OpenRaiser/Intern-Atlas huggingface dataset:
Deep Representation Learning and Clustering of Traffic Scenarios
Venue: arXiv cs.LG 2020
Year: 2020

Determining the traffic scenario space is a major challenge for the homologation and coverage assessment of automated driving functions. In contrast to current approaches that are mainly scenario-based and rely on expert knowledge, we introduce two data driven autoencoding models that learn a latent representation of traffic scenes. First is a CNN based spatio-temporal model that autoencodes a grid of traffic participants' positions. Secondly, we develop a pure temporal RNN based model that auto-encodes a sequence of sets. To handle the unordered set data, we had to incorporate the permutation invariance property. Finally, we show how the latent scenario embeddings can be used for clustering traffic scenarios and similarity retrieval.
Secondary Studies in the Academic Context: A Systematic Mapping and Survey
Venue: arXiv cs.CY 2020
Year: 2020

Context: Several researchers have reported their experiences in applying secondary studies (Systematic Literature Reviews - SLRs and Systematic Mappings - SMs) in Software Engineering (SE). However, there is still a lack of studies discussing the value of performing secondary studies in an academic context. Goal: The main goal of this study is to provide an overview on the use of secondary studies in an academic context. Method: Two empirical research methods were used. Initially, we conducted an SM to identify the available and relevant studies on the use of secondary studies as a research methodology for conducting SE research projects. Secondly, a survey was performed with 64 SE researchers to identify their perception related to the value of performing secondary studies to support their research projects. Results: Our results show benefits of using secondary studies in the academic context, such as, providing an overview of the literature as well as identifying relevant research literature on a research area enabling to find reasons to explain why a research project should be approved for a grant and/or supporting decisions made in a research project. Difficulties faced by SE graduate students with secondary studies are that they tend to be conducted by a team and it demands more effort than a traditional review. Conclusions: Secondary studies are valuable to graduate students. They should consider conducting a secondary study for their research project due to the benefits and contributions provided to develop the overall project. However, the advice of an experienced supervisor is essential to avoid bias. In addition, the acquisition of skills can increase student's motivation to pursue their research projects and prepare them for both academic or industrial careers.
AdapterHub: A Framework for Adapting Transformers
Venue: arXiv cs.CL 2020
Year: 2020

The current modus operandi in NLP involves downloading and fine-tuning pre-trained models consisting of millions or billions of parameters. Storing and sharing such large trained models is expensive, slow, and time-consuming, which impedes progress towards more general and versatile NLP methods that learn from and for many tasks. Adapters -- small learnt bottleneck layers inserted within each layer of a pre-trained model -- ameliorate this issue by avoiding full fine-tuning of the entire model. However, sharing and integrating adapter layers is not straightforward. We propose AdapterHub, a framework that allows dynamic "stitching-in" of pre-trained adapters for different tasks and languages. The framework, built on top of the popular HuggingFace Transformers library, enables extremely easy and quick adaptations of state-of-the-art pre-trained models (e.g., BERT, RoBERTa, XLM-R) across tasks and languages. Downloading, sharing, and training adapters is as seamless as possible using minimal changes to the training scripts and a specialized infrastructure. Our framework enables scalable and easy access to sharing of task-specific models, particularly in low-resource scenarios. AdapterHub includes all recent adapter architectures and can be found at https://AdapterHub.ml.
A complex network analysis of the Comprehensive R Archive Network (CRAN) package ecosystem
Venue: arXiv cs.SE 2020
Year: 2020

Free and open source software package ecosystems have existed for a long time and are among the most sophisticated human-made systems. One of the oldest and most popular software package ecosystems is CRAN, the repository of packages of the statistical language R, which is also one of the most popular environments for statistical computing nowadays. CRAN stores a large number of packages that are updated regularly and depend on a number of other packages in a complex graph of relations; such graph is empirically studied from the perspective of complex network analysis (CNA) in the current article, showing how network theory and measures proposed by previous work can help profiling the ecosystem and detecting strengths, good practices and potential risks in three perspectives: macroscopic properties of the ecosystem (structure and complexity of the network), microscopic properties of individual packages (represented as nodes), and modular properties (community detection). Results show how complex network analysis tools can be used to assess a package ecosystem and, in particular, that of CRAN.
A Survey of Aging Monitors and Reconfiguration Techniques
Venue: arXiv cs.AR 2020
Year: 2020

CMOS technology scaling makes aging effects an important concern for the design and fabrication of integrated circuits. Aging deterioration reduces the useful life of a circuit, making it fail earlier. This deterioration can affect all portions of a circuit and impacts its performance and reliability. Contemporary literature shows solutions to monitor and mitigate aging using hardware and software monitoring mechanisms and reconfiguration techniques. The goal of this review of the state-of-the-art is to identify existing monitoring and reconfiguration solutions for aging. This survey evaluates the aging research, focusing the years from 2012 to 2019, and proposes a classification for monitors and reconfiguration techniques. Results show that the most common monitor type used for aging detection is to monitor timing errors, and the most common reconfiguration technique used to deal with aging is voltage scaling. Furthermore, most of the literature contributions are in the digital field, using hardware solutions for monitoring aging in circuits. There are few literature contributions in the analog area, being the scope of this survey in the digital domain. By scrutinizing these solutions, this survey points directions for further research and development of aging monitors and reconfiguration techniques
Adaptive L2 Regularization in Person Re-Identification
Venue: arXiv cs.CV 2020
Year: 2020

We introduce an adaptive L2 regularization mechanism in the setting of person re-identification. In the literature, it is common practice to utilize hand-picked regularization factors which remain constant throughout the training procedure. Unlike existing approaches, the regularization factors in our proposed method are updated adaptively through backpropagation. This is achieved by incorporating trainable scalar variables as the regularization factors, which are further fed into a scaled hard sigmoid function. Extensive experiments on the Market-1501, DukeMTMC-reID and MSMT17 datasets validate the effectiveness of our framework. Most notably, we obtain state-of-the-art performance on MSMT17, which is the largest dataset for person re-identification. Source code is publicly available at https://github.com/nixingyang/AdaptiveL2Regularization.
A Refined Deep Learning Architecture for Diabetic Foot Ulcers Detection
Venue: arXiv cs.CV 2020
Year: 2020

Diabetic Foot Ulcers (DFU) that affect the lower extremities are a major complication of diabetes. Each year, more than 1 million diabetic patients undergo amputation due to failure to recognize DFU and get the proper treatment from clinicians. There is an urgent need to use a CAD system for the detection of DFU. In this paper, we propose using deep learning methods (EfficientDet Architectures) for the detection of DFU in the DFUC2020 challenge dataset, which consists of 4,500 DFU images. We further refined the EfficientDet architecture to avoid false negative and false positive predictions. The code for this method is available at https://github.com/Manugoyal12345/Yet-Another-EfficientDet-Pytorch.
Revisiting Data Complexity Metrics Based on Morphology for Overlap and Imbalance: Snapshot, New Overlap Number of Balls Metrics and Singular Problems Prospect
Venue: arXiv cs.LG 2020
Year: 2020

Data Science and Machine Learning have become fundamental assets for companies and research institutions alike. As one of its fields, supervised classification allows for class prediction of new samples, learning from given training data. However, some properties can cause datasets to be problematic to classify. In order to evaluate a dataset a priori, data complexity metrics have been used extensively. They provide information regarding different intrinsic characteristics of the data, which serve to evaluate classifier compatibility and a course of action that improves performance. However, most complexity metrics focus on just one characteristic of the data, which can be insufficient to properly evaluate the dataset towards the classifiers' performance. In fact, class overlap, a very detrimental feature for the classification process (especially when imbalance among class labels is also present) is hard to assess. This research work focuses on revisiting complexity metrics based on data morphology. In accordance to their nature, the premise is that they provide both good estimates for class overlap, and great correlations with the classification performance. For that purpose, a novel family of metrics have been developed. Being based on ball coverage by classes, they are named after Overlap Number of Balls. Finally, some prospects for the adaptation of the former family of metrics to singular (more complex) problems are discussed.
Model-Based Manipulation of Linear Flexible Objects with Visual Curvature Feedback
Venue: arXiv cs.RO 2020
Year: 2020

Manipulation of deformable objects is a desired skill in making robots ubiquitous in manufacturing, service, healthcare, and security. Deformable objects are common in our daily lives, e.g., wires, clothes, bed sheets, etc., and are significantly more difficult to model than rigid objects. In this study, we investigate vision-based manipulation of linear flexible objects such as cables. We propose a geometric modeling method that is based on visual feedback to develop a general representation of the linear flexible object that is subject to gravity. The model characterizes the shape of the object by combining the curvatures on two projection planes. In this approach, we achieve tracking of the position and orientation (pose) of a cable-like object, the pose of its tip, and the pose of the selected grasp point on the object, which enables closed-loop manipulation of the object. We demonstrate the feasibility of our approach by completing the Plug Task used in the 2015 DARPA Robotics Challenge Finals, which involves unplugging a power cable from one socket and plugging it into another. Experiments show that we can successfully complete the task autonomously within 30 seconds.
Starting with data: advancing spatial data science by building and sharing high-quality datasets
Venue: arXiv cs.CY 2020
Year: 2020

Spatial data science has emerged in recent years as an interdisciplinary field. This position paper discusses the importance of building and sharing high-quality datasets for spatial data science.
EfficientHRNet: Efficient Scaling for Lightweight High-Resolution Multi-Person Pose Estimation
Venue: arXiv cs.CV 2020
Year: 2020

There is an increasing demand for lightweight multi-person pose estimation for many emerging smart IoT applications. However, the existing algorithms tend to have large model sizes and intense computational requirements, making them ill-suited for real-time applications and deployment on resource-constrained hardware. Lightweight and real-time approaches are exceedingly rare and come at the cost of inferior accuracy. In this paper, we present EfficientHRNet, a family of lightweight multi-person human pose estimators that are able to perform in real-time on resource-constrained devices. By unifying recent advances in model scaling with high-resolution feature representations, EfficientHRNet creates highly accurate models while reducing computation enough to achieve real-time performance. The largest model is able to come within 4.4% accuracy of the current state-of-the-art, while having 1/3 the model size and 1/6 the computation, achieving 23 FPS on Nvidia Jetson Xavier. Compared to the top real-time approach, EfficientHRNet increases accuracy by 22% while achieving similar FPS with 1/3 the power. At every level, EfficientHRNet proves to be more computationally efficient than other bottom-up 2D human pose estimation approaches, while achieving highly competitive accuracy.
Private Approximations of a Convex Hull in Low Dimensions
Venue: arXiv cs.DS 2020
Year: 2020

We give the first differentially private algorithms that estimate a variety of geometric features of points in the Euclidean space, such as diameter, width, volume of convex hull, min-bounding box, min-enclosing ball etc. Our work relies heavily on the notion of \emph{Tukey-depth}. Instead of (non-privately) approximating the convex-hull of the given set of points $P$, our algorithms approximate the geometric features of the $κ$-Tukey region induced by $P$ (all points of Tukey-depth $κ$ or greater). Moreover, our approximations are all bi-criteria: for any geometric feature $μ$ our $(α,Δ)$-approximation is a value "sandwiched" between $(1-α)μ(D_P(κ))$ and $(1+α)μ(D_P(κ-Δ))$. Our work is aimed at producing a \emph{$(α,Δ)$-kernel of $D_P(κ)$}, namely a set $\mathcal{S}$ such that (after a shift) it holds that $(1-α)D_P(κ)\subset \mathsf{CH}(\mathcal{S}) \subset (1+α)D_P(κ-Δ)$. We show that an analogous notion of a bi-critera approximation of a directional kernel, as originally proposed by Agarwal et al~[2004], \emph{fails} to give a kernel, and so we result to subtler notions of approximations of projections that do yield a kernel. First, we give differentially private algorithms that find $(α,Δ)$-kernels for a "fat" Tukey-region. Then, based on a private approximation of the min-bounding box, we find a transformation that does turn $D_P(κ)$ into a "fat" region \emph{but only if} its volume is proportional to the volume of $D_P(κ-Δ)$. Lastly, we give a novel private algorithm that finds a depth parameter $κ$ for which the volume of $D_P(κ)$ is comparable to $D_P(κ-Δ)$. We hope this work leads to the further study of the intersection of differential privacy and computational geometry.
Layer-Wise Adaptive Updating for Few-Shot Image Classification
Venue: arXiv cs.CV 2020
Year: 2020

Few-shot image classification (FSIC), which requires a model to recognize new categories via learning from few images of these categories, has attracted lots of attention. Recently, meta-learning based methods have been shown as a promising direction for FSIC. Commonly, they train a meta-learner (meta-learning model) to learn easy fine-tuning weight, and when solving an FSIC task, the meta-learner efficiently fine-tunes itself to a task-specific model by updating itself on few images of the task. In this paper, we propose a novel meta-learning based layer-wise adaptive updating (LWAU) method for FSIC. LWAU is inspired by an interesting finding that compared with common deep models, the meta-learner pays much more attention to update its top layer when learning from few images. According to this finding, we assume that the meta-learner may greatly prefer updating its top layer to updating its bottom layers for better FSIC performance. Therefore, in LWAU, the meta-learner is trained to learn not only the easy fine-tuning model but also its favorite layer-wise adaptive updating rule to improve its learning efficiency. Extensive experiments show that with the layer-wise adaptive updating rule, the proposed LWAU: 1) outperforms existing few-shot classification methods with a clear margin; 2) learns from few images more efficiently by at least 5 times than existing meta-learners when solving FSIC.
Comprehensive Facial Expression Synthesis using Human-Interpretable Language
Venue: arXiv cs.CV 2020
Year: 2020

Recent advances in facial expression synthesis have shown promising results using diverse expression representations including facial action units. Facial action units for an elaborate facial expression synthesis need to be intuitively represented for human comprehension, not a numeric categorization of facial action units. To address this issue, we utilize human-friendly approach: use of natural language where language helps human grasp conceptual contexts. In this paper, therefore, we propose a new facial expression synthesis model from language-based facial expression description. Our method can synthesize the facial image with detailed expressions. In addition, effectively embedding language features on facial features, our method can control individual word to handle each part of facial movement. Extensive qualitative and quantitative evaluations were conducted to verify the effectiveness of the natural language.
Coupling Distant Annotation and Adversarial Training for Cross-Domain Chinese Word Segmentation
Venue: arXiv cs.CL 2020
Year: 2020

Fully supervised neural approaches have achieved significant progress in the task of Chinese word segmentation (CWS). Nevertheless, the performance of supervised models tends to drop dramatically when they are applied to out-of-domain data. Performance degradation is caused by the distribution gap across domains and the out of vocabulary (OOV) problem. In order to simultaneously alleviate these two issues, this paper proposes to couple distant annotation and adversarial training for cross-domain CWS. For distant annotation, we rethink the essence of "Chinese words" and design an automatic distant annotation mechanism that does not need any supervision or pre-defined dictionaries from the target domain. The approach could effectively explore domain-specific words and distantly annotate the raw texts for the target domain. For adversarial training, we develop a sentence-level training procedure to perform noise reduction and maximum utilization of the source domain information. Experiments on multiple real-world datasets across various domains show the superiority and robustness of our model, significantly outperforming previous state-of-the-art cross-domain CWS methods.
Inheritance software metrics on smart contracts
Venue: arXiv cs.SE 2020
Year: 2020

Blockchain systems have gained substantial traction recently, partly due to the potential of decentralized immutable mediation of economic activities. Ethereum is a prominent example that has the provision for executing stateful computing scripts known as Smart Contracts. These smart contracts resemble traditional programs, but with immutability being the core differentiating factor. Given their immutability and potential high monetary value, it becomes imperative to develop high-quality smart contracts. Software metrics have traditionally been an essential tool in determining programming quality. Given the similarity between smart contracts (written in Solidity for Ethereum) and object-oriented (OO) programming, OO metrics would appear applicable. In this paper, we empirically evaluate inheritance-based metrics as applied to smart contracts. We adopt this focus because, traditionally, inheritance has been linked to a more complex codebase which we posit is not the case with Solidity based smart contracts. In this work, we evaluate the hypothesis that, due to the differences in the context of smart contracts and OO programs, it may not be appropriate to use the same interpretation of inheritance based metrics for assessment.
Mixture of Step Returns in Bootstrapped DQN
Venue: arXiv cs.LG 2020
Year: 2020

The concept of utilizing multi-step returns for updating value functions has been adopted in deep reinforcement learning (DRL) for a number of years. Updating value functions with different backup lengths provides advantages in different aspects, including bias and variance of value estimates, convergence speed, and exploration behavior of the agent. Conventional methods such as TD-lambda leverage these advantages by using a target value equivalent to an exponential average of different step returns. Nevertheless, integrating step returns into a single target sacrifices the diversity of the advantages offered by different step return targets. To address this issue, we propose Mixture Bootstrapped DQN (MB-DQN) built on top of bootstrapped DQN, and uses different backup lengths for different bootstrapped heads. MB-DQN enables heterogeneity of the target values that is unavailable in approaches relying only on a single target value. As a result, it is able to maintain the advantages offered by different backup lengths. In this paper, we first discuss the motivational insights through a simple maze environment. In order to validate the effectiveness of MB-DQN, we perform experiments on the Atari 2600 benchmark environments, and demonstrate the performance improvement of MB-DQN over a number of baseline methods. We further provide a set of ablation studies to examine the impacts of different design configurations of MB-DQN.
Deep ahead-of-threat virtual patching
Venue: arXiv cs.CR 2020
Year: 2020

Many applications have security vulnerabilities that can be exploited. It is practically impossible to find all of them due to the NP-complete nature of the testing problem. Security solutions provide defenses against these attacks through continuous application testing, fast-patching of vulnerabilities, automatic deployment of patches, and virtual patching detection techniques deployed in network and endpoint security tools. These techniques are limited by the need to find vulnerabilities before the black-hats. We propose an innovative technique to virtually patch vulnerabilities before they are found. We leverage testing techniques for supervised-learning data generation, and show how artificial intelligence techniques can use this data to create predictive deep neural-network models that read an application's input and predict in real time whether it is a potential malicious input. We set up an ahead-of-threat experiment in which we generated data on old versions of an application, and then evaluated the predictive model accuracy on vulnerabilities found years later. Our experiments show ahead-of-threat detection on LibXML2 and LibTIFF vulnerabilities with 91.3% and 93.7% accuracy, respectively. We expect to continue work on this field of research and provide ahead-of-threat virtual patching for more libraries. Success in this research can change the current state of endless racing after application vulnerabilities and put the defenders one step ahead of the attackers
Scheduling of Real-Time Tasks with Multiple Critical Sections in Multiprocessor Systems
Venue: arXiv cs.OS 2020
Year: 2020

The performance of multiprocessor synchronization and locking protocols is a key factor to utilize the computation power of multiprocessor systems under real-time constraints. While multiple protocols have been developed in the past decades, their performance highly depends on the task partition and prioritization. The recently proposed Dependency Graph Approach showed its advantages and attracted a lot of interest. It is, however, restricted to task sets where each task has at most one critical section. In this paper, we remove this restriction and demonstrate how to utilize algorithms for the classical job shop scheduling problem to construct a dependency graph for tasks with multiple critical sections. To show the applicability, we discuss the implementation in Litmus^{RT} and report the overheads. Moreover, we provide extensive numerical evaluations under different configurations, which in many situations show significant improvement compared to the state-of-the-art.