Wirel. Commun. Mob. Comput.最新文献_第9页

Mol-BERT: An Effective Molecular Representation with BERT for Molecular Property Prediction moll -BERT:一种用于分子性质预测的有效分子表征

Wirel. Commun. Mob. Comput. Pub Date : 2021-09-02 DOI: 10.1155/2021/7181815

Juncai Li, Xiaofei Jiang

{"title":"Mol-BERT: An Effective Molecular Representation with BERT for Molecular Property Prediction","authors":"Juncai Li, Xiaofei Jiang","doi":"10.1155/2021/7181815","DOIUrl":"https://doi.org/10.1155/2021/7181815","url":null,"abstract":"Molecular property prediction is an essential task in drug discovery. Most computational approaches with deep learning techniques either focus on designing novel molecular representation or combining with some advanced models together. However, researchers pay fewer attention to the potential benefits in massive unlabeled molecular data (e.g., ZINC). This task becomes increasingly challenging owing to the limitation of the scale of labeled data. Motivated by the recent advancements of pretrained models in natural language processing, the drug molecule can be naturally viewed as language to some extent. In this paper, we investigate how to develop the pretrained model BERT to extract useful molecular substructure information for molecular property prediction. We present a novel end-to-end deep learning framework, named Mol-BERT, that combines an effective molecular representation with pretrained BERT model tailored for molecular property prediction. Specifically, a large-scale prediction BERT model is pretrained to generate the embedding of molecular substructures, by using four million unlabeled drug SMILES (i.e., ZINC 15 and ChEMBL 27). Then, the pretrained BERT model can be fine-tuned on various molecular property prediction tasks. To examine the performance of our proposed Mol-BERT, we conduct several experiments on 4 widely used molecular datasets. In comparison to the traditional and state-of-the-art baselines, the results illustrate that our proposed Mol-BERT can outperform the current sequence-based methods and achieve at least 2% improvement on ROC-AUC score on Tox21, SIDER, and ClinTox dataset.","PeriodicalId":23995,"journal":{"name":"Wirel. Commun. Mob. Comput.","volume":"26 1","pages":"7181815:1-7181815:7"},"PeriodicalIF":0.0,"publicationDate":"2021-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81519665","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: 16

Enabling Efficient Decentralized and Privacy Preserving Data Sharing in Mobile Cloud Computing 在移动云计算中实现高效分散和保护隐私的数据共享

Wirel. Commun. Mob. Comput. Pub Date : 2021-09-02 DOI: 10.1155/2021/8513869

Jiawei Zhang, Ning Lu, Teng Li, Jianfeng Ma

{"title":"Enabling Efficient Decentralized and Privacy Preserving Data Sharing in Mobile Cloud Computing","authors":"Jiawei Zhang, Ning Lu, Teng Li, Jianfeng Ma","doi":"10.1155/2021/8513869","DOIUrl":"https://doi.org/10.1155/2021/8513869","url":null,"abstract":"Mobile cloud computing (MCC) is embracing rapid development these days and able to provide data outsourcing and sharing services for cloud users with pervasively smart mobile devices. Although these services bring various conveniences, many security concerns such as illegally access and user privacy leakage are inflicted. Aiming to protect the security of cloud data sharing against unauthorized accesses, many studies have been conducted for fine-grained access control using ciphertext-policy attribute-based encryption (CP-ABE). However, a practical and secure data sharing scheme that simultaneously supports fine-grained access control, large university, key escrow free, and privacy protection in MCC with expressive access policy, high efficiency, verifiability, and exculpability on resource-limited mobile devices has not been fully explored yet. Therefore, we investigate the challenge and propose an Efficient and Multiauthority Large Universe Policy-Hiding Data Sharing (EMA-LUPHDS) scheme. In this scheme, we employ fully hidden policy to preserve the user privacy in access policy. To adapt to large scale and distributed MCC environment, we optimize multiauthority CP-ABE to be compatible with large attribute universe. Meanwhile, for the efficiency purpose, online/offline and verifiable outsourced decryption techniques with exculpability are leveraged in our scheme. In the end, we demonstrate the flexibility and high efficiency of our proposal for data sharing in MCC by extensive performance evaluation.","PeriodicalId":23995,"journal":{"name":"Wirel. Commun. Mob. Comput.","volume":"285 1","pages":"8513869:1-8513869:15"},"PeriodicalIF":0.0,"publicationDate":"2021-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76866085","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 in Industrial Internet of Things Network-Based on Deep Learning Model with Rule-Based Feature Selection 基于深度学习模型和基于规则的特征选择的工业物联网入侵检测

Wirel. Commun. Mob. Comput. Pub Date : 2021-09-02 DOI: 10.1155/2021/7154587

J. B. Awotunde, Chinmay Chakraborty, E. Adeniyi

{"title":"Intrusion Detection in Industrial Internet of Things Network-Based on Deep Learning Model with Rule-Based Feature Selection","authors":"J. B. Awotunde, Chinmay Chakraborty, E. Adeniyi","doi":"10.1155/2021/7154587","DOIUrl":"https://doi.org/10.1155/2021/7154587","url":null,"abstract":"The Industrial Internet of Things (IIoT) is a recent research area that links digital equipment and services to physical systems. The IIoT has been used to generate large quantities of data from multiple sensors, and the device has encountered several issues. The IIoT has faced various forms of cyberattacks that jeopardize its capacity to supply organizations with seamless operations. Such risks result in financial and reputational damages for businesses, as well as the theft of sensitive information. Hence, several Network Intrusion Detection Systems (NIDSs) have been developed to fight and protect IIoT systems, but the collections of information that can be used in the development of an intelligent NIDS are a difficult task; thus, there are serious challenges in detecting existing and new attacks. Therefore, the study provides a deep learning-based intrusion detection paradigm for IIoT with hybrid rule-based feature selection to train and verify information captured from TCP/IP packets. The training process was implemented using a hybrid rule-based feature selection and deep feedforward neural network model. The proposed scheme was tested utilizing two well-known network datasets, NSL-KDD and UNSW-NB15. The suggested method beats other relevant methods in terms of accuracy, detection rate, and FPR by 99.0%, 99.0%, and 1.0%, respectively, for the NSL-KDD dataset, and 98.9%, 99.9%, and 1.1%, respectively, for the UNSW-NB15 dataset, according to the results of the performance comparison. Finally, simulation experiments using various evaluation metrics revealed that the suggested method is appropriate for IIOT intrusion network attack classification.","PeriodicalId":23995,"journal":{"name":"Wirel. Commun. Mob. Comput.","volume":"107 1","pages":"7154587:1-7154587:17"},"PeriodicalIF":0.0,"publicationDate":"2021-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84736504","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: 65

MEC-Driven Fast Deformation Monitoring Based on GNSS Signal 基于GNSS信号的mec驱动快速变形监测

Wirel. Commun. Mob. Comput. Pub Date : 2021-09-02 DOI: 10.1155/2021/9517133

Bo Li, Shangwei Chen, Yi Liu, Kan Xie, Shengli Xie

引用次数: 2

An Effective Algorithm for Intrusion Detection Using Random Shapelet Forest 一种有效的随机形状森林入侵检测算法

Wirel. Commun. Mob. Comput. Pub Date : 2021-09-02 DOI: 10.1155/2021/4214784

Gongliang Li, Mingyong Yin, Siyuan Jing, Bing Guo

引用次数: 2

Influencing Factors of Athletes' Injury Rehabilitation from the Perspective of Internal Environment 内环境视角下运动员损伤康复的影响因素

Wirel. Commun. Mob. Comput. Pub Date : 2021-09-01 DOI: 10.1155/2021/2368847

Xiang Huang, Xiaoping Wang

引用次数: 0

UAV-Relaying Cooperation for Internet of Everything with CRT-Based NOMA 基于crt的NOMA的万物互联无人机中继合作

Wirel. Commun. Mob. Comput. Pub Date : 2021-09-01 DOI: 10.1155/2021/5840673

Jin-yuan Gu, Xiaohui Gu, Guoan Zhang, Wei Duan

{"title":"UAV-Relaying Cooperation for Internet of Everything with CRT-Based NOMA","authors":"Jin-yuan Gu, Xiaohui Gu, Guoan Zhang, Wei Duan","doi":"10.1155/2021/5840673","DOIUrl":"https://doi.org/10.1155/2021/5840673","url":null,"abstract":"Due to the great potential of the combination of machine learning technology and unmanned aerial vehicle (UAV) enabled wireless communications, various optimization algorithms on resource allocation have been proposed for the Internet of Things. UAVs not only can perform the missions under the extreme conditions but also enhance the overall performance of the system as an aerial relay assisting transmission in the public and civil domains, which have been received extensive attentions. However, with the limited capacity and power constraints, they are difficult to support the transmission for the big data information users. In addition, the lack of spectrum resource poses challenges to satisfy the quality of service (QoS) of mobile users in wireless networks. To contribute to these urgent problems, this article first studies the potential and effective applications of UAVs, by introducing the Chinese remainder theorem (CRT) and nonorthogonal multiple access (NOMA) technologies into UAV relay networks. Two scenarios with/without direct transmissions between the source and destination nodes are investigated, following the decomposition and reconstruction mechanisms to satisfy the big data information transmission. Considering the user fairness, we further discuss the effect of the UAV numbers to the overall system capacity. To maximize the system capacity, the designs of transmission protocol and receiver are also discussed, in various channel conditions. Finally, a low complexity and efficient two-stage power allocation scheme is established for the perspective of users and UAV relays.","PeriodicalId":23995,"journal":{"name":"Wirel. Commun. Mob. Comput.","volume":"154 1","pages":"5840673:1-5840673:7"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79055207","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

A Joint Optimization Model of s , S Inventory and Supply Strategy Using an Improved PSO-Based Algorithm 基于改进pso算法的s、s库存与供应联合优化模型

Wirel. Commun. Mob. Comput. Pub Date : 2021-08-31 DOI: 10.1155/2021/7621692

Huayang Deng, Q. Shi, Yadong Wang

引用次数: 0

Gridless Multiple Measurements Method for One-Bit DOA Estimation with a Nested Cross-Dipole Array 基于嵌套交叉偶极子阵列的位DOA估计无网格多重测量方法

Wirel. Commun. Mob. Comput. Pub Date : 2021-08-31 DOI: 10.1155/2021/6635220

Haining Long, Ting Su, Xianpeng Wang, Mengxing Huang

{"title":"Gridless Multiple Measurements Method for One-Bit DOA Estimation with a Nested Cross-Dipole Array","authors":"Haining Long, Ting Su, Xianpeng Wang, Mengxing Huang","doi":"10.1155/2021/6635220","DOIUrl":"https://doi.org/10.1155/2021/6635220","url":null,"abstract":"The gridless one-bit direction of arrival (DOA) estimator is proposed to estimate electromagnetic (EM) sources on a nested cross-dipole array, and the multiple measurement vectors (MMV) mode is introduced to improve the reliability of parameter estimation. The gridless method is based on atomic norm minimization, solved by alternating direction multiplier method (ADMM). With gridless method used, sign inconsistency caused by one-bit measurements and basis mismatches by traditional grid-based algorithms can be avoided. Furthermore, the reconstructed denoising measurements with fast convergence and stable recovery accuracy are obtained by ADMM. Finally, spatial smoothing root multiple signal classification (SSRMUSIC) and dual polynomial (DP) methods are used, respectively, to estimate the DOAs on the reconstructed denoising measurements. Numerical results show that our method one-bit ADMM-SSRMUSIC has a better performance than that of one-bit SSRMUSIC used directly. At low signal to noise ratio (SNR) and low snapshot, the one-bit ADMM-DP has an excellent performance which is even better than that of unquantized MUSIC. In addition, the proposed methods are also suitable for both completely polarized (CP) signals and partially polarized (PP) signals.","PeriodicalId":23995,"journal":{"name":"Wirel. Commun. Mob. Comput.","volume":"49 1","pages":"6635220:1-6635220:10"},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80714741","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

Compressed Sensing Reconstruction of Radar Echo Signal Based on Fractional Fourier Transform and Improved Fast Iterative Shrinkage-Thresholding Algorithm 基于分数阶傅里叶变换和改进快速迭代收缩阈值算法的雷达回波信号压缩感知重构

Wirel. Commun. Mob. Comput. Pub Date : 2021-08-31 DOI: 10.1155/2021/2272933

Rui Zhang, Chen Meng, Cheng Wang, Qiang Wang

{"title":"Compressed Sensing Reconstruction of Radar Echo Signal Based on Fractional Fourier Transform and Improved Fast Iterative Shrinkage-Thresholding Algorithm","authors":"Rui Zhang, Chen Meng, Cheng Wang, Qiang Wang","doi":"10.1155/2021/2272933","DOIUrl":"https://doi.org/10.1155/2021/2272933","url":null,"abstract":"The compressed sensing theory, which has received great attention in the field of radar technology, can effectively reduce the data rate of high-resolution radar imaging systems and solve the problem of collecting, storing, and transmitting large amounts of data in radar systems. Through the study of radar signal processing theory, it can be found that the echo of radar LFM transmit signal has sparse characteristics in the distance upward; based on this, we can consider using the theory of compressed sensing in the processing of radar echo to optimize the processing. In this paper, a fast iterative shrinkage-thresholding reconstruction algorithm based on protection coefficients is proposed. Under the new scheme, firstly, the LFM echo signal’s good sparse representation is obtained by using the time-frequency sparse characteristics of the LFM echo signal under the fractional Fourier transform; all reconstruction coefficients are analyzed in the iterative process. Then, the coefficients related to the feature will be protected from threshold shrinkage to reduce information loss. Finally, the effectiveness of the proposed method is verified through simulation experiments and application example analysis. The experimental results show that the reconstruction error of this method is lower and the reconstruction effect is better compared with the existing reconstruction algorithms.","PeriodicalId":23995,"journal":{"name":"Wirel. Commun. Mob. Comput.","volume":"43 1","pages":"2272933:1-2272933:15"},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81520740","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