pinecone/core-2020-05-10-deduplication huggingface dataset:
Empirical Research Plan: Effects of Sketching on Program Comprehension

Sketching is an important means of communication in software engineering
practice. Yet, there is little research investigating the use of sketches. We
want to contribute a better understanding of sketching, in particular its use
during program comprehension. We propose a controlled experiment to investigate
the effectiveness and efficiency of program comprehension with the support of
sketches as well as what sketches are used in what way.Comment: 5 pages, 0 figures, Proc. International Conference on Agile Software
  Development (XP'16). Volume 251 of the book series Lecture Notes in Business
  Information Processing (LNBIP). Springer, 201
OperA/ALIVE/OperettA

Comprehensive models for organizations must, on the one hand, be able to specify global goals and requirements but, on the other hand, cannot assume that particular actors will always act according to the needs and expectations of the system design. Concepts as organizational rules (Zambonelli 2002), norms and institutions (Dignum and Dignum 2001; Esteva et al. 2002), and social structures (Parunak and Odell 2002) arise from the idea that the effective engineering of organizations needs high-level, actor-independent concepts and abstractions that explicitly define the organization in which agents live (Zambonelli 2002).Peer ReviewedPostprint (author's final draft
Towards an ASM thesis for reflective sequential algorithms

Starting from Gurevich's thesis for sequential algorithms (the so-called
"sequential ASM thesis"), we propose a characterization of the behaviour of
sequential algorithms enriched with reflection. That is, we present a set of
postulates which we conjecture capture the fundamental properties of reflective
sequential algorithms (RSAs). Then we look at the plausibility of an ASM thesis
for the class of RSAs, defining a model of abstract state machine (which we
call reflective ASM) that we conjecture captures the class of RSAs as defined
by our postulates
On the Suitability of Generalized Behavioral Profiles for Process Model Comparison

Given two process models, the problem of behavioral comparison is that of determining if these models are behaviorally equivalent (e.g., by trace equivalence) and, if not, identifying how can the differences be presented in a compact manner? Behavioral profiles have been proposed as a convenient abstraction for this problem. A behavioral profile is a matrix, where each cell encodes a behavioral relation between a pair of tasks (e.g., causality or conflict). Thus, the problem of behavioral comparison can be reduced to matrix comparison. It has been observed that while behavioral profiles can be efficiently computed, they are not accurate insofar as behaviorally different process models may map to the same behavioral profile. This paper investigates the question of how accurate existing behavioral profiles are. The paper shows that behavioral profiles are fully behavior preserving for the class of acyclic unlabeled nets with respect to configuration equivalence. However, for the general class of acyclic nets, existing behavioral profiles are exponentially inaccurate, meaning that two acyclic nets with the same behavioral profile may differ in an exponential number of configurations
A Color Image Database for Haze Model and Dehazing Methods Evaluation

International audienceOne of the major issues related to dehazing methods (single or multiple image based) evaluation is the absence of the haze-free image (ground-truth). This is also a problem when it concerns the validation of Koschmieder model or its subsequent dehazing methods. To overcome this problem, we created a database called CHIC (Color Hazy Image for Comparison), consisting of two scenes in controlled environment. In addition to the haze-free image, we provide 9 images of different fog densities. Moreover, for each scene, we provide a number of parameters such as local scene depth, distance from the camera of known objects such as Macbeth Color Checkers, their radiance, and the haze level through transmittance. All of these features allow the possibility to evaluate and compare between dehazing methods by using full-reference image quality metrics regarding the haze-free image, and also to evaluate the accuracy of the Koschmieder hazy image formation model
Super-resolved enhancement of a single image and its application in cardiac MRI

Super-resolved image enhancement is of great importance in medical imaging. Conventional methods often require multiple low resolution (LR) images from different views of the same object or learn- ing from large amount of training datasets to achieve success. However, in real clinical environments, these prerequisites are rarely fulfilled. In this paper, we present a self-learning based method to perform super- resolution (SR) from a single LR input. The mappings between the given LR image and its downsampled versions are modeled using support vector regression on features extracted from sparse coded dictionaries, coupled with dual-tree complex wavelet transform based denoising. We demonstrate the efficacy of our method in application of cardiac MRI enhancement. Both quantitative and qualitative results show that our SR method is able to preserve fine textural details that can be corrupted by noise, and therefore can maintain crucial diagnostic information
Multiple-instance multiple-label learning for the classification of frog calls with acoustic event detection

Frog call classification has received increasing attention due to its importance for ecosystem. Traditionally, the classification of frog calls is solved by means of the single-instance single-label classification classifier. However, since different frog species tend to call simultaneously, classifying frog calls becomes a multiple-instance multiple-label learning problem. In this paper, we propose a novel method for the classification of frog species using multiple-instance multiple-label (MIML) classifiers. To be specific, continuous recordings are first segmented into audio clips (10 seconds). For each audio clip, acoustic event detection is used to segment frog syllables. Then, three feature sets are extracted from each syllable: mask descriptor, profile statistics, and the combination of mask descriptor and profile statistics. Next, a bag generator is applied to those extracted features. Finally, three MIML classifiers, MIML-SVM, MIML-RBF, and MIML-kNN, are employed for tagging each audio clip with different frog species. Experimental results show that our proposed method can achieve high accuracy (81.8% true positive/negatives) for frog call classification
Feature extraction based on bandpass filtering for frog call classification

In this paper, we propose an adaptive frequency scale filter bank to perform frog call classification. After preprocessing, the acoustic signal is segmented into individual syllables from which spectral peak track is extracted. Then, syllable features including track duration, dominant frequency, and oscillation rate are calculated. Next, a k-means clustering technique is applied to the dominant frequency of syllables for all frog species, whose centroids are used to construct a frequency scale. Furthermore, one novel feature named bandpass filter bank cepstral coefficients is extracted by applying a bandpass filter bank to the spectral of each syllable, where the filter bank is designed based on the generated frequency scale. Finally, a k-nearest neighbour classifier is adopted to classify frog calls based on extracted features. The experiment results show that our proposed feature can achieve an average classification accuracy of 94.3 % which outperforms Mel-frequency cepstral coefficients features (81.4 %) and syllable features (88.1 %)
An optimization scheduler in the intranet grid

Scheduling of processes is the basic task for a grid computing. This role is responsible for the allocation of time for computational agents. The calculation agent can include a wide range of devices, based on various types of computer systems. Is it possible to efficiently build a grid infrastructure in the company environment. The grid can be used in scientific and technical computing, as well as better load distribution of the individual computing systems and services. The scheduler is a major component of grid computing. The main task is to effectively distribute the load of the system and allocate tasks to places that are not sufficiently utilized at a given moment. The article also focuses on the relation between conflicting parameters, which relate to the quality of the planning process. Time calculation of the optimization algorithm affects the quality of the draft plan. It has a direct impact on the total period of the job processing. In the strategy of the scheduling there is a point where extensions of time have no effect on quality of the draft of the plan but getting worse the overall runtime of the job. The aim was to compare the common metaheuristic algorithms. From the measured values to propose a methodology for determining the optimum time for planning process. © Springer International Publishing Switzerland 2016
Technique of selecting multiversion software system structure with minimum simultaneous module version usage

Multiversion or N-version programming is well known as an effective approach, ensuring high level of software reliability. This approach is based on two fundamental strategies for enhancing the reliability of a software system—redundancy and diversity. Modules solving critical tasks are redundant and implemented in the form of functionally equivalent versions. In this connection versions can be developed by different programmer teams, in different languages, in different environment and can implement different methods and algorithms for solution of identical tasks in order to provide versions diversity. Complex software systems, as a rule, include a set of programs which can call the same modules for solving their target tasks, or to be more precise, versions of these modules. According to diversity concept call of different module versions allows to avoid identical failures. This article presents a technique of selecting optimal multiversion software system to minimize simultaneous usage of the same module versions
SPAM detection: Naïve bayesian classification and RPN expression-based LGP approaches compared

An investigation is performed of a machine learning algorithm and the Bayesian classifier in the spam-filtering context. The paper shows the advantage of the use of Reverse Polish Notation (RPN) expressions with feature extraction compared to the traditional Naïve Bayesian classifier used for spam detection assuming the same features. The performance of the two is investigated using a public corpus and a recent private spam collection, concluding that the system based on RPN LGP (Linear Genetic Programming) gave better results compared to two popularly used open source Bayesian spam filters. © Springer International Publishing Switzerland 2016
Collateral damage of Facebook apps : friends, providers, and privacy interdependence

Aquest volum de la publicació periòdica també es correspon a la monografia "ICT Systems Security and Privacy Protection"Third-party apps enable a personalized experience on social networking platforms; however, they give rise to privacy interdependence issues. Apps installed by a user’s friends can collect and potentially misuse her personal data inflicting collateral damage on the user while leaving her without proper means of control. In this paper, we present a multi-faceted study on the collateral information collection of apps in social networks. We conduct a user survey and show that Facebook users are concerned about this issue and the lack of mechanisms to control it. Based on real data, we compute the likelihood of collateral information collection affecting users; we show that the probability is significant and depends on both the friendship network and the popularity of the app. We also show its significance by computing the proportion of exposed user attributes including the case of profiling, when several apps are offered by the same provider. Finally, we propose a privacy dashboard concept enabling users to control the collateral damage
Software Defined Networking Reactive Stateful Firewall

Part 3: Cyber InfrastructureInternational audienceNetwork security is a crucial issue of Software Defined Networking (SDN). It is probably, one of the key features for the success and the future pervasion of the SDN technology. In this perspective, we propose a SDN reactive stateful firewall. Our solution is integrated into the SDN architecture. The application filters TCP communications according to the network security policies. It records and processes the different states of connections and interprets their possible transitions into OpenFlow (OF) rules. The proposition uses a reactive behavior in order to reduce the number of OpenFlow rules in the data plane devices and to mitigate some Denial of Service (DoS) attacks like SYN Flooding. The firewall processes the Finite State Machine of network protocols so as to withdraw useless traffic not corresponding to their transitions' conditions. In terms of cost efficiency, our proposal empowers the behavior of Openflow compatible devices to make them behaving like stateful firewalls. Therefore, organizations do not need to spend money and resources on buying and maintaining conventional firewalls. Furthermore, we propose an orchestrator in order to spread and to reinforce security policies in the whole network with a fine grained strategy. It is thereupon able to secure the network by filtering the traffic related to an application , a node, a subnetwork connected to a data plane device, a sub SDN network connected to a controller, traffic between different links, etc. The deployment of rules of the firewall becomes flexible according to a holistic network view provided by the management plane. In addition, the solution enlarges the security perimeter inside the network by securing accesses between its internal nodes
Seismic source modeling by clustering earthquakes and predicting earthquake magnitudes

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2016. Seismic sources are currently generated manually by experts, a process which is not efficient as the size of historical earthquake databases is growing. However, large historical earthquake databases provide an opportunity to generate seismic sources through data mining techniques. In this paper, we propose hierarchical clustering of historical earthquakes for generating seismic sources automatically. To evaluate the effectiveness of clustering in producing homogenous seismic sources, we compare the accuracy of earthquake magnitude prediction models before and after clustering. Three prediction models are experimented: decision tree, SVM, and kNN. The results show that: (1) the clustering approach leads to improved accuracy of prediction models; (2) the most accurate prediction model and the most homogenous seismic sources are achieved when earthquakes are clustered based on their non-spatial attributes; and (3) among the three prediction models experimented in this work, decision tree is the most accurate one
Why Just Boogie? Translating Between Intermediate Verification Languages

The verification systems Boogie and Why3 use their respective intermediate
languages to generate verification conditions from high-level programs. Since
the two systems support different back-end provers (such as Z3 and Alt-Ergo)
and are used to encode different high-level languages (such as C# and Java),
being able to translate between their intermediate languages would provide a
way to reuse one system's features to verify programs meant for the other. This
paper describes a translation of Boogie into WhyML (Why3's intermediate
language) that preserves semantics, verifiability, and program structure to a
large degree. We implemented the translation as a tool and applied it to 194
Boogie-verified programs of various sources and sizes; Why3 verified 83% of the
translated programs with the same outcome as Boogie. These results indicate
that the translation is often effective and practically applicable
Symbolic Reachability Analysis of B through ProB and LTSmin

We present a symbolic reachability analysis approach for B that can provide a significant speedup over traditional explicit state model checking. The symbolic analysis is implemented by linking ProB to LTSmin, a high-performance language independent model checker. The link is achieved via LTSmin's PINS interface, allowing ProB to benefit from LTSmin's analysis algorithms, while only writing a few hundred lines of glue-code, along with a bridge between ProB and C using ZeroMQ. ProB supports model checking of several formal specification languages such as B, Event-B, Z and TLA. Our experiments are based on a wide variety of B-Method and Event-B models to demonstrate the efficiency of the new link. Among the tested categories are state space generation and deadlock detection; but action detection and invariant checking are also feasible in principle. In many cases we observe speedups of several orders of magnitude. We also compare the results with other approaches for improving model checking, such as partial order reduction or symmetry reduction. We thus provide a new scalable, symbolic analysis algorithm for the B-Method and Event-B, along with a platform to integrate other model checking improvements via LTSmin in the future
Symbolic Computation and Automated Reasoning for Program Analysis

This talk describes how a combination of symbolic computation techniques with
first-order theorem proving can be used for solving some challenges of
automating program analysis, in particular for generating and proving
properties about the logically complex parts of software. The talk will first
present how computer algebra methods, such as Groebner basis computation,
quantifier elimination and algebraic recurrence solving, help us in inferring
properties of program loops with non-trivial arithmetic. Typical properties
inferred by our work are loop invariants and expressions bounding the number of
loop iterations. The talk will then describe our work to generate first-order
properties of programs with unbounded data structures, such as arrays. For
doing so, we use saturation-based first-order theorem proving and extend
first-order provers with support for program analysis. Since program analysis
requires reasoning in the combination of first-order theories of data
structures, the talk also discusses new features in firstorder theorem proving,
such as inductive reasoning and built-in boolean sort. These extensions allow
us to express program properties directly in first-order logic and hence use
further first-order theorem provers to reason about program properties.Comment: Paper published at iFM 201
Avoiding medication conflicts for patients with multimorbidities

Clinical pathways are care plans which detail essential steps in the care of patients with a specific clinical problem, usually a chronic disease. A pathway includes recommendations of medications prescribed at different stages of the care plan. For patients with three or more chronic diseases (known asmultimorbidities) the multiple pathways have to be applied together. One common problem for such patients is the adverse interaction between medications given for different diseases. This paper proposes a solution for avoiding medication conflicts for patients with multimorbidities through the use of formal methods. We introduce the notion of a pharmaceutical graph to capture the medications associated to different stages of a pathway. We then explore the use of an optimising SMT solver (Z3) to quickly find the set of medications with the minimal number and severity of conflicts which is assumed to be the safest. We evaluate the approach on a well known case of an elderly patient with five multimorbidities.Postprin
The inversion of motion of bristle bots: analytical and experimental
  analysis

Bristle bots are vibration-driven robots actuated by the motion of an
internal oscillating mass. Vibrations are translated into directed locomotion
due to the alternating friction resistance between robots' bristles and the
substrate during oscillations. Bristle bots are, in general, unidirectional
locomotion systems. In this paper we demonstrate that motion direction of
vertically vibrated bristle systems can be controlled by tuning the frequency
of their oscillatory actuation. We report theoretical and experimental results
obtained by studying an equivalent system, consisting of an inactive robot
placed on a vertically vibrating substrate
Position Kinematics of a 3-RRS Parallel Manipulator

The 3-RRS parallel manipulator presented in this study comprises of parallel revolute joint axes in each leg. The manipulator is composed of a base and a moving platform which are in the shape of equilateral triangles. Moving platform has two rotational and one translational degrees-of-freedom. This study formulates the forward and inverse kinematics of the parallel manipulator. A 16-degree polynomial in terms of one of the passive joint variables is obtained for the forward kinematic analysis. Numerical results and the corresponding pose of the manipulator for inverse and forward kinematics are presented
Machine learning based data mining for Milky Way filamentary structures
  reconstruction

We present an innovative method called FilExSeC (Filaments Extraction,
Selection and Classification), a data mining tool developed to investigate the
possibility to refine and optimize the shape reconstruction of filamentary
structures detected with a consolidated method based on the flux derivative
analysis, through the column-density maps computed from Herschel infrared
Galactic Plane Survey (Hi-GAL) observations of the Galactic plane. The present
methodology is based on a feature extraction module followed by a machine
learning model (Random Forest) dedicated to select features and to classify the
pixels of the input images. From tests on both simulations and real
observations the method appears reliable and robust with respect to the
variability of shape and distribution of filaments. In the cases of highly
defined filament structures, the presented method is able to bridge the gaps
among the detected fragments, thus improving their shape reconstruction. From a
preliminary "a posteriori" analysis of derived filament physical parameters,
the method appears potentially able to add a sufficient contribution to
complete and refine the filament reconstruction.Comment: Proceeding of WIRN 2015 Conference, May 20-22, Vietri sul Mare,
  Salerno, Italy. Published in Smart Innovation, Systems and Technology,
  Springer, ISSN 2190-3018, 9 pages, 4 figure
Constructions and In-Place Operations for MDDs Based Constraints

International audienceThis papers extends in three ways our previous work about efficient operations on Multi-valued Decision Diagrams (MDD) for building Constraint Programming models. First, we improve the existing methods for transforming a set of tuples, Global Cut Seeds or sequences of tuples into MDDs. Then, we present in-place algorithms for adding and deleting tuples from an MDD. Finally, we describe an incremental version of an algorithm which reduces an MDD. We show on a real-life application that in-place operations on MDDs combined with this incremental algorithm outperform classical operations. Furthermore, we give some experimental results showing that the creation algorithms we propose strongly improve upon existing ones