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EnhancedNet Deep Learning Model for Plant Disease Classification

EnhancedNet Deep Learning Model for Plant Disease Classifica...

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2024 1st International Conference on Logistics (ICL)

作者： Muhammad Umer Ali Javed Hussain Dawood Department of Software Engineering University of Engineering and Technology Taxila Pakistan School of Computing Skyline University College Sharjah United Arab Emirates

ISBN: (数字)9798331518844

ISBN: (纸本)9798331518851

Agricultural production vitally contribute to country's economic growth and food needs; however, it depends upon the production and quality of crops. Plants are affected easily due to climate changes and low-quality water. Plants health is examined manually by farmers or pathologists; however, it is not viable to examine each plant regularly due to larger areas of field and limited human resource. If the plant disease gets diagnosed at early stage, the precautionary measures can be taken on time and quality can be improved. There are several solutions available based on modern machine learning and deep learning techniques however, precise prediction and classification is challenging due to the limitations of low light or blurry images. We propose a deep learning-based approach which classifies the healthy and diseases plant using blurry images. Our method addresses the problem using improved layers activation, lesser parameters, using radiant pixels and active neurons. We improved the classification of smaller size and blur images by adding the pooling and dropout layers. Our solution addresses the vanishing gradient and high validation loss problems using a smaller number of parameters as compared to other convolutional neural networks. We evaluated the performance of the proposed method on plant village dataset consisting of a diverse collection of 20600 images. We achieved overall 98.3 % accuracy by minimizing the vanishing gradient, high validation loss and overfitting problem.

关键词： Deep learning Training Plant diseases Accuracy Neurons Production Time measurement Real-time systems Mobile applications Water resources

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Blockchain Development 7th

Blockchain Development

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7th International Conference on Smart computing and Communication, SmartCom 2022

作者： Xie, Siqi Cai, Jiahong Zhu, Hangyu Yang, Ce Chen, Lin Xiao, Weidong School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan411201 China Guangdong Financial High-Tech Zone "Blockchain +" Fintech Research Institute Foshan528253 China School of Software Engineering Xiamen University of Technology Xiamen361024 China

ISBN: (纸本)9783031281235

In recent years, blockchain research has set off an upsurge in academia, and it is called the next generation of value Internet. Because of its decentralization, anonymity, security, immutability, traceability and other characteristics, blockchain is gradually accepted and developed by people. With the deepening of research and the integration of technologies such as deep learning, blockchain has gradually been applied to various fields such as credit reporting, government, medical care, and industrial Internet of Things, not just the initial virtual currency field. This article mainly discusses the three important stages of blockchain public chain development, namely Bitcoin, Ethereum, and meta-verse, and introduces some basic supporting technologies of blockchain, as well as the research status and future trends of blockchain. Simple Analysis. By vertically introducing the development history of the blockchain, researchers can have a more concrete understanding of the status quo of the blockchain, and provide ideas for blockchain-related research. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Smart contract

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A Systematic Review on Fully Automated Online Exam Proctoring Approaches 24

A Systematic Review on Fully Automated Online Exam Proctorin...

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24th International Multitopic Conference, INMIC 2022

作者： Fatima, Taskeen Azam, Farooque Muzaffar, Abdul Wahab H-12 Department of Computer and Software Engineering Islamabad Pakistan College of Computing and Informatics Saudi Electronic University Riyadh11673 Saudi Arabia

ISBN: (数字)9798350397109

ISBN: (纸本)9798350397109

In the past few decades E-learning in higher education is increased and played a vital role in pandemics like COVID-19. Particularly, online examinations are conducted on e-learning platforms which leads to many security and cheating issues. For this reason, numerous research is available proposed methodologies and techniques for seamless execution of online examination. However, it is hard to find any study that provides the latest systematic literature review of anti-cheat or cheating prediction techniques and the approaches in the literature. We have analyzed 2223 studies. However, after applying inclusion and exclusion criteria 23 studies relevant studies are finalized. The review revealed that there are three types of proctoring, fully live online, recorded & reviewed and fully automated. This study provides a comparative analysis of online examination techniques & tools performed on 23 studies from the last five years 2017 to 2021. Furthermore, in this time duration five leading cheating prevention features are identified.14 important techniques which are mostly used in this time duration are found in which best frequent approach used in literature is NLP and 10 data sets including both public and private are identified. Proceeding toward the proposed solution, a total of 20 tools for the anti-cheat examinations are found. Almost 23 leading existing tools were found in the literature. To narrow down the criteria for adoption factor is analyzed and studies of the online anti-cheat examination solution adoption in different countries are also investigated. Finally, the overall cost of the e-learning infrastructure, specifically the conduction of examinations is determined by comparing the key factors of the global adoption with major online exam features. © 2022 IEEE.

关键词： E-learning

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MH-pFLID: model heterogeneous personalized federated learning via injection and distillation for medical data analysis 24

MH-pFLID: model heterogeneous personalized federated learnin...

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Proceedings of the 41st International Conference on Machine Learning

作者： Luyuan Xie Manqing Lin Tianyu Luan Cong Li Yuejian Fang Qingni Shen Zhonghai Wu School of Software and Microelectronics and National Engineering Research Center for Software Engineering and PKUOCTA Laboratory for Blockchain and Privacy Computing Peking University Beijing China State University of New York at Buffalo NY

Federated learning is widely used in medical applications for training global models without needing local data access. However, varying computational capabilities and network architectures (system heterogeneity), across clients pose significant challenges in effectively aggregating information from non-independently and identically distributed (non-IID) data. Current federated learning methods using knowledge distillation require public datasets, raising privacy and data collection issues. Additionally, these datasets require additional local computing and storage resources, which is a burden for medical institutions with limited hardware conditions. In this paper, we introduce a novel federated learning paradigm, named Model Heterogeneous personalized Federated Learning via Injection and Distillation (MH-pFLID). Our framework leverages a lightweight messenger model that carries concentrated information to collect the information from each client. We also develop a set of receiver and transmitter modules to receive and send information from the messenger model, so that the information could be injected and distilled with efficiency. Our framework eliminates the need for public datasets and efficiently share information among clients. Our experiments on various medical tasks including image classification, image segmentation, and time-series classification, show MH-pFLID outperforms state-of-the-art methods in all these areas and has good generalizability.

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HairDiffusion: Vivid Multi-Colored Hair Editing via Latent Diffusion 38

HairDiffusion: Vivid Multi-Colored Hair Editing via Latent D...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zeng, Yu Zhang, Yang Liu, Jiachen Shen, Linlin Deng, Kaijun He, Weizhao Wang, Jinbao Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with multiple hair color editing and facial preservation. Considering the advancements in diffusion models, we utilize Latent Diffusion Models (LDMs) for hairstyle editing. Our approach introduces Multi-stage Hairstyle Blend (MHB), effectively separating control of hair color and hairstyle in diffusion latent space. Additionally, we train a warping module to align the hair color with the target region. To further enhance multi-color hairstyle editing, we fine-tuned a CLIP model using a multi-color hairstyle dataset. Our method not only tackles the complexity of multi-color hairstyles but also addresses the challenge of preserving original colors during diffusion editing. Extensive experiments showcase the superiority of our method in editing multi-color hairstyles while preserving facial attributes given textual descriptions and reference images. © 2024 Neural information processing systems foundation. All rights reserved.

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Privacy Preserving Federated Learning from Multi-Input Functional Proxy Re-Encryption

Privacy Preserving Federated Learning from Multi-Input Funct...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xinyu Feng Qingni Shen Cong Li Yuejian Fang Zhonghai Wu School of Software and Microelectronics National Engineering Research Center for Software Engineering Peking University PKU-OCTA Laboratory for Blockchain and Privacy Computing Peking University School of Computer Science Peking University

Federated learning (FL) allows different participants to collaborate on model training without transmitting raw data, thereby protecting user data privacy. However, FL faces a series of security and privacy issues (e.g. the leakage of raw data from publicly shared parameters). Several privacy protection technologies, such as homomorphic encryption, differential privacy and functional encryption, are introduced for privacy enhancement in FL. Among them, the FL frameworks based on functional encryption better balance security and performance, thus receiving increasing attention. The previous FL frameworks based on functional encryption suffer from several security issues, including attacks by combining multiple rounds of ciphertexts and keys, and leakage of global parameters to the central server. To tackle these issues, we propose a novel multi-input functional proxy re-encryption (MI-FPRE) scheme and further design a new FL framework with better privacy based on MI-FPRE. Our framework allows a semi-trusted central server to aggregate the parameters without knowing the intermediate parameters and the result of aggregation, thus achieves better privacy in FL training. The experimental results indicate that our framework achieves less communication overhead and higher computational efficiency without losing accuracy.

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Automated Extraction of ABAC Policies from Natural-Language Documents in Healthcare Systems

Automated Extraction of ABAC Policies from Natural-Language ...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Xia, Yutang Zhai, Shengfang Wang, Qinting Hou, Huiting Wu, Zhonghai Shen, Qingni School of Software and Microelectronics Peking University Beijing 102600 China National Engineering Research Center for Software Engineering Peking University Beijing 100871 China PKU-OCTA Laboratory for Blockchain and Privacy Computing Peking University Beijing 100871 China

ISBN: (纸本)9781665468190

The healthcare system is a distributed collaborative system and the sensitivity of the medical data is one of the most important requirements. Preventing unauthorized access to healthcare information and data sharing security in the healthcare environment are critical processes that affect the credibility of the system. To achieve this goal and to meet the requirements of the healthcare system, access control is an important measure to realize the safe sharing of resources. The attribute-based access control (ABAC) model meets the complex security requirements of large and complex systems and provides a dynamic, flexible and scalable solution. The main obstacle to deploying ABAC is the precise development of ABAC policies. Manually developing access control policies is tedious, time-consuming and error prone. Most systems have high-level requirement specifications, which are written in natural language. These natural language (NL) documents have the intended access control policies for the systems. In this paper, we propose a new approach towards extracting policies from natural language documents. By fully taking advantage of Bidirectional Encoder Representations from Transformers (BERT) and Semantic role labeling (SRL), we are able to correctly identify access control policy (ACP) sentences with an average F1 score of 85% and correctly extract rules with an average F1 score of 72%, which outperforms the state-of-the-art and leads to a performance improvement of 7% and 2% respectively over the previously reported results. © 2022 IEEE.

关键词： Access control

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RPPFL: Robust and Privacy-Preserving Federated Learning via Trusted Execution Environments

RPPFL: Robust and Privacy-Preserving Federated Learning via ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xiaolei Zhang Zhaoyu Chen Guangpu Chen Xinyu Feng Qingni Shen Zhonghai Wu School of Computer Science Peking University National Engineering Research Center for Software Engineering Peking University PKU-OCTA Laboratory for Blockchain and Privacy Computing Peking University School of Software and Microelectronics Peking University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Federated Learning (FL) is a distributed framework that enables multi-participant collaborative model training without the need for data sharing. Despite its advantages, FL is vulnerable to poisoning and inference attacks, which compromise model accuracy and data privacy. Trusted execution environments (TEEs) offer a potential solution by providing a secure and isolated execution space to address these security and privacy concerns in FL. However, existing TEE-based FL schemes often suffer from reduced training speed and compromised model accuracy. To mitigate these issues, we propose a robust and privacy-preserving framework for federated learning (RPPFL) that leverages TEE and pseudorandom masking. In our approach, a trusted local model is trained on a secure subset of local data within the client-side TEE, which is then used for anomaly detection to resist poisoning attacks. Additionally, we employ pseudorandom masking to obfuscate local updates and global parameters. Experimental results indicate that RPPFL effectively counters both poisoning and inference attacks, with only a minimal decrease in training speed and no adverse impact on model accuracy. Compared to full-TEE approaches, our method improved local training efficiency by 10× , with less than a 9% loss in model performance under poisoning attacks.

关键词： Training Accuracy Federated learning Resists Signal processing Data models Robustness Security Speech processing Anomaly detection

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Application Deployment in Mobile Edge computing Environment Based on Microservice Chain 25

Application Deployment in Mobile Edge Computing Environment ...

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25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Li, Haiyan Tang, Bing Xu, Wei Guo, Feiyan Zhang, Xiaoyuan Hunan University of Science and Technology School of Computer Science and Engineering Hunan Laboratory for Service Computing and Novel Software Technology Xiangtan411201 China

ISBN: (纸本)9781665405270

Mobile edge computing (MEC) has become an extremely hot topic in recent years. Mobile edge cloud relies on storage and computing resources on network edge to provide users with delay-sensitive services. However, the transmission delay among microservices and the load of the servers tend to increase due to improper service placement and unreasonable resource allocation under MEC. In this paper, an edge service placement strategy based on an improved fast non-domination sorted genetic algorithm is proposed. First, a microservice placement optimization model is built with the goal of minimizing the average transmission delay and the load balance degree. Then, a genetic algorithm-based microservice placement approach called GA-MSP using improved NSGA-II is studied under the premise that a single service instance is deployed only on one container. The experiments show that the proposed GA-MSP approach is able to achieve low delay and load balance effectively, and ultimately deploy services based on the resulting sets after convergence, which outperforms several other existing representative methods. © 2022 IEEE.

关键词： Mobile edge computing

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A computer-aided speech analytics approach for pronunciation feedback using deep feature clustering

A computer-aided speech analytics approach for pronunciation...

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作者： Nazir, Faria Majeed, Muhammad Nadeem Ghazanfar, Mustansar Ali Maqsood, Muazzam Department of Software Engineering University of Engineering and Technology Taxila Taxila Pakistan Department of Data Science University of the Punjab Lahore Pakistan School of Architecture Computing and Engineering University of East London London United Kingdom Department of Computer Science COMSATS University Islamabad Attock Campus Pakistan

Nowadays, the demand for language learning is increasing because people need to communicate with other people belonging to different regions for their business deals, study, etc. During language learning, a lot of pronunciation mistakes occur due to unfamiliarity with a new language and differences in accent. In this paper, we perform speech mistakes analysis using deep feature-based clustering. We proposed two novel methods for speech analysis, one to deal with phonemic errors (confusing phonemes) and the other to deal with the prosodic errors (partially changed pronunciation variation of phones). For accurate and efficient language learning, it is important to learn both phonemic as well as prosodic error corrections. In our first method, we perform speech analysis by combining deep CNN features and clustering algorithm to detect the phonemic errors. We classify the phonemes using K-nearest neighbor, Naïve Bayes, and support vector machine (SVM). We perform experiments on the six most frequently mispronounced confusing pairs of Arabic to handle phonemic errors and achieve an accuracy of 94%. In our second method, we proposed the unsupervised phone variation model (PVM) to detect prosodic errors. In PVM, each phone is extended to represent the different types of pronunciation variation of that phone with different proficiency levels. We use an Arabic dataset of 28 individual phones for speech analysis and provide feedback based on the variation of each phone and achieves an accuracy of 97%. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Error correction

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