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32nd ACM International Conference on Multimedia, MM 2024

作者： Li, Yudong Hou, Xianxu Dezhi, Zheng Shen, Linlin Zhao, Zhe School of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China School of AI and Advanced Computing Xi'an Jiaotong-Liverpool University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Tencent AI Lab Beijing China

ISBN: (纸本)9798400706868

While significant progress has been made in multi-modal learning driven by large-scale image-text datasets, there is still a noticeable gap in the availability of such datasets within the facial domain. To facilitate and advance the field of facial representation learning, we present FLIP-80M, a large-scale visual-linguistic dataset comprising over 80 million face images paired with text descriptions. FLIP-80M is constructed by leveraging the large openly available image-text-pair dataset LAION-5B and a mixed-method approach to filter face-related pairs from both visual and linguistic perspectives. Our curation process involves face detection, face caption classification, text de-noising, and synthesis-based image augmentation. As a result, FLIP-80M stands as the largest face-text dataset to date. To evaluate the potential of our dataset, we fine-tune the CLIP model using the proposed FLIP-80M, to create FLIP (Facial Language-Image Pretraining) and assess its representation capabilities across various downstream tasks. Our experiments demonstrate that our FLIP model achieves state-of-the-art results in a range of face analysis tasks, including face parsing, face alignment, and face attribute classification. The dataset and models are available at https://***/ydli-ai/FLIP. © 2024 ACM.

关键词： Face recognition

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Hyperbolic Geometric Latent Diffusion Model for Graph Generation 41

Hyperbolic Geometric Latent Diffusion Model for Graph Genera...

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41st International Conference on Machine Learning, ICML 2024

作者： Fu, Xingcheng Gao, Yisen Wei, Yuecen Sun, Qingyun Peng, Hao Li, Jianxin Li, Xianxian Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Institute of Artificial Intelligence Beihang University Beijing China School of Software Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and Engineering Beihang University Beijing China

Diffusion models have made significant contributions to computer vision, sparking a growing interest in the community recently regarding the application of them to graph generation. Existing discrete graph diffusion models exhibit heightened computational complexity and diminished training efficiency. A preferable and natural way is to directly diffuse the graph within the latent space. However, due to the non-Euclidean structure of graphs is not isotropic in the latent space, the existing latent diffusion models effectively make it difficult to capture and preserve the topological information of graphs. To address the above challenges, we propose a novel geometrically latent diffusion framework HypDiff. Specifically, we first establish a geometrically latent space with interpretability measures based on hyperbolic geometry, to define anisotropic latent diffusion processes for graphs. Then, we propose a geometrically latent diffusion process that is constrained by both radial and angular geometric properties, thereby ensuring the preservation of the original topological properties in the generative graphs. Extensive experimental results demonstrate the superior effectiveness of HypDiff for graph generation with various topologies. Copyright 2024 by the author(s)

关键词： Graph theory

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A Real- Time Multi-Camera Auto-Adjustment Framework for Infrastructure Inspections

A Real- Time Multi-Camera Auto-Adjustment Framework for Infr...

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IEEE/SICE International Symposium on System Integration

作者： Chuong Phuoc Le Charles Ellison Steven Bunkley Hung La Anton Netchaev Department of Computer Science and Engineering Advanced Robotics and Automation (ARA) Lab University of Nevada Reno NV USA USACE Engineer Research and Development Center (ERDC) Information Technology Lab Vicksburg MS USA

In this paper, we proposed a novel multi-camera auto-adjustment framework for infrastructure inspections, specifically 3D point cloud mapping via lidar-camera fusion. Our method uses a single controller for consensus target brightness and adaptive lighting. In addition, a vignette correction filter was created to apply to images outputted from cameras to avoid dark strips on the point cloud data. This approach is designed to be a versatile, real-time friendly, and platform agnostic software stack.

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An Overview of Neuromarketing Research in Developing Countries: Prospects and Challenges 22

An Overview of Neuromarketing Research in Developing Countri...

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2nd International Conference on Computing Advancements: Age of Computing and Augmented Life, ICCA 2022

作者： Biswas, Ananna Mashrur, Fazla Rabbi Rahman, Khandoker Mahmudur Miya, Mohammad Tohidul Islam Sarker, Farhana Mamun, Khondaker A. Advanced Intelligent Multidisciplinary Systems Lab United International University Dhaka Bangladesh School of Business and Economics United International University Dhaka Bangladesh Department of Computer Science and Engineering University of Liberal Arts Dhaka Bangladesh

ISBN: (纸本)9781450397346

Neuromarketing has opened a new door in marketing research understanding behavioral economics with the help of Neuroscience. Over the past decade, Neuroscientists, Psychiatrists, Engineers, and Market-researchers have conducted several groundbreaking studies aiming to understand consumers' motivations, preferences, and decisions. However, these studies and practices are mainly based on developed countries. In this study, we outline the opportunities, real-life applications, future scenarios and shed light on the challenges faced by the researchers, marketers, and policymakers in developing countries including Bangladesh. Moreover, we have focused on the significant brain lobe involving neuromarketing research with the explanation of current technologies used in this area. We have concluded with some feasible recommendations to continue and sustain the growth of the neuromarketing field in developing countries. We expect that this study will give the directions on the inauguration of neuromarketing research in developing countries like Bangladesh that will help technologists, researchers, and marketers understand the advantages, challenges, and state-of-art of neuromarketing research. © 2022 ACM.

关键词： Brain computer interface

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Path Pooling: Train-Free Structure Enhancement for Efficient Knowledge Graph Retrieval-Augmented Generation

arXiv

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arXiv 2025年

作者： Wang, Hairu Feng, Yuan Xie, Xike Zhou, S. Kevin School of Computer Science University of Science and Technology of China China School of Biomedical Engineering USTC China Data Darkness Lab MIRACLE Center Suzhou Institute for Advanced Reasearch USTC China

Although Large Language Models achieve strong success in many tasks, they still suffer from hallucinations and knowledge deficiencies in real-world applications. Many knowledge graph-based retrieval-augmented generation (KG-RAG) methods enhance the quality and credibility of LLMs by leveraging structure and semantic information in KGs as external knowledge bases. However, these methods struggle to effectively incorporate structure information, either incurring high computational costs or underutilizing available knowledge. Inspired by smoothing operations in graph representation learning, we propose path pooling, a simple, train-free strategy that introduces structure information through a novel path-centric pooling operation. It seamlessly integrates into existing KG-RAG methods in a plug- and-play manner, enabling richer structure information utilization. Extensive experiments demonstrate that incorporating the path pooling into the state-of-the-art KG-RAG method consistently improves performance across various settings while introducing negligible additional cost. Code is coming soon at https://***/hrwang00/path-pooling. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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Real-Time Stress Detection via Photoplethysmogram Signals: Implementation of a Combined Continuous Wavelet Transform and Convolutional Neural Network on Resource-Constrained Microcontrollers

Real-Time Stress Detection via Photoplethysmogram Signals: I...

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Iranian Conference on Electrical engineering (ICEE)

作者： Yasin Hasanpoor Amin Rostami Bahram Tarvirdizadeh Khalil Alipour Mohammad Ghamari Department of Mechatronics Engineering Advanced Service Robots (ASR) Lab. Faculty of New Sciences and Technologies University of Tehran Tehran Tehran Iran Department of Electrical and Computer Engineering Kettering University Flint Michigan USA

ISBN: (数字)9798350376340

ISBN: (纸本)9798350376357

This paper introduces a robust stress detection system utilizing a Convolutional Neural Network (CNN) designed for the analysis of Photoplethysmogram (PPG) signals. Employing the WESAD dataset, we applied Continuous Wavelet Transform (CWT) to extract informative features from wrist PPG signals, demonstrating enhanced stress detection and learning compared to conventional techniques. Notably, the CNN achieved an impressive accuracy of 93.7% after five epochs, post-implementation on a resource-constrained microcontroller. The optimization process, including pruning and Post-Train Quantization, was crucial to reduce the model size to 1.6 megabytes, overcoming the microcontroller's limited resources of 2 megabytes of Flash memory and 512 kilobytes of RAM. This optimized model not only addresses resource constraints but also outperforms traditional signal processing methods, positioning it as a promising solution for real-time stress monitoring on wearable devices.

关键词： Continuous wavelet transforms Accuracy Quantization (signal) Microcontrollers Random access memory Feature extraction Real-time systems

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From Image to Language: A Critical Analysis of Visual Question Answering (VQA) Approaches, Challenges, and Opportunities

arXiv

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arXiv 2023年

作者： Ishmam, Md Farhan Shovon, Md Sakib Hossain Mridha, M.F. Dey, Nilanjan Department of Computer Science and Engineering Islamic University of Technology Dhaka Bangladesh Advanced Machine Intelligence Research Lab Dhaka Bangladesh Department of Computer Science and Engineering American International University Dhaka Bangladesh Department of Computer Science and Engineering Techno International New Town Kolkata India

The multimodal task of Visual Question Answering (VQA) encompassing elements of computer Vision (CV) and Natural Language Processing (NLP), aims to generate answers to questions on any visual input. Over time, the scope of VQA has expanded from datasets focusing on an extensive collection of natural images to datasets featuring synthetic images, video, 3D environments, and various other visual inputs. The emergence of large pre-trained networks has shifted the early VQA approaches relying on feature extraction and fusion schemes to vision language pre-training (VLP) techniques. However, there is a lack of comprehensive surveys that encompass both traditional VQA architectures and contemporary VLP-based methods. Furthermore, the VLP challenges in the lens of VQA haven’t been thoroughly explored, leaving room for potential open problems to emerge. Our work presents a survey in the domain of VQA that delves into the intricacies of VQA datasets and methods over the field’s history, introduces a detailed taxonomy to categorize the facets of VQA, and highlights the recent trends, challenges, and scopes for improvement. We further generalize VQA to multimodal question answering, explore tasks related to VQA, and present a set of open problems for future investigation. The work aims to navigate both beginners and experts by shedding light on the potential avenues of research and expanding the boundaries of the field. © 2023, CC BY.

关键词： Question answering

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A Multi-input Deep Neural Network Framework for Non-invasive Detection of Anemia using Finger Nail Images

A Multi-input Deep Neural Network Framework for Non-invasive...

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International Conference on Biosignals, Images and Instrumentation (ICBSII)

作者： Krithika S Ajay Kumar Reddy Poreddy Thunakala Balakrishna Priyanka Kokil Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India Department of Electronics and Communication Engineering Advanced Signal and Image Processing (ASIP) Lab Indian Institute of Information Technology Design and Manufacturing Kancheepuram Chennai

ISBN: (数字)9798350350951

ISBN: (纸本)9798350350968

Anemia, characterized by a deficiency in red blood corpuscles or hemoglobin, poses a significant global health challenge, particularly affecting vulnerable populations. Traditional diagnostic methods often involve invasive procedures, posing challenges in resource-limited settings. This study aims to explore non-invasive anemia detection using fingernail images and convolutional neural networks (CNNs) as a promising alternative to conventional diagnostic approaches. The study utilizes a dataset of fingernail images collected from hospitals in Ghana, comprising both anemic and non-anemic cases. The dataset undergoes preprocessing, including selective enhancement of red components, conversion to the CIElab color space, and feature extraction. A multi-input Deep Neural Network (DNN) framework employing pre-trained CNNs is proposed for anemia classification. The pre-trained CNN architectures include EfficientNet B1, EfficientNet B4, and MobileNet V3. The framework’s performance was assessed using two methodologies: The first involved random shuffling of the dataset, followed by division into training, testing, and validation sets, with evaluation metrics including Accuracy, Precision, F1 scores, and a Confusion Matrix. The second employed five-fold cross-validation, measured using accuracy. The evaluation of the proposed DNN framework using both of the methodologies indicates that EfficientNet B4 achieved the highest testing accuracy (97.87%), precision (97.88%), recall (97.87%), and F1 score (97.88%) and a cross-validation accuracy of 97.37% for the first and second methodologies respectively making it best fit for the proposed DNN framework. The findings demonstrate that the proposed framework yields promising results, especially under the second approach, and opens avenues for further exploration in transfer learning, fine-tuning of deep neural networks for multi-input feature integration, and cross-validation.

关键词： Training Accuracy Transfer learning Sociology Nails Quality assessment Convolutional neural networks

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BRNES: Enabling Security and Privacy-Aware Experience Sharing in Multiagent Robotic and Autonomous Systems

BRNES: Enabling Security and Privacy-Aware Experience Sharin...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Md Tamjid Hossain Hung Manh La Shahriar Badsha Anton Netchaev The Advanced Robotics and Automation Lab Computer Science and Engineering University of Nevada Reno Nevada USA Bosch Engineering North America The U.S. Army Corps.

Although experience sharing (ES) accelerates multiagent reinforcement learning (MARL) in an advisor-advisee framework, attempts to apply ES to decentralized multiagent systems have so far relied on trusted environments and over-looked the possibility of adversarial manipulation and inference. Nevertheless, in a real-world setting, some Byzantine attackers, disguised as advisors, may provide false advice to the advisee and catastrophically degrade the overall learning performance. Also, an inference attacker, disguised as an advisee, may conduct several queries to infer the advisors' private information and make the entire ES process questionable in terms of privacy leakage. To address and tackle these issues, we propose a novel MARL framework (BRNES) that heuristically selects a dynamic neighbor zone for each advisee at each learning step and adopts a weighted experience aggregation technique to reduce Byzantine attack impact. Furthermore, to keep the agent's private information safe from adversarial inference attacks, we leverage the local differential privacy (LDP)-induced noise during the ES process. Our experiments show that our framework outperforms the state-of-the-art in terms of the steps to goal, obtained reward, and time to goal metrics. Particularly, our evaluation shows that the proposed framework is 8.32x faster than the current non-private frameworks and 1.41x faster than the private frameworks in an adversarial setting.

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Nonlinear Model Predictive Control for Autonomous Sailboat with Optimization of Sail Angle

Nonlinear Model Predictive Control for Autonomous Sailboat w...

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OCEANS

作者： Junzhuo Wu Ya-Jun Pan Chao Shen Sean Smith Emmanuel Witrant Department of Mechanical Engineering Advanced Control and Mechatronics Lab Dalhousie University Halifax Canada Department of Systems and Computer Engineering Carleton University Ottawa Canada GIPSA-lab Université Grenoble Alpes-CNRS Grenoble France Departement of Mechanical Engineering Dalhousie University Halifax Nova Scotia Canada

ISBN: (数字)9798331540081

ISBN: (纸本)9798331540098

This paper studies trajectory tracking control problems of sailing vessels using a nonlinear model predictive control (NMPC) approach with a novel sail angle optimization approach. The proposed sail angle optimization method accounts for physical constraints during practical implementation such as operational sail angle bound and rate of change in sail angles. This technique also emphasizes the potential to use the sail to plan a safe trajectory, while a simple proportional-derivative (PD) controller is used for rudder angle regulation. The NMPC is implemented for trajectory tracking control in the simulation. Results show that the proposed controller can achieve excellent tracking performance in the presence of environmental disturbances in terms of ocean waves.

关键词： Trajectory tracking Oceans Optimization methods Ocean waves Predictive models Regulation Trajectory PD control Predictive control

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