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Distortion risk measures for tail-risk multicriteria decision-making based on a probabilistic linguistic environment

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 8389-8409页

作者： Chen, Jie Yin, Chuancun School of Statistics and Data Science Qufu Normal University Qufu China School of Mathematics and Computer Applied Technology Jining University Qufu China

Probabilistic linguistic term sets (PLTSs) provide a flexible tool to express linguistic preferences, and several multi-criteria decision models based on PLTSs have been recently developed. In this framework, distortion risk measures are extensively used in finance and insurance applications, but are rarely applied in fuzzy systems. In this paper, distortion risk measures are applied to fuzzy tail decisions. In particular, three tail risk measurement methods are put forward, referred to as probabilistic linguistic VaR (PLVaR), expected probability linguistic VaR (EPLVaR), and Wang tail risk measure and extensively study their properties. Our novel methods help to clarify the connections between distortion risk measure and fuzzy tail decision-making. In particular, the Wang tail risk measure is characterized by consistency and stability of decision results. The criteria and expert weights are unknown or only partially known during the decision making process, and the maximising PLTSs deviations are showed how to determine them. The theoretical results are showcased on an optimal stock fund selection problem, where the three tail risk measures are compared and analyzed. © 2024 - IOS Press. All rights reserved.

关键词： Linguistics

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REVISITING ROBUSTNESS IN GRAPH MACHINE LEARNING 11

REVISITING ROBUSTNESS IN GRAPH MACHINE LEARNING

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11th International Conference on Learning Representations, ICLR 2023

作者： Gosch, Lukas Sturm, Daniel Geisler, Simon Günnemann, Stephan Department of Computer Science Munich Data Science Institute Technical University of Munich Germany

Many works show that node-level predictions of Graph Neural Networks (GNNs) are unrobust to small, often termed adversarial, changes to the graph structure. However, because manual inspection of a graph is difficult, it is unclear if the studied perturbations always preserve a core assumption of adversarial examples: that of unchanged semantic content. To address this problem, we introduce a more principled notion of an adversarial graph, which is aware of semantic content change. Using Contextual Stochastic Block Models (CSBMs) and real-world graphs, our results uncover: i) for a majority of nodes the prevalent perturbation models include a large fraction of perturbed graphs violating the unchanged semantics assumption;ii) surprisingly, all assessed GNNs show over-robustness - that is robustness beyond the point of semantic change. We find this to be a complementary phenomenon to adversarial examples and show that including the label-structure of the training graph into the inference process of GNNs significantly reduces over-robustness, while having a positive effect on test accuracy and adversarial robustness. Theoretically, leveraging our new semantics-aware notion of robustness, we prove that there is no robustness-accuracy tradeoff for inductively classifying a newly added node. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Graph neural networks

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Geometric prior guided hybrid deep neural network for facial beauty analysis

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CAAI Transactions on Intelligence Technology 2024年第2期9卷 467-480页

作者： Tianhao Peng Mu Li Fangmei Chen Yong Xu David Zhang The School of Computer Science and Technology Guizhou UniversityGuiyangChina Department of Automation Moutai InstituteRenhuaiGuizhouChina The School of Computer Science and Technology Harbin Institute of TechnologyShenzhenShenzhenChina The Information and Communication Engineering Department Dalian Minzu UniversityDalianChina The School of Data Science The Chinese University of Hong KongShenzhenShenzhenChina

Facial beauty analysis is an important topic in human *** may be used as a guidance for face beautification applications such as cosmetic *** neural networks(DNNs)have recently been adopted for facial beauty analysis and have achieved remarkable ***,most existing DNN-based models regard facial beauty analysis as a normal classification *** ignore important prior knowledge in traditional machine learning models which illustrate the significant contribution of the geometric features in facial beauty *** be specific,landmarks of the whole face and facial organs are introduced to extract geometric features to make the *** by this,we introduce a novel dual-branch network for facial beauty analysis:one branch takes the Swin Transformer as the backbone to model the full face and global patterns,and another branch focuses on the masked facial organs with the residual network to model the local patterns of certain facial ***,the designed multi-scale feature fusion module can further facilitate our network to learn complementary semantic information between the two *** model optimisation,we propose a hybrid loss function,where especially geometric regulation is introduced by regressing the facial landmarks and it can force the extracted features to convey facial geometric *** performed on the SCUT-FBP5500 dataset and the SCUT-FBP dataset demonstrate that our model outperforms the state-of-the-art convolutional neural networks models,which proves the effectiveness of the proposed geometric regularisation and dual-branch structure with the hybrid *** the best of our knowledge,this is the first study to introduce a Vision Transformer into the facial beauty analysis task.

关键词： deep neural networks face analysis face biometrics image analysis

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Optimal trained ensemble of classification model for satellite image classification

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Multimedia Tools and Applications 2025年第7期84卷 3745-3766页

作者： Singh, Sarabjeet Kundra, Harish Kundra, Sheetal Pratima, P. Venkata Devi, M. V. Anjana Kumar, Sushil Hassan, Mariya Department of Computer Science and Engineering Chandigarh College of Engineering and Technology Chandigarh160019 India Department of Computer Science and Engineering Guru Nanak Institutions Technical Campus Ranga Reddy District Telangana Ibrahimpatnam501506 India Department of Artificial Intelligence and Data Science Guru Nanak Institute of Technology Ranga Reddy District Telangana Ibrahimpatnam501506 India Department of Computer Science and Engineering Central University of Haryana Jant-Pali Mahendergarh123031 India

Satellite image classification is the most significant remote sensing method for computerized analysis and pattern detection of satellite data. This method relies on the image's diversity structures and necessitates a thorough validation of the training data using the chosen classification algorithm. The classifying of remotely sensed images refers to the process of classifying pixels into a fixed set of distinct classes based on their data values. The pixel is categorized into that class if it complies with a specific set of requirements. A new SIC scheme is suggested in this work, where, Contrast limited adaptive histogram equalization (CLAHE) is deployed for pre-processing the image. Further, statistical features, Vegetation Indices (VI) and Improved Hybrid Vegetation Index (IHVI) are extracted from the preprocessed image. Then, we deploy 2 phase classification process for classifying the image. In the first phase, Bi-Directional Gated Recurrent Unit (BI-GRU), Convolutional Neural Network (CNN) and Deep Max Out (DMO) are used. In 2nd phase, optimized RNN is used for determining the final classification result. Also, the training of the model is done by the new Chaotic Opposition Behavior Improvised Coot Algorithm (COBI-CA) optimization via tuning the optimal weights. The COOT method has been enhanced and is now the suggested algorithm. Lastly, we conduct an analysis to compare the suggested Satellite Image Classification (SIC) model's performance to the traditional approaches in terms of statistical analysis, precision, accuracy, and error measure. In comparison to other conventional methods, the accuracy of the suggested model is 97%. © This is a U.S. Government work and not under copyright protection in the US;foreign copyright protection may apply 2024.

关键词： Pixels

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SAMPLING-FREE INFERENCE FOR AB-INITIO POTENTIAL ENERGY SURFACE NETWORKS 11

SAMPLING-FREE INFERENCE FOR AB-INITIO POTENTIAL ENERGY SURFA...

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11th International Conference on Learning Representations, ICLR 2023

作者： Gao, Nicholas Günnemann, Stephan Department of Computer Science Munich Data Science Institute Technical University of Munich Germany

Recently, it has been shown that neural networks not only approximate the ground-state wave functions of a single molecular system well but can also generalize to multiple geometries. While such generalization significantly speeds up training, each energy evaluation still requires Monte Carlo integration which limits the evaluation to a few geometries. In this work, we address the inference shortcomings by proposing the Potential learning from ab-initio Networks (PlaNet) framework, in which we simultaneously train a surrogate model in addition to the neural wave function. At inference time, the surrogate avoids expensive Monte-Carlo integration by directly estimating the energy, accelerating the process from hours to milliseconds. In this way, we can accurately model high-resolution multi-dimensional energy surfaces for larger systems that previously were unobtainable via neural wave functions. Finally, we explore an additional inductive bias by introducing physically-motivated restricted neural wave function models. We implement such a function with several additional improvements in the new PESNet++ model. In our experimental evaluation, PlaNet accelerates inference by 7 orders of magnitude for larger molecules like ethanol while preserving accuracy. Compared to previous energy surface networks, PESNet++ reduces energy errors by up to 74 %. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Wave functions

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Language Identification System: Employing ReLu for India's Regional Languages (ReLu) 2

Language Identification System: Employing ReLu for India's R...

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2nd International Conference for Advancement in Technology, ICONAT 2023

作者： Saini, Lekhraj Defence Institute of Advanced Technology School of Computer Science and Data Science Pune India

ISBN: (纸本)9781665475174

In this paper, I provide a model for language identification that makes use of neural networks. The approach is intended to distinguish between Indian regional and dialectal languages. Individuals of Indian descent are the target population for this model. I train the model using a variety of data sources, including corpora of written and spoken material in a variety of languages. In addition, I use a variety of additional data sources. As a result, we can train the model to be more accurate. When I compare the performance of our model to that of a naive Bayes classifier, I find that the results produced by our model are superior to those produced by the naive Bayes classifier. This is because our model considers more information than the naïve Bayes classifier. The ReLu activation function is employed on each neuron throughout our simulation, and the neural network design comprises several layers. Each neuron receives the ReLu activation function. This allows the model to capture the relationships and correlations that exist between the input data, which improves its capacity to recognize the language employed in a specific sentence. Furthermore, our technology can deal with ambiguous cases, such as sentences written in the devnagri script, which is utilized in a number of Indian languages. This is a benefit provided by our software. Using our system has several advantages, and this is only one of them. © 2023 IEEE.

关键词： Chemical activation

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SEGMENT+: Long Text Processing with Short-Context Language Models

SEGMENT+: Long Text Processing with Short-Context Language M...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Shi, Wei Li, Shuang Yu, Kerun Chen, Jinglei Liang, Zujie Wu, Xinhui Qian, Yuxi Wei, Feng Zheng, Bo Liang, Jiaqing Chen, Jiangjie Xiao, Yanghua Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China Columbia University United States MYbank Ant Group China School of Data Science Fudan University China

ISBN: (纸本)9798891761643

There is a growing interest in expanding the input capacity of language models (LMs) across various domains. However, simply increasing the context window does not guarantee robust performance across diverse long-input processing tasks, such as understanding extensive documents and extracting detailed information from lengthy and noisy data. In response, we introduce SEGMENT+, a general framework that enables LMs to handle extended inputs within limited context windows efficiently. SEGMENT+ utilizes structured notes and a filtering module to manage information flow, resulting in a system that is both controllable and interpretable. Our extensive experiments across various model sizes, focusing on long-document question-answering and Needle-in-a-Haystack tasks, demonstrate the effectiveness of SEGMENT+ in improving performance. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Graph Augmentation Empowered Contrastive Learning for Recommendation

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ACM Transactions on Information Systems 2025年第2期43卷 1-27页

作者： Xu, Lixiang Liu, Yusheng Xu, Tong Chen, Enhong Tang, Yuanyan School of Artificial Intelligence and Big Data Hefei University Hefei China State Key Laboratory of Cognitive Intelligence School of Computer Science and Technology University of Science and Technology of China Hefei China Zhuhai UM Science and Technology Research Institute FST University of Macau China

The application of contrastive learning (CL) to collaborative filtering (CF) in recommender systems has achieved remarkable success. CL-based recommendation models mainly focus on creating multiple augmented views by employing different graph augmentation methods and utilizing these views for self-supervised learning. However, current CL methods for recommender systems usually struggle to fully address the problem of noisy data. To address this problem, we propose the Graph Augmentation Empowered Contrastive Learning (GAECL) for recommendation framework, which uses graph augmentation based on topological and semantic dual adaptation and global co-modeling via structural optimization to co-create contrasting views for better augmentation of the CF paradigm. Specifically, we strictly filter out unimportant topologies by reconstructing the adjacency matrix and mask unimportant attributes in nodes according to the PageRank centrality principle to generate an augmented view that filters out noisy data. Additionally, GAECL achieves global collaborative modeling through structural optimization and generates another augmented view based on the PageRank centrality principle. This helps to filter the noisy data while preserving the original semantics of the data for more effective data augmentation. Extensive experiments are conducted on five datasets to demonstrate the superior performance of our model over various recommendation models. © 2025 Copyright held by the owner/author(s).

关键词： Self-supervised learning

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A Novelty Framework in Image-Captioning with Visual Attention-Based Refined Visual Features

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computers, Materials & Continua 2025年第3期82卷 3943-3964页

作者： Alaa Thobhani Beiji Zou Xiaoyan Kui Amr Abdussalam Muhammad Asim Mohammed ELAffendi Sajid Shah School of Computer Science and Engineering Central South UniversityChangsha410083China Electronic Engineering and Information ScienceDepartment University of Science and Technology of ChinaHefei230026China EIAS Data Science Lab College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia

Image captioning,the task of generating descriptive sentences for images,has advanced significantly with the integration of semantic ***,traditional models still rely on static visual features that do not evolve with the changing linguistic context,which can hinder the ability to form meaningful connections between the image and the generated *** limitation often leads to captions that are less accurate or *** this paper,we propose a novel approach to enhance image captioning by introducing dynamic interactions where visual features continuously adapt to the evolving linguistic *** model strengthens the alignment between visual and linguistic elements,resulting in more coherent and contextually appropriate ***,we introduce two innovative modules:the Visual Weighting Module(VWM)and the Enhanced Features Attention Module(EFAM).The VWM adjusts visual features using partial attention,enabling dynamic reweighting of the visual inputs,while the EFAM further refines these features to improve their relevance to the generated *** continuously adjusting visual features in response to the linguistic context,our model bridges the gap between static visual features and dynamic language *** demonstrate the effectiveness of our approach through experiments on the MS-COCO dataset,where our method outperforms state-of-the-art techniques in terms of caption quality and contextual *** results show that dynamic visual-linguistic alignment significantly enhances image captioning performance.

关键词： Image-captioning visual attention deep learning visual features

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GAN-IE: Generative Adversarial Network for Information Extraction with Limited Annotated data 1

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24th International Conference on Web Information Systems Engineering, WISE 2023

作者： Talukder, Ahmed Shoeb Nayak, Richi Bashar, Md Abul Centre for Data Science School of Computer Science Queensland University of Technology Brisbane Australia

ISBN: (数字)9789819972548

ISBN: (纸本)9789819972531

Extracting valuable information from a large corpus of unstructured data poses a formidable challenge in many applications. Transformer-based architectures e.g. BERT, employing transfer learning techniques, have exhibited promising results across diverse NLP tasks. Nonetheless, the practical implementation of these models presents a significant hurdle given the substantial demand for annotated data during the training phase. In this paper, we present GAN-IE, a novel GAN-based model architecture, designed specifically for information extraction from unstructured textual data while accounting for limited annotated resources. In a generative adversarial setting, GAN-IE leverages BERT’s rich semantic and contextual knowledge obtained from unlabelled data while fine-tuning. Experimental results show that GAN-IE achieves a level of accuracy that surpasses the current state-of-the-art models when trained using a fraction of labelled data (∼ 100–200 annotated samples). © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Generative adversarial networks

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