OpenRaiser/Intern-Atlas huggingface dataset:
Identification and Control of a Soft-Robotic Bladder Towards Impedance-Style Haptic Terrain Display
Venue: arXiv cs.RO 2022
Year: 2022

This paper evaluates the capabilities of a soft robotic pneumatic actuator derived from the terrain display haptic device, "The Smart Shoe." The bladder design of the Smart Shoe is upgraded to include a pressure supply and greater output flow capabilities. A bench top setup is created to rigorously test this new type of actuator. The bandwidth and stiffness capability of this new actuator are evaluated relative to forces and displacements encountered during human gait. Four force vs. displacement profiles relevant to haptic terrain display are proposed and tested using sliding-mode tracking control. It was found that the actuator could sustain a stiffness similar to a soft-soled shoe on concrete, as well as other terrain (sand, dirt, etc.), while the bandwidth of 7.3 Hz fell short of the goal bandwidth of 10 Hz. Compressions of the bladder done at 20 mm/s, which is similar to the speed of human gait, showed promising results in tracking a desired force trajectory. The results in this paper show this actuator is capable of displaying haptic terrain trajectories, providing a basis for futurep wearable haptic terrain display devices.
AuNa: Modularly Integrated Simulation Framework for Cooperative Autonomous Navigation
Venue: arXiv cs.RO 2022
Year: 2022

In the near future, the development of autonomous driving will get more complex as the vehicles will not only rely on their own sensors but also communicate with other vehicles and the infrastructure to cooperate and improve the driving experience. Towards this, several research areas, such as robotics, communication, and control, are required to collaborate in order to implement future-ready methods. However, each area focuses on the development of its own components first, while the effects the components may have on the whole system are only considered at a later stage. In this work, we integrate the simulation tools of robotics, communication and control namely ROS2, OMNeT++, and MATLAB to evaluate cooperative driving scenarios. The framework can be utilized to develop the individual components using the designated tools, while the final evaluation can be conducted in a complete scenario, enabling the simulation of advanced multi-robot applications for cooperative driving. Furthermore, it can be used to integrate additional tools, as the integration is done in a modular way. We showcase the framework by demonstrating a platooning scenario under cooperative adaptive cruise control (CACC) and the ETSI ITS-G5 communication architecture. Additionally, we compare the differences of the controller performance between the theoretical analysis and practical case study.
Investigating the Impact of Independent Rule Fitnesses in a Learning Classifier System
Venue: arXiv cs.LG 2022
Year: 2022

Achieving at least some level of explainability requires complex analyses for many machine learning systems, such as common black-box models. We recently proposed a new rule-based learning system, SupRB, to construct compact, interpretable and transparent models by utilizing separate optimizers for the model selection tasks concerning rule discovery and rule set composition.This allows users to specifically tailor their model structure to fulfil use-case specific explainability requirements. From an optimization perspective, this allows us to define clearer goals and we find that -- in contrast to many state of the art systems -- this allows us to keep rule fitnesses independent. In this paper we investigate this system's performance thoroughly on a set of regression problems and compare it against XCSF, a prominent rule-based learning system. We find the overall results of SupRB's evaluation comparable to XCSF's while allowing easier control of model structure and showing a substantially smaller sensitivity to random seeds and data splits. This increased control can aid in subsequently providing explanations for both training and final structure of the model.
Abstraction Logic: A New Foundation for (Computer) Mathematics
Venue: arXiv cs.LO 2022
Year: 2022

Abstraction logic is a new logic, serving as a foundation of mathematics. It combines features of both predicate logic and higher-order logic: abstraction logic can be viewed both as higher-order logic minus static types as well as predicate logic plus operators and variable binding. We argue that abstraction logic is the best foundational logic possible because it maximises both simplicity and practical expressivity. This argument is supported by the observation that abstraction logic has simpler terms and a simpler notion of proof than all other general logics. At the same time, abstraction logic can formalise both intuitionistic and classical abstraction logic, and is sound and complete for these logics and all other logics extending deduction logic with equality.
Mechanizing Refinement Types (extended)
Venue: arXiv cs.PL 2022
Year: 2022

Practical checkers based on refinement types use the combination of implicit semantic sub-typing and parametric polymorphism to simplify the specification and automate the verification of sophisticated properties of programs. However, a formal meta-theoretic accounting of the soundness of refinement type systems using this combination has proved elusive. We present λ_RF a core refinement calculus that combines semantic sub-typing and parametric polymorphism. We develop a meta-theory for this calculus and prove soundness of the type system. Finally, we give a full mechanization of our meta-theory using the refinement-type based LiquidHaskell as a proof checker, showing how refinements can be used for mechanization.
SafeDrones: Real-Time Reliability Evaluation of UAVs using Executable Digital Dependable Identities
Venue: arXiv cs.RO 2022
Year: 2022

The use of Unmanned Arial Vehicles (UAVs) offers many advantages across a variety of applications. However, safety assurance is a key barrier to widespread usage, especially given the unpredictable operational and environmental factors experienced by UAVs, which are hard to capture solely at design-time. This paper proposes a new reliability modeling approach called SafeDrones to help address this issue by enabling runtime reliability and risk assessment of UAVs. It is a prototype instantiation of the Executable Digital Dependable Identity (EDDI) concept, which aims to create a model-based solution for real-time, data-driven dependability assurance for multi-robot systems. By providing real-time reliability estimates, SafeDrones allows UAVs to update their missions accordingly in an adaptive manner.
Approximate Distance Oracles for Planar Graphs with Subpolynomial Error Dependency
Venue: arXiv cs.DS 2022
Year: 2022

Thorup [FOCS'01, JACM'04] and Klein [SODA'01] independently showed that there exists a $(1+ε)$-approximate distance oracle for planar graphs with $O(n (\log n)ε^{-1})$ space and $O(ε^{-1})$ query time. While the dependency on $n$ is nearly linear, the space-query product of their oracles depend quadratically on $1/ε$. Many follow-up results either improved the space \emph{or} the query time of the oracles while having the same, sometimes worst, dependency on $1/ε$. Kawarabayashi, Sommer, and Thorup [SODA'13] were the first to improve the dependency on $1/ε$ from quadratic to nearly linear (at the cost of $\log^*(n)$ factors). It is plausible to conjecture that the linear dependency on $1/ε$ is optimal: for many known distance-related problems in planar graphs, it was proved that the dependency on $1/ε$ is at least linear. In this work, we disprove this conjecture by reducing the dependency of the space-query product on $1/ε$ from linear all the way down to \emph{subpolynomial} $(1/ε)^{o(1)}$. More precisely, we construct an oracle with $O(n\log(n)(ε^{-o(1)} + \log^*n))$ space and $\log^{2+o(1)}(1/ε)$ query time. Our construction is the culmination of several different ideas developed over the past two decades.
Time synchronization protocol for the KLJN secure key exchange scheme
Venue: arXiv cs.CR 2022
Year: 2022

The information theoretically secure Kirchhoff-law-Johnson-noise (KLJN) key exchange scheme, similarly to quantum key distribution (QKD), is also potentially vulnerable against clock attacks, where Eve takes over the control of clock synchronization in the channel. This short note aims to introduce a time synchronization protocol scheme for Alice and Bob, which is resistant against arbitrary time delay attacks, both symmetric and asymmetric ones. We propose and explore various ways of clock synchronization for the KLJN system and propose an ultimate protocol that preserves time and hardware integrity under arbitrary attacks.
PAC-Bayesian Domain Adaptation Bounds for Multiclass Learners
Venue: arXiv cs.LG 2022
Year: 2022

Multiclass neural networks are a common tool in modern unsupervised domain adaptation, yet an appropriate theoretical description for their non-uniform sample complexity is lacking in the adaptation literature. To fill this gap, we propose the first PAC-Bayesian adaptation bounds for multiclass learners. We facilitate practical use of our bounds by also proposing the first approximation techniques for the multiclass distribution divergences we consider. For divergences dependent on a Gibbs predictor, we propose additional PAC-Bayesian adaptation bounds which remove the need for inefficient Monte-Carlo estimation. Empirically, we test the efficacy of our proposed approximation techniques as well as some novel design-concepts which we include in our bounds. Finally, we apply our bounds to analyze a common adaptation algorithm that uses neural networks.
ReLyMe: Improving Lyric-to-Melody Generation by Incorporating Lyric-Melody Relationships
Venue: arXiv cs.SD 2022
Year: 2022

Lyric-to-melody generation, which generates melody according to given lyrics, is one of the most important automatic music composition tasks. With the rapid development of deep learning, previous works address this task with end-to-end neural network models. However, deep learning models cannot well capture the strict but subtle relationships between lyrics and melodies, which compromises the harmony between lyrics and generated melodies. In this paper, we propose ReLyMe, a method that incorporates Relationships between Lyrics and Melodies from music theory to ensure the harmony between lyrics and melodies. Specifically, we first introduce several principles that lyrics and melodies should follow in terms of tone, rhythm, and structure relationships. These principles are then integrated into neural network lyric-to-melody models by adding corresponding constraints during the decoding process to improve the harmony between lyrics and melodies. We use a series of objective and subjective metrics to evaluate the generated melodies. Experiments on both English and Chinese song datasets show the effectiveness of ReLyMe, demonstrating the superiority of incorporating lyric-melody relationships from the music domain into neural lyric-to-melody generation.
Learning Joint Detection, Equalization and Decoding for Short-Packet Communications
Venue: arXiv cs.IT 2022
Year: 2022

We propose and practically demonstrate a joint detection and decoding scheme for short-packet wireless communications in scenarios that require to first detect the presence of a message before actually decoding it. For this, we extend the recently proposed serial Turbo-autoencoder neural network (NN) architecture and train it to find short messages that can be, all "at once", detected, synchronized, equalized and decoded when sent over an unsynchronized channel with memory. The conceptional advantage of the proposed system stems from a holistic message structure with superimposed pilots for joint detection and decoding without the need of relying on a dedicated preamble. This results not only in higher spectral efficiency, but also translates into the possibility of shorter messages compared to using a dedicated preamble. We compare the detection error rate (DER), bit error rate (BER) and block error rate (BLER) performance of the proposed system with a hand-crafted state-of-the-art conventional baseline and our simulations show a significant advantage of the proposed autoencoder-based system over the conventional baseline in every scenario up to messages conveying k = 96 information bits. Finally, we practically evaluate and confirm the improved performance of the proposed system over-the-air (OTA) using a software-defined radio (SDR)-based measurement testbed.
The Untold Impact of Learning Approaches on Software Fault-Proneness Predictions
Venue: arXiv cs.SE 2022
Year: 2022

Software fault-proneness prediction is an active research area, with many factors affecting prediction performance extensively studied. However, the impact of the learning approach (i.e., the specifics of the data used for training and the target variable being predicted) on the prediction performance has not been studied, except for one initial work. This paper explores the effects of two learning approaches, useAllPredictAll and usePrePredictPost, on the performance of software fault-proneness prediction, both within-release and across-releases. The empirical results are based on data extracted from 64 releases of twelve open-source projects. Results show that the learning approach has a substantial, and typically unacknowledged, impact on the classification performance. Specifically, using useAllPredictAll leads to significantly better performance than using usePrePredictPost learning approach, both within-release and across-releases. Furthermore, this paper uncovers that, for within-release predictions, this difference in classification performance is due to different levels of class imbalance in the two learning approaches. When class imbalance is addressed, the performance difference between the learning approaches is eliminated. Our findings imply that the learning approach should always be explicitly identified and its impact on software fault-proneness prediction considered. The paper concludes with a discussion of potential consequences of our results for both research and practice.
AGBoost: Attention-based Modification of Gradient Boosting Machine
Venue: arXiv cs.LG 2022
Year: 2022

A new attention-based model for the gradient boosting machine (GBM) called AGBoost (the attention-based gradient boosting) is proposed for solving regression problems. The main idea behind the proposed AGBoost model is to assign attention weights with trainable parameters to iterations of GBM under condition that decision trees are base learners in GBM. Attention weights are determined by applying properties of decision trees and by using the Huber's contamination model which provides an interesting linear dependence between trainable parameters of the attention and the attention weights. This peculiarity allows us to train the attention weights by solving the standard quadratic optimization problem with linear constraints. The attention weights also depend on the discount factor as a tuning parameter, which determines how much the impact of the weight is decreased with the number of iterations. Numerical experiments performed for two types of base learners, original decision trees and extremely randomized trees with various regression datasets illustrate the proposed model.
Enhancing Fairness of Visual Attribute Predictors
Venue: arXiv cs.CV 2022
Year: 2022

The performance of deep neural networks for image recognition tasks such as predicting a smiling face is known to degrade with under-represented classes of sensitive attributes. We address this problem by introducing fairness-aware regularization losses based on batch estimates of Demographic Parity, Equalized Odds, and a novel Intersection-over-Union measure. The experiments performed on facial and medical images from CelebA, UTKFace, and the SIIM-ISIC melanoma classification challenge show the effectiveness of our proposed fairness losses for bias mitigation as they improve model fairness while maintaining high classification performance. To the best of our knowledge, our work is the first attempt to incorporate these types of losses in an end-to-end training scheme for mitigating biases of visual attribute predictors. Our code is available at https://github.com/nish03/FVAP.
1st Place Solution to the EPIC-Kitchens Action Anticipation Challenge 2022
Venue: arXiv cs.CV 2022
Year: 2022

In this report, we describe the technical details of our submission to the EPIC-Kitchens Action Anticipation Challenge 2022. In this competition, we develop the following two approaches. 1) Anticipation Time Knowledge Distillation using the soft labels learned by the teacher model as knowledge to guide the student network to learn the information of anticipation time; 2) Verb-Noun Relation Module for building the relationship between verbs and nouns. Our method achieves state-of-the-art results on the testing set of EPIC-Kitchens Action Anticipation Challenge 2022.
PAC Reinforcement Learning for Predictive State Representations
Venue: arXiv cs.LG 2022
Year: 2022

In this paper we study online Reinforcement Learning (RL) in partially observable dynamical systems. We focus on the Predictive State Representations (PSRs) model, which is an expressive model that captures other well-known models such as Partially Observable Markov Decision Processes (POMDP). PSR represents the states using a set of predictions of future observations and is defined entirely using observable quantities. We develop a novel model-based algorithm for PSRs that can learn a near optimal policy in sample complexity scaling polynomially with respect to all the relevant parameters of the systems. Our algorithm naturally works with function approximation to extend to systems with potentially large state and observation spaces. We show that given a realizable model class, the sample complexity of learning the near optimal policy only scales polynomially with respect to the statistical complexity of the model class, without any explicit polynomial dependence on the size of the state and observation spaces. Notably, our work is the first work that shows polynomial sample complexities to compete with the globally optimal policy in PSRs. Finally, we demonstrate how our general theorem can be directly used to derive sample complexity bounds for special models including $m$-step weakly revealing and $m$-step decodable tabular POMDPs, POMDPs with low-rank latent transition, and POMDPs with linear emission and latent transition.
Domain Gap Estimation for Source Free Unsupervised Domain Adaptation with Many Classifiers
Venue: arXiv cs.CV 2022
Year: 2022

In theory, the success of unsupervised domain adaptation (UDA) largely relies on domain gap estimation. However, for source free UDA, the source domain data can not be accessed during adaptation, which poses great challenge of measuring the domain gap. In this paper, we propose to use many classifiers to learn the source domain decision boundaries, which provides a tighter upper bound of the domain gap, even if both of the domain data can not be simultaneously accessed. The source model is trained to push away each pair of classifiers whilst ensuring the correctness of the decision boundaries. In this sense, our many classifiers model separates the source different categories as far as possible which induces the maximum disagreement of many classifiers in the target domain, thus the transferable source domain knowledge is maximized. For adaptation, the source model is adapted to maximize the agreement among pairs of the classifiers. Thus the target features are pushed away from the decision boundaries. Experiments on several datasets of UDA show that our approach achieves state of the art performance among source free UDA approaches and can even compete to source available UDA methods.
Look-ups are not (yet) all you need for deep learning inference
Venue: arXiv cs.LG 2022
Year: 2022

Fast approximations to matrix multiplication have the potential to dramatically reduce the cost of neural network inference. Recent work on approximate matrix multiplication proposed to replace costly multiplications with table-lookups by fitting a fast hash function from training data. In this work, we propose improvements to this previous work, targeted to the deep learning inference setting, where one has access to both training data and fixed (already learned) model weight matrices. We further propose a fine-tuning procedure for accelerating entire neural networks while minimizing loss in accuracy. Finally, we analyze the proposed method on a simple image classification task. While we show improvements to prior work, overall classification accuracy remains substantially diminished compared to exact matrix multiplication. Our work, despite this negative result, points the way towards future efforts to accelerate inner products with fast nonlinear hashing methods.
A new hope for network model generalization
Venue: arXiv cs.NI 2022
Year: 2022

Generalizing machine learning (ML) models for network traffic dynamics tends to be considered a lost cause. Hence for every new task, we design new models and train them on model-specific datasets closely mimicking the deployment environments. Yet, an ML architecture called_Transformer_ has enabled previously unimaginable generalization in other domains. Nowadays, one can download a model pre-trained on massive datasets and only fine-tune it for a specific task and context with comparatively little time and data. These fine-tuned models are now state-of-the-art for many benchmarks. We believe this progress could translate to networking and propose a Network Traffic Transformer (NTT), a transformer adapted to learn network dynamics from packet traces. Our initial results are promising: NTT seems able to generalize to new prediction tasks and environments. This study suggests there is still hope for generalization, though it calls for a lot of future research.
Sockeye 3: Fast Neural Machine Translation with PyTorch
Venue: arXiv cs.CL 2022
Year: 2022

Sockeye 3 is the latest version of the Sockeye toolkit for Neural Machine Translation (NMT). Now based on PyTorch, Sockeye 3 provides faster model implementations and more advanced features with a further streamlined codebase. This enables broader experimentation with faster iteration, efficient training of stronger and faster models, and the flexibility to move new ideas quickly from research to production. When running comparable models, Sockeye 3 is up to 126% faster than other PyTorch implementations on GPUs and up to 292% faster on CPUs. Sockeye 3 is open source software released under the Apache 2.0 license.
A Conceptual Framework for Using Machine Learning to Support Child Welfare Decisions
Venue: arXiv cs.CY 2022
Year: 2022

Human services systems make key decisions that impact individuals in the society. The U.S. child welfare system makes such decisions, from screening-in hotline reports of suspected abuse or neglect for child protective investigations, placing children in foster care, to returning children to permanent home settings. These complex and impactful decisions on children's lives rely on the judgment of child welfare decisionmakers. Child welfare agencies have been exploring ways to support these decisions with empirical, data-informed methods that include machine learning (ML). This paper describes a conceptual framework for ML to support child welfare decisions. The ML framework guides how child welfare agencies might conceptualize a target problem that ML can solve; vet available administrative data for building ML; formulate and develop ML specifications that mirror relevant populations and interventions the agencies are undertaking; deploy, evaluate, and monitor ML as child welfare context, policy, and practice change over time. Ethical considerations, stakeholder engagement, and avoidance of common pitfalls underpin the framework's impact and success. From abstract to concrete, we describe one application of this framework to support a child welfare decision. This ML framework, though child welfare-focused, is generalizable to solving other public policy problems.