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Explainable Topic-Enhanced Argument Mining from Heterogeneous Sources

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arXiv 2023年

作者： Si, Jiasheng Zhu, Yingjie Shi, Xingyu Zhou, Deyu He, Yulan School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China Department of informatics King’s College London United Kingdom Alan Turing Insitute United Kingdom

Given a controversial target such as "nuclear energy", argument mining aims to identify the argumentative text from heterogeneous sources. Current approaches focus on exploring better ways of integrating the target-associated semantic information with the argumentative text. Despite their empirical successes, two issues remain unsolved: (i) a target is represented by a word or a phrase, which is insufficient to cover a diverse set of target-related subtopics;(ii) the sentence-level topic information within an argument, which we believe is crucial for argument mining, is ignored. To tackle the above issues, we propose a novel explainable topic-enhanced argument mining approach. Specifically, with the use of the neural topic model and the language model, the target information is augmented by explainable topic representations. Moreover, the sentence-level topic information within the argument is captured by minimizing the distance between its latent topic distribution and its semantic representation through mutual learning. Experiments have been conducted on the benchmark dataset in both the in-target setting and the cross-target setting. Results demonstrate the superiority of the proposed model against the state-of-the-art baselines. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

Bridging Evolutionary Algorithms and Reinforcement Learning: A Comprehensive Survey on Hybrid Algorithms

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arXiv 2024年

作者： Li, Pengyi Hao, Jianye Tang, Hongyao Fu, Xian Zheng, Yan Tang, Ke The College of Intelligence and Computing Tianjin University Tianjin300350 China QuebecH3A 0G4 Canada The Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

Evolutionary Reinforcement Learning (ERL), which integrates Evolutionary Algorithms (EAs) and Reinforcement Learning (RL) for optimization, has demonstrated remarkable performance advancements. By fusing both approaches, ERL has emerged as a promising research direction. This survey offers a comprehensive overview of the diverse research branches in ERL. Specifically, we systematically summarize recent advancements in related algorithms and identify three primary research directions: EA-assisted Optimization of RL, RL-assisted Optimization of EA, and synergistic optimization of EA and RL. Following that, we conduct an in-depth analysis of each research direction, organizing multiple research branches. We elucidate the problems that each branch aims to tackle and how the integration of EAs and RL addresses these challenges. In conclusion, we discuss potential challenges and prospective future research directions across various research directions. To facilitate researchers in delving into ERL, we organize the algorithms and codes involved on https://***/yeshenpy/Awesome-Evolutionary-Reinforcement-Learning Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

A Probability-Based Scheme for Generating Robust Internet of Things

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A Probability-Based Scheme for Generating Robust Internet of...

International Conference on computer Supported Cooperative Work in Design

作者： Jingchen Sun Ning Chen Songwei Zhang Zhaolong Ning Tie Qiu College of Intelligence and Computing Tianjin University Tianjin China Department of Computer Science City University of Hong Kong Hong Kong China School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China School of Computer Qinghai Minzu Univerisity Xining China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

With the scale of Internet of Things (IoT) continually expanding, the topology is growing rapidly and the probability of cascading collapse due to node failures or malicious attacks is increasing. The decrease in the Quality of Service (QoS) of IoT could be mitigated by robust topology. Existing optimization strategies usually use heuristic algorithms to enhance topology robustness. However, when the scale of topology is large, these algorithms involve a significant amount of iterative searching for the optimal solution, which is time-consuming and prone to getting stuck into local optimum. To tackle this situation, this study introduces arithmetic encoding and proposes a novel probability-based robust topology generation model that can quickly generate IoT robust topology. We losslessly compress robust topologies using arithmetic encoding and extract their features. Based on the extracting features, we design a unique probability-based topology generation approach that avoids the time overhead of iterative calculations. Experimental results demonstrate that the proposed solution in this paper can construct robust topologies in less time for different network scales.

关键词： Network topology Heuristic algorithms Quality of service Reinforcement learning Feature extraction Encoding Robustness

RAFO-4 MRI: SIMULTANEOUS CARTILAGE MORPHOLOGY AND 3D T 2 MAPPING WITH MACHINE LEARNING

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Osteoarthritis and Cartilage 2025年第6期33卷 791-792页

作者： Krithika Balaji Michael Mendoza Pedro M. Vicente Christoforos Galazis Simran Kukran Anil A. Bharath Peter J. Lally Neal K. Bangerter Department of Bioengineering Imperial College London London Please select an option below United Kingdom Department of Brain Sciences Imperial College London London United Kingdom Department of Computing Imperial College London London United Kingdom School of Medicine Case Western Reserve University Cleveland Ohio United States IN-CYPHER Imperial Global: Singapore Singapore Singapore Department of Electrical and Computer Engineering Boise State University Boise Idaho United States

OCU-Net: A Novel U-Net Architecture for Enhanced Oral Cancer Segmentation

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arXiv 2023年

作者： Albishri, Ahmed Shah, Syed Jawad Lee, Yugyung Wang, Rong Computer Science School of Science and Engineering University of Missouri-Kansas City Kansas City United States College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Department of Oral and Craniofacial Sciences School of Dentistry University of Missouri - Kansas City Kansas City United States

Accurate detection of oral cancer is crucial for improving patient outcomes. However, the field faces two key challenges: the scarcity of deep learning-based image segmentation research specifically targeting oral cancer and the lack of annotated data. Our study proposes OCU-Net, a pioneering U-Net image segmentation architecture exclusively designed to detect oral cancer in hematoxylin and eosin (H&E) stained image datasets. OCU-Net incorporates advanced deep learning modules, such as the Channel and Spatial Attention Fusion (CSAF) module, a novel and innovative feature that emphasizes important channel and spatial areas in H&E images while exploring contextual information. In addition, OCU-Net integrates other innovative components such as Squeeze-and-Excite (SE) attention module, Atrous Spatial Pyramid Pooling (ASPP) module, residual blocks, and multi-scale fusion. The incorporation of these modules showed superior performance for oral cancer segmentation for two datasets used in this research. Furthermore, we effectively utilized the efficient ImageNet pre-trained MobileNet-V2 model as a backbone of our OCU-Net to create OCU-Netm, an enhanced version achieving state-of-the-art results. Comprehensive evaluation demonstrates that OCU-Net and OCU-Netm outperformed existing segmentation methods, highlighting their precision in identifying cancer cells in H&E images from OCDC and ORCA datasets. © 2023, CC BY.

关键词： Image segmentation

Analytical Study of the Features and Parameters of Off-Angle Iris Image vs. Deep Learning Model

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Analytical Study of the Features and Parameters of Off-Angle...

Decision Aid Sciences and Application (DASA), International Conference on

作者： Aghiad Khedr Suleiman Darin Shafek Mahmoud Mahfuri Mohammad Khishe Ali Saeed Sameh Ghwanmeh Cybersecurity and Cloud Computing Technologies Engineering Dept. Alma'moon University College Baghdad iraq Computer Engineering Techniques Dept. Alma'moon University College Baghdad iraq Computer and information Technology Department International University for Science and Technology Innovation Center for Artificial Intelligence Applications Yuan Ze University Taiwan Jadara research center Jadara University Jordan Computer Center Northern Technical University Ninevah Iraq Computer Science Dept College of Computer Information Technology American University in the Emirates

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

This paper presents an analytical study and a technique for extracting the features of a common case of images of the iris called off-angle iris which was taken for persons identification systems. The main problem when using biological iris measurements to identify the persons is the difficulty of identifying and extracting features of the iris. This problem is increasing when dealing with off-angle iris and it leads to decreased system accuracy and increased system rate error. In return, all the transfer learning techniques face difficulties in the case of heavily degraded data and partially occluded, and off-angle. A new method has a whole new methodology to deal with the image as it is without transformation processes. A new algorithm has been included for extracting features of the iris through the pupil switching points. The most discriminating points of the iris depend on the biological human eye statistics and analytical study. It has been trained and tested on the common images of the off-angle iris database so-called: “CASIA Iris 1.0”. It has been implemented in the MATLAB environment. The results showed the efficiency of this technique, high precision and more importantly low failed acceptance rate. It emphasized that it is adaptive as well as efficiency improvement of the system.

关键词： Deep learning Analytical models Adaptation models Accuracy Transfer learning Feature extraction Cameras Mathematical models Pupils Iris recognition

Multi sensor based monitoring of paralyzed using Emperor Penguin Optimizer and Deep Maxout Network

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Scientific Reports 2025年第1期15卷 1-19页

作者： Gunturu, Vijaya Kavitha, J. Thouti, Swapna Senthil Kumar, N.K. Poon, Kamal Alharbi, Ayman A. Jaffar, Amar Y. Saravanan, V. Department of ECE Indian Institute of Information Technology - Design and Manufacturing (IIITDM) Andhra pradesh Jagannathagattu Hill Kurnool 518007 India Department of Computer Science & Engineering Koneru Lakshmaiah Education Foundation Telangana Bowrampet Hyderabad 500043 India Department of Electronics and Communication Engineering CVR College of Engineering Telangana Hyderabad India Department of Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Chennai 600 062 India Department of College of Science and Engineering Southern Arkansas University Magnolia 71753 AR United States Computer and Network Engineering Department College of Computing Umm Al-Qura University Makkah 21955 Saudi Arabia Department of Computer Science Dambi Dollo University Dambi Dollo Ethiopia

The correct sitting posture in a wheelchair is crucial for paralyzed people. This helps prevent problems such as pressure ulcers, muscle contractures, and respiratory problems. A paralyzed person with poor sitting posture is highly likely to slip out of their wheelchair. To prevent this from happening and consistently maintain paralyzed individuals under observation, a new model, the Emperor Penguin Optimized Sensor-Infused Wheelchair (EPIC), has been designed to monitor the position and health of the individual in the wheelchair in real-time. A Force Sensitive Resistor (FSR) sensor and an ultrasonic sensor continuously transmit information to the Arduino UNO R4 Wi-Fi board. The Emperor Penguin Optimizer Algorithm (EPOA) was used to select the features sent from the Arduino board to the ESP8266-Wi-Fi module. A Deep Maxout Network (DMN) was used to predict the posture of a wheelchair-using patient following the feature selection phase. A mobile application for Android collects data from the ESP32 module and estimates posture to inform the caretaker about the user’s current posture and health status. Evaluation metrics such as precision, accuracy, sensitivity, and specificity have been used to determine the efficiency in the EPIC framework, which improves overall accuracy by 10.1%, 7.73%, and 2.84% for better posture recognition. © The Author(s) 2025.

关键词： Deep learning Deep Maxout Network Emperor Penguin Optimizer Feature selection Healthcare monitoring system Sensor networks

Simulated Annealing for Optimal Path Selection and Scheduling in Energy Routing for Smart Grids

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Simulated Annealing for Optimal Path Selection and Schedulin...

International Conference on computer and Applications (ICCA)

作者： Assala Nacef Lemia Louail Djamila Mechta Lina Benchikh Saad Harous LRSD laboratory Faculty of Sciences University Ferhat Abbas setif 1 setif Algeria Université de Lorraine CRAN F-54000 Nancy France Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah UAE

Technological advancements in electronics and power generation devices have led to the development of smart grids and the energy internet. Energy routing is a critical step in creating a reliable and functional energy system capable of harnessing the full potential of renewable energy. One of the major challenges in energy routing is the optimal path determination problem, which involves finding the path with the least power loss. An optimal path can alleviate network congestion and minimize energy costs for grid users. In this paper, we present an innovative path discovery protocol based on simulated annealing. We evaluate the efficiency of our protocol by comparing it to two other existing methods in the literature using simulation. The findings highlight the effectiveness of our approach in achieving optimal results for both large and small networks.

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A SURVEY ON COMPUTATIONAL SOLUTIONS FOR RECONSTRUCTING COMPLETE OBJECTS BY REASSEMBLING THEIR FRACTURED PARTS

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arXiv 2024年

作者： Lu, Jiaxin Liang, Yongqing Han, Huijun Hua, Jiacheng Jiang, Junfeng Li, Xin Huang, Qixing Computer Science Department The University of Texas at Austin 2317 Speedway AustinTX United States Department of Computer Science and Engineering Texas A&M University 435 Nagle St College StationTX United States Department of Computer Science and Technology Tsinghua University 30 Shuangqing Road Beijing China College of Artificial Intelligence and Automation Hohai University 1915 Hohai Avenue Jiangsu Changzhou China Section of Visual Computing & Computational Media Department of Computer Science and Engineering Texas A&M University 789 Ross Street College StationTX United States

Reconstructing a complete object from its parts is a fundamental problem in many scientific domains. The purpose of this article is to provide a systematic survey on this topic. This reassembly problem requires understanding the attributes of individual pieces and establishing matches between different pieces. Many approaches also model priors of the underlying complete object. Existing approaches are tightly connected problems of shape segmentation, shape matching, and learning shape priors. We provide existing algorithms in this context and emphasize their similarities and differences to general-purpose approaches. We also survey the trends from early procedural approaches to more recent deep learning approaches. In addition to algorithms, this survey will also describe existing datasets, open-source software packages, and applications. To the best of our knowledge, this is the first comprehensive survey on this topic in computer graphics. Copyright © 2024, The Authors. All rights reserved.

关键词： Open source software