OpenRaiser/Intern-Atlas huggingface dataset:
Linear codes over Fq which are equivalent to LCD codes
Venue: arXiv cs.IT 2017
Year: 2017

Linear codes with complementary duals (abbreviated LCD) are linear codes whose intersection with their dual are trivial. When they are binary, they play an important role in armoring implementations against side-channel attacks and fault injection attacks. Non-binary LCD codes in characteristic 2 can be transformed into binary LCD codes by expansion. In this paper, we introduce a general construction of LCD codes from any linear codes. Further, we show that any linear code over $\mathbb F_{q} (q>3)$ is equivalent to an Euclidean LCD code and any linear code over $\mathbb F_{q^2} (q>2)$ is equivalent to a Hermitian LCD code. Consequently an $[n,k,d]$-linear Euclidean LCD code over $\mathbb F_q$ with $q>3$ exists if there is an $[n,k,d]$-linear code over $\mathbb F_q$ and an $[n,k,d]$-linear Hermitian LCD code over $\mathbb F_{q^2}$ with $q>2$ exists if there is an $[n,k,d]$-linear code over $\mathbb F_{q^2}$. Hence, when $q>3$ (resp.$q>2$) $q$-ary Euclidean (resp. $q^2$-ary Hermitian) LCD codes possess the same asymptotical bound as $q$-ary linear codes (resp. $q^2$-ary linear codes). Finally, we present an approach of constructing LCD codes by extending linear codes.
A Localisation-Segmentation Approach for Multi-label Annotation of Lumbar Vertebrae using Deep Nets
Venue: arXiv cs.CV 2017
Year: 2017

Multi-class segmentation of vertebrae is a non-trivial task mainly due to the high correlation in the appearance of adjacent vertebrae. Hence, such a task calls for the consideration of both global and local context. Based on this motivation, we propose a two-staged approach that, given a computed tomography dataset of the spine, segments the five lumbar vertebrae and simultaneously labels them. The first stage employs a multi-layered perceptron performing non-linear regression for locating the lumbar region using the global context. The second stage, comprised of a fully-convolutional deep network, exploits the local context in the localised lumbar region to segment and label the lumbar vertebrae in one go. Aided with practical data augmentation for training, our approach is highly generalisable, capable of successfully segmenting both healthy and abnormal vertebrae (fractured and scoliotic spines). We consistently achieve an average Dice coefficient of over 90 percent on a publicly available dataset of the xVertSeg segmentation challenge of MICCAI 2016. This is particularly noteworthy because the xVertSeg dataset is beset with severe deformities in the form of vertebral fractures and scoliosis.
Deep Learning for Skin Lesion Classification
Venue: arXiv cs.CV 2017
Year: 2017

Melanoma, a malignant form of skin cancer is very threatening to life. Diagnosis of melanoma at an earlier stage is highly needed as it has a very high cure rate. Benign and malignant forms of skin cancer can be detected by analyzing the lesions present on the surface of the skin using dermoscopic images. In this work, an automated skin lesion detection system has been developed which learns the representation of the image using Google's pretrained CNN model known as Inception-v3 \cite{cnn}. After obtaining the representation vector for our input dermoscopic images we have trained two layer feed forward neural network to classify the images as malignant or benign. The system also classifies the images based on the cause of the cancer either due to melanocytic or non-melanocytic cells using a different neural network. These classification tasks are part of the challenge organized by International Skin Imaging Collaboration (ISIC) 2017. Our system learns to classify the images based on the model built using the training images given in the challenge and the experimental results were evaluated using validation and test sets. Our system has achieved an overall accuracy of 65.8\% for the validation set.
Cost-Based Intuitionist Probabilities on Spaces of Graphs, Hypergraphs and Theorems
Venue: arXiv cs.AI 2017
Year: 2017

A novel partial order is defined on the space of digraphs or hypergraphs, based on assessing the cost of producing a graph via a sequence of elementary transformations. Leveraging work by Knuth and Skilling on the foundations of inference, and the structure of Heyting algebras on graph space, this partial order is used to construct an intuitionistic probability measure that applies to either digraphs or hypergraphs. As logical inference steps can be represented as transformations on hypergraphs representing logical statements, this also yields an intuitionistic probability measure on spaces of theorems. The central result is also extended to yield intuitionistic probabilities based on more general weighted rule systems defined over bicartesian closed categories.
Opening Scholarly Communication in Social Sciences by Connecting Collaborative Authoring to Peer Review
Venue: arXiv cs.DL 2017
Year: 2017

The objective of the OSCOSS research project on "Opening Scholarly Communication in the Social Sciences" is to build a coherent collaboration environment that facilitates scholarly communication workflows of social scientists in the roles of authors, reviewers, editors and readers. This paper presents the implementation of the core of this environment: the integration of the Fidus Writer academic word processor with the Open Journal Systems (OJS) submission and review management system.
Bicriteria Rectilinear Shortest Paths among Rectilinear Obstacles in the Plane
Venue: arXiv cs.CG 2017
Year: 2017

Given a rectilinear domain $\mathcal{P}$ of $h$ pairwise-disjoint rectilinear obstacles with a total of $n$ vertices in the plane, we study the problem of computing bicriteria rectilinear shortest paths between two points $s$ and $t$ in $\mathcal{P}$. Three types of bicriteria rectilinear paths are considered: minimum-link shortest paths, shortest minimum-link paths, and minimum-cost paths where the cost of a path is a non-decreasing function of both the number of edges and the length of the path. The one-point and two-point path queries are also considered. Algorithms for these problems have been given previously. Our contributions are threefold. First, we find a critical error in all previous algorithms. Second, we correct the error in a not-so-trivial way. Third, we further improve the algorithms so that they are even faster than the previous (incorrect) algorithms when $h$ is relatively small. For example, for the minimum-link shortest paths, we obtain the following results. Our algorithm computes a minimum-link shortest $s$-$t$ path in $O(n+h\log^{3/2} h)$ time. For the one-point queries, we build a data structure of size $O(n+ h\log h)$ in $O(n+h\log^{3/2} h)$ time for a source point $s$, such that given any query point $t$, a minimum-link shortest $s$-$t$ path can be determined in $O(\log n)$ time. For the two-point queries, with $O(n+h^2\log^2 h)$ time and space preprocessing, a minimum-link shortest $s$-$t$ path can be determined in $O(\log n+\log^2 h)$ time for any two query points $s$ and $t$; alternatively, with $O(n+h^2\cdot \log^{2} h \cdot 4^{\sqrt{\log h}})$ time and $O(n+h^2\cdot \log h \cdot 4^{\sqrt{\log h}})$ space preprocessing, we can answer each two-point query in $O(\log n)$ time.
Evaluation of 50 Greek Science and Engineering University Departments using Google Scholar
Venue: arXiv cs.DL 2017
Year: 2017

In this paper, the scientometric evaluation of faculty members of 50 Greek Science and Engineering University Departments is presented. 1978 academics were examined in total. The number of papers, citations, h-index and i10-index have been collected for each academic, department, school and university using Google Scholar and the citations analysis program Publish or Perish. Analysis of the collected data showed that departments of the same academic discipline are characterized by significant differences on the scientific outcome. In addition, in the majority of the evaluated departments a significant difference in h-index between academics who report scientific activity on the departments website and those who do not, was observed. Moreover, academics who earned their PhD title in the USA demonstrate higher indices in comparison to scholars who obtained their PhD title in Europe or in Greece. Finally, the correlation between the academic rank and the scholars h-index (or the number of their citations) is quite low in some departments, which, under specific circumstances, could be an indication of the lack of meritocracy.
Social Fingerprinting: detection of spambot groups through DNA-inspired behavioral modeling
Venue: arXiv cs.SI 2017
Year: 2017

Spambot detection in online social networks is a long-lasting challenge involving the study and design of detection techniques capable of efficiently identifying ever-evolving spammers. Recently, a new wave of social spambots has emerged, with advanced human-like characteristics that allow them to go undetected even by current state-of-the-art algorithms. In this paper, we show that efficient spambots detection can be achieved via an in-depth analysis of their collective behaviors exploiting the digital DNA technique for modeling the behaviors of social network users. Inspired by its biological counterpart, in the digital DNA representation the behavioral lifetime of a digital account is encoded in a sequence of characters. Then, we define a similarity measure for such digital DNA sequences. We build upon digital DNA and the similarity between groups of users to characterize both genuine accounts and spambots. Leveraging such characterization, we design the Social Fingerprinting technique, which is able to discriminate among spambots and genuine accounts in both a supervised and an unsupervised fashion. We finally evaluate the effectiveness of Social Fingerprinting and we compare it with three state-of-the-art detection algorithms. Among the peculiarities of our approach is the possibility to apply off-the-shelf DNA analysis techniques to study online users behaviors and to efficiently rely on a limited number of lightweight account characteristics.
EmbedInsight: Automated Grading of Embedded Systems Assignments
Venue: arXiv cs.CY 2017
Year: 2017

Grading in embedded systems courses typically requires a face-to-face appointment between the student and the instructor because of experimental setups that are only available in laboratory facilities. Such a manual grading process is an impediment to both students and instructors. Students have to wait for several days to get feedback, and instructors may spend valuable time evaluating trivial aspects of the assignment. As seen with software courses, an automated grading system can significantly improve the insights available to the instructor and encourage students to learn quickly with iterative testing. We have designed and implemented EmbedInsight, an automated grading system for embedded system courses that accommodates a wide variety of experimental setups and is scalable to MOOC-style courses. EmbedInsight employs a modular web services design that separates the user interface and the experimental setup that evaluates student assignments. We deployed and evaluated EmbedInsight for our university embedded systems course. We show that our system scales well to a large number of submissions, and students are satisfied with their overall experience.
MWTP: Monterrey Weather, Traffic and Pollution Database for Geospatial Analysis
Venue: arXiv cs.CY 2017
Year: 2017

The MWTP: Monterrey Weather, Traffic and Pollution Database contains a collection of historic weather conditions, and recent traffic and air pollution data of the metropolitan area of Monterrey, Mexico. Collected data includes: temperature, humidity, raining conditions, wind speed, travel distance, travel time, PM2.5, PM10, O3, among many others. As of February 2017, the MWTP database contained more than 4 million records of weather data, more than 700 thousand records of traffic data and around 57 thousand of air pollution measurements. Here, it is described how the data is been collected and structured into a databased, so it can be used for statistical and geospatial analysis of the Monterrey area.
Citation histories of papers: sometimes the rich get richer, sometimes they don't
Venue: arXiv cs.DL 2017
Year: 2017

We describe a simple model of how a publication's citations change over time, based on pure-birth stochastic processes with a linear cumulative advantage effect. The model is applied to citation data from the Physical Review corpus provided by APS. Our model reveals that papers fall into three different clusters: papers that have rapid initial citations and ultimately high impact (fast-hi), fast to rise but quick to plateau (fast-flat), or late bloomers (slow-late), which may either never achieve many citations, or do so many years after publication. In "fast-hi" and "slow-late", there is a rich-get-richer effect: papers that have many citations accumulate additional citations more rapidly while the "fast-flat" papers do not display this effect. We conclude by showing that only a few years of post-publication statistics are needed to identify high impact ("fast-hi") papers.
Approximation Schemes for Independent Set and Sparse Subsets of Polygons
Venue: arXiv cs.CG 2017
Year: 2017

We present an $(1+\varepsilon)$-approximation algorithm with quasi-polynomial running time for computing the maximum weight independent set of polygons out of a given set of polygons in the plane (specifically, the running time is $n^{O( \mathrm{poly}( \log n, 1/\varepsilon))}$). Contrasting this, the best known polynomial time algorithm for the problem has an approximation ratio of~$n^{\varepsilon}$. Surprisingly, we can extend the algorithm to the problem of computing the maximum weight subset of the given set of polygons whose intersection graph fulfills some sparsity condition. For example, we show that one can approximate the maximum weight subset of polygons, such that the intersection graph of the subset is planar or does not contain a cycle of length $4$ (i.e., $K_{2,2}$). Our algorithm relies on a recursive partitioning scheme, whose backbone is the existence of balanced cuts with small complexity that intersect polygons from the optimal solution of a small total weight. For the case of large axis-parallel rectangles, we provide a polynomial time $(1+\varepsilon)$-approximation for the maximum weight independent set. Specifically, we consider the problem where each rectangle has one edge whose length is at least a constant fraction of the length of the corresponding edge of the bounding box of all the input elements. This is now the most general case for which a PTAS is known, and it requires a new and involved partitioning scheme, which should be of independent interest.
In Search of a Dataset for Handwritten Optical Music Recognition: Introducing MUSCIMA++
Venue: arXiv cs.CV 2017
Year: 2017

Optical Music Recognition (OMR) has long been without an adequate dataset and ground truth for evaluating OMR systems, which has been a major problem for establishing a state of the art in the field. Furthermore, machine learning methods require training data. We analyze how the OMR processing pipeline can be expressed in terms of gradually more complex ground truth, and based on this analysis, we design the MUSCIMA++ dataset of handwritten music notation that addresses musical symbol recognition and notation reconstruction. The MUSCIMA++ dataset version 0.9 consists of 140 pages of handwritten music, with 91255 manually annotated notation symbols and 82261 explicitly marked relationships between symbol pairs. The dataset allows training and evaluating models for symbol classification, symbol localization, and notation graph assembly, both in isolation and jointly. Open-source tools are provided for manipulating the dataset, visualizing the data and further annotation, and the dataset itself is made available under an open license.
A Quasi-Linear Time Algorithm Deciding Whether Weak Büchi Automata Reading Vectors of Reals Recognize Saturated Languages
Venue: arXiv cs.FL 2017
Year: 2017

This work considers weak deterministic Büchi automata reading encodings of non-negative $d$-vectors of reals in a fixed base. A saturated language is a language which contains all encoding of elements belonging to a set of $d$-vectors of reals. A Real Vector Automaton is an automaton which recognizes a saturated language. It is explained how to decide in quasi-linear time whether a minimal weak deterministic Büchi automaton is a Real Vector Automaton. The problem is solved both for the two standard encodings of vectors of numbers: the sequential encoding and the parallel encoding. This algorithm runs in linear time for minimal weak Büchi automata accepting set of reals. Finally, the same problem is also solved for parallel encoding of automata reading vectors of relative reals.
Revisiting Frequency Reuse towards Supporting Ultra-Reliable Ubiquitous-Rate Communication
Venue: arXiv cs.IT 2017
Year: 2017

One of the goals of 5G wireless systems stated by the NGMN alliance is to provide moderate rates (50+ Mbps) everywhere and with very high reliability. We term this service Ultra-Reliable Ubiquitous-Rate Communication (UR2C). This paper investigates the role of frequency reuse in supporting UR2C in the downlink. To this end, two frequency reuse schemes are considered: user-specific frequency reuse (FRu) and BS-specific frequency reuse (FRb). For a given unit frequency channel, FRu reduces the number of serving user equipments (UEs), whereas FRb directly decreases the number of interfering base stations (BSs). This increases the distance from the interfering BSs and the signal-to-interference ratio (SIR) attains ultra-reliability, e.g. 99% SIR coverage at a randomly picked UE. The ultra-reliability is, however, achieved at the cost of the reduced frequency allocation, which may degrade overall downlink rate. To fairly capture this reliability-rate tradeoff, we propose ubiquitous rate defined as the maximum downlink rate whose required SIR can be achieved with ultra-reliability. By using stochastic geometry, we derive closed-form ubiquitous rate as well as the optimal frequency reuse rules for UR2C.
Skin lesion segmentation based on preprocessing, thresholding and neural networks
Venue: arXiv cs.CV 2017
Year: 2017

This abstract describes the segmentation system used to participate in the challenge ISIC 2017: Skin Lesion Analysis Towards Melanoma Detection. Several preprocessing techniques have been tested for three color representations (RGB, YCbCr and HSV) of 392 images. Results have been used to choose the better preprocessing for each channel. In each case a neural network is trained to predict the Jaccard Index based on object characteristics. The system includes black frames and reference circle detection algorithms but no special treatment is done for hair removal. Segmentation is performed in two steps first the best channel to be segmented is chosen by selecting the best neural network output. If this output does not predict a Jaccard Index over 0.5 a more aggressive preprocessing is performed using open and close morphological operations and the segmentation of the channel that obtains the best output from the neural networks is selected as the lesion.
Exploring the Combination Rules of D Numbers From a Perspective of Conflict Redistribution
Venue: arXiv cs.AI 2017
Year: 2017

Dempster-Shafer theory of evidence is widely applied to uncertainty modelling and knowledge reasoning because of its advantages in dealing with uncertain information. But some conditions or requirements, such as exclusiveness hypothesis and completeness constraint, limit the development and application of that theory to a large extend. To overcome the shortcomings and enhance its capability of representing the uncertainty, a novel model, called D numbers, has been proposed recently. However, many key issues, for example how to implement the combination of D numbers, remain unsolved. In the paper, we have explored the combination of D Numbers from a perspective of conflict redistribution, and proposed two combination rules being suitable for different situations for the fusion of two D numbers. The proposed combination rules can reduce to the classical Dempster's rule in Dempster-Shafer theory under a certain conditions. Numerical examples and discussion about the proposed rules are also given in the paper.
Riemannian stochastic quasi-Newton algorithm with variance reduction and its convergence analysis
Venue: arXiv cs.LG 2017
Year: 2017

Stochastic variance reduction algorithms have recently become popular for minimizing the average of a large, but finite number of loss functions. The present paper proposes a Riemannian stochastic quasi-Newton algorithm with variance reduction (R-SQN-VR). The key challenges of averaging, adding, and subtracting multiple gradients are addressed with notions of retraction and vector transport. We present convergence analyses of R-SQN-VR on both non-convex and retraction-convex functions under retraction and vector transport operators. The proposed algorithm is evaluated on the Karcher mean computation on the symmetric positive-definite manifold and the low-rank matrix completion on the Grassmann manifold. In all cases, the proposed algorithm outperforms the state-of-the-art Riemannian batch and stochastic gradient algorithms.
Ensemble of Neural Classifiers for Scoring Knowledge Base Triples
Venue: arXiv cs.CL 2017
Year: 2017

This paper describes our approach for the triple scoring task at the WSDM Cup 2017. The task required participants to assign a relevance score for each pair of entities and their types in a knowledge base in order to enhance the ranking results in entity retrieval tasks. We propose an approach wherein the outputs of multiple neural network classifiers are combined using a supervised machine learning model. The experimental results showed that our proposed method achieved the best performance in one out of three measures (i.e., Kendall's tau), and performed competitively in the other two measures (i.e., accuracy and average score difference).
Greedy-Merge Degrading has Optimal Power-Law
Venue: arXiv cs.IT 2017
Year: 2017

Consider a channel with a given input alphabet size and a given input distribution. Our aim is to degrade or upgrade it to a channel with at most L output letters. The paper contains four main results. The first result, from which the paper title is derived, deals with the so called "greedy-merge" algorithm. We derive an upper bound on the reduction in mutual information between input and output. This upper bound is within a constant factor of an algorithm-independent lower bound. Thus, we establish that greedy-merge is optimal in the power-law sense. The other main results deal with upgrading. The second result shows that a certain sequence of channels that was previously shown to be "hard" for degrading, displays the same hardness in the context of upgrading. That is, suppose we are given such a channel and a corresponding input distribution. If we upgrade (degrade) to a new channel with L output letters, we incur an increase (decrease) in mutual information between input and output. We show that a previously derived bound on the decrease in mutual information for the degrading case is also a lower bound on the increase for the upgrading case. The third result is an efficient algorithm for optimal upgrading, in the binary-input case. That is, we are given a channel and an input distribution. We must find an upgraded channel with L output letters, for which the increase in mutual information is minimal. We give a simple characterization of such a channel, which implies an efficient algorithm. The fourth result is an analog of the first result for the upgrading case, when the input is binary. That is, we first present a sub-optimal algorithm for the setting considered in the third result. By analyzing the algorithm, we show that the increase incurred in mutual information is within a constant factor of the lower bound derived in the second result.