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检索条件"任意字段=6th International Conference on the Art, Science, and Engineering of Programming, Programming 2022"

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A Systematic Literature Review of Machine Learning to Predict Location in Social Media

A Systematic Literature Review of Machine Learning to Predic...

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international conference on Information Technology, Information Systems and Electrical engineering (ICITISEE)

作者： Bayu Prasetiyo Utomo Widyawan Muhammad Nur Rizal Department of Electrical Engineering and Information Technology Universitas Gadjah Mada Yogyakarta Indonesia

Predicting the location is a critical issue for conducting any analyses. Location analysis is one of analysis which processes Geographic Information (GI). Example in social media, users could include their posts or tweets with latitude and longitude using the Global Position System (GPS) in their devices. Unfortunately, not all users or content creators provide their latitude and longitude. As a result, we investigate the literature in journals and conferences while being aware of the best methodology and algorithm employed by current researchers. Based on the predetermined inclusion and exclusion criteria, thirty-six location prediction research articles published between 2013 and 2022 were kept and selected to be investigated further. A strategy for identifying, assessing, and interpreting all study materials accessible with the goal of responding to specific questions is known as a Systematic Literature Review (SLR). the SLR result show that the topic of location prediction is still high in publication level. User text post is the most popular approach used by researchers, followed by user information and user trajectory. Almost all reviewed paper use Twitter to get the social media data. It mostly caused because of the availability throughout the Application programming Interface (API), tool library, and datasets on the internet. Out of the twenty-six ways, four of the most popular algorithms for predicting the location were found (RNN, LSTM, CNN and Random Forest). To raise the performance of machine learning classifiers for position prediction, some researchers combining any machine learning algorithms. therefore, the researcher suggests to using any combined algorithm, approach, and source of data in social media to get deeper optimization of location prediction.

关键词： Machine learning algorithms Systematics Text analysis Social networking (online) Bibliographies Prediction algorithms Trajectory

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Classroom Student Action Recognition Based on Dynamic Head

Classroom Student Action Recognition Based on Dynamic Head

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international conference on Data science and Business Analytics (ICDSBA)

作者： Xiudong Qu Wenxing Bao Yongjun Jing Xu Chen Chuanming Li School of Computer Science and Engineering North Minzu University Yinchuan China School of Computer and Information Hefei University of Technology Hefei China

Students' behavior reflects their status and the degree of participation in the classroom. therefore, it is particularly crucial to use the classroom camera to automatically detect the student's behavior. However, there are situations where students are densely distributed and there are many small objects in the classroom. To solve this problem, the dynamic head was introduced to effectively detect student behavior. Firstly, we collect a large number of videos from real university classrooms for data annotation to form a student behavior dataset and treat student behavior recognition as an object detection task. Secondly, we propose a model called YOLOv5-Dy, which includes a dynamic head. the dynamic head includes scale, space, and task attention mechanisms, which focus on the importance of different features from different perspectives. Experimental results show that the improved model is more adaptable to the student behavior detection task. Finally, we compare our method with state-of-the-art detectors, and it turns out that the proposed approach performs better on the student behavior dataset.

关键词： Adaptation models Detectors Object detection Data science Cameras Data models Behavioral sciences

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Application of reverse engineering in software engineering teaching: Combining case teaching with task driven teaching 2021

Application of reverse engineering in software engineering t...

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6th international conference on Distance Education and Learning, ICDEL 2021

作者： Liu, Huaming Bi, Xuehui Wang, Xiuyou Li, Huaimin Yang, Ying Liu, Zhengyan Liu, Kunzhe School of Computer and Information Engineering Fuyang Normal University Anhui Fuyang236037 China School of Education Fuyang Normal University Anhui Fuyang236037 China

ISBN: (纸本)9781450390033

In the teaching of software engineering for computer related majors, there is a gap between high-level logic design and implementation, which leads to the students' unreasonable software design, and the implementation code does not follow the design scheme. therefore, it is necessary to carry out heuristic teaching for students, starting from the programming students are most familiar with, gradually transition to software design, using reverse thinking teaching mode, so that students can master the transformation relationship between design and implementation. In the implementation of case teaching, it is necessary to restore high-level design or demand analysis through code, realize the conversion from code to document, and effectively improve the theoretical level of students;in terms of practical skills, implement task driven teaching, deepen the understanding of cases and knowledge transfer, and exercise the practical ability of students. through the performance analysis of 2015 and 2016 students, the reverse engineering case teaching and task driven practice can significantly improve students' practical ability, stimulate students' enthusiasm for software development, and highlight students' software development ability in computer skills competition, college students' innovation training project and graduation design. © 2021 ACM.

关键词： Students

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Power Curve Estimation of a Wind Turbine Considering Different Weather Conditions using Machine Learning Algorithms

Power Curve Estimation of a Wind Turbine Considering Differe...

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international Middle-East Power System conference, MEPCON

作者： Mostafa Al-Gabalawy Haitham S. Ramadan Mohammed A. Mostafa Shimaa A. Hussien Electronics Communications Computers Engineering Department El-Safawa High Institute of Engineering Cairo Egypt Electrical Power and Machines Dept Faculty of Engineering Zagazig University Zagazig Egypt Electrical Power and Machines Dept Faculty of Engineering Must University 6th October Cairo Egypt Electrical Power and Control Department Pyramids Higher Institute for Engineering and Technology 6th October Cairo Egypt

ISBN: (纸本)9781665463645

theoretically, the output of the wind turbine might be estimated based the most known power equation that depends mainly in the wind speed. there are many issues appeared in the phase of the estimating and control while applying this equation due to ignoring many weather conditions. this paper introduces a multivariate estimation for the power curve of the wind turbine considering the weather conditions such as wind speed, air density, wind turbulence, and wind share. there variables are termed features, and a lot of measurement has been occurred to collect all possible data for these features, where measurements (system data) exceed 47,000 points. this data is proceeded mainly by three steps of the data sciences; exploratory data analysis (EDA), data processing, and building the model, using Python programming language, where it gives more flexibility more than the other languages. the power curve estimation is executed using different machine learning tools such as linear regression, polynomial regression, random forest regression, gradient boost (G Boost), and extreme gradient regression (XGBoost). A comparative study is introduced considering the R-square and the root mean square error. From the results, XGBoost learning tool provides the best performance in terms of root mean square error (RMSE). the RMSE value decreases to 6.404 while using the proposed algorithm compared to (6.631, 6.6721, and 9.072) attained through the alternative G Boost, forest random, and 4th-degree polynomial respectively.

关键词： Machine learning algorithms Wind energy Wind speed Estimation Mathematical models Wind turbines Power systems

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Improving Chinese Named Entity Recognition Based on Lexical Information Adjustment 22

Improving Chinese Named Entity Recognition Based on Lexical ...

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Proceedings of the 2022 6th international conference on Computer science and artificial Intelligence

作者： Jinshang Luo Mengshu Hou Information Center University of Electronic Science and Technology of China China School of Computer Science and Engineering University of Electronic Science and Technology of China China

ISBN: (纸本)9781450397773

Lexical enhancement has been proven effective for Chinese Named Entity Recognition (NER), allowing for the use of word boundary information and the reduction of segmentation errors. Most present methods incorporate the lexical information by lattices but neglect the impact of candidate lexicon conflicts. the conflicts may misguide the model to make different predictions. In the work, a novel Long Short-Term Memory (LSTM) model based on the lexical information adjustment strategy (LIA-LSTM) is proposed. Firstly, all potential words are categorized into corresponding word sets. To lessen the influence of lexicon conflicts, the lexical features are introduced for the character sequence via elaborate weight adjustment. then the LSTM and self-attention mechanism are applied to improve contextual awareness. Experiments on benchmark datasets demonstrate that LIA-LSTM outperforms the state-of-the-art methods compared. LIA-LSTM improves F1 score by 1.4% over baseline Lattice-LSTM on the MSRA dataset.

关键词： Attention Mechanism Lexical Information Adjustment Chinese Named Entity Recognition Natural Language Processing

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Fast Streaming Algorithms for k-Submodular Maximization under a Knapsack Constraint

Fast Streaming Algorithms for k-Submodular Maximization unde...

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international conference on Data science and Advanced Analytics (DSAA)

作者： Canh V. Pham Dung K.T. Ha Huan X. Hoang Tan D. Tran ORLab Faculty of Computer Science Phenikaa University Hanoi Vietnam Faculty of Information Technology VNU University of Engineering and Technology Hanoi Vietnam Faculty of Information Technology Halong University Vietnam VNU University of Engineering and Technology Ha Noi Vietnam People’s Security Academy Hanoi Vietnam

ISBN: (纸本)9781665473316

this paper proposes two fast streaming algorithms for the problem of k-submodular maximization over the ground set of n elements under the knapsack constraint which is important and popular in combinatorial optimization and machine learning. Our algorithms are the first ones that provide constant-approximation ratios within O(nk) query complexity. the first algorithm is a single-pass streaming algorithm that returns a 1/10-approximation solution, the second one is a multi-pass streaming algorithm and improves the approximation ratio to nearly 1/4. Although these ratios are simply near to the state-of-the-art algorithms yet the number of queries can diminish by a large factor. We further investigate the performance of our algorithms by directing several experiments on instances of the issue: Influence Maximization and Sensor Placement. the outcomes confirm that our algorithms not only methodology in the quality arrangement of the cutting edge techniques including streaming and non-streaming algorithms yet in addition significantly reduce the number of queries.

关键词： Sensor placement Machine learning algorithms Costs Machine learning Data science Approximation algorithms Linear programming

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Research on Service Function Chain Deployment Algorithm Based on Proximal Policy Optimization 22

Research on Service Function Chain Deployment Algorithm Base...

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Proceedings of the 2022 6th international conference on Electronic Information Technology and Computer engineering

作者： Peng Sun Hai Lin Huailong Yu You Zhong Xiaoping Wu Bing Sun Information and Communication Branch of State Grid Hunan Electric Power Company Limited China College of Electrical and Information Engineering Hunan University China State Grid Hunan Electric Power Company Limited China

ISBN: (纸本)9781450397148

In the future, business scenarios will become diversified, but it is difficult for the existing network architecture to provide strong support for them. Network function virtualization (NFV) technology decouples network functions from dedicated hardware devices and provides customized services for users in the form of service function chain (SFC). At present, the deployment of SFC has been proved to be a NP-hard problem. Most of the solutions are integer linear programming algorithms, but the process of such algorithms is complex. When the network topology scale becomes larger, the calculation process is very time-consuming, and the results sometimes fall into local optimal solutions, which makes it difficult to achieve the desired effect. In this case, reinforcement learning (RL) algorithms show great advantages, learning strategies through interaction with the environment to maximize rewards or achieve specific goals. therefore, this paper proposes a SFC deployment algorithm based on proximal policy optimization (PPO) reinforcement learning, which aims at maximizing access rate and minimizing resource consumption. the simulation results show that the proposed algorithm has good convergence and stability, which is more conducive to the actual deployment of the SFC.

关键词： reinforcement learning (RL) Proximal policy optimization (PPO) Service function chain (SFC) Deployment algorithm Network function virtualization (NFV)

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Adder Wavelet for Better Image Classification under Adder Neural Network 22

Adder Wavelet for Better Image Classification under Adder Ne...

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Proceedings of the 2022 6th international conference on Computer science and artificial Intelligence

作者： Guangping Li Dingkai Liang School of Information Engineering Guangdong University of Technology China

ISBN: (纸本)9781450397773

Adder neural network (AdderNet) is a new kind of deep learning model in which the original massive multiplications in convolutions are replaced by additions. the overall energy consumption using adder network is reduced significantly. However, there is a classification accuracy drop when using AdderNet in image classification task. In this paper, we present an adder wavelet transform (AWT) layer instead of the existing down-sampling operations. Based on the AWT layer, we propose a novel adder neural network (AddWaveNet) to improve classification accuracy. Experimental results on CIFAR-10 and CIFAR-100 datasets show that our proposed AddWaveNets achieves significant improvements in classification accuracy and powerful ability of feature learning compared to state-of-the-art quantization networks. Specifically, based on ResNet-32 backbones, AddWaveNet achieves 0.24% accuracy improvement on the CIFAR-10 benchmark and 0.52% accuracy improvement on the CIFAR-100 benchmark.

关键词： image classification wavelet translation deep learning neural network

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A Hierarchical Optimization-Based Model for Microgrids with Energy Storage Considering Operation Cost and Net Load Mean Square Error

A Hierarchical Optimization-Based Model for Microgrids with ...

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IEEE conference on Energy Internet and Energy System Integration (EI2)

作者： Zexiang Guan Dongsheng Cai Olusola Bamisile Jian Li Zhenyuan Zhang Qi Huang School of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu China Sichuan Provincial Key Lab of Power System Wide-Area Measurement and Control University of Electronic Science and Technology of China Chengdu China

When a high proportion of renewable energy is connected to the grid, the net load fluctuation can be suppressed by configuring energy storage. However, the current cost of energy storage is high. this paper proposes a bi-level optimization model that takes into account the dynamic allocation of shared energy storage (SES) capacity in response to electric load demand. the upper-layer objective function adopts EO optimization algorithm, which is to maximize the income of the shared energy storage operator (SESO). the lower-layer multi-objective function is to minimize the sum of the operating cost and the net load mean square error of multiple microgrids, which can be classified as the mixed integer linear programming problem. then, the solution flow of the algorithm is given. Finally, under the constraints of the hierarchical optimization-based model, solving the problem by the CPLEX solver on the MATLAB platform. the results demonstrate that the proposed method can allocate optimal capacity and reduce operating costs for microgrids. Simultaneously, this method increases the revenue of SESO and promotes the consumption of renewable energy.

关键词： Renewable energy sources Costs Fluctuations Microgrids Mean square error methods Mathematical models Classification algorithms

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A State Estimation Solution Method for Electricity-Gas Integrated Energy System Based on AWPSO

A State Estimation Solution Method for Electricity-Gas Integ...

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IEEE conference on Energy Internet and Energy System Integration (EI2)

作者： Kaicheng Liu Zhengyang Lin Fei Jiang Sen Li China Electric Power Research Institute Beijing China School of Electrical and Information Engineering Changsha University of Science and Technology Changsha China

the electricity-gas integrated energy system (EGIES) can not only realize the energy conversion between electricity and natural gas, but also effectively improve the energy utilization rate and promote the low-carbon transformation of the energy system. State estimation technology can provide accurate operation state data for the system, and is the key cornerstone of safe and reliable operation of the system. However, the improper selection of the initial value of the gas grid in the EGIES will cause the convergence difficulty of the state estimation solution, and bring challenges to the stable operation of the EGIES. therefore, a state estimation solution method of EGIES based on (adaptive weight particle swarm optimization) AWPSO is proposed in this paper. the EGIES measurement model was established; the EGIES state estimation model is constructed based on (weighted least square) WLS; the constraint is treated as a penalty function, and an unconstrained EGIES state estimation model is established and solved by AWPSO. the effectiveness and feasibility of the proposed method have been verified by a case study of an EGIES coupled with a14-node power grid and a 7-node gas grid.

关键词： Weight measurement Adaptation models System integration programming Particle measurements Power grids Reliability

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