Emotion recognition based on Electroencephalogram (EEG) is of great importance for cognitive psychology, disease therapy, etc. However, most mainstream recognition models are trained using batch learning, which fails to adapt to the dynamic and non-stationary nature of real-time EEG streams. In contrast, online learning can adjust model parameters continuously, but it typically relies on labeled data and a fully trained initial model, which are unavailable in practical scenarios. To tackle this challenge, we propose a novel learn-review-challenge online meta-learning framework (LRCOMF) for unlabeled online EEG learning. This framework incorporates a meta updating module through a multi-task cache and a customized sampling strategy to improve the model's generalization during online learning. A sample judgement module is implemented based on a prototype weight being designed to estimate the confidence of predicted labels during the "learn-review" step. Additionally, a challenge module using a clustering quality metric determines whether low-confidence samples can be reconsidered during the "challenge" phase. The validation employed the DEAP and DREAMER datasets. In comparison to the initial baseline condition, the recognition model exhibited substantial enhancements 3.59%/3.87%/1.98% (p $< $ 0.001) for arousal, valence, dominance labels in the DEAP dataset. Substantial increases of 3.42%/3.63%/2.93% (p $< $ 0.001) were also noted in DREAMER's evaluation of arousal/valence/dominance labels. This research is essential for tackling unlabeled online learning in authentic EEG contexts and enhancing the advancement of EEG practical applications.

Graph contrastive learning (GCL) has drawn much research attention for its ability to learn node representations in a self-supervised manner. However, the homophily assumption inherent in GNN encoders limits the direction (macro-level) and the process (micro-level) of message passing in current GCL frameworks, impairing the expressive power of GCL in non-homophilous graphs. This paper presents a novel framework that employs Macro and Micro Message Passing in GCL (M3P-GCL) to overcome these limitations and advance performance in both homophilous and non-homophilous graphs. Specifically, at the macro-level, we integrate structural and attribute views to enhance the direction of message passing, and employ an Aligned Priority-Supporting View Encoding (APS-VE) strategy to facilitate contrastive training; at the micro-level, we propose an Adaptive Self-Propagation (ASP) strategy based on role segmentation of self-loops to diversify the process of message passing in the encoder. These enhancements effectively address the limitations imposed by the homophily assumption. Experiments demonstrate that M3P-GCL outperforms both supervised and unsupervised baselines in the node classification task on various datasets with different levels of homophily.

Scan is a fundamental operation widely used in main-memory analytical database systems. To accelerate scans, previous studies build either record-order or sort-order structures known as scan indices. While achieving good performance, scan indices often incur significant space overhead, limiting their use in main-memory databases. For example, the most recent and best performing scan index, BinDex, consists of a sort-order position array, which is an array of rowIDs in the value order, and a set of record-order bit vectors, representing records in pre-defined value intervals. The structures can be much larger than the base data column size. In this paper, we propose a novel scan index, Cabin, that exploits the following three techniques for better time-space tradeoff. 1) filter sketches that represent every 2^w-2 value intervals with a w-bit sketched vector, thereby exponentially reducing the space for the bit vectors; 2) selective position array that removes the rowID array for a fraction of intervals in order to lower the space overhead for the position array; and 3) data-aware intervals that judiciously select interval boundaries based on the data characteristics to better support popular values in skewed data distributions or categorical attributes. Experimental results show that compared with state-of-the-art scan solutions, Cabin achieves better time-space tradeoff, and attains 1.70 -- 4.48x improvement for average scan performance given the same space budget.

Abstract To protect the satellite communication uplink signal (SCUS) from being intercepted by an eavesdropper, this study proposes several tactics to ensure SCUS being in the low probability of intercept (LPI) state. First, based on the satellite link equation, an optimisation model for LPI SCUS is established with coding and synchronisation of symbol and carrier frequency. However, when the satellite is on the synchronisation orbit, the LPI model of SCUS is still very likely to be intercepted by an eavesdropper that might be close to the SCUS. Aiming at this problem, the second tactic is to mask LPI SCUS with directed and power limited jamming. The purpose of using directed and power limited jamming is to reduce direction‐finding accuracy of the eavesdropper, and to achieve the optimal LPI performance with the minimum radiation energy. The high‐order cumulate algorithm is used to predict the signal‐to‐noise ratio of mixed signal in the second tactic. The simulation results show that the model of LPI SCUS can effectively reduce the detection probability compared with the traditional satellite uplink signals, and the second tactic can effectively reduce the direction‐finding accuracy of the SCUS by the eavesdropper.

Feature selection (FS) is one of the fundamental data processing techniques in various machine learning algorithms, especially for classification of healthcare data. However, it is a challenging issue due to the large search space. Binary Particle Swarm Optimization (BPSO) is an efficient evolutionary computation technique, and has been widely used in FS. In this paper, we proposed a Confidence-based and Cost-effective feature selection (CCFS) method using BPSO to improve the performance of healthcare data classification. Specifically, first, CCFS improves search effectiveness by developing a new updating mechanism that designs the feature confidence to explicitly take into account the fine-grained impact of each dimension in the particle on the classification performance. The feature confidence is composed of two measurements: the correlation between feature and categories, and historically selected frequency of each feature. Second, considering the fact that the acquisition costs of different features are naturally different, especially for medical data, and should be fully taken into account in practical applications, besides the classification performance, the feature cost and the feature reduction ratio are comprehensively incorporated into the design of fitness function. The proposed method has been verified in various UCI public datasets and compared with various benchmark schemes. The thoroughly experimental results show the effectiveness of the proposed method, in terms of accuracy and feature selection cost.