OpenRaiser/Intern-Atlas huggingface dataset:
Fast influencers in complex networks
Venue: arXiv cs.SI 2019
Year: 2019

Influential nodes in complex networks are typically defined as those nodes that maximize the asymptotic reach of a spreading process of interest. However, for practical applications such as viral marketing and online information spreading, one is often interested in maximizing the reach of the process in a short amount of time. The traditional definition of influencers in network-related studies from diverse research fields narrows down the focus to the late-time state of the spreading processes, leaving the following question unsolved: which nodes are able to initiate large-scale spreading processes, in a limited amount of time? Here, we find that there is a fundamental difference between the nodes -- which we call "fast influencers" -- that initiate the largest-reach processes in a short amount of time, and the traditional, "late-time" influencers. Stimulated by this observation, we provide an extensive benchmarking of centrality metrics with respect to their ability to identify both the fast and late-time influencers. We find that local network properties can be used to uncover the fast influencers. In particular, a parsimonious, local centrality metric (which we call social capital) achieves optimal or nearly-optimal performance in the fast influencer identification for all the analyzed empirical networks. Local metrics tend to be also competitive in the traditional, late-time influencer identification task.
Quality-aware Unpaired Image-to-Image Translation
Venue: arXiv cs.CV 2019
Year: 2019

Generative Adversarial Networks (GANs) have been widely used for the image-to-image translation task. While these models rely heavily on the labeled image pairs, recently some GAN variants have been proposed to tackle the unpaired image translation task. These models exploited supervision at the domain level with a reconstruction process for unpaired image translation. On the other hand, parallel works have shown that leveraging perceptual loss functions based on high level deep features could enhance the generated image quality. Nevertheless, as these GAN-based models either depended on the pretrained deep network structure or relied on the labeled image pairs, they could not be directly applied to the unpaired image translation task. Moreover, despite the improvement of the introduced perceptual losses from deep neural networks, few researchers have explored the possibility of improving the generated image quality from classical image quality measures. To tackle the above two challenges, in this paper, we propose a unified quality-aware GAN-based framework for unpaired image-to-image translation, where a quality-aware loss is explicitly incorporated by comparing each source image and the reconstructed image at the domain level. Specifically, we design two detailed implementations of the quality loss. The first method is based on a classical image quality assessment measure by defining a classical quality-aware loss. The second method proposes an adaptive deep network based loss. Finally, extensive experimental results on many real-world datasets clearly show the quality improvement of our proposed framework, and the superiority of leveraging classical image quality measures for unpaired image translation compared to the deep network based model.
Get rid of inline assembly through verification-oriented lifting
Venue: arXiv cs.PL 2019
Year: 2019

Formal methods for software development have made great strides in the last two decades, to the point that their application in safety-critical embedded software is an undeniable success. Their extension to non-critical software is one of the notable forthcoming challenges. For example, C programmers regularly use inline assembly for low-level optimizations and system primitives. This usually results in driving state-of-the-art formal analyzers developed for C ineffective. We thus propose TInA, an automated, generic, trustable and verification-oriented lifting technique turning inline assembly into semantically equivalent C code, in order to take advantage of existing C analyzers. Extensive experiments on real-world C code with inline assembly (including GMP and ffmpeg) show the feasibility and benefits of TInA.
Optimal Puncturing of Polar Codes With a Fixed Information Set
Venue: arXiv cs.IT 2019
Year: 2019

For a given polar code construction, the existing literature on puncturing for polar codes focuses in finding the optimal puncturing pattern, and then re-selecting the information set. This paper devotes itself to find the optimal puncturing pattern when the information set is fixed. Puncturing the coded bits corresponding to the worst quality bit channels, called the worst quality puncturing (WQP), is proposed, which is analyzed to minimize the bit channel quality loss at the punctured positions. Simulation results show that WQP outperforms the best existing puncturing schemes when the information set is fixed.
Proportionally dense subgraph of maximum size: complexity and approximation
Venue: arXiv cs.CC 2019
Year: 2019

We define a proportionally dense subgraph (PDS) as an induced subgraph of a graph with the property that each vertex in the PDS is adjacent to proportionally as many vertices in the subgraph as in the graph. We prove that the problem of finding a PDS of maximum size is APX-hard on split graphs, and NP-hard on bipartite graphs. We also show that deciding if a PDS is inclusion-wise maximal is co-NP-complete on bipartite graphs. Nevertheless, we present a simple polynomial-time $(2-\frac{2}{Δ+1})$-approximation algorithm for the problem, where $Δ$ is the maximum degree of the graph. Finally, we show that all Hamiltonian cubic graphs with $n$ vertices (except two) have a PDS of size $\lfloor \frac{2n+1}{3} \rfloor$, which we prove to be an upper bound on the size of a PDS in cubic graphs.
Unsupervised and interpretable scene discovery with Discrete-Attend-Infer-Repeat
Venue: arXiv cs.CV 2019
Year: 2019

In this work we present Discrete Attend Infer Repeat (Discrete-AIR), a Recurrent Auto-Encoder with structured latent distributions containing discrete categorical distributions, continuous attribute distributions, and factorised spatial attention. While inspired by the original AIR model andretaining AIR model's capability in identifying objects in an image, Discrete-AIR provides direct interpretability of the latent codes. We show that for Multi-MNIST and a multiple-objects version of dSprites dataset, the Discrete-AIR model needs just one categorical latent variable, one attribute variable (for Multi-MNIST only), together with spatial attention variables, for efficient inference. We perform analysis to show that the learnt categorical distributions effectively capture the categories of objects in the scene for Multi-MNIST and for Multi-Sprites.
Selective Kernel Networks
Venue: arXiv cs.CV 2019
Year: 2019

In standard Convolutional Neural Networks (CNNs), the receptive fields of artificial neurons in each layer are designed to share the same size. It is well-known in the neuroscience community that the receptive field size of visual cortical neurons are modulated by the stimulus, which has been rarely considered in constructing CNNs. We propose a dynamic selection mechanism in CNNs that allows each neuron to adaptively adjust its receptive field size based on multiple scales of input information. A building block called Selective Kernel (SK) unit is designed, in which multiple branches with different kernel sizes are fused using softmax attention that is guided by the information in these branches. Different attentions on these branches yield different sizes of the effective receptive fields of neurons in the fusion layer. Multiple SK units are stacked to a deep network termed Selective Kernel Networks (SKNets). On the ImageNet and CIFAR benchmarks, we empirically show that SKNet outperforms the existing state-of-the-art architectures with lower model complexity. Detailed analyses show that the neurons in SKNet can capture target objects with different scales, which verifies the capability of neurons for adaptively adjusting their receptive field sizes according to the input. The code and models are available at https://github.com/implus/SKNet.
Distribution-Sensitive Bounds on Relative Approximations of Geometric Ranges
Venue: arXiv cs.CG 2019
Year: 2019

A family $\mathcal{R}$ of ranges and a set $X$ of points together define a range space $(X, \mathcal{R}|_X)$, where $\mathcal{R}|_X = \{X \cap h \mid h \in \mathcal{R}\}$. We want to find a structure to estimate the quantity $|X \cap h|/|X|$ for any range $h \in \mathcal{R}$ with the $(ρ, ε)$-guarantee: (i) if $|X \cap h|/|X| > ρ$, the estimate must have a relative error $ε$; (ii) otherwise, the estimate must have an absolute error $ρε$. The objective is to minimize the size of the structure. Currently, the dominant solution is to compute a relative $(ρ, ε)$-approximation, which is a subset of $X$ with $\tilde{O}(λ/(ρε^2))$ points, where $λ$ is the VC-dimension of $(X, \mathcal{R}|_X)$, and $\tilde{O}$ hides polylog factors. This paper shows a more general bound sensitive to the content of $X$. We give a structure that stores $O(\log (1/ρ))$ integers plus $\tilde{O}(θ\cdot (λ/ε^2))$ points of $X$, where $θ$ - called the disagreement coefficient - measures how much the ranges differ from each other in their intersections with $X$. The value of $θ$ is between 1 and $1/ρ$, such that our space bound is never worse than that of relative $(ρ, ε)$-approximations, but we improve the latter's $1/ρ$ term whenever $θ= o(\frac{1}{ρ\log (1/ρ)})$. We also prove that, in the worst case, summaries with the $(ρ, 1/2)$-guarantee must consume $Ω(θ)$ words even for $d = 2$ and $λ\le 3$. We then constrain $\mathcal{R}$ to be the set of halfspaces in $\mathbb{R}^d$ for a constant $d$, and prove the existence of structures with $o(1/(ρε^2))$ size offering $(ρ,ε)$-guarantees, when $X$ is generated from various stochastic distributions. This is the first formal justification on why the term $1/ρ$ is not compulsory for "realistic" inputs.
Surface Compression Using Dynamic Color Palettes
Venue: arXiv cs.GR 2019
Year: 2019

Off-chip memory traffic is a major source of power and energy consumption on mobile platforms. A large amount of this off-chip traffic is used to manipulate graphics framebuffer surfaces. To cut down the cost of accessing off-chip memory, framebuffer surfaces are compressed to reduce the bandwidth consumed on surface manipulation when rendering or displaying. In this work, we study the compression properties of framebuffer surfaces and highlight the fact that surfaces from different applications have different compression characteristics. We use the results of our analysis to propose a scheme, Dynamic Color Palettes (DCP), which achieves higher compression rates with UI and 2D surfaces. DCP is a hardware mechanism for exploiting inter-frame coherence in lossless surface compression; it implements a scheme that dynamically constructs color palettes, which are then used to efficiently compress framebuffer surfaces. To evaluate DCP, we created an extensive set of OpenGL workload traces from 124 Android applications. We found that DCP improves compression rates by 91% for UI and 20% for 2D applications compared to previous proposals. We also evaluate a hybrid scheme that combines DCP with a generic compression scheme. We found that compression rates improve over previous proposals by 161%, 124% and 83% for UI, 2D and 3D applications, respectively.
kPAM: KeyPoint Affordances for Category-Level Robotic Manipulation
Venue: arXiv cs.RO 2019
Year: 2019

We would like robots to achieve purposeful manipulation by placing any instance from a category of objects into a desired set of goal states. Existing manipulation pipelines typically specify the desired configuration as a target 6-DOF pose and rely on explicitly estimating the pose of the manipulated objects. However, representing an object with a parameterized transformation defined on a fixed template cannot capture large intra-category shape variation, and specifying a target pose at a category level can be physically infeasible or fail to accomplish the task -- e.g. knowing the pose and size of a coffee mug relative to some canonical mug is not sufficient to successfully hang it on a rack by its handle. Hence we propose a novel formulation of category-level manipulation that uses semantic 3D keypoints as the object representation. This keypoint representation enables a simple and interpretable specification of the manipulation target as geometric costs and constraints on the keypoints, which flexibly generalizes existing pose-based manipulation methods. Using this formulation, we factor the manipulation policy into instance segmentation, 3D keypoint detection, optimization-based robot action planning and local dense-geometry-based action execution. This factorization allows us to leverage advances in these sub-problems and combine them into a general and effective perception-to-action manipulation pipeline. Our pipeline is robust to large intra-category shape variation and topology changes as the keypoint representation ignores task-irrelevant geometric details. Extensive hardware experiments demonstrate our method can reliably accomplish tasks with never-before seen objects in a category, such as placing shoes and mugs with significant shape variation into category level target configurations.
Module-per-Object: a Human-Driven Methodology for C++-based High-Level Synthesis Design
Venue: arXiv cs.DC 2019
Year: 2019

High-Level Synthesis (HLS) brings FPGAs to audiences previously unfamiliar to hardware design. However, achieving the highest Quality-of-Results (QoR) with HLS is still unattainable for most programmers. This requires detailed knowledge of FPGA architecture and hardware design in order to produce FPGA-friendly codes. Moreover, these codes are normally in conflict with best coding practices, which favor code reuse, modularity, and conciseness. To overcome these limitations, we propose Module-per-Object (MpO), a human-driven HLS design methodology intended for both hardware designers and software developers with limited FPGA expertise. MpO exploits modern C++ to raise the abstraction level while improving QoR, code readability and modularity. To guide HLS designers, we present the five characteristics of MpO classes. Each characteristic exploits the power of HLS-supported modern C++ features to build C++-based hardware modules. These characteristics lead to high-quality software descriptions and efficient hardware generation. We also present a use case of MpO, where we use C++ as the intermediate language for FPGA-targeted code generation from P4, a packet processing domain specific language. The MpO methodology is evaluated using three design experiments: a packet parser, a flow-based traffic manager, and a digital up-converter. Based on experiments, we show that MpO can be comparable to hand-written VHDL code while keeping a high abstraction level, human-readable coding style and modularity. Compared to traditional C-based HLS design, MpO leads to more efficient circuit generation, both in terms of performance and resource utilization. Also, the MpO approach notably improves software quality, augmenting parametrization while eliminating the incidence of code duplication.
Graph Processing on FPGAs: Taxonomy, Survey, Challenges
Venue: arXiv cs.DC 2019
Year: 2019

Graph processing has become an important part of various areas, such as machine learning, computational sciences, medical applications, social network analysis, and many others. Various graphs, for example web or social networks, may contain up to trillions of edges. The sheer size of such datasets, combined with the irregular nature of graph processing, poses unique challenges for the runtime and the consumed power. Field Programmable Gate Arrays (FPGAs) can be an energy-efficient solution to deliver specialized hardware for graph processing. This is reflected by the recent interest in developing various graph algorithms and graph processing frameworks on FPGAs. To facilitate understanding of this emerging domain, we present the first survey and taxonomy on graph computations on FPGAs. Our survey describes and categorizes existing schemes and explains key ideas. Finally, we discuss research and engineering challenges to outline the future of graph computations on FPGAs.
Analysis of a Stochastic Model for Coordinated Platooning of Heavy-duty Vehicles
Venue: arXiv cs.SY 2019
Year: 2019

Platooning of heavy-duty vehicles (HDVs) is a key component of smart and connected highways and is expected to bring remarkable fuel savings and emission reduction. In this paper, we study the coordination of HDV platooning on a highway section. We model the arrival of HDVs as a Poisson process. Multiple HDVs are merged into one platoon if their headways are below a given threshold. The merging is done by accelerating the following vehicles to catch up with the leading ones. We characterize the following random variables: (i) platoon size, (ii) headway between platoons, and (iii) travel time increment due to platoon formation. We formulate and solve an optimization problem to determine the headway threshold for platooning that leads to minimal cost (time plus fuel). We also compare our results with that from Simulation of Urban MObility (SUMO).
Atari-HEAD: Atari Human Eye-Tracking and Demonstration Dataset
Venue: arXiv cs.LG 2019
Year: 2019

Large-scale public datasets have been shown to benefit research in multiple areas of modern artificial intelligence. For decision-making research that requires human data, high-quality datasets serve as important benchmarks to facilitate the development of new methods by providing a common reproducible standard. Many human decision-making tasks require visual attention to obtain high levels of performance. Therefore, measuring eye movements can provide a rich source of information about the strategies that humans use to solve decision-making tasks. Here, we provide a large-scale, high-quality dataset of human actions with simultaneously recorded eye movements while humans play Atari video games. The dataset consists of 117 hours of gameplay data from a diverse set of 20 games, with 8 million action demonstrations and 328 million gaze samples. We introduce a novel form of gameplay, in which the human plays in a semi-frame-by-frame manner. This leads to near-optimal game decisions and game scores that are comparable or better than known human records. We demonstrate the usefulness of the dataset through two simple applications: predicting human gaze and imitating human demonstrated actions. The quality of the data leads to promising results in both tasks. Moreover, using a learned human gaze model to inform imitation learning leads to an 115\% increase in game performance. We interpret these results as highlighting the importance of incorporating human visual attention in models of decision making and demonstrating the value of the current dataset to the research community. We hope that the scale and quality of this dataset can provide more opportunities to researchers in the areas of visual attention, imitation learning, and reinforcement learning.
A Methodology for Using GitLab for Software Engineering Learning Analytics
Venue: arXiv cs.SE 2019
Year: 2019

To bridge the digital skills gap, we need to train more people in Software Engineering techniques. This paper reports on a project exploring the way students solve tasks using collaborative development platforms and version control systems, such as GitLab, to find patterns and evaluation metrics that can be used to improve the course content and reflect on the most common issues the students are facing. In this paper, we explore Learning Analytics approaches that can be used with GitLab and similar tools, and discuss the challenges raised when applying those approaches in Software Engineering Education, with the objective of building a pipeline that supports the full Learning Analytics cycle, from data extraction to data analysis. We focus in particular on the data anonymisation step of the proposed pipeline to explore the available alternatives to satisfy the data protection requirements when handling personal information in academic environments for research purposes.
An "On The Fly" Framework for Efficiently Generating Synthetic Big Data Sets
Venue: arXiv cs.DC 2019
Year: 2019

Collecting, analyzing and gaining insight from large volumes of data is now the norm in an ever increasing number of industries. Data analytics techniques, such as machine learning, are powerful tools used to analyze these large volumes of data. Synthetic data sets are routinely relied upon to train and develop such data analytics methods for several reasons: to generate larger data sets than are available, to generate diverse data sets, to preserve anonymity in data sets with sensitive information, etc. Processing, transmitting and storing data is a key issue faced when handling large data sets. This paper presents an "On the fly" framework for generating big synthetic data sets, suitable for these data analytics methods, that is both computationally efficient and applicable to a diverse set of problems. An example application of the proposed framework is presented along with a mathematical analysis of its computational efficiency, demonstrating its effectiveness.
Detecting GAN generated Fake Images using Co-occurrence Matrices
Venue: arXiv cs.CV 2019
Year: 2019

The advent of Generative Adversarial Networks (GANs) has brought about completely novel ways of transforming and manipulating pixels in digital images. GAN based techniques such as Image-to-Image translations, DeepFakes, and other automated methods have become increasingly popular in creating fake images. In this paper, we propose a novel approach to detect GAN generated fake images using a combination of co-occurrence matrices and deep learning. We extract co-occurrence matrices on three color channels in the pixel domain and train a model using a deep convolutional neural network (CNN) framework. Experimental results on two diverse and challenging GAN datasets comprising more than 56,000 images based on unpaired image-to-image translations (cycleGAN [1]) and facial attributes/expressions (StarGAN [2]) show that our approach is promising and achieves more than 99% classification accuracy in both datasets. Further, our approach also generalizes well and achieves good results when trained on one dataset and tested on the other.
Directional PointNet: 3D Environmental Classification for Wearable Robotics
Venue: arXiv cs.CV 2019
Year: 2019

Environmental information can provide reliable prior information about human motion intent, which can aid the subject with wearable robotics to walk in complex environments. Previous researchers have utilized 1D signal and 2D images to classify environments, but they may face the problems of self-occlusion. Comparatively, 3D point cloud can be more appropriate to depict environments, thus we propose a directional PointNet to classify 3D point cloud directly. By utilizing the orientation information of the point cloud, the directional PointNet can classify daily terrains, including level ground, up stairs, and down stairs, and the classification accuracy achieves 99% for testing set. Moreover, the directional PointNet is more efficient than the previous PointNet because the T-net, which is utilized to estimate the transformation of the point cloud, is removed in this research and the length of the global feature is optimized. The experimental results demonstrate that the directional PointNet can classify the environments robustly and efficiently.