Studies in Informatics and Control最新文献_第9页

Integrated Information System for the Management of Activities in the Organization 管理本组织活动的综合信息系统

IF 1.6 4区计算机科学

Studies in Informatics and Control Pub Date : 2022-06-30 DOI: 10.24846/v31i2y202203

A. Udroiu, Ionut Sandu, M. Dumitrache

引用次数: 1

On a Practical Algorithm of P-IMC Type 一种实用的P-IMC型算法

IF 1.6 4区计算机科学

Studies in Informatics and Control Pub Date : 2022-06-30 DOI: 10.24846/v31i2y202206

V. Cîrtoaje, A. Baiesu

引用次数: 0

Fused-Grain Feature Learning for Unsupervised Person Re-identification 用于无监督人员再识别的融合粒度特征学习

IF 1.6 4区计算机科学

Studies in Informatics and Control Pub Date : 2022-06-30 DOI: 10.24846/v31i2y202204

Hua Han, Li Huang, Yujin Zhang, Jiamin Tang

{"title":"Fused-Grain Feature Learning for Unsupervised Person Re-identification","authors":"Hua Han, Li Huang, Yujin Zhang, Jiamin Tang","doi":"10.24846/v31i2y202204","DOIUrl":"https://doi.org/10.24846/v31i2y202204","url":null,"abstract":": Most supervised learning methods are currently used to solve the task of person re-identification (Re-ID) and yield excellent results. But these methods usually need manual annotation of training data. Especially for large data sets, they need too high cost of manual annotation and the data are difficult to obtain for fully pairwise labeling. So unsupervised learning becomes a necessarily trend for person Re-ID. This paper is trying to solve the problem by unsupervised learning method. Moreover, global features focus on spatial integrity of person features, and local ones help to highlight discriminative features of different patches. Therefore, fused-grained unsupervised (FGU) learning framework of global and local branches’ feature learning is proposed to solve Re-ID task. Specifically, for the local branch, one extracts patches from a feature map which learned on a PatchNet network of images, and learns their fine-grained features to pull close the similar patches and push away the dissimilar ones. For the global branch, one maximizes the diversity between classes by repelled loss and similarity within classes through attracted loss, then similarity and diversity in the unlabeled data sets are used as information for unsupervised cluster merging and learning their coarse-grained features. The two branches are used to jointly achieve the effect of increasing inter-class differences and intra-class similarity. A large number of experiments verify the superiority of the proposed method for unsupervised person re-identification.","PeriodicalId":49466,"journal":{"name":"Studies in Informatics and Control","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43907249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Industrial Intrusion Detection Classifier Pruning via Hybrid-order Difference of Weights Based on Poisson Distribution 基于泊松分布的混合阶权差的工业入侵检测分类器剪枝

IF 1.6 4区计算机科学

Studies in Informatics and Control Pub Date : 2022-06-30 DOI: 10.24846/v31i2y202201

Xu Liu, Hongya Wang, Hai-ying Luan, Yong Yan, Yun Sha

{"title":"Industrial Intrusion Detection Classifier Pruning via Hybrid-order Difference of Weights Based on Poisson Distribution","authors":"Xu Liu, Hongya Wang, Hai-ying Luan, Yong Yan, Yun Sha","doi":"10.24846/v31i2y202201","DOIUrl":"https://doi.org/10.24846/v31i2y202201","url":null,"abstract":": Pruning Techniques can greatly reduce the number of parameters and the computational load related to convolutional neural networks, which makes them suitable for edge industrial control systems with limited resources. However, they face the problem that the detection accuracy will be greatly reduced after pruning. Given the above, this paper proposes filter pruning via a technique called hybrid-order difference, which is based on Poisson distribution. According to this technique, some filters in each convolutional layer are removed, thus the number of parameters of the employed classifiers is highly reduced. The first-order and second-order difference for the L1-norm of filter parameters are calculated, they are given weights and they are converted into activity indices through the Min-Max function. The proposed method can fully explore the relationship between the weights and avoid the problem of threshold selection in pruning. Experiments were carried out on the LeNet-5, VGG16, ResNet18 and ResNet50 convolutional neural networks based on the 2019 Distributed Denial of Service dataset (the DDoS dataset) of the Canadian Institute for Cybersecurity, the 2014 experimental dataset of the Mississippi State University related to a natural gas pipeline (the gas dataset), and the dataset for an oil depot (the oil dataset). The results showed that the proposed method can effectively prune the employed intrusion detection classifiers, such as removing 83.74% of the Floating Point Operations (FLOPs) for VGG16 with only a 0.10% reduction of accuracy. As such, it greatly alleviates the load pressure generated by the above-mentioned classifiers in the context of edge industrial control systems.","PeriodicalId":49466,"journal":{"name":"Studies in Informatics and Control","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44496603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient Intrusion Detection and Prevention Model in Cloud Environment Using Sgd-LSTM and C2HA 基于Sgd-LSTM和C2HA的云环境下高效入侵检测和防御模型

IF 1.6 4区计算机科学

Studies in Informatics and Control Pub Date : 2022-06-30 DOI: 10.24846/v31i2y202209

Ponnuviji NAMAKKAL PONNUSAMY, Vigilson Prem Monickaraj, Ezhumalai Periyathambi

{"title":"Efficient Intrusion Detection and Prevention Model in Cloud Environment Using Sgd-LSTM and C2HA","authors":"Ponnuviji NAMAKKAL PONNUSAMY, Vigilson Prem Monickaraj, Ezhumalai Periyathambi","doi":"10.24846/v31i2y202209","DOIUrl":"https://doi.org/10.24846/v31i2y202209","url":null,"abstract":": Cloud computing is an attractive technology paradigm that has been widely used as a tool for storing and analyzing the data of different users. Since access to the cloud is achieved through the Internet, data stored in clouds is susceptible to attacks from external as well as internal intruders. Henceforth, cloud service providers (CSPs) need to take action in order to provide a secure framework that would detect intrusion in the cloud and protect and secure customer information against hackers and intruders. This paper proposes a Sgd-LSTM and signature-based access control policy based Intrusion Detection and Prevention System (IDPS) model which is meant to detect and prevent various intrusions in the cloud. The proposed system includes three phases: the user registration phase, intrusion detection phase, and intrusion prevention phase. Initially, user registration is performed based on a unique ID and password, and then, the password is converted into hashcode by using the C2HA algorithm and then stored in the cloud for authentication purposes. In the intrusion detection phase, the status of cloud data is predicted by employing the Sgd-LSTM classifier in order to discard the intruder data packets from the cloud. At last, in the intrusion prevention phase, data access to the cloud environment is controlled by using signature-based user authentication in order to authenticate the legitimate user. The proposed classifier can effectively detect the intruders, which was experimentally proved by comparing it with the existing classifiers.","PeriodicalId":49466,"journal":{"name":"Studies in Informatics and Control","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42548332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancement of Very Fast Decision Tree for Data Stream Mining 数据流挖掘中快速决策树的增强

IF 1.6 4区计算机科学

Studies in Informatics and Control Pub Date : 2022-06-30 DOI: 10.24846/v31i2y202205

Mai Lefa, Hatem Abd-Elkader, Rashed K. Salem

{"title":"Enhancement of Very Fast Decision Tree for Data Stream Mining","authors":"Mai Lefa, Hatem Abd-Elkader, Rashed K. Salem","doi":"10.24846/v31i2y202205","DOIUrl":"https://doi.org/10.24846/v31i2y202205","url":null,"abstract":": Traditional machine learning (ML) algorithms use static datasets to model knowledge. Nowadays, there is an increasing demand for machine learning based solutions that can handle very huge amounts of data in the shape of streams that never stop. The Very Fast Decision Tree (VFDT) is one of the most widely utilized data stream mining algorithms (DSM), despite the fact that it wastes a huge amount of energy on trivial calculations. The machine learning community has come first in terms of accuracy and execution time while designing algorithms like this. When assessing data mining algorithms, numerous types of studies include energy usage as a crucial factor. The purpose of this research is to create a hyper model to optimize the VFDT algorithm, which reduces the waste of energy while maintaining accuracy. In the proposed method, some fixed algorithm parameters were changed to dynamic parameters after analyzing each of them separately and knowing the extent of their positive impact on reducing energy consumption in several cases in algorithm. The practical experiment was conducted on both the algorithm in its basic form and the algorithm in the proposed form on several different types of datasets in the same application environment The main advantage of the results of the proposed method compared to the results of the basic algorithm is that there was a noticeable development in the performance of the algorithm in terms of reducing its energy consumption and maintaining its accuracy levels.","PeriodicalId":49466,"journal":{"name":"Studies in Informatics and Control","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46236823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing the Generalization Performance of Few-Shot Image Classification with Self-Knowledge Distillation 利用自知识精馏提高小样本图像分类的泛化性能

IF 1.6 4区计算机科学

Studies in Informatics and Control Pub Date : 2022-06-30 DOI: 10.24846/v31i2y202207

Liang Li, Weidong Jin, Yingkun Huang, Junxiao Ren

{"title":"Enhancing the Generalization Performance of Few-Shot Image Classification with Self-Knowledge Distillation","authors":"Liang Li, Weidong Jin, Yingkun Huang, Junxiao Ren","doi":"10.24846/v31i2y202207","DOIUrl":"https://doi.org/10.24846/v31i2y202207","url":null,"abstract":": Though deep learning has succeeded in various fields, its performance on tasks without a large-scale dataset is always unsatisfactory. The meta-learning based few-shot learning has been used to address the limited data situation. Because of its fast adaptation to the new concepts, meta-learning fully utilizes the prior transferrable knowledge to recognize the unseen instances. The general belief is that meta-learning leverages a large quantity of few-shot tasks sampled from the base dataset to quickly adapt the learner to an unseen task. In this paper, the teacher model is distilled to transfer the features using the same architecture. Following the standard-setting in few-shot learning, the proposed model was trained from scratch and the distribution was transferred to a better generalization. Feature similarity matching was proposed to compensate for the inner feature similarities. Besides, the prediction from the teacher model was further corrected in the self-knowledge distillation period. The proposed approach was evaluated on several commonly used benchmarks in few-shot learning and performed best among all prior works.","PeriodicalId":49466,"journal":{"name":"Studies in Informatics and Control","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46727896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Real-Time Optimization for an AVR System Using Enhanced Harris Hawk and IIoT 基于增强型哈里斯鹰和工业物联网的AVR系统实时优化

IF 1.6 4区计算机科学

Studies in Informatics and Control Pub Date : 2022-06-30 DOI: 10.24846/v31i2y202208

Amr M. Saber, Mohamed H. Behiry, Mohamed Amin

{"title":"Real-Time Optimization for an AVR System Using Enhanced Harris Hawk and IIoT","authors":"Amr M. Saber, Mohamed H. Behiry, Mohamed Amin","doi":"10.24846/v31i2y202208","DOIUrl":"https://doi.org/10.24846/v31i2y202208","url":null,"abstract":": Recently, several research studies have used standard metaheuristic optimization algorithms rather than traditional algorithms and the Ziegler-Nichols (Z-N) method for tuning PID controller parameters. However, these studies have directly implemented these algorithms in order to configure the cascade control system one time. This paper presents a novel real- time monitoring and optimization architecture based on the Enhanced Harris Hawk Algorithm (EHHOA) and the Industrial Internet of Things (IIoT) for tuning the PID controller parameters for an Automatic Voltage Regulator (AVR) system. The EHHOA is based on a Chaotic map and an opposition-based learning technique that is linked to the IIoT layers. The proposed algorithm was implemented through Simulink in the MATLAB environment and it was compared with the Z-N method, the classical HHO/PID algorithm and the PSO/PID algorithm. The simulation results show that the proposed algorithm managed to enhance tuning with an insignificant difference in comparison with the other employed algorithms and EHHOA gave satisfactory results in adjusting the parameters of the PID controller, especially in IIoT real-time scenarios.","PeriodicalId":49466,"journal":{"name":"Studies in Informatics and Control","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48528645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Multi-Attribute Approach for Cyber Threat Intelligence Product and Services Selection 网络威胁情报产品和服务选择的多属性方法

IF 1.6 4区计算机科学

Studies in Informatics and Control Pub Date : 2022-03-30 DOI: 10.24846/v31i1y202202

A. Vevera, Carmen Elena Cîrnu, C. Rădulescu

{"title":"A Multi-Attribute Approach for Cyber Threat Intelligence Product and Services Selection","authors":"A. Vevera, Carmen Elena Cîrnu, C. Rădulescu","doi":"10.24846/v31i1y202202","DOIUrl":"https://doi.org/10.24846/v31i1y202202","url":null,"abstract":": Cyber Threat Intelligence (CTI) is a significant field in Cyber Security research. It enables organizations to share threat data and allow a proactive defence against sophisticated intrusion attempts. The wide variety in the CTI products and services offered by different providers from the market, makes it difficult for the security experts to decide which CTI provider is the most suitable according to their security program requirements. CTI products and services provider selection is a complex decision-making problem that involves multiple criteria. The aim of the present paper is to propose a multi- attribute approach based on the VIKOR method for CTI providers ranking and selection, according to a set of criteria. A case study based on the users’ evaluations reviews about the security threats intelligence providers is studied. The impact of the VIKOR user parameter variation on the CTI providers ranking is analysed. The proposed approach is a support tool for the security program leaders faced with the decision of selecting the CTI providers. It also helps the CTI service providers to improve the quality of their products and services.","PeriodicalId":49466,"journal":{"name":"Studies in Informatics and Control","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2022-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49196583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Tuning Convolutional Neural Network Hyperparameters by Bare Bones Fireworks Algorithm 用裸骨烟花算法调优卷积神经网络超参数

IF 1.6 4区计算机科学

Studies in Informatics and Control Pub Date : 2022-03-30 DOI: 10.24846/v31i1y202203

Ira Tuba, M. Veinovic, Eva Tuba, Romana Capor Hrosik, M. Tuba

{"title":"Tuning Convolutional Neural Network Hyperparameters by Bare Bones Fireworks Algorithm","authors":"Ira Tuba, M. Veinovic, Eva Tuba, Romana Capor Hrosik, M. Tuba","doi":"10.24846/v31i1y202203","DOIUrl":"https://doi.org/10.24846/v31i1y202203","url":null,"abstract":": Digital image classification is an important component in various applications. Lately, convolutional neural networks have been widely used as a classifier since they achieve superior results, while their application is relatively simple. In order to achieve the best possible results, tuning of the network’s hyperparameters is necessary but that represents an exponentially hard optimization problem with computationally very expensive fitness function. The swarm intelligence algorithms have been proven to be effective in solving such exponentially hard optimization problems, however their application to this particular problem has not been sufficiently studied. In this paper, convolutional neural network hyperparameters were tuned by the bare bones fireworks algorithm. The quality of the proposed method was tested on two standard benchmark datasets, CIFAR-10 and MNIST. The results were compared to CIFAR-Net, LeNet-5 and the networks optimized by the harmony search algorithm and the proposed method achieved better results considering the classification accuracy. The proposed method for CNN hyperparameter tuning improved the classification accuracy up to 99.34% on the MNIST dataset and up to 75.51% on the CIFAR-10 dataset compared to 99.25% and 74.76% reported by another method from the specialized literature.","PeriodicalId":49466,"journal":{"name":"Studies in Informatics and Control","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2022-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47848886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5