OpenRaiser/Intern-Atlas huggingface dataset:
Modular Semantics and Characteristics for Bipolar Weighted Argumentation Graphs
Venue: arXiv cs.AI 2018
Year: 2018

This paper addresses the semantics of weighted argumentation graphs that are bipolar, i.e. contain both attacks and supports for arguments. It builds on previous work by Amgoud, Ben-Naim et. al. We study the various characteristics of acceptability semantics that have been introduced in these works, and introduce the notion of a modular acceptability semantics. A semantics is modular if it cleanly separates aggregation of attacking and supporting arguments (for a given argument $a$) from the computation of their influence on $a$'s initial weight. We show that the various semantics for bipolar argumentation graphs from the literature may be analysed as a composition of an aggregation function with an influence function. Based on this modular framework, we prove general convergence and divergence theorems. We demonstrate that all well-behaved modular acceptability semantics converge for all acyclic graphs and that no sum-based semantics can converge for all graphs. In particular, we show divergence of Euler-based semantics (Amgoud et al.) for certain cyclic graphs. Further, we provide the first semantics for bipolar weighted graphs that converges for all graphs.
Supermodular Locality Sensitive Hashes
Venue: arXiv cs.DM 2018
Year: 2018

In this work, we show deep connections between Locality Sensitive Hashability and submodular analysis. We show that the LSHablility of the most commonly analyzed set similarities is in one-to-one correspondance with the supermodularity of these similarities when taken with respect to the symmetric difference of their arguments. We find that the supermodularity of equivalent LSHable similarities can be dependent on the set encoding. While monotonicity and supermodularity does not imply the metric condition necessary for supermodularity, this condition is guaranteed for the more restricted class of supermodular Hamming similarities that we introduce. We show moreover that LSH preserving transformations are also supermodular-preserving, yielding a way to generate families of similarities both LSHable and supermodular. Finally, we show that even the more restricted family of cardinality-based supermodular Hamming similarities presents promising aspects for the study of the link between LSHability and supermodularity. We hope that the several bridges that we introduce between LSHability and supermodularity paves the way to a better understanding both of supermodular analysis and LSHability, notably in the context of large-scale supermodular optimization.
Integrating Algorithmic Planning and Deep Learning for Partially Observable Navigation
Venue: arXiv cs.RO 2018
Year: 2018

We propose to take a novel approach to robot system design where each building block of a larger system is represented as a differentiable program, i.e. a deep neural network. This representation allows for integrating algorithmic planning and deep learning in a principled manner, and thus combine the benefits of model-free and model-based methods. We apply the proposed approach to a challenging partially observable robot navigation task. The robot must navigate to a goal in a previously unseen 3-D environment without knowing its initial location, and instead relying on a 2-D floor map and visual observations from an onboard camera. We introduce the Navigation Networks (NavNets) that encode state estimation, planning and acting in a single, end-to-end trainable recurrent neural network. In preliminary simulation experiments we successfully trained navigation networks to solve the challenging partially observable navigation task.
Psychological constraints on string-based methods for pattern discovery in polyphonic corpora
Venue: arXiv cs.SD 2018
Year: 2018

Researchers often divide symbolic music corpora into contiguous sequences of n events (called n-grams) for the purposes of pattern discovery, key finding, classification, and prediction. What is more, several studies have reported improved task performance when using psychologically motivated weighting functions, which adjust the count to privilege n-grams featuring more salient or memorable events (e.g., Krumhansl, 1990). However, these functions have yet to appear in harmonic pattern discovery algorithms, which attempt to discover the most recurrent chord progressions in complex polyphonic corpora. This study examines whether psychologically-motivated weighting functions can improve harmonic pattern discovery algorithms. Models using various n-gram selection methods, weighting functions, and ranking algorithms attempt to discover the most conventional closing harmonic progression in the common-practice period, ii6-"I64"-V7-I, with the progression's mean reciprocal rank serving as an evaluation metric for model comparison.
Security Mental Model: Cognitive map approach
Venue: arXiv cs.HC 2018
Year: 2018

Security models have been designed to ensure data is accessed and used in proper manner according to the security policies. Unfortunately, human role in designing security models has been ignored. Human behavior relates to many security breaches and plays a significant part in many security situations.In this paper, we study users' security decision making toward security and usability through the mental model approach. To elicit and depict users' security and usability mental models, crowd sourcing techniques and cognitive map method are applied and we have performed an experiment to evaluate our findings using Amazon MTurk.
The MOEADr Package - A Component-Based Framework for Multiobjective Evolutionary Algorithms Based on Decomposition
Venue: arXiv cs.NE 2018
Year: 2018

Multiobjective Evolutionary Algorithms based on Decomposition (MOEA/D) represent a widely used class of population-based metaheuristics for the solution of multicriteria optimization problems. We introduce the MOEADr package, which offers many of these variants as instantiations of a component-oriented framework. This approach contributes for easier reproducibility of existing MOEA/D variants from the literature, as well as for faster development and testing of new composite algorithms. The package offers an standardized, modular implementation of MOEA/D based on this framework, which was designed aiming at providing researchers and practitioners with a standard way to discuss and express MOEA/D variants. In this paper we introduce the design principles behind the MOEADr package, as well as its current components. Three case studies are provided to illustrate the main aspects of the package.
Deep Content-User Embedding Model for Music Recommendation
Venue: arXiv cs.IR 2018
Year: 2018

Recently deep learning based recommendation systems have been actively explored to solve the cold-start problem using a hybrid approach. However, the majority of previous studies proposed a hybrid model where collaborative filtering and content-based filtering modules are independently trained. The end-to-end approach that takes different modality data as input and jointly trains the model can provide better optimization but it has not been fully explored yet. In this work, we propose deep content-user embedding model, a simple and intuitive architecture that combines the user-item interaction and music audio content. We evaluate the model on music recommendation and music auto-tagging tasks. The results show that the proposed model significantly outperforms the previous work. We also discuss various directions to improve the proposed model further.
Learning Interpretable Anatomical Features Through Deep Generative Models: Application to Cardiac Remodeling
Venue: arXiv cs.CV 2018
Year: 2018

Alterations in the geometry and function of the heart define well-established causes of cardiovascular disease. However, current approaches to the diagnosis of cardiovascular diseases often rely on subjective human assessment as well as manual analysis of medical images. Both factors limit the sensitivity in quantifying complex structural and functional phenotypes. Deep learning approaches have recently achieved success for tasks such as classification or segmentation of medical images, but lack interpretability in the feature extraction and decision processes, limiting their value in clinical diagnosis. In this work, we propose a 3D convolutional generative model for automatic classification of images from patients with cardiac diseases associated with structural remodeling. The model leverages interpretable task-specific anatomic patterns learned from 3D segmentations. It further allows to visualise and quantify the learned pathology-specific remodeling patterns in the original input space of the images. This approach yields high accuracy in the categorization of healthy and hypertrophic cardiomyopathy subjects when tested on unseen MR images from our own multi-centre dataset (100%) as well on the ACDC MICCAI 2017 dataset (90%). We believe that the proposed deep learning approach is a promising step towards the development of interpretable classifiers for the medical imaging domain, which may help clinicians to improve diagnostic accuracy and enhance patient risk-stratification.
Melanoma Recognition with an Ensemble of Techniques for Segmentation and a Structural Analysis for Classification
Venue: arXiv cs.CV 2018
Year: 2018

An approach to lesion recognition is described that for lesion localization uses an ensemble of segmentation techniques and for lesion classification an exhaustive structural analysis. For localization, candidate regions are obtained from global thresholding of the chromatic maps and from applying the K-Means algorithm to the RGB image; the candidate regions are then integrated. For classification, a relatively exhaustive structural analysis of contours and regions is carried out.
Evaluation of CT Scan Usability for Saudi Arabian Users
Venue: arXiv cs.HC 2018
Year: 2018

- Like consumer electronic products, medical devices are becoming more complicated, with performance doubling every two years. With multiple commands and systems to negotiate, cognitive load can make it difficult for users to execute commands effectively. In the case of medical devices, which use advanced technology and require multidisciplinary inputs for design and development, cognitive workload is a significant factor. As these devices are very expensive and operators require specialized training, effective and economical methods are needed to evaluate the user experience. Heuristic evaluation is an effective method of identifying major usability problems and related issues. This study used heuristic evaluation to assess the usability of a CT scan and associated physical and mental loads for Saudi Arabian users. The findings indicate a gender difference in terms of consistency, flexibility, and document attributes, with a statistically significant gender difference in mental load.
Time-Bounded Influence Diffusion with Incentives
Venue: arXiv cs.DS 2018
Year: 2018

A widely studied model of influence diffusion in social networks represents the network as a graph $G=(V,E)$ with an influence threshold $t(v)$ for each node. Initially the members of an initial set $S\subseteq V$ are influenced. During each subsequent round, the set of influenced nodes is augmented by including every node $v$ that has at least $t(v)$ previously influenced neighbours. The general problem is to find a small initial set that influences the whole network. In this paper we extend this model by using \emph{incentives} to reduce the thresholds of some nodes. The goal is to minimize the total of the incentives required to ensure that the process completes within a given number of rounds. The problem is hard to approximate in general networks. We present polynomial-time algorithms for paths, trees, and complete networks.
Development of SageMath filter for Moodle
Venue: arXiv cs.HC 2018
Year: 2018

Research goals: determine the characteristics of the development process, installation, configuration and usage of the filter SageMath for learning support system Moodle. Research objectives: to prove the feasibility of using Moodle system as a tool to support the process of competency formation in technical objects simulation of future bachelors in electromechanical engineering; to analyze existing support tools of technical objects simulation and to identify the ways of it's integration into Moodle; to describe the structure and features of the software implementation of the new SageMath filter for Moodle; to provide the guidance on installing and configuring developed filter; to describe the examples of filter usage. Research subject: text filter development process for learning support system Moodle to processing the commands of computer mathematics system SageMath. Research results. Designed SageMath filter allows to execute the Sage code on the external SageMathCell public server, to view the execution results at the Moodle pages without reloading by using AJAX technology, to stave off XSS attacks and ready for use with Moodle. The main conclusions and recommendations: 1. The perspective direction of learning environment development for bachelors in electromechanical engineering is the integration of learning support system Moodle and computer mathematics system SageMath. 2. An effective tool for embedded a computer mathematics systems SageMath models into Moodle is a text filter. The software engineering process for this filter is presented in the article. 3. Promising area of future research is the use of a developed filter in the process of bachelor's in electromechanical engineering competencies in technical objects simulation by embedding into Moodle learning courses the interactive labs programmed in Sage.
Method for motion artifact reduction using a convolutional neural network for dynamic contrast enhanced MRI of the liver
Venue: arXiv cs.CV 2018
Year: 2018

Purpose: To improve the quality of images obtained via dynamic contrast-enhanced MRI (DCE-MRI) that include motion artifacts and blurring using a deep learning approach. Methods: A multi-channel convolutional neural network (MARC) based method is proposed for reducing the motion artifacts and blurring caused by respiratory motion in images obtained via DCE-MRI of the liver. The training datasets for the neural network included images with and without respiration-induced motion artifacts or blurring, and the distortions were generated by simulating the phase error in k-space. Patient studies were conducted using a multi-phase T1-weighted spoiled gradient echo sequence for the liver containing breath-hold failures during data acquisition. The trained network was applied to the acquired images to analyze the filtering performance, and the intensities and contrast ratios before and after denoising were compared via Bland-Altman plots. Results: The proposed network was found to significantly reduce the magnitude of the artifacts and blurring induced by respiratory motion, and the contrast ratios of the images after processing via the network were consistent with those of the unprocessed images. Conclusion: A deep learning based method for removing motion artifacts in images obtained via DCE-MRI in the liver was demonstrated and validated.
Quantifying Biases in Online Information Exposure
Venue: arXiv cs.SI 2018
Year: 2018

Our consumption of online information is mediated by filtering, ranking, and recommendation algorithms that introduce unintentional biases as they attempt to deliver relevant and engaging content. It has been suggested that our reliance on online technologies such as search engines and social media may limit exposure to diverse points of view and make us vulnerable to manipulation by disinformation. In this paper, we mine a massive dataset of Web traffic to quantify two kinds of bias: (i) homogeneity bias, which is the tendency to consume content from a narrow set of information sources, and (ii) popularity bias, which is the selective exposure to content from top sites. Our analysis reveals different bias levels across several widely used Web platforms. Search exposes users to a diverse set of sources, while social media traffic tends to exhibit high popularity and homogeneity bias. When we focus our analysis on traffic to news sites, we find higher levels of popularity bias, with smaller differences across applications. Overall, our results quantify the extent to which our choices of online systems confine us inside "social bubbles."
HyLine: a Simple and Practical Flow Scheduling for Commodity Datacenters
Venue: arXiv cs.NI 2018
Year: 2018

Today's datacenter networks (DCNs) have been built upon multipath topologies where each path contains multiple links. However, flow scheduling schemes proposed to minimize flow completion times (FCT) in DCNs are based on algorithms which are optimum or close-to-optimum only over single link. Moreover, most of these scheduling schemes seek either fully centralized approaches having overhead of communicating to a central entity or fully distributed approaches requiring changes in the fabric. Motivated by these shortcomings, we present HyLine a simple scheduling design for commodity DCNs which is equipped with a joint load-balancing and flow scheduling (path-aware) design exploiting the multipath nature of DCNs. HyLine takes a hybrid approach and uses the global-awareness of centralized and agility of distributed techniques without requiring any changes in the fabric. To that end, it determines a threshold margin identifying flows for which using centralized approach is beneficial. We have shown through extensive ns2 simulations that despite HyLine's simplicity, it significantly outperforms existing schemes and achieves lower average and 99th percentile FCTs. For instance, compared to Qjump-state-of-the-art practical scheme-and pFabric-one of the best performing flow scheduling schemes-HyLine reduces average FCT up to 68% and 31%, respectively, under a production datacenter workload.
Minimum distance computation of linear codes via genetic algorithms with permutation encoding
Venue: arXiv cs.IT 2018
Year: 2018

We design a heuristic method, a genetic algorithm, for the computation of an upper bound of the minimum distance of a linear code over a finite field. By the use of the row reduced echelon form, we obtain a permutation encoding of the problem, so that its space of solutions does not depend on the size of the base field or the dimension of the code. Actually, the efficiency of our method only grows non-polynomially with respect to the length of the code.
Approximation Schemes for Low-Rank Binary Matrix Approximation Problems
Venue: arXiv cs.DS 2018
Year: 2018

We provide a randomized linear time approximation scheme for a generic problem about clustering of binary vectors subject to additional constrains. The new constrained clustering problem encompasses a number of problems and by solving it, we obtain the first linear time-approximation schemes for a number of well-studied fundamental problems concerning clustering of binary vectors and low-rank approximation of binary matrices. Among the problems solvable by our approach are \textsc{Low GF(2)-Rank Approximation}, \textsc{Low Boolean-Rank Approximation}, and various versions of \textsc{Binary Clustering}. For example, for \textsc{Low GF(2)-Rank Approximation} problem, where for an $m\times n$ binary matrix $A$ and integer $r>0$, we seek for a binary matrix $B$ of $GF_2$ rank at most $r$ such that $\ell_0$ norm of matrix $A-B$ is minimum, our algorithm, for any $ε>0$ in time $ f(r,ε)\cdot n\cdot m$, where $f$ is some computable function, outputs a $(1+ε)$-approximate solution with probability at least $(1-\frac{1}{e})$. Our approximation algorithms substantially improve the running times and approximation factors of previous works. We also give (deterministic) PTASes for these problems running in time $n^{f(r)\frac{1}{ε^2}\log \frac{1}ε}$, where $f$ is some function depending on the problem. Our algorithm for the constrained clustering problem is based on a novel sampling lemma, which is interesting in its own.
How Consumer Empathy Assist Power Grid in Demand Response
Venue: arXiv cs.GT 2018
Year: 2018

This paper investigates interaction among residential electricity users and utility company in a distribution network with the capability of two-way communication provided by smart grid. The energy consumption scheduling of electricity users is formulated as a game-theoretic problem within a group where all players are not totally selfish. Considering altruistic behavior of human decision-making, altruistic player action to other players actions can be influenced by recognizing the well being of others. The proposed model captures the empathy of electricity users in energy consumption scheduling and how this behavior affect peak demand and electricity prices. Numerical results demonstrate that both residential users and utility company can benefit through the channel of empathy.
Tomlinson-Harashima Precoded Rate-Splitting for Multiuser MIMO Systems
Venue: arXiv cs.IT 2018
Year: 2018

In this work, we investigate the performance of Rate-Splitting (RS) based on Tomlinson-Harashima Precoding (THP) in a multiple-antenna broadcast channel with perfect and imperfect Channel State Information at the Transmitter (CSIT). In particular, we consider RS using centralized and decentralized THP structures, where only one user splits its message into a common and private part, and develop expressions to describe the signal-to-interference-plus-noise (SINR) ratio and the sum rates associated with these schemes. Furthermore, we also assess the performance achieved by RS combined with Dirty-Paper Coding (DPC). Simulations show that RS with THP outperforms existing standard THP and RS with linear precoding schemes.
Deep Adaptive Proposal Network for Object Detection in Optical Remote Sensing Images
Venue: arXiv cs.CV 2018
Year: 2018

Object detection is a fundamental and challenging problem in aerial and satellite image analysis. More recently, a two-stage detector Faster R-CNN is proposed and demonstrated to be a promising tool for object detection in optical remote sensing images, while the sparse and dense characteristic of objects in remote sensing images is complexity. It is unreasonable to treat all images with the same region proposal strategy, and this treatment limits the performance of two-stage detectors. In this paper, we propose a novel and effective approach, named deep adaptive proposal network (DAPNet), address this complexity characteristic of object by learning a new category prior network (CPN) on the basis of the existing Faster R-CNN architecture. Moreover, the candidate regions produced by DAPNet model are different from the traditional region proposal network (RPN), DAPNet predicts the detail category of each candidate region. And these candidate regions combine the object number, which generated by the category prior network to achieve a suitable number of candidate boxes for each image. These candidate boxes can satisfy detection tasks in sparse and dense scenes. The performance of the proposed framework has been evaluated on the challenging NWPU VHR-10 data set. Experimental results demonstrate the superiority of the proposed framework to the state-of-the-art.