OpenRaiser/Intern-Atlas huggingface dataset:
Personalized Federated Learning on Heterogeneous and Long-Tailed Data via Expert Collaborative Learning
Venue: arXiv cs.LG 2024
Year: 2024

Personalized Federated Learning (PFL) aims to acquire customized models for each client without disclosing raw data by leveraging the collective knowledge of distributed clients. However, the data collected in real-world scenarios is likely to follow a long-tailed distribution. For example, in the medical domain, it is more common for the number of general health notes to be much larger than those specifically relatedto certain diseases. The presence of long-tailed data can significantly degrade the performance of PFL models. Additionally, due to the diverse environments in which each client operates, data heterogeneity is also a classic challenge in federated learning. In this paper, we explore the joint problem of global long-tailed distribution and data heterogeneity in PFL and propose a method called Expert Collaborative Learning (ECL) to tackle this problem. Specifically, each client has multiple experts, and each expert has a different training subset, which ensures that each class, especially the minority classes, receives sufficient training. Multiple experts collaborate synergistically to produce the final prediction output. Without special bells and whistles, the vanilla ECL outperforms other state-of-the-art PFL methods on several benchmark datasets under different degrees of data heterogeneity and long-tailed distribution.
Mining Path Association Rules in Large Property Graphs (with Appendix)
Venue: arXiv cs.DB 2024
Year: 2024

How can we mine frequent path regularities from a graph with edge labels and vertex attributes? The task of association rule mining successfully discovers regular patterns in item sets and substructures. Still, to our best knowledge, this concept has not yet been extended to path patterns in large property graphs. In this paper, we introduce the problem of path association rule mining (PARM). Applied to any \emph{reachability path} between two vertices within a large graph, PARM discovers regular ways in which path patterns, identified by vertex attributes and edge labels, co-occur with each other. We develop an efficient and scalable algorithm PIONEER that exploits an anti-monotonicity property to effectively prune the search space. Further, we devise approximation techniques and employ parallelization to achieve scalable path association rule mining. Our experimental study using real-world graph data verifies the significance of path association rules and the efficiency of our solutions.
Enhancing Human Action Recognition and Violence Detection Through Deep Learning Audiovisual Fusion
Venue: arXiv cs.CV 2024
Year: 2024

This paper proposes a hybrid fusion-based deep learning approach based on two different modalities, audio and video, to improve human activity recognition and violence detection in public places. To take advantage of audiovisual fusion, late fusion, intermediate fusion, and hybrid fusion-based deep learning (HFBDL) are used and compared. Since the objective is to detect and recognize human violence in public places, Real-life violence situation (RLVS) dataset is expanded and used. Simulating results of HFBDL show 96.67\% accuracy on validation data, which is more accurate than the other state-of-the-art methods on this dataset. To showcase our model's ability in real-world scenarios, another dataset of 54 sounded videos of both violent and non-violent situations was recorded. The model could successfully detect 52 out of 54 videos correctly. The proposed method shows a promising performance on real scenarios. Thus, it can be used for human action recognition and violence detection in public places for security purposes.
Recovering the state and dynamics of autonomous system with partial states solution using neural networks
Venue: arXiv cs.LG 2024
Year: 2024

In this paper we explore the performance of deep hidden physics model (M. Raissi 2018) for autonomous systems. These systems are described by set of ordinary differential equations which do not explicitly depend on time. Such systems can be found in nature and have applications in modeling chemical concentrations, population dynamics, n-body problems in physics etc. In this work we consider dynamics of states, which explain how the states will evolve are unknown to us. We approximate state and dynamics both using neural networks. We have considered examples of 2D linear/nonlinear and Lorenz systems. We observe that even without knowing all the states information, we can estimate dynamics of certain states whose state information are known.
Why distinctiveness centrality is distinctive
Venue: arXiv cs.SI 2024
Year: 2024

This paper responds to a commentary by Neal (2024) regarding the Distinctiveness centrality metrics introduced by Fronzetti Colladon and Naldi (2020). Distinctiveness centrality offers a novel reinterpretation of degree centrality, particularly emphasizing the significance of direct connections to loosely connected peers within (social) networks. This response paper presents a more comprehensive analysis of the correlation between Distinctiveness and the Beta and Gamma measures. All five distinctiveness measures are considered, as well as a more meaningful range of the alpha parameter and different network topologies, distinguishing between weighted and unweighted networks. Findings indicate significant variability in correlations, supporting the viability of Distinctiveness as alternative or complementary metrics within social network analysis. Moreover, the paper presents computational complexity analysis and simplified R code for practical implementation. Encouraging initial findings suggest potential applications in diverse domains, inviting further exploration and comparative analyses.
Secure and Transparent Medical Record Management System Using Python and Blockchain
Venue: arXiv cs.CR 2024
Year: 2024

In this paper, we propose a robust health record storage and management system built on blockchain technology to address the challenges faced by traditional healthcare record systems. The primary advantage of employing blockchain in healthcare record management is its ability to provide a secure and decentralized platform. Unlike traditional centralized databases, where a single point of failure can compromise data integrity and security, blockchain distributes data across a network of nodes, ensuring redundancy and resilience against cyber-attacks. This distributed nature of blockchain enhances data security and privacy, crucial considerations when dealing with sensitive health information. Central to our proposed system is the utilization of smart contracts, which are self-executing contracts with predefined rules and conditions. Smart contracts automate processes related to health record management, such as data access, sharing, and updating, based on predefined permissions and protocols. This automation not only streamlines administrative tasks but also reduces the risk of human errors and ensures data accuracy and consistency. Furthermore, our system prioritizes patient empowerment by granting individuals complete control over their health records. Patients can securely access and manage their data using cryptographic keys, granting permission to healthcare providers or other authorized entities as needed. Overall, our proposed health record storage and management system on the blockchain offer significant advantages over traditional systems, including enhanced security, data integrity, transparency, and patient control. By leveraging blockchain technology and smart contracts, healthcare organizations can revolutionize their record management practices, and maintaining secure ecosystems.
RICA2: Rubric-Informed, Calibrated Assessment of Actions
Venue: arXiv cs.CV 2024
Year: 2024

The ability to quantify how well an action is carried out, also known as action quality assessment (AQA), has attracted recent interest in the vision community. Unfortunately, prior methods often ignore the score rubric used by human experts and fall short of quantifying the uncertainty of the model prediction. To bridge the gap, we present RICA^2 - a deep probabilistic model that integrates score rubric and accounts for prediction uncertainty for AQA. Central to our method lies in stochastic embeddings of action steps, defined on a graph structure that encodes the score rubric. The embeddings spread probabilistic density in the latent space and allow our method to represent model uncertainty. The graph encodes the scoring criteria, based on which the quality scores can be decoded. We demonstrate that our method establishes new state of the art on public benchmarks, including FineDiving, MTL-AQA, and JIGSAWS, with superior performance in score prediction and uncertainty calibration. Our code is available at https://abrarmajeedi.github.io/rica2_aqa/
Unsupervised Domain Adaption Harnessing Vision-Language Pre-training
Venue: arXiv cs.CV 2024
Year: 2024

This paper addresses two vital challenges in Unsupervised Domain Adaptation (UDA) with a focus on harnessing the power of Vision-Language Pre-training (VLP) models. Firstly, UDA has primarily relied on ImageNet pre-trained models. However, the potential of VLP models in UDA remains largely unexplored. The rich representation of VLP models holds significant promise for enhancing UDA tasks. To address this, we propose a novel method called Cross-Modal Knowledge Distillation (CMKD), leveraging VLP models as teacher models to guide the learning process in the target domain, resulting in state-of-the-art performance. Secondly, current UDA paradigms involve training separate models for each task, leading to significant storage overhead and impractical model deployment as the number of transfer tasks grows. To overcome this challenge, we introduce Residual Sparse Training (RST) exploiting the benefits conferred by VLP's extensive pre-training, a technique that requires minimal adjustment (approximately 0.1\%$\sim$0.5\%) of VLP model parameters to achieve performance comparable to fine-tuning. Combining CMKD and RST, we present a comprehensive solution that effectively leverages VLP models for UDA tasks while reducing storage overhead for model deployment. Furthermore, CMKD can serve as a baseline in conjunction with other methods like FixMatch, enhancing the performance of UDA. Our proposed method outperforms existing techniques on standard benchmarks. Our code will be available at: https://github.com/Wenlve-Zhou/VLP-UDA.
Synergistic Learning with Multi-Task DeepONet for Efficient PDE Problem Solving
Venue: arXiv cs.LG 2024
Year: 2024

Multi-task learning (MTL) is an inductive transfer mechanism designed to leverage useful information from multiple tasks to improve generalization performance compared to single-task learning. It has been extensively explored in traditional machine learning to address issues such as data sparsity and overfitting in neural networks. In this work, we apply MTL to problems in science and engineering governed by partial differential equations (PDEs). However, implementing MTL in this context is complex, as it requires task-specific modifications to accommodate various scenarios representing different physical processes. To this end, we present a multi-task deep operator network (MT-DeepONet) to learn solutions across various functional forms of source terms in a PDE and multiple geometries in a single concurrent training session. We introduce modifications in the branch network of the vanilla DeepONet to account for various functional forms of a parameterized coefficient in a PDE. Additionally, we handle parameterized geometries by introducing a binary mask in the branch network and incorporating it into the loss term to improve convergence and generalization to new geometry tasks. Our approach is demonstrated on three benchmark problems: (1) learning different functional forms of the source term in the Fisher equation; (2) learning multiple geometries in a 2D Darcy Flow problem and showcasing better transfer learning capabilities to new geometries; and (3) learning 3D parameterized geometries for a heat transfer problem and demonstrate the ability to predict on new but similar geometries. Our MT-DeepONet framework offers a novel approach to solving PDE problems in engineering and science under a unified umbrella based on synergistic learning that reduces the overall training cost for neural operators.
Evaluating the Performance of Large Language Models for SDG Mapping (Technical Report)
Venue: arXiv cs.LG 2024
Year: 2024

The use of large language models (LLMs) is expanding rapidly, and open-source versions are becoming available, offering users safer and more adaptable options. These models enable users to protect data privacy by eliminating the need to provide data to third parties and can be customized for specific tasks. In this study, we compare the performance of various language models on the Sustainable Development Goal (SDG) mapping task, using the output of GPT-4o as the baseline. The selected open-source models for comparison include Mixtral, LLaMA 2, LLaMA 3, Gemma, and Qwen2. Additionally, GPT-4o-mini, a more specialized version of GPT-4o, was included to extend the comparison. Given the multi-label nature of the SDG mapping task, we employed metrics such as F1 score, precision, and recall with micro-averaging to evaluate different aspects of the models' performance. These metrics are derived from the confusion matrix to ensure a comprehensive evaluation. We provide a clear observation and analysis of each model's performance by plotting curves based on F1 score, precision, and recall at different thresholds. According to the results of this experiment, LLaMA 2 and Gemma still have significant room for improvement. The other four models do not exhibit particularly large differences in performance. The outputs from all seven models are available on Zenodo: https://doi.org/10.5281/zenodo.12789375.
SoK: Fighting Counterfeits with Cyber-Physical Synergy Based on Physically-Unclonable Identifiers of Paper Surface
Venue: arXiv cs.CR 2024
Year: 2024

Counterfeit products cause severe harm to public safety and health by penetrating untrusted supply chains. Numerous anti-counterfeiting techniques have been proposed, among which the use of inherent, unclonable irregularities of paper surfaces has shown considerable potential as a high-performance economical solution. Prior works do not consider supply chains cohesively, either focusing on creating or improving unclonable identifiers or on securing digital records of products. This work aims to systematically unify these two separate but connected research areas by comprehensively analyzing the needs of supply chains. We construct a generalized paper-based authentication framework and identify important shortcomings and promising ideas in the existing literature. Next, we do a stage-wise security analysis of our consolidated framework by drawing inspiration from works in signal processing, cryptography, and biometric systems. Finally, we examine key representative scenarios that illustrate the range of practical and technical challenges in real-world supply chains, and we outline the best practices to guide future research.
Cross-modulated Attention Transformer for RGBT Tracking
Venue: arXiv cs.CV 2024
Year: 2024

Existing Transformer-based RGBT trackers achieve remarkable performance benefits by leveraging self-attention to extract uni-modal features and cross-attention to enhance multi-modal feature interaction and template-search correlation computation. Nevertheless, the independent search-template correlation calculations ignore the consistency between branches, which can result in ambiguous and inappropriate correlation weights. It not only limits the intra-modal feature representation, but also harms the robustness of cross-attention for multi-modal feature interaction and search-template correlation computation. To address these issues, we propose a novel approach called Cross-modulated Attention Transformer (CAFormer), which performs intra-modality self-correlation, inter-modality feature interaction, and search-template correlation computation in a unified attention model, for RGBT tracking. In particular, we first independently generate correlation maps for each modality and feed them into the designed Correlation Modulated Enhancement module, modulating inaccurate correlation weights by seeking the consensus between modalities. Such kind of design unifies self-attention and cross-attention schemes, which not only alleviates inaccurate attention weight computation in self-attention but also eliminates redundant computation introduced by extra cross-attention scheme. In addition, we propose a collaborative token elimination strategy to further improve tracking inference efficiency and accuracy. Extensive experiments on five public RGBT tracking benchmarks show the outstanding performance of the proposed CAFormer against state-of-the-art methods.
Do Large Language Models Speak All Languages Equally? A Comparative Study in Low-Resource Settings
Venue: arXiv cs.CL 2024
Year: 2024

Large language models (LLMs) have garnered significant interest in natural language processing (NLP), particularly their remarkable performance in various downstream tasks in resource-rich languages. Recent studies have highlighted the limitations of LLMs in low-resource languages, primarily focusing on binary classification tasks and giving minimal attention to South Asian languages. These limitations are primarily attributed to constraints such as dataset scarcity, computational costs, and research gaps specific to low-resource languages. To address this gap, we present datasets for sentiment and hate speech tasks by translating from English to Bangla, Hindi, and Urdu, facilitating research in low-resource language processing. Further, we comprehensively examine zero-shot learning using multiple LLMs in English and widely spoken South Asian languages. Our findings indicate that GPT-4 consistently outperforms Llama 2 and Gemini, with English consistently demonstrating superior performance across diverse tasks compared to low-resource languages. Furthermore, our analysis reveals that natural language inference (NLI) exhibits the highest performance among the evaluated tasks, with GPT-4 demonstrating superior capabilities.
Towards Identifying Code Proficiency through the Analysis of Python Textbooks
Venue: arXiv cs.SE 2024
Year: 2024

Python, one of the most prevalent programming languages today, is widely utilized in various domains, including web development, data science, machine learning, and DevOps. Recent scholarly efforts have proposed a methodology to assess Python competence levels, similar to how proficiency in natural languages is evaluated. This method involves assigning levels of competence to Python constructs, for instance, placing simple 'print' statements at the most basic level and abstract base classes at the most advanced. The aim is to gauge the level of proficiency a developer must have to understand a piece of source code. This is particularly crucial for software maintenance and evolution tasks, such as debugging or adding new features. For example, in a code review process, this method could determine the competence level required for reviewers. However, categorizing Python constructs by proficiency levels poses significant challenges. Prior attempts, which relied heavily on expert opinions and developer surveys, have led to considerable discrepancies. In response, this paper presents a new approach to identifying Python competency levels through the systematic analysis of introductory Python programming textbooks. By comparing the sequence in which Python constructs are introduced in these textbooks with the current state of the art, we have uncovered notable discrepancies in the order of introduction of Python constructs. Our study underscores a misalignment in the sequences, demonstrating that pinpointing proficiency levels is not trivial. Insights from the study serve as pivotal steps toward reinforcing the idea that textbooks serve as a valuable source for evaluating developers' proficiency, and particularly in terms of their ability to undertake maintenance and evolution tasks.
SNFinLLM: Systematic and Nuanced Financial Domain Adaptation of Chinese Large Language Models
Venue: arXiv cs.CL 2024
Year: 2024

Large language models (LLMs) have become powerful tools for advancing natural language processing applications in the financial industry. However, existing financial LLMs often face challenges such as hallucinations or superficial parameter training, resulting in suboptimal performance, particularly in financial computing and machine reading comprehension (MRC). To address these issues, we propose a novel large language model specifically designed for the Chinese financial domain, named SNFinLLM. SNFinLLM excels in domain-specific tasks such as answering questions, summarizing financial research reports, analyzing sentiment, and executing financial calculations. We then perform the supervised fine-tuning (SFT) to enhance the model's proficiency across various financial domains. Specifically, we gather extensive financial data and create a high-quality instruction dataset composed of news articles, professional papers, and research reports of finance domain. Utilizing both domain-specific and general datasets, we proceed with continuous pre-training on an established open-source base model, resulting in SNFinLLM-base. Following this, we engage in supervised fine-tuning (SFT) to bolster the model's capability across multiple financial tasks. Crucially, we employ a straightforward Direct Preference Optimization (DPO) method to better align the model with human preferences. Extensive experiments conducted on finance benchmarks and our evaluation dataset demonstrate that SNFinLLM markedly outperforms other state-of-the-art financial language models. For more details, check out our demo video here: https://www.youtube.com/watch?v=GYT-65HZwus.
Mixture-of-Noises Enhanced Forgery-Aware Predictor for Multi-Face Manipulation Detection and Localization
Venue: arXiv cs.CV 2024
Year: 2024

With the advancement of face manipulation technology, forgery images in multi-face scenarios are gradually becoming a more complex and realistic challenge. Despite this, detection and localization methods for such multi-face manipulations remain underdeveloped. Traditional manipulation localization methods either indirectly derive detection results from localization masks, resulting in limited detection performance, or employ a naive two-branch structure to simultaneously obtain detection and localization results, which cannot effectively benefit the localization capability due to limited interaction between two tasks. This paper proposes a new framework, namely MoNFAP, specifically tailored for multi-face manipulation detection and localization. The MoNFAP primarily introduces two novel modules: the Forgery-aware Unified Predictor (FUP) Module and the Mixture-of-Noises Module (MNM). The FUP integrates detection and localization tasks using a token learning strategy and multiple forgery-aware transformers, which facilitates the use of classification information to enhance localization capability. Besides, motivated by the crucial role of noise information in forgery detection, the MNM leverages multiple noise extractors based on the concept of the mixture of experts to enhance the general RGB features, further boosting the performance of our framework. Finally, we establish a comprehensive benchmark for multi-face detection and localization and the proposed \textit{MoNFAP} achieves significant performance. The codes will be made available.