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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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SBiPMU-Based Power Data Encryption Traffic Attack Forecasting

SBiPMU-Based Power Data Encryption Traffic Attack Forecastin...

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

作者： Songming Han Ying Ling Ming Xie Shaofeng Ming Electric Power Research Institute of Guangxi Power Grid Nan Ning China Information Department of Guangxi Power Grid Nan Ning China

ISBN: (数字)9798350354973

ISBN: (纸本)9798350354980

Amidst the exponential growth of information technology, the pervasive integration of Internet of things (IoT) solutions has become an integral part of both the everyday lives and professional routines for many individuals. However, in the information systems of the power industry, the characteristics of data flow present a phased and relatively centralized situation, which makes traditional encryption behaviors and abnormal communication behavior detection technologies difficult to deal with. Although existing attack prediction methods based on neural networks have partially solved the above problems, the process entails extensive application of nonlinear transformations for data learning and profiling, which can lead to considerable attrition of feature details. Addressing this issue, this paper design a new neural network model based on bidirectional parsimonious memory unit (BIPMU), namely the bidirectional supplementary gate minimal unit (SBiPMU). In contrast to the conventional BIPMU, the SBiPMU mitigates the degradation of feature integrity stemming from nonlinear data transformations in the learning and profiling of network traffic, employing a compensatory mechanism to preserve feature richness. then, a new attack prediction method is proposed using SBiPMU to monitor network security. Ultimately, we have constructed a pertinent prototype system and assessed its functionality. the experimental findings suggest that our proposed strategy for anticipating attacks holds a higher degree of precision in comparison to antecedent techniques.

关键词： Neural networks Intrusion detection Prototypes Prediction methods Telecommunication traffic Real-time systems Encryption Internet of things Load flow Resilience

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Enhancing Arabic information Retrieval for Question Answering 23

Enhancing Arabic Information Retrieval for Question Answerin...

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Proceedings of the 7th international conference on Future Networks and Distributed Systems

作者： Muath Alghamdi Mohammed Abushawarib Mahmoud Ellouh Mustafa Ghaleb Muhamad Felemban Information & Computer Science Department King Fahd University of Petroleum and Minerals (KFUPM) Saudi Arabia Information & Computer Science Department KFUPM Saudi Arabia Center for Intelligent Secure Systems KFUPM Saudi Arabia Computer Engineering Department KFUPM Saudi Arabia and Information & Computer Science Department KFUPM Saudi Arabia and Center for Intelligent Secure Systems KFUPM Saudi Arabia

ISBN: (纸本)9798400709036

In the modern landscape of Natural language processing (NLP), intelligent chatbots like ChatGPT 3.5 and Google’s Bard have shown remarkable competence in generic question-answering (QA) tasks. However, their performance falters when navigating domain-specific QA, particularly in the Arabic language, which is celebrated for its complex morphology and syntax. this paper presents a comprehensive approach to address these issues. the aim of this research is to build a chatbot tailored for a university community. We first create an extensive Arabic Q&A dataset by extracting data from academic documents, employing state-of-the-art Optical Character Recognition (OCR) tools. then, we evaluate multiple text similarity measures like Pooled FastText Word embedding, BM25 ranking functions, and various semantic sentence embedding models. A thorough performance assessment reveals that the domain-specific model excels at both sentence-level similarity and context-relevance tasks. the developed web application chatbot, leveraging LangChain library and Retrieval Augmented Generation (RAG) methods, outperforms existing chatbots in domain-specific, Arabic language QA scenarios.

关键词： information Retrieval

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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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A web-based Tool for Semi-interactively Karyotyping the Chromosome Images for Analyzing Chromosome Abnormalities

A Web-based Tool for Semi-interactively Karyotyping the Chro...

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National Foundation for Science and technology Development conference on information and Computer Science (NICS)

作者： Nguyen Hong thinh Nguyen Huu Hoang Son Pham thi Viet Huong Nguyen thi Cuc Nhung Do thi Ram Nguyen thanh Binh Minh Luu Manh Ha FET University of Engineering and Technology VNU Hanoi Vietnam AVITECH University of Engineering and Technology VNU Hanoi Vietnam International School VNU Hanoi Vietnam Hematology and Blood Transfusion Center Bach Mai Hospital Hanoi Vietnam

ISBN: (数字)9780738105536

ISBN: (纸本)9781665405188

Chromosome abnormalities relate to several genetic diseases. these abnormalities can be diagnosed based on the analysis of karyogram of the human chromosomes. However, the manual chromosome karyotyping process is often time consuming. this paper presents a novel web-based tool, Biochrom, to assist the cytogeneticist in producing the karyogram. Biochrom is a semi-automated tool, which provides both manual and automated functions in chromosomes segmentation and classification using image processing combined with machine learning techniques. the study is carried on 612 metaphase images with 48 of those containing abnormal chromosomes. We compare the proposed tool to a conventional public tool, Metasel, for karyotyping based on performance by two cytogeneticists using user experience metrics such as number of manual interactions and processing time. Moreover, we quantitatively evaluate the accuracy of the classification of two approaches: Support Vector Machine (SVM) and deep learning. the evaluation results show that the deep learning classification outperforms SVM classification, and our proposed tool requires fewer interactions and less time consuming to complete the karyotyping task on average.

关键词： Support vector machines Deep learning Manuals Tools User experience Task analysis Biological cells

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Exploring Hierarchical Multi-Label Text Classification Models using Attention-Based Approaches for Vietnamese language 23

Exploring Hierarchical Multi-Label Text Classification Model...

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Proceedings of the 2023 7th international conference on Natural language processing and information Retrieval

作者： Van Bich Lam Khoi Minh Quach Long Hong Buu Nguyen Dien Dinh Faculty of Information Technology University of Science Vietnam National University Vietnam

ISBN: (纸本)9798400709227

the Hierarchical Attention-based Recurrent Neural Network (HARNN) is a system designed to categorize documents efficiently, taking into account both the content of the texts and their hierarchical category structure. this system is comprised of three primary components: the Document Representation Layer (DRL), which is used for semantic encoding, the Hierarchical Attention-based Recurrent Layer (HARL), that models dependencies between different hierarchical levels, and the Hybrid Predicting Layer (HPL), which is responsible for accurate category predictions. In this research, we put HARNN to the test, using a dataset of Vietnamese articles from VnExpress. We then contrast the performance of four different word embeddings (Word2Vec, FastText, PhoBERT, and BERT multilingual). Additionally, we introduce a domain-based approach for the HARNN model to compare the accuracy with the original manner. Experimental findings indicate that HARNN performs effectively in the context of Vietnamese language and that our domain-based approach can be advantageous in specific domains HMTC task.

关键词： Hierarchical Attention-based Recurrent Neural Network Vietnamese articles Word Embedding

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A Comparative ResNet-50, InceptionV3 and EfficientNetB3 with Retinal Disease 23

A Comparative ResNet-50, InceptionV3 and EfficientNetB3 with...

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Proceedings of the 2023 7th international conference on Natural language processing and information Retrieval

作者： Warameth Nuipian Phayung Meesad Sarunya Kanjanawattana Department of Information Technology King Mongkut's University of Technology North Bangkok Thailand School of Computer Engineering Suranaree University of Technology Thailand

ISBN: (纸本)9798400709227

Globally, a staggering 2.2 billion individuals suffer from either near or distance vision impairment, of which at least 1 billion cases could have been prevented or remain unaddressed. the predominant causes of such vision impairment at a global scale are refractive errors and cataracts. It is noteworthy that only 36% of individuals with distance vision impairment due to refractive errors and a mere 17% with vision impairment due to cataracts have gained access to suitable interventions. the escalating prevalence of eye diseases underscores the necessity for advanced AI algorithms capable of analyzing intricate image data beyond the reach of conventional methods. For example, the complex nature of retinal imagery necessitates the use of Convolutional Neural Networks (CNNs), which rely on convolution operations for feature extraction. Networks such as ResNet-50, InceptionV3, and EfficientNetB3 exemplify such architectures. In this study, EfficientNetB3, with a 30-epoch training cycle, demonstrated superior performance in retinal disease classification, achieving training and testing accuracies of 99.9% and 96%, respectively. While these findings attest to the potential of using CNNs, particularly EfficientNetB3, for diagnosing retinal diseases, additional research is essential for validating these results across diverse populations and healthcare environments. the integration of these models into telemedicine platforms holds promise for alleviating healthcare system burdens and enhancing patient outcomes.

关键词： CNN Deep Learning EfficientNetB3 InceptionV3 ResNet-50 Retina

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Extracting structured information from the textual description of geometry word problems 23

Extracting structured information from the textual descripti...

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Proceedings of the 2023 7th international conference on Natural language processing and information Retrieval

作者： Archana Narayandas Boob Prajakta Dnyaneshwar Bodakhe Mansi Anup Radke Umesh Ashok Deshpande Department of Computer Science and Engineering Visvesvaraya National Institute of Technology India CUEMATH Tutor CUEMATH Tutor India

ISBN: (纸本)9798400709227

AI (Artificial Intelligence) is playing its role in every field as automation is the way forward. Solving geometry mathematics word problems (MWPs) automatically can help in smart tutoring and has a plethora of applications in many fields. To solve a geometry MWPs, multi-modal methods are needed to parse the question text using Natural language processing (NLP) and diagram using Image processing (IP) techniques and combine the information from both. the information is then to be structured and appropriate axioms and theorems need to be applied to transform the question into intermediate equations which could be solved by equation solvers. In this entire pipeline, text parsing of the question content is a crucial component which is precisely the main focus of this paper. there have been attempts in the literature and techniques to solve geometry MWPs have been proposed, however they rely on input predicates and do not generate them automatically. In cases where they are generated, regular expressions are used which lack generalisation and scalability. this paper models the text parsing problem as a Relation Extraction and Natural language Generation problem where from the question text structured information is generated in the form of predicates. We basically generate appropriate geometric tags by using a Named Entity Recognition (NER) annotator and then utilize these tags to generate predicates that represent the question in a machine readable formal language. To test the proposed approach, a custom dataset is created containing about 500 questions from standard elementary school-level Indian text books. It has been demonstrated through experiments on the custom dataset that the suggested method is capable of extracting geometric relations. To evaluate the generated predicates, we create ground truth predicates for each of the questions through skilled domain experts. We also design a unique method to evaluate the generated predicates with the METEOR (Metric for Ev

关键词： Artificial Intelligence Mathematics (Geometry) word problems Named Entity Recognition Natural language processing Predicate Generation

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Design and Implementation of Adaptive Gaussian Denoising Algorithm Based on FPGA

Design and Implementation of Adaptive Gaussian Denoising Alg...

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IEEE international conference on Automation, Electronics and Electrical Engineering (AUTEEE)

作者： Xinghao Chen School of Electronic Information Engineering Beihang University Beijing China

ISBN: (数字)9798350377033

ISBN: (纸本)9798350377040

With the continuous progress of image processing and machine vision technology, the demand for efficient and real-time processing is becoming more and more prominent, especially in the field of high-noise image processing. In this study, an adaptive Gaussian filtering algorithm is proposed, which is implemented based on FPGA and aims to improve the computational efficiency and real-time performance of the image processing system. Compared with the traditional fixed-weight filter, this algorithm is able to dynamically adjust the filtering parameters according to different noise environments, effectively balancing noise suppression and image detail retention. We coded the algorithm using Verilog hardware description language and verified it on PYNQ-Z2 FPGA platform. the experimental results show that the adaptive algorithm outperforms the fixed-weight filtering method in terms of performance, especially in terms of noise suppression and detail preservation. Meanwhile, the FPGA hardware implements the reduction of filtering delay and optimization of resource consumption, making it well suited for real-time applications. this study demonstrates the promise of FPGA adaptive filtering for applications in medical imaging, remote sensing, and intelligent surveillance, which have stringent requirements for high-performance and high-efficiency processing. this research provides new hardware solutions for real-time, high-quality image processing in constrained environments.

关键词： Filtering Heuristic algorithms Noise reduction Adaptive filters Filtering algorithms Real-time systems Hardware Field programmable gate arrays Remote sensing Optimization

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Safetydesk: Accelerating the Production of Toxicological Reports

Safetydesk: Accelerating the Production of Toxicological Rep...

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Experiment@ international conference (exp.at)

作者： José Pereira Bruno Carlos Luís Ferreira Ângela Laranjeiro Bruno Caceiro Hugo Amaro Hugo Gonçalo Oliveira Catarina Silva LIS Instituto Pedro Nunes Coimbra Portugal DEI University of Coimbra CISUC Coimbra Portugal TalentIngredient Miranda do Corvo Portugal

ISBN: (数字)9798350314403

ISBN: (纸本)9798350314410

the task of handling and processing large volumes of textual data has emerged as a challenge for companies and organizations. As such, intelligent algorithms have increasingly been employed to reduce human effort, minimize costs, and enhance overall process efficiency. this is particularly pertinent in the realm of toxicological analysis of chemical substances, where the extraction of critical information from diverse sources requires extensive manual effort. Automated assistance provided by intelligent algorithms has become a tool for organizations seeking to streamline their data analysis workflows and accelerate decision-making processes. We developed Safetydesk, a web-based platform that addresses the challenges associated with the time-consuming and labor-intensive process of producing comprehensive reports on chemical substances. With Safetydesk, we reduce the amount of time and resources required to conduct extensive research on chemical substances. Safetydesk provides access to an array of chemical databases, including structured and unstructured sources, and uses advanced Natural language processing algorithms that in combination can rapidly extract relevant information from these sources. By automating the research process and leveraging intelligent algorithms, Safetydesk eliminates the need for extensive manual searches and enables users to quickly and easily compile detailed reports on chemical substances, improving analysis of chemical data, efficiency, reducing costs, and supporting more informed decision-making.

关键词： Analytical models Costs Decision making Refining Manuals Natural language processing User experience

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Combining BERT and Blockchain technology to Improve Network Authentication Security

Combining BERT and Blockchain Technology to Improve Network ...

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IEEE international conference on Automation, Electronics and Electrical Engineering (AUTEEE)

作者： Xiaojun Pan School of Information Engineering Anhui Business and Technology College Hefei Anhui China

ISBN: (数字)9798350377033

ISBN: (纸本)9798350377040

With the rapid development of the Internet, network identity authentication faces increasingly severe security challenges, such as identity theft, data tampering, and privacy leakage. To address these issues, this study introduces a method that combines the BERT (Bidirectional Encoder Representations from Transformers) model in natural language processing with blockchain technology to build an efficient and secure network identity authentication system. In the method part, first, the BERT model is used to perform deep semantic understanding and feature extraction of user identity information to improve the accuracy and robustness of identity authentication. Next, blockchain technology is used to build a distributed ledger to ensure that the data in the identity authentication process cannot be tampered with and is transparent and traceable, thereby enhancing the overall security of the system. Finally, smart contracts are used to automate the identity authentication process, reduce the risk of human intervention, and improve the response speed and efficiency of the system. Experimental results show that the accuracy of traditional methods fluctuates between 80% and 90%, while the accuracy of BERT methods is mostly above 90%. the response time of the BERT+Blockchain system fluctuates slightly, ranging from 0.51 seconds to 0.99 seconds. In summary, the network identity authentication scheme based on BERT and blockchain adopted in this paper not only improves the security and reliability of identity authentication, but also provides a new technical path for future network security protection.

关键词： Industries Accuracy Identity theft Authentication Bidirectional control Network security Encoding Robustness Blockchains Security

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