This study explores how fitness technologies support users in achieving their fitness goals. As performing fitness activities is an intermediate process related to the realization of fitness goals, it is essential to understand how fitness technologies facilitate users’ exercise experience. To that end, this study draws on affordance actualization theory and the concept of exercise engagement to develop and empirically test a model measuring the impact of four actualized affordances on users’ cognitive and emotional engagement in fitness activities, and subsequently on their fitness goal attainment. Data from two survey waves involving 221 matched responses were analyzed using CB-SEM. The results indicate that while fitness technologies embed multiple features, users predominantly actualized four key affordances—self-appraisal, social appraisal, rewarding, and reminding. Among these, self-appraisal and social appraisal affordances significantly enhance emotional exercise engagement, which in turn has a positive effect on fitness goal achievement. This study contributes to the literature by highlighting the importance of actualized affordances in fitness technology use context, focusing on how specific actualized affordances foster emotional exercise engagement, a critical driver of goal attainment. By integrating the affordance actualization theory with exercise engagement, this research provides new insights into how fitness technologies can effectively support users’ fitness journeys.

Social recommendation leverages social relations to complement user–item interactions, alleviating data sparsity in recommendation systems. However, existing approaches still suffer from two critical issues: social graphs inevitably contain noisy or redundant edges, which can hinder user representation learning, and the results of recommendations are biased toward popular items, failing to capture user preferences for long-tail items. To address these challenges, we propose a novel model named denoised and bias-aware framework for social recommendation (DBA-SR). DBA-SR incorporates two key innovations. First, a structure-aware social graph pruning module adaptively removes weak or redundant edges to construct a denoised and structurally informative social graph. Second, a tail- and popularity-aware preference modeling mechanism jointly enhances long-tail preference learning and suppresses popularity bias through dedicated regularization strategies. The overall model is trained under a unified dual graph reconstruction and variational regularization objective. Extensive experiments on three real-world benchmark datasets demonstrate that DBA-SR consistently outperforms baselines in top-$N$recommendation tasks. Further ablation studies confirm the complementary effects of structure-aware pruning and preference specialization. This work provides new insights into the role of structural denoising in social graphs and demonstrates an effective solution to mitigating popularity bias.

This study describes the design and evaluation journey for a mental health education app for individuals experiencing paternal postpartum depression (PPD), employing the double diamond model of design thinking and design science research. We explore the problem space with qualitative analysis of primary and secondary data; and design and evaluate two versions of an artifact – one with a traditional interface and the other incorporating gamification elements. Our findings highlight unique challenges faced by fathers with PPD and an assessment of utility and usability of both prototypes. This work makes three key contributions: (1) a tentative “theory of the problem” of paternal PPD as a specific mental health concern, (2) a comparative assessment of traditional and gamified app designs for educating about paternal PPD, and (3) recommendations for designing engaging and effective mental health educational tools. The educational app has practical implications for providing personalized content about paternal PPD with/without gamification.

Cross-modal hashing is favored in the field of large-scale cross-modal retrieval for its fast query and low storage cost. Existing cross-modal hashing methods are typically based on an idealized assumption that data from different modalities are fully and completely paired. However, in practical scenarios, the occurrence of missing modal data is common due to technical difficulties, hardware failures, and challenges in data collection, contradicting the aforementioned assumption. In this paper, we propose an innovative unsupervised cross-modal hashing frame-work, named Semantic Reconstruction Guided Missing Hashing (SRGMH). Specifically, we utilize Dual-Variational Autoencoders (D-VAEs) to map features of different modalities into a shared low-dimensional latent representation, better bridging the gaps between modalities. Notably, we generate pseudo-representations of other modalities corresponding to the missing modality, effectively solving the problem of missing modal data. Moreover, we construct a refined adjacency similarity matrix to align the latent representations of different modalities, enhancing the quality of completed data and ensuring semantic consistency across modalities. Finally, we embed the adjacency similarity matrices in a shared latent representation space to jointly learn hash functions and hash codes, which enriches the semantic content and maintains structural similarity between hash functions and codes. Experiments on three real-world datasets demonstrate the superior performance of the proposed method on both retrieval accuracy and efficiency.

Sexually transmitted diseases (STDs) significantly impact public health, affecting one in five U.S. adults and imposing substantial economic burdens. Many at-risk individuals seek information and support online rather than through regular testing, facing challenges due to fragmented information and a lack of personalized recommendations. This study develops an online health recommendation system (OHRS) tailored for STD patients, integrating informational and emotional support based on their disease journey. Using a BERT-based named entity recognition (NER) algorithm, the system identifies patient emotions and stages from online posts, providing relevant support resources. Data collection for designing a recommendation engine included analyzing online posts and consulting healthcare experts. The system's effectiveness was validated through user-based simulation studies. Key contributions include developing text-mining algorithms, creating a knowledge-based recommendation system, and proposing design principles for AI-driven support systems for stigmatized conditions.

Hyperspectral image (HSI) classification attempts to classify each pixel, which is an important means of obtaining land–cover knowledge. Hyperspectral images are cubic data with spectral–spatial knowledge and can generally be considered as sequential data alongside spectral dimension. Unlike convolutional neural networks (CNNs), which mainly focus on local relationship models in images, transformers have been shown to be a powerful structure for qualifying sequence data. However, it lacks the excellent ability of CNNs in establishing local relationships in images and cannot perform good generalization in case of insufficient data. In addition, the gradient disappearance problem hinders the convergence stability of deep learning networks as the layers get deeper. To address these problems, we propose a Cascaded Convolution-based Transformer with Densely Connected Mechanism (CDCformer) for hyperspectral image classification. First, we propose a cascaded convolution feature tokenization to extract spectral–spatial information, which will introduce some inductive bias properties of CNN into the transformer. In addition, we design a simple and effective densely connected transformer to enhance feature propagation and transfer memorable information from shallow to deep layers. It efficiently improves the performance of the transformer and extracts more discriminative spectral–spatial features from the HSI. Extensive experimental evaluation of three public hyperspectral data sets shows that CDCformer achieves competitive classification results.

Attributed graphs have attracted much attention in recent years. Different from conventional graphs, attributed graphs involve two different types of heterogeneous information, i.e., structural information, which represents the links between the nodes, and attribute information on each of the nodes. Clustering on attributed graphs usually requires the fusion of both types of information in order to identify meaningful clusters. However, most of existing works implement the combination of these two types of information in a "global" manner by treating all nodes equally and learning a global weight for the information fusion. To address this issue, this paper proposed a novel weighted K -means algorithm with "local" learning for attributed graph clustering, called adaptive fusion of structural and attribute information (Adapt-SA) and analyzed the convergence property of the algorithm. The key advantage of this model is to automatically balance the structural connections and attribute information of each node to learn a fusion weight, and get densely connected clusters with high attribute semantic similarity. Experimental study of weights on both synthetic and real-world data sets showed that the weights learned by Adapt-SA were reasonable, and they reflected which one of these two types of information was more important to decide the membership of a node. We also compared Adapt-SA with the state-of-the-art algorithms on the real-world networks with varieties of characteristics. The experimental results demonstrated that our method outperformed the other algorithms in partitioning an attributed graph into a community structure or other general structures.

Graph clustering has attracted much attention in recent years, which has wide applications in social and biological networks. Recent approaches on graph clustering mainly focus on either certain graphs with node attributes or uncertain graphs without node attributes. However, many real-world graphs have both uncertainty on the edges and attributes on the nodes. We refer to such networks as \emph{attributed uncertain graphs}. Different from conventional graphs, attributed uncertain graphs post two major challenges for graph clustering: 1) uncertainty on the edges, which makes it difficult to extract reliable clusters; 2) high dimensional attributes on the nodes, which contain irrelevant and noisy information. In this paper, we study the problem of node clustering on attributed uncertain graphs, where we exploit both the uncertain edges and a set of important attributes for graph clustering. The uncertain edges can help identify the set of relevant attributes in the nodes, which are called focus attributes. While the focus attributes can help reduce the uncertainty in edges for graph clustering. We propose two novel approaches: AUG-I based upon integrated attribute induced graphs and AUG-U based upon the unified partition over possible worlds of a uncertain graph. Extensive empirical studies on real-world datasets demonstrate the effectiveness of our approaches for clustering tasks on attributed uncertain graphs.

BACKGROUND: Identifying subpopulations within a study and inferring intercontinental ancestry of the samples are important steps in genome wide association studies. Two software packages are widely used in analysis of substructure: Structure and Eigenstrat. Structure assigns each individual to a population by using a Bayesian method with multiple tuning parameters. It requires considerable computational time when dealing with thousands of samples and lacks the ability to create scores that could be used as covariates. Eigenstrat uses a principal component analysis method to model all sources of sampling variation. However, it does not readily provide information directly relevant to ancestral origin; the eigenvectors generated by Eigenstrat are sample specific and thus cannot be generalized to other individuals. RESULTS: We developed FastPop, an efficient R package that fills the gap between Structure and Eigenstrat. It can: 1, generate PCA scores that identify ancestral origins and can be used for multiple studies; 2, infer ancestry information for data arising from two or more intercontinental origins. We demonstrate the use of FastPop using 2318 SNP markers selected from the genome based on high variability among European, Asian and West African (African) populations. We conducted an analysis of 505 Hapmap samples with European, African or Asian ancestry along with 19661 additional samples of unknown ancestry. The results from FastPop are highly consistent with those obtained by Structure across the 19661 samples we studied. The correlations of the results between FastPop and Structure are 0.99, 0.97 and 0.99 for European, African and Asian ancestry scores, respectively. Compared with Structure, FastPop is more efficient as it finished ancestry inference for 19661 samples in 16 min compared with 21-24 h required by Structure. FastPop also provided scores based on SNP weights so the scores of reference population can be applied to other studies provided the same set of markers are used. We also present application of the method for studying four continental populations (European, Asian, African, and Native American). CONCLUSIONS: We developed an algorithm that can infer ancestries on data involving two or more intercontinental origins. It is efficient for analyzing large datasets. Additionally the PCA derived scores can be applied to multiple data sets to ensure the same ancestry analysis is applied to all studies.