OpenRaiser/Intern-Atlas huggingface dataset:
Compression Artifacts Removal Using Convolutional Neural Networks
Venue: arXiv cs.CV 2016
Year: 2016

This paper shows that it is possible to train large and deep convolutional neural networks (CNN) for JPEG compression artifacts reduction, and that such networks can provide significantly better reconstruction quality compared to previously used smaller networks as well as to any other state-of-the-art methods. We were able to train networks with 8 layers in a single step and in relatively short time by combining residual learning, skip architecture, and symmetric weight initialization. We provide further insights into convolution networks for JPEG artifact reduction by evaluating three different objectives, generalization with respect to training dataset size, and generalization with respect to JPEG quality level.
A Linear-Time Algorithm for the Weighted Paired-Domination Problem on Block Graphs
Venue: arXiv cs.DS 2016
Year: 2016

In a graph $G = (V,E)$, a vertex subset $S\subseteq V(G)$ is said to be a dominating set of $G$ if every vertex not in $S$ is adjacent to a vertex in $S$. A dominating set $S$ of $G$ is called a paired-dominating set of $G$ if the induced subgraph $G[S]$ contains a perfect matching. In this paper, we propose an $O(n+m)$-time algorithm for the weighted paired-domination problem on block graphs using dynamic programming, which strengthens the results in [Theoret. Comput. Sci., 410(47--49):5063--5071, 2009] and [J. Comb. Optim., 19(4):457--470, 2010]. Moreover, the algorithm can be completed in $O(n)$ time if the block-cut-vertex structure of $G$ is given.
Revisiting Human Action Recognition: Personalization vs. Generalization
Venue: arXiv cs.CV 2016
Year: 2016

By thoroughly revisiting the classic human action recognition paradigm, this paper aims at proposing a new approach for the design of effective action classification systems. Taking as testbed publicly available three-dimensional (MoCap) action/activity datasets, we analyzed and validated different training/testing strategies. In particular, considering that each human action in the datasets is performed several times by different subjects, we were able to precisely quantify the effect of inter- and intra-subject variability, so as to figure out the impact of several learning approaches in terms of classification performance. The net result is that standard testing strategies consisting in cross-validating the algorithm using typical splits of the data (holdout, k-fold, or one-subject-out) is always outperformed by a "personalization" strategy which learns how a subject is performing an action. In other words, it is advantageous to customize (i.e., personalize) the method to learn the actions carried out by each subject, rather than trying to generalize the actions executions across subjects. Consequently, we finally propose an action recognition framework consisting of a two-stage classification approach where, given a test action, the subject is first identified before the actual recognition of the action takes place. Despite the basic, off-the-shelf descriptors and standard classifiers adopted, we noted a relevant increase in performance with respect to standard state-of-the-art algorithms, so motivating the usage of personalized approaches for designing effective action recognition systems.
Well-Rounded Lattices for Reliability and Security in Rayleigh Fading SISO Channels
Venue: arXiv cs.IT 2016
Year: 2016

For many wiretap channel models asymptotically optimal coding schemes are known, but less effort has been put into actual realizations of wiretap codes for practical parameters. Bounds on the mutual information and error probability when using coset coding on a Rayleigh fading channel were recently established by Oggier and Belfiore, and the results in this paper build on their work. However, instead of using their ultimate inverse norm sum approximation, a more precise expression for the eavesdropper's probability of correct decision is used in order to determine a general class of good coset codes. The code constructions are based on well-rounded lattices arising from simple geometric criteria. In addition to new coset codes and simulation results, novel number-theoretic results on well-rounded ideal lattices are presented.
Follow Spam Detection based on Cascaded Social Information
Venue: arXiv cs.SI 2016
Year: 2016

In the last decade we have witnessed the explosive growth of online social networking services (SNSs) such as Facebook, Twitter, RenRen and LinkedIn. While SNSs provide diverse benefits for example, forstering interpersonal relationships, community formations and news propagation, they also attracted uninvited nuiance. Spammers abuse SNSs as vehicles to spread spams rapidly and widely. Spams, unsolicited or inappropriate messages, significantly impair the credibility and reliability of services. Therefore, detecting spammers has become an urgent and critical issue in SNSs. This paper deals with Follow spam in Twitter. Instead of spreading annoying messages to the public, a spammer follows (subscribes to) legitimate users, and followed a legitimate user. Based on the assumption that the online relationships of spammers are different from those of legitimate users, we proposed classification schemes that detect follow spammers. Particularly, we focused on cascaded social relations and devised two schemes, TSP-Filtering and SS-Filtering, each of which utilizes Triad Significance Profile (TSP) and Social status (SS) in a two-hop subnetwork centered at each other. We also propose an emsemble technique, Cascaded-Filtering, that combine both TSP and SS properties. Our experiments on real Twitter datasets demonstrated that the proposed three approaches are very practical. The proposed schemes are scalable because instead of analyzing the whole network, they inspect user-centered two hop social networks. Our performance study showed that proposed methods yield significantly better performance than prior scheme in terms of true positives and false positives.
On Reductions from Multi-Domain Noninterference to the Two-Level Case
Venue: arXiv cs.CR 2016
Year: 2016

The literature on information flow security with respect to transitive policies has been concentrated largely on the case of policies with two security domains, High and Low, because of a presumption that more general policies can be reduced to this two-domain case. The details of the reduction have not been the subject of careful study, however. Many works in the literature use a reduction based on a quantification over "Low-down" partitionings of domains into those below and those not below a given domain in the information flow order. A few use "High-up" partitionings of domains into those above and those not above a given domain. Our paper argues that more general "cut" partitionings are also appropriate, and studies the relationships between the resulting multi-domain notions of security when the basic notion for the two-domain case to which we reduce is either Nondeducibility on Inputs or Generalized Noninterference. The Low-down reduction is shown to be weaker than the others, and while the High-up reduction is sometimes equivalent to the cut reduction, both it and the Low-down reduction may have an undesirable property of non-monotonicity with respect to a natural ordering on policies. These results suggest that the cut-based partitioning yields a more robust general approach for reduction to the two-domain case.
Inspector: A Data Provenance Library for Multithreaded Programs
Venue: arXiv cs.DC 2016
Year: 2016

Data provenance strives for explaining how the computation was performed by recording a trace of the execution. The provenance trace is useful across a wide-range of workflows to improve the dependability, security, and efficiency of software systems. In this paper, we present Inspector, a POSIX-compliant data provenance library for shared-memory multithreaded programs. The Inspector library is completely transparent and easy to use: it can be used as a replacement for the pthreads library by a simple exchange of libraries linked, without even recompiling the application code. To achieve this result, we present a parallel provenance algorithm that records control, data, and schedule dependencies using a Concurrent Provenance Graph (CPG). We implemented our algorithm to operate at the compiled binary code level by leveraging a combination of OS-specific mechanisms, and recently released Intel PT ISA extensions as part of the Broadwell micro-architecture. Our evaluation on a multicore platform using applications from multithreaded benchmarks suites (PARSEC and Phoenix) shows reasonable provenance overheads for a majority of applications. Lastly, we briefly describe three case-studies where the generic interface exported by Inspector is being used to improve the dependability, security, and efficiency of systems. The Inspector library is publicly available for further use in a wide range of other provenance workflows.
Parameterized complexity of the MINCCA problem on graphs of bounded decomposability
Venue: arXiv cs.DS 2016
Year: 2016

In an edge-colored graph, the cost incurred at a vertex on a path when two incident edges with different colors are traversed is called reload or changeover cost. The "Minimum Changeover Cost Arborescence" (MINCCA) problem consists in finding an arborescence with a given root vertex such that the total changeover cost of the internal vertices is minimized. It has been recently proved by Gözüpek et al. [TCS 2016] that the problem is FPT when parameterized by the treewidth and the maximum degree of the input graph. In this article we present the following results for the MINCCA problem: - the problem is W[1]-hard parameterized by the treedepth of the input graph, even on graphs of average degree at most 8. In particular, it is W[1]-hard parameterized by the treewidth of the input graph, which answers the main open problem of Gözüpek et al. [TCS 2016]; - it is W[1]-hard on multigraphs parameterized by the tree-cutwidth of the input multigraph; - it is FPT parameterized by the star tree-cutwidth of the input graph, which is a slightly restricted version of tree-cutwidth. This result strictly generalizes the FPT result given in Gözüpek et al. [TCS 2016]; - it remains NP-hard on planar graphs even when restricted to instances with at most 6 colors and 0/1 symmetric costs, or when restricted to instances with at most 8 colors, maximum degree bounded by 4, and 0/1 symmetric costs.
Robust measurement-based buffer overflow probability estimators for QoS provisioning and traffic anomaly prediction applicationm
Venue: arXiv cs.IT 2016
Year: 2016

Suitable estimators for a class of Large Deviation approximations of rare event probabilities based on sample realizations of random processes have been proposed in our earlier work. These estimators are expressed as non-linear multi-dimensional optimization problems of a special structure. In this paper, we develop an algorithm to solve these optimization problems very efficiently based on their characteristic structure. After discussing the nature of the objective function and constraint set and their peculiarities, we provide a formal proof that the developed algorithm is guaranteed to always converge. The existence of efficient and provably convergent algorithms for solving these problems is a prerequisite for using the proposed estimators in real time problems such as call admission control, adaptive modulation and coding with QoS constraints, and traffic anomaly detection in high data rate communication networks.
Radio Transformer Networks: Attention Models for Learning to Synchronize in Wireless Systems
Venue: arXiv cs.LG 2016
Year: 2016

We introduce learned attention models into the radio machine learning domain for the task of modulation recognition by leveraging spatial transformer networks and introducing new radio domain appropriate transformations. This attention model allows the network to learn a localization network capable of synchronizing and normalizing a radio signal blindly with zero knowledge of the signals structure based on optimization of the network for classification accuracy, sparse representation, and regularization. Using this architecture we are able to outperform our prior results in accuracy vs signal to noise ratio against an identical system without attention, however we believe such an attention model has implication far beyond the task of modulation recognition.
A propagation matting method based on the Local Sampling and KNN Classification with adaptive feature space
Venue: arXiv cs.CV 2016
Year: 2016

Closed Form is a propagation based matting algorithm, functioning well on images with good propagation . The deficiency of the Closed Form method is that for complex areas with poor image propagation , such as hole areas or areas of long and narrow structures. The right results are usually hard to get. On these areas, if certain flags are provided, it can improve the effects of matting. In this paper, we design a matting algorithm by local sampling and the KNN classifier propagation based matting algorithm. First of all, build the corresponding features space according to the different components of image colors to reduce the influence of overlapping between the foreground and background, and to improve the classification accuracy of KNN classifier. Second, adaptively use local sampling or using local KNN classifier for processing based on the pros and cons of the sample performance of unknown image areas. Finally, based on different treatment methods for the unknown areas, we will use different weight for augmenting constraints to make the treatment more effective. In this paper, by combining qualitative observation and quantitative analysis, we will make evaluation of the experimental results through online standard set of evaluation tests. It shows that on images with good propagation , this method is as effective as the Closed Form method, while on images in complex regions, it can perform even better than Closed Form.
Walk-Unlock: Zero-Interaction Authentication Protected with Multi-Modal Gait Biometrics
Venue: arXiv cs.CR 2016
Year: 2016

Zero-interaction authentication (ZIA) refers to a form of user-transparent login mechanism using which a terminal (e.g., a desktop computer) can be unlocked by the mere proximity of an authentication token (e.g., a smartphone). Given its appealing usability, ZIA has already been deployed in many real-world applications. However, ZIA contains one major security weakness - unauthorized physical access to the token, e.g., during lunch-time or upon theft, allows the attacker to have unfettered access to the terminal. In this paper, we address this gaping vulnerability with ZIA systems by (un)locking the authentication token with the user's walking pattern as she approaches the terminal to access it. Since a user's walking or gait pattern is believed to be unique, only that user (no imposter) would be able to unlock the token to gain access to the terminal in a ZIA session. While walking-based biometrics schemes have been studied in prior literature for other application settings, our main novelty lies in the careful use of: (1) multiple sensors available on the current breed of devices (e.g., accelerometer, gyroscope and magnetometer), and (2) multiple devices carried by the user (in particular, an "in-pocket" smartphone and a "wrist-worn" smartwatch), that all capture unique facets of user's walking pattern. Our contributions are three-fold. First, we introduce, design and implement WUZIA ("Walk-Unlock ZIA"), a multi-modal walking biometrics approach tailored to enhance the security of ZIA systems (still with zero interaction). Second, we demonstrate that WUZIA offers a high degree of detection accuracy, based on multi-sensor and multi-device fusion. Third, we show that WUZIA can resist active attacks that attempt to mimic a user's walking pattern, especially when multiple devices are used.
Bandwidth and delay issue s on the network routing
Venue: arXiv cs.NI 2016
Year: 2016

N/W has always been intended to be incredibly strong. H/W and software changes affect basic structure on the N/W capacity. N/W-Routing-protocols have been used to determine the shortest path as the goal. N/W information is deployed using dominant interior N/W-Routing-protocols. Although in N/W little instability is associated with bandwidth and latency this requires analysis. Statistical analysis of the samples have been taken for identifying the relationship between dependent and independent variables. Study has been made to see the relations with the delay, bandwidth, and N/W-Routing stability to draw to the conclusions for a positive relationship. Thus, for getting the better performance and results, bandwidth and delay should be reduced, as suggested in this paper.
Spatially Aware Dictionary Learning and Coding for Fossil Pollen Identification
Venue: arXiv cs.CV 2016
Year: 2016

We propose a robust approach for performing automatic species-level recognition of fossil pollen grains in microscopy images that exploits both global shape and local texture characteristics in a patch-based matching methodology. We introduce a novel criteria for selecting meaningful and discriminative exemplar patches. We optimize this function during training using a greedy submodular function optimization framework that gives a near-optimal solution with bounded approximation error. We use these selected exemplars as a dictionary basis and propose a spatially-aware sparse coding method to match testing images for identification while maintaining global shape correspondence. To accelerate the coding process for fast matching, we introduce a relaxed form that uses spatially-aware soft-thresholding during coding. Finally, we carry out an experimental study that demonstrates the effectiveness and efficiency of our exemplar selection and classification mechanisms, achieving $86.13\%$ accuracy on a difficult fine-grained species classification task distinguishing three types of fossil spruce pollen.
Recurrent Convolutional Neural Network Regression for Continuous Pain Intensity Estimation in Video
Venue: arXiv cs.CV 2016
Year: 2016

Automatic pain intensity estimation possesses a significant position in healthcare and medical field. Traditional static methods prefer to extract features from frames separately in a video, which would result in unstable changes and peaks among adjacent frames. To overcome this problem, we propose a real-time regression framework based on the recurrent convolutional neural network for automatic frame-level pain intensity estimation. Given vector sequences of AAM-warped facial images, we used a sliding-window strategy to obtain fixed-length input samples for the recurrent network. We then carefully design the architecture of the recurrent network to output continuous-valued pain intensity. The proposed end-to-end pain intensity regression framework can predict the pain intensity of each frame by considering a sufficiently large historical frames while limiting the scale of the parameters within the model. Our method achieves promising results regarding both accuracy and running speed on the published UNBC-McMaster Shoulder Pain Expression Archive Database.
Hierarchical Modeling of Multidimensional Data in Regularly Decomposed Spaces: Main Principles
Venue: arXiv cs.CV 2016
Year: 2016

The described works have been carried out in the framework of a mid-term study initiated by the Centre Electronique de l'Armement and led by ADERSA, a French company of research under contract. The aim was to study the techniques of regular dividing of numerical data sets so as to provide tools for problem solving enabling to model multidimensional numerical objects and to be used in computer-aided design and manufacturing, in robotics, in image analysis and synthesis, in pattern recognition, in decision making, in cartography and numerical data base management. These tools are relying on the principle of regular hierarchical decomposition and led to the implementation of a multidimensional generalization of quaternary and octernary trees: the trees of order 2**k or 2**k-trees mapped in binary trees. This first tome, dedicated to the hierarchical modeling of multidimensional numerical data, describes the principles used for building, transforming, analyzing and recognizing patterns on which is relying the development of the associated algorithms. The whole so developed algorithms are detailed in pseudo-code at the end of this document. The present publication especially describes: - a building method adapted disordered and overcrowded data streams ; - its extension in inductive limits ; - the computation of the homographic transformation of a tree ; - the attribute calculus based on generalized moments and the provision of Eigen trees ; - perception procedures of objects without any covering in affine geometry ; - several supervised and unsupervised pattern recognition methods.
Implementing Strassen's Algorithm with BLIS
Venue: arXiv cs.MS 2016
Year: 2016

We dispel with "street wisdom" regarding the practical implementation of Strassen's algorithm for matrix-matrix multiplication (DGEMM). Conventional wisdom: it is only practical for very large matrices. Our implementation is practical for small matrices. Conventional wisdom: the matrices being multiplied should be relatively square. Our implementation is practical for rank-k updates, where k is relatively small (a shape of importance for libraries like LAPACK). Conventional wisdom: it inherently requires substantial workspace. Our implementation requires no workspace beyond buffers already incorporated into conventional high-performance DGEMM implementations. Conventional wisdom: a Strassen DGEMM interface must pass in workspace. Our implementation requires no such workspace and can be plug-compatible with the standard DGEMM interface. Conventional wisdom: it is hard to demonstrate speedup on multi-core architectures. Our implementation demonstrates speedup over conventional DGEMM even on an Intel(R) Xeon Phi(TM) coprocessor utilizing 240 threads. We show how a distributed memory matrix-matrix multiplication also benefits from these advances.
WEPSAM: Weakly Pre-Learnt Saliency Model
Venue: arXiv cs.CV 2016
Year: 2016

Visual saliency detection tries to mimic human vision psychology which concentrates on sparse, important areas in natural image. Saliency prediction research has been traditionally based on low level features such as contrast, edge, etc. Recent thrust in saliency prediction research is to learn high level semantics using ground truth eye fixation datasets. In this paper we present, WEPSAM : Weakly Pre-Learnt Saliency Model as a pioneering effort of using domain specific pre-learing on ImageNet for saliency prediction using a light weight CNN architecture. The paper proposes a two step hierarchical learning, in which the first step is to develop a framework for weakly pre-training on a large scale dataset such as ImageNet which is void of human eye fixation maps. The second step refines the pre-trained model on a limited set of ground truth fixations. Analysis of loss on iSUN and SALICON datasets reveal that pre-trained network converges much faster compared to randomly initialized network. WEPSAM also outperforms some recent state-of-the-art saliency prediction models on the challenging MIT300 dataset.