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共 801 条记录，以下是271-280 订阅

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Peer to Peer Real-Time Mobile Payment Scheme 23

Peer to Peer Real-Time Mobile Payment Scheme

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23rd international Arab conference on Information Technology, ACIT 2022

作者： Obaid, Mahmoud Hodrob, Rami Computer Systems Engineering Arab American University Jenin Palestine Computer Scince Arab American University Jenin Palestine

ISBN: (纸本)9798350320244

A novel real-Time mobile payment (MOP) system that utilizes mobile phones together with interactive voice response technology (IVR) is proposed. The system consists of a communication network built upon existing banking infrastructures, thus minimizing the cost of adoption by banks. A sender or receiver communicates only with his/her bank, using a regular mobile phone, and banks talk to each other through a central authority, which acts as a broker. The communication of each component with other components in the system is clearly specified. Provision is made for the security of the whole system, making use of existing security arrangements between each participating bank and its customers, as well as established security practices of the banking industry. Rollbacks of transactions are also available. Our proposed system MOP will establish connections between banks so that all mobile phone-based transactions and payments may be made safely and easily. By developing an effective and secure system, it will meet the needs of all stakeholders. As long as they are logged into the system, users will be able to send, track, manage, and receive payments. © 2022 IEEE.

关键词： Electronic money

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A Novel Fatigue Driving Detection Method Under the Mask-Wearing Condition 23

A Novel Fatigue Driving Detection Method Under the Mask-Wear...

引用

23rd IEEE international conference on High Performance Computing and Communications, 7th IEEE international conference on Data Science and systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Lv, Shengyi Wang, Hui Ni, Sirui Li, Jiangpeng Zhao, Zeli Zhang, Yuhan Cheng, Yuqian Zhang, Yunchuang Liu, Bernie Li, Xiulai Xie, Gengquan School of Cyberspace Security Hainan University Haikou China RobAI-Lab Hainan University Haikou China School of Information and Communication Engineering Hainan University Haikou China School of Computer Science and Technology Hainan University Haikou China College of Foreign Language Hainan University Haikou China Hainan Hairui Zhong Chuang Technol Co Ltd China

ISBN: (纸本)9781665494571

Fatigue driving is one of the major contributors to road accidents. Nowadays, COVID-19 is reaching epidemic proportions, which directly leads to the phenomenon of mask-wearing becomes ordinary among drivers. Most of the existing fatigue detection systems are unable to effectively determine the factual fatigue status of a driver that wearing a mask. Therefore, we propose a quick-witted fatigue detection system to counteract the obstruction of masks. The system detects faces by means of a pyramidbox-based approach. Then a modified PFLD-based method will predict the facial landmarks, from which the eye aspect ratio (EAR) is calculated. Ultimately, our self-made FDUM dataset was tested by using the evaluation method that combined PERCLOS and a method for blink frequency based on Gaussian distribution. Our system can achieve 97.06% accuracy in determining the fatigue status of the driver under the mask, which represents an excellent recognition rate of the system. © 2021 IEEE.

关键词： COVID-19 Epidemics Road accidents Ear Gaussian distribution Fatigue Faces

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ASTC: An Adaptive Gradient Compression Scheme for Communication-Efficient Edge Computing 23

ASTC: An Adaptive Gradient Compression Scheme for Communicat...

引用

作者： Wu, Jun Mao, Yingchi Xu, Xuesong School of Computer and Information Hohai University Nanjing China Hohai University Key Laboratory of Water Big Data Technology of Ministry of Water Resources Nanjing China

ISBN: (纸本)9781665494571

In edge computing, though distributed training of Deep Neural Network (DNN) is expected to exchange massive gradients between the parameter servers and workers, the high communication cost constrains the training speed. To break this limitation, the gradient compression algorithms employ the ultimate compression ratio at the expense of the accuracy of the trained model. Therefore, new gradient compression techniques are necessary to ensure both communication efficiency and model accuracy. This paper introduced a novel technique - an Adaptive Sparse Ternary Gradient Compression (ASTC) scheme to enhance the communication efficiency and training accuracy during the DNN training. Specifically, ASTC firstly establishes the model compression selection criterion by the amount of gradients to compress the network layer that meets the criterion. Secondly, ASTC evaluates the gradients' importance based on entropy to adaptively perform sparse compression. Finally, ASTC performs ternary quantization compression and a lossless code scheme to further reduce the communication cost. Numerical results demonstrate that the average iteration time of ASTC can be reduced largely compared to the state-of-art benchmark schemes. Furthermore, ASTC improves an average of about 1.9% in training accuracy. © 2021 IEEE.

关键词： Training Adaptation models Costs Quantization (signal) Smart cities Computational modeling Benchmark testing

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AP3: Adaptive Power Prediction Framework based on Spatial Partition Multi-Phase Model 23

AP3: Adaptive Power Prediction Framework based on Spatial Pa...

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作者： Chen, Juan Ou, Zhixin Guo, Yifei Qi, Xinxin Sun, Yuyang Deng, Lin Chen, Hongyu Lin, Zihan College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781665494571

The accuracy of processor power modeling is an important foundation for power management and optimization on parallel computing system. It is difficult to build a high-accuracy instantaneous CPU/DRAM power prediction model. One of the main reasons for the low accuracy is that the processor architecture sometimes influence greatly on the accuracy of power prediction. For example, the accuracy of processor power model is affected by inaccurate modeling of the uncore part of processor. Another reason comes from the limitation of static models for instantaneous power prediction. Despite the existence of various optional linear/nonlinear models, the fixed training set and model coefficients are insufficient to make high-accuracy instantaneous predictions for various target programs. Aiming at the above two issues, this paper proposes an Adaptive Power Prediction framework based on spatial Partition multi-phase model (AP^{3}). Spatial partition mainly solves the impact of uncore power on the prediction accuracy, and adaptability solves the limitation of the static model on the power prediction accuracy. According to the experimental results on both ARM-based and x86-based processor platforms, AP^{3} greatly increases the CPU and DRAM power instantaneous prediction accuracy. Spatial partition reduce the prediction error (MRE) by 0.3%-8.2% compared to previous single model, while adaptive update further reduces the error (MRE) by 1.7%-7.1% compared to previous static model. © 2021 IEEE.

关键词： Training Adaptation models Smart cities Multicore processing Computational modeling Power system management Random access memory

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Organs-At-Risk Segmentation in Medical Imaging Based on the U-Net with Residual and Attention Mechanisms 23

Organs-At-Risk Segmentation in Medical Imaging Based on the ...

引用

作者： Lin, Xiao-Wei Li, Ni Qi, Qi School of Computer Science and Technology Hainan University Haikou China School of Mathematics and Statistics Hainan Normal University Hainan Normal University Key Laboratory of Data Science and Intelligence Education of Ministry of Education Haikou China

ISBN: (纸本)9781665494571

In this paper, we proposed a new organs-at-risk segmentation method for Medical images of heads and necks based on the U-Net with residuals and attention mechanism. A Project & Excite (PE) block combined with a residual method is proposed as Residual-PE Block in the U-Net encoding stage in order to enhance the feature expression ability. Attention Gates (AGs) are introduced in the decoding stage to increase the relevance of feature weighting in accord with segmentation tasks. The proposed method has been evaluated through a set of real world medical images including 22 organs at risk in the head and neck. Experimental results showed that our proposed method improved the average segmentation accuracy by 3-4% compared with current existing methods. The average Dice Similarity Coefficient (DSC) score on the segmentation of the 22 types of head-and-neck related organs at risk is 79.5% and the average 95% Hausdorff Distance (HD) is 6.99mm. It demonstrates that this approach is promising for clinical doctors to improve their accuracy and efficiency in radiotherapy. © 2021 IEEE.

关键词： Image segmentation Head Three-dimensional displays Smart cities Medical services Logic gates Encoding

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Driving Gaze Behavior Prediction at S-Curve Based on Driver Experience using Machine Learning 23

Driving Gaze Behavior Prediction at S-Curve Based on Driver ...

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作者： Oroni, Chrispus Zacharia Zhu, Yishui Wang, Luyang Wu, Bo School of Information Engineering Chang'an University Xi'an China School of Computer Science Tokyo University of Technology Hachioji Japan

ISBN: (纸本)9781665494571

Transport is an essential activity that plays a key role worldwide, leading to a rise in infrastructure growth for sustainable growth. The speedy growth has directly led to an increase in the number of vehicles which demands the drivers to have a complete awareness while driving to reduce traffic accidents. For safe and comfortable driving, it would be helpful to detect the driver's attention using a gaze pattern. For this reason, we used Tobii Glass 2 to capture and record the gaze behavior from 10 drivers, each driving ten times on the S-Curve, and used the Extreme Gradient Boosting algorithm (XGBoost) to identify the essential features that influence the driver behavior. We developed Machine Learning models to predict the driver's gaze behaviors related to their experience. Our results showed the best performance for SVM with 93% accuracy and 94% Precision in classifying the driver performance based on experience while driving on S-Curve. Furthermore, this study established that while at the S-Curve, there was a significant difference between the Experienced and the Novice drivers on the gaze direction pattern. The Novice driver's gaze concentration was often diverted away and took a significant amount of time to return their gaze which is a danger to safe driving. Therefore, detecting the drivers' gaze behavior is vital since failure by the drivers to distribute their attention effectively in driving situations leads to havoc. © 2021 IEEE.

关键词： Support vector machines Machine learning algorithms Roads Static VAr compensators Glass Behavioral sciences Safety

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An Effective Metaheuristic for Partial Offloading and Resource Allocation in Multi-Device Mobile Edge Computing 23

An Effective Metaheuristic for Partial Offloading and Resour...

引用

作者： He, Weirong Wu, Suxiang Sun, Jin School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

ISBN: (纸本)9781665494571

Mobile edge computing (MEC) can provide mobile devices with high-quality computing services by offloading computation-intensive tasks to MEC servers, which are close to mobile devices, instead of submitting them to the cloud datacenter. The offloading strategy has a significant impact on the quality-of-service provided by the MEC system. Oriented toward a multi-device MEC system, this work takes into account the processing delay of computation tasks and the total energy consumption of the MEC system, and studies the partial offloading and resource allocation problem for minimizing the delay under the energy constraint. To solve the above-mentioned optimization problem, which is formulated as an NP-complete integer program, we propose an effective scheduling metaheuristic based on the solution exploration mechanism of artificial bee colony (ABC) algorithm. With a task sequence representing the scheduling solution, the ABC-based metaheuristic uses a position-based mapping method to convert each individual bee in ABC into a valid solution. For each converted solution, we further use a greedy search strategy to determine the order of tasks to be offloaded and the number of computing resources allocated for processing each offloaded task. The resultant task processing delay is used to evaluate the quality of the nectar source found by the bee. Our proposed method iteratively employs the ABC's population updating rule to update the positions of nectar sources, and identifies the current best solution corresponding to the best nectar source in the population. Experimental results demonstrate the advantageous of the proposed algorithm over baseline algorithms in reducing the task processing delay. © 2021 IEEE.

关键词： Multi-access edge computing Processor scheduling Metaheuristics Sociology Mobile handsets Delays Servers

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MDistMult: A Multiple Scoring Functions Model for Link Prediction on Antiviral Drugs Knowledge Graph 23

MDistMult: A Multiple Scoring Functions Model for Link Predi...

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作者： Wang, Weichuan Xie, Zhiwen Liu, Jin Duan, YuCong Huang, Bo Zhang, Junsheng School of Computer Science Wuhan University Wuhan China College of Information Science and Technology Hainan University Haikou China School of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai China Institute of Scientific and Technical Information of China Beijing China

ISBN: (纸本)9781665494571

Knowledge graphs (KGs) on COVID-19 have been constructed to accelerate the research process of COVID-19. However, KGs are always incomplete, especially the new constructed COVID-19 KGs. Link prediction task aims to predict missing entities for (e, r, t) or (h, r, e), where h and t are certain entities, e is an entity that needs to be predicted and r is a relation. This task also has the potential to solve COVID-19 related KGs' incomplete problem. Although various knowledge graph embedding (KGE) approaches have been proposed to the link prediction task, these existing methods suffer from the limitation of using a single scoring function, which fails to capture rich features of COVID-19 KGs. In this work, we propose the MDistMult model that leverages multiple scoring functions to extract more features from existing triples. We employ experiments on the CCKS2020 COVID-19 Antiviral Drugs Knowledge Graph (CADKG). The experimental results demonstrate that our MDistMult achieves state-of-the-art performance in link prediction task on the CADKG dataset. © 2021 IEEE.

关键词： COVID-19 Drugs Smart cities Natural languages Predictive models Feature extraction Task analysis

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A High-Precision DQDS Algorithm 23

A High-Precision DQDS Algorithm

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作者： Li, Chuanying Xiao, Xiong Du, Peibing Jiang, Hao Barrio, Roberto Quan, Zhe Li, Kenli College of Information Science and Engineering Hunan University Changsha China Northwest Institute of Nuclear Technology Xi'an China College of Computer National University of Defense Technology Changsha China University of Zaragoza Departamento de Matemática Aplicada and Iuma Zaragoza Spain

ISBN: (纸本)9781665494571

With the rise of high-performance computing, the precision requirements for numerical calculations are getting higher and higher. Rounding errors cannot be avoided in floating point operations, and therefore the calculation result can be inaccurate and even incorrect when rounding errors are not taken into account. We study and analyze in detail the Differential Quotient Difference with Shifts (DQDS) algorithm, used in algorithms to calculate the eigenvalues of tridiagonal matrices, and propose a novel High Precision DQDS algorithm (HDQDS) based on Error Free Transformations (EFT). We use EFT to reduce the accumulation of errors and obtain a much more precise algorithm. The relative error of the HDQDS algorithm remains constant at the machine precision error level, while the relative error of the DQDS algorithm has fluctuations in the same experimental scenario. Numerical experiments show that the proposed high-precision HDQDS algorithm allows for higher precision and more stability than the original alagrithm. © 2021 IEEE.

关键词： Fluctuations Scientific computing Stability analysis Eigenvalues and eigenfunctions Numerical stability

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SA_3AM: A Novel Sentiment Analysis Approach Integrating Three-Level Attention Mechanism 23

SA_3AM: A Novel Sentiment Analysis Approach Integrating Thre...

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作者： Wang, Qingren Sun, Liang Cui, Jie Zhang, Jing School of Computer Science and Technology Anhui University Hefei China Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9781665494571

Nowadays, social networks have become the primary ways for people to communicate in their daily lives. Due to the great value of short texts generated by social networks, sentiment analysis on them has been attracting more attention from academics and business. However, emojis, which have taken the world by storm, have been challenging sentiment analysis because of their flexible ability to convey emotions. Therefore, this paper proposes a novel sentiment analysis approach by integrating three-level attention mechanism, namely SA_3AM. Particularly, SA_3AM can be divided into three phases: i) attention-oriented word sentiment semantics learning, where SA_3AM utilizes Bi-LSTM to capture bidirectional semantic dependencies within various dimensions conveyed in pure words;ii) attention-oriented emoji sentiment seantics mining, where SA_3AM employs column max-pooling to obtain rich sentiment semantics of emojis by fusing the sentiment dependencies of words;and iii) attention-oriented microblog sentiment semantics calculating, where SA_3AM uses attention mechanism to mine implicit intentions conveyed by microblogs. A real-world dataset Weibo_SL containing 6,436 Chinese microblogs from January 2017 to July 2020 was built to evaluate our SA_3AM. Our experimental results not only show that SA_3AM outperforms the peers, but also demonstrate the feasibility of the dataset Weibo SL. © 2021 IEEE.

关键词： Training Deep learning Sentiment analysis Social networking (online) Storms Smart cities Semantics

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