2023 IEEE International Conference on Smart Computing (SMARTCOMP)最新文献_第2页

SMARTCOMP 2023 Organizing Committee SMARTCOMP 2023组委会

2023 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2023-06-01 DOI: 10.1109/smartcomp58114.2023.00006

引用次数: 0

CA-Wav2Lip: Coordinate Attention-based Speech To Lip Synthesis In The Wild CA-Wav2Lip:在野外协调基于注意的语音到嘴唇合成

2023 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2023-06-01 DOI: 10.1109/SMARTCOMP58114.2023.00018

Kuan-Chien Wang, J. Zhang, Jingquan Huang, Qi Li, Minmin Sun, Kazuya Sakai, Wei-Shinn Ku

引用次数: 0

SMARTCOMP 2023 Technical Program Committee SMARTCOMP 2023技术计划委员会

2023 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2023-06-01 DOI: 10.1109/smartcomp58114.2023.00007

引用次数: 0

Privacy-preserving Real-world Video Anomaly Detection 保护隐私的真实世界视频异常检测

2023 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2023-06-01 DOI: 10.1109/SMARTCOMP58114.2023.00067

Ghazal Alinezhad Noghre

引用次数: 0

Message from the General and TPC Co-Chairs 总主席和结核结核联合主席的致辞

2023 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2023-06-01 DOI: 10.1109/smartcomp58114.2023.00005

引用次数: 0

SSC 2023 Message from Workshop Co-Chairs SSC 2023研讨会联合主席致辞

2023 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2023-06-01 DOI: 10.1109/smartcomp58114.2023.00016

引用次数: 0

A Model Based Decision Support System for Smart Cities 基于模型的智慧城市决策支持系统

2023 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2023-06-01 DOI: 10.1109/SMARTCOMP58114.2023.00065

Mostafa Zaman

{"title":"A Model Based Decision Support System for Smart Cities","authors":"Mostafa Zaman","doi":"10.1109/SMARTCOMP58114.2023.00065","DOIUrl":"https://doi.org/10.1109/SMARTCOMP58114.2023.00065","url":null,"abstract":"The escalating population growth and urbanization have led to a surge in the demand for smart cities. Nonetheless, handling and evaluating the vast data produced by Internet of Things (IoT) sensors requires significant effort. Therefore, implementing intelligent decision support systems is crucial for analyzing real-time data and optimizing city operations while tackling uncertain events. This study discusses the architectural flow diagram of a smart city decision support system that employs reinforcement learning techniques to enhance traffic management, minimize energy consumption, elevate public safety, and reduce risks in a constantly changing and unpredictable environment. This system comprises various components that work in tandem to provide customized real-time recommendations for a given situation. The capacity of the system to produce recommendations in real-time while taking into account the likelihood of various outcomes has the potential to enhance performance and facilitate more efficient decision-making in intricate settings. In general, this system will exhibit the capability to improve emergency response and public safety to a considerable extent in smart cities.","PeriodicalId":163556,"journal":{"name":"2023 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116881000","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

A Prototype for QKD-secure Serverless Computing with ETSI MEC 基于ETSI MEC的qkd安全无服务器计算原型

2023 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2023-06-01 DOI: 10.1109/SMARTCOMP58114.2023.00043

C. Cicconetti, M. Conti, E. Lella, Pietro Noviello, Gennaro Davide Paduanelli, Andrea Passarella, Elisabetta Storelli

引用次数: 0

Robust Detection of Social Isolation in Older Adults by Combining Biometrics with Social Interaction Data 结合生物识别与社会互动数据对老年人社会隔离的稳健检测

2023 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2023-06-01 DOI: 10.1109/smartcomp58114.2023.00057

Raghav Mehrotra-Venkat, N. Dutt, J. Rousseau

{"title":"Robust Detection of Social Isolation in Older Adults by Combining Biometrics with Social Interaction Data","authors":"Raghav Mehrotra-Venkat, N. Dutt, J. Rousseau","doi":"10.1109/smartcomp58114.2023.00057","DOIUrl":"https://doi.org/10.1109/smartcomp58114.2023.00057","url":null,"abstract":"Several recent studies, in the aftermath of Covid-19, point to dangers of social isolation that negatively impacts both mental and physical health especially amongst older adults. Isolation often leads to self-destructive behaviour such as drug and alcohol misuse deteriorating the quality of life and compounding health complications. Recent research has explored several technologies to detect the onset of isolation and to trigger interventions (e.g., nudge caregivers, etc.) to mitigate its impact. Such mechanisms span a range from using survey instruments, using biometrics to detect stress (an effect of isolation), and those using monitoring social interactions to detect loneliness amongst individuals. This paper studies biometric based and social interaction-based methods with the objective to understand their relative benefits/disadvantages and explores their combined usage to create a robust isolation detection mechanism. In particular, we explore the design of an integrated system based on both biometric (via wearables) and social interaction data (using call log analysis) to study their efficacy both individually and in combination.","PeriodicalId":163556,"journal":{"name":"2023 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"135 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134022433","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

Feature Engineering in Machine Learning-Based Intrusion Detection Systems for OT Networks 基于机器学习的OT网络入侵检测系统特征工程

2023 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2023-06-01 DOI: 10.1109/SMARTCOMP58114.2023.00086

Alex Howe, M. Papa

{"title":"Feature Engineering in Machine Learning-Based Intrusion Detection Systems for OT Networks","authors":"Alex Howe, M. Papa","doi":"10.1109/SMARTCOMP58114.2023.00086","DOIUrl":"https://doi.org/10.1109/SMARTCOMP58114.2023.00086","url":null,"abstract":"This paper evaluates the importance of feature exploration and engineering when applying machine learning for intrusion detection in OT (Operational Technology) networks. Data used consisted of raw network traffic captures from a simulated OT environment communicating over the Modbus/TCP protocol. Feature engineering efforts identified thirty eight attributes of interest at the different layers of the network stack. The Random Forest algorithm was used to analyze the importance of each feature for the detection of anomalous network behavior. Both supervised and unsupervised learning methods were evaluated including Random Forest, Support Vector Machines, K-Nearest Neighbors, K-Means Clustering, and Isolation Forest. Results indicate that statistical based features as well as features derived from the protocol and application layers contained information best suited for detecting anomalous OT behavior. Additionally, variable importance-based feature selection helped reduce complexity and improved detection rate when compared with models trained on the original high dimensional data. Random Forest and Support Vector Machines had the best detection performance but required a large amount of labeled data for training and validation. Notably, Isolation Forest shows potential for anomaly detection in OT networks as it requires no labeled data and produced promising results.","PeriodicalId":163556,"journal":{"name":"2023 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131951926","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