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Pervasive Computing and Social Networking (ICPCSN), International Conference on

作者： Kavitha C.R. Rajarajan S J R. Jothilakshmi Kamal Alaskar Mohammad Ishrat V. Chithra Devi Department of Computer Science Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India Department of Mechanical Engineering S.A. Engineering College (Autonomous) Thiruverkadu Tamil Nadu India Department of Information Technology R.M.D. Engineering College Kavaraipettai Tamil Nadu India Department of Computer Application BharatiVidyapeeth (Deemed to be University) Institute of Management Kolhapur Maharashtra India Department of Computer Science and Engineering KoneruLakshmaiah Education Foundation Vaddeswaram Guntur Andhra Pradesh India Department of English PSNA College of Engineering and Technology (Autonomous) Dindigul Tamil Nadu India

ISBN: (数字)9798350322842

ISBN: (纸本)9798350322859

The study of opinions, sentiments, assessments, perspectives, and feelings as they are expressed in written language is the focus of sentiment analysis and evaluation mining. It is commonly the subject of text mining, information mining, and web mining because it is one of the most active study fields in natural language processing. Sentiment analysis has been applied in many different contexts, including the study of how events affect unofficial groups and the exploration of presumptions about goods and services. The importance of sentiment analysis is increasing at the same time as virtual entertainment such as surveys, group chats, web blogs, micro sites, Twitter, and interpersonal organizations. In this work, we provide the results of our preliminary studies on twitter sentiment analysis. This study seeks to distinguish subjects from sentiment in tweets. It recognizes the sentiment that refers to the particular problem using Natural Language Processing techniques. Our analysis consists of three main processes, subjective grouping, semantic affiliation, and extreme order, to characterize sentiment. By describing the syntactic relationship between sentiment dictionaries and subject, the study makes use of sentiment vocabularies. Exploratory results demonstrate that the proposed framework performs better than existing message sentiment analysis tools because tweets don't have the same architecture as traditional messages.

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Information Entropy Regularized Subspace Support Vector Data Description

SSRN

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SSRN 2023年

作者： Hu, Wenjun Wang, Chuang Wang, Juan Qian, Pengjiang Wang, Shitong School of Information Engineering Huzhou University Huzhou313000 China Zhejiang Province Key Laboratory of Smart Management & Application of Modern Agricultural Resources Huzhou University Huzhou313000 China School of Artificial Intelligence & Computer Science Jiangnan University Wuxi214122 China

Since it is very difficult or expensive to obtain abnormal sample in many practical applications, one-class classification (OCC) problem is always a hot topic. As one of typical approaches, SVDD has received great attention. However, it has an inherent drawback, i.e. sensitivity to sample distribution, which is reflected in sensitivity to feature redundancy and sensitivity to the tradeoff parameter. To reduce the impact of feature redundancy, we use subspace learning to obtain the essential characteristics to reduce the coupling and mutual interference between features. To solve sensitivity to the tradeoff parameter, a probability is designed for each sample to evaluate the possibility belonging to normal sample in the subspace and then it is used to generate an information entropy-based term for penalizing each sample. As a result, a novel SVDD method is proposed, called information entropy regularized subspace SVDD (IS-SVDD). Finally, we arrange extensive experiments on real-world datasets. Experimental results demonstrate that our approach is effective and more competitive than other algorithms. © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

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Ultrasonic Index Modulation With Spread Spectrum for Intra-Body Communications

Ultrasonic Index Modulation With Spread Spectrum for Intra-B...

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IEEE International Conference on Communications (ICC)

作者： Qianqian Wang Quansheng Guan Julian Cheng Xiangdong Jia Mangang Xie Yuankun Tang College of Computer Science and Engineering Northwest Normal University Lanzhou China School of Electronic and Information Engineering South China University of Technology Guangzhou China Key Laboratory of Marine Environmental Survey Technology and Application Ministry of Natural Resources Guangzhou China School of Engineering The University of British Columbia Kelowna BC Canada

Ultrasonic intra-body communication (IBC) is an emerging technique to enable revolutionary healthcare applications. This paper proposes an ultrasonic index modulation with spread spectrum (UsIM-SS) scheme for IBCs. The information bits consist of modulation bits and index bits. The core idea of UsIM-SS is that the modulation bits are spread by spreading codes and the index bits are leveraged to select the time-hoping sequence to transmit the spreading bits. We also find that the existing ultrasonic wideband (UsWB) and UsIM can be regarded as special cases of the proposed UsIM-SS. Furthermore, the soft decision-based maximum likelihood (S-ML) receiver and hard decision-based ML (H-ML) receiver are proposed for UsIM-SS, providing a trade-off between bit-error rate (BER) and complexity. Theoretical spectrum efficiency (SE) and BER expressions for UsIM-SS using S-ML and H-ML are also derived and verified by extensive Monte Carlo simulations. Both theoretical and simulation results show that UsIM-SS can achieve lower BER and higher SE than existing UsWB and Us 1M techniques.

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Graph Structure Prefix Injection Transformer for Multi-Modal Entity Alignment

SSRN

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SSRN 2024年

作者： Li, Zhifei Luo, Xiangyu Zhang, Miao Zhang, Yan Xiao, Kui School of Computer Science and Information Engineering Hubei University Wuhan430062 China School of Cyber Science and Technology Hubei University Wuhan430062 China Key Laboratory of Intelligent Sensing System and Security Hubei University Ministry of Education Wuhan430062 China Hubei Key Laboratory of Big Data Intelligent Analysis and Application Hubei University Wuhan430062 China

Multi-modal entity alignment aims to integrate equivalent entities in diverse multi-modal knowledge graphs. However, previous studies have primarily focused on multi-modal fusion methods without considering the impact of graph structure heterogeneity on entity alignment tasks due to the distinct structural features of different knowledge graphs. Moreover, the multi-modal information that enriches entity representations, including image, textual content, and numerical attributes, frequently incorporates noise due to modal discrepancies. To tackle these challenges, we introduce the Graph Structural Prefix Injection transformer (GSIEA) for multi-modal entity alignment. This novel approach minimizes structural heterogeneity’s impact and optimizes structural data utilization by integrating a structural prefix inject interaction module within the multi-head attention mechanism. Then, we adopt a convolutional enhancement module to extract local features of multi-modal information and complete feature fusion by cross-modal weights. Extensive experiments on two datasets demonstrate that GSIEA outperforms previous baseline models, showcasing an average performance improvement of 3.26% on MRR, with a maximum enhancement of 12.5%. Additionally, the average performance improvement on Hits@1 is 4.96%, with a maximum enhancement of 16.92%. Our code is available at https://***/HubuKG/GSIEA. © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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Camouflaged Object Detection via Context-aware Cross-level Fusion

arXiv

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arXiv 2022年

作者： Chen, Geng Liu, Si-Jie Sun, Yu-Jia Ji, Ge-Peng Wu, Ya-Feng Zhou, Tao Northwestern Polytechnical University Xi’an710072 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering China Data Processing Center School of Power and Energy China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China School of Computer Science Inner Mongolia University Hohhot010021 China Artificial Intelligence Institute School of Computer Science Wuhan University Wuhan430072 China

Camouflaged object detection (COD) aims to identify the objects that conceal themselves in natural scenes. Accurate COD suffers from a number of challenges associated with low boundary contrast and the large variation of object appearances, e.g., object size and shape. To address these challenges, we propose a novel Context-aware Cross-level Fusion Network (C2F-Net), which fuses context-aware cross-level features for accurately identifying camouflaged objects. Specifically, we compute informative attention coefficients from multi-level features with our Attention-induced Cross-level Fusion Module (ACFM), which further integrates the features under the guidance of attention coefficients. We then propose a Dual-branch Global Context Module (DGCM) to refine the fused features for informative feature representations by exploiting rich global context information. Multiple ACFMs and DGCMs are integrated in a cascaded manner for generating a coarse prediction from high-level features. The coarse prediction acts as an attention map to refine the low-level features before passing them to our Camouflage Inference Module (CIM) to generate the final prediction. We perform extensive experiments on three widely used benchmark datasets and compare C2F-Net with state-of-the-art (SOTA) models. The results show that C2F-Net is an effective COD model and outperforms SOTA models remarkably. Further, an evaluation on polyp segmentation datasets demonstrates the promising potentials of our C2F-Net in COD downstream applications. Our code is publicly available at: https://***/Ben57882/C2FNet-TSCVT. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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Privacy-Preserving Decentralized Signature-Based Access Control

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Han, Jinguang Chen, Liqun Susilo, Willy Southeast University School of Cyber Science and Engineering Jiangsu Nanjing210096 China Ministry of Education Engineering Research Center of Blockchain Application China University of Surrey Department of Computer Science GuildfordGU2 7XH United Kingdom University of Wollongong School of Computing & Information Technology New South Walse WollongongNSW2522 Australia

Decentralized access control reduces trust on the central authority. Considering privacy issues, privacy-preserving decentralized access control (PPDAC) schemes were proposed. However, existing schemes did not consider the unforgeability and aggregtability of access permissions and the traceability of illegal users. In this paper, we propose a privacy-preserving decentralized signature-based access control (PPDSBAC) scheme to fix the above problems. In our scheme, each access permission is a multi- signatures on a keyword, time period and pseudonym, hence is unforgeable and aggregatable. Services are encrypted by using different keywords and time periods. A user can use a pseudonym to access a service directly if he/she gets required access permissions from required authorization authorities. Furthermore, in the case that trace is required, a trace authority can de-anonymise users and identify their real identities. We formalize both the definition and security model of our scheme, and propose a concrete construction. Then, the implementation and evaluation are conducted to show its efficiency. Finally, the security of our scheme is formally proven. The novelty is to construct a PPDAC scheme with unlinkable access requests, unforgeable and aggregatable access permissions, non-interactive authentication and traceability. © 2004-2012 IEEE.

关键词： Authentication

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Integration of AI and Cloud-Native Technologies for Personalized Mobile Banking Experiences

Integration of AI and Cloud-Native Technologies for Personal...

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Recent Innovation in Smart and Sustainable Technology (ICRISST), International Conference on

作者： N. Legapriyadharshini T. Thirumalaikumari K. Senbagam R. Sarasu H. Anwer Basha P. Malathi Department of Computer Application Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai Tamil Nadu India Department of Computer Science and Engineering Dhanalakshmi Srinivasan College of Engineering and Technology Mamallapuram Tamil Nadu India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Computer Science Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Data Analytics Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical And Technical Sciences Chennai Tamil Nadu India

ISBN: (数字)9798350309140

ISBN: (纸本)9798350309157

In today's rapidly evolving digital landscape, using Artificial Intelligence (AI) and Cloud-Native Technologies is essential for improving personalized mobile banking experiences. The limitations and errors of existing financial systems necessitate such integration. Existing systems often struggle to adapt to modern consumers dynamic tastes and expectations, resulting in unsatisfactory customer experiences. Identifying the gap, the proposed system revolutionizes mobile banking applications by leveraging AI algorithms such as machine learning and natural language processing within a microservices-based cloud infrastructure. As a result, the proposed system solves the limitations of outdated architectures by providing personalized and efficient services while addressing security issues. The study indicates considerable gains in terms of user pleasure, system scalability, and security robustness through an in-depth evaluation of the results. The results highlight AI's and Cloud-Native Technologies transformational potential in revolutionizing the landscape of tailored mobile banking experiences. The study provides helpful information to financial institutions looking to adopt innovation and fulfill the evolving needs of modern consumers.

关键词： Technological innovation Scalability Microservice architectures Banking Predictive models User experience Robustness Natural language processing Security Artificial intelligence

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The State of AI-Empowered Backscatter Communications: A Comprehensive Survey

TechRxiv

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TechRxiv 2023年

作者： Ahmed, Manzoor Hussain, Touseef Ali, Khurshed Mirza, Muhammad Ayzed Khan, Wali Ullah Ihsan, Asim Xu, Fang Han, Zhu The School of Computer and Information Science Institute for AI Industrial Technology Research Hubei Engineering University Xiaogan City432000 China The with College of Computer Science and Technology Qingdao University Qingdao266071 China The DTU Compute Technical University of Denmark Denmark The BUPT-QMUL EM Theory and Application International Research Lab Beijing University of Posts and Telecommunications Beijing100876 China University of Luxembourg Luxembourg City1855 Luxembourg The School of Computer Science and Electronic Engineering Bangor University BangorLL57 1UT United Kingdom The University of Houston TX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul Korea Republic of

The Internet of Things (IoT) is undergoing significant advancements, driven by the emergence of Backscatter Communication (BC) and Artificial Intelligence (AI). BC is an energy-saving and cost-effective communication method where passive backscatter devices communicate by modulating ambient Radio-Frequency (RF) carriers. AI has the potential to transform our way of communicating and interacting and represents a powerful tool for enabling the next generation of IoT devices and networks. By integrating AI with BC, we can create new opportunities for energy-efficient and low-cost communication and open the door to a range of innovative applications that were previously not possible. This paper brings these two technologies together to investigate the current state of AI-powered BC. We begin with an introduction to BC and an overview of the AI algorithms employed in BC. Then, we delve into the recent advances in AI-based BC, covering key areas such as backscatter signal detection, channel estimation, and jammer control to ensure security, mitigate interference, and improve throughput and latency. We also explore the exciting frontiers of AI in BC using B5G/6G technologies, including backscatter-assisted relay and cognitive communication networks, backscatter-assisted MEC networks, and BC with RIS, UAV, and vehicular networks. Finally, we highlight the challenges and present new research opportunities in AI-powered BC. This survey provides a comprehensive overview of the potential of AI-powered BC and its insightful impact on the future of IoT. © 2023, CC BY.

关键词： Backscattering

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Short-term Traffic Flow Prediction by Graph Deep Learning with Spatial Temporal Modeling 2

Short-term Traffic Flow Prediction by Graph Deep Learning wi...

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2nd International Conference on Electronic Information Technology and Smart Agriculture, ICEITSA 2022

作者： Yuan, Lilian Fang, Weilin Xiao, Hongbo Xiao, Jianhua Shi, Yuanquan Yang, Yujun School of Computer Science and Engineering Huaihua University Hunan Huaihua418000 China Intelligent Processing and Application in Hunan Province Universities Key Laboratory of Wuling-Mountain Health Big Data Hunan Huaihua418000 China Wuling-Mountain Ecological Agriculture in Hunan Province Key Laboratory of Intelligent Control Technology for Huaihua Hunan418000 China

ISBN: (纸本)9781665464017

An extensive amount of research applications on traffic flow prediction models taking into account spatio-temporal modeling have been undertaken in recent years. Relative to traditional spatio-temporal modeling approaches, good application results have been achieved in predicting traffic flow. However, in real life, traffic flow proves to be time- and space-dependent, which poses difficulties for accurate modeling of traffic flow prediction. To address these problems, we propose the ST-GNN-GRU model, which mainly contains S-GNN and T-GRU modules. The model uses a combination of spatio-temporal modeling and deep learning, which can not only cope with the spatio-temporal modeling problem, but also improve the traffic flow prediction accuracy. The experiments are conducted using the METR-LA dataset, and the outcomes show that the proposed method performs more effectively than other baseline approaches. © 2022 IEEE.

关键词： Graph neural networks

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Towards Clinician-Preferred Segmentation: Leveraging Human-in-the-Loop for Test Time Adaptation in Medical Image Segmentation

arXiv

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arXiv 2024年

作者： Hu, Shishuai Liao, Zehui Liu, Zeyou Xia, Yong National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China Ningbo Institute of Northwestern Polytechnical University Ningbo315048 China Research and Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen518057 China

Deep learning-based medical image segmentation models often face performance degradation when deployed across various medical centers, largely due to the discrepancies in data distribution. Test Time Adaptation (TTA) methods, which adapt pre-trained models to test data, have been employed to mitigate such discrepancies. However, existing TTA methods primarily focus on manipulating Batch Normalization (BN) layers or employing prompt and adversarial learning, which may not effectively rectify the inconsistencies arising from divergent data distributions. In this paper, we propose a novel Human-in-the-loop TTA (HiTTA) framework that stands out in two significant ways. First, it capitalizes on the largely overlooked potential of clinician-corrected predictions, integrating these corrections into the TTA process to steer the model towards predictions that coincide more closely with clinical annotation preferences. Second, our framework conceives a divergence loss, designed specifically to diminish the prediction divergence instigated by domain disparities, through the careful calibration of BN parameters. Our HiTTA is distinguished by its dual-faceted capability to acclimatize to the distribution of test data whilst ensuring the model’s predictions align with clinical expectations, thereby enhancing its relevance in a medical context. Extensive experiments on a public dataset underscore the superiority of our HiTTA over existing TTA methods, emphasizing the advantages of integrating human feedback and our divergence loss in enhancing the model’s performance and adaptability across diverse medical centers. © 2024, CC BY-NC-ND.

关键词： Forecasting

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