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5th International Conference on Bio-engineering for Smart Technologies, BioSMART 2023

作者： Cherifi, Dalila Salah, Imane Si Chihaoui, Thiziri Moudoud, Massyl Boubchir, Larbi Nait-Ali, Amine Institute of Electrical and Electronic Engineering University of Boumerdes Algeria University of Paris8 Laboratory for Artificial Intelligence and Semantic Data France Ea 3956 France

ISBN: (纸本)9798350338492

Electromyography (EMG) signals are anatomical and physiological properties representation of muscles;their analysis is crucial for diagnosing neuromuscular diseases, and it typically involves the manual inspection of the signals. However, in recent years, experts have developed automatic classification systems to support practitioners and enhance the diagnostic process. In this study, we have used diverse methodologies to extract features from the raw EMG signal in the time and time-frequency domain, using DWT, WPD and a combination of both, with and without preprocessing and for varying levels of decomposition from level 3 to 10, with the support of higher order stats. We have used optimized ensemble tree and optimized SVM classifiers with Bayesian optimization of hyper parameters. The experiments were conducted using the publicly available dataset EMG lab. We got good training results reaching a 100% accuracy from many combinations and testing accuracy of 78.35% when using DWT without preprocessing at level 9 with the linear SVM classifier. The obtained outcomes demonstrate the effectiveness of the suggested method in accurately categorizing the EMG signals. We have also highlighted that the classifier ensemble trees discriminate better between ALS and healthy and doesn't recognize well Myopathy. Furthermore, the suggested framework has the potential to support clinicians in the diagnosis of neuromuscular disorders. © 2023 IEEE.

关键词： Support vector machines

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Paid User data Mining Approach for a System to Assist with Volunteering for the College Entrance Examination 13

Paid User Data Mining Approach for a System to Assist with V...

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13th International Conference of Information and Communication Technology, ICTech 2024

作者： Gao, Cen Wang, Sheng Fu, Lijun Zhang, Yonghong Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Laboratory of Big Data and Artificial Intelligence Technology Shandong University Jinan250100 China Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang110168 China Institute of Future Artificial Intelligence Technology and Innovative Applications Beijing100025 China

ISBN: (纸本)9798350376258

Addressing the dilemma that the current paid user mining techniques are complex and unsuitable for some specific applications, the present study suggests a rule-based paid user mining approach to streamline the implementation process. The main process consists of a buried design to produce JSON format logs detailing user usage. These logs are obtained as raw data in topics created with Kafka. Real-time user behavior data is then analyzed, using Spark Streaming computation engine, and matching calculation is executed based on predefined rules to determine a likelihood threshold for the conversion of nonpaying users to paying users. If the conversion probability threshold surpasses the preset level, the system identifies the user as a potential paying customer. Consequently, a message is delivered to the customer based on the outcome. The system might configure the coupon amount contingent upon different thresholds and distinct specified indications. Additionally, it persists in updating the customer's labeling status, ensuring adequately responsive, targeted advertising strategies are present at each customer visit. This method doesn't require intricate machine learning algorithms, making implementation more straightforward and providing crucial technical support for subsequent user operations. The implementation of this design improves the likelihood of user payments and boosts product profits. ©2024 IEEE.

关键词： Sales

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Enhancing digital security using Signa-Deep for online signature verification and identity authentication

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2024年第2期8卷 58-69页

作者： Ch, Ravikumar Sridevi, Mulagundla Ramchander, M. Ramesh, Vankudoth Kumar, Vadapally Praveen Department of Artificial Intelligence &Data Science Chaitanya Bharathi Institute of Technology Hyderabad 500075 India Department of Computer Science and Engineering CVR College of Engineering Hyderabad 501510 India Department of Computer Applications Chaitanya Bharathi Institute of Technology Hyderabad 500075 India Department of Emerging Technologies CVR College of Engineering Hyderabad 500039 India

In the contemporary digital realm, the utilization of online services has surged, facilitated by the seamless integration of deep learning technology, which is paramount in applications demanding precision and efficiency. A pivotal use case in this context is online handwritten signature verification, where the need for exceptional accuracy is indisputable. This paper introduces ‘Signa-Deep, ’ an innovative approach designed to address the challenge of online signature verification and the determination of an individual’s authorization status. The study explores a range of methodologies, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), GoogleNet, and MobileNet, to discern the authenticity of signatures and affirm the identity of the signatory. The results of our proposed method are promising, showcasing its potential to significantly enhance the security of digital transactions and identity verification processes. In summary, ‘Signa-Deep’ harnesses deep learning technology to bolster the accuracy and reliability of online signature verification, thereby contributing to the overall robustness of digital interactions and identity validation processes. © (2024), (Society of Sytematic Innovation). All rights reserved.

关键词： Authorization Status Deep Learning Digital Transactions Security Identity Authentication Online Signature Verification

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A Queuing Model for Software-Defined Networking in Cloud and Multi-Controller Environments 6

A Queuing Model for Software-Defined Networking in Cloud and...

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6th International Conference on Pattern Analysis and Intelligent Systems, PAIS 2024

作者： Teboub, Islem Aissani, Amar Merazka, Fatiha Usthb University Riima Laboratory Department of Artificial Intelligence And Data Sciences Computer Engineering Faculty Algiers16111 Algeria Usthb University Lisic Lab. Department of Telecommunications Electrical Engineering Faculty Algiers16111 Algeria

ISBN: (纸本)9798350350265

Enterprises are increasingly adopting Software-Defined Networking (SDN) for advanced infrastructure management, cost reduction, and adaptation to technological challenges. Cloud-based SDN controllers play a pivotal role in ensuring flexibility, security, and scalability. The transition from a single-controller to a multi-controller paradigm in cloud networks not only improves security and availability but also reinforces the overall resilience of SDN. Our scientific study aims to propose a novel approach for deploying SDN networks and to introduce a queueing model for this proposed concept. The work focuses on modeling SDN networks using Markovian stochastic processes, taking into consideration the availability criterion through the multiplicity of SDN controllers at the control layer, specifically utilizing queuing theory. The proposed model brings improvement and optimization compared to existing ones. The evaluation of Quality of Service (QoS), based on response time measurements, has demonstrated the efficiency of the model. Additionally, observing the model through the conducted experiments has led us to conclude that scalability is ensured concerning the multiplicity of controllers. Our model has successfully addressed the multi-criteria constraint and ensured an optimal combination that incorporates cloud concepts, QoS, and multi-controllers. © 2024 IEEE.

关键词： Quality of service

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Discovering Reliable Information Extraction Patterns with Pre-Trained Model for Text with Writing Style

Discovering Reliable Information Extraction Patterns with Pr...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Bu, Chenyang Liu, Jiacheng Liu, Jiaxuan Ji, Shengwei Yang, Hongbin School of Computer Science and Information Engineering Ministry of Education Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Anhui Hefei230027 China School of Artificial Intelligence and Big Data Hefei University China

ISBN: (纸本)9798350337020

Large-scale pre-trained models such as GPT and BERT have demonstrated remarkable performance in information extraction tasks. However, their black-box nature poses challenges for reliability and interpretability. In contrast, rule- based extraction methods have better interpretability, but typically require domain experts to manually establish rules, limiting their generalization ability. In industry, there is often a demand for reliable knowledge extraction to reduce the time spent on manual verification of each piece of knowledge. In this paper, we explore the idea of combining GPT and symbolic-based methods to automatically discover reliable extraction patterns in text with a particular writing style. This method leverages the characteristics of high information density and similar writing patterns in text with a specific writing style to generate verifiable and reliable patterns. We conduct experiments on two datasets with a specific writing style to demonstrate its effectiveness, validating the idea of combining large models for reliable information extraction pattern discovery in the tested datasets. © 2023 IEEE.

关键词： Information extraction Reliable pattern Writing style data

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StoryLLaVA: Enhancing Visual Storytelling with Multi-Modal Large Language Models 31

StoryLLaVA: Enhancing Visual Storytelling with Multi-Modal L...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Yang, Li Xiao, Zhiding Huang, Wenxin Zhong, Xian Hubei Key Laboratory of Big Data Intelligent Analysis and Application School of Computer Science and Information Engineering Hubei University China Hubei Key Laboratory of Transportation Internet of Things School of Computer Science and Artificial Intelligence Wuhan University of Technology China

ISBN: (纸本)9798891761964

The rapid development of multi-modal large language models (MLLMs) has positioned visual storytelling as a crucial area in content creation. However, existing models often struggle to maintain temporal, spatial, and narrative coherence across image sequences and frequently lack the depth and engagement of human-authored stories. To address these challenges, we propose Story with Large Language and Vision Assistant (StoryLLaVA), a novel framework for enhancing visual storytelling. Our approach introduces a Topic-Driven Narrative Optimizer (TDNO) that improves both the training data and MLLM models by integrating image descriptions, topic generation, and GPT-4-based refinements. Furthermore, we employ a preference-based ranked story sampling method that aligns model outputs with human storytelling preferences through positive-negative pairing. These two phases of the framework differ in their training methods: the former uses supervised fine-tuning, while the latter incorporates reinforcement learning with positive and negative sample pairs. Experimental results demonstrate that StoryLLaVA outperforms current models in visual relevance, coherence, and fluency, with LLM-based evaluations confirming the generation of richer and more engaging narratives. The enhanced dataset and model are available at https://***/XxxZzD/StoryLLaVA. © 2025 Association for Computational Linguistics.

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MEMORY-ASSISTED SUB-PROTOTYPE MINING FOR UNIVERSAL DOMAIN ADAPTATION 12

MEMORY-ASSISTED SUB-PROTOTYPE MINING FOR UNIVERSAL DOMAIN AD...

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12th International Conference on Learning Representations, ICLR 2024

作者： Lai, Yuxiang Zhou, Yi Liu, Xinghong Zhou, Tao School of Computer Science and Engineering Southeast University China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education China School of Computer Science and Engineering Nanjing University of Science and Technology China

Universal domain adaptation aims to align the classes and reduce the feature gap between the same category of the source and target domains. The target private category is set as the unknown class during the adaptation process, as it is not included in the source domain. However, most existing methods overlook the intra-class structure within a category, especially in cases where there exists significant concept shift between the samples belonging to the same category. When samples with large concept shifts are forced to be pushed together, it may negatively affect the adaptation performance. Moreover, from the interpretability aspect, it is unreasonable to align visual features with significant differences, such as fighter jets and civil aircraft, into the same category. Unfortunately, due to such semantic ambiguity and annotation cost, categories are not always classified in detail, making it difficult for the model to perform precise adaptation. To address these issues, we propose a novel Memory-Assisted Sub-Prototype Mining (MemSPM) method that can learn the differences between samples belonging to the same category and mine sub-classes when there exists significant concept shift between them. By doing so, our model learns a more reasonable feature space that enhances the transferability and reflects the inherent differences among samples annotated as the same category. We evaluate the effectiveness of our MemSPM method over multiple scenarios, including UniDA, OSDA, and PDA. Our method achieves state-of-the-art performance on four benchmarks in most cases. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Benchmarking

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Pest Detection and Identification Guided by Feature Maps 12

Pest Detection and Identification Guided by Feature Maps

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12th International Conference on Image Processing Theory, Tools and applications, IPTA 2023

作者： Chen, Miao Chen, Yanan Guo, Minghui Wang, Jianji Xi'an Jiaotong University National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics China Xi'an Jiaotong University School of Software Engineering Xi'an China

ISBN: (纸本)9798350325416

Effective pest detection and identification are of great significance for agricultural activities, and morden machine learning methods, especially the deep neural network, undoubtedly provide convenient and effective ways for this issue. Most of the mainstream methods for pest detection require annotation of manual bounding box. However, the public pest datasets usually lack the labeling information. In this work, we propose a method for detecting pests with weak supervision. We obtain the key regions of the pest targets by deeply mining the information in the feature maps of the convolutional neural networks (CNNs). First, using the class activation mapping technique to generate activation maps of the target class during forward propagation of the model, and then obtaining the key regions of the target based on the hotspots extracted from the activation maps. In addition, our proposed method can be deployed as a general strategy to other CNNs, and experimental results demonstrate that our method can steadily improve the classification accuracy of the model. By applying our method to the single Resnet50, we can achieve 74.27% classification accuracy on the publicly accessible IP102 dataset. Another attribution of this paper is that we propose a lightweight approach to find solutions for pest detection in agricultural automation applications. © 2023 IEEE.

关键词： Deep neural networks

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Clause-level Relationship-aware Math Word Problems Solver

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Machine intelligence Research 2022年第5期19卷 425-438页

作者： Chang-Yang Wu Xin Lin Zhen-Ya Huang Yu Yin Jia-Yu Liu Qi Liu Gang Zhou Anhui Province Key Laboratory of Big Data Analysis and Application School of Data ScienceUniversity of Science and Technology of ChinaHefei 230026China Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefei 230088China Laboratory of Mathematical Engineering and Advanced Computing Information Engineering UniversityZhengzhou 450001China

Automatically solving math word problems,which involves comprehension,cognition,and reasoning,is a crucial issue in artificial intelligence *** math word problem solvers mainly work on word-level relationship extraction and the generation of expression solutions while lacking consideration of the clause-level *** this end,inspired by the theory of two levels of process in comprehension,we propose a novel clause-level relationship-aware math solver(CLRSolver)to mimic the process of human comprehension from lower level to higher ***,in the lower-level processes,we split problems into clauses according to their natural division and learn their *** the higher-level processes,following human′s multi-view understanding of clause-level relationships,we first apply a CNN-based module to learn the dependency relationships between clauses from word relevance in a local ***,we propose two novel relationship-aware mechanisms to learn dependency relationships from the clause semantics in a global ***,we enhance the representation of clauses based on the learned clause-level dependency *** expression generation,we develop a tree-based decoder to generate the mathematical *** conduct extensive experiments on two datasets,where the results demonstrate the superiority of our framework.

关键词： artificial intelligence(AI) artificial neural network(ANN) computational mathematics machine intelligence machine learning

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MIKE: A New Benchmark for Fine-grained Multimodal Entity Knowledge Editing 62

MIKE: A New Benchmark for Fine-grained Multimodal Entity Kno...

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Findings of the 62nd Annual Meeting of the Association for Computational Linguistics, ACL 2024

作者： Li, Jiaqi Du, Miaozeng Zhang, Chuanyi Chen, Yongrui Hu, Nan Qi, Guilin Jiang, Haiyun Cheng, Siyuan Tian, Bozhong School of Cyber Science and Engineering Southeast University Nanjing China School of Computer Science and Engineering Southeast University Nanjing China College of Artificial Intelligence and Automation Hohai University Nanjing China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education China Tencent AI Lab China Zhejiang University China

ISBN: (纸本)9798891760998

Multimodal knowledge editing represents a critical advancement in enhancing the capabilities of Multimodal Large Language Models (MLLMs). Despite its potential, current benchmarks predominantly focus on coarse-grained knowledge, leaving the intricacies of fine-grained (FG) multimodal entity knowledge largely unexplored. This gap presents a notable challenge, as FG entity recognition is pivotal for the practical deployment and effectiveness of MLLMs in diverse real-world scenarios. To bridge this gap, we introduce MIKE, a comprehensive benchmark and dataset specifically designed for the FG multimodal entity knowledge editing. MIKE encompasses a suite of tasks tailored to assess different perspectives, including Vanilla Name Answering, Entity-Level Caption, and Complex-Scenario Recognition. In addition, a new form of knowledge editing, Multi-Step Editing, is introduced to evaluate the editing efficiency. Through our extensive evaluations, we demonstrate that the current state-of-the-art methods face significant challenges in tackling our proposed benchmark, underscoring the complexity of FG knowledge editing in MLLMs. Our findings spotlight the urgent need for novel approaches in this domain, setting a clear agenda for future research and development efforts within the community. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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