Miracle-dz/UniFAR-FTUs huggingface dataset:
Data logging: a method for efficient data updates in constantly active RAIDs

RAIDs (Redundant Arrays of Independent Disks) are a set of disks organized to achieve parallel I/O to multiple disks and to provide tolerance of disk failures. RAIDs offer these advantages at the cost of additional space and additional disk I/O for writes. Previous methods of reducing this I/O overhead suffered from such problems as requiring periods during which data is reorganized and not available, destroying the physical locality of data, or weakening the RAID's fault-tolerance properties. We propose a new method called data logging which reduces the I/O overhead without requiring periodic downtime for reorganization. Instead, incremental maintenance can be performed concurrently with routine processing. This is particularly advantageous in applications requiring "24/spl times/7" uptime. Data logging preserves both physical locality of data and RAID fault tolerance. The major cost of our method is a moderate amount of nonvolatile RAM. This paper describes our method, as well as two schemes for efficient encoding of the information that must be stored in nonvolatile RAM.
Pattern Recognition 42 (2009) 3215 -- 3223

: In order to facilitate sketch recognition, most online existing works assume that people will not start to draw a new symbol before the current one has been finished. We propose in this paper a method that relaxes this constraint. The proposed methodology relies on a two-dimensional dynamic programming (2D-DP) technique allowing symbol hypothesis generation, which can correctly segment and recognize interspersed symbols. In addition, as discriminative classifiers usually have limited capability to reject outliers, some domain specific knowledge is included to circumvent those errors due to untrained patterns corresponding to erroneous segmentation hypotheses. With a point-level measurement, the experiment shows that the proposed novel approach is able to achieve an accuracy of more than 90 percent.
Watermarking security

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Watermarking security Teddy Furon
Puppetnets: Misusing Web Browsers as a Distributed Attack Infrastructure

Most of the recent work on Web security focuses on preventing attacks that directly harm the browser’s host machine and user. In this paper we attempt to quantify the threat of browsers being indirectly misused for attacking third parties. Specifically, we look at how the existing Web infrastructure (e.g., the languages, protocols, and security policies) can be exploited by malicious or subverted Web sites to remotely instruct browsers to orchestrate actions including denial of service attacks, worm propagation, and reconnaissance scans. We show that attackers are able to create powerful botnet-like infrastructures that can cause significant damage. We explore the effectiveness of countermeasures including anomaly detection and more fine-grained browser security policies.
Issues in Information Systems

We compare the performance of three growth models – Gompertz, Logistics, and Exponential -using security breaches data from the fifteenth annual report of the Computer Security Institute. Unlike other studies, multiple components (frequency of attacks and dollar loss value) are utilized to model the growth of security breaches thereby providing a balanced understanding of the undesirable phenomenon. The findings indicate that multipledimensional approach to modeling security breach approach seems more rigorous than the single-dimension found in the literature. The two components seem to be different and each explains an aspect of security breach growth rate. Frequency of attacks provides a slightly better fit than dollar loss value in all three models.
Computers and Chemical Engineering

Detailed numerical simulations of detailed combustion systems require substantial computational resources, which limit their use for optimization and uncertainty quantiﬁcation studies. Starting from a limited number of CFD simulations, reduced-order models can be derived using a few detailed function evaluations. In this work, the combination of Principal Component Analysis (PCA) with Kriging is considered to identify accurate low-order models. PCA is used to identify and separate invariants of the system, the PCA modes, from the coeﬃcients that are instead related to the characteristic operating conditions. Kriging is then used to ﬁnd a response surface for these coeﬃcients. This leads to a surrogate model that allows performing parameter exploration with reduced computational cost. Variations of the classical PCA approach, namely Local and Constrained PCA, are also presented. This methodology is demonstrated on 1D and 2D ﬂames produced by OpenSmoke++ and OpenFoam, respectively, for which accurate surrogate models have been developed.
Declining Radial Growth Response of Coastal Forests to Hurricanes and Nor'easters

The Mid‐Atlantic coastal forests in Virginia are stressed by episodic disturbance from hurricanes and nor'easters. Using annual tree ring data, we adopt a dendroclimatic and statistical modeling approach to understand the response and resilience of a coastal pine forest to extreme storm events, over the past few decades. Results indicate that radial growth of trees in the study area is influenced by age, regional climate trends, and individual tree effects but dominated periodically by growth disturbance due to storms. We evaluated seven local extreme storm events to understand the effect of nor'easters and hurricanes on radial growth. A general decline in radial growth was observed in the year of the extreme storm and 3 years following it, after which the radial growth started recovering. The decline in radial growth showed a statistically significant correlation with the magnitude of the extreme storm (storm surge height and wind speed). This study contributes to understanding declining tree growth response and resilience of coastal forests to past disturbances. Given the potential increase in hurricanes and storm surge severity in the region, this can help predict vegetation response patterns to similar disturbances in the future.
On the Conversion Between Binary Code and Binary-Reflected Gray Code on Binary Cubes

We present a new algorithm for conversion between binary code and binary-reflected Gray code that requires approximately 2 K/3 element transfers in sequence for K elements per node, compared to K element transfers for previously known algorithms. For a binary cube of n=2 dimensions the new algorithm degenerates to yield a complexity of 2 K/+1 element a transfers, which is optimal. The new algorithm is optimal to within a multiplicative factor of 4/3 with respect to the best known 3 lower bound for any routing strategy. We show that the minimum number of element transfers for minimum path length routing is K with concurrent communication on all channels of every node of a binary cube. >
ADOPT: A tool for automatic detection of tectonic plates at the surface of convection models

Mantle convection models with plate‐like behavior produce surface structures comparable to Earth's plate boundaries. However, analyzing those structures is a difficult task, since convection models produce, as on Earth, diffuse deformation and elusive plate boundaries. Therefore we present here and share a quantitative tool to identify plate boundaries and produce plate polygon layouts from results of numerical models of convection: Automatic Detection Of Plate Tectonics (ADOPT). This digital tool operates within the free open‐source visualization software Paraview. It is based on image segmentation techniques to detect objects. The fundamental algorithm used in ADOPT is the watershed transform. We transform the output of convection models into a topographic map, the crest lines being the regions of deformation (plate boundaries) and the catchment basins being the plate interiors. We propose two generic protocols (the field and the distance methods) that we test against an independent visual detection of plate polygons. We show that ADOPT is effective to identify the smaller plates and to close plate polygons in areas where boundaries are diffuse or elusive. ADOPT allows the export of plate polygons in the standard OGR‐GMT format for visualization, modification, and analysis under generic softwares like GMT or GPlates.
Project-Team TEXMEX Efficient Exploitation of Multimedia Documents: exploring, indexing and searching in very large databases

1. Team TEXMEX is a joint project with CNRS and University of Rennes 1. The team has been created on January 1st, 2002 and became an INRIA project on November 1st, 2002. Head of Project-team Patrick Gros [research scientist CNRS] Administrative assistant Maryse Auffray [AJT INRIA, partial position in the project-team] Faculty members (University of Rennes 1) Laure Berti-Équille [associate professor] Annie Morin [associate professor] Pascale Sébillot [associate professor] Faculty member (CNRS) Laurent Amsaleg [research scientist] Ph.D. students Sid-Ahmed Berrani [CIFRE with Thomson] Nicolas Bonnel [CIFRE with France Télécom R&D] Vincent Claveau [MJENR grant, teaching assistant since Oct 10th] Manolis Delakis [MJENR grant, since Oct 1st] Ewa Kijak [CIFRE with Thomson until Sep 30th] Anicet Kouomou-Choupo [MJENR grant] Fabienne Moreau [INRIA grant, since Oct 1st] Anthony Remazeilles [MJENR grant, also with VISTA] Hervé Renault [since Oct 1st, 2003] Mathias Rossignol [MJENR grant] François Tonnin [INRIA-Britany grant, also with TEMICS] Project technical staff David Bonnaud [ANNAPURNA project] Christophe Garcia [FERIA project, since Feb. 1st until Nov 30th] Sophie Le Delliou [DIPHONET project, also with TEMICS, since Nov 1st] 2. Overall Objectives The explosion of the quantity of numerical documents raises the problem of the management of these documents. Beyond the problem of storage, we are interested in problems related with the management of the contents: how to exploit large bases of documents, how to classify them, how to index them to be able to search documents, how to visualize their contents? To solve these problems, we propose a multi-field work gathering within the same team specialists of the various media: image, video, text, and specialists in exploitation techniques of the data and metadata extracted from these data: databases, statistics, information retrieval. Our work is at the intersection of these fields and relates more particularly to 3 points: searching in large image databases, adding semantics to search engines, coupling media for multimedia document description. The exploitation of the contents of large databases of digital multimedia documents is a problem with multiple facets, and the construction of a system exploiting such a database calls upon many techniques: study and description of documents, organization of the bases, search algorithms, classification, visualization, but also adapted management of the primary and secondary memories, interfaces and interaction with the user. The five major challenges of the field appear to us to be the following ones: 2 Activity Report INRIA 2003 • it is necessary, first of all, …
TABLE 1 A comparison of high resolution palmprint matching algorithms proposed in the literature

With the availability of live-scan palmprint technology, high resolution palmprint recognition has started to receive significant attention in forensics and law enforcement. In forensic applications, latent palmprints provide critical evidence as it is estimated that about 30 percent of the latents recovered at crime scenes are those of palms. Most of the available high resolution palmprint matching algorithms essentially follow the minutiae-based fingerprint matching strategy. Considering the large number of minutiae (about 1,000 minutiae in a full palmprint compared to about 100 minutiae in a rolled fingerprint) and large area of foreground region in full palmprints, novel strategies need to be developed for efficient and robust latent palmprint matching. In this paper, a coarse to fine matching strategy based on minutiae clustering and minutiae match propagation is designed specifically for palmprint matching. To deal with the large number of minutiae, a local feature based minutiae clustering algorithm is designed to cluster minutiae into several groups such that minutiae belonging to the same group have similar local characteristics. The coarse matching is then performed within each cluster to establish initial minutiae correspondences between two palmprints. Starting with each initial correspondence, a minutiae match propagation algorithm searches for mated minutiae in the full palmprint. The proposed palmprint matching algorithm has been evaluated on a latent-to-full palmprint database consisting of 446 latents and 12,489 background full prints. The matching results show a rank-1 identification accuracy of 79.4%, which is significantly higher than the 60.8% identification accuracy of a state of the art latent palmprint matching algorithm on the same latent database. The average computation time of our algorithm for a single latent-to-full match is about 141ms for genuine match and 50ms for impostor match, on a Windows XP desktop system with 2.2GHz CPU and 1.00GB RAM. The computation time of our algorithm is an order of magnitude faster than a previously published state of the art
Videojuegos: máquinas del tiempo y mutaciones de la subjetividad

El articulo revisa las implicaciones de la relacion con la tecnologia en las sociedades contemporaneas. Esta reflexion se centra en el impacto que tienen los videojuegos en la relacion tiempo-subjetividad-conocimiento, por medio de las transformaciones narrativas y de sus consecuentes alteraciones en la sensibilidad social y cultural. Asi mismo, se plantea la idea de que los videojuegos se configuran como la realizacion de las propuestas de la ciencia ficcion, considerada como metanarrativa de la modernidad, a traves de la consolidacion de la metafora del cyborg como cuerpo alterado y reinventado.
Compositional verification of middleware-based software architecture descriptions

In this paper we present a compositional reasoning to verify middleware-based software architecture descriptions. We consider a nowadays typical software system development, namely the development of a software application A on a middleware M. Our goal is to efficiently integrate verification techniques, like model checking, in the software life cycle in order to improve the overall software quality. The approach exploits the structure imposed on the system by the software architecture in order to develop an assume-guarantee methodology to reduce properties verification from global to local. We apply the methodology on a non-trivial case study namely the development of a Gnutella system on top of the SIENA event-notification middleware.
Bilateral Blue Noise Sampling

We present bilateral blue noise sampling, a method that considers both spatial and feature properties. Our core idea is a sample distance measure that incorporates both sample positions and domain features. This idea, inspired by bilateral filtering, is very simple and can be easily combined with prior sampling algorithms. We describe corresponding synthesis methods for sample generation and analysis methods for distribution quality. We demonstrate the efficacy of bilateral sampling for a variety of applications including geometry sampling, image stippling, and dynamic domains.
Evaluating Content Based Image Retrieval Techniques with the One Million Images CLIC TestBed

Pattern recognition and image recognition methods are commonly developed and tested using testbeds, which contain known responses to a query set. Until now, testbeds available for image analysis and content-based image retrieval (CBIR) have been scarce and small-scale. Here we present the one million images CEAList Image Collection (CLIC) testbed that we have produced, and report on our use of this testbed to evaluate image analysis merging techniques. This testbed will soon be made publicly available through the EU MUSCLE Network of Excellence. Keywords—CBIR, CLIC, evaluation, image indexing and retrieval, testbed.
Deep Generative Models for Fast Shower Simulation in ATLAS

Detectors of High Energy Physics experiments, such as the ATLAS dectector [1] at the Large Hadron Collider [2], serve as cameras that take pictures of the particles produced in the collision events. One of the key detector technologies used for measuring the energy of particles are calorimeters. Particles will lose their energy in a cascade (called a shower) of electromagnetic and hadronic interactions with a dense absorbing material. The number of the particles produced in this showering process is subsequently measured across the sampling layers of the calorimeter. The deposition of energy in the calorimeter due to a developing shower is a stochastic process that can not be described from first principles and rather relies on a precise simulation of the detector response. It requires the modeling of particles interactions with matter at the microscopic level as implemented using the Geant4 toolkit [3]. This simulation process is inherently slow and thus presents a bottleneck in the ATLAS simulation pipeline [4]. The current work addresses this limitation. To meet the growing analysis demands, ATLAS already relies strongly on fast calorimeter simulation techniques based on thousands of individual parametrizations of the calorimeter response [5]. The algorithms currently employed for physics analyses by the ATLAS collaboration achieve a significant speedup over the full simulation of the detector response at the cost of accuracy. Current developments [6] [7] aim at improving the modeling of taus, jet-substructure-based boosted objects or wrongly identified objects in the calorimeter and will benefit from an improved detector description following data taking and a more detailed forward calorimeter geometry. Deep Learning techniques have been improving state of the art results in various science areas such as: astrophysics [8], cosmology [9] and medical imaging [10]. These techniques are able to describe complex data structures and scale well with highdimensionality problems. Generative models are powerful deep learning algorithms to map complex distributions into a lower dimensional space, to generate samples of higher dimensionality and to approximate the underlying probability densities. Among the most promising approaches are Variational Auto-Encoders [11] [12] and Generative Adversarial Networks [13]. In this context, the talk presents the first application of such models to the fast simulation of the calorimeter response in the ATLAS detector. This work [14] demonstrates the feasibility of using such algorithms for large scale high energy physics experiments in the future, and opens the possibility to complement current techniques.
A Clustering model for gadgets and apps used in patient monitoring in HIOT environment

Background: with increasing demand for treatment, patients are monitored with help of Internet of Things(IOT). Patient's monitoring devices and technologies include heart rate measurement, blood pressure measurement, blood glucose and other vital signs. The purpose of study is to provide a model of clustering patient physical monitoring gadgets and apps in Healthcare Internet of Things (HIOT) environment using data mining techniques, so based on the needs and characteristics of the user, the more appropriate results of choosing technologies acquired. Materials and methods: This study is a review and functional since its result. The data includes 6 unique features of 60 selected technologies including function, price, connectivity route, power supply, location and type of use that has been extracted from R&D and advertising sites of technologies and also relevant articles. data analysis method is clustering technique and K-medoids algorithm. to identify the most effective features, random forest algorithm has been used. Results: the proposed clustering model takes into account 6 as inputs and clusters gadgets and apps in accordance with selected characteristics as the model outputs. clustering problem data is clustered in 4 categories. Silhouette index is 0.45, which indicates the validity of the model. The type of application and then the price had the greatest impact on clustering. Conclusion: By this model, patients or users can find the most appropriate technology based on the type of disease and other effective features, such as price. So with accurate physical and momentary monitoring, disease progression decrease and prevention of disease will improve.
Real-Time Enveloping with Rotational Regression by

Enveloping (or skinning) is the process that relates a skele ton, which an animator controls, to a 3-D surface mesh, which the audience sees. This process i s necessary in most computer graphics applications that involve animated charact e s. The complexity (and speed) of enveloping solutions vary from photo-realistic muscle s imulations used for movie production, to artifact-ridden heuristics such as linear blen d skinning used for video games and training simulations. We propose a method for example-based enveloping of 3-D char a ters. We can approximate the output of muscle simulations or other high-qu ality enveloping tools with a model that can be evaluated at speeds comparable to the faste st env loping techniques. Our technique introduces a rotational regression model that ca n a curately capture common skinning behaviors such as muscle bulging, twisting, and ch allenging areas such as the shoulders. Our better treatment of rotational quantities i s made possible by a framework that predicts mesh deformation gradients instead of mesh ve rtex positions. We reconstruct the vertex positions from deformation gradients in an addit ional step by solving a Poisson equation. We show that our model is significantly more expressive than l inear blend skinning and capable of capturing a wider variety of effects without g eneralization problems. Our method is also comparable in run-time speed to linear blend s kinning. All in all, we are proposing a more general, less artifact-ridden, replaceme nt for linear blend skinning that will allow for more realistic enveloping in real-time appli cations. Thesis Supervisor: Jovan Popovic Title: Associate Professor