OpenRaiser/Intern-Atlas huggingface dataset:
EM and EM'-index sequence: Construction and application in scientific assessment of scholars
Venue: arXiv cs.DL 2018
Year: 2018

Most of the scientometric indicators use only the total number of citations of an article and produce a single number for scientific assessment of scholars. Although this concept is very simple to compute, it fails to show the scientific productivity and impact of scholars during a time-span or in a year. To overcome this, several time series indicators have been proposed that consider the citations from the entire research career of a scholar. However, these indicators fail to give a comparative assessment of two scholars having same or very similar index value. To overcome this shortcoming, h-index sequence was proposed to assess the impact of scholars during a particular time-span and to compare multiple scholars at a similar stage in their careers. The h-index sequence is based on the h-index formulation. One of the main issues related to the h-index is that it completely ignores the excess citation in scientific assessment; h-index sequence also exhibits a similar behaviour. To overcome these limitations, in this article, we have discussed the EM and $EM^{'}$-index sequence, and performed an empirical study based on yearly citation count earned from all publications of 89 scholars' publication data. The element of the EM and $EM^{'}$-index sequence for a given year shows the impact of a scholar for that year. We conclude that the EM and $EM^{'}$-index sequence could be used as an alternative metrics to asses the impact of scholars.
Requirements for Ethereum Private Sidechains
Venue: arXiv cs.CR 2018
Year: 2018

The Enterprise Ethereum Client Specification by the Enterprise Ethereum Alliance defines the requirements which Ethereum Clients offering private smart contract capabilities should comply with. This specification though ground breaking, misses some important blockchain requirements and does not fully consider the requirements of Ethereum Clients offering Private Sidechain capabilities. This paper presents the case for Private Sidechains and defines requirements to be complied with to deliver this technology. The capabilities of three blockchain clients have been analysed based on the requirements: Quorum, Parity, and Hyperledger Fabric. Quorum and Hyperledger Fabric operate as private consortium blockchains where as Parity delivers private transaction capabilities on top of Ethereum MainNet. These differing approaches has led to different strengths and weaknesses which has resulted in each client not complying with one or more key requirement. In particular, none of the reviewed blockchain clients support the ability to determine bootstrap information to establish on-demand blockchains and none of the clients support secure management and pinning from Ethereum MainNet. This paper presents Ethereum Private Sidechains and a range of technologies which allow it to deliver on complex sidechain requirements. Ethereum Registration Authorities are presented, which allow entities which have not previously interacted to securely obtain information to bootstrap a sidechain, and a Management and Pinning strategy is described which allows the state of a sidechain to be securely pinned to Ethereum MainNet without compromising privacy.
Transmission Delay Minimization in Wireless Powered Communication Systems
Venue: arXiv cs.IT 2018
Year: 2018

We study transmission delay minimization of a wireless powered communication (WPC) system in a point-to-point scenario with one hybrid access point (HAP) and one WPC node. In this type of communications, the HAP sends energy to the node at the downlink (DL) for a given time duration and the WPC node harvests enough radio frequency (RF) power. Then, at the uplink (UL) channel, the WPC node transmits its collected data in a given time duration to the HAP. Minimizing such round trip delay is our concern here. So, we have defined four optimization problems to minimize this delay by applying the optimal DL and UL time durations and also the optimal power at the HAP. These optimization problems are investigated here with thorough comparison of the obtained results. After that, we extend our study to the multiuser case with one HAP and $K$ nodes and two different optimization problems are studied again in these cases.
Multi-modal Image Processing based on Coupled Dictionary Learning
Venue: arXiv cs.CV 2018
Year: 2018

In real-world scenarios, many data processing problems often involve heterogeneous images associated with different imaging modalities. Since these multimodal images originate from the same phenomenon, it is realistic to assume that they share common attributes or characteristics. In this paper, we propose a multi-modal image processing framework based on coupled dictionary learning to capture similarities and disparities between different image modalities. In particular, our framework can capture favorable structure similarities across different image modalities such as edges, corners, and other elementary primitives in a learned sparse transform domain, instead of the original pixel domain, that can be used to improve a number of image processing tasks such as denoising, inpainting, or super-resolution. Practical experiments demonstrate that incorporating multimodal information using our framework brings notable benefits.
Conditioning Deep Generative Raw Audio Models for Structured Automatic Music
Venue: arXiv cs.SD 2018
Year: 2018

Existing automatic music generation approaches that feature deep learning can be broadly classified into two types: raw audio models and symbolic models. Symbolic models, which train and generate at the note level, are currently the more prevalent approach; these models can capture long-range dependencies of melodic structure, but fail to grasp the nuances and richness of raw audio generations. Raw audio models, such as DeepMind's WaveNet, train directly on sampled audio waveforms, allowing them to produce realistic-sounding, albeit unstructured music. In this paper, we propose an automatic music generation methodology combining both of these approaches to create structured, realistic-sounding compositions. We consider a Long Short Term Memory network to learn the melodic structure of different styles of music, and then use the unique symbolic generations from this model as a conditioning input to a WaveNet-based raw audio generator, creating a model for automatic, novel music. We then evaluate this approach by showcasing results of this work.
Text-Independent Speaker Verification Based on Deep Neural Networks and Segmental Dynamic Time Warping
Venue: arXiv cs.SD 2018
Year: 2018

In this paper we present a new method for text-independent speaker verification that combines segmental dynamic time warping (SDTW) and the d-vector approach. The d-vectors, generated from a feed forward deep neural network trained to distinguish between speakers, are used as features to perform alignment and hence calculate the overall distance between the enrolment and test utterances.We present results on the NIST 2008 data set for speaker verification where the proposed method outperforms the conventional i-vector baseline with PLDA scores and outperforms d-vector approach with local distances based on cosine and PLDA scores. Also score combination with the i-vector/PLDA baseline leads to significant gains over both methods.
A Tensor Based Data Model for Polystore: An Application to Social Networks Data
Venue: arXiv cs.DB 2018
Year: 2018

In this article, we show how the mathematical object tensor can be used to build a multi-paradigm model for the storage of social data in data warehouses. From an architectural point of view, our approach allows to link different storage systems (polystore) and limits the impact of ETL tools performing model transformations required to feed different analysis algorithms. Therefore, systems can take advantage of multiple data models both in terms of query execution performance and the semantic expressiveness of data representation. The proposed model allows to reach the logical independence between data and programs implementing analysis algorithms. With a concrete case study on message virality on Twitter during the French presidential election of 2017, we highlight some of the contributions of our model.
Finding a Maximum-Weight Convex Set in a Chordal Graph
Venue: arXiv cs.DS 2018
Year: 2018

We consider a natural combinatorial optimization problem on chordal graphs, the class of graphs with no induced cycle of length four or more. A subset of vertices of a chordal graph is (monophonically) convex if it contains the vertices of all chordless paths between any two vertices of the set. The problem is to find a maximum-weight convex subset of a given vertex-weighted chordal graph. It generalizes previously studied special cases in trees and split graphs. It also happens to be closely related to the closure problem in partially ordered sets and directed graphs. We give the first polynomial-time algorithm for the problem.
Crowd Counting with Density Adaption Networks
Venue: arXiv cs.CV 2018
Year: 2018

Crowd counting is one of the core tasks in various surveillance applications. A practical system involves estimating accurate head counts in dynamic scenarios under different lightning, camera perspective and occlusion states. Previous approaches estimate head counts despite that they can vary dramatically in different density settings; the crowd is often unevenly distributed and the results are therefore unsatisfactory. In this paper, we propose a lightweight deep learning framework that can automatically estimate the crowd density level and adaptively choose between different counter networks that are explicitly trained for different density domains. Experiments on two recent crowd counting datasets, UCF_CC_50 and ShanghaiTech, show that the proposed mechanism achieves promising improvements over state-of-the-art methods. Moreover, runtime speed is 20 FPS on a single GPU.
Semantically Enhanced Dynamic Bayesian Network for Detecting Sepsis Mortality Risk in ICU Patients with Infection
Venue: arXiv cs.LG 2018
Year: 2018

Although timely sepsis diagnosis and prompt interventions in Intensive Care Unit (ICU) patients are associated with reduced mortality, early clinical recognition is frequently impeded by non-specific signs of infection and failure to detect signs of sepsis-induced organ dysfunction in a constellation of dynamically changing physiological data. The goal of this work is to identify patient at risk of life-threatening sepsis utilizing a data-centered and machine learning-driven approach. We derive a mortality risk predictive dynamic Bayesian network (DBN) guided by a customized sepsis knowledgebase and compare the predictive accuracy of the derived DBN with the Sepsis-related Organ Failure Assessment (SOFA) score, the Quick SOFA (qSOFA) score, the Simplified Acute Physiological Score (SAPS-II) and the Modified Early Warning Score (MEWS) tools. A customized sepsis ontology was used to derive the DBN node structure and semantically characterize temporal features derived from both structured physiological data and unstructured clinical notes. We assessed the performance in predicting mortality risk of the DBN predictive model and compared performance to other models using Receiver Operating Characteristic (ROC) curves, area under curve (AUROC), calibration curves, and risk distributions. The derived dataset consists of 24,506 ICU stays from 19,623 patients with evidence of suspected infection, with 2,829 patients deceased at discharge. The DBN AUROC was found to be 0.91, which outperformed the SOFA (0.843), qSOFA (0.66), MEWS (0.73), and SAPS-II (0.77) scoring tools. Continuous Net Reclassification Index and Integrated Discrimination Improvement analysis supported the superiority DBN. Compared with conventional rule-based risk scoring tools, the sepsis knowledgebase-driven DBN algorithm offers improved performance for predicting mortality of infected patients in ICUs.
Dual SVM Training on a Budget
Venue: arXiv cs.LG 2018
Year: 2018

We present a dual subspace ascent algorithm for support vector machine training that respects a budget constraint limiting the number of support vectors. Budget methods are effective for reducing the training time of kernel SVM while retaining high accuracy. To date, budget training is available only for primal (SGD-based) solvers. Dual subspace ascent methods like sequential minimal optimization are attractive for their good adaptation to the problem structure, their fast convergence rate, and their practical speed. By incorporating a budget constraint into a dual algorithm, our method enjoys the best of both worlds. We demonstrate considerable speed-ups over primal budget training methods.
Generalized Reversible Computing
Venue: arXiv cs.ET 2018
Year: 2018

Landauer's Principle that information loss from a computation implies entropy increase can be rigorously proved from mathematical physics. However, carefully examining its detailed formulation reveals that the traditional identification of logically reversible computational operations with bijective transformations of the full digital state space is actually not the correct logical-level characterization of the full set of classical computational operations that can be carried out physically with asymptotically zero energy dissipation. To find the correct logical conditions for physical reversibility, we must account for initial-state probabilities when applying the Principle. The minimal logical-level requirement for the physical reversibility of deterministic computational operations is that the subset of initial states that exhibit nonzero probability in a given statistical operating context must be transformed one-to-one into final states. Thus, any computational operation is conditionally reversible relative to any sufficiently-restrictive precondition on its initial state, and the minimum dissipation required for any deterministic operation by Landauer's Principle asymptotically approaches 0 when the probability of meeting any preselected one of its suitable preconditions approaches 1. This realization facilitates simpler designs for asymptotically thermodynamically reversible computational hardware, compared to designs that are restricted to using only fully-bijective operations such as Toffoli type operations. Thus, this more general framework for reversible computing provides a more effective theoretical foundation to use for the design of practical reversible computers than does the more restrictive traditional model of reversible logic. In this paper, we formally develop the theoretical foundations of the generalized model, and briefly survey some of its applications.
Learning a Saliency Evaluation Metric Using Crowdsourced Perceptual Judgments
Venue: arXiv cs.CV 2018
Year: 2018

In the area of human fixation prediction, dozens of computational saliency models are proposed to reveal certain saliency characteristics under different assumptions and definitions. As a result, saliency model benchmarking often requires several evaluation metrics to simultaneously assess saliency models from multiple perspectives. However, most computational metrics are not designed to directly measure the perceptual similarity of saliency maps so that the evaluation results may be sometimes inconsistent with the subjective impression. To address this problem, this paper first conducts extensive subjective tests to find out how the visual similarities between saliency maps are perceived by humans. Based on the crowdsourced data collected in these tests, we conclude several key factors in assessing saliency maps and quantize the performance of existing metrics. Inspired by these factors, we propose to learn a saliency evaluation metric based on a two-stream convolutional neural network using crowdsourced perceptual judgements. Specifically, the relative saliency score of each pair from the crowdsourced data is utilized to regularize the network during the training process. By capturing the key factors shared by various subjects in comparing saliency maps, the learned metric better aligns with human perception of saliency maps, making it a good complement to the existing metrics. Experimental results validate that the learned metric can be generalized to the comparisons of saliency maps from new images, new datasets, new models and synthetic data. Due to the effectiveness of the learned metric, it also can be used to facilitate the development of new models for fixation prediction.
Feature-less Stitching of Cylindrical Tunnel
Venue: arXiv cs.CV 2018
Year: 2018

Traditional image stitching algorithms use transforms such as homography to combine different views of a scene. They usually work well when the scene is planar or when the camera is only rotated, keeping its position static. This severely limits their use in real world scenarios where an unmanned aerial vehicle (UAV) potentially hovers around and flies in an enclosed area while rotating to capture a video sequence. We utilize known scene geometry along with recorded camera trajectory to create cylindrical images captured in a given environment such as a tunnel where the camera rotates around its center. The captured images of the inner surface of the given scene are combined to create a composite panoramic image that is textured onto a 3D geometrical object in Unity graphical engine to create an immersive environment for end users.
Attention to Head Locations for Crowd Counting
Venue: arXiv cs.CV 2018
Year: 2018

Occlusions, complex backgrounds, scale variations and non-uniform distributions present great challenges for crowd counting in practical applications. In this paper, we propose a novel method using an attention model to exploit head locations which are the most important cue for crowd counting. The attention model estimates a probability map in which high probabilities indicate locations where heads are likely to be present. The estimated probability map is used to suppress non-head regions in feature maps from several multi-scale feature extraction branches of a convolution neural network for crowd density estimation, which makes our method robust to complex backgrounds, scale variations and non-uniform distributions. In addition, we introduce a relative deviation loss to compensate a commonly used training loss, Euclidean distance, to improve the accuracy of sparse crowd density estimation. Experiments on Shanghai-Tech, UCF_CC_50 and World-Expo'10 data sets demonstrate the effectiveness of our method.
CeMNet: Self-supervised learning for accurate continuous ego-motion estimation
Venue: arXiv cs.CG 2018
Year: 2018

In this paper, we propose a novel self-supervised learning model for estimating continuous ego-motion from video. Our model learns to estimate camera motion by watching RGBD or RGB video streams and determining translational and rotation velocities that correctly predict the appearance of future frames. Our approach differs from other recent work on self-supervised structure-from-motion in its use of a continuous motion formulation and representation of rigid motion fields rather than direct prediction of camera parameters. To make estimation robust in dynamic environments with multiple moving objects, we introduce a simple two-component segmentation process that isolates the rigid background environment from dynamic scene elements. We demonstrate state-of-the-art accuracy of the self-trained model on several benchmark ego-motion datasets and highlight the ability of the model to provide superior rotational accuracy and handling of non-rigid scene motions.
Exploiting Spatial-Temporal Modelling and Multi-Modal Fusion for Human Action Recognition
Venue: arXiv cs.CV 2018
Year: 2018

In this report, our approach to tackling the task of ActivityNet 2018 Kinetics-600 challenge is described in detail. Though spatial-temporal modelling methods, which adopt either such end-to-end framework as I3D \cite{i3d} or two-stage frameworks (i.e., CNN+RNN), have been proposed in existing state-of-the-arts for this task, video modelling is far from being well solved. In this challenge, we propose spatial-temporal network (StNet) for better joint spatial-temporal modelling and comprehensively video understanding. Besides, given that multi-modal information is contained in video source, we manage to integrate both early-fusion and later-fusion strategy of multi-modal information via our proposed improved temporal Xception network (iTXN) for video understanding. Our StNet RGB single model achieves 78.99\% top-1 precision in the Kinetics-600 validation set and that of our improved temporal Xception network which integrates RGB, flow and audio modalities is up to 82.35\%. After model ensemble, we achieve top-1 precision as high as 85.0\% on the validation set and rank No.1 among all submissions.
Game-Theoretic Spectrum Trading in RF Relay-Assisted Free-Space Optical Communications
Venue: arXiv cs.IT 2018
Year: 2018

This work proposes a novel hybrid RF/FSO system based on a game theoretic spectrum trading process. It is assumed that no RF spectrum is preallocated to the FSO link and only when the link availability is severely impaired by the infrequent adverse weather conditions, i.e. fog, etc., the source can borrow a portion of licensed RF spectrum from one of the surrounding RF nodes. Using the leased spectrum, the source establishes a dual-hop RF/FSO hybrid link to maintain its throughout to the destination. The proposed system is considered to be both spectrum- and power-efficient. A market-equilibrium-based pricing process is proposed for the spectrum trading between the source and RF nodes. Through extensive performance analysis, it is demonstrated that the proposed scheme can significantly improve the average capacity of the system, especially when the surrounding RF nodes are with low traffic loads. In addition, the system benefits from involving more RF nodes into the spectrum trading process by means of diversity, particularly when the surrounding RF nodes have high probability of being in heavy traffic loads. Furthermore, the application of the proposed system in a realistic scenario is presented based on the weather statistics in the city of Edinburgh, UK. It is demonstrated that the proposed system can substantially enhance the link availability towards the carrier-class requirement.
Implementation of a Near-Optimal Complex Root Clustering Algorithm
Venue: arXiv cs.MS 2018
Year: 2018

We describe Ccluster, a software for computing natural $ε$-clusters of complex roots in a given box of the complex plane. This algorithm from Becker et al.~(2016) is near-optimal when applied to the benchmark problem of isolating all complex roots of an integer polynomial. It is one of the first implementations of a near-optimal algorithm for complex roots. We describe some low level techniques for speeding up the algorithm. Its performance is compared with the well-known MPSolve library and Maple.