OpenRaiser/Intern-Atlas huggingface dataset:
Towards a Comprehensive Framework for Telemetry Data in HPC Environments
Venue: arXiv cs.DC 2017
Year: 2017

Current HPC platforms do not provide the infrastructure, interfaces and conceptual models to collect, store, analyze, and access such data. Today, applications depend on application and platform specific techniques for collecting telemetry data; introducing significant development overheads that inhibit portability and mobility. The development and adoption of adaptive, context-aware strategies is thereby impaired. To facilitate 2nd generation applications, more efficient application development, and swift adoption of adaptive applications in production, a comprehensive framework for telemetry data management must be provided by future HPC systems and services. We introduce a conceptual model and a software framework to collect, store, analyze, and exploit streams of telemetry data generated by HPC systems and their applications. We show how this framework can be integrated with HPC platform architectures and how it enables common application execution strategies.
Cybersecurity Cost of Quality: Managing the Costs of Cybersecurity Risk Management
Venue: arXiv cs.CR 2017
Year: 2017

There is no standard yet for measuring and controlling the costs associated with implementing cybersecurity programs. To advance research and practice towards this end, we develop a mapping using the well-known concept of quality costs and the Framework Core within the Cybersecurity Framework produced by the National Institute of Standards and Technology (NIST) in response to the Cybersecurity Enhancement Act of 2014. This mapping can be easily adopted by organizations that are already using the NIST CSF for cybersecurity risk management to plan, manage, and continually improve cybersecurity operations. If an organization is not using the NIST CSF, this mapping may still be useful for linking elements in accounting systems that are associated with cybersecurity operations and risk management to a quality cost model.
A New Algorithm to Automate Inductive Learning of Default Theories
Venue: arXiv cs.LO 2017
Year: 2017

In inductive learning of a broad concept, an algorithm should be able to distinguish concept examples from exceptions and noisy data. An approach through recursively finding patterns in exceptions turns out to correspond to the problem of learning default theories. Default logic is what humans employ in common-sense reasoning. Therefore, learned default theories are better understood by humans. In this paper, we present new algorithms to learn default theories in the form of non-monotonic logic programs. Experiments reported in this paper show that our algorithms are a significant improvement over traditional approaches based on inductive logic programming.
Object Handover Prediction using Gaussian Processes clustered with Trajectory Classification
Venue: arXiv cs.RO 2017
Year: 2017

A robotic system which approximates the user intention and appropriate complimentary motion is critical for successful human-robot interaction. %While the existing wearable sensors can monitor human movements in real-time, prediction of human movement is a significant challenge due to its highly non-linear motions optimised through the redundancy in the degrees of freedom. Here, we demonstrate robustness of the Gaussian Process (GP) clustered with a stochastic classification technique for trajectory prediction using an object handover scenario. By parametrising real 6D hand movements during human-human object handover using dual quaternions, variations of handover configurations were classified in real-time and then the remaining hand trajectory was predicted using the GP. The results highlights that our method can classify the handover configuration at an average of $43.4\%$ of the trajectory and the final hand configuration can be predicted within the normal variation of human movement. In conclusion, we demonstrate that GPs combined with a stochastic classification technique is a robust tool for proactively estimating human motions for human-robot interaction.
A Local-Search Algorithm for Steiner Forest
Venue: arXiv cs.DS 2017
Year: 2017

In the Steiner Forest problem, we are given a graph and a collection of source-sink pairs, and the goal is to find a subgraph of minimum total length such that all pairs are connected. The problem is APX-Hard and can be 2-approximated by, e.g., the elegant primal-dual algorithm of Agrawal, Klein, and Ravi from 1995. We give a local-search-based constant-factor approximation for the problem. Local search brings in new techniques to an area that has for long not seen any improvements and might be a step towards a combinatorial algorithm for the more general survivable network design problem. Moreover, local search was an essential tool to tackle the dynamic MST/Steiner Tree problem, whereas dynamic Steiner Forest is still wide open. It is easy to see that any constant factor local search algorithm requires steps that add/drop many edges together. We propose natural local moves which, at each step, either (a) add a shortest path in the current graph and then drop a bunch of inessential edges, or (b) add a set of edges to the current solution. This second type of moves is motivated by the potential function we use to measure progress, combining the cost of the solution with a penalty for each connected component. Our carefully-chosen local moves and potential function work in tandem to eliminate bad local minima that arise when using more traditional local moves.
Compressed Representation of Dynamic Binary Relations with Applications
Venue: arXiv cs.DS 2017
Year: 2017

We introduce a dynamic data structure for the compact representation of binary relations $\mathcal{R} \subseteq A \times B$. The data structure is a dynamic variant of the k$^2$-tree, a static compact representation that takes advantage of clustering in the binary relation to achieve compression. Our structure can efficiently check whether two objects $(a,b) \in A \times B$ are related, and list the objects of $B$ related to some $a \in A$ and vice versa. Additionally, our structure allows inserting and deleting pairs $(a,b)$ in the relation, as well as modifying the base sets $A$ and $B$. We test our dynamic data structure in different contexts, including the representation of Web graphs and RDF databases. Our experiments show that our dynamic data structure achieves good compression ratios and fast query times, close to those of a static representation, while also providing efficient support for updates in the represented binary relation.
Learning to Compose Task-Specific Tree Structures
Venue: arXiv cs.CL 2017
Year: 2017

For years, recursive neural networks (RvNNs) have been shown to be suitable for representing text into fixed-length vectors and achieved good performance on several natural language processing tasks. However, the main drawback of RvNNs is that they require structured input, which makes data preparation and model implementation hard. In this paper, we propose Gumbel Tree-LSTM, a novel tree-structured long short-term memory architecture that learns how to compose task-specific tree structures only from plain text data efficiently. Our model uses Straight-Through Gumbel-Softmax estimator to decide the parent node among candidates dynamically and to calculate gradients of the discrete decision. We evaluate the proposed model on natural language inference and sentiment analysis, and show that our model outperforms or is at least comparable to previous models. We also find that our model converges significantly faster than other models.
Tabula: A Language to Model Spreadsheet Tables
Venue: arXiv cs.SE 2017
Year: 2017

Spreadsheets provide a flexible and easy to use software development environment, but that leads to error proneness. Work has been done to prevent errors in spreadsheets, including using models to specify distinct parts of a spreadsheet as it is done with model-driven software development. Previous model languages for spreadsheets offer a limited expressiveness, and cannot model several features present in most real world spreadsheets. In this paper, the modeling language Tabula is introduced. It extends previous spreadsheet models with features like type constraints and nested classes with repetitions. Tabula is not only more expressive than other models but it can also be extended with more features. Moreover, Tabula includes a bidirectional transformation engine that guarantees synchronization after an update either in the model or spreadsheet.
No-idle, no-wait: when shop scheduling meets dominoes, eulerian and hamiltonian paths
Venue: arXiv cs.DM 2017
Year: 2017

In shop scheduling, several applications exist where it is required that some components perform consecutively. We refer to no-idle schedules if machines are required to operate with no inserted idle time and no-wait schedules if tasks cannot wait between the end of an operation and the start of the following one. We consider here no-idle/no-wait shop scheduling problems with makespan as performance measure and determine related complexity results. We first analyze the two-machine no-idle/no-wait flow shop problem and show that it is equivalent to a special version of the game of dominoes which is polynomially solvable by tackling an Eulerian path problem on a directed graph. We present for this problem an O(n) exact algorithm. As a byproduct we show that the Hamiltonian Path problem on a digraph G(V,A) with a special structure (where every pair of vertices i,j either has all successors in common or has no common successors) reduces to the two-machine no-idle/no-wait flow shop problem. Correspondingly, we provide a new polynomially solvable special case of the Hamiltonian Path problem. Then, we show that also the corresponding $m$-machine no-idle no-wait flow shop problem is polynomially solvable and provide an O(mn log n) exact algorithm. Finally we prove that the 2-machine no-idle/no-wait job shop problem and the 2-machine no-idle/no-wait open shop problem are NP-Hard in the strong sense.
How to cut a cake with a gram matrix
Venue: arXiv cs.GT 2017
Year: 2017

In this article we study the problem of fair division. In particular we study a notion introduced by J. Barbanel that generalizes super envy-free fair division. We give a new proof of his result. Our approach allows us to give an explicit bound for this kind of fair division. Furthermore, we also give a theoretical answer to an open problem posed by Barbanel in 1996. Roughly speaking, this question is: how can we decide if there exists a fair division satisfying some inequalities constraints? Furthermore, when all the measures are given with piecewise constant density functions then we show how to construct effectively such a fair division.
Deep Bilateral Learning for Real-Time Image Enhancement
Venue: arXiv cs.GR 2017
Year: 2017

Performance is a critical challenge in mobile image processing. Given a reference imaging pipeline, or even human-adjusted pairs of images, we seek to reproduce the enhancements and enable real-time evaluation. For this, we introduce a new neural network architecture inspired by bilateral grid processing and local affine color transforms. Using pairs of input/output images, we train a convolutional neural network to predict the coefficients of a locally-affine model in bilateral space. Our architecture learns to make local, global, and content-dependent decisions to approximate the desired image transformation. At runtime, the neural network consumes a low-resolution version of the input image, produces a set of affine transformations in bilateral space, upsamples those transformations in an edge-preserving fashion using a new slicing node, and then applies those upsampled transformations to the full-resolution image. Our algorithm processes high-resolution images on a smartphone in milliseconds, provides a real-time viewfinder at 1080p resolution, and matches the quality of state-of-the-art approximation techniques on a large class of image operators. Unlike previous work, our model is trained off-line from data and therefore does not require access to the original operator at runtime. This allows our model to learn complex, scene-dependent transformations for which no reference implementation is available, such as the photographic edits of a human retoucher.
Wavelet-based Reflection Symmetry Detection via Textural and Color Histograms
Venue: arXiv cs.CV 2017
Year: 2017

Symmetry is one of the significant visual properties inside an image plane, to identify the geometrically balanced structures through real-world objects. Existing symmetry detection methods rely on descriptors of the local image features and their neighborhood behavior, resulting incomplete symmetrical axis candidates to discover the mirror similarities on a global scale. In this paper, we propose a new reflection symmetry detection scheme, based on a reliable edge-based feature extraction using Log-Gabor filters, plus an efficient voting scheme parameterized by their corresponding textural and color neighborhood information. Experimental evaluation on four single-case and three multiple-case symmetry detection datasets validates the superior achievement of the proposed work to find global symmetries inside an image.
Backscatter Communications for Wireless Powered Sensor Networks with Collision Resolution
Venue: arXiv cs.IT 2017
Year: 2017

Wireless powered backscatter communications is an attractive technology for next-generation low-powered sensor networks such as the Internet of Things. However, backscattering suffers from collisions due to multiple simultaneous transmissions and a dyadic backscatter channel, which greatly attenuate the received signal at the reader. This letter deals with backscatter communications in sensor networks from a large-scale point-of-view and considers various collision resolution techniques: directional antennas, ultra-narrow band transmissions and successive interference cancellation. We derive analytical expressions for the decoding probability and our results show the significant gains, which can be achieved from the aforementioned techniques.
Choosing Requirements for Experimentation with User Interfaces of Requirements Modeling Tools
Venue: arXiv cs.SE 2017
Year: 2017

When designing a new presentation front-end called FlexiView for requirements modeling tools, we encountered a general problem: designing such an interface requires a lot of experimentation which is costly when the code of the tool needs to be adapted for every experiment. On the other hand, when using simplified user interface (UI) tools, the results are difficult to generalize. To improve this situation, we are developing a UI experimentation tool which is based on so-called ImitGraphs. ImitGraphs can act as a simple, but an accurate substitute for a modeling tool. In this paper, we define requirements for such a UI experimentation tool based on an analysis of the features of existing requirements modeling tools.
Ensemble Estimation of Distributional Functionals via $k$-Nearest Neighbors
Venue: arXiv cs.IT 2017
Year: 2017

The problem of accurate nonparametric estimation of distributional functionals (integral functionals of one or more probability distributions) has received recent interest due to their wide applicability in signal processing, information theory, machine learning, and statistics. In particular, $k$-nearest neighbor (nn) based methods have received a lot of attention due to their adaptive nature and their relatively low computational complexity. We derive the mean squared error (MSE) convergence rates of leave-one-out $k$-nn plug-in density estimators of a large class of distributional functionals without boundary correction. We then apply the theory of optimally weighted ensemble estimation to obtain weighted ensemble estimators that achieve the parametric MSE rate under assumptions that are competitive with the state of the art. The asymptotic distributions of these estimators, which are unknown for all other $k$-nn based distributional functional estimators, are also presented which enables us to perform hypothesis testing.
Online Handwritten Mathematical Expressions Recognition System Using Fuzzy Neural Network
Venue: arXiv cs.CV 2017
Year: 2017

The article describes developed information technology for online recognition of handwritten mathematical expressions that based on proposed approaches to handwritten symbols recognition and structural analysis.
Refining Raw Sentence Representations for Textual Entailment Recognition via Attention
Venue: arXiv cs.CL 2017
Year: 2017

In this paper we present the model used by the team Rivercorners for the 2017 RepEval shared task. First, our model separately encodes a pair of sentences into variable-length representations by using a bidirectional LSTM. Later, it creates fixed-length raw representations by means of simple aggregation functions, which are then refined using an attention mechanism. Finally it combines the refined representations of both sentences into a single vector to be used for classification. With this model we obtained test accuracies of 72.057% and 72.055% in the matched and mismatched evaluation tracks respectively, outperforming the LSTM baseline, and obtaining performances similar to a model that relies on shared information between sentences (ESIM). When using an ensemble both accuracies increased to 72.247% and 72.827% respectively.
Distance-to-Mean Continuous Conditional Random Fields to Enhance Prediction Problem in Traffic Flow Data
Venue: arXiv cs.CV 2017
Year: 2017

The increase of vehicle in highways may cause traffic congestion as well as in the normal roadways. Predicting the traffic flow in highways especially, is demanded to solve this congestion problem. Predictions on time-series multivariate data, such as in the traffic flow dataset, have been largely accomplished through various approaches. The approach with conventional prediction algorithms, such as with Support Vector Machine (SVM), is only capable of accommodating predictions that are independent in each time unit. Hence, the sequential relationships in this time series data is hardly explored. Continuous Conditional Random Field (CCRF) is one of Probabilistic Graphical Model (PGM) algorithms which can accommodate this problem. The neighboring aspects of sequential data such as in the time series data can be expressed by CCRF so that its predictions are more reliable. In this article, a novel approach called DM-CCRF is adopted by modifying the CCRF prediction algorithm to strengthen the probability of the predictions made by the baseline regressor. The result shows that DM-CCRF is superior in performance compared to CCRF. This is validated by the error decrease of the baseline up to 9% significance. This is twice the standard CCRF performance which can only decrease baseline error by 4.582% at most.
Foreground Detection in Camouflaged Scenes
Venue: arXiv cs.CV 2017
Year: 2017

Foreground detection has been widely studied for decades due to its importance in many practical applications. Most of the existing methods assume foreground and background show visually distinct characteristics and thus the foreground can be detected once a good background model is obtained. However, there are many situations where this is not the case. Of particular interest in video surveillance is the camouflage case. For example, an active attacker camouflages by intentionally wearing clothes that are visually similar to the background. In such cases, even given a decent background model, it is not trivial to detect foreground objects. This paper proposes a texture guided weighted voting (TGWV) method which can efficiently detect foreground objects in camouflaged scenes. The proposed method employs the stationary wavelet transform to decompose the image into frequency bands. We show that the small and hardly noticeable differences between foreground and background in the image domain can be effectively captured in certain wavelet frequency bands. To make the final foreground decision, a weighted voting scheme is developed based on intensity and texture of all the wavelet bands with weights carefully designed. Experimental results demonstrate that the proposed method achieves superior performance compared to the current state-of-the-art results.
Towards an automated method based on Iterated Local Search optimization for tuning the parameters of Support Vector Machines
Venue: arXiv cs.AI 2017
Year: 2017

We provide preliminary details and formulation of an optimization strategy under current development that is able to automatically tune the parameters of a Support Vector Machine over new datasets. The optimization strategy is a heuristic based on Iterated Local Search, a modification of classic hill climbing which iterates calls to a local search routine.
Securing Content Sharing over ICN
Venue: arXiv cs.CR 2017
Year: 2017

The emerging Information-Centric Networking (ICN) paradigm is expected to facilitate content sharing among users. ICN will make it easy for users to appoint storage nodes, in various network locations, perhaps owned or controlled by them, where shared content can be stored and disseminated from. These storage nodes should be (somewhat) trusted since not only they have (some level of) access to user shared content, but they should also properly enforce access control. Traditional forms of encryption introduce significant overhead when it comes to sharing content with large and dynamic groups of users. To this end, proxy re-encryption provides a convenient solution. In this paper, we use Identity-Based Proxy Re-Encryption (IB-PRE) to provide confidentiality and access control for content items shared over ICN, realizing secure content distribution among dynamic sets of users. In contrast to similar IB-PRE based solutions, our design allows each user to generate the system parameters and the secret keys required by the underlay encryption scheme using their own Private Key Generator, therefore, our approach does not suffer from the key escrow problem. Moreover, our design further relaxes the trust requirements on the storage nodes by preventing them from sharing usable content with unauthorized users. Finally, our scheme does not require out-of-band secret key distribution.
Tensor-based approach to accelerate deformable part models
Venue: arXiv cs.CV 2017
Year: 2017

This article provides next step towards solving speed bottleneck of any system that intensively uses convolutions operations (e.g. CNN). Method described in the article is applied on deformable part models (DPM) algorithm. Method described here is based on multidimensional tensors and provides efficient tradeoff between DPM performance and accuracy. Experiments on various databases, including Pascal VOC, show that the proposed method allows decreasing a number of convolutions up to 4.5 times compared with DPM v.5, while maintaining similar accuracy. If insignificant accuracy degradation is allowable, higher computational gain can be achieved. The method consists of filters tensor decomposition and convolutions shortening using the decomposed filter. Mathematical overview of the proposed method as well as simulation results are provided.