Concurrency and Computation: Practice and Experience最新文献

Open‐domain event schema induction via weighted attentive hypergraph neural network 通过加权殷勤超图神经网络归纳开放域事件模式

Concurrency and Computation: Practice and Experience Pub Date : 2024-02-13 DOI: 10.1002/cpe.8029

Wei Qin, Haozhe Jasper Wang, Xiangfeng Luo

{"title":"Open‐domain event schema induction via weighted attentive hypergraph neural network","authors":"Wei Qin, Haozhe Jasper Wang, Xiangfeng Luo","doi":"10.1002/cpe.8029","DOIUrl":"https://doi.org/10.1002/cpe.8029","url":null,"abstract":"Event schema refers to the use of a template to depict similar events, and it is a necessary prerequisite for event causality extractions. The induction of event schemas is a difficult task, especially for texts in the open domain, due to the complex and diverse manifestations of events. Previous models considered participants in event mentions are independent or compositional, ignoring the high‐order correlations among participants, which limit their capability of induce event schema. To remedy this, we propose constructing an Event Structure Hypergraph (ESH) to better utilizes the event structural information for event schema induction. In particular, we first extract event mentions from the open‐domain corpus. and then construct an ESH by representing event mentions as a hyperedges. ESH contains high‐order information between participants in event mention. To, learn event mentions representation based on ESH, we propose a weighted attentive hypergraph neural network (WHGNN) to model event high‐order correlations and then integrate node‐category weight matrix into the training of network by improving event representation. By applying jointly cluster algorithm on the event mentions representation, we can induce reliable event schemas. Experimental results on three datasets demonstrate that our approach can induce salient and high‐quality event schemas on open‐domain corpus.","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"81 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139781514","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}

引用次数: 0

Fused GEMMs towards an efficient GPU implementation of the ADER‐DG method in SeisSol 在 SeisSol 中融合 GEMMs 以实现 ADER-DG 方法的高效 GPU 实施

Concurrency and Computation: Practice and Experience Pub Date : 2024-02-13 DOI: 10.1002/cpe.8037

Ravil Dorozhinskii, G. B. Gadeschi, Michael Bader

引用次数: 0

Simulation method for infrared radiation transmission characteristics of typical ship targets based on optical remote sensing 基于光学遥感的典型舰船目标红外辐射传输特性仿真方法

Concurrency and Computation: Practice and Experience Pub Date : 2022-11-28 DOI: 10.1002/cpe.7515

Zheng Jiang, Ming Xu, Hao Shi, Liang Chen

引用次数: 0

A novel approach to QoS‐aware resource allocation in NOMA cellular HetNets using multi‐layer optimization 基于多层优化的NOMA蜂窝HetNets中QoS感知资源分配的新方法

Concurrency and Computation: Practice and Experience Pub Date : 2022-11-07 DOI: 10.1007/s10586-022-03734-9

A. Mirzaei

引用次数: 5

The usage of cybernetic in complex software systems and its application to the deterministic multithreading 控制论在复杂软件系统中的应用及其在确定性多线程中的应用

Concurrency and Computation: Practice and Experience Pub Date : 2022-10-31 DOI: 10.1002/cpe.7375

E. M. Bozkurt

{"title":"The usage of cybernetic in complex software systems and its application to the deterministic multithreading","authors":"E. M. Bozkurt","doi":"10.1002/cpe.7375","DOIUrl":"https://doi.org/10.1002/cpe.7375","url":null,"abstract":"In this paper, a new cybernetic control technology that can be used in complex software systems will be introduced. In this approach, the software systems are governed by cybernetic control objects and the class libraries defining the types of these cybernetic control objects are produced by special meta‐programming platforms. In this approach, the requirements of the software to be developed are received from the programmer by meta‐programming systems before coding. Actually, the cybernetic control objects have standard design and properties and the programmers only determine the quantities and the locations of these properties before library production process. Then, the meta‐programming platforms build project‐specific class libraries based on previously determined code templates. By this way, the cybernetic control objects are constructed with optimal memory and they can receive feedback about ongoing operations on the process. With the help of the feedback coming from the process, the control objects steer the process in the line of the programmer directives. By this way, the control of the programmer on the software increases significantly. In addition, in this paper, a typical application of this approach to the multithread programming will be introduced.","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80212365","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}

引用次数: 0

Intrusion detection framework using stacked auto encoder based deep neural network in IOT network 物联网中基于堆叠自编码器的深度神经网络入侵检测框架

Concurrency and Computation: Practice and Experience Pub Date : 2022-10-27 DOI: 10.1002/cpe.7401

G. Sugitha, B. C. Preethi, G. Kavitha

{"title":"Intrusion detection framework using stacked auto encoder based deep neural network in IOT network","authors":"G. Sugitha, B. C. Preethi, G. Kavitha","doi":"10.1002/cpe.7401","DOIUrl":"https://doi.org/10.1002/cpe.7401","url":null,"abstract":"Security is of paramount importance in the number of systems affiliated with increased IoT. Therefore, in this manuscript, a Stacked Auto Encoder based Deep Neural Network (DNN) fostered Intrusion Detection Framework is proposed to secure the IoT Environment. Here, the data is given to the preprocessing stage, in which redundancy elimination and replacement of missing value are done. Then, the preprocessed output is given to the feature selection process. Wherein, the Golden eagle optimization (GEO) algorithm selects the optimum features from pre‐processed data sets. Then selected features are given to the Stacked Auto encoder based deep neural network for classification, which classified the data, like normal, anomalies. Here, the proposed approach is implemented in Python language. To check the robustness of the proposed approach, the performance metrics, like accuracy, specificity, sensitivity, F‐measure, precision, and recall is measured. The simulation outcome show that the proposed Stacked Auto Encoder based Deep Neural Network based Intrusion Detection Framework (IDS‐FS‐GEO‐SAENN) method attains higher accuracy 99.75%, 97.85%, 95.13%, and 98.79, higher sensitivity 96.34%, 91.23%, 89.12%, and 87.25%, higher specificity 93.67%, 92.37%, 98.47%, and 94.78% compared with the existing methods, like FS‐SMO‐SDPN, FS‐WO‐RNNLSTM, FS‐hybrid GWOPSO‐RF, and FS‐CNNLSTMGRU, respectively.","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80682677","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}

引用次数: 2

Smart bin: Waste segregation system using deep learning‐Internet of Things for sustainable smart cities 智能垃圾箱:使用深度学习-物联网的垃圾分类系统，用于可持续的智能城市

Concurrency and Computation: Practice and Experience Pub Date : 2022-10-23 DOI: 10.1002/cpe.7378

K. O. M. Aarif, C. M. Yousuff, B. Hashim, C. M. Hashim, Poruran Sivakumar

{"title":"Smart bin: Waste segregation system using deep learning‐Internet of Things for sustainable smart cities","authors":"K. O. M. Aarif, C. M. Yousuff, B. Hashim, C. M. Hashim, Poruran Sivakumar","doi":"10.1002/cpe.7378","DOIUrl":"https://doi.org/10.1002/cpe.7378","url":null,"abstract":"Waste management is a major issue with the emerging growth in the world population, and we need to find efficient ways to recycle and reuse waste. Segregating waste has become a primary need in waste management as different types of waste like Bio & Non‐Bio‐degradable waste should be processed differently. Effective waste isolation at the fundamental level is especially required for this. Several Smart cities oriented smart garbage management systems are also proposed using Internet of Things (IoT) and GSM. The existing smart bins using IoT and wireless sensor network (WSN) are dependent significantly on two major things. First, multiple types of sensors, as a single sensor may not be able to detect different material waste, and second, the console (Microcontroller, Arduino Raspberry Pi) and connectivity which in turn dependent on programming and operating system. These limitations of the embedded smart bin are overcome by combining IoT with artificial intelligence approaches such as deep neural network (DNN) systems. In this paper, we have presented a Friendly Waste Segregator Using Deep Learning and the IoT to classify and isolate the waste objects as biodegradable and nonbiodegradable. Our proposed method utilizes, a robust deep learning network to classify the waste accurately and IoT for monitoring and connectivity using various sensors. Our proposed method with initial training can identify and segregte real‐time waste objects without human intervention with an average accuracy of 97.49 %. Our smart bin intends to provide optimized waste management of bio and non‐bio‐waste and help to build an ecologically safe society.","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78213199","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}

引用次数: 1

Graph convolutional neural networks‐based assessment of students' collaboration ability 基于卷积神经网络的学生协作能力评估

Concurrency and Computation: Practice and Experience Pub Date : 2022-10-23 DOI: 10.1002/cpe.7395

Jinjiao Lin, Tianqi Gao, Yuhua Wen, Xianmiao Yu, Bi-Zhen You, Yanfang Yin, Yanze Zhao, Haitao Pu

{"title":"Graph convolutional neural networks‐based assessment of students' collaboration ability","authors":"Jinjiao Lin, Tianqi Gao, Yuhua Wen, Xianmiao Yu, Bi-Zhen You, Yanfang Yin, Yanze Zhao, Haitao Pu","doi":"10.1002/cpe.7395","DOIUrl":"https://doi.org/10.1002/cpe.7395","url":null,"abstract":"As 21st‐century skills have become increasingly important, collaboration ability is now considered essential in many areas of life. Different theoretical frameworks and assessment tools have emerged to measure this skill. However, more applied studies on its implementation and assessment in current educational settings are required. This research accordingly uses Graph Convolutional Neural Networks (GCNs) to assess students' collaboration ability from students' assignments. The Pearson correlation coefficient is used to measure the similarity of the level of students' collaboration ability, and similar students are linked together to establish an adjacency matrix. By sorting through relevant literature and selecting the feature words that represent the strength of collaboration ability, calculating the similarity between the preprocessed student data and each selected feature word, after which the highest value of the similarity as the feature value of the student for this feature and establish the student feature matrix. Finally, the GCNs are jointly trained by the adjacency matrix and the feature matrix. The results show that this method can effectively assess students' collaboration ability. Moreover, compared with other text classification methods, the GCNs selected in this paper has higher accuracy.","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87335141","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}

引用次数: 0

Bitcoin price prediction using optimized multiplicative long short term memory with attention mechanism using modified cuckoo search optimization 基于改进布谷鸟搜索优化的优化乘法长短期记忆与注意机制的比特币价格预测

Concurrency and Computation: Practice and Experience Pub Date : 2022-10-21 DOI: 10.1002/cpe.7384

Aarif Ahamed Shahul Hameed, Chandrasekar Ravi

{"title":"Bitcoin price prediction using optimized multiplicative long short term memory with attention mechanism using modified cuckoo search optimization","authors":"Aarif Ahamed Shahul Hameed, Chandrasekar Ravi","doi":"10.1002/cpe.7384","DOIUrl":"https://doi.org/10.1002/cpe.7384","url":null,"abstract":"For the past few years, Bitcoin plays a vital role in both the economical and financial industries. In order to gain a huge return on investment, the investors are eager to forecast the future value of Bitcoin. However, Bitcoin price variation is quite nonlinear and chaotic in nature, so it creates more difficulty in forecasting future value. Researchers found that the multiplicative long short term memory (LSTM) model will be more efficient for predicting those complex variations. So, target mission is about to develop an optimized multiplicative LSTM with an Attention mechanism using Technical Indicators derived from historical data. A modified cuckoo search optimization model is proposed to tune the hyperparameter of the Deep Learning model. This novel optimization algorithm eliminates the local optimum and slower convergence problem of the cuckoo search optimization algorithm. Deibold Mariano test is performed to statistically evaluate the proposed model and it is inferred that the recommended methodology is statistically fit. Regression metrics such as root mean square error, mean square error and mean absolute error has been used for comparative evaluation with related benchmark techniques such as genetic algorithm optimized LSTM (GA–LSTM), particle swarm optimized LSTM (PSO–LSTM) and cuckoo search optimized LSTM (CSO–LSTM). The empirical result shows that the recommended methodology outperforms the taken benchmark models and provides better accuracy.","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"74 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86361937","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}

引用次数: 0

An effective blockchain‐based smart contract system for securing electronic medical data in smart healthcare application 一个有效的基于区块链的智能合约系统，用于保护智能医疗应用中的电子医疗数据

Concurrency and Computation: Practice and Experience Pub Date : 2022-10-21 DOI: 10.1002/cpe.7363

Ansar Sonya, G. Kavitha

{"title":"An effective blockchain‐based smart contract system for securing electronic medical data in smart healthcare application","authors":"Ansar Sonya, G. Kavitha","doi":"10.1002/cpe.7363","DOIUrl":"https://doi.org/10.1002/cpe.7363","url":null,"abstract":"In today's world, data management plays a key role in smart healthcare applications. However, data availability, trustworthiness, confidentiality, and security are the major issues faced by current healthcare data management systems. The modern healthcare systems manage Electronic Medical Records (EMR) using a centralized manner, which increases the single point of failure in the event of a natural catastrophe. In this paper, a new robust Blockchain‐based Medical Cloud (BC‐MedCl) framework has been proposed to provide secure EMR sharing between patient and doctor. Primarily, Internet of Things (IoT) devices will gather the health‐related data of the patient periodically. The proposed framework then stores the encrypted EMRs in cloud storage while their correlating hash values are placed into the blockchain. Finally, a decentralized selective smart contract‐based access control mechanism is developed to enhance the security of the proposed system. The prototype file‐sharing performance of the proposed architecture has been evaluated using the Ethereum platform. The performance results manifest that the proposed blockchain framework is more effective to handle EMR in the real‐time healthcare system with a superior accuracy ratio of 98.7% and a lesser latency ratio of 25% as compared with the existing systems.","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"145 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86208402","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}

引用次数: 1