2021 13th International Conference on Machine Learning and Computing最新文献_第9页

Pedestrian Trajectory Prediction with MLP-social-GRU 基于MLP-social-GRU的行人轨迹预测

2021 13th International Conference on Machine Learning and Computing Pub Date : 2021-02-26 DOI: 10.1145/3457682.3457737

Yanbo Zhang, Liying Zheng

{"title":"Pedestrian Trajectory Prediction with MLP-social-GRU","authors":"Yanbo Zhang, Liying Zheng","doi":"10.1145/3457682.3457737","DOIUrl":"https://doi.org/10.1145/3457682.3457737","url":null,"abstract":"When crossing a crowded area, a person can predict dangers or collisions in advance around him/her, and then makes a suitable decision which direction he/she should take. The pedestrian trajectory prediction aims at simulating such ability of humans in a crowded environment. Most of the existing trajectory predictions are all based on the traditional hand-crafted methods that often ignore critical factors and can only be adapted to specific environments. Based on deep learning technology, this paper proposes a data-driven pedestrian trajectory predictor called MLP-social-GRU. First, the proposed predictor processes a pedestrian trajectory with a Multilayer Perceptron (MLP). Then, it adopts Gated Recurrent Units (GRU) to get hidden features of a pedestrian motion patterns, from which relationships between pedestrians can be simulated. Next, the social-pooling is used to receive and merge the hidden status information to get the mutual influence of adjacent pedestrians. Finally, a unified pedestrian trajectory prediction framework is designed based on abovementioned modules. We evaluate our predictor on two publicly available datasets, ETH and UCY, and the results show that it is superior to popular models such as LSTM, social-LSTM, and goal-social-array.","PeriodicalId":142045,"journal":{"name":"2021 13th International Conference on Machine Learning and Computing","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132772021","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: 3

Diverse Conversation Generation System with Sentence Function Classification 具有句子功能分类的多元会话生成系统

2021 13th International Conference on Machine Learning and Computing Pub Date : 2021-02-26 DOI: 10.1145/3457682.3457761

Zuning Fan, Liangwei Chen

{"title":"Diverse Conversation Generation System with Sentence Function Classification","authors":"Zuning Fan, Liangwei Chen","doi":"10.1145/3457682.3457761","DOIUrl":"https://doi.org/10.1145/3457682.3457761","url":null,"abstract":"This paper mainly studies the implementation of diverse conversation generation system based on end-to-end neural networks and sentence classification on sentence function. In existing work, the output of the conversational system is mostly in the form of one type of sentence, for instance, declarative sentence. The type of sentence function is not well distributed. There is still insufficient diversity in the output of conversational system, which could be unattractive to users. A good conversational system could well interact with users by generating diverse output, including asking and responding, driving conversations to go further. Generating different type of output sentence is necessary to conversational systems, which is also challenging. Therefore, this paper introduces the idea of diverse conversation into the generative system, and designs the Diverse Conversation Generation (DCG) model. The model adopts a sentence function classifier trained independently to supervise the model output with modified loss function and back-propagation. The DCG model increases the diversity of output sentence, which could guide user to chat more with the system, extend the quality of conversation, and improve the user experience. The model is experimented on two different sequence-to sequence models, evaluated with perplexity and classify entropy, achieves better performance compared with two base models.","PeriodicalId":142045,"journal":{"name":"2021 13th International Conference on Machine Learning and Computing","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115716901","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

AEE-Net: An Efficient End-to-End Dehazing Network in UAV Imaging System AEE-Net:无人机成像系统中高效的端到端除雾网络

2021 13th International Conference on Machine Learning and Computing Pub Date : 2021-02-26 DOI: 10.1145/3457682.3457739

Tianxiao Cai, Sheng Zhang, Bo Tan

{"title":"AEE-Net: An Efficient End-to-End Dehazing Network in UAV Imaging System","authors":"Tianxiao Cai, Sheng Zhang, Bo Tan","doi":"10.1145/3457682.3457739","DOIUrl":"https://doi.org/10.1145/3457682.3457739","url":null,"abstract":"Because it can provide real-time images for the first time, UAV plays a massive role in disaster relief, environmental observation, and information collection. However, the quality of images collected by UAV is always affected by fog. Therefore, the research on how to remove the fog in the image becomes more and more critical. In recent years, the role of convolutional neural networks (CNN), which can automatically extract features and efficiently process high-dimensional data, has received more and more attention in many disciplines. To improve the imaging quality of UAV in a foggy environment, this paper proposes an image dehazing model built with a convolutional neural network (CNN), called an effective end-to-end dehazing Network (AEE-Net). Our proposed method has a faster running speed than traditional models due to the simple structure of the model and the design based on the modified atmospheric scattering model. Our method combines the characteristics of dehazing processes and the advantages of deep learning. Experimental results on the training set and raw images show that the proposed method has better performance than traditional methods. This method can improve the quality of UAV-captured images under foggy conditions and can meet the input requirements of UAV vision tasks.","PeriodicalId":142045,"journal":{"name":"2021 13th International Conference on Machine Learning and Computing","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116317446","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

Using Deep Learning to Construct Auto Web Penetration Test 利用深度学习构建汽车Web渗透测试

2021 13th International Conference on Machine Learning and Computing Pub Date : 2021-02-26 DOI: 10.1145/3457682.3457691

Jian Jiao, Haini Zhao, Hongsheng Cao

引用次数: 1

Using Video Restoration to Improve Face Forgery Detection Based on Low-quality Video 基于低质量视频的视频复原改进人脸伪造检测

2021 13th International Conference on Machine Learning and Computing Pub Date : 2021-02-26 DOI: 10.1145/3457682.3457753

Jianyang Qi, Peng Liang, Gang Hao, Yuting Wu

引用次数: 0

Toward an Effective Analysis of COVID-19 Moroccan Business Survey Data using Machine Learning Techniques 利用机器学习技术有效分析COVID-19摩洛哥商业调查数据

2021 13th International Conference on Machine Learning and Computing Pub Date : 2021-02-26 DOI: 10.1145/3457682.3457690

Imane Lasri, Anouar Riadsolh, Mourad Elbelkacemi

{"title":"Toward an Effective Analysis of COVID-19 Moroccan Business Survey Data using Machine Learning Techniques","authors":"Imane Lasri, Anouar Riadsolh, Mourad Elbelkacemi","doi":"10.1145/3457682.3457690","DOIUrl":"https://doi.org/10.1145/3457682.3457690","url":null,"abstract":"COVID-19 pandemic has gravely affected our societies and economies with severe consequences. To contain the spread of the disease, most governments around the world authorized unprecedented measures, including Morocco, which has closed the borders and adopted full lockdown between March and June 2020. However, these measures have resulted in economic loss and have led to dramatic changes in how businesses act and consumers behave. The main focus of this study was to examine the impact of the full lockdown on Moroccan enterprises based on the COVID-19 Moroccan business survey carried out by the High Commission for Planning (HCP). A three-stage analysis method was employed. First, multiple correspondence analysis (MCA) was used to reduce the dimensionality of the categorical variables, and k-means clustering algorithm was used to cluster the data, then decision tree algorithm was performed in order to interpret each cluster and the maximum accuracy achieved is 84.45%. Compared with the decision tree algorithm, an artificial neural network (ANN) with stratified 10-fold cross-validation was applied to the dataset and has reached an accuracy of 83.4%. The simulation results confirm the effectiveness of the proposed techniques for analyzing survey data.","PeriodicalId":142045,"journal":{"name":"2021 13th International Conference on Machine Learning and Computing","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130571348","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

Inter-domain Link Inference with Confidence Using Naïve Bayes Classifier 使用Naïve贝叶斯分类器的域间链接推理

2021 13th International Conference on Machine Learning and Computing Pub Date : 2021-02-26 DOI: 10.1145/3457682.3457715

Yi Zhao, Yan Liu, Xiaoyu Guo, ZhongHang Sui

引用次数: 0

Single-Pass On-Line Event Detection in Twitter Streams Twitter流中的单次在线事件检测

2021 13th International Conference on Machine Learning and Computing Pub Date : 2021-02-26 DOI: 10.1145/3457682.3457762

Xingfa Qiu, Qiaosha Zou, C. Richard Shi

{"title":"Single-Pass On-Line Event Detection in Twitter Streams","authors":"Xingfa Qiu, Qiaosha Zou, C. Richard Shi","doi":"10.1145/3457682.3457762","DOIUrl":"https://doi.org/10.1145/3457682.3457762","url":null,"abstract":"Intensive information is emerged in social media every second. Many breaking news often appear first in social media, much earlier than they appear in traditional news media. Through the technology of event detection on social media data streams, scatter information can be gathered together to inform us the popular events discussing online. An event is often modeled as a cluster of documents which discuss the same subject. Traditional event detection methods perform poorly on social media because of their huge amount of data and irregular expressions. In this paper, we propose a simple yet efficient event detection method towards social media. An event is represented by a sequence of keywords extracted from social media. We use a single-pass incremental clustering method with a trained encoder mapping documents and events into the same semantic space, which is helpful for the similarity calculation between them. We consider the similarity calculation between a tweet and an event as a matching process and construct a relevance matching dataset with tweet-event pairs. We finetune BERT (Bidirectional Encoder Representations from Transformers) model in the matching dataset to get an appropriate semantic encoder. Keywords are dynamically changed to represent an event for capturing the development of the event. Our proposed method achieves 0.86 on NMI (Normed Mutual Information), 0.69 on ARI (Adjusted Rand Index) and 0.70 on F1-score on a public twitter dataset, which shows the superiority of our method compared with baseline methods.","PeriodicalId":142045,"journal":{"name":"2021 13th International Conference on Machine Learning and Computing","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127207776","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

ICodeNet - A Hierarchical Neural Network Approach For Source Code Author Identification 源代码作者识别的层次神经网络方法

2021 13th International Conference on Machine Learning and Computing Pub Date : 2021-01-30 DOI: 10.1145/3457682.3457709

Pranali Bora, Tulika Awalgaonkar, Himanshu Palve, Raviraj Joshi, Purvi Goel

引用次数: 4

Content-Based Textual File Type Detection at Scale 大规模基于内容的文本文件类型检测

2021 13th International Conference on Machine Learning and Computing Pub Date : 2021-01-21 DOI: 10.1145/3457682.3457756

Francesca Del Bonifro, M. Gabbrielli, Stefano Zacchiroli

{"title":"Content-Based Textual File Type Detection at Scale","authors":"Francesca Del Bonifro, M. Gabbrielli, Stefano Zacchiroli","doi":"10.1145/3457682.3457756","DOIUrl":"https://doi.org/10.1145/3457682.3457756","url":null,"abstract":"Programming language detection is a common need in the analysis of large source code bases. It is supported by a number of existing tools that rely on several features, and most notably file extensions, to determine file types. We consider the problem of accurately detecting the type of files commonly found in software code bases, based solely on textual file content. Doing so is helpful to classify source code that lack file extensions (e.g., code snippets posted on the Web or executable scripts), to avoid misclassifying source code that has been recorded with wrong or uncommon file extensions, and also shed some light on the intrinsic recognizability of source code files. We propose a simple model that (a) use a language-agnostic word tokenizer for textual files, (b) group tokens in 1-/2-grams, (c) build feature vectors based on N-gram frequencies, and (d) use a simple fully connected neural network as classifier. As training set we use textual files extracted from GitHub repositories with at least 1000 stars, using existing file extensions as ground truth. Despite its simplicity the proposed model reaches ≈ 85% in our experiments for a relatively high number of recognized classes (more than 130 file types).","PeriodicalId":142045,"journal":{"name":"2021 13th International Conference on Machine Learning and Computing","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116766747","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