OpenRaiser/Intern-Atlas huggingface dataset:
HyperMix: Out-of-Distribution Detection and Classification in Few-Shot Settings
Venue: arXiv cs.LG 2023
Year: 2023

Out-of-distribution (OOD) detection is an important topic for real-world machine learning systems, but settings with limited in-distribution samples have been underexplored. Such few-shot OOD settings are challenging, as models have scarce opportunities to learn the data distribution before being tasked with identifying OOD samples. Indeed, we demonstrate that recent state-of-the-art OOD methods fail to outperform simple baselines in the few-shot setting. We thus propose a hypernetwork framework called HyperMix, using Mixup on the generated classifier parameters, as well as a natural out-of-episode outlier exposure technique that does not require an additional outlier dataset. We conduct experiments on CIFAR-FS and MiniImageNet, significantly outperforming other OOD methods in the few-shot regime.
Two Steps Forward and One Step Back: The Right to Opt-out of Sale under CPRA
Venue: arXiv cs.CY 2023
Year: 2023

The California Privacy Rights Act (CPRA) was a ballot initiative that revised the California Consumer Privacy Act (CCPA). Although often framed as expanding and enhancing privacy rights, a close analysis of textual revisions -- both changes from the earlier law and changes from earlier drafts of the CPRA guidelines -- suggest that the reality might be more nuanced. In this work, we identify three textual revisions that have potential to negatively impact the right to opt-out of sale under CPRA and evaluate the effect of these textual revisions using (1) a large-scale longitudinal measurement study of 25,000 websites over twelve months and (2) an experimental user study with 775 participants recruited through Prolific. We find that all revisions negatively impacted the usability, scope, and visibility of the right to opt-out of sale. Our results provide the first comprehensive evaluation of the impact of CPRA on Internet privacy. They also emphasize the importance of continued evaluation of legal requirements as guidelines and case law evolve after a law goes into effect.
UAS-based Automated Structural Inspection Path Planning via Visual Data Analytics and Optimization
Venue: arXiv cs.RO 2023
Year: 2023

Unmanned Aerial Systems (UAS) have gained significant traction for their application in infrastructure inspections. However, considering the enormous scale and complex nature of infrastructure, automation is essential for improving the efficiency and quality of inspection operations. One of the core problems in this regard is electing an optimal automated flight path that can achieve the mission objectives while minimizing flight time. This paper presents an effective formulation for the path planning problem in the context of structural inspections. Coverage is guaranteed as a constraint to ensure damage detectability and path length is minimized as an objective, thus maximizing efficiency while ensuring inspection quality. A two-stage algorithm is then devised to solve the path planning problem, composed of a genetic algorithm for determining the positions of viewpoints and a greedy algorithm for calculating the poses. A comprehensive sensitivity analysis is conducted to demonstrate the proposed algorithm's effectiveness and range of applicability. Applied examples of the algorithm, including partial space inspection with no-fly zones and focused inspection, are also presented, demonstrating the flexibility of the proposed method to meet real-world structural inspection requirements. In conclusion, the results of this study highlight the feasibility of the proposed approach and establish the groundwork for incorporating automation into UAS-based structural inspection mission planning.
Personalized Federated Learning with Attention-based Client Selection
Venue: arXiv cs.LG 2023
Year: 2023

Personalized Federated Learning (PFL) relies on collective data knowledge to build customized models. However, non-IID data between clients poses significant challenges, as collaborating with clients who have diverse data distributions can harm local model performance, especially with limited training data. To address this issue, we propose FedACS, a new PFL algorithm with an Attention-based Client Selection mechanism. FedACS integrates an attention mechanism to enhance collaboration among clients with similar data distributions and mitigate the data scarcity issue. It prioritizes and allocates resources based on data similarity. We further establish the theoretical convergence behavior of FedACS. Experiments on CIFAR10 and FMNIST validate FedACS's superiority, showcasing its potential to advance personalized federated learning. By tackling non-IID data challenges and data scarcity, FedACS offers promising advances in the field of personalized federated learning.
Multilingual Bias Detection and Mitigation for Indian Languages
Venue: arXiv cs.CL 2023
Year: 2023

Lack of diverse perspectives causes neutrality bias in Wikipedia content leading to millions of worldwide readers getting exposed by potentially inaccurate information. Hence, neutrality bias detection and mitigation is a critical problem. Although previous studies have proposed effective solutions for English, no work exists for Indian languages. First, we contribute two large datasets, mWikiBias and mWNC, covering 8 languages, for the bias detection and mitigation tasks respectively. Next, we investigate the effectiveness of popular multilingual Transformer-based models for the two tasks by modeling detection as a binary classification problem and mitigation as a style transfer problem. We make the code and data publicly available.
ZO-AdaMU Optimizer: Adapting Perturbation by the Momentum and Uncertainty in Zeroth-order Optimization
Venue: arXiv cs.LG 2023
Year: 2023

Lowering the memory requirement in full-parameter training on large models has become a hot research area. MeZO fine-tunes the large language models (LLMs) by just forward passes in a zeroth-order SGD optimizer (ZO-SGD), demonstrating excellent performance with the same GPU memory usage as inference. However, the simulated perturbation stochastic approximation for gradient estimate in MeZO leads to severe oscillations and incurs a substantial time overhead. Moreover, without momentum regularization, MeZO shows severe over-fitting problems. Lastly, the perturbation-irrelevant momentum on ZO-SGD does not improve the convergence rate. This study proposes ZO-AdaMU to resolve the above problems by adapting the simulated perturbation with momentum in its stochastic approximation. Unlike existing adaptive momentum methods, we relocate momentum on simulated perturbation in stochastic gradient approximation. Our convergence analysis and experiments prove this is a better way to improve convergence stability and rate in ZO-SGD. Extensive experiments demonstrate that ZO-AdaMU yields better generalization for LLMs fine-tuning across various NLP tasks than MeZO and its momentum variants.
Personalized Federated Learning with Contextual Modulation and Meta-Learning
Venue: arXiv cs.LG 2023
Year: 2023

Federated learning has emerged as a promising approach for training machine learning models on decentralized data sources while preserving data privacy. However, challenges such as communication bottlenecks, heterogeneity of client devices, and non-i.i.d. data distribution pose significant obstacles to achieving optimal model performance. We propose a novel framework that combines federated learning with meta-learning techniques to enhance both efficiency and generalization capabilities. Our approach introduces a federated modulator that learns contextual information from data batches and uses this knowledge to generate modulation parameters. These parameters dynamically adjust the activations of a base model, which operates using a MAML-based approach for model personalization. Experimental results across diverse datasets highlight the improvements in convergence speed and model performance compared to existing federated learning approaches. These findings highlight the potential of incorporating contextual information and meta-learning techniques into federated learning, paving the way for advancements in distributed machine learning paradigms.
Revealing Shadows: Low-Light Image Enhancement Using Self-Calibrated Illumination
Venue: arXiv cs.CV 2023
Year: 2023

In digital imaging, enhancing visual content in poorly lit environments is a significant challenge, as images often suffer from inadequate brightness, hidden details, and an overall reduction in quality. This issue is especially critical in applications like nighttime surveillance, astrophotography, and low-light videography, where clear and detailed visual information is crucial. Our research addresses this problem by enhancing the illumination aspect of dark images. We have advanced past techniques by using varied color spaces to extract the illumination component, enhance it, and then recombine it with the other components of the image. By employing the Self-Calibrated Illumination (SCI) method, a strategy initially developed for RGB images, we effectively intensify and clarify details that are typically lost in low-light conditions. This method of selective illumination enhancement leaves the color information intact, thus preserving the color integrity of the image. Crucially, our method eliminates the need for paired images, making it suitable for situations where they are unavailable. Implementing the modified SCI technique represents a substantial shift from traditional methods, providing a refined and potent solution for low-light image enhancement. Our approach sets the stage for more complex image processing techniques and extends the range of possible real-world applications where accurate color representation and improved visibility are essential.
Enhancing Code Intelligence Tasks with ChatGPT
Venue: arXiv cs.SE 2023
Year: 2023

Pre-trained code models have emerged as crucial tools in various code intelligence tasks. However, their effectiveness depends on the quality of the pre-training dataset, particularly the human reference comments, which serve as a bridge between the programming language and natural language. One significant challenge is that such comments can become inconsistent with the corresponding code as the software evolves. This discrepancy can lead to suboptimal training of the models, decreasing their performances. LLMs have demonstrated superior capabilities in generating high-quality code comments. In light of that, we try to tackle the quality issue of the dataset by harnessing the power of LLMs. Specifically, we raise the question: Can we rebuild the pre-training dataset by substituting the original comments with LLM-generated ones for more effective pre-trained code models? To answer the question, we first conduct a comprehensive evaluation to compare ChatGPT-generated comments with human reference comments. As existing reference-based metrics treat the reference comments as gold standards, we introduce two auxiliary tasks as novel reference-free metrics to assess the quality of comments, i.e., code-comment inconsistency detection and code search. Experimental results show that ChatGPT-generated comments demonstrate superior semantic consistency with the code compared to human references, indicating the potential of utilizing ChatGPT to enhance the quality of the pre-training dataset. We rebuilt the widely used dataset, CodeSearchNet, with ChatGPT-generated comments. Subsequent experiments involve re-pre-training the CodeT5 with our refined dataset.Evaluation results on four generation tasks and one understanding code intelligence tasks show that the model pre-trained by ChatGPT-enhanced data outperforms its counterpart on code summarization, code generation, and code translation tasks.
Sensing-Enhanced Secure Communication: Joint Time Allocation and Beamforming Design
Venue: arXiv cs.IT 2023
Year: 2023

The integration of sensing and communication enables wireless communication systems to serve environment-aware applications. In this paper, we propose to leverage sensing to enhance physical layer security (PLS) in multiuser communication systems in the presence of a suspicious target. To this end, we develop a two-phase framework to first estimate the location of the potential eavesdropper by sensing and then utilize the estimated information to enhance PLS for communication. In particular, in the first phase, a dual-functional radar and communication (DFRC) base station (BS) exploits a sensing signal to mitigate the sensing information uncertainty of the potential eavesdropper. Then, in the second phase, to facilitate joint sensing and secure communication, the DFRC BS employs beamforming and artificial noise to enhance secure communication. The design objective is to maximize the system sum rate while alleviating the information leakage by jointly optimizing the time allocation and beamforming policy. Capitalizing on monotonic optimization theory, we develop a two-layer globally optimal algorithm to reveal the performance upper bound of the considered system. Simulation results show that the proposed scheme achieves a significant sum rate gain over two baseline schemes that adopt existing techniques. Moreover, our results unveil that ISAC is a promising paradigm for enhancing secure communication in wireless networks.
NoPose-NeuS: Jointly Optimizing Camera Poses with Neural Implicit Surfaces for Multi-view Reconstruction
Venue: arXiv cs.CV 2023
Year: 2023

Learning neural implicit surfaces from volume rendering has become popular for multi-view reconstruction. Neural surface reconstruction approaches can recover complex 3D geometry that are difficult for classical Multi-view Stereo (MVS) approaches, such as non-Lambertian surfaces and thin structures. However, one key assumption for these methods is knowing accurate camera parameters for the input multi-view images, which are not always available. In this paper, we present NoPose-NeuS, a neural implicit surface reconstruction method that extends NeuS to jointly optimize camera poses with the geometry and color networks. We encode the camera poses as a multi-layer perceptron (MLP) and introduce two additional losses, which are multi-view feature consistency and rendered depth losses, to constrain the learned geometry for better estimated camera poses and scene surfaces. Extensive experiments on the DTU dataset show that the proposed method can estimate relatively accurate camera poses, while maintaining a high surface reconstruction quality with 0.89 mean Chamfer distance.
Measuring Value Alignment
Venue: arXiv cs.AI 2023
Year: 2023

As artificial intelligence (AI) systems become increasingly integrated into various domains, ensuring that they align with human values becomes critical. This paper introduces a novel formalism to quantify the alignment between AI systems and human values, using Markov Decision Processes (MDPs) as the foundational model. We delve into the concept of values as desirable goals tied to actions and norms as behavioral guidelines, aiming to shed light on how they can be used to guide AI decisions. This framework offers a mechanism to evaluate the degree of alignment between norms and values by assessing preference changes across state transitions in a normative world. By utilizing this formalism, AI developers and ethicists can better design and evaluate AI systems to ensure they operate in harmony with human values. The proposed methodology holds potential for a wide range of applications, from recommendation systems emphasizing well-being to autonomous vehicles prioritizing safety.
Efficient Deformable Tissue Reconstruction via Orthogonal Neural Plane
Venue: arXiv cs.CV 2023
Year: 2023

Intraoperative imaging techniques for reconstructing deformable tissues in vivo are pivotal for advanced surgical systems. Existing methods either compromise on rendering quality or are excessively computationally intensive, often demanding dozens of hours to perform, which significantly hinders their practical application. In this paper, we introduce Fast Orthogonal Plane (Forplane), a novel, efficient framework based on neural radiance fields (NeRF) for the reconstruction of deformable tissues. We conceptualize surgical procedures as 4D volumes, and break them down into static and dynamic fields comprised of orthogonal neural planes. This factorization iscretizes the four-dimensional space, leading to a decreased memory usage and faster optimization. A spatiotemporal importance sampling scheme is introduced to improve performance in regions with tool occlusion as well as large motions and accelerate training. An efficient ray marching method is applied to skip sampling among empty regions, significantly improving inference speed. Forplane accommodates both binocular and monocular endoscopy videos, demonstrating its extensive applicability and flexibility. Our experiments, carried out on two in vivo datasets, the EndoNeRF and Hamlyn datasets, demonstrate the effectiveness of our framework. In all cases, Forplane substantially accelerates both the optimization process (by over 100 times) and the inference process (by over 15 times) while maintaining or even improving the quality across a variety of non-rigid deformations. This significant performance improvement promises to be a valuable asset for future intraoperative surgical applications. The code of our project is now available at https://github.com/Loping151/ForPlane.
MARS: Multi-Scale Adaptive Robotics Vision for Underwater Object Detection and Domain Generalization
Venue: arXiv cs.RO 2023
Year: 2023

Underwater robotic vision encounters significant challenges, necessitating advanced solutions to enhance performance and adaptability. This paper presents MARS (Multi-Scale Adaptive Robotics Vision), a novel approach to underwater object detection tailored for diverse underwater scenarios. MARS integrates Residual Attention YOLOv3 with Domain-Adaptive Multi-Scale Attention (DAMSA) to enhance detection accuracy and adapt to different domains. During training, DAMSA introduces domain class-based attention, enabling the model to emphasize domain-specific features. Our comprehensive evaluation across various underwater datasets demonstrates MARS's performance. On the original dataset, MARS achieves a mean Average Precision (mAP) of 58.57\%, showcasing its proficiency in detecting critical underwater objects like echinus, starfish, holothurian, scallop, and waterweeds. This capability holds promise for applications in marine robotics, marine biology research, and environmental monitoring. Furthermore, MARS excels at mitigating domain shifts. On the augmented dataset, which incorporates all enhancements (+Domain +Residual+Channel Attention+Multi-Scale Attention), MARS achieves an mAP of 36.16\%. This result underscores its robustness and adaptability in recognizing objects and performing well across a range of underwater conditions. The source code for MARS is publicly available on GitHub at https://github.com/LyesSaadSaoud/MARS-Object-Detection/
TMAP: A Threat Modeling and Attack Path Analysis Framework for Industrial IoT Systems (A Case Study of IoM and IoP)
Venue: arXiv cs.CR 2023
Year: 2023

Industrial cyber-physical systems (ICPS) are gradually integrating information technology and automating industrial processes, leading systems to become more vulnerable to malicious actors. Thus, to deploy secure Industrial Control and Production Systems (ICPS) in smart factories, cyber threats and risks must be addressed. To identify all possible threats, Threat Modeling is a promising solution. Despite the existence of numerous methodological solutions for threat modeling in cyber-physical systems (CPS), current approaches are ad hoc and inefficient in providing clear insights to researchers and organizations involved in IIoT technologies. These approaches lack a comprehensive analysis of cyber threats and fail to facilitate effective path analysis across the ICPS lifecycle, incorporating smart manufacturing technologies and tools. To address these gaps, a novel quantitative threat modeling approach is proposed, aiming to identify probable attack vectors, assess the path of attacks, and evaluate the magnitude of each vector. This paper also explains the execution of the proposed approach with two case studies, namely the industrial manufacturing line, i.e., the Internet of Manufacturing (IoM), and the power and industry, i.e., the Internet of Production (IoP).
Towards a Microservice-based Middleware for a Multi-hazard Early Warning System
Venue: arXiv cs.DC 2023
Year: 2023

Environmental hazards like water and air pollution, extreme weather, or chemical exposures can affect human health in a number of ways, and it is a persistent apprehension in communities surrounded by mining operations. The application of modern technologies in the environmental monitoring of these Human-made hazards is critical, because while not immediately health-threatening may turn out detrimental with unwanted negative effects. Enabling technologies needed to realise this concept is multifaceted and most especially involves deploying interconnected Internet of Things (IoT) sensors, existing legacy systems, enterprise networks, multi layered software architecture (middleware), and event processing engines, amongst others. Currently, the integration of several early warning systems has inherent challenges, mostly due to the heterogeneity of components. This paper proposes transversal microservice-based middleware aiming at increasing data integration, interoperability, scalability, high availability, and reusability of adopted systems using a container orchestration framework for a multi-hazard early warning system. Devised within the scope of the ICMHEWS project, the proposed platform aims at improving known challenges.
Scout-Net: Prospective Personalized Estimation of CT Organ Doses from Scout Views
Venue: arXiv cs.CV 2023
Year: 2023

Purpose: Estimation of patient-specific organ doses is required for more comprehensive dose metrics, such as effective dose. Currently, available methods are performed retrospectively using the CT images themselves, which can only be done after the scan. To optimize CT acquisitions before scanning, rapid prediction of patient-specific organ dose is needed prospectively, using available scout images. We, therefore, devise an end-to-end, fully-automated deep learning solution to perform real-time, patient-specific, organ-level dosimetric estimation of CT scans. Approach: We propose the Scout-Net model for CT dose prediction at six different organs as well as for the overall patient body, leveraging the routinely obtained frontal and lateral scout images of patients, before their CT scans. To obtain reference values of the organ doses, we used Monte Carlo simulation and 3D segmentation methods on the corresponding CT images of the patients. Results: We validate our proposed Scout-Net model against real patient CT data and demonstrate the effectiveness in estimating organ doses in real-time (only 27 ms on average per scan). Additionally, we demonstrate the efficiency (real-time execution), sufficiency (reasonable error rates), and robustness (consistent across varying patient sizes) of the Scout-Net model. Conclusions: An effective, efficient, and robust Scout-Net model, once incorporated into the CT acquisition plan, could potentially guide the automatic exposure control for balanced image quality and radiation dose.