Requirement traceability is the process of identifying the inter-dependencies
between requirements. It poses a significant challenge when conducted manually,
especially when dealing with requirements at various levels of abstraction. In
this work, we propose a novel approach to automate the task of linking
high-level business requirements with more technical system requirements. The
proposed approach begins by representing each requirement using a Bag of-Words
(BOW) model combined with the Term Frequency-Inverse Document Frequency
(TF-IDF) scoring function. Then, we suggested an enhanced cosine similarity
that uses recent advances in word embedding representation to correct
traditional cosine similarity function limitations. To evaluate the
effectiveness of our approach, we conducted experiments on three well-known
datasets: COEST, WARC(NFR), and WARC(FRS). The results demonstrate that our
approach significantly improves efficiency compared to existing methods. We
achieved better results with an increase of approximately 18.4% in one of the
datasets, as measured by the F2 score.

我们提出了一种新的方法来自动化将高级业务需求与更高级的技术系统需求关联起来，该方法使用词袋模型和 TF-IDF 得分函数表示每个需求，并使用最近的词嵌入表达方式改进余弦相似度函数以提高效率。实验证明，与现有方法相比，我们的方法在某个数据集中的 F2 分数增加了约 18.4%。

跨级需求追踪：一种融合词袋模型和词向量的新方法，用于增强相似性功能

Cross-level Requirement Traceability: A Novel Approach Integrating  Bag-of-Words and Word Embedding for Enhanced Similarity Functionality

The accurate forecasting of infectious epidemic diseases is the key to
effective control of the epidemic situation in a region. Most existing methods
ignore potential dynamic dependencies between regions or the importance of
temporal dependencies and inter-dependencies between regions for prediction. In
this paper, we propose an Inter- and Intra-Series Embeddings Fusion Network
(SEFNet) to improve epidemic prediction performance. SEFNet consists of two
parallel modules, named Inter-Series Embedding Module and Intra-Series
Embedding Module. In Inter-Series Embedding Module, a multi-scale unified
convolution component called Region-Aware Convolution is proposed, which
cooperates with self-attention to capture dynamic dependencies between time
series obtained from multiple regions. The Intra-Series Embedding Module uses
Long Short-Term Memory to capture temporal relationships within each time
series. Subsequently, we learn the influence degree of two embeddings and fuse
them with the parametric-matrix fusion method. To further improve the
robustness, SEFNet also integrates a traditional autoregressive component in
parallel with nonlinear neural networks. Experiments on four real-world
epidemic-related datasets show SEFNet is effective and outperforms
state-of-the-art baselines.

本文提出了一种基于 Inter- and Intra-Series Embeddings Fusion Network (SEFNet) 的方法，该方法通过捕捉来自多个地区的时间序列之间的动态依赖关系和时间依赖关系来提高预测流行病的性能，并在四个真实数据集上实验证明，该方法优于其他最先进的基线方法。

基于序列嵌入融合的流行病预测网络

Inter- and Intra-Series Embeddings Fusion Network for Epidemiological Forecasting

A reliable and efficient representation of multivariate time series is
crucial in various downstream machine learning tasks. In multivariate time
series forecasting, each variable depends on its historical values and there
are inter-dependencies among variables as well. Models have to be designed to
capture both intra- and inter-relationships among the time series. To move
towards this goal, we propose the Time Series Attention Transformer (TSAT) for
multivariate time series representation learning. Using TSAT, we represent both
temporal information and inter-dependencies of multivariate time series in
terms of edge-enhanced dynamic graphs. The intra-series correlations are
represented by nodes in a dynamic graph; a self-attention mechanism is modified
to capture the inter-series correlations by using the super-empirical mode
decomposition (SMD) module. We applied the embedded dynamic graphs to times
series forecasting problems, including two real-world datasets and two
benchmark datasets. Extensive experiments show that TSAT clearly outerperforms
six state-of-the-art baseline methods in various forecasting horizons. We
further visualize the embedded dynamic graphs to illustrate the graph
representation power of TSAT. We share our code at
this https URL.

该研究提出了一个名为 TSAT 的多元时间序列表示学习方法，使用动态图来表示时间信息和各变量间的相互关系，并将其应用于时间序列预测问题，实验证明 TSAT 的预测性能显著优于六种先进方法。