Revisiting the latent structure of negative symptoms in schizophrenia: Evidence from two second-generation clinical assessments.

BACKGROUND: Negative symptoms are core symptom of schizophrenia, and many previous research studied the latent structure of negative symptoms based on a single measurement scale. Applying two second-generation negative symptom scales to the same sample can address measurement-invariance of latent structure. METHODS: Three-hundred-and-five schizophrenia patients were assessed using the CAINS and the BNSS. Confirmatory Factor Analysis (CFA) tested four competing factor-models: (1) a 1-factor model; (2) a 2-factor model comprising the motivation and pleasure (MAP) domain and the diminished expression (EXP) domain; (3) a 5-factor model comprising anhedonia, avolition, asociality, blunted affect and alogia; (4) a hierarchical model comprising the "first-order" 5-domain factors and the "second-order" MAP & EXP factors. RESULTS: The CFA results for the data of the CAINS showed that the 2-factor model had the best data fit over the other competing models. The CFA using the BNSS data in the same sample also supported the superiority of the 2-factor model. Lastly, after combining the items of the BNSS and CAINS together in the same sample for CFA, the 2-factor model prevailed over the other competing models. CONCLUSIONS: The 2-factor model appears to be measurement-invariant latent structure of negative symptoms. The novel method of combining the items of the CAINS and BNSS might have circumvented the possible imperfect construct of a single scale. Our findings support the MAP and EXP factors as the latent structure for negative symptoms.

TKRD: Trusted kernel rootkit detection for cybersecurity of VMs based on machine learning and memory forensic analysis.

The promotion of cloud computing makes the virtual machine (VM) increasingly a target of malware attacks in cybersecurity such as those by kernel rootkits. Memory forensic, which observes the malicious tracks from the memory aspect, is a useful way for malware detection. In this paper, we propose a novel TKRD method to automatically detect kernel rootkits in VMs from private cloud, by combining VM memory forensic analysis with bio-inspired machine learning technology. Malicious features are extracted from the memory dumps of the VM through memory forensic analysis method. Based on these features, various machine learning classifiers are trained including Decision tree, Rule based classifiers, Bayesian and Support vector machines (SVM). The experiment results show that the Random Forest classifier has the best performance which can effectively detect unknown kernel rootkits with an Accuracy of 0.986 and an AUC value (the area under the receiver operating characteristic curve) of 0.998.