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arXiv 2024年

作者： Wang, Wenjie Huang, Biwei Liu, Feng You, Xinge Liu, Tongliang Zhang, Kun Gong, Mingming School of Mathematics and Statistics The University of Melbourne Australia Department of Machine Learning Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates University of California San Diego United States School of Computing and Information Systems The University of Melbourne Australia Huazhong University of Science and Technology China School of Computer Science Faculty of Engineering The University of Sydney Australia Department of Philosophy Carnegie Mellon University United States

Score-based methods have demonstrated their effectiveness in discovering causal relationships by scoring different causal structures based on their goodness of fit to the data. Recently, Huang et al. (2018) proposed a generalized score function that can handle general data distributions and causal relationships by modeling the relations in reproducing kernel Hilbert space (RKHS). The selection of an appropriate kernel within this score function is crucial for accurately characterizing causal relationships and ensuring precise causal discovery. However, the current method involves manual heuristic selection of kernel parameters, making the process tedious and less likely to ensure optimality. In this paper, we propose a kernel selection method within the generalized score function that automatically selects the optimal kernel that best fits the data. Specifically, we model the generative process of the variables involved in each step of the causal graph search procedure as a mixture of independent noise variables. Based on this model, we derive an automatic kernel selection method by maximizing the marginal likelihood of the variables involved in each search step. We conduct experiments on both synthetic data and real-world benchmarks, and the results demonstrate that our proposed method outperforms heuristic kernel selection methods. © 2024, CC BY.

关键词： Optimization

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Exploring Traditional, Deep Learning and Hybrid Methods for Hyperspectral Image Classification: A Review

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Journal of Information and intelligence 2025年

作者： Mosa Haidarh Caihong Mu Yi Liu Xinyu He School of Artificial Intelligence Xidian University Xi'an China Faculty of Administration Science Data Science Department Taiz University Taiz Yemen School of Electronic Engineering Xidian University Xi'an China

Hyperspectral image (HSI) classification has appeared as an important area in remote sensing due to the rich spectral and spatial information that enables accurate object classification. Traditional methods such as sparse representation, random forest, support vector machines, and Markov random fields have long been used for HSI classification, offering remarkable efficiency and precision. Regardless, the ingrained complexity and nonlinearity of HSI data pose challenges to these traditional methods. Deep learning (DL) methods, including convolutional neural networks, recurrent neural networks, autoencoders, generative adversarial networks, and transformers, have shown super performance by leveraging their ability to capture both the spatial and spectral features separately or together. In recent years, hybrid methods that combine traditional methods and DL algorithms or integrate two types of DL algorithms have demonstrated superior performance by utilizing the strengths of different methods. This paper presents a comprehensive review of traditional approaches, DL approaches, and hybrid approaches in HSI classification and an overview of hyperspectral datasets. In addition, we compare these methods via evaluation metrics, highlighting the strengths and limitations of each type. Our work provides helpful guidelines for future research directions and work in this area.

关键词： Hyperspectral image classification Support vector machines Markov random fields Deep learning Convolutional neural network Transformer Hybrid methods

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RETRACTED ARTICLE: A Systematic Survey on Implementation of Fuzzy Regression Models for Real Life Applications (Archives of Computational Methods in engineering, (2024), 31, 1, (291-311), 10.1007/s11831-023-09978-x)

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Archives of Computational Methods in engineering 2025年

作者： Khan, Mufala Kumar, Rakesh Aledaily, Arwa N. Kariri, Elham Viriyasitavat, Wattana Yadav, Kusum Dhiman, Gaurav Kaur, Amandeep Sharma, Ashutosh Vimal, S. Department of Mathematics Lovely Professional University Punjab Phagwara144411 India Department of Computer Science Government Bikram College of Commerce Punjab Patiala147001 India College of Computer Science and Engineering University of Ha’il Hail Saudi Arabia College of Computer Science and Engineering Prince Sattam Bin Abdulaziz University Al‑Kharj Saudi Arabia Business Information Technology Division Department of Statistics Faculty of Commerce and Accountancy Chulalongkorn University Bangkok Thailand Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon University Centre for Research and Development Department of Computer Science and Engineering Chandigarh University Mohali Gharuan140413 India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun248002 India Division of Research and Development Lovely Professional University Phagwara India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India School of Computer Science University of Petroleum and Energy Studies Dehradun India Department of Artificial Intelligence and Data Science Ramco Institute of Technology Tamil Nadu Rajaplayam India

The Editors-in-Chief have retracted this article. After publication, concerns were raised about the originality of the study reported. An investigation conducted by the Publisher confirmed that the article contains material that substantially overlaps with a previous study by different authors [1]. In particular, the research methodology, research questions, numerous formulations, and discussion are almost identical with those in [1]. The Editors-in-Chief therefore consider this article redundant. None of the authors have responded to correspondence from the Publisher about this retraction. © The Author(s) under exclusive licence to International Center for Numerical Methods in engineering (CIMNE) 2025.

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Construction and Evaluation of Benchmark Da-Tasets For Adversarial Ai Research

Construction and Evaluation of Benchmark Da-Tasets For Adver...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Parikshit N. Mahalle P. K. Miniappan Ashmeet Kaur S. Ganga Yuvraj Parmar Sahana B S Department of Artificial Intelligence & Data Science Vishwakarma Institute of Technology Pune India Department of Mechanical Engineering Karpagam Academy of Higher Education Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of IT Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Mangement UG Management JAIN (Deemed to be University) Bangalore Karnataka India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Standard offensive AI datasets need to be made and tested to improve the performance of AI models. Using adversarial diversity-driven dataset building (ADDC), this work shows new ways to create datasets and ways to measure safety and power. The DCA fakes several aggressive events to make sure that the benchmark dataset has a variety of strikes and pauses within it. When the REA is run, it shows how well AI models can handle certain threats. A vulnerability avoidance algorithm (VMA) finds holes in an AI model and stops them from being used. We compare the suggested plan to ADS, APDA, RMAAI, AVP, CABG, and AASF based on several factors that affect results. The suggested method both lowers the number of mistakes and raises the variety, which improves the dataset and makes sure that a full review is done. artificial intelligence models that use the suggested method are less likely to be hacked online. First, well-known datasets for study into hostile artificial intelligence are put together and studied.

关键词： Power measurement Reviews Stability criteria Government Benchmark testing Solids Robustness Safety artificial intelligence Standards

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Methodology for Real Estate Recommendation Based on Customer Behavior Knowledge as Context-Approach 15th

Methodology for Real Estate Recommendation Based on Customer...

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15th International Joint Conference on Knowledge Discovery, Knowledge engineering and Knowledge Management, IC3K 2023

作者： Vu, Sang Nguyen, Tinh Nguyen, Truc Pham, Vuong T. Huynh, Tai Nguyen, Hien D. University of Information Technology Ho Chi Minh City Viet Nam Faculty of Mathematics and Computer Science University of Science Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam Institute of Data Science and Artificial Intelligence Sai Gon University Ho Chi Minh City Viet Nam Kyanon Digital Ho Chi Minh City Viet Nam Computer Science Department New Mexico State University Las Cruces United States

ISBN: (纸本)9783031875687

Real estate recommendation systems present a significant challenge due to the multifaceted nature of buyer decision-making. This growing field necessitates the development of a holistic recommendation approach. This study proposes the Est-Model, which captures customer behavior related to real estate purchase decisions. The Est-Model leverages a relational knowledge model to organize property features. This model is then integrated with a database containing user profiles and real estate information. To address the cold-start problem for both items and users, the study introduces a novel real estate recommendation methodology. Utilizing a session-based recommendation framework, the system effectively manages sequential and contextual data to predict user preferences for future interactions. Empirical evaluations demonstrate the superiority of this approach over existing methodologies within the top-n recommendation paradigm. These findings are validated through experiments conducted on a real estate dataset. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Knowledge acquisition

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Blockchain-Based Homomorphic Transaction Framework for Enhanced Consumer Security and Business Scalability

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IEEE Transactions on Consumer Electronics 2024年

作者： Guo, Tao Shang, Fengjun Dai, Xiangguang Liu, Qilie Chongqing University of Posts and Telecommunications School of Computer Science and Technology Chongqing400065 China Key Laboratory of Big Data Intelligent Computing Chongqing400065 China Chongqing Three Gorges University Key Lab. of Intelligent Info. Proc. and Contr. of Chongqing Munic. Institutions of Higher Education Chongqing Engineering Research Center of Internet of Things and Intelligent Control Technology School of Three Gorges Artificial Intelligence Chongqing400044 China Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400065 China

Energy trading in distributed microgrids represents an effective means of enhancing the utilization of renewable energy. However, the aggregation of large-scale consumption data may encounter business scalability issues and potential privacy exposure concerns, necessitating the development of novel secure and scalable frameworks for integrating consumption demands between prosumer and microgrid entities. we research focuses on providing a user-friendly transaction model for consumers and microgrid operators. Firstly, a privacy aggregation scheme for user resource requests is designed to ensure the security and decentralization of user data. In addition, the model employs a bidirectional heterogeneous resource allocation mechanism to support the optimal allocation of multiple resources, such as renewable energy, power assets, energy storage devices, and carbon credits, among different microgrids. Finally, a microgrid transaction prototype has been constructed on the Truffle development framework. In this context, smart contracts has been designed to ensure that the transactions are carried out automatically. Experimental results indicate that the scheme provides enhanced security for multi-resource trading in a cluster context relative to existing methodologies. The number of transactions increases linearly with the resource overhead, resulting in more efficient utilization of energy trading. Furthermore, the framework's scalability has been demonstrated through both temporal and spatial considerations. © 1975-2011 IEEE.

关键词： Decentralized finance

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Tri-Band and High Fom Thz Metamaterial Absorber for Food/Agricultural Safety Sensing Applications

SSRN

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SSRN 2023年

作者： Ge, Hongyi Ji, Xiaodi Jiang, Yuying Wu, Xuyang Li, Li Jia, Zhiyuan Sun, Zhenyu Bu, Yuwei Guo, Chunyan Zhang, Yuan Key Laboratory of Grain Information Processing and Control Ministry of Education Henan University of Technology Henan Zhengzhou450001 China Henan Provincial Key Laboratory of Grain Photoelectric Detection and Control Henan Zhengzhou450001 China College of Information Science and Engineering Henan University of Technology Henan Zhengzhou450001 China School of Artificial Intelligence and Big Data Henan University of Technology Henan Zhengzhou450001 China

Multi-band based terahertz metamaterial absorber is of great importance in current research and applications because of its ability to achieve multi-band absorption of electromagnetic waves and multi-point matching of detection material information. In this work, a terahertz metamaterial absorber with narrow harp peak is designed by combining various coupling modes such as higher-order resonance, surface lattice effect and so on. By optimizing the structure parameters, the Q-factor is as high as 528 and the FOM value is 153.2, which is better than the FOM of the currently reported absorbers. Five pesticides and solutions of amino acids (proline) with different concentrations (2-10g/100ml) were detected and analyzed. These results show that our multi-band TMA has good performance indexes for markerless sensing and non-contact detection of trace substances. © 2023, The Authors. All rights reserved.

关键词： Metamaterials

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Comparisons of Deep Learning Models to predict Energy Consumption of an Educational Building

Comparisons of Deep Learning Models to predict Energy Consum...

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Machine Learning and Applied Network Technologies (ICMLANT), IEEE International Conference on

作者： Julio Barzola-Monteses Wendy Yanez-Pazmino Eduardo Flores-Moran Franklin Parrales-Bravo Artificial Intelligence and Information Technology research group University of Guayaquil Guayaquil Ecuador Department of Computer Science and Artificial Intelligence Escuela Tecnica Superior de Ingenierias Informatica y de Telecomunicacion Universidad de Granada Granada Spain Faculty of Mathematics and Physical Sciences University of Guayaquil Guayaquil Ecuador Faculty of Electrical and Computer Engineering Escuela Superior Politecnica del Litoral Guayaquil Ecuador Faculty of Computer Science Complutense University of Madrid Madrid Spain

ISBN: (数字)9781665487016

ISBN: (纸本)9781665487023

According to the latest United Nations Environment Programme report, in 2020, the construction and operation of buildings globally accounted for more than a third (36%) of final energy used and 37% of carbon dioxide emissions. This has given rise to research interest in energy efficiency in buildings in recent years, considering different approaches. In this work, black-box approaches based on bio-inspired techniques to predict the energy consumption of a university building are proposed. Three artificial neural network (ANN) architectures. Feed Forward Neural Network (FFNN) such as multilayer perceptron (MLP) and recurrent neural network (RNN) such as long short-term memory (LSTM), and gated recurrent unit (GRU). Three scenarios with different resolution timesteps and forecast horizons multi-step are tested for each architecture. The results according to the comparison of analyzed metric and computational execution times, the MLP model ranks first. These models can be very useful in predictive control systems considered in buildings to forecast the behavior of buildings' electric load in the short term with very good precision.

关键词： Measurement Deep learning Energy consumption Recurrent neural networks Computational modeling Buildings Energy resolution

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User Alignment across Dynamic Social Networks based on Heuristic Algorithm

User Alignment across Dynamic Social Networks based on Heuri...

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International Conference on Systems and Informatics (ICSAI)

作者： Jiawei He Li Liu Zihan Yan Zhiqiang Wang Min Xiao Youmin Zhang School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China College of Big Data and Artificial Intelligence Chongqing Institute of Engineering Chongqing China

ISBN: (纸本)9781665426251

User alignment across social networks, whose main goal is to fuse user information in different network platforms, is a fundamental task in social network analysis. It can benefit social network applications such as user recommendation and information diffusion. Attributed to the inherent dynamic characteristic of the social networks, aligning users in dynamic networks is a key issue in practice. However, most of the alignment models encounter model retraining when the network is updated, thus result in the consumption of time and resources. To address this problem, a heuristic algorithm is proposed to align users in a dynamic environment. Firstly, the attention mechanism is leveraged to obtain the local importance weight of the new node in a single network. Secondly, the anchor nodes are adopted as supervised information for heuristically learning the alignment task-driven local influence of new nodes. Finally, by preserving the second-order similarity of the network, the model aligns users across networks. Experimental results conducted on realworld datasets prove that the proposed model has a comparable performance but lower time complexity compared with several state-of-the-art algorithms.

关键词： Representation learning Social networking (online) Fuses Heuristic algorithms data models Time complexity Task analysis

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MetaMask: Revisiting Dimensional Confounder for Self-Supervised Learning

arXiv

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arXiv 2022年

作者： Li, Jiangmeng Qiang, Wenwen Zhang, Yanan Mo, Wenyi Zheng, Changwen Su, Bing Xiong, Hui University of Chinese Academy of Sciences Institute of Software Chinese Academy of Sciences Southern Marine Science and Engineering Guangdong Laboratory Guangzhou China Gaoling School of Artificial Intelligence Renmin University of China China Institute of Software Chinese Academy of Sciences Southern Marine Science and Engineering Guangdong Laboratory Guangzhou China Gaoling School of Artificial Intelligence Renmin University of China Beijing Key Laboratory of Big Data Management and Analysis Methods China Thrust of Artificial Intelligence The Hong Kong University of Science and Technology Guangzhou China Guangzhou HKUST Fok Ying Tung Research Institute China

As a successful approach to self-supervised learning, contrastive learning aims to learn invariant information shared among distortions of the input sample. While contrastive learning has yielded continuous advancements in sampling strategy and architecture design, it still remains two persistent defects: the interference of task-irrelevant information and sample inefficiency, which are related to the recurring existence of trivial constant solutions. From the perspective of dimensional analysis, we find out that the dimensional redundancy and dimensional confounder are the intrinsic issues behind the phenomena, and provide experimental evidence to support our viewpoint. We further propose a simple yet effective approach MetaMask, short for the dimensional Mask learned by Meta-learning, to learn representations against dimensional redundancy and confounder. MetaMask adopts the redundancy-reduction technique to tackle the dimensional redundancy issue and innovatively introduces a dimensional mask to reduce the gradient effects of specific dimensions containing the confounder, which is trained by employing a meta-learning paradigm with the objective of improving the performance of masked representations on a typical self-supervised task. We provide solid theoretical analyses to prove MetaMask can obtain tighter risk bounds for downstream classification compared to typical contrastive methods. Empirically, our method achieves state-of-the-art performance on various benchmarks. Copyright © 2022, The Authors. All rights reserved.

关键词： Redundancy

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