OpenRaiser/Intern-Atlas huggingface dataset:
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Venue: arXiv cs.DC 2016
Year: 2016

TensorFlow is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms. A computation expressed using TensorFlow can be executed with little or no change on a wide variety of heterogeneous systems, ranging from mobile devices such as phones and tablets up to large-scale distributed systems of hundreds of machines and thousands of computational devices such as GPU cards. The system is flexible and can be used to express a wide variety of algorithms, including training and inference algorithms for deep neural network models, and it has been used for conducting research and for deploying machine learning systems into production across more than a dozen areas of computer science and other fields, including speech recognition, computer vision, robotics, information retrieval, natural language processing, geographic information extraction, and computational drug discovery. This paper describes the TensorFlow interface and an implementation of that interface that we have built at Google. The TensorFlow API and a reference implementation were released as an open-source package under the Apache 2.0 license in November, 2015 and are available at www.tensorflow.org.
An approach towards debiasing user ratings
Venue: arXiv cs.IR 2016
Year: 2016

With increasing importance of e-commerce, many websites have emerged where users can express their opinions about products, such as movies, books, songs, etc. Such interactions can be modeled as bipartite graphs where the weight of the directed edge from a user to a product denotes a rating that the user imparts to the product. These graphs are used for recommendation systems and discovering most reliable (trusted) products. For these applications, it is important to capture the bias of a user when she is rating a product. Users have inherent bias---many users always impart high ratings while many others always rate poorly. It is necessary to know the bias of a reviewer while reading the review of a product. It is equally important to compensate for this bias while assigning a ranking for an object. In this paper, we propose an algorithm to capture the bias of a user and then subdue it to compute the true rating a product deserves. Experiments show the efficiency and effectiveness of our system in capturing the bias of users and then computing the true ratings of a product.
Effective Computer Model For Recognizing Nationality From Frontal Image
Venue: arXiv cs.CV 2016
Year: 2016

We are introducing new effective computer model for extracting nationality from frontal image candidate using face part color, size and distances based on deep research. Determining face part size, color, and distances is depending on a variety of factors including image quality, lighting condition, rotation angle, occlusion and facial emotion. Therefore, first we need to detect a face on the image then convert an image into the real input. After that, we can determine image candidate gender, face shape, key points and face parts. Finally, we will return the result, based on the comparison of sizes and distances with the sample measurement table database. While we were measuring samples, there were big differences between images by their gender and face shapes. Input images must be the frontal face image that has smooth lighting and does not have any rotation angle. The model can be used in military, police, defense, healthcare, and technology sectors. Finally, Computer can distinguish nationality from the face image.
Classification with Repulsion Tensors: A Case Study on Face Recognition
Venue: arXiv cs.CV 2016
Year: 2016

We consider dimensionality reduction methods for face recognition in a supervised setting, using an image-as-matrix representation. A common procedure is to project image matrices into a smaller space in which the recognition is performed. These methods are often called "two-dimensional" in the literature and there exist counterparts that use an image-as-vector representation. When two face images are close to each other in the input space they may remain close after projection - but this is not desirable in the situation when these two images are from different classes, and this often affects the recognition performance. We extend a previously developed `repulsion Laplacean' technique based on adding terms to the objective function with the goal or creation a repulsion energy between such images in the projected space. This scheme, which relies on a repulsion graph, is generic and can be incorporated into various two-dimensional methods. It can be regarded as a multilinear generalization of the repulsion strategy by Kokiopoulou and Saad [Pattern Recog., 42 (2009), pp. 2392--2402]. Experimental results demonstrate that the proposed methodology offers significant recognition improvement relative to the underlying two-dimensional methods.
Testing Interestingness Measures in Practice: A Large-Scale Analysis of Buying Patterns
Venue: arXiv cs.DB 2016
Year: 2016

Understanding customer buying patterns is of great interest to the retail industry and has shown to benefit a wide variety of goals ranging from managing stocks to implementing loyalty programs. Association rule mining is a common technique for extracting correlations such as "people in the South of France buy rosé wine" or "customers who buy paté also buy salted butter and sour bread." Unfortunately, sifting through a high number of buying patterns is not useful in practice, because of the predominance of popular products in the top rules. As a result, a number of "interestingness" measures (over 30) have been proposed to rank rules. However, there is no agreement on which measures are more appropriate for retail data. Moreover, since pattern mining algorithms output thousands of association rules for each product, the ability for an analyst to rely on ranking measures to identify the most interesting ones is crucial. In this paper, we develop CAPA (Comparative Analysis of PAtterns), a framework that provides analysts with the ability to compare the outcome of interestingness measures applied to buying patterns in the retail industry. We report on how we used CAPA to compare 34 measures applied to over 1,800 stores of Intermarché, one of the largest food retailers in France.
Centralized Repair of Multiple Node Failures with Applications to Communication Efficient Secret Sharing
Venue: arXiv cs.IT 2016
Year: 2016

This paper considers a distributed storage system, where multiple storage nodes can be reconstructed simultaneously at a centralized location. This centralized multi-node repair (CMR) model is a generalization of regenerating codes that allow for bandwidth-efficient repair of a single failed node. This work focuses on the trade-off between the amount of data stored and repair bandwidth in this CMR model. In particular, repair bandwidth bounds are derived for the minimum storage multi-node repair (MSMR) and the minimum bandwidth multi-node repair (MBMR) operating points. The tightness of these bounds are analyzed via code constructions. The MSMR point is characterized through codes achieving this point under functional repair for general set of CMR parameters, as well as with codes enabling exact repair for certain CMR parameters. The MBMR point, on the other hand, is characterized with exact repair codes for all CMR parameters for systems that satisfy a certain entropy accumulation property. Finally, the model proposed here is utilized for the secret sharing problem, where the codes for the multi-node repair problem is used to construct communication efficient secret sharing schemes with the property of bandwidth efficient share repair.
Obstacle avoidance via B-spline parameterizations of flat trajectories
Venue: arXiv cs.SY 2016
Year: 2016

This paper considers the collision avoidance problem in a multi-agent multi-obstacle framework. The originality in solving this intensively studied problem resides in the proposed geometrical view combined with differential flatness for trajectory generation and B-splines for the flat output parametrization. Using some important properties of these theoretical tools we show that the constraints can be validated at all times. Exact and sub-optimal constructions of the collision avoidance optimization problem are provided. The results are validated through extensive simulations over standard autonomous aerial vehicle dynamics.
Data Clustering and Graph Partitioning via Simulated Mixing
Venue: arXiv cs.LG 2016
Year: 2016

Spectral clustering approaches have led to well-accepted algorithms for finding accurate clusters in a given dataset. However, their application to large-scale datasets has been hindered by computational complexity of eigenvalue decompositions. Several algorithms have been proposed in the recent past to accelerate spectral clustering, however they compromise on the accuracy of the spectral clustering to achieve faster speed. In this paper, we propose a novel spectral clustering algorithm based on a mixing process on a graph. Unlike the existing spectral clustering algorithms, our algorithm does not require computing eigenvectors. Specifically, it finds the equivalent of a linear combination of eigenvectors of the normalized similarity matrix weighted with corresponding eigenvalues. This linear combination is then used to partition the dataset into meaningful clusters. Simulations on real datasets show that partitioning datasets based on such linear combinations of eigenvectors achieves better accuracy than standard spectral clustering methods as the number of clusters increase. Our algorithm can easily be implemented in a distributed setting.
On the Complexity of One-class SVM for Multiple Instance Learning
Venue: arXiv cs.LG 2016
Year: 2016

In traditional multiple instance learning (MIL), both positive and negative bags are required to learn a prediction function. However, a high human cost is needed to know the label of each bag---positive or negative. Only positive bags contain our focus (positive instances) while negative bags consist of noise or background (negative instances). So we do not expect to spend too much to label the negative bags. Contrary to our expectation, nearly all existing MIL methods require enough negative bags besides positive ones. In this paper we propose an algorithm called "Positive Multiple Instance" (PMI), which learns a classifier given only a set of positive bags. So the annotation of negative bags becomes unnecessary in our method. PMI is constructed based on the assumption that the unknown positive instances in positive bags be similar each other and constitute one compact cluster in feature space and the negative instances locate outside this cluster. The experimental results demonstrate that PMI achieves the performances close to or a little worse than those of the traditional MIL algorithms on benchmark and real data sets. However, the number of training bags in PMI is reduced significantly compared with traditional MIL algorithms.
Mitigation Procedures to Rank Experts through Information Retrieval Measures
Venue: arXiv cs.IR 2016
Year: 2016

In order to find experts, different approaches build rankings of people, assuming that they are ranked by level of expertise, and use typical Information Retrieval (IR) measures to evaluate their effectiveness. However, we figured out that expert rankings (i) tend to be partially ordered, (ii) incomplete, and (iii) consequently provide more an order rather than absolute ranks, which is not what usual IR measures exploit. To improve this state of the art, we propose to revise the formalism used in IR to design proper measures for comparing expert rankings. In this report, we investigate a first step by providing mitigation procedures for the three issues, and we analyse IR measures with the help of these procedures to identify interesting revisions and remaining limitations. From this analysis, we see that most of the measures can be exploited for this more generic context because of our mitigation procedures. Moreover, measures based on precision and recall, usually unable to consider the order of the ranked items, are of first interest if we represent a ranking as a set of ordered pairs. Cumulative measures, on the other hand, are specifically designed for considering the order but suffer from a higher complexity, motivating the use of precision/recall measures with the right representation.
Online Optimization in Dynamic Environments: Improved Regret Rates for Strongly Convex Problems
Venue: arXiv cs.LG 2016
Year: 2016

In this paper, we address tracking of a time-varying parameter with unknown dynamics. We formalize the problem as an instance of online optimization in a dynamic setting. Using online gradient descent, we propose a method that sequentially predicts the value of the parameter and in turn suffers a loss. The objective is to minimize the accumulation of losses over the time horizon, a notion that is termed dynamic regret. While existing methods focus on convex loss functions, we consider strongly convex functions so as to provide better guarantees of performance. We derive a regret bound that captures the path-length of the time-varying parameter, defined in terms of the distance between its consecutive values. In other words, the bound represents the natural connection of tracking quality to the rate of change of the parameter. We provide numerical experiments to complement our theoretical findings.
Guitar Solos as Networks
Venue: arXiv cs.SD 2016
Year: 2016

This paper presents an approach to model melodies (and music pieces in general) as networks. Notes of a melody can be seen as nodes of a network that are connected whenever these are played in sequence. This creates a directed graph. By using complex network theory, it is possible to extract some main metrics, typical of networks, that characterize the piece. Using this framework, we provide an analysis on a set of guitar solos performed by main musicians. The results of this study indicate that this model can have an impact on multimedia applications such as music classification, identification, and automatic music generation.
Substitutability of Spectrum and Cloud-based Antennas in Virtualised Wireless Networks
Venue: arXiv cs.NI 2016
Year: 2016

Some of the new trends emerging in future wireless networks enable a vastly increased fluidity in accessing a wide range of resources, thus supporting flexible network composition and dynamic allocation of resources to virtual network operators (VNOs). In this work we study a new resource allocation opportunity that is enabled by the cloud radio access network architecture. In particular, we investigate the relationship between the cloud-based antennas and spectrum as two important resources in virtualized wireless networks. We analyze the interplay between spectrum and antennas in the context of an auction-based allocation mechanism through which VNOs can bid for a combination of the two types of resources. Our analysis shows that the complementarity and partial substitutability of the two resources significantly impact the results of the allocation of those resources and uncovers the possibility of divergent interests between the spectrum and the infrastructure providers.
Improved Bounds on the Epidemic Threshold of Exact SIS Models on Complex Networks
Venue: arXiv cs.SI 2016
Year: 2016

The SIS (susceptible-infected-susceptible) epidemic model on an arbitrary network, without making approximations, is a $2^n$-state Markov chain with a unique absorbing state (the all-healthy state). This makes analysis of the SIS model and, in particular, determining the threshold of epidemic spread quite challenging. It has been shown that the exact marginal probabilities of infection can be upper bounded by an $n$-dimensional linear time-invariant system, a consequence of which is that the Markov chain is "fast-mixing" when the LTI system is stable, i.e. when $\fracβδ<\frac{1}{λ_{\max}(A)}$ (where $β$ is the infection rate per link, $δ$ is the recovery rate, and $λ_{\max}(A)$ is the largest eigenvalue of the network's adjacency matrix). This well-known threshold has been recently shown not to be tight in several cases, such as in a star network. In this paper, We provide tighter upper bounds on the exact marginal probabilities of infection, by also taking pairwise infection probabilities into account. Based on this improved bound, we derive tighter eigenvalue conditions that guarantee fast mixing (i.e., logarithmic mixing time) of the chain. We demonstrate the improvement of the threshold condition by comparing the new bound with the known one on various networks and epidemic parameters.
Cooperative Planning for Coupled Multi-Agent Systems under Timed Temporal Specifications
Venue: arXiv cs.SY 2016
Year: 2016

This paper presents a fully automated procedure for controller synthesis for multi-agent systems under coupled constraints. Each agent has dynamics consisting of two terms: the first one models the coupled constraints and the other one is an additional control input. We aim to design these inputs so that each agent meets an individual high-level specification given as a Metric Interval Temporal Logic (MITL). First, a decentralized abstraction that provides a time and space discretization of the multi-agent system is designed. Second, by utilizing this abstraction and techniques from formal veri- fication, we provide an algorithm that computes the individual runs which provably satisfy the high-level tasks. The overall approach is demonstrated in a simulation example.
2D Discrete Fourier Transform with Simultaneous Edge Artifact Removal for Real-Time Applications
Venue: arXiv cs.CV 2016
Year: 2016

Two-Dimensional (2D) Discrete Fourier Transform (DFT) is a basic and computationally intensive algorithm, with a vast variety of applications. 2D images are, in general, non-periodic, but are assumed to be periodic while calculating their DFTs. This leads to cross-shaped artifacts in the frequency domain due to spectral leakage. These artifacts can have critical consequences if the DFTs are being used for further processing. In this paper we present a novel FPGA-based design to calculate high-throughput 2D DFTs with simultaneous edge artifact removal. Standard approaches for removing these artifacts using apodization functions or mirroring, either involve removing critical frequencies or a surge in computation by increasing image size. We use a periodic-plus-smooth decomposition based artifact removal algorithm optimized for FPGA implementation, while still achieving real-time ($\ge$23 frames per second) performance for a 512$\times$512 size image stream. Our optimization approach leads to a significant decrease in external memory utilization thereby avoiding memory conflicts and simplifies the design. We have tested our design on a PXIe based Xilinx Kintex 7 FPGA system communicating with a host PC which gives us the advantage to further expand the design for industrial applications.
Analysis of the Cuban journal Bibliotecas: Anales de Investigacion
Venue: arXiv cs.DL 2016
Year: 2016

The objective of this article is to describe the academic impact, the editorial process quality, and the editorial and visibility strategies of Bibliotecas. Anales de Investigacion (BAI), a scientific Cuban journal edited by National Library of Cuba Jose Marti. The academic impact is determined through a citation analysis, which considers Google Scholar database as reference source. The bibliometric indicators applied are: citation per year, citation vs. self-citation, citable journals vs. non-citable documents, Hirsch Index, and impact factor. The editorial process quality and the visibility strategies are determined through a self-evaluation which takes into account the SciELO, Scopus, CLASE, Redalyc, Latindex, Dialnet, and ERIH PLUS methodologies. The results reveal an ascending citation line that highlights citing journals from the field of Library and Information Science, Medicine and Health Sciences, and Education. Aspects related content and format have negatively influenced on editorial process quality. Some strategies are proposed to improve scientific visibility through the inclusion in databases, directories, and social and academic networks. In general, this study contributes to the editorial decision taking, an issue that could augment the impact and scientific visibility of BAI.
Persistent Homology of Attractors For Action Recognition
Venue: arXiv cs.CG 2016
Year: 2016

In this paper, we propose a novel framework for dynamical analysis of human actions from 3D motion capture data using topological data analysis. We model human actions using the topological features of the attractor of the dynamical system. We reconstruct the phase-space of time series corresponding to actions using time-delay embedding, and compute the persistent homology of the phase-space reconstruction. In order to better represent the topological properties of the phase-space, we incorporate the temporal adjacency information when computing the homology groups. The persistence of these homology groups encoded using persistence diagrams are used as features for the actions. Our experiments with action recognition using these features demonstrate that the proposed approach outperforms other baseline methods.
Towards the Emulation of the Cardiac Conduction System for Pacemaker Testing
Venue: arXiv cs.SY 2016
Year: 2016

The heart is a vital organ that relies on the orchestrated propagation of electrical stimuli to coordinate each heart beat. Abnormalities in the heart's electrical behaviour can be managed with a cardiac pacemaker. Recently, the closed-loop testing of pacemakers with an emulation (real-time simulation) of the heart has been proposed. An emulated heart would provide realistic reactions to the pacemaker as if it were a real heart. This enables developers to interrogate their pacemaker design without having to engage in costly or lengthy clinical trials. Many high-fidelity heart models have been developed, but are too computationally intensive to be simulated in real-time. Heart models, designed specifically for the closed-loop testing of pacemakers, are too abstract to be useful in the testing of physical pacemakers. In the context of pacemaker testing, this paper presents a more computationally efficient heart model that generates realistic continuous-time electrical signals. The heart model is composed of cardiac cells that are connected by paths. Significant improvements were made to an existing cardiac cell model to stabilise its activation behaviour and to an existing path model to capture the behaviour of continuous electrical propagation. We provide simulation results that show our ability to faithfully model complex re-entrant circuits (that cause arrhythmia) that existing heart models can not.