Data stream classification is widely used in Internet of Things (IoT) scenarios such as health monitoring, anomaly detection and online diagnosis. Due to the continuous data stream changing dynamically over time, it is impossible to classify all the data simultaneously. Moreover, labeling each sample in practical data stream applications is time-and resource-consuming. The realistic situation is that only a few instances in a data stream are labeled. Therefore, classifying data streams with limited labels has become challenging in IoT scenarios. In this paper, we propose an incremental dynamic weighted semi-supervised method for classifying IoT data streams. Considering the dynamics and continuity in data streams, we use a chunk-based approach to learn the features in the data stream and assign weights to the classifier dynamically. Moreover, we deploy incremental learning methods to continuously learn from the sampled labeled data stream to update the classifier model, which can take advantage of newly incoming labeled data to improve learning performance. Experimental evaluations on seven IoT datasets show that the proposed method outperforms semi-supervised methods in accuracy, precision, and geometric mean (Gmean) by 10% and 5% over supervised methods, respectively.

Anomaly detection has become a key focus in maintaining the stability and reliability of the cloud environment. Although with excellent feature extraction ability, deep learning-based anomaly detection methods entail a time-consuming training process. Broad learning system (BLS) provides an alternative supervised way for efficient training. However, due to the imbalance of the collected cloud computing data in which anomaly accounts for a low proportion, sufficient feature extraction from anomaly behaviors with BLS becomes a challenge. Moreover, the input generation of BLS only considers the independence of data, and the generalization of BLS in the correlation modeling of cloud computing data is limited. To tackle the above issues, we introduce an effective anomaly detector, CatBRS, an improved BLS with rebalance operations. Initially, we employ a hybrid resampling method of SMOTE-Tomek to mitigate data imbalance, retain non-synthetic samples for training, and involve synthetic samples in the input generation later. Subsequently, we extend BLS by refining the process of input generation. This enhanced system employs a simple recurrent architecture to model temporal dynamics. Additionally, it integrates an autoencoder-based model with metric learning to obtain category-constrained discriminant features. The improvement in BLS facilitates more comprehensive feature extraction. Finally, extensive experiments are conducted to evaluate the performance of CatBRS on four benchmark datasets. CatBRS shows improvements of up to 3.81% in AUC and 6.09% in F1 compared to suboptimal baseline methods with a low training cost.

The anomaly detection techniques for cloud service focus on alerting the operation engineers about the anomalous running state. However, their shortcoming of anomaly interpretability is an obstacle to understanding and further removing the anomalies. To overcome the abovementioned challenge, we propose a tree-like attention-based detection framework CauseFormer that provides both the metric and sample interpretations. Firstly, we develop stepwise attention based on the multi-head attention mechanism, which imitates the rule-based tree formation process. This network block extracts the higher-order features and generates the metric contribution that can be regarded as metric interpretation. Meanwhile, we design the hyper-circle loss function rather than cross-entropy-based approaches to optimize the representation. Then we introduce the majority voting rule into the classifier. This neighbor classification criterion raises the alarms of anomalies and achieves the sample interpretation. Finally, we conduct extensive experiments in four datasets collected from cloud application cases. The experimental results reveal the superiority of CauseFormer in improving detection accuracy and embodying practical interpretability.

Anomaly detection is indispensable for achieving higher availability and reliability in the cloud computing. The traditional autoencoder-based method only models the historical normal samples and then identifies the current online anomaly samples by the fixed threshold of anomaly score. Although more advances have been made in recent years, two main challenges remain: (i) ignoring the historical anomaly samples, (ii) poor self-adaptive ability for online detection. To address the above challenges, we propose a unified detector, namely BroadCAE, which integrates autoencoder with contrastive learning and broad network. Specifically, the reconstruction loss is first replaced by contrastive loss, which equally formulates both normal and anomaly samples. These samples belonging to the same class become closer in a lower-dimensional space. Conversely, different classes of samples are far away from each other. Next, we apply the anomaly-score-based pseudo thresholds to train the dynamic threshold selection, which generates the threshold according to the coming sample. The broad network in dynamic threshold selection takes the place of the deep network, which overcomes catastrophic forgetting and adapts to new online samples. Finally, validation experiments are conducted on four benchmark datasets. Our BroadCAE outperforms the comparative baseline methods by averaging over 4% of the f1-score.

This paper reports our work in developing a suitable application to engage groups of students in a debate with the aim of fostering their debating skills and level of critical awareness, and making them more aware of the substantive issues involved. A new dialogue model is proposed for group argumentation. A web application that realizes the proposed model has been iteratively constructed. A user-based evaluation has been carried out. The user evaluation reveals that users are generally satisfied with the proposed dialogue model and the education value of the application in facilitating collaborative argumentation.

In the development and application of intelligent learning environments, an important trend is to integrate characteristics proper of human, such as personality and emotion, into the intelligent interface agents, with the aim of providing the student with a more personalized and friendly environment. The learning companion is a kind of very useful intelligent interface agent in the learning environment. Therefore, exploring the model of personality and emotion of learning companion agent is the crucial problem to make it more hominine and believable. In this paper, the related works on personality and emotions in psychology and artificial intelligence are reviewed briefly, and a framework of learning companion agent with Personality and Emotions is proposed. Based on the OCEAN model and the OCC model, the model of personality and emotion of learning companion agent is defined and formalized. Moreover, the computation and the coming implementation of the model are described in detail.