OpenRaiser/Intern-Atlas huggingface dataset:
Simulating and Evaluating Rebalancing Strategies for Dockless Bike-Sharing Systems
Venue: arXiv cs.CY 2020
Year: 2020

Following the growth of dock-based bike sharing systems as an eco-friendly solution for transportation in urban areas, Dockless systems are revolutionizing the market for the increased flexibility they offer to users. Bike redistribution is a common approach to improve service, and there exists extensive research considering static and dynamic rebalancing strategies for dock-based systems. We approach the dockless problem by defining abstract stations from trip start and end location frequency. This paper offers an optimizing Mixed Integer Program framework to model the effects of various bike repositioning strategies for dockless systems. We process 30 days worth of Singapore-based dockless bike data from September 2017 to extract trips. Pairing our mixed integer program with a demand model built from the processed data, we unveil trends between fleet size, lost demand, and magnitude of repositioning proper to the repositioning strategy employed. We also show that increasing repositioning potential does not always improve service performance.
KYPO4INDUSTRY: A Testbed for Teaching Cybersecurity of Industrial Control Systems
Venue: arXiv cs.CR 2020
Year: 2020

There are different requirements on cybersecurity of industrial control systems and information technology systems. This fact exacerbates the global issue of hiring cybersecurity employees with relevant skills. In this paper, we present KYPO4INDUSTRY training facility and a course syllabus for beginner and intermediate computer science students to learn cybersecurity in a simulated industrial environment. The training facility is built using open-source hardware and software and provides reconfigurable modules of industrial control systems. The course uses a flipped classroom format with hands-on projects: the students create educational games that replicate real cyber attacks. Throughout the semester, they learn to understand the risks and gain capabilities to respond to cyber attacks that target industrial control systems. Our described experience from the design of the testbed and its usage can help any educator interested in teaching cybersecurity of cyber-physical systems.
Invariants for Continuous Linear Dynamical Systems
Venue: arXiv cs.LO 2020
Year: 2020

Continuous linear dynamical systems are used extensively in mathematics, computer science, physics, and engineering to model the evolution of a system over time. A central technique for certifying safety properties of such systems is by synthesising inductive invariants. This is the task of finding a set of states that is closed under the dynamics of the system and is disjoint from a given set of error states. In this paper we study the problem of synthesising inductive invariants that are definable in o-minimal expansions of the ordered field of real numbers. In particular, assuming Schanuel's conjecture in transcendental number theory, we establish effective synthesis of o-minimal invariants in the case of semi-algebraic error sets. Without using Schanuel's conjecture, we give a procedure for synthesizing o-minimal invariants that contain all but a bounded initial segment of the orbit and are disjoint from a given semi-algebraic error set. We further prove that effective synthesis of semi-algebraic invariants that contain the whole orbit, is at least as hard as a certain open problem in transcendental number theory.
On averaging the best samples in evolutionary computation
Venue: arXiv cs.NE 2020
Year: 2020

Choosing the right selection rate is a long standing issue in evolutionary computation. In the continuous unconstrained case, we prove mathematically that a single parent $μ=1$ leads to a sub-optimal simple regret in the case of the sphere function. We provide a theoretically-based selection rate $μ/λ$ that leads to better progress rates. With our choice of selection rate, we get a provable regret of order $O(λ^{-1})$ which has to be compared with $O(λ^{-2/d})$ in the case where $μ=1$. We complete our study with experiments to confirm our theoretical claims.
Variance Reduction for Better Sampling in Continuous Domains
Venue: arXiv cs.NE 2020
Year: 2020

Design of experiments, random search, initialization of population-based methods, or sampling inside an epoch of an evolutionary algorithm use a sample drawn according to some probability distribution for approximating the location of an optimum. Recent papers have shown that the optimal search distribution, used for the sampling, might be more peaked around the center of the distribution than the prior distribution modelling our uncertainty about the location of the optimum. We confirm this statement, provide explicit values for this reshaping of the search distribution depending on the population size $λ$ and the dimension $d$, and validate our results experimentally.
Dynamic topic modeling of the COVID-19 Twitter narrative among U.S. governors and cabinet executives
Venue: arXiv cs.SI 2020
Year: 2020

A combination of federal and state-level decision making has shaped the response to COVID-19 in the United States. In this paper we analyze the Twitter narratives around this decision making by applying a dynamic topic model to COVID-19 related tweets by U.S. Governors and Presidential cabinet members. We use a network Hawkes binomial topic model to track evolving sub-topics around risk, testing and treatment. We also construct influence networks amongst government officials using Granger causality inferred from the network Hawkes process.
The magnetic field from a homogeneously magnetized cylindrical tile
Venue: arXiv cs.CE 2020
Year: 2020

The magnetic field of a homogeneously magnetized cylindrical tile geometry, i.e. an angular section of a finite hollow cylinder, is found. The field is expressed as the product between a tensor field describing the geometrical part of the problem and a column vector holding the magnetization of the tile. Outside the tile, the tensor is identical to the demagnetization tensor. We find that four components of the tensor, $N_{xy},N_{xz},N_{yz}$ and $N_{zy}$, can be expressed fully analytically, while the five remaining components, $N_{xx},N_{yx},N_{yy},N_{zx}$ and $N_{zz}$, contain integrals that have to be evaluated numerically. When evaluated numerically the tensor is symmetric. A comparison between the found solution, implemented in the open source magnetic framework MagTense, and a finite element calculation of the magnetic flux density of a cylindrical tile shows excellent agreement.
Target specific mining of COVID-19 scholarly articles using one-class approach
Venue: arXiv cs.LG 2020
Year: 2020

In recent years, several research articles have been published in the field of corona-virus caused diseases like severe acute respiratory syndrome (SARS), middle east respiratory syndrome (MERS) and COVID-19. In the presence of numerous research articles, extracting best-suited articles is time-consuming and manually impractical. The objective of this paper is to extract the activity and trends of corona-virus related research articles using machine learning approaches. The COVID-19 open research dataset (CORD-19) is used for experiments, whereas several target-tasks along with explanations are defined for classification, based on domain knowledge. Clustering techniques are used to create the different clusters of available articles, and later the task assignment is performed using parallel one-class support vector machines (OCSVMs). Experiments with original and reduced features validate the performance of the approach. It is evident that the k-means clustering algorithm, followed by parallel OCSVMs, outperforms other methods for both original and reduced feature space.
A Survey on Edge Performance Benchmarking
Venue: arXiv cs.DC 2020
Year: 2020

Edge computing is the next Internet frontier that will leverage computing resources located near users, sensors, and data stores to provide more responsive services. Therefore, it is envisioned that a large-scale, geographically dispersed, and resource-rich distributed system will emerge and play a key role in the future Internet. However, given the loosely coupled nature of such complex systems, their operational conditions are expected to change significantly over time. In this context, the performance characteristics of such systems will need to be captured rapidly, which is referred to as performance benchmarking, for application deployment, resource orchestration, and adaptive decision-making. Edge performance benchmarking is a nascent research avenue that has started gaining momentum over the past five years. This article first reviews articles published over the past three decades to trace the history of performance benchmarking from tightly coupled to loosely coupled systems. It then systematically classifies previous research to identify the system under test, techniques analyzed, and benchmark runtime in edge performance benchmarking.
A 12/7-approximation algorithm for the discrete Bamboo Garden Trimming problem
Venue: arXiv cs.DS 2020
Year: 2020

We study the discrete Bamboo Garden Trimming problem (BGT), where we are given n bamboos with different growth rates. At the end of each day, one can cut down one bamboo to height zero. The goal in BGT is to make a perpetual schedule of cuts such that the height of the tallest bamboo ever is minimized. Here, we improve the current best approximation guarantee by designing a 12/7-approximation algorithm. This result is based on a reduction to the Pinwheel Scheduling problem. We show that a guarantee of 12/7 is essentially the best we can hope for if our algorithm is based on this type of reduction.
Algebra-based Loop Synthesis
Venue: arXiv cs.PL 2020
Year: 2020

We present an algorithm for synthesizing program loops satisfying a given polynomial loop invariant. The class of loops we consider can be modeled by a system of algebraic recurrence equations with constant coefficients. We turn the task of loop synthesis into a polynomial constraint problem by precisely characterizing the set of all loops satisfying the given invariant. We prove soundness of our approach, as well as its completeness with respect to an a priori fixed upper bound on the number of program variables. Our work has applications towards program verification, as well as generating number sequences from algebraic relations. We implemented our work in the Absynth tool and report on our initial experiments with loop synthesis.
A Hybrid Approach Combining Control Theory and AI for Engineering Self-Adaptive Systems
Venue: arXiv cs.SE 2020
Year: 2020

Control theoretical techniques have been successfully adopted as methods for self-adaptive systems design to provide formal guarantees about the effectiveness and robustness of adaptation mechanisms. However, the computational effort to obtain guarantees poses severe constraints when it comes to dynamic adaptation. In order to solve these limitations, in this paper, we propose a hybrid approach combining software engineering, control theory, and AI to design for software self-adaptation. Our solution proposes a hierarchical and dynamic system manager with performance tuning. Due to the gap between high-level requirements specification and the internal knob behavior of the managed system, a hierarchically composed components architecture seek the separation of concerns towards a dynamic solution. Therefore, a two-layered adaptive manager was designed to satisfy the software requirements with parameters optimization through regression analysis and evolutionary meta-heuristic. The optimization relies on the collection and processing of performance, effectiveness, and robustness metrics w.r.t control theoretical metrics at the offline and online stages. We evaluate our work with a prototype of the Body Sensor Network (BSN) in the healthcare domain, which is largely used as a demonstrator by the community. The BSN was implemented under the Robot Operating System (ROS) architecture, and concerns about the system dependability are taken as adaptation goals. Our results reinforce the necessity of performing well on such a safety-critical domain and contribute with substantial evidence on how hybrid approaches that combine control and AI-based techniques for engineering self-adaptive systems can provide effective adaptation.
On Sparsifying Encoder Outputs in Sequence-to-Sequence Models
Venue: arXiv cs.CL 2020
Year: 2020

Sequence-to-sequence models usually transfer all encoder outputs to the decoder for generation. In this work, by contrast, we hypothesize that these encoder outputs can be compressed to shorten the sequence delivered for decoding. We take Transformer as the testbed and introduce a layer of stochastic gates in-between the encoder and the decoder. The gates are regularized using the expected value of the sparsity-inducing L0penalty, resulting in completely masking-out a subset of encoder outputs. In other words, via joint training, the L0DROP layer forces Transformer to route information through a subset of its encoder states. We investigate the effects of this sparsification on two machine translation and two summarization tasks. Experiments show that, depending on the task, around 40-70% of source encodings can be pruned without significantly compromising quality. The decrease of the output length endows L0DROP with the potential of improving decoding efficiency, where it yields a speedup of up to 1.65x on document summarization tasks against the standard Transformer. We analyze the L0DROP behaviour and observe that it exhibits systematic preferences for pruning certain word types, e.g., function words and punctuation get pruned most. Inspired by these observations, we explore the feasibility of specifying rule-based patterns that mask out encoder outputs based on information such as part-of-speech tags, word frequency and word position.
Investigations on $c$-Boomerang Uniformity and Perfect Nonlinearity
Venue: arXiv cs.IT 2020
Year: 2020

We defined in~\cite{EFRST20} a new multiplicative $c$-differential, and the corresponding $c$-differential uniformity and we characterized the known perfect nonlinear functions with respect to this new concept, as well as the inverse in any characteristic. The work was continued in~\cite{RS20}, investigating the $c$-differential uniformity for some further APN functions. Here, we extend the concept to the boomerang uniformity, introduced at Eurocrypt '18 by Cid et al.~\cite{Cid18}, to evaluate S-boxes of block ciphers, and investigate it in the context of perfect nonlinearity and related functions.
Towards A Domain-Customized Automated Machine Learning Framework For Networks and Systems
Venue: arXiv cs.NI 2020
Year: 2020

Clouds gather a vast volume of telemetry from their networked systems which contain valuable information that can help solve many of the problems that continue to plague them. However, it is hard to extract useful information from such raw data. Machine Learning (ML) models are useful tools that enable operators to either leverage this data to solve such problems or develop intuition about whether/how they can be solved. Building practical ML models is time-consuming and requires experts in both ML and networked systems to tailor the model to the system/network (a.k.a "domain-customize" it). The number of applications we deploy exacerbates the problem. The speed with which our systems evolve and with which new monitoring systems are deployed (deprecated) means these models often need to be adapted to keep up. Today, the lack of individuals with both sets of expertise is becoming one of the bottlenecks for adopting ML in cloud operations. This paper argues it is possible to build a domain-customized automated ML framework for networked systems that can help save valuable operator time and effort.
Towards Differentiable Resampling
Venue: arXiv cs.LG 2020
Year: 2020

Resampling is a key component of sample-based recursive state estimation in particle filters. Recent work explores differentiable particle filters for end-to-end learning. However, resampling remains a challenge in these works, as it is inherently non-differentiable. We address this challenge by replacing traditional resampling with a learned neural network resampler. We present a novel network architecture, the particle transformer, and train it for particle resampling using a likelihood-based loss function over sets of particles. Incorporated into a differentiable particle filter, our model can be end-to-end optimized jointly with the other particle filter components via gradient descent. Our results show that our learned resampler outperforms traditional resampling techniques on synthetic data and in a simulated robot localization task.
From elusive to ubiquitous: understanding smart cities
Venue: arXiv cs.CY 2020
Year: 2020

Converting the city into a "smart" one is the emerging strategy of alleviating the problems generated by the rapid population growth in most urban areas, i.e., urbanisation. However, as the rate in which the different concepts of the smart city architecture are implemented is very high, academic research pertaining these advancements simply can not keep up. To improve the existing knowledge-base regarding the concept of smart cities, this paper reviews the existing definitions, as well as its architectures, based on an in-depth literature review of relevant studies and research fields alike. Additionally, we also provide our own definition of the smart city concept.
DALES: A Large-scale Aerial LiDAR Data Set for Semantic Segmentation
Venue: arXiv cs.CV 2020
Year: 2020

We present the Dayton Annotated LiDAR Earth Scan (DALES) data set, a new large-scale aerial LiDAR data set with over a half-billion hand-labeled points spanning 10 square kilometers of area and eight object categories. Large annotated point cloud data sets have become the standard for evaluating deep learning methods. However, most of the existing data sets focus on data collected from a mobile or terrestrial scanner with few focusing on aerial data. Point cloud data collected from an Aerial Laser Scanner (ALS) presents a new set of challenges and applications in areas such as 3D urban modeling and large-scale surveillance. DALES is the most extensive publicly available ALS data set with over 400 times the number of points and six times the resolution of other currently available annotated aerial point cloud data sets. This data set gives a critical number of expert verified hand-labeled points for the evaluation of new 3D deep learning algorithms, helping to expand the focus of current algorithms to aerial data. We describe the nature of our data, annotation workflow, and provide a benchmark of current state-of-the-art algorithm performance on the DALES data set.
Reconfigurable ULAs for Line-of-Sight MIMO Transmission
Venue: arXiv cs.IT 2020
Year: 2020

This paper establishes an upper bound on the capacity of line-of-sight multiantenna channels over all possible antenna arrangements and shows that uniform linear arrays (ULAs) with an SNR-dependent rotation of transmitter or receiver can closely approach such capacity---and in fact achieve it at low and high SNR, and asymptotically in the numbers of antennas. Then, as an alternative to mechanically rotating ULAs, we propose to electronically select among multiple ULAs having a radial disposition at either transmitter or receiver, and we bound the shortfall from capacity as a function of the number of such ULAs. With only three ULAs, properly angled, 96% of the capacity can be achieved. Finally, we further introduce reduced-complexity precoders and linear receivers that capitalize on the structure of the channels spawned by these configurable ULA architectures.
Deep Photon Mapping
Venue: arXiv cs.GR 2020
Year: 2020

Recently, deep learning-based denoising approaches have led to dramatic improvements in low sample-count Monte Carlo rendering. These approaches are aimed at path tracing, which is not ideal for simulating challenging light transport effects like caustics, where photon mapping is the method of choice. However, photon mapping requires very large numbers of traced photons to achieve high-quality reconstructions. In this paper, we develop the first deep learning-based method for particle-based rendering, and specifically focus on photon density estimation, the core of all particle-based methods. We train a novel deep neural network to predict a kernel function to aggregate photon contributions at shading points. Our network encodes individual photons into per-photon features, aggregates them in the neighborhood of a shading point to construct a photon local context vector, and infers a kernel function from the per-photon and photon local context features. This network is easy to incorporate in many previous photon mapping methods (by simply swapping the kernel density estimator) and can produce high-quality reconstructions of complex global illumination effects like caustics with an order of magnitude fewer photons compared to previous photon mapping methods.
Deep Multimodal Neural Architecture Search
Venue: arXiv cs.CV 2020
Year: 2020

Designing effective neural networks is fundamentally important in deep multimodal learning. Most existing works focus on a single task and design neural architectures manually, which are highly task-specific and hard to generalize to different tasks. In this paper, we devise a generalized deep multimodal neural architecture search (MMnas) framework for various multimodal learning tasks. Given multimodal input, we first define a set of primitive operations, and then construct a deep encoder-decoder based unified backbone, where each encoder or decoder block corresponds to an operation searched from a predefined operation pool. On top of the unified backbone, we attach task-specific heads to tackle different multimodal learning tasks. By using a gradient-based NAS algorithm, the optimal architectures for different tasks are learned efficiently. Extensive ablation studies, comprehensive analysis, and comparative experimental results show that the obtained MMnasNet significantly outperforms existing state-of-the-art approaches across three multimodal learning tasks (over five datasets), including visual question answering, image-text matching, and visual grounding.
Clustering by Constructing Hyper-Planes
Venue: arXiv cs.CV 2020
Year: 2020

As a kind of basic machine learning method, clustering algorithms group data points into different categories based on their similarity or distribution. We present a clustering algorithm by finding hyper-planes to distinguish the data points. It relies on the marginal space between the points. Then we combine these hyper-planes to determine centers and numbers of clusters. Because the algorithm is based on linear structures, it can approximate the distribution of datasets accurately and flexibly. To evaluate its performance, we compared it with some famous clustering algorithms by carrying experiments on different kinds of benchmark datasets. It outperforms other methods clearly.
Randomized Nested Polar Subcode Constructions for Privacy, Secrecy, and Storage
Venue: arXiv cs.IT 2020
Year: 2020

We consider polar subcodes (PSCs), which are polar codes (PCs) with dynamically-frozen symbols, to increase the minimum distance as compared to corresponding PCs. A randomized nested PSC construction with a low-rate PSC and a high-rate PC, is proposed for list and sequential successive cancellation decoders. This code construction aims to perform lossy compression with side information. Nested PSCs are used in the key agreement problem with physical identifiers. Gains in terms of the secret-key vs. storage rate ratio as compared to nested PCs with the same list size are illustrated to show that nested PSCs significantly improve on nested PCs. The performance of the nested PSCs is shown to improve with larger list sizes, which is not the case for nested PCs considered.