The study of special vehicle crews' mission responses is essential for optimizing their working conditions and enhancing combat effectiveness. This study investigates the physiological, behavioral, and subjective workload responses of crews with varying experience levels in visual/motion tasks with different demands. A virtual simulation system was employed with 120 participants categorized into professionals, short-term trainees, and novices. EEG, eye movement and NASA-TLX scores were measured across task conditions. Results indicate that as crew experience increases, NASA-TLX scores decrease, physiological responses stabilize, but task completion times lengthen, with distinct behavioral patterns. Novices in low-demand motion tasks produce physiological data statistically like professionals. Short-term trainees in high-demand motion tasks produce coefficient of variation results of physiological data statistically like professionals. These findings suggest that short-term trainees can substitute for professionals in high-demand motion mechanical tasks, with implications for research data reliability assessment and increasing training and operational efficiency.

In the human-machine system of armored vehicles, the cognitive performance state of crews is crucial for the personnel security and combat efficiency. The purpose of this research was to establish a real-time assessment system for cognitive performances of armored vehicle crews, consisting of the data input module, data processing module, data visualization module, and scheduling module. Forty subjects were recruited to cooperate and execute the cross-platform strike task in a virtual simulation platform. The physiological data and operation behavior data was collected during the experiment process. To realize the accurate classification of different cognitive performance states, a multi-source information fusion algorithm was developed based on linear discriminant analysis (LDA) and D-S evidence theory, which included the information collection module, the feature extraction module, and the information fusion module. The results indicated that there existed a significant correlation between the extractive feature indicators (i.e., EOG, ECG, and task performance indicators) and the cognitive performance. The recognition accuracy and the data efficiency of the proposed assessment system were 91.25% and 96.69% respectively by using the complementarity of different evidences, which were higher than the others using partial information sources. This study can provide a reference for the comprehensive assessment of cognitive performance of human operators in military and industrial domains.

Multimodal Sentiment Analysis (MSA) has gained wide attention in many fields in recent years. However, the problem of heterogeneity and redundant information among different signals seriously affects the extraction and fusion of sentiment features. To address this challenge, we propose a Multilevel Representational Disentanglement Framework (MRDF) to achieve effective modality fusion and produce refined joint multimodal representations. Specifically, we design a refined semantic decomposition module for learning task-shared representations and modality-exclusive representations by crossmodal translations and task semantic reconstruction. Furthermore, we propose a contrastive learning-based distribution alignment mechanism and an adversarial learning-based distribution alignment strategy to utilize contrastive adversarial learning paradigms to further align the disentangled task-shared representations Experimental results show that the MRDF framework significantly outperforms existing state-of-the-art methods on the MOSI and MOSEI benchmarks.

Abstract. Let [Formula: see text] and [Formula: see text] be positive integers such that [Formula: see text], [Formula: see text], and [Formula: see text]. An [Formula: see text]-matching [Formula: see text] in a [Formula: see text]-uniform hypergraph is a set of [Formula: see text] pairwise disjoint edges. The anti-Ramsey number [Formula: see text] of an [Formula: see text]-matching is the smallest integer [Formula: see text] such that each edge-coloring of the [Formula: see text]-vertex [Formula: see text]-uniform complete hypergraph with exactly [Formula: see text] colors contains an [Formula: see text]-matching with distinct colors. In 2013, Özkahya and Young proposed a conjecture on the exact value of [Formula: see text] for all [Formula: see text] and [Formula: see text]. A 2019 result by Frankl and Kupavskii verified this conjecture for all [Formula: see text] and [Formula: see text]. We aim to determine the value of [Formula: see text] for [Formula: see text] in this paper. Namely, we prove that if [Formula: see text] and [Formula: see text] is large enough, then [Formula: see text]. Here [Formula: see text] is the Turán number of an [Formula: see text]-matching. Thus this result confirms the conjecture of Özkahya and Young for [Formula: see text], [Formula: see text] and sufficiently large [Formula: see text]. For [Formula: see text] and [Formula: see text], we present a new construction for the lower bound of [Formula: see text] which shows the conjecture by Özkahya and Young is not true. In particular, for [Formula: see text], we prove that [Formula: see text] for sufficiently large [Formula: see text].

Let $n,s,k$ be three positive integers such that $1\leq s\leq(n-k+1)/k$ and let $[n]=\{1,\ldots,n\}$. Let $H$ be a $k$-graph with vertex set $[n]$, and let $e(H)$ denote the number of edges of $H$. Let $\nu(H)$ and $\tau(H)$ denote the size of the largest matching and the size of a minimum vertex cover in $H$, respectively. Define $A^k_i(n,s):=\{e\in\binom{[n]}{k}:|e\cap[(s+1)i-1]|\geq i\}$ for $2\leq i\leq k$ and $HM^k_{n,s}:=\big\{e\in\binom{[n]}{k}:e\cap[s-1]\neq\emptyset\big\} \cup\big\{S\big\}\cup \big\{e\in\binom{[n]}{k}: s\in e, e\cap S\neq \emptyset\}$, where $S=\{s+1,\ldots,s+k\}$. Frankl and Kupavskii proposed a conjecture that if $\nu(H)\leq s$ and $\tau(H)>s$, then $e(H)\leq \max\{|A^k_2(n,s)|,\ldots ,|A^k_k(n,s)|,|HM^k_{n,s}|\}$. In this paper, we prove this conjecture for $k=3$ and sufficiently large $n$.