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Multi-layered access control based auto tuning relational key implications in enterprise-level multi-tenancy

Multimedia Tools and Applications 2025年第17期84卷 17805-17836页

作者： Henge, Santosh Kumar Rajakumar, R. Prasanna, P. Parivazhagan, A. Hu, Yu-Chen Chen, Wu-Lin Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation KL University AP Vaddeswaram India Centre for Automation School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering Veltech Rangarajan Dr Sagunthala R &D Institute of Science and Technology Chennai India Department of CSE Kalasalingam Academy of Research and Education Tamilnadu Krishnankoil India Department of Computer Science Xitun District Tunghai University No. 1727 Sec. 4 Boulevard Taichung City407224 Taiwan Department of Computer Science and Information Management Providence University Taichung City Taiwan

This article discusses the importance of cloud-based multi-tenancy in private–public-private secure cloud environments, which is achieved through the isolation of end-user data and resources into tenants to ensure data confidentiality and integrity. Maintaining secure access control is a complex and critical task in multi-tenant cloud environments, and researchers have proposed innovative methods for implementing security, privacy, and authenticity. However, several challenges remain. The proposed research addresses these challenges by presenting a layered approach to multi-tenancy in an enterprise-level cloud environment. The study has three main objectives: (1) to describe and analyze the complexities of enterprise-based multi-tenancy and proposed technical solutions in a tabular format;(2) to propose multi-tenant test cases across application, software, infrastructure, and network as a service environment;and (3) to present a collaborative, relational optimal key-based multi-tenancy access control model. The proposed access control model involves integrating various secure layers, such as intranet, extranet, intra-extranet, and using feasible relational optimal keys to provide secure multi-tenant access control among various tenants of single and multiple enterprises. This model provides secure data transmission by integrating various multi-layer data filters and secure blades. The layered multi-tenancy approach offers many benefits and technical feasibilities to avoid security and privacy issues in enterprise-based multi-tenant environments. The process achieved a 93% secure mapping rate to authenticate all IN and OUT data access transactions among the various multi-tenancy of similar and distinct enterprises. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Virtualization

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Modeling a fuzzy expert system for predicting customers’ behavioral intention for restaurant business through parallel prediction of perceived values and satisfactions

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International Journal of Information technology (Singapore) 2025年第2期17卷 1007-1014页

作者： Akhtaruzzaman, M. Shahryer, Fahim Halder, Sachitra Tanvin, Jamal Uddin Department of Computer Science and Engineering Military Institute of Science and Technology (MIST) Mirpur Cantonment Dhaka 1216 Bangladesh Quantum Robotics and Automation Research Group (QRARG) Dhaka Bangladesh

This paper focuses on the modeling of a recommendation process for the restaurant industry. It is a challenge job for a restaurant owner to ascertain the intentions of customers to evaluate their services, further improve their profit margin. So, the objective of this study is to design a fuzzy-based prediction model of customers’ intentions for a fine dining experience. To facilitate a comparative analysis and to identify the optimal approach, various membership functions (MF) and de-fuzzification methods are employed along with the analysis of the correlational behavior among various input subsets. Results have shown that CoA method with triangular MF are well enough to implement the model. The fuzzy-correlation analysis presents various levels of membership strengths for a same recommendation score in some cases. Moreover, the evaluation of the proposed model based on a custom dataset shows prominent results with as 0.938. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Customer intension Customer satisfaction FIS Perceived value Recommendation

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Wind disturbance compensated path-following control for fixed-wing UAVs in arbitrarily strong winds

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Chinese Journal of Aeronautics 2024年第2期37卷 431-445页

作者： Hao LU Lan GAO Yunda YAN Mingzhe HOU Chenliang WANG Hangzhou Innovation Institute Beihang UniversityHangzhou 310051China School of Information Science and Technology Hangzhou Normal UniversityHangzhou 311121China Department of Computer Science University College LondonLondon WC1E 6BTUnited Kingdom Center for Control Theory and Guidance Technology Harbin Institute of TechnologyHarbin 150001China School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China

Wind is the primary challenge for low-speed fixed-wing unmanned aerial vehicles to follow a predefined flight *** cope with various wind conditions,this paper proposes a wind disturbance compensated path following control strategy where the wind disturbance estimate is incorporated with the nominal guiding vector field to provide the desired airspeed direction for the *** the control input vector for the outer-loop kinematic subsystem needs to satisfy a magnitude constraint,a scaling mechanism is introduced to tune the proportions of the compensation and nominal ***,an optimization problem is formulated to pursue a maximum wind compensation in strong winds,which can be solved analytically to yield two scaling factors.A cascaded inner-loop tracking controller is also designed to fulfill the outer-loop wind disturbance compensated guiding vector ***-fidelity simulation results under sensor noises and realistic winds demonstrate that the proposed path following algorithm is less sensitive to sensor noises,achieves promising accuracy in normal winds,and mitigates the deviation from a desired path in wild winds.

关键词： Path following Guiding vector field Wind disturbance Disturbance rejection Fixed wings

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Region-Based Optimization in Continual Learning for Audio Deepfake Detection 39

Region-Based Optimization in Continual Learning for Audio De...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Chen, Yujie Yi, Jiangyan Fan, Cunhang Tao, Jianhua Ren, Yong Zeng, Siding Zhang, Chu Yuan Yan, Xinrui Gu, Hao Xue, Jun Wang, Chenglong Lv, Zhao Zhang, Xiaohui School of Computer Science and Technology Anhui University China Institute of Automation Chinese Academy of Sciences China Department of Automation Tsinghua University China Beijing National Research Center for lnformation Science and Technology Tsinghua University China

ISBN: (纸本)157735897X

Rapid advancements in speech synthesis and voice conversion bring convenience but also new security risks, creating an urgent need for effective audio deepfake detection. Although current models perform well, their effectiveness diminishes when confronted with the diverse and evolving nature of real-world deepfakes. To address this issue, we propose a continual learning method named Region-Based Optimization (RegO) for audio deepfake detection. Specifically, we use the Fisher information matrix to measure important neuron regions for real and fake audio detection, dividing them into four regions. First, we directly fine-tune the less important regions to quickly adapt to new tasks. Next, we apply gradient optimization in parallel for regions important only to real audio detection, and in orthogonal directions for regions important only to fake audio detection. For regions that are important to both, we use sample proportion-based adaptive gradient optimization. This region-adaptive optimization ensures an appropriate trade-off between memory stability and learning plasticity. Additionally, to address the increase of redundant neurons from old tasks, we further introduce the Ebbinghaus forgetting mechanism to release them, thereby promoting the model’s ability to learn more generalized discriminative features. Experimental results show our method achieves a 21.3 percent improvement in EER over the state-of-the-art continual learning approach RWM for audio deepfake detection. Moreover, the effectiveness of RegO extends beyond the audio deepfake detection domain, showing potential significance in other tasks, such as image recognition. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Deep learning

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Knowledge Graph Enhanced Large Language Model Editing

Knowledge Graph Enhanced Large Language Model Editing

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Zhang, Mengqi Ye, Xiaotian Liu, Qiang Ren, Pengjie Wu, Shu Chen, Zhumin School of Computer Science and Technology Shandong University China School of Computer Science Beijing University of Posts and Telecommunications China Institute of Automation Chinese Academy of Sciences China

ISBN: (纸本)9798891761643

Large language models (LLMs) are pivotal in advancing natural language processing (NLP) tasks, yet their efficacy is hampered by inaccuracies and outdated knowledge. Model editing emerges as a promising solution to address these challenges. However, existing editing methods struggle to track and incorporate changes in knowledge associated with edits, which limits the generalization ability of post-edit LLMs in processing edited knowledge. To tackle these problems, we propose a novel model editing method that leverages knowledge graphs for enhancing LLM editing, namely GLAME. Specifically, we first utilize a knowledge graph augmentation module to uncover associated knowledge that has changed due to editing, obtaining its internal representations within LLMs. This approach allows knowledge alterations within LLMs to be reflected through an external graph structure. Subsequently, we design a graph-based knowledge edit module to integrate structured knowledge into the model editing. This ensures that the updated parameters reflect not only the modifications of the edited knowledge but also the changes in other associated knowledge resulting from the editing process. Comprehensive experiments conducted on GPT-J and GPT-2 XL demonstrate that GLAME significantly improves the generalization capabilities of post-edit LLMs in employing edited knowledge. © 2024 Association for Computational Linguistics.

关键词： Knowledge graph

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DAO to HANOI via DeSci: AI Paradigm Shifts from AlphaGo to ChatGPT

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 877-897页

作者： Qinghai Miao Wenbo Zheng Yisheng Lv Min Huang Wenwen Ding Fei-Yue Wang School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China School of Computer Science and Artificial Intelligence Wuhan University of TechnologyWuhan 430070China Institute of Automation Chinese Academy of SciencesBeijing 100190China Institute of System Engineering Macao University of Science and TechnologyMacaoChina

From AlphaGo to ChatGPT,the field of AI has launched a series of remarkable achievements in recent ***,comparing,and summarizing these achievements at the paradigm level is important for future AI innovation,but has not received sufficient *** this paper,we give an overview and perspective on machine learning ***,we propose a paradigm taxonomy with three levels and seven dimensions from a knowledge ***,we give an overview on three basic and twelve extended learning paradigms,such as Ensemble Learning,Transfer Learning,etc.,with figures in unified *** further analyze three advanced paradigms,i.e.,AlphaGo,AlphaFold and ***,to enable more efficient and effective scientific discovery,we propose to build a new ecosystem that drives AI paradigm shifts through the decentralized science(DeSci)movement based on decentralized autonomous organization(DAO).To this end,we design the Hanoi framework,which integrates human factors,parallel intelligence based on a combination of artificial systems and the natural world,and the DAO to inspire AI innovations.

关键词： ChatGPT decentralized science(DeSci) decentralized autonomous organization(DAO) machine learning paradigm shift

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Wearable IMUs: Advancing Human Motion Analysis with Deep Learning 3rd

Wearable IMUs: Advancing Human Motion Analysis with Deep Le...

引用

3rd International Conference on Computing and Communication Networks, ICCCN 2023

作者： Das, Satyesh Das, Divyesh Parashar, Ashana Computer Science and Engineering Rajiv Gandhi Institute of Petroleum Technology Jais India Electronics and Computer Engineering Thapar Institute of Engineering and Technology Patiala India Mobile Automation Vodafone Intelligent Solutions Nashik India

ISBN: (纸本)9789819726707

In recent times, there has been a notable surge in the exploration of studying human body movements through the utilization of inertial measurement units that can be worn. This trend stems from its substantial impact on academic and industrial circles, encompassing applications ranging from portable healthcare solutions to sports and interfacing with computers through human interaction. Developing human activity recognition (HAR) systems that achieve remarkable accuracy in identification while relying on just one sensor remains an ongoing technological hurdle. In this paper, we explore both supervised and partially supervised approaches using convolutional neural networks and denoising autoencoders approaches. The goal of our study revolves around increasing the classification precision pertaining to previous related works and decreasing reliance on human-engineered features. Since the raw IMU data is composed of time series, it is split using a running window approach and segments of roughly one second of data are fed to the neural networks. The above approaches are tested with two different variations of the original dataset, obtained with data augmentation approaches, to balance uneven class distribution. The dataset contains measurements of one single IMU sensor positioned in the belt of different users performing seven actions: running, jumping, walking, falling, sitting, standing, and lying. The score of best 8-layer CNN-based system was 0.972. The worst represented class, falling, with just 2 min of recorded data, has an F1-score of 0.919. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Incremental Object Detection With Image-Level Labels

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第5期5卷 2331-2341页

作者： Yu, Qianzi Zhu, Kai Wang, Wen Cao, Yang Kang, Yu University of Science and Technology of China Department of Automation School of Information Science and Technology Hefei230026 China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230039 China Zhejiang University College of Computer Science and Technology State Key Lab of CAD&CG Hangzhou310027 China University of Science and Technology of China School of Information Science and Technology Hefei230026 China

Incremental object detection (IOD) aims to achieve simultaneous prediction of old and new samples on localization and classification when new concepts are provided. It is a challenging task due to the need for a joint interpretation of semantic and spatial information. While the existing work accomplishes the detector generalization with the help of annotated sample classes and bounding boxes, this article presents one astonishing finding on the unnecessity of new bounding boxes, which will significantly reduce the annotation cost and condition constraints in IOD. To enable the incremental detection process with image-level labels, we propose a multibranch decoupling scheme in which the representation, classification, and regression branches are customized to accommodate new concepts with class labels with different semantic levels. The regression branch is first frozen to ensure generalizable localization while maintaining the stability of representation optimization. Then, the representation branch is gradient-inverted to estimate the distribution drift from the old object-level set to the new image-level one, improving the effectiveness of feature updates in the absence of homogeneous semantic and spatial supervision. Finally, the classification branch is calibrated to balance the contribution of different images to the gradient, in which the frequency ratios of old instances and new images are calculated as a postprocessing factor for the logits margin. Extensive experiments on two standard incremental detection benchmarks demonstrate the above-par recognition and localization performance while reducing the burden on new class annotation, outperforming state-of-the-art methods by 2% and 3%, respectively. © 2023 IEEE.

关键词： Feature extraction

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Clinical Learning Order-Guided Deep Neural Network for Brain Tumor Segmentation

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IEICE Transactions on Information and Systems 2025年第6期E108.D卷 615-628页

作者： Zhang, Pengfei Wang, Jinke Cheng, Yuanzhi Tamura, Shinichi School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China School of Automation Harbin University of Science and Technology Harbin150080 China Weihai Research Institute Harbin University of Science and Technology Weihai264300 China School of Information Science and Technology Qingdao University of Science and Technology Shandong Qingdao266061 China Graduate School of Medicine Osaka University Suita-shi565-0871 Japan NBL Technovator Co. Ltd. Sennan590-0522 Japan

In this paper, we present an innovative multi-label local-to-global learning segmentation (MLLGL-Seg) neural network model for brain tumor segmentation. Our framework is grounded on a clinical learning order from the whole tumor (WT) to the tumor core (TC) and then to the enhanced tumor (ET). Thus, we first propose a multi-label segmentation network to embed the clinical learning sequence of WT-TC-ET during training. We then introduce a local-to-global learning algorithm and integrate it into multi-label to construct the MLLGL-Seg model. In addition, considering the hierarchical structure of the output tumor region, we further perform hierarchical consistency transformation on the network output to ensure that it complies with hierarchical constraints. The novelty of this paper lies in the proposal of the MLLGL-Seg by introducing curriculum learning based on category space into segmentation, in the construction of a learning sequence based on boundary difficulty and category similarity based on clinical experience, and in the device of learning sequences based on anti-similarity and label noise. Experimental results show that the proposed method achieves one of the most competitive results in brain tumor segmentation accuracy on three publicly available datasets. Copyright © 2025 The institute of Electronics, Information and Communication Engineers.

关键词： Curricula

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Event-triggered adaptive neural network control design for stochastic nonlinear systems with output constraint

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International Journal of Adaptive Control and Signal Processing 2024年第1期38卷 342-358页

作者： Shen, Fei Wang, Xinjun Pan, Xinxin School of Electrical and Automation Changshu Institute of Technology Suzhou China College of Information Science and Engineering Shandong Normal University Jinan China College of Computer and Information Hohai University Nanjing China

This paper is concerned with the adaptive neural network event-triggered control (ETC) problem for stochastic nonlinear systems with output constraint. The influence of stochastic disturbance inevitably exists in many practical systems, which leads to system instability. Meanwhile, a novel tan type barrier Lyapunov function (Tan-BLF) structure is proposed to deal with the constraint requirements of stochastic systems. In the sense of probability, the output constraints will not be violated during the operation of the system. In addition, the ETC strategy is adopted to reduce the burden of communication. The asymptotic stability of the closed-loop system is guaranteed without violating output constraints. Meanwhile, the tracking error converges to a small region of the origin. Two simulations results demonstrate the effectiveness of theoretical analysis. © 2023 John Wiley & Sons Ltd.

关键词： Lyapunov functions

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