pinecone/core-2020-05-10-deduplication huggingface dataset:
Diffusion Component Analysis: Unraveling Functional Topology in Biological Networks

Complex biological systems have been successfully modeled by biochemical and genetic interaction networks, typically gathered from high-throughput (HTP) data. These networks can be used to infer functional relationships between genes or pro- teins. Using the intuition that the topological role of a gene in a network relates to its biological function, local or diffusion-based “guilt-by-association” and graph- theoretic methods have had success in inferring gene functions [1, 2, 3]. Here we seek to improve function prediction by integrating diffusion-based methods with a novel dimensionality reduction technique to overcome the incomplete and noisy nature of network data. A  type  of  diffusion  algorithm,  also  known  as  random  walk  with  restart (RWR), has been extensively studied in the context of biological networks and effectively applied to protein function prediction (e.g., [1]). The key idea is to propagate information along the network, in order to exploit both direct and indirect linkages between genes. Typically, a distribution of topological similar- ity is computed for each gene, in relation to other genes in the network, so that researchers can select the most related genes in the resulting distribution or, rather, select genes that share the most similar distributions. Though successful, these approaches are susceptible to noise in the input networks due to the high dimensionality of the computed distributions
Linearly Homomorphic Encryption from DDH

International audienceWe design a linearly homomorphic encryption scheme whose security relies on the hardness of the decisional Diffie-Hellman problem. Our approach requires some special features of the underlying group. In particular, its order is unknown and it contains a subgroup in which the discrete logarithm problem is tractable. Therefore, our instantiation holds in the class group of a non maximal order of an imaginary quadratic field. Its algebraic structure makes it possible to obtain such a linearly homomorphic scheme whose message space is the whole set of integers modulo a prime p and which supports an unbounded number of additions modulo p from the ciphertexts. A notable difference with previous works is that, for the first time, the security does not depend on the hardness of the factorization of integers. As a consequence, under some conditions, the prime p can be scaled to fit the application needs
On the Spectrum of Volume Integral Operators in Acoustic Scattering

International audienceVolume integral equations have been used as a theoretical tool in scattering theory for a long time. A classical application is an existence proof for the scattering problem based on the theory of Fredholm integral equations. This approach is described for acoustic and electromagnetic scattering in the books by Colton and Kress [CoKr83, CoKr98] where volume integral equations appear under the name "Lippmann-Schwinger equations". In electromagnetic scattering by penetrable objects, the volume integral equation (VIE) method has also been used for numerical computations. In particular the class of discretization methods known as "discrete dipole approximation" [PuPe73, DrFl94] has become a standard tool in computational optics applied to atmospheric sciences, astrophysics and recently to nano-science under the keyword "optical tweezers", see the survey article [YuHo07] and the literature quoted there. In sharp contrast to the abundance of articles by physicists describing and analyzing applications of the VIE method, the mathematical literature on the subject consists only of a few articles. An early spectral analysis of a VIE for magnetic problems was given in [FrPa84], and more recently [Ki07, KiLe09] have found sufficient conditions for well-posedness of the VIE in electromagnetic and acoustic scattering with variable coefficients. In [CoDK10, CoDS12], we investigated the essential spectrum of the VIE in electromagnetic scattering under general conditions on the complex-valued coefficients, finding necessary and sufficient conditions for well-posedness in the sense of Fredholm in the physically relevant energy spaces. A detailed presentation of these results can be found in the thesis [Sa14]. Publications based on the thesis are in preparation. Curiously, whereas the study of VIE in electromagnetic scattering has thus been completed as far as questions of Fredholm properties are concerned, the simpler case of acoustic scattering does not seem to have been covered in the same depth. It is the purpose of the present paper to close this gap
Spatial Bloom filters: Enabling privacy in location-aware applications

The wide availability of inexpensive positioning systems made it possible to embed them into smartphones and other personal devices. This marked the beginning of location-aware applications, where users request personalized services based on their geographic position. The location of a user is, however, highly sensitive information: the user’s privacy can be preserved if only the minimum amount of information needed to provide the service is disclosed at any time. While some applications, such as navigation systems, are based on the users’ movements and therefore require constant tracking, others only require knowledge of the user’s position in relation to a set of points or areas of interest. In this paper we focus on the latter kind of services, where location information is essentially used to determine membership in one or more geographic sets. We address this problem using Bloom Filters (BF), a compact data structure for representing sets. In particular, we present an extension of the original Bloom filter idea: the Spatial Bloom Filter (SBF). SBF’s are designed to manage spatial and geographical information in a space efficient way, and are well-suited for enabling privacy in location-aware applications. We show this by providing two multi-party protocols for privacy-preserving computation of location information, based on the known homomorphic properties of public key encryption schemes. The protocols keep the user’s exact position private, but allow the provider of the service to learn when the user is close to specific points of interest, or inside predefined areas. At the same time, the points and areas of interest remain oblivious to the user
Memory Forensics of a Java Card Dump

International audienceNowadays several papers have shown the ability to dump the EEPROM area of several Java Cards leading to the disclosure of already loaded applet and data structure of the card. Such a reverse engineering process is costly and prone to errors. Currently there are no tools available to help the process. We propose here an approach to find in the raw data obtained after a dump, the area containing the code and the data. Then, once the code area has been identified, we propose to rebuilt the original binary Cap file in order to be able to obtain the source code of the applet stored in the card
Heap . . . Hop! Heap Is Also Vulnerable

International audienceSeveral logical attacks against Java based smart card have been published recently. Most of them are based on the hypothesis that the type verification was not performed, thus allowing to obtain dynamically a type confusion. To mitigate such attacks, typed stack have been introduced on recent smart card. We propose here a new attack path for performing a type confusion even in presence of a typed stack. Then we propose using a Fault Tree Analysis a way to design efficiently counter measure in a top down approach. These counter measures are then evaluated on a Java Card virtual machin
Efficient Stochastic Methods: Profiled Attacks Beyond 8 Bits

Template attacks and stochastic models are among the most powerful side-channel attacks. However, they can be computationally expensive when processing a large number of samples. Various compression techniques have been used very successfully to reduce the data dimensionality prior to applying template attacks, most notably Principal Component Analysis (PCA) and Fisher’s Linear Discriminant Analysis (LDA). These make the attacks more efficient computationally and help the profiling phase to converge faster. We show how these ideas can also be applied to implement stochastic models more efficiently, and we also show that they can be applied and evaluated even for more than eight unknown data bits at once.This is the author accepted manuscript. The final version is available from Springer via http://dx.doi.org/10.1007/978-3-319-16763-3_
Usability of Scientific Workflow in Dynamically Changing Environment

Scientific workflow management systems are mainly data-flow oriented, which face several challenges due to the huge amount of data and the required computational capacity which cannot be predicted before enactment. Other problems may arise due to the dynamic access of the data storages or other data sources and the distributed nature of the scientific workflow computational infrastructures (cloud, cluster, grid, HPC), which status may change even during running of a single workflow instance. Many of these failures could be avoided with workflow management systems that provide provenance based dynamism and adaptivity to the unforeseen scenarios arising during enactment. In our work we summarize and categorize the failures that can arise in cloud environment during enactment and show the possibility of prediction and avoidance of failures with dynamic and provenance support
Risk Analysis and Behavior of Electricity Portfolio Aggregator

The scope of this paper is to adapt the standard mean-variance model of Henry Markowitz theory, creating a simulation tool to find the optimal configuration of the portfolio aggregator, calculate its profitability and risk. Currently, there is a deep discussion going on among the power system society about the structure and architecture of the future electric system. In this environment, policy makers and electric utilities find new approaches to access the electricity market; this configures new challenging positions in order to find innovative strategies and methodologies. Decentralized power generation is gaining relevance in liberalized markets, and small and medium size electricity consumers are also become producers (“prosumers”). In this scenario an electric aggregator is an entity that joins a group of electric clients, customers, producers, “prosumers” together as a single purchasing unit to negotiate the purchase and sale of electricity. The aggregator conducts research on electricity prices, contract terms and conditions in order to promote better energy prices for their clients and allows small and medium customers to benefit improved market prices
Simulation of Offshore Wind System with Three-Level Converters: HVDC Power Transmission in Cloud Scope

This paper is on a simulation for offshore wind systems in deep water
under cloud scope. The system is equipped with a permanent magnet synchronous
generator and a full-power three-level converter, converting the electric energy at
variable frequency in one at constant frequency. The control strategies for the
three-level are based on proportional integral controllers. The electric energy is injected
through a HVDC transmission submarine cable into the grid. The drive train
is modeled by a three-mass model taking into account the resistant stiffness torque,
structure and tower in the deep water due to the moving surface elevation. Conclusions
are taken on the influence of the moving surface on the energy conversion
Fast super-resolution via dense local training and inverse regressor search

Regression-based Super-Resolution (SR) addresses the upscaling problem by learning a mapping function (i.e. regressor) from the low-resolution to the high-resolution manifold. Under the locally linear assumption, this complex non-linear mapping can be properly modeled by a set of linear regressors distributed across the manifold. In such methods, most of the testing time is spent searching for the right regressor within this trained set. In this paper we propose a novel inverse-search approach for regression-based SR. Instead of performing a search from the image to the dictionary of regressors, the search is done inversely from the regressors’ dictionary to the image patches. We approximate this framework by applying spherical hashing to both image and regressors, which reduces the inverse search into computing a trained function. Additionally, we propose an improved training scheme for SR linear regressors which improves perceived and objective quality. By merging both contributions we improve speed and quality compared to the state-of-the-art.Peer ReviewedPostprint (author's final draft
Gesture Modeling by Hanklet-based Hidden Markov Model

In this paper we propose a novel approach for gesture modeling. We aim at decomposing a gesture into sub-trajectories that are the output of a sequence of atomic linear time invariant (LTI) systems, and we use a Hidden Markov Model to model the transitions from the LTI system to another. For this purpose, we represent the human body motion in a temporal window as a set of body joint trajectories that we assume are the output of an LTI system. We describe the set of trajectories in a temporal window by the corresponding Hankel matrix (Hanklet), which embeds the observability matrix of the LTI system that produced it. We train a set of HMMs (one for each gesture class) with a discriminative approach. To account for the sharing of body motion templates we allow the HMMs to share the same state space. We demonstrate by means of experiments on two publicly available datasets that, even with just considering the trajectories of the 3D joints, our method achieves state-of-the-art accuracy while competing well with methods that employ more complex models and feature representations
Automatic wrinkle detection using hybrid hessian filter

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-16811-1_40Aging as a natural phenomenon affects different parts of the human body under the influence of various biological and environmental factors. The most pronounced changes that occur on the face is the appearance of wrinkles, which are the focus of this research. Accurate wrinkle detection is an important task in face analysis. Some have been proposed in the literature, but the poor localization limits the performance of wrinkle detection. It will lead to false wrinkle detection and consequently affect the processes such as age estimation and clinician score assessment. Therefore, we propose a hybrid Hessian filter (HHF) to cope with the identified problem. HHF is composed of the directional gradient and Hessian matrix. The proposed filter is conceptually simple, however, it significantly increases the true wrinkle localization when compared with the conventional methods. In the experimental setup, three coders have been instructed to annotate the wrinkle of 2D forehead image manually. The inter-reliability among three coders is 93 % of Jaccard similarity index (JSI). In comparison to the state-of-the-art Cula method (CLM) and Frangi filter, HHF yielded the best result with a mean JSI of 75.67 %. We noticed that the proposed method is capable of detecting the medium to coarse wrinkle but not the fine wrinkle. Although there is a gap between human annotation and automated detection, this work demonstrates that HHF is a remarkably strong filter for wrinkle detection. From the experimental results, we believe that our findings are notable in terms of the JSI
A high performance CRF model for clothes parsing

In this paper we tackle the problem of clothing parsing: Our goal is to segment and classify different garments a person is wearing. We frame the problem as the one of inference in a pose-aware Conditional Random Field (CRF) which exploits appearance, figure/ground segmentation, shape and location priors for each garment as well as similarities between segments, and symmetries between different human body parts. We demonstrate the effectiveness of our approach on the Fashionista dataset and show that we can obtain a significant improvement over the state-of-the-art.Peer ReviewedPostprint (published version
Action-Gons: Action recognition with a discriminative dictionary of structured elements with varying granularity

LNCS v. 9007 entitled: Computer Vision -- ACCV 2014: 12th Asian Conference on Computer ..., Part 5This paper presents “Action-Gons”, a middle level representation for action recognition in videos. Actions in videos exhibit a reasonable level of regularity seen in human behavior, as well as a large degree of variation. One key property of action, compared with image scene, might be the amount of interaction among body parts, although scenes also observe structured patterns in 2D images. Here, we study highorder statistics of the interaction among regions of interest in actions and propose a mid-level representation for action recognition, inspired by the Julesz school of n-gon statistics. We propose a systematic learning process to build an over-complete dictionary of “Action-Gons”. We first extract motion clusters, named as action units, then sequentially learn a pool of action-gons with different granularities modeling different degree of interactions among action units. We validate the discriminative power of our learned action-gons on three challenging video datasets and show evident advantages over the existing methods. © Springer International Publishing Switzerland 2015.postprin
Data mining for action recognition

© Springer International Publishing Switzerland 2015. In recent years, dense trajectories have shown to be an efficient representation for action recognition and have achieved state-of-the art results on a variety of increasingly difficult datasets. However, while the features have greatly improved the recognition scores, the training process and machine learning used hasn’t in general deviated from the object recognition based SVM approach. This is despite the increase in quantity and complexity of the features used. This paper improves the performance of action recognition through two data mining techniques, APriori association rule mining and Contrast Set Mining. These techniques are ideally suited to action recognition and in particular, dense trajectory features as they can utilise the large amounts of data, to identify far shorter discriminative subsets of features called rules. Experimental results on one of the most challenging datasets, Hollywood2 outperforms the current state-of-the-art
2D Or Not 2D: Bridging the Gap Between Tracking and Structure from Motion

In this paper, we address the problem of tracking an unknown object in 3D space. Online 2D tracking often fails for strong outof-plane rotation which results in considerable changes in appearance beyond those that can be represented by online update strategies. However, by modelling and learning the 3D structure of the object explicitly, such effects are mitigated. To address this, a novel approach is presented, combining techniques from the fields of visual tracking, structure from motion (SfM) and simultaneous localisation and mapping (SLAM). This algorithm is referred to as TMAGIC (Tracking, Modelling And Gaussianprocess Inference Combined). At every frame, point and line features are tracked in the image plane and are used, together with their 3D correspondences, to estimate the camera pose. These features are also used to model the 3D shape of the object as a Gaussian process. Tracking determines the trajectories of the object in both the image plane and 3D space, but the approach also provides the 3D object shape. The approach is validated on several video-sequences used in the tracking literature, comparing favourably to state-of-the-art trackers for simple scenes (error reduced by 22 %) with clear advantages in the case of strong out-of-plane rotation, where 2D approaches fail (error reduction of 58 %)
Pixel-level hand detection with shape-aware structured forests

LNCS v. 9006 entitled: Computer Vision -- ACCV 2014: 12th Asian Conference on Computer Vision, Singapore, Singapore, November 1-5, 2014, Revised Selected Papers, Part IVHand detection has many important applications in HCI, yet it is a challenging problem because the appearance of hands can vary greatly in images. In this paper, we propose a novel method for efficient pixel-level hand detection. Unlike previous method which assigns a binary label to every pixel independently, our method estimates a probability shape mask for a pixel using structured forests. This approach can better exploit hand shape information in the training data, and enforce shape constraints in the estimation. Aggregation of multiple predictions generated from neighboring pixels further improves the robustness of our method. We evaluate our method on both ego-centric videos and unconstrained still images. Experiment results show that our method can detect hands efficiently and outperform other state-of-the-art methods.postprin
Learning detectors quickly with stationary statistics

Computer vision is increasingly becoming interested in the\ud
rapid estimation of object detectors. The canonical strategy of using Hard Negative Mining to train a Support Vector Machine is slow, since the large negative set must be traversed at least once per detector. Recent work has demonstrated that, with an assumption of signal stationarity, Linear Discriminant Analysis is able to learn comparable detectors without ever revisiting the negative set. Even with this insight, the time to learn a detector can still be on the order of minutes. Correlation filters,\ud
on the other hand, can produce a detector in under a second. However, this involves the unnatural assumption that the statistics are periodic, and requires the negative set to be re-sampled per detector size. These two methods differ chie y in the structure which they impose on the co-\ud
variance matrix of all examples. This paper is a comparative study which develops techniques (i) to assume periodic statistics without needing to revisit the negative set and (ii) to accelerate the estimation of detectors\ud
with aperiodic statistics. It is experimentally verified that periodicity is detrimental
Microstructural evaluation of suspension thermally sprayed WC-Co nanocomposite coatings.

Microstructural and sliding wear evaluations of nanostructured coatings deposited by Suspension High Velocity Oxy-Fuel (S-HVOF) spraying were conducted in as-sprayed and HIPed (Hot Isostatically Pressed) conditions. S-HVOF coatings were nanostructured via ball milling of the WC-12Co start powder, and deposited via an aqueous based suspension using modified HVOF (TopGun) spraying. Microstructural evaluations of these hardmetal coatings included TEM (Transmission Electron Microscopy), X-ray Diffraction (XRD) and Scanning Electron Microscopy (SEM). Sliding wear tests were conducted using a ball-on-flat test rig. Results indicated that nanostructured features inherited from the start powder in S-HVOF spraying were retained in the resulting coatings. The decarburisation of WC due to a higher surface area to volume ratio was also observed in the S-HVOF coatings. Nanostructured and amorphous phases caused by the high cooling rates during thermal spraying crystallized into complex eta-phases after the HIPing treatment. Sliding wear performance indicated that the coating wear was lower for the HIPed coatings