OpenRaiser/Intern-Atlas huggingface dataset:
On MDS Condition and Erased Lines Recovery of Generalized Expanded-Blaum-Roth Codes and Generalized Blaum-Roth Codes
Venue: arXiv cs.IT 2022
Year: 2022

Generalized Expanded-Blaum-Roth (GEBR) codes [1] are designed for large-scale distributed storage systems that have larger recoverability for single-symbol failures, multi-column failures and multi-row failures, compared with locally recoverable codes (LRC). GEBR codes encode an $α\times k$ information array into a $pτ\times (k+r)$ array such that lines of slope $i$ with $0\leq i\leq r-1$ have even parity and each column contains $pτ-α$ local parity symbols, where $p$ is an odd prime and $k+r\leq pτ$. Necessary and sufficient conditions for GEBR codes to be $(n,k)$ recoverable (i.e., any $k$ out of $n=k+r$ columns can retrieve all information symbols) are given in [2] for $α=(p-1)τ$. However, the $(n,k)$ recoverable condition of GEBR codes is unknown when $α<(p-1)τ$. In this paper, we present the $(n,k)$ recoverable condition for GEBR codes for $α< (p-1)τ$. In addition, we present a sufficient condition for enabling GEBR codes to recover some erased lines of any slope $i$ ($0\leq i\leq pτ-1$) for any parameter $r$ when $τ$ is a power of $p$. Moreover, we present the construction of Generalized Blaum-Roth (GBR) codes that encode an $α\times k$ information array into an $α\times (k+r)$ array. We show that GBR codes share the same MDS condition as the $(n,k)$ recoverable condition of GEBR codes, and we also present a sufficient condition for GBR codes to recover some erased lines of any slope $i$ ($0\leq i\leq α-1$).
Proprioceptive State Estimation of Legged Robots with Kinematic Chain Modeling
Venue: arXiv cs.RO 2022
Year: 2022

Legged robot locomotion is a challenging task due to a myriad of sub-problems, such as the hybrid dynamics of foot contact and the effects of the desired gait on the terrain. Accurate and efficient state estimation of the floating base and the feet joints can help alleviate much of these issues by providing feedback information to robot controllers. Current state estimation methods are highly reliant on a conjunction of visual and inertial measurements to provide real-time estimates, thus being handicapped in perceptually poor environments. In this work, we show that by leveraging the kinematic chain model of the robot via a factor graph formulation, we can perform state estimation of the base and the leg joints using primarily proprioceptive inertial data. We perform state estimation using a combination of preintegrated IMU measurements, forward kinematic computations, and contact detections in a factor-graph based framework, allowing our state estimate to be constrained by the robot model. Experimental results in simulation and on hardware show that our approach out-performs current proprioceptive state estimation methods by 27% on average, while being generalizable to a variety of legged robot platforms. We demonstrate our results both quantitatively and qualitatively on a wide variety of trajectories.
Software-Hardware Codesign for Efficient In-Memory Regular Pattern Matching
Venue: arXiv cs.FL 2022
Year: 2022

Regular pattern matching is used in numerous application domains, including text processing, bioinformatics, and network security. Patterns are typically expressed with an extended syntax of regular expressions that include the computationally challenging construct of bounded iteration or counting, which describes the repetition of a pattern a fixed number of times. We develop a design for a specialized in-memory hardware architecture for NFA execution that integrates counter and bit vector elements. The design is inspired by the theoretical model of nondeterministic counter automata (NCA). A key feature of our approach is that we statically analyze regular expressions to determine bounds on the amount of memory needed for the occurrences of counting. The results of this analysis are used by a regex-to-hardware compiler in order to make an appropriate selection of counter or bit vector elements. We evaluate the performance of our hardware implementation on a simulator based on circuit parameters collected by SPICE simulation using a TSMC 28nm process. We find the usage of counter and bit vector quickly outperforms unfolding solutions by orders of magnitude with small counting quantifiers. Experiments concerning realistic workloads show up to 76% energy reduction and 58% area reduction in comparison to traditional in-memory NFA processors.
Ground-to-Air Communications Beyond 5G: Coordinated Multi-Point Transmission Based on Poisson-Delaunay Triangulation
Venue: arXiv cs.IT 2022
Year: 2022

This paper designs a novel ground-to-air communication scheme to serve unmanned aerial vehicles (UAVs) through legacy terrestrial base stations (BSs). In particular, a tractable coordinated multi-point (CoMP) transmission based on the geometry of Poisson-Delaunay triangulation is developed, which provides reliable and seamless connectivity for UAVs. An effective dynamic frequency allocation scheme is designed to eliminate inter-cell interference by using the theory of circle packing. For exact performance evaluation, the handoff probability of a typical UAV is characterized, and then the coverage probability with handoffs is attained. Simulation and numerical results corroborate that the proposed scheme outperforms the conventional CoMP scheme with three nearest cooperating BSs in terms of handoff and coverage probabilities. Moreover, as each UAV has a fixed and unique CoMP BS set, it avoids the real-time dynamic BS searching process, thus reducing the feedback overhead.
A Capsule Network for Hierarchical Multi-Label Image Classification
Venue: arXiv cs.CV 2022
Year: 2022

Image classification is one of the most important areas in computer vision. Hierarchical multi-label classification applies when a multi-class image classification problem is arranged into smaller ones based upon a hierarchy or taxonomy. Thus, hierarchical classification modes generally provide multiple class predictions on each instance, whereby these are expected to reflect the structure of image classes as related to one another. In this paper, we propose a multi-label capsule network (ML-CapsNet) for hierarchical classification. Our ML-CapsNet predicts multiple image classes based on a hierarchical class-label tree structure. To this end, we present a loss function that takes into account the multi-label predictions of the network. As a result, the training approach for our ML-CapsNet uses a coarse to fine paradigm while maintaining consistency with the structure in the classification levels in the label-hierarchy. We also perform experiments using widely available datasets and compare the model with alternatives elsewhere in the literature. In our experiments, our ML-CapsNet yields a margin of improvement with respect to these alternative methods.
Ruleformer: Context-aware Differentiable Rule Mining over Knowledge Graph
Venue: arXiv cs.LO 2022
Year: 2022

Rule mining is an effective approach for reasoning over knowledge graph (KG). Existing works mainly concentrate on mining rules. However, there might be several rules that could be applied for reasoning for one relation, and how to select appropriate rules for completion of different triples has not been discussed. In this paper, we propose to take the context information into consideration, which helps select suitable rules for the inference tasks. Based on this idea, we propose a transformer-based rule mining approach, Ruleformer. It consists of two blocks: 1) an encoder extracting the context information from subgraph of head entities with modified attention mechanism, and 2) a decoder which aggregates the subgraph information from the encoder output and generates the probability of relations for each step of reasoning. The basic idea behind Ruleformer is regarding rule mining process as a sequence to sequence task. To make the subgraph a sequence input to the encoder and retain the graph structure, we devise a relational attention mechanism in Transformer. The experiment results show the necessity of considering these information in rule mining task and the effectiveness of our model.
Continuous Design Control for Machine Learning in Certified Medical Systems
Venue: arXiv cs.SE 2022
Year: 2022

Continuous software engineering has become commonplace in numerous fields. However, in regulating intensive sectors, where additional concerns needs to be taken into account, it is often considered difficult to apply continuous development approaches, such as devops. In this paper, we present an approach for using pull requests as design controls, and apply this approach to machine learning in certified medical systems leveraging model cards, a novel technique developed to add explainability to machine learning systems, as a regulatory audit trail. The approach is demonstrated with an industrial system that we have used previously to show how medical systems can be developed in a continuous fashion.
Multi-stage Distillation Framework for Cross-Lingual Semantic Similarity Matching
Venue: arXiv cs.CL 2022
Year: 2022

Previous studies have proved that cross-lingual knowledge distillation can significantly improve the performance of pre-trained models for cross-lingual similarity matching tasks. However, the student model needs to be large in this operation. Otherwise, its performance will drop sharply, thus making it impractical to be deployed to memory-limited devices. To address this issue, we delve into cross-lingual knowledge distillation and propose a multi-stage distillation framework for constructing a small-size but high-performance cross-lingual model. In our framework, contrastive learning, bottleneck, and parameter recurrent strategies are combined to prevent performance from being compromised during the compression process. The experimental results demonstrate that our method can compress the size of XLM-R and MiniLM by more than 50\%, while the performance is only reduced by about 1%.
Federated Meta-Learning for Traffic Steering in O-RAN
Venue: arXiv cs.NI 2022
Year: 2022

The vision of 5G lies in providing high data rates, low latency (for the aim of near-real-time applications), significantly increased base station capacity, and near-perfect quality of service (QoS) for users, compared to LTE networks. In order to provide such services, 5G systems will support various combinations of access technologies such as LTE, NR, NR-U and Wi-Fi. Each radio access technology (RAT) provides different types of access, and these should be allocated and managed optimally among the users. Besides resource management, 5G systems will also support a dual connectivity service. The orchestration of the network therefore becomes a more difficult problem for system managers with respect to legacy access technologies. In this paper, we propose an algorithm for RAT allocation based on federated meta-learning (FML), which enables RAN intelligent controllers (RICs) to adapt more quickly to dynamically changing environments. We have designed a simulation environment which contains LTE and 5G NR service technologies. In the simulation, our objective is to fulfil UE demands within the deadline of transmission to provide higher QoS values. We compared our proposed algorithm with a single RL agent, the Reptile algorithm and a rule-based heuristic method. Simulation results show that the proposed FML method achieves higher caching rates at first deployment round 21% and 12% respectively. Moreover, proposed approach adapts to new tasks and environments most quickly amongst the compared methods.
SVNet: Where SO(3) Equivariance Meets Binarization on Point Cloud Representation
Venue: arXiv cs.CV 2022
Year: 2022

Efficiency and robustness are increasingly needed for applications on 3D point clouds, with the ubiquitous use of edge devices in scenarios like autonomous driving and robotics, which often demand real-time and reliable responses. The paper tackles the challenge by designing a general framework to construct 3D learning architectures with SO(3) equivariance and network binarization. However, a naive combination of equivariant networks and binarization either causes sub-optimal computational efficiency or geometric ambiguity. We propose to locate both scalar and vector features in our networks to avoid both cases. Precisely, the presence of scalar features makes the major part of the network binarizable, while vector features serve to retain rich structural information and ensure SO(3) equivariance. The proposed approach can be applied to general backbones like PointNet and DGCNN. Meanwhile, experiments on ModelNet40, ShapeNet, and the real-world dataset ScanObjectNN, demonstrated that the method achieves a great trade-off between efficiency, rotation robustness, and accuracy. The codes are available at https://github.com/zhuoinoulu/svnet.
Does Road Diversity Really Matter in Testing Automated Driving Systems? -- A Registered Report
Venue: arXiv cs.SE 2022
Year: 2022

Background/Context. The use of automated driving systems (ADSs) in the real world requires rigorous testing to ensure safety. To increase trust, ADSs should be tested on a large set of diverse road scenarios. Literature suggests that if a vehicle is driven along a set of geometrically diverse roads-measured using various diversity measures (DMs)-it will react in a wide range of behaviours, thereby increasing the chances of observing failures (if any), or strengthening the confidence in its safety, if no failures are observed. To the best of our knowledge, however, this assumption has never been tested before, nor have road DMs been assessed for their properties. Objective/Aim. Our goal is to perform an exploratory study on 47 currently used and new, potentially promising road DMs. Specifically, our research questions look into the road DMs themselves, to analyse their properties (e.g. monotonicity, computation efficiency), and to test correlation between DMs. Furthermore, we look at the use of road DMs to investigate whether the assumption that diverse test suites of roads expose diverse driving behaviour holds. Method. Our empirical analysis relies on a state-of-the-art, open-source ADSs testing infrastructure and uses a data set containing over 97,000 individual road geometries and matching simulation data that were collected using two driving agents. By sampling random test suites of various sizes and measuring their roads' geometric diversity, we study road DMs properties, the correlation between road DMs, and the correlation between road DMs and the observed behaviour.
Adversarial Inter-Group Link Injection Degrades the Fairness of Graph Neural Networks
Venue: arXiv cs.LG 2022
Year: 2022

We present evidence for the existence and effectiveness of adversarial attacks on graph neural networks (GNNs) that aim to degrade fairness. These attacks can disadvantage a particular subgroup of nodes in GNN-based node classification, where nodes of the underlying network have sensitive attributes, such as race or gender. We conduct qualitative and experimental analyses explaining how adversarial link injection impairs the fairness of GNN predictions. For example, an attacker can compromise the fairness of GNN-based node classification by injecting adversarial links between nodes belonging to opposite subgroups and opposite class labels. Our experiments on empirical datasets demonstrate that adversarial fairness attacks can significantly degrade the fairness of GNN predictions (attacks are effective) with a low perturbation rate (attacks are efficient) and without a significant drop in accuracy (attacks are deceptive). This work demonstrates the vulnerability of GNN models to adversarial fairness attacks. We hope our findings raise awareness about this issue in our community and lay a foundation for the future development of GNN models that are more robust to such attacks.
Assessing User Interface Design Artifacts: A Tool-Supported Behavior-Based Approach
Venue: arXiv cs.SE 2022
Year: 2022

Behaviour-Driven Development (BDD) has emerged in the last years as a powerful methodology to specify testable and executable user requirements through stories and scenarios. With the support of external testing frameworks, BDD stories can be used to automatically assess the behavior of a fully functional software system. This article describes a toolset which extends BDD with the aim of providing automated assessment also for user interface design artifacts to ensure their consistency with the user requirements since the beginning of a software project. The approach has been evaluated by exploiting previously specified user requirements for a web system to book business trips. Such requirements gave rise to a set of BDD stories that have been refined and used to automatically assess the consistency of task models, graphical user interface (GUI) prototypes, and final GUIs of the system. The results have shown that our approach was able to identify different types of inconsistencies in the set of analyzed artifacts and consistently keep the semantic traces between them.
Learning to Solve Multiple-TSP with Time Window and Rejections via Deep Reinforcement Learning
Venue: arXiv cs.LG 2022
Year: 2022

We propose a manager-worker framework based on deep reinforcement learning to tackle a hard yet nontrivial variant of Travelling Salesman Problem (TSP), \ie~multiple-vehicle TSP with time window and rejections (mTSPTWR), where customers who cannot be served before the deadline are subject to rejections. Particularly, in the proposed framework, a manager agent learns to divide mTSPTWR into sub-routing tasks by assigning customers to each vehicle via a Graph Isomorphism Network (GIN) based policy network. A worker agent learns to solve sub-routing tasks by minimizing the cost in terms of both tour length and rejection rate for each vehicle, the maximum of which is then fed back to the manager agent to learn better assignments. Experimental results demonstrate that the proposed framework outperforms strong baselines in terms of higher solution quality and shorter computation time. More importantly, the trained agents also achieve competitive performance for solving unseen larger instances.
Generate synthetic samples from tabular data
Venue: arXiv cs.LG 2022
Year: 2022

Generating new samples from data sets can mitigate extra expensive operations, increased invasive procedures, and mitigate privacy issues. These novel samples that are statistically robust can be used as a temporary and intermediate replacement when privacy is a concern. This method can enable better data sharing practices without problems relating to identification issues or biases that are flaws for an adversarial attack.
Verified Compositions of Neural Network Controllers for Temporal Logic Control Objectives
Venue: arXiv cs.RO 2022
Year: 2022

This paper presents a new approach to design verified compositions of Neural Network (NN) controllers for autonomous systems with tasks captured by Linear Temporal Logic (LTL) formulas. Particularly, the LTL formula requires the system to reach and avoid certain regions in a temporal/logical order. We assume that the system is equipped with a finite set of trained NN controllers. Each controller has been trained so that it can drive the system towards a specific region of interest while avoiding others. Our goal is to check if there exists a temporal composition of the trained NN controllers - and if so, to compute it - that will yield composite system behaviors that satisfy a user-specified LTL task for any initial system state belonging to a given set. To address this problem, we propose a new approach that relies on a novel integration of automata theory and recently proposed reachability analysis tools for NN-controlled systems. We note that the proposed method can be applied to other controllers, not necessarily modeled by NNs, by appropriate selection of the reachability analysis tool. We focus on NN controllers due to their lack of robustness. The proposed method is demonstrated on navigation tasks for aerial vehicles.
Characterizing the Performance of Node-Aware Strategies for Irregular Point-to-Point Communication on Heterogeneous Architectures
Venue: arXiv cs.DC 2022
Year: 2022

Supercomputer architectures are trending toward higher computational throughput due to the inclusion of heterogeneous compute nodes. These multi-GPU nodes increase on-node computational efficiency, while also increasing the amount of data to be communicated and the number of potential data flow paths. In this work, we characterize the performance of irregular point-to-point communication with MPI on heterogeneous compute environments through performance modeling, demonstrating the limitations of standard communication strategies for both device-aware and staging-through-host communication techniques. Presented models suggest staging communicated data through host processes then using node-aware communication strategies for high inter-node message counts. Notably, the models also predict that node-aware communication utilizing all available CPU cores to communicate inter-node data leads to the most performant strategy when communicating with a high number of nodes. Model validation is provided via a case study of irregular point-to-point communication patterns in distributed sparse matrix-vector products. Importantly, we include a discussion on the implications model predictions have on communication strategy design for emerging supercomputer architectures.
Mandator and Sporades: Robust Wide-Area Consensus with Efficient Request Dissemination
Venue: arXiv cs.DC 2022
Year: 2022

Consensus algorithms are deployed in the wide area to achieve high availability for geographically replicated applications. Wide-area consensus is challenging due to two main reasons: (1) low throughput due to the high latency overhead of client request dissemination and (2) network asynchrony that causes consensus protocols to lose liveness. In this paper, we propose Mandator and Sporades, a modular state machine replication algorithm that enables high performance and resiliency in the wide-area setting. To address the high client request dissemination overhead challenge, we propose Mandator, a novel consensus-agnostic asynchronous dissemination layer. Mandator separates client request dissemination from the critical path of consensus to obtain high performance. Composing Mandator with Multi-Paxos (Mandator-Paxos) delivers significantly high throughput under synchronous networks. However, under asynchronous network conditions, Mandator-Paxos loses liveness which results in high latency. To achieve low latency and robustness under asynchrony, we propose Sporades, a novel omission fault-tolerant consensus algorithm. Sporades consists of two modes of operations -- synchronous and asynchronous -- that always ensure liveness. The combination of Mandator and Sporades (Mandator-Sporades) provides a robust and high-performing state machine replication system. We implement and evaluate Mandator-Sporades in a wide-area deployment running on Amazon EC2. Our evaluation shows that in the synchronous execution, Mandator-Sporades achieves 300k tx/sec throughput in less than 900ms latency, outperforming Multi-Paxos, EPaxos and Rabia by 650\% in throughput, at a modest expense of latency. Furthermore, we show that Mandator-Sporades outperforms Mandator-Paxos, Multi-Paxos, and EPaxos in the face of targeted distributed denial-of-service attacks.