OpenRaiser/Intern-Atlas huggingface dataset:
Dronecrypt - An Efficient Cryptographic Framework for Small Aerial Drones
Venue: arXiv cs.CR 2019
Year: 2019

Aerial drones are becoming an integral part of application domains including but not limited to, military operations, package delivery, construction, monitoring and search/rescue operations. It is critical to ensure the cyber security of networked aerial drone systems in these applications. Standard cryptographic services can be deployed to provide basic security services; however, they have been shown to be inefficient in terms of energy and time consumption, especially for small aerial drones with resource-limited processors. Therefore, there is a significant need for an efficient cryptographic framework that can meet the requirements of small aerial drones. We propose an improved cryptographic framework for small aerial drones, which offers significant energy efficiency and speed advantages over standard cryptographic techniques. (i) We create (to the best of our knowledge) the first optimized public key infrastructure (PKI) based framework for small aerial drones, which provides energy efficient techniques by harnessing special precomputation methods and optimized elliptic curves. (ii) We also integrate recent light-weight symmetric primitives into our PKI techniques to provide a full-fledged cryptographic framework. (iii) We implemented standard counterparts and our proposed techniques on an actual small aerial drone (Crazyflie 2.0), and provided an in-depth energy analysis. Our experiments showed that our improved cryptographic framework achieves up to 35x lower energy consumption than its standard counterpart.
Expanding across time to deliver bandwidth efficiency and low latency
Venue: arXiv cs.NI 2019
Year: 2019

Datacenters need networks that support both low-latency and high-bandwidth packet delivery to meet the stringent requirements of modern applications. We present Opera, a dynamic network that delivers latency-sensitive traffic quickly by relying on multi-hop forwarding in the same way as expander-graph-based approaches, but provides near-optimal bandwidth for bulk flows through direct forwarding over time-varying source-to-destination circuits. The key to Opera's design is the rapid and deterministic reconfiguration of the network, piece-by-piece, such that at any moment in time the network implements an expander graph, yet, integrated across time, the network provides bandwidth-efficient single-hop paths between all racks. We show that Opera supports low-latency traffic with flow completion times comparable to cost-equivalent static topologies, while delivering up to 4x the bandwidth for all-to-all traffic and supporting 60% higher load for published datacenter workloads.
ESFNet: Efficient Network for Building Extraction from High-Resolution Aerial Images
Venue: arXiv cs.CV 2019
Year: 2019

Building footprint extraction from high-resolution aerial images is always an essential part of urban dynamic monitoring, planning and management. It has also been a challenging task in remote sensing research. In recent years, deep neural networks have made great achievement in improving accuracy of building extraction from remote sensing imagery. However, most of existing approaches usually require large amount of parameters and floating point operations for high accuracy, it leads to high memory consumption and low inference speed which are harmful to research. In this paper, we proposed a novel efficient network named ESFNet which employs separable factorized residual block and utilizes the dilated convolutions, aiming to preserve slight accuracy loss with low computational cost and memory consumption. Our ESFNet obtains a better trade-off between accuracy and efficiency, it can run at over 100 FPS on single Tesla V100, requires 6x fewer FLOPs and has 18x fewer parameters than state-of-the-art real-time architecture ERFNet while preserving similar accuracy without any additional context module, post-processing and pre-trained scheme. We evaluated our networks on WHU Building Dataset and compared it with other state-of-the-art architectures. The result and comprehensive analysis show that our networks are benefit for efficient remote sensing researches, and the idea can be further extended to other areas. The code is public available at: https://github.com/mrluin/ESFNet-Pytorch
Capacity of Fading Channels without Channel Side Information
Venue: arXiv cs.IT 2019
Year: 2019

There are currently a plurality of capacity theories of fading channels, including the ergodic capacity for fast fading channels and outage capacity for slow fading channels. However, analyses show that the outage capacity is a misconception. In this paper we use the 1st order Gaussian-Markov process with coherence coefficient $α$ as the unified model for slow and fast fading channels, the capacity of which without channel side information is studied. We demonstrate that the information rate of a fading channel has a structure that the rate of user message is always accompanied by a rate of channel information. The formula for the channel information rate is derived and turns out to be a non-increasing function of $α$. We prove that there is an asymptotically monotonic behavior of the user information rate with respect to $α$ when the input is independent, identically distributed and Gaussian in the high signal to noise ratio regime. It is further conjectured that the monotonic behavior of the user information rate with respect to $α$ is universal.
Corrigendum to "Counting Database Repairs that Satisfy Conjunctive Queries with Self-Joins"
Venue: arXiv cs.DB 2019
Year: 2019

The helping Lemma 7 in [Maslowski and Wijsen, ICDT, 2014] is false. The lemma is used in (and only in) the proof of Theorem 3 of that same paper. In this corrigendum, we provide a new proof for the latter theorem.
Trustworthy Experimentation Under Telemetry Loss
Venue: arXiv cs.OH 2019
Year: 2019

Failure to accurately measure the outcomes of an experiment can lead to bias and incorrect conclusions. Online controlled experiments (aka AB tests) are increasingly being used to make decisions to improve websites as well as mobile and desktop applications. We argue that loss of telemetry data (during upload or post-processing) can skew the results of experiments, leading to loss of statistical power and inaccurate or erroneous conclusions. By systematically investigating the causes of telemetry loss, we argue that it is not practical to entirely eliminate it. Consequently, experimentation systems need to be robust to its effects. Furthermore, we note that it is nontrivial to measure the absolute level of telemetry loss in an experimentation system. In this paper, we take a top-down approach towards solving this problem. We motivate the impact of loss qualitatively using experiments in real applications deployed at scale, and formalize the problem by presenting a theoretical breakdown of the bias introduced by loss. Based on this foundation, we present a general framework for quantitatively evaluating the impact of telemetry loss, and present two solutions to measure the absolute levels of loss. This framework is used by well-known applications at Microsoft, with millions of users and billions of sessions. These general principles can be adopted by any application to improve the overall trustworthiness of experimentation and data-driven decision making.
A Local Approach to Forward Model Learning: Results on the Game of Life Game
Venue: arXiv cs.AI 2019
Year: 2019

This paper investigates the effect of learning a forward model on the performance of a statistical forward planning agent. We transform Conway's Game of Life simulation into a single-player game where the objective can be either to preserve as much life as possible or to extinguish all life as quickly as possible. In order to learn the forward model of the game, we formulate the problem in a novel way that learns the local cell transition function by creating a set of supervised training data and predicting the next state of each cell in the grid based on its current state and immediate neighbours. Using this method we are able to harvest sufficient data to learn perfect forward models by observing only a few complete state transitions, using either a look-up table, a decision tree or a neural network. In contrast, learning the complete state transition function is a much harder task and our initial efforts to do this using deep convolutional auto-encoders were less successful. We also investigate the effects of imperfect learned models on prediction errors and game-playing performance, and show that even models with significant errors can provide good performance.
Ham-Sandwich cuts and center transversals in subspaces
Venue: arXiv cs.CG 2019
Year: 2019

The Ham-Sandwich theorem is a well-known result in geometry. It states that any $d$ mass distributions in $\mathbb{R}^d$ can be simultaneously bisected by a hyperplane. The result is tight, that is, there are examples of $d+1$ mass distributions that cannot be simultaneously bisected by a single hyperplane. In this abstract we will study the following question: given a continuous assignment of mass distributions to certain subsets of $\mathbb{R}^d$, is there a subset on which we can bisect more masses than what is guaranteed by the Ham-Sandwich theorem? We investigate two types of subsets. The first type are linear subspaces of $\mathbb{R}^d$, i.e., $k$-dimensional flats containing the origin. We show that for any continuous assignment of $d$ mass distributions to the $k$-dimensional linear subspaces of $\mathbb{R}^d$, there is always a subspace on which we can simultaneously bisect the images of all $d$ assignments. We extend this result to center transversals, a generalization of Ham-Sandwich cuts. As for Ham-Sandwich cuts, we further show that for $d-k+2$ masses, we can choose $k-1$ of the vectors defining the $k$-dimensional subspace in which the solution lies. The second type of subsets we consider are subsets that are determined by families of $n$ hyperplanes in $\mathbb{R}^d$. Also in this case, we find a Ham-Sandwich-type result. In an attempt to solve a conjecture by Langerman about bisections with several cuts, we show that our underlying topological result can be used to prove this conjecture in a relaxed setting.
Practical Synthesis of Reactive Systems from LTL Specifications via Parity Games
Venue: arXiv cs.LO 2019
Year: 2019

The synthesis of reactive systems from linear temporal logic (LTL) specifications is an important aspect in the design of reliable software and hardware. We present our adaption of the classic automata-theoretic approach to LTL synthesis, implemented in the tool Strix which has won the two last synthesis competitions (Syntcomp2018/2019). The presented approach is (1) structured, meaning that the states used in the construction have a semantic structure that is exploited in several ways, it performs a (2) forward exploration such that it often constructs only a small subset of the reachable states, and it is (3) incremental in the sense that it reuses results from previous inconclusive solution attempts. Further, we present and study different guiding heuristics that determine where to expand the on-demand constructed arena. Moreover, we show several techniques for extracting an implementation (Mealy machine or circuit) from the witness of the tree-automaton emptiness check. Lastly, the chosen constructions use a symbolic representation of the transition functions to reduce runtime and memory consumption. We evaluate the proposed techniques on the Syntcomp2019 benchmark set and show in more detail how the proposed techniques compare to the techniques implemented in other leading LTL synthesis tools.
Engineering problems in machine learning systems
Venue: arXiv cs.SE 2019
Year: 2019

Fatal accidents are a major issue hindering the wide acceptance of safety-critical systems that employ machine learning and deep learning models, such as automated driving vehicles. In order to use machine learning in a safety-critical system, it is necessary to demonstrate the safety and security of the system through engineering processes. However, thus far, no such widely accepted engineering concepts or frameworks have been established for these systems. The key to using a machine learning model in a deductively engineered system is decomposing the data-driven training of machine learning models into requirement, design, and verification, particularly for machine learning models used in safety-critical systems. Simultaneously, open problems and relevant technical fields are not organized in a manner that enables researchers to select a theme and work on it. In this study, we identify, classify, and explore the open problems in engineering (safety-critical) machine learning systems --- that is, in terms of requirement, design, and verification of machine learning models and systems --- as well as discuss related works and research directions, using automated driving vehicles as an example. Our results show that machine learning models are characterized by a lack of requirements specification, lack of design specification, lack of interpretability, and lack of robustness. We also perform a gap analysis on a conventional system quality standard SQuARE with the characteristics of machine learning models to study quality models for machine learning systems. We find that a lack of requirements specification and lack of robustness have the greatest impact on conventional quality models.
M2FPA: A Multi-Yaw Multi-Pitch High-Quality Database and Benchmark for Facial Pose Analysis
Venue: arXiv cs.CV 2019
Year: 2019

Facial images in surveillance or mobile scenarios often have large view-point variations in terms of pitch and yaw angles. These jointly occurred angle variations make face recognition challenging. Current public face databases mainly consider the case of yaw variations. In this paper, a new large-scale Multi-yaw Multi-pitch high-quality database is proposed for Facial Pose Analysis (M2FPA), including face frontalization, face rotation, facial pose estimation and pose-invariant face recognition. It contains 397,544 images of 229 subjects with yaw, pitch, attribute, illumination and accessory. M2FPA is the most comprehensive multi-view face database for facial pose analysis. Further, we provide an effective benchmark for face frontalization and pose-invariant face recognition on M2FPA with several state-of-the-art methods, including DR-GAN, TP-GAN and CAPG-GAN. We believe that the new database and benchmark can significantly push forward the advance of facial pose analysis in real-world applications. Moreover, a simple yet effective parsing guided discriminator is introduced to capture the local consistency during GAN optimization. Extensive quantitative and qualitative results on M2FPA and Multi-PIE demonstrate the superiority of our face frontalization method. Baseline results for both face synthesis and face recognition from state-of-theart methods demonstrate the challenge offered by this new database.
A "poor man's" approach for high-resolution three-dimensional topology optimization of natural convection problems
Venue: arXiv cs.CE 2019
Year: 2019

This paper treats topology optimization of natural convection problems. A simplified model is suggested to describe the flow of an incompressible fluid in steady state conditions, similar to Darcy's law for fluid flow in porous media. The equations for the fluid flow are coupled to the thermal convection-diffusion equation through the Boussinesq approximation. The coupled non-linear system of equations is discretized with stabilized finite elements and solved in a parallel framework that allows for the optimization of high resolution three-dimensional problems. A density-based topology optimization approach is used, where a two-material interpolation scheme is applied to both the permeability and conductivity of the distributed material. Due to the simplified model, the proposed methodology allows for a significant reduction of the computational effort required in the optimization. At the same time, it is significantly more accurate than even simpler models that rely on convection boundary conditions based on Newton's law of cooling. The methodology discussed herein is applied to the optimization-based design of three-dimensional heat sinks. The final designs are formally compared with results of previous work obtained from solving the full set of Navier-Stokes equations. The results are compared in terms of performance of the optimized designs and computational cost. The computational time is shown to be decreased to around 5-20% in terms of core-hours, allowing for the possibility of generating an optimized design during the workday on a small computational cluster and overnight on a high-end desktop.
FO = FO3 for linear orders with monotone binary relations
Venue: arXiv cs.LO 2019
Year: 2019

We show that over the class of linear orders with additional binary relations satisfying some monotonicity conditions, monadic first-order logic has the three-variable property. This generalizes (and gives a new proof of) several known results, including the fact that monadic first-order logic has the three-variable property over linear orders, as well as over (R,<,+1), and answers some open questions mentioned in a paper from Antonopoulos, Hunter, Raza and Worrell [FoSSaCS 2015]. Our proof is based on a translation of monadic first-order logic formulas into formulas of a star-free variant of Propositional Dynamic Logic, which are in turn easily expressible in monadic first-order logic with three variables.
Exploiting SIFT Descriptor for Rotation Invariant Convolutional Neural Network
Venue: arXiv cs.CV 2019
Year: 2019

This paper presents a novel approach to exploit the distinctive invariant features in convolutional neural network. The proposed CNN model uses Scale Invariant Feature Transform (SIFT) descriptor instead of the max-pooling layer. Max-pooling layer discards the pose, i.e., translational and rotational relationship between the low-level features, and hence unable to capture the spatial hierarchies between low and high level features. The SIFT descriptor layer captures the orientation and the spatial relationship of the features extracted by convolutional layer. The proposed SIFT Descriptor CNN therefore combines the feature extraction capabilities of CNN model and rotation invariance of SIFT descriptor. Experimental results on the MNIST and fashionMNIST datasets indicates reasonable improvements over conventional methods available in literature.
A HVS-inspired Attention to Improve Loss Metrics for CNN-based Perception-Oriented Super-Resolution
Venue: arXiv cs.CV 2019
Year: 2019

Deep Convolutional Neural Network (CNN) features have been demonstrated to be effective perceptual quality features. The perceptual loss, based on feature maps of pre-trained CNN's has proven to be remarkably effective for CNN based perceptual image restoration problems. In this work, taking inspiration from the the Human Visual System (HVS) and visual perception, we propose a spatial attention mechanism based on the dependency human contrast sensitivity on spatial frequency. We identify regions in input images, based on the underlying spatial frequency, which are not generally well reconstructed during Super-Resolution but are most important in terms of visual sensitivity. Based on this prior, we design a spatial attention map that is applied to feature maps in the perceptual loss and its variants, helping them to identify regions that are of more perceptual importance. The results demonstrate the our technique improves the ability of the perceptual loss and contextual loss to deliver more natural images in CNN based super-resolution.
Classification of Motorcycles using Extracted Images of Traffic Monitoring Videos
Venue: arXiv cs.CV 2019
Year: 2019

Due to the great growth of motorcycles in the urban fleet and the growth of the study on its behavior and of how this vehicle affects the flow of traffic becomes necessary the development of tools and techniques different from the conventional ones to identify its presence in the traffic flow and be able to extract your information. The article in question attempts to contribute to the study on this type of vehicle by generating a motorcycle image bank and developing and calibrating a motorcycle classifier by combining the LBP techniques to create the characteristic vectors and the classification technique LinearSVC to perform the predictions. In this way the classifier of vehicles of the type motorcycle developed in this research can classify the images of vehicles extracted of videos of monitoring between two classes motorcycles and non-motorcycles with a precision and an accuracy superior to 0,9.
Convex Stochastic Dominance in Bayesian Localization, Filtering and Controlled Sensing POMDPs
Venue: arXiv cs.SY 2019
Year: 2019

This paper provides conditions on the observation probability distribution in Bayesian localization and optimal filtering so that the conditional mean estimate satisfies convex stochastic dominance. Convex dominance allows us to compare the unconditional mean square error between two optimal Bayesian state estimators over arbitrary time horizons instead of using brute force Monte-Carlo computations. The proof uses two key ideas from microeconomics, namely, integral precision dominance and aggregation of single crossing.The convex dominance result is then used to give sufficient conditions so that the optimal policy of a controlled sensing two-state partially observed Markov decision process (POMDP) is lower bounded by a myopic policy. Numerical examples are presented where the Shannon capacity of the observation distribution using one sensor dominates that of another, and convex dominance holds but Blackwell dominance does not hold. These illustrate the usefulness of the main result in localization, filtering and controlled sensing applications.
Two-phase flow regime prediction using LSTM based deep recurrent neural network
Venue: arXiv cs.CV 2019
Year: 2019

Long short-term memory (LSTM) and recurrent neural network (RNN) has achieved great successes on time-series prediction. In this paper, a methodology of using LSTM-based deep-RNN for two-phase flow regime prediction is proposed, motivated by previous research on constructing deep RNN. The method is featured with fast response and accuracy. The built RNN networks are trained and tested with time-series void fraction data collected using impedance void meter. The result shows that the prediction accuracy depends on the depth of network and the number of layer cells. However, deeper and larger network consumes more time in predicting.
Using Scratch to Teach Undergraduate Students' Skills on Artificial Intelligence
Venue: arXiv cs.AI 2019
Year: 2019

This paper presents a educational workshop in Scratch that is proposed for the active participation of undergraduate students in contexts of Artificial Intelligence. The main objective of the activity is to demystify the complexity of Artificial Intelligence and its algorithms. For this purpose, students must realize simple exercises of clustering and two neural networks, in Scratch. The detailed methodology to get that is presented in the article.
Modeling Drug-Disease Relations with Linguistic and Knowledge Graph Constraints
Venue: arXiv cs.CL 2019
Year: 2019

FDA drug labels are rich sources of information about drugs and drug-disease relations, but their complexity makes them challenging texts to analyze in isolation. To overcome this, we situate these labels in two health knowledge graphs: one built from precise structured information about drugs and diseases, and another built entirely from a database of clinical narrative texts using simple heuristic methods. We show that Probabilistic Soft Logic models defined over these graphs are superior to text-only and relation-only variants, and that the clinical narratives graph delivers exceptional results with little manual effort. Finally, we release a new dataset of drug labels with annotations for five distinct drug-disease relations.