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Summary Single-cell RNA-seq (scRNA-seq) is a powerful technique for decoding the complex cellular compositions in the tumor microenvironment (TME). As previous studies have defined many meaningful cell subtypes in several tumor types, there is a great need to computationally transfer these labels to new datasets. Also, different studies used different approaches or criteria to define the cell subtypes for the same major cell lineages. The relationships between the cell subtypes defined in different studies should be carefully evaluated. In this updated package scCancer2, designed for integrative tumor scRNA-seq data analysis, we developed a supervised machine learning framework to annotate TME cells with annotated cell subtypes from 15 scRNA-seq datasets with 594 samples in total. Based on the trained classifiers, we quantitatively constructed the similarity maps between the cell subtypes defined in different references by testing on all the 15 datasets. Secondly, to improve the identification of malignant cells, we designed a classifier by integrating large-scale pan-cancer TCGA bulk gene expression datasets and scRNA-seq datasets (10 cancer types, 175 samples, 663 857 cells). This classifier shows robust performances when no internal confidential reference cells are available. Thirdly, scCancer2 integrated a module to process the spatial transcriptomic data and analyze the spatial features of TME. Availability and implementation The package and user documentation are available at http://lifeome.net/software/sccancer2/ and https://doi.org/10.5281/zenodo.10477296. [ABSTRACT FROM AUTHOR]

Potential topics include but are not limited to the following: (1) Security machine learning modelling and architecture (2) Secure multiparty computation techniques for machine learning (3) Attacks against machine learning (4) Machine learning threat intelligence (5) Machine learning for cybersecurity (6) Machine learning for intrusion detection and response (7) Machine learning for multimedia data security (8) Machine learning for public safety After a thorough review process, this special issue has selected a set of eight papers to provide new insights on the abovementioned research areas. Machine learning (ML) has recently gained a renewed interest as the technology powering it has become more widely available and accessible to organizations of all sizes. [Extracted from the article]

Industrial Control Systems (ICS) are hardware, network, and software, upon which a facility depends to allow daily operations to function. In most cases society takes the operation of such systems, for example public transport, tap water or electricity, for granted. However, the disruption of those systems might have serious consequences across different sectors. In this paper, we propose a supervised energy-based approach for anomaly detection in a clean water supply system using a new dataset which is physically modelled in the Festo MPA workstation rig. The novelty relies on the set of engineered features collected from the testbed, including voltage, current and power from the sensors that compose the ICS. These values are obtained from independent current sensors that we have physically wired to the testbed. Five machine learning algorithms; Support Vector Machine, k-Nearest Neighbours, Multilayer Perceptron, Decision Tree and Random Forest are employed to evaluate the effectiveness of our proposed features. The metrics used to present the performance of the selected machine learning algorithms are F1-Score, G-Mean, False Positive Rate (FPR) and False Negative Rate (FNR). The results show that machine learning algorithms can classify the variations of energy produced by the execution of cyber-attacks as anomalous by achieving 95.5% F1-Score, and 6.8% FNR with the Multilayer Perceptron classifier. [ABSTRACT FROM AUTHOR]