OpenRaiser/Intern-Atlas huggingface dataset:
Towards Privacy-preserving Explanations in Medical Image Analysis
Venue: arXiv cs.CV 2021
Year: 2021

The use of Deep Learning in the medical field is hindered by the lack of interpretability. Case-based interpretability strategies can provide intuitive explanations for deep learning models' decisions, thus, enhancing trust. However, the resulting explanations threaten patient privacy, motivating the development of privacy-preserving methods compatible with the specifics of medical data. In this work, we analyze existing privacy-preserving methods and their respective capacity to anonymize medical data while preserving disease-related semantic features. We find that the PPRL-VGAN deep learning method was the best at preserving the disease-related semantic features while guaranteeing a high level of privacy among the compared state-of-the-art methods. Nevertheless, we emphasize the need to improve privacy-preserving methods for medical imaging, as we identified relevant drawbacks in all existing privacy-preserving approaches.
Evolutionary Innovation Viewed as Novel Physical Phenomena and Hierarchical Systems Building
Venue: arXiv cs.NE 2021
Year: 2021

In previous work I proposed a framework for thinking about open-ended evolution. The framework characterised the basic processes required for Darwinian evolution as: (1) the generation of a phenotype from a genetic description; (2) the evaluation of that phenotype; and (3) the reproduction with variation of successful genotype-phenotypes. My treatment emphasized the potential influence of the biotic and abiotic environment, and of the laws of physics/chemistry, on each of these processes. I demonstrated the conditions under which these processes can allow for ongoing exploration of a space of possible phenotypes (which I labelled exploratory open-endedness). However, these processes by themselves cannot expand the space of possible phenotypes and therefore cannot account for the more interesting and unexpected kinds of evolutionary innovation (such as those I labelled expansive and transformational open-endedness). In the previous work I looked at ways in which expansive and transformational innovations could arise. I proposed transdomain bridges and non-additive compositional systems as two mechanisms by which these kinds of innovations could arise. In the current paper I wish to generalise and expand upon these two concepts. I do this by adopting the Parameter Space-Organisation Space-Action Space (POA) perspective, as suggested at in my previous work, and proposing that all evolutionary innovations can be viewed as either capturing some novel physical phenomena that had previously been unused, or as the creation of new persistent systems within the environment.
Uncertainty Estimation and Out-of-Distribution Detection for Counterfactual Explanations: Pitfalls and Solutions
Venue: arXiv cs.LG 2021
Year: 2021

Whilst an abundance of techniques have recently been proposed to generate counterfactual explanations for the predictions of opaque black-box systems, markedly less attention has been paid to exploring the uncertainty of these generated explanations. This becomes a critical issue in high-stakes scenarios, where uncertain and misleading explanations could have dire consequences (e.g., medical diagnosis and treatment planning). Moreover, it is often difficult to determine if the generated explanations are well grounded in the training data and sensitive to distributional shifts. This paper proposes several practical solutions that can be leveraged to solve these problems by establishing novel connections with other research works in explainability (e.g., trust scores) and uncertainty estimation (e.g., Monte Carlo Dropout). Two experiments demonstrate the utility of our proposed solutions.
An Exploration of Exploration: Measuring the ability of lexicase selection to find obscure pathways to optimality
Venue: arXiv cs.NE 2021
Year: 2021

Parent selection algorithms (selection schemes) steer populations through a problem's search space, often trading off between exploitation and exploration. Understanding how selection schemes affect exploitation and exploration within a search space is crucial to tackling increasingly challenging problems. Here, we introduce an "exploration diagnostic" that diagnoses a selection scheme's capacity for search space exploration. We use our exploration diagnostic to investigate the exploratory capacity of lexicase selection and several of its variants: epsilon lexicase, down-sampled lexicase, cohort lexicase, and novelty-lexicase. We verify that lexicase selection out-explores tournament selection, and we show that lexicase selection's exploratory capacity can be sensitive to the ratio between population size and the number of test cases used for evaluating candidate solutions. Additionally, we find that relaxing lexicase's elitism with epsilon lexicase can further improve exploration. Both down-sampling and cohort lexicase -- two techniques for applying random subsampling to test cases -- degrade lexicase's exploratory capacity; however, we find that cohort partitioning better preserves lexicase's exploratory capacity than down-sampling. Finally, we find evidence that novelty-lexicase's addition of novelty test cases can degrade lexicase's capacity for exploration. Overall, our findings provide hypotheses for further exploration and actionable insights and recommendations for using lexicase selection. Additionally, this work demonstrates the value of selection scheme diagnostics as a complement to more conventional benchmarking approaches to selection scheme analysis.
Track based Offline Policy Learning for Overtaking Maneuvers with Autonomous Racecars
Venue: arXiv cs.RO 2021
Year: 2021

The rising popularity of driver-less cars has led to the research and development in the field of autonomous racing, and overtaking in autonomous racing is a challenging task. Vehicles have to detect and operate at the limits of dynamic handling and decisions in the car have to be made at high speeds and high acceleration. One of the most crucial parts in autonomous racing is path planning and decision making for an overtaking maneuver with a dynamic opponent vehicle. In this paper we present the evaluation of a track based offline policy learning approach for autonomous racing. We define specific track portions and conduct offline experiments to evaluate the probability of an overtaking maneuver based on speed and position of the ego vehicle. Based on these experiments we can define overtaking probability distributions for each of the track portions. Further, we propose a switching MPCC controller setup for incorporating the learnt policies to achieve a higher rate of overtaking maneuvers. By exhaustive simulations, we show that our proposed algorithm is able to increase the number of overtakes at different track portions.
NeurObfuscator: A Full-stack Obfuscation Tool to Mitigate Neural Architecture Stealing
Venue: arXiv cs.CR 2021
Year: 2021

Neural network stealing attacks have posed grave threats to neural network model deployment. Such attacks can be launched by extracting neural architecture information, such as layer sequence and dimension parameters, through leaky side-channels. To mitigate such attacks, we propose NeurObfuscator, a full-stack obfuscation tool to obfuscate the neural network architecture while preserving its functionality with very limited performance overhead. At the heart of this tool is a set of obfuscating knobs, including layer branching, layer widening, selective fusion and schedule pruning, that increase the number of operators, reduce/increase the latency, and number of cache and DRAM accesses. A genetic algorithm-based approach is adopted to orchestrate the combination of obfuscating knobs to achieve the best obfuscating effect on the layer sequence and dimension parameters so that the architecture information cannot be successfully extracted. Results on sequence obfuscation show that the proposed tool obfuscates a ResNet-18 ImageNet model to a totally different architecture (with 44 layer difference) without affecting its functionality with only 2% overall latency overhead. For dimension obfuscation, we demonstrate that an example convolution layer with 64 input and 128 output channels can be obfuscated to generate a layer with 207 input and 93 output channels with only a 2% latency overhead.
Separating Colored Points with Minimum Number of Rectangles
Venue: arXiv cs.CG 2021
Year: 2021

In this paper we study the following problem: Given $k$ disjoint sets of points, $P_1, \ldots, P_k$ on the plane, find a minimum cardinality set $\mathcal{T}$ of arbitrary rectangles such that each rectangle contains points of just one set $P_i$ but not the others. We prove the NP-hardness of this problem.
Guided Generation of Cause and Effect
Venue: arXiv cs.CL 2021
Year: 2021

We present a conditional text generation framework that posits sentential expressions of possible causes and effects. This framework depends on two novel resources we develop in the course of this work: a very large-scale collection of English sentences expressing causal patterns CausalBank; and a refinement over previous work on constructing large lexical causal knowledge graphs Cause Effect Graph. Further, we extend prior work in lexically-constrained decoding to support disjunctive positive constraints. Human assessment confirms that our approach gives high-quality and diverse outputs. Finally, we use CausalBank to perform continued training of an encoder supporting a recent state-of-the-art model for causal reasoning, leading to a 3-point improvement on the COPA challenge set, with no change in model architecture.
Preventing dataset shift from breaking machine-learning biomarkers
Venue: arXiv cs.LG 2021
Year: 2021

Machine learning brings the hope of finding new biomarkers extracted from cohorts with rich biomedical measurements. A good biomarker is one that gives reliable detection of the corresponding condition. However, biomarkers are often extracted from a cohort that differs from the target population. Such a mismatch, known as a dataset shift, can undermine the application of the biomarker to new individuals. Dataset shifts are frequent in biomedical research, e.g. because of recruitment biases. When a dataset shift occurs, standard machine-learning techniques do not suffice to extract and validate biomarkers. This article provides an overview of when and how dataset shifts breaks machine-learning extracted biomarkers, as well as detection and correction strategies.
Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies
Venue: arXiv cs.LG 2021
Year: 2021

Objective: Temporal electronic health records (EHRs) can be a wealth of information for secondary uses, such as clinical events prediction or chronic disease management. However, challenges exist for temporal data representation. We therefore sought to identify these challenges and evaluate novel methodologies for addressing them through a systematic examination of deep learning solutions. Methods: We searched five databases (PubMed, EMBASE, the Institute of Electrical and Electronics Engineers [IEEE] Xplore Digital Library, the Association for Computing Machinery [ACM] digital library, and Web of Science) complemented with hand-searching in several prestigious computer science conference proceedings. We sought articles that reported deep learning methodologies on temporal data representation in structured EHR data from January 1, 2010, to August 30, 2020. We summarized and analyzed the selected articles from three perspectives: nature of time series, methodology, and model implementation. Results: We included 98 articles related to temporal data representation using deep learning. Four major challenges were identified, including data irregularity, data heterogeneity, data sparsity, and model opacity. We then studied how deep learning techniques were applied to address these challenges. Finally, we discuss some open challenges arising from deep learning. Conclusion: Temporal EHR data present several major challenges for clinical prediction modeling and data utilization. To some extent, current deep learning solutions can address these challenges. Future studies can consider designing comprehensive and integrated solutions. Moreover, researchers should incorporate additional clinical domain knowledge into study designs and enhance the interpretability of the model to facilitate its implementation in clinical practice.
Memorization in Deep Neural Networks: Does the Loss Function matter?
Venue: arXiv cs.LG 2021
Year: 2021

Deep Neural Networks, often owing to the overparameterization, are shown to be capable of exactly memorizing even randomly labelled data. Empirical studies have also shown that none of the standard regularization techniques mitigate such overfitting. We investigate whether the choice of the loss function can affect this memorization. We empirically show, with benchmark data sets MNIST and CIFAR-10, that a symmetric loss function, as opposed to either cross-entropy or squared error loss, results in significant improvement in the ability of the network to resist such overfitting. We then provide a formal definition for robustness to memorization and provide a theoretical explanation as to why the symmetric losses provide this robustness. Our results clearly bring out the role loss functions alone can play in this phenomenon of memorization.
Multi-Agent Belief Sharing through Autonomous Hierarchical Multi-Level Clustering
Venue: arXiv cs.RO 2021
Year: 2021

Coordination in multi-agent systems is challenging for agile robots such as unmanned aerial vehicles (UAVs), where relative agent positions frequently change due to unconstrained movement. The problem is exacerbated through the individual take-off and landing of agents for battery recharging leading to a varying number of active agents throughout the whole mission. This work proposes autonomous hierarchical multi-level clustering (MLC), which forms a clustering hierarchy utilizing decentralized methods. Through periodic cluster maintenance executed by cluster-heads, stable multi-level clustering is achieved. The resulting hierarchy is used as a backbone to solve the communication problem for locally-interactive applications such as UAV tracking problems. Using observation aggregation, compression, and dissemination, agents share local observations throughout the hierarchy, giving every agent a total system belief with spatially dependent resolution and freshness. Extensive simulations show that MLC yields a stable cluster hierarchy under different motion patterns and that the proposed belief sharing is highly applicable in wildfire front monitoring scenarios.
A Tandem Framework Balancing Privacy and Security for Voice User Interfaces
Venue: arXiv cs.CR 2021
Year: 2021

Speech synthesis, voice cloning, and voice conversion techniques present severe privacy and security threats to users of voice user interfaces (VUIs). These techniques transform one or more elements of a speech signal, e.g., identity and emotion, while preserving linguistic information. Adversaries may use advanced transformation tools to trigger a spoofing attack using fraudulent biometrics for a legitimate speaker. Conversely, such techniques have been used to generate privacy-transformed speech by suppressing personally identifiable attributes in the voice signals, achieving anonymization. Prior works have studied the security and privacy vectors in parallel, and thus it raises alarm that if a benign user can achieve privacy by a transformation, it also means that a malicious user can break security by bypassing the anti-spoofing mechanism. In this paper, we take a step towards balancing two seemingly conflicting requirements: security and privacy. It remains unclear what the vulnerabilities in one domain imply for the other, and what dynamic interactions exist between them. A better understanding of these aspects is crucial for assessing and mitigating vulnerabilities inherent with VUIs and building effective defenses. In this paper,(i) we investigate the applicability of the current voice anonymization methods by deploying a tandem framework that jointly combines anti-spoofing and authentication models, and evaluate the performance of these methods;(ii) examining analytical and empirical evidence, we reveal a duality between the two mechanisms as they offer different ways to achieve the same objective, and we show that leveraging one vector significantly amplifies the effectiveness of the other;(iii) we demonstrate that to effectively defend from potential attacks against VUIs, it is necessary to investigate the attacks from multiple complementary perspectives(security and privacy).
Interpreting diffusion score matching using normalizing flow
Venue: arXiv cs.LG 2021
Year: 2021

Scoring matching (SM), and its related counterpart, Stein discrepancy (SD) have achieved great success in model training and evaluations. However, recent research shows their limitations when dealing with certain types of distributions. One possible fix is incorporating the original score matching (or Stein discrepancy) with a diffusion matrix, which is called diffusion score matching (DSM) (or diffusion Stein discrepancy (DSD)). However, the lack of interpretation of the diffusion limits its usage within simple distributions and manually chosen matrix. In this work, we plan to fill this gap by interpreting the diffusion matrix using normalizing flows. Specifically, we theoretically prove that DSM (or DSD) is equivalent to the original score matching (or Stein discrepancy) evaluated in the transformed space defined by the normalizing flow, where the diffusion matrix is the inverse of the flow's Jacobian matrix. In addition, we also build its connection to Riemannian manifolds and further extend it to continuous flows, where the change of DSM is characterized by an ODE.
Peer Selection with Noisy Assessments
Venue: arXiv cs.GT 2021
Year: 2021

In the peer selection problem a group of agents must select a subset of themselves as winners for, e.g., peer-reviewed grants or prizes. Here, we take a Condorcet view of this aggregation problem, i.e., that there is a ground-truth ordering over the agents and we wish to select the best set of agents, subject to the noisy assessments of the peers. Given this model, some agents may be unreliable, while others might be self-interested, attempting to influence the outcome in their favour. In this paper we extend PeerNomination, the most accurate peer reviewing algorithm to date, into WeightedPeerNomination, which is able to handle noisy and inaccurate agents. To do this, we explicitly formulate assessors' reliability weights in a way that does not violate strategyproofness, and use this information to reweight their scores. We show analytically that a weighting scheme can improve the overall accuracy of the selection significantly. Finally, we implement several instances of reweighting methods and show empirically that our methods are robust in the face of noisy assessments.
Secure Random Sampling in Differential Privacy
Venue: arXiv cs.CR 2021
Year: 2021

Differential privacy is among the most prominent techniques for preserving privacy of sensitive data, oweing to its robust mathematical guarantees and general applicability to a vast array of computations on data, including statistical analysis and machine learning. Previous work demonstrated that concrete implementations of differential privacy mechanisms are vulnerable to statistical attacks. This vulnerability is caused by the approximation of real values to floating point numbers. This paper presents a practical solution to the finite-precision floating point vulnerability, where the inverse transform sampling of the Laplace distribution can itself be inverted, thus enabling an attack where the original value can be retrieved with non-negligible advantage. The proposed solution has the advantages of being generalisable to any infinitely divisible probability distribution, and of simple implementation in modern architectures. Finally, the solution has been designed to make side channel attack infeasible, because of inherently exponential, in the size of the domain, brute force attacks.
JEFL: Joint Embedding of Formal Proof Libraries
Venue: arXiv cs.LO 2021
Year: 2021

The heterogeneous nature of the logical foundations used in different interactive proof assistant libraries has rendered discovery of similar mathematical concepts among them difficult. In this paper, we compare a previously proposed algorithm for matching concepts across libraries with our unsupervised embedding approach that can help us retrieve similar concepts. Our approach is based on the fasttext implementation of Word2Vec, on top of which a tree traversal module is added to adapt its algorithm to the representation format of our data export pipeline. We compare the explainability, customizability, and online-servability of the approaches and argue that the neural embedding approach has more potential to be integrated into an interactive proof assistant.
Multi-institution encrypted medical imaging AI validation without data sharing
Venue: arXiv cs.CR 2021
Year: 2021

Adoption of artificial intelligence medical imaging applications is often impeded by barriers between healthcare systems and algorithm developers given that access to both private patient data and commercial model IP is important to perform pre-deployment evaluation. This work investigates a framework for secure, privacy-preserving and AI-enabled medical imaging inference using CrypTFlow2, a state-of-the-art end-to-end compiler allowing cryptographically secure 2-party Computation (2PC) protocols between the machine learning model vendor and target patient data owner. A common DenseNet-121 chest x-ray diagnosis model was evaluated on multi-institutional chest radiographic imaging datasets both with and without CrypTFlow2 on two test sets spanning seven sites across the US and India, and comprising 1,149 chest x-ray images. We measure comparative AUROC performance between secure and insecure inference in multiple pathology classification tasks, and explore model output distributional shifts and resource constraints introduced by secure model inference. Secure inference with CrypTFlow2 demonstrated no significant difference in AUROC for all diagnoses, and model outputs from secure and insecure inference methods were distributionally equivalent. The use of CrypTFlow2 may allow off-the-shelf secure 2PC between healthcare systems and AI model vendors for medical imaging, without changes in performance, and can facilitate scalable pre-deployment infrastructure for real-world secure model evaluation without exposure to patient data or model IP.
A low-overhead approach for self-sovereign identity in IoT
Venue: arXiv cs.CR 2021
Year: 2021

We present a low-overhead mechanism for self-sovereign identification and communication of IoT agents in constrained networks. Our main contribution is to enable native use of Decentralized Identifiers (DIDs) and DID-based secure communication on constrained networks, whereas previous works either did not consider the issue or relied on proxy-based architectures. We propose a new extension to DIDs along with a more concise serialization method for DID metadata. Moreover, in order to reduce the security overhead over transmitted messages, we adopted a binary message envelope. We implemented these proposals within the context of Swarm Computing, an approach for decentralized IoT. Results showed that our proposal reduces the size of identity metadata in almost four times and security overhead up to five times. We observed that both techniques are required to enable operation on constrained networks.