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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Meng, Zizhuo Li, Boyu Fan, Xuhui Li, Zhidong Wang, Yang Chen, Fang Zhou, Feng Data Science Institute University of Technology Sydney Australia School of Computing Macquarie University Australia Center for Applied Statistics School of Statistics Renmin University of China China Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing China

ISBN: (纸本)9781643685489

The classical temporal point process (TPP) constructs an intensity function by taking the occurrence times into account. Nevertheless, occurrence time may not be the only relevant factor, other contextual data, termed covariates, may also impact the event evolution. Incorporating such covariates into the model is beneficial, while distinguishing their relevance to the event dynamics is of great practical significance. In this work, we propose a Transformer-based covariate temporal point process (TransFeat-TPP) model to improve the interpretability of deep covariate-TPPs while maintaining powerful expressiveness. TransFeat-TPP can effectively model complex relationships between events and covariates, and provide enhanced interpretability by discerning the importance of various covariates. Experimental results on synthetic and real datasets demonstrate improved prediction accuracy and consistently interpretable feature importance when compared to existing deep covariate-TPPs. Our code is available at https://***/waystogetthere/***. © 2024 The Authors.

关键词： Spatio-temporal data

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A survey for light field super-resolution

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High-Confidence computing 2024年第1期4卷 118-129页

作者： Mingyuan Zhao Hao Sheng Da Yang Sizhe Wang Ruixuan Cong Zhenglong Cui Rongshan Chen Tun Wang Shuai Wang Yang Huang Jiahao Shen State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Key Laboratory of Data Science and Intelligent Computing International Innovation InstituteBeihang UniversityHangzhou 311115China State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Compared to 2D imaging data,the 4D light field(LF)data retains richer scene’s structure information,which can significantly improve the computer’s perception capability,including depth estimation,semantic segmentation,and LF ***,there is a contradiction between spatial and angular resolution during the LF image acquisition *** overcome the above problem,researchers have gradually focused on the light field super-resolution(LFSR).In the traditional solutions,researchers achieved the LFSR based on various optimization frameworks,such as Bayesian and Gaussian *** learning-based methods are more popular than conventional methods because they have better performance and more robust generalization *** this paper,the present approach can mainly divided into conventional methods and deep learning-based *** discuss these two branches in light field spatial super-resolution(LFSSR),light field angular super-resolution(LFASR),and light field spatial and angular super-resolution(LFSASR),***,this paper also introduces the primary public datasets and analyzes the performance of the prevalent approaches on these ***,we discuss the potential innovations of the LFSR to propose the progress of our research field.

关键词： Light field super-resolution Convolutional neural network Transformer Sub-aperture image Epipolar-plane image

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Research progress of quantum artificial intelligence in smart city

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Intelligent and Converged Networks 2024年第2期5卷 116-133页

作者： Sumin Wang Ning Wang Tongyu Ji Yiyun Shi Chao Wang the Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai UniversityShanghai 200444China School of Information Engineering Ganzhou Key Laboratory of Cloud Computing and Big Data ResearchGannan University of Science and TechnologyGanzhou 341000China

The rapid accumulation of big data in the Internet era has gradually decelerated the progress of Artificial Intelligence(AI).As Moore’s Law approaches its limit,it is imperative to break the constraints that are holding back artificial *** computing and artificial intelligence have been advancing along the highway of human civilization for many years,emerging as new engines driving economic and social *** article delves into the integration of quantum computing and artificial intelligence in both research and *** introduces the capabilities of both universal quantum computers and special-purpose quantum computers that leverage quantum *** discussion further explores how quantum computing enhances classical artificial intelligence from four perspectives:quantum supervised learning,quantum unsupervised learning,quantum reinforcement learning,and quantum deep *** an effort to address the limitations of smart cities,this article explores the formidable potential of quantum artificial intelligence in the realm of smart *** does so by examining aspects such as intelligent transportation,urban operation assurance,urban planning,and information communication,showcasing a plethora of practical achievements in the *** the foreseeable future,Quantum Artificial Intelligence(QAI)is poised to bring about revolutionary development to smart *** urgency lies in developing quantum artificial intelligence algorithms that are compatible with quantum computers,constructing an efficient,stable,and adaptive hybrid computing architecture that integrates quantum and classical computing,preparing quantum data as needed,and advancing controllable qubit hardware equipment to meet actual *** ultimate goal is to shape the next generation of artificial intelligence that possesses common sense cognitive abilities,robustness,excellent generalization capabilities,and interpretability.

关键词： quantum artificial intelligence smart city D-Wave quantum computer

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data Processing Efficiency Aware User Association and Resource Allocation in Blockchain Enabled Metaverse over Wireless Communications

arXiv

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arXiv 2024年

作者： Qian, Liangxin Zhao, Jun College of Computing and Data Science Nanyang Technological University Singapore

In the rapidly evolving landscape of the Metaverse, enhanced by blockchain technology, the efficient processing of data has emerged as a critical challenge, especially in wireless communication systems. Addressing this need, our paper introduces the innovative concept of data processing efficiency (DPE), aiming to maximize processed bits per unit of resource consumption in blockchain-empowered Metaverse environments. To achieve this, we propose the DPE-Aware User Association and Resource Allocation (DAUR) algorithm, a tailored solution for these complex systems. The DAUR algorithm transforms the challenging task of optimizing the sum of DPE ratios into a solvable convex optimization problem. It uniquely alternates the optimization of key variables like user association, work offloading ratios, task-specific computing resource distribution, bandwidth allocation, user power usage ratios, and server computing resource allocation ratios. Our extensive numerical results demonstrate the DAUR algorithm’s effectiveness in DPE. Copyright © 2024, The Authors. All rights reserved.

关键词： Convex optimization

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data statistical analysis on Amazon e-commerce platform for recommender system

Data statistical analysis on Amazon e-commerce platform for ...

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作者： Zheng Huo College of Computing Data Science and Society University of California Berkeley

Recommendation systems are a crucial element in engaging users and maintaining their engagement with e-commerce platforms. By recommending products or services that are likely to be relevant to each user's interests and preferences, the system can help maintain user interest and encourage them to spend more time on the platform. This work analyzes the principles of several off-the-shelf recommendation models, including the collaborative filtering model, the singular value decomposition model, and the rating-based collaborative filtering model. These models play a crucial role in the field of recommendation systems for ecommerce. To gain further insights from the comments of various merchandise items,sentiment analysis techniques and word cloud analysis are applied. Evaluation of these results demonstrates the critical role of recommender systems in shaping the future landscape of ecommerce. Sentiment analysis allows us to identify patterns in user feedback and understand how different factors influence user satisfaction with products or services. Word cloud analysis provides a visual representation of the most frequently mentioned features or keywords in the comments, allowing us to identify trends and patterns in user behavior. By combining these techniques with traditional recommendation models, more accurate and personalized recommendations could be made that better meet user needs and enhance their shopping experience on e-commerce platforms.

关键词： Recommendation System data Analysis E-Commerce Platform

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Long Short-Term Memory (LSTM) model for Indian sign language recognition

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 11185-11203页

作者： Nihalani, Rahul Chouhan, Siddharth Singh Mittal, Devansh Vadula, Jai Thakur, Shwetank Chakraborty, Sandeepan Patel, Rajneesh Kumar Singh, Uday Pratap Ghosh, Rajdeep Singh, Pritpal Saxena, Akash School of Computing Science and Engineering VIT Bhopal University Kothrikalan Madhya Pradesh Sehore India Department of Mathematics Central University of Jammu UT of JK India Department of Data Science and Analytics Central University of Rajasthan Rajasthan Ajmer India School of Engineering and Technology Central University of Haryana Haryana Mahendragarh India

The human-computer interaction process is a vital task in attaining artificial intelligence, especially for a person suffering from hearing or speaking disabilities. Recognizing actions more traditionally known as sign language is a common way for them to interact. Computer vision and Deep learning models are capable of understanding these actions and can simulate them to build up a sustainable learning process. This sign language mechanism will be helpful for both the persons with disabilities and the machines to unbound the gap to achieve intelligence. Therefore, in the proposed work, a real-Time sign language system is introduced that is capable of identifying numbers ranging from 0 to 9. The database is acquired from the 8 different subjects respectively and processed to achieve approximately 200k amount of data. Further, a deep learning model named LSTM is used for sign recognition. The results were compared with different approaches and on distinct databases proving the supremacy of the proposed work with 91.50% accuracy. Collection of daily life useful signs and further improving the efficiency of the LSTM model is the research direction for future work. The code and data will be available at https://***/rahuln2002/Sign-Language-Recognition-using-LSTM-model. © 2024-IOS Press. All rights reserved.

关键词： Long short-term memory

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Creating Pelargonium dataset for Species Classification Task

Creating Pelargonium Dataset for Species Classification Task

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AITU: Digital Generation, IEEE

作者： Aruzhan Zhumatay Almas Alzhanov Department of Computing and Data Science Astana IT University Astana Kazakhstan

ISBN: (数字)9798350364378

ISBN: (纸本)9798350364385

This paper presents a comprehensive dataset of pelargonium species that can be used to train classification and recognition models. The provided dataset consists of 1600 images divided by 4 Pelargonium species. The collection process involved manual photography and conducting inquiries with individuals possessing imagery and knowledge regarding Pelargonium species. In addition, the paper provides a baseline method for pelargonium classification that uses Resnet50 deep convolutional neural network architecture achieving an F1-score of 73,2%. This dataset and the results of the baseline model could be used as a benchmark for further research and enhancements in the field. It is particularly useful for researchers in data science and artificial intelligence to create competitive machine learning models. The objective of the study is to create a dataset to contribute to the development of flower monitoring and flower recognition in general which can prove beneficial in the fields of botanical research, agriculture, and commerce.

关键词： Photography Transfer learning Manuals Flowering plants data science Benchmark testing Robustness

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Pharmacophore-guided de novo drug design with diffusion bridge

arXiv

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arXiv 2024年

作者： Wang, Conghao Rajapakse, Jagath C. College of Computing and Data Science Nanyang Technological University Singapore

De novo design of bioactive drug molecules with potential to treat desired biological targets is a profound task in the drug discovery process. Existing approaches tend to leverage the pocket structure of the target protein to condition the molecule generation. However, even the pocket area of the target protein may contain redundant information since not all atoms in the pocket is responsible for the interaction with the ligand. In this work, we propose PharmacoBridge, a phamacophoreguided de novo design approach to generate drug candidates inducing desired bioactivity via diffusion bridge. Our method adapts the diffusion bridge to effectively convert pharmacophore arrangements in the spatial space into molecular structures under the manner of SE(3)-equivariant transformation, providing sophisticated control over optimal biochemical feature arrangements on the generated molecules. PharmacoBridge is demonstrated to generate hit candidates that exhibit high binding affinity with potential protein targets. © 2024, CC BY.

关键词： Bridge approaches

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Two Tropical data Center Testbeds and data Yields from 2018 to 2024 24

Two Tropical Data Center Testbeds and Data Yields from 2018 ...

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Proceedings of the 15th ACM International Conference on Future and Sustainable Energy Systems

作者： Rongrong Wang Duc Van Le Rui Tan College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)9798400704802

The ever-increasing energy consumption of data centers (DCs) has emerged as a global concern. Currently, many DCs follow conventional practices, characterized by low temperature setpoints, resulting in considerable energy consumption for cooling purposes. In pursuit of energy-efficient DC operation practices, we have built two DC testbeds with full control of the cooling system to facilitate experiments under various operating conditions. The test server rooms are instrumented with sensors in different modalities at both room and server levels to monitor real-time operating conditions. Our experiment results indicate that the air-cooled DCs operated in enclosed buildings can increase temperature setpoint to improve energy efficiency with insignificant impact on server computing performance and reliability. The collected environmental and energy traces from our testbeds have been released as two public datasets. Our analytical results and datasets will contribute to the future design and operation of energy-efficient DCs.

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An Objective Metric Towards Music Mood Visualization

An Objective Metric Towards Music Mood Visualization

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International Conference on Cyberworlds

作者： Hanqin Wang Alexei Sourin College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798331527174

ISBN: (纸本)9798331527181

Music visualization is presented in different formats. The mainstream music visualization remains focused on providing animated images for visual pleasure or for enhancing the perception of sheet music with colors. Static visualization of music, on the other hand, has the potential to provide quick insights into the music’s attributes, particularly its mood. However, despite our previous work exploring several approaches to music mood visualization, a major problem still exists in how to objectively evaluate the mood of music pieces and the generated images. Thus, this paper proposes IMEMNextan improvement to the existing framework IMEMNet [1], which serves as a novel and objective metric for music mood visualization. Not only does it allow for a user study-free evaluation of music mood visualization, but the new network also enables for creation of datasets of music-image pairs with the same mood. The new IMEMNext network achieved a significant performance improvement compared to its original implementation.

关键词： Measurement Hands Visualization Computer vision Mood Image color analysis Computational modeling Transformers Music information retrieval Image classification

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