Accurate electrical load forecasting is of great significance to help power companies in better scheduling and efficient management. Since high levels of uncertainties exist in the load time series, it is a challenging task to make accurate short-term load forecast (STLF). In recent years, deep learning approaches provide better performance to predict electrical load in real world cases. The convolutional neural network (CNN) can extract the local trend and capture the same pattern, and the long short-term memory (LSTM) is proposed to learn the relationship in time steps. In this paper, a new deep neural network framework that integrates the hidden feature of the CNN model and the LSTM model is proposed to improve the forecasting accuracy. The proposed model was tested in a real-world case, and detailed experiments were conducted to validate its practicality and stability. The forecasting performance of the proposed model was compared with the LSTM model and the CNN model. The Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) were used as the evaluation indexes. The experimental results demonstrate that the proposed model can achieve better and stable performance in STLF.

Web of Things (WoT) facilitates the discovery and interoperability of Internet of Things (IoT) devices in a cyber-physical system (CPS). Moreover, a uniform knowledge representation of physical resources is quite necessary for further composition, collaboration, and decision-making process in CPS. Though several efforts have integrated semantics with WoT, such as knowledge engineering methods based on semantic sensor networks (SSN), it still could not represent the complex relationships between devices when dynamic composition and collaboration occur, and it totally depends on manual construction of a knowledge base with low scalability. In this paper, to addresses these limitations, we propose the semantic Web of Things (SWoT) framework for CPS (SWoT4CPS). SWoT4CPS provides a hybrid solution with both ontological engineering methods by extending SSN and machine learning methods based on an entity linking (EL) model. To testify to the feasibility and performance, we demonstrate the framework by implementing a temperature anomaly diagnosis and automatic control use case in a building automation system. Evaluation results on the EL method show that linking domain knowledge to DBpedia has a relative high accuracy and the time complexity is at a tolerant level. Advantages and disadvantages of SWoT4CPS with future work are also discussed.

The rapid progress of 6G mobile communication technologies has sparked a great deal research interests. The 6G core network architecture faces formidable challenges due to the escalating complexity of network service demands and diverse application scenarios. In response, our research endeavors to tackle these challenges by proposing a self-evolving architecture based on intelligent decision making. Inspired by the principles of biological morphological evolution, our architecture empowers the core network to dynamically adapt and reshape itself in order to effectively address the evolving communication environments. To facilitate this self-evolutionary process, we introduce a comprehensive framework encompassing mechanisms, architecture, agents, and algorithms that enable the network to autonomously generate and optimize its own structure, thereby ensuring adaptability to a wide range of application scenarios. By conducting concept proof simulation experiments, we have demonstrated the effectiveness of our self-evolution algorithm, which enables the 6G core network to make rational evolving decisions and exhibit remarkable adaptability to various application scenarios.

As the core of the Knowledge Tracking (KT) task, assessing students' dynamic mastery of knowledge concepts is crucial for both offline teaching and online educational applications. Since students' mastery of knowledge concepts is often unlabeled, existing KT methods focus on predicting students' responses to practices. However, purely predicting student responses without imposing specific constraints on hidden concept mastery values does not guarantee the accuracy of these intermediate values as concept mastery values. To address this issue, we propose a principled approach called Counterfactual Monotonic Knowledge Tracing (CMKT), which builds on the implicit paradigm described above by using a counterfactual assumption to constrain the evolution of students' mastery of knowledge concepts. Specifically, CMKT first assesses students' knowledge concept mastery value based on their historical practice sequences. Then, CMKT sets the answer of the most recent practice as the opposite of the actual answer and, based on this counterfactual answer, assesses the student's corresponding counterfactual knowledge mastery value. During the model training process, CMKT constrains the update of the student's knowledge states by ensuring that the two types of knowledge mastery values of students satisfy a fundamental educational theory, the monotonicity theory, to provide specific semantics for the assessed mastery values by the model. Finally, extensive experiments on five datasets demonstrate the superiority of CMKT over baseline models.

Word association, revealing mental representations and connections of human, has been widely studied in psychology. However, the scale of available associative cue-response words is severely restricted due to the traditional manually collecting methodology. Meanwhile, with the tremendous success in Natural Language Process (NLP) tasks, an extremely large amount of plain texts can be easily acquired. This suggests an insight about the potential to find association words automatically from the text corpus instead of manually collection. As an original attempt, this paper takes a small step toward proposing a deep learning based framework for automatic association word extraction. The framework mainly consists of two stages of association word detection and machine association network construction. In particular, attention mechanism based Reading Comprehension (RC) algorithm is explored to find valuable association words automatically. To validate the value of the extracted association words, the correlation coefficient between semantic similarities of machine and human association words is introduced as an effective measurement for evaluating association consistence. The experiments are conducted on two text datasets from which together about 20k association words, more than the existing largest human association word dataset, are finally derived. The experiment further verifies that the machine association words are generally consistent with human association words with respect to semantic similarity, which highlights the promising utilization of the machine association words in the future researches of both psychology and NLP.