OpenRaiser/Intern-Atlas huggingface dataset:
Scalable Approximation Algorithm for Graph Summarization
Venue: arXiv cs.DS 2018
Year: 2018

Massive sizes of real-world graphs, such as social networks and web graph, impose serious challenges to process and perform analytics on them. These issues can be resolved by working on a small summary of the graph instead . A summary is a compressed version of the graph that removes several details, yet preserves it's essential structure. Generally, some predefined quality measure of the summary is optimized to bound the approximation error incurred by working on the summary instead of the whole graph. All known summarization algorithms are computationally prohibitive and do not scale to large graphs. In this paper we present an efficient randomized algorithm to compute graph summaries with the goal to minimize reconstruction error. We propose a novel weighted sampling scheme to sample vertices for merging that will result in the least reconstruction error. We provide analytical bounds on the running time of the algorithm and prove approximation guarantee for our score computation. Efficiency of our algorithm makes it scalable to very large graphs on which known algorithms cannot be applied. We test our algorithm on several real world graphs to empirically demonstrate the quality of summaries produced and compare to state of the art algorithms. We use the summaries to answer several structural queries about original graph and report their accuracies.
CapsGAN: Using Dynamic Routing for Generative Adversarial Networks
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we propose a novel technique for generating images in the 3D domain from images with high degree of geometrical transformations. By coalescing two popular concurrent methods that have seen rapid ascension to the machine learning zeitgeist in recent years: GANs (Goodfellow et. al.) and Capsule networks (Sabour, Hinton et. al.) - we present: \textbf{CapsGAN}. We show that CapsGAN performs better than or equal to traditional CNN based GANs in generating images with high geometric transformations using rotated MNIST. In the process, we also show the efficacy of using capsules architecture in the GANs domain. Furthermore, we tackle the Gordian Knot in training GANs - the performance control and training stability by experimenting with using Wasserstein distance (gradient clipping, penalty) and Spectral Normalization. The experimental findings of this paper should propel the application of capsules and GANs in the still exciting and nascent domain of 3D image generation, and plausibly video (frame) generation.
Writing Style Invariant Deep Learning Model for Historical Manuscripts Alignment
Venue: arXiv cs.CV 2018
Year: 2018

Historical manuscript alignment is a widely known problem in document analysis. Finding the differences between manuscript editions is mostly done manually. In this paper, we present a writer independent deep learning model which is trained on several writing styles, and able to achieve high detection accuracy when tested on writing styles not present in training data. We test our model using cross validation, each time we train the model on five manuscripts, and test it on the other two manuscripts, never seen in the training data. We've applied cross validation on seven manuscripts, netting 21 different tests, achieving average accuracy of $\%92.17$. We also present a new alignment algorithm based on dynamic sized sliding window, which is able to successfully handle complex cases.
Real-time coherent diffraction inversion using deep generative networks
Venue: arXiv cs.CV 2018
Year: 2018

Phase retrieval, or the process of recovering phase information in reciprocal space to reconstruct images from measured intensity alone, is the underlying basis to a variety of imaging applications including coherent diffraction imaging (CDI). Typical phase retrieval algorithms are iterative in nature, and hence, are time-consuming and computationally expensive, precluding real-time imaging. Furthermore, iterative phase retrieval algorithms struggle to converge to the correct solution especially in the presence of strong phase structures. In this work, we demonstrate the training and testing of CDI NN, a pair of deep deconvolutional networks trained to predict structure and phase in real space of a 2D object from its corresponding far-field diffraction intensities alone. Once trained, CDI NN can invert a diffraction pattern to an image within a few milliseconds of compute time on a standard desktop machine, opening the door to real-time imaging.
Learning to Estimate Indoor Lighting from 3D Objects
Venue: arXiv cs.CV 2018
Year: 2018

In this work, we propose a step towards a more accurate prediction of the environment light given a single picture of a known object. To achieve this, we developed a deep learning method that is able to encode the latent space of indoor lighting using few parameters and that is trained on a database of environment maps. This latent space is then used to generate predictions of the light that are both more realistic and accurate than previous methods. To achieve this, our first contribution is a deep autoencoder which is capable of learning the feature space that compactly models lighting. Our second contribution is a convolutional neural network that predicts the light from a single image of a known object. To train these networks, our third contribution is a novel dataset that contains 21,000 HDR indoor environment maps. The results indicate that the predictor can generate plausible lighting estimations even from diffuse objects.
Semantically Selective Augmentation for Deep Compact Person Re-Identification
Venue: arXiv cs.CV 2018
Year: 2018

We present a deep person re-identification approach that combines semantically selective, deep data augmentation with clustering-based network compression to generate high performance, light and fast inference networks. In particular, we propose to augment limited training data via sampling from a deep convolutional generative adversarial network (DCGAN), whose discriminator is constrained by a semantic classifier to explicitly control the domain specificity of the generation process. Thereby, we encode information in the classifier network which can be utilized to steer adversarial synthesis, and which fuels our CondenseNet ID-network training. We provide a quantitative and qualitative analysis of the approach and its variants on a number of datasets, obtaining results that outperform the state-of-the-art on the LIMA dataset for long-term monitoring in indoor living spaces.
Improving Whole Slide Segmentation Through Visual Context - A Systematic Study
Venue: arXiv cs.CV 2018
Year: 2018

While challenging, the dense segmentation of histology images is a necessary first step to assess changes in tissue architecture and cellular morphology. Although specific convolutional neural network architectures have been applied with great success to the problem, few effectively incorporate visual context information from multiple scales. With this paper, we present a systematic comparison of different architectures to assess how including multi-scale information affects segmentation performance. A publicly available breast cancer and a locally collected prostate cancer datasets are being utilised for this study. The results support our hypothesis that visual context and scale play a crucial role in histology image classification problems.
Opportunistic Edge Computing: Concepts, Opportunities and Research Challenges
Venue: arXiv cs.NI 2018
Year: 2018

The growing need for low-latency access to computing resources has motivated the introduction of edge computing, where resources are strategically placed at the access networks. Unfortunately, edge computing infrastructures like fogs and cloudlets have limited scalability and may be prohibitively expensive to install given the vast edge of the Internet. In this paper, we present Opportunistic Edge Computing (OEC), a new computing paradigm that provides a framework to create scalable infrastructures at the edge using end-user contributed resources. One of the goals of OEC is to place resources where there is high demand for them by incentivizing people to share their resources. This paper defines the OEC paradigm and the involved stakeholders and puts forward a management framework to build, manage and monitor scalable edge infrastructures. It also highlights the key differences between the OEC computing models and the existing computing models and shed the light on early works on the topic. The paper also presents preliminary experimental results that highlight the benefits and the limitations of OEC compared to the regular cloud computing deployment and the Fog deployment. It finally summarizes key research directions pertaining to resource management in OEC environments.
Automatic Ship Detection of Remote Sensing Images from Google Earth in Complex Scenes Based on Multi-Scale Rotation Dense Feature Pyramid Networks
Venue: arXiv cs.CV 2018
Year: 2018

Ship detection has been playing a significant role in the field of remote sensing for a long time but it is still full of challenges. The main limitations of traditional ship detection methods usually lie in the complexity of application scenarios, the difficulty of intensive object detection and the redundancy of detection region. In order to solve such problems above, we propose a framework called Rotation Dense Feature Pyramid Networks (R-DFPN) which can effectively detect ship in different scenes including ocean and port. Specifically, we put forward the Dense Feature Pyramid Network (DFPN), which is aimed at solving the problem resulted from the narrow width of the ship. Compared with previous multi-scale detectors such as Feature Pyramid Network (FPN), DFPN builds the high-level semantic feature-maps for all scales by means of dense connections, through which enhances the feature propagation and encourages the feature reuse. Additionally, in the case of ship rotation and dense arrangement, we design a rotation anchor strategy to predict the minimum circumscribed rectangle of the object so as to reduce the redundant detection region and improve the recall. Furthermore, we also propose multi-scale ROI Align for the purpose of maintaining the completeness of semantic and spatial information. Experiments based on remote sensing images from Google Earth for ship detection show that our detection method based on R-DFPN representation has a state-of-the-art performance.
Reversible Codes and Its Application to Reversible DNA Codes over $F_{4^k}$
Venue: arXiv cs.IT 2018
Year: 2018

Coterm polynomials are introduced by Oztas et al. [a novel approach for constructing reversible codes and applications to DNA codes over the ring $F_2[u]/(u^{2k}-1)$, Finite Fields and Their Applications 46 (2017).pp. 217-234.], which generate reversible codes. In this paper, we generalize the coterm polynomials and construct some reversible codes which are optimal codes by using $m$-quasi-reciprocal polynomials. Moreover, we give a map from DNA $k$-bases to the elements of $F_{4^k}$, and construct reversible DNA codes over $F_{4^k}$ by DNA-$m$-quasi-reciprocal polynomials.
Explaining and Generalizing Back-Translation through Wake-Sleep
Venue: arXiv cs.CL 2018
Year: 2018

Back-translation has become a commonly employed heuristic for semi-supervised neural machine translation. The technique is both straightforward to apply and has led to state-of-the-art results. In this work, we offer a principled interpretation of back-translation as approximate inference in a generative model of bitext and show how the standard implementation of back-translation corresponds to a single iteration of the wake-sleep algorithm in our proposed model. Moreover, this interpretation suggests a natural iterative generalization, which we demonstrate leads to further improvement of up to 1.6 BLEU.
Is India's Unique Identification Number a legally valid identification?
Venue: arXiv cs.CY 2018
Year: 2018

A legally valid identification document allows impartial arbitration of the identification of individuals. It protects individuals from a violation of their dignity, justice, liberty and equality. It protects the nation from a destruction of its republic, democratic, sovereign status. In order to test the ability of an identification document to establish impartial identification of individuals, it must be evaluated for its ability to establish identity, undertake identification and build confidence to impartial, reliable and valid identification. The processes of issuing, using and validating identification documents alter the ability of the document to establish identity, undertake identification and build confidence to impartial and valid identification. These processes alter the ability of the document to serve as proof of identity, proof of address, proof of being a resident, or even the proof of existence of a person. We examine the ability of the UID number to serve as an identification document with the ability to impartially arbitrate the identification of individuals and serve as proof of identity, address, and demonstrate existence of a person. We evaluate the implications of the continued use UID system on our ability to undertake legally valid identification ensure integrity of the identity and address databases across the world.
Sample Dropout for Audio Scene Classification Using Multi-Scale Dense Connected Convolutional Neural Network
Venue: arXiv cs.CV 2018
Year: 2018

Acoustic scene classification is an intricate problem for a machine. As an emerging field of research, deep Convolutional Neural Networks (CNN) achieve convincing results. In this paper, we explore the use of multi-scale Dense connected convolutional neural network (DenseNet) for the classification task, with the goal to improve the classification performance as multi-scale features can be extracted from the time-frequency representation of the audio signal. On the other hand, most of previous CNN-based audio scene classification approaches aim to improve the classification accuracy, by employing different regularization techniques, such as the dropout of hidden units and data augmentation, to reduce overfitting. It is widely known that outliers in the training set have a high negative influence on the trained model, and culling the outliers may improve the classification performance, while it is often under-explored in previous studies. In this paper, inspired by the silence removal in the speech signal processing, a novel sample dropout approach is proposed, which aims to remove outliers in the training dataset. Using the DCASE 2017 audio scene classification datasets, the experimental results demonstrates the proposed multi-scale DenseNet providing a superior performance than the traditional single-scale DenseNet, while the sample dropout method can further improve the classification robustness of multi-scale DenseNet.
Google Scholar as a data source for research assessment
Venue: arXiv cs.DL 2018
Year: 2018

The launch of Google Scholar (GS) marked the beginning of a revolution in the scientific information market. This search engine, unlike traditional databases, automatically indexes information from the academic web. Its ease of use, together with its wide coverage and fast indexing speed, have made it the first tool most scientists currently turn to when they need to carry out a literature search. Additionally, the fact that its search results were accompanied from the beginning by citation counts, as well as the later development of secondary products which leverage this citation data (such as Google Scholar Metrics and Google Scholar Citations), made many scientists wonder about its potential as a source of data for bibliometric analyses. The goal of this chapter is to lay the foundations for the use of GS as a supplementary source (and in some disciplines, arguably the best alternative) for scientific evaluation. First, we present a general overview of how GS works. Second, we present empirical evidences about its main characteristics (size, coverage, and growth rate). Third, we carry out a systematic analysis of the main limitations this search engine presents as a tool for the evaluation of scientific performance. Lastly, we discuss the main differences between GS and other more traditional bibliographic databases in light of the correlations found between their citation data. We conclude that Google Scholar presents a broader view of the academic world because it has brought to light a great amount of sources that were not previously visible.
Dank Learning: Generating Memes Using Deep Neural Networks
Venue: arXiv cs.CL 2018
Year: 2018

We introduce a novel meme generation system, which given any image can produce a humorous and relevant caption. Furthermore, the system can be conditioned on not only an image but also a user-defined label relating to the meme template, giving a handle to the user on meme content. The system uses a pretrained Inception-v3 network to return an image embedding which is passed to an attention-based deep-layer LSTM model producing the caption - inspired by the widely recognised Show and Tell Model. We implement a modified beam search to encourage diversity in the captions. We evaluate the quality of our model using perplexity and human assessment on both the quality of memes generated and whether they can be differentiated from real ones. Our model produces original memes that cannot on the whole be differentiated from real ones.
Online Parallel Portfolio Selection with Heterogeneous Island Model
Venue: arXiv cs.NE 2018
Year: 2018

We present an online parallel portfolio selection algorithm based on the island model commonly used for parallelization of evolutionary algorithms. In our case each of the islands runs a different optimization algorithm. The distributed computation is managed by a central planner which periodically changes the running methods during the execution of the algorithm -- less successful methods are removed while new instances of more successful methods are added. We compare different types of planners in the heterogeneous island model among themselves and also to the traditional homogeneous model on a wide set of problems. The tests include experiments with different representations of the individuals and different duration of fitness function evaluations. The results show that heterogeneous models are a more general and universal computational tool compared to homogeneous models.
Application Component Placement in NFV-based Hybrid Cloud/Fog Systems
Venue: arXiv cs.NI 2018
Year: 2018

Applications are sets of interacting components that can be executed in sequence, in parallel, or by using more complex constructs such as selections and loops. They can, therefore, be modeled as structured graphs with sub-structures consisting of these constructs. Fog computing can reduce the latency induced by distant clouds by enabling the deployment of some components at the edge of the network (i.e., closer to end-devices) while keeping others in the cloud. Network Functions Virtualization (NFV) decouples software from hardware and enables an agile deployment of network services and applications as Virtual Network Functions (VNFs). In NFV settings, efficient placement algorithms are required to map the structured graphs representing the VNF Forwarding Graphs (VNF-FGs) onto the infrastructure of the hybrid cloud/fog system. Only deterministic graphs with sequence and parallel sub-structures have been considered thus to date. However, several real-life applications do require non-deterministic graphs with sub-structures as selections and loops. This paper focuses on application component placement in NFV-based hybrid cloud/fog systems, with the assumption that the graph representing the application is non-deterministic. The objective is to minimize an aggregated weighted function of makespan and cost. The problem is modeled as an Integer Linear Programming (ILP) and evaluated over small-scale scenarios using the CPLEX optimization tool.
Nanoseconds Timing System Based on IEEE 1588 FPGA Implementation
Venue: arXiv cs.NI 2018
Year: 2018

Clock synchronization procedures are mandatory in most physical experiments where event fragments are readout by spatially dislocated sensors and must be glued together to reconstruct key parameters (e.g. energy, interaction vertex etc.) of the process under investigation. These distributed data readout topologies rely on an accurate time information available at the frontend, where raw data are acquired and tagged with a precise timestamp prior to data buffering and central data collecting. This makes the network complexity and latency, between frontend and backend electronics, negligible within upper bounds imposed by the frontend data buffer capability. The proposed research work describes an FPGA implementation of IEEE 1588 Precision Time Protocol (PTP) that exploits the CERN Timing, Trigger and Control (TTC) system as a multicast messaging physical and data link layer. The hardware implementation extends the clock synchronization to the nanoseconds range, overcoming the typical accuracy limitations inferred by computers Ethernet based Local Area Network (LAN). Establishing a reliable communication between master and timing receiver nodes is essential in a message-based synchronization system. In the backend electronics, the serial data streams synchronization with the global clock domain is guaranteed by an hardware-based finite state machine that scans the bit period using a variable delay chain and finds the optimal sampling point. The validity of the proposed timing system has been proved in point-to-point data links as well as in star topology configurations over standard CAT-5e cables. The results achieved together with weaknesses and possible improvements are hereby detailed.
On Greedy Routing in Dynamic UAV Networks
Venue: arXiv cs.NI 2018
Year: 2018

Unmanned aerial vehicles (UAVs), commonly known as drones, are becoming increasingly popular for various applications. Freely flying drones create highly dynamic environments, where conventional routing algorithms which rely on stationary network contact graphs fail to perform efficiently. Also, link establishment through exploring optimal paths using hello messages (as is used in AODV algorithm) deems extremely inefficient and costly for rapidly changing network topologies. In this paper, we present a distance-based greedy routing algorithm for UAV networks solely based on UAVs' local observations of their surrounding subnetwork. Thereby, neither a central decision maker nor a time-consuming route setup and maintenance mechanism are required. To evaluate the proposed method, we derive an analytical bound for the expected number of hops that a packet traverses. Also, we find the expected end-to-end distance traveled by each packet as well as the probability of successful delivery. The simulation results verify the accuracy of the developed analytical expressions and show considerable improvement compared to centralized shortest path routing algorithms.