We propose a novel resident identification framework to identify residents in
a multi-occupant smart environment. The proposed framework employs a feature
extraction model based on the concepts of positional encoding. The feature
extraction model considers the locations of homes as a graph. We design a novel
algorithm to build such graphs from layout maps of smart environments. The
Node2Vec algorithm is used to transform the graph into high-dimensional node
embeddings. A Long Short-Term Memory (LSTM) model is introduced to predict the
identities of residents using temporal sequences of sensor events with the node
embeddings. Extensive experiments show that our proposed scheme effectively
identifies residents in a multi-occupant environment. Evaluation results on two
real-world datasets demonstrate that our proposed approach achieves 94.5% and
87.9% accuracy, respectively.

我们提出了一种新颖的居民识别框架，用于在多居住者智能环境中识别居民。该框架利用了基于位置编码概念的特征提取模型，该模型将住宅位置视为一个图形。我们设计了一种新颖的算法，从智能环境的布局图构建这样的图形。Node2Vec 算法用于将图形转换为高维节点嵌入。引入了长短期记忆（LSTM）模型，使用节点嵌入与传感器事件的时间序列来预测居民的身份。大量的实验证明我们提出的方案有效地在多居住者环境中识别居民。两个实际数据集的评估结果分别达到 94.5％和 87.9％的准确率。

基于位置编码的多住户智能家居居民识别

Positional Encoding-based Resident Identification in Multi-resident  Smart Homes

Energy prediction in buildings plays a crucial role in effective energy
management. Precise predictions are essential for achieving optimal energy
consumption and distribution within the grid. This paper introduces a Long
Short-Term Memory (LSTM) model designed to forecast building energy consumption
using historical energy data, occupancy patterns, and weather conditions. The
LSTM model provides accurate short, medium, and long-term energy predictions
for residential and commercial buildings compared to existing prediction
models. We compare our LSTM model with established prediction methods,
including linear regression, decision trees, and random forest. Encouragingly,
the proposed LSTM model emerges as the superior performer across all metrics.
It demonstrates exceptional prediction accuracy, boasting the highest R2 score
of 0.97 and the most favorable mean absolute error (MAE) of 0.007. An
additional advantage of our developed model is its capacity to achieve
efficient energy consumption forecasts even when trained on a limited dataset.
We address concerns about overfitting (variance) and underfitting (bias)
through rigorous training and evaluation on real-world data. In summary, our
research contributes to energy prediction by offering a robust LSTM model that
outperforms alternative methods and operates with remarkable efficiency,
generalizability, and reliability.

本研究通过使用历史能源数据、占用模式和天气条件，引入了一种基于长短期记忆（LSTM）模型的建筑能源消耗预测方法。该 LSTM 模型对于住宅和商业建筑的短期、中期和长期能源预测相比现有的预测模型具有更高的准确性。通过与线性回归、决策树和随机森林等已有预测方法的比较，我们的 LSTM 模型在所有指标上表现出色，具有 0.97 的最高 R2 得分和 0.007 的最佳平均绝对误差（MAE）。我们的模型还具有在受限数据集上实现高效能源消耗预测的能力。通过在实际数据上的严格训练和评估，我们解决了过拟合（方差）和欠拟合（偏差）的问题。总结而言，我们的研究通过提供一种优于其他方法的、具有出色效率、普适性和可靠性的强大 LSTM 模型，为能源预测做出了贡献。

基于历史数据和环境因素的数据驱动能耗预测

DECODE: Data-driven Energy Consumption Prediction leveraging Historical  Data and Environmental Factors in Buildings

Sentiment analysis is the task of mining the authors' opinions about specific
entities. It allows organizations to monitor different services in real time
and act accordingly. Reputation is what is generally said or believed about
people or things. Informally, reputation combines the measure of reliability
derived from feedback, reviews, and ratings gathered from users, which reflect
their quality of experience (QoE) and can either increase or harm the
reputation of the provided services. In this study, we propose to perform
sentiment analysis on web microservices reviews to exploit the provided
information to assess and score the microservices' reputation. Our proposed
approach uses the Long Short-Term Memory (LSTM) model to perform sentiment
analysis and the Net Brand Reputation (NBR) algorithm to assess reputation
scores for microservices. This approach is tested on a set of more than 10,000
reviews related to 15 Amazon Web microservices, and the experimental results
have shown that our approach is more accurate than existing approaches, with an
accuracy and precision of 93% obtained after applying an oversampling strategy
and a resulting reputation score of the considered microservices community of
89%.

本研究提出了一种使用长短期记忆（LSTM）模型进行情感分析以及使用净品牌声誉（NBR）算法评估微服务声誉分数的方法，实验结果显示该方法在准确性上超过现有方法，通过应用过采样策略获得 93% 的准确率和精确度，并得出所考虑的微服务社区的声誉分数为 89%。