OpenRaiser/Intern-Atlas huggingface dataset:
Proactive Fraud Defense: Machine Learning's Evolving Role in Protecting Against Online Fraud
Venue: arXiv cs.LG 2024
Year: 2024

As online fraud becomes more sophisticated and pervasive, traditional fraud detection methods are struggling to keep pace with the evolving tactics employed by fraudsters. This paper explores the transformative role of machine learning in addressing these challenges by offering more advanced, scalable, and adaptable solutions for fraud detection and prevention. By analyzing key models such as Random Forest, Neural Networks, and Gradient Boosting, this paper highlights the strengths of machine learning in processing vast datasets, identifying intricate fraud patterns, and providing real-time predictions that enable a proactive approach to fraud prevention. Unlike rule-based systems that react after fraud has occurred, machine learning models continuously learn from new data, adapting to emerging fraud schemes and reducing false positives, which ultimately minimizes financial losses. This research emphasizes the potential of machine learning to revolutionize fraud detection frameworks by making them more dynamic, efficient, and capable of handling the growing complexity of fraud across various industries. Future developments in machine learning, including deep learning and hybrid models, are expected to further enhance the predictive accuracy and applicability of these systems, ensuring that organizations remain resilient in the face of new and emerging fraud tactics.
On performance bounds for topology optimization
Venue: arXiv cs.CE 2024
Year: 2024

Topology optimization has matured to become a powerful engineering design tool that is capable of designing extraordinary structures and materials taking into account various physical phenomena. Despite the method's great advancements in recent years, several unanswered questions remain. This paper takes a step towards answering one of the larger questions, namely: How far from the global optimum is a given topology optimized design? Typically this is a hard question to answer, as almost all interesting topology optimization problems are non-convex. Unfortunately, this non-convexity implies that local minima may plague the design space, resulting in optimizers ending up in suboptimal designs. In this work, we investigate performance bounds for topology optimization via a computational framework that utilizes Lagrange duality theory. This approach provides a viable measure of how \say{close} a given design is to the global optimum for a subset of optimization formulations. The method's capabilities are exemplified via several numerical examples, including the design of mode converters and resonating plates.
Multiple kernel concept factorization algorithm based on global fusion
Venue: arXiv cs.LG 2024
Year: 2024

Non-negative Matrix Factorization(NMF) algorithm can only be used to find low rank approximation of original non-negative data while Concept Factorization(CF) algorithm extends matrix factorization to single non-linear kernel space, improving learning ability and adaptability of matrix factorization. In unsupervised environment, to design or select proper kernel function for specific dataset, a new algorithm called Globalized Multiple Kernel CF(GMKCF)was proposed. Multiple candidate kernel functions were input in the same time and learned in the CF framework based on global linear fusion, obtaining a clustering result with high quality and stability and solving the problem of kernel function selection that the CF faced. The convergence of the proposed algorithm was verified by solving the model with alternate iteration. The experimental results on several real databases show that the proposed algorithm outperforms comparison algorithms in data clustering, such as Kernel K-Means(KKM), Spectral Clustering(SC), Kernel CF(KCF), Co-regularized multi-view spectral clustering(Coreg), and Robust Multiple KKM(RMKKM).
Unsupervised Feature Selection Algorithm Based on Dual Manifold Re-ranking
Venue: arXiv cs.LG 2024
Year: 2024

High-dimensional data is commonly encountered in numerous data analysis tasks. Feature selection techniques aim to identify the most representative features from the original high-dimensional data. Due to the absence of class label information, it is significantly more challenging to select appropriate features in unsupervised learning scenarios compared to supervised ones. Traditional unsupervised feature selection methods typically score the features of samples based on certain criteria, treating samples indiscriminately. However, these approaches fail to fully capture the internal structure of the data. The importance of different samples should vary, and there is a dual relationship between the weight of samples and features that will influence each other. Therefore, an unsupervised feature selection algorithm based on dual manifold re-ranking (DMRR) is proposed in this paper. Different similarity matrices are constructed to depict the manifold structures among samples, between samples and features, and among features themselves. Then, manifold re-ranking is performed by combining the initial scores of samples and features. By comparing DMRR with three original unsupervised feature selection algorithms and two unsupervised feature selection post-processing algorithms, experimental results confirm that the importance information of different samples and the dual relationship between sample and feature are beneficial for achieving better feature selection.
Lodge++: High-quality and Long Dance Generation with Vivid Choreography Patterns
Venue: arXiv cs.CV 2024
Year: 2024

We propose Lodge++, a choreography framework to generate high-quality, ultra-long, and vivid dances given the music and desired genre. To handle the challenges in computational efficiency, the learning of complex and vivid global choreography patterns, and the physical quality of local dance movements, Lodge++ adopts a two-stage strategy to produce dances from coarse to fine. In the first stage, a global choreography network is designed to generate coarse-grained dance primitives that capture complex global choreography patterns. In the second stage, guided by these dance primitives, a primitive-based dance diffusion model is proposed to further generate high-quality, long-sequence dances in parallel, faithfully adhering to the complex choreography patterns. Additionally, to improve the physical plausibility, Lodge++ employs a penetration guidance module to resolve character self-penetration, a foot refinement module to optimize foot-ground contact, and a multi-genre discriminator to maintain genre consistency throughout the dance. Lodge++ is validated by extensive experiments, which show that our method can rapidly generate ultra-long dances suitable for various dance genres, ensuring well-organized global choreography patterns and high-quality local motion.
Causal Modeling in Multi-Context Systems: Distinguishing Multiple Context-Specific Causal Graphs which Account for Observational Support
Venue: arXiv cs.LG 2024
Year: 2024

Causal structure learning with data from multiple contexts carries both opportunities and challenges. Opportunities arise from considering shared and context-specific causal graphs enabling to generalize and transfer causal knowledge across contexts. However, a challenge that is currently understudied in the literature is the impact of differing observational support between contexts on the identifiability of causal graphs. Here we study in detail recently introduced [6] causal graph objects that capture both causal mechanisms and data support, allowing for the analysis of a larger class of context-specific changes, characterizing distribution shifts more precisely. We thereby extend results on the identifiability of context-specific causal structures and propose a framework to model context-specific independence (CSI) within structural causal models (SCMs) in a refined way that allows to explore scenarios where these graph objects differ. We demonstrate how this framework can help explaining phenomena like anomalies or extreme events, where causal mechanisms change or appear to change under different conditions. Our results contribute to the theoretical foundations for understanding causal relations in multi-context systems, with implications for generalization, transfer learning, and anomaly detection. Future work may extend this approach to more complex data types, such as time-series.
NT-VOT211: A Large-Scale Benchmark for Night-time Visual Object Tracking
Venue: arXiv cs.CV 2024
Year: 2024

Many current visual object tracking benchmarks such as OTB100, NfS, UAV123, LaSOT, and GOT-10K, predominantly contain day-time scenarios while the challenges posed by the night-time has been less investigated. It is primarily because of the lack of a large-scale, well-annotated night-time benchmark for rigorously evaluating tracking algorithms. To this end, this paper presents NT-VOT211, a new benchmark tailored for evaluating visual object tracking algorithms in the challenging night-time conditions. NT-VOT211 consists of 211 diverse videos, offering 211,000 well-annotated frames with 8 attributes including camera motion, deformation, fast motion, motion blur, tiny target, distractors, occlusion and out-of-view. To the best of our knowledge, it is the largest night-time tracking benchmark to-date that is specifically designed to address unique challenges such as adverse visibility, image blur, and distractors inherent to night-time tracking scenarios. Through a comprehensive analysis of results obtained from 42 diverse tracking algorithms on NT-VOT211, we uncover the strengths and limitations of these algorithms, highlighting opportunities for enhancements in visual object tracking, particularly in environments with suboptimal lighting. Besides, a leaderboard for revealing performance rankings, annotation tools, comprehensive meta-information and all the necessary code for reproducibility of results is made publicly available. We believe that our NT-VOT211 benchmark will not only be instrumental in facilitating field deployment of VOT algorithms, but will also help VOT enhancements and it will unlock new real-world tracking applications. Our dataset and other assets can be found at: {https://github.com/LiuYuML/NV-VOT211.
A Deconfounding Framework for Human Behavior Prediction: Enhancing Robotic Systems in Dynamic Environments
Venue: arXiv cs.RO 2024
Year: 2024

Accurate prediction of human behavior is crucial for effective human-robot interaction (HRI) systems, especially in dynamic environments where real-time decisions are essential. This paper addresses the challenge of forecasting future human behavior using multivariate time series data from wearable sensors, which capture various aspects of human movement. The presence of hidden confounding factors in this data often leads to biased predictions, limiting the reliability of traditional models. To overcome this, we propose a robust predictive model that integrates deconfounding techniques with advanced time series prediction methods, enhancing the model's ability to isolate true causal relationships and improve prediction accuracy. Evaluation on real-world datasets demonstrates that our approach significantly outperforms traditional methods, providing a more reliable foundation for responsive and adaptive HRI systems.
MARS: Multi-sample Allocation through Russian roulette and Splitting
Venue: arXiv cs.GR 2024
Year: 2024

Multiple importance sampling (MIS) is an indispensable tool in rendering that constructs robust sampling strategies by combining the respective strengths of individual distributions. Its efficiency can be greatly improved by carefully selecting the number of samples drawn from each distribution, but automating this process remains a challenging problem. Existing works are mostly limited to mixture sampling, in which only a single sample is drawn in total, and the works that do investigate multi-sample MIS only optimize the sample counts at a per-pixel level, which cannot account for variations beyond the first bounce. Recent work on Russian roulette and splitting has demonstrated how fixed-point schemes can be used to spatially vary sample counts to optimize image efficiency but is limited to choosing the same number of samples across all sampling strategies. Our work proposes a highly flexible sample allocation strategy that bridges the gap between these areas of work. We show how to iteratively optimize the sample counts to maximize the efficiency of the rendered image using a lightweight data structure, which allows us to make local and individual decisions per technique. We demonstrate the benefits of our approach in two applications, path guiding and bidirectional path tracing, in both of which we achieve consistent and substantial speedups over the respective previous state-of-the-art.
TEAFormers: TEnsor-Augmented Transformers for Multi-Dimensional Time Series Forecasting
Venue: arXiv cs.LG 2024
Year: 2024

Multi-dimensional time series data, such as matrix and tensor-variate time series, are increasingly prevalent in fields such as economics, finance, and climate science. Traditional Transformer models, though adept with sequential data, do not effectively preserve these multi-dimensional structures, as their internal operations in effect flatten multi-dimensional observations into vectors, thereby losing critical multi-dimensional relationships and patterns. To address this, we introduce the Tensor-Augmented Transformer (TEAFormer), a novel method that incorporates tensor expansion and compression within the Transformer framework to maintain and leverage the inherent multi-dimensional structures, thus reducing computational costs and improving prediction accuracy. The core feature of the TEAFormer, the Tensor-Augmentation (TEA) module, utilizes tensor expansion to enhance multi-view feature learning and tensor compression for efficient information aggregation and reduced computational load. The TEA module is not just a specific model architecture but a versatile component that is highly compatible with the attention mechanism and the encoder-decoder structure of Transformers, making it adaptable to existing Transformer architectures. Our comprehensive experiments, which integrate the TEA module into three popular time series Transformer models across three real-world benchmarks, show significant performance enhancements, highlighting the potential of TEAFormers for cutting-edge time series forecasting.
Deep Reinforcement Learning Agents for Strategic Production Policies in Microeconomic Market Simulations
Venue: arXiv cs.LG 2024
Year: 2024

Traditional economic models often rely on fixed assumptions about market dynamics, limiting their ability to capture the complexities and stochastic nature of real-world scenarios. However, reality is more complex and includes noise, making traditional models assumptions not met in the market. In this paper, we explore the application of deep reinforcement learning (DRL) to obtain optimal production strategies in microeconomic market environments to overcome the limitations of traditional models. Concretely, we propose a DRL-based approach to obtain an effective policy in competitive markets with multiple producers, each optimizing their production decisions in response to fluctuating demand, supply, prices, subsidies, fixed costs, total production curve, elasticities and other effects contaminated by noise. Our framework enables agents to learn adaptive production policies to several simulations that consistently outperform static and random strategies. As the deep neural networks used by the agents are universal approximators of functions, DRL algorithms can represent in the network complex patterns of data learnt by trial and error that explain the market. Through extensive simulations, we demonstrate how DRL can capture the intricate interplay between production costs, market prices, and competitor behavior, providing insights into optimal decision-making in dynamic economic settings. The results show that agents trained with DRL can strategically adjust production levels to maximize long-term profitability, even in the face of volatile market conditions. We believe that the study bridges the gap between theoretical economic modeling and practical market simulation, illustrating the potential of DRL to revolutionize decision-making in market strategies.
SympCam: Remote Optical Measurement of Sympathetic Arousal
Venue: arXiv cs.CV 2024
Year: 2024

Recent work has shown that a person's sympathetic arousal can be estimated from facial videos alone using basic signal processing. This opens up new possibilities in the field of telehealth and stress management, providing a non-invasive method to measure stress only using a regular RGB camera. In this paper, we present SympCam, a new 3D convolutional architecture tailored to the task of remote sympathetic arousal prediction. Our model incorporates a temporal attention module (TAM) to enhance the temporal coherence of our sequential data processing capabilities. The predictions from our method improve accuracy metrics of sympathetic arousal in prior work by 48% to a mean correlation of 0.77. We additionally compare our method with common remote photoplethysmography (rPPG) networks and show that they alone cannot accurately predict sympathetic arousal "out-of-the-box". Furthermore, we show that the sympathetic arousal predicted by our method allows detecting physical stress with a balanced accuracy of 90% - an improvement of 61% compared to the rPPG method commonly used in related work, demonstrating the limitations of using rPPG alone. Finally, we contribute a dataset designed explicitly for the task of remote sympathetic arousal prediction. Our dataset contains synchronized face and hand videos of 20 participants from two cameras synchronized with electrodermal activity (EDA) and photoplethysmography (PPG) measurements. We will make this dataset available to the community and use it to evaluate the methods in this paper. To the best of our knowledge, this is the first dataset available to other researchers designed for remote sympathetic arousal prediction.
Language Models And A Second Opinion Use Case: The Pocket Professional
Venue: arXiv cs.CR 2024
Year: 2024

This research tests the role of Large Language Models (LLMs) as formal second opinion tools in professional decision-making, particularly focusing on complex medical cases where even experienced physicians seek peer consultation. The work analyzed 183 challenging medical cases from Medscape over a 20-month period, testing multiple LLMs' performance against crowd-sourced physician responses. A key finding was the high overall score possible in the latest foundational models (>80% accuracy compared to consensus opinion), which exceeds most human metrics reported on the same clinical cases (450 pages of patient profiles, test results). The study rates the LLMs' performance disparity between straightforward cases (>81% accuracy) and complex scenarios (43% accuracy), particularly in these cases generating substantial debate among human physicians. The research demonstrates that LLMs may be valuable as generators of comprehensive differential diagnoses rather than as primary diagnostic tools, potentially helping to counter cognitive biases in clinical decision-making, reduce cognitive loads, and thus remove some sources of medical error. The inclusion of a second comparative legal dataset (Supreme Court cases, N=21) provides added empirical context to the AI use to foster second opinions, though these legal challenges proved considerably easier for LLMs to analyze. In addition to the original contributions of empirical evidence for LLM accuracy, the research aggregated a novel benchmark for others to score highly contested question and answer reliability between both LLMs and disagreeing human practitioners. These results suggest that the optimal deployment of LLMs in professional settings may differ substantially from current approaches that emphasize automation of routine tasks.
Ant Detective: An Automated Approach for Counting Ants in Densely Populated Images and Gaining Insight into Ant Foraging Behavior
Venue: arXiv cs.CV 2024
Year: 2024

Ant foraging behavior is essential to understanding ecological dynamics and developing effective pest management strategies, but quantifying this behavior is challenging due to the labor-intensive nature of manual counting, especially in densely populated images. This study presents an automated approach using computer vision to count ants and analyze their foraging behavior. Leveraging the YOLOv8 model, the system was calibrated and evaluated on datasets encompassing various imaging scenarios and densities. The study results demonstrate that the system achieves average precision and recall of up to 87.96% and 87,78%, respectively, with only 64 calibration images provided when the both calibration and evaluation images share similar imaging backgrounds. When the background is more complex than the calibration images, the system requires a larger calibration set to generalize effectively, with 1,024 images yielding the precision and recall of up to 83.60% and 78.88, respectively. In more challenging scenarios where more than one thousand ants are present in a single image, the system significantly improves detection accuracy by slicing images into smaller patches, reaching a precision and recall of 77.97% and 71.36%, respectively. The system's ability to generate heatmaps visualizes the spatial distribution of ant activity over time, providing valuable insights into their foraging patterns. This spatial-temporal analysis enables a more comprehensive understanding of ant behavior, which is crucial for ecological studies and improving pest control methods. By automating the counting process and offering detailed behavioral analysis, this study provides an efficient tool for researchers and pest control professionals to develop more effective strategies.
Convergences and Divergences in the 2024 Judicial Reform in Mexico: A Neural Network Analysis of Transparency, Judicial Autonomy, and Public Acceptance
Venue: arXiv cs.CY 2024
Year: 2024

This study utilizes neural networks to evaluate the 2024 judicial reform in Mexico, a proposal designed to overhaul the judicial system by increasing transparency, judicial autonomy, and introducing the popular election of judges. The neural network model analyzes both converging and diverging factors that influence the reforms viability and public acceptance. Key areas of convergence include enhanced transparency and judicial autonomy, which are seen as improvements to the system. However, major points of divergence, such as the high costs of implementation and concerns about the legitimacy of electing judges, pose significant challenges. By integrating variables like transparency, decision quality, judicial independence, and implementation costs, the model predicts levels of public and professional acceptance of the reform. The neural networks multilayered structure allows for the modeling of complex relationships, offering predictive insights into how the reform may impact the Mexican judicial system. Initial findings suggest that while the reform could strengthen judicial autonomy, the risks of politicizing the judiciary and the financial burden it entails may reduce its overall acceptance. This research highlights the importance of using advanced AI tools to simulate public policy outcomes, providing valuable data to guide lawmakers in refining their proposals.
CIB-SE-YOLOv8: Optimized YOLOv8 for Real-Time Safety Equipment Detection on Construction Sites
Venue: arXiv cs.CV 2024
Year: 2024

Ensuring safety on construction sites is critical, with helmets playing a key role in reducing injuries. Traditional safety checks are labor-intensive and often insufficient. This study presents a computer vision-based solution using YOLO for real-time helmet detection, leveraging the SHEL5K dataset. Our proposed CIB-SE-YOLOv8 model incorporates SE attention mechanisms and modified C2f blocks, enhancing detection accuracy and efficiency. This model offers a more effective solution for promoting safety compliance on construction sites.