Space-air-ground integrated networks (SAGINs) are key elements for facilitating high-speed seamless connectivity to the devices/users in infrastructure-less environments, where the traditional terrestrial networks are critically infeasible or uneconomical to be fully deployed. This article comprehensively surveys the advanced computing technologies that support the utilization of SAGINs for infrastructure-less environments. The advanced computing technologies refer to the emerging computing techniques, tools, and the processes that can be utilized to support SAGINs in handling the increasing computing tasks. The main contents include: (1) background of SAGINs, (2) typical use cases of SAGINs in infrastructure-less environments, (3) advanced computing technologies to assist SAGINs to meet the requirements of various services in infrastructure-less environments, (4) the related practical initiatives, and (5) the open research challenges and the future research directions.

Visual information shows to empower accurately named entity recognition in short texts, such as posts from social media. Previous work on multimodal named entity recognition (MNER) often regards an image as a set of visual objects, trying to explicitly align visual objects and entities. However, these methods may suffer the bias introduced by visual objects when they are not identical to entities in quantity and entity type. Different from this kind of explicit alignment, we argue that implicit alignment is effective in optimizing the shared semantic space learning between text and image for improving MNER. To this end, we propose a de-bias contrastive learning based approach for MNER, which studies modality alignment enhanced by cross-modal contrastive learning. Specifically, our contrastive learning adopts a hard sample mining strategy and a debiased contrastive loss to alleviate the bias of quantity and entity type, respectively, which globally learns to align the feature spaces from text and image. Finally, the learned semantic space works with a NER decoder to recognize entities in text. Conducted on two benchmark datasets, experimental results show that our approach outperforms the current state-of-the-art methods.

The happiness level of citizen groups has been widely analyzed using machine learning methods with explanation, aiming to support informed decision-making in our society. However, caused of complex correlations between happiness factors, there is inconsistency in case-by-case explanations provided by different models. In response, we propose a novel and trustworthy explanation solution for happiness prediction that can identify a broadly acceptable key factor set to improve explanation consistency across various models. First, the factor decorrelation is employed to ensure competitively high prediction accuracy. Second, we utilized a happiness prediction model pool that includes trained models with competitive accuracy, contributing to consistent explanations. The factor contribution is then computed using a post-hoc method based on the Shapley value with theoretical properties. The final key factor set is determined by the intersection of sets across different models. Experimental results using the Chinese General Social Survey (CGSS) and the European Social Survey (ESS) datasets validate the 2-fold increase in explanation consistency. Represented by specific citizen groups built on age, comprised of young group ($\leq$40) and elder group ($>$40), and health, comprised of bad health (1-3) and good health (4-5), we demonstrate how these demographics exhibit different contributions in terms of factors. Additionally, we leverage four objective metrics to further evaluate the explanation quality and a human perspective metric for evaluating explanation consistency by comparing our results against explanatory and descriptive studies to provide qualitative reliability measures.

Happiness prediction based on large-scale online data and machine learning models is an emerging research topic that underpins a range of issues, from personal growth to social stability. Many advanced machine learning (ML) models with explanations are used for happiness online assessment while maintaining high accuracy of results. However, expert feedback and sociological theory may be absent from these models, which limits the association between prediction results and the right reasons for why they occurred. Sociological studies have shown that primary and secondary relations are inherent in happiness factors, which can be used as domain knowledge to guide model training. Inspired by such insights, this article attempts to provide new insights into the explanation consistency from an empirical study perspective. Then this article studies how to represent and introduce domain knowledge constraints to make ML models more trustworthy. We achieve this by 1) proving that multiple prediction models with additive factor attributions will have the desirable property of primary and secondary relations consistency; and 2) showing that factor relations with quantity can be represented as an importance distribution for encoding domain knowledge. Factor explanation difference is penalized by the Wasserstein distance among prediction models. Experimental results using two online datasets show that domain knowledge of stable factor relations exists. Using this knowledge not only improves happiness prediction accuracy but also reveals more significant happiness factors for assisting decisions.

The domain shift of crowd scenes significantly hinders the application of crowd counting models in open scenarios. Although domain adaptation methods for crowd counting have bridged this gap to some extent, they ignore one of the significant causes of domain shift, which is the inter-domain data distribution bias. We discover a connection between known and unknown distributions, offering an opportunity for similarity mining to address domain shift. However, there are still challenges related to insufficient and inaccurate similarity mining. In this paper, we propose a novel Fine-grained Inter-domain Similarity Mining (FSIM) framework. To comprehensively explore similar distributions between source and target domains, we propose a Multi-scale Distribution Alignment (MDA) module based on diffusion retrieval. To enhance the reliability of inter-domain similarity mining, we propose a Multi-retrieval Refinement (MR) module based on evidence theory, serving as an uncertainty measurement method. Eventually, to eliminate data distribution bias, we perform model retraining using similar distributions. Extensive experiments conducted on five standard crowd counting benchmarks, SHA, SHB, QNRF, NWPU, and JHU-CROWD++, demonstrate that the proposed FSIM has robust generalizability. Our code will be available at: https://github.com/HopooLinZ/FSIM/