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A Multi-Head Collaborative Matrix Joint Extraction Method for the Chinese E-Government Domain

A Multi-Head Collaborative Matrix Joint Extraction Method fo...

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Symposium of Image, Signal Processing, and Artificial Vision (STSIVA)

作者： Runbo Li Bo Zhu Lan Qiu Siqi Chou Rong Yuan Jianling Li School of Mechanical and Electrical Engineering Kunming University of Science and Technology Kunming City Yunnan Province China

ISBN: (数字)9798350363203

ISBN: (纸本)9798350363210

In Chinese government texts, the efficient extraction of entities and relationships is crucial for tasks such as information retrieval and intelligent question answering in e-government systems. Most current entity-relationship extraction methods often overlook the importance of entity type information in relation extraction, making it difficult to effectively capture complex multi-type entities and relationships, particularly in the government domain where the semantics are complex and diverse. To address this issue, this paper proposes a multi-head collaborative matrix joint extraction method, tailored to the characteristics of Chinese government texts. The approach leverages the Mengzi pre-trained model, which is more suited for Chinese entity-relationship extraction tasks, to dynamically encode word vectors from government texts, effectively solving the problem of insufficient information mining. Additionally, a multi-head collaborative matrix labeling scheme is introduced, allowing the model to incorporate entity type information from the text as an influencing factor in relation extraction. This enhances the model's ability to capture entity relationships and addresses issues of complex overlapping relationships and word ambiguity in Chinese texts. Experimental results show that this method significantly outperforms other benchmark models in terms of accuracy, recall, and F1 score on Chinese government datasets, providing more comprehensive and enriched information, and demonstrating strong advantages.

关键词： Semantics Collaboration Tail Information retrieval Solids Vectors Question answering (information retrieval) data mining Labeling Electronic government

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Real-time Coordinated Motion Generation: A Hierarchical Deep Predictive Learning Model for Bimanual Tasks

Real-time Coordinated Motion Generation: A Hierarchical Deep...

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ieee/RSJ international conference on Intelligent Robots and Systems (IROS)

作者： Genki Shikada Simon Armleder Hiroshi Ito Gordon Cheng Tetsuya Ogata Department of Intermedia Art and Science Waseda University Tokyo Japan Institute for Cognitive System (ICS) Technical University of Munich Germany National Institute of Advanced Industrial Science and Technology (AIST) Tokyo Japan

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Robots that autonomously operate in human living environments require the ability to adapt to unpredictable changes and flexibly handle a variety of tasks. Particularly, coordinated bimanual motions are essential for enabling tasks that are difficult with just one hand, such as grasping bulky objects, transporting heavy loads, and precision work. Traditional methods of generating robot motions typically involve executing pre-programmed motions, making it challenging to adapt to complex and unpredictable environmental changes. To address this issue, our research focuses on generating diverse motions that can flexibly adapt to environmental changes based on Deep Predictive Learning from a small amount of real-world data. Previous Deep Predictive Learning models have generated the motions of a robot’s left and right arms by a single LSTM, making it difficult to operate them independently. Therefore, we propose a new Hierarchical Deep Predictive Learning model specialized for generating coordinated bimanual motions. This model comprises three components: a Left-LSTM, which learns the body and visual information on the robot’s left side, a Right-LSTM that performs a similar function for the right side, and a Union-LSTM which integrates this information at a higher level. To verify the effectiveness of the proposed model, we conducted bimanual grasping experiments with multiple different objects using two different robots. The experimental results showed that independent of hardware, our model demonstrated a higher success rate compared to the traditional approach, indicating its enhanced capability in coordinating bimanual motions.

关键词： Robot motion Visualization Robot kinematics Grasping Predictive models Transformers Hardware Real-time systems Long short term memory Periodic structures

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The Role of AI and Machine Learning in the Evolution of 5G and Beyond Networks

The Role of AI and Machine Learning in the Evolution of 5G a...

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Intelligent Signal Processing and Effective Communication Technologies (INSPECT), ieee international conference on

作者： Y.L. Malathi Latha Sk. Rabbani Chiranjeevi Manike Sandip J. Gami M Kishore Kumar Siva Koteswara Rao Katta Department of Information Technology Stanely College of Engineering and Technology for Women (A) Hyderabad India Department of Computer Science and Engineering MallaReddy Engineering College(A) Maisammaguda India Data Science MLR Institute of Technology Hyderabad Independent Researcher Brambleton VA Department of ECE Aditya College of Engineering & Technology Surampalem AP Department of Mechanical KITEW Kakinada

ISBN: (数字)9798350379525

ISBN: (纸本)9798350379532

This article will examine the current capabilities of 5G and B5G networks in terms of artificial intelligence and machine learning. The research delves into several applications of AI and ML within the telecom industry. Neural networks are introduced as a class of tools for decision-making and statistical data modelling that are often nonlinear. data pattern discovery and the representation of complex connections between input and output parameters in systems are common uses for these. This class of neural networks includes recurrent, convolutional, feed-forward, and deep neural networks. When improving a system property or maximising a collective reward, reinforcement learning focuses on the behaviours that intelligent agents must do. One advantage of deep reinforcement learning is its ability to handle unstructured input. This is achieved by combining deep neural networks. We offer hybrid solutions, like expert knowledge aided machine learning and mixed analytical and machine learning models. Lastly, further targeted approaches are showcased, including GANs, unsupervised learning, and clustering. CF M-MIMO technology, which stands for cell-free massive MIMO, is the most promising idea in B5G. The appealing aspects of CF M-MIMO systems, such as power distribution and channel estimation, are with their downsides. There have been numerous successful applications of deep learning (DL) across many scientific domains, including wireless communications. According to the research by Lazaros Alexios Iliadis et al., CF M-MIMO networks use cutting-edge DL techniques. We provide the most popular DL models and review cell-free network fundamentals here. The endnotes emphasise the potential avenues for further study.

关键词： Wireless communication Cloud computing 5G mobile communication Biological system modeling Tourism industry Artificial neural networks Signal processing Telecommunications Trajectory Unsupervised learning

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Single-Precision Floating-Point Adder Based on ieee 754 Standard Through Verilog

Single-Precision Floating-Point Adder Based on IEEE 754 Stan...

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2nd international conference on Artificial Intelligence and Communication Technologies, ICAICT 2024

作者： Guo, Cong Jia, Zhouchi Lin, Yechen Yang, Haozhe College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing China School of Information Science and Technology NorthWest University Xi’an China School of Chips Xi’an Jiaotong-Liverpool University Suzhou China College of Artificial Intelligence and Automation Hohai University Nanjing China

ISBN: (纸本)9789819600915

Floating-point number operations are widely used in scientific computing, engineering simulations, financial modeling, and data analysis because of its ability of quick processing while maintaining the accuracy. Compared with integer and fixed-point representations, they enable more accuracy and manipulation of real numbers in various computational tasks, ranging from numerical analysis to machine learning algorithms. However, among the arithmetic operations, the most complex ones are the addition/subtraction operations compared to multiplication/division operations due to the unique way of processing data in binary systems, which differs a lot from human calculations. In this paper, a floating-number calculator is programmed using finite-state machine (FSM) triggered by clock period through Verilog hardware description language (VHDL) and implemented on FPGA. The Verilog code is in accord with ieee 754 standard, and the standard will be illustrated visually. The states of the finite-state machine along with the conditions required to transmogrify are also to be discussed in this article, using pictures to help readers fully comprehend the code. Also, simulations will be provided in this paper and analyzed. Future applications as well as possible adaptations will also be discussed due to the deficiencies of the existing calculator. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Computer hardware description languages

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Investigating Physiological Responses During Driving as Potential Biomarkers of cognitive Decline in Seniors Using Decision Tree Ensemble Modeling

Investigating Physiological Responses During Driving as Pote...

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ieee EMBS international conference on Information Technology Applications in Biomedicine (ITAB)

作者： Anu M. Kumar Yi Lu Murphey Amanda Cook Maher Carol Persad Robert Koeppe Bruno Giordani Dept. of Electrical and Computer Engineering University of Michigan MI USA Dept. of Psychiatry University of Michigan Medical School MI USA Dept. of Radiology University of Michigan Medical School MI USA

ISBN: (数字)9798350351552

ISBN: (纸本)9798350351569

Early identification of individuals at risk for Alzheimer's disease is essential to improve treatment effectiveness. Cerebrospinal fluid analyses and positron emission tomography (PET) scans are commonly used to detect the presence of beta-amyloid and tau, which are associated with an increased risk of conversion to Alzheimer's disease. However, these biomarker tests are expensive and involve invasive procedures. Researchers are working towards discovering easily measurable biomarkers to detect individuals at risk, but only a few have been identified thus far. There is a need to discover biomarkers that are cost-efficient and non-invasive to test. We propose a machine learning approach for discovering potential risk biomarkers of Alzheimer's disease through the analysis of physiological responses to the cognitively complex task of driving by using decision tree ensemble techniques. Though driving patterns in early Alzheimer's have been previously studied, physiological responses of cognitively normal seniors during driving remain unexplored. As a first step, we measure heart rate, electrodermal activity, and temperature responses to several driving events, such as right turns and roundabouts, of seniors with and without elevated PET beta-amyloid levels to explore the relationship between these physiological responses and amyloid level. data were collected from 26 participants with elevated beta-amyloid and 28 without. We used four machine learning algorithms for classification: Random Forest, Extra Trees, AdaBoost, and XGBoost, and developed a novel methodology to extract significant features from these models. By doing so, we successfully identified five risk biomarkers most influential in differentiating the two groups with and without elevated beta-amyloid.

关键词： Temperature measurement Accuracy Biological system modeling Roads Physiology Decision trees Alzheimer's disease Random forests Testing Vehicles

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Artificial Intelligence Meets Tactical Autonomy: Challenges and Perspectives

Artificial Intelligence Meets Tactical Autonomy: Challenges ...

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ieee international conference on cognitive Machine Intelligence (CogMI)

作者： Danda B. Rawat Department of Electrical Engineering and Computer Science (EECS) DoD Center of Excellence in Artificial Intelligence & Machine Learning Howard University Washington DC 20059 USA

Artificial Intelligence (AI) enabled systems have shown tremendous impact in our national defense and in our society due to recent advances in artificial neural networks, deep learning, machine learning, and Internet of Things, big data, computing and communications. New AI capabilities can improve efficiency, trust, and efficacy for mission critical applications for tactical autonomy with minimal supervision from human operators in multi-domain battlefield (MDB) environments that are complex, contested and unpredictable. Although AI-enabled tools have been responsive to people and complementary to human capabilities, in order to realize its full potential in tactical applications, there are several challenges to be addressed for making trustworthy, ethical, fair, real-time explainable AI-enabled autonomous systems. Collaborations between platforms/systems as well as joint human-machine learning/teaming could address many of these issues to provide trusted and shared understanding and delivering cost-effective and adaptive systems to assist operations across military domains (space, air, land, maritime, and cyber) at combat speed using a shared set of resources. In this paper, we present some challenges and perspectives for AI enabled tactical autonomy.

关键词： Deep learning Ethics Mission critical systems Collaboration Big data Real-time systems Internet of Things

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Cross-Domain Few-Shot Learning with Label Propagation for Hyperspectral Image Classification

Cross-Domain Few-Shot Learning with Label Propagation for Hy...

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Symposium of Image, Signal Processing, and Artificial Vision (STSIVA)

作者： Haochen Zhang Wenxiu Diao Lingwu Meng Zhongjie Qian Zhixin Zhao Jingzhou Chen School of Computer Science and Technology Nanjing University of Science and Technology Nanjing China Huzhou Municipal Bureau of Natural Resources and Planning Huzhou China

ISBN: (数字)9798350363203

ISBN: (纸本)9798350363210

In practical applications, the acquisition cost of hyperspectral images is high, and acquiring substantial labeled data is difficult, which results in a restricted number of labeled samples using for training. Few-shot learning (FSL) serves as an efficient method to tackle the classification challenges of hyperspectral images (HSI) when labeled samples are scarce. It acquires transferable knowledge from abundant labeled data, that can be transferred to new domains. However, the distributional differences between domains with abundant labeled data and those with limited labeled data make the generalizable knowledge learned from the former less applicable to the classification tasks of the latter. To address this issue, this paper proposes a cross-domain few-shot hyperspectral image classification method based on label propagation. Firstly, the method employs a feature extraction module to capture complex information within samples, then applies a graph structure to the label propagation algorithm. The graph structure effectively captures local relationships between samples and avoids over-reliance on distant samples, thereby reducing the risk of overfitting. Furthermore, it incorporates a Tri-training structure to learn from different perspectives and complement each other, ultimately achieving classification of cross-domain few-shot data. Experimental results on hyperspectral datasets with Chikusei as the source domain, and Pavia University and Salinas as the target domains demonstrate that our method is effective and has good performance.

关键词： Training Adaptation models Network architecture Feature extraction data models Few shot learning Informatics Hyperspectral imaging Image classification Overfitting

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Game-Based Discovery: Harnessing Mini-Games within Primary Games for Scientific data Collection and Problem Solving

Game-Based Discovery: Harnessing Mini-Games within Primary G...

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international Computer Sciences and Information Technologies (CSIT)

作者： Abhishek Phadke Mamta Yadav Stanislav Ustymenko School of Engineering and Computing Christopher Newport University Newport News Virginia USA Department of Computer Science Texas A&M University-Corpus Christi Corpus Christi Texas USA School of CARDS Saint Leo University Saint Leo FL USA

ISBN: (数字)9798331542634

ISBN: (纸本)9798331542641

In the popular video game Batman: Arkham Knight, produced by Rocksteady Studios and released in 2015, the primary protagonist of the game is Batman, a vigilante dressed as a bat, fighting crime from the shadows in the fictitious city of Gotham. The game involves a real-world player who takes up the role of Batman to solve a peculiar side mission wherein they have to reconstruct the clean DNA sequence of a human and separate it from mutant DNA to manufacture an antidote to cure the villain. Although this is undoubtedly a fascinating part of the game, one that was absent in previous Batman games, it showcases an interesting notion of using mini-games embedded within primary games to achieve a particular real-world research objective. Although the DNA data used in this case was not real, there are multiple such instances in video games where mini-games have been used for an underlying motive besides entertainment. Based on popular case studies incorporating a similar method, this study characterizes the methodology of designing mini-games within primary games for research purposes into a descriptive framework, highlighting the process's advantages and limitations. It is concluded that these mini-games not only facilitate a deeper understanding of complex scientific concepts but also accelerate data processing and analysis by leveraging crowd-sourced human intuition and pattern recognition capabilities. This paper argues for strategically incorporating miniaturized, gamified elements into established video games that are mainly intended for recreational purposes.

关键词： Computer science Video games Urban areas Entertainment industry Games data collection Pattern recognition Problem-solving Information technology

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Real-time In-situ Defect Detection in 3D Printing of Continuous Fiber-Reinforced Composites

Real-time In-situ Defect Detection in 3D Printing of Continu...

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Digital Twins and Parallel Intelligence (DTPI), ieee international conference on

作者： Zhijian Yue Zhen Shen Xiangyang Dong Qihang Fang Weixing Wang Xisong Dong Gang Xiong Fei-Yue Wang School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China Intelligent Manufacturing Center Qingdao Academy of Intelligent Industries Qingdao China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China State Key Laboratory for Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350349252

ISBN: (纸本)9798350349269

With the development of continuous fiber-reinforced composites (CFRCs) 3D printing technology, timely, efficient and accurate detection of fiber path defects is essential for ensuring product quality and performance. However, challenges still need to be addressed in this field, such as inadequate training data coverage and the complexity of fiber path structures. These factors hinder the capability of traditional methods to accurately assess the status of printed products. Therefore, developing accurate and robust fiber path defect detection techniques is crucial for CFRCs 3D printing technology. In this paper, we propose a precise detection method for fiber path defects in CFRCs 3D printing using the YOLOv7 object detection model combined with a Squeeze-and-Excitation (SE) attention mechanism. By introducing the SE attention mechanism, we enhance the perception capability of the YOLOv7 model for crucial features, thereby improving the accuracy of fiber path defect detection. The experimental results show that the YOLOv7 model, when augmented with an attention mechanism, achieves an average accuracy of 93.9% in identifying fiber path defects during the printing process. Compared to models without this attention mechanism, the results show that there is 10.7% increase, with a notably enhanced learning capacity during the training phase, which substantiates the efficacy and practicality of the proposed approach.

关键词： Training Solid modeling Accuracy Attention mechanisms Training data Three-dimensional printing Real-time systems Quality assessment Optical fiber testing Defect detection

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Military News Event Extraction Based on MRC

Military News Event Extraction Based on MRC

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Frontiers Technology of Information and Computer (ICFTIC), ieee international conference on

作者： Zongyuan Sun Ning Li Jingbo Wang Lin Miao Computer School Beijing Information Science and Technology University Beijing China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

Due to the problems of diverse event types, complex argument roles and frequent emergence of new event types in the military news domain. For the classification-based approach in the event extraction task in the military news domain, there is the problem of not being able to explicitly model the semantics of event labels, which makes it difficult to adequately capture the deep correlation between event types and argument roles, and thus leads to the problem of insufficient generalization ability of the model as well as the problem of error propagation. In this paper, we propose an event extraction framework based on MRC (Machine Reading Comprehension). The framework divides the event extraction process is divided into two components: event detection and recognizing event argument extraction, and designs specialized problem templates for different subtasks. Specifically, the first TriEE model extracts event trigger words and their event types from the input sentences, while the second ArgEE model identifies candidate event theses in the sentences by means of multiple rounds of question and answer. Our experimental comparative analysis with the other four baseline models on the CMNEE dataset shows that the MRC-based event extraction framework achieves better results than the other models in both event detection and thesis element recognition tasks. There is a certain improvement in the recall rate and F1 value. Furthermore the framework effectively leverages the dependency between event arguments and event types, demonstrating enhanced generalization capabilities.

关键词： Adaptation models Analytical models Correlation Event detection Military computing Computational modeling Semantics Computer architecture Robustness data mining

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