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Variational Bayesian Learning based Joint Localization and Channel Parameter Estimation in Wireless Sensor Networks with Distance-dependent Noise

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IEEE Transactions on Vehicular Technology 2024年第5期74卷 6877-6891页

作者： Li, Yunfei Luo, Yiting Tan, Weiqiang Li, Chunguo Ma, Shaodan Yang, Guanghua Anhui Polytechnic University Department of Electrical Engineering Wuhu China Guangzhou University School of Computer Science and Cyber Engineering Guangzhou China Southeast University School of Information Science and Engineering Nanjing China Purple Mountain Laboratories Nanjing China University of Macau State Key Lab. of Internet of Things for Smart City and the Dept. of Elec. and Computer Engineering Taipa China Jinan University School of Intelligent Systems Science and Engineering Zhuhai519070 China

This paper focuses on the challenge of jointly optimizing location and path loss exponent (PLE) in distancedependent noise. Departing from the conventional independent noise model used in localization and path loss exponent estimation problems, we consider a more realistic model incorporating distance-dependent noise variance, as revealed in recent theoretical analyses and experimental results. The distance-dependent noise introduces a complex noise model with unknown noise power and PLE, resulting in an exceptionally challenging nonconvex and nonlinear optimization problem. In this study, we address a joint localization and path loss exponent estimation problem encompassing distance-dependent noise, unknown parameters, and uncertainties in sensor node locations. To surmount the intractable nonlinear and non-convex objective function inherent in the problem, we introduce a variational Bayesian learning-based framework that enables the joint optimization of localization, path loss exponent, and reference noise parameters by leveraging an effective approximation to the true posterior distribution. Furthermore, the proposed joint learning algorithm provides an iterative closed-form solution and exhibits superior performance in terms of computational complexity compared to existing algorithms. computer simulation results demonstrate that the proposed algorithm approaches the performance of the Bayesian Cramer-Rao bound (BCRB), achieves localization performance comparable to the (maximum likelihood-Gaussian message passing) ML-GMP algorithm in some cases, and outperforms the other comparison algorithm in all cases. © 1967-2012 IEEE.

关键词： Iterative methods

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Correction to: Improving time and energy efficiency in multi-UAV coverage operations by optimizing the UAVs’ initial positions (International Journal of intelligent Robotics and Applications, (2024), 10.1007/s41315-024-00333-2)

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International Journal of intelligent Robotics and Applications 2024年第1期9卷 365-373页

作者： Stefanopoulou, Aliki Raptis, Emmanuel K. Apostolidis, Savvas D. Gkelios, Socratis Kapoutsis, Athanasios Ch. Chatzichristofis, Savvas A. Vrochidis, Stefanos Kosmatopoulos, Elias B. Information Technologies Institute The Centre for Research & ampTechnology Hellas 6th km Harilaou - Thermis Thessaloniki57001 Greece Department of Electrical and Computer Engineering Democritus University of Thrace Kimmeria Campus Xanthi67100 Greece Intelligent Systems Laboratory Department of Computer Science Neapolis University Pafos 2 Danais Avenue PafosCY 8042 Cyprus

In the original publication, the author has found few errors in the figures 2, 4, 11 and 12 which significantly impact both the paper's quality and the reputation of the journal. These issues undermine the clarity of the methodology and the novelty of the research. 1. Each column represents a different case in the experiment, corresponding to the number of UAVs in both the obstacle-free and obstacle areas. However, in Fig. 2, the first row contains identical subfigures across all columns, while the subfigures in the remaining rows are identical to each other The incorrect Fig. 2 appears as below: (Figure presented.) However the corrected Fig. 2 should appear as below. (Figure presented.) 2. In Fig. 4, every row showcase the average QoF for the testing scenarios (obstacle-free and obstacle area). However the first row has the exact same figures with the second row. The incorrect Fig. 4 appears as below: (Figure presented.) However the corrected Fig. 4 should appear as below. (Figure presented.) 3. Figures 11 and 12 showcase the distribution of each Quality of Flight (QoF) metric value for every UAV in the conducted trials during the testing scenarios. However, Fig. 11 is the same with Fig. 12. The incorrect Figs. 11 and 12 appears as below: (Figure presented.) However the corrected Figs. 11 and 12 should appear as below. (Figure presented.) Distribution of each Quality of Flight metric value for every UAV in the conducted trials during the coverageof the obstacle-free area Distribution of each Quality of Flight metric value for every UAV in the conducted trials during the coverageof the obstacle area The original article has been corrected. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

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Building a Reinforcement Learning Environment from Limited Data to Optimize Teachable Robot Interventions 15

Building a Reinforcement Learning Environment from Limited D...

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15th International Conference on Educational Data Mining, EDM 2022

作者： Maidment, Tristan Yu, Mingzhi Lobczowski, Nikki Kovashka, Adriana Walker, Erin Litman, Diane Nokes-Malach, Timothy Intelligent Systems Program University of Pittsburgh United States Computer Science Department University of Pittsburgh United States Learning Research and Development Center University of Pittsburgh United States

ISBN: (纸本)9781733673631

Working collaboratively in groups can positively impact performance and student engagement. intelligent social agents can provide a source of personalized support for students, and their benefits likely extend to collaborative settings, but it is difficult to determine how these agents should interact with students. Reinforcement learning (RL) offers an opportunity for adapting the interactions between the social agent and the students to better support collaboration and learning. However, using RL in education with social agents typically involves training using real students. In this work, we train an RL agent in a high-quality simulated environment to learn how to improve students’ collaboration. Data was collected during a pilot study with dyads of students who worked together to tutor an intelligent teachable robot. We explore the process of building an environment from the data, training a policy, and the impact of the policy on different students, compared to various baselines. © 2022 Copyright is held by the author(s).

关键词： Reinforcement learning

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HighlightRemover: Spatially Valid Pixel Learning for Image Specular Highlight Removal 24

HighlightRemover: Spatially Valid Pixel Learning for Image S...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhang, Ling Ma, Yidong Jiang, Zhi He, Weilei Bao, Zhongyun Fu, Gang Xu, Wenju Xiao, Chunxia School of Computer Science and Technology Hubei Key Laboratory of Intelligent Information Processing and Realtime Industrial Systems Wuhan University of Science and Technology Wuhan China School of Computer Science Wuhan University Wuhan China Department of Computing Hong Kong Polytechnic University Hong Kong Amazon Palo Alto United States

ISBN: (纸本)9798400706868

Recently, learning-based methods have made significant progress for image specular highlight removal. However, many of these approaches treat all the image pixels uniformly, overlooking the negative impact of invalid pixels on feature reconstruction. This oversight often leads to undesirable outcomes, such as color distortion or residual highlights. In this paper, we propose a novel image specular highlight removal network called HighlightRNet, which utilizes valid pixels as references to reconstruct the highlight-free image. To achieve this, we introduce a context-aware fusion block (CFBlock) that aggregates information in four directions, effectively capturing global contextual information. Additionally, we introduce a location-aware feature transformation module (LFTModule) to adaptively learn the valid pixels for feature reconstruction, thereby avoiding information errors caused by invalid pixels. With these modules, our method can produce high-quality highlight-free results without color distortion and highlight residual. Furthermore, we develop a multiple light image-capturing system to construct a large-scale highlight dataset called NSH, which exhibits minimal misalignment in image pairs and minimal brightness variation in non-highlight regions. Experimental results on various datasets demonstrate the superiority of our method over state-of-the-art methods, both qualitatively and quantitatively. © 2024 ACM.

关键词： Pixels

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Semantic Literature Review on Non-Fungible Token: Expansion Area of Usage & Trends 8

Semantic Literature Review on Non-Fungible Token: Expansion ...

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8th International Conference on Information Management and Technology, ICIMTech 2023

作者： Martha, Gede Indra Raditya Warnars, Harco Leslie Hendric Spits Prabowo, Harjanto Meyliana Hidayanto, Achmad Nizar Bina Nusantara University Binus Graduate Program - Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia Binus Business School Undergraduate Program Bina Nusantara University Management Department Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information System Department Jakarta11480 Indonesia University of Indonesia Faculty of Computer Science Depok Indonesia

ISBN: (纸本)9798350326093

Blockchain, Metaverse & NFT are technologies that were booming during the Pandemic. As a derivative product of blockchain, the Non-Fungible Token (NFT) is one of the technologies that has attracted the most interest from investors and the public because of its potential benefits and has always been closely associated with the Metaverse as a main purposes research of the NFTs. This paper is to explore the possibilities and provide an overview of the current use of NFT technology so that it can provide insight for further research & gap with the big picture that it is trying to present in this paper. However, after exploring further related to NFT in this research, it was found that the problem that most people are trying to solve through NFT is related to the topic of Decentralization & Web3 not as a Metaverse main backbone. © 2023 IEEE.

关键词： Blockchain

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Boosting Weakly Supervised Object Detection using Fusion and Priors from Hallucinated Depth

arXiv

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arXiv 2023年

作者： Gungor, Cagri Kovashka, Adriana Intelligent Systems Program University of Pittsburgh United States Department of Computer Science University of Pittsburgh United States

Despite recent attention to depth for various tasks, it is still an unexplored modality for weakly-supervised object detection (WSOD). We propose an amplifier method for enhancing the performance of WSOD by integrating depth information. Our approach can be applied to different WSOD methods based on multiple-instance learning, without necessitating additional annotations or inducing large computational cost. Our proposed method employs monocular depth estimation to obtain hallucinated depth information, which is then incorporated into a Siamese WSOD network using contrastive loss and fusion. By analyzing the relationship between language context and depth, we calculate depth priors to identify the bounding box proposals that may contain an object of interest. These depth priors are then utilized to update the list of pseudo ground-truth boxes, or adjust the confidence of per-box predictions. We evaluate our proposed method on three datasets (COCO, PASCAL VOC, and Conceptual Captions) by implementing it on top of two state-of-the-art WSOD methods, and we demonstrate a substantial enhancement in performance. © 2023, CC BY.

关键词： Object detection

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Evidence Theory and Fuzzy Discriminations in Dependable and Resilient Interactive MADM. Application in Emergency Psychiatric Diagnostics 13

Evidence Theory and Fuzzy Discriminations in Dependable and ...

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13th International Conference on Dependable systems, Services and Technologies, DESSERT 2023

作者： Sirbiladze, Gia Kacprzyk, Janusz Midodashvili, Bidzina Ghvaberidze, Bezhan Lozano, Jose Antonio Davitashvili, Tinatin Javakhishvili Tbilsi State University Department of Computer Sciences Iv. Tbilisi Georgia Polish Academy of Sciences Intelligent Systems Laboratory Systems Research Institute Warsaw Poland University of the Basque Country Department of Computer Science and Artificial Intelligence Donostia-San Sebastian Spain

ISBN: (纸本)9798350396119

Today, much attention is paid to the engineering of dependable and resilient intelligent systems and models. This direction includes multi-attribute decision-making (MADM) models and systems in emergency situations. A new interactive MADM approach based on evidence theory and fuzzy discrimination analysis is developed. MADM's utilities in the discrimination q-rung orthopair fuzzy values in the Dempster-Shaper's believe structure environment are transformed. New aggregation operators - extensions of Dempster's extremal expectations under discrimination q-rung orthopair fuzzy information are constructed. MADM's alternatives by new constructed operators are ranked. For the illustration of obtained results, a numerical example is created from Emergency Psychiatric Diagnostics (EPD). © 2023 IEEE.

关键词： intelligent systems

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Fetal Health Risk Classification Using Important Feature Selection and Cart Model on Cardiotocography Data

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第1期49卷 193-206页

作者： Ilham, Ahmad Nagara, Thahta Ardhika Prabu Kamaruddin, Mudyawati Khikmah, Laelatul Mantoro, Teddy Department of Informatics Universitas Muhammadiyah Semarang Semarang Indonesia Intelligent Data Science Research Group Universitas Muhammadiyah Semarang Semarang Indonesia Postgraduate Program of Medical Laboratory Science Universitas Muhammadiyah Semarang Semarang Indonesia Interdisciplinary Research Laboratory for Experimental Plasma Medicine Universitas Muhammadiyah Semarang Semarang Indonesia Department of Statistics Institut Teknologi Statistika dan Bisnis Muhammadiyah Semarang Semarang Indonesia School of Computer Science Nusa Putra University West Java Indonesia

Fetal mortality and newborn health issues require urgent attention because of high maternal and infant mortality rates during labor, highlighting the critical need for accurate fetal condition monitoring to reduce complications. This study proposes the development of a fetal health risk classification model based on Important Feature Selection (IFS) and a Classification and Regression Tree (CART) using cardiotocography (CTG) data from the UCI Machine Learning Repository. The IFS method was used to select the most relevant features, reduce model complexity, and increase generalization to prevent overfitting. The IFS-CART model was tested with 10-fold cross-validation and showed an accuracy of 94.50%, superior to the conventional CART, which only reached 93.83%. In addition, the average values of the True Positive Rate (TPR) and True Negative Rate (TNR) also increased, indicating that this model is effective in distinguishing normal, suspected, and pathological fetal conditions. Evaluation using the area under the curve receiver operating characteristic (AUC-ROC) showed that the model had high performance in detecting at-risk conditions, with an AUC of 0.981 for the"suspect" class. This finding confirmed that IFS-CART is not only accurate but also has high interpretability, making it easy for medical personnel to use for clinical decision support. The results of this study show that IFS-CART can serve as a reliable decision support system for real-time fetal health monitoring. Further implementation is expected to improve diagnostic accuracy and prevent complications during pregnancy and labor © 2025 Slovene Society Informatika. All rights reserved.

关键词： Fetal monitoring

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Multi-modal classification of breast cancer lesions in Digital Mammography and contrast enhanced spectral mammography images

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computers in Biology and Medicine 2024年 183卷 109266-109266页

作者： Bouzarjomehri, Narjes Barzegar, Mohammad Rostami, Habib Keshavarz, Ahmad Asghari, Ahmad Navid Azad, Saeed Talatian Department of Computer Engineering Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr*** Iran Department of Electrical Engineering Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr*** Iran

Breast cancer ranks as the second most prevalent cancer in women, recognized as one of the most dangerous types of cancer, and is on the rise globally. Regular screenings are essential for early-stage treatment. Digital mammography (DM) is the most recognized and widely used technique for breast cancer screening. Contrast-Enhanced Spectral Mammography (CESM or CM) is used in conjunction with DM to detect and identify hidden abnormalities, particularly in dense breast tissue where DM alone might not be as effective. In this work, we explore the effectiveness of each modality (CM, DM, or both) in detecting breast cancer lesions using deep learning methods. We introduce an architecture for detecting and classifying breast cancer lesions in DM and CM images in Craniocaudal (CC) and Mediolateral Oblique (MLO) views. The proposed architecture (JointNet) consists of a convolution module for extracting local features, a transformer module for extracting long-range features, and a feature fusion layer to fuse the local features, global features, and global features weighted based on the local ones. This significantly enhances the accuracy of classifying DM and CM images into normal or abnormal categories and lesion classification into benign or malignant. Using our architecture as a backbone, three lesion classification pipelines are introduced that utilize attention mechanisms focused on lesion shape, texture, and overall breast texture, examining the critical features for effective lesion classification. The results demonstrate that our proposed methods outperform their components in classifying images as normal or abnormal and mitigate the limitations of independently using the transformer module or the convolution module. An ensemble model is also introduced to explore the effect of each modality and each view to increase our baseline architecture's accuracy. The results demonstrate superior performance compared with other similar works. The best performance on DM images

关键词： Image enhancement

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Dependable and Resilient Emergency MAGDM Approach for the Selection of HADCs 13

Dependable and Resilient Emergency MAGDM Approach for the Se...

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13th International Conference on Dependable systems, Services and Technologies, DESSERT 2023

作者： Sirbiladze, Gia Kacprzyk, Janusz Midodashvili, Bidzina Ghvaberidze, Bezhan Lozano, Jose Antonio Davitashvili, Tinatin Iv. Javakhishvili Tbilsi State University Department of Computer Sciences Tbilisi Georgia Research Institute Polish Academy of Sciences Intelligent Systems Laboratory Systems Warsaw Poland University of the Basque Country Department of Computer Science and Artificial Intelligence Donostia-San Sebastian Spain

ISBN: (纸本)9798350396119

Today, much attention is paid to the engineering of dependable and resilient intelligent systems and models. This direction includes multi-attribute group decision making (MAGDM) models and systems in emergency situations. As a rule, they are characterized by insufficient objective data and lack of response time to the solution of the problem. We consider in this direction a new MAGDM of facilities locations selection problem (FLSP) based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approach. Expert's evaluations of each candidate of humanitarian aid distribution center (HADC) relative to uncertain attributes in q-rung orthopair fuzzy numbers (q-ROFNs) are presented. The offered model is examined with a numerical example. © 2023 IEEE.

关键词： intelligent systems

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