2019 IEEE International Conference on Smart Computing (SMARTCOMP)最新文献_第9页

Identifying Security Bug Reports Based Solely on Report Titles and Noisy Data 仅根据报告标题和噪声数据识别安全漏洞报告

2019 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2019-06-01 DOI: 10.1109/SMARTCOMP.2019.00026

Mayana Pereira, Alok Kumar, Scott Cristiansen

引用次数: 6

SmartSys 2019 Committees

2019 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2019-06-01 DOI: 10.1109/smartcomp.2019.00014

引用次数: 0

Exact and Approximate Drone Warehouse for a Mixed Landscape Delivery System 混合景观运输系统的精确和近似无人机仓库

2019 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2019-06-01 DOI: 10.1109/SMARTCOMP.2019.00062

Luca Bartoli, Francesco Betti Sorbelli, Federico Coró, M. C. Pinotti, Anil M. Shende

引用次数: 8

Indoor Person Identification and Fall Detection through Non-intrusive Floor Seismic Sensing 基于非侵入式地板地震传感的室内人员识别和跌倒检测

2019 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2019-06-01 DOI: 10.1109/SMARTCOMP.2019.00081

José Clemente, Wenzhan Song, Maria Valero, Fangyu Li, Xiangyang Li

{"title":"Indoor Person Identification and Fall Detection through Non-intrusive Floor Seismic Sensing","authors":"José Clemente, Wenzhan Song, Maria Valero, Fangyu Li, Xiangyang Li","doi":"10.1109/SMARTCOMP.2019.00081","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2019.00081","url":null,"abstract":"This paper presents a novel in-network person identification and fall detection system that uses floor seismic data produced by footsteps and fall downs as an only source for recognition. Compared with other existing methods, our approach is done in real-time, which means the system is able to identify a person almost immediately with only one or two footsteps. An adapted in-network localization method is proposed in which sensors collaborate among them to recognize the person walking, and most importantly, detect if the person falls down at any moment. We also introduce a voting system among sensor nodes to improve accuracy in person identification. Our system is innovative since it can be robust to identify fall downs from other possible events, like jumps, door close, objects fall down, etc. Such a smart system can also be connected to smart commercial devices (like Google Home or Amazon Alexa) for emergency notifications. Our approach represents an advance in smart technology for elder people who live alone. Evaluation of the system shows it is able to identify people with one or two steps in an average of 93.75% (higher accuracy than other methods that use more footsteps), and it detects fall downs with an acceptance rate of 95.14% (distinguishing from other possible events). The fall down localization error is smaller than 0.28 meters, which it is acceptable compared to the height of a person.","PeriodicalId":253364,"journal":{"name":"2019 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116141957","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: 19

Spatial Attention Mechanism for Weakly Supervised Fire and Traffic Accident Scene Classification 弱监督火灾和交通事故现场分类的空间注意机制

2019 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2019-06-01 DOI: 10.1109/SMARTCOMP.2019.00061

M. Moniruzzaman, Zhaozheng Yin, Ruwen Qin

{"title":"Spatial Attention Mechanism for Weakly Supervised Fire and Traffic Accident Scene Classification","authors":"M. Moniruzzaman, Zhaozheng Yin, Ruwen Qin","doi":"10.1109/SMARTCOMP.2019.00061","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2019.00061","url":null,"abstract":"During the past ten years, on average there were near 16.5 thousands of hazardous materials (hazmat) transport incidents per year resulting in $82 millions of damages. Prompt, accurate, objective assessment on hazmat incidents is important for the first-responders to take appropriate actions timely, which will reduce the damage of hazmat incidents and protect the safety of people and the environment. Therefore, one of the most important steps is to automatically detect transport incidents, such as fire and traffic accidents. In this paper, we introduce a simple and yet effective framework that integrates the convolutional feature maps of deep Convolutional Neural Network with a spatial attention mechanism for fire and traffic accident scene classification. Our spatial attention model learns to highlight the most discriminative convolutional features, which is related to the regions of interest in the input image. We train our network in a weakly supervised way. In other words, without the requirement of precise bounding box annotating the exact location of fire or traffic accidents in the image, our network can be learned from the only image-level label. In addition to the image-based traffic scene classification, the model is also applied on a set of collected videos for real-world applications. The proposed model, a simple end-to-end architecture, achieves promising performance on fire scene classification from images, and traffic accident scene classification from both images and videos.","PeriodicalId":253364,"journal":{"name":"2019 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125075322","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

Smart Assistance for Students and People Living in a Campus 为学生和校园居民提供智能帮助

2019 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2019-06-01 DOI: 10.1109/SMARTCOMP.2019.00042

S. Gaglio, G. Re, M. Morana, Claudio Ruocco

引用次数: 16

DeepCEP: Deep Complex Event Processing Using Distributed Multimodal Information DeepCEP:使用分布式多模态信息的深度复杂事件处理

2019 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2019-06-01 DOI: 10.1109/SMARTCOMP.2019.00034

Tianwei Xing, Marc Roig Vilamala, L. Garcia, Federico Cerutti, Lance M. Kaplan, A. Preece, M. Srivastava

{"title":"DeepCEP: Deep Complex Event Processing Using Distributed Multimodal Information","authors":"Tianwei Xing, Marc Roig Vilamala, L. Garcia, Federico Cerutti, Lance M. Kaplan, A. Preece, M. Srivastava","doi":"10.1109/SMARTCOMP.2019.00034","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2019.00034","url":null,"abstract":"Deep learning models typically make inferences over transient features of the latent space, i.e., they learn data representations to make decisions based on the current state of the inputs over short periods of time. Such models would struggle with state-based events, or complex events, that are composed of simple events with complex spatial and temporal dependencies. In this paper, we propose DeepCEP, a framework that integrates the concepts of deep learning models with complex event processing engines to make inferences across distributed, multimodal information streams with complex spatial and temporal dependencies. DeepCEP utilizes deep learning to detect primitive events. A user can define a complex event to be detected as a particular sequence or pattern of primitive events as well as any other logical predicates that constrain the definition of such an event. The integration of human logic not only increases robustness and interpretability, but also greatly reduces the amount of training data required. Further, we demonstrate how the uncertainty of a model can be propagated throughout the complex event detection pipeline. Finally, we enumerate the future directions of research enabled by DeepCEP. In particular, we detail how an end-to-end training model for complex event processing with deep learning may be realized.","PeriodicalId":253364,"journal":{"name":"2019 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132141006","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: 18

Using Delay Tolerant Networks as a Backbone for Low-Cost Smart Cities 用容延迟网络作为低成本智慧城市的骨干

2019 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2019-05-14 DOI: 10.1109/SMARTCOMP.2019.00090

Oluwashina Madamori, Esther Max-Onakpoya, Christan Earl Grant, C. Baker

引用次数: 9

On Maximizing Task Throughput in IoT-Enabled 5G Networks Under Latency and Bandwidth Constraints 时延和带宽限制下物联网5G网络任务吞吐量最大化研究

2019 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2019-04-10 DOI: 10.1109/SMARTCOMP.2019.00056

A. Pratap, Ragini Gupta, V. S. S. Nadendla, Sajal K. Das

引用次数: 14

A Reference Architecture for Smart and Software-Defined Buildings 智能和软件定义建筑的参考体系结构

2019 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2019-02-25 DOI: 10.1109/SMARTCOMP.2019.00048

M. Mazzara, Ilya M. Afanasyev, S. Sarangi, Salvatore Distefano, Vivek Kumar

引用次数: 17