OpenRaiser/Intern-Atlas huggingface dataset:
All one needs to know about shared micromobility simulation: a complete survey
Venue: arXiv cs.CY 2022
Year: 2022

As the shared micromobility becomes a part of our daily life and environment, we expect the number of low-speed modes for first-and-last mile trips to grow rapidly. The shared micomobility is expected to serve billions of humans, bringing us considerable advantages. With this growth, shared micromobility simulation such as docked stations based shared bikes, dockless shared bikes and e-scooters, are regarded as promising solutions to deal with a large number of first-and-last mile trips. In this paper, we first provide a comprehensive overview of shared micromobility simulation and its related validation metrics. Next, we classify the research topics of shared micromobility simulation, summarize, and classify the existing works. Finally, challenges and future directions are provided for further research.
Cloud Computing-based Higher Education Platforms during the COVID-19 Pandemic
Venue: arXiv cs.CY 2022
Year: 2022

Cloud computing has become the infrastructure that supports people's daily activities, business operations, and education delivery around the world. Cloud computing-based education platforms have been widely applied to assist online teaching during the COVID-19 pandemic. This paper examines the impact and importance of cloud computing in remote learning and education. This study conducted multiple-case analyses of 22 online platforms of higher education in Chinese universities during the epidemic. A comparative analysis of the 22 platforms revealed that they applied different cloud computing models and tools based on their unique requirements and needs. The study results provide strategic insights to higher education institutions regarding effective approaches to applying cloud computing-based platforms for remote education, especially during crisis situations.
Needs and Artificial Intelligence
Venue: arXiv cs.CY 2022
Year: 2022

Throughout their history, homo sapiens have used technologies to better satisfy their needs. The relation between needs and technology is so fundamental that the US National Research Council defined the distinguishing characteristic of technology as its goal "to make modifications in the world to meet human needs". Artificial intelligence (AI) is one of the most promising emerging technologies of our time. Similar to other technologies, AI is expected "to meet [human] needs". In this article, we reflect on the relationship between needs and AI, and call for the realisation of needs-aware AI systems. We argue that re-thinking needs for, through, and by AI can be a very useful means towards the development of realistic approaches for Sustainable, Human-centric, Accountable, Lawful, and Ethical (HALE) AI systems. We discuss some of the most critical gaps, barriers, enablers, and drivers of co-creating future AI-based socio-technical systems in which [human] needs are well considered and met. Finally, we provide an overview of potential threats and HALE considerations that should be carefully taken into account, and call for joint, immediate, and interdisciplinary efforts and collaborations.
A Push-Relabel Based Additive Approximation for Optimal Transport
Venue: arXiv cs.LG 2022
Year: 2022

Optimal Transport is a popular distance metric for measuring similarity between distributions. Exact algorithms for computing Optimal Transport can be slow, which has motivated the development of approximate numerical solvers (e.g. Sinkhorn method). We introduce a new and very simple combinatorial approach to find an $\varepsilon$-approximation of the OT distance. Our algorithm achieves a near-optimal execution time of $O(n^2/\varepsilon^2)$ for computing OT distance and, for the special case of the assignment problem, the execution time improves to $O(n^2/\varepsilon)$. Our algorithm is based on the push-relabel framework for min-cost flow problems. Unlike the other combinatorial approach (Lahn, Mulchandani and Raghvendra, NeurIPS 2019) which does not have a fast parallel implementation, our algorithm has a parallel execution time of $O(\log n/\varepsilon^2)$. Interestingly, unlike the Sinkhorn algorithm, our method also readily provides a compact transport plan as well as a solution to an approximate version of the dual formulation of the OT problem, both of which have numerous applications in Machine Learning. For the assignment problem, we provide both a CPU implementation as well as an implementation that exploits GPU parallelism. Experiments suggest that our algorithm is faster than the Sinkhorn algorithm, both in terms of CPU and GPU implementations, especially while computing matchings with a high accuracy.
CrowdFormer: Weakly-supervised Crowd counting with Improved Generalizability
Venue: arXiv cs.CV 2022
Year: 2022

Convolutional neural networks (CNNs) have dominated the field of computer vision for nearly a decade due to their strong ability to learn local features. However, due to their limited receptive field, CNNs fail to model the global context. On the other hand, transformer, an attention-based architecture can model the global context easily. Despite this, there are limited studies that investigate the effectiveness of transformers in crowd counting. In addition, the majority of the existing crowd counting methods are based on the regression of density maps which requires point-level annotation of each person present in the scene. This annotation task is laborious and also error-prone. This has led to increased focus on weakly-supervised crowd counting methods which require only the count-level annotations. In this paper, we propose a weakly-supervised method for crowd counting using a pyramid vision transformer. We have conducted extensive evaluations to validate the effectiveness of the proposed method. Our method is comparable to the state-of-the-art on the benchmark crowd datasets. More importantly, it shows remarkable generalizability.
A Unified Framework of Medical Information Annotation and Extraction for Chinese Clinical Text
Venue: arXiv cs.CL 2022
Year: 2022

Medical information extraction consists of a group of natural language processing (NLP) tasks, which collaboratively convert clinical text to pre-defined structured formats. Current state-of-the-art (SOTA) NLP models are highly integrated with deep learning techniques and thus require massive annotated linguistic data. This study presents an engineering framework of medical entity recognition, relation extraction and attribute extraction, which are unified in annotation, modeling and evaluation. Specifically, the annotation scheme is comprehensive, and compatible between tasks, especially for the medical relations. The resulted annotated corpus includes 1,200 full medical records (or 18,039 broken-down documents), and achieves inter-annotator agreements (IAAs) of 94.53%, 73.73% and 91.98% F 1 scores for the three tasks. Three task-specific neural network models are developed within a shared structure, and enhanced by SOTA NLP techniques, i.e., pre-trained language models. Experimental results show that the system can retrieve medical entities, relations and attributes with F 1 scores of 93.47%, 67.14% and 90.89%, respectively. This study, in addition to our publicly released annotation scheme and code, provides solid and practical engineering experience of developing an integrated medical information extraction system.
GANSpiration: Balancing Targeted and Serendipitous Inspiration in User Interface Design with Style-Based Generative Adversarial Network
Venue: arXiv cs.HC 2022
Year: 2022

Inspiration from design examples plays a crucial role in the creative process of user interface design. However, current tools and techniques that support inspiration usually only focus on example browsing with limited user control or similarity-based example retrieval, leading to undesirable design outcomes such as focus drift and design fixation. To address these issues, we propose the GANSpiration approach that suggests design examples for both targeted and serendipitous inspiration, leveraging a style-based Generative Adversarial Network. A quantitative evaluation revealed that the outputs of GANSpiration-based example suggestion approaches are relevant to the input design, and at the same time include diverse instances. A user study with professional UI/UX practitioners showed that the examples suggested by our approach serve as viable sources of inspiration for overall design concepts and specific design elements. Overall, our work paves the road of using advanced generative machine learning techniques in supporting the creative design practice.
An Efficient Two-Stage SPARC Decoder for Massive MIMO Unsourced Random Access
Venue: arXiv cs.IT 2022
Year: 2022

In this paper, we study a concatenate coding scheme based on sparse regression code (SPARC) and tree code for unsourced random access in massive multiple-input and multiple-output systems. Our focus is concentrated on efficient decoding for the inner SPARC with practical concerns. A two-stage method is proposed to achieve near-optimal performance while maintaining low computational complexity. Specifically, a one-step thresholding-based algorithm is first used for reducing large dimensions of the SPARC decoding, after which a relaxed maximum-likelihood estimator is employed for refinement. Adequate simulation results are provided to validate the near-optimal performance and the low computational complexity. Besides, for covariance-based sparse recovery method, theoretical analyses are given to characterize the upper bound of the number of active users supported when convex relaxation is considered, and the probability of successful dimension reduction by the one-step thresholding-based algorithm.
Occupancy Flow Fields for Motion Forecasting in Autonomous Driving
Venue: arXiv cs.RO 2022
Year: 2022

We propose Occupancy Flow Fields, a new representation for motion forecasting of multiple agents, an important task in autonomous driving. Our representation is a spatio-temporal grid with each grid cell containing both the probability of the cell being occupied by any agent, and a two-dimensional flow vector representing the direction and magnitude of the motion in that cell. Our method successfully mitigates shortcomings of the two most commonly-used representations for motion forecasting: trajectory sets and occupancy grids. Although occupancy grids efficiently represent the probabilistic location of many agents jointly, they do not capture agent motion and lose the agent identities. To this end, we propose a deep learning architecture that generates Occupancy Flow Fields with the help of a new flow trace loss that establishes consistency between the occupancy and flow predictions. We demonstrate the effectiveness of our approach using three metrics on occupancy prediction, motion estimation, and agent ID recovery. In addition, we introduce the problem of predicting speculative agents, which are currently-occluded agents that may appear in the future through dis-occlusion or by entering the field of view. We report experimental results on a large in-house autonomous driving dataset and the public INTERACTION dataset, and show that our model outperforms state-of-the-art models.
Incentivizing Data Contribution in Cross-Silo Federated Learning
Venue: arXiv cs.GT 2022
Year: 2022

In cross-silo federated learning, clients (e.g., organizations) train a shared global model using local data. However, due to privacy concerns, the clients may not contribute enough data points during training. To address this issue, we propose a general incentive framework where the profit/benefit obtained from the global model can be appropriately allocated to clients to incentivize data contribution. We formulate the clients' interactions as a data contribution game and study its equilibrium. We characterize conditions for an equilibrium to exist, and prove that each client's equilibrium data contribution increases in its data quality and decreases in the privacy sensitivity. We further conduct experiments using CIFAR-10 and show that the results are consistent with the analysis. Moreover, we show that practical allocation mechanisms such as linearly proportional, leave-one-out, and Shapley-value incentivize more data contribution from clients with higher-quality data, in which leave-one-out tends to achieve the highest global model accuracy at equilibrium.
End-to-end system for object detection from sub-sampled radar data
Venue: arXiv cs.CV 2022
Year: 2022

Robust and accurate sensing is of critical importance for advancing autonomous automotive systems. The need to acquire situational awareness in complex urban conditions using sensors such as radar has motivated research on power and latency-efficient signal acquisition methods. In this paper, we present an end-to-end signal processing pipeline, capable of operating in extreme weather conditions, that relies on sub-sampled radar data to perform object detection in vehicular settings. The results of the object detection are further utilized to sub-sample forthcoming radar data, which stands in contrast to prior work where the sub-sampling relies on image information. We show robust detection based on radar data reconstructed using 20% of samples under extreme weather conditions such as snow or fog, and on low-illuminated nights. Additionally, we generate 20% sampled radar data in a fine-tuning set and show 1.1% gain in AP50 across scenes and 3% AP50 gain in motorway condition.
The Internet of Things enabling communication technologies, applications and challenges: a survey
Venue: arXiv cs.NI 2022
Year: 2022

Recently, the IoT has gained great importance, which results in the evolution of communication technologies to meet the needs of different IoT applications. In addition, several domains integrate the IoT technologies in several applications of our professional life and daily activities. However, the IoT still presents many challenges and issues. This article describes in details the emerging communication technologies used in the IoT networks and enumerates the common domains of their application. It also describes the main challenges of the IoT and its use in order to exploit its advantages at most.
Adaptor: Objective-Centric Adaptation Framework for Language Models
Venue: arXiv cs.CL 2022
Year: 2022

Progress in natural language processing research is catalyzed by the possibilities given by the widespread software frameworks. This paper introduces Adaptor library that transposes the traditional model-centric approach composed of pre-training + fine-tuning steps to objective-centric approach, composing the training process by applications of selected objectives. We survey research directions that can benefit from enhanced objective-centric experimentation in multitask training, custom objectives development, dynamic training curricula, or domain adaptation. Adaptor aims to ease reproducibility of these research directions in practice. Finally, we demonstrate the practical applicability of Adaptor in selected unsupervised domain adaptation scenarios.
Semi-Random Sparse Recovery in Nearly-Linear Time
Venue: arXiv cs.DS 2022
Year: 2022

Sparse recovery is one of the most fundamental and well-studied inverse problems. Standard statistical formulations of the problem are provably solved by general convex programming techniques and more practical, fast (nearly-linear time) iterative methods. However, these latter "fast algorithms" have previously been observed to be brittle in various real-world settings. We investigate the brittleness of fast sparse recovery algorithms to generative model changes through the lens of studying their robustness to a "helpful" semi-random adversary, a framework which tests whether an algorithm overfits to input assumptions. We consider the following basic model: let $\mathbf{A} \in \mathbb{R}^{n \times d}$ be a measurement matrix which contains an unknown subset of rows $\mathbf{G} \in \mathbb{R}^{m \times d}$ which are bounded and satisfy the restricted isometry property (RIP), but is otherwise arbitrary. Letting $x^\star \in \mathbb{R}^d$ be $s$-sparse, and given either exact measurements $b = \mathbf{A} x^\star$ or noisy measurements $b = \mathbf{A} x^\star + ξ$, we design algorithms recovering $x^\star$ information-theoretically optimally in nearly-linear time. We extend our algorithm to hold for weaker generative models relaxing our planted RIP assumption to a natural weighted variant, and show that our method's guarantees naturally interpolate the quality of the measurement matrix to, in some parameter regimes, run in sublinear time. Our approach differs from prior fast iterative methods with provable guarantees under semi-random generative models: natural conditions on a submatrix which make sparse recovery tractable are NP-hard to verify. We design a new iterative method tailored to the geometry of sparse recovery which is provably robust to our semi-random model. We hope our approach opens the door to new robust, efficient algorithms for natural statistical inverse problems.
Metal Wire Manipulation Planning for 3D Curving -- How a Low Payload Robot Can Use a Bending Machine to Bend Stiff Metal Wire
Venue: arXiv cs.RO 2022
Year: 2022

This paper presents a combined task and motion planner for a robot arm to carry out 3D metal wire curving tasks by collaborating with a bending machine. We assume a collaborative robot that is safe to work in a human environment but has a weak payload to bend objects with large stiffness, and developed a combined planner for the robot to use a bending machine. Our method converts a 3D curve to a bending set and generates the feasible bending sequence, machine usage, robotic grasp poses, and pick-and-place arm motion considering the combined task and motion level constraints. Compared with previous deformable linear object shaping work that relied on forces provided by robotic arms, the proposed method is suitable for the material with high stiffness. We evaluate the system using different tasks. The results show that the proposed system is flexible and robust to generate robotic motion to corporate with the designed bending machine.
Towards Safe and Efficient Swarm-Human Collaboration: A Hierarchical Multi-Agent Pickup and Delivery framework
Venue: arXiv cs.MA 2022
Year: 2022

The multi-Agent Pickup and Delivery (MAPD) problem is crucial in the realm of Intelligent Storage Systems (ISSs), where multiple robots are assigned with time-varying, heterogeneous, and potentially uncertain tasks. When it comes to Human-Swarm Hybrid System ((HS)$_2$), robots and human workers will accomplish the MAPD tasks in collaboration. Herein, we propose a Human-Swarm Hybrid System Pickup and Delivery ((HS)$_2$PD) framework, which is predominant in future ISSs. A two-layer decision framework based on the prediction horizon window is established in light of the unpredictability of human behavior and the dynamic changes of tasks. The first layer is a two-level programming problem to solve the problems of mode assignment and TA. The second layer is devoted to the exact path of each agent via solving mixed-integer programming (MIP) problems. An integrated algorithm for the (HS)$_2$PD problem is summarized. The practicality and validity of the above algorithm are illustrated via a numerical simulation example towards (HS)$_2$PD tasks.
Counting with Adaptive Auxiliary Learning
Venue: arXiv cs.CV 2022
Year: 2022

This paper proposes an adaptive auxiliary task learning based approach for object counting problems. Unlike existing auxiliary task learning based methods, we develop an attention-enhanced adaptively shared backbone network to enable both task-shared and task-tailored features learning in an end-to-end manner. The network seamlessly combines standard Convolution Neural Network (CNN) and Graph Convolution Network (GCN) for feature extraction and feature reasoning among different domains of tasks. Our approach gains enriched contextual information by iteratively and hierarchically fusing the features across different task branches of the adaptive CNN backbone. The whole framework pays special attention to the objects' spatial locations and varied density levels, informed by object (or crowd) segmentation and density level segmentation auxiliary tasks. In particular, thanks to the proposed dilated contrastive density loss function, our network benefits from individual and regional context supervision in terms of pixel-independent and pixel-dependent feature learning mechanisms, along with strengthened robustness. Experiments on seven challenging multi-domain datasets demonstrate that our method achieves superior performance to the state-of-the-art auxiliary task learning based counting methods. Our code is made publicly available at: https://github.com/smallmax00/Counting_With_Adaptive_Auxiliary
Analyzing General-Purpose Deep-Learning Detection and Segmentation Models with Images from a Lidar as a Camera Sensor
Venue: arXiv cs.RO 2022
Year: 2022

Over the last decade, robotic perception algorithms have significantly benefited from the rapid advances in deep learning (DL). Indeed, a significant amount of the autonomy stack of different commercial and research platforms relies on DL for situational awareness, especially vision sensors. This work explores the potential of general-purpose DL perception algorithms, specifically detection and segmentation neural networks, for processing image-like outputs of advanced lidar sensors. Rather than processing the three-dimensional point cloud data, this is, to the best of our knowledge, the first work to focus on low-resolution images with 360\textdegree field of view obtained with lidar sensors by encoding either depth, reflectivity, or near-infrared light in the image pixels. We show that with adequate preprocessing, general-purpose DL models can process these images, opening the door to their usage in environmental conditions where vision sensors present inherent limitations. We provide both a qualitative and quantitative analysis of the performance of a variety of neural network architectures. We believe that using DL models built for visual cameras offers significant advantages due to the much wider availability and maturity compared to point cloud-based perception.
Urban Vehicle Mobility Characteristic Mining and Trip Generation Based on Knowledge Graph
Venue: arXiv cs.CY 2022
Year: 2022

The operation of urban transportation produces massive traffic data, which contains abundant information and is of great significance for the study of intelligent transportation systems. In particular, with the improvement of perception technology, it has become possible to obtain trip data in individual-level of vehicles. It has finer granularity and greater research potential, but at the same time requires higher requirements in terms of data organization and analysis. More importantly it cannot be made public due to privacy issues. To handle individual-level urban vehicle trip big data better, we introduce the knowledge graph for the study. For organization of individual level trip data, we designed and constructed an individual-level trip knowledge graph which greatly improves the efficiency of obtaining data. Then we used the trip knowledge graph as the data engine and designed logical rules to mine the trip characteristics of vehicles by combining the transportation domain knowledge. Finally, we further propose an individual-level trip synthesis method based on knowledge graph generation to address the privacy issue of individual-level traffic data. The experiment shows that the final generated trip data are similar to the historical one in mobility patterns and vehicle associations, and have high spatial continuity.