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检索条件"任意字段=7th International Conference on Advanced Language Processing and Web Information Technology"

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Learning from Auxiliary Sources in Argumentative Revision Classification 24th

Learning from Auxiliary Sources in Argumentative Revision Cl...

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24th international conference on Artificial Intelligence in Education (AIED)

作者： Afrin, Tazin Litman, Diane Univ Pittsburgh Pittsburgh PA 15260 USA

ISBN: (纸本)9783031363351;9783031363368

We develop models to classify desirable reasoning revisions in argumentative writing. We explore two approaches - multi-task learning and transfer learning - to take advantage of auxiliary sources of revision data for similar tasks. Results of intrinsic and extrinsic evaluations show that both approaches can indeed improve classifier performance over baselines. While multi-task learning shows that training on different sources of data at the same time may improve performance, transfer-learning better represents the relationship between the data.

关键词： Writing Revision Natural language processing

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Improving Signer-Independence Using Pose Estimation and Transfer Learning for Sign language Recognition 14th

Improving Signer-Independence Using Pose Estimation and Tra...

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12th international advanced Computing conference, IACC 2022

作者： Marais, Marc Brown, Dane Connan, James Boby, Alden Department of Computer Science Rhodes University Grahamstown South Africa

ISBN: (纸本)9783031356438

Automated Sign language Recognition (SLR) aims to bridge the communication gap between the hearing and the hearing disabled. Computer vision and deep learning lie at the forefront in working toward these systems. Most SLR research focuses on signer-dependent SLR and fails to account for variations in varying signers who gesticulate naturally. this paper investigates signer-independent SLR on the LSA64 dataset, focusing on different feature extraction approaches. Two approaches are proposed an InceptionV3-GRU architecture, which uses raw images as input, and a pose estimation LSTM architecture. MediaPipe Holistic is implemented to extract pose estimation landmark coordinates. A final third model applies augmentation and transfer learning using the pose estimation LSTM model. the research found that the pose estimation LSTM approach achieved the best performance with an accuracy of 80.22%. MediaPipe Holistic struggled with the augmentations introduced in the final experiment. thus, looking into introducing more subtle augmentations may improve the model. Overall, the system shows significant promise toward addressing the real-world signer-independence issue in SLR. © 2023, Springer Nature Switzerland AG.

关键词： Audition

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Empowering Cross-Device Data Security Verification for IoT Sensor Nodes

Empowering Cross-Device Data Security Verification for IoT S...

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Machine Learning and Natural language processing (MLNLP), international conference on

作者： Xin Shi Sheng He Ting Wang Chong Zhang Yang Wang Xiao Zhang School of Computer Science and Software Engineering Southwest Petroleum University Chengdu China School of Computer Science Chengdu University of Information Technology Chengdu China School of Information Engineering Mianyang Teachers̕ College Mianyang China

ISBN: (数字)9798350354973

ISBN: (纸本)9798350354980

In the process of realizing the Internet of Everything through the Internet of things (IoT), the massive deployment of sensor nodes poses significant security challenges. this paper introduces a novel approach to anomaly detection. the proposed method focuses on generating secure replicas of sensor data, which are compared with real-time data to identify anomalies. this process ensures robust detection across multiple devices and terminals, a key challenge in the realization of the Internet of Everything. the model combines Long Short-Term Memory (LSTM) networks and Variational Autoencoders (VAE), leveraging their strengths in capturing temporal dependencies and learning latent feature representations from sensor data. To enhance detection accuracy in complex environments, an adaptive threshold algorithm is employed, dynamically adjusting thresholds based on real-time data's statistical properties. A crossattention mechanism is also integrated, allowing the model to effectively capture spatio-temporal relationships between sensors. Additionally, we employed a weighted smoothing algorithm to reduce noise while preserving data integrity, ensuring accurate anomaly detection even in noisy environments. Finally, we evaluated our method using the real-world dataset of the Belgian railway bridge KW51. the results indicate that our system can generate sensor data replicas with 99.02% accuracy and detect abnormal data with 99.8% precision. Based on these findings, we believe that this novel approach can effectively perform anomaly detection and provide robust support for IoT security across multiple terminals and devices.

关键词： Adaptation models Accuracy Structural panels Heuristic algorithms Autoencoders Data models Real-time systems Internet of things Anomaly detection Long short term memory

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Access Control Design Based on User Role Type in Telemedicine System Using Ethereum Blockchain 21

Access Control Design Based on User Role Type in Telemedicin...

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Proceedings of the 7th international conference on Communication and information processing

作者： Muhammad Hamzah Diyanatul Husna Fransiskus Astha Ekadiyanto Ketut Purnama Afif Nurul Hidayati Mauridhi Hery Purnomo Supeno Mardi Susiki Nugroho Reza Fuad Rachmadi Ingrid Nurtanio Anak Agung Ratna Department of Electrical Engineering Universitas Indonesia Indonesia Department of Computer Engineering Sepuluh November Institute of Technology Indonesia Department of Dermatology and Venereology Airlangga University Indonesia Department of Informatics Hassanudin University Indonesia

ISBN: (纸本)9781450385190

To fulfill health as basic human needs, health care services technology is always improved but also must maintain security and privacy of the data due to huge amount of data is created and distributed. Telemedicine with telecommunication technology offers convenience and cheaper cost, but still is vulnerable to cyber attacks, making it a threat for patients’ data's privacy. One approach that can be used to secure the data is applying access control to the data. Immutable blockchain can helps to enforce access control so it cannot be violated. We propose a system which implements Ethereum for the blockchain and React web application for the interface of the system. Data management and the access control are provided through Ethereum smart contracts, and the access control requires different role-based permission to upload and access the patients’ data. the system works successfully with average time taken to verify the role is 1.8033 seconds per session. the privacy of the patients’ data is ensured because only the patients that are allowed to keep the ID of their own data.

关键词： Blockchain web Smart Contract Telemedicine User Role

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Smart Recruitment System

Smart Recruitment System

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international conference on Computing and Networking technology (ICCNT)

作者： Rakshitha Dugyala Vinay Kumar Gaddam Harika Eroju Mohana Varma Dantuluri Madhubabu Ch dept. of Computer Science Engineering BV Raju Institute Of Technology Narsapur Medak Telangana India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

this project proposes the development of an advanced job applicants assessment and recruitment system that harnesses the power of artificial intelligence (AI) to streamline and enhance the hiring process. the system integrates cuttingedge AI algorithms, including natural language processing (NLP) and machine learning, to automate and optimize key stages of recruitment. the system begins with an AI-driven resume screening process that analyzes candidate resumes, extracting relevant information and evaluating qualifications against predefined criteria. Additionally, NLP algorithms are employed to comprehend the context and nuances of the resumes, enabling a more accurate assessment of candidates’ skills and experiences. Furthermore, the system incorporates machine learning models to dynamically adapt and learn from past recruitment data, continually improving its ability to identify suitable candidates. this iterative learning process ensures a more effective and efficient selection of candidates over time. the intelligent-based system also facilitates automated interview scheduling, taking into account both candidate availability and interviewer preferences. By automating this process, the system optimizes the coordination of interviews, reducing the time and effort required from human resources personnel. Collaboration with HR professionals and data scientists is crucial for refining the system’s algorithms and ensuring alignment with industry standards and ethical considerations. the proposed solution aims to revolutionize the traditional recruitment process by providing a robust, intelligent, and data-hy7driven approach to talent acquisition.

关键词： Ethics Machine learning algorithms Accuracy Heuristic algorithms Resumes Collaboration Machine learning Natural language processing Interviews Recruitment

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Knowledge Graphs Enhanced Large language Model Prompt for Electric Power Question Answering 23

Knowledge Graphs Enhanced Large Language Model Prompt for El...

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Proceedings of the 2023 7th international conference on Electronic information technology and Computer Engineering

作者： Chen Wang Min Hua Jiale Song Xue-song Tang State Grid Shanghai Electric Power Research Institute China Donghua University China

ISBN: (纸本)9798400708305

With the continuous development and digital transformation in the field of electric power, the application of large language models in the electric power industry has become a remarkable trend. the electric power industry is an information-intensive domain involving extensive data processing, predictive analysis, and decision-making. therefore, the application of large language models in the electric power sector is of great significance. Current large language models such as GPT3.5 and GLM can perform well in tasks such as question answering dialogues. However, these models still face challenges such as answer hallucination and inaccurate responses. this paper proposes a method to enhance question answering in large language models using knowledge graphs, aiming to improve the accuracy and reliability of these models in question answering tasks in the electric power *** proposed method first utilizes local electric power data to extract triplets and generate a question answering dataset specific to the electric power domain using a large language model. then, the relationships of the knowledge graph triplets are incorporated into the question prompt to enhance the quality of the model's answers. Furthermore, we fine-tune the large language model using the expanded question set derived from the triplets as knowledge enhanced data. Subsequently, we conduct experiments on both an electric power question answering dataset and a knowledge graph question answering dataset. the experimental results demonstrate that our method significantly improves various metrics of the large language model in the electric power question answering task. this research provides new insights and approaches to enhance the effectiveness of question answering systems in the electric power domain. Future studies can further explore and optimize this prompt expansion method for application in broader domains and tasks.

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Exploring the Incremental Improvements of YOLOv7 over YOLOv5 for Character Recognition 14th

Exploring the Incremental Improvements of YOLOv7 over YOL...

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12th international advanced Computing conference, IACC 2022

作者： Boby, Alden Brown, Dane Connan, James Marias, Marc Department of Computer Science Rhodes University Grahamstown South Africa

ISBN: (纸本)9783031356438

Technological advances are being applied to aspects of life to improve quality of living and efficiency. this speaks specifically to automation, especially in the industry. the growing number of vehicles on the road has presented a need to monitor more vehicles than ever to enforce traffic rules. One way to identify a vehicle is through its licence plate, which contains a unique string of characters that make it identifiable within an external database. Detecting characters on a licence plate using an object detector has only recently been explored. this paper uses the latest versions of the YOLO object detector to perform character recognition on licence plate images. this paper expands upon existing object detection-based character recognition by investigating how improvements in the framework translate to licence plate character recognition accuracy compared to character recognition based on older architectures. Results from this paper indicate that the newer YOLO models have increased performance over older YOLO-based character recognition models such as CRNET. © 2023, Springer Nature Switzerland AG.

关键词： Object detection

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A UNIVERSAL JAVA INTERFACE TO NATIVE 3D RENDERING PLATFORMS USING MULTIPLE SCENEGRAPH REPRESENTATIONS

A UNIVERSAL JAVA INTERFACE TO NATIVE 3D RENDERING PLATFORMS ...

引用

the 7th international conference on Computer-Aided Industrial Design and Conceptual Design (CAID&CD’2006)

作者： Mark Rudolph Information Technology University Copenhagen Rued Langgaards Vej 72300 Copenhagen Rovsing Aerospace Dyregardsvej 2 2740 Copenhagen PhD Dr

there are many 3D formats used for displaying 3D objects and supporting interactivity, sophisticated lighting and cinematography, and media integration of geometry, text, images, sound, vector graphics and video. Some of these also permit access to virtual scenes on the web, and many permit some sort of programmatic scripting of the scenes. A few of these are VRML, X3D, MPEG4 and a variety of game engine formats. For reasons of processing speed and availability of 3D libraries these formats are generally implemented in native C/C++ code. However, these languages may not be the most elegant languages to use for artificially intelligent control of 3D for dramatic narrative, believable speaking characters, dynamic cinematography and stage management. A good alternative is the Java language which provides very productive syntax and a body of developed application code for control and management of 3D scenes. We first present an analysis of the problems of interfacing Java to 3D rendering systems, and some preliminary solutions in the context of interfacing Java to VRML. Finally we present a Java interface which is defined independent to the underlying 3D representation and format. this solution makes it possible to abstract out the particular field types used in the 3D rendering platform. therefore a Java control system using the interface presented here should be simple to port and integrate with any 3D rendering system using any 3D format.

关键词： VRML, EAI, JNI, Octaga, Scenegraph

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An Object-oriented Approach to Ontological Engineering of Complex Cyber-physical systems Based on Meta-associative Graphs

An Object-oriented Approach to Ontological Engineering of Co...

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international conference on information Technologies in Engineering Education (Inforino)

作者： A.E. Misnik Information Technology Software Inter-State Educational Institution of Higher Education ‘Belarusian-Russian University” Mogilev Belarus

ISBN: (数字)9798350371369

ISBN: (纸本)9798350371376

this article delves into the pivotal role of cyberphysical systems in the fabric of modern digital and automated environments, serving as a cornerstone of the Industrial Internet of things and the Industry 4.0 revolution. these systems represent a synthesis of computational algorithms and physical processes, resulting in intelligent, autonomous systems equipped for self-correction and adaptation. the ability of cyber-physical systems to monitor environmental changes, adapt to the physical world, and manage complex tasks marks a significant leap in automating production and ensuring safety in intelligent transportation, among other applications. At the heart of developing and deploying cyber-physical systems is the ontological approach, which employs structured frameworks to delineate the interactions between cyber and physical elements. Ontologies, by categorizing and organizing knowledge, are vital for modeling relationships within cyber-physical systems, enhancing interoperability and communication across various domains. the quasi-hierarchical structure of cyber-physical systems ontologies, where a child node may link to multiple parents, introduces a nuanced representation of complex system interdependencies, surpassing the capabilities of traditional hierarchical models. Ontological engineering for cyber-physical systems merges object-oriented and structural analysis methodologies, focusing on key entity classes and their interrelations to dictate system evolution and behavior. the object-oriented approach divides into structuring data and knowledge, organizing system data, and developing user interfaces that support cyber-physical systems business processes, culminating in a coherent, functional system model. the article posits meta-associative graphs as a superior ontological engineering tool for cyber-physical systems compared to the web Ontology language (OWL) and frames, particularly due to their flexibility in representing quasi-hierarchical structures an

关键词： Object oriented modeling OWL Semantics Cyber-physical systems Ontologies User interfaces Software

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An Analysis Employing Various Machine Learning Algorithms for Detection of Malicious URLs 14th

An Analysis Employing Various Machine Learning Algorithms fo...

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12th international advanced Computing conference, IACC 2022

作者： Rizvi, Fizza Mohi ud din, Saika Sharma, Nonita Sharma, Deepak Kumar Department of Information Technology Indira Gandhi Delhi Technical University for Women Kashmere Gate Delhi India

ISBN: (纸本)9783031356438

Currently, there are millions and trillions of webpages online. therefore, in order to protect their data, users must be able to distinguish between trustworthy and dangerous websites. Since they are on the rise, malicious URLs (Uniform Resource Locators) are one of the most common cybersecurity concerns nowadays. they host free content (like malware, ransomware, trojans, password sniffers, etc.), and by clicking on this free content, especially the links found in emails, messages, and other formats, unwary users become the targets of these scams, have their personal information compromised, or have their system infected with malware, resulting in the loss of billions of rupees. there are more threats to cybersecurity than ever before, so a quick fix is required. In this study, we investigate how well different machine learning methods work in spotting potentially dangerous URLs. To identify both dangerous and benign URLs, this study employs a variety of machine learning methods. Decision Tree classifier, Random Forest classifier, AdaBoost classifier, K-Nearest Neighbors classifier, Gaussian Nave Bayes classifier, and XGB classifier are just a few of the machine learning methods that are used. Finding the most effective algorithm for accurately detecting fraudulent URLs is the main objective of this project. the accuracy metric was utilised as the evaluation tool to determine the accuracy of the various algorithms, with Random Forest having the highest accuracy (92.6%) of all of them. © 2023, Springer Nature Switzerland AG.

关键词： websites

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