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10th International Symposium on System security, Safety, and Reliability, ISSSR 2024

作者： Liang, Xiaoyan He, Jingsha Zhu, Nafei Beijing University of Technology School of Software Beijing China Software Technology and Network Security Lab Beijing China

ISBN: (纸本)9798350362930

In the cloud server, ranked searchable encryption allows the cloud server to search for the first k most relevant documents based on the correlation scores between the query keyword and the document. OPE (Orderpreserving encryption) is a widely used technique in sorted searchable encryption to protect privacy. However, recent studies have revealed potential privacy vulnerabilities in OPE schemes. To address this issue, our research paper proposes a novel approach called DP-SSE (Differential Privacy-based Ranked Searchable Encryption). By incorporating differential privacy into ranked searchable encryption, our proposed approach offers a more robust and secure solution to privacy preservation, mitigating the potential privacy leakage associated with traditional OPEbased techniques. Finally, the security proof, efficiency and accuracy analysis show that the scheme is feasible. © 2024 IEEE.

关键词： Cloud computing

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Secure Searchable Encryption Based on Local Differential Privacy

Secure Searchable Encryption Based on Local Differential Pri...

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International Symposium on System and software Reliability (ISSSR)

作者： Xiaoyan Liang Jingsha He Nafei Zhu School of Software Beijing University of Technology Beijing China Software Technology and Network Security Lab Beijing China

ISBN: (数字)9798350362930

ISBN: (纸本)9798350362947

关键词： Privacy Differential privacy Correlation Accuracy Noise Search problems Encryption

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A Novel Meta-Hierarchical Active Inference Reinforcement Learning Approach for QoS-Driven Resource Allocation in Dynamic Clouds

A Novel Meta-Hierarchical Active Inference Reinforcement Lea...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Xian, Peijie He, Ying Yu, F. Richard Du, Jianbo Shenzhen Univ. College of Computer Science and Software Engineering Shenzhen China Xi'an Univ. of Posts & Telecomm. Shaanxi Key Lab. of Information Comm. Network and Security Shaanxi Xi'an China Carleton Univ. School of Information Technology OttawaON Canada

ISBN: (纸本)9798350351255

The cloud computing environment is highly dynamic due to a variety of external factors such as seasonal changes, market trends, and social events. In this case, tenant behavior patterns exhibit significant variability. Therefore, the cloud computing resource allocation model must continuously adapt to these changes. To this end, we propose an adaptive algorithm for dynamic cloud resource allocation based on meta-hierarchical active inference reinforcement learning (MHAIRL). The algorithm combines active inference with meta-hierarchical reinforcement learning. It improves the overall performance of the algorithm, as well as quickly adapts to environmental changes, and improves the generalization performance. In addition, we design a novel polling scheduling framework combined with long short-term memory (LSTM) network. The framework ensures scheduling fairness and flexibility while greatly reducing the state and action space dimensions of the agent. Extensive simulation results show that our method outperforms baseline algorithms in quality of service (QoS) metrics and significantly improves system performance in highly dynamic cloud resource allocation. © 2024 IEEE.

关键词： Active learning

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Early Detection of Autism in Children Using Transfer Learning

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Intelligent Automation & Soft Computing 2023年第4期36卷 11-22页

作者： Taher M.Ghazal Sundus Munir Sagheer Abbas Atifa Athar Hamza Alrababah Muhammad Adnan Khan School of Information Technology Skyline University CollegeSharjah1797UAE Network and Communication Technology Lab Center for Cyber SecurityFaculty of Information Science and Technology Universiti Kebangsaan Malaysia43600Malaysia School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Lahore Garrison University Lahore54000Pakistan Department of Computer Science COMSATS University Islamabad–Lahore CampusLahore54000Pakistan Department of Software Pattern Recognition and Machine Learning LabGachon UniversitySeongnamGyeonggido13120Korea

Autism spectrum disorder(ASD)is a challenging and complex neurodevelopment syndrome that affects the child’s language,speech,social skills,communication skills,and logical thinking *** early detection of ASD is essential for delivering effective,timely *** facial features such as a lack of eye contact,showing uncommon hand or body movements,bab-bling or talking in an unusual tone,and not using common gestures could be used to detect and classify ASD at an early *** study aimed to develop a deep transfer learning model to facilitate the early detection of ASD based on facial fea-tures.A dataset of facial images of autistic and non-autistic children was collected from the Kaggle data repository and was used to develop the transfer learning AlexNet(ASDDTLA)*** model achieved a detection accuracy of 87.7%and performed better than other established ASD detection ***,this model could facilitate the early detection of ASD in clinical practice.

关键词： Autism spectrum disorder convolutional neural network loss rate transfer learning AlexNet deep learning

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Smart Energy Management System Using Machine Learning

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Computers, Materials & Continua 2024年第1期78卷 959-973页

作者： Ali Sheraz Akram Sagheer Abbas Muhammad Adnan Khan Atifa Athar Taher M.Ghazal Hussam Al Hamadi School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnamGyeonggido13120Korea Department of Computer Science COMSATS University IslamabadLahore CampusLahore54000Pakistan School of Information Technology Skyline University CollegeSharjah1797United Arab Emirates Network and Communication Technology Lab Center for Cyber SecurityFaculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangiSelangor43600Malaysia College of Engineering and IT University of DubaiDubaiUnited Arab Emirates

Energy management is an inspiring domain in developing of renewable energy ***,the growth of decentralized energy production is revealing an increased complexity for power grid managers,inferring more quality and reliability to regulate electricity flows and less imbalance between electricity production and *** major objective of an energy management system is to achieve optimum energy procurement and utilization throughout the organization,minimize energy costs without affecting production,and minimize environmental *** energy management is an essential and complex subject because of the excessive consumption in residential buildings,which necessitates energy optimization and increased user *** address the issue of energy management,many researchers have developed various frameworks;while the objective of each framework was to sustain a balance between user comfort and energy consumption,this problem hasn’t been fully solved because of how difficult it is to solve *** inclusive and Intelligent Energy Management System(IEMS)aims to provide overall energy efficiency regarding increased power generation,increase flexibility,increase renewable generation systems,improve energy consumption,reduce carbon dioxide emissions,improve stability,and reduce energy *** Learning(ML)is an emerging approach that may be beneficial to predict energy efficiency in a better way with the assistance of the Internet of Energy(IoE)*** IoE network is playing a vital role in the energy sector for collecting effective data and usage,resulting in smart resource *** this research work,an IEMS is proposed for Smart Cities(SC)using the ML technique to better resolve the energy management *** proposed system minimized the energy consumption with its intelligent nature and provided better outcomes than the previous approaches in terms of 92.11% accuracy,and 7.89% miss-rate.

关键词： Intelligent energy management system smart cities machine learning

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An Ensemble of CapsNet and KSVM Using Feature Fusion: Application to COVID-19 Detection 3

An Ensemble of CapsNet and KSVM Using Feature Fusion: Applic...

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3rd International Conference on Pattern Recognition and Machine Learning, PRML 2022

作者： Aggrey, Esther Stacy E. B. Zhen, Qin Kodjiku, Seth Larweh Aidoo, Evans Fiasam, Linda Delali Mensah, Acheampong Edward University of Electronic Science & Technology of China School of Information & Software Engineering Chengdu China University of Electronic Science & Technology of China School of Information & Software Engineering Network and Data Security Key Lab of Sichuan Province Chengdu China Zhejiang Gongshang University School of Computer & Information Engineering Hangzhou China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

COVID-19 virus is a major worldwide pandemic that is growing at a fast pace throughout the world. The usual approach for diagnosing COVID-19 is the use of a real-time polymerase chain reaction (RT-PCR) based nucleic acid test. However, RT-PCR has lower sensitivity in the early phases of COVID-19 detection. Recent studies have indicated that X-ray images may be useful throughout the early detection of the virus. Human screening has been shown to be cost-effective, susceptible to mistakes, and time-demanding, which has sparked an interest in using Convolutional Neural networks (CNNs) to automate the process. CNNs, on the other hand, fail to view the exact placement of features as advantageous in medical imaging. Furthermore, for successful training and prediction, CNNs need a huge quantity of datasets. CNNs are rapidly reducing picture resolution, resulting in worsening accuracy in classification. We used newly created capsule networks (CapsNets) in our study to circumvent these disadvantages. The primary contribution is to improve the identification of SARS-CoV-2 with images obtained from X-ray by coupling capsule network with a kernel support vector machine (KSVM). The technique was evaluated using a publicly available dataset, and the proposed model shows that the accuracy of the CapsNet-KSVM based model is improved by 94.6% accuracy, 95% sensitivity, and 98% specificity, which outperforms the traditional CNN and other existing ensemble models. The proposed CapsNet-KSVM based system can be employed to identify the presence of COVID-19 in the human body using X-ray images. © 2022 IEEE.

关键词： COVID-19

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Single and Mitochondrial Gene Inheritance Disorder Prediction Using Machine Learning

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Computers, Materials & Continua 2022年第10期73卷 953-963页

作者： Muhammad Umar Nasir Muhammad Adnan Khan Muhammad Zubair Taher MGhazal Raed A.Said Hussam Al Hamadi Riphah School of Computing&Innovation Faculty of ComputingRiphah International University Lahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13120GyeonggidoKorea Faculty of Computing Riphah International UniversityIslamabad45000Pakistan School of Information Technology Skyline University CollegeSharjah1797UAE Network and Communication Technology Lab Center for Cyber SecurityFaculty of Information Science and Technology Universiti Kebangsaan Malaysia43600Malaysia Canadian University Dubai00000UAE Cyber-Physical Systems Khalifa UniversityAbu Dhabi127788UAE

One of the most difficult jobs in the post-genomic age is identifying a genetic disease from a massive amount of genetic ***,the complicated genetic disease has a very diverse genotype,making it challenging to find genetic *** is a challenging process since it must be completed effectively and *** research article focuses largely on which patients are more likely to have a genetic disorder based on numerous medical *** the patient’s medical history,we used a genetic disease prediction algorithm that predicts if the patient is likely to be diagnosed with a genetic *** predict and categorize the patient with a genetic disease,we utilize several deep and machine learning techniques such as Artificial neural network(ANN),K-nearest neighbors(KNN),and Support vector machine(SVM).To enhance the accuracy of predicting the genetic disease in any patient,a highly efficient approach was utilized to control how the model can be *** predict genetic disease,deep and machine learning approaches are *** most productive tool model provides more precise *** simulation results demonstrate that by using the proposed model with the ANN,we achieve the highest model performance of 85.7%,84.9%,84.3%accuracy of training,testing and validation *** approach will undoubtedly transform genetic disorder prediction and give a real competitive strategy to save patients’lives.

关键词： Genetic disorder machine learning deep learning single gene inheritance gene disorder mitochondrial gene inheritance disorder

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OAA: An Abstraction for Efficient Accelerator Adaptation in Deep Learning Frameworks

OAA: An Abstraction for Efficient Accelerator Adaptation in ...

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International Joint Conference on Neural networks (IJCNN)

作者： Zhengxian Lu Chengkun Du Xueshuo Xie Qiankun Dong Cheng Gong Tao Li College of Computer Science Nankai University Tianjin China Tianjin Key Laboratory of Network and Data Security Technology Tianjin China Huawei Technologies Zhejiang China Haihe Lab of ITAI Tianjin China College of Software Nankai University Tianjin China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Deep learning frameworks rely on specific runtime and computation libraries to rewrite the backend for the adaptation of specialized accelerators, which is inefficient and hard to guarantee performance. This paper solves the issue by proposing an Operator Adaptation Abstraction (OAA) that lies between the framework and the libraries. In addition, this paper optimizes training performance based on the hardware characteristics of the accelerator. We designed experiments based on Ascend 910 and OneFlow to verify the effectiveness of OAA. Our adaptation includes more than 50 Ascend operators, achieving up to 2.0x throughput compared to the official adaptation version of PyTorch. The experiments validate that the adaptation method proposed in this paper can effectively retain the advantages of both the framework and the accelerator.

关键词： Deep learning Training Runtime Neural networks Throughput Libraries Computational efficiency

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Deep Feature Surgery: Towards Accurate and Efficient Multi-Exit networks

arXiv

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arXiv 2024年

作者： Gong, Cheng Chen, Yao Luo, Qiuyang Lu, Ye Li, Tao Zhang, Yuzhi Sun, Yufei Zhang, Le College of Software Nankai University China College of Computer Science Nankai University China National University of Singapore Singapore School of Information and Communication Engineering University of Electronic Science and Technology of China China HAIHE Lab of ITAI China Tianjin Key Laboratory of Network and Data Security Technology China Key Laboratory of Data and Intelligent System Security Ministry of Education

Multi-exit network is a promising architecture for efficient model inference by sharing backbone networks and weights among multiple exits. However, the gradient conflict of the shared weights results in sub-optimal accuracy. This paper introduces Deep Feature Surgery (DFS), which consists of feature partitioning and feature referencing approaches to resolve gradient conflict issues during the training of multi-exit networks. The feature partitioning separates shared features along the depth axis among all exits to alleviate gradient conflict while simultaneously promoting joint optimization for each exit. Subsequently, feature referencing enhances multi-scale features for distinct exits across varying depths to improve the model accuracy. Furthermore, DFS reduces the training operations with the reduced complexity of backpropagation. Experimental results on Cifar100 and ImageNet datasets exhibit that DFS provides up to a 50.00% reduction in training time and attains up to a 6.94% enhancement in accuracy when contrasted with baseline methods across diverse models and tasks. Budgeted batch classification evaluation on MSDNet demonstrates that DFS uses about 2× fewer average FLOPs per image to achieve the same classification accuracy as baseline methods on Cifar100. The code is available at https: //***/GongCheng1919/dfs. Copyright © 2024, The Authors. All rights reserved.

关键词： Budget control

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SemiCD-VL: Visual-Language Model Guidance Makes Better Semi-supervised Change Detector

arXiv

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arXiv 2024年

作者： Li, Kaiyu Cao, Xiangyong Deng, Yupeng Song, Jiayi Liu, Junmin Meng, Deyu Wang, Zhi School of Software Engineering Xi’an Jiaotong University Xi’an710049 China School of Computer Science and Technology Ministry of Education Key Lab For Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an710049 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Mathematics and Statistics Ministry of Education Key Lab of Intelligent Networks and Network Security Xi’an Jiaotong University Shaanxi Xi’an China Guangdong Guangzhou China

Change Detection (CD) aims to identify pixels with semantic changes between images. However, annotating massive numbers of pixel-level images is labor-intensive and costly, especially for multi-temporal images, which require pixel-wise comparisons by human experts. Considering the excellent performance of visual language models (VLMs) for zero-shot, open-vocabulary, etc. with prompt-based reasoning, it is promising to utilize VLMs to make better CD under limited labeled data. In this paper, we propose a VLM guidance-based semisupervised CD method, namely SemiCD-VL. The insight of SemiCD-VL is to synthesize free change labels using VLMs to provide additional supervision signals for unlabeled data. However, almost all current VLMs are designed for single-temporal images and cannot be directly applied to bi- or multi-temporal images. Motivated by this, we first propose a VLM-based mixed change event generation (CEG) strategy to yield pseudo labels for unlabeled CD data. Since the additional supervised signals provided by these VLM-driven pseudo labels may conflict with the original pseudo labels from the consistency regularization paradigm (e.g. FixMatch), we propose the dual projection head for de-entangling different signal sources. Further, we explicitly decouple the bi-temporal images semantic representation through two auxiliary segmentation decoders, which are also guided by VLM. Finally, to make the model more adequately capture change representations, we introduce contrastive consistency regularization by constructing feature-level contrastive loss in auxiliary branches. Extensive experiments show the advantage of SemiCD-VL. For instance, SemiCD-VL improves the FixMatch baseline by +5.3 IoUc on WHU-CD and by +2.4 IoUc on LEVIR-CD with 5% labels, and SemiCD-VL requires only 5% to 10% of the labels to achieve performance similar to the supervised methods. In addition, our CEG strategy, in an un-supervised manner, can achieve performance far superior to state-of-t

关键词： Visual languages

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