OpenRaiser/Intern-Atlas huggingface dataset:
Magnetic Resonance Spectroscopy Quantification using Deep Learning
Venue: arXiv cs.CV 2018
Year: 2018

Magnetic resonance spectroscopy (MRS) is an important technique in biomedical research and it has the unique capability to give a non-invasive access to the biochemical content (metabolites) of scanned organs. In the literature, the quantification (the extraction of the potential biomarkers from the MRS signals) involves the resolution of an inverse problem based on a parametric model of the metabolite signal. However, poor signal-to-noise ratio (SNR), presence of the macromolecule signal or high correlation between metabolite spectral patterns can cause high uncertainties for most of the metabolites, which is one of the main reasons that prevents use of MRS in clinical routine. In this paper, quantification of metabolites in MR Spectroscopic imaging using deep learning is proposed. A regression framework based on the Convolutional Neural Networks (CNN) is introduced for an accurate estimation of spectral parameters. The proposed model learns the spectral features from a large-scale simulated data set with different variations of human brain spectra and SNRs. Experimental results demonstrate the accuracy of the proposed method, compared to state of the art standard quantification method (QUEST), on concentration of 20 metabolites and the macromolecule.
Authentication as a service: Shamir Secret Sharing with byzantine components
Venue: arXiv cs.CR 2018
Year: 2018

We present a practical methodology for securing the password-based authentication scheme. We propose a solution based on the well-known (k,n) threshold scheme of Shamir for sharing a secret, where in our case the secret is the password itself and (k,n) threshold scheme means that n password-derived secrets (shares) are created and k less than n shares are necessary and sufficient for reconstructing the password, while k-1 are not sufficient. The scheme is information-theoretic secure. Since each of the n shares is stored on a different host (shareholder), an attacker will need to compromise k different shareholders for obtaining an amount of data sufficient for reconstructing the secret. Furthermore, in order to be resistant to the compromising of the server (dealer) coordinating the shareholders we define a variant of the classic Shamir, where the Shamir's abscissae are unknown to dealer and shareholders, making the reconstruction impossible even in the case of dealer and shareholders compromised. In addition we use the Pedersen technique for allowing the verification of shares. For the described scenario we have designed two protocols: the registration (user's sign-up, to be carried out once), and authentication (user's login). We analyse several scenarios where dealer and/or shareholders are partially/totally compromised and confirm that none of them is enabling the attacker to break the authentication. Furthermore we focus on cases where one or more byzantine servers are presented, analysing the impact on the framework and show the adopted mechanisms to be safe against these kinds of attacks. We have developed a prototype demonstrating that our framework works correctly, effectively and efficiently. It provides a first feasibility study that will provide a base for structured and engineered cloud-based implementations aiming at providing an authentication-as-a-service.
Trust Anchors in Software Defined Networks
Venue: arXiv cs.NI 2018
Year: 2018

Advances in software virtualization and network processing lead to increasing network softwarization. Software network elements running on commodity platforms replace or complement hardware components in cloud and mobile network infrastructure. However, such com- modity platforms have a large attack surface and often lack granular control and tight integration of the underlying hardware and software stack. Often, software network elements are either themselves vulnerable to software attacks or can be compromised through the bloated trusted computing base. To address this, we protect the core security assets of network elements - authentication credentials and cryptographic context - by provisioning them to and maintaining them exclusively in isolated execution environments. We complement this with a secure and scalable mechanism to enroll network elements into software defined networks. Our evaluation results show a negligible impact on run-time performance and only a moderate performance impact at the deployment stage.
Reducing Property Graph Queries to Relational Algebra for Incremental View Maintenance
Venue: arXiv cs.DB 2018
Year: 2018

The property graph data model of modern graph database systems is increasingly adapted for storing and processing heterogeneous datasets like networks. Many challenging applications with near real-time requirements -- e.g. financial fraud detection, recommendation systems, and on-the-fly validation -- can be captured with graph queries, which are evaluated repeatedly. To ensure quick response time for a changing data set, these applications would benefit from applying incremental view maintenance (IVM) techniques, which can perform continuous evaluation of queries and calculate the changes in the result set upon updates. However, currently, no graph databases provide support for incremental views. While IVM problems have been studied extensively over relational databases, views on property graph queries require operators outside the scope of standard relational algebra. Hence, tackling this problem requires the integration of numerous existing IVM techniques and possibly further extensions. In this paper, we present an approach to perform IVM on property graphs, using a nested relational algebraic representation for property graphs and graph operations. Then we define a chain of transformations to reduce most property graph queries to flat relational algebra and use techniques from discrimination networks (used in rule-based expert systems) to evaluate them. We demonstrate the approach using our prototype tool, ingraph, which uses openCypher, an open graph query language specified as part of an industry initiative. However, several aspects of our approach can be generalised to other graph query languages such as G-CORE and PGQL.
Localizing and Quantifying Damage in Social Media Images
Venue: arXiv cs.CV 2018
Year: 2018

Traditional post-disaster assessment of damage heavily relies on expensive GIS data, especially remote sensing image data. In recent years, social media has become a rich source of disaster information that may be useful in assessing damage at a lower cost. Such information includes text (e.g., tweets) or images posted by eyewitnesses of a disaster. Most of the existing research explores the use of text in identifying situational awareness information useful for disaster response teams. The use of social media images to assess disaster damage is limited. In this paper, we propose a novel approach, based on convolutional neural networks and class activation maps, to locate damage in a disaster image and to quantify the degree of the damage. Our proposed approach enables the use of social network images for post-disaster damage assessment and provides an inexpensive and feasible alternative to the more expensive GIS approach.
G2D: from GTA to Data
Venue: arXiv cs.CV 2018
Year: 2018

This document describes G2D, a software that enables capturing videos from Grand Theft Auto V (GTA V), a popular role playing game set in an expansive virtual city. The target users of our software are computer vision researchers who wish to collect hyper-realistic computer-generated imagery of a city from the street level, under controlled 6DOF camera poses and varying environmental conditions (weather, season, time of day, traffic density, etc.). G2D accesses/calls the native functions of the game; hence users can directly interact with G2D while playing the game. Specifically, G2D enables users to manipulate conditions of the virtual environment on the fly, while the gameplay camera is set to automatically retrace a predetermined 6DOF camera pose trajectory within the game coordinate system. Concurrently, automatic screen capture is executed while the virtual environment is being explored. G2D and its source code are publicly available at https://goo.gl/SS7fS6 In addition, we demonstrate an application of G2D to generate a large-scale dataset with groundtruth camera poses for testing structure-from-motion (SfM) algorithms. The dataset and generated 3D point clouds are also made available at https://goo.gl/DNzxHx
Two Stream Self-Supervised Learning for Action Recognition
Venue: arXiv cs.CV 2018
Year: 2018

We present a self-supervised approach using spatio-temporal signals between video frames for action recognition. A two-stream architecture is leveraged to tangle spatial and temporal representation learning. Our task is formulated as both a sequence verification and spatio-temporal alignment tasks. The former task requires motion temporal structure understanding while the latter couples the learned motion with the spatial representation. The self-supervised pre-trained weights effectiveness is validated on the action recognition task. Quantitative evaluation shows the self-supervised approach competence on three datasets: HMDB51, UCF101, and Honda driving dataset (HDD). Further investigations to boost performance and generalize validity are still required.
Improved Image Selection for Stack-Based HDR Imaging
Venue: arXiv cs.CV 2018
Year: 2018

Stack-based high dynamic range (HDR) imaging is a technique for achieving a larger dynamic range in an image by combining several low dynamic range images acquired at different exposures. Minimizing the set of images to combine, while ensuring that the resulting HDR image fully captures the scene's irradiance, is important to avoid long image acquisition and post-processing times. The problem of selecting the set of images has received much attention. However, existing methods either are not fully automatic, can be slow, or can fail to fully capture more challenging scenes. In this paper, we propose a fully automatic method for selecting the set of exposures to acquire that is both fast and more accurate. We show on an extensive set of benchmark scenes that our proposed method leads to improved HDR images as measured against ground truth using the mean squared error, a pixel-based metric, and a visible difference predictor and a quality score, both perception-based metrics.
Social media usage patterns during natural hazards
Venue: arXiv cs.SI 2018
Year: 2018

Natural hazards are becoming increasingly expensive as climate change and development are exposing communities to greater risks. Preparation and recovery are critical for climate change resilience, and social media are being used more and more to communicate before, during, and after disasters. While there is a growing body of research aimed at understanding how people use social media surrounding disaster events, most existing work has focused on a single disaster case study. In the present study, we analyze five of the costliest disasters in the last decade in the United States (Hurricanes Irene and Sandy, two sets of tornado outbreaks, and flooding in Louisiana) through the lens of Twitter. In particular, we explore the frequency of both generic and specific food-security related terms, and quantify the relationship between network size and Twitter activity during disasters. We find differences in tweet volume for keywords depending on disaster type, with people using Twitter more frequently in preparation for Hurricanes, and for real-time or recovery information for tornado and flooding events. Further, we find that people share a host of general disaster and specific preparation and recovery terms during these events. Finally, we find that among all account types, individuals with "average" sized networks are most likely to share information during these disasters, and in most cases, do so more frequently than normal. This suggests that around disasters, an ideal form of social contagion is being engaged in which average people rather than outsized influentials are key to communication. These results provide important context for the type of disaster information and target audiences that may be most useful for disaster communication during varying extreme events.
iCLAP: Shape Recognition by Combining Proprioception and Touch Sensing
Venue: arXiv cs.RO 2018
Year: 2018

For humans, both the proprioception and touch sensing are highly utilized when performing haptic perception. However, most approaches in robotics use only either proprioceptive data or touch data in haptic object recognition. In this paper, we present a novel method named Iterative Closest Labeled Point (iCLAP) to link the kinesthetic cues and tactile patterns fundamentally and also introduce its extensions to recognize object shapes. In the training phase, the iCLAP first clusters the features of tactile readings into a codebook and assigns these features with distinct label numbers. A 4D point cloud of the object is then formed by taking the label numbers of the tactile features as an additional dimension to the 3D sensor positions; hence, the two sensing modalities are merged to achieve a synthesized perception of the touched object. Furthermore, we developed and validated hybrid fusion strategies, product based and weighted sum based, to combine decisions obtained from iCLAP and single sensing modalities. Extensive experimentation demonstrates a dramatic improvement of object recognition using the proposed methods and it shows great potential to enhance robot perception ability.
Internet of Things (IoT) and Cloud Computing Enabled Disaster Management
Venue: arXiv cs.CY 2018
Year: 2018

Disaster management demands a near real-time information dissemina-tion so that the emergency services can be provided to the right people at the right time. Recent advances in information and communication technologies enable collection of real-time information from various sources. For example, sensors deployed in the fields collect data about the environment. Similarly, social networks like Twitter and Facebook can help to collect data from people in the disaster zone. On one hand, inadequate situation awareness in disasters has been identified as one of the primary factors in human errors with grave consequences such as loss of lives and destruction of critical infrastructure. On the other hand, the growing ubiquity of social media and mobile devices, and pervasive nature of the Internet-of-Things means that there are more sources of outbound traffic, which ultimately results in the creation of a data deluge, beginning shortly after the onset of disaster events, leading to the problem of information tsunami. In addition, security and privacy has crucial role to overcome the misuse of the system for either intrusions into data or overcome the misuse of the information that was meant for a specified purpose. .... In this chapter, we provide such a situation aware application to support disaster management data lifecycle, i.e. from data ingestion and processing to alert dissemination. We utilize cloud computing, Internet of Things and social computing technologies to achieve a scalable, effi-cient, and usable situation-aware application called Cloud4BigData.
Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems
Venue: arXiv cs.AI 2018
Year: 2018

Several researchers have argued that a machine learning system's interpretability should be defined in relation to a specific agent or task: we should not ask if the system is interpretable, but to whom is it interpretable. We describe a model intended to help answer this question, by identifying different roles that agents can fulfill in relation to the machine learning system. We illustrate the use of our model in a variety of scenarios, exploring how an agent's role influences its goals, and the implications for defining interpretability. Finally, we make suggestions for how our model could be useful to interpretability researchers, system developers, and regulatory bodies auditing machine learning systems.
Automatic detection of lumen and media in the IVUS images using U-Net with VGG16 Encoder
Venue: arXiv cs.CV 2018
Year: 2018

Coronary heart disease is one of the top rank leading cause of mortality in the world which can be because of plaque burden inside the arteries. Intravascular Ultrasound (IVUS) has been recognized as power- ful imaging technology which captures the real time and high resolution images of the coronary arteries and can be used for the analysis of these plaques. The IVUS segmentation involves the extraction of two arterial walls components namely, lumen and media. In this paper, we investi- gate the effectiveness of Convolutional Neural Networks including U-Net to segment ultrasound scans of arteries. In particular, the proposed seg- mentation network was built based on the the U-Net with the VGG16 encoder. Experiments were done for evaluating the proposed segmen- tation architecture which show promising quantitative and qualitative results.
An Analytics Tool for Exploring Scientific Software and Related Publications
Venue: arXiv cs.DL 2018
Year: 2018

Scientific software is one of the key elements for reproducible research. However, classic publications and related scientific software are typically not (sufficiently) linked, and it lacks tools to jointly explore these artefacts. In this paper, we report on our work on developing an analytics tool for jointly exploring software and publications. The presented prototype, a concept for automatic code discovery, and two use cases demonstrate the feasibility and usefulness of the proposal. Submitted to TPDL 2018 as Demonstration Paper.
Large-Scale Demonstration of Precise Demand Response Provided by Residential Heating Systems
Venue: arXiv cs.SY 2018
Year: 2018

Being able to adjust the demand of electricity can be an effective means for power system operators to compensate fluctuating renewable generation, to avoid grid congestion, and to cope with other contingencies. Electric heating and cooling systems of buildings can provide different demand response services because their electricity consumption is inherently flexible because of their thermal inertia. This paper reports on the results of a large-scale demand response demonstration involving a population of more than 300 residential buildings with heat pump installations. We show how the energetic behavior and flexibility of individual systems can be identified autonomously based only on energy meter data and outdoor air temperature measurements, and how the aggregate demand response potential of the population can be quantified. Various load reduction and rebound damping experiments illustrate the effectiveness of the approach: the load reductions can be predicted precisely and amount to 40-65% of the aggregate load, and the rebound can be damped efficiently.
Combinatorial Testing for Deep Learning Systems
Venue: arXiv cs.SE 2018
Year: 2018

Deep learning (DL) has achieved remarkable progress over the past decade and been widely applied to many safety-critical applications. However, the robustness of DL systems recently receives great concerns, such as adversarial examples against computer vision systems, which could potentially result in severe consequences. Adopting testing techniques could help to evaluate the robustness of a DL system and therefore detect vulnerabilities at an early stage. The main challenge of testing such systems is that its runtime state space is too large: if we view each neuron as a runtime state for DL, then a DL system often contains massive states, rendering testing each state almost impossible. For traditional software, combinatorial testing (CT) is an effective testing technique to reduce the testing space while obtaining relatively high defect detection abilities. In this paper, we perform an exploratory study of CT on DL systems. We adapt the concept in CT and propose a set of coverage criteria for DL systems, as well as a CT coverage guided test generation technique. Our evaluation demonstrates that CT provides a promising avenue for testing DL systems. We further pose several open questions and interesting directions for combinatorial testing of DL systems.
Hide and Seek tracker: Real-time recovery from target loss
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we examine the real-time recovery of a video tracker from a target loss, using information that is already available from the original tracker and without a significant computational overhead. More specifically, before using the tracker output to update the target position we estimate the detection confidence. In the case of a low confidence, the position update is rejected and the tracker passes to a single-frame failure mode, during which the patch low-level visual content is used to swiftly update the object position, before recovering from the target loss in the next frame. Orthogonally to this improvement, we further enhance the running average method used for creating the query model in tracking-through-similarity. The experimental evidence provided by evaluation on standard tracking datasets (OTB-50, OTB-100 and OTB-2013) validate that target recovery can be successfully achieved without compromising the real-time update of the target position.
Log-sum-exp neural networks and posynomial models for convex and log-log-convex data
Venue: arXiv cs.NE 2018
Year: 2018

We show in this paper that a one-layer feedforward neural network with exponential activation functions in the inner layer and logarithmic activation in the output neuron is an universal approximator of convex functions. Such a network represents a family of scaled log-sum exponential functions, here named LSET. Under a suitable exponential transformation, the class of LSET functions maps to a family of generalized posynomials GPOST, which we similarly show to be universal approximators for log-log-convex functions. A key feature of an LSET network is that, once it is trained on data, the resulting model is convex in the variables, which makes it readily amenable to efficient design based on convex optimization. Similarly, once a GPOST model is trained on data, it yields a posynomial model that can be efficiently optimized with respect to its variables by using geometric programming (GP). The proposed methodology is illustrated by two numerical examples, in which, first, models are constructed from simulation data of the two physical processes (namely, the level of vibration in a vehicle suspension system, and the peak power generated by the combustion of propane), and then optimization-based design is performed on these models.
Using Core-Periphery Structure to Predict High Centrality Nodes in Time-Varying Networks
Venue: arXiv cs.SI 2018
Year: 2018

Vertices with high betweenness and closeness centrality represent influential entities in a network. An important problem for time varying networks is to know a-priori, using minimal computation, whether the influential vertices of the current time step will retain their high centrality, in the future time steps, as the network evolves. In this paper, based on empirical evidences from several large real world time varying networks, we discover a certain class of networks where the highly central vertices are part of the innermost core of the network and this property is maintained over time. As a key contribution of this work, we propose novel heuristics to identify these networks in an optimal fashion and also develop a two-step algorithm for predicting high centrality vertices. Consequently, we show for the first time that for such networks, expensive shortest path computations in each time step as the network changes can be completely avoided; instead we can use time series models (e.g., ARIMA as used here) to predict the overlap between the high centrality vertices in the current time step to the ones in the future time steps. Moreover, once the new network is available in time, we can find the high centrality vertices in the top core simply based on their high degree.