OpenRaiser/Intern-Atlas huggingface dataset:
Weakly-Supervised Image Semantic Segmentation Using Graph Convolutional Networks
Venue: arXiv cs.CV 2021
Year: 2021

This work addresses weakly-supervised image semantic segmentation based on image-level class labels. One common approach to this task is to propagate the activation scores of Class Activation Maps (CAMs) using a random-walk mechanism in order to arrive at complete pseudo labels for training a semantic segmentation network in a fully-supervised manner. However, the feed-forward nature of the random walk imposes no regularization on the quality of the resulting complete pseudo labels. To overcome this issue, we propose a Graph Convolutional Network (GCN)-based feature propagation framework. We formulate the generation of complete pseudo labels as a semi-supervised learning task and learn a 2-layer GCN separately for every training image by back-propagating a Laplacian and an entropy regularization loss. Experimental results on the PASCAL VOC 2012 dataset confirm the superiority of our scheme to several state-of-the-art baselines. Our code is available at https://github.com/Xavier-Pan/WSGCN.
Robust Trajectory Tracking Error Model-Based Predictive Control for Unmanned Ground Vehicles
Venue: arXiv cs.RO 2021
Year: 2021

This paper proposes a new robust trajectory tracking error-based control approach for unmanned ground vehicles. A trajectory tracking error-based model is used to design a linear model predictive controller and its control action is combined with feedforward and robust control actions. The experimental results show that the proposed control structure is capable to let a tractor-trailer system track both linear and curvilinear target trajectories with low tracking error.
Robust Beamforming Design for Covert Communications
Venue: arXiv cs.IT 2021
Year: 2021

In this paper, we consider a common unicast beamforming network where Alice utilizes the communication to Carol as a cover and covertly transmits a message to Bob without being recognized by Willie. We investigate the beamformer design of Alice to maximize the covert rate to Bob when Alice has either perfect or imperfect knowledge about Willie's channel state information (WCSI). For the perfect WCSI case, the problem is formulated under the perfect covert constraint, and we develop a covert beamformer by applying semidefinite relaxation and the bisection method. Then, to reduce the computational complexity, we further propose a zero-forcing beamformer design with a single iteration processing. For the case of the imperfect WCSI, the robust beamformer is developed based on a relaxation and restriction approach by utilizing the property of Kullback-Leibler divergence. Furthermore, we derive the optimal decision threshold of Willie, and analyze the false alarm and the missed detection probabilities in this case. Finally, the performance of the proposed beamformer designs is evaluated through numerical experiments.
Ising formulations for two-dimensional cutting stock problem with setup cost
Venue: arXiv cs.DS 2021
Year: 2021

We proposed the method that translates the 2-D CSP for minimizing the number of cuts to the Ising model. After that, we conducted computer experiments of the proposed model using the benchmark problem. From the above, the following results are obtained. (1) The proposed Ising model adequately represents the target problem. (2) Acceptance rates were low as 0.2% to 9.8% and from 21.8% to 49.4%. (3) Error rates from optimal solution were broad as 0% to 25.9%. As the future work, (1) Improve the Hamiltonian for Constraints. (2) Improve the proposed model to adjust more complex 2-D CSP and reduce the number of spins when it deals with large materials and components. (3) Conduct experiments using a quantum annealer.
QUEST: Queue Simulation for Content Moderation at Scale
Venue: arXiv cs.LG 2021
Year: 2021

Moderating content in social media platforms is a formidable challenge due to the unprecedented scale of such systems, which typically handle billions of posts per day. Some of the largest platforms such as Facebook blend machine learning with manual review of platform content by thousands of reviewers. Operating a large-scale human review system poses interesting and challenging methodological questions that can be addressed with operations research techniques. We investigate the problem of optimally operating such a review system at scale using ideas from queueing theory and simulation.
Physics-Based Modeling and Predictive Simulation of Powder Bed Fusion Additive Manufacturing Across Length Scales
Venue: arXiv cs.CE 2021
Year: 2021

Powder bed fusion additive manufacturing (PBFAM) of metals has the potential to enable new paradigms of product design, manufacturing and supply chains while accelerating the realization of new technologies in the medical, aerospace, and other industries. Currently, wider adoption of PBFAM is held back by difficulty in part qualification, high production costs and low production rates, as extensive process tuning, post-processing, and inspection are required before a final part can be produced and deployed. Physics-based modeling and predictive simulation of PBFAM offers the potential to advance fundamental understanding of physical mechanisms that initiate process instabilities and cause defects. In turn, these insights can help link process and feedstock parameters with resulting part and material properties, thereby predicting optimal processing conditions and inspiring the development of improved processing hardware, strategies and materials. This work presents recent developments of our research team in the modeling of metal PBFAM processes spanning length scales, namely mesoscale powder modeling, mesoscale melt pool modeling, macroscale thermo-solid-mechanical modeling and microstructure modeling. Ongoing work in experimental validation of these models is also summarized. In conclusion, we discuss the interplay of these individual submodels within an integrated overall modeling approach, along with future research directions.
Scheduling Applications on Containers Based on Dependency of The Applications
Venue: arXiv cs.DC 2021
Year: 2021

Cloud computing technology has been one of the most critical developments in provisioning both hardware and software infrastructure in recent years. Container technology is a new cloud technology that boosts the booting of applications, increases the ability to deploy applications on containers and improves the host machine resource sharing. Thus, enhancing a cloud container system needs a robust algorithm that deploys the applications efficiently. Most of the schedulers associated with container technology are focused on load balancing for increasing container performance. The traffic over networks plays a significant role in the performance of containers. Container deployment considering only load balancing may not be the best scheduling strategy due to the dependency between the applications that might be deployed in different pods (zones) in the container's cloud. This project aims to develop an algorithm that deploys applications into containers by considering the dependencies between applications as well as load balancing. The proposed algorithm performs better in terms of improving the throughput and reducing the network traffic as compared to state-of-the-art container scheduling algorithms.
Managing smartphone crowdsensing campaigns through the Organicity smart city platform
Venue: arXiv cs.DC 2021
Year: 2021

We briefly present the design and architecture of a system that aims to simplify the process of organizing, executing and administering crowdsensing campaigns in a smart city context over smartphones volunteered by citizens. We built our system on top of an Android app substrate on the end-user level, which enables us to utilize smartphone resources. Our system allows researchers and other developers to manage and distribute their "mini" smart city applications, gather data and publish their results through the Organicity smart city platform. We believe this is the first time such a tool is paired with a large scale IoT infrastructure, to enable truly city-scale IoT and smart city experimentation.
$α$-Geodesical Skew Divergence
Venue: arXiv cs.IT 2021
Year: 2021

The asymmetric skew divergence smooths one of the distributions by mixing it, to a degree determined by the parameter $λ$, with the other distribution. Such divergence is an approximation of the KL divergence that does not require the target distribution to be absolutely continuous with respect to the source distribution. In this paper, an information geometric generalization of the skew divergence called the $α$-geodesical skew divergence is proposed, and its properties are studied.
Personal Data Gentrification
Venue: arXiv cs.CY 2021
Year: 2021

We live in an era in which the most valued services are not paid for in money, but in personal data. Every day, service providers collect the personal information of billions of individuals, information that sustain their infrastructure by marketing profiles labeled with this information to personal data consumers, such as advertisers. Not all uses of this personal data are for marketing; data consumers can also include, for instance, public health authorities tracking pandemics. In either case, individuals have undergone a process of Personal Data Gentrification, as data ownership has shifted from individuals to service providers and data consumers, as if the data is worth nothing to the individuals; these new owners then harness the data to obtain large profits. Current privacy-enhancing technologies are beginning to allow individuals to control and share less information. However, not sharing individuals' personal information at all could lead to Personal Data Blight, in which the potential of personal data in applications that benefit all of society remains forever latent. In this paper, we propose Personal Data Enfranchisement as a middle ground, empowering individuals to control the sharing of their personal information to shift the business flows of personal information. Based on these insights, we propose a model to gradually and incrementally make a shift from our current situation towards one of Personal Data Enfranchisement. Finally, we present a roadmap and some challenges towards achieving this bold vision.
Globally Distributed Development during COVID-19
Venue: arXiv cs.SE 2021
Year: 2021

Due to the global pandemic, in March 2020 we in academia and industry were abruptly forced into working from home. Yet teaching never stopped, and neither did developing software, fixing software, and expanding into new markets. Demands for flexible ways of working, responding to new requirements, have never been so high. How did we manage to continue working, when we had to suddenly switch all communication to online and virtual forms of contact? In this short paper we describe how Ocuco Ltd., a medium-sized organization headquartered in Ireland, managed our software development teams--distributed throughout Ireland, Europe, Asia and America during the COVID-19 pandemic. We describe how we expanded, kept our customers happy, and our teams motivated. We made changes, some large, such as providing emergency financial support; others small, like implementing regular online social pizza evenings. Technology and process changes were minor, an advantage of working in globally distributed teams since 2016, when development activities were coordinated according to the Scaled Agile Framework (SAFe). The results of implementing the changes were satisfying; productivity went up, we gained new customers, and preliminary results from our wellness survey indicate that everyone feels extremely well-supported by management to achieve their goals. However, the anonymised survey responses did show some developers' anxiety levels were slightly raised, and many are working longer hours. Administering this survey is very beneficial, as now we know, so we can act.
An effective and friendly tool for seed image analysis
Venue: arXiv cs.CV 2021
Year: 2021

Image analysis is an essential field for several topics in the life sciences, such as biology or botany. In particular, the analysis of seeds (e.g. fossil research) can provide significant information on their evolution, the history of agriculture, plant domestication and knowledge of diets in ancient times. This work aims to present software that performs image analysis for feature extraction and classification from images containing seeds through a novel and unique framework. In detail, we propose two plugins \emph{ImageJ}, one able to extract morphological, textual and colour features from seed images, and another to classify seeds into categories using the extracted features. The experimental results demonstrated the correctness and validity of both the extracted features and the classification predictions. The proposed tool is easily extendable to other fields of image analysis.
Lower bound on the Voronoi diagram of lines in $\mathbb{R}^d$
Venue: arXiv cs.CG 2021
Year: 2021

This note gives a lower bound of $Ω(n^{\lceil 2d/3\rceil})$ on the maximal complexity of the Euclidean Voronoi diagram of $n$ non-intersecting lines in $\mathbb{R}^d$ for $d>2$.
Smartphone Camera Oximetry in an Induced Hypoxemia Study
Venue: arXiv cs.LG 2021
Year: 2021

Hypoxemia, a medical condition that occurs when the blood is not carrying enough oxygen to adequately supply the tissues, is a leading indicator for dangerous complications of respiratory diseases like asthma, COPD, and COVID-19. While purpose-built pulse oximeters can provide accurate blood-oxygen saturation (SpO$_2$) readings that allow for diagnosis of hypoxemia, enabling this capability in unmodified smartphone cameras via a software update could give more people access to important information about their health, as well as improve physicians' ability to remotely diagnose and treat respiratory conditions. In this work, we take a step towards this goal by performing the first clinical development validation on a smartphone-based SpO$_2$ sensing system using a varied fraction of inspired oxygen (FiO$_2$) protocol, creating a clinically relevant validation dataset for solely smartphone-based methods on a wide range of SpO$_2$ values (70%-100%) for the first time. This contrasts with previous studies, which evaluated performance on a far smaller range (85%-100%). We build a deep learning model using this data to demonstrate accurate reporting of SpO$_2$ level with an overall MAE=5.00% SpO$_2$ and identifying positive cases of low SpO$_2$<90% with 81% sensitivity and 79% specificity. We ground our analysis with a summary of recent literature in smartphone-based SpO2 monitoring, and we provide the data from the FiO$_2$ study in open-source format, so that others may build on this work.
Hierarchical Road Topology Learning for Urban Map-less Driving
Venue: arXiv cs.RO 2021
Year: 2021

The majority of current approaches in autonomous driving rely on High-Definition (HD) maps which detail the road geometry and surrounding area. Yet, this reliance is one of the obstacles to mass deployment of autonomous vehicles due to poor scalability of such prior maps. In this paper, we tackle the problem of online road map extraction via leveraging the sensory system aboard the vehicle itself. To this end, we design a structured model where a graph representation of the road network is generated in a hierarchical fashion within a fully convolutional network. The method is able to handle complex road topology and does not require a user in the loop.
LIFT-SLAM: a deep-learning feature-based monocular visual SLAM method
Venue: arXiv cs.CV 2021
Year: 2021

The Simultaneous Localization and Mapping (SLAM) problem addresses the possibility of a robot to localize itself in an unknown environment and simultaneously build a consistent map of this environment. Recently, cameras have been successfully used to get the environment's features to perform SLAM, which is referred to as visual SLAM (VSLAM). However, classical VSLAM algorithms can be easily induced to fail when either the motion of the robot or the environment is too challenging. Although new approaches based on Deep Neural Networks (DNNs) have achieved promising results in VSLAM, they still are unable to outperform traditional methods. To leverage the robustness of deep learning to enhance traditional VSLAM systems, we propose to combine the potential of deep learning-based feature descriptors with the traditional geometry-based VSLAM, building a new VSLAM system called LIFT-SLAM. Experiments conducted on KITTI and Euroc datasets show that deep learning can be used to improve the performance of traditional VSLAM systems, as the proposed approach was able to achieve results comparable to the state-of-the-art while being robust to sensorial noise. We enhance the proposed VSLAM pipeline by avoiding parameter tuning for specific datasets with an adaptive approach while evaluating how transfer learning can affect the quality of the features extracted.
Rate-Splitting Multiple Access for Multigroup Multicast Cellular and Satellite Communications: PHY Layer Design and Link-Level Simulations
Venue: arXiv cs.IT 2021
Year: 2021

Rate-splitting multiple access (RSMA), relying on linearly precoded rate-splitting (RS) at the transmitter and successive interference cancellation (SIC) at the receivers has emerged as a powerful and flexible multiple access strategy for downlink multi-user multi-antenna systems. Through message splitting and the transmission of both common and private messages, RSMA has been demonstrated to be a robust interference management strategy which enables partially decoding interference and partially treating interference as noise. In this work, we consider the application of RSMA in a multigroup multicast scenario, where each message is intended to a group of users. By leveraging the recent results on the max-min fair (MMF) optimization problem of RSMA-based multigroup multicast beamforming with imperfect channel state information at the transmitter (CSIT), we investigate the design of the physical (PHY) layer including finite length polar coding, finite alphabet modulation, adaptive modulation and coding (AMC) algorithm, and SIC receivers, etc. Link-level simulation (LLS) results verify the superiority of RSMA-based multigroup multicast transmission compared with space-division multiple access (SDMA)-based strategy in both cellular systems and multibeam satellite systems.
Perspective, Survey and Trends: Public Driving Datasets and Toolsets for Autonomous Driving Virtual Test
Venue: arXiv cs.RO 2021
Year: 2021

Owing to the merits of early safety and reliability guarantee, autonomous driving virtual testing has recently gains increasing attention compared with closed-loop testing in real scenarios. Although the availability and quality of autonomous driving datasets and toolsets are the premise to diagnose the autonomous driving system bottlenecks and improve the system performance, due to the diversity and privacy of the datasets and toolsets, collecting and featuring the perspective and quality of them become not only time-consuming but also increasingly challenging. This paper first proposes a Systematic Literature review approach for Autonomous driving tests (SLA), then presents an overview of existing publicly available datasets and toolsets from 2000 to 2020. Quantitative findings with the scenarios concerned, perspectives and trend inferences and suggestions with 35 automated driving test tool sets and 70 test data sets are also presented. To the best of our knowledge, we are the first to perform such recent empirical survey on both the datasets and toolsets using a SLA based survey approach. Our multifaceted analyses and new findings not only reveal insights that we believe are useful for system designers, practitioners and users, but also can promote more researches on a systematic survey analysis in autonomous driving surveys on dataset and toolsets.
Many-to-English Machine Translation Tools, Data, and Pretrained Models
Venue: arXiv cs.CL 2021
Year: 2021

While there are more than 7000 languages in the world, most translation research efforts have targeted a few high-resource languages. Commercial translation systems support only one hundred languages or fewer, and do not make these models available for transfer to low resource languages. In this work, we present useful tools for machine translation research: MTData, NLCodec, and RTG. We demonstrate their usefulness by creating a multilingual neural machine translation model capable of translating from 500 source languages to English. We make this multilingual model readily downloadable and usable as a service, or as a parent model for transfer-learning to even lower-resource languages.
Unsupervised Sound Localization via Iterative Contrastive Learning
Venue: arXiv cs.CV 2021
Year: 2021

Sound localization aims to find the source of the audio signal in the visual scene. However, it is labor-intensive to annotate the correlations between the signals sampled from the audio and visual modalities, thus making it difficult to supervise the learning of a machine for this task. In this work, we propose an iterative contrastive learning framework that requires no data annotations. At each iteration, the proposed method takes the 1) localization results in images predicted in the previous iteration, and 2) semantic relationships inferred from the audio signals as the pseudo-labels. We then use the pseudo-labels to learn the correlation between the visual and audio signals sampled from the same video (intra-frame sampling) as well as the association between those extracted across videos (inter-frame relation). Our iterative strategy gradually encourages the localization of the sounding objects and reduces the correlation between the non-sounding regions and the reference audio. Quantitative and qualitative experimental results demonstrate that the proposed framework performs favorably against existing unsupervised and weakly-supervised methods on the sound localization task.
Federated Few-Shot Learning with Adversarial Learning
Venue: arXiv cs.LG 2021
Year: 2021

We are interested in developing a unified machine learning model over many mobile devices for practical learning tasks, where each device only has very few training data. This is a commonly encountered situation in mobile computing scenarios, where data is scarce and distributed while the tasks are distinct. In this paper, we propose a federated few-shot learning (FedFSL) framework to learn a few-shot classification model that can classify unseen data classes with only a few labeled samples. With the federated learning strategy, FedFSL can utilize many data sources while keeping data privacy and communication efficiency. There are two technical challenges: 1) directly using the existing federated learning approach may lead to misaligned decision boundaries produced by client models, and 2) constraining the decision boundaries to be similar over clients would overfit to training tasks but not adapt well to unseen tasks. To address these issues, we propose to regularize local updates by minimizing the divergence of client models. We also formulate the training in an adversarial fashion and optimize the client models to produce a discriminative feature space that can better represent unseen data samples. We demonstrate the intuitions and conduct experiments to show our approaches outperform baselines by more than 10% in learning vision tasks and 5% in language tasks.