OpenRaiser/Intern-Atlas huggingface dataset:
FedVAE: Trajectory privacy preserving based on Federated Variational AutoEncoder
Venue: arXiv cs.AI 2024
Year: 2024

The use of trajectory data with abundant spatial-temporal information is pivotal in Intelligent Transport Systems (ITS) and various traffic system tasks. Location-Based Services (LBS) capitalize on this trajectory data to offer users personalized services tailored to their location information. However, this trajectory data contains sensitive information about users' movement patterns and habits, necessitating confidentiality and protection from unknown collectors. To address this challenge, privacy-preserving methods like K-anonymity and Differential Privacy have been proposed to safeguard private information in the dataset. Despite their effectiveness, these methods can impact the original features by introducing perturbations or generating unrealistic trajectory data, leading to suboptimal performance in downstream tasks. To overcome these limitations, we propose a Federated Variational AutoEncoder (FedVAE) approach, which effectively generates a new trajectory dataset while preserving the confidentiality of private information and retaining the structure of the original features. In addition, FedVAE leverages Variational AutoEncoder (VAE) to maintain the original feature space and generate new trajectory data, and incorporates Federated Learning (FL) during the training stage, ensuring that users' data remains locally stored to protect their personal information. The results demonstrate its superior performance compared to other existing methods, affirming FedVAE as a promising solution for enhancing data privacy and utility in location-based applications.
Deep Adversarial Defense Against Multilevel-Lp Attacks
Venue: arXiv cs.LG 2024
Year: 2024

Deep learning models have shown considerable vulnerability to adversarial attacks, particularly as attacker strategies become more sophisticated. While traditional adversarial training (AT) techniques offer some resilience, they often focus on defending against a single type of attack, e.g., the $\ell_\infty$-norm attack, which can fail for other types. This paper introduces a computationally efficient multilevel $\ell_p$ defense, called the Efficient Robust Mode Connectivity (EMRC) method, which aims to enhance a deep learning model's resilience against multiple $\ell_p$-norm attacks. Similar to analytical continuation approaches used in continuous optimization, the method blends two $p$-specific adversarially optimal models, the $\ell_1$- and $\ell_\infty$-norm AT solutions, to provide good adversarial robustness for a range of $p$. We present experiments demonstrating that our approach performs better on various attacks as compared to AT-$\ell_\infty$, E-AT, and MSD, for datasets/architectures including: CIFAR-10, CIFAR-100 / PreResNet110, WideResNet, ViT-Base.
Predicting and Understanding Human Action Decisions: Insights from Large Language Models and Cognitive Instance-Based Learning
Venue: arXiv cs.AI 2024
Year: 2024

Large Language Models (LLMs) have demonstrated their capabilities across various tasks, from language translation to complex reasoning. Understanding and predicting human behavior and biases are crucial for artificial intelligence (AI) assisted systems to provide useful assistance, yet it remains an open question whether these models can achieve this. This paper addresses this gap by leveraging the reasoning and generative capabilities of the LLMs to predict human behavior in two sequential decision-making tasks. These tasks involve balancing between exploitative and exploratory actions and handling delayed feedback, both essential for simulating real-life decision processes. We compare the performance of LLMs with a cognitive instance-based learning (IBL) model, which imitates human experiential decision-making. Our findings indicate that LLMs excel at rapidly incorporating feedback to enhance prediction accuracy. In contrast, the cognitive IBL model better accounts for human exploratory behaviors and effectively captures loss aversion bias, i.e., the tendency to choose a sub-optimal goal with fewer step-cost penalties rather than exploring to find the optimal choice, even with limited experience. The results highlight the benefits of integrating LLMs with cognitive architectures, suggesting that this synergy could enhance the modeling and understanding of complex human decision-making patterns.
MetaFood CVPR 2024 Challenge on Physically Informed 3D Food Reconstruction: Methods and Results
Venue: arXiv cs.CV 2024
Year: 2024

The increasing interest in computer vision applications for nutrition and dietary monitoring has led to the development of advanced 3D reconstruction techniques for food items. However, the scarcity of high-quality data and limited collaboration between industry and academia have constrained progress in this field. Building on recent advancements in 3D reconstruction, we host the MetaFood Workshop and its challenge for Physically Informed 3D Food Reconstruction. This challenge focuses on reconstructing volume-accurate 3D models of food items from 2D images, using a visible checkerboard as a size reference. Participants were tasked with reconstructing 3D models for 20 selected food items of varying difficulty levels: easy, medium, and hard. The easy level provides 200 images, the medium level provides 30 images, and the hard level provides only 1 image for reconstruction. In total, 16 teams submitted results in the final testing phase. The solutions developed in this challenge achieved promising results in 3D food reconstruction, with significant potential for improving portion estimation for dietary assessment and nutritional monitoring. More details about this workshop challenge and access to the dataset can be found at https://sites.google.com/view/cvpr-metafood-2024.
Structuring Authenticity Assessments on Historical Documents using LLMs
Venue: arXiv cs.DL 2024
Year: 2024

Given the wide use of forgery throughout history, scholars have and are continuously engaged in assessing the authenticity of historical documents. However, online catalogues merely offer descriptive metadata for these documents, relegating discussions about their authenticity to free-text formats, making it difficult to study these assessments at scale. This study explores the generation of structured data about documents' authenticity assessment from natural language texts. Our pipeline exploits Large Language Models (LLMs) to select, extract and classify relevant claims about the topic without the need for training, and Semantic Web technologies to structure and type-validate the LLM's results. The final output is a catalogue of documents whose authenticity has been debated, along with scholars' opinions on their authenticity. This process can serve as a valuable resource for integration into catalogues, allowing room for more intricate queries and analyses on the evolution of these debates over centuries.
Counterfactual Explainable Incremental Prompt Attack Analysis on Large Language Models
Venue: arXiv cs.CR 2024
Year: 2024

This study sheds light on the imperative need to bolster safety and privacy measures in large language models (LLMs), such as GPT-4 and LLaMA-2, by identifying and mitigating their vulnerabilities through explainable analysis of prompt attacks. We propose Counterfactual Explainable Incremental Prompt Attack (CEIPA), a novel technique where we guide prompts in a specific manner to quantitatively measure attack effectiveness and explore the embedded defense mechanisms in these models. Our approach is distinctive for its capacity to elucidate the reasons behind the generation of harmful responses by LLMs through an incremental counterfactual methodology. By organizing the prompt modification process into four incremental levels: (word, sentence, character, and a combination of character and word) we facilitate a thorough examination of the susceptibilities inherent to LLMs. The findings from our study not only provide counterfactual explanation insight but also demonstrate that our framework significantly enhances the effectiveness of attack prompts.
SS-SfP:Neural Inverse Rendering for Self Supervised Shape from (Mixed) Polarization
Venue: arXiv cs.CV 2024
Year: 2024

We present a novel inverse rendering-based framework to estimate the 3D shape (per-pixel surface normals and depth) of objects and scenes from single-view polarization images, the problem popularly known as Shape from Polarization (SfP). The existing physics-based and learning-based methods for SfP perform under certain restrictions, i.e., (a) purely diffuse or purely specular reflections, which are seldom in the real surfaces, (b) availability of the ground truth surface normals for direct supervision that are hard to acquire and are limited by the scanner's resolution, and (c) known refractive index. To overcome these restrictions, we start by learning to separate the partially-polarized diffuse and specular reflection components, which we call reflectance cues, based on a modified polarization reflection model and then estimate shape under mixed polarization through an inverse-rendering based self-supervised deep learning framework called SS-SfP, guided by the polarization data and estimated reflectance cues. Furthermore, we also obtain the refractive index as a non-linear least squares solution. Through extensive quantitative and qualitative evaluation, we establish the efficacy of the proposed framework over simple single-object scenes from DeepSfP dataset and complex in-the-wild scenes from SPW dataset in an entirely self-supervised setting. To the best of our knowledge, this is the first learning-based approach to address SfP under mixed polarization in a completely self-supervised framework.
Pre-training Point Cloud Compact Model with Partial-aware Reconstruction
Venue: arXiv cs.CV 2024
Year: 2024

The pre-trained point cloud model based on Masked Point Modeling (MPM) has exhibited substantial improvements across various tasks. However, two drawbacks hinder their practical application. Firstly, the positional embedding of masked patches in the decoder results in the leakage of their central coordinates, leading to limited 3D representations. Secondly, the excessive model size of existing MPM methods results in higher demands for devices. To address these, we propose to pre-train Point cloud Compact Model with Partial-aware \textbf{R}econstruction, named Point-CPR. Specifically, in the decoder, we couple the vanilla masked tokens with their positional embeddings as randomly masked queries and introduce a partial-aware prediction module before each decoder layer to predict them from the unmasked partial. It prevents the decoder from creating a shortcut between the central coordinates of masked patches and their reconstructed coordinates, enhancing the robustness of models. We also devise a compact encoder composed of local aggregation and MLPs, reducing the parameters and computational requirements compared to existing Transformer-based encoders. Extensive experiments demonstrate that our model exhibits strong performance across various tasks, especially surpassing the leading MPM-based model PointGPT-B with only 2% of its parameters.
Bipartizing (Pseudo-)Disk Graphs: Approximation with a Ratio Better than 3
Venue: arXiv cs.DS 2024
Year: 2024

In a disk graph, every vertex corresponds to a disk in $\mathbb{R}^2$ and two vertices are connected by an edge whenever the two corresponding disks intersect. Disk graphs form an important class of geometric intersection graphs, which generalizes both planar graphs and unit-disk graphs. We study a fundamental optimization problem in algorithmic graph theory, Bipartization (also known as Odd Cycle Transversal), on the class of disk graphs. The goal of Bipartization is to delete a minimum number of vertices from the input graph such that the resulting graph is bipartite. A folklore (polynomial-time) $3$-approximation algorithm for Bipartization on disk graphs follows from the classical framework of Goemans and Williamson [Combinatorica'98] for cycle-hitting problems. For over two decades, this result has remained the best known approximation for the problem (in fact, even for Bipartization on unit-disk graphs). In this paper, we achieve the first improvement upon this result, by giving a $(3-α)$-approximation algorithm for Bipartization on disk graphs, for some constant $α>0$. Our algorithm directly generalizes to the broader class of pseudo-disk graphs. Furthermore, our algorithm is robust in the sense that it does not require a geometric realization of the input graph to be given.
Mitigating Entity-Level Hallucination in Large Language Models
Venue: arXiv cs.CL 2024
Year: 2024

The emergence of Large Language Models (LLMs) has revolutionized how users access information, shifting from traditional search engines to direct question-and-answer interactions with LLMs. However, the widespread adoption of LLMs has revealed a significant challenge known as hallucination, wherein LLMs generate coherent yet factually inaccurate responses. This hallucination phenomenon has led to users' distrust in information retrieval systems based on LLMs. To tackle this challenge, this paper proposes Dynamic Retrieval Augmentation based on hallucination Detection (DRAD) as a novel method to detect and mitigate hallucinations in LLMs. DRAD improves upon traditional retrieval augmentation by dynamically adapting the retrieval process based on real-time hallucination detection. It features two main components: Real-time Hallucination Detection (RHD) for identifying potential hallucinations without external models, and Self-correction based on External Knowledge (SEK) for correcting these errors using external knowledge. Experiment results show that DRAD demonstrates superior performance in both detecting and mitigating hallucinations in LLMs. All of our code and data are open-sourced at https://github.com/oneal2000/EntityHallucination.
Rethinking temporal self-similarity for repetitive action counting
Venue: arXiv cs.CV 2024
Year: 2024

Counting repetitive actions in long untrimmed videos is a challenging task that has many applications such as rehabilitation. State-of-the-art methods predict action counts by first generating a temporal self-similarity matrix (TSM) from the sampled frames and then feeding the matrix to a predictor network. The self-similarity matrix, however, is not an optimal input to a network since it discards too much information from the frame-wise embeddings. We thus rethink how a TSM can be utilized for counting repetitive actions and propose a framework that learns embeddings and predicts action start probabilities at full temporal resolution. The number of repeated actions is then inferred from the action start probabilities. In contrast to current approaches that have the TSM as an intermediate representation, we propose a novel loss based on a generated reference TSM, which enforces that the self-similarity of the learned frame-wise embeddings is consistent with the self-similarity of repeated actions. The proposed framework achieves state-of-the-art results on three datasets, i.e., RepCount, UCFRep, and Countix.
A Distance for Geometric Graphs via the Labeled Merge Tree Interleaving Distance
Venue: arXiv cs.DS 2024
Year: 2024

Geometric graphs appear in many real-world data sets, such as road networks, sensor networks, and molecules. We investigate the notion of distance between embedded graphs and present a metric to measure the distance between two geometric graphs via merge trees. In order to preserve as much useful information as possible from the original data, we introduce a way of rotating the sublevel set to obtain the merge trees via the idea of the directional transform. We represent the merge trees using a surjective multi-labeling scheme and then compute the distance between two representative matrices. We show some theoretically desirable qualities and present two methods of computation: approximation via sampling and exact distance using a kinetic data structure, both in polynomial time. We illustrate its utility by implementing it on two data sets.
Learning Outcomes, Assessment, and Evaluation in Educational Recommender Systems: A Systematic Review
Venue: arXiv cs.HC 2024
Year: 2024

In this paper, we analyse how learning is measured and optimized in Educational Recommender Systems (ERS). In particular, we examine the target metrics and evaluation methods used in the existing ERS research, with a particular focus on the pedagogical effect of recommendations. While conducting this systematic literature review (SLR), we identified 1395 potentially relevant papers, then filtered them through the inclusion and exclusion criteria, and finally selected and analyzed 28 relevant papers. Rating-based relevance is the most popular target metric, while less than a half of papers optimize learning-based metrics. Only a third of the papers used outcome-based assessment to measure the pedagogical effect of recommendations, mostly within a formal university course. This indicates a gap in ERS research with respect to assessing the pedagogical effect of recommendations at scale and in informal education settings.
1-Lipschitz Neural Distance Fields
Venue: arXiv cs.CV 2024
Year: 2024

Neural implicit surfaces are a promising tool for geometry processing that represent a solid object as the zero level set of a neural network. Usually trained to approximate a signed distance function of the considered object, these methods exhibit great visual fidelity and quality near the surface, yet their properties tend to degrade with distance, making geometrical queries hard to perform without the help of complex range analysis techniques. Based on recent advancements in Lipschitz neural networks, we introduce a new method for approximating the signed distance function of a given object. As our neural function is made 1- Lipschitz by construction, it cannot overestimate the distance, which guarantees robustness even far from the surface. Moreover, the 1-Lipschitz constraint allows us to use a different loss function, called the hinge-Kantorovitch-Rubinstein loss, which pushes the gradient as close to unit-norm as possible, thus reducing computation costs in iterative queries. As this loss function only needs a rough estimate of occupancy to be optimized, this means that the true distance function need not to be known. We are therefore able to compute neural implicit representations of even bad quality geometry such as noisy point clouds or triangle soups. We demonstrate that our methods is able to approximate the distance function of any closed or open surfaces or curves in the plane or in space, while still allowing sphere tracing or closest point projections to be performed robustly.
Is GPT-4 conscious?
Venue: arXiv cs.AI 2024
Year: 2024

GPT-4 is often heralded as a leading commercial AI offering, sparking debates over its potential as a steppingstone toward artificial general intelligence. But does it possess consciousness? This paper investigates this key question using the nine qualitative measurements of the Building Blocks theory. GPT-4's design, architecture and implementation are compared to each of the building blocks of consciousness to determine whether it has achieved the requisite milestones to be classified as conscious or, if not, how close to consciousness GPT-4 is. Our assessment is that, while GPT-4 in its native configuration is not currently conscious, current technological research and development is sufficient to modify GPT-4 to have all the building blocks of consciousness. Consequently, we argue that the emergence of a conscious AI model is plausible in the near term. The paper concludes with a comprehensive discussion of the ethical implications and societal ramifications of engineering conscious AI entities.
UAV Networks Surveillance Implementing an Effective Load-Aware Multipath Routing Protocol (ELAMRP)
Venue: arXiv cs.NI 2024
Year: 2024

In this work uses innovative multi-channel load-sensing techniques to deploy unmanned aerial vehicles (UAVs) for surveillance. The research aims to improve the quality of data transmission methods and improve the efficiency and reliability of surveillance systems by exploiting the mobility and adaptability of UAVs does the proposed protocol intelligently distribute network traffic across multiple channels, considering the load of each channel, While addressing challenges such as load balancing, this study investigates the effectiveness of the protocol by simulations or practical tests on The expected results have improved UAV-based surveillance systems, more flexible and efficient networks for applications such as security, emergency response and the environment alignment of monitoring -Offering infrastructures, which contribute to efficient and reliable monitoring solutions.
Assessing Annotation Accuracy in Ice Sheets Using Quantitative Metrics
Venue: arXiv cs.CV 2024
Year: 2024

The increasing threat of sea level rise due to climate change necessitates a deeper understanding of ice sheet structures. This study addresses the need for accurate ice sheet data interpretation by introducing a suite of quantitative metrics designed to validate ice sheet annotation techniques. Focusing on both manual and automated methods, including ARESELP and its modified version, MARESELP, we assess their accuracy against expert annotations. Our methodology incorporates several computer vision metrics, traditionally underutilized in glaciological research, to evaluate the continuity and connectivity of ice layer annotations. The results demonstrate that while manual annotations provide invaluable expert insights, automated methods, particularly MARESELP, improve layer continuity and alignment with expert labels.
A parallel evolutionary algorithm to optimize dynamic memory managers in embedded systems
Venue: arXiv cs.NE 2024
Year: 2024

For the last thirty years, several Dynamic Memory Managers (DMMs) have been proposed. Such DMMs include first fit, best fit, segregated fit and buddy systems. Since the performance, memory usage and energy consumption of each DMM differs, software engineers often face difficult choices in selecting the most suitable approach for their applications. This issue has special impact in the field of portable consumer embedded systems, that must execute a limited amount of multimedia applications (e.g., 3D games, video players and signal processing software, etc.), demanding high performance and extensive memory usage at a low energy consumption. Recently, we have developed a novel methodology based on genetic programming to automatically design custom DMMs, optimizing performance, memory usage and energy consumption. However, although this process is automatic and faster than state-of-the-art optimizations, it demands intensive computation, resulting in a time consuming process. Thus, parallel processing can be very useful to enable to explore more solutions spending the same time, as well as to implement new algorithms. In this paper we present a novel parallel evolutionary algorithm for DMMs optimization in embedded systems, based on the Discrete Event Specification (DEVS) formalism over a Service Oriented Architecture (SOA) framework. Parallelism significantly improves the performance of the sequential exploration algorithm. On the one hand, when the number of generations are the same in both approaches, our parallel optimization framework is able to reach a speed-up of 86.40x when compared with other state-of-the-art approaches. On the other, it improves the global quality (i.e., level of performance, low memory usage and low energy consumption) of the final DMM obtained in a 36.36% with respect to two well-known general-purpose DMMs and two state-of-the-art optimization methodologies.