pinecone/core-2020-05-10-deduplication huggingface dataset:
A formal semantics for complete UML state machines with communications

UML is a widely used notation, and formalizing its semantics is an important issue. Here, we concentrate on formalizing UML state machines, used to express the dynamic behaviour of software systems. We propose a formal operational semantics covering all features of the latest version (2.4.1) of UML state machines specification. We use labelled transition systems as the semantic model, so as to use automatic verification techniques like model checking. Furthermore, our proposed semantics includes synchronous and asynchronous communications between state machines. We implement our approach in USM2C, a model checker supporting editing, simulation and automatic verification of UML state machines. Experiments show the effectiveness of our approach.Full Tex
Automated Anonymity Verification of ThreeBallot Voting System

In recent years, a large number of secure voting protocols have been proposed in the literature. Often these protocols contain flaws, but because they are complex protocols, rigorous formal analysis has proven hard to come by. Rivest’s ThreeBallot voting system is important because it aims to provide security (voter anonymity and voter verifiability) without requiring cryptography. In this paper, we construct a CSP model of ThreeBallot, and use it to produce the first automated formal analysis of its anonymity property. Along the way, we discover that one of the crucial assumptions under which ThreeBallot (and many other voting systems) operates-the Short Ballot Assumption-is highly ambiguous in the literature.We give various plausible precise interpretations, and discover that in each case, the interpretation either is unrealistically strong, or else fails to ensure anonymity. Therefore, we give a version of the Short Ballot Assumption for ThreeBallot that is realistic but still provides a guarantee of anonymity
Solving the Shortest Vector Problem in Lattices Faster Using Quantum
  Search

By applying Grover's quantum search algorithm to the lattice algorithms of
Micciancio and Voulgaris, Nguyen and Vidick, Wang et al., and Pujol and
Stehl\'{e}, we obtain improved asymptotic quantum results for solving the
shortest vector problem. With quantum computers we can provably find a shortest
vector in time $2^{1.799n + o(n)}$, improving upon the classical time
complexity of $2^{2.465n + o(n)}$ of Pujol and Stehl\'{e} and the $2^{2n +
o(n)}$ of Micciancio and Voulgaris, while heuristically we expect to find a
shortest vector in time $2^{0.312n + o(n)}$, improving upon the classical time
complexity of $2^{0.384n + o(n)}$ of Wang et al. These quantum complexities
will be an important guide for the selection of parameters for post-quantum
cryptosystems based on the hardness of the shortest vector problem.Comment: 19 page
A Data Fusion Perspective on Human Motion Analysis Including Multiple Camera Applications

Proceedings of: 5th International Work-Conference on the Interplay Between Natural and Artificial Computation, (IWINAC 2013). Mallorca, Spain, June 10-14.Human motion analysis methods have received increasing attention during the last two decades. In parallel, data fusion technologies have emerged as a powerful tool for the estimation of properties of objects in the real world. This papers presents a view of human motion analysis from the viewpoint of data fusion. JDL process model and Dasarathy's input-output hierarchy are employed to categorize the works in the area. A survey of the literature in human motion analysis from multiple cameras is included. Future research directions in the area are identified after this review.Publicad
Experimental study of the stress level at the workplace using an smart testbed of wireless sensor networks and ambient intelligence techniques

"Natural and artificial computation in engineering and medical applications : 5th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2013, Mallorca, Spain, June 10-14, 2013. Proceedings, Part II", ISBN 978-364238621-3This paper combines techniques of ambient intelligence and wireless sensor networks with the objective of obtain important conclusions to increase the quality of life of people. In particular, we oriented our study to the stress at the workplace, because stress is a leading cause of illness and disease. This article presents a wireless sensor network obtaining information of the environment, a pulse sensor obtaining hear rate values and a complete data analysis applying techniques of ambient intelligence to predict stress from these environment variables and people attributes. Results show promise on the identification of stressful situations as well as stress inference through the use of predictive algorithm
Detection of indoor and outdoor stairs

There are a few applications in which stairs must be detected, for example for aiding blind persons during navigation and for autonomous robots which must rely on vision. . In this paper we present
a very simple algorithm for detecting stairs, both in indoor and outdoor
environments, when the camera is at a distance of approximately ﬁve meters from the stairs. The problem is hard to solve due to diﬀerent
factors like lighting, diﬀerent constructions, materials and view angles.
However, ﬁrst results are very promising, although false positive and
negative detections cannot yet be avoided
Fractal Analysis for Symmetry Plane Detection  in Neuroimages

Despite the considerable amount of research, brain symmetry plane detection is still an open problem. In this paper, we present a novel method for symmetry plane detection in magnetic resonance (MR) neuroimages based on the textural information and underlying brain's physiological structure. Fractal dimension and lacunarity analysis are used to locate the symmetry plane of the brain. The method was tested on MR data while analyzing the robustness against intensity non-uniformity, noise, and pathology. The proposed method does not need skull-stripping like pre-processing of MR images. The method was compared with another commonly used technique. The results were evaluated by an expert. The experimental results show the viability of our approach.Griffith Sciences, School of Information and Communication TechnologyFull Tex
MirBot: A Multimodal Interactive Image Retrieval System

This study presents a multimodal interactive image retrieval system for smartphones (MirBot). The application is designed as a collaborative game where users can categorize photographs according to the WordNet hierarchy. After taking a picture, the region of interest of the target can be selected, and the image information is sent with a set of metadata to a server in order to classify the object. The user can validate the category proposed by the system to improve future queries. The result is a labeled database with a structure similar to ImageNet, but with contents selected by the users, fully marked with regions of interest, and with novel metadata that can be useful to constrain the search space in a future work. The MirBot app is freely available on the Apple app store.This study was supported by the Consolider Ingenio 2010 program (MIPRCV, CSD2007-00018), the PASCAL2 Network of Excellence IST-2007-216886, and the Spanish CICyT TIN2009-14205-C04-C1
Real-Time Object Recognition Based on Cortical Multi-scale Keypoints

In recent years, a large number of impressive object categorisation algorithms have surfaced, both computational and biologically motivated. While results on standardised benchmarks are impressive, very few of the best-performing algorithms took run-time performance into account, rendering most of them useless for real-time active vision scenarios such as cognitive robots. In this paper, we combine cortical keypoints based on primate area V1 with a state-of-the-art nearest neighbour classifier, and show that such a system can approach state-of-the-art categorisation performance while meeting the real-time constraint
Which fast nearest neighbour search algorithm to use?

Choosing which fast Nearest Neighbour search algorithm to use depends on the task we face. Usually kd-tree search algorithm is selected when the similarity function is the Euclidean or the Manhattan distances. Generic fast search algorithms (algorithms that works with any distance function) are only used when there is not specific fast search algorithms for the involved distance function. In this work we show that in real data problems generic search algorithms (i.e. MDF-tree) can be faster that specific ones (i.e. kd-tree).The authors thank the Spanish CICyT for partial support of this work through project TIN2009-14205-C04-C1 and la Consellería de Educación de la Comunidad Valenciana through project PROMETEO/2012/01
Efficient Optimization Algorithm for Space-Variant Mixture of Vector Fields

This paper presents a new algorithm for trajectory classifi- cation of human activities. The presented framework uses a mixture of parametric space-variant vector fields to describe pedestrian’s trajecto- ries. An advantage of the proposed method is that the vector fields are not constant and depend on the pedestrian’s localization. This means that the switching motion among vector fields may occur at any image location and should be accurately estimated. In this paper, the model is equipped with a novel methodology to estimate the switching probabilities among motion regimes. More specifically, we propose an iterative optimization of switching probabilities based on the natural gradient vector, with respect to the Fisher information metric. This approach follows an information geometric framework and contrasts with more traditional approaches of constrained optimization in which euclidean gradient based methods are used combined with probability simplex constraints. We testify the per- formance superiority of the proposed approach in the classification of pedestrian’s trajectories in synthetic and real data sets concerning farfield surveillance scenarios
The intelligent room for elderly care

Daily life assistance for elderly is one of the most promising\ud
and interesting scenarios for advanced technologies in the present and\ud
near future. Improving the quality of life of elderly is also some of the\ud
first priorities in modern countries and societies where the percentage of\ud
elder people is rapidly increasing due mainly to great improvements in\ud
medicine during the last decades. In this paper, we present an overview of\ud
our informationally structured room that supports daily life activities of\ud
elderly. Our environment contains different distributed sensors including\ud
a floor sensing system and several intelligent cabinets. Sensor information\ud
is sent to a centralized management system which processes the data and\ud
makes it available to a service robot which assists the people in the room.\ud
One important restriction in our intelligent environment is to maintain\ud
a small number of sensors to avoid interfering with the daily activities of\ud
people and to reduce as much as possible the invasion of their privacy.\ud
In addition we discuss some experiments using our real environment and\ud
robot
Hybrid tabu search for fuzzy job shop

International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2013 (5th. 2013, Mallorca, España)We consider the fuzzy job shop scheduling problem, which is a variant of the well-known job shop problem, with uncertainty in task durations that we model using fuzzy numbers. We propose a tabu search algorithm for minimising the expected makespan based on reversing arcs within critical blocks. We test the algorithm and then combine it with a genetic algorithm from the literature so we can observe the synergy effect, obtaining better results with the hybrid algorithm than with its components by separate. Finally we compare our hybrid algorithm with a memetic algorithm from the literature and show that even in similar times, our method is better in terms of expected makespa
Diffusion methods for wind power ramp detection

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-38679-4_9Proceedings of 12th International Work-Conference on Artificial Neural Networks, IWANN 2013, Puerto de la Cruz, Tenerife, Spain, June 12-14, 2013, Part IThe prediction and management of wind power ramps is currently receiving large attention as it is a crucial issue for both system operators and wind farm managers. However, this is still an issue far from being solved and in this work we will address it as a classification problem working with delay vectors of the wind power time series and applying local Mahalanobis K-NN search with metrics derived from Anisotropic Diffusion methods. The resulting procedures clearly outperform a random baseline method and yield good sensitivity but more work is needed to improve on specificity and, hence, precision.With partial support from Spain's grant TIN2010-21575-
C02-01 and the UAM-ADIC Chair for Machine Learning. The  rst author is also
supported by an FPI-UAM grant and kindly thanks the Applied Mathematics
Department of Yale University for receiving her during her visits. The second
author is supported by the FPU-MEC grant AP2008-00167
Improving 3D Keypoint Detection from Noisy Data Using Growing Neural Gas

3D sensors provides valuable information for mobile robotic tasks like scene classification or object recognition, but these sensors often produce noisy data that makes impossible applying classical keypoint detection and feature extraction techniques. Therefore, noise removal and downsampling have become essential steps in 3D data processing. In this work, we propose the use of a 3D filtering and down-sampling technique based on a Growing Neural Gas (GNG) network. GNG method is able to deal with outliers presents in the input data. These features allows to represent 3D spaces, obtaining an induced Delaunay Triangulation of the input space. Experiments show how the state-of-the-art keypoint detectors improve their performance using GNG output representation as input data. Descriptors extracted on improved keypoints perform better matching in robotics applications as 3D scene registration
3D Hand Pose Estimation with Neural Networks

We propose the design of a real-time system to recognize and interprethand gestures. The acquisition devices are low cost 3D sensors. 3D hand pose will be segmented, characterized and track using growing neural gas (GNG) structure. The capacity of the system to obtain information with a high degree of freedom allows the encoding of many gestures and a very accurate motion capture. The use of hand pose models combined with motion information provide with GNG permits to deal with the problem of the hand motion representation. A natural interface applied to a virtual mirrorwriting system and to a system to estimate hand pose will be designed to demonstrate the validity of the system
Understanding process behaviours in a large insurance company in Australia : a case study

Having a reliable understanding about the behaviours, problems, and performance of existing processes is important in enabling a targeted process improvement initiative. Recently, there has been an increase in the application of innovative process mining techniques to facilitate evidence-based understanding about organizations' business processes. Nevertheless, the application of these techniques in the domain of finance in Australia is, at best, scarce. This paper details a 6-month case study on the application of process mining in one of the largest insurance companies in Australia. In particular, the challenges encountered, the lessons learned, and the results obtained from this case study are detailed. Through this case study, we not only validated existing `lessons learned' from other similar case studies, but also added new insights that can be beneficial to other practitioners in applying process mining in their respective fields
Profiling event logs to configure risk indicators for process delays

Risk identification is one of the most challenging stages in the risk management process. Conventional risk management approaches provide little guidance and companies often rely on the knowledge of experts for risk identification. In this paper we demonstrate how risk indicators can be used to predict process delays via a method for configuring so-called Process Risk Indicators(PRIs). The method learns suitable configurations from past process behaviour recorded in event logs. To validate the approach we have implemented it as a plug-in of the ProM process mining framework and have conducted experiments using various data sets from a major insurance company
Supporting risk-informed decisions during business process execution

This paper proposes a technique that supports process participants in making risk-informed decisions, with the aim to reduce the process risks. Risk reduction involves decreasing the likelihood and severity of a process fault from occurring. Given a process exposed to risks, e.g. a financial process exposed to a risk of reputation loss, we enact this process and whenever a process participant needs to provide input to the process, e.g. by selecting the next task to execute or by filling out a form, we prompt the participant with the expected risk that a given fault will occur given the particular input. These risks are predicted by traversing decision trees generated from the logs of past process executions and considering process data, involved resources, task durations and contextual information like task frequencies. The approach has been implemented in the YAWL system and its effectiveness evaluated. The results show that the process instances executed in the tests complete with substantially fewer faults and with lower fault severities, when taking into account the recommendations provided by our technique.\u
iDataCool: HPC with Hot-Water Cooling and Energy Reuse

iDataCool is an HPC architecture jointly developed by the University of
Regensburg and the IBM Research and Development Lab B\"oblingen. It is based on
IBM's iDataPlex platform, whose air-cooling solution was replaced by a custom
water-cooling solution that allows for cooling water temperatures of 70C/158F.
The system is coupled to an adsorption chiller by InvenSor that operates
efficiently at these temperatures. Thus a significant portion of the energy
spent on HPC can be recovered in the form of chilled water, which can then be
used to cool other parts of the computing center. We describe the architecture
of iDataCool and present benchmarks of the cooling performance and the energy
(reuse) efficiency.Comment: 12 pages, 7 figures, proceedings of ISC 201
VM-MAD: A cloud/cluster software for service-oriented academic environments

The availability of powerful computing hardware in IaaS clouds makes cloud computing attractive also for computational workloads that were up to now almost exclusively run on HPC clusters.\ud
In this paper we present the VM-MAD Orchestrator software: an open source framework for cloudbursting Linux-based HPC clusters into IaaS clouds but also computational grids. The Orchestrator is completely modular, allowing flexible configurations of cloudbursting policies. It can be used with any batch system or cloud infrastructure, dynamically extending the cluster when needed. A distinctive feature of our framework is that the policies can be tested and tuned in a simulation mode based on historical or synthetic cluster accounting data.\ud
In the paper we also describe how the VM-MAD Orchestrator was used in a production environment at the Functional Genomics Center Zurich to speed up the analysis of mass spectrometry-based protein data by cloudbursting to the Amazon Elastic Compute Cloud. The advantages of this hybrid system are shown with a large evaluation run using about hundred large (EC2) nodes
Achieving efficient strong scaling with PETSc using hybrid MPI/OpenMP optimisations

22.04.15 KB. Ok to add accepted version to spiral, 12 month embargo period expiredThe increasing number of processing elements and decreasing memory to core ratio in modern high-performance platforms makes efficient strong scaling a key requirement for numerical algorithms. In order to achieve efficient scalability on massively parallel systems scientific software must evolve across the entire stack to exploit the multiple levels of parallelism exposed in modern architectures. In this paper we demonstrate the use of hybrid MPI/OpenMP parallelisation to optimise parallel sparse matrix-vector multiplication in PETSc, a widely used scientific library for the scalable solution of partial differential equations. Using large matrices generated by Fluidity, an open source CFD application code which uses PETSc as its linear solver engine, we evaluate the effect of explicit communication overlap using task-based parallelism and show how to further improve performance by explicitly load balancing threads within MPI processes. We demonstrate a significant speedup over the pure-MPI mode and efficient strong scaling of sparse matrix-vector multiplication on Fujitsu PRIMEHPC FX10 and Cray XE6 systems. © 2013 Springer-Verlag
A Fast Algorithm Finding the Shortest Reset Words

In this paper we present a new fast algorithm finding minimal reset words for
finite synchronizing automata. The problem is know to be computationally hard,
and our algorithm is exponential. Yet, it is faster than the algorithms used so
far and it works well in practice. The main idea is to use a bidirectional BFS
and radix (Patricia) tries to store and compare resulted subsets. We give both
theoretical and practical arguments showing that the branching factor is
reduced efficiently. As a practical test we perform an experimental study of
the length of the shortest reset word for random automata with $n$ states and 2
input letters. We follow Skvorsov and Tipikin, who have performed such a study
using a SAT solver and considering automata up to $n=100$ states. With our
algorithm we are able to consider much larger sample of automata with up to
$n=300$ states. In particular, we obtain a new more precise estimation of the
expected length of the shortest reset word $\approx 2.5\sqrt{n-5}$.Comment: COCOON 2013. The final publication is available at
  http://link.springer.com/chapter/10.1007%2F978-3-642-38768-5_1
Time/Memory/Data Tradeoffs for Variants of the RSA Problem

International audienceIn this paper, we study the security of the Micali-Schnorr pseudorandom number generator. The security of this cryptographic scheme is based on two computational problems which are variants of the RSA problem. The RSA problem essentially aims at recovering the plaintext from a random ciphertext. In the analysis of the Micali-Schnorr pseudorandom generator, we are interested in instances of this problem where the plaintext is small and where the ciphertext is not entirely known. We will describe time / memory tradeoff techniques to solve these hard problems which provides the first analysis of this pseudoran-dom generator 25 years after its publication
A Fresh Approach to Learning Register Automata

International audienceThis paper provides an Angluin-style learning algorithm for a class of register automata supporting the notion of fresh data values. More specifically, we introduce session automata which are well suited for modeling protocols in which sessions using fresh values are of major interest, like in security protocols or ad-hoc networks. We show that session automata (i) have an expressiveness partly extending, partly reducing that of register automata, (ii) admit a symbolic regular representation, and (iii) have a decidable equivalence problem. We use these results to establish a learning algorithm that can infer inherently non-deterministic session automata. Moreover, in the case of deterministic automata, it has a better complexity wrt. membership queries than existing learning algorithms for register automata. We strengthen the importance of our automaton model by its characterization in monadic second-order logic