Computers, materials & continua最新文献

{"title":"Two-Stage Edge-Side Fault Diagnosis Method Based on Double Knowledge Distillation","authors":"Yang Yang, Yuhan Long, Yijing Lin, Zhipeng Gao, Lanlan Rui, Peng Yu","doi":"10.32604/cmc.2023.040250","DOIUrl":"https://doi.org/10.32604/cmc.2023.040250","url":null,"abstract":"With the rapid development of the Internet of Things (IoT), the automation of edge-side equipment has emerged as a significant trend. The existing fault diagnosis methods have the characteristics of heavy computing and storage load, and most of them have computational redundancy, which is not suitable for deployment on edge devices with limited resources and capabilities. This paper proposes a novel two-stage edge-side fault diagnosis method based on double knowledge distillation. First, we offer a clustering-based self-knowledge distillation approach (Cluster KD), which takes the mean value of the sample diagnosis results, clusters them, and takes the clustering results as the terms of the loss function. It utilizes the correlations between faults of the same type to improve the accuracy of the teacher model, especially for fault categories with high similarity. Then, the double knowledge distillation framework uses ordinary knowledge distillation to build a lightweight model for edge-side deployment. We propose a two-stage edge-side fault diagnosis method (TSM) that separates fault detection and fault diagnosis into different stages: in the first stage, a fault detection model based on a denoising auto-encoder (DAE) is adopted to achieve fast fault responses; in the second stage, a diverse convolution model with variance weighting (DCMVW) is used to diagnose faults in detail, extracting features from micro and macro perspectives. Through comparison experiments conducted on two fault datasets, it is proven that the proposed method has high accuracy, low delays, and small computation, which is suitable for intelligent edge-side fault diagnosis. In addition, experiments show that our approach has a smooth training process and good balance.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136053660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Smart Obfuscation Approach to Protect Software in Cloud","authors":"Lei Yu, Yucong Duan","doi":"10.32604/cmc.2023.038970","DOIUrl":"https://doi.org/10.32604/cmc.2023.038970","url":null,"abstract":"Cloud computing and edge computing brought more software, which also brought a new danger of malicious software attacks. Data synchronization mechanisms of software can further help reverse data modifications. Based on the mechanisms, attackers can cover themselves behind the network and modify data undetected. Related knowledge of software reverse engineering can be organized as rules to accelerate the attacks, when attackers intrude cloud server to access the source or binary codes. Therefore, we proposed a novel method to resist this kind of reverse engineering by breaking these rules. Our method is based on software obfuscations and encryptions to enhance the security of distributed software and cloud services in the 5G era. Our method is capable of (1) replacing the original assembly codes of the protected program with equivalent assembly instructions in an iteration way, (2) obfuscating the control flow of the protected program to confuse attackers meanwhile keeps the program producing the same outputs, (3) encrypting data to confuse attackers. In addition, the approach can periodically and automatically modify the protected software binary codes, and the binary codes of the protected software are encrypted to resist static analysis and dynamic analysis. Furthermore, a simplified virtual machine is implemented to make the protected codes unreadable to attackers. Cloud game is one of the specific scenarios which needs low latency and strong data consistency. Cheat engine, Ollydbg, and Interactive Disassembler Professional (IDA) are used prevalently for games. Our improved methods can protect the software from the most vulnerable aspects. The improved dynamic code swapping and the simplified virtual machine technologies for cloud games are the main innovations. We inductively learned that our methods have been working well according to the security mechanisms and time complexity analysis. Experiments show that hidden dangers can be eliminated with efficient methods: Execution time and file sizes of the target codes can be multiple times than that of the original program codes which depend on specific program functions.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136053661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Hybrid Deep Learning Approach to Classify the Plant Leaf Species","authors":"Javed Rashid, Imran Khan, Irshad Ahmed Abbasi, Muhammad Rizwan Saeed, Mubbashar Saddique, Mohamed Abbas","doi":"10.32604/cmc.2023.040356","DOIUrl":"https://doi.org/10.32604/cmc.2023.040356","url":null,"abstract":"Many plant species have a startling degree of morphological similarity, making it difficult to split and categorize them reliably. Unknown plant species can be challenging to classify and segment using deep learning. While using deep learning architectures has helped improve classification accuracy, the resulting models often need to be more flexible and require a large dataset to train. For the sake of taxonomy, this research proposes a hybrid method for categorizing guava, potato, and java plum leaves. Two new approaches are used to form the hybrid model suggested here. The guava, potato, and java plum plant species have been successfully segmented using the first model built on the MobileNetV2-UNET architecture. As a second model, we use a Plant Species Detection Stacking Ensemble Deep Learning Model (PSD-SE-DLM) to identify potatoes, java plums, and guava. The proposed models were trained using data collected in Punjab, Pakistan, consisting of images of healthy and sick leaves from guava, java plum, and potatoes. These datasets are known as PLSD and PLSSD. Accuracy levels of 99.84% and 96.38% were achieved for the suggested PSD-SE-DLM and MobileNetV2-UNET models, respectively.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136053954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decentralized Heterogeneous Federal Distillation Learning Based on Blockchain","authors":"Hong Zhu, Lisha Gao, Yitian Sha, Nan Xiang, Yue Wu, Shuo Han","doi":"10.32604/cmc.2023.040731","DOIUrl":"https://doi.org/10.32604/cmc.2023.040731","url":null,"abstract":"Load forecasting is a crucial aspect of intelligent Virtual Power Plant (VPP) management and a means of balancing the relationship between distributed power grids and traditional power grids. However, due to the continuous emergence of power consumption peaks, the power supply quality of the power grid cannot be guaranteed. Therefore, an intelligent calculation method is required to effectively predict the load, enabling better power grid dispatching and ensuring the stable operation of the power grid. This paper proposes a decentralized heterogeneous federated distillation learning algorithm (DHFDL) to promote trusted federated learning (FL) between different federates in the blockchain. The algorithm comprises two stages: common knowledge accumulation and personalized training. In the first stage, each federate on the blockchain is treated as a meta-distribution. After aggregating the knowledge of each federate circularly, the model is uploaded to the blockchain. In the second stage, other federates on the blockchain download the trained model for personalized training, both of which are based on knowledge distillation. Experimental results demonstrate that the DHFDL algorithm proposed in this paper can resist a higher proportion of malicious code compared to FedAvg and a Blockchain-based Federated Learning framework with Committee consensus (BFLC). Additionally, by combining asynchronous consensus with the FL model training process, the DHFDL training time is the shortest, and the training efficiency of decentralized FL is improved.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136053958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Blockchain Security Threats and Collaborative Defense: A Literature Review","authors":"Xiulai Li, Jieren Cheng, Zhaoxin Shi, Jingxin Liu, Bin Zhang, Xinbing Xu, Xiangyan Tang, Victor S. Sheng","doi":"10.32604/cmc.2023.040596","DOIUrl":"https://doi.org/10.32604/cmc.2023.040596","url":null,"abstract":"As a distributed database, the system security of the blockchain is of great significance to prevent tampering, protect privacy, prevent double spending, and improve credibility. Due to the decentralized and trustless nature of blockchain, the security defense of the blockchain system has become one of the most important measures. This paper comprehensively reviews the research progress of blockchain security threats and collaborative defense, and we first introduce the overview, classification, and threat assessment process of blockchain security threats. Then, we investigate the research status of single-node defense technology and multi-node collaborative defense technology and summarize the blockchain security evaluation indicators and evaluation methods. Finally, we discuss the challenges of blockchain security and future research directions, such as parallel detection and federated learning. This paper aims to stimulate further research and discussion on blockchain security, providing more reliable security guarantees for the use and development of blockchain technology to face changing threats and challenges through continuous updating and improvement of defense technologies.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136054170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reliability Analysis of Correlated Competitive and Dependent Components Considering Random Isolation Times","authors":"Shuo Cai, Tingyu Luo, Fei Yu, Pradip Kumar Sharma, Weizheng Wang, Lairong Yin","doi":"10.32604/cmc.2023.037825","DOIUrl":"https://doi.org/10.32604/cmc.2023.037825","url":null,"abstract":"In the Internet of Things (IoT) system, relay communication is widely used to solve the problem of energy loss in long-distance transmission and improve transmission efficiency. In Body Sensor Network (BSN) systems, biosensors communicate with receiving devices through relay nodes to improve their limited energy efficiency. When the relay node fails, the biosensor can communicate directly with the receiving device by releasing more transmitting power. However, if the remaining battery power of the biosensor is insufficient to enable it to communicate directly with the receiving device, the biosensor will be isolated by the system. Therefore, a new combinatorial analysis method is proposed to analyze the influence of random isolation time (RIT) on system reliability, and the competition relationship between biosensor isolation and propagation failure is considered. This approach inherits the advantages of common combinatorial algorithms and provides a new approach to effectively address the impact of RIT on system reliability in IoT systems, which are affected by competing failures. Finally, the method is applied to the BSN system, and the effect of RIT on the system reliability is analyzed in detail.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136054178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Internet of Things (IoT) Security Enhancement Using XGboost Machine Learning Techniques","authors":"Dana F. Doghramachi, Siddeeq Y. Ameen","doi":"10.32604/cmc.2023.041186","DOIUrl":"https://doi.org/10.32604/cmc.2023.041186","url":null,"abstract":"The rapid adoption of the Internet of Things (IoT) across industries has revolutionized daily life by providing essential services and leisure activities. However, the inadequate software protection in IoT devices exposes them to cyberattacks with severe consequences. Intrusion Detection Systems (IDS) are vital in mitigating these risks by detecting abnormal network behavior and monitoring safe network traffic. The security research community has shown particular interest in leveraging Machine Learning (ML) approaches to develop practical IDS applications for general cyber networks and IoT environments. However, most available datasets related to Industrial IoT suffer from imbalanced class distributions. This study proposes a methodology that involves dataset preprocessing, including data cleaning, encoding, and normalization. The class imbalance is addressed by employing the Synthetic Minority Oversampling Technique (SMOTE) and performing feature reduction using correlation analysis. Multiple ML classifiers, including Logistic Regression, multi-layer perceptron, Decision Trees, Random Forest, and XGBoost, are employed to model IoT attacks. The effectiveness and robustness of the proposed method evaluate using the IoTID20 dataset, which represents current imbalanced IoT scenarios. The results highlight that the XGBoost model, integrated with SMOTE, achieves outstanding attack detection accuracy of 0.99 in binary classification, 0.99 in multi-class classification, and 0.81 in multiple sub-classifications. These findings demonstrate our approach’s significant improvements to attack detection in imbalanced IoT datasets, establishing its superiority over existing IDS frameworks.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135317106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Solving Algebraic Problems with Geometry Diagrams Using Syntax-Semantics Diagram Understanding","authors":"Litian Huang, Xinguo Yu, Lei Niu, Zihan Feng","doi":"10.32604/cmc.2023.041206","DOIUrl":"https://doi.org/10.32604/cmc.2023.041206","url":null,"abstract":"Solving Algebraic Problems with Geometry Diagrams (APGDs) poses a significant challenge in artificial intelligence due to the complex and diverse geometric relations among geometric objects. Problems typically involve both textual descriptions and geometry diagrams, requiring a joint understanding of these modalities. Although considerable progress has been made in solving math word problems, research on solving APGDs still cannot discover implicit geometry knowledge for solving APGDs, which limits their ability to effectively solve problems. In this study, a systematic and modular three-phase scheme is proposed to design an algorithm for solving APGDs that involve textual and diagrammatic information. The three-phase scheme begins with the application of the state-transformer paradigm, modeling the problem-solving process and effectively representing the intermediate states and transformations during the process. Next, a generalized APGD-solving approach is introduced to effectively extract geometric knowledge from the problem’s textual descriptions and diagrams. Finally, a specific algorithm is designed focusing on diagram understanding, which utilizes the vectorized syntax-semantics model to extract basic geometric relations from the diagram. A method for generating derived relations, which are essential for solving APGDs, is also introduced. Experiments on real-world datasets, including geometry calculation problems and shaded area problems, demonstrate that the proposed diagram understanding method significantly improves problem-solving accuracy compared to methods relying solely on simple diagram parsing.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"2015 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135317298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization of CNC Turning Machining Parameters Based on Bp-DWMOPSO Algorithm","authors":"Jiang Li, Jiutao Zhao, Qinhui Liu, Laizheng Zhu, Jinyi Guo, Weijiu Zhang","doi":"10.32604/cmc.2023.042429","DOIUrl":"https://doi.org/10.32604/cmc.2023.042429","url":null,"abstract":"Cutting parameters have a significant impact on the machining effect. In order to reduce the machining time and improve the machining quality, this paper proposes an optimization algorithm based on Bp neural network-Improved Multi-Objective Particle Swarm (Bp-DWMOPSO). Firstly, this paper analyzes the existing problems in the traditional multi-objective particle swarm algorithm. Secondly, the Bp neural network model and the dynamic weight multi-objective particle swarm algorithm model are established. Finally, the Bp-DWMOPSO algorithm is designed based on the established models. In order to verify the effectiveness of the algorithm, this paper obtains the required data through equal probability orthogonal experiments on a typical Computer Numerical Control (CNC) turning machining case and uses the Bp-DWMOPSO algorithm for optimization. The experimental results show that the Cutting speed is 69.4 mm/min, the Feed speed is 0.05 mm/r, and the Depth of cut is 0.5 mm. The results show that the Bp-DWMOPSO algorithm can find the cutting parameters with a higher material removal rate and lower spindle load while ensuring the machining quality. This method provides a new idea for the optimization of turning machining parameters.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135317499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AnimeNet: A Deep Learning Approach for Detecting Violence and Eroticism in Animated Content","authors":"Yixin Tang","doi":"10.32604/cmc.2023.041550","DOIUrl":"https://doi.org/10.32604/cmc.2023.041550","url":null,"abstract":"Cartoons serve as significant sources of entertainment for children and adolescents. However, numerous animated videos contain unsuitable content, such as violence, eroticism, abuse, and vehicular accidents. Current content detection methods rely on manual inspection, which is resource-intensive, time-consuming, and not always reliable. Therefore, more efficient detection methods are necessary to safeguard young viewers. This paper addresses this significant problem by proposing a novel deep learning-based system, AnimeNet, designed to detect varying degrees of violent and erotic content in videos. AnimeNet utilizes a novel Convolutional Neural Network (CNN) model to extract image features effectively, classifying violent and erotic scenes in videos and images. The novelty of the work lies in the introduction of a novel channel-spatial attention module, enhancing the feature extraction performance of the CNN model, an advancement over previous efforts in the literature. To validate the approach, I compared AnimeNet with state-of-the-art classification methods, including ResNet, RegNet, ConvNext, ViT, and MobileNet. These were used to identify violent and erotic scenes within specific video frames. The results showed that AnimeNet outperformed these models, proving it to be well-suited for real-time applications in videos or images. This work presents a significant leap forward in automatic content detection in animation, offering a high-accuracy solution that is less resource-intensive and more reliable than current methods. The proposed approach enables it possible to better protect young audiences from exposure to unsuitable content, underlining its importance and potential for broad social impact.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135317508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}