OpenRaiser/Intern-Atlas huggingface dataset:
Routing in Small Satellite Networks: A GNN-based Learning Approach
Venue: arXiv cs.NI 2021
Year: 2021

Small satellite networks (SSNs), which are constructed by large number of small satellites in low earth orbits (LEO), are considered as promising ways to provide ubiquitous Internet access. To handle stochastic Internet traffic, on-board routing is necessary in SSNs. However, large-scale, high dynamic SSN topologies and limited resources make on-board routing in SSNs face great challenges. To address this issue, we turn to graph neural network (GNN), a deep learning network inherently designed for graph data, motivated by the fact that SSNs can be naturally modeled as graphs. By exploiting GNN's topology extraction capabilities, we propose a GNN-based learning routing approach (GLR) to achieve near-optimal on-board routing with low complexity. We design high-order and low-order feature extractor and cross process to deal with high dynamic topologies of SSNs, even those topologies that have never been seen in training. Simulation results demonstrate that GLR results in a significant reduction in routing computation cost while achieves near-optimal routing performance in SSNs with different scales compared with typical existing satellite routing algorithms.
Inter-Species Cell Detection: Datasets on pulmonary hemosiderophages in equine, human and feline specimens
Venue: arXiv cs.HC 2021
Year: 2021

Pulmonary hemorrhage (P-Hem) occurs among multiple species and can have various causes. Cytology of bronchoalveolarlavage fluid (BALF) using a 5-tier scoring system of alveolar macrophages based on their hemosiderin content is considered the most sensitive diagnostic method. We introduce a novel, fully annotated multi-species P-Hem dataset which consists of 74 cytology whole slide images (WSIs) with equine, feline and human samples. To create this high-quality and high-quantity dataset, we developed an annotation pipeline combining human expertise with deep learning and data visualisation techniques. We applied a deep learning-based object detection approach trained on 17 expertly annotated equine WSIs, to the remaining 39 equine, 12 human and 7 feline WSIs. The resulting annotations were semi-automatically screened for errors on multiple types of specialised annotation maps and finally reviewed by a trained pathologists. Our dataset contains a total of 297,383 hemosiderophages classified into five grades. It is one of the largest publicly availableWSIs datasets with respect to the number of annotations, the scanned area and the number of species covered.
Multi-task Federated Learning for Heterogeneous Pancreas Segmentation
Venue: arXiv cs.CV 2021
Year: 2021

Federated learning (FL) for medical image segmentation becomes more challenging in multi-task settings where clients might have different categories of labels represented in their data. For example, one client might have patient data with "healthy'' pancreases only while datasets from other clients may contain cases with pancreatic tumors. The vanilla federated averaging algorithm makes it possible to obtain more generalizable deep learning-based segmentation models representing the training data from multiple institutions without centralizing datasets. However, it might be sub-optimal for the aforementioned multi-task scenarios. In this paper, we investigate heterogeneous optimization methods that show improvements for the automated segmentation of pancreas and pancreatic tumors in abdominal CT images with FL settings.
Byzantine Cluster-Sending in Expected Constant Communication
Venue: arXiv cs.DB 2021
Year: 2021

Traditional resilient systems operate on fully-replicated fault-tolerant clusters, which limits their scalability and performance. One way to make the step towards resilient high-performance systems that can deal with huge workloads, is by enabling independent fault-tolerant clusters to efficiently communicate and cooperate with each other, as this also enables the usage of high-performance techniques such as sharding and parallel processing. Recently, such inter-cluster communication was formalized as the Byzantine cluster-sending problem, and worst-case optimal protocols have been proposed that solve this problem. Unfortunately, these protocols have an all-case linear complexity in the size of the clusters involved. In this paper, we propose probabilistic cluster-sending techniques that can reliably send messages from one Byzantine fault-tolerant cluster to another with only an expected constant message complexity, this independent of the size of the clusters involved. Depending on the robustness of the clusters involved, our techniques require only two-to-four message round-trips. Furthermore, our protocols can support worst-case linear communication between clusters, which is optimal, and deal with asynchronous and unreliable communication. As such, our work provides a strong foundation for the further development of resilient high-performance systems.
Monitoring weeder robots and anticipating their functioning by using advanced topological data analysis
Venue: arXiv cs.RO 2021
Year: 2021

The present paper aims at analyzing the topological content of the complex trajectories that weeder-autonomous robots follow in operation. We will prove that the topological descriptors of these trajectories are affected by the robot environment as well as by the robot state, with respect to maintenance operations. Topological Data Analysis will be used for extracting the trajectory descriptors, based on homology persistence. Then, appropriate metrics will be applied in order to compare that topological representation of the trajectories, for classifying them or for making efficient pattern recognition.
Successive cohorts of Twitter users show increasing activity and shrinking content horizons
Venue: arXiv cs.SI 2021
Year: 2021

The global public sphere has changed dramatically over the past decades: a significant part of public discourse now takes place on algorithmically driven platforms owned by a handful of private companies. Despite its growing importance, there is scant large-scale academic research on the long-term evolution of user behaviour on these platforms, because the data are often proprietary to the platforms. Here, we evaluate the individual behaviour of 600,000 Twitter users between 2012 and 2019 and find empirical evidence for an acceleration of the way Twitter is used on an individual level. This manifests itself in the fact that cohorts of Twitter users behave differently depending on when they joined the platform. Behaviour within a cohort is relatively consistent over time and characterised by strong internal interactions, but over time behaviour from cohort to cohort shifts towards increased activity. Specifically, we measure this in terms of more tweets per user over time, denser interactions with others via retweets, and shorter content horizons, expressed as an individual's decaying autocorrelation of topics over time. Our observations are explained by a growing proportion of active users who not only tweet more actively but also elicit more retweets. These behaviours suggest a collective contribution to an increased flow of information through each cohort's news feed -- an increase that potentially depletes available collective attention over time. Our findings complement recent, empirical work on social acceleration, which has been largely agnostic about individual user activity.
Max-min Fairness Based Faucet Design for Blockchains
Venue: arXiv cs.DC 2021
Year: 2021

In order to have transactions executed and recorded on blockchains such as the Ethereum Mainnet, fees expressed in crypto-currency units of the blockchain must be paid. One can buy crypto-currency called Ether of the Ethereum blockchain from exchanges and pay for the transaction fees. In the case of test networks (such as Rinkeby) or scientific research blockchains (such as Bloxberg), free crypto-currency, Ether, is distributed to users via faucets. Since transaction slots on the blocks, storage and smart contract executions are consuming blockchain resources, Ethers are distributed by fixed small amounts to users. Users may have different amount of Ether requirements; some small amounts and some large amounts during different times. As a result, rather than allowing the user to get a fixed small amount of Ether, a more general distribution mechanism that allows a user to demand and claim arbitrary amounts of Ether, while satisfying fairness among users, is needed. For this end, Max-min Fairness based schemes have been used in centralized settings. Our work contributes a Max-min Fairness based algorithm and its Solidity smart contract implementation that requires low transaction costs independent of the number of users. This is important on the Ethereum blockchain, since a smart contract execution with transaction costs depending on the number of users would mean block gas limit exhaustion problem will eventually be met, making the smart contract ineffective. We report tests which confirm that the low transaction cost aims have been achieved by our algorithm.
A Survey on Domain-Specific Memory Architectures
Venue: arXiv cs.DC 2021
Year: 2021

The never-ending demand for high performance and energy efficiency is pushing designers towards an increasing level of heterogeneity and specialization in modern computing systems. In such systems, creating efficient memory architectures is one of the major opportunities for optimizing modern workloads (e.g., computer vision, machine learning, graph analytics, etc.) that are extremely data-driven. However, designers demand proper design methods to tackle the increasing design complexity and address several new challenges, like the security and privacy of the data to be elaborated. This paper overviews the current trend for the design of domain-specific memory architectures. Domain-specific architectures are tailored for the given application domain, with the introduction of hardware accelerators and custom memory modules while maintaining a certain level of flexibility. We describe the major components, the common challenges, and the state-of-the-art design methodologies for building domain-specific memory architectures. We also discuss the most relevant research projects, providing a classification based on our main topics.
The Legislative Recipe: Syntax for Machine-Readable Legislation
Venue: arXiv cs.CL 2021
Year: 2021

Legal interpretation is a linguistic venture. In judicial opinions, for example, courts are often asked to interpret the text of statutes and legislation. As time has shown, this is not always as easy as it sounds. Matters can hinge on vague or inconsistent language and, under the surface, human biases can impact the decision-making of judges. This raises an important question: what if there was a method of extracting the meaning of statutes consistently? That is, what if it were possible to use machines to encode legislation in a mathematically precise form that would permit clearer responses to legal questions? This article attempts to unpack the notion of machine-readability, providing an overview of both its historical and recent developments. The paper will reflect on logic syntax and symbolic language to assess the capacity and limits of representing legal knowledge. In doing so, the paper seeks to move beyond existing literature to discuss the implications of various approaches to machine-readable legislation. Importantly, this study hopes to highlight the challenges encountered in this burgeoning ecosystem of machine-readable legislation against existing human-readable counterparts.
A Cuckoo Quantum Evolutionary Algorithm for the Graph Coloring Problem
Venue: arXiv cs.NE 2021
Year: 2021

Based on the framework of the quantum-inspired evolutionary algorithm, a cuckoo quantum evolutionary algorithm (CQEA) is proposed for solving the graph coloring problem (GCP). To reduce iterations for the search of the chromatic number, the initial quantum population is generated by random initialization assisted by inheritance. Moreover, improvement of global exploration is achieved by incorporating the cuckoo search strategy, and a local search operation, as well as a perturbance strategy, is developed to enhance its performance on GCPs. Numerical results demonstrate that CQEA operates with strong exploration and exploitation abilities, and is competitive to the compared state-of-the-art heuristic algorithms.
5G System Security Analysis
Venue: arXiv cs.NI 2021
Year: 2021

Fifth generation mobile networks (5G) are currently being deployed by mobile operators around the globe. 5G acts as an enabler for various use cases and also improves the security and privacy over 4G and previous network generations. However, as recent security research has revealed, the standard still has security weaknesses that may be exploitable by attackers. In addition, the migration from 4G to 5G systems is taking place by first deploying 5G solutions in a non-standalone (NSA) manner where the first step of the 5G deployment is restricted to the new radio aspects of 5G, while the control of the user equipment is still based on 4G protocols, i.e. the core network is still the legacy 4G evolved packet core (EPC) network. As a result, many security vulnerabilities of 4G networks are still present in current 5G deployments. This paper presents a systematic risk analysis of standalone and non-standalone 5G networks. We first describe an overview of the 5G system specification and the new security features of 5G compared to 4G. Then, we define possible threats according to the STRIDE threat classification model and derive a risk matrix based on the likelihood and impact of 12 threat scenarios that affect the radio access and the network core. Finally, we discuss possible mitigations and security controls. Our analysis is generic and does not account for the specifics of particular 5G network vendors or operators. Further work is required to understand the security vulnerabilities and risks of specific 5G implementations and deployments.
How to cheat with metrics in single-image HDR reconstruction
Venue: arXiv cs.CV 2021
Year: 2021

Single-image high dynamic range (SI-HDR) reconstruction has recently emerged as a problem well-suited for deep learning methods. Each successive technique demonstrates an improvement over existing methods by reporting higher image quality scores. This paper, however, highlights that such improvements in objective metrics do not necessarily translate to visually superior images. The first problem is the use of disparate evaluation conditions in terms of data and metric parameters, calling for a standardized protocol to make it possible to compare between papers. The second problem, which forms the main focus of this paper, is the inherent difficulty in evaluating SI-HDR reconstructions since certain aspects of the reconstruction problem dominate objective differences, thereby introducing a bias. Here, we reproduce a typical evaluation using existing as well as simulated SI-HDR methods to demonstrate how different aspects of the problem affect objective quality metrics. Surprisingly, we found that methods that do not even reconstruct HDR information can compete with state-of-the-art deep learning methods. We show how such results are not representative of the perceived quality and that SI-HDR reconstruction needs better evaluation protocols.
Improving Semi-Supervised Learning for Remaining Useful Lifetime Estimation Through Self-Supervision
Venue: arXiv cs.LG 2021
Year: 2021

RUL estimation suffers from a server data imbalance where data from machines near their end of life is rare. Additionally, the data produced by a machine can only be labeled after the machine failed. Semi-Supervised Learning (SSL) can incorporate the unlabeled data produced by machines that did not yet fail. Previous work on SSL evaluated their approaches under unrealistic conditions where the data near failure was still available. Even so, only moderate improvements were made. This paper proposes a novel SSL approach based on self-supervised pre-training. The method can outperform two competing approaches from the literature and a supervised baseline under realistic conditions on the NASA C-MAPSS dataset.
Feature-weighted Stacking for Nonseasonal Time Series Forecasts: A Case Study of the COVID-19 Epidemic Curves
Venue: arXiv cs.LG 2021
Year: 2021

We investigate ensembling techniques in forecasting and examine their potential for use in nonseasonal time-series similar to those in the early days of the COVID-19 pandemic. Developing improved forecast methods is essential as they provide data-driven decisions to organisations and decision-makers during critical phases. We propose using late data fusion, using a stacked ensemble of two forecasting models and two meta-features that prove their predictive power during a preliminary forecasting stage. The final ensembles include a Prophet and long short term memory (LSTM) neural network as base models. The base models are combined by a multilayer perceptron (MLP), taking into account meta-features that indicate the highest correlation with each base model's forecast accuracy. We further show that the inclusion of meta-features generally improves the ensemble's forecast accuracy across two forecast horizons of seven and fourteen days. This research reinforces previous work and demonstrates the value of combining traditional statistical models with deep learning models to produce more accurate forecast models for time-series from different domains and seasonality.
Learning General Halfspaces with General Massart Noise under the Gaussian Distribution
Venue: arXiv cs.LG 2021
Year: 2021

We study the problem of PAC learning halfspaces on $\mathbb{R}^d$ with Massart noise under the Gaussian distribution. In the Massart model, an adversary is allowed to flip the label of each point $\mathbf{x}$ with unknown probability $η(\mathbf{x}) \leq η$, for some parameter $η\in [0,1/2]$. The goal is to find a hypothesis with misclassification error of $\mathrm{OPT} + ε$, where $\mathrm{OPT}$ is the error of the target halfspace. This problem had been previously studied under two assumptions: (i) the target halfspace is homogeneous (i.e., the separating hyperplane goes through the origin), and (ii) the parameter $η$ is strictly smaller than $1/2$. Prior to this work, no nontrivial bounds were known when either of these assumptions is removed. We study the general problem and establish the following: For $η<1/2$, we give a learning algorithm for general halfspaces with sample and computational complexity $d^{O_η(\log(1/γ))}\mathrm{poly}(1/ε)$, where $γ=\max\{ε, \min\{\mathbf{Pr}[f(\mathbf{x}) = 1], \mathbf{Pr}[f(\mathbf{x}) = -1]\} \}$ is the bias of the target halfspace $f$. Prior efficient algorithms could only handle the special case of $γ= 1/2$. Interestingly, we establish a qualitatively matching lower bound of $d^{Ω(\log(1/γ))}$ on the complexity of any Statistical Query (SQ) algorithm. For $η= 1/2$, we give a learning algorithm for general halfspaces with sample and computational complexity $O_ε(1) d^{O(\log(1/ε))}$. This result is new even for the subclass of homogeneous halfspaces; prior algorithms for homogeneous Massart halfspaces provide vacuous guarantees for $η=1/2$. We complement our upper bound with a nearly-matching SQ lower bound of $d^{Ω(\log(1/ε))}$, which holds even for the special case of homogeneous halfspaces.
Mr. TyDi: A Multi-lingual Benchmark for Dense Retrieval
Venue: arXiv cs.CL 2021
Year: 2021

We present Mr. TyDi, a multi-lingual benchmark dataset for mono-lingual retrieval in eleven typologically diverse languages, designed to evaluate ranking with learned dense representations. The goal of this resource is to spur research in dense retrieval techniques in non-English languages, motivated by recent observations that existing techniques for representation learning perform poorly when applied to out-of-distribution data. As a starting point, we provide zero-shot baselines for this new dataset based on a multi-lingual adaptation of DPR that we call "mDPR". Experiments show that although the effectiveness of mDPR is much lower than BM25, dense representations nevertheless appear to provide valuable relevance signals, improving BM25 results in sparse-dense hybrids. In addition to analyses of our results, we also discuss future challenges and present a research agenda in multi-lingual dense retrieval. Mr. TyDi can be downloaded at https://github.com/castorini/mr.tydi.
Image Inpainting using Partial Convolution
Venue: arXiv cs.CV 2021
Year: 2021

Image Inpainting is one of the very popular tasks in the field of image processing with broad applications in computer vision. In various practical applications, images are often deteriorated by noise due to the presence of corrupted, lost, or undesirable information. There have been various restoration techniques used in the past with both classical and deep learning approaches for handling such issues. Some traditional methods include image restoration by filling gap pixels using the nearby known pixels or using the moving average over the same. The aim of this paper is to perform image inpainting using robust deep learning methods that use partial convolution layers.
Lifelong Computing
Venue: arXiv cs.SE 2021
Year: 2021

Computing systems form the backbone of many aspects of our life, hence they are becoming as vital as water, electricity, and road infrastructures for our society. Yet, engineering long running computing systems that achieve their goals in ever-changing environments pose significant challenges. Currently, we can build computing systems that adjust or learn over time to match changes that were anticipated. However, dealing with unanticipated changes, such as anomalies, novelties, new goals or constraints, requires system evolution, which remains in essence a human-driven activity. Given the growing complexity of computing systems and the vast amount of highly complex data to process, this approach will eventually become unmanageable. To break through the status quo, we put forward a new paradigm for the design and operation of computing systems that we coin "lifelong computing." The paradigm starts from computing-learning systems that integrate computing/service modules and learning modules. Computing warehouses offer such computing elements together with data sheets and usage guides. When detecting anomalies, novelties, new goals or constraints, a lifelong computing system activates an evolutionary self-learning engine that runs online experiments to determine how the computing-learning system needs to evolve to deal with the changes, thereby changing its architecture and integrating new computing elements from computing warehouses as needed. Depending on the domain at hand, some activities of lifelong computing systems can be supported by humans. We motivate the need for lifelong computing with a future fish farming scenario, outline a blueprint architecture for lifelong computing systems, and highlight key research challenges to realise the vision of lifelong computing.