OpenRaiser/Intern-Atlas huggingface dataset:
DARTS-PRIME: Regularization and Scheduling Improve Constrained Optimization in Differentiable NAS
Venue: arXiv cs.LG 2021
Year: 2021

Differentiable Architecture Search (DARTS) is a recent neural architecture search (NAS) method based on a differentiable relaxation. Due to its success, numerous variants analyzing and improving parts of the DARTS framework have recently been proposed. By considering the problem as a constrained bilevel optimization, we present and analyze DARTS-PRIME, a variant including improvements to architectural weight update scheduling and regularization towards discretization. We propose a dynamic schedule based on per-minibatch network information to make architecture updates more informed, as well as proximity regularization to promote well-separated discretization. Our results in multiple domains show that DARTS-PRIME improves both performance and reliability, comparable to state-of-the-art in differentiable NAS.
Test Distribution-Aware Active Learning: A Principled Approach Against Distribution Shift and Outliers
Venue: arXiv cs.LG 2021
Year: 2021

Expanding on MacKay (1992), we argue that conventional model-based methods for active learning - like BALD - have a fundamental shortfall: they fail to directly account for the test-time distribution of the input variables. This can lead to pathologies in the acquisition strategy, as what is maximally informative for model parameters may not be maximally informative for prediction: for example, when the data in the pool set is more dispersed than that of the final prediction task, or when the distribution of pool and test samples differs. To correct this, we revisit an acquisition strategy that is based on maximizing the expected information gained about possible future predictions, referring to this as the Expected Predictive Information Gain (EPIG). As EPIG does not scale well for batch acquisition, we further examine an alternative strategy, a hybrid between BALD and EPIG, which we call the Joint Expected Predictive Information Gain (JEPIG). We consider using both for active learning with Bayesian neural networks on a variety of datasets, examining the behavior under distribution shift in the pool set.
Fog computing state of the art: concept and classification of platforms to support distributed computing systems
Venue: arXiv cs.DC 2021
Year: 2021

As the Internet of Things (IoT) becomes a part of our daily life, there is a rapid growth in connected devices. A well-established approach based on cloud computing technologies cannot provide the necessary quality of service in such an environment, particularly in terms of reducing data latency. Today, fog computing technology is seen as a novel approach for processing large amounts of critical and time-sensitive data. This article reviews cloud computing technology and analyzes the prerequisites for the evolution of this approach and the emergence of the concept of fog computing. As part of an overview of the critical features of fog computing, we analyze the frequent confusion of the concepts of fog and edge computing. We provide an overview of fog computing technologies: virtualization, containerization, orchestration, scalability, parallel computing environments, as well as a systematic analysis of the most popular platforms that support fog computing. As a result of the analysis, we offer two approaches to classification of the fog computing platforms: by the principle of openness/closure of components and a three-level classification based on the provided platform functionality (Deploy-, Platform- and Ecosystem as a Service).
Experiments in Artificial Culture: from noisy imitation to storytelling robots
Venue: arXiv cs.RO 2021
Year: 2021

This paper presents a series of experiments in collective social robotics, spanning more than 10 years, with the long-term aim of building embodied models of (aspects) of cultural evolution. Initial experiments demonstrated the emergence of behavioural traditions in a group of social robots programmed to imitate each other's behaviours (we call these Copybots). These experiments show that the noisy (i.e. less than perfect fidelity) imitation that comes for free with real physical robots gives rise naturally to variation in social learning. More recent experimental work extends the robots' cognitive capabilities with simulation-based internal models, equipping them with a simple artificial theory of mind. With this extended capability we explore, in our current work, social learning not via imitation but robot-robot storytelling, in an effort to model this very human mode of cultural transmission. In this paper we give an account of the methods and inspiration for these experiments, the experiments and their results, and an outline of possible directions for this programme of research. It is our hope that this paper stimulates not only discussion but suggestions for hypotheses to test with the Storybots.
Trinity: A No-Code AI platform for complex spatial datasets
Venue: arXiv cs.SE 2021
Year: 2021

We present a no-code Artificial Intelligence (AI) platform called Trinity with the main design goal of enabling both machine learning researchers and non-technical geospatial domain experts to experiment with domain-specific signals and datasets for solving a variety of complex problems on their own. This versatility to solve diverse problems is achieved by transforming complex Spatio-temporal datasets to make them consumable by standard deep learning models, in this case, Convolutional Neural Networks (CNNs), and giving the ability to formulate disparate problems in a standard way, eg. semantic segmentation. With an intuitive user interface, a feature store that hosts derivatives of complex feature engineering, a deep learning kernel, and a scalable data processing mechanism, Trinity provides a powerful platform for domain experts to share the stage with scientists and engineers in solving business-critical problems. It enables quick prototyping, rapid experimentation and reduces the time to production by standardizing model building and deployment. In this paper, we present our motivation behind Trinity and its design along with showcasing sample applications to motivate the idea of lowering the bar to using AI.
The Interplay of Reconfigurable Intelligent Surfaces and Mobile Edge Computing in Future Wireless Networks: A Win-Win Strategy to 6G
Venue: arXiv cs.NI 2021
Year: 2021

Reconfigurable intelligent surface (RIS)-empowered communication is being considered as an enabling technology for sixth generation (6G) wireless networks. The key idea of RIS-assisted communication is to enhance the capacity, coverage, energy efficiency, physical layer security, and many other aspects of modern wireless networks. At the same time, mobile edge computing (MEC) has already shown its huge potential by extending the computation, communication, and caching capabilities of a standalone cloud server to the network edge. In this article, we first provide an overview of how MEC and RIS can benefit each other. We envision that the integration of MEC and RIS will bring an unprecedented transformation to the future evolution of wireless networks. We provide a system-level perspective on the MEC-aided RIS (and RIS-assisted MEC) that will evolve wireless network towards 6G. We also outline some of the fundamental challenges that pertain to the implementation of MEC-aided RIS (and RIS-assisted MEC) networks. Finally, the key research trends in the RIS-assisted MEC are discussed.
Evaluation of a Region Proposal Architecture for Multi-task Document Layout Analysis
Venue: arXiv cs.CV 2021
Year: 2021

Automatically recognizing the layout of handwritten documents is an important step towards useful extraction of information from those documents. The most common application is to feed downstream applications such as automatic text recognition and keyword spotting; however, the recognition of the layout also helps to establish relationships between elements in the document which allows to enrich the information that can be extracted. Most of the modern document layout analysis systems are designed to address only one part of the document layout problem, namely: baseline detection or region segmentation. In contrast, we evaluate the effectiveness of the Mask-RCNN architecture to address the problem of baseline detection and region segmentation in an integrated manner. We present experimental results on two handwritten text datasets and one handwritten music dataset. The analyzed architecture yields promising results, outperforming state-of-the-art techniques in all three datasets.
Linear Logic, the $π$-calculus, and their Metatheory: A Recipe for Proofs as Processes
Venue: arXiv cs.LO 2021
Year: 2021

Initiated by Abramsky [1994], the Proofs as Processes agenda is to establish a solid foundation for the study of concurrent languages, by researching the connection between linear logic and the $π$-calculus. To date, Proofs as Processes is still a partial success. Caires and Pfenning [2010] showed that linear propositions correspond to session types, which prescribe the observable behaviour of processes. Further, Carbone et al. [2018] demonstrated that adopting devices from hypersequents [Avron 1991] shapes proofs such that they correspond to the expected syntactic structure of processes in the $π$-calculus. What remains is reconstructing the expected metatheory of session types and the $π$-calculus. In particular, the hallmark of session types, session fidelity, still has to be reconstructed: a correspondence between the observable behaviours of processes and their session types, in terms of labelled transitions. In this article, we present $π$LL, a new process calculus rooted in linear logic. The key novelty of $π$LL is that it comes with a carefully formulated design recipe, based on a dialgebraic view of labelled transition systems. Thanks to our recipe, $π$LL offers the expected transition systems of session types, which we use to establish session fidelity. We use $π$LL to carry out the first thorough investigation of the metatheoretical properties enforced by linear logic on the observable behaviour of processes, uncovering connections with similarity and bisimilarity. We also prove that $π$LL and our recipe form a robust basis for the further exploration of Proofs as Processes, by considering different features: polymorphism, process mobility, and recursion.
High Performance Optimization at the Door of the Exascale
Venue: arXiv cs.DC 2021
Year: 2021

quest for processing speed potential. In fact, we always get a fraction of the technically available computing power (so-called {\em theoretical peak}), and the gap is likely to go hand-to-hand with the hardware complexity of the target system. Among the key aspects of this complexity, we have: the {\em heterogeneity} of the computing units, the {\em memory hierarchy and partitioning} including the non-uniform memory access (NUMA) configuration, and the {\em interconnect} for data exchanges among the computing nodes. Scientific investigations and cutting-edge technical activities should ideally scale-up with respect to sustained performance. The special case of quantitative approaches for solving (large-scale) problems deserves a special focus. Indeed, most of common real-life problems, even when considering the artificial intelligence paradigm, rely on optimization techniques for the main kernels of algorithmic solutions. Mathematical programming and pure combinatorial methods are not easy to implement efficiently on large-scale supercomputers because of {\em irregular control flow}, {\em complex memory access patterns}, {\em heterogeneous kernels}, {\em numerical issues}, to name a few. We describe and examine our thoughts from the standpoint of large-scale supercomputers.
Off-Policy Reinforcement Learning with Delayed Rewards
Venue: arXiv cs.LG 2021
Year: 2021

We study deep reinforcement learning (RL) algorithms with delayed rewards. In many real-world tasks, instant rewards are often not readily accessible or even defined immediately after the agent performs actions. In this work, we first formally define the environment with delayed rewards and discuss the challenges raised due to the non-Markovian nature of such environments. Then, we introduce a general off-policy RL framework with a new Q-function formulation that can handle the delayed rewards with theoretical convergence guarantees. For practical tasks with high dimensional state spaces, we further introduce the HC-decomposition rule of the Q-function in our framework which naturally leads to an approximation scheme that helps boost the training efficiency and stability. We finally conduct extensive experiments to demonstrate the superior performance of our algorithms over the existing work and their variants.
Towards Automated Evaluation of Explanations in Graph Neural Networks
Venue: arXiv cs.AI 2021
Year: 2021

Explaining Graph Neural Networks predictions to end users of AI applications in easily understandable terms remains an unsolved problem. In particular, we do not have well developed methods for automatically evaluating explanations, in ways that are closer to how users consume those explanations. Based on recent application trends and our own experiences in real world problems, we propose automatic evaluation approaches for GNN Explanations.
Latency-Aware Neural Architecture Search with Multi-Objective Bayesian Optimization
Venue: arXiv cs.LG 2021
Year: 2021

When tuning the architecture and hyperparameters of large machine learning models for on-device deployment, it is desirable to understand the optimal trade-offs between on-device latency and model accuracy. In this work, we leverage recent methodological advances in Bayesian optimization over high-dimensional search spaces and multi-objective Bayesian optimization to efficiently explore these trade-offs for a production-scale on-device natural language understanding model at Facebook.
MuseumViz -- Towards Visualizing Online Museum Collections
Venue: arXiv cs.HC 2021
Year: 2021

Despite the growth of online museums for India's cultural heritage data, there is limited increase in terms of visitors. Over the years, online museums adopted many techniques to improve the overall user experience. However, many Indian online museums display artifacts as lists and grids with basic search functionality, making it less visually appealing and difficult to comprehend. Our work aims to enhance the user experience of accessing Indian online museums by utilizing advancements in information visualization. Hence, we propose MuseumViz, a framework which processes data from online museums and visualizes it using four different interactive visualizations: the Network Graph, TreepMap, Polygon Chart and SunBurst Chart. We demonstrate MuseumViz on a total of 723 cultural heritage artifacts present in the Archaeological Survey of India, Goa. Based on our evaluation with 25 users, about 83% of them find it easier and more comprehensible to browse cultural heritage artifacts through MuseumViz.
Selecting Miners within Blockchain-based Systems Using Evolutionary Algorithms for Energy Optimisation
Venue: arXiv cs.NE 2021
Year: 2021

In this paper, we represent the problem of selecting miners within a blockchain-based system as a subset selection problem. We formulate the problem of minimising blockchain energy consumption as an optimisation problem with two conflicting objectives: energy consumption and trust. The proposed model is compared across different algorithms to demonstrate its performance.
Analysis of Executional and Procedural Errors in Dry-lab Robotic Surgery Experiments
Venue: arXiv cs.RO 2021
Year: 2021

Background Analyzing kinematic and video data can help identify potentially erroneous motions that lead to sub-optimal surgeon performance and safety-critical events in robot-assisted surgery. Methods We develop a rubric for identifying task and gesture-specific Executional and Procedural errors and evaluate dry-lab demonstrations of Suturing and Needle Passing tasks from the JIGSAWS dataset. We characterize erroneous parts of demonstrations by labeling video data, and use distribution similarity analysis and trajectory averaging on kinematic data to identify parameters that distinguish erroneous gestures. Results Executional error frequency varies by task and gesture, and correlates with skill level. Some predominant error modes in each gesture are distinguishable by analyzing error-specific kinematic parameters. Procedural errors could lead to lower performance scores and increased demonstration times but also depend on surgical style. Conclusions This study provides insights into context-dependent errors that can be used to design automated error detection mechanisms and improve training and skill assessment.
Listen to Your Favorite Melodies with img2Mxml, Producing MusicXML from Sheet Music Image by Measure-based Multimodal Deep Learning-driven Assembly
Venue: arXiv cs.CV 2021
Year: 2021

Deep learning has recently been applied to optical music recognition (OMR). However, currently OMR processing from various sheet music images still lacks precision to be widely applicable. Here, we present an MMdA (Measure-based Multimodal deep learning (DL)-driven Assembly) method allowing for end-to-end OMR processing from various images including inclined photo images. Using this method, measures are extracted by a deep learning model, aligned, and resized to be used for inference of given musical symbol components by using multiple deep learning models in sequence or in parallel. Use of each standardized measure enables efficient training of the models and accurate adjustment of five staff lines in each measure. Multiple musical symbol component category models with a small number of feature types can represent a diverse set of notes and other musical symbols including chords. This MMdA method provides a solution to end-to-end OMR processing with precision.
Automatic Head Overcoat Thickness Measure with NASNet-Large-Decoder Net
Venue: arXiv cs.CV 2021
Year: 2021

Transmission electron microscopy (TEM) is one of the primary tools to show microstructural characterization of materials as well as film thickness. However, manual determination of film thickness from TEM images is time-consuming as well as subjective, especially when the films in question are very thin and the need for measurement precision is very high. Such is the case for head overcoat (HOC) thickness measurements in the magnetic hard disk drive industry. It is therefore necessary to develop software to automatically measure HOC thickness. In this paper, for the first time, we propose a HOC layer segmentation method using NASNet-Large as an encoder and then followed by a decoder architecture, which is one of the most commonly used architectures in deep learning for image segmentation. To further improve segmentation results, we are the first to propose a post-processing layer to remove irrelevant portions in the segmentation result. To measure the thickness of the segmented HOC layer, we propose a regressive convolutional neural network (RCNN) model as well as orthogonal thickness calculation methods. Experimental results demonstrate a higher dice score for our model which has lower mean squared error and outperforms current state-of-the-art manual measurement.
Regret-optimal Estimation and Control
Venue: arXiv cs.LG 2021
Year: 2021

We consider estimation and control in linear time-varying dynamical systems from the perspective of regret minimization. Unlike most prior work in this area, we focus on the problem of designing causal estimators and controllers which compete against a clairvoyant noncausal policy, instead of the best policy selected in hindsight from some fixed parametric class. We show that the regret-optimal estimator and regret-optimal controller can be derived in state-space form using operator-theoretic techniques from robust control and present tight,data-dependent bounds on the regret incurred by our algorithms in terms of the energy of the disturbances. Our results can be viewed as extending traditional robust estimation and control, which focuses on minimizing worst-case cost, to minimizing worst-case regret. We propose regret-optimal analogs of Model-Predictive Control (MPC) and the Extended KalmanFilter (EKF) for systems with nonlinear dynamics and present numerical experiments which show that our regret-optimal algorithms can significantly outperform standard approaches to estimation and control.
A Review of Assistive Technologies for Activities of Daily Living of Elderly
Venue: arXiv cs.HC 2021
Year: 2021

One of the distinct features of this century has been the population of older adults which has been on a constant rise. Elderly people have several needs and requirements due to physical disabilities, cognitive issues, weakened memory and disorganized behavior, that they face with increasing age. The extent of these limitations also differs according to the varying diversities in elderly, which include age, gender, background, experience, skills, knowledge and so on. These varying needs and challenges with increasing age, limits abilities of older adults to perform Activities of Daily Living (ADLs) in an independent manner. To add to it, the shortage of caregivers creates a looming need for technology-based services for elderly people, to assist them in performing their daily routine tasks to sustain their independent living and active aging. To address these needs, this work consists of making three major contributions in this field. First, it provides a rather comprehensive review of assisted living technologies aimed at helping elderly people to perform ADLs. Second, the work discusses the challenges identified through this review, that currently exist in the context of implementation of assisted living services for elderly care in Smart Homes and Smart Cities. Finally, the work also outlines an approach for implementation, extension and integration of the existing works in this field for development of a much-needed framework that can provide personalized assistance and user-centered behavior interventions to elderly as per their varying and ever-changing needs.
What makes a good Node.js package? Investigating Users, Contributors, and Runnability
Venue: arXiv cs.SE 2021
Year: 2021

The Node.js Package Manager (i.e., npm) archive repository serves as a critical part of the JavaScript community and helps support one of the largest developer ecosystems in the world. However, as a developer, selecting an appropriate npm package to use or contribute to can be difficult. To understand what features users and contributors consider important when searching for a good npm package, we conduct a survey asking Node.js developers to evaluate the importance of 30 features derived from existing work, including GitHub activity, software usability, and properties of the repository and documentation. We identify that both user and contributor perspectives share similar views on which features they use to assess package quality. We then extract the 30 features from 104,364 npm packages and analyse the correlations between them, including three software features that measure package ``runnability"; ability to install, build, and execute a unit test. We identify which features are negatively correlated with runnability-related features and find that predicting the runnability of packages is viable. Our study lays the groundwork for future work on understanding how users and contributors select appropriate npm packages.