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Semantic and secure search over encrypted outsourcing cloud based on BERT

Frontiers of Computer Science 2022年第2期16卷 152-159页

作者： Zhangjie FU Yan WANG Xingming SUN Xiaosong ZHANG Engineering Research Center of Digital Forensice Ministry of EducationNanjing University of Information Science and TechnologyNanjing 210044China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 210044China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China

Searchable encryption provides an effective way for data security and privacy in cloud *** can retrieve encrypted data in the cloud under the premise of protecting their own data security and ***,most of the current content-based retrieval schemes do not contain enough semantic information of the article and cannot fully reflect the semantic information of the *** this paper,we propose two secure and semantic retrieval schemes based on BERT(bidirectional encoder representations from transformers)named SSRB-1,*** training the documents with BERT,the keyword vector is generated to contain more semantic information of the documents,which improves the accuracy of retrieval and makes the retrieval result more consistent with the user’s ***,through testing on real data sets,it is shown that both of our solutions are feasible and effective.

关键词： cloud computing semantic search BERT model searchable encryption

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EMARec: a sequential recommendation with exponential moving average

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Neural Computing and Applications 2024年第21期36卷 12917-12933页

作者： Chen, Rui Wang, Zonglin Tang, Cundong Zhang, Jianwei Li, Pu Kong, Xiangjie Huang, Min College of Software Engineering Zhengzhou University of Light Industry Zhengzhou450001 China Henan Joint International Research Laboratory of Computer Animation Implementation Technologies Zhengzhou450001 China School of Information Science and Technology Northwest University Xi’an710127 China Faculty of Information and Engineering Xuchang Vocational Technical College Xuchang461000 China College of Computer Science and Technology Zhejiang University of Technology Hangzhou310023 China

Capturing dynamic preference features from user historical behavioral data is widely applied to improve the accuracy of recommendations in sequential recommendation tasks. However, existing deep neural network-based sequential recommendation methods often ignore the noise information in user behavioral data that influence recommendation effectiveness, making recommendation models sensitive to noisy data. Additionally, these deep learning-based recommendation models often require a large number of parameters to capture user behavior patterns, leading to higher time complexity and susceptibility to overfitting due to noise fluctuations. To address these issues, in this paper, we apply the moving average concept from the field of time-series analysis to sequential recommendation tasks. The approach smooths sequence data, removes noise, making the data more stable and reliable, increases the model’s insensitivity to noise information, and better understands the evolution of user interests and behavioral patterns. Specifically, in our experiments, the moving average algorithm effectively denoises sequential data with low time complexity. Therefore, we propose a Sequential Recommendation with Exponential Moving Average with MLP architecture that makes the model more competitive in terms of time complexity. Experimental results conducted on two datasets demonstrate that EMARec outperforms state-of-the-art sequential recommendation methods across various common evaluation metrics. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Recommender systems

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Real-Time VPN Anomaly Detection System 25

Real-Time VPN Anomaly Detection System

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25th International Arab Conference on information technology, ACIT 2024

作者： Alamleh, Amneh Nasir, Ola Hannoun, Ayman Abu Darwish, Mustafa Alallawi, Bashar Alshurafa, Zaid Khouj, Mohammed Zarqa University Faculty of Information Technology Department of Artificial Intelligence Zarqa Jordan Zarqa University Faculty of Information Technology Department of Internet Technology Zarqa Jordan Zarqa University Faculty of Information Technology Department of Software Engineering Zarqa Jordan University of Business and Technology Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

This paper implements a network traffic analysis system with a focus on cybersecurity applications. The novelty lies in its combined approach of real-time traffic monitoring and machine learning-driven anomaly detection. Using the Scapy library, the core system extracts features such as packet rates, inter-arrival times, port usage, and TTL distributions from live network traffic. Four classifiers were evaluated for their effectiveness in detecting potential threats or unusual patterns, such as VPN usage. The Random Forest classifier achieved the highest accuracy at (0.976), followed by Gradient Boosting with an accuracy of (0.967). Logistic Regression demonstrated an accuracy of (0.902), while the Support Vector Machine (SVM) recorded a lower accuracy of (0.694). These results underscore the system's potential to accurately identify network anomalies, making it a valuable tool for cybersecurity professionals. © 2024 IEEE.

关键词： Cybersecurity

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Fortifying Healthcare Data Security in the Cloud:A Comprehensive Examination of the EPM-KEA Encryption Protocol

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Computers, Materials & Continua 2024年第5期79卷 3397-3416页

作者： Umi Salma Basha Shashi Kant Gupta Wedad Alawad SeongKi Kim Salil Bharany Computer Science and Information Technology Jazan UniversityJazanSaudi Arabia CSE Eudoxia Research UniversityNew CastleUSA Department of Information Technology College of Computer ScienceQassim UniversityBuraydahSaudi Arabia National Center of Excellence in Software Sangmyung UniversitySeoulKorea Department of Computer Science and Engineering Lovely Professional UniversityPhagwaraPunjabIndia

A new era of data access and management has begun with the use of cloud computing in the healthcare *** the efficiency and scalability that the cloud provides, the security of private patient data is still a majorconcern. Encryption, network security, and adherence to data protection laws are key to ensuring the confidentialityand integrity of healthcare data in the cloud. The computational overhead of encryption technologies could leadto delays in data access and processing rates. To address these challenges, we introduced the Enhanced ParallelMulti-Key Encryption Algorithm (EPM-KEA), aiming to bolster healthcare data security and facilitate the securestorage of critical patient records in the cloud. The data was gathered from two categories Authorization forHospital Admission (AIH) and Authorization for High Complexity *** use Z-score normalization forpreprocessing. The primary goal of implementing encryption techniques is to secure and store massive amountsof data on the cloud. It is feasible that cloud storage alternatives for protecting healthcare data will become morewidely available if security issues can be successfully fixed. As a result of our analysis using specific parametersincluding Execution time (42%), Encryption time (45%), Decryption time (40%), Security level (97%), and Energyconsumption (53%), the system demonstrated favorable performance when compared to the traditional *** suggests that by addressing these security concerns, there is the potential for broader accessibility to cloudstorage solutions for safeguarding healthcare data.

关键词： Cloud computing healthcare data security enhanced parallel multi-key encryption algorithm(EPM-KEA)

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Distributed Hierarchical Multi-Agent Reinforcement Learning for Real-World Adversarial Team Games 21

Distributed Hierarchical Multi-Agent Reinforcement Learning ...

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21st International Computer Conference on Wavelet Active Media technology and information Processing, ICCWAMTIP 2024

作者： Wei, Li Bei, Hui Ke, Lu School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9798331519254

Real-world applications often require teams of agents to coordinate strategies against adversaries. However, existing MARL models overlook resource constraints and task complexity, limiting their practical use. To address this, we propose the Distributed Hierarchical Multi-agent Learning (DHML) framework, which uses distributed neural networks for efficient computation. A mean-field mechanism and an attentional communication module are further incorporated for scalability, and a hierarchical task decomposition is adopted based on the option-critic framework for handling complexity. Experiments on a simulation platform modeling real communication networks demonstrate DHML's effectiveness across various adversarial scenarios. © 2024 IEEE.

关键词： Adversarial machine learning

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Empowering Precision Machine: Deep Learning Technique for Lung Cancer Diagnosis 2

Empowering Precision Machine: Deep Learning Technique for Lu...

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2nd International Conference on Computing and Data Analytics, ICCDA 2024

作者： Jamil, Danish Shah, Syed Mehr Ali Khan, Muhammad Numan Ali Aljarwani, Fatma Mahfoodh Mohammed University of Santiago de Compostela Spain Sir Syed University of Engineering & Technology Department of Software Engineering India College of Computing & Information Sciences University of Technology and Applied Sciences Musandam Oman

ISBN: (纸本)9798331542573

This research paper delves into the realm of lung cancer diagnosis, a critical area in modern healthcare owing to its substantial global impact. Leveraging cutting-edge technology and machine learning methodologies, specifically employing the VGG16 model, our study aims to enhance the early detection of lung cancer through the analysis of CT scan images. The dataset utilized comprises CT scan images sourced from the Lung Image Database Consortium (LIDC/IDRI) Image Dataset, categorized into three classes: malignant, benign, and normal. By harnessing the capabilities of the VGG16 model, we seek to accurately predict the classification of CT scan images into these distinct categories. Through rigorous experimentation and analysis, our findings shed light on the efficacy of this approach in aiding medical professionals in the timely identification and classification of lung cancer cases. Ultimately, our research contributes to advancing the field of medical imaging and underscores the potential of deep learning in revolutionizing healthcare diagnostics. © 2024 IEEE.

关键词： Lung cancer

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Emotional Synchronization for Audio-Driven Talking-Head Generation 24

Emotional Synchronization for Audio-Driven Talking-Head Gene...

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24th IEEE International Conference on Data Mining, ICDM 2024

作者： Zhang, Zhao Luo, Yan Zuo, Zhichao Hong, Richang Yang, Yi Wang, Meng School of Computer Science and Information Engineering University of Technology Hefei China Yunnan Key Laboratory of Software Engineering Yunan China School of Computer Science and Technology Zhejiang University Hangzhou China

ISBN: (纸本)9798331506681

Audio-driven talking-head synthesis has become a significant focus in the field of virtual human applications. However, existing methodologies face challenges in effectively synchronizing audio and video, especially in maintaining emotional consistency. Additionally, there is a notable inefficiency in leveraging emotional prompts to guide expression generation. To address these limitations, this paper introduces an Emotion Synchronized audio-driven Talking-head synthesis (EST) approach. The EST approach aims to enhance the emotion-agnostic talking-head models by enabling emotion control, and it incorporates a diffusion module to learn diverse latent rep-resentations. Furthermore, EST utilizes null-text embedding to align the latent code with emotional prompts. Additionally, a novel Sync Attention Block (SAB) is developed to broaden the spatial perceptual field, thus preventing the loss of critical information. Extensive experiments demonstrate the effectiveness of the EST method, showcasing state-of-the-art performance across widely-adopted datasets. Moreover, the EST approach exhibits exceptional generalization capabilities, even in scenarios where emotional training videos are unavailable. © 2024 IEEE.

关键词： Synchronization

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On Automatic Generation of Executable Domain Models for Domain-Driven Design 15

On Automatic Generation of Executable Domain Models for Doma...

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15th International Conference on Knowledge and Systems engineering, KSE 2023

作者： Le, Van-Vinh Be, Nghia-Trong Dang, Duc-Hanh University of Engineering and Technology Vietnam National University Department of Software Engineering Hanoi Viet Nam Vinh University of Technology Education Department of Information Technology Viet Nam

ISBN: (纸本)9798350329742

Domain-driven design (DDD) aims to iteratively develop software based on a realistic model of the problem domain. This approach requires a thorough understanding of the domain's requirements and ensuring that the implementation is technically feasible for deployment. Currently, such a domain model is often represented using programming languages such as Java and is manually defined by taking as input a specification in natural language and UML/OCL diagrams. It is very tedious to obtain a domain model in that way. This paper introduces a technique to automatically generate a domain model from a high-level specification in the form of UML/OCL diagrams. Specifically, we take as input the Activity diagram, which incorporates a Class diagram to describe the domain, and transforms it into an AGL+ specification. The generated AGL+ specification is executable based on the Java software framework jDomainApp. We develop tool support for our method using the Acceleo transformation language and illustrate our method with a case study. © 2023 IEEE.

关键词： Specifications

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FedQP: Towards Accurate Federated Learning using Quadratic Programming Guided Mutation 36

FedQP: Towards Accurate Federated Learning using Quadratic P...

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36th International Conference on software engineering and Knowledge engineering, SEKE 2024

作者： Weng, Jiawen Xia, Zeke Li, Ran Hu, Ming Chen, Mingsong Software/Hardware Co-Design Engineering Research Center Ministry of Education East China Normal University Shanghai China School of Software Nanjing University of Information Science and Technology Nanjing China Research Lab for Intelligent Software Engineering Singapore Management University Singapore

ISBN: (纸本)1891706594

Due to the advantages of privacy-preserving, Federated Learning (FL) is widely used in distributed machine learning systems. However, existing FL methods suffer from low-inference performance caused by data heterogeneity. Specifically, due to heterogeneous data, the optimization directions of different local models vary greatly, making it difficult for the traditional FL method to get a generalized global model that performs well on all clients. As one of the state-of-the-art FL methods, the mutation-based FL method attempts to adopt a stochastic mutation strategy to guide the model training towards a well-generalized area (i.e., flat area in the loss landscape). Specifically, mutation allows the model to shift within the solution space, providing an opportunity to escape areas with poor generalization (i.e., sharp area). However, the stochastic mutation strategy easily results in diverse optimal directions of mutated models, which limits the performance of the existing mutation-based FL method. To achieve higher performance, this paper proposes a novel mutation-based FL approach named FedQP, utilizing a quadratic programming strategy to regulate the mutation directions wisely. By biasing the model mutation towards the direction of gradient update rather than traditional random mutation, FedQP can effectively guide the model to optimize towards a well-generalized area (i.e., flat area). Experiments on multiple well-known datasets show that our quadratic programming-guided mutation strategy effectively improves the inference accuracy of the global model in various heterogeneous data scenarios. © 2024 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Federated learning

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A Weighted Topic Modeling Approach Based on Word Embedding 2

A Weighted Topic Modeling Approach Based on Word Embedding

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2nd International Conference on Artificial Intelligence and Computer information technology, AICIT 2023

作者： Yuan, Conghui Zhang, Shengnan Information Science and Engineering School Shenyang University of Technology Shenyang China Software School Shenyang University of Technology Shenyang China

ISBN: (纸本)9798350302950

Topic modeling is commonly used to discover potential semantic structures in different domain corpora, and it is an essential tool for semantic retrieval, feature extraction, and target classification of large amounts of text. As a widely used probabilistic topic generation model, Latent Dirichlet Allocation (LDA) is powerful in extracting salient topics, but LDA makes assumptions based on bag-of-words model, which ignores the semantic information implied between words, makes inter-word disorder in the process of topic extraction, and results in poor quality of generated topics. In this paper, we propose a weighted topic modeling method based on word embedding, which first constructs the directed semantic graph between feature words, calculates weight values of nodes, and then improves LDA topic model by combining word embedding. The experimental results show that the presented method can effectively enhance the contextual semantic association and improve the distinction between topics compared with TF-LDA and ETM. © 2023 IEEE.

关键词： Embeddings

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