OpenRaiser/Intern-Atlas huggingface dataset:
Introduction of a Hybrid Monitor to Cyber-Physical Systems
Venue: arXiv cs.CR 2019
Year: 2019

Computing and technology have vastly changed since the introduction of firewall technology in 1988. The internet has grown from a simple network of networks to a cyber and physical entity that encompasses the entire planet. Cyber-physical systems(CPS) now control most of the day to day operations of human civilization from autonomous cars to nuclear energy plants. While phenomenal, this growth spurt has created new security threats. These are threats that cannot be blocked by a firewall for they are not only cyber but cyber-physical. In light of these new cyber-physical threats, this text proposes a security measure that promises to enhance the security of cyber-physical systems. Using theoretical cyber, physical, and cyber-physical attack scenarios, this text will highlight the need for some sort of monitor in cyber-physical systems as an extra security measure. Additionally, this text will illustrate the efficiency of said monitor using a Shannon entropy proof, and a multiple security domain nondeducibility(MSDND) proof.
Ring Compute-and-Forward over Block-Fading Channels
Venue: arXiv cs.IT 2019
Year: 2019

The Compute-and-Forward protocol in quasi-static channels normally employs lattice codes based on the rational integers $\mathbb{Z}$, Gaussian integers $\mathbb{Z}\left[i\right]$ or Eisenstein integers $\mathbb{Z}\left[ω\right]$, while its extension to more general channels often assumes channel state information at transmitters (CSIT). In this paper, we propose a novel scheme for Compute-and-Forward in block-fading channels without CSIT, which is referred to as Ring Compute-and-Forward because the fading coefficients are quantized to the canonical embedding of a ring of algebraic integers. Thanks to the multiplicative closure of the algebraic lattices employed, a relay is able to decode an algebraic-integer linear combination of lattice codewords. We analyze its achievable computation rates and show it outperforms conventional Compute-and-Forward based on $\mathbb{Z}$-lattices. By investigating the effect of Diophantine approximation by algebraic conjugates, we prove that the degrees-of-freedom (DoF) of the optimized computation rate is ${n}/{L}$, where $n$ is the number of blocks and $L$ is the number of users.
The Online Best Reply Algorithm for Resource Allocation Problems
Venue: arXiv cs.DS 2019
Year: 2019

We study the performance of a best reply algorithm for online resource allocation problems with a diseconomy of scale. In an online resource allocation problem, we are given a set of resources and a set of requests that arrive in an online manner. Each request consists of a set of feasible allocations and an allocation is a set of resources. The total cost of an allocation vector is given by the sum of the resources' costs, where each resource's cost depends on the total load on the resource under the allocation vector. We analyze the natural online procedure where each request is allocated greedily to a feasible set of resources that minimizes the individual cost of that particular request. In the literature, this algorithm is also known as a one-round walk-in congestion games starting from the empty state. For unweighted resource allocation problems with polynomial cost functions with maximum degree $d$, upper bounds on the competitive ratio of this greedy algorithm were known only for the special cases $d\in\{1, 2, 3\}$. In this paper, we show a general upper bound on the competitive ratio of $d(d / W(\frac{1.2d-1}{d+1}))^{d+1}$ for the unweighted case where $W$ denotes the Lambert-W function on $\mathbb{R}_{\geq 0}$. For the weighted case, we show that the competitive ratio of the greedy algorithm is bounded from above by $(d/W(\frac{d}{d+1}))^{d+1}$.
Relational Network for Skeleton-Based Action Recognition
Venue: arXiv cs.CV 2019
Year: 2019

With the fast development of effective and low-cost human skeleton capture systems, skeleton-based action recognition has attracted much attention recently. Most existing methods use Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) to extract spatio-temporal information embedded in the skeleton sequences for action recognition. However, these approaches are limited in the ability of relational modeling in a single skeleton, due to the loss of important structural information when converting the raw skeleton data to adapt to the input format of CNN or RNN. In this paper, we propose an Attentional Recurrent Relational Network-LSTM (ARRN-LSTM) to simultaneously model spatial configurations and temporal dynamics in skeletons for action recognition. We introduce the Recurrent Relational Network to learn the spatial features in a single skeleton, followed by a multi-layer LSTM to learn the temporal features in the skeleton sequences. Between the two modules, we design an adaptive attentional module to focus attention on the most discriminative parts in the single skeleton. To exploit the complementarity from different geometries in the skeleton for sufficient relational modeling, we design a two-stream architecture to learn the structural features among joints and lines simultaneously. Extensive experiments are conducted on several popular skeleton datasets and the results show that the proposed approach achieves better results than most mainstream methods.
Closed-form Preintegration Methods for Graph-based Visual-Inertial Navigation
Venue: arXiv cs.RO 2019
Year: 2019

In this paper we propose a new analytical preintegration theory for graph-based sensor fusion with an inertial measurement unit (IMU) and a camera (or other aiding sensors).Rather than using discrete sampling of the measurement dynamics as in current methods,we derive the closed-form solutions to the preintegration equations, yielding improved accuracy in state estimation.We advocate two new different inertial models for preintegration: (i) the model that assumes piecewise constant measurements, and (ii) the model that assumes piecewise constant local true acceleration.We show through extensive Monte-Carlo simulations the effect that the choice of preintegration model has on estimation performance.To validate the proposed preintegration theory, we develop both direct and indirect visual-inertial navigation systems (VINS) that leverage our preintegration.In the first, within a tightly-coupled, sliding-window optimization framework, we jointly estimate the features in the window and the IMU states while performing marginalization to bound the computational cost.In the second, we loosely-couple the IMU preintegration with a direct image alignment that estimates relative camera motion by minimizing the photometric errors (i.e., image intensity difference), allowing for efficient and informative loop closures. Both systems are extensively validated in real-world experiments and are shown to offer competitive performance to state-of-the-art methods.
DeepWalking: Enabling Smartphone-based Walking Speed Estimation Using Deep Learning
Venue: arXiv cs.CY 2019
Year: 2019

Walking speed estimation is an essential component of mobile apps in various fields such as fitness, transportation, navigation, and health-care. Most existing solutions are focused on specialized medical applications that utilize body-worn motion sensors. These approaches do not serve effectively the general use case of numerous apps where the user holding a smartphone tries to find his or her walking speed solely based on smartphone sensors. However, existing smartphone-based approaches fail to provide acceptable precision for walking speed estimation. This leads to a question: is it possible to achieve comparable speed estimation accuracy using a smartphone over wearable sensor based obtrusive solutions? We find the answer from advanced neural networks. In this paper, we present DeepWalking, the first deep learning-based walking speed estimation scheme for smartphone. A deep convolutional neural network (DCNN) is applied to automatically identify and extract the most effective features from the accelerometer and gyroscope data of smartphone and to train the network model for accurate speed estimation. Experiments are performed with 10 participants using a treadmill. The average root-mean-squared-error (RMSE) of estimated walking speed is 0.16m/s which is comparable to the results obtained by state-of-the-art approaches based on a number of body-worn sensors (i.e., RMSE of 0.11m/s). The results indicate that a smartphone can be a strong tool for walking speed estimation if the sensor data are effectively calibrated and supported by advanced deep learning techniques.
A User-Friendly Hybrid Sparse Matrix Class in C++
Venue: arXiv cs.MS 2019
Year: 2019

When implementing functionality which requires sparse matrices, there are numerous storage formats to choose from, each with advantages and disadvantages. To achieve good performance, several formats may need to be used in one program, requiring explicit selection and conversion between the formats. This can be both tedious and error-prone, especially for non-expert users. Motivated by this issue, we present a user-friendly sparse matrix class for the C++ language, with a high-level application programming interface deliberately similar to the widely used MATLAB language. The class internally uses two main approaches to achieve efficient execution: (i) a hybrid storage framework, which automatically and seamlessly switches between three underlying storage formats (compressed sparse column, coordinate list, Red-Black tree) depending on which format is best suited for specific operations, and (ii) template-based meta-programming to automatically detect and optimise execution of common expression patterns. To facilitate relatively quick conversion of research code into production environments, the class and its associated functions provide a suite of essential sparse linear algebra functionality (eg., arithmetic operations, submatrix manipulation) as well as high-level functions for sparse eigendecompositions and linear equation solvers. The latter are achieved by providing easy-to-use abstractions of the low-level ARPACK and SuperLU libraries. The source code is open and provided under the permissive Apache 2.0 license, allowing unencumbered use in commercial products.
Game Development-Based Learning Experience: Gender Differences in Game Design
Venue: arXiv cs.CY 2019
Year: 2019

Learning theories emphasize the importance of intrinsic and extrinsic motivators in curricula, and games are a promising way to provide both while constructing the game and presenting or sharing it in public or with a community. New technologies and the emerging mobile gaming sector further the case that learning should be promoted everywhere and anytime. What seems to be a promising opportunity for all pupils to learn coding in an entertaining way raises the question of whether such game based concepts also help to fix the gender gap of women in IT related fields. Gender differences are already present in secondary schools. These are the years where first career choices but also low levels of participation in technical subjects occur. To address this gender bias, a goal of the European project No One Left Behind (NOLB) was to integrate Pocket Code, an app developed by Catrobat, a free open source non-profit project at the University of Technology in Graz/Austria, into different school subjects, thus making coding more accessible and attractive to female pupils. During the period of this project (2015-2017), teachers were supported to guide their pupils in the learning processes by constructing ideas and realizing them through game design. For this paper an analysis of submitted programs according to their game design has been performed. In detail, the evaluation considered formal game elements, gaming structures, and used graphics, as well as Pocket Code specific aspects. The programs analysis showed commonly used design patterns by genders and suggests preferred game design characteristics of female teenagers. This analysis helps to build a more creative and inclusive Game Development-Based Learning (GDBL) environment that provides room for self-expression and inspires by building on intrinsic and extrinsic motivators by constructing personal experiences.
Social networks and geography: a review of the literature and its implications
Venue: arXiv cs.SI 2019
Year: 2019

The spatial metaphor of the network along with its accompanying abstractions, such as flow, movement, and connectivity, have been central themes throughout the relational turn in human geography. However, to date networks in geography have been primarily explored either through actor-network theory or assemblage thinking, both of which embrace the network metaphor without specifically and formally interrogating networks themselves. We seek to problematize the treatment of networks in geography by exploring the largely underutilized literature on social networks as an alternative to the now dominant actor-network and assemblage frameworks. Our paper discusses the conceptual connections between key concepts in geography, such as place, distance, scale, and power, and those in network theory, such as centrality, density, and homophily. Voluntarily written from the periphery of human geography, our paper opens new directions for geographers that are interested in more than the metaphor of the network.
Unsupervised Intuitive Physics from Visual Observations
Venue: arXiv cs.CV 2019
Year: 2019

While learning models of intuitive physics is an increasingly active area of research, current approaches still fall short of natural intelligences in one important regard: they require external supervision, such as explicit access to physical states, at training and sometimes even at test times. Some authors have relaxed such requirements by supplementing the model with an handcrafted physical simulator. Still, the resulting methods are unable to automatically learn new complex environments and to understand physical interactions within them. In this work, we demonstrated for the first time learning such predictors directly from raw visual observations and without relying on simulators. We do so in two steps: first, we learn to track mechanically-salient objects in videos using causality and equivariance, two unsupervised learning principles that do not require auto-encoding. Second, we demonstrate that the extracted positions are sufficient to successfully train visual motion predictors that can take the underlying environment into account. We validate our predictors on synthetic datasets; then, we introduce a new dataset, ROLL4REAL, consisting of real objects rolling on complex terrains (pool table, elliptical bowl, and random height-field). We show that in all such cases it is possible to learn reliable extrapolators of the object trajectories from raw videos alone, without any form of external supervision and with no more prior knowledge than the choice of a convolutional neural network architecture.
Prophets and Secretaries with Overbooking
Venue: arXiv cs.GT 2019
Year: 2019

The prophet and secretary problems demonstrate online scenarios involving the optimal stopping theory. In a typical prophet or secretary problem, selection decisions are assumed to be immediate and irrevocable. However, many online settings accommodate some degree of revocability. To study such scenarios, we introduce the $\ell-out-of-k$ setting, where the decision maker can select up to $k$ elements immediately and irrevocably, but her performance is measured by the top $\ell$ elements in the selected set. Equivalently, the decision makes can hold up to $\ell$ elements at any given point in time, but can make up to $k-\ell$ returns as new elements arrive. We give upper and lower bounds on the competitive ratio of $\ell$-out-of-$k$ prophet and secretary scenarios. These include a single-sample prophet algorithm that gives a competitive ratio of $1-\ell\cdot e^{-Θ\left(\frac{\left(k-\ell\right)^2}{k}\right)}$, which is asymptotically tight for $k-\ell=Θ(\ell)$. For secretary settings, we devise an algorithm that obtains a competitive ratio of $1-\ell e^{-\frac{k-8\ell}{2+2\ln \ell}} - e^{-k/6}$, and show that no secretary algorithm obtains a better ratio than $1-e^{-k}$ (up to negligible terms). In passing, our results lead to an improvement of the results of Assaf et al. [2000] for $1-out-of-k$ prophet scenarios. Beyond the contribution to online algorithms and optimal stopping theory, our results have implications to mechanism design. In particular, we use our prophet algorithms to derive {\em overbooking} mechanisms with good welfare and revenue guarantees; these are mechanisms that sell more items than the seller's capacity, then allocate to the agents with the highest values among the selected agents.
A Chaos Engineering System for Live Analysis and Falsification of Exception-handling in the JVM
Venue: arXiv cs.SE 2019
Year: 2019

Software systems contain resilience code to handle those failures and unexpected events happening in production. It is essential for developers to understand and assess the resilience of their systems. Chaos engineering is a technology that aims at assessing resilience and uncovering weaknesses by actively injecting perturbations in production. In this paper, we propose a novel design and implementation of a chaos engineering system in Java called ChaosMachine. It provides a unique and actionable analysis on exception-handling capabilities in production, at the level of try-catch blocks. To evaluate our approach, we have deployed ChaosMachine on top of 3 large-scale and well-known Java applications totaling 630k lines of code. Our results show that ChaosMachine reveals both strengths and weaknesses of the resilience code of a software system at the level of exception handling.
Photorealistic Image Reconstruction from Hybrid Intensity and Event based Sensor
Venue: arXiv cs.CV 2019
Year: 2019

Event sensors output a stream of asynchronous brightness changes (called ``events'') at a very high temporal rate. Previous works on recovering the lost intensity information from the event sensor data have heavily relied on the event stream, which makes the reconstructed images non-photorealistic and also susceptible to noise in the event stream. We propose to reconstruct photorealistic intensity images from a hybrid sensor consisting of a low frame rate conventional camera, which has the scene texture information, along with the event sensor. To accomplish our task, we warp the low frame rate intensity images to temporally dense locations of the event data by estimating a spatially dense scene depth and temporally dense sensor ego-motion. The results obtained from our algorithm are more photorealistic compared to any of the previous state-of-the-art algorithms. We also demonstrate our algorithm's robustness to abrupt camera motion and noise in the event sensor data.
Convex Hull Formation for Programmable Matter
Venue: arXiv cs.DC 2019
Year: 2019

We envision programmable matter as a system of nano-scale agents (called particles) with very limited computational capabilities that move and compute collectively to achieve a desired goal. We use the geometric amoebot model as our computational framework, which assumes particles move on the triangular lattice. Motivated by the problem of sealing an object using minimal resources, we show how a particle system can self-organize to form an object's convex hull. We give a distributed, local algorithm for convex hull formation and prove that it runs in $\mathcal{O}(B)$ asynchronous rounds, where $B$ is the length of the object's boundary. Within the same asymptotic runtime, this algorithm can be extended to also form the object's (weak) $\mathcal{O}$-hull, which uses the same number of particles but minimizes the area enclosed by the hull. Our algorithms are the first to compute convex hulls with distributed entities that have strictly local sensing, constant-size memory, and no shared sense of orientation or coordinates. Ours is also the first distributed approach to computing restricted-orientation convex hulls. This approach involves coordinating particles as distributed memory; thus, as a supporting but independent result, we present and analyze an algorithm for organizing particles with constant-size memory as distributed binary counters that efficiently support increments, decrements, and zero-tests --- even as the particles move.
On-Demand Virtual Research Environments using Microservices
Venue: arXiv cs.DC 2019
Year: 2019

The computational demands for scientific applications are continuously increasing. The emergence of cloud computing has enabled on-demand resource allocation. However, relying solely on infrastructure as a service does not achieve the degree of flexibility required by the scientific community. Here we present a microservice-oriented methodology, where scientific applications run in a distributed orchestration platform as software containers, referred to as on-demand, virtual research environments. The methodology is vendor agnostic and we provide an open source implementation that supports the major cloud providers, offering scalable management of scientific pipelines. We demonstrate applicability and scalability of our methodology in life science applications, but the methodology is general and can be applied to other scientific domains.
Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification
Venue: arXiv cs.LG 2019
Year: 2019

Correctly identifying sleep stages is important in diagnosing and treating sleep disorders. This work proposes a joint classification-and-prediction framework based on CNNs for automatic sleep staging, and, subsequently, introduces a simple yet efficient CNN architecture to power the framework. Given a single input epoch, the novel framework jointly determines its label (classification) and its neighboring epochs' labels (prediction) in the contextual output. While the proposed framework is orthogonal to the widely adopted classification schemes, which take one or multiple epochs as contextual inputs and produce a single classification decision on the target epoch, we demonstrate its advantages in several ways. First, it leverages the dependency among consecutive sleep epochs while surpassing the problems experienced with the common classification schemes. Second, even with a single model, the framework has the capacity to produce multiple decisions, which are essential in obtaining a good performance as in ensemble-of-models methods, with very little induced computational overhead. Probabilistic aggregation techniques are then proposed to leverage the availability of multiple decisions. We conducted experiments on two public datasets: Sleep-EDF Expanded with 20 subjects, and Montreal Archive of Sleep Studies dataset with 200 subjects. The proposed framework yields an overall classification accuracy of 82.3% and 83.6%, respectively. We also show that the proposed framework not only is superior to the baselines based on the common classification schemes but also outperforms existing deep-learning approaches. To our knowledge, this is the first work going beyond the standard single-output classification to consider multitask neural networks for automatic sleep staging. This framework provides avenues for further studies of different neural-network architectures for automatic sleep staging.