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arXiv 2023年

作者： Xiao, Meng Wang, Dongjie Wu, Min Wang, Pengfei Zhou, Yuanchun Fu, Yanjie Computer Network Information Center Chinese Academy of Sciences China University of Chinese Academy of Sciences China Department of Computer Science University of Central Florida United States Singapore Arizona State University School of Computing and AI United States

The goal of Feature Selection - comprising filter, wrapper, and embedded approaches - is to find the optimal feature subset for designated downstream tasks. Nevertheless, current feature selection methods are limited by: 1) the selection criteria of these methods are varied for different domains, leading them hard to be generalized;2) the selection performance of these approaches drops significantly when processing high-dimensional feature space coupled with small sample size. In light of these challenges, we pose the question: can selected feature subsets be more robust, accurate, and input dimensionality agnostic? In this paper, we reformulate the feature selection problem as a deep differentiable optimization task and propose a new research perspective: conceptualizing discrete feature subsetting as continuous embedding space optimization. We introduce a novel and principled framework that encompasses a sequential encoder, an accuracy evaluator, a sequential decoder, and a gradient ascent optimizer. This comprehensive framework includes four important steps: preparation of features-accuracy training data, deep feature subset embedding, gradient-optimized search, and feature subset reconstruction. Specifically, we utilize reinforcement feature selection learning to generate diverse and high-quality training data and enhance generalization. By optimizing reconstruction and accuracy losses, we embed feature selection knowledge into a continuous space using an encoder-evaluator-decoder model structure. We employ a gradient ascent search algorithm to find better embeddings in the learned embedding space. Furthermore, we reconstruct feature selection solutions using these embeddings and select the feature subset with the highest performance for downstream tasks as the optimal subset. Finally, extensive experimental results demonstrate the effectiveness of our proposed method, showcasing significant enhancements in feature selection robustness and accuracy. To improve t

关键词： Feature Selection

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AIM-CP: AI-Based Multi-visual Components Analysis for Crowd Dynamics Prediction

AIM-CP: AI-Based Multi-Visual Components Analysis for Crowd ...

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International Conference on Image and Vision computing New Zealand, IVCNZ

作者： Elham Basunduwah Muhammad Mohzary Sejun Song Baek-Young Choi School of Science and Engineering University of Missouri-Kansas City MO USA College of Engineering and Computing in Al-Qunfudhah Umm Al-Qura University Makkah Saudi Arabia Department of Computer Science Jazan University Jazan Saudi Arabia Engineering and Technology Research Center Jazan University Jazan Saudi Arabia School of Computer and Cyber Science Augusta University Augusta GA USA

ISBN: (数字)9798331518776

ISBN: (纸本)9798331518783

Accurate prediction of crowd mobility dynamics is essential for effective crowd safety management. Traditional models, such as the Random Waypoint Model (RWM), have been widely employed for mobility simulation. However, they suffer inherent limitations, including non-uniform node distribution and speed decay, which reduce their predictive accuracy. Although vision-based crowd mobility tracking methods show promise, movement prediction still faces significant challenges in achieving accuracy, scalability, generalization, and computational efficiency. This paper introduces a novel AI-based multi-visual components analysis approach for crowd dynamics prediction called AIM-CP. This approach combines the YOLOv8 object detection system with a rule-based expert system to enhance prediction accuracy. AIM-CP integrates multi-modal data from face, body, and pose movements across 12 distinct classes, improving real-time processing capabilities and leveraging more scalable and robust AI models. To support this approach, we developed high-resolution datasets, including mall datasets, the CrowdHuman dataset, selected video frames from YouTube, and images from Unsplash. Experimental results demonstrate that AIM-CP is highly effective in detecting mobility dynamics, significantly improving single-modal prediction models with an accuracy of 90.12% in real-world mobility scenarios. These findings suggest that AIM -CP offers a powerful tool for improving crowd safety and mobility management in dynamic environments.

关键词： Accuracy Video on demand Computational modeling Object detection Predictive models Real-time systems Web sites Expert systems Artificial intelligence Faces

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Human-centered GeoAI foundation models: where GeoAI meets human dynamics

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Urban Informatics 2025年第1期4卷 1-15页

作者： Ye, Xinyue Du, Jiaxin Li, Xinyu Shaw, Shih-Lung Fu, Yanjie Dong, Xishuang Zhang, Zhe Wu, Ling Department of Landscape Architecture and Urban Planning & Center for Geospatial Sciences Applications and Technology Texas A&M University College Station USA Department of Computer Science and Engineering Texas A&M University College Station USA College of Computing Grand Valley State University Allendale USA Department of Geography & Sustainability University of Tennessee Knoxville USA School of Computing and AI Arizona State University Tempe USA Department of Electrical & Computer Engineering Prairie View A&M University Prairie View USA Department of Geography Texas A&M University College Station USA Department of Justice Studies Prairie View A&M University Prairie View USA

This study examines the role of human dynamics within Geospatial Artificial Intelligence (GeoAI), highlighting its potential to reshape the geospatial research field. GeoAI, emerging from the confluence of geospatial technologies and artificial intelligence, is revolutionizing our comprehension of human-environmental interactions. This revolution is powered by large-scale models trained on extensive geospatial datasets, employing deep learning to analyze complex geospatial phenomena. Our findings highlight the synergy between human intelligence and AI. Particularly, the humans-as-sensors approach enhances the accuracy of geospatial data analysis by leveraging human-centric AI, while the evolving GeoAI landscape underscores the significance of human–robot interaction and the customization of GeoAI services to meet individual needs. The concept of mixed-experts GeoAI, integrating human expertise with AI, plays a crucial role in conducting sophisticated data analyses, ensuring that human insights remain at the forefront of this field. This paper also tackles ethical issues such as privacy and bias, which are pivotal for the ethical application of GeoAI. By exploring these human-centric considerations, we discuss how the collaborations between humans and AI transform the future of work at the human-technology frontier and redefine the role of AI in geospatial contexts.

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Roopkotha: A Companion Robot for Enhancing Interactive Storytelling with Natural Interaction

Roopkotha: A Companion Robot for Enhancing Interactive Story...

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Image Processing and Robotics (ICIP), International Conference on

作者： Kazi Mayesha Mehzabin Md. Zahidul Islam Md. Ashaduzzaman Nur Mohammad Shidujaman Hooman Samani Haipeng Mi RIoT Center and Department of Computer Science & Engineering Independent University Bangladesh Dhaka Bangladesh Creative Computing Institute University of the Arts London London United Kingdom Media and Interaction Lab Academy of Arts and Design Tsinghua University Beijing China

ISBN: (数字)9798350374766

ISBN: (纸本)9798350374773

Roopkotha is a storytelling robot that seamlessly combines traditional storytelling methods and technology, creating a captivating robot storyteller. We are creating a special prototype in the world of robots that tells stories in a way that is easy to understand and enjoy. In this era of technological advancement, Roopkotha combines voice recognition with Bangla Language processing, emotion recognition, human behavior detection. Roopkotha aims to revolutionize the way stories are told and engage with users on a deep emotional level. Furthermore, Roopkotha is equipped with advanced facial expressions and emotion recognition technology. The emotion recognition feature helps the robot to have a profound connection with the users.

关键词： Emotion recognition Image recognition Human-robot interaction Prototypes Speech recognition Linguistics Feature extraction

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Uncovering Neural Scaling Laws in Molecular Representation Learning

arXiv

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arXiv 2023年

作者： Chen, Dingshuo Zhu, Yanqiao Zhang, Jieyu Du, Yuanqi Li, Zhixun Liu, Qiang Wu, Shu Wang, Liang Center for Research on Intelligent Perception and Computing Institute of Automation Chinese Academy of Sciences China Department of Computer Science University of California Los Angeles United States The Paul G. Allen School of Computer Science and Engineering University of Washington United States Department of Computer Science Cornell University United States Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong

Molecular Representation Learning (MRL) has emerged as a powerful tool for drug and materials discovery in a variety of tasks such as virtual screening and inverse design. While there has been a surge of interest in advancing model-centric techniques, the influence of both data quantity and quality on molecular representations is not yet clearly understood within this field. In this paper, we delve into the neural scaling behaviors of MRL from a data-centric viewpoint, examining four key dimensions: (1) data modalities, (2) dataset splitting, (3) the role of pre-training, and (4) model capacity. Our empirical studies confirm a consistent power-law relationship between data volume and MRL performance across these dimensions. Additionally, through detailed analysis, we identify potential avenues for improving learning efficiency. To challenge these scaling laws, we adapt seven popular data pruning strategies to molecular data and benchmark their performance. Our findings underline the importance of data-centric MRL and highlight possible directions for future research. Copyright © 2023, The Authors. All rights reserved.

关键词： Benchmarking

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Logical Relation Modeling and Mining in Hyperbolic Space for Recommendation

Logical Relation Modeling and Mining in Hyperbolic Space for...

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International Conference on Data Engineering

作者： Yanchao Tan Hang Lv Zihao Zhou Wenzhong Guo Bo Xiong Weiming Liu Chaochao Chen Shiping Wang Carl Yang College of Computer and Data Science Fuzhou University Fuzhou China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Institute for Artificial Intelligence University of Stuttgart Stuttgart Germany College of Computer Science Zhejiang University Hangzhou China Department of Computer Science Emory University Atlanta United States

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

The sparse interactions between users and items have aggravated the difficulty of their representations in recommender systems. Existing methods leverage tags to alleviate the sparsity problem but ignore prevalent logical relations among items and tags (e.g., membership, hierarchy, and exclusion), which can be leveraged to enhance the accuracy of modeling user preferences and conducting recommendations. To this end, we propose to extract logical relations among item tags from existing tag taxonomies and exploit the individual strengths of the Poincaré and the Lorentz models in hyperbolic space for logical relation modeling towards enhanced recommendations. Moreover, we find that the logical relations directly extracted from existing tag taxonomies can be inaccurate and coarse. Therefore, we further devise innovative consistency-based and granularity- based weighting mechanisms based on user behavior patterns for data-driven logical relation mining that can be jointly optimized along with recommendations in an end-to-end fashion. Extensive experiments on four real-world benchmark datasets show drastic performance gains brought by our proposed framework, which constantly achieves an average of 8.25% improvement over state-of-the-art competitors regarding both Recall and NDCG metrics. Insightful case studies further demonstrate that our automatically refined logical relations are highly accurate and interpretable.

关键词： Measurement Accuracy Taxonomy Semantics Predictive models Performance gain Linguistics

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TD-NET: A TRI-DOMAIN NETWORK FOR SPARSE-VIEW CT RECONSTRUCTION

arXiv

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arXiv 2023年

作者： Wang, Xinyuan Su, Changqing Xiong, Bo University College London Department of Computer Science LondonWC1E 6BT United Kingdom Peking University National Engineering Research Center of Visual Technology Beijing100871 China

Sparse-view CT reconstruction, aimed at reducing X-ray radiation risks, frequently suffers from image quality degradation, manifested as noise and artifacts. Existing post-processing and dual-domain techniques, although effective in radiation reduction, often lead to over-smoothed results, compromising diagnostic clarity. Addressing this, we introduce TD-Net, a pioneering tri-domain approach that unifies sinogram, image, and frequency domain optimizations. By incorporating Frequency Supervision Module(FSM), TD-Net adeptly preserves intricate details, overcoming the prevalent over-smoothing issue. Extensive evaluations demonstrate TD-Net’s superior performance in reconstructing high-quality CT images from sparse views, efficiently balancing radiation safety and image fidelity. The enhanced capabilities of TD-Net in varied noise scenarios highlight its potential as a breakthrough in medical imaging. Copyright © 2023, The Authors. All rights reserved.

关键词： Medical imaging

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Video Mobile-Former: Video Recognition with Efficient Global Spatial-temporal Modeling

arXiv

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arXiv 2022年

作者： Wang, Rui Wu, Zuxuan Chen, Dongdong Chen, Yinpeng Dai, Xiyang Liu, Mengchen Zhou, Luowei Yuan, Lu Jiang, Yu-Gang Shanghai Key Lab of Intelligent Info. Processing School of Computer Science Fudan University China Shanghai Collaborative Innovation Center on Intelligent Visual Computing China Microsoft Cloud&AI China

Transformer-based models have achieved top performance on major video recognition benchmarks. Benefiting from the self-attention mechanism, these models show stronger ability of modeling long-range dependencies compared to CNN-based models. However, significant computation overheads, resulted from the quadratic complexity of self-attention on top of a tremendous number of tokens, limit the use of existing video transformers in applications with limited resources like mobile devices. In this paper, we extend Mobile-Former to Video Mobile-Former, which decouples the video architecture into a lightweight 3D-CNNs for local context modeling and a Transformer modules for global interaction modeling in a parallel fashion. To avoid significant computational cost incurred by computing self-attention between the large number of local patches in videos, we propose to use very few global tokens (e.g., 6) for a whole video in Transformers to exchange information with 3D-CNNs with a cross-attention mechanism. Through efficient global spatial-temporal modeling, Video Mobile-Former significantly improves the video recognition performance of alternative lightweight baselines, and outperforms other efficient CNN-based models at the low FLOP regime from 500M to 6G total FLOPs on various video recognition tasks. It is worth noting that Video Mobile-Former is the first Transformer-based video model which constrains the computational budget within 1G FLOPs. Copyright © 2022, The Authors. All rights reserved.

关键词： Benchmarking

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Metaverse: A Vision, Architectural Elements, and Future Directions for Scalable and Realtime Virtual Worlds

arXiv

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arXiv 2023年

作者： Ismail, Leila Buyya, Rajkumar Lab School of Computing and Information Systems The University of Melbourne Australia Lab Department of Computer Science and Software Engineering National Water and Energy Center United Arab Emirates University United Arab Emirates

With the emergence of Cloud computing, Internet of Things-enabled Human-computer Interfaces, Generative Artificial Intelligence, and high-accurate Machine and Deep-learning recognition and predictive models, along with the Post Covid-19 proliferation of social networking, and remote communications, the Metaverse gained a lot of popularity. Metaverse has the prospective to extend the physical world using virtual and augmented reality so the users can interact seamlessly with the real and virtual worlds using avatars and holograms. It has the potential to impact people in the way they interact on social media, collaborate in their work, perform marketing and business, teach, learn, and even access personalized healthcare. Several works in the literature examine Metaverse in terms of hardware wearable devices, and virtual reality gaming applications. However, the requirements of realizing the Metaverse in realtime and at a large-scale need yet to be examined for the technology to be usable. To address this limitation, this paper presents the temporal evolution of Metaverse definitions and captures its evolving requirements. Consequently, we provide insights into Metaverse requirements. In addition to enabling technologies, we lay out architectural elements for scalable, reliable, and efficient Metaverse systems, and a classification of existing Metaverse applications along with proposing required future research *** Codes ***, *** © 2023, CC BY.

关键词： Energy efficiency

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READFake: Reflection and Environment-Aware DeepFake Detection

READFake: Reflection and Environment-Aware DeepFake Detectio...

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IEEE visual Communications and Image Processing (VCIP)

作者： Muhammad Mohzary Elham Basunduwah Sejun Song Baek-Young Choi Department of Computer Science Jazan University Jazan Saudi Arabia Engineering and Technology Research Center Jazan University Jazan Saudi Arabia School of Science and Engineering University of Missouri-Kansas City MO USA College of Engineering and Computing in Al-Qunfudhah Umm Al-Qura University Makkah Saudi Arabia School of Computer and Cyber Science Augusta University Augusta GA USA

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

This paper presents a novel Reflection and Environment-Aware DeepFake (READFake) detection technique. Using reflections on various body parts (e.g., eyes, nose, cheeks, etc.) and environmental factors, we validate the hypothesis that the existing DeepFake creation methods, including reenactment, replacement, and synthesis, fail to coordinate their counterfeits with the reflective components along with the given environmental mapping. We detect various features from the specular highlight images, including color components, shapes, and textures, to check the coordination with the surrounding environmental factors, such as indoor/outdoor, bright/dark backgrounds, and light strength. We have conducted extensive experiments to evaluate the performance of READFake using various input parameters and advanced Deep Neural Network (DNN) architectures on multiple public DeepFake datasets. The empirical results show that READFake achieves high accuracy (99.00%) in detecting sophisticated DeepFake images.

关键词： Deepfakes Accuracy Shape Image color analysis visual communication Nose Artificial neural networks Feature extraction Reflection Environmental factors

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