João Phillipe Cardenuto, Jing Yang, Rafael Padilha, Renjie Wan, Daniel Moreira, Haoliang Li, Shiqi Wang, Fernanda Andaló, Sébastien Marcel, Anderson Rocha
{"title":"The Age of Synthetic Realities: Challenges and Opportunities","authors":"João Phillipe Cardenuto, Jing Yang, Rafael Padilha, Renjie Wan, Daniel Moreira, Haoliang Li, Shiqi Wang, Fernanda Andaló, Sébastien Marcel, Anderson Rocha","doi":"10.1561/116.00000138","DOIUrl":"https://doi.org/10.1561/116.00000138","url":null,"abstract":"","PeriodicalId":44812,"journal":{"name":"APSIPA Transactions on Signal and Information Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135447314","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}
Jiaying Liu, Wen-Huang Cheng, Jenq-Neng Hwang, lvan V. Bajic, Shiqi Wang, Junseok Kwon, Ngai-Man Cheung, Rei Kawakami
The continuously expanding urban environment introduces a significant amount of both physical and digital infrastructure. The accompanying solution that collects environmental big data through the Internet of Things (IoT) holds great promise, opening up new opportunities as well as challenges. On one hand, billions of sensors and devices continuously collect, process, and transmit data. The data volume poses the challenge for supporting the decision-making in an automatic and intelligent way. On the other hand, the dynamism of data, the complexity of the environment, and the diversity of tasks also set the barrier to the intelligent processing paradigm of smart infrastructure. Fortunately, recent advancements in AI technologies offer cost-effective solutions that are capable of substantially improving modern metropolitan smart infrastructure. This special issue focuses smart sensors, smart communications, smart analytics, and applications for smart infrastructure, introducing the relevant background and discussing potential beneficial technical routes. This special issue has collected seven excellent articles recognized by the reviewers and highly recommended by the editors.
{"title":"Editorial for Special Issue on Emerging AI Technologies for Smart Infrastructure","authors":"Jiaying Liu, Wen-Huang Cheng, Jenq-Neng Hwang, lvan V. Bajic, Shiqi Wang, Junseok Kwon, Ngai-Man Cheung, Rei Kawakami","doi":"10.1561/116.00001101","DOIUrl":"https://doi.org/10.1561/116.00001101","url":null,"abstract":"The continuously expanding urban environment introduces a significant amount of both physical and digital infrastructure. The accompanying solution that collects environmental big data through the Internet of Things (IoT) holds great promise, opening up new opportunities as well as challenges. On one hand, billions of sensors and devices continuously collect, process, and transmit data. The data volume poses the challenge for supporting the decision-making in an automatic and intelligent way. On the other hand, the dynamism of data, the complexity of the environment, and the diversity of tasks also set the barrier to the intelligent processing paradigm of smart infrastructure. Fortunately, recent advancements in AI technologies offer cost-effective solutions that are capable of substantially improving modern metropolitan smart infrastructure. This special issue focuses smart sensors, smart communications, smart analytics, and applications for smart infrastructure, introducing the relevant background and discussing potential beneficial technical routes. This special issue has collected seven excellent articles recognized by the reviewers and highly recommended by the editors.","PeriodicalId":44812,"journal":{"name":"APSIPA Transactions on Signal and Information Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135450871","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}
{"title":"PointFlowHop: Green and Interpretable Scene Flow Estimation from Consecutive Point Clouds","authors":"Pranav Kadam, Jiahao Gu, Shan Liu, C.-C. Jay Kuo","doi":"10.1561/116.00000006","DOIUrl":"https://doi.org/10.1561/116.00000006","url":null,"abstract":"","PeriodicalId":44812,"journal":{"name":"APSIPA Transactions on Signal and Information Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135733701","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}
Kun-Chih (Jimmy) Chen, Wen-Hsiao Peng, Chris Gwo Giun Lee
{"title":"Overview of Intelligent Signal Processing Systems","authors":"Kun-Chih (Jimmy) Chen, Wen-Hsiao Peng, Chris Gwo Giun Lee","doi":"10.1561/116.00000053","DOIUrl":"https://doi.org/10.1561/116.00000053","url":null,"abstract":"","PeriodicalId":44812,"journal":{"name":"APSIPA Transactions on Signal and Information Processing","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67081078","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}
{"title":"Missing Data Completion of Multi-channel Signals Using Autoencoder for Acoustic Scene Classification","authors":"Yuki Shiroma, Yuma Kinoshita, Keisuke Imoto, Sayaka Shiota, Nobutaka Ono, H. Kiya","doi":"10.1561/116.00000074","DOIUrl":"https://doi.org/10.1561/116.00000074","url":null,"abstract":"","PeriodicalId":44812,"journal":{"name":"APSIPA Transactions on Signal and Information Processing","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67081183","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}
Ganning Zhao, Vasileios Magoulianitis, Suya You, C. J. Kuo
Although there are metrics to evaluate the performance of generative models, little research is conducted on the quality evaluation of individual generated samples. A lightweight generated sample quality evaluation (LGSQE) method is proposed in this work. LGSQE trains a binary classifier to differentiate real and synthetic images from a generative model and, then, uses it to assign a soft label between zero and one to a generated sample as its quality index. LGSQE can reject poor generations and serve as a post-processing module for quality control. Furthermore, by aggregating quality indices of a large number of generated samples, LGSQE offers four metrics (i.e., classification accuracy (Acc), the area under the curve (AUC), precision, and recall) to evaluate the performance of a generative model as a byproduct. LGSQE demands a significantly smaller memory size and faster evaluation time while preserving the same rank order predicted by the Fréchet Inception Distance (FID). Extensive experiments are conducted to demonstrate the effectiveness and efficiency of LGSQE.
{"title":"Lightweight Quality Evaluation of Generated Samples and Generative Models","authors":"Ganning Zhao, Vasileios Magoulianitis, Suya You, C. J. Kuo","doi":"10.1561/116.00000076","DOIUrl":"https://doi.org/10.1561/116.00000076","url":null,"abstract":"Although there are metrics to evaluate the performance of generative models, little research is conducted on the quality evaluation of individual generated samples. A lightweight generated sample quality evaluation (LGSQE) method is proposed in this work. LGSQE trains a binary classifier to differentiate real and synthetic images from a generative model and, then, uses it to assign a soft label between zero and one to a generated sample as its quality index. LGSQE can reject poor generations and serve as a post-processing module for quality control. Furthermore, by aggregating quality indices of a large number of generated samples, LGSQE offers four metrics (i.e., classification accuracy (Acc), the area under the curve (AUC), precision, and recall) to evaluate the performance of a generative model as a byproduct. LGSQE demands a significantly smaller memory size and faster evaluation time while preserving the same rank order predicted by the Fréchet Inception Distance (FID). Extensive experiments are conducted to demonstrate the effectiveness and efficiency of LGSQE.","PeriodicalId":44812,"journal":{"name":"APSIPA Transactions on Signal and Information Processing","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67081197","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}
{"title":"A Real-Time DDoS Attack Detection and Classification System Using Hierarchical Temporal Memory","authors":"Yu-Kuen Lai, M. Nguyen","doi":"10.1561/116.00000147","DOIUrl":"https://doi.org/10.1561/116.00000147","url":null,"abstract":"","PeriodicalId":44812,"journal":{"name":"APSIPA Transactions on Signal and Information Processing","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67081614","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}
Min Zhang, Jintang Xue, Pranav Kadam, Hardik Prajapati, Shan Liu, C.-C. Jay Kuo
{"title":"A Tiny Machine Learning Model for Point Cloud Object Classification","authors":"Min Zhang, Jintang Xue, Pranav Kadam, Hardik Prajapati, Shan Liu, C.-C. Jay Kuo","doi":"10.1561/116.00000114","DOIUrl":"https://doi.org/10.1561/116.00000114","url":null,"abstract":"","PeriodicalId":44812,"journal":{"name":"APSIPA Transactions on Signal and Information Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136137115","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}
{"title":"ExAD-GNN: Explainable Graph Neural Network for Alzheimer’s Disease State Prediction from Single-cell Data","authors":"Ziheng Duan, Cheyu Lee, Jing Zhang","doi":"10.1561/116.00000239","DOIUrl":"https://doi.org/10.1561/116.00000239","url":null,"abstract":"","PeriodicalId":44812,"journal":{"name":"APSIPA Transactions on Signal and Information Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135594886","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}
Yao Zhu, Xinyu Wang, Hong-Shuo Chen, Ronald Salloum, C.-C. Jay Kuo
{"title":"Green Steganalyzer: A Green Learning Approach to Image Steganalysis","authors":"Yao Zhu, Xinyu Wang, Hong-Shuo Chen, Ronald Salloum, C.-C. Jay Kuo","doi":"10.1561/116.00000136","DOIUrl":"https://doi.org/10.1561/116.00000136","url":null,"abstract":"","PeriodicalId":44812,"journal":{"name":"APSIPA Transactions on Signal and Information Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135846624","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：