OpenRaiser/Intern-Atlas huggingface dataset:
Open-environment Machine Learning
Venue: arXiv cs.LG 2022
Year: 2022

Conventional machine learning studies generally assume close-environment scenarios where important factors of the learning process hold invariant. With the great success of machine learning, nowadays, more and more practical tasks, particularly those involving open-environment scenarios where important factors are subject to change, called open-environment machine learning (Open ML) in this article, are present to the community. Evidently it is a grand challenge for machine learning turning from close environment to open environment. It becomes even more challenging since, in various big data tasks, data are usually accumulated with time, like streams, while it is hard to train the machine learning model after collecting all data as in conventional studies. This article briefly introduces some advances in this line of research, focusing on techniques concerning emerging new classes, decremental/incremental features, changing data distributions, varied learning objectives, and discusses some theoretical issues.
helyOS: A customized off-the-shelf solution for autonomous driving applications in delimited areas
Venue: arXiv cs.SE 2022
Year: 2022

Microservice Architectures (MSA), known to successfully handle complex software systems, are emerging as the new paradigm for automotive software. The design of an MSA requires correct subdivision of the software system and implementation of the communication between components. These tasks demand both software expertise and domain knowledge. In this context, we developed an MSA framework pre-tailored to meet the requirements of autonomous driving applications in delimited areas - the helyOS framework. The framework decomposes complex applications in predefined microservice domains and provides a communication backbone for event messages and data. This paper demonstrates how such a tailored MSA framework can accelerate the development by prompting a quick start for the integration of motion planning algorithms, device controllers, vehicles simulators and web-browser interfaces.
Differentially Private Shapley Values for Data Evaluation
Venue: arXiv cs.LG 2022
Year: 2022

The Shapley value has been proposed as a solution to many applications in machine learning, including for equitable valuation of data. Shapley values are computationally expensive and involve the entire dataset. The query for a point's Shapley value can also compromise the statistical privacy of other data points. We observe that in machine learning problems such as empirical risk minimization, and in many learning algorithms (such as those with uniform stability), a diminishing returns property holds, where marginal benefit per data point decreases rapidly with data sample size. Based on this property, we propose a new stratified approximation method called the Layered Shapley Algorithm. We prove that this method operates on small (O(\polylog(n))) random samples of data and small sized ($O(\log n)$) coalitions to achieve the results with guaranteed probabilistic accuracy, and can be modified to incorporate differential privacy. Experimental results show that the algorithm correctly identifies high-value data points that improve validation accuracy, and that the differentially private evaluations preserve approximate ranking of data.
Amodal Cityscapes: A New Dataset, its Generation, and an Amodal Semantic Segmentation Challenge Baseline
Venue: arXiv cs.CV 2022
Year: 2022

Amodal perception terms the ability of humans to imagine the entire shapes of occluded objects. This gives humans an advantage to keep track of everything that is going on, especially in crowded situations. Typical perception functions, however, lack amodal perception abilities and are therefore at a disadvantage in situations with occlusions. Complex urban driving scenarios often experience many different types of occlusions and, therefore, amodal perception for automated vehicles is an important task to investigate. In this paper, we consider the task of amodal semantic segmentation and propose a generic way to generate datasets to train amodal semantic segmentation methods. We use this approach to generate an amodal Cityscapes dataset. Moreover, we propose and evaluate a method as baseline on Amodal Cityscapes, showing its applicability for amodal semantic segmentation in automotive environment perception. We provide the means to re-generate this dataset on github.
When happy accidents spark creativity: Bringing collaborative speculation to life with generative AI
Venue: arXiv cs.HC 2022
Year: 2022

Generative AI techniques like those that synthesize images from text (text-to-image models) offer new possibilities for creatively imagining new ideas. We investigate the capabilities of these models to help communities engage in conversations about their collective future. In particular, we design and deploy a facilitated experience where participants collaboratively speculate on utopias they want to see, and then produce AI-generated imagery from those speculations. In a series of in-depth user interviews, we invite participants to reflect on the generated images and refine their visions for the future. We synthesize findings with a bespoke community zine on the experience. We observe that participants often generated ideas for implementing their vision and drew new lateral considerations as a result of viewing the generated images. Critically, we find that the unexpected difference between the participant's imagined output and the generated image is what facilitated new insight for the participant. We hope our experimental model for co-creation, computational creativity, and community reflection inspires the use of generative models to help communities and organizations envision better futures.
Learning to Sequence and Blend Robot Skills via Differentiable Optimization
Venue: arXiv cs.RO 2022
Year: 2022

In contrast to humans and animals who naturally execute seamless motions, learning and smoothly executing sequences of actions remains a challenge in robotics. This paper introduces a novel skill-agnostic framework that learns to sequence and blend skills based on differentiable optimization. Our approach encodes sequences of previously-defined skills as quadratic programs (QP), whose parameters determine the relative importance of skills along the task. Seamless skill sequences are then learned from demonstrations by exploiting differentiable optimization layers and a tailored loss formulated from the QP optimality conditions. Via the use of differentiable optimization, our work offers novel perspectives on multitask control. We validate our approach in a pick-and-place scenario with planar robots, a pouring experiment with a real humanoid robot, and a bimanual sweeping task with a human model.
Cooling Down FaaS: Towards Getting Rid of Warm Starts
Venue: arXiv cs.DC 2022
Year: 2022

Serverless execution and most notably the Function as a Service (FaaS) model got quite some attention during the recent years. As of today, all commercial and open source implementations follow the common practice of keeping the execution environments running to achieve low function execution latency. In this paper we compare the startup latency of different available virtualization technologies, then we implement and benchmark an FaaS prototype system using IncludeOS unikernels for function execution. We show that our system can start and execute functions with essentially the same latency as AWS Lambda with its continuously running executor units. Due to the low overhead, this approach opens the possibility for simplified FaaS platforms without the resource waste and extensive monitoring requirements of existing solutions.
Dual-stream spatiotemporal networks with feature sharing for monitoring animals in the home cage
Venue: arXiv cs.CV 2022
Year: 2022

This paper presents a spatiotemporal deep learning approach for mouse behavioural classification in the home-cage. Using a series of dual-stream architectures with assorted modifications to increase performance, we introduce a novel feature sharing approach that jointly processes the streams at regular intervals throughout the network. To investigate the efficacy of this approach, models were evaluated by dissociating the streams and training/testing in the same rigorous manner as the main classifiers. Using an annotated, publicly available dataset of a singly-housed mice, we achieve prediction accuracy of 86.47% using an ensemble of a Inception-based network and an attention-based network, both of which utilize this feature sharing. We also demonstrate through ablation studies that for all models, the feature-sharing architectures consistently perform better than conventional ones having separate streams. The best performing models were further evaluated on other activity datasets, both mouse and human. Future work will investigate the effectiveness of feature sharing to behavioural classification in the unsupervised anomaly detection domain.
Graph Machine Learning for Design of High-Octane Fuels
Venue: arXiv cs.LG 2022
Year: 2022

Fuels with high-knock resistance enable modern spark-ignition engines to achieve high efficiency and thus low CO2 emissions. Identification of molecules with desired autoignition properties indicated by a high research octane number and a high octane sensitivity is therefore of great practical relevance and can be supported by computer-aided molecular design (CAMD). Recent developments in the field of graph machine learning (graph-ML) provide novel, promising tools for CAMD. We propose a modular graph-ML CAMD framework that integrates generative graph-ML models with graph neural networks and optimization, enabling the design of molecules with desired ignition properties in a continuous molecular space. In particular, we explore the potential of Bayesian optimization and genetic algorithms in combination with generative graph-ML models. The graph-ML CAMD framework successfully identifies well-established high-octane components. It also suggests new candidates, one of which we experimentally investigate and use to illustrate the need for further auto-ignition training data.
Graph Neural Networks with Precomputed Node Features
Venue: arXiv cs.LG 2022
Year: 2022

Most Graph Neural Networks (GNNs) cannot distinguish some graphs or indeed some pairs of nodes within a graph. This makes it impossible to solve certain classification tasks. However, adding additional node features to these models can resolve this problem. We introduce several such augmentations, including (i) positional node embeddings, (ii) canonical node IDs, and (iii) random features. These extensions are motivated by theoretical results and corroborated by extensive testing on synthetic subgraph detection tasks. We find that positional embeddings significantly outperform other extensions in these tasks. Moreover, positional embeddings have better sample efficiency, perform well on different graph distributions and even outperform learning with ground truth node positions. Finally, we show that the different augmentations perform competitively on established GNN benchmarks, and advise on when to use them.
Studying the Practices of Deploying Machine Learning Projects on Docker
Venue: arXiv cs.SE 2022
Year: 2022

Docker is a containerization service that allows for convenient deployment of websites, databases, applications' APIs, and machine learning (ML) models with a few lines of code. Studies have recently explored the use of Docker for deploying general software projects with no specific focus on how Docker is used to deploy ML-based projects. In this study, we conducted an exploratory study to understand how Docker is being used to deploy ML-based projects. As the initial step, we examined the categories of ML-based projects that use Docker. We then examined why and how these projects use Docker, and the characteristics of the resulting Docker images. Our results indicate that six categories of ML-based projects use Docker for deployment, including ML Applications, MLOps/ AIOps, Toolkits, DL Frameworks, Models, and Documentation. We derived the taxonomy of 21 major categories representing the purposes of using Docker, including those specific to models such as model management tasks (e.g., testing, training). We then showed that ML engineers use Docker images mostly to help with the platform portability, such as transferring the software across the operating systems, runtimes such as GPU, and language constraints. However, we also found that more resources may be required to run the Docker images for building ML-based software projects due to the large number of files contained in the image layers with deeply nested directories. We hope to shed light on the emerging practices of deploying ML software projects using containers and highlight aspects that should be improved.
Not so immutable: Upgradeability of Smart Contracts on Ethereum
Venue: arXiv cs.CR 2022
Year: 2022

A smart contract that is deployed to a blockchain system like Ethereum is, under reasonable circumstances, expected to be immutable and tamper-proof. This is both a feature (promoting integrity and transparency) and a bug (preventing security patches and feature updates). Modern smart contracts use software tricks to enable upgradeability, raising the research questions of how upgradeability is achieved and who is authorized to make changes. In this paper, we summarize and evaluate six upgradeability patterns. We develop a measurement framework for finding how many upgradeable contracts are on Ethereum that use certain prominent upgrade patters. We find 1.4 million proxy contracts which 8,225 of them are unique upgradeable proxy contracts. We also measure how they implement access control over their upgradeability: about 50% are controlled by a single Externally Owned Address (EOA), and about 14% are controlled by multi-signature wallets in which a limited number of persons can change the whole logic of the contract.
Robots in healthcare as envisioned by care professionals
Venue: arXiv cs.RO 2022
Year: 2022

As AI-enabled robots enter the realm of healthcare and caregiving, it is important to consider how they will address the dimensions of care and how they will interact not just with the direct receivers of assistance, but also with those who provide it (e.g., caregivers, healthcare providers etc.). Caregiving in its best form addresses challenges in a multitude of dimensions of a person's life: from physical, to social-emotional and sometimes even existential dimensions (such as issues surrounding life and death). In this study we use semi-structured qualitative interviews administered to healthcare professions with multidisciplinary backgrounds (physicians, public health professionals, social workers, and chaplains) to understand their expectations regarding the possible roles robots may play in the healthcare ecosystem in the future. We found that participants drew inspiration in their mental models of robots from both works of science fiction but also from existing commercial robots. Participants envisioned roles for robots in the full spectrum of care, from physical to social-emotional and even existential-spiritual dimensions, but also pointed out numerous limitations that robots have in being able to provide comprehensive humanistic care. While no dimension of care was deemed as exclusively the realm of humans, participants stressed the importance of caregiving humans as the primary providers of comprehensive care, with robots assisting with more narrowly focused tasks. Throughout the paper we point out the encouraging confluence of ideas between the expectations of healthcare providers and research trends in the human-robot interaction (HRI) literature.
Delivering Document Conversion as a Cloud Service with High Throughput and Responsiveness
Venue: arXiv cs.DL 2022
Year: 2022

Document understanding is a key business process in the data-driven economy since documents are central to knowledge discovery and business insights. Converting documents into a machine-processable format is a particular challenge here due to their huge variability in formats and complex structure. Accordingly, many algorithms and machine-learning methods emerged to solve particular tasks such as Optical Character Recognition (OCR), layout analysis, table-structure recovery, figure understanding, etc. We observe the adoption of such methods in document understanding solutions offered by all major cloud providers. Yet, publications outlining how such services are designed and optimized to scale in the cloud are scarce. In this paper, we focus on the case of document conversion to illustrate the particular challenges of scaling a complex data processing pipeline with a strong reliance on machine-learning methods on cloud infrastructure. Our key objective is to achieve high scalability and responsiveness for different workload profiles in a well-defined resource budget. We outline the requirements, design, and implementation choices of our document conversion service and reflect on the challenges we faced. Evidence for the scaling behavior and resource efficiency is provided for two alternative workload distribution strategies and deployment configurations. Our best-performing method achieves sustained throughput of over one million PDF pages per hour on 3072 CPU cores across 192 nodes.
Neural Decoding with Optimization of Node Activations
Venue: arXiv cs.IT 2022
Year: 2022

The problem of maximum likelihood decoding with a neural decoder for error-correcting code is considered. It is shown that the neural decoder can be improved with two novel loss terms on the node's activations. The first loss term imposes a sparse constraint on the node's activations. Whereas, the second loss term tried to mimic the node's activations from a teacher decoder which has better performance. The proposed method has the same run time complexity and model size as the neural Belief Propagation decoder, while improving the decoding performance by up to $1.1dB$ on BCH codes.
Adaptive Sampling-based Motion Planning with Control Barrier Functions
Venue: arXiv cs.RO 2022
Year: 2022

Sampling-based algorithms, such as Rapidly Exploring Random Trees (RRT) and its variants, have been used extensively for motion planning. Control barrier functions (CBFs) have been recently proposed to synthesize controllers for safety-critical systems. In this paper, we combine the effectiveness of RRT-based algorithms with the safety guarantees provided by CBFs in a method called CBF-RRT$^\ast$. CBFs are used for local trajectory planning for RRT$^\ast$, avoiding explicit collision checking of the extended paths. We prove that CBF-RRT$^\ast$ preserves the probabilistic completeness of RRT$^\ast$. Furthermore, in order to improve the sampling efficiency of the algorithm, we equip the algorithm with an adaptive sampling procedure, which is based on the cross-entropy method (CEM) for importance sampling (IS). The procedure exploits the tree of samples to focus the sampling in promising regions of the configuration space. We demonstrate the efficacy of the proposed algorithms through simulation examples.
Learning code summarization from a small and local dataset
Venue: arXiv cs.SE 2022
Year: 2022

Foundation models (e.g., CodeBERT, GraphCodeBERT, CodeT5) work well for many software engineering tasks. These models are pre-trained (using self-supervision) with billions of code tokens, and then fine-tuned with hundreds of thousands of labeled examples, typically drawn from many projects. However, software phenomena can be very project-specific. Vocabulary, and other phenomena vary substantially with each project. Thus, training on project-specific data, and testing on the same project, is a promising idea. This hypothesis has to be evaluated carefully, e.g., in a time-series setting, to prevent training-test leakage. We compare several models and training approaches, including same-project training, cross-project training, training a model especially designed to be sample efficient (and thus prima facie well-suited for learning in a limited-sample same-project setting) and a maximalist hybrid approach, fine-tuning first on many projects in many languages and then training on the same-project. We find that the maximalist hybrid setting provides consistent, substantial gains over the state-of-the-art, on many different projects in both Java and Python.
XBound-Former: Toward Cross-scale Boundary Modeling in Transformers
Venue: arXiv cs.CV 2022
Year: 2022

Skin lesion segmentation from dermoscopy images is of great significance in the quantitative analysis of skin cancers, which is yet challenging even for dermatologists due to the inherent issues, i.e., considerable size, shape and color variation, and ambiguous boundaries. Recent vision transformers have shown promising performance in handling the variation through global context modeling. Still, they have not thoroughly solved the problem of ambiguous boundaries as they ignore the complementary usage of the boundary knowledge and global contexts. In this paper, we propose a novel cross-scale boundary-aware transformer, \textbf{XBound-Former}, to simultaneously address the variation and boundary problems of skin lesion segmentation. XBound-Former is a purely attention-based network and catches boundary knowledge via three specially designed learners. We evaluate the model on two skin lesion datasets, ISIC-2016\&PH$^2$ and ISIC-2018, where our model consistently outperforms other convolution- and transformer-based models, especially on the boundary-wise metrics. We extensively verify the generalization ability of polyp lesion segmentation that has similar characteristics, and our model can also yield significant improvement compared to the latest models.
Reinforcement learning based parameters adaption method for particle swarm optimization
Venue: arXiv cs.NE 2022
Year: 2022

Particle swarm optimization (PSO) is a well-known optimization algorithm that shows good performance in solving different optimization problems. However, PSO usually suffers from slow convergence. In this article, a reinforcement learning-based online parameters adaption method(RLAM) is developed to enhance PSO in convergence by designing a network to control the coefficients of PSO. Moreover, based on RLAM, a new RLPSO is designed. In order to investigate the performance of RLAM and RLPSO, experiments on 28 CEC 2013 benchmark functions are carried out when comparing with other online adaption method and PSO variants. The reported computational results show that the proposed RLAM is efficient and effictive and that the the proposed RLPSO is more superior compared with several state-of-the-art PSO variants.
Bi-convolution matrix factorization algorithm based on improved ConvMF
Venue: arXiv cs.IR 2022
Year: 2022

With the rapid development of information technology, "information overload" has become the main theme that plagues people's online life. As an effective tool to help users quickly search for useful information, a personalized recommendation is more and more popular among people. In order to solve the sparsity problem of the traditional matrix factorization algorithm and the problem of low utilization of review document information, this paper proposes a Bicon-vMF algorithm based on improved ConvMF. This algorithm uses two parallel convolutional neural networks to extract deep features from the user review set and item review set respectively and fuses these features into the decomposition of the rating matrix, so as to construct the user latent model and the item latent model more accurately. The experimental results show that compared with traditional recommendation algorithms like PMF, ConvMF, and DeepCoNN, the method proposed in this paper has lower prediction error and can achieve a better recommendation effect. Specifically, compared with the previous three algorithms, the prediction errors of the algorithm proposed in this paper are reduced by 45.8%, 16.6%, and 34.9%, respectively.