OpenRaiser/Intern-Atlas huggingface dataset:
EdgeBench: A Workflow-based Benchmark for Edge Computing
Venue: arXiv cs.DC 2020
Year: 2020

Edge computing has been developed to utilize multiple tiers of resources for privacy, cost and Quality of Service (QoS) reasons. Edge workloads have the characteristics of data-driven and latency-sensitive. Because of this, edge systems have developed to be both heterogeneous and distributed. The unique characteristics of edge workloads and edge systems have motivated EdgeBench, a workflow-based benchmark aims to provide the ability to explore the full design space of edge workloads and edge systems. EdgeBench is both customizable and representative. It allows users to customize the workflow logic of edge workloads, the data storage backends, and the distribution of the individual workflow stages to different computing tiers. To illustrate the usability of EdgeBench, we also implements two representative edge workflows, a video analytics workflow and an IoT hub workflow that represents two distinct but common edge workloads. Both workflows are evaluated using the workflow-level and function-level metrics reported by EdgeBench to illustrate both the performance bottlenecks of the edge systems and the edge workloads.
Synthetic Training for Monocular Human Mesh Recovery
Venue: arXiv cs.CV 2020
Year: 2020

Recovering 3D human mesh from monocular images is a popular topic in computer vision and has a wide range of applications. This paper aims to estimate 3D mesh of multiple body parts (e.g., body, hands) with large-scale differences from a single RGB image. Existing methods are mostly based on iterative optimization, which is very time-consuming. We propose to train a single-shot model to achieve this goal. The main challenge is lacking training data that have complete 3D annotations of all body parts in 2D images. To solve this problem, we design a multi-branch framework to disentangle the regression of different body properties, enabling us to separate each component's training in a synthetic training manner using unpaired data available. Besides, to strengthen the generalization ability, most existing methods have used in-the-wild 2D pose datasets to supervise the estimated 3D pose via 3D-to-2D projection. However, we observe that the commonly used weak-perspective model performs poorly in dealing with the external foreshortening effect of camera projection. Therefore, we propose a depth-to-scale (D2S) projection to incorporate the depth difference into the projection function to derive per-joint scale variants for more proper supervision. The proposed method outperforms previous methods on the CMU Panoptic Studio dataset according to the evaluation results and achieves comparable results on the Human3.6M body and STB hand benchmarks. More impressively, the performance in close shot images gets significantly improved using the proposed D2S projection for weak supervision, while maintains obvious superiority in computational efficiency.
Blockchain-enabled Identity Verification for Safe Ridesharing Leveraging Zero-Knowledge Proof
Venue: arXiv cs.CR 2020
Year: 2020

The on-demand mobility market, including ridesharing, is becoming increasingly important with e-hailing fares growing at a rate of approximately 130% per annum since 2013. By increasing utilization of existing vehicles and empty seats, ridesharing can provide many benefits including reduced traffic congestion and environmental impact from vehicle usage and production. However, the safety of riders and drivers has become of paramount concern and a method for privacy-preserving identity verification between untrusted parties is essential for protecting users. To this end, we propose a novel privacy-preserving identity verification system, extending zero-knowledge proof (ZKP) and blockchain for use in ridesharing applications. We design a permissioned blockchain network to perform the ZKP verification of a driver's identity, which also acts as an immutable ledger to store ride logs and ZKP records. For the ZKP module, we design a protocol to facilitate user verification without requiring the exchange of any private information. We prototype the proposed system on the Hyperledger Fabric platform, with the Hyperledger Ursa cryptography library, and conduct extensive experimentation. To measure the prototype's performance, we utilize the Hyperledger Caliper benchmark tool to perform extensive analysis and the results show that our system is suitable for use in real-world ridesharing applications.
Enhanced Classification Accuracy for Cardiotocogram Data with Ensemble Feature Selection and Classifier Ensemble
Venue: arXiv cs.LG 2020
Year: 2020

In this paper ensemble learning based feature selection and classifier ensemble model is proposed to improve classification accuracy. The hypothesis is that good feature sets contain features that are highly correlated with the class from ensemble feature selection to SVM ensembles which can be achieved on the performance of classification accuracy. The proposed approach consists of two phases: (i) to select feature sets that are likely to be the support vectors by applying ensemble based feature selection methods; and (ii) to construct an SVM ensemble using the selected features. The proposed approach was evaluated by experiments on Cardiotocography dataset. Four feature selection techniques were used: (i) Correlation-based, (ii) Consistency-based, (iii) ReliefF and (iv) Information Gain. Experimental results showed that using the ensemble of Information Gain feature selection and Correlation-based feature selection with SVM ensembles achieved higher classification accuracy than both single SVM classifier and ensemble feature selection with SVM classifier.
A robust low data solution: dimension prediction of semiconductor nanorods
Venue: arXiv cs.LG 2020
Year: 2020

Precise control over dimension of nanocrystals is critical to tune the properties for various applications. However, the traditional control through experimental optimization is slow, tedious and time consuming. Herein a robust deep neural network-based regression algorithm has been developed for precise prediction of length, width, and aspect ratios of semiconductor nanorods (NRs). Given there is limited experimental data available (28 samples), a Synthetic Minority Oversampling Technique for regression (SMOTE-REG) has been employed for the first time for data generation. Deep neural network is further applied to develop regression model which demonstrated the well performed prediction on both the original and generated data with a similar distribution. The prediction model is further validated with additional experimental data, showing accurate prediction results. Additionally, Local Interpretable Model-Agnostic Explanations (LIME) is used to interpret the weight for each variable, which corresponds to its importance towards the target dimension, which is approved to be well correlated well with experimental observations.
In-situ data analytics for highly scalable cloud modelling on Cray machines
Venue: arXiv cs.DC 2020
Year: 2020

MONC is a highly scalable modelling tool for the investigation of atmospheric flows, turbulence and cloud microphysics. Typical simulations produce very large amounts of raw data which must then be analysed for scientific investigation. For performance and scalability reasons this analysis and subsequent writing to disk should be performed in-situ on the data as it is generated however one does not wish to pause the computation whilst analysis is carried out. In this paper we present the analytics approach of MONC, where cores of a node are shared between computation and data analytics. By asynchronously sending their data to an analytics core, the computational cores can run continuously without having to pause for data writing or analysis. We describe our IO server framework and analytics workflow, which is highly asynchronous, along with solutions to challenges that this approach raises and the performance implications of some common configuration choices. The result of this work is a highly scalable analytics approach and we illustrate on up to 32768 computational cores of a Cray XC30 that there is minimal performance impact on the runtime when enabling data analytics in MONC and also investigate the performance and suitability of our approach on the KNL.
Jiffy: A Fast, Memory Efficient, Wait-Free Multi-Producers Single-Consumer Queue
Venue: arXiv cs.DB 2020
Year: 2020

In applications such as sharded data processing systems, sharded in-memory key-value stores, data flow programming and load sharing applications, multiple concurrent data producers are feeding requests into the same data consumer. This can be naturally realized through concurrent queues, where each consumer pulls its tasks from its dedicated queue. For scalability, wait-free queues are often preferred over lock based structures. The vast majority of wait-free queue implementations, and even lock-free ones, support the multi-producer multi-consumer model. Yet, this comes at a premium, since implementing wait-free multi-producer multi-consumer queues requires utilizing complex helper data structures. The latter increases the memory consumption of such queues and limits their performance and scalability. Additionally, many such designs employ (hardware) cache unfriendly memory access patterns. In this work we study the implementation of wait-free multi-producer single-consumer queues. Specifically, we propose Jiffy, an efficient memory frugal novel wait-free multi-producer single-consumer queue and formally prove its correctness. We then compare the performance and memory requirements of Jiffy with other state of the art lock-free and wait-free queues. We show that indeed Jiffy can maintain good performance with up to 128 threads, delivers up to 50% better throughput than the next best construction we compared against, and consumes ~90% less memory.
Spiking Neural Networks -- Part III: Neuromorphic Communications
Venue: arXiv cs.NE 2020
Year: 2020

Synergies between wireless communications and artificial intelligence are increasingly motivating research at the intersection of the two fields. On the one hand, the presence of more and more wirelessly connected devices, each with its own data, is driving efforts to export advances in machine learning (ML) from high performance computing facilities, where information is stored and processed in a single location, to distributed, privacy-minded, processing at the end user. On the other hand, ML can address algorithm and model deficits in the optimization of communication protocols. However, implementing ML models for learning and inference on battery-powered devices that are connected via bandwidth-constrained channels remains challenging. This paper explores two ways in which Spiking Neural Networks (SNNs) can help address these open problems. First, we discuss federated learning for the distributed training of SNNs, and then describe the integration of neuromorphic sensing, SNNs, and impulse radio technologies for low-power remote inference.
GMACO-P: GPU assisted Preemptive MACO algorithm for enabling Smart Transportation
Venue: arXiv cs.NI 2020
Year: 2020

Vehicular Ad-hoc NETworks (VANETs) are developing at a very fast pace to enable smart transportation in urban cities, by designing some mechanisms for decreasing travel time for commuters by reducing congestion. Inefficient Traffic signals and routing mechanisms are the major factors that contribute to the increase of road congestion. For smoother traffic movement and reducing congestion on the roads, the waiting time at intersections must be reduced and an optimal path should be chosen simultaneously. In this paper, A GPU assisted Preemptive MACO (GMACO-P) algorithm has been proposed to minimize the total travel time of the commuters. GMACO-P is an improvement of MACO-P algorithm that uses the harnessing the power of the GPU to provide faster computations for further minimizing the travel time. The MACO-P algorithm is based on an existing MACO algorithm that avoid the path with the congestion. The MACO-P algorithm reduces the average queue length at intersections by incorporating preemption that ensures less waiting time. In this paper, GMACO-P algorithm is proposed harnessing the power of GPU to improve MACO-P to further reduce the travel time. The GMACO-P algorithm is executed with CUDA toolkit 7.5 using C language and the obtained results were compared with existing Dijkstra, ACO, MACO, MACO-P, parallel implementation of the Dijkstra, ACO and MACO algorithms. Obtained results show the significant reduction in the travel time after using the proposed GMACO-P algorithm.
$μ$NAS: Constrained Neural Architecture Search for Microcontrollers
Venue: arXiv cs.LG 2020
Year: 2020

IoT devices are powered by microcontroller units (MCUs) which are extremely resource-scarce: a typical MCU may have an underpowered processor and around 64 KB of memory and persistent storage, which is orders of magnitude fewer computational resources than is typically required for deep learning. Designing neural networks for such a platform requires an intricate balance between keeping high predictive performance (accuracy) while achieving low memory and storage usage and inference latency. This is extremely challenging to achieve manually, so in this work, we build a neural architecture search (NAS) system, called $μ$NAS, to automate the design of such small-yet-powerful MCU-level networks. $μ$NAS explicitly targets the three primary aspects of resource scarcity of MCUs: the size of RAM, persistent storage and processor speed. $μ$NAS represents a significant advance in resource-efficient models, especially for "mid-tier" MCUs with memory requirements ranging from 0.5 KB to 64 KB. We show that on a variety of image classification datasets $μ$NAS is able to (a) improve top-1 classification accuracy by up to 4.8%, or (b) reduce memory footprint by 4--13x, or (c) reduce the number of multiply-accumulate operations by at least 2x, compared to existing MCU specialist literature and resource-efficient models.
A Simple and Efficient Registration of 3D Point Cloud and Image Data for Indoor Mobile Mapping System
Venue: arXiv cs.CV 2020
Year: 2020

Registration of 3D LiDAR point clouds with optical images is critical in the combination of multi-source data. Geometric misalignment originally exists in the pose data between LiDAR point clouds and optical images. To improve the accuracy of the initial pose and the applicability of the integration of 3D points and image data, we develop a simple but efficient registration method. We firstly extract point features from LiDAR point clouds and images: point features is extracted from single-frame LiDAR and point features from images using classical Canny method. Cost map is subsequently built based on Canny image edge detection. The optimization direction is guided by the cost map where low cost represents the the desired direction, and loss function is also considered to improve the robustness of the the purposed method. Experiments show pleasant results.
Fast Local Attack: Generating Local Adversarial Examples for Object Detectors
Venue: arXiv cs.CV 2020
Year: 2020

The deep neural network is vulnerable to adversarial examples. Adding imperceptible adversarial perturbations to images is enough to make them fail. Most existing research focuses on attacking image classifiers or anchor-based object detectors, but they generate globally perturbation on the whole image, which is unnecessary. In our work, we leverage higher-level semantic information to generate high aggressive local perturbations for anchor-free object detectors. As a result, it is less computationally intensive and achieves a higher black-box attack as well as transferring attack performance. The adversarial examples generated by our method are not only capable of attacking anchor-free object detectors, but also able to be transferred to attack anchor-based object detector.
What Color is this? Explaining Art Restoration Research Methods using Interactive Museum Installations
Venue: arXiv cs.HC 2020
Year: 2020

This case study describes an approach to designing interactive museum installations as a student project with the aim of presenting the research results of the restoration process of paintings to a wide range of visitors. During one and a half years, the Chair of Media Design created five interactive media stations in two lectures to enrich the special exhibition "Veronese: The Cuccina Cycle. The Restored Masterpiece". The project was realised in close communication with the conservators of the Dresden State Art Collections and the employees of the Science and Archaeometric Laboratory of the Dresden University of Fine Arts. The students had to learn about the foreign content and how to translate it into a media-related environment. With suitable teaching methods, we pushed the students towards a deeper understanding of the matter.
Pixel-based Facial Expression Synthesis
Venue: arXiv cs.CV 2020
Year: 2020

Facial expression synthesis has achieved remarkable advances with the advent of Generative Adversarial Networks (GANs). However, GAN-based approaches mostly generate photo-realistic results as long as the testing data distribution is close to the training data distribution. The quality of GAN results significantly degrades when testing images are from a slightly different distribution. Moreover, recent work has shown that facial expressions can be synthesized by changing localized face regions. In this work, we propose a pixel-based facial expression synthesis method in which each output pixel observes only one input pixel. The proposed method achieves good generalization capability by leveraging only a few hundred training images. Experimental results demonstrate that the proposed method performs comparably well against state-of-the-art GANs on in-dataset images and significantly better on out-of-dataset images. In addition, the proposed model is two orders of magnitude smaller which makes it suitable for deployment on resource-constrained devices.
Can Reinforcement Learning for Continuous Control Generalize Across Physics Engines?
Venue: arXiv cs.LG 2020
Year: 2020

Reinforcement learning (RL) algorithms should learn as much as possible about the environment but not the properties of the physics engines that generate the environment. There are multiple algorithms that solve the task in a physics engine based environment but there is no work done so far to understand if the RL algorithms can generalize across physics engines. In this work, we compare the generalization performance of various deep reinforcement learning algorithms on a variety of control tasks. Our results show that MuJoCo is the best engine to transfer the learning to other engines. On the other hand, none of the algorithms generalize when trained on PyBullet. We also found out that various algorithms have a promising generalizability if the effect of random seeds can be minimized on their performance.
Asymptotic Bounds on the Rate of Locally Repairable Codes
Venue: arXiv cs.IT 2020
Year: 2020

New asymptotic upper bounds are presented on the rate of sequences of locally repairable codes (LRCs) with a prescribed relative minimum distance and locality over a finite field $F$. The bounds apply to LRCs in which the recovery functions are linear; in particular, the bounds apply to linear LRCs over $F$. The new bounds are shown to improve on previously published results, especially when the repair groups are disjoint, namely, they form a partition of the set of coordinates.
The State of Industrial Robotics: Emerging Technologies, Challenges, and Key Research Directions
Venue: arXiv cs.RO 2020
Year: 2020

Robotics and related technologies are central to the ongoing digitization and advancement of manufacturing. In recent years, a variety of strategic initiatives around the world including "Industry 4.0", introduced in Germany in 2011 have aimed to improve and connect manufacturing technologies in order to optimize production processes. In this work, we study the changing technological landscape of robotics and "internet-of-things" (IoT)-based connective technologies over the last 7-10 years in the wake of Industry 4.0. We interviewed key players within the European robotics ecosystem, including robotics manufacturers and integrators, original equipment manufacturers (OEMs), and applied industrial research institutions and synthesize our findings in this paper. We first detail the state-of-the-art robotics and IoT technologies we observed and that the companies discussed during our interviews. We then describe the processes the companies follow when deciding whether and how to integrate new technologies, the challenges they face when integrating these technologies, and some immediate future technological avenues they are exploring in robotics and IoT. Finally, based on our findings, we highlight key research directions for the robotics community that can enable improved capabilities in the context of manufacturing.
Language ID in the Wild: Unexpected Challenges on the Path to a Thousand-Language Web Text Corpus
Venue: arXiv cs.CL 2020
Year: 2020

Large text corpora are increasingly important for a wide variety of Natural Language Processing (NLP) tasks, and automatic language identification (LangID) is a core technology needed to collect such datasets in a multilingual context. LangID is largely treated as solved in the literature, with models reported that achieve over 90% average F1 on as many as 1,366 languages. We train LangID models on up to 1,629 languages with comparable quality on held-out test sets, but find that human-judged LangID accuracy for web-crawl text corpora created using these models is only around 5% for many lower-resource languages, suggesting a need for more robust evaluation. Further analysis revealed a variety of error modes, arising from domain mismatch, class imbalance, language similarity, and insufficiently expressive models. We propose two classes of techniques to mitigate these errors: wordlist-based tunable-precision filters (for which we release curated lists in about 500 languages) and transformer-based semi-supervised LangID models, which increase median dataset precision from 5.5% to 71.2%. These techniques enable us to create an initial data set covering 100K or more relatively clean sentences in each of 500+ languages, paving the way towards a 1,000-language web text corpus.
On the diminishing return of labeling clinical reports
Venue: arXiv cs.CL 2020
Year: 2020

Ample evidence suggests that better machine learning models may be steadily obtained by training on increasingly larger datasets on natural language processing (NLP) problems from non-medical domains. Whether the same holds true for medical NLP has by far not been thoroughly investigated. This work shows that this is indeed not always the case. We reveal the somehow counter-intuitive observation that performant medical NLP models may be obtained with small amount of labeled data, quite the opposite to the common belief, most likely due to the domain specificity of the problem. We show quantitatively the effect of training data size on a fixed test set composed of two of the largest public chest x-ray radiology report datasets on the task of abnormality classification. The trained models not only make use of the training data efficiently, but also outperform the current state-of-the-art rule-based systems by a significant margin.