Article
Automation & Control Systems
Amber Srivastava, Srinivasa M. Salapaka
Summary: This paper addresses a class of sequential decision-making problems with dynamic parameters, where the dynamics are pre-specified for some parameters and manipulable for others. The objective is to determine the manipulable parameter dynamics and the time-varying optimal policy to minimize the associated sequential decision-making cost at each time instant.
Article
Geography, Physical
Xiao Ling, Rongjun Qin
Summary: In this paper, a fully automated georegistration method for wide-area 3D data generation in complex urban environments is proposed. The method utilizes semantically segmented object boundaries as view invariant features and performs registration through graph-matching. The experiment shows promising results on a large-scale cross-view dataset.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Wei Liu, Xiaodong Yue, Yufei Chen, Thierry Denoeux
Summary: Multi-view deep learning is performed based on the deep fusion of data from multiple sources. However, due to the differences and inconsistency of data sources, the fusion results may be uncertain and unreliable. This study proposes a trusted multi-view deep learning method by using evidence theory to reduce uncertainty and achieve reliable results.
THIRTY-SIXTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FOURTH CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE / TWELVETH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE
(2022)
Article
Engineering, Electrical & Electronic
Mengyang Zhang, Guohui Tian, Ying Zhang, Hong Liu
Summary: Incorporating cooking logic into ingredient recognition is beneficial for food cognition. Our paper proposes a sequential learning method to guide a neural network model on producing ingredients following the corresponding cooking logic in recipes. Reinforcement learning is employed to optimize the model's ability to associate images and sequential ingredients.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2023)
Article
Computer Science, Information Systems
Ahmad Jalal, Abrar Ahmed, Adnan Ahmed Rafique, Kibum Kim
Summary: The study introduces a novel scene semantic recognition framework, which intelligently segments object locations, generates a novel Bag of Features, and utilizes Maximum Entropy for scene recognition. During experiments, the system demonstrated high accuracy rates on various datasets and shows promising applications in different fields.
Article
Computer Science, Software Engineering
Zhongzhu Chen, Marcia Fampa, Amelie Lambert, Jon Lee
Summary: The maximum-entropy sampling problem is a fundamental and challenging combinatorial-optimization problem with applications in spatial statistics. It involves finding the maximum-determinant order-s principal submatrix of an order-n covariance matrix. Exact solutions for this NP-hard problem are based on a branch-and-bound framework, with many known upper bounds relying on convex optimization. Mixing these bounds can lead to improved overall bounds.
MATHEMATICAL PROGRAMMING
(2021)
Article
Computer Science, Artificial Intelligence
Yancheng Wang, Yang Xiao, Junyi Lu, Bo Tan, Zhiguo Cao, Zhenjun Zhang, Joey Tianyi Zhou
Summary: The article addresses the challenge of dramatic imaging viewpoint variation for action recognition in depth video, proposing a discriminative MVDI fusion method via multi-instance learning to enhance cross-view 3-D action recognition performance. The method emphasizes enhancing view-tolerance of visual features and utilizing Fisher vector for better discriminative power.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Engineering, Civil
Wei Shen, Makoto Ohsaki, Makoto Yamakawa
Summary: In this study, a reliability-based shape and topology optimization method for plane frames is proposed, with the quantile estimated using the maximum entropy method and an iterative scheme employed to solve deterministic optimization problems. The force density method is applied for simultaneous shape and topology optimization. The results demonstrate the effectiveness and feasibility of the proposed method.
Article
Computer Science, Artificial Intelligence
Deepayan Chakrabarti
Summary: The RoLin algorithm, which combines top principal components with robust optimization, does not require user choice of norms and is applicable to various loss functions, outperforming dimensionality reduction and regularization.
Article
Chemistry, Physical
Denise Medeiros Selegato, Cesare Bracco, Carlotta Giannelli, Giacomo Parigi, Claudio Luchinat, Luca Sgheri, Enrico Ravera
Summary: Conformational variability and heterogeneity play vital roles in determining the function of biological macromolecules. Experimental access to this information is limited by the lack of observable parameters compared to the potentially infinite number of available conformational states. Computational methods, such as reweighting the initial conformational ensemble, have been proposed to address this challenge.
Article
Computer Science, Artificial Intelligence
Sarang Kapoor, Dhish Kumar Saxena, Matthijs van Leeuwen
Summary: Dynamic graph summarization is a significant issue in describing network evolution, with existing methods focusing on using objective measures to discover fixed structures. However, approaches for online summarization are relatively scarce, and we propose a novel framework based on subjective interestingness and information gain measure to discover compact summaries without the need to determine the number of patterns.
DATA MINING AND KNOWLEDGE DISCOVERY
(2021)
Article
Automation & Control Systems
Bin Gu, Yingying Shan, Xin Quan, Guansheng Zheng
Summary: This paper introduces a generalized framework for accelerating Sequential Minimal Optimization (SMO) using Stochastic Subgradient Descent (SSGD), and explores the effectiveness of this approach through experimental results on various datasets and learning applications.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Article
Environmental Sciences
Jili Sun, Lingdong Geng, Yize Wang
Summary: This paper proposes a hybrid classification model based on superpixel entropy discrimination in polarimetric synthetic aperture radar (PolSAR) image classification, achieving good results. By introducing information entropy to evaluate the quality of superpixel classification and using a complex-valued convolutional neural network to reclassify high-entropy superpixels, some problems in existing classification methods are solved.
Article
Energy & Fuels
Markus Rosenfelder, Moritz Wussow, Gunther Gust, Roger Cremades, Dirk Neumann
Summary: Reducing the electricity consumption of buildings through modeling based on aerial and street view images is an effective approach in predicting energy consumption and outperforms traditional models. The innovative method presented in the study is significant in addressing the lack of building electricity consumption data.
Article
Computer Science, Information Systems
Jie Hu, Yi Pan, Tianrui Li, Yan Yang
Summary: This study proposes a novel Two-level Weighted Collaborative Multi-view Fuzzy Clustering (TW-Co-MFC) approach to address the challenges in multi-view clustering, showcasing the effectiveness of the method through experiments on real-world datasets.
TSINGHUA SCIENCE AND TECHNOLOGY
(2021)
Article
Computer Science, Artificial Intelligence
C. Lopez-Molina, S. Iglesias-Rey, B. De Baets
Summary: Quantitative image comparison is a critical topic in image processing literature, with diverse applications. Existing measures of comparison often overlook the context in which the comparison takes place. This paper presents a context-aware comparison method for binary images, tested on the BSDS500 benchmark.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Lorenz Linhardt, Klaus-Robert Mueller, Gregoire Montavon
Summary: This paper investigates the issue of mismatches between the decision strategy of the explainable model and the user's domain knowledge, and proposes a new method EGEM to mitigate hidden flaws in the model. Experimental results demonstrate that the approach can significantly reduce reliance on Clever Hans strategies and improve the accuracy of the model on new data.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Zhimin Shao, Weibei Dou, Yu Pan
Summary: This paper proposes a novel algorithm, Dual-level Deep Evidential Fusion (DDEF), to integrate multimodal information at both the BBA level and multimodal level, aiming to enhance accuracy, robustness, and reliability. The DDEF approach utilizes the Dirichlet framework and BBA methods for effective uncertainty estimation and employs the Dempster-Shafer Theory for dual-level fusion. The experimental results show that the proposed DDEF outperforms existing methods in synthetic digit classification and real-world medical prognosis after BCI treatment.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Abhishek K. Ghosh, Danilo S. Catelli, Samuel Wilson, Niamh C. Nowlan, Ravi Vaidyanathan
Summary: The inability of current FM monitoring methods to be used outside clinical environments has made it challenging to understand the nature and evolution of FM. This investigation introduces a novel wearable FM monitor with a heterogeneous sensor suite and a data fusion architecture to efficiently capture and separate FM from interfering artifacts. The performance of the device and architecture were validated through at-home use, demonstrating high accuracy in detecting and recognizing FM events. This research is a major milestone in the development of low-cost wearable FM monitors for pervasive monitoring of FM in unsupervised environments.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Jianlei Kong, Xiaomeng Fan, Min Zuo, Muhammet Deveci, Xuebo Jin, Kaiyang Zhong
Summary: In this study, we propose an intelligent traffic flow prediction framework based on the adaptive dual-graphic transformer with a cross-fusion strategy, aiming to uncover latent graphic feature representations that transcend temporal and spatial limitations. By establishing a traffic spatiotemporal prediction model using a cross-fusion attention mechanism, our proposed model achieves superior prediction performance on practical urban traffic flow datasets, particularly for long-term predictions.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Huilai Zhi, Jinhai Li
Summary: This article addresses the issue that conflict analysis based on single-valued information systems is no longer valid. It proposes a conflict analysis method based on component similarity, which uses three-way n-valued concept lattices to handle set-valued formal contexts and realizes fast conflict analysis from an information fusion viewpoint. Experimental results verify the effectiveness of this method in reducing time consumption.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Huchang Liao, Jiaxin Qi, Jiawei Zhang, Chonghui Zhang, Fan Liu, Weiping Ding
Summary: In this paper, a hospital selection approach based on a fuzzy multi-criterion decision-making method is proposed. This approach considers sentiment evaluation values of unstructured data from online reviews and structured data of public indexes simultaneously. The methodology involves collecting and processing online reviews, classifying topics and sentiments, quantifying sentiment analysis results using fuzzy numbers, and obtaining final preference scores of hospitals based on patients' preferences. A case study and robustness analysis are conducted to validate the effectiveness of the method.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Faramarz Farhangian, Rafael M. O. Cruz, George D. C. Cavalcanti
Summary: The proliferation of social networks has posed challenges in combating fake news, but automatic fake news detection using artificial intelligence has become more feasible. This paper revisits the definitions and perspectives of fake news and proposes an updated taxonomy, based on multiple criteria, for the field. The study finds that optimal feature extraction techniques vary depending on the dataset, and context-dependent models based on transformer models consistently exhibit superior performance.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Mariana A. Souza, Robert Sabourin, George D. C. Cavalcanti, Rafael M. O. Cruz
Summary: In this study, a dynamic selection technique is proposed to handle sparse and overlapped data. The technique leverages the relationships between instances and classifiers to learn a dynamic classifier combination rule. Experimental results show that the proposed method outperforms static selection and other dynamic selection techniques.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Bin Yu, Ruihui Xu, Mingjie Cai, Weiping Ding
Summary: This paper introduces a clustering method based on non-Euclidean metric and multi-granularity staged clustering to address the challenges posed by complex spatial structure data to traditional clustering methods. The method improves the similarity measure and employs an attenuation-diffusion pattern for local to global clustering, achieving good clustering results.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Jian Zhu, Pengbo Hu, Bingqian Li, Yi Zhou
Summary: The acquisition of multi-view hash representation for heterogeneous data is highly important for multimedia retrieval. Current approaches suffer from limited retrieval precision due to insufficient integration of multi-view features and failure to effectively utilize metric information from diverse samples. In this paper, we propose an innovative method called Fast Metric Multi-View Hashing (FMMVH), which demonstrates the superiority of gate-based fusion over traditional methods. We also introduce a novel deep metric loss function to leverage metric information from dissimilar samples. By optimizing and employing model compression techniques, our FMMVH method significantly outperforms existing state-of-the-art methods on benchmark datasets, with up to 7.47% improvement in mean Average Precision (mAP).
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Fayaz Ali Dharejo, Iyyakutti Iyappan Ganapathi, Muhammad Zawish, Basit Alawode, Moath Alathbah, Naoufel Werghi, Sajid Javed
Summary: The resource-limited nature of underwater vision equipment affects underwater robotics and ocean engineering tasks. Super-resolution methods, particularly using Vision Transformers (ViTs), have emerged to enhance low-resolution underwater images. However, ViTs face challenges in handling severe degradation in underwater imaging. In contrast, Multi-scale ViTs (MViTs) overcome these challenges by preserving long-range dependencies through evolving channel capacity. This study proposes a novel algorithm, SwinWave-SR, for efficient and accurate multi-scale super-resolution for underwater images.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Weiwei Jiang, Haoyu Han, Yang Zhang, Jianbin Mu
Summary: This study incorporates federated learning and split learning paradigms with satellite-terrestrial integrated networks and introduces a split-then-federated learning framework and federated split learning with long short-term memory to handle sequential data in STINs. The proposed solution is demonstrated to be effective through a case study of electricity theft detection based on a real-world dataset.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Najah Abuali, Mohammad Bilal Khan, Farman Ullah, Mohammad Hayajneh, Hikmat Ullah, Shahid Mumtaz
Summary: The demand for innovative solutions in biomedical systems for precise diagnosis and management of critical diseases is increasing. A promising technology, non-invasive and intelligent Internet of Medical Things (IoMT) system, emerges to assess patients with reduced health risks. This research introduces a comprehensive framework for early diagnosis of respiratory abnormalities through RF sensing and SDR technology. The results highlight the superior performance of deep learning frameworks in classifying respiratory anomalies.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Shichen Huang, Weina Fu, Zhaoyue Zhang, Shuai Liu
Summary: In the era of adversarial machine learning (AML), developing robust and generalized algorithms has become a key research topic. This study proposes a global similarity matching module and a global-local cognition fusion training mechanism based on relationship adversarial sample generation to improve image-text matching algorithm. Experimental results show significant improvements in accuracy and robustness, performing well in facing security challenges and promoting the fusion of visual and linguistic modalities.
INFORMATION FUSION
(2024)