the image, similar to the Itti and Koch model. Finally, a diffusion process is used to sharpen the resulting regions. Evaluation on a large saliency dataset shows a significant improvement of our method over the baseline Itti and Koch model.Postprin
Patch-level spatial layout for classification and weakly supervised localization

International audienceWe propose a discriminative patch-level spatial layout model suitable for training with weak supervision. We start from a block-sparse model of patch appearance based on the normalized Fisher vector representation. The appearance model is responsible for i) selecting a discriminative subset of visual words, and ii) identifying distinctive patches assigned to the selected subset. These patches are further filtered by a sparse spatial model operating on a novel representation of pairwise patch layout. We have evaluated the proposed pipeline in image classification and weakly supervised localization experiments on a public traffic sign dataset. The results show significant advantage of the proposed spatial model over state of the art appearance models
Explicit Model Checking of Very Large MDP using Partitioning and
  Secondary Storage

The applicability of model checking is hindered by the state space explosion
problem in combination with limited amounts of main memory. To extend its
reach, the large available capacities of secondary storage such as hard disks
can be exploited. Due to the specific performance characteristics of secondary
storage technologies, specialised algorithms are required. In this paper, we
present a technique to use secondary storage for probabilistic model checking
of Markov decision processes. It combines state space exploration based on
partitioning with a block-iterative variant of value iteration over the same
partitions for the analysis of probabilistic reachability and expected-reward
properties. A sparse matrix-like representation is used to store partitions on
secondary storage in a compact format. All file accesses are sequential, and
compression can be used without affecting runtime. The technique has been
implemented within the Modest Toolset. We evaluate its performance on several
benchmark models of up to 3.5 billion states. In the analysis of time-bounded
properties on real-time models, our method neutralises the state space
explosion induced by the time bound in its entirety.Comment: The final publication is available at Springer via
  http://dx.doi.org/10.1007/978-3-319-24953-7_1
Marimba: A Tool for Verifying Properties of Hidden Markov Models

The formal verification of properties of Hidden Markov Models (HMMs) is
highly desirable for gaining confidence in the correctness of the model and the
corresponding system. A significant step towards HMM verification was the
development by Zhang et al. of a family of logics for verifying HMMs, called
POCTL*, and its model checking algorithm. As far as we know, the verification
tool we present here is the first one based on Zhang et al.'s approach. As an
example of its effective application, we verify properties of a handover task
in the context of human-robot interaction. Our tool was implemented in Haskell,
and the experimental evaluation was performed using the humanoid robot Bert2.Comment: Tool paper accepted in the 13th International Symposium on Automated
  Technology for Verification and Analysis (ATVA 2015
Test Case Generation of Actor Systems

Testing is a vital part of the software development process. It is even more so in the context of concurrent languages, since due to undesired task interleavings and to unexpected behaviours of the underlying  task scheduler, errors can go easily undetected. Test case generation (TCG) is the process of automatically generating test inputs for interesting coverage criteria, which are then applied to the system under test. This paper presents a TCG framework for actor systems, which consists of three main elements, which are the original contributions of this work: (1) a symbolic execution calculus, which allows symbolically executing the program (i.e., executing the program for unknown input data), (2) improved techniques to avoid performing redundant computations during symbolic execution, (3) new termination and coverage criteria, which ensure the termination of symbolic execution and guarantee that the test cases provide the desired degree of code coverage. Finally, our framework has been implemented and evaluated within the aPET system
Unfolding-based process discovery

This paper presents a novel technique for process discovery. In contrast to the current trend, which only considers an event log for discovering a process model, we assume two additional inputs: an independence relation on the set of logged activities, and a collection of negative traces. After deriving an intermediate net unfolding from them, we perform a controlled folding giving rise to a Petri net which contains both the input log and all independence-equivalent traces arising from it. Remarkably, the derived Petri net cannot execute any trace from the negative collection. The entire chain of transformations is fully automated. A tool has been developed and experimental results are provided that witness the significance of the contribution of this paper.Peer ReviewedPostprint (author's final draft
A Mechanically Checked Generation of Correlating Programs Directed by Structured Syntactic Differences

International audienceWe present a new algorithm for the construction of a correlating program from the syntactic difference between the original and modified versions of a program. This correlating program exhibits the semantics of the two input programs and can then be used to compute their semantic differences, following an approach of Partush and Yahav [12]. We show that Partush and Yahav's correlating program is unsound on loops that include an early exit. Our algorithm is defined on an imperative language with while-loops, break, and continue. To guarantee its correctness, it is formalized and mechanically checked within the Coq proof assistant. On a series of examples, we experimentally find that the static analyzer dizy is at least as precise on our correlating program as on Partush and Yahav's
Ontology-Based Integration of Cross-Linked Datasets

In this paper we tackle the problem of answering SPARQL queries over virtually integrated databases. We assume that the entity resolution problem has already been solved and explicit information is available about which records in the different databases refer to the same real world entity. Surprisingly, to the best of our knowledge, there has been no attempt to extend the standard Ontology-Based Data Access (OBDA) setting to take into account these DB links for SPARQL query-answering and consistency checking. This is partly because the OWL built-in owl:sameAs property, the most natural representation of links between data sets, is not included in OWL 2 QL, the de facto ontology language for OBDA. We formally treat several fundamental questions in this context: how links over database identifiers can be represented in terms of owl:sameAs statements, how to recover rewritability of SPARQL into SQL (lost because of owl:sameAs statements), and how to check consistency. Moreover, we investigate how our solution can be made to scale up to large enterprise datasets. We have implemented the approach, and carried out an extensive set of experiments showing its scalability.

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-25007-6_1
Improving Entity Retrieval on Structured Data

The increasing amount of data on the Web, in particular of Linked Data, has
led to a diverse landscape of datasets, which make entity retrieval a
challenging task. Explicit cross-dataset links, for instance to indicate
co-references or related entities can significantly improve entity retrieval.
However, only a small fraction of entities are interlinked through explicit
statements. In this paper, we propose a two-fold entity retrieval approach. In
a first, offline preprocessing step, we cluster entities based on the
\emph{x--means} and \emph{spectral} clustering algorithms. In the second step,
we propose an optimized retrieval model which takes advantage of our
precomputed clusters. For a given set of entities retrieved by the BM25F
retrieval approach and a given user query, we further expand the result set
with relevant entities by considering features of the queries, entities and the
precomputed clusters. Finally, we re-rank the expanded result set with respect
to the relevance to the query. We perform a thorough experimental evaluation on
the Billions Triple Challenge (BTC12) dataset. The proposed approach shows
significant improvements compared to the baseline and state of the art
approaches
Opportunistic linked data querying through approximate membership metadata

Between URI dereferencing and the SPARQL protocol lies a largely unexplored axis of possible interfaces to Linked Data, each with its own combination of trade-offs. One of these interfaces is Triple Pattern Fragments, which allows clients to execute SPARQL queries against low-cost servers, at the cost of higher bandwidth. Increasing a client's efficiency means lowering the number of requests, which can among others be achieved through additional metadata in responses. We noted that typical SPARQL query evaluations against Triple Pattern Fragments require a significant portion of membership subqueries, which check the presence of a specific triple, rather than a variable pattern. This paper studies the impact of providing approximate membership functions, i.e., Bloom filters and Golomb-coded sets, as extra metadata. In addition to reducing HTTP requests, such functions allow to achieve full result recall earlier when temporarily allowing lower precision. Half of the tested queries from a WatDiv benchmark test set could be executed with up to a third fewer HTTP requests with only marginally higher server cost. Query times, however, did not improve, likely due to slower metadata generation and transfer. This indicates that approximate membership functions can partly improve the client-side query process with minimal impact on the server and its interface
Substring filtering for low-cost linked data interfaces

Recently, Triple Pattern Fragments (TPFS) were introduced as a low-cost server-side interface when high numbers of clients need to evaluate SPARQL queries. Scalability is achieved by moving part of the query execution to the client, at the cost of elevated query times. Since the TPFS interface purposely does not support complex constructs such as SPARQL filters, queries that use them need to be executed mostly on the client, resulting in long execution times. We therefore investigated the impact of adding a literal substring matching feature to the TPFS interface, with the goal of improving query performance while maintaining low server cost. In this paper, we discuss the client/server setup and compare the performance of SPARQL queries on multiple implementations, including Elastic Search and case-insensitive FM-index. Our evaluations indicate that these improvements allow for faster query execution without significantly increasing the load on the server. Offering the substring feature on TPF servers allows users to obtain faster responses for filter-based SPARQL queries. Furthermore, substring matching can be used to support other filters such as complete regular expressions or range queries
Timely Semantics: A Study of a Stream-based Ranking System for Entity Relationships

In recent years, search engines have started presenting se- mantically relevant entity information together with document search results. Entity ranking systems are used to compute recommendations for related entities that a user might also be interested to explore. Typically, this is done by ranking relationships between entities in a semantic knowledge graph using signals found in a data source as well as type annotations on the nodes and links of the graph. However, the process of producing these rankings can take a substantial amount of time. As a result, entity ranking systems typically lag behind real-world events and present relevant entities with outdated relationships to the search term or even outdated entities that should be replaced with more recent relations or entities.\ud
This paper presents a study using a real-world stream-processing based implementation of an entity ranking system, to understand the effect of data timeliness on entity rankings. We describe the system and the data it processes in detail. Using a longitudinal case-study, we demonstrate (i) that low-latency, large-scale entity relationship ranking is feasible using moderate resources and (ii) that stream-based entity ranking improves the freshness of related entities while maintaining relevance
Assessing and refining mappings to RDF to improve dataset quality

RDF dataset quality assessment is currently performed primarily after data is published. However, there is neither a systematic way to incorporate its results into the dataset nor the assessment into the publishing workflow. Adjustments are manually -but rarely- applied. Nevertheless, the root of the violations which often derive from the mappings that specify how the RDF dataset will be generated, is not identified. We suggest an incremental, iterative and uniform validation workflow for RDF datasets stemming originally from (semi-) structured data (e.g., CSV, XML, JSON). In this work, we focus on assessing and improving their mappings. We incorporate (i) a test-driven approach for assessing the mappings instead of the RDF dataset itself, as mappings reflect how the dataset will be formed when generated; and (ii) perform semi-automatic mapping refinements based on the results of the quality assessment. The proposed workflow is applied to diverse cases, e.g., large, crowdsourced datasets such as DBpedia, or newly generated, such as iLastic. Our evaluation indicates the efficiency of our workflow, as it significantly improves the overall quality of an RDF dataset in the observed cases