OpenRaiser/Intern-Atlas huggingface dataset:
Data-Adaptive Dimensional Analysis for Accurate Interpolation and Extrapolation in Computer Experiments
Venue: arXiv cs.LG 2023
Year: 2023

Dimensional analysis (DA) pays attention to fundamental physical dimensions such as length and mass when modelling scientific and engineering systems. It goes back at least a century to Buckingham's Pi theorem, which characterizes a scientifically meaningful model in terms of a limited number of dimensionless variables. The methodology has only been exploited relatively recently by statisticians for design and analysis of experiments, however, and computer experiments in particular. The basic idea is to build models in terms of new dimensionless quantities derived from the original input and output variables. A scientifically valid formulation has the potential for improved prediction accuracy in principle, but the implementation of DA is far from straightforward. There can be a combinatorial number of possible models satisfying the conditions of the theory. Empirical approaches for finding effective derived variables will be described, and improvements in prediction accuracy will be demonstrated. As DA's dimensionless quantities for a statistical model typically compare the original variables rather than use their absolute magnitudes, DA is less dependent on the choice of experimental ranges in the training data. Hence, we are also able to illustrate sustained accuracy gains even when extrapolating substantially outside the training data.
Enhancing Cognitive Diagnosis using Un-interacted Exercises: A Collaboration-aware Mixed Sampling Approach
Venue: arXiv cs.CY 2023
Year: 2023

Cognitive diagnosis is a crucial task in computational education, aimed at evaluating students' proficiency levels across various knowledge concepts through exercises. Current models, however, primarily rely on students' answered exercises, neglecting the complex and rich information contained in un-interacted exercises. While recent research has attempted to leverage the data within un-interacted exercises linked to interacted knowledge concepts, aiming to address the long-tail issue, these studies fail to fully explore the informative, un-interacted exercises related to broader knowledge concepts. This oversight results in diminished performance when these models are applied to comprehensive datasets. In response to this gap, we present the Collaborative-aware Mixed Exercise Sampling (CMES) framework, which can effectively exploit the information present in un-interacted exercises linked to un-interacted knowledge concepts. Specifically, we introduce a novel universal sampling module where the training samples comprise not merely raw data slices, but enhanced samples generated by combining weight-enhanced attention mixture techniques. Given the necessity of real response labels in cognitive diagnosis, we also propose a ranking-based pseudo feedback module to regulate students' responses on generated exercises. The versatility of the CMES framework bolsters existing models and improves their adaptability. Finally, we demonstrate the effectiveness and interpretability of our framework through comprehensive experiments on real-world datasets.
Review of Unsupervised POS Tagging and Its Implications on Language Acquisition
Venue: arXiv cs.CL 2023
Year: 2023

An ability that underlies human syntactic knowledge is determining which words can appear in the similar structures (i.e. grouping words by their syntactic categories). These groupings enable humans to combine structures in order to communicate complex meanings. A foundational question is how do children acquire this ability underlying syntactic knowledge. In exploring this process, we will review various engineering approaches whose goal is similar to that of a child's -- without prior syntactic knowledge, correctly identify the parts of speech (POS) of the words in a sample of text. In reviewing these unsupervised tagging efforts, we will discuss common themes that support the advances in the models and their relevance for language acquisition. For example, we discuss how each model judges success (evaluation metrics), the "additional information" that constrains the POS learning (such as orthographic information), and the context used to determine POS (only previous word, words before and after the target, etc). The identified themes pave the way for future investigations into the cognitive processes that underpin the acquisition of syntactic categories and provide a useful layout of current state of the art unsupervised POS tagging models.
3FM: Multi-modal Meta-learning for Federated Tasks
Venue: arXiv cs.LG 2023
Year: 2023

We present a novel approach in the domain of federated learning (FL), particularly focusing on addressing the challenges posed by modality heterogeneity, variability in modality availability across clients, and the prevalent issue of missing data. We introduce a meta-learning framework specifically designed for multimodal federated tasks. Our approach is motivated by the need to enable federated models to robustly adapt when exposed to new modalities, a common scenario in FL where clients often differ in the number of available modalities. The effectiveness of our proposed framework is demonstrated through extensive experimentation on an augmented MNIST dataset, enriched with audio and sign language data. We demonstrate that the proposed algorithm achieves better performance than the baseline on a subset of missing modality scenarios with careful tuning of the meta-learning rates. This is a shortened report, and our work will be extended and updated soon.
Video-based Surgical Skill Assessment using Tree-based Gaussian Process Classifier
Venue: arXiv cs.CV 2023
Year: 2023

This paper aims to present a novel pipeline for automated surgical skill assessment using video data and to showcase the effectiveness of the proposed approach in evaluating surgeon proficiency, its potential for targeted training interventions, and quality assurance in surgical departments. The pipeline incorporates a representation flow convolutional neural network and a novel tree-based Gaussian process classifier, which is robust to noise, while being computationally efficient. Additionally, new kernels are introduced to enhance accuracy. The performance of the pipeline is evaluated using the JIGSAWS dataset. Comparative analysis with existing literature reveals significant improvement in accuracy and betterment in computation cost. The proposed pipeline contributes to computational efficiency and accuracy improvement in surgical skill assessment using video data. Results of our study based on comments of our colleague surgeons show that the proposed method has the potential to facilitate skill improvement among surgery fellows and enhance patient safety through targeted training interventions and quality assurance in surgical departments.
Healthcare Policy Compliance: A Blockchain Smart Contract-Based Approach
Venue: arXiv cs.CR 2023
Year: 2023

This paper addresses the critical challenge of ensuring healthcare policy compliance in the context of Electronic Health Records (EHRs). Despite stringent regulations like HIPAA, significant gaps in policy compliance often remain undetected until a data breach occurs. To bridge this gap, we propose a novel blockchain-powered, smart contract-based access control model. This model is specifically designed to enforce patient-provider agreements (PPAs) and other relevant policies, thereby ensuring both policy compliance and provenance. Our approach integrates components of informed consent into PPAs, employing blockchain smart contracts to automate and secure policy enforcement. The authorization module utilizes these contracts to make informed access decisions, recording all actions in a transparent, immutable blockchain ledger. This system not only ensures that policies are rigorously applied but also maintains a verifiable record of all actions taken, thus facilitating an easy audit and proving compliance. We implement this model in a private Ethereum blockchain setup, focusing on maintaining the integrity and lineage of policies and ensuring that audit trails are accurately and securely recorded. The Proof of Compliance (PoC) consensus mechanism enables decentralized, independent auditor nodes to verify compliance status based on the audit trails recorded. Experimental evaluation demonstrates the effectiveness of the proposed model in a simulated healthcare environment. The results show that our approach not only strengthens policy compliance and provenance but also enhances the transparency and accountability of the entire process. In summary, this paper presents a comprehensive, blockchain-based solution to a longstanding problem in healthcare data management, offering a robust framework for ensuring policy compliance and provenance through smart contracts and blockchain technology.
Hypothesis Testing for Class-Conditional Noise Using Local Maximum Likelihood
Venue: arXiv cs.LG 2023
Year: 2023

In supervised learning, automatically assessing the quality of the labels before any learning takes place remains an open research question. In certain particular cases, hypothesis testing procedures have been proposed to assess whether a given instance-label dataset is contaminated with class-conditional label noise, as opposed to uniform label noise. The existing theory builds on the asymptotic properties of the Maximum Likelihood Estimate for parametric logistic regression. However, the parametric assumptions on top of which these approaches are constructed are often too strong and unrealistic in practice. To alleviate this problem, in this paper we propose an alternative path by showing how similar procedures can be followed when the underlying model is a product of Local Maximum Likelihood Estimation that leads to more flexible nonparametric logistic regression models, which in turn are less susceptible to model misspecification. This different view allows for wider applicability of the tests by offering users access to a richer model class. Similarly to existing works, we assume we have access to anchor points which are provided by the users. We introduce the necessary ingredients for the adaptation of the hypothesis tests to the case of nonparametric logistic regression and empirically compare against the parametric approach presenting both synthetic and real-world case studies and discussing the advantages and limitations of the proposed approach.
On selecting a fraction of leaves with disjoint neighborhoods in a plane tree
Venue: arXiv cs.CG 2023
Year: 2023

We present a generalization of a combinatorial result by Aggarwal, Guibas, Saxe and Shor [Discrete & Computational Geometry, 1989] on a linear-time algorithm that selects a constant fraction of leaves, with pairwise disjoint neighborhoods, from a binary tree embedded in the plane. This result of Aggarwal et al. is essential to the linear-time framework, which they also introduced, that computes certain Voronoi diagrams of points with a tree structure in linear time. An example is the diagram computed while updating the Voronoi diagram of points after deletion of one site. Our generalization allows that only a fraction of the tree leaves is considered, and it is motivated by linear-time Voronoi constructions for non-point sites. We are given a plane tree $T$ of $n$ leaves, $m$ of which have been marked, and each marked leaf is associated with a neighborhood (a subtree of $T$) such that any two topologically consecutive marked leaves have disjoint neighborhoods. We show how to select in linear time a constant fraction of the marked leaves having pairwise disjoint neighborhoods.
VoCopilot: Voice-Activated Tracking of Everyday Interactions
Venue: arXiv cs.SD 2023
Year: 2023

Voice plays an important role in our lives by facilitating communication, conveying emotions, and indicating health. Therefore, tracking vocal interactions can provide valuable insight into many aspects of our lives. This paper presents our ongoing efforts to design a new vocal tracking system we call VoCopilot. VoCopilot is an end-to-end system centered around an energy-efficient acoustic hardware and firmware combined with advanced machine learning models. As a result, VoCopilot is able to continuously track conversations, record them, transcribe them, and then extract useful insights from them. By utilizing large language models, VoCopilot ensures the user can extract useful insights from recorded interactions without having to learn complex machine learning techniques. In order to protect the privacy of end users, VoCopilot uses a novel wake-up mechanism that only records conversations of end users. Additionally, all the rest of pipeline can be run on a commodity computer (Mac Mini M2). In this work, we show the effectiveness of VoCopilot in real-world environment for two use cases.
User Authentication and Identity Inconsistency Detection via Mouse-trajectory Similarity Measurement
Venue: arXiv cs.CR 2023
Year: 2023

Completely Automated Public Turing Test To Tell Computers and Humans Apart (CAPTCHA) is a type of challenge-response test widely used in authentication systems. A well-known challenge it faces is the CAPTCHA farm, where workers are hired to solve CAPTCHAs manually. In this work, we propose to tackle this challenge from a novel perspective, converting CAPTCHA farm detection to identity inconsistency detection, which essentially becomes an authentication process. Specifically, we develop a novel embedding model, which measures the similarity between mouse trajectories collected during the session and when registering/solving CAPTCHA, to authenticate and detect identity inconsistency. Moreover, unlike most existing works that employ a separate mouse movement classifier for each individual user, which brings in considerable costs when serving a large number of users, our model performs detection tasks using only one classifier for all users, significantly reducing the cost. Experiment results validate the superiority of our method over the state-of-the-art time series classification methods, achieving 94.3% and 97.7% of AUC in identity and authentication inconsistency detection, respectively.
Active Reinforcement Learning for Robust Building Control
Venue: arXiv cs.LG 2023
Year: 2023

Reinforcement learning (RL) is a powerful tool for optimal control that has found great success in Atari games, the game of Go, robotic control, and building optimization. RL is also very brittle; agents often overfit to their training environment and fail to generalize to new settings. Unsupervised environment design (UED) has been proposed as a solution to this problem, in which the agent trains in environments that have been specially selected to help it learn. Previous UED algorithms focus on trying to train an RL agent that generalizes across a large distribution of environments. This is not necessarily desirable when we wish to prioritize performance in one environment over others. In this work, we will be examining the setting of robust RL building control, where we wish to train an RL agent that prioritizes performing well in normal weather while still being robust to extreme weather conditions. We demonstrate a novel UED algorithm, ActivePLR, that uses uncertainty-aware neural network architectures to generate new training environments at the limit of the RL agent's ability while being able to prioritize performance in a desired base environment. We show that ActivePLR is able to outperform state-of-the-art UED algorithms in minimizing energy usage while maximizing occupant comfort in the setting of building control.
Mapping Housing Stock Characteristics from Drone Images for Climate Resilience in the Caribbean
Venue: arXiv cs.CV 2023
Year: 2023

Comprehensive information on housing stock is crucial for climate adaptation initiatives aiming to reduce the adverse impacts of climate-extreme hazards in high-risk regions like the Caribbean. In this study, we propose a workflow for rapidly generating critical baseline housing stock data using very high-resolution drone images and deep learning techniques. Specifically, our work leverages the Segment Anything Model and convolutional neural networks for the automated generation of building footprints and roof classification maps. By strengthening local capacity within government agencies to leverage AI and Earth Observation-based solutions, this work seeks to improve the climate resilience of the housing sector in small island developing states in the Caribbean.
scBiGNN: Bilevel Graph Representation Learning for Cell Type Classification from Single-cell RNA Sequencing Data
Venue: arXiv cs.LG 2023
Year: 2023

Single-cell RNA sequencing (scRNA-seq) technology provides high-throughput gene expression data to study the cellular heterogeneity and dynamics of complex organisms. Graph neural networks (GNNs) have been widely used for automatic cell type classification, which is a fundamental problem to solve in scRNA-seq analysis. However, existing methods do not sufficiently exploit both gene-gene and cell-cell relationships, and thus the true potential of GNNs is not realized. In this work, we propose a bilevel graph representation learning method, named scBiGNN, to simultaneously mine the relationships at both gene and cell levels for more accurate single-cell classification. Specifically, scBiGNN comprises two GNN modules to identify cell types. A gene-level GNN is established to adaptively learn gene-gene interactions and cell representations via the self-attention mechanism, and a cell-level GNN builds on the cell-cell graph that is constructed from the cell representations generated by the gene-level GNN. To tackle the scalability issue for processing a large number of cells, scBiGNN adopts an Expectation Maximization (EM) framework in which the two modules are alternately trained via the E-step and M-step to learn from each other. Through this interaction, the gene- and cell-level structural information is integrated to gradually enhance the classification performance of both GNN modules. Experiments on benchmark datasets demonstrate that our scBiGNN outperforms a variety of existing methods for cell type classification from scRNA-seq data.
STELLAR: Siamese Multi-Headed Attention Neural Networks for Overcoming Temporal Variations and Device Heterogeneity with Indoor Localization
Venue: arXiv cs.LG 2023
Year: 2023

Smartphone-based indoor localization has emerged as a cost-effective and accurate solution to localize mobile and IoT devices indoors. However, the challenges of device heterogeneity and temporal variations have hindered its widespread adoption and accuracy. Towards jointly addressing these challenges comprehensively, we propose STELLAR, a novel framework implementing a contrastive learning approach that leverages a Siamese multi-headed attention neural network. STELLAR is the first solution that simultaneously tackles device heterogeneity and temporal variations in indoor localization, without the need for retraining the model (re-calibration-free). Our evaluations across diverse indoor environments show 8-75% improvements in accuracy compared to state-of-the-art techniques, to effectively address the device heterogeneity challenge. Moreover, STELLAR outperforms existing methods by 18-165% over 2 years of temporal variations, showcasing its robustness and adaptability.
Building AI and Human Capital for Road Safety
Venue: arXiv cs.HC 2023
Year: 2023

AI is about learning algorithms and huge amounts of data and are drivers of economic growth -- what does this mean for the field of development studies? Can we re-orient to twin AI studies and development theory and practice to generate how development challenges are identified and researched? To do this a good grasp is needed of AI internal mechanisms and outcomes in addressing development issues -- this argument will be developed through a case study of the ADAS [Advanced Driver Assistance System] deployment in India. Over and above discussing the ADAS we bring an anthropological lens to understand the social context that surrounds the system. Focusing on bus drivers, we offer findings from a qualitative and ethnographic study of drivers in a collaborative effort to achieve road safety by deploying AI-driven technology and empowering stakeholders in the transport industry in India especially, bus drivers as critical actors in the city transport network.
Perturbation-Invariant Adversarial Training for Neural Ranking Models: Improving the Effectiveness-Robustness Trade-Off
Venue: arXiv cs.IR 2023
Year: 2023

Neural ranking models (NRMs) have shown great success in information retrieval (IR). But their predictions can easily be manipulated using adversarial examples, which are crafted by adding imperceptible perturbations to legitimate documents. This vulnerability raises significant concerns about their reliability and hinders the widespread deployment of NRMs. By incorporating adversarial examples into training data, adversarial training has become the de facto defense approach to adversarial attacks against NRMs. However, this defense mechanism is subject to a trade-off between effectiveness and adversarial robustness. In this study, we establish theoretical guarantees regarding the effectiveness-robustness trade-off in NRMs. We decompose the robust ranking error into two components, i.e., a natural ranking error for effectiveness evaluation and a boundary ranking error for assessing adversarial robustness. Then, we define the perturbation invariance of a ranking model and prove it to be a differentiable upper bound on the boundary ranking error for attainable computation. Informed by our theoretical analysis, we design a novel \emph{perturbation-invariant adversarial training} (PIAT) method for ranking models to achieve a better effectiveness-robustness trade-off. We design a regularized surrogate loss, in which one term encourages the effectiveness to be maximized while the regularization term encourages the output to be smooth, so as to improve adversarial robustness. Experimental results on several ranking models demonstrate the superiority of PITA compared to existing adversarial defenses.
Controlling Data Access Load in Distributed Systems
Venue: arXiv cs.DC 2023
Year: 2023

Distributed systems store data objects redundantly to balance the data access load over multiple nodes. Load balancing performance depends mainly on 1) the level of storage redundancy and 2) the assignment of data objects to storage nodes. We analyze the performance implications of these design choices by considering four practical storage schemes that we refer to as clustering, cyclic, block and random design. We formulate the problem of load balancing as maintaining the load on any node below a given threshold. Regarding the level of redundancy, we find that the desired load balance can be achieved in a system of $n$ nodes only if the replication factor $d = Ω(\log(n)^{1/3})$, which is a necessary condition for any storage design. For clustering and cyclic designs, $d = Ω(\log(n))$ is necessary and sufficient. For block and random designs, $d = Ω(\log(n))$ is sufficient but unnecessary. Whether $d = Ω(\log(n)^{1/3})$ is sufficient remains open. The assignment of objects to nodes essentially determines which objects share the access capacity on each node. We refer to the number of nodes jointly shared by a set of objects as the \emph{overlap} between those objects. We find that many consistently slight overlaps between the objects (block, random) are better than few but occasionally significant overlaps (clustering, cyclic). However, when the demand is ''skewed beyond a level'' the impact of overlaps becomes the opposite. We derive our results by connecting the load-balancing problem to mathematical constructs that have been used to study other problems. For a class of storage designs containing the clustering and cyclic design, we express load balance in terms of the maximum of moving sums of i.i.d. random variables, which is known as the scan statistic. For random design, we express load balance by using the occupancy metric for random allocation with complexes.