Gait Recognition as a Service for Unobtrusive User Identification in Smart Spaces

Recently, Internet of Things (IoT) has raised as an important research area that combines the environmental sensing and machine learning capabilities to flourish the concept of smart spaces, in which intelligent and customized services can be provided to users in a smart manner. In smart spaces, one fundamental service that needs to be provided is accurate and unobtrusive user identification. In this work, to address this challenge, we propose a Gait Recognition as a Service (GRaaS) model, which is an instantiation of the traditional Sensing as a Service (S2aaS) model, and is specially deigned for user identification using gait in smart spaces. To illustrate the idea, a Radio Frequency Identification (RFID)-based gait recognition service is designed and implemented following the GRaaS concept. Novel tag selection algorithms and attention-based Long Short-term Memory (At-LSTM) models are designed to realize the device layer and edge layer, achieving a robust recognition with 96.3% accuracy. Extensive evaluations are provided, which show that the proposed service has accurate and robust performance and has great potential to support future smart space applications

Embracing Analytics in the Drinking Water Industry

Analytics can support numerous aspects of water industry planning, management, and operations. Given this wide range of touchpoints and applications, it is becoming increasingly imperative that the championship and capability of broad-based analytics needs to be developed and practically integrated to address the current and transitional challenges facing the drinking water industry. Analytics will contribute substantially to future efforts to provide innovative solutions that make the water industry more sustainable and resilient. The purpose of this book is to introduce analytics to practicing water engineers so they can deploy the covered subjects, approaches, and detailed techniques in their daily operations, management, and decision-making processes. Also, undergraduate students as well as early graduate students who are in the water concentrations will be exposed to established analytical techniques, along with many methods that are currently considered to be new or emerging/maturing. This book covers a broad spectrum of water industry analytics topics in an easy-to-follow manner. The overall background and contexts are motivated by (and directly drawn from) actual water utility projects that the authors have worked on numerous recent years. The authors strongly believe that the water industry should embrace and integrate data-driven fundamentals and methods into their daily operations and decision-making process(es) to replace established ìrule-of-thumbî and weak heuristic approaches ñ and an analytics viewpoint, approach, and culture is key to this industry transformation

A Survey of Face Recognition

Recent years witnessed the breakthrough of face recognition with deep convolutional neural networks. Dozens of papers in the field of FR are published every year. Some of them were applied in the industrial community and played an important role in human life such as device unlock, mobile payment, and so on. This paper provides an introduction to face recognition, including its history, pipeline, algorithms based on conventional manually designed features or deep learning, mainstream training, evaluation datasets, and related applications. We have analyzed and compared state-of-the-art works as many as possible, and also carefully designed a set of experiments to find the effect of backbone size and data distribution. This survey is a material of the tutorial named The Practical Face Recognition Technology in the Industrial World in the FG2023

Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities

Graph Neural Networks (GNNs) have become the leading paradigm for learning on (static) graph-structured data. However, many real-world systems are dynamic in nature, since the graph and node/edge attributes change over time. In recent years, GNN-based models for temporal graphs have emerged as a promising area of research to extend the capabilities of GNNs. In this work, we provide the first comprehensive overview of the current stateof-the-art of temporal GNN, introducing a rigorous formalization of learning settings and tasks and a novel taxonomy categorizing existing approaches in terms of how the temporal aspect is represented and processed. We conclude the survey with a discussion of the most relevant open challenges for the field, from both research and application perspectives

Proteogenomic characterization reveals therapeutic vulnerabilities in lung adenocarcinoma

To explore the biology of lung adenocarcinoma (LUAD) and identify new therapeutic opportunities, we performed comprehensive proteogenomic characterization of 110 tumors and 101 matched normal adjacent tissues (NATs) incorporating genomics, epigenomics, deep-scale proteomics, phosphoproteomics, and acetylproteomics. Multi-omics clustering revealed four subgroups defined by key driver mutations, country, and gender. Proteomic and phosphoproteomic data illuminated biology downstream of copy number aberrations, somatic mutations, and fusions and identified therapeutic vulnerabilities associated with driver events involving KRAS, EGFR, and ALK. Immune subtyping revealed a complex landscape, reinforced the association of STK11 with immune-cold behavior, and underscored a potential immunosuppressive role of neutrophil degranulation. Smoking-associated LUADs showed correlation with other environmental exposure signatures and a field effect in NATs. Matched NATs allowed identification of differentially expressed proteins with potential diagnostic and therapeutic utility. This proteogenomics dataset represents a unique public resource for researchers and clinicians seeking to better understand and treat lung adenocarcinomas

System identification of computer networks with random service

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High-dimensional non-Gaussian data analysis based on sample relationship

High-dimensional data are omnipresent. Although many statistical methods developed for analysing high-dimensional data adopt the normality assumption, the Gaussian distribution could be a poor approximation of real data in many applications. In this thesis, we investigate how to properly analyse such high-dimensional non-Gaussian data. As quantifying sample relationships, such as measuring the inter-sample proximity and determining neighbours for samples, is an important step in numerous statistical approaches, this thesis develops three methods for analysing different high-dimensional non-Gaussian data types based on the sample relationship: dimension reduction for single cell RNA-sequencing data with missingness with a proposed proximity measure, dimension reduction for data of small counts with a developed proximity measure, and modelling skewed survival data with a proposed procedure of identifying neighbours for samples. In chapter 3, I develop an unbiased estimator of the Gram matrix, which characterises the proximity between samples. The proposed estimator improves a broad spectrum of dimension reduction methods when applied to single cell RNA-sequencing data with missingness. In addition, the consequences of directly applying existing dimension reduction methods to data with missingness are empirically and theoretically clarified. In chapter 4, I develop a dissimilarity measure for count data with an excess of zeros based on the Kullback-Leibler divergence and the empirical Bayes estimators. The proposed measure is shown to have better discriminative power compared with other popular measures. The proposed measure boosts the performance of standard dimension reduction methods on count data containing many zeros. In chapter 5, I clarify that graphs derived from features themselves can be beneficial for the analysis of high-dimensional survival data when used in graph convolutional networks. Besides, a sequential forward floating selection algorithm is proposed to simultaneously perform survival analysis and unveil the local neighbourhoods of samples with the aid of graph convolutional networks