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2021 2nd International Conference on Computing and Data Science (CDS)最新文献

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Brief Review on Computing Resource Allocation Algorithms in Mobile Edge Computing 移动边缘计算中计算资源分配算法综述
2021 2nd International Conference on Computing and Data Science (CDS) Pub Date : 2021-01-01 DOI: 10.1109/CDS52072.2021.00025
Ning Lyu
{"title":"Brief Review on Computing Resource Allocation Algorithms in Mobile Edge Computing","authors":"Ning Lyu","doi":"10.1109/CDS52072.2021.00025","DOIUrl":"https://doi.org/10.1109/CDS52072.2021.00025","url":null,"abstract":"Over the past few years, the Internet has greatly developed. And due to the expansion of its application range, huge amount of data will be generated in our daily life. In order to be able to process large-scale data quickly, the concept of Mobile Edge Computing (MEC) was proposed. Mobile Edge Computing refers to dealing with data at the edge of the network. In this way, MEC can reduce response time, extend battery life and shorten bandwidth. This article focuses on the problems of computing resource allocation in MEC. This article summarizes the existing related research from the perspective of single node and multiple nodes, and analyzes the advantages and disadvantages of methods used in existing research to reduce time delay and energy consumption. Finally, it reviews how existing methods solve the problems of latency and energy consumption in practical scenarios.","PeriodicalId":380426,"journal":{"name":"2021 2nd International Conference on Computing and Data Science (CDS)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117157875","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: 4
A Feature-based Deep Neural Framework for Poverty Prediction 基于特征的深度神经网络贫困预测框架
2021 2nd International Conference on Computing and Data Science (CDS) Pub Date : 2021-01-01 DOI: 10.1109/CDS52072.2021.00103
Sheng B, Silan Chen, Huayou Si, Yuesheng Zhu, Zhiqiang Bai, Shuo Li
{"title":"A Feature-based Deep Neural Framework for Poverty Prediction","authors":"Sheng B, Silan Chen, Huayou Si, Yuesheng Zhu, Zhiqiang Bai, Shuo Li","doi":"10.1109/CDS52072.2021.00103","DOIUrl":"https://doi.org/10.1109/CDS52072.2021.00103","url":null,"abstract":"Poverty eradication has been a challenge for humanity, especially when it comes to sustainability, if a family with a tendency to sink back into poverty can be detected, with necessary assistance provided in advance, such situation may be avoided. In order to detect them, we design a data-driven procedure to capture the relevance between poverty and related data, putting forward a deep neural multi-channel model to encode multi-type features, named Deep Poverty Forecast(DPF). In this paper, we also introduce a star graph to represent the relationship between family members and a data labeling method for supervised learning. Extensive experiments are conducted over five city datasets and our results show that our proposed framework has achieved much gain over previous methods. This solution applied at Data Platform of Poverty Reduction in Guangxi province has covered 84% of new families falling into poverty while the search space is reduced by over 90% according to the survey conducted by the local government. Our proposed framework can be easily applied to other family-related scenarios as well.","PeriodicalId":380426,"journal":{"name":"2021 2nd International Conference on Computing and Data Science (CDS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127321861","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
Undecidability of Underfitting in Learning Algorithms 学习算法中欠拟合的不确定性
2021 2nd International Conference on Computing and Data Science (CDS) Pub Date : 2021-01-01 DOI: 10.1109/CDS52072.2021.00107
Sonia Sehra, David Flores, George D. Montañez
{"title":"Undecidability of Underfitting in Learning Algorithms","authors":"Sonia Sehra, David Flores, George D. Montañez","doi":"10.1109/CDS52072.2021.00107","DOIUrl":"https://doi.org/10.1109/CDS52072.2021.00107","url":null,"abstract":"Using recent machine learning results that present an information-theoretic perspective on underfitting and overfitting, we prove that deciding whether an encodable learning algorithm will always underfit a dataset, even if given unlimited training time, is undecidable. We discuss the importance of this result and potential topics for further research, including information-theoretic and probabilistic strategies for bounding learning algorithm fit.","PeriodicalId":380426,"journal":{"name":"2021 2nd International Conference on Computing and Data Science (CDS)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116118183","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: 7
A Survey of Sentiment Analysis Based on Product Review 基于产品评论的情感分析研究综述
2021 2nd International Conference on Computing and Data Science (CDS) Pub Date : 2021-01-01 DOI: 10.1109/CDS52072.2021.00017
Yu-Chen Wei
{"title":"A Survey of Sentiment Analysis Based on Product Review","authors":"Yu-Chen Wei","doi":"10.1109/CDS52072.2021.00017","DOIUrl":"https://doi.org/10.1109/CDS52072.2021.00017","url":null,"abstract":"Sentiment analysis (SA) is becoming more and more irreplaceable when we talk about recommendation or analyze guest's preference. Sentiment classification gives prediction ratings or classifying the emotion into two or more poles. We are now looking forward to perfect the precision of the prediction of models. Nowadays, with the development of neural network, deep learning has been broadly used in sentiment analysis. In this paper, we survey large number of papers concerning sentiment classification with deep learning. We propose a new venue to make a division of all the models into 6 kinds. Moreover, we make a comparison of these results of models and draw a conclusion.","PeriodicalId":380426,"journal":{"name":"2021 2nd International Conference on Computing and Data Science (CDS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121884832","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
Chat-Oriented Social Engineering Attack Detection Using Attention-based Bi-LSTM and CNN 基于注意力的Bi-LSTM和CNN的面向聊天的社会工程攻击检测
2021 2nd International Conference on Computing and Data Science (CDS) Pub Date : 2021-01-01 DOI: 10.1109/CDS52072.2021.00089
Yuanyuan Lan
{"title":"Chat-Oriented Social Engineering Attack Detection Using Attention-based Bi-LSTM and CNN","authors":"Yuanyuan Lan","doi":"10.1109/CDS52072.2021.00089","DOIUrl":"https://doi.org/10.1109/CDS52072.2021.00089","url":null,"abstract":"As more traditional businesses, such as banking and finance, are transferred to online platforms or the cloud, the deepening of system interaction with users and the improvement of technology-based defence system make cyber attackers focus more on human beings, leading to serious financial consequences. This attack utilising social engineering often exploits human nature's weakness. Its complexity, language variability and inductivity are difficult to defend effectively. Therefore, this paper proposes a model for detecting social engineering attacks based on deep neural network by reviewing current methods for social engineering detection, in terms of phishing, deception and content-based detection, in addition to examining deep learning algorithms with excellent data performance. Through the processing and analysis of natural language in chat history, the attention-based Bi-LSTM is used to capture and mine the context semantics, and the ResNet is used to integrate user characteristics and content characteristics for classification and judgment. By describing the features of social engineering attacks and online conversations, the feasibility and effectiveness of the proposed model are demonstrated from the perspective of algorithm selection and applicability.","PeriodicalId":380426,"journal":{"name":"2021 2nd International Conference on Computing and Data Science (CDS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124650787","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
Multidimensional Scaling for Gene Sequence Data with Autoencoders 基于自编码器的基因序列数据多维缩放
2021 2nd International Conference on Computing and Data Science (CDS) Pub Date : 2021-01-01 DOI: 10.1109/CDS52072.2021.00095
P. Wickramasinghe, G. Fox
{"title":"Multidimensional Scaling for Gene Sequence Data with Autoencoders","authors":"P. Wickramasinghe, G. Fox","doi":"10.1109/CDS52072.2021.00095","DOIUrl":"https://doi.org/10.1109/CDS52072.2021.00095","url":null,"abstract":"Multidimensional scaling of gene sequence data has long played a vital role in analysing gene sequence data to identify clusters and patterns. However the computation complexities and memory requirements of state-of-the-art dimensional scaling algorithms make it infeasible to scale to large datasets. In this paper we present an autoencoder-based dimensional reduction model which can easily scale to datasets containing millions of gene sequences, while attaining results comparable to state-of-the-art MDS algorithms with minimal resource requirements. The model also supports out-of-sample data points with a 99.5%+ accuracy based on our experiments. The proposed model is evaluated against DAMDS with a real world fungi gene sequence dataset. The presented results showcase the effectiveness of the autoencoder-based dimension reduction model and its advantages.","PeriodicalId":380426,"journal":{"name":"2021 2nd International Conference on Computing and Data Science (CDS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129445861","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
2021 2nd International Conference on Computing and Data Science CDS 2021 - Title page 第二届计算与数据科学国际会议光盘2021 -标题页
2021 2nd International Conference on Computing and Data Science (CDS) Pub Date : 2021-01-01 DOI: 10.1109/cds52072.2021.00002
{"title":"2021 2nd International Conference on Computing and Data Science CDS 2021 - Title page","authors":"","doi":"10.1109/cds52072.2021.00002","DOIUrl":"https://doi.org/10.1109/cds52072.2021.00002","url":null,"abstract":"","PeriodicalId":380426,"journal":{"name":"2021 2nd International Conference on Computing and Data Science (CDS)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114207700","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
Object Tracking for Automatic Driving 自动驾驶的目标跟踪
2021 2nd International Conference on Computing and Data Science (CDS) Pub Date : 2021-01-01 DOI: 10.1109/CDS52072.2021.00053
Zhonghao Luo
{"title":"Object Tracking for Automatic Driving","authors":"Zhonghao Luo","doi":"10.1109/CDS52072.2021.00053","DOIUrl":"https://doi.org/10.1109/CDS52072.2021.00053","url":null,"abstract":"With the development of automatic driving technology, the object tracking based on computer vision is being widely used nowadays. In this paper an overview of object tracking methods in automatic driving are presented. Kalman filtering, LSTM CNN, correlation filtering and Deep Affinity Network will be introduced. Kalman filtering and Kalman filtering extension algorithms and Correlation filtering have been combined with deep learning algorithms about object detection. Learning goals in end-to-end way the appearance of the object characteristics and correlation in several frame, including its appearance modeling study on the hierarchical characteristics of the object and its surrounding. Finally, we conclude the object tracking in automatic driving.","PeriodicalId":380426,"journal":{"name":"2021 2nd International Conference on Computing and Data Science (CDS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131365848","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
Distantly Supervision for Relation Extraction via LayerNorm Gated Recurrent Neural Networks 基于分层范数门控递归神经网络的关系抽取远程监控
2021 2nd International Conference on Computing and Data Science (CDS) Pub Date : 2021-01-01 DOI: 10.1109/CDS52072.2021.00022
Siheng Wei
{"title":"Distantly Supervision for Relation Extraction via LayerNorm Gated Recurrent Neural Networks","authors":"Siheng Wei","doi":"10.1109/CDS52072.2021.00022","DOIUrl":"https://doi.org/10.1109/CDS52072.2021.00022","url":null,"abstract":"Relation extraction is a classic task in the NLP field which aims to predict the relation between two entities in given sentences. Convolutional neural network (CNN) is one of the typical neural network structures applied to this task. However, the existing CNN model used for extraction is not able to capture the time information in sentences which leads a great contribution to predict the right directionality between the two entities. Therefore, I propose a new gated recurrent neural networks with layer normalization (LNGRU) to obtain the background information of the future and the past in sentences. Experiments demonstrate that my model is effective and superior to several comparable baseline models.","PeriodicalId":380426,"journal":{"name":"2021 2nd International Conference on Computing and Data Science (CDS)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131502423","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
The Application of ADMM Algorithm in Optimization Problem with Absolute Terms ADMM算法在绝对项优化问题中的应用
2021 2nd International Conference on Computing and Data Science (CDS) Pub Date : 2021-01-01 DOI: 10.1109/CDS52072.2021.00038
Chenyang Wang
{"title":"The Application of ADMM Algorithm in Optimization Problem with Absolute Terms","authors":"Chenyang Wang","doi":"10.1109/CDS52072.2021.00038","DOIUrl":"https://doi.org/10.1109/CDS52072.2021.00038","url":null,"abstract":"Optimizations for objective functions with absolute terms appear frequently in practical problems, like classical least square method with absolute penalty (lasso), least absolute deviation (LAD) regression and graphical lasso with absolute penalty all have absolute terms in their objective functions. Corresponding algorithms have been given when the problems were proposed, for example, least angle regression (LARS) and coordinate descent algorithms for lasso, linear programming for LAD and glasso for Gaussian graphical model Although they solve the problems correctly, they are not uniform and can be dramatically improved in efficiency. Using the alternating direction method of multipliers (ADMM) algorithms, we established a general framework to solve problems like these. And we have conducted simulation experiments under different parameter settings, and the simulation results showed that the efficiency of ADMM algorithm is higher than or comparable to, that of existing methods.","PeriodicalId":380426,"journal":{"name":"2021 2nd International Conference on Computing and Data Science (CDS)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115051228","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
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