2019 IEEE International Conference on Smart Computing (SMARTCOMP)最新文献_第8页

SmartStudents 2019 Committees 智慧学生2019委员会

2019 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2019-06-01 DOI: 10.1109/smartcomp.2019.00019

引用次数: 0

Message from the SmartStudents 2019 Advisory Chairs and Student Workshop Chairs 2019智能学生咨询主席和学生研讨会主席的信息

2019 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2019-06-01 DOI: 10.1109/smartcomp.2019.00018

引用次数: 0

Precept-Based Framework for Using Crowdsourcing in IoT-Based Systems 在物联网系统中使用众包的基于规则的框架

2019 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2019-06-01 DOI: 10.1109/SMARTCOMP.2019.00077

Urjaswala Vora, Peeyush Chomal, Avani Vakharwala

{"title":"Precept-Based Framework for Using Crowdsourcing in IoT-Based Systems","authors":"Urjaswala Vora, Peeyush Chomal, Avani Vakharwala","doi":"10.1109/SMARTCOMP.2019.00077","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2019.00077","url":null,"abstract":"Cyber-physical systems (CPS) provide the foundation of critical infrastructure, form the basis of emerging and future smart services, and improve quality of life. The technological advances such as edge-centric computing have pushed the frontier of computing applications, data, and services away from the centralized nodes to the logical extremes of a network. It enables analytics and knowledge generation to occur at the source of the data. In this paper we present the amalgamation of three paradigms, each at different level of system development. Precept is a design paradigm for modeling complex control structures of the activities. It is a declaratively modeled control component that prohibits undesired couplings that are otherwise unavoidable in imperatively programmed control components. Crowdsourcing for system integration harnesses the collective intelligence or knowledge base of crowd's wisdom when given the right set of conditions. Here we discuss the aptness of crowdsourcing in the development of IoT-based systems that integrate the smart components from varied domains. We define a precept based software development framework that uses crowdsourcing to develop and to continuously extend a more sustainable and adaptive IoT-based system.","PeriodicalId":253364,"journal":{"name":"2019 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"31 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":"122402024","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

Towards Trustless Prediction-as-a-Service 迈向无信任预测即服务

2019 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2019-06-01 DOI: 10.1109/SMARTCOMP.2019.00068

G. Santhosh, Dario Bruneo, F. Longo, A. Puliafito

{"title":"Towards Trustless Prediction-as-a-Service","authors":"G. Santhosh, Dario Bruneo, F. Longo, A. Puliafito","doi":"10.1109/SMARTCOMP.2019.00068","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2019.00068","url":null,"abstract":"Prediction-as-a-Service is a promising new paradigm that brings the advantages of Software-as-a-Service's business model to the world of prediction APIs. In such a scenario, prediction API providers can leverage a Cloud provider's infrastructure to offer their inference service to the general public without having to worry about infrastructure acquisition and operation costs. Indeed, in the case of prediction APIs, self-hosting costs could be much higher than usual due to the fact that inference models, e.g., deep learning models, need specific hardware (e.g., graphical processing units) for an efficient execution. In such a context, trust is of great importance as the prediction API provider's most valuable asset, i.e., the inference model, is transferred to the Cloud provider. Thus, specific countermeasures should be designed to mitigate the possible attacks. In this paper, we analyze this scenario identifying the peculiar threat models. Then, we present a decentralized blockchain-based system, implemented on top of the popular Tendermint framework, that provides countermeasures to some of the main attacks. Numerical results, obtained executing deep neural network models, demonstrate that the overhead with respect to a centralized approach is negligible if compared with the advantages in terms of prevention of malicious behaviors.","PeriodicalId":253364,"journal":{"name":"2019 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"40 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":"127821499","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

SmartSys 2019 Keynote

2019 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2019-06-01 DOI: 10.1109/smartcomp.2019.00015

引用次数: 0

SMARTCOMP 2019 Organizing Committee SMARTCOMP 2019组委会

2019 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2019-06-01 DOI: 10.1109/smartcomp.2019.00006

引用次数: 0

Developing Machine Learning Based Predictive Models for Smart Policing 开发基于机器学习的智能警务预测模型

2019 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2019-06-01 DOI: 10.1109/SMARTCOMP.2019.00053

Lavanya Elluri, V. Mandalapu, Nirmalya Roy

{"title":"Developing Machine Learning Based Predictive Models for Smart Policing","authors":"Lavanya Elluri, V. Mandalapu, Nirmalya Roy","doi":"10.1109/SMARTCOMP.2019.00053","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2019.00053","url":null,"abstract":"Crimes are problematic where normal social issues are confronted and influence personal satisfaction, financial development, and quality-of-life of a region. There has been a surge in the crime rate over the past couple of years. To reduce the offense rate, law enforcement needs to embrace innovative preventive technological measures. Accurate crime forecasts help to decrease the crime rate. However, predicting criminal activities is difficult due to the high complexity associated with modeling numerous intricate elements. In this work, we employ statistical analysis methods and machine learning models for predicting different types of crimes in New York City, based on 2018 crime datasets. We combine weather, and its temporal attributes like cloud cover, lighting and time of day to identify relevance to crime data. We note that weatherrelated attributes play a negligible role in crime forecasting. We have evaluated the various performance metrics of crime prediction, with and without the consideration of weather datasets, on different types of crime committed. Our proposed methodology will enable law enforcement to make effective decisions on appropriate resource allocation, including backup officers related to crime type and location.","PeriodicalId":253364,"journal":{"name":"2019 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"39 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":"121430076","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: 11

Towards Automatic Semantic Models by Extraction of Relevant Information from Online Text 基于在线文本相关信息提取的自动语义模型研究

2019 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2019-06-01 DOI: 10.1109/SMARTCOMP.2019.00094

L. Krupp, Agnes Grünerbl, G. Bahle, P. Lukowicz

{"title":"Towards Automatic Semantic Models by Extraction of Relevant Information from Online Text","authors":"L. Krupp, Agnes Grünerbl, G. Bahle, P. Lukowicz","doi":"10.1109/SMARTCOMP.2019.00094","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2019.00094","url":null,"abstract":"Monitoring of human activities is an essential capability of many smart systems. In recent years much progress has been achieved. One of the key remaining challenges is the availability of labeled training data, in particular taking into account the degree of variability in human activities. A possible solution is to leverage large scale online data repositories. This has been previously attempted with image and sound data, as both microphones and cameras are widely used sensing modalities. In this paper, we describe a first step towards the use of online, text-based activity descriptions to support general sensor-based activity recognition systems. The idea is to extract semantic information from online texts about the way complex activities are composed of simple ones that have to be performed (e.g. a manual for assembling a furniture piece) and use such a semantic description in conjunction with sensor based, statistical classifiers of basic actions to recognize the complex activities and compose them into semantic trees. Extraction of domain relevant information evaluated in 11 different text-based manuals from different domains reached an average recall of 77%, and precision of 88%. Actual structural error-rate in the construction of respective trees was around 1%.","PeriodicalId":253364,"journal":{"name":"2019 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"8 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":"127649267","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

A Scalable Approach for Dynamic Evacuation Routing in Large Smart Buildings 大型智能建筑动态疏散路径的可扩展方法

2019 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2019-06-01 DOI: 10.1109/SMARTCOMP.2019.00065

Van-Quyet Nguyen, Huu-Duy Nguyen, H. Thang, N. Venkatasubramanian, Kyungbaek Kim

{"title":"A Scalable Approach for Dynamic Evacuation Routing in Large Smart Buildings","authors":"Van-Quyet Nguyen, Huu-Duy Nguyen, H. Thang, N. Venkatasubramanian, Kyungbaek Kim","doi":"10.1109/SMARTCOMP.2019.00065","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2019.00065","url":null,"abstract":"This paper considers the problem of dynamic evacuation routing in large smart buildings. We investigate a scalable routing approach which not only generates effective routes for evacuees but also quickly updates routes as the disaster status and building conditions could change during the evacuation time. We first design a flexible and scalable evacuation system for large smart buildings with multiple levels of computational support. Given such a system, we develop a novel distributed algorithm for finding effective evacuation routes dynamically by using an LCDT (Length-Capacity-Density-Trustiness) weighted graph model, which is built upon the current disaster information and building conditions. Finally, we propose a caching strategy which expedites dynamic route generation with the current effective route part(s) in order to improve the performance of dynamic evacuation in large buildings. To validate our approach, we test the proposed algorithm with our implementation of an evacuation simulator and compare the results with other approaches. Experimental results show that our approach outperforms other ones in the aspect of the evacuation time reduction and the maximum number of people being evacuated in each time span.","PeriodicalId":253364,"journal":{"name":"2019 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"2017 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":"133115861","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

Fully Homomorphic Encryption with Table Lookup for Privacy-Preserving Smart Grid 隐私保护智能电网的全同态表查找加密

2019 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2019-06-01 DOI: 10.1109/SMARTCOMP.2019.00023

Ruixiao Li, Yu Ishimaki, H. Yamana

{"title":"Fully Homomorphic Encryption with Table Lookup for Privacy-Preserving Smart Grid","authors":"Ruixiao Li, Yu Ishimaki, H. Yamana","doi":"10.1109/SMARTCOMP.2019.00023","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2019.00023","url":null,"abstract":"Smart grids are indispensable applications in smart connected communities (SCC). To construct privacy-preserving anomaly detection systems on a smart grid, we adopt fully homomorphic encryption (FHE) to protect users' sensitive data. Although FHE allows a third party to perform calculations on encrypted data without decryption, FHE only supports addition and multiplication on encrypted data. In anomaly detection, we must calculate both harmonic and arithmetic means consisting of logarithms. A naïve implementation of such arithmetic operations with FHE is a bitwise operation; thus, it requires huge computation time. To speed up such calculations, we propose an efficient protocol to evaluate any functions with FHE using a lookup table (LUT). Our protocol allows integer encoding, i.e., a set of integers is encrypted as a single ciphertext, rather than using bitwise encoding. Our experimental results in a multi-threaded environment show that the runtime of our protocol is approximately 51 s when the size of the LUT is 448,000. Our protocol is more practical than the previously proposed bitwise implementation.","PeriodicalId":253364,"journal":{"name":"2019 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"103 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":"114850453","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: 6