OpenRaiser/Intern-Atlas huggingface dataset:
Visualizing Environmental Justice Issues in Urban Areas with a Community-based Approach
Venue: arXiv cs.CY 2021
Year: 2021

According to environmental justice, environmental degradation and benefits should not be disproportionately shared between communities. Identifying disparities in the spatial distribution of environmental degradation is therefore a prerequisite for validating the state of environmental justice in a geographic region. Under ideal circumstances, environmental risk assessment is a preferred metric, but only when exposure levels have been quantified reliably after estimating the risk. In this study, we adopt a proximity burden metric caused by adjacent hazardous sources, allowing us to evaluate the environmental burden distribution and vulnerability to pollution sources. In close collaboration with a predominantly Latinx community in Chicago, we highlight the usefulness of our approach through a case study that shows how certain community areas in the city are likely to bear a disproportionate burden of environmental pollution caused by industrial roads.
MVLayoutNet:3D layout reconstruction with multi-view panoramas
Venue: arXiv cs.CV 2021
Year: 2021

We present MVLayoutNet, an end-to-end network for holistic 3D reconstruction from multi-view panoramas. Our core contribution is to seamlessly combine learned monocular layout estimation and multi-view stereo (MVS) for accurate layout reconstruction in both 3D and image space. We jointly train a layout module to produce an initial layout and a novel MVS module to obtain accurate layout geometry. Unlike standard MVSNet [33], our MVS module takes a newly-proposed layout cost volume, which aggregates multi-view costs at the same depth layer into corresponding layout elements. We additionally provide an attention-based scheme that guides the MVS module to focus on structural regions. Such a design considers both local pixel-level costs and global holistic information for better reconstruction. Experiments show that our method outperforms state-of-the-arts in terms of depth rmse by 21.7% and 20.6% on the 2D-3D-S [1] and ZInD [5] datasets. Finally, our method leads to coherent layout geometry that enables the reconstruction of an entire scene.
Pixel-wise Deep Image Stitching
Venue: arXiv cs.CV 2021
Year: 2021

Image stitching aims at stitching the images taken from different viewpoints into an image with a wider field of view. Existing methods warp the target image to the reference image using the estimated warp function, and a homography is one of the most commonly used warping functions. However, when images have large parallax due to non-planar scenes and translational motion of a camera, the homography cannot fully describe the mapping between two images. Existing approaches based on global or local homography estimation are not free from this problem and suffer from undesired artifacts due to parallax. In this paper, instead of relying on the homography-based warp, we propose a novel deep image stitching framework exploiting the pixel-wise warp field to handle the large-parallax problem. The proposed deep image stitching framework consists of two modules: Pixel-wise Warping Module (PWM) and Stitched Image Generating Module (SIGMo). PWM employs an optical flow estimation model to obtain pixel-wise warp of the whole image, and relocates the pixels of the target image with the obtained warp field. SIGMo blends the warped target image and the reference image while eliminating unwanted artifacts such as misalignments, seams, and holes that harm the plausibility of the stitched result. For training and evaluating the proposed framework, we build a large-scale dataset that includes image pairs with corresponding pixel-wise ground truth warp and sample stitched result images. We show that the results of the proposed framework are qualitatively superior to those of the conventional methods, especially when the images have large parallax. The code and the proposed dataset will be publicly available soon.
Parallel Batch-Dynamic $k$d-Trees
Venue: arXiv cs.DS 2021
Year: 2021

$k$d-trees are widely used in parallel databases to support efficient neighborhood/similarity queries. Supporting parallel updates to $k$d-trees is therefore an important operation. In this paper, we present BDL-tree, a parallel, batch-dynamic implementation of a $k$d-tree that allows for efficient parallel $k$-NN queries over dynamically changing point sets. BDL-trees consist of a log-structured set of $k$d-trees which can be used to efficiently insert or delete batches of points in parallel with polylogarithmic depth. Specifically, given a BDL-tree with $n$ points, each batch of $B$ updates takes $O(B\log^2{(n+B)})$ amortized work and $O(\log(n+B)\log\log{(n+B)})$ depth (parallel time). We provide an optimized multicore implementation of BDL-trees. Our optimizations include parallel cache-oblivious $k$d-tree construction and parallel bloom filter construction. Our experiments on a 36-core machine with two-way hyper-threading using a variety of synthetic and real-world datasets show that our implementation of BDL-tree achieves a self-relative speedup of up to $34.8\times$ ($28.4\times$ on average) for batch insertions, up to $35.5\times$ ($27.2\times$ on average) for batch deletions, and up to $46.1\times$ ($40.0\times$ on average) for $k$-nearest neighbor queries. In addition, it achieves throughputs of up to 14.5 million updates/second for batch-parallel updates and 6.7 million queries/second for $k$-NN queries. We compare to two baseline $k$d-tree implementations and demonstrate that BDL-trees achieve a good tradeoff between the two baseline options for implementing batch updates.
Report on A Formally-Founded Model-Based Approach to Engineer Self-Adaptive Systems
Venue: arXiv cs.SE 2021
Year: 2021

Self-adaptive systems manage themselves to deal with uncertainties that can only be resolved during operation. A common approach to realize self-adaptation is by adding a feedback loop to the system that monitors the system and adapts it to realize a set of adaptation goals. ActivFORMS (Active FORmal Models for Self-adaptation) provides an end-to-end approach for engineering self-adaptive systems. ActivFORMS relies on feedback loops that consists of formally verified models that are directly deployed and executed at runtime to realize self-adaptation. At runtime, the approach relies on statistical verification techniques that allow efficient analysis of the possible options for adaptation. Further, ActivFORMS supports on-the-fly changes of adaptation goals and updates of the verified models to to meet the new goals. ActivFORMSi provides a tool-supported instance of ActivFORMS. The approach has been validates using an IoT application for building security monitoring. This report provides complementary material to the paper ``ActivFORMS: A Formally-Founded Model-Based Approach to Engineer Self-Adaptive Systems'' [Weyns and Iftikhar 2019].
Joint Sensing, Communication, and Computation Resource Allocation for Cooperative Perception in Fog-Based Vehicular Networks
Venue: arXiv cs.NI 2021
Year: 2021

To enlarge the perception range and reliability of individual autonomous vehicles, cooperative perception has been received much attention. However, considering the high volume of shared messages, limited bandwidth and computation resources in vehicular networks become bottlenecks. In this paper, we investigate how to balance the volume of shared messages and constrained resources in fog-based vehicular networks. To this end, we first characterize sum satisfaction of cooperative perception taking account of its spatial-temporal value and latency performance. Next, the sensing block message, communication resource block, and computation resource are jointly allocated to maximize the sum satisfaction of cooperative perception, while satisfying the maximum latency and sojourn time constraints of vehicles. Owing to its non-convexity, we decouple the original problem into two separate sub-problems and devise corresponding solutions. Simulation results demonstrate that our proposed scheme can effectively boost the sum satisfaction of cooperative perception compared with existing baselines.
Boosting the Capability of Intelligent Vulnerability Detection by Training in a Human-Learning Manner
Venue: arXiv cs.CR 2021
Year: 2021

Due to its powerful automatic feature extraction, deep learning (DL) has been widely used in source code vulnerability detection. However, although it performs well on artificial datasets, its performance is not satisfactory when detecting real-world vulnerabilities due to the high complexity of real-world samples. In this paper, we propose to train DL-based vulnerability detection models in a human-learning manner, that is, start with the simplest samples and then gradually transition to difficult knowledge. Specifically, we design a novel framework (Humer) that can enhance the detection ability of DL-based vulnerability detectors. To validate the effectiveness of Humer, we select five state-of-the-art DL-based vulnerability detection models (TokenCNN, VulDeePecker, StatementGRU, ASTGRU, and Devign) to complete our evaluations. Through the results, we find that the use of Humer can increase the F1 of these models by an average of 10.5%. Moreover, Humer can make the model detect up to 16.7% more real-world vulnerabilities. Meanwhile, we also conduct a case study to uncover vulnerabilities from real-world open source products by using these enhanced DL-based vulnerability detectors. Through the results, we finally discover 281 unreported vulnerabilities in NVD, of which 98 have been silently patched by vendors in the latest version of corresponding products, but 159 still exist in the products.
SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with Sparsification
Venue: arXiv cs.LG 2021
Year: 2021

Federated learning is inherently vulnerable to model poisoning attacks because its decentralized nature allows attackers to participate with compromised devices. In model poisoning attacks, the attacker reduces the model's performance on targeted sub-tasks (e.g. classifying planes as birds) by uploading "poisoned" updates. In this report we introduce \algoname{}, a novel defense that uses global top-k update sparsification and device-level gradient clipping to mitigate model poisoning attacks. We propose a theoretical framework for analyzing the robustness of defenses against poisoning attacks, and provide robustness and convergence analysis of our algorithm. To validate its empirical efficacy we conduct an open-source evaluation at scale across multiple benchmark datasets for computer vision and federated learning.
Reading Task Classification Using EEG and Eye-Tracking Data
Venue: arXiv cs.CL 2021
Year: 2021

The Zurich Cognitive Language Processing Corpus (ZuCo) provides eye-tracking and EEG signals from two reading paradigms, normal reading and task-specific reading. We analyze whether machine learning methods are able to classify these two tasks using eye-tracking and EEG features. We implement models with aggregated sentence-level features as well as fine-grained word-level features. We test the models in within-subject and cross-subject evaluation scenarios. All models are tested on the ZuCo 1.0 and ZuCo 2.0 data subsets, which are characterized by differing recording procedures and thus allow for different levels of generalizability. Finally, we provide a series of control experiments to analyze the results in more detail.
Evaluation of Security Training and Awareness Programs: Review of Current Practices and Guideline
Venue: arXiv cs.CR 2021
Year: 2021

Evaluating the effectiveness of security awareness and training programs is critical for minimizing organizations' human security risk. Based on a literature review and industry interviews, we discuss current practices and devise guidelines for measuring the effectiveness of security training and awareness initiatives used by organizations
Embracing Single Stride 3D Object Detector with Sparse Transformer
Venue: arXiv cs.CV 2021
Year: 2021

In LiDAR-based 3D object detection for autonomous driving, the ratio of the object size to input scene size is significantly smaller compared to 2D detection cases. Overlooking this difference, many 3D detectors directly follow the common practice of 2D detectors, which downsample the feature maps even after quantizing the point clouds. In this paper, we start by rethinking how such multi-stride stereotype affects the LiDAR-based 3D object detectors. Our experiments point out that the downsampling operations bring few advantages, and lead to inevitable information loss. To remedy this issue, we propose Single-stride Sparse Transformer (SST) to maintain the original resolution from the beginning to the end of the network. Armed with transformers, our method addresses the problem of insufficient receptive field in single-stride architectures. It also cooperates well with the sparsity of point clouds and naturally avoids expensive computation. Eventually, our SST achieves state-of-the-art results on the large scale Waymo Open Dataset. It is worth mentioning that our method can achieve exciting performance (83.8 LEVEL 1 AP on validation split) on small object (pedestrian) detection due to the characteristic of single stride. Codes will be released at https://github.com/TuSimple/SST
Does Fully Homomorphic Encryption Need Compute Acceleration?
Venue: arXiv cs.CR 2021
Year: 2021

Fully Homomorphic Encryption (FHE) allows arbitrarily complex computations on encrypted data without ever needing to decrypt it, thus enabling us to maintain data privacy on third-party systems. Unfortunately, sustaining deep computations with FHE requires a periodic noise reduction step known as bootstrapping. The cost of the bootstrapping operation is one of the primary barriers to the wide-spread adoption of FHE. In this paper, we present an in-depth architectural analysis of the bootstrapping step in FHE. First, we observe that secure implementations of bootstrapping exhibit a low arithmetic intensity (<1 Op/byte), require large caches (>100 MB), and are heavily bound by the main memory bandwidth. Consequently, we demonstrate that existing workloads observe marginal performance gains from the design of bespoke high-throughput arithmetic units tailored to FHE. Second, we propose several cache-friendly algorithmic optimizations that improve the throughput in FHE bootstrapping by enabling up to 3.2x higher arithmetic intensity and 4.6x lower memory bandwidth. Our optimizations apply to a wide range of structurally similar computations such as private evaluation and training of machine learning models. Finally, we incorporate these optimizations into an architectural tool which, given a cache size, memory subsystem, the number of functional units and a desired security level, selects optimal cryptosystem parameters to maximize the bootstrapping throughput. Our optimized bootstrapping implementation represents a best-case scenario for compute acceleration of FHE. We show that despite these optimizations, bootstrapping continues to be bottlenecked by main memory bandwidth. We propose new research directions to address the underlying memory bottleneck. In summary, our answer to the titular question is: yes, but only after addressing the memory bottleneck!
A Benchmark for Low-Switching-Cost Reinforcement Learning
Venue: arXiv cs.LG 2021
Year: 2021

A ubiquitous requirement in many practical reinforcement learning (RL) applications, including medical treatment, recommendation system, education and robotics, is that the deployed policy that actually interacts with the environment cannot change frequently. Such an RL setting is called low-switching-cost RL, i.e., achieving the highest reward while reducing the number of policy switches during training. Despite the recent trend of theoretical studies aiming to design provably efficient RL algorithms with low switching costs, none of the existing approaches have been thoroughly evaluated in popular RL testbeds. In this paper, we systematically studied a wide collection of policy-switching approaches, including theoretically guided criteria, policy-difference-based methods, and non-adaptive baselines. Through extensive experiments on a medical treatment environment, the Atari games, and robotic control tasks, we present the first empirical benchmark for low-switching-cost RL and report novel findings on how to decrease the switching cost while maintain a similar sample efficiency to the case without the low-switching-cost constraint. We hope this benchmark could serve as a starting point for developing more practically effective low-switching-cost RL algorithms. We release our code and complete results in https://sites.google.com/view/low-switching-cost-rl.
GM Score: Incorporating inter-class and intra-class generator diversity, discriminability of disentangled representation, and sample fidelity for evaluating GANs
Venue: arXiv cs.LG 2021
Year: 2021

While generative adversarial networks (GAN) are popular for their higher sample quality as opposed to other generative models like the variational autoencoders (VAE) and Boltzmann machines, they suffer from the same difficulty of the evaluation of generated samples. Various aspects must be kept in mind, such as the quality of generated samples, the diversity of classes (within a class and among classes), the use of disentangled latent spaces, agreement of said evaluation metric with human perception, etc. In this paper, we propose a new score, namely, GM Score, which takes into various factors such as sample quality, disentangled representation, intra-class and inter-class diversity, and other metrics such as precision, recall, and F1 score are employed for discriminability of latent space of deep belief network (DBN) and restricted Boltzmann machine (RBM). The evaluation is done for different GANs (GAN, DCGAN, BiGAN, CGAN, CoupledGAN, LSGAN, SGAN, WGAN, and WGAN Improved) trained on the benchmark MNIST dataset.
Software-Hardware Evolution and birth of Multicore Processors
Venue: arXiv cs.AR 2021
Year: 2021

This paper presents a brief journey to the evolution of computer hardware and software, and underlines that shift to multicore technology is natual part of the evolution, and highlights the various laws governing the advancement of computer industry. Looking to these, it appears that the HW-SW industry trend can be represented by a mathematical model, for which future developments are predictable. Finally, the paper establishes that future of computer industry lies in more thrust in software to exploit parallelism available in software to utilize the heterogeneity in multicore processors.
Real Time Action Recognition from Video Footage
Venue: arXiv cs.CV 2021
Year: 2021

Crime rate is increasing proportionally with the increasing rate of the population. The most prominent approach was to introduce Closed-Circuit Television (CCTV) camera-based surveillance to tackle the issue. Video surveillance cameras have added a new dimension to detect crime. Several research works on autonomous security camera surveillance are currently ongoing, where the fundamental goal is to discover violent activity from video feeds. From the technical viewpoint, this is a challenging problem because analyzing a set of frames, i.e., videos in temporal dimension to detect violence might need careful machine learning model training to reduce false results. This research focuses on this problem by integrating state-of-the-art Deep Learning methods to ensure a robust pipeline for autonomous surveillance for detecting violent activities, e.g., kicking, punching, and slapping. Initially, we designed a dataset of this specific interest, which contains 600 videos (200 for each action). Later, we have utilized existing pre-trained model architectures to extract features, and later used deep learning network for classification. Also, We have classified our models' accuracy, and confusion matrix on different pre-trained architectures like VGG16, InceptionV3, ResNet50, Xception and MobileNet V2 among which VGG16 and MobileNet V2 performed better.
Machine Learning-Based Heart Disease Diagnosis: A Systematic Literature Review
Venue: arXiv cs.LG 2021
Year: 2021

Heart disease is one of the significant challenges in today's world and one of the leading causes of many deaths worldwide. Recent advancement of machine learning (ML) application demonstrates that using electrocardiogram (ECG) and patient data, detecting heart disease during the early stage is feasible. However, both ECG and patient data are often imbalanced, which ultimately raises a challenge for the traditional ML to perform unbiasedly. Over the years, several data level and algorithm level solutions have been exposed by many researchers and practitioners. To provide a broader view of the existing literature, this study takes a systematic literature review (SLR) approach to uncover the challenges associated with imbalanced data in heart diseases predictions. Before that, we conducted a meta-analysis using 451 referenced literature acquired from the reputed journals between 2012 and November 15, 2021. For in-depth analysis, 49 referenced literature has been considered and studied, taking into account the following factors: heart disease type, algorithms, applications, and solutions. Our SLR study revealed that the current approaches encounter various open problems/issues when dealing with imbalanced data, eventually hindering their practical applicability and functionality.
Aerial Chasing of a Dynamic Target in Complex Environments
Venue: arXiv cs.RO 2021
Year: 2021

Rapidly generating an optimal chasing motion of a drone to follow a dynamic target among obstacles is challenging due to numerical issues rising from multiple conflicting objectives and non-convex constraints. This study proposes to resolve the difficulties with a fast and reliable pipeline that incorporates 1) a target movement forecaster and 2) a chasing planner. They are based on a sample-and-check approach that consists of the generation of high-quality candidate primitives and the feasibility tests with a light computation load. We forecast the movement of the target by selecting an optimal prediction among a set of candidates built from past observations. Based on the prediction, we construct a set of prospective chasing trajectories which reduce the high-order derivatives, while maintaining the desired relative distance from the predicted target movement. Then, the candidate trajectories are tested on safety of the chaser and visibility toward the target without loose approximation of the constraints. The proposed algorithm is thoroughly evaluated in challenging scenarios involving dynamic obstacles. Also, the overall process from the target recognition to the chasing motion planning is implemented fully onboard on a drone, demonstrating real-world applicability.