Most recent paradigms of generative model-based recommendation still face challenges related to the cold-start problem. Existing models addressing cold item recommendations mainly focus on acquiring more knowledge to enrich embeddings or model inputs. However, many models do not assess the efficiency with which they utilize the available training knowledge, leading to the extraction of significant knowledge that is not fully used, thus limiting improvements in cold-start performance. To address this, we introduce the concept of epistemic uncertainty (which refers to uncertainty caused by a lack of knowledge of the best model) to indirectly define how efficiently a model uses the training knowledge. Since epistemic uncertainty represents the reducible part of the total uncertainty, we can optimize the recommendation model further based on epistemic uncertainty to improve its performance. To this end, we propose a Cold-Start Recommendation based on Epistemic Uncertainty (CREU) framework. Additionally, CREU is inspired by Pairwise-Distance Estimators (PaiDEs) to efficiently and accurately measure epistemic uncertainty by evaluating the mutual information between model outputs and weights in high-dimensional spaces. The proposed method is evaluated through extensive offline experiments on public datasets, which further demonstrate the advantages and robustness of CREU. The source code is available at https://github.com/EsiksonX/CREU.

Utilizing visualization techniques for opinion mining and sentiment exploration of social media texts is a pivotal concern in the visualization field. Research predominantly concentrates on the collective expression of numerous users on popular topics within the public domain. However, there is a need to focus on changing or evolving sentiments for individuals. This paper tries to integrate sentiment analysis with visualization techniques to enable individual users to visually explore the underlying sentiment stories behind their social media texts. Therefore, the SentiSand system is designed and developed based on user requirements. It employs innovative visual design to transform the text from users' social media into a series of visual stories to facilitate users tracking their sentiment changes. The system provides the functionalities of visualizing the overview of the sentiment evolution over a specified period, the sentiment polarity distribution, and words used over that period as well. In a user study, eighteen participants with diverse backgrounds are invited to evaluate the system. The results demonstrate that SentiSand can effectively and intuitively depict sentiment states and changes. All participants gave the system high ratings across dimensions of creativity, practicality, and narrative coherence. Overall, this paper introduces a novel perspective and methodology for visualizing personal sentiments, thereby contributing to an enhanced comprehension of and intervention in individual sentiment states.

In the digital era, traditional methods of historical learning are undergoing a transition, as there is a growing demand for intuitive and visually engaging approaches. Vi-sualization, particularly timeline-based visualization, is critical in bridging the gap between extensive and complex historical events and history learners due to its intuitive features. Despite the advances in existing timeline visualization tools, they often face challenges: visualization creators spend much time in the materials preparation stage, especially for designers unfamiliar with the history; the lack of flexible interaction limits users from designing customized representations, which decreases the user's interest. To address these issues, we have developed Create3DHistory, a system that leverages the advanced Artificial Intelligence Generated Content and visualization technologies, enabling users to easily generate historical events content and create 3D and 2D historical event timeline representation. Users only need to input the name of a historical event, and the system will automatically generate corresponding content and deploy it onto the timeline representation. Moreover, users can edit the generated content or upload self-prepared multimedia materials for customized event visualization. The user study has demonstrated that Create3DHistory can effectively assist users in customizing personalized historical event timelines. Overall, the proposed tool simplifies the timeline creation process using AIGC technology, providing an overview and detailed information on-demand functions for various tasks.

Digital humanity visualization, as an innovative trend that combines historical and cultural studies with visualization, aims to provide the public with intuitive and engaging cultural exploration experiences. However, it is still challenging in this field due to the intrinsic complex and cumbersome textual data, such as how to intuitively and comprehensively present complex relationship networks and spatiotemporal evolution among the data. In this paper, we take the Jinling-related dataset as an example to design a composite visual storytelling tool that utilizes a narrative framework that smoothly changes between macro and micro perspectives. Simultaneously, AI-generated intuitive images are employed to represent the textual data, collectively narrating an engaging cultural story of Jinling. Through user studies, we validate the effectiveness and usability of the tool and demonstrate that the tool possesses excellent storytelling capabilities to improve users' cultural experiences.

The digitization of cultural heritage offers opportunities for preservation and dissemination but also presents challenges in fostering public engagement and narrative coherence. This paper proposes an interactive storytelling system that integrates AI technologies, including AI agents, LLMs, and visualization tools, to enhance user engagement with cultural heritage. Using the Jataka story murals from the Dunhuang grottoes as a case study, the proposed system features three core modules: mural segmentation with cultural context explanations, an interactive knowledge graph that enables users to engage with the murals through storytelling, and user-driven creative reconstruction through comic-style narratives. An AI agent supports these modules by offering conversational interactions that provide real-time explanations, personalized guidance, and creative suggestions. User evaluations based on the User Experience Questionnaire demonstrate the system’s effectiveness in promoting user engagement, with particularly high scores in attractiveness, novelty, and stimulation. These results suggest that the system increases accessibility and interactivity, helping users form deeper emotional and intellectual connections with cultural heritage. The prototype system demonstrates the framework’s potential to make cultural heritage more accessible and participatory, fostering richer connections through interaction, storytelling, and creative expression. These findings highlight the potential of this approach to connect traditional heritage with contemporary audiences, offering new avenues for participation, learning, and creative engagement.

This paper mainly uses the stochastic forest model and multiple linear models to forecast stock return rates. Based on the ten risk factors of the Barra framework, the above two models are used to predict the predicted return rate of each stock. The main conclusions of this paper are as follows. First, in the single-factor sequencing analysis of stock return prediction, the long-short combination of return prediction based on a random forest model can obtain a return rate of 1.66% per month, which is significant at a 1% significance level. The long-short combination has a Sharp rate of 1.78, which is 1.24 higher than that of the linear model. Second, in the case of controlling six classical factors, such as market factors and market value factors, the long-short combination based on the random forest model can still obtain 1.63% excess return per month and is significant at the 5% significance level. Finally, the results based on Fama-MacBeth regression show that the prediction result of random forest predicted rate of return on the real rate of return is long-term stable.

Hyperspectral image (HSI) characteristics of the abundant spectral information are favored by many scholars, but the challenge is how to select relevant features from such high-dimensional data. Band selection (BS), one of the most fundamental dimensionality reduction (DR) techniques, removes redundant bands while providing a subset of bands that can preserve high information content and low noise for further HSI classification. Cuckoo search (CS) algorithm is well known for its high performance of searching relevant features but struggles to get rid of local extremes in the late iteration. Therefore, in this article, an unsupervised BS method based on the heterogeneous CS algorithm with matched filter (HCS-MF) is proposed for HSI classification, in which an optimization model is constructed based on the sensitivity of the matching filter to noise. To reduce the similarity between selected bands, a mapping method based on neighborhood band grouping (NBG) is proposed. In addition, an automatic recommendation strategy based on sliding spectrum decomposition (SSD) is proposed to determine the minimum recommended number of selected bands in different scenes. The superiority of the selected subset of bands is verified by random forest, support vector machine (SVM), and edge-preserving filtering-based SVM (EPF-SVM) classifiers. Experimental results on three well-known datasets demonstrate the robustness and superiority of the proposed HCS-MF algorithm compared with the state-of-the-art methods, such as marginalized graph self-representation (MGSR), neighborhood grouping normalized matched filter (NGNMF), and region-wise multiple graph fusion (RMGF).

This paper aims to explore the use of color and color harmony in Vincent van Gogh’s paintings and present the results in the form of a web-based digital museum to provide an intuitive and immersive experience to the public audience. To achieve this goal, we tested four different color extraction algorithms and determined the extcolors library to be the most effective method for extracting theme colors after interviewing ten random viewers. This study then conducted a visual analysis of the extracted colors, revealing that the predominant colors of van Gogh’s paintings were yellow and green, followed by blue and red. We also identified the main types of color harmony used in van Gogh’s paintings as monochromatic, analogous, and complementary harmony. The ultimate outcome of this research is a digital gallery that showcases the different periods of van Gogh’s work via a timeline representation, highlighting the distinctive color schemes that were employed during each period. This presentation gives the exhibits a clear context and provides the viewer with a clear and vivid experience. Overall, the study’s findings reveal insightful information about how color is employed in van Gogh’s artwork and suggest a useful application which is in the form of a timeline representation that can be used to improve the audience’s exposure to the art and cultural heritage.

In a recent work, a neighborhood polarimetric covariance matrix [N] was proposed to detect ships from polarimetric SAR (PolSAR) imagery. However, its computational complexity is extremely high. Besides, the heterogeneity surrounding ship edges is also not well considered in [N]. To cure these draw-backs, we construct a novel superpixels-based neighborhood polarimetric covariance matrix [SN] in this paper. Specifically, the simple linear iterative clustering (SLIC) is first used to obtain superpixels. Then, the vector vmeancorresponding to the mean value of superpixel is further computed so as to characterize the neighborhood information of each pixel in superpixel. Finally, by combining the original scattering vector v and vmeantogether, the vector t12is built to calculate [SN]. The experiment tested on one L-Band ALOS PolSAR imagery shows that i) the polarimetric whitening filter derived from [SN] (i.e., PWFSN) has a better detection performance than that derived from [N] (i.e., PWFN); ii) the calculation process of [SN] takes much less time than that of [N].

Abstract Electroencephalography (EEG) coherence networks represent functional brain connectivity, and are constructed by calculating the coherence between pairs of electrode signals as a function of frequency. Visualization of such networks can provide insight into unexpected patterns of cognitive processing and help neuroscientists to understand brain mechanisms. However, visualizing dynamic EEG coherence networks is a challenge for the analysis of brain connectivity, especially when the spatial structure of the network needs to be taken into account. In this paper, we present a design and implementation of a visualization framework for such dynamic networks. First, requirements for supporting typical tasks in the context of dynamic functional connectivity network analysis were collected from neuroscience researchers. In our design, we consider groups of network nodes and their corresponding spatial location for visualizing the evolution of the dynamic coherence network. We introduce an augmented timeline‐based representation to provide an overview of the evolution of functional units (FUs) and their spatial location over time. This representation can help the viewer to identify relations between functional connectivity and brain regions, as well as to identify persistent or transient functional connectivity patterns across the whole time window. In addition, we introduce the time‐annotated FU map representation to facilitate comparison of the behaviour of nodes between consecutive FU maps. A colour coding is designed that helps to distinguish distinct dynamic FUs. Our implementation also supports interactive exploration. The usefulness of our visualization design was evaluated by an informal user study. The feedback we received shows that our design supports exploratory analysis tasks well. The method can serve as a first step before a complete analysis of dynamic EEG coherence networks.