OpenRaiser/Intern-Atlas huggingface dataset:
Segmenting root systems in X-ray computed tomography images using level sets
Venue: arXiv cs.CV 2018
Year: 2018

The segmentation of plant roots from soil and other growing media in X-ray computed tomography images is needed to effectively study the root system architecture without excavation. However, segmentation is a challenging problem in this context because the root and non-root regions share similar features. In this paper, we describe a method based on level sets and specifically adapted for this segmentation problem. In particular, we deal with the issues of using a level sets approach on large image volumes for root segmentation, and track active regions of the front using an occupancy grid. This method allows for straightforward modifications to a narrow-band algorithm such that excessive forward and backward movements of the front can be avoided, distance map computations in a narrow band context can be done in linear time through modification of Meijster et al.'s distance transform algorithm, and regions of the image volume are iteratively used to estimate distributions for root versus non-root classes. Results are shown of three plant species of different maturity levels, grown in three different media. Our method compares favorably to a state-of-the-art method for root segmentation in X-ray CT image volumes.
Hidden Markov Model Estimation-Based Q-learning for Partially Observable Markov Decision Process
Venue: arXiv cs.LG 2018
Year: 2018

The objective is to study an on-line Hidden Markov model (HMM) estimation-based Q-learning algorithm for partially observable Markov decision process (POMDP) on finite state and action sets. When the full state observation is available, Q-learning finds the optimal action-value function given the current action (Q function). However, Q-learning can perform poorly when the full state observation is not available. In this paper, we formulate the POMDP estimation into a HMM estimation problem and propose a recursive algorithm to estimate both the POMDP parameter and Q function concurrently. Also, we show that the POMDP estimation converges to a set of stationary points for the maximum likelihood estimate, and the Q function estimation converges to a fixed point that satisfies the Bellman optimality equation weighted on the invariant distribution of the state belief determined by the HMM estimation process.
Computing Wasserstein Distance for Persistence Diagrams on a Quantum Computer
Venue: arXiv cs.CG 2018
Year: 2018

Persistence diagrams are a useful tool from topological data analysis which can be used to provide a concise description of a filtered topological space. What makes them even more useful in practice is that they come with a notion of a metric, the Wasserstein distance (closely related to but not the same as the homonymous metric from probability theory). Further, this metric provides a notion of stability; that is, small noise in the input causes at worst small differences in the output. In this paper, we show that the Wasserstein distance for persistence diagrams can be computed through quantum annealing. We provide a formulation of the problem as a Quadratic Unconstrained Binary Optimization problem, or QUBO, and prove correctness. Finally, we test our algorithm, exploring parameter choices and problem size capabilities, using a D-Wave 2000Q quantum annealing computer.
LMap: Shape-Preserving Local Mappings for Biomedical Visualization
Venue: arXiv cs.CV 2018
Year: 2018

Visualization of medical organs and biological structures is a challenging task because of their complex geometry and the resultant occlusions. Global spherical and planar mapping techniques simplify the complex geometry and resolve the occlusions to aid in visualization. However, while resolving the occlusions these techniques do not preserve the geometric context, making them less suitable for mission-critical biomedical visualization tasks. In this paper, we present a shape-preserving local mapping technique for resolving occlusions locally while preserving the overall geometric context. More specifically, we present a novel visualization algorithm, LMap, for conformally parameterizing and deforming a selected local region-of-interest (ROI) on an arbitrary surface. The resultant shape-preserving local mappings help to visualize complex surfaces while preserving the overall geometric context. The algorithm is based on the robust and efficient extrinsic Ricci flow technique, and uses the dynamic Ricci flow algorithm to guarantee the existence of a local map for a selected ROI on an arbitrary surface. We show the effectiveness and efficacy of our method in three challenging use cases: (1) multimodal brain visualization, (2) optimal coverage of virtual colonoscopy centerline flythrough, and (3) molecular surface visualization.
Ground vehicle odometry using a non-intrusive inertial speed sensor
Venue: arXiv cs.SY 2018
Year: 2018

This paper describes the design and development of a non-intrusive inertial speed sensor that can be reliably used to replace a conventional optical or hall effect-based speedometer on any kind of ground vehicle. The design allows for simple assembly-disassembly from tyre rims. The sensor design and data flow are explained. Algorithms and filters for pre-processing and processing the data are detailed. Comparison with a real optical encoder proves the accuracy of the proposed sensor. Finally, it is shown that factor graph-based localization is possible with the developed sensor.
Transfer Entropy in MDPs with Temporal Logic Specifications
Venue: arXiv cs.SY 2018
Year: 2018

Emerging applications in autonomy require control techniques that take into account uncertain environments, communication and sensing constraints, while satisfying highlevel mission specifications. Motivated by this need, we consider a class of Markov decision processes (MDPs), along with a transfer entropy cost function. In this context, we study highlevel mission specifications as co-safe linear temporal logic (LTL) formulae. We provide a method to synthesize a policy that minimizes the weighted sum of the transfer entropy and the probability of failure to satisfy the specification. We derive a set of coupled non-linear equations that an optimal policy must satisfy. We then use a modified Arimoto-Blahut algorithm to solve the non-linear equations. Finally, we demonstrated the proposed method on a navigation and path planning scenario of a Mars rover.
HashTran-DNN: A Framework for Enhancing Robustness of Deep Neural Networks against Adversarial Malware Samples
Venue: arXiv cs.CR 2018
Year: 2018

Adversarial machine learning in the context of image processing and related applications has received a large amount of attention. However, adversarial machine learning, especially adversarial deep learning, in the context of malware detection has received much less attention despite its apparent importance. In this paper, we present a framework for enhancing the robustness of Deep Neural Networks (DNNs) against adversarial malware samples, dubbed Hashing Transformation Deep Neural Networks} (HashTran-DNN). The core idea is to use hash functions with a certain locality-preserving property to transform samples to enhance the robustness of DNNs in malware classification. The framework further uses a Denoising Auto-Encoder (DAE) regularizer to reconstruct the hash representations of samples, making the resulting DNN classifiers capable of attaining the locality information in the latent space. We experiment with two concrete instantiations of the HashTran-DNN framework to classify Android malware. Experimental results show that four known attacks can render standard DNNs useless in classifying Android malware, that known defenses can at most defend three of the four attacks, and that HashTran-DNN can effectively defend against all of the four attacks.
Performance Analysis of Symmetric Key Ciphers in Linear and Grid Based Sensor Networks
Venue: arXiv cs.CR 2018
Year: 2018

The linear and grid based Wireless Sensor Networks (WSN) are formed by applications where objects being monitored are either placed in linear or grid based form. E.g. monitoring oil, water or gas pipelines; perimeter surveillance; monitoring traffic level of city streets, goods warehouse monitoring. The security of data is a critical issue for all such applications and as the devices used for the monitoring purpose have several resource constraints (bandwidth, storage capacity, battery life); it is significant to have a lightweight security solution. Therefore, we consider symmetric key based solutions proposed in the literature as asymmetric based solutions require more computation, energy and storage of keys. We analyse the symmetric ciphers with respect to the performance parameters: RAM, ROM consumption and number of CPU cycles. We perform this simulation analysis in Contiki Cooja by considering an example scenario on two different motes namely: Sky and Z1. The aim of this analysis is to come up with the best suited symmetric key based cipher for the linear and grid based WSN.
Talking to myself: self-dialogues as data for conversational agents
Venue: arXiv cs.CL 2018
Year: 2018

Conversational agents are gaining popularity with the increasing ubiquity of smart devices. However, training agents in a data driven manner is challenging due to a lack of suitable corpora. This paper presents a novel method for gathering topical, unstructured conversational data in an efficient way: self-dialogues through crowd-sourcing. Alongside this paper, we include a corpus of 3.6 million words across 23 topics. We argue the utility of the corpus by comparing self-dialogues with standard two-party conversations as well as data from other corpora.
Local Reconstruction Codes: A Class of MDS-PIR Capacity-Achieving Codes
Venue: arXiv cs.IT 2018
Year: 2018

We prove that a class of distance-optimal local reconstruction codes (LRCs), an important family of repair-efficient codes for distributed storage systems, achieve the maximum distance separable private information retrieval capacity for the case of noncolluding nodes. This particular class of codes includes Pyramid codes and other LRCs proposed in the literature.
Adding Cues to Binary Feature Descriptors for Visual Place Recognition
Venue: arXiv cs.RO 2018
Year: 2018

In this paper we propose an approach to embed continuous and selector cues in binary feature descriptors used for visual place recognition. The embedding is achieved by extending each feature descriptor with a binary string that encodes a cue and supports the Hamming distance metric. Augmenting the descriptors in such a way has the advantage of being transparent to the procedure used to compare them. We present two concrete applications of our methodology, demonstrating the two considered types of cues. In addition to that, we conducted on these applications a broad quantitative and comparative evaluation covering five benchmark datasets and several state-of-the-art image retrieval approaches in combination with various binary descriptor types.
Visual Diagnostics for Deep Reinforcement Learning Policy Development
Venue: arXiv cs.LG 2018
Year: 2018

Modern vision-based reinforcement learning techniques often use convolutional neural networks (CNN) as universal function approximators to choose which action to take for a given visual input. Until recently, CNNs have been treated like black-box functions, but this mindset is especially dangerous when used for control in safety-critical settings. In this paper, we present our extensions of CNN visualization algorithms to the domain of vision-based reinforcement learning. We use a simulated drone environment as an example scenario. These visualization algorithms are an important tool for behavior introspection and provide insight into the qualities and flaws of trained policies when interacting with the physical world. A video may be seen at https://sites.google.com/view/drlvisual .
HDTCat: let's make HDT scale
Venue: arXiv cs.DB 2018
Year: 2018

HDT (Header, Dictionary, Triples) is a serialization for RDF. HDT has become very popular in the last years because it allows to store RDF data with a small disk footprint, while remaining at the same time queriable. For this reason HDT is often used when scalability becomes an issue. Once RDF data is serialized into HDT, the disk footprint to store it and the memory footprint to query it are very low. However, generating HDT files from raw text RDF serializations (like N-Triples) is a time-consuming and (especially) memory-consuming task. In this publication we present HDTCat, an algorithm and command line tool to join two HDT files with low memory footprint. HDTCat can be used in a divide-and-conquer strategy to generate HDT files from huge datasets using a low-memory footprint.
Security and Protocol Exploit Analysis of the 5G Specifications
Venue: arXiv cs.CR 2018
Year: 2018

The Third Generation Partnership Project (3GPP) released its first 5G security specifications in March 2018. This paper reviews the 5G security architecture, requirements and main processes and evaluates them in the context of known and new protocol exploits. Although the security has been enhanced when compared to previous generations to tackle known protocol exploits, our analysis identifies some potentially unrealistic system assumptions that are critical for security as well as a number protocol edge cases that could render 5G systems vulnerable to adversarial attacks. For example, null encryption and null authentication are supported and can be used in valid system configurations, and certain key security functions are still left outside of the scope of the specifications. Moreover, the prevention of pre-authentcation message exploits appears to rely on the implicit assumption of impractical carrier and roaming agreements and the management of public keys from all global operators. In parallel, existing threats such as International Mobile Subscriber Identity (IMSI) catchers are prevented only if the serving network enforces optional security features and if the UE knows the public key of the home network operator. The comparison with 4G LTE protocol exploits reveals that the 5G security specifications, as of Release 15, do not fully address the user privacy and network availability concerns, where one edge case can compromise the privacy, security and availability of 5G users and services.
Program Analysis of Commodity IoT Applications for Security and Privacy: Challenges and Opportunities
Venue: arXiv cs.CR 2018
Year: 2018

Recent advances in Internet of Things (IoT) have enabled myriad domains such as smart homes, personal monitoring devices, and enhanced manufacturing. IoT is now pervasive---new applications are being used in nearly every conceivable environment, which leads to the adoption of device-based interaction and automation. However, IoT has also raised issues about the security and privacy of these digitally augmented spaces. Program analysis is crucial in identifying those issues, yet the application and scope of program analysis in IoT remains largely unexplored by the technical community. In this paper, we study privacy and security issues in IoT that require program-analysis techniques with an emphasis on identified attacks against these systems and defenses implemented so far. Based on a study of five IoT programming platforms, we identify the key insights that result from research efforts in both the program analysis and security communities and relate the efficacy of program-analysis techniques to security and privacy issues. We conclude by studying recent IoT analysis systems and exploring their implementations. Through these explorations, we highlight key challenges and opportunities in calibrating for the environments in which IoT systems will be used.
Approximate Nash Region of the Gaussian Interference Channel with Noisy Output Feedback
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, an achievable $η$-Nash equilibrium ($η$-NE) region for the two-user Gaussian interference channel with noisy channel-output feedback is presented for all $η\geqslant 1$. This result is obtained in the scenario in which each transmitter-receiver pair chooses its own transmit-receive configuration in order to maximize its own individual information transmission rate. At an $η$-NE, any unilateral deviation by either of the pairs does not increase the corresponding individual rate by more than $η$ bits per channel use.
FastDeepIoT: Towards Understanding and Optimizing Neural Network Execution Time on Mobile and Embedded Devices
Venue: arXiv cs.LG 2018
Year: 2018

Deep neural networks show great potential as solutions to many sensing application problems, but their excessive resource demand slows down execution time, pausing a serious impediment to deployment on low-end devices. To address this challenge, recent literature focused on compressing neural network size to improve performance. We show that changing neural network size does not proportionally affect performance attributes of interest, such as execution time. Rather, extreme run-time nonlinearities exist over the network configuration space. Hence, we propose a novel framework, called FastDeepIoT, that uncovers the non-linear relation between neural network structure and execution time, then exploits that understanding to find network configurations that significantly improve the trade-off between execution time and accuracy on mobile and embedded devices. FastDeepIoT makes two key contributions. First, FastDeepIoT automatically learns an accurate and highly interpretable execution time model for deep neural networks on the target device. This is done without prior knowledge of either the hardware specifications or the detailed implementation of the used deep learning library. Second, FastDeepIoT informs a compression algorithm how to minimize execution time on the profiled device without impacting accuracy. We evaluate FastDeepIoT using three different sensing-related tasks on two mobile devices: Nexus 5 and Galaxy Nexus. FastDeepIoT further reduces the neural network execution time by $48\%$ to $78\%$ and energy consumption by $37\%$ to $69\%$ compared with the state-of-the-art compression algorithms.
Optimal Deployment of Drone Base Stations for Cellular Communication by Network-based Localization
Venue: arXiv cs.SY 2018
Year: 2018

Drone base stations can assist cellular networks in a variety of scenarios. To serve the maximum number of users in an area without apriori user distribution information, we proposed a two-stage algorithm to find the optimal deployment of drone base stations. The algorithm involves UTDOA positioning, coverage control and collision avoidance. To the best of our knowledge, the concept that uses network-based localization to optimize the deployment of drone-BSs has not been analyzed in the previous literature. Simulations are presented showing that the proposed algorithm outperforms random search algorithm in terms of the maximum number of severed users under the deployment of drone-BSs they found, with limited user densities.
New approach for solar tracking systems based on computer vision, low cost hardware and deep learning
Venue: arXiv cs.LG 2018
Year: 2018

In this work, a new approach for Sun tracking systems is presented. Due to the current system limitations regarding costs and operational problems, a new approach based on low cost, computer vision open hardware and deep learning has been developed. The preliminary tests carried out successfully in Plataforma solar de Almeria (PSA), reveal the great potential and show the new approach as a good alternative to traditional systems. The proposed approach can provide key variables for the Sun tracking system control like cloud movements prediction, block and shadow detection, atmospheric attenuation or measures of concentrated solar radiation, which can improve the control strategies of the system and therefore the system performance.
Latent Topic Conversational Models
Venue: arXiv cs.CL 2018
Year: 2018

Latent variable models have been a preferred choice in conversational modeling compared to sequence-to-sequence (seq2seq) models which tend to generate generic and repetitive responses. Despite so, training latent variable models remains to be difficult. In this paper, we propose Latent Topic Conversational Model (LTCM) which augments seq2seq with a neural latent topic component to better guide response generation and make training easier. The neural topic component encodes information from the source sentence to build a global "topic" distribution over words, which is then consulted by the seq2seq model at each generation step. We study in details how the latent representation is learnt in both the vanilla model and LTCM. Our extensive experiments contribute to better understanding and training of conditional latent models for languages. Our results show that by sampling from the learnt latent representations, LTCM can generate diverse and interesting responses. In a subjective human evaluation, the judges also confirm that LTCM is the overall preferred option.
Counting the uncountable: deep semantic density estimation from Space
Venue: arXiv cs.CV 2018
Year: 2018

We propose a new method to count objects of specific categories that are significantly smaller than the ground sampling distance of a satellite image. This task is hard due to the cluttered nature of scenes where different object categories occur. Target objects can be partially occluded, vary in appearance within the same class and look alike to different categories. Since traditional object detection is infeasible due to the small size of objects with respect to the pixel size, we cast object counting as a density estimation problem. To distinguish objects of different classes, our approach combines density estimation with semantic segmentation in an end-to-end learnable convolutional neural network (CNN). Experiments show that deep semantic density estimation can robustly count objects of various classes in cluttered scenes. Experiments also suggest that we need specific CNN architectures in remote sensing instead of blindly applying existing ones from computer vision.
Distributed transient frequency control in power networks
Venue: arXiv cs.SY 2018
Year: 2018

Modern power networks face increasing challenges in controlling their transient frequency behavior at acceptable levels due to low inertia and highly-dynamic units. This paper presents a distributed control strategy regulated on a subset of buses in a power network to maintain their transient frequencies in safe regions while preserving asymptotic stability of the overall system. Building on Lyapunov stability and set invariance theory, we formulate the transient frequency requirement and the asymptotic stability requirement as two separate constraints for the control input. Hereby, for each bus of interest, we synthesize a controller satisfying both constraints simultaneously. The controller is distributed and Lipschitz, guaranteeing the existence and uniqueness of the trajectories of the closed-loop system. Simulations on the IEEE 39-bus power network illustrate the results.