Open Set Recognition (OSR) aims to accurately classify known classes and effectively reject unknown classes in open-world scenarios, which is essential for building safe and reliable intelligent systems. Existing studies have shown that auxiliary data-based methods—utilizing outlier exposure or prompt-based guidance to strengthen OSR models—have achieved significant improvements. However, these methods are highly sensitive to the selection of data. Considering the demand for domain expertise and the cost implications, synthesizing data from known samples becomes a preferable alternative. To achieve performance on par with methods that incorporate auxiliary data, it is essential to address the following key challenges: (1) obtaining meaningful pseudo-unknown samples and prompts without manual intervention, and (2) establishing a reliable recognition mechanism during the inference phase. In this paper, we propose a novel Dual-Space Prompt Learning with Context Bias Decoupling (DSPL) method to address the above issues. Specifically, we symmetrically model the same number of unknown classes as known classes to achieve a balanced class distribution in the embedding space. Learnable textual templates are designed for both types of data. Meanwhile, boundary samples are synthesized and treated as pseudo-unknown samples by decoupling discriminative and co-occurring features. Additionally, unknown sample detection is performed by integrating the maximum posterior over known and unknown classes, thereby extending the advantages of symmetric modeling from the training phase to inference. Extensive experiments demonstrate that the proposed method DSPL achieves state-of-the-art performance compared with the most advanced OSR methods.

Cognitive diagnosis (CD) is an important task in the field of intelligent education, aiming to discover the proficiency of students on knowledge concepts with response logs. In applications, different users of the tutoring system demand for a diagnosis of knowledge concepts at different granularities. However, recent methods assume that the concepts are of the same granularity and use explicit correlations between same-granularity concepts to improve the diagnosis performance. If required for diagnosing multi-granularity concepts, these methods will face diminished performance or partial invalidation. To this end, we make the first attempt for multiple-granularity cognitive diagnosis, i.e., diagnosis on coarse- and fine-grained concepts simultaneously. Specifically, in a skillful way, the same-granularity correlations are captured and embedded into concept representations in view of concept semantics and cross-granularity correlations to model the proficiency influence between concepts implicitly. Then, the specific loss for single-granularity diagnosis and the general loss for the consistency of multi-granularity are designed to train the model jointly, achieving multiple-granularity diagnosis. Extensive experiments demonstrate that our method can achieve state-of-the-art accuracy on both coarse- and fine-grained concepts.

Real-world applications have produced massive short text streams. Contrary to the traditional normal texts, they present the characteristics such as short length, only having few labeled data, high-velocity, high-volume and dynamic data distributions, which deteriorate the issues of data sparseness, label missing and concept drift. Obviously, it is a huge challenge for existing short text (stream) classification algorithms due to the poor effectiveness, where they always assume all short texts are completely labeled and little attention is paid on the concept drift issue hidden in short text streams. Therefore, we propose a novel semi-supervised short text steam classification method based on the drift-aware incremental deep learning ensemble model. Specifically, with the sliding window mechanism, we firstly fuse three types of statistical, semantic and structure information to solve the data sparseness issue. Secondly, a semi-supervised incremental deep learning ensemble model based on GCN and the refined LSTM is developed to adapt to the high-volume, high-velocity and label missing short text streams. Thirdly, a label-probability distribution based concept drift detector is introduced to distinguish concept drifts. Finally, as compared with eleven well-known classification methods, extensive experiments demonstrate the effectiveness of the proposed method in the handling of short text streams with limited labeled data.

Time series data are ubiquitous in a variety of disciplines. Early classification of time series, which aims to predict the class label of a time series as early and accurately as possible, is a significant but challenging task in many time-sensitive applications. Existing approaches mainly utilize heuristic stopping rules to capture stopping signals from the prediction results of time series classifiers. However, heuristic stopping rules can only capture obvious stopping signals, which makes these approaches give either correct but late predictions or early but incorrect predictions. To tackle the problem, we propose a novel second-order confidence network for early classification of time series, which can automatically learn to capture implicit stopping signals in early time series in a unified framework. The proposed model leverages deep neural models to capture temporal patterns and outputs second-order confidence to reflect the implicit stopping signals. Specifically, our model exploits the data not only from a time step but also from the probability sequence to capture stopping signals. By combining stopping signals from the classifier output and the second-order confidence, we design a more robust trigger to decide whether or not to request more observations from future time steps. Experimental results show that our approach can achieve superior results in early classification compared to state-of-the-art approaches.

In educational data mining, knowledge tracing (KT) aims to model learning performance based on student knowledge mastery. Deep-learning-based KT models perform remarkably better than traditional KT and have attracted considerable attention. However, most of them lack interpretability, making it challenging to explain why the model performed well in the prediction. In this paper, we propose an interpretable deep KT model, referred to as fuzzy deep knowledge tracing (FDKT) via fuzzy reasoning. Specifically, we formalize continuous scores into several fuzzy scores using the fuzzification module. Then, we input the fuzzy scores into the fuzzy reasoning module (FRM). FRM is designed to deduce the current cognitive ability, based on which the future performance was predicted. FDKT greatly enhanced the intrinsic interpretability of deep-learning-based KT through the interpretation of the deduction of student cognition. Furthermore, it broadened the application of KT to continuous scores. Improved performance with regard to both the advantages of FDKT was demonstrated through comparisons with the state-of-the-art models.

Exercise recommendation is a fundamental and important task in the E-learning system, facilitating students' personalized learning. Most existing exercise recommendation algorithms design a scoring criterion (e.g., weakest mastery, lowest historical correctness) in conjunction with experience, and then recommend the recommended knowledge concepts (KCs). These algorithms rely entirely on the scoring criteria by treating exercise recommendations as a centralized system. However, it is a complex problem for the centralized system to choose a limited number of exercises in a period of time to consolidate and learn the KCs efficiently. Moreover, different groups of students (e.g., different countries, schools, or classes) have different solutions for the same group of KCs according to their own situations, in the spirit of competency-based instructing. Therefore, we propose Meta Multi-Agent Exercise Recommendation (MMER). Specifically, we design the multi-agent exercise recommendation module, in which the KCs involved in exercises are considered agents with competition and cooperation among them. And the meta-training stage is designed to learn a robust recommendation module for new student groups. Extensive experiments on real-world datasets validate the satisfactory performance of the proposed model. Furthermore, the effectiveness of the multi-agent and meta-training part is demonstrated for the model in recommendation applications.

Micro-video recommendation has attracted extensive research attention with the increasing popularity of micro-video sharing platforms. Recently, graph contrastive learning (GCL) is adopted for enhancing the performance of graph neural network based micro-video recommendation. However, these GCL methods may suffer from the following problems: (1) they fail to fully exploit the potential of contrastive learning for ignoring or misjudging highly similar samples, and (2) the complementary recommendation effects between graph structure information and multi-modal feature information are not effectively utilized. In this paper, we propose a novel Cross-View Sample-Enriched Graph Contrastive Learning Network (CSGCL) for micro-video recommendation. Specifically, we build a collaborative learning view and a semantic learning view to learn node representations. For the collaborative learning view, we leverage similar nodes at the structure level to construct an effective collaborative contrastive objective. For the semantic learning view, we derive the k-nearest neighbor graph generated from multi-modal features as the semantic graphs and build a semantic contrastive objective for learning high-quality micro-video representations. Finally, a cross-view contrastive objective is designed to consider the mutually complementary recommendation effects by maximizing the agreement between the two above views. Extensive experiments on three real-world datasets demonstrate that the proposed model outperforms the baselines.

Crowdsourcing truth inference aims to assign a correct answer to each task from candidate answers that are provided by crowdsourced workers. A common approach is to generate workers’ reliabilities to represent the quality of answers. Although crowdsourced triples can be converted into various crowdsourced relationships, the available related methods are not effective in capturing these relationships to alleviate the harm to inference that is caused by conflicting answers. In this research, we propose aReliability-drivenMulti-viewGraphEmbedding framework forTruthinference (TiReMGE), which explores multiple crowdsourced relationships by organically integrating worker reliabilities into a graph space that is constructed from crowdsourced triples. Specifically, to create an interactive environment, we propose a reliability-driven initialization criterion for initializing vectors of tasks and workers as interactive carriers of reliabilities. From the perspective of multiple crowdsourced relationships, a multi-view graph embedding framework is proposed for reliability information interaction on a task-worker graph, which encodes latent crowdsourced relationships into vectors of workers and tasks for reliability update and truth inference. A heritable reliability updating method based on the Lagrange multiplier method is proposed to obtain reliabilities that match the quality of workers for interaction by a novel constraint law. Our ultimate goal is to minimize the Euclidean distance between the encoded task vector and the answer that is provided by a worker with high reliability. Extensive experimental results on nine real-world datasets demonstrate that TiReMGE significantly outperforms the nine state-of-the-art baselines.

Multi-label data streams such as Web texts and images have been popular on the Web. These data present the characteristics of multiple label, high dimensionality, high volume, high velocity and especial concept drift etc. Thus, multi-label data stream classification is a very challenging and significant task especially in the handling of high-dimensional data with concept drifts. However, this challenge has received little attention from the research community. Therefore, we propose the max-relevance and min-redundancy based algorithm adaptation approach for the efficient and effective classification on multi-label data streams with high-dimensional attributes and concept drifts .11.Source codes and data sets are available at below. https://github.com/peipeilihfut/MLStreamClassificationIn order to reduce the impact from the high-dimensional data with noisy attributes, we first refine the minimal-redundancy-maximal-relevance criterion based on mutual information to select qualified features in multi-label data streams. Secondly, we propose the data distribution based concept drifting detection approach to distinguish concept drifts hidden in data streams. Finally, we build an incremental ensemble classification model for efficiently classifying multi-label data streams. Extensive studies show that our approach can get optimal subsets of features while maintaining a good performance in the multi-label classification, as compared to several state-of-the-art multi-label feature selection algorithms using two efficient multi-label classification methods as base classifiers. Meanwhile, our approach is superior to three well-known multi-label data stream classification approaches in the effectiveness and efficiency.

Short text stream classification is a challengingand significant task due to the characteristics of short length, weak signal, high velocity and especially topic drifting in short text stream. However, this challenge has received little attention from the research community. Motivated by this, we propose a new feature extension approach for short text stream classification using a large scale, general purpose semantic network obtained from a web corpus. Our approach is built on an incremental ensemble classification model. First, in terms of the open semantic network, we introduce more semantic contexts in short texts to make up of the data sparsity. Meanwhile, we disambiguate terms by their semantics to reduce the noise impact. Second, to effectively track hidden topic drifts, we propose a concept cluster based topic drifting detection method. Finally, extensive experiments demonstratethat our approach can detect topic drifts effectively compared to several well-known concept drifting detection methods in data streams. Meanwhile, our approach can perform best in the classification of text data streams compared to several stateof-the-art short text classification approaches.

Measuring semantic similarity between two terms is essential for a variety of text analytics and understanding applications. Currently, there are two main approaches for this task, namely the knowledge based and the corpus based approaches. However, existing approaches are more suitable for semantic similarity between words rather than the more general multi-word expressions (MWEs), and they do not scale very well. Contrary to these existing techniques, we propose an efficient and effective approach for semantic similarity using a large scale semantic network. This semantic network is automatically acquired from billions of web documents. It consists of millions of concepts, which explicitly model the context of semantic relationships. In this paper, we first show how to map two terms into the concept space, and compare their similarity there. Then, we introduce a clustering approach to orthogonalize the concept space in order to improve the accuracy of the similarity measure. Finally, we conduct extensive studies to demonstrate that our approach can accurately compute the semantic similarity between terms of MWEs and with ambiguity, and significantly outperforms 12 competing methods under Pearson Correlation Coefficient. Meanwhile, our approach is much more efficient than all competing algorithms, and can be used to compute semantic similarity in a large scale.

Online selection of dynamic features has attracted intensive interest in recent years. However, existing online feature selection methods evaluate features individually and ignore the underlying structure of a feature stream. For instance, in image analysis, features are generated in groups which represent color, texture, and other visual information. Simply breaking the group structure in feature selection may degrade performance. Motivated by this observation, we formulate the problem as an online group feature selection. The problem assumes that features are generated individually but there are group structures in the feature stream. To the best of our knowledge, this is the first time that the correlation among streaming features has been considered in the online feature selection process. To solve this problem, we develop a novel online group feature selection method named OGFS. Our proposed approach consists of two stages: online intra-group selection and online inter-group selection. In the intra-group selection, we design a criterion based on spectral analysis to select discriminative features in each group. In the inter-group selection, we utilize a linear regression model to select an optimal subset. This two-stage procedure continues until there are no more features arriving or some predefined stopping conditions are met. Finally, we apply our method to multiple tasks including image classification and face verification. Extensive empirical studies performed on real-world and benchmark data sets demonstrate that our method outperforms other state-of-the-art online feature selection methods.

The research on community structure is a key to analyze the network functionality and topology, and thus it is significant to detect and analysis the community structure. During the abstract process from an actual system to a network, especially for a large-scale network, it is inevitable to have mistaken connections between nodes or have connection missing. In addition, in real applications, from time to time we can obtain prior information in the form of pairwise constraints between nodes besides topology information, although they may be inaccurate or conflicted. These noises in the network-related information will dramatically reduce the accuracy of community detection. Hence, in this paper, we introduce a dissimilarity index to determine the trustworthiness of pairwise constraints and settle the conflict of pairwise constraints. Then, focusing on the community detection with false connections or conflicted connections, we propose a pairwise constrained structure-enhanced extremal optimization-based semi-supervised algorithm (PCSEO-SS algorithm). Compared with existing semi-supervised community detection approaches, the experimental results executed on real networks and synthetic networks, show that PCSEO-SS can solve the problem of false connections or conflicted connections to some extent and detect the community structure more precisely.

In addition to the news content, most web news pages also contain navigation panels, advertisements, related news links etc. These non-news items not only exist outside the news region, but are also present in the news content region. Effectively extracting the news content and filtering the noise have important effects on the follow-up activities of content management and analysis. Our extensive case studies have indicated that there exists potential relevance between web content layouts and their tag paths. Based on this observation, we design two tag path features to measure the importance of nodes: Text to tag Path Ratio (TPR) and Extended Text to tag Path Ratio (ETPR), and describe the calculation process of TPR by traversing the parsing tree of a web news page. In this paper, we present Content Extraction via Path Ratios (CEPR) - a fast, accurate and general on-line method for distinguishing news content from non-news content by the TPR/ETPR histogram effectively. In order to improve the ability of CEPR in extracting short texts, we propose a Gaussian smoothing method weighted by a tag path edit distance. This approach can enhance the importance of internal-link nodes but ignore noise nodes existing in news content. Experimental results on the CleanEval datasets and web news pages randomly selected from well-known websites show that CEPR can extract across multi-resources, multi-styles, and multi-languages. The average F and average score with CEPR is 8.69% and 14.25% higher than CETR, which demonstrates better web news extraction performance than most existing methods.

Tracking recurring concept drifts is a significant issue for machine learning and data mining that frequently appears in real-world stream classification problems. It is a challenge for many streaming classification algorithms to learn recurring concepts in a data stream environment with unlabeled data, and this challenge has received little attention from the research community. Motivated by this challenge, this article focuses on the problem of recurring contexts in streaming environments with limited labeled data. We propose a semi-supervised classification algorithm for data streams with REcurring concept Drifts and Limited LAbeled data, called REDLLA, in which a decision tree is adopted as the classification model. When growing a tree, a clustering algorithm based on k -means is installed to produce concept clusters and unlabeled data are labeled in the method of majority-class at leaves. In view of deviations between history and new concept clusters, potential concept drifts are distinguished and recurring concepts are maintained. Extensive studies on both synthetic and real-world data confirm the advantages of our REDLLA algorithm over three state-of-the-art online classification algorithms of CVFDT, DWCDS, and CDRDT and several known online semi-supervised algorithms, even in the case with more than 90% unlabeled data.