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Hybrid Deep Learning Model for Cyber-Attack Detection

Hybrid Deep Learning Model for Cyber-Attack Detection

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international conference on ent Computing and Control Systems (ICICCS)

作者： M. Premkumar R Lakshmi P. Velrajkumar S.Gayathri Priya Rama Chaithanya Tanguturi S Murali M. Sivaramkrishnan Department of Information Technology Mahendra Institute of Technology (Autonomous) Mallasamudharam Namakkal India Department of Computer Science and Engineering RNS Institute of Technology Bengaluru affiliated to VTU Belgaum India CMR Institute of Technology Bengaluru India Department of Electronics and Communication Engineering R. M. D. Engineering College Kavaraipettai Chennai India Department of Computer Science and Engineering PACE Institute of Technology and Sciences Ongole Andhra Pradesh India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India Department of EEE Karpagam College of Engineering Coimbatore India

Better detection and avoidance of intrusions solutions are in high demand because of the worldwide upsurge in hacking on computer networks. New technologies like fog computing, cloud computing, and the Internet of things have dramatically increased the potential for cyberattacks and other forms of cyber risk. these attacks can compromise computer network infrastructures, web services, and social media platforms, resulting in economic and reputation loss. Because of its usefulness in detecting and halting malicious actions, intrusion detection systems (IDS) play a crucial part in network defence mechanisms. In this study, a hybrid Deep Learning (DL) network was used to identify the cyberattack. this effort began with the collection and processing of cyber-attack data from the NSL-KDD. Convolutional Neural Networks (CNNs), Bidirectional Long Short-Term Memory (Bi-LS TMs), and hybrid CNN+Bi-LSTMs are the DL models trained with 80% of processed data. the remaining 20% of the data is used for testing the models after they have been trained. Both positive and negative metrics are used to assess the results of the testing phase. When compared to other networks, the recommended CNN+Bi-LSTM model achieves the highest score for positive metrics and the lowest score for negative metrics.

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MRLKG: Multimodal Representation Learning with Keyword-Based Graph for Hashtag Recommendation

MRLKG: Multimodal Representation Learning with Keyword-Based...

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

作者： Mian Muhammad Yasir Khalil Qingxian Wang Bo Chen Muhammad Arslan Rauf Weidong Wang School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350354973

ISBN: (纸本)9798350354980

the increasing volume of social media content has made hashtag recommendation a critical task for enhancing content discoverability and user engagement. this paper presents MRLKG (Multimodal Representation Learning with Keywordbased Graph) for hashtag recommendation. Our approach integrates cross-modality joint representation with a keyword-guided graph. the joint embedding provides a contextually rich representation of the content. In parallel, a keyword-guided Graph Convolutional Network (GCN) generates a refined representation of each microblog post content by emphasizing key concepts and their relational context with other microblog posts, essential for understanding the broader context and trends that hashtags often represent. By integrating the contextually rich joint embedding provided by transformers with the KeyBERT-GCN's focus on key concepts and their relational structure, the model can recommend hashtags that are both contextually relevant and aligned with the key trends represented by other microblog posts. Experimental results demonstrate the effectiveness of our method, showing improved performance over existing approaches. this method offers a robust solution for hashtag recommendation by leveraging both contextual understanding and graph-based reasoning.

关键词： Representation learning Social networking (online) Graph convolutional networks Blogs Network analyzers Market research Transformers Natural language processing Cognition Context modeling

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A Study on Non-Autoregressive Mongolian-Chinese Neural Machine Translation for Multilingual Pre-Training

A Study on Non-Autoregressive Mongolian-Chinese Neural Machi...

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

作者： Xiaoli Zheng Yonghong Tian Chang Ma Kangkang Sun College of data science and Application Inner Mongolia University of Technology Hohhot China

ISBN: (数字)9798350354973

ISBN: (纸本)9798350354980

Aiming at the problem that most of the current Mongolian-Chinese Neural Machine Translation (NMT) adopts autoregressive generation, which is prone to error accumulation and slow generation speed, we propose the study of non-autoregressive Mongolian-Chinese NMT. However, non-autoregressive generation generally suffers from the phenomenon of poor translation quality, and its performance depends largely on the quantity and quality of data, so we resort to the multilingual pre-trained model CeMAT to assist the non-autoregressive Mongolian-Chinese NMT task, and then use CeMAT-based autoregressive Mongolian-Chinese NMT as teacher model, adopt multi-level knowledge distillation to train the CeMAT-based non-autoregressive Mongolian-Chinese NMT model, and finally incorporating Graph Convolutional Networks(GCN) to supplement the semantic information into the word embedding layer by modelling word-to-word relationships. the experimental results show that the methods used obtains a BLEU value of +9.99 and an increase in translation speed by a factor of 1.94 compared to the autoregressive model, i.e., it ensures an increase in the translation speed of the research model along with a significant increase in the quality of the translated text.

关键词： Training Knowledge engineering Translation Semantics Neural machine translation Machine learning Data models Natural language processing Multilingual Periodic structures

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AI-Powered Legal Documentation Assistant

AI-Powered Legal Documentation Assistant

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Pervasive Computing and Social Networking (ICPCSN), international conference on

作者： Kuldeep Vayadande Aditi Bhat Pranav Bachhav Aditya Bhoyar Zulfikar Charoliya Aayush Chavan Department of Information Technology Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798350386349

ISBN: (纸本)9798350386356

Legal systems worldwide vary in structure and principles, reflecting the diverse legal traditions of different countries. the legal system, inherently complex and reliant on meticulous documentation, often faces challenges related to time-consuming manual processes and the potential for human errors. the system proposed provides a transformative solution to the above problems. the system emerges as a groundbreaking solution within the intricate landscape of legal systems which responds to these challenges by seamlessly integrating advanced AI techniques. At its core, OpenAI embeddings takes center stage, demonstrating unparalleled proficiency in document generation, comprehension, and abnormality detection, addressing the complexities ingrained in legal documentation. In contrast to traditional approaches, this system maximizes the versatility of ChatGPT 3.5, allowing it to not only issue commands but also proficiently generate a diverse array of legal documents. By incorporating an understanding module equipped with PyPDF, Amazon Textract, and langchain utilities, the system adeptly handles document intricacies. the utilization of OpenAI Embeddings further enhances natural language understanding. Leveraging sentiment analysis and Named Entity Recognition (NER) in its natural language processing (NLP) toolkit, the system employs an intuitive web interface for irregularities detection. the exploration of AI for automated irregularity detection showcases its transformative potential in ensuring document accuracy within the legal domain. this project, therefore, stands as a beacon of innovation, promising to reshape the dynamics of legal document processing by merging advanced AI capabilities with the unique demands of legal systems.

关键词： Pervasive computing Technological innovation Sentiment analysis Law Social networking (online) Documentation Named entity recognition

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Design of Document Secret Level Consistency Check Software Based on OCR

Design of Document Secret Level Consistency Check Software B...

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Computer information Science and Application technology (CISAT), international conference on

作者： Shuhao Qi Qiao Sun Chen Cheng Zhenjie Wang Ziwen Gao Juntao Liu Wuhan Institute of Digital Engineering Wuhan Hubei China

ISBN: (数字)9798350375107

ISBN: (纸本)9798350375114

the secret level of secret documents should be correctly labeled, especially when they are printed or copied from the company’s internal network to mobile media. At this point, it is necessary to perform a consistency check on the file name of the document, the document cover, and the content of the document to ensure the consistency of the secret levels among the three. Traditional consistency checks require manual labor and can be time-consuming when there are many documents. this article uses Python language and OCR to design a document secret level consistency check software. the software determines the secret level of the document cover by extracting the cover image from a doc/docx or PDF file and recognizing text in the upper left corner. the content secret level of the document is also determined through character matching. the test shows that the software can replace manual verification of secret level consistency, and the processing speed meets the requirements.

关键词： information science Image recognition Accuracy Text recognition Optical character recognition Manuals Media Portable document format Character recognition Python

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Dialogue Segmentation based on Dynamic Context Coherence 23

Dialogue Segmentation based on Dynamic Context Coherence

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

作者： Hengfeng Pu Liqing Wang School of Information Science and Engineering Yunnan University China School of Information Science and Engineering & Digital Media Technology Key Laboratory of Universitys in Yunnan Yunnan University China

ISBN: (纸本)9798400709227

Dialogue segmentation plays a crucial role in various dialogue modeling tasks. Semantic coherence-based methods are a major trend, but previous methods have tended to take fixed-size units in assessing semantic coherence, which may suffer from semantic loss or noise introduction, thus obtaining inaccurate coherence scores. In this paper, we propose a simple but effective approach for dynamically assessing semantic coherence, which can improve the accuracy of coherence scoring by dynamically expanding the assessment unit from a single utterance to a semantically related context. Experimental results on three benchmark datasets in English and Chinese demonstrate that our proposal can achieve competitive performance over strong baselines.

关键词： Dialogue segmentation Dialogue semantic analysis Text segmentation

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CoPrompt: A Contrast-prompt Tuning Method for Multiparty Dialogue Character Relationship Extraction 23

CoPrompt: A Contrast-prompt Tuning Method for Multiparty Dia...

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

作者： Yu Li Yuru Jiang Jie Chen Liangguo Wang Yuyang Tao Yangsen Zhang School of Computer Science Beijing Information Science and Technology University China

ISBN: (纸本)9798400709227

information extraction is a fundamental task in natural language processing, and relation extraction is one of its primary subfields. However, most previous research focuses on extracting relations from standard texts, such as news articles and Wikipedia entries, while overlooking the challenges present in dialogues - sparse details, cross-sentence links, and complex character relationships. To address these challenges, we propose a contrastive-prompt tuning method (CoPrompt) to better capture the relationships between characters in dialogues. Our method improves the performance of the relationship extraction model by constructing positive and negative sample pairs to obtain better embedding representations of relationship features through prompt learning. To evaluate our method, we separately built manual and continuous templates and conducted experiments on the DialogRE and CRECIL datasets. Our method consistently outperformed other methods, notably achieving state-of-the-art results on Chinese datasets(DialogRE_cn and CRECIL), underscoring its robust efficiency in Chinese language relation extraction tasks. We also demonstrate the effectiveness of our method in low-resource scenarios. Our code is available at https://***/LIyu810/CoPrompt_main.

关键词： Multiparty Dialogue Prompt-tuning Relation Extraction

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Ar-SLoTE: A Recognizing Textual Entailment Tool for Arabic Question/Answering Systems

Ar-SLoTE: A Recognizing Textual Entailment Tool for Arabic Q...

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international conference on information and Communication technology and Accessibility (ICTA)

作者： Mabrouka Ben-Sghaier Wided Bakari Mahmoud Neji Faculty of Economics and Management MIR@CL Sfax Tunisia

ISBN: (数字)9781728161839

ISBN: (纸本)9781728161846

Recognizing the relation of entailment between sentences is an important and common part of linguistic communication. the recognizing textual entailment task has been proposed as a solution for this problem. In this paper, we present an Arabic Recognizing Textual Entailment Tool called Ar-SLoTE "Arabic Semantic Logical Textual Entailment Tool". the proposed tool is composed of five modules: pretreatment, linguistic analysis, first-order logic representation, features extraction and entailment decision modules. It extracts the logical representations of the hypothesis/text pairs in order to extract valuable and informative features namely, predicates-arguments overlap, semantic similarity and named entity matching. Ar-SLoTE is destined especially to Arabic factual question/answering systems and the attained result is very encouraging.

关键词： Semantics Feature extraction Tools Linguistics Task analysis Natural language processing

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A Brain Function Connection Method for Tone processing of Deaf Children Based on One-dimensional Convolutional Network 23

A Brain Function Connection Method for Tone Processing of De...

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

作者： Mengrui Shi Qiang Li Hongge Cao Qiuli Li TECHNICAL COLLEGE FOR THE DEAF Tianjin University of Technology China Tianjin University of Technology China Royal Melbourne Institute of Technology Australia

ISBN: (纸本)9798400709227

the purpose of this study is to investigate the differences in brain functional connectivity between deaf and typically developing children when processing three different tones (tone 1, tone 2, tone 3) using deep learning methods. Additionally, the goal is to identify variations in activated brain regions during tone processing between these two groups. Five deaf children and two typically developing children were selected as participants. Resting-state functional magnetic resonance imaging (fMRI) scans were conducted using an fMRI scanner. Subsequently, data preprocessing and fMRI data analysis were performed. A one-dimensional convolutional network was employed to extract features and classify the input brain functional connectivity. through a series of theoretical and comparative analyses, it was determined that the one-dimensional convolutional network is capable of capturing local patterns within sequential data. this network can extract information related to connectivity patterns from the one-dimensional sequence of connection weights, making it more suitable for processing brain functional connectivity data in deaf children.

关键词： Brain functional connectivity Deaf children One-dimensional convolutional network Tones fMRI

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PERCEPTION OF SYNthESIZED SINGING VOICES WIth FINE FLUCTUATIONS IN thEIR FUNDAMENTAL FREQUENCY CONTOURS

PERCEPTION OF SYNTHESIZED SINGING VOICES WITH FINE FLUCTUATI...

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6～(th) international conference on Spoken language processing

作者： Masato Akagi Hironori Kitakaze Japan Advanced Institute of Science and Technology 1-1 Asahidai Tatsunokuchi Ishikawa 923-1292 Japan Japan Advanced Institute of Science and Technology 1-1 Asahidai Tatsunokuchi Ishikawa 923-1292 Japan

this paper demonstrates the importance of fine fluctuations quantitatively by measuring the detection thresholds of fine fluctuations in singing-voice F0s, in which voice quality is particularly important. We analyzed the fine fluctuations left by subtracting the melody and vibrato components from estimated F0s, focuslng on the modulation frequency (MF) and modulation amplitude (MA). To test a hypothesis that the fine fluctuations in the F0 of singing voices affect the perception of quality, and that the magnitude of this effect depends on the MF and MA, we performed four psychoacoustic experiments using synthesized stimuli. the experimental results indicate that our hypothesis was correct, and suggest that, to produce high-quality synthesized speech, one should extract F0s containing fine fluctuations with an MF of over 7 Hz in the analysis and add not only melody and vibrato but also fine fluctuation components to the F0 contours in the synthesis.

关键词： singing voice, fundamental frequency, fine fluctua-tion. and voice quality

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