OpenRaiser/Intern-Atlas huggingface dataset:
Spectrum Matching in Licensed Spectrum Sharing
Venue: arXiv cs.IT 2018
Year: 2018

Spectrum sharing is one of the promising solutions to meet the spectrum demand in 5G networks that results from the emerging services like machine to machine and vehicle to infrastructure communication. The idea is to allow a set of entities access the spectrum whenever and wherever it is unused by the licensed users. In the proposed framework, different spectrum provider (SP) networks with surplus spectrum available may rank the operators requiring the spectrum, called spectrum users (SUs) hereafter, differently in terms of their preference to lease spectrum, based for example on target business market considerations of the SUs. Similarly, SUs rank SPs depending on a number of criteria, for example based on coverage and availability in a service area. Ideally, both SPs and SUs prefer to provide/get spectrum to/from the operator of their first choice, but this is not necessarily always possible due to conflicting preferences. We apply matching theory algorithms with the aim to resolve the conflicting preferences of the SPs and SUs and quantify the effect of the proposed matching theory approach on establishing preferred (spectrum) provider-user network pairs. We discuss both one-to-one and many-to-one spectrum sharing scenarios and evaluate the performance using Monte Carlo simulations. The results show that comprehensive gains in terms of preferred matching of the provider-user network pairs can be achieved by applying matching theory for spectrum sharing as compared to uncoordinated spectrum allocation of the available spectrum to the SUs.
An experiment in distributed Internet address management using blockchains
Venue: arXiv cs.NI 2018
Year: 2018

The current system to manage the global pool of IP addresses is centralized in five transnational organizations, the Regional Internet Registries (RIRs). Each of these RIRs manage the address pool for a large number of countries. Because the RIRs are private organizations, they are subject to the legal framework of the country where they are based. This configuration results in a jurisdictional overflow from the legal framework of the countries where the RIR is based to all the countries that the RIRs are serving (the countries served by the RIRs de facto become subjects of the legal system of the country where the RIR is hosted). The situation is aggravated by the deployment of new security techniques such as the RPKI and BGPsec, that enable enforcement of allocations by the RIRs. In this paper we present InBlock, a blockchain-based distributed governance body aimed to provide de-centralized management of IP addresses. InBlock also aims to fulfil the same objectives as the current IP address allocation system, namely, uniqueness, fairness, conservation, aggregation, registration and minimized overhead. InBlock is implemented as a Decentralized Autonomous Organization, i.e., as a set of blockchain's smart contracts in Ethereum. Any entity may request an allocation of addresses to the InBlock registry by solely performing a (crypto)currency transfer to the InBlock. The fee required, along with the annual renewal fee, serves as a mechanism to deter stockpiling and other wasteful practices. As with any novel technology, there are many open questions about the usage of blockchains to build an IP address registry. For this reason, we believe that practical experimentation is required in order to have hands-on experiences about such a system. We propose to conduct an experiment on distributed address management using InBlock as a starting point to inform future directions in this area.
X2Face: A network for controlling face generation by using images, audio, and pose codes
Venue: arXiv cs.CV 2018
Year: 2018

The objective of this paper is a neural network model that controls the pose and expression of a given face, using another face or modality (e.g. audio). This model can then be used for lightweight, sophisticated video and image editing. We make the following three contributions. First, we introduce a network, X2Face, that can control a source face (specified by one or more frames) using another face in a driving frame to produce a generated frame with the identity of the source frame but the pose and expression of the face in the driving frame. Second, we propose a method for training the network fully self-supervised using a large collection of video data. Third, we show that the generation process can be driven by other modalities, such as audio or pose codes, without any further training of the network. The generation results for driving a face with another face are compared to state-of-the-art self-supervised/supervised methods. We show that our approach is more robust than other methods, as it makes fewer assumptions about the input data. We also show examples of using our framework for video face editing.
LiDAR and Camera Detection Fusion in a Real Time Industrial Multi-Sensor Collision Avoidance System
Venue: arXiv cs.CV 2018
Year: 2018

Collision avoidance is a critical task in many applications, such as ADAS (advanced driver-assistance systems), industrial automation and robotics. In an industrial automation setting, certain areas should be off limits to an automated vehicle for protection of people and high-valued assets. These areas can be quarantined by mapping (e.g., GPS) or via beacons that delineate a no-entry area. We propose a delineation method where the industrial vehicle utilizes a LiDAR {(Light Detection and Ranging)} and a single color camera to detect passive beacons and model-predictive control to stop the vehicle from entering a restricted space. The beacons are standard orange traffic cones with a highly reflective vertical pole attached. The LiDAR can readily detect these beacons, but suffers from false positives due to other reflective surfaces such as worker safety vests. Herein, we put forth a method for reducing false positive detection from the LiDAR by projecting the beacons in the camera imagery via a deep learning method and validating the detection using a neural network-learned projection from the camera to the LiDAR space. Experimental data collected at Mississippi State University's Center for Advanced Vehicular Systems (CAVS) shows the effectiveness of the proposed system in keeping the true detection while mitigating false positives.
Deep Learning Hyperspectral Image Classification Using Multiple Class-based Denoising Autoencoders, Mixed Pixel Training Augmentation, and Morphological Operations
Venue: arXiv cs.CV 2018
Year: 2018

Herein, we present a system for hyperspectral image segmentation that utilizes multiple class--based denoising autoencoders which are efficiently trained. Moreover, we present a novel hyperspectral data augmentation method for labelled HSI data using linear mixtures of pixels from each class, which helps the system with edge pixels which are almost always mixed pixels. Finally, we utilize a deep neural network and morphological hole-filling to provide robust image classification. Results run on the Salinas dataset verify the high performance of the proposed algorithm.
A Capsule Network for Traffic Speed Prediction in Complex Road Networks
Venue: arXiv cs.CV 2018
Year: 2018

This paper proposes a deep learning approach for traffic flow prediction in complex road networks. Traffic flow data from induction loop sensors are essentially a time series, which is also spatially related to traffic in different road segments. The spatio-temporal traffic data can be converted into an image where the traffic data are expressed in a 3D space with respect to space and time axes. Although convolutional neural networks (CNNs) have been showing surprising performance in understanding images, they have a major drawback. In the max pooling operation, CNNs are losing important information by locally taking the highest activation values. The inter-relationship in traffic data measured by sparsely located sensors in different time intervals should not be neglected in order to obtain accurate predictions. Thus, we propose a neural network with capsules that replaces max pooling by dynamic routing. This is the first approach that employs the capsule network on a time series forecasting problem, to our best knowledge. Moreover, an experiment on real traffic speed data measured in the Santander city of Spain demonstrates the proposed method outperforms the state-of-the-art method based on a CNN by 13.1% in terms of root mean squared error.
Learning associations between clinical information and motion-based descriptors using a large scale MR-derived cardiac motion atlas
Venue: arXiv cs.CV 2018
Year: 2018

The availability of large scale databases containing imaging and non-imaging data, such as the UK Biobank, represents an opportunity to improve our understanding of healthy and diseased bodily function. Cardiac motion atlases provide a space of reference in which the motion fields of a cohort of subjects can be directly compared. In this work, a cardiac motion atlas is built from cine MR data from the UK Biobank (~ 6000 subjects). Two automated quality control strategies are proposed to reject subjects with insufficient image quality. Based on the atlas, three dimensionality reduction algorithms are evaluated to learn data-driven cardiac motion descriptors, and statistical methods used to study the association between these descriptors and non-imaging data. Results show a positive correlation between the atlas motion descriptors and body fat percentage, basal metabolic rate, hypertension, smoking status and alcohol intake frequency. The proposed method outperforms the ability to identify changes in cardiac function due to these known cardiovascular risk factors compared to ejection fraction, the most commonly used descriptor of cardiac function. In conclusion, this work represents a framework for further investigation of the factors influencing cardiac health.
Sound Transpilation from Binary to Machine-Independent Code
Venue: arXiv cs.PL 2018
Year: 2018

In order to handle the complexity and heterogeneity of mod- ern instruction set architectures, analysis platforms share a common design, the adoption of hardware-independent intermediate representa- tions. The usage of these platforms to verify systems down to binary-level is appealing due to the high degree of automation they provide. How- ever, it introduces the need for trusting the correctness of the translation from binary code to intermediate language. Achieving a high degree of trust is challenging since this transpilation must handle (i) all the side effects of the instructions, (ii) multiple instruction encoding (e.g. ARM Thumb), and (iii) variable instruction length (e.g. Intel). We overcome these problems by formally modeling one of such intermediate languages in the interactive theorem prover HOL4 and by implementing a proof- producing transpiler. This tool translates ARMv8 programs to the in- termediate language and generates a HOL4 proof that demonstrates the correctness of the translation in the form of a simulation theorem. We also show how the transpiler theorems can be used to transfer properties verified on the intermediate language to the binary code.
An Algorithm for Learning Shape and Appearance Models without Annotations
Venue: arXiv cs.CV 2018
Year: 2018

This paper presents a framework for automatically learning shape and appearance models for medical (and certain other) images. It is based on the idea that having a more accurate shape and appearance model leads to more accurate image registration, which in turn leads to a more accurate shape and appearance model. This leads naturally to an iterative scheme, which is based on a probabilistic generative model that is fit using Gauss-Newton updates within an EM-like framework. It was developed with the aim of enabling distributed privacy-preserving analysis of brain image data, such that shared information (shape and appearance basis functions) may be passed across sites, whereas latent variables that encode individual images remain secure within each site. These latent variables are proposed as features for privacy-preserving data mining applications. The approach is demonstrated qualitatively on the KDEF dataset of 2D face images, showing that it can align images that traditionally require shape and appearance models trained using manually annotated data (manually defined landmarks etc.). It is applied to MNIST dataset of handwritten digits to show its potential for machine learning applications, particularly when training data is limited. The model is able to handle ``missing data'', which allows it to be cross-validated according to how well it can predict left-out voxels. The suitability of the derived features for classifying individuals into patient groups was assessed by applying it to a dataset of over 1,900 segmented T1-weighted MR images, which included images from the COBRE and ABIDE datasets.
Attention-based Active Visual Search for Mobile Robots
Venue: arXiv cs.RO 2018
Year: 2018

We present an active visual search model for finding objects in unknown environments. The proposed algorithm guides the robot towards the sought object using the relevant stimuli provided by the visual sensors. Existing search strategies are either purely reactive or use simplified sensor models that do not exploit all the visual information available. In this paper, we propose a new model that actively extracts visual information via visual attention techniques and, in conjunction with a non-myopic decision-making algorithm, leads the robot to search more relevant areas of the environment. The attention module couples both top-down and bottom-up attention models enabling the robot to search regions with higher importance first. The proposed algorithm is evaluated on a mobile robot platform in a 3D simulated environment. The results indicate that the use of visual attention significantly improves search, but the degree of improvement depends on the nature of the task and the complexity of the environment. In our experiments, we found that performance enhancements of up to 42\% in structured and 38\% in highly unstructured cluttered environments can be achieved using visual attention mechanisms.
CUPSMAN: Control User Plane Separation Based Routing in Ad-hoc Networks
Venue: arXiv cs.NI 2018
Year: 2018

Separation of user (data) plane from the control plane in networks helps scale resources independently, increase the quality of service and facilitate autonomy by employing software-defined networking techniques. Clustering introduces hierarchy in ad hoc networks where control functions can be carried out by some designated cluster heads. It is also an effective solution to handle challenges due to lack of centralized controllers and infrastructure in ad-hoc networks. Clustered network topologies gain a significant amount of scalability and reliability in comparison to flat topologies. Different roles that nodes have in a clustered network can be effectively used for routing as well. In this paper, we propose a novel plane-separated routing algorithm, Cluster-based Hybrid Routing Algorithm (CHRA). In CHRA, we take advantage of the hierarchical clustered structure through control and user plane separation (CUPS) in mobile ad-hoc networks. In the cluster neighborhood with a particular size, a link-state routing is used to minimize delay, control overhead, and also utilize energy consumption. For facilitating the communication with distant nodes, we form a routing backbone that is responsible for both control and data messages. The results show that CHRA outperforms its opponents in terms of fair energy consumption and end-to-end delay.
False Positive Reduction by Actively Mining Negative Samples for Pulmonary Nodule Detection in Chest Radiographs
Venue: arXiv cs.CV 2018
Year: 2018

Generating large quantities of quality labeled data in medical imaging is very time consuming and expensive. The performance of supervised algorithms for various tasks on imaging has improved drastically over the years, however the availability of data to train these algorithms have become one of the main bottlenecks for implementation. To address this, we propose a semi-supervised learning method where pseudo-negative labels from unlabeled data are used to further refine the performance of a pulmonary nodule detection network in chest radiographs. After training with the proposed network, the false positive rate was reduced to 0.1266 from 0.4864 while maintaining sensitivity at 0.89.
Gated Fusion Network for Joint Image Deblurring and Super-Resolution
Venue: arXiv cs.CV 2018
Year: 2018

Single-image super-resolution is a fundamental task for vision applications to enhance the image quality with respect to spatial resolution. If the input image contains degraded pixels, the artifacts caused by the degradation could be amplified by super-resolution methods. Image blur is a common degradation source. Images captured by moving or still cameras are inevitably affected by motion blur due to relative movements between sensors and objects. In this work, we focus on the super-resolution task with the presence of motion blur. We propose a deep gated fusion convolution neural network to generate a clear high-resolution frame from a single natural image with severe blur. By decomposing the feature extraction step into two task-independent streams, the dual-branch design can facilitate the training process by avoiding learning the mixed degradation all-in-one and thus enhance the final high-resolution prediction results. Extensive experiments demonstrate that our method generates sharper super-resolved images from low-resolution inputs with high computational efficiency.
Holographic Sensing
Venue: arXiv cs.IT 2018
Year: 2018

Holographic representations of data encode information in packets of equal importance that enable progressive recovery. The quality of recovered data improves as more and more packets become available. This progressive recovery of the information is independent of the order in which packets become available. Such representations are ideally suited for distributed storage and for the transmission of data packets over networks with unpredictable delays and or erasures. Several methods for holographic representations of signals and images have been proposed over the years and multiple description information theory also deals with such representations. Surprisingly, however, these methods had not been considered in the classical framework of optimal least-squares estimation theory, until very recently. We develop a least-squares approach to the design of holographic representation for stochastic data vectors, relying on the framework widely used in modeling signals and images.
Energy Contract Settlements through Automated Negotiation in Residential Cooperatives
Venue: arXiv cs.MA 2018
Year: 2018

This paper presents an automated peer-to-peer (P2P) negotiation strategy for settling energy contracts among prosumers in a Residential Energy Cooperative (REC) considering heterogeneous prosumer preferences. The heterogeneity arises from prosumers' evaluation of energy contracts through multiple societal and environmental criteria and the prosumers' private preferences over those criteria. The prosumers engage in bilateral negotiations with peers to mutually agree on periodical energy contracts/loans that consist of an energy volume to be exchanged at that period and the return time of the exchanged energy. The prosumers keep an ordered preference profile of possible energy contracts by evaluating the contracts from their own valuations on the entailed criteria, and iteratively offer the peers contracts until an agreement is formed. A prosumer embeds the valuations into a utility function that further considers uncertainties imposed by demand and generation profiles. Empirical evaluation on real demand, generation and storage profiles illustrates that the proposed negotiation based strategy is able to increase the system efficiency (measured by utilitarian social welfare) and fairness (measured by Nash social welfare) over a baseline strategy and an individual flexibility control strategy. We thus elicit system benefits from P2P flexibility exchange already with few agents and without central coordination, providing a simple yet flexible and effective paradigm that may complement existing markets.
A Comprehensive Review of Technologies Used for Screening, Assessment, and Rehabilitation of Autism Spectrum Disorder
Venue: arXiv cs.HC 2018
Year: 2018

Autism Spectrum Disorder (ASD) is an umbrella term for a wide range of developmental disorders. For the past two decades, researchers proposed the use of various technologies in order to tackle specific symptoms of the disorder. Although there exist many literature reviews about screening, assessment, and rehabilitation of ASD, no comprehensive survey of types of technologies in all defined symptoms of ASD has been presented. Therefore, in this paper a comprehensive survey of previous studies has been presented in which the studies are classified into three main categories, and several sub-categories, and three main technologies. An analysis of the number of studies in each category and sub-category is given to help researchers decide on areas which need further investigation. The analysis show that the majority of studies fall into the software-based systems technology category. Finally, a brief review of studies in each category of ASD is presented for each type of technology. As a result, this paper also helps researchers to obtain an overview of the typical methods of using a specific technology in ASD screening, assessment, and rehabilitation.
U-Finger: Multi-Scale Dilated Convolutional Network for Fingerprint Image Denoising and Inpainting
Venue: arXiv cs.CV 2018
Year: 2018

This paper studies the challenging problem of fingerprint image denoising and inpainting. To tackle the challenge of suppressing complicated artifacts (blur, brightness, contrast, elastic transformation, occlusion, scratch, resolution, rotation, and so on) while preserving fine textures, we develop a multi-scale convolutional network, termed U- Finger. Based on the domain expertise, we show that the usage of dilated convolutions as well as the removal of padding have important positive impacts on the final restoration performance, in addition to multi-scale cascaded feature modules. Our model achieves the overall ranking of No.2 in the ECCV 2018 Chalearn LAP Inpainting Competition Track 3 (Fingerprint Denoising and Inpainting). Among all participating teams, we obtain the MSE of 0.0231 (rank 2), PSNR 16.9688 dB (rank 2), and SSIM 0.8093 (rank 3) on the hold-out testing set.
Towards Good Practices on Building Effective CNN Baseline Model for Person Re-identification
Venue: arXiv cs.CV 2018
Year: 2018

Person re-identification is indeed a challenging visual recognition task due to the critical issues of human pose variation, human body occlusion, camera view variation, etc. To address this, most of the state-of-the-art approaches are proposed based on deep convolutional neural network (CNN), being leveraged by its strong feature learning power and classification boundary fitting capacity. Although the vital role towards person re-identification, how to build effective CNN baseline model has not been well studied yet. To answer this open question, we propose 3 good practices in this paper from the perspectives of adjusting CNN architecture and training procedure. In particular, they are adding batch normalization after the global pooling layer, executing identity categorization directly using only one fully-connected, and using Adam as optimizer. The extensive experiments on 3 widely-used benchmark datasets demonstrate that, our propositions essentially facilitate the CNN baseline model to achieve the state-of-the-art performance without any other high-level domain knowledge or low-level technical trick.