OpenRaiser/Intern-Atlas huggingface dataset:
LPOS: Location Privacy for Optimal Sensing in Cognitive Radio Networks
Venue: arXiv cs.NI 2018
Year: 2018

Cognitive Radio Networks (CRNs) enable opportunistic access to the licensed channel resources by allowing unlicensed users to exploit vacant channel opportunities. One effective technique through which unlicensed users, often referred to as Secondary Users (SUs), acquire whether a channel is vacant is cooperative spectrum sensing. Despite its effectiveness in enabling CRN access, cooperative sensing suffers from location privacy threats, merely because the sensing reports that need to be exchanged among the SUs to perform the sensing task are highly correlated to the SUs' locations. In this paper, we develop a new Location Privacy for Optimal Sensing (LPOS) scheme that preserves the location privacy of SUs while achieving optimal sensing performance through voting-based sensing. In addition, LPOS is the only alternative among existing CRN location privacy preserving schemes (to the best of our knowledge) that ensures high privacy, achieves fault tolerance, and is robust against the highly dynamic and wireless nature of CRNs.
CuCoTrack: Cuckoo Filter Based Connection Tracking
Venue: arXiv cs.DS 2018
Year: 2018

This paper introduces CuCoTrack, a cuckoo hash based data structure designed to efficiently implement connection tracking. The proposed scheme exploits the fact that queries always match one existing connection to compress the 5-tuple that identifies the connection. This reduces significantly the amount of memory needed to store the connections and also the memory bandwidth needed for lookups. CuCoTrack uses a dynamic fingerprint to avoid collisions thus ensuring that queries are completed in at most two memory accesses and facilitating a hardware implementation. The proposed scheme has been analyzed theoretically and validated by simulation. The results show that using 16 bits for the fingerprint is enough to avoid collisions in practical configurations.
Improving Surgical Training Phantoms by Hyperrealism: Deep Unpaired Image-to-Image Translation from Real Surgeries
Venue: arXiv cs.CY 2018
Year: 2018

Current `dry lab' surgical phantom simulators are a valuable tool for surgeons which allows them to improve their dexterity and skill with surgical instruments. These phantoms mimic the haptic and shape of organs of interest, but lack a realistic visual appearance. In this work, we present an innovative application in which representations learned from real intraoperative endoscopic sequences are transferred to a surgical phantom scenario. The term hyperrealism is introduced in this field, which we regard as a novel subform of surgical augmented reality for approaches that involve real-time object transfigurations. For related tasks in the computer vision community, unpaired cycle-consistent Generative Adversarial Networks (GANs) have shown excellent results on still RGB images. Though, application of this approach to continuous video frames can result in flickering, which turned out to be especially prominent for this application. Therefore, we propose an extension of cycle-consistent GANs, named tempCycleGAN, to improve temporal consistency.The novel method is evaluated on captures of a silicone phantom for training endoscopic reconstructive mitral valve procedures. Synthesized videos show highly realistic results with regard to 1) replacement of the silicone appearance of the phantom valve by intraoperative tissue texture, while 2) explicitly keeping crucial features in the scene, such as instruments, sutures and prostheses. Compared to the original CycleGAN approach, tempCycleGAN efficiently removes flickering between frames. The overall approach is expected to change the future design of surgical training simulators since the generated sequences clearly demonstrate the feasibility to enable a considerably more realistic training experience for minimally-invasive procedures.
Neural Disease Named Entity Extraction with Character-based BiLSTM+CRF in Japanese Medical Text
Venue: arXiv cs.CL 2018
Year: 2018

We propose an 'end-to-end' character-based recurrent neural network that extracts disease named entities from a Japanese medical text and simultaneously judges its modality as either positive or negative; i.e., the mentioned disease or symptom is affirmed or negated. The motivation to adopt neural networks is to learn effective lexical and structural representation features for Entity Recognition and also for Positive/Negative classification from an annotated corpora without explicitly providing any rule-based or manual feature sets. We confirmed the superiority of our method over previous char-based CRF or SVM methods in the results.
Generative Adversarial Network Architectures For Image Synthesis Using Capsule Networks
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we propose Generative Adversarial Network (GAN) architectures that use Capsule Networks for image-synthesis. Based on the principal of positional-equivariance of features, Capsule Network's ability to encode spatial relationships between the features of the image helps it become a more powerful critic in comparison to Convolutional Neural Networks (CNNs) used in current architectures for image synthesis. Our proposed GAN architectures learn the data manifold much faster and therefore, synthesize visually accurate images in significantly lesser number of training samples and training epochs in comparison to GANs and its variants that use CNNs. Apart from analyzing the quantitative results corresponding the images generated by different architectures, we also explore the reasons for the lower coverage and diversity explored by the GAN architectures that use CNN critics.
Synthetic Perfusion Maps: Imaging Perfusion Deficits in DSC-MRI with Deep Learning
Venue: arXiv cs.CV 2018
Year: 2018

In this work, we present a novel convolutional neural net- work based method for perfusion map generation in dynamic suscepti- bility contrast-enhanced perfusion imaging. The proposed architecture is trained end-to-end and solely relies on raw perfusion data for inference. We used a dataset of 151 acute ischemic stroke cases for evaluation. Our method generates perfusion maps that are comparable to the target maps used for clinical routine, while being model-free, fast, and less noisy.
Dual Pattern Learning Networks by Empirical Dual Prediction Risk Minimization
Venue: arXiv cs.CV 2018
Year: 2018

Motivated by the observation that humans can learn patterns from two given images at one time, we propose a dual pattern learning network architecture in this paper. Unlike conventional networks, the proposed architecture has two input branches and two loss functions. Instead of minimizing the empirical risk of a given dataset, dual pattern learning networks is trained by minimizing the empirical dual prediction loss. We show that this can improve the performance for single image classification. This architecture forces the network to learn discriminative class-specific features by analyzing and comparing two input images. In addition, the dual input structure allows the network to have a considerably large number of image pairs, which can help address the overfitting issue due to limited training data. Moreover, we propose to associate each input branch with a random interest value for learning corresponding image during training. This method can be seen as a stochastic regularization technique, and can further lead to generalization performance improvement. State-of-the-art deep networks can be adapted to dual pattern learning networks without increasing the same number of parameters. Extensive experiments on CIFAR-10, CIFAR- 100, FI-8, Google commands dataset, and MNIST demonstrate that our DPLNets exhibit better performance than original networks. The experimental results on subsets of CIFAR- 10, CIFAR-100, and MNIST demonstrate that dual pattern learning networks have good generalization performance on small datasets.
Analysis of Average Consensus Algorithm for Asymmetric Regular Networks
Venue: arXiv cs.SY 2018
Year: 2018

Average consensus algorithms compute the global average of sensor data in a distributed fashion using local sensor nodes. Simple execution, decentralized philosophy make these algorithms suitable for WSN scenarios. Most of the researchers have studied the average consensus algorithms by modeling the network as an undirected graph. But, WSNs in practice consist of asymmetric links and the undirected graph cannot model the asymmetric links. Therefore, these studies fail to study the actual performance of consensus algorithms on WSNs. In this paper, we model the WSN as a directed graph and derive the explicit formulas of the ring, torus, $r$-nearest neighbor ring, and $m$-dimensional torus networks. Numerical results subsequently demonstrate the accuracy of directed graph modeling. Further, we study the effect of asymmetric links, the number of nodes, network dimension, and node overhead on the convergence rate of average consensus algorithms.
Latent Space Representation for Shape Analysis and Learning
Venue: arXiv cs.GR 2018
Year: 2018

We propose a novel shape representation useful for analyzing and processing shape collections, as well for a variety of learning and inference tasks. Unlike most approaches that capture variability in a collection by using a template model or a base shape, we show that it is possible to construct a full shape representation by using the latent space induced by a functional map net- work, allowing us to represent shapes in the context of a collection without the bias induced by selecting a template shape. Key to our construction is a novel analysis of latent functional spaces, which shows that after proper regularization they can be endowed with a natural geometric structure, giving rise to a well-defined, stable and fully informative shape representation. We demonstrate the utility of our representation in shape analysis tasks, such as highlighting the most distorted shape parts in a collection or separating variability modes between shape classes. We further exploit our representation in learning applications by showing how it can naturally be used within deep learning and convolutional neural networks for shape classi cation or reconstruction, signi cantly outperforming existing point-based techniques.
End to End Brain Fiber Orientation Estimation using Deep Learning
Venue: arXiv cs.CV 2018
Year: 2018

In this work, we explore the various Brain Neuron tracking techniques, which is one of the most significant applications of Diffusion Tensor Imaging. Tractography provides us with a non-invasive method to analyze underlying tissue micro-structure. Understanding the structure and organization of the tissues facilitates us with a diagnosis method to identify any aberrations and provide acute information on the occurrences of brain ischemia or stroke, the mutation of neurological diseases such as Alzheimer, multiple sclerosis and so on. Time if of essence and accurate localization of the aberrations can help save or change a diseased life. Following up with the limitations introduced by the current Tractography techniques such as computational complexity, reconstruction errors during tensor estimation and standardization, we aim to elucidate these limitations through our research findings. We introduce an end to end Deep Learning framework which can accurately estimate the most probable likelihood orientation at each voxel along a neuronal pathway. We use Probabilistic Tractography as our baseline model to obtain the training data and which also serve as a Tractography Gold Standard for our evaluations. Through experiments we show that our Deep Network can do a significant improvement over current Tractography implementations by reducing the run-time complexity to a significant new level. Our architecture also allows for variable sized input DWI signals eliminating the need to worry about memory issues as seen with the traditional techniques. The advantage of this architecture is that it is perfectly desirable to be processed on a cloud setup and utilize the existing multi GPU frameworks to perform whole brain Tractography in minutes rather than hours. We evaluate our network with Gold Standard and benchmark its performance across several parameters.
A Multi-task Deep Learning Architecture for Maritime Surveillance using AIS Data Streams
Venue: arXiv cs.LG 2018
Year: 2018

In a world of global trading, maritime safety, security and efficiency are crucial issues. We propose a multi-task deep learning framework for vessel monitoring using Automatic Identification System (AIS) data streams. We combine recurrent neural networks with latent variable modeling and an embedding of AIS messages to a new representation space to jointly address key issues to be dealt with when considering AIS data streams: massive amount of streaming data, noisy data and irregular timesampling. We demonstrate the relevance of the proposed deep learning framework on real AIS datasets for a three-task setting, namely trajectory reconstruction, anomaly detection and vessel type identification.
The History of Software Architecture - In the Eye of the Practitioner
Venue: arXiv cs.SE 2018
Year: 2018

Software architecture (SA) is celebrating 25 years. This is so if we consider the seminal papers establishing SA as a distinct discipline and scientific publications that have identified cornerstones of both research and practice, like architecture views, architecture description languages, and architecture evaluation. With the pervasive use of cloud provisioning, the dynamic integration of multi-party distributed services, and the steep increase in the digitalization of business and society, making sound design decisions encompasses an increasingly-large and complex problem space. The role of SA is essential as never before, so much so that no organization undertakes `serious' projects without the support of suitable architecture practices. But, how did SA practice evolve in the past 25 years? and What are the challenges ahead? There have been various attempts to summarize the state of research and practice of SA. Still, we miss the practitioners' view on the questions above. To fill this gap, we have first extracted the top-10 topics resulting from the analysis of 5,622 scientific papers. Then, we have used such topics to design an online survey filled out by 57 SA practitioners with 5 to 20+ years of experience. We present the results of the survey with a special focus on the SA topics that SA practitioners perceive, in the past, present and future, as the most impactful. We finally use the results to draw preliminary takeaways.
Constructing Social Networks From Binary Data
Venue: arXiv cs.SI 2018
Year: 2018

Much of applied network analysis concerns with studying the existing relationships between a set of agents; however, little focus has been given to the considerations of how to represent observed phenomena as a network object. In the case of physical structures such as electric grids or transportation flows, the construction of a network model is fairly straightforward as the nodes and edges usually correspond to some physical structure themselves. On the other hand, construction of a social network is much less defined; while nodes may correspond to well defined social agents, such as people or groups of people, there much more liberty in defining the relationship that edges should represent. This paper studies the intricacies of constructing a social network from data, in particular, binary data, applicable to a wide range of social science contexts. We examine several methods of constructing social networks in prior literature and discuss the methods under a common framework. Finally, using a data set of meetings among technologists in New York, we show that the different constructions of social networks arising from various interpretations of the underlying social relationships can result in vastly different network structures. These findings highlight the significance of understanding the precise relationships of interest when building a network model.
NeuroNet: Fast and Robust Reproduction of Multiple Brain Image Segmentation Pipelines
Venue: arXiv cs.CV 2018
Year: 2018

NeuroNet is a deep convolutional neural network mimicking multiple popular and state-of-the-art brain segmentation tools including FSL, SPM, and MALPEM. The network is trained on 5,000 T1-weighted brain MRI scans from the UK Biobank Imaging Study that have been automatically segmented into brain tissue and cortical and sub-cortical structures using the standard neuroimaging pipelines. Training a single model from these complementary and partially overlapping label maps yields a new powerful "all-in-one", multi-output segmentation tool. The processing time for a single subject is reduced by an order of magnitude compared to running each individual software package. We demonstrate very good reproducibility of the original outputs while increasing robustness to variations in the input data. We believe NeuroNet could be an important tool in large-scale population imaging studies and serve as a new standard in neuroscience by reducing the risk of introducing bias when choosing a specific software package.
Lecture Notes on Fair Division
Venue: arXiv cs.AI 2018
Year: 2018

Fair division is the problem of dividing one or several goods amongst two or more agents in a way that satisfies a suitable fairness criterion. These Notes provide a succinct introduction to the field. We cover three main topics. First, we need to define what is to be understood by a "fair" allocation of goods to individuals. We present an overview of the most important fairness criteria (as well as the closely related criteria for economic efficiency) developed in the literature, together with a short discussion of their axiomatic foundations. Second, we give an introduction to cake-cutting procedures as an example of methods for fairly dividing a single divisible resource amongst a group of individuals. Third, we discuss the combinatorial optimisation problem of fairly allocating a set of indivisible goods to a group of agents, covering both centralised algorithms (similar to auctions) and a distributed approach based on negotiation. While the classical literature on fair division has largely developed within Economics, these Notes are specifically written for readers with a background in Computer Science or similar, and who may be (or may wish to be) engaged in research in Artificial Intelligence, Multiagent Systems, or Computational Social Choice. References for further reading, as well as a small number of exercises, are included. Notes prepared for a tutorial at the 11th European Agent Systems Summer School (EASSS-2009), Torino, Italy, 31 August and 1 September 2009. Updated for a tutorial at the COST-ADT Doctoral School on Computational Social Choice, Estoril, Portugal, 9--14 April 2010.
Augmenting Stream Constraint Programming with Eventuality Conditions
Venue: arXiv cs.AI 2018
Year: 2018

Stream constraint programming is a recent addition to the family of constraint programming frameworks, where variable domains are sets of infinite streams over finite alphabets. Previous works showed promising results for its applicability to real-world planning and control problems. In this paper, motivated by the modelling of planning applications, we improve the expressiveness of the framework by introducing 1) the "until" constraint, a new construct that is adapted from Linear Temporal Logic and 2) the @ operator on streams, a syntactic sugar for which we provide a more efficient solving algorithm over simple desugaring. For both constructs, we propose corresponding novel solving algorithms and prove their correctness. We present competitive experimental results on the Missionaries and Cannibals logic puzzle and a standard path planning application on the grid, by comparing with Apt and Brand's method for verifying eventuality conditions using a CP approach.
Sparse, Collaborative, or Nonnegative Representation: Which Helps Pattern Classification?
Venue: arXiv cs.CV 2018
Year: 2018

The use of sparse representation (SR) and collaborative representation (CR) for pattern classification has been widely studied in tasks such as face recognition and object categorization. Despite the success of SR/CR based classifiers, it is still arguable whether it is the $\ell_{1}$-norm sparsity or the $\ell_{2}$-norm collaborative property that brings the success of SR/CR based classification. In this paper, we investigate the use of nonnegative representation (NR) for pattern classification, which is largely ignored by previous work. Our analyses reveal that NR can boost the representation power of homogeneous samples while limiting the representation power of heterogeneous samples, making the representation sparse and discriminative simultaneously and thus providing a more effective solution to representation based classification than SR/CR. Our experiments demonstrate that the proposed NR based classifier (NRC) outperforms previous representation based classifiers. With deep features as inputs, it also achieves state-of-the-art performance on various visual classification tasks.
Projecting Embeddings for Domain Adaptation: Joint Modeling of Sentiment Analysis in Diverse Domains
Venue: arXiv cs.CL 2018
Year: 2018

Domain adaptation for sentiment analysis is challenging due to the fact that supervised classifiers are very sensitive to changes in domain. The two most prominent approaches to this problem are structural correspondence learning and autoencoders. However, they either require long training times or suffer greatly on highly divergent domains. Inspired by recent advances in cross-lingual sentiment analysis, we provide a novel perspective and cast the domain adaptation problem as an embedding projection task. Our model takes as input two mono-domain embedding spaces and learns to project them to a bi-domain space, which is jointly optimized to (1) project across domains and to (2) predict sentiment. We perform domain adaptation experiments on 20 source-target domain pairs for sentiment classification and report novel state-of-the-art results on 11 domain pairs, including the Amazon domain adaptation datasets and SemEval 2013 and 2016 datasets. Our analysis shows that our model performs comparably to state-of-the-art approaches on domains that are similar, while performing significantly better on highly divergent domains. Our code is available at https://github.com/jbarnesspain/domain_blse
Rate Control under Finite Blocklength for Downlink Cellular Networks with Reliability Constraints
Venue: arXiv cs.NI 2018
Year: 2018

Coming cellular systems are envisioned to open up to new services and applications with high reliability and low latency requirements. In this paper we focus on the rate allocation problem in downlink cellular networks with Rayleigh fading and stringent reliability constraints. We propose a rate control strategy to cope with those requirements making use only of topological characteristics of the scenario, the reliability constraint and the number of antennas that are available at the receiver side. Numerical results show the feasibility of the ultra-reliable operation when the number of antennas increases, and also that our results remain valid even when operating at short blocklength as far as the amount of information to be transmitted is not too small.