OpenRaiser/Intern-Atlas huggingface dataset:
Digital Fingerprinting on Multimedia: A Survey
Venue: arXiv cs.MM 2024
Year: 2024

The explosive growth of multimedia content in the digital economy era has brought challenges in content recognition, copyright protection, and data management. As an emerging content management technology, perceptual hash-based digital fingerprints, serving as compact summaries of multimedia content, have been widely adopted for efficient multimedia content identification and retrieval across different modalities (e.g., text, image, video, audio), attracting significant attention from both academia and industry. Despite the increasing applications of digital fingerprints, there is a lack of systematic and comprehensive literature review on multimedia digital fingerprints. This survey aims to fill this gap and provide an important resource for researchers studying the details and related advancements of multimedia digital fingerprints. The survey first introduces the definition, characteristics, and related concepts (including hash functions, granularity, similarity measures, etc.) of digital fingerprints. It then focuses on analyzing and summarizing the algorithms for extracting unimodal fingerprints of different types of digital content, including text fingerprints, image fingerprints, video fingerprints, and audio fingerprints. Particularly, it provides an in-depth review and summary of deep learning-based fingerprints. Additionally, the survey elaborates on the various practical applications of digital fingerprints and outlines the challenges and potential future research directions. The goal is to promote the continued development of multimedia digital fingerprint research.
DynamicRouteGPT: A Real-Time Multi-Vehicle Dynamic Navigation Framework Based on Large Language Models
Venue: arXiv cs.AI 2024
Year: 2024

Real-time dynamic path planning in complex traffic environments presents challenges, such as varying traffic volumes and signal wait times. Traditional static routing algorithms like Dijkstra and A* compute shortest paths but often fail under dynamic conditions. Recent Reinforcement Learning (RL) approaches offer improvements but tend to focus on local optima, risking dead-ends or boundary issues. This paper proposes a novel approach based on causal inference for real-time dynamic path planning, balancing global and local optimality. We first use the static Dijkstra algorithm to compute a globally optimal baseline path. A distributed control strategy then guides vehicles along this path. At intersections, DynamicRouteGPT performs real-time decision-making for local path selection, considering real-time traffic, driving preferences, and unexpected events. DynamicRouteGPT integrates Markov chains, Bayesian inference, and large-scale pretrained language models like Llama3 8B to provide an efficient path planning solution. It dynamically adjusts to traffic scenarios and driver preferences and requires no pre-training, offering broad applicability across road networks. A key innovation is the construction of causal graphs for counterfactual reasoning, optimizing path decisions. Experimental results show that our method achieves state-of-the-art performance in real-time dynamic path planning for multiple vehicles while providing explainable path selections, offering a novel and efficient solution for complex traffic environments.
Harnessing the Digital Revolution: A Comprehensive Review of mHealth Applications for Remote Monitoring in Transforming Healthcare Delivery
Venue: arXiv cs.SE 2024
Year: 2024

The utilization of mHealth applications for remote monitoring has the potential to revolutionize healthcare delivery by enhancing patient outcomes, increasing access to healthcare services, and reducing healthcare costs. This literature review aims to provide a comprehensive overview of the current state of knowledge on mHealth applications for remote monitoring, including their types, benefits, challenges, and limitations, as well as future directions and research gaps. A systematic search of databases such as PubMed, MEDLINE, EMBASE, CINAHL, and Google Scholar was conducted to identify relevant articles published within the last 5 years. Thematic analysis was used to synthesize the findings. The review highlights various types of mHealth applications used for remote monitoring, such as telemedicine platforms, mobile apps for chronic disease management, and wearable devices. The benefits of these applications include improved patient outcomes, increased access to healthcare, reduced healthcare costs, and addressing healthcare disparities. However, challenges and limitations, such as privacy and security concerns, lack of technical infrastructure, regulatory is-sues, data accuracy, user adherence, and the digital divide, need to be addressed to ensure successful adoption and utilization of mHealth applications. Further research is required in areas such as the long-term effects of mHealth applications on patient outcomes, integration of mHealth data with electronic health records, and the development of artificial intelligence-driven mHealth applica-tions. By harnessing the potential of mHealth applications and addressing the existing challenges, healthcare delivery can be transformed towards a more accessible, cost-effective, and patient-centered model.
MagicMan: Generative Novel View Synthesis of Humans with 3D-Aware Diffusion and Iterative Refinement
Venue: arXiv cs.CV 2024
Year: 2024

Existing works in single-image human reconstruction suffer from weak generalizability due to insufficient training data or 3D inconsistencies for a lack of comprehensive multi-view knowledge. In this paper, we introduce MagicMan, a human-specific multi-view diffusion model designed to generate high-quality novel view images from a single reference image. As its core, we leverage a pre-trained 2D diffusion model as the generative prior for generalizability, with the parametric SMPL-X model as the 3D body prior to promote 3D awareness. To tackle the critical challenge of maintaining consistency while achieving dense multi-view generation for improved 3D human reconstruction, we first introduce hybrid multi-view attention to facilitate both efficient and thorough information interchange across different views. Additionally, we present a geometry-aware dual branch to perform concurrent generation in both RGB and normal domains, further enhancing consistency via geometry cues. Last but not least, to address ill-shaped issues arising from inaccurate SMPL-X estimation that conflicts with the reference image, we propose a novel iterative refinement strategy, which progressively optimizes SMPL-X accuracy while enhancing the quality and consistency of the generated multi-views. Extensive experimental results demonstrate that our method significantly outperforms existing approaches in both novel view synthesis and subsequent 3D human reconstruction tasks.
Fact Probability Vector Based Goal Recognition
Venue: arXiv cs.AI 2024
Year: 2024

We present a new approach to goal recognition that involves comparing observed facts with their expected probabilities. These probabilities depend on a specified goal g and initial state s0. Our method maps these probabilities and observed facts into a real vector space to compute heuristic values for potential goals. These values estimate the likelihood of a given goal being the true objective of the observed agent. As obtaining exact expected probabilities for observed facts in an observation sequence is often practically infeasible, we propose and empirically validate a method for approximating these probabilities. Our empirical results show that the proposed approach offers improved goal recognition precision compared to state-of-the-art techniques while reducing computational complexity.
Cascaded Temporal Updating Network for Efficient Video Super-Resolution
Venue: arXiv cs.CV 2024
Year: 2024

Existing video super-resolution (VSR) methods generally adopt a recurrent propagation network to extract spatio-temporal information from the entire video sequences, exhibiting impressive performance. However, the key components in recurrent-based VSR networks significantly impact model efficiency, e.g., the alignment module occupies a substantial portion of model parameters, while the bidirectional propagation mechanism significantly amplifies the inference time. Consequently, developing a compact and efficient VSR method that can be deployed on resource-constrained devices, e.g., smartphones, remains challenging. To this end, we propose a cascaded temporal updating network (CTUN) for efficient VSR. We first develop an implicit cascaded alignment module to explore spatio-temporal correspondences from adjacent frames. Moreover, we propose a unidirectional propagation updating network to efficiently explore long-range temporal information, which is crucial for high-quality video reconstruction. Specifically, we develop a simple yet effective hidden updater that can leverage future information to update hidden features during forward propagation, significantly reducing inference time while maintaining performance. Finally, we formulate all of these components into an end-to-end trainable VSR network. Extensive experimental results show that our CTUN achieves a favorable trade-off between efficiency and performance compared to existing methods. Notably, compared with BasicVSR, our method obtains better results while employing only about 30% of the parameters and running time. The source code and pre-trained models will be available at https://github.com/House-Leo/CTUN.
Collaborative XRTactics: A Formative Study on Tactical Communication in Outdoor Team Sports
Venue: arXiv cs.HC 2024
Year: 2024

In team sports, effective tactical communication is crucial for success, particularly in the fast-paced and complex environment of outdoor athletics. This paper investigates the challenges faced in transmitting strategic plans to players and explores potential solutions using eXtended Reality (XR) technologies. We conducted a formative study involving interviews with 4 Division I professional soccer coaches, 4 professional players, 2 college club coaches, and 2 college club players, as well as a survey among 17 Division I players. The study identified key requirements for tactical communication tools, including the need for rapid communication, minimal disruption to game flow, reduced cognitive load, clear visualization for all players, and enhanced auditory clarity. Based on these insights, we propose a potential solution - a Mobile Augmented Reality (AR) system designed to address these challenges by providing real-time, intuitive tactical visualization and communication. The system aims to improve strategic planning and execution, thereby enhancing team performance and cohesion. This work represents a significant step towards integrating XR technologies into sports coaching, offering a modern and practical solution for real-time tactical communication.
The Power of Proportional Fairness for Non-Clairvoyant Scheduling under Polyhedral Constraints
Venue: arXiv cs.DS 2024
Year: 2024

The Polytope Scheduling Problem (PSP) was introduced by Im, Kulkarni, and Munagala (JACM 2018) as a very general abstraction of resource allocation over time and captures many well-studied problems including classical unrelated machine scheduling, multidimensional scheduling, and broadcast scheduling. In PSP, jobs with different arrival times receive processing rates that are subject to arbitrary packing constraints. An elegant and well-known algorithm for instantaneous rate allocation with good fairness and efficiency properties is the Proportional Fairness algorithm (PF), which was analyzed for PSP by Im et al. We drastically improve the analysis of the PF algorithm for both the general PSP and several of its important special cases subject to the objective of minimizing the sum of weighted completion times. We reduce the upper bound on the competitive ratio from 128 to 27 for general PSP and to 4 for the prominent class of monotone PSP. For certain heterogeneous machine environments we even close the substantial gap to the lower bound of 2 for non-clairvoyant scheduling. Our analysis also gives the first polynomial-time improvements over the nearly 30-year-old bounds on the competitive ratio of the doubling framework by Hall, Shmoys, and Wein (SODA 1996) for clairvoyant online preemptive scheduling on unrelated machines. Somewhat surprisingly, we achieve this improvement by a non-clairvoyant algorithm, thereby demonstrating that non-clairvoyance is not a (significant) hurdle. Our improvements are based on exploiting monotonicity properties of PSP, providing tight dual fitting arguments on structured instances, and showing new additivity properties on the optimal objective value for scheduling on unrelated machines. Finally, we establish new connections of PF to matching markets, and thereby provide new insights on equilibria and their computational complexity.
Model Predictive Parkour Control of a Monoped Hopper in Dynamically Changing Environments
Venue: arXiv cs.RO 2024
Year: 2024

A great advantage of legged robots is their ability to operate on particularly difficult and obstructed terrain, which demands dynamic, robust, and precise movements. The study of obstacle courses provides invaluable insights into the challenges legged robots face, offering a controlled environment to assess and enhance their capabilities. Traversing it with a one-legged hopper introduces intricate challenges, such as planning over contacts and dealing with flight phases, which necessitates a sophisticated controller. A novel model predictive parkour controller is introduced, that finds an optimal path through a real-time changing obstacle course with mixed integer motion planning. The execution of this optimized path is then achieved through a state machine employing a PD control scheme with feedforward torques, ensuring robust and accurate performance.
User-Access Point Association for High Density MIMO Wireless LANs
Venue: arXiv cs.IT 2024
Year: 2024

Wireless local area network (WLAN) access points (APs) are being deployed in high density to improve coverage and throughput. The emerging multiple-input multiple-output (MIMO) implementation for uplink (UL) transmissions promises high per-user throughput and improved aggregate network throughput. However, the high throughput potential of dense UL-MIMO WLAN is impaired by multiple access channel interference and high contention among densely distributed user stations (STAs). We investigate the problem of actualizing the throughput potential of UL-MIMO in high density WLANs via user-AP association. Since user-AP association influences interference and STA contention, a method to optimally distribute STAs among APs is proposed to maximize aggregate users' throughput utility. This problem is transformed into a graph matching problem with the throughput utility function as the graph edge weights. The graph matching problem is solved as a combinatorial problem using a modified classical Kuhn-Munkres algorithm. A dynamic implementation of the proposed algorithm is used to periodically update user-AP associations when there are changes in the network due to new entrants and/or user mobility. Simulated dense UL-MIMO WLAN scenarios reveal that the proposed scheme achieves an average of $36.9 \%$, $33.5 \%$, $20.4 \%$ and $11.3 \%$ gains over the default strongest signal first (SSF) association scheme used in conventional WLAN, Greedy [14], SmartAssoc [13] and best performance first (BPF) [5] algorithms, respectively.
Towards Better Comprehension of Breaking Changes in the NPM Ecosystem
Venue: arXiv cs.SE 2024
Year: 2024

Breaking changes cause a lot of effort to both downstream and upstream developers: downstream developers need to adapt to breaking changes and upstream developers are responsible for identifying and documenting them. In the NPM ecosystem, characterized by frequent code changes and a high tolerance for making breaking changes, the effort is larger. For better comprehension of breaking changes in the NPM ecosystem and to enhance breaking change detection tools, we conduct a large-scale empirical study to investigate breaking changes in the NPM ecosystem. We construct a dataset of explicitly documented breaking changes from 381 popular NPM projects. We find that 95.4% of the detected breaking changes can be covered by developers' documentation, and about 19% of the breaking changes cannot be detected by regression testing. Then in the process of investigating source code of our collected breaking changes, we yield a taxonomy of JavaScript and TypeScript-specific syntactic breaking changes and a taxonomy of major types of behavioral breaking changes. Additionally, we investigate the reasons why developers make breaking changes in NPM and find three major reasons, i.e., to reduce code redundancy, to improve identifier name, and to improve API design, and each category contains several sub-items. We provide actionable implications for future research, e.g., automatic naming and renaming techniques should be applied in JavaScript projects to improve identifier names, future research can try to detect more types of behavioral breaking changes. By presenting the implications, we also discuss the weakness of automatic renaming and breaking change detection approaches.
Explicit Inductive Inference using Large Language Models
Venue: arXiv cs.CL 2024
Year: 2024

Large Language Models (LLMs) are reported to hold undesirable attestation bias on inference tasks: when asked to predict if a premise P entails a hypothesis H, instead of considering H's conditional truthfulness entailed by P, LLMs tend to use the out-of-context truth label of H as a fragile proxy. In this paper, we propose a pipeline that exploits this bias to do explicit inductive inference. Our pipeline uses an LLM to transform a premise into a set of attested alternatives, and then aggregate answers of the derived new entailment inquiries to support the original inference prediction. On a directional predicate entailment benchmark, we demonstrate that by applying this simple pipeline, we can improve the overall performance of LLMs on inference and substantially alleviate the impact of their attestation bias.
Evolvable Psychology Informed Neural Network for Memory Behavior Modeling
Venue: arXiv cs.LG 2024
Year: 2024

Memory behavior modeling is a core issue in cognitive psychology and education. Classical psychological theories typically use memory equations to describe memory behavior, which exhibits insufficient accuracy and controversy, while data-driven memory modeling methods often require large amounts of training data and lack interpretability. Knowledge-informed neural network models have shown excellent performance in fields like physics, but there have been few attempts in the domain of behavior modeling. This paper proposed a psychology theory informed neural networks for memory behavior modeling named PsyINN, where it constructs a framework that combines neural network with differentiating sparse regression, achieving joint optimization. Specifically, to address the controversies and ambiguity of descriptors in memory equations, a descriptor evolution method based on differentiating operators is proposed to achieve precise characterization of descriptors and the evolution of memory theoretical equations. Additionally, a buffering mechanism for the sparse regression and a multi-module alternating iterative optimization method are proposed, effectively mitigating gradient instability and local optima issues. On four large-scale real-world memory behavior datasets, the proposed method surpasses the state-of-the-art methods in prediction accuracy. Ablation study demonstrates the effectiveness of the proposed refinements, and application experiments showcase its potential in inspiring psychological research.
Applying graph neural network to SupplyGraph for supply chain network
Venue: arXiv cs.LG 2024
Year: 2024

Supply chain networks describe interactions between products, manufacture facilities, storages in the context of supply and demand of the products. Supply chain data are inherently under graph structure; thus, it can be fertile ground for applications of graph neural network (GNN). Very recently, supply chain dataset, SupplyGraph, has been released to the public. Though the SupplyGraph dataset is valuable given scarcity of publicly available data, there was less clarity on description of the dataset, data quality assurance process, and hyperparameters of the selected models. Further, for generalizability of findings, it would be more convincing to present the findings by performing statistical analyses on the distribution of errors rather than showing the average value of the errors. Therefore, this study assessed the supply chain dataset, SupplyGraph, with better clarity on analyses processes, data quality assurance, machine learning (ML) model specifications. After data quality assurance procedures, this study compared performance of Multilayer Perceptions (MLP), Graph Convolution Network (GCN), and Graph Attention Network (GAT) on a demanding forecasting task while matching hyperparameters as feasible as possible. The analyses revealed that GAT performed best, followed by GCN and MLP. Those performance improvements were statistically significant at $α= 0.05$ after correction for multiple comparisons. This study also discussed several considerations in applying GNN to supply chain networks. The current study reinforces the previous study in supply chain benchmark dataset with respect to description of the dataset and methodology, so that the future research in applications of GNN to supply chain becomes more reproducible.
Variational autoencoder-based neural network model compression
Venue: arXiv cs.LG 2024
Year: 2024

Variational Autoencoders (VAEs), as a form of deep generative model, have been widely used in recent years, and shown great great peformance in a number of different domains, including image generation and anomaly detection, etc.. This paper aims to explore neural network model compression method based on VAE. The experiment uses different neural network models for MNIST recognition as compression targets, including Feedforward Neural Network (FNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM). These models are the most basic models in deep learning, and other more complex and advanced models are based on them or inherit their features and evolve. In the experiment, the first step is to train the models mentioned above, each trained model will have different accuracy and number of total parameters. And then the variants of parameters for each model are processed as training data in VAEs separately, and the trained VAEs are tested by the true model parameters. The experimental results show that using the latent space as a representation of the model compression can improve the compression rate compared to some traditional methods such as pruning and quantization, meanwhile the accuracy is not greatly affected using the model parameters reconstructed based on the latent space. In the future, a variety of different large-scale deep learning models will be used more widely, so exploring different ways to save time and space on saving or transferring models will become necessary, and the use of VAE in this paper can provide a basis for these further explorations.