Decoding human movement intentions from electroencephalography (EEG) signals is critical for brain-computer interface (BCI) applications in motor neurorehabilitation, active assistance, and functional augmentation. However, current BCI models face two challenges for motor-impaired patients: 1) prolonged EEG data collection from patients is difficult; 2) differences in brain functional structures and motor behaviors between healthy individuals and patients limit the generalizability of models trained on healthy individuals' EEG data. To address these challenges, this study proposes a transfer learning-based model, TL-ME, to bridge the gap between healthy individuals' and patients' EEG data and improve movement decoding accuracy for patients. TL-ME integrates an attention-based feature extractor, adversarial domain discriminator, multi-source selection, and movement classifier to transfer knowledge from healthy individuals' EEG data (source domain) to patients' EEG data (target domain). Temporal and spectral visualizations are used to inspect brain activation patterns for shared motor tasks between healthy individuals and patients. Experimental results show a 10.8% improvement in upper-limb movement decoding's accuracy using TL-ME, with each module contributing to performance gains. Visualization analyses also demonstrate similar brain activation patterns across domains, validating the transferability of healthy individuals' EEG data to patient-specific models. This work introduces a novel cross-population transfer learning approach that leverages healthy individuals' EEG data to enhance neural decoding for motor-impaired patients, bridging the gap between experimental studies and real-world applications in BCI-based neurorehabilitation.

Implementation-level distributed system model checkers (DMCKs) have proven valuable in verifying the correctness of real distributed systems. However, they primarily focus on state space reduction, and often have a bottleneck on another crucial dimension: exploration speed. To scale DMCK, we introduce SandTable, a technique for lifting state-space exploration from the implementation level to the specification level, and confirming bugs at the implementation level. We made SandTable practical through a methodology consisting of four essential parts: (1) writing specifications that adhere to the implementation, (2) checking conformance to enhance specification quality and reduce false positives and false negatives, (3) exploring the state space with heuristics for effectiveness and efficiency, and (4) confirming bugs and verifying their fixes in the implementation.

Multi-modal data have recently been utilized to improve the performance of knowledge graph completion (KGC), attracting widespread research interest. However, they have been ignored in few-shot knowledge graph completion (FKGC), which aims to discover potential facts involving unseen relations that only appear in few-shot triples. The most relevant FKGC study simply concatenates various modal features, but the performance is still limited due to the following problems: (1) lack of exploiting significant multi-modal features in neighborhoods, and (2) ineffectively modeling inter-modal interactions in a few-shot setting. To tackle these problems, we propose a novel relational learning model entitled MMSN (Multi-Modal Siamese Network) for few-shot knowledge graph completion, which is composed of the following two primary modules: the Siamese multi-modal neighbor encoder (SMNE) and the meta-learning multi-modal knowledge representation decoder (MKRD). The module SMNE is developed to encode diverse modalities of neighbors by a Siamese attention network, fuse multi-modal information through a gating fusion network, and learn effective relational embeddings using an aggregator. The module MKRD is introduced to handle inter-modal interactions between multiple modalities and train the proposed model in a few-shot scenario. Extensive experiments demonstrate that our proposed model MMSN outperforms the state-of-the-art FKGC models, including uni-modal and multi-modal models, on two real-world few-shot multi-modal datasets.

Knowledge graph embedding (KGE) plays an important role in downstream tasks, such as question answering, recommendation system, and entity recognition. Most existing KGE methods focus on modeling static knowledge graphs. However, many knowledge graphs are incremental in reality. Existing KGE methods are time-consuming to update the embedding space incrementally, and have difficulty in keeping the timeliness of the knowledge graph embedding. To address this problem, we propose a novel knowledge graph embedding method by rotating on hyperplane (RotatH), which supports updating the embedding space incrementally and ensures the timeliness and accuracy of knowledge graph embedding. Specifically, our proposed method first employs relation-specific hyperplanes to update the incremental entities into the trained vector space efficiently. Meanwhile, by combining hyperplane and rotation, our method can deal with complex relations, such as many-to-many and symmetry relations, and has high performance in both incremental and static environments. Moreover, our method introduces a mean-based method to constraint the density of incremental entities. We conduct extensive link prediction experiments on two real-world incremental datasets and two benchmark datasets. The experimental results show that our model incrementally updates embedding space efficiently and outperforms static models on benchmarks.

In the course of recommending locations for establishing new facilities on urban planning or commercial programming, the location prediction offers the optimal candidates, which maximizes the number of served customers or minimize customer inconvenience, therefore brings the maximum profits. In most existing studies, only the spatial-temporal features are recognized to evaluate the location popularity, where social relationships of customers, which are significant factors for popularity assessing, have been ignored. Additionally, current researches also fail to take capacities and categories of the facilities into consideration. To overcome the drawbacks, we introduce a novel model of Multi-characteristic Information based Top-k Location Prediction (MITLP), it captures the spatio-temporal behaviors of customers based on historical trajectories, exploits the social relevancy from their friend relationships, as well as examines the category competitiveness of specific facilities thoroughly. Subsequently, by drawing on the feature evaluation and popularity quantization, MITLP will be implemented within a hybrid B-tree-liked recommending framework, Constrained Location and Social-Trajectory Clustered forest (CLSTC-forest), which can not only produce better performance in practice but also address the facility service constraints. Finally, extensive experiments conducted on real-world datasets demonstrate the higher efficiency and effectiveness of the proposed model.

Microservice architecture refers to the use of numerous small-scale and independently deployed services, instead of encapsulating all functions into one monolith. It has been a challenge in software engineering to decompose a monolithic system into smaller parts. In this paper, we propose the Feature Table approach, a structured approach to service decomposition based on the correlation between functional features and microservices: (1) we defined the concept of Feature Cards and 12 instances of such cards; (2) we formulated Decomposition Rules to decompose monolithic applications; (3) we designed the Feature Table Analysis Tool to provide semi-automatic analysis for identification of microservices; and (4) we formulated Mapping Rules to help developers implement microservice candidates. We performed a case study on Cargo Tracking System to validate our microservice-oriented decomposition approach. Cargo Tracking System is a typical case that has been decomposed by other related methods (dataflow-driven approach, Service Cutter, and API Analysis). Through comparison with the related methods in terms of specific coupling and cohesion metrics, the results show that the proposed Feature Table approach can deliver more reasonable microservice candidates, which are feasible in implementation with semi-automatic support.