OpenRaiser/Intern-Atlas huggingface dataset:
A Policy Model and Framework for Context-Aware Access Control to Information Resources
Venue: arXiv cs.CR 2017
Year: 2017

In today's dynamic ICT environments, the ability to control users' access to resources becomes ever important. On the one hand, it should adapt to the users' changing needs; on the other hand, it should not be compromised. Therefore, it is essential to have a flexible access control model, incorporating dynamically changing context information. Towards this end, this paper introduces a policy framework for context-aware access control (CAAC) applications that extends the role-based access control model with both dynamic associations of user-role and role-permission capabilities. We first present a formal model of CAAC policies for our framework. Using this model, we then introduce an ontology-based approach and a software prototype for modelling and enforcing CAAC policies. In addition, we evaluate our policy ontology model and framework by considering (i) the completeness of the ontology concepts, specifying different context-aware user-role and role-permission assignment policies from the healthcare scenarios; (ii) the correctness and consistency of the ontology semantics, assessing the core and domain-specific ontologies through the healthcare case study; and (iii) the performance of the framework by means of response time. The evaluation results demonstrate the feasibility of our framework and quantify the performance overhead of achieving context-aware access control to information resources.
Using Deep Learning Method for Classification: A Proposed Algorithm for the ISIC 2017 Skin Lesion Classification Challenge
Venue: arXiv cs.CV 2017
Year: 2017

Skin cancer, the most common human malignancy, is primarily diagnosed visually by physicians [1]. Classification with an automated method like CNN [2, 3] shows potential for challenging tasks [1]. By now, the deep convolutional neural networks are on par with human dermatologist [1]. This abstract is dedicated on developing a Deep Learning method for ISIC [5] 2017 Skin Lesion Detection Competition hosted at [6] to classify the dermatology pictures, which is aimed at improving the diagnostic accuracy rate and general level of the human health. The challenge falls into three sub-challenges, including Lesion Segmentation, Lesion Dermoscopic Feature Extraction and Lesion Classification. This project only participates in the Lesion Classification part. This algorithm is comprised of three steps: (1) original images preprocessing, (2) modelling the processed images using CNN [2, 3] in Caffe [4] framework, (3) predicting the test images and calculating the scores that represent the likelihood of corresponding classification. The models are built on the source images are using the Caffe [4] framework. The scores in prediction step are obtained by two different models from the source images.
Deterministic Temporal Logics and Interval Constraints
Venue: arXiv cs.LO 2017
Year: 2017

In temporal logics, a central question is about the choice of modalities and their relative expressive power, in comparison to the complexity of decision problems such as satisfiability. In this tutorial, we will illustrate the study of such questions over finite word models, first with logics for Unambiguous Starfree Regular Languages (UL), originally defined by Schutzenberger, and then for extensions with constraints, which appear in interval logics. We present Deterministic temporal logics, with diverse sets of modalities, which also characterize UL. The tools and techniques used go under the name of "Turtle Programs" or "Rankers". These are simple kinds of automata. We use properties such as Ranker Directionality and Ranker Convexity to show that all these logics have NP satisfiability. A recursive extension of some of these modalities gives us the full power of first-order logic over finite linear orders. We also discuss Interval Constraint modalities extending Deterministic temporal logics, with intermediate expressiveness. These allow counting or simple algebraic operations on paths. The complexity of these extended logics is PSpace, as of full temporal logic (and ExpSpace when using binary notation).
Finite Model Reasoning in Expressive Fragments of First-Order Logic
Venue: arXiv cs.LO 2017
Year: 2017

Over the past two decades several fragments of first-order logic have been identified and shown to have good computational and algorithmic properties, to a great extent as a result of appropriately describing the image of the standard translation of modal logic to first-order logic. This applies most notably to the guarded fragment, where quantifiers are appropriately relativized by atoms, and the fragment defined by restricting the number of variables to two. The aim of this talk is to review recent work concerning these fragments and their popular extensions. When presenting the material special attention is given to decision procedures for the finite satisfiability problems, as many of the fragments discussed contain infinity axioms. We highlight most effective techniques used in this context, their advantages and limitations. We also mention a few open directions of study.
Removal of Salt and Pepper noise from Gray-Scale and Color Images: An Adaptive Approach
Venue: arXiv cs.CV 2017
Year: 2017

An efficient adaptive algorithm for the removal of Salt and Pepper noise from gray scale and color image is presented in this paper. In this proposed method first a 3X3 window is taken and the central pixel of the window is considered as the processing pixel. If the processing pixel is found as uncorrupted, then it is left unchanged. And if the processing pixel is found corrupted one, then the window size is increased according to the conditions given in the proposed algorithm. Finally the processing pixel or the central pixel is replaced by either the mean, median or trimmed value of the elements in the current window depending upon different conditions of the algorithm. The proposed algorithm efficiently removes noise at all densities with better Peak Signal to Noise Ratio (PSNR) and Image Enhancement Factor (IEF). The proposed algorithm is compared with different existing algorithms like MF, AMF, MDBUTMF, MDBPTGMF and AWMF.
Shape DNA: Basic Generating Functions for Geometric Moment Invariants
Venue: arXiv cs.CV 2017
Year: 2017

Geometric moment invariants (GMIs) have been widely used as basic tool in shape analysis and information retrieval. Their structure and characteristics determine efficiency and effectiveness. Two fundamental building blocks or generating functions (GFs) for invariants are discovered, which are dot product and vector product of point vectors in Euclidean space. The primitive invariants (PIs) can be derived by carefully selecting different products of GFs and calculating the corresponding multiple integrals, which translates polynomials of coordinates of point vectors into geometric moments. Then the invariants themselves are expressed in the form of product of moments. This procedure is just like DNA encoding proteins. All GMIs available in the literature can be decomposed into linear combinations of PIs. This paper shows that Hu's seven well known GMIs in computer vision have a more deep structure, which can be further divided into combination of simpler PIs. In practical uses, low order independent GMIs are of particular interest. In this paper, a set of PIs for similarity transformation and affine transformation in 2D are presented, which are simpler to use, and some of which are newly reported. The discovery of the two generating functions provides a new perspective of better understanding shapes in 2D and 3D Euclidean spaces, and the method proposed can be further extended to higher dimensional spaces and different manifolds, such as curves, surfaces and so on.
An annotated bibliography on 1-planarity
Venue: arXiv cs.CG 2017
Year: 2017

The notion of 1-planarity is among the most natural and most studied generalizations of graph planarity. A graph is 1-planar if it has an embedding where each edge is crossed by at most another edge. The study of 1-planar graphs dates back to more than fifty years ago and, recently, it has driven increasing attention in the areas of graph theory, graph algorithms, graph drawing, and computational geometry. This annotated bibliography aims to provide a guiding reference to researchers who want to have an overview of the large body of literature about 1-planar graphs. It reviews the current literature covering various research streams about 1-planarity, such as characterization and recognition, combinatorial properties, and geometric representations. As an additional contribution, we offer a list of open problems on 1-planar graphs.
Convolutional Recurrent Neural Networks for Bird Audio Detection
Venue: arXiv cs.SD 2017
Year: 2017

Bird sounds possess distinctive spectral structure which may exhibit small shifts in spectrum depending on the bird species and environmental conditions. In this paper, we propose using convolutional recurrent neural networks on the task of automated bird audio detection in real-life environments. In the proposed method, convolutional layers extract high dimensional, local frequency shift invariant features, while recurrent layers capture longer term dependencies between the features extracted from short time frames. This method achieves 88.5% Area Under ROC Curve (AUC) score on the unseen evaluation data and obtains the second place in the Bird Audio Detection challenge.
Performance Analysis of Near-Optimal Energy Buffer Aided Wireless Powered Communication
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we consider a wireless powered communication system, where an energy harvesting (EH) node harvests energy from a radio frequency (RF) signal broadcasted by an access point (AP) in the downlink (DL). The node stores the harvested energy in an energy buffer and uses the stored energy to transmit data to the AP in the uplink (UL). We investigate two simple online transmission policies for the EH node, namely a best-effort policy and an on-off policy, which do not require knowledge of the EH profile nor channel knowledge. In particular, for both policies, the EH node transmits in each time slot with a constant desired power if sufficient energy is available in its energy buffer. Otherwise, the node transmits with the maximum possible power in the best-effort policy and remains silent in the on-off policy. For both policies, we use the theory of discrete-time continuous-state Markov chains to analyze the limiting distribution of the stored energy for finite- and infinite-size energy buffers. We provide this limiting distribution in closed form for a Nakagami-m fading DL channel and analyze the outage probability for a Nakagami-m fading UL channel. All derived analytical results are not limited to EH via RF WPT but are applicable for any independent and identically distributed EH process, originating from e.g. solar and wind energy. Our results reveal that, for low outage probabilities, the best-effort policy is superior to the on-off policy and the optimal constant UL transmit power of the EH node that minimizes the outage probability is always less than the average harvested power but increases with the capacity of the energy buffer. The opposite behaviour is observed for high outage probabilities. Furthermore, we show that the minimum outage probability of the two proposed policies closely approaches the outage probability of the optimal offline power allocation policy.
Deep Learning based Large Scale Visual Recommendation and Search for E-Commerce
Venue: arXiv cs.CV 2017
Year: 2017

In this paper, we present a unified end-to-end approach to build a large scale Visual Search and Recommendation system for e-commerce. Previous works have targeted these problems in isolation. We believe a more effective and elegant solution could be obtained by tackling them together. We propose a unified Deep Convolutional Neural Network architecture, called VisNet, to learn embeddings to capture the notion of visual similarity, across several semantic granularities. We demonstrate the superiority of our approach for the task of image retrieval, by comparing against the state-of-the-art on the Exact Street2Shop dataset. We then share the design decisions and trade-offs made while deploying the model to power Visual Recommendations across a catalog of 50M products, supporting 2K queries a second at Flipkart, India's largest e-commerce company. The deployment of our solution has yielded a significant business impact, as measured by the conversion-rate.
Vocabulary Alignment in Openly Specified Interactions
Venue: arXiv cs.MA 2017
Year: 2017

The problem of achieving common understanding between agents that use different vocabularies has been mainly addressed by designing techniques that explicitly negotiate mappings between their vocabularies, requiring agents to share a meta-language. In this paper we consider the case of agents that use different vocabularies and have no meta-language in common, but share the knowledge of how to perform a task, given by the specification of an interaction protocol. For this situation, we present a framework that lets agents learn a vocabulary alignment from the experience of interacting. Unlike previous work in this direction, we use open protocols that constrain possible actions instead of defining procedures, making our approach more general. We present two techniques that can be used either to learn an alignment from scratch or to repair an existent one, and we evaluate experimentally their performance.
A Simple Deterministic Distributed MST Algorithm, with Near-Optimal Time and Message Complexities
Venue: arXiv cs.DS 2017
Year: 2017

Distributed minimum spanning tree (MST) problem is one of the most central and fundamental problems in distributed graph algorithms. Garay et al. \cite{GKP98,KP98} devised an algorithm with running time $O(D + \sqrt{n} \cdot \log^* n)$, where $D$ is the hop-diameter of the input $n$-vertex $m$-edge graph, and with message complexity $O(m + n^{3/2})$. Peleg and Rubinovich \cite{PR99} showed that the running time of the algorithm of \cite{KP98} is essentially tight, and asked if one can achieve near-optimal running time **together with near-optimal message complexity**. In a recent breakthrough, Pandurangan et al. \cite{PRS16} answered this question in the affirmative, and devised a **randomized** algorithm with time $\tilde{O}(D+ \sqrt{n})$ and message complexity $\tilde{O}(m)$. They asked if such a simultaneous time- and message-optimality can be achieved by a **deterministic** algorithm. In this paper, building upon the work of \cite{PRS16}, we answer this question in the affirmative, and devise a **deterministic** algorithm that computes MST in time $O((D + \sqrt{n}) \cdot \log n)$, using $O(m \cdot \log n + n \log n \cdot \log^* n)$ messages. The polylogarithmic factors in the time and message complexities of our algorithm are significantly smaller than the respective factors in the result of \cite{PRS16}. Also, our algorithm and its analysis are very **simple** and self-contained, as opposed to rather complicated previous sublinear-time algorithms \cite{GKP98,KP98,E04b,PRS16}.
Object classification in images of Neoclassical furniture using Deep Learning
Venue: arXiv cs.CV 2017
Year: 2017

This short paper outlines research results on object classification in images of Neoclassical furniture. The motivation was to provide an object recognition framework which is able to support the alignment of furniture images with a symbolic level model. A data-driven bottom-up research routine in the Neoclassica research framework is the main use-case. It strives to deliver tools for analyzing the spread of aesthetic forms which are considered as a cultural transfer process.
GPU parallel simulation algorithm of Brownian particles with excluded volume using Delaunay triangulations
Venue: arXiv cs.DC 2017
Year: 2017

A novel parallel simulation algorithm on the GPU, implemented in CUDA and C++, is presented for the simulation of Brownian particles that display excluded volume repulsion and interact with long and short range forces. When an explicit Euler-Maruyama integration step is performed to take into account the pairwise forces and Brownian motion, particle overlaps can appear. The excluded volume property brings up the need for correcting these overlaps as they happen, since predicting them is not feasible due to the random displacement of Brownian particles. The proposed solution handles, at each time step, a Delaunay triangulation of the particle positions because it allows us to efficiently solve overlaps between particles by checking just their neighborhood. The algorithm starts by generating a Delaunay triangulation of the particle initial positions on CPU, but after that the triangulation is always kept on GPU memory. We used a parallel edge-flip implementation to keep the triangulation updated during each time step, checking previously that the triangulation was not rendered invalid due to the particle displacements. The algorithm is validated with two models of active colloidal particles. Upon testing the parallel implementation of a long range forces simulation, the results show a performance improvement of up to two orders of magnitude when compared to the previously existing sequential solution.
Introducing big data topics: a multicourse experience report from Norway
Venue: arXiv cs.CY 2017
Year: 2017

In the last few years, we have witnessed an explosion of interest in Big Data in both academic and industry arenas. Big Data is about the capture, storage, analysis and visualization of huge volumes of data in both structured and unstructured forms generated from a myriad of applications and devices in a wide set of scenarios. Focusing on the need of academia to develop professional posing the competencies that industry demands, the paper presents the approach adopted by the Faculty of Computer Sciences at Østfold University College, Norway to deploy Big Data related contents throughout its studies in the computing field. This paper describes initiatives in bachelor and master programs along with continuous education courses with regards to Big Data topics. New master courses were implemented in the 2014-2015 academic year, while bachelor and continuous education courses will be deployed the year after. Initial results in terms of course assessments and students' acceptation unveil promising perspectives for the initiative.
Energy-Aware Disk Storage Management: Online Approach with Application in DBMS
Venue: arXiv cs.DB 2017
Year: 2017

Energy consumption has become a first-class optimization goal in design and implementation of data-intensive computing systems. This is particularly true in the design of database management systems (DBMS), which was found to be the major consumer of energy in the software stack of modern data centers. Among all database components, the storage system is one of the most power-hungry elements. In previous work, dynamic power management (DPM) techniques that make real-time decisions to transition the disks to low-power modes are normally used to save energy in storage systems. In this paper, we tackle the limitations of DPM proposals in previous contributions. We introduced a DPM optimization model integrated with model predictive control (MPC) strategy to minimize power consumption of the disk-based storage system while satisfying given performance requirements. It dynamically determines the state of disks and plans for inter-disk data fragment migration to achieve desirable balance between power consumption and query response time. Via analyzing our optimization model to identify structural properties of optimal solutions, we propose a fast-solution heuristic DPM algorithm that can be integrated in large-scale disk storage systems for efficient state configuration and data migration. We evaluate our proposed ideas by running simulations using extensive set of synthetic workloads based on popular TPC benchmarks. Our results show that our solution significantly outperforms the best existing algorithm in both energy savings and response time.
Linguistic Knowledge as Memory for Recurrent Neural Networks
Venue: arXiv cs.CL 2017
Year: 2017

Training recurrent neural networks to model long term dependencies is difficult. Hence, we propose to use external linguistic knowledge as an explicit signal to inform the model which memories it should utilize. Specifically, external knowledge is used to augment a sequence with typed edges between arbitrarily distant elements, and the resulting graph is decomposed into directed acyclic subgraphs. We introduce a model that encodes such graphs as explicit memory in recurrent neural networks, and use it to model coreference relations in text. We apply our model to several text comprehension tasks and achieve new state-of-the-art results on all considered benchmarks, including CNN, bAbi, and LAMBADA. On the bAbi QA tasks, our model solves 15 out of the 20 tasks with only 1000 training examples per task. Analysis of the learned representations further demonstrates the ability of our model to encode fine-grained entity information across a document.
Structural Data Recognition with Graph Model Boosting
Venue: arXiv cs.LG 2017
Year: 2017

This paper presents a novel method for structural data recognition using a large number of graph models. In general, prevalent methods for structural data recognition have two shortcomings: 1) Only a single model is used to capture structural variation. 2) Naive recognition methods are used, such as the nearest neighbor method. In this paper, we propose strengthening the recognition performance of these models as well as their ability to capture structural variation. The proposed method constructs a large number of graph models and trains decision trees using the models. This paper makes two main contributions. The first is a novel graph model that can quickly perform calculations, which allows us to construct several models in a feasible amount of time. The second contribution is a novel approach to structural data recognition: graph model boosting. Comprehensive structural variations can be captured with a large number of graph models constructed in a boosting framework, and a sophisticated classifier can be formed by aggregating the decision trees. Consequently, we can carry out structural data recognition with powerful recognition capability in the face of comprehensive structural variation. The experiments shows that the proposed method achieves impressive results and outperforms existing methods on datasets of IAM graph database repository.