Traditional log anomaly detection systems are centralized, which poses the risk of privacy leakage during data transmission. Previous research mainly focuses on single-domain logs, requiring domain-specific models and retraining, which limits flexibility and scalability. In this paper, we propose a unified federated cross-domain log anomaly detection approach, FLUXLog, which is based on MoE (Mixture of Experts) to handle heterogeneous log data. Based on our insights, we establish a two phase training process: pre-training the gating network to assign expert weights based on data distribution, followed by expert driven top-down feature fusion. The following training of the gating network is based on fine-tuning the adapters, providing the necessary flexibility for the model to adapt across domains while maintaining expert specialization. This training paradigm enables a Hybrid Specialization Strategy, fostering both domain-specific expertise and cross-domain generalization. The Cross Gated Experts Module (CGEM) then fuses expert weights and dual channel outputs. Experiments on public datasets demonstrate that our model outperforms baseline models in handling unified cross-domain log data.

Aboveground biomass (AGB) is vital for assessing productivity and carbon storage. The area-based approach (ABA) and individual tree approach (ITA) are commonly used in remote sensing for AGB estimation, but their combined application remains underexplored. This study introduced a practical method that combined ABA and ITA using airborne laser scanning (ALS) data to estimate AGB of natural secondary forests of northeastern China. The results demonstrated that combining ABA and ITA features significantly enhanced AGB estimation accuracy. CNN achieved the highest accuracy and substantially improved compared to using ABA features alone. Among ITA features, tree height-related features were the most influential, and combining multiple types of ITA features proved to be more successful than using a single type. Therefore, using CNN based on ABA and all selected ITA features is recommended in practical applications. This method simplifies the complex ITA process and can be applied for large-scale, efficient forest inventory, sustainable forest management, and precise forest monitoring and protection in the future.

Personality not only plays essential roles in people's real lives, but also becomes an important factor for various online services. Traditional approaches to personality assessment usually ask users to answer a long list of questions and are thus not practical in many online systems. It is more desirable to use some universally available data in the system to perform personality assessment. This article reports a controlled study to investigate common mouse operations as a potential new type of data source for online personality assessment. We establish an elaborate personality-mouse behavior dataset from 146 subjects and propose kinematic and adjustment features to characterize mouse motion patterns. Statistical approaches and machine learning algorithms are employed to examine the connections between personality preferences and mouse motion features via correlation analysis, discernibility analysis, and personality recognition experiments. The results reveal some interesting expressions of cognitive personality perspectives reflected in mouse motion patterns, such asfast starting accelerationandbetter controller. Performance evaluation shows it is possible to recognize different personality preferences using mouse motion features with accuracies ranging from 60.6 to 78.3 percent. Our findings suggest a potential to use mouse operational behaviors as a new data source for personality assessment in various information systems.

Dictionary learning methods, such as K-SVD, have recently been introduced to learn a specific dictionary matrix that best fits a set of training data vectors. These methods are flexible in that any preferred pursuit method of sparse coding can be used to represent the data. Incremental in-situ learning using unseen data, however, requires complete retraining and hence increased computational efforts. In this paper, we present a new recursive algorithm to jointly perform dictionary learning and sparse coding using a fast orthogonal matching pursuit (OMP) when unseen data samples are encountered. The incremental in-situ learning is accomplished while maintaining the performance and with substantially reduced computations. Simulation results are presented to show the effectiveness and benefits of the proposed algorithms.

Individual tree crown delineation (ITCD), which includes treetop detection and/or tree crown delineation, plays a significant role in modern forest resources management and precise forestry. Recent years, amount of ITCD algorithms have been proposed based on passive and active remotely sensed data. However, since there is no standardized accuracy assessment procedure for ITCD, it is extremely difficult to compare ITCD algorithms unless multiple approaches are tested on a single study area using the same datasets and accuracy metrics. This study proposes a standard accuracy assessment procedure on both plot and individual tree levels, and develops an accuracy assessment tool for ITCD studies, including treetop detection and crown delineation procedures. The tool provides a scientific and efficient approach for accuracy assessment of ITCD.

This paper uses the canonical correlation decomposition (CCD) framework to investigate the spatial correlation of sources captured using two spatially separated sensor arrays. The relationship between the canonical correlations of the observed signals and the spatial correlation coefficients of the source signals are first derived, including an analysis of the changes seen in this relationship under certain noise level and array geometry assumptions. Additionally, simulation results are presented that demonstrate the effects of different noise levels and array geometries on the canonical correlations for the case of two uniform linear sparse arrays.

Wireless sensor networks (WSNs) are emerging as essential and popular ways of providing pervasive computing environments for various applications. In all these environments energy constraint is the most critical problem that must be considered. Clustering is introduced to WSNs because of its network scalability, energy-saving attributes and network topology stabilities. However, there also exist some disadvantages associated with individual clustering scheme, such as additional overheads during cluster-head (CH) selection, assignment and cluster construction process. In this paper, we discuss and compare several aspects and characteristics of some widely explored clustering algorithms in WSNs, e.g. clustering timings, attributes, metrics, advantages and disadvantages of corresponding clustering algorithms. This paper also presents a discussion on the future research topics and the challenges of clustering in WSNs.