7 research outputs found
Efficient Partitioning Method of Large-Scale Public Safety Spatio-Temporal Data based on Information Loss Constraints
The storage, management, and application of massive spatio-temporal data are
widely applied in various practical scenarios, including public safety.
However, due to the unique spatio-temporal distribution characteristics of
re-al-world data, most existing methods have limitations in terms of the
spatio-temporal proximity of data and load balancing in distributed storage.
There-fore, this paper proposes an efficient partitioning method of large-scale
public safety spatio-temporal data based on information loss constraints
(IFL-LSTP). The IFL-LSTP model specifically targets large-scale spatio-temporal
point da-ta by combining the spatio-temporal partitioning module (STPM) with
the graph partitioning module (GPM). This approach can significantly reduce the
scale of data while maintaining the model's accuracy, in order to improve the
partitioning efficiency. It can also ensure the load balancing of distributed
storage while maintaining spatio-temporal proximity of the data partitioning
results. This method provides a new solution for distributed storage of
mas-sive spatio-temporal data. The experimental results on multiple real-world
da-tasets demonstrate the effectiveness and superiority of IFL-LSTP
A Relational Triple Extraction Method Based on Feature Reasoning for Technological Patents
The relation triples extraction method based on table filling can address the
issues of relation overlap and bias propagation. However, most of them only
establish separate table features for each relationship, which ignores the
implicit relationship between different entity pairs and different relationship
features. Therefore, a feature reasoning relational triple extraction method
based on table filling for technological patents is proposed to explore the
integration of entity recognition and entity relationship, and to extract
entity relationship triples from multi-source scientific and technological
patents data. Compared with the previous methods, the method we proposed for
relational triple extraction has the following advantages: 1) The table filling
method that saves more running space enhances the speed and efficiency of the
model. 2) Based on the features of existing token pairs and table relations,
reasoning the implicit relationship features, and improve the accuracy of
triple extraction. On five benchmark datasets, we evaluated the model we
suggested. The result suggest that our model is advanced and effective, and it
performed well on most of these datasets
Unsupervised Semantic Representation Learning of Scientific Literature Based on Graph Attention Mechanism and Maximum Mutual Information
Since most scientific literature data are unlabeled, this makes unsupervised
graph-based semantic representation learning crucial. Therefore, an
unsupervised semantic representation learning method of scientific literature
based on graph attention mechanism and maximum mutual information (GAMMI) is
proposed. By introducing a graph attention mechanism, the weighted summation of
nearby node features make the weights of adjacent node features entirely depend
on the node features. Depending on the features of the nearby nodes, different
weights can be applied to each node in the graph. Therefore, the correlations
between vertex features can be better integrated into the model. In addition,
an unsupervised graph contrastive learning strategy is proposed to solve the
problem of being unlabeled and scalable on large-scale graphs. By comparing the
mutual information between the positive and negative local node representations
on the latent space and the global graph representation, the graph neural
network can capture both local and global information. Experimental results
demonstrate competitive performance on various node classification benchmarks,
achieving good results and sometimes even surpassing the performance of
supervised learning
Reinforcement Federated Learning Method Based on Adaptive OPTICS Clustering
Federated learning is a distributed machine learning technology, which
realizes the balance between data privacy protection and data sharing
computing. To protect data privacy, feder-ated learning learns shared models by
locally executing distributed training on participating devices and aggregating
local models into global models. There is a problem in federated learning, that
is, the negative impact caused by the non-independent and identical
distribu-tion of data across different user terminals. In order to alleviate
this problem, this paper pro-poses a strengthened federation aggregation method
based on adaptive OPTICS clustering. Specifically, this method perceives the
clustering environment as a Markov decision process, and models the adjustment
process of parameter search direction, so as to find the best clus-tering
parameters to achieve the best federated aggregation method. The core
contribution of this paper is to propose an adaptive OPTICS clustering
algorithm for federated learning. The algorithm combines OPTICS clustering and
adaptive learning technology, and can effective-ly deal with the problem of
non-independent and identically distributed data across different user
terminals. By perceiving the clustering environment as a Markov decision
process, the goal is to find the best parameters of the OPTICS cluster without
artificial assistance, so as to obtain the best federated aggregation method
and achieve better performance. The reliability and practicability of this
method have been verified on the experimental data, and its effec-tiveness and
superiority have been proved
Aspect-Based Sentiment Analysis using Local Context Focus Mechanism with DeBERTa
Text sentiment analysis, also known as opinion mining, is research on the
calculation of people's views, evaluations, attitude and emotions expressed by
entities. Text sentiment analysis can be divided into text-level sentiment
analysis, sen-tence-level sentiment analysis and aspect-level sentiment
analysis. Aspect-Based Sentiment Analysis (ABSA) is a fine-grained task in the
field of sentiment analysis, which aims to predict the polarity of aspects. The
research of pre-training neural model has significantly improved the
performance of many natural language processing tasks. In recent years, pre
training model (PTM) has been applied in ABSA. Therefore, there has been a
question, which is whether PTMs contain sufficient syntactic information for
ABSA. In this paper, we explored the recent DeBERTa model (Decoding-enhanced
BERT with disentangled attention) to solve Aspect-Based Sentiment Analysis
problem. DeBERTa is a kind of neural language model based on transformer, which
uses self-supervised learning to pre-train on a large number of original text
corpora. Based on the Local Context Focus (LCF) mechanism, by integrating
DeBERTa model, we purpose a multi-task learning model for aspect-based
sentiment analysis. The experiments result on the most commonly used the laptop
and restaurant datasets of SemEval-2014 and the ACL twitter dataset show that
LCF mechanism with DeBERTa has significant improvement