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5th International Conference on Advancements in Computing, ICAC 2023

作者： Tennakoon, Suvin Wickramasinha, Pamuditha Ranathunga, Hasini Ulpathakumbura, Chathuranga Kasthurirathna, Dharshana Samaratunge Arachchillage, Udara Srimath S. Sri Lanka Institute Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute Information Technology Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350358131

The world of vehicle service and troubleshooting can be daunting for individuals without specialized training. Vehicle manuals are often complex and challenging to comprehend, while relying on experienced mechanics for assistance can be inconvenient and expensive. To address these challenges, this research paper presents the development of an interactive vehicle service assistance system that empowers vehicle owners to independently service and troubleshoot their vehicles. The proposed system encompasses four key components: a knowledgebase construction, write semantic rules (SWRL), real-time object identification using YOLO model, audio feedback and stepwise guidance for customer. The knowledgebase is used for stepwise guidance for disassembling engine through employing marker-less Augmented Reality (AR). The knowledgebase component offers user-friendly access to troubleshooting techniques based on insights from experienced mechanics. By providing stepwise instructions accompanied by AR based visualization, users can effectively learn the process of deconstructing and assembling different components of their automobiles. The proposed system enhances vehicle troubleshooting experiences by enabling owners to acquire the necessary skills to self-troubleshooting without involving external assistance and empower individuals to take control of their own vehicles. © 2023 IEEE.

关键词： Augmented reality

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Multi-View User Preference Modeling for Personalized Text-to-Image Generation

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IEEE Transactions on Multimedia 2025年 27卷 3082-3091页

作者： Zhang, Huaiwen Wu, Tianci Wei, Yinwei Inner Mongolia University College of Computer Science Hohhot010021 China National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot010021 China Inner Mongolia Key Laboratory of Mongolian Information Processing Technology Hohhot010021 China Shandong University School of Software Jinan250100 China

Personalized text-to-image generation aims to synthesize images tailored to individual user preferences. Existing methods primarily generate customized content using a few reference images, which often struggle to mine user preferences from historical records, and thus fail to synthesize truly personalized content. In addition, it is difficult to directly incorporate the extracted feature of user preferences into the feature space of the generation model, since there exists a considerable gap between them. In this paper, we propose a novel multi-view personalized text-to-image generation method based on the diffusion model, named MVP-Diffusion, which learns instance- and user-level preferences from historical records and integrates them into the generation model. For instance-level user preference modeling, we employ a chain-of-thought prompting strategy to deduce preference keywords and integrate them into input prompts with the aid of a large language model. For user-level preference modeling, we construct a learnable embedding for each user to capture more comprehensive preferences by analyzing their historical records. An adaptive user preference fusion module is proposed to inject user preferences into the generation model via a set of learnable parameters. Experimental results demonstrate that the proposed method significantly enhances the personalization of the generated images compared to the other personalized text-to-image generation methods. © 1999-2012 IEEE.

关键词： Adaptation models User preference Text to image Large language models Training Analytical models Image synthesis Feature extraction Diffusion models Symbols

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Tackling Real-world Complexity: Hierarchical Modeling and Dynamic Prompting for Multimodal Long Document Classification

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IEEE Transactions on Circuits and Systems for Video technology 2025年第6期35卷 5776-5790页

作者： Liu, Tengfei Hu, Yongli Li, Mingjie Yi, Junfei Chang, Xiaojun Gao, Junbin Yin, Baocai Beijing University of Technology Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing Institute of Artificial Intelligence School of Information Science and Technology Beijing100124 China Stanford University Department of Radiation Oncology StanfordCA94305 United States Hunan University National Engineering Laboratory of Robot Visual Perception and Control Technology College of Electrical and Information Engineering Hunan Changsha410082 China University of Technology Sydney ReLER Laboratory Australian Artificial Intelligence Institute Faculty of Engineering and Information Technology UltimoNSW2007 Australia The University of Sydney Business School The University of Sydney Discipline of Business Analytics CamperdownNSW2006 Australia

With the rapid growth of internet content, multimodal long document data has become increasingly prominent, drawing significant attention from researchers. However, most existing methods primarily focus on scenarios where all modalities are present, often overlooking more challenging and realistic cases involving missing image modality. To address this limitation, we propose a robust multimodal long document classification (MLDC) framework that integrates hierarchical modeling and dynamic prompting to handle complex multimodal long document data. Our approach begins by leveraging hierarchical modeling combined with an Adaptive Correlation Multimodal Transformer (ACMT) to effectively capture relationships between text and images at both section and sentence levels. We also introduce a Dynamic Prompt Generation (DPG) module at both levels to enhance the model's robustness in handling missing image data. By evaluating sample uncertainty, the DPG module dynamically adjusts both the number of prompts and the prompts themselves, allowing the model to better adapt to the varying needs of different samples. Finally, a Hierarchical Heterogeneous Graph (HHG) is introduced to enhance feature interactions across levels, further improving the coherence and accuracy of the model. Extensive experiments on four multi-modal long document datasets demonstrate that our model shows superior performance compared to existing state-of-the-art MLDC classification methods in various conditions. © 1991-2012 IEEE.

关键词： Adaptation models Transformers Data models Correlation Circuits and systems Electronic mail Robustness Uncertainty Training Complexity theory

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CSCVD: A Method of Source Code Vulnerability Detection Based on Semantic Feature and Context Dependence 9

CSCVD: A Method of Source Code Vulnerability Detection Based...

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9th International Conference on Computer and Communications, ICCC 2023

作者： Chenxi, Yang Le, Wei Chengdu University of Information Technology School of Software Engineering Chengdu China Sichuan Manufacturing Information Industry Collaborative Innovation Center Chengdu University of Information Technology Chengdu China

ISBN: (纸本)9798350317251

Most current source code vulnerability detection methods use the single sequence or graph representation, which cannot effectively extract multi-dimensional code features. To solve this problem, we propose a novel vulnerability detection method CSCVD (Combining Semantic feature and Context dependency for Vulnerability Detection) based on semantic features and context dependency. Firstly, we utilize the CB-BL model to serialize and embed code statement segments to extract local semantic features, and the source code is analyzed by data flow and control flow to construct control flow diagram, as well as the local semantic features are embedded into the control flow diagram as nodes. Then the graph attention network is used to extract the information of different types of edges in the control flow graph to capture the code context dependencies. Finally, the multi-dimensional features are fused to obtain more abundant vulnerability features, and the vulnerability probability is calculated using multi-layer perceptron to complete the source code vulnerability detection task. The experimental results show that CSCVD improves the performance of vulnerability detection compared with static analysis tool Flwafinder, program-slic-based detection method VulDeepecker and graph neural network-based detection method. Specifically, CSCVD obtains 63.59% accuracy and 65.91% recall on the real-world benchmark dataset from CodeXGLUE for vulnerability detection. © 2023 IEEE.

关键词： Semantics

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SA-ResNet:An Intrusion Detection Method Based on Spatial Attention Mechanism and Residual Neural Network Fusion

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Computers, Materials & Continua 2025年第5期83卷 3335-3350页

作者： Zengyu Cai Yuming Dai Jianwei Zhang Yuan Feng School of Computer Science and Technology Zhengzhou University of Light IndustryZhengzhou450066China College of Software Engineering Zhengzhou University of Light IndustryZhengzhou450066China Faculty of Information Engineering Xuchang Vocational Technical CollegeXuchang461000China School of Electronic Information Zhengzhou University of Light IndustryZhengzhou450066China

The rapid development and widespread adoption of Internet technology have significantly increased Internet traffic,highlighting the growing importance of network *** Detection Systems(IDS)are essential for safeguarding network *** address the low accuracy of existing intrusion detection models in identifying network attacks,this paper proposes an intrusion detection method based on the fusion of Spatial Attention mechanism and Residual Neural Network(SA-ResNet).Utilizing residual connections can effectively capture local features in the data;by introducing a spatial attention mechanism,the global dependency relationships of intrusion features can be extracted,enhancing the intrusion recognition model’s focus on the global features of intrusions,and effectively improving the accuracy of intrusion *** proposed model in this paper was experimentally verified on theNSL-KDD *** experimental results showthat the intrusion recognition accuracy of the intrusion detection method based on SA-ResNet has reached 99.86%,and its overall accuracy is 0.41% higher than that of traditional Convolutional Neural Network(CNN)models.

关键词： Intrusion detection deep learning residual neural network spatial attention mechanism

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Iterative Segmentation and Propagation Based Interactive Video Object Segmentation

Iterative Segmentation and Propagation Based Interactive Vid...

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2023 International Conference on Algorithm, Imaging Processing, and Machine Vision, AIPMV 2023

作者： Luo, Sihan Yang, Sizhe Yuan, Xia School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu Nanjing China School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu China

ISBN: (纸本)9781510672444

Interactive video object segmentation (iVOS), which aims to efficiently produce high-quality segmentation masks of the target object in a video with user interactions. Recently, numerous works are proposed to advance the task of iVOS. However, their usages on user intent are limited. First, typical modules usually try to direct generate the segmentation without any further exploration on the input interaction, which misses valuable information. Second, recent iVOS approaches also do not consider the raw interactive information. As a result, the final segmentation results will be poisoned by the erroneous information given by the previous round’s segmentation masks. To solve the aforementioned weaknesses, in this paper, an Iterative Segmentation and Propagation based iVOS method is proposed to conduct better user intent exploration, namely ISP. ISP directly models user intent into the PGI2M module and TP module. Specifically, ISP first extracts a coarse-grained segmentation mask by analyzing the user’s input. Subsequently, this mask is used as a prior to aid the PGI2M module. Secondly, ISP presents a new interaction-driven self-attention module to recall the user’s intent in the TP module. Extensive experiments on two public datasets show the superiority of ISP over existing methods. © 2024 SPIE.

关键词： Iterative methods

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An Approach to Optimize Symbolic Execution in Ethereum Smart Contracts 24

An Approach to Optimize Symbolic Execution in Ethereum Smart...

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24th IEEE International Conference on software Quality, Reliability and Security Companion, QRS-C 2024

作者： Liu, Yi Cai, Lizhi Chai, Chenchen Ma, Yuan School of Information Science and Engineering East China University of Science and Technology Shanghai China Shanghai Development Center of Computer Software Technology Shanghai Key Laboratory of Computer Software Testing & Evaluating Shanghai China

ISBN: (纸本)9798350365658

As the application of smart contracts in blockchain technology becomes increasingly widespread, their security issues have emerged as a focal point of both research and practice. Although symbolic execution technology has been extensively applied for the detection of security vulnerabilities in smart contracts, its limitations in terms of execution efficiency and code coverage remain significant. In response to these challenges, this study introduces an innovative method named HoneyToken-DetectorRev, which optimizes traditional symbolic execution through program slicing technology, aiming to enhance the efficiency and effectiveness of smart contract security detection. By selecting ten smart contracts from the Ethereum platform, which vary in business logic and complexity, we conducted an empirical analysis of HoneyToken-DetectorRev. The experimental results demonstrate that this method significantly improves the efficiency of smart contract detection without sacrificing precision. This research not only provides an effective technical approach for the security detection of smart contracts but also offers theoretical and practical guidance for further enhancing the security of blockchain applications. © 2024 IEEE.

关键词： Smart contract

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HistoMind: AI-Based Historical Place Navigator 5

HistoMind: AI-Based Historical Place Navigator

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Ranasinghe, Chathra Navoda Ranasinghe, Kavindu Ushan Munasighe, Niketh Priyaratne, Manul Harshanath, Buddika Rajapakse, Samantha Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350358131

This research addresses a critical gap in Sri Lanka's tourism infrastructure: the absence of a comprehensive smart location-based recommendation system with a route planner and a multilingual AI chatbot. To date, no integrated solution has been developed to cater to the diverse preferences of tourists, offer community-validated recommendations, and bridge language barriers. This research is pioneering the creation of these novel systems, with a clear focus on enhancing the tourism experience and supporting the country's economy. The smart recommendation system harmonizes various recommendation techniques, encompassing user preferences, community-approved choices, and hidden gems. By integrating these aspects, the system aims to provide tourists with personalized, community-endorsed recommendations while introducing them to unique destinations, thus addressing the existing void in Sri Lanka's tourism landscape. Furthermore, the AI-based multilingual chatbot tackles the language barrier head-on, enabling tourists to communicate effortlessly in their native language, a feature hitherto unavailable in the Sri Lankan tourism sector. This innovation aims to break down language barriers, making the country more accessible to a global audience. Through advanced methodologies and cutting-edge technology, this research strives to fill these critical gaps, promising a brighter future for Sri Lanka's tourism and its economic prosperity. © 2023 IEEE.

关键词： Tourism

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A Model of Development of a System for Monitoring Students' Graduation Qualification Work Projects 3

A Model of Development of a System for Monitoring Students' ...

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3rd IEEE International Conference on Problems of Informatics, Electronics and Radio engineering, PIERE 2024

作者： Bekmurod, Khujamuratov Ruza, Sharifbaeva Bakhodir, Radjapov Munisa, Otaboyeva Ziyodulla, Abdukarimov Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Departmentent of Software Engineering Urgench Uzbekistan Urgench Branch of Tashkent University Information Technology named after Muhammad al Khwarizmi Departpment of Software Engineering Urgench Uzbekistan Urganch State University Department of Technology Urgench Uzbekistan

ISBN: (纸本)9798331516321

In order to facilitate the remote completion of final qualification works, it is essential to understand how this process is currently progressing. Currently, students do not have the opportunity to choose their final qualification or course work;instead, they are provided with a predetermined list by university educators, and students are obliged to work on topics assigned to them. Through our system, educators contribute topics, and students select those they find acceptable. This process is monitored by the department heads' statistics. The quality assurance department will also have the ability to view statistics and issue directives. The system integrates all information of both educators and students with the current HEMIS system implemented in all universities in Uzbekistan. With the help of the system, students can choose graduation qualification works, and it is distinguished from other systems by facilitating work between students and teachers, ensuring transparency and quality. © 2024 IEEE.

关键词： Students

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Evaluating Public Sentiments of Covid-19 Vaccine Tweets Using Machine Learning Techniques

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Informatica (Slovenia) 2022年第1期46卷 69-75页

作者： Akpatsa, Samuel Kofi Lei, Hang Li, Xiaoyu Obeng, Victor-Hillary Kofi Setornyo School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Province Chengdu610054 China

The quest to create a vaccine for covid-19 has rekindled hope for most people worldwide, with the anticipation that a vaccine breakthrough would be one step closer to the end of the deadly Covid-19. The pandemic has had a bearing on the use of Twitter as a communication medium to reach a wider audience. This study examines Covid-19 vaccine-related discussions, concerns, and Twitter-emerged sentiments about the Covid-19 vaccine rollout program. Natural Language Processing (NLP) techniques were applied to analyze Covid-19 vaccine-related tweets. Our analysis identified popular n-grams and salient themes such as "vaccine health information," "vaccine distribution and administration," "vaccine doses required for immunity," and "vaccine availability." We apply machine learning algorithms and evaluate their performance using the standard metrics, namely accuracy, precision, recall, and f1-score. Support Vector Machine (SVM) classifier proves to be the best fit on the dataset with 84.32% accuracy. The research demonstrates how Twitter data and machine learning methods can study the evolving public discussions and sentiments concerning the Covid-19 vaccine rollout program. © 2022 Slovene Society Informatika. All rights reserved.

关键词： Social networking (online)

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