Partial multi-view classification (PMvC) poses a significant challenge due to the incomplete nature of multi-view data, which complicates effective information fusion and accurate classification. Existing PMvC methods typically rely on heuristic evaluations of view informativeness to achieve global alignment for downstream classification tasks. However, these approaches suffer from two critical issues: information redundancy and semantic misalignment. The complexity of missing data not only leads to over-reliance on redundant or less informative views but also exacerbates semantic misalignment across views, making it difficult for existing methods to effectively capture and discriminate the class-related features. To address these issues, this work proposes a novel GLobal-semantic Alignment Distillation (GLAD) model for partial multi-view classification without requiring imputation. Our approach incorporates a self-distillation mechanism that enables the model to extract informative features and achieve global semantic alignment across views. The key insight of GLAD is leveraging labels as semantic anchors to guide the alignment of partial multi-view features. By integrating labels with extracted features via a cross-attention mechanism, we generate ideal embeddings that consistently capture global semantics across views. These embeddings then serve as intermediate supervision for distilling the student model, ensuring robust semantic alignment even with missing views. We further introduce a margin-aware weighting strategy to enhance the model's discriminative ability. Extensive experimental results validate the effectiveness and superiority of the proposed method, showcasing significant improvements in classification performance over existing techniques.

Existing research on potential-based reward shaping (PBRS) relies on optimal policy in Markov decision process (MDP) where optimal policy is regarded as the ground truth. However, in some practical application scenarios, there is an extrapolation error challenge between the computed optimal policy and the real-world optimal policy. At this time, the optimal policy is unreliable. To address this challenge, we design a Reward Shaping based on Optimal-Policy-Free to get rid of the dependence on the optimal policy. We view reinforcement learning as probabilistic inference on a directed graph. Essentially, this inference propagates information from the rewarding states in the MDP and results in a function which is leveraged as a potential function for PBRS. Our approach utilizes a contrastive learning technique on directed graph Laplacian. Here, this technique does not change the structure of the directed graph. Then, the directed graph Laplacian is used to approximate the true state transition matrix in MDP. The potential function in PBRS can be learned through the message passing mechanism which is built on this directed graph Laplacian. The experiments on Atari, MuJoCo and MiniWorld show that our approach outperforms the competitive algorithms.

Leveraging domain-specific knowledge from pre-trained large language models and knowledge graphs for reasoning in the medical question answering task has emerged as a prominent research field. However, the accuracy of the inference results is restricted by multiple factors, including the quality of analyzed topic entities, the selected inference path in the knowledge graph, and the absence of mutual updating for embedding representations from large language models and knowledge graphs. In this paper, we propose a novel medical question answering framework based on the domain-specific large language model, aiming to enhance the quality of topic entities by implementing the retrieval augmentation technique. Inspired by the concept of chain-of-thought reasoning, we introduce a joint reasoning approach based on analyzed topic entities to facilitate the generation of accurate inference paths. Additionally, we design a unified embedding mechanism that combines representations from both the large language model and graph neural networks, incorporating a pooling operation for predicting answers to input questions. To the best of our knowledge, this work signifies the pioneering efforts in implementing the retrieval augmentation technique and the joint reasoning approach within the context of the medical question answering task. Experimental results on public benchmark datasets demonstrate that the introduced method outperforms state-of-the-art baseline approaches.

Public incidents necessitate prompt proactive measures by the government and pertinent departments, posing substantial challenges to emergency management capabilities. With the advancement of internet technologies, social media platforms have played a pivotal role in shaping the landscape of public incidents, progressively emerging as primary conduits for authentic expression and sentiment sharing among individuals. The sentiment polarities and emotion categories manifested on social media platforms serve as the correspondence to real-world societal behaviors and performance. This article presents a novel framework leveraging the Transformer-based pretrained language model for conducting large-scale analysis of publicly available short text data sourced from social media platforms. The research aims to comprehensively understand the dynamic fluctuations across fourteen dimensions of sentiment polarities and emotion categories extracted from short text data expressed by people on public incidents over temporal periods. The study seeks to elucidate the relation between the enactment of relevant policies and the observed sentiment polarities as well as emotion categories. One sentiment polarity and two emotion categories related to policies on public incidents are outlined. This research contributes to the government and pertinent departments by providing insights into the text content on social media platforms concerning public incidents, thereby facilitating the understanding of the evolving sentiment polarities and emotion categories.

Semi-supervised multi-view learning is a remarkable but challenging task. Existing semi-supervised multi-view classification (SMVC) approaches mainly focus on performance improvement while ignoring decision reliability, which limits their deployment in safety-critical applications. Although several trusted multi-view classification methods are proposed recently, they rely on manual annotations. Therefore, this work emphasizes trusted multi-view classification learning under semi-supervised conditions. Different from existing SMVC methods, this work jointly models class probabilities and uncertainties based on evidential deep learning to formulate view-specific opinions. Moreover, unlike previous works that explore cross-view consistency in a single schema, this work proposes a multi-level consistency constraint. Specifically, we explore instance-level consistency on the view-specific representation space and category-level consistency on opinions from multiple views. Our proposed trusted graph-based contrastive loss nicely establishes the relationship between joint opinions and view-specific representations, which enables view-specific representations to enjoy a good manifold to improve classification performance. Overall, the proposed approach provides reliable and superior semi-supervised multiview classification decisions. Extensive experiments demonstrate the effectiveness, reliability and robustness of the proposed model.

Semi-supervised multi-view learning has been an important research topic due to its capability to exploit complementary information from unlabeled multi-view data. This work proposes MMatch, a new semi-supervised discriminative representation learning method for multi-view classification. Unlike existing multi-view representation learning methods that seldom consider the negative impact caused by particular views with unclear classification structures (weak discriminative views). MMatch jointly learns view-specific representations and class probabilities of training data. The representations concatenated to integrate multiple views’ information to form a global representation. Moreover, MMatch performs the smoothness constraint on the class probabilities of the global representation to improve pseudo labels, whereas the pseudo labels regularize the structure of view-specific representations. A discriminative global representation is mined with the training process, and the negative impact of weak discriminative views is overcome. Besides, MMatch learns consistent classification while preserving diverse information from multiple views. Experiments on several multi-view datasets demonstrate the effectiveness of MMatch.

Selfish herd optimizer (SHO) is a new optimization algorithm. However, its optimization performance is not satisfactory. The main reason for this phenomenon is the weak global search ability of SHO. In this paper, in order to increase the global search ability of SHO, we add Levy-flight distribution strategy. To verify the performance of the proposed algorithm, we use 10 benchmark functions as test cases. Experiment results show that our algorithm is more competitive.

Internet forums and public social media, such as online healthcare forums, provide a convenient channel for users (people/patients) concerned about health issues to discuss and share information with each other. In late December 2019, an outbreak of a novel coronavirus (infection from which results in the disease named COVID-19) was reported, and, due to the rapid spread of the virus in other parts of the world, the World Health Organization declared a state of emergency. In this paper, we used automated extraction of COVID-19-related discussions from social media and a natural language process (NLP) method based on topic modeling to uncover various issues related to COVID-19 from public opinions. Moreover, we also investigate how to use LSTM recurrent neural network for sentiment classification of COVID-19 comments. Our findings shed light on the importance of using public opinions and suitable computational techniques to understand issues surrounding COVID-19 and to guide related decision-making. In addition, experiments demonstrated that the research model achieved an accuracy of 81.15% - a higher accuracy than that of several other well-known machine-learning algorithms for COVID-19-Sentiment Classification.

Active learning algorithms attempt to overcome the labeling bottleneck by asking queries from large collection of unlabeled examples. Existing batch mode active learning algorithms sufferfrom three limitations: (1) The methods that are based on similarityfunction or optimizing certain diversity measurement, in which may lead to suboptimal performance and produce the selected set with redundant examples. (2) The models with assumption on data are hard in finding images that are both informative and representative. (3) The problem of noise labels has been an obstacle for algorithms. In this paper, we propose a novel active learning method that makes embeddings of labeled examples to those of unlabeled ones and back via deep neural networks. The active scheme makes correct association cycles that end up at the same class from that the association was started, which considers both the informativeness and representativeness of examples, as well as being robust to the noise labels. We apply our active learning method to semi-supervised classification and clustering. The submodular function is designed to reduce the redundancy of the selected examples. Specifically, we incorporate our batch mode active scheme into the classification approaches, in which the generalization ability is improved. For semi-supervised clustering, we try to use our active scheme for constraints to make fast convergence and perform better than unsupervised clustering. Finally, we apply our active learning method to data filtering. To validate the effectiveness of the proposed algorithms, extensive experiments are conducted on diversity benchmark datasets for different tasks, i.e., classification, clustering, and data filtering, and the experimental results demonstrate consistent and substantial improvements over the state-of-the-art approaches.

Nowadays, wireless body area networks (WBANs) systems have adopted cloud computing (CC) technology to overcome limitations such as power, storage, scalability, management, and computing. This amalgamation of WBANs systems and CC technology, as sensor‐cloud infrastructure (S‐CI), is aiding the healthcare domain through real‐time monitoring of patients and the early diagnosis of diseases. Hence, the distributed environment of S‐CI presents new threats to patient data privacy and security. In this paper, we review the techniques for patient data privacy and security in S‐CI. Existing techniques are classified as multibiometric key generation, pairwise key establishment, hash function, attribute‐based encryption, chaotic maps, hybrid encryption, Number Theory Research Unit, Tri‐Mode Algorithm, Dynamic Probability Packet Marking, and Priority‐Based Data Forwarding techniques, according to their application areas. Their pros and cons are presented in chronological order. We also provide our six‐step generic framework for patient physiological parameters (PPPs) privacy and security in S‐CI: (1) selecting the preliminaries; (2) selecting the system entities; (3) selecting the technique; (4) accessing PPPs; (5) analysing the security; and (6) estimating performance. Meanwhile, we identify and discuss PPPs utilized as datasets and provide the performance evolution of this research area. Finally, we conclude with the open challenges and future directions for this flourishing research area.

The field of biomarkers in cancer research has recently gained widespread interest, for its potential to improve diagnosis accuracy, prognosis, and make cancer treatments to be more personalized. However, the detection of multi-tumor markers method still has many problems, such as limited applicability, need to different model for different tumor markers, a simple series or parallel method cannot effectively take advantage of different tumor markers. This paper proposed a data fusion method for multi-tumor markers, which can be adapted to different scene. It can effectively use the different markers to give an adjuvant diagnosis. With the new markers continue to be found, we can provide guidance for the combined detection.