OpenRaiser/Intern-Atlas huggingface dataset:
BundleMoCap: Efficient, Robust and Smooth Motion Capture from Sparse Multiview Videos
Venue: arXiv cs.CV 2023
Year: 2023

Capturing smooth motions from videos using markerless techniques typically involves complex processes such as temporal constraints, multiple stages with data-driven regression and optimization, and bundle solving over temporal windows. These processes can be inefficient and require tuning multiple objectives across stages. In contrast, BundleMoCap introduces a novel and efficient approach to this problem. It solves the motion capture task in a single stage, eliminating the need for temporal smoothness objectives while still delivering smooth motions. BundleMoCap outperforms the state-of-the-art without increasing complexity. The key concept behind BundleMoCap is manifold interpolation between latent keyframes. By relying on a local manifold smoothness assumption, we can efficiently solve a bundle of frames using a single code. Additionally, the method can be implemented as a sliding window optimization and requires only the first frame to be properly initialized, reducing the overall computational burden. BundleMoCap's strength lies in its ability to achieve high-quality motion capture results with simplicity and efficiency. More details can be found at https://moverseai.github.io/bundle/.
Fair Text Classification with Wasserstein Independence
Venue: arXiv cs.CL 2023
Year: 2023

Group fairness is a central research topic in text classification, where reaching fair treatment between sensitive groups (e.g. women vs. men) remains an open challenge. This paper presents a novel method for mitigating biases in neural text classification, agnostic to the model architecture. Considering the difficulty to distinguish fair from unfair information in a text encoder, we take inspiration from adversarial training to induce Wasserstein independence between representations learned to predict our target label and the ones learned to predict some sensitive attribute. Our approach provides two significant advantages. Firstly, it does not require annotations of sensitive attributes in both testing and training data. This is more suitable for real-life scenarios compared to existing methods that require annotations of sensitive attributes at train time. Second, our approach exhibits a comparable or better fairness-accuracy trade-off compared to existing methods.
Cascade Learning Localises Discriminant Features in Visual Scene Classification
Venue: arXiv cs.CV 2023
Year: 2023

Lack of interpretability of deep convolutional neural networks (DCNN) is a well-known problem particularly in the medical domain as clinicians want trustworthy automated decisions. One way to improve trust is to demonstrate the localisation of feature representations with respect to expert labeled regions of interest. In this work, we investigate the localisation of features learned via two varied learning paradigms and demonstrate the superiority of one learning approach with respect to localisation. Our analysis on medical and natural datasets show that the traditional end-to-end (E2E) learning strategy has a limited ability to localise discriminative features across multiple network layers. We show that a layer-wise learning strategy, namely cascade learning (CL), results in more localised features. Considering localisation accuracy, we not only show that CL outperforms E2E but that it is a promising method of predicting regions. On the YOLO object detection framework, our best result shows that CL outperforms the E2E scheme by $2\%$ in mAP.
Attacking Motion Planners Using Adversarial Perception Errors
Venue: arXiv cs.RO 2023
Year: 2023

Autonomous driving (AD) systems are often built and tested in a modular fashion, where the performance of different modules is measured using task-specific metrics. These metrics should be chosen so as to capture the downstream impact of each module and the performance of the system as a whole. For example, high perception quality should enable prediction and planning to be performed safely. Even though this is true in general, we show here that it is possible to construct planner inputs that score very highly on various perception quality metrics but still lead to planning failures. In an analogy to adversarial attacks on image classifiers, we call such inputs \textbf{adversarial perception errors} and show they can be systematically constructed using a simple boundary-attack algorithm. We demonstrate the effectiveness of this algorithm by finding attacks for two different black-box planners in several urban and highway driving scenarios using the CARLA simulator. Finally, we analyse the properties of these attacks and show that they are isolated in the input space of the planner, and discuss their implications for AD system deployment and testing.
Exploring Graph Classification Techniques Under Low Data Constraints: A Comprehensive Study
Venue: arXiv cs.LG 2023
Year: 2023

This survey paper presents a brief overview of recent research on graph data augmentation and few-shot learning. It covers various techniques for graph data augmentation, including node and edge perturbation, graph coarsening, and graph generation, as well as the latest developments in few-shot learning, such as meta-learning and model-agnostic meta-learning. The paper explores these areas in depth and delves into further sub classifications. Rule based approaches and learning based approaches are surveyed under graph augmentation techniques. Few-Shot Learning on graphs is also studied in terms of metric learning techniques and optimization-based techniques. In all, this paper provides an extensive array of techniques that can be employed in solving graph processing problems faced in low-data scenarios.
Learn to Augment Network Simulators Towards Digital Network Twins
Venue: arXiv cs.NI 2023
Year: 2023

Digital network twin (DNT) is a promising paradigm to replicate real-world cellular networks toward continual assessment, proactive management, and what-if analysis. Existing discussions have been focusing on using only deep learning techniques to build DNTs, which raises widespread concerns regarding their generalization, explainability, and transparency. In this paper, we explore an alternative approach to augment network simulators with context-aware neural agents. The main challenge lies in the non-trivial simulation-to-reality (sim-to-real) discrepancy between offline simulators and real-world networks. To solve the challenge, we propose a new learn-to-bridge algorithm to cost-efficiently bridge the sim-to-real discrepancy in two alternative stages. In the first stage, we select states to query performances in real-world networks by using newly-designed cost-aware Bayesian optimization. In the second stage, we train the neural agent to learn the state context and bridge the probabilistic discrepancy based on Bayesian neural networks (BNN). In addition, we build a small-scale end-to-end network testbed based on OpenAirInterface RAN and Core with USRP B210 and a smartphone, and replicate the network in NS-3. The evaluation results show that, our proposed solution substantially outperforms existing methods, with more than 92\% reduction in the sim-to-real discrepancy.
An Improved Line-Point Low-Degree Test
Venue: arXiv cs.CC 2023
Year: 2023

We prove that the most natural low-degree test for polynomials over finite fields is ``robust'' in the high-error regime for linear-sized fields. Specifically we consider the ``local'' agreement of a function $f: \mathbb{F}_q^m \to \mathbb{F}_q$ from the space of degree-$d$ polynomials, i.e., the expected agreement of the function from univariate degree-$d$ polynomials over a randomly chosen line in $\mathbb{F}_q^m$, and prove that if this local agreement is $ε\geq Ω((\frac{d}{q})^τ))$ for some fixed $τ> 0$, then there is a global degree-$d$ polynomial $Q: \mathbb{F}_q^m \to \mathbb{F}_q$ with agreement nearly $ε$ with $f$. This settles a long-standing open question in the area of low-degree testing, yielding an $O(d)$-query robust test in the ``high-error'' regime (i.e., when $ε< \frac{1}{2}$). The previous results in this space either required $ε> \frac{1}{2}$ (Polishchuk \& Spielman, STOC 1994), or $q = Ω(d^4)$ (Arora \& Sudan, Combinatorica 2003), or needed to measure local distance on $2$-dimensional ``planes'' rather than one-dimensional lines leading to $Ω(d^2)$-query complexity (Raz \& Safra, STOC 1997). Our analysis follows the spirit of most previous analyses in first analyzing the low-variable case ($m = O(1)$) and then ``bootstrapping'' to general multivariate settings. Our main technical novelty is a new analysis in the bivariate setting that exploits a previously known connection between multivariate factorization and finding (or testing) low-degree polynomials, in a non ``black-box'' manner. A second contribution is a bootstrapping analysis which manages to lift analyses for $m=2$ directly to analyses for general $m$, where previous works needed to work with $m = 3$ or $m = 4$ -- arguably this bootstrapping is significantly simpler than those in prior works.
Iris Presentation Attack: Assessing the Impact of Combining Vanadium Dioxide Films with Artificial Eyes
Venue: arXiv cs.CV 2023
Year: 2023

Iris recognition systems, operating in the near infrared spectrum (NIR), have demonstrated vulnerability to presentation attacks, where an adversary uses artifacts such as cosmetic contact lenses, artificial eyes or printed iris images in order to circumvent the system. At the same time, a number of effective presentation attack detection (PAD) methods have been developed. These methods have demonstrated success in detecting artificial eyes (e.g., fake Van Dyke eyes) as presentation attacks. In this work, we seek to alter the optical characteristics of artificial eyes by affixing Vanadium Dioxide (VO2) films on their surface in various spatial configurations. VO2 films can be used to selectively transmit NIR light and can, therefore, be used to regulate the amount of NIR light from the object that is captured by the iris sensor. We study the impact of such images produced by the sensor on two state-of-the-art iris PA detection methods. We observe that the addition of VO2 films on the surface of artificial eyes can cause the PA detection methods to misclassify them as bonafide eyes in some cases. This represents a vulnerability that must be systematically analyzed and effectively addressed.
Calibration System and Algorithm Design for a Soft Hinged Micro Scanning Mirror with a Triaxial Hall Effect Sensor
Venue: arXiv cs.RO 2023
Year: 2023

Micro scanning mirrors (MSM) extend the range and field of view of LiDARs, medical imaging devices, and laser projectors. However, a new class of soft-hinged MSMs contains out-of-plane translation in addition to the 2 degree-of-freedom rotations, which presents a cabliration challenge. We report a new calibration system and algorithm design to address the challenge. In the calibration system, a new low-cost calibration rig design employs a minimal 2-laser beam approach. The new new algorithm builds on the reflection principle and an optimization approach to precisely measure MSM poses. To establish the mapping between Hall sensor readings and MSM poses, we propose a self-synchronizing periodicity-based model fitting calibration approach. We achieve an MSM poses estimation accuracy of 0.020° with a standard deviation of 0.011°.
Prompting Frameworks for Large Language Models: A Survey
Venue: arXiv cs.SE 2023
Year: 2023

Since the launch of ChatGPT, a powerful AI Chatbot developed by OpenAI, large language models (LLMs) have made significant advancements in both academia and industry, bringing about a fundamental engineering paradigm shift in many areas. While LLMs are powerful, it is also crucial to best use their power where "prompt'' plays a core role. However, the booming LLMs themselves, including excellent APIs like ChatGPT, have several inherent limitations: 1) temporal lag of training data, and 2) the lack of physical capabilities to perform external actions. Recently, we have observed the trend of utilizing prompt-based tools to better utilize the power of LLMs for downstream tasks, but a lack of systematic literature and standardized terminology, partly due to the rapid evolution of this field. Therefore, in this work, we survey related prompting tools and promote the concept of the "Prompting Framework" (PF), i.e. the framework for managing, simplifying, and facilitating interaction with large language models. We define the lifecycle of the PF as a hierarchical structure, from bottom to top, namely: Data Level, Base Level, Execute Level, and Service Level. We also systematically depict the overall landscape of the emerging PF field and discuss potential future research and challenges. To continuously track the developments in this area, we maintain a repository at https://github.com/lxx0628/Prompting-Framework-Survey, which can be a useful resource sharing platform for both academic and industry in this field.
Intrinsic Image Decomposition via Ordinal Shading
Venue: arXiv cs.CV 2023
Year: 2023

Intrinsic decomposition is a fundamental mid-level vision problem that plays a crucial role in various inverse rendering and computational photography pipelines. Generating highly accurate intrinsic decompositions is an inherently under-constrained task that requires precisely estimating continuous-valued shading and albedo. In this work, we achieve high-resolution intrinsic decomposition by breaking the problem into two parts. First, we present a dense ordinal shading formulation using a shift- and scale-invariant loss in order to estimate ordinal shading cues without restricting the predictions to obey the intrinsic model. We then combine low- and high-resolution ordinal estimations using a second network to generate a shading estimate with both global coherency and local details. We encourage the model to learn an accurate decomposition by computing losses on the estimated shading as well as the albedo implied by the intrinsic model. We develop a straightforward method for generating dense pseudo ground truth using our model's predictions and multi-illumination data, enabling generalization to in-the-wild imagery. We present an exhaustive qualitative and quantitative analysis of our predicted intrinsic components against state-of-the-art methods. Finally, we demonstrate the real-world applicability of our estimations by performing otherwise difficult editing tasks such as recoloring and relighting.
A Fine-Grained Image Description Generation Method Based on Joint Objectives
Venue: arXiv cs.CV 2023
Year: 2023

The goal of fine-grained image description generation techniques is to learn detailed information from images and simulate human-like descriptions that provide coherent and comprehensive textual details about the image content. Currently, most of these methods face two main challenges: description repetition and omission. Moreover, the existing evaluation metrics cannot clearly reflect the performance of models on these two issues. To address these challenges, we propose an innovative Fine-grained Image Description Generation model based on Joint Objectives. Furthermore, we introduce new object-based evaluation metrics to more intuitively assess the model's performance in handling description repetition and omission. This novel approach combines visual features at both the image level and object level to maximize their advantages and incorporates an object penalty mechanism to reduce description repetition. Experimental results demonstrate that our proposed method significantly improves the CIDEr evaluation metric, indicating its excellent performance in addressing description repetition and omission issues.
End-to-end Phase Field Model Discovery Combining Experimentation, Crowdsourcing, Simulation and Learning
Venue: arXiv cs.CV 2023
Year: 2023

The availability of tera-byte scale experiment data calls for AI driven approaches which automatically discover scientific models from data. Nonetheless, significant challenges present in AI-driven scientific discovery: (i) The annotation of large scale datasets requires fundamental re-thinking in developing scalable crowdsourcing tools. (ii) The learning of scientific models from data calls for innovations beyond black-box neural nets. (iii) Novel visualization and diagnosis tools are needed for the collaboration of experimental and theoretical physicists, and computer scientists. We present Phase-Field-Lab platform for end-to-end phase field model discovery, which automatically discovers phase field physics models from experiment data, integrating experimentation, crowdsourcing, simulation and learning. Phase-Field-Lab combines (i) a streamlined annotation tool which reduces the annotation time (by ~50-75%), while increasing annotation accuracy compared to baseline; (ii) an end-to-end neural model which automatically learns phase field models from data by embedding phase field simulation and existing domain knowledge into learning; and (iii) novel interfaces and visualizations to integrate our platform into the scientific discovery cycle of domain scientists. Our platform is deployed in the analysis of nano-structure evolution in materials under extreme conditions (high temperature and irradiation). Our approach reveals new properties of nano-void defects, which otherwise cannot be detected via manual analysis.
Towards the generation of synchronized and believable non-verbal facial behaviors of a talking virtual agent
Venue: arXiv cs.CV 2023
Year: 2023

This paper introduces a new model to generate rhythmically relevant non-verbal facial behaviors for virtual agents while they speak. The model demonstrates perceived performance comparable to behaviors directly extracted from the data and replayed on a virtual agent, in terms of synchronization with speech and believability. Interestingly, we found that training the model with two different sets of data, instead of one, did not necessarily improve its performance. The expressiveness of the people in the dataset and the shooting conditions are key elements. We also show that employing an adversarial model, in which fabricated fake examples are introduced during the training phase, increases the perception of synchronization with speech. A collection of videos demonstrating the results and code can be accessed at: https://github.com/aldelb/non_verbal_facial_animation.
DeepCompass: AI-driven Location-Orientation Synchronization for Navigating Platforms
Venue: arXiv cs.CV 2023
Year: 2023

In current navigating platforms, the user's orientation is typically estimated based on the difference between two consecutive locations. In other words, the orientation cannot be identified until the second location is taken. This asynchronous location-orientation identification often leads to our real-life question: Why does my navigator tell the wrong direction of my car at the beginning? We propose DeepCompass to identify the user's orientation by bridging the gap between the street-view and the user-view images. First, we explore suitable model architectures and design corresponding input configuration. Second, we demonstrate artificial transformation techniques (e.g., style transfer and road segmentation) to minimize the disparity between the street-view and the user's real-time experience. We evaluate DeepCompass with extensive evaluation in various driving conditions. DeepCompass does not require additional hardware and is also not susceptible to external interference, in contrast to magnetometer-based navigator. This highlights the potential of DeepCompass as an add-on to existing sensor-based orientation detection methods.
Combining low-dose CT-based radiomics and metabolomics for early lung cancer screening support
Venue: arXiv cs.CV 2023
Year: 2023

Due to its predominantly asymptomatic or mildly symptomatic progression, lung cancer is often diagnosed in advanced stages, resulting in poorer survival rates for patients. As with other cancers, early detection significantly improves the chances of successful treatment. Early diagnosis can be facilitated through screening programs designed to detect lung tissue tumors when they are still small, typically around 3mm in size. However, the analysis of extensive screening program data is hampered by limited access to medical experts. In this study, we developed a procedure for identifying potential malignant neoplastic lesions within lung parenchyma. The system leverages machine learning (ML) techniques applied to two types of measurements: low-dose Computed Tomography-based radiomics and metabolomics. Using data from two Polish screening programs, two ML algorithms were tested, along with various integration methods, to create a final model that combines both modalities to support lung cancer screening.
Personalized Guidelines for Design, Implementation and Evaluation of Anti-phishing Interventions
Venue: arXiv cs.CR 2023
Year: 2023

Background: Current anti-phishing interventions, which typically involve one-size-fits-all solutions, suffer from limitations such as inadequate usability and poor implementation. Human-centric challenges in anti-phishing technologies remain little understood. Research shows a deficiency in the comprehension of end-user preferences, mental states, and cognitive requirements by developers and practitioners involved in the design, implementation, and evaluation of anti-phishing interventions. Aims: This study addresses the current lack of resources and guidelines for the design, implementation and evaluation of anti-phishing interventions, by presenting personalized guidelines to the developers and practitioners. Method: Through an analysis of 53 academic studies and 16 items of grey literature studies, we systematically identified the challenges and recommendations within the anti-phishing interventions, across different practitioner groups and intervention types. Results: We identified 22 dominant factors at the individual, technical, and organizational levels, that affected the effectiveness of anti-phishing interventions and, accordingly, reported 41 guidelines based on the suggestions and recommendations provided in the studies to improve the outcome of anti-phishing interventions. Conclusions: Our dominant factors can help developers and practitioners enhance their understanding of human-centric, technical and organizational issues in anti-phishing interventions. Our customized guidelines can empower developers and practitioners to counteract phishing attacks.
HPC-GPT: Integrating Large Language Model for High-Performance Computing
Venue: arXiv cs.DC 2023
Year: 2023

Large Language Models (LLMs), including the LLaMA model, have exhibited their efficacy across various general-domain natural language processing (NLP) tasks. However, their performance in high-performance computing (HPC) domain tasks has been less than optimal due to the specialized expertise required to interpret the model responses. In response to this challenge, we propose HPC-GPT, a novel LLaMA-based model that has been supervised fine-tuning using generated QA (Question-Answer) instances for the HPC domain. To evaluate its effectiveness, we concentrate on two HPC tasks: managing AI models and datasets for HPC, and data race detection. By employing HPC-GPT, we demonstrate comparable performance with existing methods on both tasks, exemplifying its excellence in HPC-related scenarios. Our experiments on open-source benchmarks yield extensive results, underscoring HPC-GPT's potential to bridge the performance gap between LLMs and HPC-specific tasks. With HPC-GPT, we aim to pave the way for LLMs to excel in HPC domains, simplifying the utilization of language models in complex computing applications.