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2nd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2022

作者： Popli, Renu Singh, Daljeet Kumar, Rajeev Saini, Gurpreet Singh Garg, Kanwal Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Center for Space Research DRD Lovely Professional University Phagwara India School of Electronics and Electrical Engineering Lovely Professional University Phagwara India Kurukshetra University Department of Computer Science and Application Kurukshetra India

ISBN: (纸本)9781665437899

Gone are the days when agriculture was said to be a profession of uneducated people who used only basic mechanical tools to survive. Use of technology in agriculture has transformed it into a high-tech, smart and efficient business. The abrupt increase in population has pushed the efficiency of agriculture activities to achieve higher and better yield from given set of resources. In this paper, a comprehensive survey on the role of contemporary techniques used in agricultural development is presented. Various aspects of smart agriculture involving the use of Wireless Sensor Networks (WSNs) to gather information, big data analysis for processing the collected information, Internet of Things (IOT) framework for communication, image processing techniques and mobile phone application developed for agriculture are discussed. Landmark studies published in each of these related fields are also presented. © 2022 IEEE.

关键词： Wireless sensor networks

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Pre-trained language models in Spanish for health insurance coverage 5

Pre-trained language models in Spanish for health insurance ...

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5th Workshop on Clinical Natural Language Processing, ClinicalNLP 2023. held at ACL 2023

作者： Aracena, Claudio Rodríguez, Nicolás Rocco, Victor Dunstan, Jocelyn Faculty of Physical and Mathematical Sciences University of Chile Chile Millennium Institute Foundational Research on Data Chile Chilean Safety Association Chile Department of Computer Science Catholic University of Chile Chile Institute for Mathematical and Computational Engineering Catholic University of Chile Chile Center for Mathematical Modeling University of Chile Chile

ISBN: (纸本)9781959429883

The field of clinical natural language processing (NLP) can extract useful information from clinical text. Since 2017, the NLP field has shifted towards using pre-trained language models (PLMs), improving performance in several tasks. Most of the research in this field has focused on English text, but there are some available PLMs in Spanish. In this work, we use clinical PLMs to analyze text from admission and medical reports in Spanish for an insurance and health provider to give a probability of no coverage in a labor insurance process. Our results show that fine-tuning a PLM pretrained with the provider's data leads to better results, but this process is time-consuming and computationally expensive. At least for this task, fine-tuning publicly available clinical PLM leads to comparable results to a custom PLM, but in less time and with fewer resources. Analyzing large volumes of insurance requests is burdensome for employers, and models can ease this task by pre-classifying reports that are likely not to have coverage. Our approach of entirely using clinical-related text improves the current models while reinforcing the idea of clinical support systems that simplify human labor but do not replace it. To our knowledge, the clinical corpus collected for this study is the largest one reported for the Spanish language. © 2023 Association for Computational Linguistics.

关键词： Health insurance

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Tensor Graph Convolutional Network for Dynamic Graph Representation Learning

arXiv

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arXiv 2024年

作者： Wang, Ling Yuan, Ye The School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China The Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Engineering Research Center of Big Data Application for Smart Cities Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China The College of Computer and Information Science Southwest University Chongqing400715 China

Dynamic graphs (DG) describe dynamic interactions between entities in many practical scenarios. Most existing DG representation learning models combine graph convolutional network and sequence neural network, which model spatial-temporal dependencies through two different types of neural networks. However, this hybrid design cannot well capture the spatial-temporal continuity of a DG. In this paper, we propose a tensor graph convolutional network to learn DG representations in one convolution framework based on the tensor product with the following two-fold ideas: a) representing the information of DG by tensor form;b) adopting tensor product to design a tensor graph convolutional network modeling spatial-temporal feature simultaneously. Experiments on real-world DG datasets demonstrate that our model obtains state-of-the-art performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Tensors

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Sequence-to-Sequence Multi-Hop Knowledge Reasoning Based on Retentive Network

Sequence-to-Sequence Multi-Hop Knowledge Reasoning Based on ...

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computer Information science and application Technology (CISAT), International Conference on

作者： Ruilin Su Wanshan Zhang Dunhui Yu Hubei University Wuhan China Wuhan China School of Computer Science and Information Engineering Hubei University Wuhan China Hubei Key Laboratory of Big Data Intelligent Analysis and Application Hubei University Wuhan China Engineering and Technical Research Center of Hubei Province in Software Engineering Wuhan China

ISBN: (数字)9798350375107

ISBN: (纸本)9798350375114

Multi-hop Knowledge Reasoning is a task that involves generating an answer given a query and a knowledge graph. Existing sequence-to-sequence reasoning models use the Transformer to encode and decode sequences, but these models have some flaws, such as the inability to effectively handle long-sequence reasoning and susceptibility to exposure bias. To address these issues, we provides a sequence-to-sequence reasoning model named STSR, which is based on Retentive Network. aiming at improving training efficiency through parallel training, this model leverages the advantages in Retentive Network, reduce time overhead, and enhance reasoning efficiency through iterative reasoning. It effectively mitigates the problems of high spatial overhead and low efficiency in long-sequence reasoning faced by the Transformer. Moreover, it retains and updates historical information during the encoding and decoding process, enhancing the model’s memory and generalization capabilities. Additionally, the Scheduled Sampling method is adopted to alleviate the exposure bias introduced during reasoning, which involves using the model’s own output as the input for the next moment with a certain probability during training, instead of using the true label. We conduct the experiment on six public datasets, with the results showing that the proposed model outperforms existing baseline models in terms of precision and generation quality. This paper provides a new solution for the task of sequence-to-sequence multihop knowledge reasoning and also demonstrates the potential application of the Retentive Network in natural language processing.

关键词： Training Knowledge engineering Accuracy Spread spectrum communication Knowledge graphs Transformers Sampling methods Cognition Natural language processing Iterative decoding

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An ADRC-Incorporated Stochastic Gradient Descent Algorithm for Latent Factor Analysis

arXiv

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arXiv 2024年

作者： Li, Jinli Yuan, Ye The School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China The Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Engineering Research Center of Big Data Application for Smart Cities Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China The College of Computer and Information Science Southwest University Chongqing400715 China

High-dimensional and incomplete (HDI) matrix contains many complex interactions between numerous nodes. A stochastic gradient descent (SGD)-based latent factor analysis (LFA) model is remarkably effective in extracting valuable information from an HDI matrix. However, such a model commonly encounters the problem of slow convergence because a standard SGD algorithm only considers the current learning error to compute the stochastic gradient without considering the historical and future state of the learning error. To address this critical issue, this paper innovatively proposes an ADRC-incorporated SGD (ADS) algorithm by refining the instance learning error by considering the historical and future state by following the principle of an ADRC controller. With it, an ADS-based LFA model is further achieved for fast and accurate latent factor analysis on an HDI matrix. Empirical studies on two HDI datasets demonstrate that the proposed model outperforms the state-of-the-art LFA models in terms of computational efficiency and accuracy for predicting the missing data of an HDI matrix. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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What improves the generalization of graph transformers? a theoretical dive into the self-attention and positional encoding 24

What improves the generalization of graph transformers? a th...

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Proceedings of the 41st International Conference on Machine Learning

作者： Hongkang Li Meng Wang Tengfei Ma Sijia Liu Zaixi Zhang Pin-Yu Chen Department of Electrical Computer and System Engineering Rensselaer Polytechnic Institute Troy NY Department of Biomedical Informatics Stony Brook University Stony Brook NY Department of Computer Science and Engineering Michigan State University East Lansing MI and MIT-IBM Watson AI Lab IBM Research MA Department of Computer Science and Engineering University of Science and Technology of China Hefei Anhui China IBM Thomas J. Watson Research Center Yorktown Heights NY

Graph Transformers, which incorporate self-attention and positional encoding, have recently emerged as a powerful architecture for various graph learning tasks. Despite their impressive performance, the complex non-convex interactions across layers and the recursive graph structure have made it challenging to establish a theoretical foundation for learning and generalization. This study introduces the first theoretical investigation of a shallow Graph Transformer for semi-supervised node classification, comprising a self-attention layer with relative positional encoding and a two-layer perceptron. Focusing on a graph data model with discriminative nodes that determine node labels and non-discriminative nodes that are class-irrelevant, we characterize the sample complexity required to achieve a desirable generalization error by training with stochastic gradient descent (SGD). This paper provides the quantitative characterization of the sample complexity and number of iterations for convergence dependent on the fraction of discriminative nodes, the dominant patterns, and the initial model errors. Furthermore, we demonstrate that self-attention and positional encoding enhance generalization by making the attention map sparse and promoting the core neighborhood during training, which explains the superior feature representation of Graph Transformers. Our theoretical results are supported by empirical experiments on synthetic and real-world benchmarks.

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ARBS-YOLOv8 for Annotation Box Detection in Colonoscopy CADe Systems

ARBS-YOLOv8 for Annotation Box Detection in Colonoscopy CADe...

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International Conference on e-health Networking, applications and Services (HealthCom)

作者： Ruiyao Zhang Naohisa Yoshida Osamu Dohi Boyuan Peng Xinkai Liu Jie Huang Xin Zhu Graduate School of Computer Science and Engineering The University of Aizu Aizu-Wakamatsu Japan Department of Molecular Gastroenterology and Hepatology Graduate School of Medical Science Kyoto Prefectural University of Medicine Kyoto Japan Department of AI Technology Development M&D Data Science Center Institute of Science Tokyo Tokyo Japan

ISBN: (数字)9798350350548

ISBN: (纸本)9798350350555

Colonoscopy is the gold standard for colorectal lesion detection, with its accuracy directly influencing patient prog-nosis. The accurate detection of Annotation Boxes in computer-aided Detection (CADe) systems is important for improving their performance in colonoscopy procedures. This research introduces a novel algorithm that combines active learning with a modified YOLOv8 algorithm, named ARBS-YOLOv8 (Active learning with RepVGG Block and SIoU loss function base on YOLOv8), for the detection of Annotation Boxes generated by colonoscopy CADe systems. We compared standard YOLOv8, YOLOv6, YOLOX algorithms and their active learning variants using colonoscopy video data from 31 patients. The active learning method involves analyzing false positives generated by the initially trained model on an active learning dataset. These false detection samples are then reintroduced into the training set as negative examples. This active learning method led to substantial performance improvements across all algorithms, with particularly notable increase in precision. Notably, the application of active learning to YOLOv6 resulted in a significant increase in Precision by 23.28%, reaching 95.79%. Furthermore, when active learning was integrated with YOLOv8, it achieved an impressive F1 Score of 99.70% on the test set. Compared to the standard YOLOv8, ARBS-YOLOv8 demonstrated 0.88% increase in F1 Score while maintaining high Sensitivity. This improvement suggests that ARBS-YOLOv8 is more effective at extracting features from the data and increase the F1 score in detecting Annotation Boxes.

关键词： Training Accuracy Sensitivity Annotations Active learning Colonoscopy Feature extraction Lesions data mining Standards

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Electrochemical impedance behavior of implantable neural interface following the change of equivalent circuit element 12

Electrochemical impedance behavior of implantable neural int...

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12th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2022

作者： Zhang, Jing Liu, Lusheng Wang, aiping Wang, Junkongshuai Fang, Tao Niu, Lan Bin, Jianxiong Wang, Pengchao Mu, Wei Jia, Jie Zhang, Lihua Kang, Xiaoyang Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of Ai and Robotics Ministry of Education Shanghai Engineering Research Center of Ai and Robotics Moe Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Institute of Ai and Robotics Academy for Engineering and Technology Fudan University Shanghai200433 China Ji Hua Laboratory Guangdong Province Foshan528200 China Department of Rehabilitation Medicine Huashan Hospital Fudan University Shanghai200040 China Yiwu Research Institute of Fudan University Zhejiang Yiwu City322000 China Research Center for Intelligent Sensing Zhejiang Lab Hangzhou311121 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

The simulation of two basic equivalent circuit models of the implantable neural interface is described in this paper. The simulated equivalent circuits are created based on the physicochemical mechanism of charge transfer on the surface of the implantable neural interface. Each parameter of the equivalent circuit model was varied to investigate the regular pattern of individual element affecting the performance of the implantable neural interface. In this paper, one circuit model is based on the charge transfer process, and another circuit model is based on the mixed charge transfer and diffusion process. The equivalent circuits are split into two parts. The neural interface of the implantable neural interface device is simulated in one part, and the rest of that is simulated in another part. The electrochemical impedance data is acquired firstly by simulating the equivalent circuit with Z-view software. Then, the simulated data was analyzed to investigate the change tendency of a single parameter, providing a theoretical method for developing customized implantable neural interface with specific electrochemical impedance. © 2022 IEEE.

关键词： Equivalent circuits

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Multi-agent Collaboration for Vehicular Task Offloading Using Federated Deep Reinforcement Learning

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IEEE Transactions on Mobile Computing 2025年

作者： Chen, Xing Xiao, Bohuai Lin, Xinyu Chen, Zheyi Min, Geyong Fuzhou University College of Computer and Data Science Fuzhou350116 China Ministry of Education Engineering Research Center of Big Data Intelligence Fuzhou350002 China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350116 China University of Exeter Department of Computer Science ExeterEX4 4QF United Kingdom

Mobile Edge Computing (MEC) distributes resources such as computing, storage, and bandwidth to the side close to users, which can provide low-latency services to in-vehicle users, thus promising a more efficient and safer driving environment. However, due to the dynamic scale of vehicle and the variability of resource requirements, it is a significant challenge to quickly obtain effective task offloading in large-scale vehicle scenarios. The existing studies generally adopt the centralized decision-making method, with long decision-making time and high computational overhead, which cannot effectively achieve good offloading decisions in large-scale scenarios. To address these problems, we propose a Multi-agent Collaborative Method for vehicular task offloading using Federated Deep Reinforcement Learning called MCM-FDRL. First, each vehicle as an agent, independently makes offloading decisions based on local information. Next, the offloading decision model of each vehicle is obtained through federated reinforcement learning training. At runtime, an effective vehicle offloading plan can be gradually developed through multi-agent collaboration. Using two real-world datasets, experiments show that the MCM-FDRL has good adaptability and scalability. Moreover, compared to the state-of-the-art methods, the task's average response time of the MCM-FDRL is reduced by 9.75%-64.90%, respectively. © 2002-2012 IEEE.

关键词： Federated learning

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Wireless Sensor Networks and Green Networking for 6G communication- An Overview

Wireless Sensor Networks and Green Networking for 6G communi...

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Computational Intelligence, Communication Technology and Networking (CICTN), International Conference on

作者： Ramiz Salama Fadi Al-Turjman Dibyahash Bordoloi Satya Prakash Yadav Department of Computer Engineering AI and Robotics Institute Research Center for AI and IoT Near East University Nicosia Turkey Artificial Intelligence Engineering Dept. AI and Robotics Institute Near East University Nicosia Turkey Faculty of Engineering Research Center for AI and IoT University of Kyrenia Kyrenia Turkey Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun RS CSE Graphic Era University Dehradun Department of Computer Science and Engineering G.L. Bajaj Institute of technology and Management (GLBITM) Greater Noida India

Wireless sensor networks and green networking have been major research areas in the field of communication technology for the past few decades. In particular, the development of 6G communication systems has brought renewed focus on these areas, as the demands for higher data rates, more diverse applications, and lower energy consumption continue to increase. The use of wireless sensor networks in 6G communication systems has been widely explored in the literature, and has shown great potential for a wide range of applications, including environmental monitoring, industrial process control, and healthcare. However, a key challenge in the deployment of these networks is ensuring their energy efficiency, as the demands on the network will be much higher than in previous generations. To address this challenge, researchers have proposed various green networking techniques, such as energy harvesting and low-power communication protocols, which have been shown to significantly reduce the energy consumption of the sensors. In addition to improving the energy efficiency of wireless sensor networks, researchers have also studied their reliability, scalability, and adaptability. These properties are important in 6G communication systems, as the demands on the network are expected to be much higher than in previous generations. researchers have proposed various methods to improve the reliability, scalability, and adaptability of wireless sensor networks, including the use of error-correction codes, redundant communication channels, and machine learning algorithms.

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