pinecone/core-2020-05-10-deduplication huggingface dataset:
An Efficient Solution for Processing Skewed MapReduce Jobs

International audienceAlthough MapReduce has been praised for its high scalability and fault tolerance, it has been criticized in some points, in particular, its poor performance in the case of data skew. There are important cases where a high percentage of processing in the reduce side is done by a few nodes, or even one node, while the others remain idle. There have been some attempts to address the problem of data skew, but only for specific cases. In particular, there is no proposed solution for the cases where most of the intermediate values correspond to a single key, or when the number of keys is less than the number of reduce workers. In this paper, we propose FP-Hadoop, a system that makes the reduce side of MapReduce more parallel, and efficiently deals with the problem of data skew in the reduce side. In FP-Hadoop, there is a new phase, called intermediate reduce (IR), in which blocks of intermediate values, constructed dynamically, are processed by intermediate reduce workers in parallel, by using a scheduling strategy. By using the IR phase, even if all intermediate values belong to only one key, the main part of the reducing work can be done in parallel by using the computing resources of all available workers. We implemented a prototype of FP-Hadoop, and conducted extensive experiments over synthetic and real datasets. We achieved excellent performance gains compared to native Hadoop, e.g. more than 10 times in reduce time and 5 times in total execution time
A Privacy-Aware Framework for Decentralized Online Social Networks

Online social networks based on a single service provider suffer several drawbacks, first of all the privacy issues arising from the delegation of user data to a single entity. Distributed online social networks (DOSN) have been recently proposed as an alternative solution allowing users to keep control of theirprivate data. However, the lack of a centralized entity introduces new problems, like the need of defining proper privacy policies for data access and of guaranteeing the availability of user\u27s data when the user disconnects from the social network. This paper introduces a privacy-aware support for DOSN enabling users to define a set of privacy policies which describe who is entitled to access the data in their social profile. These policies are exploited by the DOSN support to decide the re-allocation of the profile when the user disconnects from the socialnetwork.The proposed approach is validated through a set of simulations performed on real traces logged from Facebook
An approach for characterizing the operating modes in dynamic hybrid control architectures

Nowadays, manufacturing control system faces the challenge of featuring optimal and reactive mechanisms to respond to volatile environments. In automation domain, hybrid control architectures solve these requirements as it allows coupling predictive/proactive and reactive techniques in manufacturing operations. However, to include dynamic coupling features, it is necessary to characterize the possible new operating modes and visualize its potential when a switching is needed. This paper presents an approach to characterize the operating modes of dynamic hybrid control architectures to support the dynamic switching process. The results, obtained through a simulation in a multi agent platform of flexible manufacturing systems, showed the interest of our approach in terms of including the characterization of operating modes as decisional criteria towards a system switching.info:eu-repo/semantics/publishedVersio
Behavioural validation of the ADACOR2 Self-organized holonic multi-agent manufacturing system

Global economy is driving manufacturing companies into a paradigm
revolution. Highly customizable products at lower prices and with higher quality
are among the most imposed influence factors. To respond properly to these external
and internal constraints, such as work absence and machine failures, companies
must be in a constant adaptation phase. Several manufacturing control architectures
have been proposed throughout the years displaying more or less success
to adapt into different manufacturing situations. These architectures follow
different design paradigms but recently the decentralization and distribution of
the processing power into a set of cooperating and collaborative entities is becoming
the trend. Despite of the effort spent, there is still the need to empower
those architectures with evolutionary capabilities and self-organization mechanisms
to enable the constant adaption to disturbances. This paper presents a behavioural
mechanism embed in the ADACOR2 holons. A validation procedure
for this mechanism is also presented and results extracted. This validation is
achieved through the use of a benchmark and results are compared with classical
hierarchical and heterarchical architectures as also with the ADACOR.info:eu-repo/semantics/publishedVersio
Technical Aspects of Cyber Kill Chain

Recent trends in targeted cyber-attacks has increased the interest of
research in the field of cyber security. Such attacks have massive disruptive
effects on rganizations, enterprises and governments. Cyber kill chain is a
model to describe cyber-attacks so as to develop incident response and analysis
capabilities. Cyber kill chain in simple terms is an attack chain, the path
that an intruder takes to penetrate information systems over time to execute an
attack on the target. This paper broadly categories the methodologies,
techniques and tools involved in cyber-attacks. This paper intends to help a
cyber security researcher to realize the options available to an attacker at
every stage of a cyber-attack.Comment: 7 pages, 1 figure, 4 tables, The final publication is available at
  Springer via http://dx.doi.org/10.1007/978-3-319-22915-7_4
Improved Multimodal Emotion Recognition for Better Game-Based Learning

This paper has been already published on the Springer website and is available online in the following link: http://link.springer.com/chapter/10.1007/978-3-319-22960-7_11This paper introduces the integration of the face emotion recognition part and the voice emotion recognition part of our FILTWAM framework that uses webcams and microphones. This framework enables real-time multimodal emotion recognition of learners during game-based learning for triggering feedback towards improved learning. The main goal of this study is to validate the integration of webcam and microphone data for a real-time and adequate interpretation of facial and vocal expressions into emotional states where the software modules are calibrated with end users. This integration aims to improve timely and relevant feedback, which is expected to increase learners’ awareness of their own behavior. Twelve test persons received the same computer-based tasks in which they were requested to mimic specific facial and vocal expressions. Each test person mimicked 80 emotions, which led to a dataset of 960 emotions. All sessions were recorded on video. An overall accuracy of Kappa value based on the requested emotions, expert opinions, and the recognized emotions is 0.61, of the face emotion recognition software is 0.76, and of the voice emotion recognition software is 0.58. A multimodal fusion between the software modules can increase the accuracy to 78%. In contrast with existing software our software modules allow real-time, continuously and unobtrusively monitoring of learners’ face expressions and voice intonations and convert these into emotional states. This inclusion of learner's emotional states paves the way for more effective, efficient and enjoyable game-based learning.The Netherlands Laboratory for Lifelong Learning (NELLL) of the Open University Netherland
Model-Based Robustness Testing in Event-B Using Mutation

International audienceRobustness testing aims at finding errors in a system under invalid conditions, such as unexpected inputs. We propose a robust-ness testing approach for Event-B based on specification mutation and model-based testing. We assume that a specification describes the valid inputs of a system. By applying negation rules, we mutate the precondition of events to explore invalid behaviour. Tests are generated from the mutated specification using ProB. ProB has been adapted to efficiently process mutated events. Mutated events are statically checked for satisfiability and enability using constraint satisfaction, to prune the transition search space. This has dramatically improve the performance of test generation. The approach is applied to the Java Card bytecode verifier. Large mutated specifications (containing 921 mutated events) can be easily tackled to ensure a good coverage of the robustness test space
Fluid model checking of timed properties

We address the problem of verifying timed properties of Markovian models of large populations of interacting agents, modelled as finite state automata. In particular, we focus on time-bounded properties of (random) individual agents specified by Deterministic Timed Automata (DTA) endowed with a single clock. Exploiting ideas from fluid approximation, we estimate the satisfaction probability of the DTA properties by reducing it to the computation of the transient probability of a subclass of Time-Inhomogeneous Markov Renewal Processes with exponentially and deterministically-timed transitions, and a small state space. For this subclass of models, we show how to derive a set of Delay Differential Equations (DDE), whose numerical solution provides a fast and accurate estimate of the satisfaction probability. In the paper, we also prove the asymptotic convergence of the approach, and exemplify the method on a simple epidemic spreading model. Finally, we also show how to construct a system of DDEs to efficiently approximate the average number of agents that satisfy the DTA specification
Quantitative Analysis of Concurrent Reversible Computations

Reversible computing is a paradigm of computation that extends the standard forward-only programming to reversible programming, so that programs can be executed both in the standard, forward direction, and backward, going back to past states. In this paper we present novel quantitative stochastic model for concurrent and cooperating computations. More precisely, we introduce the class of ρ-reversible stochastic automata and define a semantics for the synchronization ensuring that this class of models is closed under composition. For this class of automata we give an efficient way of deriving the equilibrium distribution. Moreover, we prove that the equilibrium distribution of the composition of reversible automata can be derived as the product of the equilibrium distributions of each automaton in isolation
Performance evaluation of an emergency call center: tropical polynomial
  systems applied to timed Petri nets

We analyze a timed Petri net model of an emergency call center which
processes calls with different levels of priority. The counter variables of the
Petri net represent the cumulated number of events as a function of time. We
show that these variables are determined by a piecewise linear dynamical
system. We also prove that computing the stationary regimes of the associated
fluid dynamics reduces to solving a polynomial system over a tropical
(min-plus) semifield of germs. This leads to explicit formul{\ae} expressing
the throughput of the fluid system as a piecewise linear function of the
resources, revealing the existence of different congestion phases. Numerical
experiments show that the analysis of the fluid dynamics yields a good
approximation of the real throughput.Comment: 21 pages, 4 figures. A shorter version can be found in the
  proceedings of the conference FORMATS 201
On the Scalability of Constraint Solving for Static/Off-Line Real-Time Scheduling

International audienceRecent papers have reported on successful application of constraint solving techniques to off-line real-time scheduling problems, with realistic size and complexity. Success allegedly came for two reasons: major recent advances in solvers efficiency and use of optimized, problem-specific constraint representations. Our current objective is to assess further the range of applicability and the scalability of such constraint solving techniques based on a more general and agnostic evaluation campaign. For this, we have considered a large number of synthetic scheduling problems and a few real-life ones, and attempted to solve them using 3 state-of-the-art solvers, namely CPLEX, Yices2, and MiniZinc/G12. Our findings were that, for all problems considered, constraint solving does scale to a certain limit, then diverges rapidly. This limit greatly depends on the specificity of the scheduling problem type. All experimental data (synthetic task systems, SMT/ILP models) are provided so as to allow experimental reproducibility
The Vertical Optic Flow: An Additional Cue for Stabilizing Beerotor Robot’s Flight Without IMU

International audienceBio-inspired guidance principles involving no reference frame are presented here and were implemented in a rotorcraft called Beerotor, which was equipped with a minimalistic panoramic optic flow sensor and no accelerometer, no inertial measurement unit (IMU) [9], as in flying insects (Dipterian only uses rotation rates). In the present paper, the vertical optic flow was used as an additional cue whereas the previously published Beerotor II's visuo-motor system only used translational op-tic flow cues [9]. To test these guidance principles, we built a tethered tandem rotorcraft called Beerotor (80g), which flies along a high-roofed tunnel. The aerial robot adjusts its pitch and hence its speed, hugs the ground and lands safely without any need for an inertial reference frame. The rotorcraft's altitude and forward speed are adjusted via several op-tic flow feedback loops piloting respectively the lift and the pitch angle on the basis of the common-mode and differential rotor speeds, respectively as well as an active system of reorientation of a quasi-panoramic eye which constantly realigns its gaze, keeping it parallel to the nearest surface followed. Safe automatic terrain following and landing were obtained with the active eye-reorientation system over rugged terrain, without any need for an inertial reference frame
Comprehensible video acquisition for caregiving scenes—How multimedia can support caregiver training

Proceedings of the third International Conference Innovation in Medicine and Healthcare 2015 held 11-12 September 2015, in Kyoto, JapanInnovation in Medicine and Healthcare 2015Book Title: Innovation in Medicine and Healthcare 2015Caregiving requires a variety of techniques that directly affect carereceivers’ quality of life. However, caregivers have difficulties in objectively observing and confirming their skills and whether they are providing care-receivers with appropriate and acceptable care. Videotaping is often used in caregiver training for this purpose. This paper introduces a novel method for enhancing such videotaping using information media technology, capturing video using multiple cameras and editing the videos automatically into a single-stream video to achieve a recording purpose. The purpose is to provide an effectivemeans of observing and learning caregiving from typical important points of view. Our experiments showed that videos obtained through our proposed method are effective for the intended purposes
Complex Symbolic Sequence Encodings for Predictive Monitoring of Business Processes

This paper addresses the problem of predicting the outcome of an ongoing case of a business process based on event logs. In this setting, the outcome of a case may refer for example to the achievement of a performance objective or the fulfillment of a compliance rule upon completion of the case. Given a log consisting of traces of completed cases, given a trace of an ongoing case, and given two or more possible out- comes (e.g., a positive and a negative outcome), the paper addresses the problem of determining the most likely outcome for the case in question. Previous approaches to this problem are largely based on simple symbolic sequence classification, meaning that they extract features from traces seen as sequences of event labels, and use these features to construct a classifier for runtime prediction. In doing so, these approaches ignore the data payload associated to each event. This paper approaches the problem from a different angle by treating traces as complex symbolic sequences, that is, sequences of events each carrying a data payload. In this context, the paper outlines different feature encodings of complex symbolic sequences and compares their predictive accuracy on real-life business process event logs
BPMN task instance streaming for efficient micro-task crowdsourcing processes

The Business Process Model and Notation (BPMN) is a standard for modeling and executing business processes with human or machine tasks. The semantics of tasks is usually discrete: a task has exactly one start event and one end event; for multi-instance tasks, all instances must complete before an end event is emitted. We propose a new task type and streaming connector for crowdsourcing able to run hundreds or thousands of micro-task instances in parallel. The two constructs provide for task streaming semantics that is new to BPMN, enable the modeling and efficient enactment of complex crowdsourcing scenarios, and are applicable also beyond the special case of crowdsourcing. We implement the necessary design and runtime support on top of Crowd- Flower, demonstrate the viability of the approach via a case study, and report on a set of runtime performance experiments
Log delta analysis: Interpretable differencing of Business process event logs

This paper addresses the problem of identifying and explaining behavioral differences between two business process event logs. The paper presents a method that, given two event logs, returns a set of statements in natural language capturing behavior that is present or frequent in one log, while absent or infrequent in the other. This log delta analysis method allows users to diagnose differences between normal and deviant executions of a process or between two versions or variants of a process. The method relies on a novel approach to losslessly encode an event log as an event structure, combined with a frequency-enhanced technique for differencing pairs of event structures. A validation of the proposed method shows that it accurately diagnoses typical change patterns and can explain differences between normal and deviant cases in a real-life log, more compactly and precisely than previously proposed methods
Efficient Process Model Discovery Using Maximal Pattern Mining

In recent years, process mining has become one of the most important and promising areas of research in the field of business process management as it helps businesses understand, analyze, and improve their business processes. In particular, several proposed techniques and algorithms have been proposed to discover and construct process models from workflow execution logs (i.e., event logs). With the existing techniques, mined models can be built based on analyzing the relationship between any two events seen in event logs. Being restricted by that, they can only handle special cases of routing constructs and often produce unsound models that do not cover all of the traces seen in the log. In this paper, we propose a novel technique for process discovery using Maximal Pattern Mining (MPM) where we construct patterns based on the whole sequence of events seen on the traces—ensuring the soundness of the mined models. Our MPM technique can handle loops (of any length), duplicate tasks, non-free choice constructs, and long distance dependencies. Our evaluation shows that it consistently achieves better precision, replay fitness and efficiency than the existing techniques
Log-based simplification of process models

The visualization of models is essential for user-friendly human-machine interactions during Process Mining. A simple graphical representation contributes to give intuitive information about the behavior of a system. However, complex systems cannot always be represented with succinct models that can be easily visualized. Quality-preserving model simplifications can be of paramount importance to alleviate the complexity of finding useful and attractive visualizations.\ud
This paper presents a collection of log-based techniques to simplify process models. The techniques trade off visual-friendly properties with quality metrics related to logs, such as fitness and precision, to avoid degrading the resulting model. The algorithms, either cast as optimization problems or heuristically guided, find simplified versions of the initial process model, and can be applied in the final stage of the process mining life-cycle, between the discovery of a process model and the deployment to the final user. A tool has been developed and tested on large logs, producing simplified process models that are one order of magnitude smaller while keeping fitness and precision under reasonable margins.Peer ReviewedPostprint (author's final draft
Incorporating negative information in process discovery

The discovery of a formal process model from event logs describing real process executions is a challenging problem that has been studied from several angles. Most of the contributions consider the extraction of a model as a semi-supervised problem where only positive information is available. In this paper we present a fresh look at process discovery where also negative information can be taken into account. This feature may be crucial for deriving process models which are not only simple, fitting and precise, but also good on generalizing the right behavior underlying an event log. The technique is based on numerical abstract domains and Satisfiability Modulo Theories (SMT), and can be combined with any process discovery technique. As an example, we show in detail how to supervise a recent technique that uses numerical abstract domains. Experiments performed in our prototype implementation show the effectiveness of the techniques and the ability to improve the results produced by selected discovery techniques.Peer ReviewedPostprint (author's final draft
Filamentation and Pulse Self-compression in the Anomalous Dispersion Region of Glasses

International audienceThe propagation of near-infrared ultra-short laser pulses in the regime of anomalous dispersion of transparent solids is associated with a host of self-induced effects including a significant spectral broadening extending from the ultraviolet into the infrared region, pulse self-compression down to few-cycle pulse durations, free and driven third harmonic generation, conical emission and the formation of stable filaments over several cm showing the emergence of conical light bullets. We review measurements performed in different experimental conditions and results of numerical simulations of unidirectional propagation models showing that the interpretation of all these phenomena proceed from the formation of non-spreading conical light bullets during filamentation