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2024 International Conference on Electronics, Electrical and Information engineering, ICEEIE 2024

作者： Karim, Shahid Qadir, Akeel Han, Gao Li, Jing Ibrar, Muhammad Bibi, Irfana School of Information Engineering Xi'an Eurasia University 710065 China Software College Shenyang Normal University Shenyang China Department of Computer Science Faculty of Computing and Information Technology University of Punjab Punjab Lahore Pakistan

ISBN: (数字)9781510686809

ISBN: (纸本)9781510686793

This paper introduces a new method for traffic anomaly identification within the Faster R-CNN framework by combining the DenseNet module with the region proposals (RPs) processing block. The suggested approach seeks to improve feature extraction and RP creation in aerial imagery or CCTV frames to get beyond the drawbacks of the current methods. Discriminative features are extracted through DenseNet, and the RPs block makes it easier to provide a wide range of accurate and varied area proposals. Contrast correction techniques are applied to overcome the difficulties presented by low-resolution and low-contrast images frequently seen in surveillance scenarios. Comparing experimental results to state-of-the-art techniques, one can observe notable resilience and detection accuracy increases. The suggested method works exceptionally well in various traffic scenarios and environmental circumstances, indicating its potential for practical implementation in traffic surveillance systems. Overall, the study contributes to advancing the efficacy and reliability of anomaly detection in urban traffic environments. © 2024 SPIE.

关键词： Aerial photography

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SHT-based public auditing protocol with error tolerance in FDL-empowered IoVs

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Digital Communications and Networks 2024年第1期10卷 142-149页

作者： Kui Zhu Yongjun Ren Jian Shen Pandi Vijayakumar Pradip Kumar Sharma School of Computer and Software Nanjing University of Information Science&TechnologyNanjing210044China Peng Cheng Laboratory Shenzhen518000China Department of Computer Science and Engineering University College of Engineering TindivanamTamil Nadu604001India Department of Computing Science University of AberdeenAberdeenAB243UEUK

With the intelligentization of the Internet of Vehicles(lovs),Artificial Intelligence(Al)technology is becoming more and more essential,especially deep *** Deep Learning(FDL)is a novel distributed machine learning technology and is able to address the challenges like data security,privacy risks,and huge communication overheads from big raw data ***,FDL can only guarantee data security and privacy among multiple clients during data *** the data sets stored locally in clients are corrupted,including being tampered with and lost,the training results of the FDL in intelligent IoVs must be negatively *** this paper,we are the first to design a secure data auditing protocol to guarantee the integrity and availability of data sets in FDL-empowered ***,the cuckoo filter and Reed-Solomon codes are utilized to guarantee error tolerance,including efficient corrupted data locating and *** addition,a novel data structure,Skip Hash Table(SHT)is designed to optimize data ***,we illustrate the security of the scheme with the Computational Diffie-Hellman(CDH)assumption on bilinear *** theoretical analyses and performance evaluations demonstrate the security and efficiency of our scheme for data sets in FDL-empowered IoVs.

关键词： Internet of vehicles Federated deep learning Data security Data auditing Data locating and recovery

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Recursive Confidence Training for Pseudo-Labeling Calibration in Semi-Supervised Few-Shot Learning

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IEEE Transactions on Image Processing 2025年 34卷 3194-3208页

作者： Jing, Kunlei Ma, Hebo Zhang, Chen Wen, Lei Zhang, Zhaorui Xi’an Jiaotong University School of Software Engineering National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Xi’an China The Hong Kong Polytechnic University Department of Computing Hong Kong Xi’an Jiaotong University Institute of Science and Education Development Xi’an China The Hong Kong Polytechnic University Department of Computing School of Hotel and Tourism Management Hong Kong Guangzhou China

Semi-Supervised Few-Shot Learning (SSFSL) aims to address the data scarcity in few-shot learning by leveraging both a few labeled support data and abundant unlabeled data. In SSFSL, a classifier trained on scarce support data is often biased and thus assigns inaccurate pseudo-labels to the unlabeled data, which will mislead downstream learning tasks. To combat this issue, we introduce a novel method called Certainty-Aware Recursive Confidence Training (CARCT). CARCT hinges on the insight that selecting pseudo-labeled data based on confidence levels can yield more informative support data, which is crucial for retraining an unbiased classifier to achieve accurate pseudo-labeling—a process we term pseudo-labeling calibration. We observe that accurate pseudo-labels typically exhibit smaller certainty entropy, indicating high-confidence pseudo-labeling compared to those of inaccurate pseudo-labels. Accordingly, CARCT constructs a joint double-Gaussian model to fit the certainty entropies collected across numerous SSFSL tasks. Thereby, A semi-supervised Prior Confidence Distribution (ssPCD) is learned to aid in distinguishing between high-confidence and low-confidence pseudo-labels. During an SSFSL task, ssPCD guides the selection of both high-confidence and low-confidence pseudo-labeled data to retrain the classifier that then assigns more accurate pseudo-labels to the low-confidence pseudo-labeled data. Such recursive confidence training continues until the low-confidence ones are exhausted, terminating the pseudo-labeling calibration. The unlabeled data all receive accurate pseudo-labels to expand the few support data to generalize the downstream learning task, which in return meta-refines the classifier, named self-training, to boost the pseudo-labeling in subsequent tasks. Extensive experiments on basic and extended SSFSL setups showcase the superiority of CARCT versus state-of-the-art methods, and comprehensive ablation studies and visualizations justify our insigh

关键词： Semi-supervised learning

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Fingerprint Agreement Using Enhanced Kerberos Authentication Protocol on M-Health

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Computer Systems science & engineering 2022年第11期43卷 833-847页

作者： A.S.Anakath S.Ambika S.Rajakumar R.Kannadasan K.S.Sendhil Kumar School of Computing E.G.S.Pillay Engineering CollegeNagapattinam611002India Department of Computer Science and Engineering University College of Engineering AriyalurAriyalur621705India Department of Mathematics University College of Engineering AriyalurAriyalur621705India Department of Software Systems School of Computer Science and Engineering(SCOPE)Vellore Institute of Technology UniversityVellore632014India Department of IoT School of Computer Science and Engineering(SCOPE)Vellore Institute of Technology UniversityVellore632014India

Cloud computing becomes an important application development platform for processing user data with high *** providers are accustomed to providing storage centers outside the trusted location preferred by the data ***,ensuring the security and confidentiality of the data while processing in the centralized network is very *** secured key transmission between the sender and the receiver in the network is a huge challenge in managing most of the sensitive data transmission among the cloud *** are very active over the network like real authenticated user to hack the personal sensitive data,such as bank balance,health data,personal data,and confidential documents over the cloud *** this research,a secured key agreement between the sender and the receiver using Kerberos authentication protocol with fingerprint is proposed to ensure security in *** of patients are monitored using wireless sensor devices and are then transferred to the *** protocol helps in avoiding unnecessary communication of authenticated data over the cloud *** security process is a procedure with the best security in most of the authentication *** node is responsible in carrying data packets from the sender to the receiver in the cloud *** Kerberos protocol is used in trust node to ensure *** communication between the local health center and the healthcare server is ensured by using a fingerprint feature called minutiae form,which refers to the fingerprint image of both sender and *** computational and communicational cost of the proposed system is lesser when compared with other existing authentication methods.

关键词： Protocol security m-health cloud computing biometric fingerprint kerberos protocol

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Accumulative Time Based Ranking Method to Reputation Evaluation in Information Networks

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Journal of Computer science & Technology 2022年第4期37卷 960-974页

作者： Hao Liao Qi-Xin Liu Ze-Cheng Huang Ke-Zhong Lu Chi Ho Yeung Yi-Cheng Zhang National Engineering Laboratory on Big Data System Computing Technology College of Computer Science and SoftwareEngineeringShenzhen UniversityShenzhen 518060China Guangdong Province Key Laboratory of Popular High Performance Computers Shenzhen UniversityShenzhen 518060China Guangdong Province Engineering Center of China-Made High Performance Data Computing System College of ComputerScience and Software EngineeringShenzhen UniversityShenzhen 518060China Institute of Big Data Intelligent Management and Decision Shenzhen UniversityShenzhen 518060China Department of Science and Environmental Studies The Education University of Hong KongHong Kong 999077China Department of Physics University of FribourgFribourg 1700Switzerland

Due to over-abundant information on the Web, information filtering becomes a key task for online users to obtain relevant suggestions and how to extract the most related item is always a key topic for researchers in various fields. In this paper, we adopt tools used to analyze complex networks to evaluate user reputation and item quality. In our proposed Accumulative Time Based Ranking (ATR) algorithm, we take into account the growth record of the network to identify the evolution of the reputation of users and the quality of items, by incorporating two behavior weighting factors which can capture the hidden facts on reputation and quality dynamics for each user and item respectively. Our proposed ATR algorithm mainly combines the iterative approach to rank user reputation and item quality with temporal dependence compared with other reputation evaluation methods. We show that our algorithm outperforms other benchmark ranking algorithms in terms of precision and robustness on empirical datasets from various online retailers and the citation datasets among research publications. Therefore, our proposed method has the capability to effectively evaluate user reputation and item quality.

关键词： temporal network behavior dynamics reputation evaluation ranking algorithm

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PraK Tool V3: Enhancing Video Item Search Using Localized Text and Texture Queries 31st

PraK Tool V3: Enhancing Video Item Search Using Localized Te...

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31st International Conference on Multimedia Modeling, MMM 2025

作者： Stroh, Michael Kloda, Vojtěch Verner, Benjamin Vopálková, Zuzana Buchmüller, Raphael Jäckl, Bastian Hajko, Jakub Lokoč, Jakub Visual Computing Department of Computer Science Faculty of Natural Sciences Konstanz University Konstanz Germany SIRET Research Group Department of Software Engineering Faculty of Mathematics and Physics Charles University Prague Czech Republic Data Analysis and Visualization Department of Computer Science Faculty of Natural Sciences Konstanz University Konstanz Germany

ISBN: (纸本)9789819620739

We present a third version of the PraK system designed around an effective text-image and image-image search model. The system integrates sub-image search options for localized context search for CLIP and image color/texture and interfaces tailored to specific task types. A user study shows the benefits of sub-image search in homogeneous video collections. We further enhance the user experience of the PraK framework by re-designing the frontend and introducing task-specific views. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Video analysis

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Workload Prediction of Virtual Machines Using Integrated Deep Learning Approaches Over Cloud Data Centers 1st

Workload Prediction of Virtual Machines Using Integrated Dee...

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1st International Conference on Human-Centric Smart computing, ICHCSC 2022

作者： Leka, Habte Lejebo Fengli, Zhang Kenea, Ayantu Tesfaye Sharma, Durga P. Tegene, Abebe Tamrat School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Department of Computer Science and Engineering School of Electrical Engineering and Computing Adama Science and Technology University Adama1888 Ethiopia AMUIT MOEFDRE Under UNDP & MAISM RTU Kota Kota India

ISBN: (纸本)9789811954023

Exponential growth in the use of cloud computing services makes it difficult to forecast loads of virtual machines (VMs). Accurate virtual machine (VM) workload forecasting is the most critical task in appropriately managing cloud resources such as memory and central processing units while minimizing energy usage. To address this problem, an integrated deep learning model is proposed in this research paper. The model employs two popular neural networks: a bidirectional Long Short-Term Memory Network (BiLSTM) with a convolutional neural networks (CNN). The CNN component pulls high-level attributes from all VM workload data, whereas the BiLSTM component forecasts future VM workload. The experimental results reveal that the suggested model outperforms commonly used workload prediction methods in terms of forecasting accuracy of VMs workloads in cloud computing environments. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cloud computing

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An IT/OT Converged Cyber Range Framework Supporting Ensemble of Large Multimodal Models-Human Teaming and RAG Capability for Bigdata Information Retrieval - 6G Perspective

An IT/OT Converged Cyber Range Framework Supporting Ensemble...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Rahman, M. Abdur Islam, M. Saiful Islam, M. Repon Hossain, M. Shamim Al-Hazzaa, Selwa University of Prince Mugrin Cyber Security and Forensic Computing Department Medina Saudi Arabia UoL Worldwide University of London London United Kingdom Khulna University of Engineering & Technology Department of Computer Science and Engineering Khulna Bangladesh King Saud University Department of Software Engineering Riyadh Saudi Arabia Riyadh Saudi Arabia

ISBN: (纸本)9798350351255

In IT/OT converged enterprise networks, the management of multimodal big data has surpassed human capabilities, presenting critical challenges in business decision-making. This paper introduces an innovative IT/OT Converged Cyber Range Framework, leveraging the power of 6G technology alongside an ensemble of large multimodal models (LMMs) equipped with Retrieval Augmented Generation (RAG) capabilities. Our proposed framework excels at parsing and retrieving information from diverse and massive multimodal datasets in near real-time, driven by the integration of LMMs and human interactions in natural language. The framework's advanced configuration with RAG achieved top performance with an average score of 80.5 across key metrics, including an 86 in answer relevancy and an 88 in faithfulness, while effectively reducing hallucinations to a score of 26. By combining 6G's connectivity with advanced LMM capabilities, we significantly improve the efficiency of automated big data processing and information retrieval, thereby transforming decision-making processes in IT/OT environments. The implementation of our framework, validated through real-life big data sources, and the encouraging test results showcase its effectiveness and transformative potential in managing and utilizing big data far beyond traditional human capabilities. © 2024 IEEE.

关键词： 6G Technology Human-LMM Teaming IT/OT Converged Network Large Multimodal Models (LMMs) Multimedia Bigdata Retrieval Augmented Generation (RAG)

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Exploring Deep Learning and Grad-CAM for Speech-Based Detection of Mild Traumatic Brain Injury

Exploring Deep Learning and Grad-CAM for Speech-Based Detect...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Rojas, Fredy Madanian, Samaneh Templeton, John Michael Poellabauer, Christian Schneider, Sandra L. Department of Computer Science and Software Engineering Auckland New Zealand University of South Florida Department of Computer Science and Engineering TampaFL United States Florida International University School of Computing and Information Sciences MiamiFL United States Saint Mary's College Department of Communicative Sciences & Disorders Notre DameIN United States

ISBN: (纸本)9798350362480

Mild traumatic brain injury (mTBI) is challenging to diagnose due to its subtle and transient symptoms, making noninvasive diagnostic tools crucial for early detection. This study explores the use of a custom ResNet deep learning model combined with the Grad-CAM interpretability technique for mTBI detection via speech analysis. Speech data were transformed into Mel-spectrograms and fed into the model for binary classification between concussed and control individuals. The Grad-CAM method provided insights into which frequency regions of the Mel-spectrogram were most important for the model's predictions, with higher-frequency regions identified as significant for the model in detecting mTBI. Using Monte Carlo Cross-Validation (MCCV), we evaluated 50 different subject train-test split configurations to gain insights into the model's performance stability and variability. This analysis can assess the model's ability to learn consistent patterns within the dataset and suggest potential generalization tendencies. The variability observed in the performance metrics distribution underscores the importance of robust evaluation methods, particularly when working with small datasets. The combination of deep learning, robust evaluation technique and interpretability in this study contributes to the development of clinically viable, speech-based tools for mTBI detection, with potential applications in sports and healthcare settings. © 2024 IEEE.

关键词： Speech analysis

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Classification of Helpful and Unhelpful Online Customer Reviews Using XLNet and BERT Variants 1

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International Conference on Green Energy, computing, and Intelligent Technology, GEn-CITy 2023

作者： Bilal, Muhammad Arshad, Muhammad Haseeb Ramzan, Muhammad Department of Computing and Information Systems School of Engineering and Technology Sunway University Petaling Jaya Malaysia Department of Computer Science National University of Computer and Emerging Sciences Chiniot-Faisalabad Campus Chiniot Pakistan Department of Software Engineering University of Sargodha Sargodha Pakistan School of Electronics and Computer Science University of Southampton Malaysia Iskandar Puteri Malaysia

ISBN: (数字)9789819998333

ISBN: (纸本)9789819998326

The majority of businesses have made public appearances on various social media platforms as a result of recent advances in e-commerce and the popularity of social media websites. Customers can share their experiences in the form of online customer reviews, which can assist potential customers in determining the quality of a company and making purchasing decisions. Due to the large volume of published reviews, it becomes difficult for customers to read all of the reviews and assess the quality of the business, resulting in the problem of information overload. Several solutions have been proposed in the literature by researchers using statistical and machine learning techniques to predict the helpfulness of online customer reviews. However, most of the existing solutions are based on the use of various business, review, and reviewer features, which lead to generalizability issues. Moreover, very limited studies have examined the effectiveness of state-of-the-art pre-trained language models for the classification of helpful and unhelpful reviews. Therefore, this study aims to examine the effectiveness of XLNet, Albert, DistilBert, and Roberta for review helpfulness prediction using textual features. The models were fine-tuned using a publicly available dataset of Yelp reviews. The results showed that XLNet achieved the highest F1-score of 0.730 compared to a benchmark of 0.717 achieved by the BERT base model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Sales

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