OpenRaiser/Intern-Atlas huggingface dataset:
Next Generation Loss Function for Image Classification
Venue: arXiv cs.CV 2024
Year: 2024

Neural networks are trained by minimizing a loss function that defines the discrepancy between the predicted model output and the target value. The selection of the loss function is crucial to achieve task-specific behaviour and highly influences the capability of the model. A variety of loss functions have been proposed for a wide range of tasks affecting training and model performance. For classification tasks, the cross entropy is the de-facto standard and usually the first choice. Here, we try to experimentally challenge the well-known loss functions, including cross entropy (CE) loss, by utilizing the genetic programming (GP) approach, a population-based evolutionary algorithm. GP constructs loss functions from a set of operators and leaf nodes and these functions are repeatedly recombined and mutated to find an optimal structure. Experiments were carried out on different small-sized datasets CIFAR-10, CIFAR-100 and Fashion-MNIST using an Inception model. The 5 best functions found were evaluated for different model architectures on a set of standard datasets ranging from 2 to 102 classes and very different sizes. One function, denoted as Next Generation Loss (NGL), clearly stood out showing same or better performance for all tested datasets compared to CE. To evaluate the NGL function on a large-scale dataset, we tested its performance on the Imagenet-1k dataset where it showed improved top-1 accuracy compared to models trained with identical settings and other losses. Finally, the NGL was trained on a segmentation downstream task for Pascal VOC 2012 and COCO-Stuff164k datasets improving the underlying model performance.
Eye-tracking in Mixed Reality for Diagnosis of Neurodegenerative Diseases
Venue: arXiv cs.HC 2024
Year: 2024

Parkinson's disease ranks as the second most prevalent neurodegenerative disorder globally. This research aims to develop a system leveraging Mixed Reality capabilities for tracking and assessing eye movements. In this paper, we present a medical scenario and outline the development of an application designed to capture eye-tracking signals through Mixed Reality technology for the evaluation of neurodegenerative diseases. Additionally, we introduce a pipeline for extracting clinically relevant features from eye-gaze analysis, describing the capabilities of the proposed system from a medical perspective. The study involved a cohort of healthy control individuals and patients suffering from Parkinson's disease, showcasing the feasibility and potential of the proposed technology for non-intrusive monitoring of eye movement patterns for the diagnosis of neurodegenerative diseases. Clinical relevance - Developing a non-invasive biomarker for Parkinson's disease is urgently needed to accurately detect the disease's onset. This would allow for the timely introduction of neuroprotective treatment at the earliest stage and enable the continuous monitoring of intervention outcomes. The ability to detect subtle changes in eye movements allows for early diagnosis, offering a critical window for intervention before more pronounced symptoms emerge. Eye tracking provides objective and quantifiable biomarkers, ensuring reliable assessments of disease progression and cognitive function. The eye gaze analysis using Mixed Reality glasses is wireless, facilitating convenient assessments in both home and hospital settings. The approach offers the advantage of utilizing hardware that requires no additional specialized attachments, enabling examinations through personal eyewear.
Aquaculture field robotics: Applications, lessons learned and future prospects
Venue: arXiv cs.RO 2024
Year: 2024

Aquaculture is a big marine industry and contributes to securing global food demands. Underwater vehicles such as remotely operated vehicles (ROVs) are commonly used for inspection, maintenance, and intervention (IMR) tasks in fish farms. However, underwater vehicle operations in aquaculture face several unique and demanding challenges, such as navigation in dynamically changing environments with time-varying sealoads and poor hydroacoustic sensor capabilities, challenges yet to be properly addressed in research. This paper will present various endeavors to address these questions and improve the overall autonomy level in aquaculture robotics, with a focus on field experiments. We will also discuss lessons learned during field trials and potential future prospects in aquaculture robotics.
Goal Exploration via Adaptive Skill Distribution for Goal-Conditioned Reinforcement Learning
Venue: arXiv cs.LG 2024
Year: 2024

Exploration efficiency poses a significant challenge in goal-conditioned reinforcement learning (GCRL) tasks, particularly those with long horizons and sparse rewards. A primary limitation to exploration efficiency is the agent's inability to leverage environmental structural patterns. In this study, we introduce a novel framework, GEASD, designed to capture these patterns through an adaptive skill distribution during the learning process. This distribution optimizes the local entropy of achieved goals within a contextual horizon, enhancing goal-spreading behaviors and facilitating deep exploration in states containing familiar structural patterns. Our experiments reveal marked improvements in exploration efficiency using the adaptive skill distribution compared to a uniform skill distribution. Additionally, the learned skill distribution demonstrates robust generalization capabilities, achieving substantial exploration progress in unseen tasks containing similar local structures.
A Mobile Additive Manufacturing Robot Framework for Smart Manufacturing Systems
Venue: arXiv cs.RO 2024
Year: 2024

Recent technological innovations in the areas of additive manufacturing and collaborative robotics have paved the way toward realizing the concept of on-demand, personalized production on the shop floor. Additive manufacturing process can provide the capability of printing highly customized parts based on various customer requirements. Autonomous, mobile systems provide the flexibility to move custom parts around the shop floor to various manufacturing operations, as needed by product requirements. In this work, we proposed a mobile additive manufacturing robot framework for merging an additive manufacturing process system with an autonomous mobile base. Two case studies showcase the potential benefits of the proposed mobile additive manufacturing framework. The first case study overviews the effect that a mobile system can have on a fused deposition modeling process. The second case study showcases how integrating a mobile additive manufacturing machine can improve the throughput of the manufacturing system. The major findings of this study are that the proposed mobile robotic AM has increased throughput by taking advantage of the travel time between operations/processing sites. It is particularly suited to perform intermittent operations (e.g., preparing feedstock) during the travel time of the robotic AM. One major implication of this study is its application in manufacturing structural components (e.g., concrete construction, and feedstock preparation during reconnaissance missions) in remote or extreme terrains with on-site or on-demand feedstocks.
FPGA Divide-and-Conquer Placement using Deep Reinforcement Learning
Venue: arXiv cs.AR 2024
Year: 2024

This paper introduces the problem of learning to place logic blocks in Field-Programmable Gate Arrays (FPGAs) and a learning-based method. In contrast to previous search-based placement algorithms, we instead employ Reinforcement Learning (RL) with the goal of minimizing wirelength. In addition to our preliminary learning results, we also evaluated a novel decomposition to address the nature of large search space when placing many blocks on a chipboard. Empirical experiments evaluate the effectiveness of the learning and decomposition paradigms on FPGA placement tasks.
Towards Efficient Resume Understanding: A Multi-Granularity Multi-Modal Pre-Training Approach
Venue: arXiv cs.CL 2024
Year: 2024

In the contemporary era of widespread online recruitment, resume understanding has been widely acknowledged as a fundamental and crucial task, which aims to extract structured information from resume documents automatically. Compared to the traditional rule-based approaches, the utilization of recently proposed pre-trained document understanding models can greatly enhance the effectiveness of resume understanding. The present approaches have, however, disregarded the hierarchical relations within the structured information presented in resumes, and have difficulty parsing resumes in an efficient manner. To this end, in this paper, we propose a novel model, namely ERU, to achieve efficient resume understanding. Specifically, we first introduce a layout-aware multi-modal fusion transformer for encoding the segments in the resume with integrated textual, visual, and layout information. Then, we design three self-supervised tasks to pre-train this module via a large number of unlabeled resumes. Next, we fine-tune the model with a multi-granularity sequence labeling task to extract structured information from resumes. Finally, extensive experiments on a real-world dataset clearly demonstrate the effectiveness of ERU.
Relational Graph Convolutional Networks for Sentiment Analysis
Venue: arXiv cs.CL 2024
Year: 2024

With the growth of textual data across online platforms, sentiment analysis has become crucial for extracting insights from user-generated content. While traditional approaches and deep learning models have shown promise, they cannot often capture complex relationships between entities. In this paper, we propose leveraging Relational Graph Convolutional Networks (RGCNs) for sentiment analysis, which offer interpretability and flexibility by capturing dependencies between data points represented as nodes in a graph. We demonstrate the effectiveness of our approach by using pre-trained language models such as BERT and RoBERTa with RGCN architecture on product reviews from Amazon and Digikala datasets and evaluating the results. Our experiments highlight the effectiveness of RGCNs in capturing relational information for sentiment analysis tasks.
A process mining-based error correction approach to improve data quality of an IoT-sourced event log
Venue: arXiv cs.DB 2024
Year: 2024

Internet of Things (IoT) systems are vulnerable to data collection errors and these errors can significantly degrade the quality of collected data, impact data analysis and lead to inaccurate or distorted results. This article emphasizes the importance of evaluating data quality and errors before proceeding with analysis and considering the effectiveness of error correction methods for a smart home use case.
Reducing Redundant Computation in Multi-Agent Coordination through Locally Centralized Execution
Venue: arXiv cs.MA 2024
Year: 2024

In multi-agent reinforcement learning, decentralized execution is a common approach, yet it suffers from the redundant computation problem. This occurs when multiple agents redundantly perform the same or similar computation due to overlapping observations. To address this issue, this study introduces a novel method referred to as locally centralized team transformer (LCTT). LCTT establishes a locally centralized execution framework where selected agents serve as leaders, issuing instructions, while the rest agents, designated as workers, act as these instructions without activating their policy networks. For LCTT, we proposed the team-transformer (T-Trans) architecture that allows leaders to provide specific instructions to each worker, and the leadership shift mechanism that allows agents autonomously decide their roles as leaders or workers. Our experimental results demonstrate that the proposed method effectively reduces redundant computation, does not decrease reward levels, and leads to faster learning convergence.
Robotic deployment on construction sites: considerations for safety and productivity impact
Venue: arXiv cs.RO 2024
Year: 2024

Deploying mobile robots in construction sites to collaborate with workers or perform automated tasks such as surveillance and inspections carries the potential to greatly increase productivity, reduce human errors, and save costs. However ensuring human safety is a major concern, and the rough and dynamic construction environments pose multiple challenges for robot deployment. In this paper, we present the insights we obtained from our collaborations with construction companies in Canada and discuss our experiences deploying a semi-autonomous mobile robot in real construction scenarios.
A universal material model subroutine for soft matter systems
Venue: arXiv cs.CE 2024
Year: 2024

Soft materials play an integral part in many aspects of modern life including autonomy, sustainability, and human health, and their accurate modeling is critical to understand their unique properties and functions. Today's finite element analysis packages come with a set of pre-programmed material models, which may exhibit restricted validity in capturing the intricate mechanical behavior of these materials. Regrettably, incorporating a modified or novel material model in a finite element analysis package requires non-trivial in-depth knowledge of tensor algebra, continuum mechanics, and computer programming, making it a complex task that is prone to human error. Here we design a universal material subroutine, which automates the integration of novel constitutive models of varying complexity in non-linear finite element packages, with no additional analytical derivations and algorithmic implementations. We demonstrate the versatility of our approach to seamlessly integrate innovative constituent models from the material point to the structural level through a variety of soft matter case studies: a frontal impact to the brain; reconstructive surgery of the scalp; diastolic loading of arteries and the human heart; and the dynamic closing of the tricuspid valve. Our universal material subroutine empowers all users, not solely experts, to conduct reliable engineering analysis of soft matter systems. We envision that this framework will become an indispensable instrument for continued innovation and discovery within the soft matter community at large.
DeepFake-O-Meter v2.0: An Open Platform for DeepFake Detection
Venue: arXiv cs.CR 2024
Year: 2024

Deepfakes, as AI-generated media, have increasingly threatened media integrity and personal privacy with realistic yet fake digital content. In this work, we introduce an open-source and user-friendly online platform, DeepFake-O-Meter v2.0, that integrates state-of-the-art methods for detecting Deepfake images, videos, and audio. Built upon DeepFake-O-Meter v1.0, we have made significant upgrades and improvements in platform architecture design, including user interaction, detector integration, job balancing, and security management. The platform aims to offer everyday users a convenient service for analyzing DeepFake media using multiple state-of-the-art detection algorithms. It ensures secure and private delivery of the analysis results. Furthermore, it serves as an evaluation and benchmarking platform for researchers in digital media forensics to compare the performance of multiple algorithms on the same input. We have also conducted detailed usage analysis based on the collected data to gain deeper insights into our platform's statistics. This involves analyzing two-month trends in user activity and evaluating the processing efficiency of each detector.
Hybrid Quantum Tabu Search for Solving the Vehicle Routing Problem
Venue: arXiv cs.ET 2024
Year: 2024

There has never been a more exciting time for the future of quantum computing than now. Near-term quantum computing usage is now the next XPRIZE. With that challenge in mind we have explored a new approach as a hybrid quantum-classical algorithm for solving NP-Hard optimization problems. We have focused on the classic problem of the Capacitated Vehicle Routing Problem (CVRP) because of its real-world industry applications. Heuristics are often employed to solve this problem because it is difficult. In addition, meta-heuristic algorithms have proven to be capable of finding reasonable solutions to optimization problems like the CVRP. Recent research has shown that quantum-only and hybrid quantum/classical approaches to solving the CVRP are possible. Where quantum approaches are usually limited to minimal optimization problems, hybrid approaches have been able to solve more significant problems. Still, the hybrid approaches often need help finding solutions as good as their classical counterparts. In our proposed approach, we created a hybrid quantum/classical metaheuristic algorithm capable of finding the best-known solution to a classic CVRP problem. Our experimental results show that our proposed algorithm often outperforms other hybrid approaches.
Robustness and Accuracy in Pipelined Bi-Conjugate Gradient Stabilized Method: A Comparative Study
Venue: arXiv cs.MS 2024
Year: 2024

In this article, we propose an accuracy-assuring technique for finding a solution for unsymmetric linear systems. Such problems are related to different areas such as image processing, computer vision, and computational fluid dynamics. Parallel implementation of Krylov subspace methods speeds up finding approximate solutions for linear systems. In this context, the refined approach in pipelined BiCGStab enhances scalability on distributed memory machines, yielding to substantial speed improvements compared to the standard BiCGStab method. However, it's worth noting that the pipelined BiCGStab algorithm sacrifices some accuracy, which is stabilized with the residual replacement technique. This paper aims to address this issue by employing the ExBLAS-based reproducible approach. We validate the idea on a set of matrices from the SuiteSparse Matrix Collection.
Preserving History through Augmented Reality
Venue: arXiv cs.HC 2024
Year: 2024

Extended reality can weave together the fabric of the past, present, and future. A two-day design hackathon was held to bring the community together through a love for history and a common goal to use technology for good. Through interviewing an influential community elder, Emile Pitre, and referencing his book Revolution to Evolution, my team developed an augmented reality artifact to tell his story and preserve on revolutionary's legacy that impacted the University of Washington's history forever.
Comparative Analysis on Snowmelt-Driven Streamflow Forecasting Using Machine Learning Techniques
Venue: arXiv cs.LG 2024
Year: 2024

The rapid advancement of machine learning techniques has led to their widespread application in various domains including water resources. However, snowmelt modeling remains an area that has not been extensively explored. In this study, we propose a state-of-the-art (SOTA) deep learning sequential model, leveraging the Temporal Convolutional Network (TCN), for snowmelt-driven discharge modeling in the Himalayan basin of the Hindu Kush Himalayan Region. To evaluate the performance of our proposed model, we conducted a comparative analysis with other popular models including Support Vector Regression (SVR), Long Short Term Memory (LSTM), and Transformer. Furthermore, Nested cross-validation (CV) is used with five outer folds and three inner folds, and hyper-parameter tuning is performed on the inner folds. To evaluate the performance of the model mean absolute error (MAE), root mean square error (RMSE), R square ($R^{2}$), Kling-Gupta Efficiency (KGE), and Nash-Sutcliffe Efficiency (NSE) are computed for each outer fold. The average metrics revealed that TCN outperformed the other models, with an average MAE of 0.011, RMSE of 0.023, $R^{2}$ of 0.991, KGE of 0.992, and NSE of 0.991. The findings of this study demonstrate the effectiveness of the deep learning model as compared to traditional machine learning approaches for snowmelt-driven streamflow forecasting. Moreover, the superior performance of TCN highlights its potential as a promising deep learning model for similar hydrological applications.
Socialized Learning: A Survey of the Paradigm Shift for Edge Intelligence in Networked Systems
Venue: arXiv cs.NI 2024
Year: 2024

Amidst the robust impetus from artificial intelligence (AI) and big data, edge intelligence (EI) has emerged as a nascent computing paradigm, synthesizing AI with edge computing (EC) to become an exemplary solution for unleashing the full potential of AI services. Nonetheless, challenges in communication costs, resource allocation, privacy, and security continue to constrain its proficiency in supporting services with diverse requirements. In response to these issues, this paper introduces socialized learning (SL) as a promising solution, further propelling the advancement of EI. SL is a learning paradigm predicated on social principles and behaviors, aimed at amplifying the collaborative capacity and collective intelligence of agents within the EI system. SL not only enhances the system's adaptability but also optimizes communication, and networking processes, essential for distributed intelligence across diverse devices and platforms. Therefore, a combination of SL and EI may greatly facilitate the development of collaborative intelligence in the future network. This paper presents the findings of a literature review on the integration of EI and SL, summarizing the latest achievements in existing research on EI and SL. Subsequently, we delve comprehensively into the limitations of EI and how it could benefit from SL. Special emphasis is placed on the communication challenges and networking strategies and other aspects within these systems, underlining the role of optimized network solutions in improving system efficiency. Based on these discussions, we elaborate in detail on three integrated components: socialized architecture, socialized training, and socialized inference, analyzing their strengths and weaknesses. Finally, we identify some possible future applications of combining SL and EI, discuss open problems and suggest some future research.
HiVG: Hierarchical Multimodal Fine-grained Modulation for Visual Grounding
Venue: arXiv cs.CV 2024
Year: 2024

Visual grounding, which aims to ground a visual region via natural language, is a task that heavily relies on cross-modal alignment. Existing works utilized uni-modal pre-trained models to transfer visual or linguistic knowledge separately while ignoring the multimodal corresponding information. Motivated by recent advancements in contrastive language-image pre-training and low-rank adaptation (LoRA) methods, we aim to solve the grounding task based on multimodal pre-training. However, there exists significant task gaps between pre-training and grounding. Therefore, to address these gaps, we propose a concise and efficient hierarchical multimodal fine-grained modulation framework, namely HiVG. Specifically, HiVG consists of a multi-layer adaptive cross-modal bridge and a hierarchical multimodal low-rank adaptation (HiLoRA) paradigm. The cross-modal bridge can address the inconsistency between visual features and those required for grounding, and establish a connection between multi-level visual and text features. HiLoRA prevents the accumulation of perceptual errors by adapting the cross-modal features from shallow to deep layers in a hierarchical manner. Experimental results on five datasets demonstrate the effectiveness of our approach and showcase the significant grounding capabilities as well as promising energy efficiency advantages. The project page: https://github.com/linhuixiao/HiVG.