Suicide remains a pressing global public health concern. While social media platforms offer opportunities for early risk detection through online conversation trees, existing approaches face two major limitations: (1) They rely on predefined rules (e.g., quotes or relies) to log conversations that capture only a narrow spectrum of user interactions, and (2) They overlook hidden influences such as user conformity and suicide copycat behavior, which can significantly affect suicidal expression and propagation in online communities. To address these limitations, we propose a Multi-Agent Causal Reasoning (MACR) framework that collaboratively employs a Reasoning Agent to scale user interactions and a Bias-aware Decision-Making Agent to mitigate harmful biases arising from hidden influences. The Reasoning Agent integrates cognitive appraisal theory to generate counterfactual user reactions to posts, thereby scaling user interactions. It analyses these reactions through structured dimensions, i.e., cognitive, emotional, and behavioral patterns, with a dedicated sub-agent responsible for each dimension. The Bias-aware Decision-Making Agent mitigates hidden biases through a front-door adjustment strategy, leveraging the counterfactual user reactions produced by the Reasoning Agent. Through the collaboration of reasoning and bias-aware decision making, the proposed MACR framework not only alleviates hidden biases, but also enriches contextual information of user interactions with counterfactual knowledge. Extensive experiments on real-world conversational datasets demonstrate the effectiveness and robustness of MACR in identifying suicide risk.

Educational programs emphasizing experiential learning and personal development increasingly rely on student reflections to assess learning outcomes beyond traditional grade-based measures. However, manual analysis of open-ended textual reflections is time-consuming, subjective, and particularly challenging for short texts. This paper applies an automated topic detection method using sentence embeddings and unsupervised machine learning to analyze student reflections and identify learning gains expressed in students’ own words. Our approach combines sentence embedding techniques to capture semantic information from short textual reflections with K-means clustering to automatically partition embeddings into meaningful topic clusters. This methodology enables systematic identification of themes that students emphasize when describing their learning experiences, providing insights into personalized learning outcomes that traditional assessment methods might miss. We demonstrate this approach using a corpus of short reflections from service-learning programs. The method is broadly applicable to any educational context where understanding student experiences and feelings takes precedence over conventional assessment metrics. Experimental results show the method effectively uncovers meaningful topics from brief, open-ended reflections. Analysis of frequent keywords within each topic cluster provides fine-grained insights into student perspectives on their learning outcomes. Our findings reveal that students with higher self-reported learning outcomes tend to discuss a broader range of topics in their reflections. This computational approach offers educators and researchers a scalable tool for understanding what aspects of educational experiences most significantly impact student learning from the learners’ own perspectives, with applications extending beyond service-learning to diverse experiential education programs.

The growth of synchronous online education has introduced new forms of communication, collaboration, and engagement in real-time learning environments. To understand these processes, analytic approaches are needed that move beyond traditional assessments and make use of the multimodal signals available in virtual classrooms. This study investigates online service-learning programs as a representative setting where university students interact with peers and service recipients via videoconferencing. We construct a multimodal dataset of synchronous service-learning sessions, comprising transcripts, speech recordings, and screen recording content aligned at the utterance level. Using this dataset, we compare unimodal models with a range of multimodal fusion strategies to evaluate how linguistic, acoustic, and visual information contribute to predicting students’ self-reported learning gains. The results show that transcript features yielded higher predictive performance than other single modalities, while their integration with voice features and screen content produced more consistent and reliable predictions. These findings suggest that combining linguistic, acoustic, and visual information—capturing what students say, how they use their voices, and the learning materials they share on screen—offers a more grounded understanding of student learning processes in synchronous online education.

Backchannel responses play an essential role in human communication, which are often expressed by listeners to show their attention and engagement to speakers without interrupting their speech. Their automatic detection is crucial for developing conversational AI agents that engage in human-like, responsive communication. Backchanneling can be conveyed via a combination of various non-verbal cues, such as head nodding and facial expressions. However, these cues are often subtle, brief and sparse during conversations, posing significant challenge in the accurate detection of backchannel responses. This study introduces TMAN, a sequential three-stage multimodal temporal network designed to effectively encode behavioral features from four human visual modalities. It incorporates three attention modules to encode subtle "micro" actions, such as specific gestures or facial expressions, that occur at each frame, as well as temporal "macro" behavior patterns, such as sustained body and head movements, into a final representation for backchannel detection. These are often expressed in backchannel responses, thereby enhancing the detection capabilities. Comprehensive experiments conducted on two public datasets demonstrate that TMAN significantly enhances performance and achieves state-of-the-art results. Extensive ablation studies validate the contribution of each attention module and visual modality employed in our model, and identify the appropriate feature transformation and implementation setups for effective backchannel detection. An in-depth investigation of the model inference process further demonstrates the effectiveness of TMAN attention modules, particularly in processing both "micro" and temporal "macro" behavior patterns in multimodal visual cues.

Locomotion has a marked impact on user experience in VR, but currently, common to-go techniques such as steering and teleportation have their limitations. Particularly, steering is prone to cybersickness, while teleportation trades presence for mitigating cybersickness. Inspired by how we manipulate a picture on a mobile phone, we propose illumotion, an optical-illusion-based method that, we believe, can provide an alternative to these two typical techniques. Instead of zooming in a picture by pinching two fingers, we can move forward by "zooming" toward part of the 3D virtual scene with pinched hands. Not only is the proposed technique easy to use, it also seems to minimize cybersickness to some degree. illumotion relies on the manipulation of optics; as such, it requires solving motion parameters in screen space and a model of how we perceive depth. To evaluate it, a comprehensive user study with 66 users was conducted. Results show that, compared with either teleportation, steering or both, illumotion has better performance, presence, usability, user experience and cybersickness alleviation. We believe the result is a clear indication that our novel optically-driven method is a promising candidate for generalized locomotion.

This overview presents one of the cup challenges of IEEE BigData 2024, with the topic of suicide risk level detection on social media posts. Given a training set of N = 2000 posts (N = 500 labelled and N = 1500 unlabelled posts) from r/SuicideWatch subreddits, the task of this challenge is to develop a predictive model capable of classifying the suicidal posts into four levels (i.e., indicator, ideation, behaviour, and attempt). The dataset provided simulated the obstacles existed in relevant fields (e.g., model overfitting, data scarcity and class imbalance), participating teams are supposed to tackle these issues while exploring the effectiveness of various model architectures. We received submissions from 21 teams and works of 13 teams underwent final evaluation. Teams addressed key challenges in suicide risk detection including limited suicidal data and suicidal risk imbalance. They employed novel approaches to overcome these obstacles, leveraging a diverse range of models from foundational base language models (BLMs) to state-of-the-art large language models (LLMs). In the competition, the highest weighted F1-score achieved under the final evaluation was 0.7605. The findings of this challenge can provide technical implications to social media suicide detection and contribute the clinical effectiveness to the applications of machine learning in digital suicide or mental healthcare management.

Neural rendering is an exciting topic injecting machine learning methodologies into the classical computer graphics rendering pipeline. Although recent works have achieved remarkable fidelity, discussion on how to enable it for interactive scenarios like video games seems to be lacking. Aside from an editable 3D model and UV-mapping, an interactive application will demand the neural rendering to handle animatable 3D content with interactive speed. This is currently a gap in neural rendering and our solution to this problem is a novel neural rendering pipeline involving a primitive named NeRFahedron. It localizes a NeRF field and as such effectively reduces the number of expensive network sampling operations to improve speed. Our pipeline involves tetrahedron rasterization, localized ray marching and near-surface particle sampling. The result is a method that can enable animatable content for neural rendering with interactive speed, which has been shown to be competitive in rendering animation. We will also showcase its ability to enable interactive applications via a real-time demo.

Neural rendering is an exciting topic injecting machine learning methodologies into the classical computer graphics rendering pipeline. Although recent works have achieved remarkable fidelity, discussion on how to enable it for interactive scenarios like video games seems to be lacking. Aside from an editable 3D model and UV-mapping, an interactive application will demand the neural rendering to handle animatable 3D content with interactive speed. This is currently a gap in neural rendering and our solution to this problem is a novel neural rendering pipeline involving a primitive named NeRFahedron. It localizes a NeRF field and as such effectively reduces the number of expensive network sampling operations to improve speed. Our pipeline involves tetrahedron rasterization, localized ray marching and near-surface particle sampling. The result is a method that can enable animatable content for neural rendering with interactive speed, which has been shown to be competitive in rendering animation. We will also showcase its ability to enable interactive applications via a real-time demo.

Increasing number of people work long hours with computers under high cognitive load. This could potentially cause mental stress in workplaces. Prolonged exposure to mental stress contributes to poor working experience and even severe health problems. Despite the growing demand, the existing intelligent stress detection methods are limited when applied to actual workplaces. They often measure physiological and physical signals, via intrusive devices, to detect stress. The intrusiveness hampers their accessibility and applicability in daily life and workplaces. To overcome that, behavior-based methods were proposed. Models that explore mouse and gaze behaviors during computer usages were demonstrated to be particularly effective. However, the current methods rely on using prior knowledge of the user interface (UI) layout to construct models. Their applicability thus is limited, especially in real workplaces where task UI is often dynamic. This paper presents a novel stress detection method to address the challenges. It attains non-intrusiveness and UI-agnostic by modeling the relative movement and coordination of mouse and gaze. The method is evaluated on a dynamic-UI task, namely, web searching. An accuracy of 78.8% is achieved using a commercial eye-tracker for gaze estimation, beating the state-of-the-art approaches by around 20%. We further use webcam to estimate gaze locations substituting for the eye-tracker, to enhance the model accessibility. The method yields 68.6% accuracy of stress detection without using any special devices. Experimental results demonstrate the effectiveness and applicability of our method. It opens up a new avenue for cognitive-aware adaptive user interface, intelligent working environment, and related applications.

This paper presents the development of an attention based bi-directional gated recurrent unit model, P-Flashv2, for the prediction of potential occurrence of flashover in a traditional 111 m2 single story ranch-style family home. Synthetic temperature data for more than 110 000 fire cases with a wide range of fire and vent opening conditions are collected. Temperature limit to heat detectors is applied to mimic the loss of temperature data in real fire scenarios. P-Flashv2 is shown to be able to make predictions with a maximum lead time of 60 s and its performance is benchmarked against eight different model architectures. Results show that P-Flashv2 has an overall accuracy of -87.7 % and -89.5% for flashover predictions with a lead time setting of 30 s and 60 s, respectively. Additional model testing is conducted to assess P-Flashv2 prediction capability in real fire scenarios. Evaluating the model again with full-scale experimental data, P-Flashv2 has an overall prediction accuracy of -82.7 % and -85.6 % for cases with the lead time of setting 30 s and 60 s, respectively. Results from this study show that the proposed machine learning based model, P-Flashv2, can be used to facilitate data-driven fire fighting and reduce fire fighter deaths and injuries.

As a high-impact educational practice, service-learning has demonstrated success in positively influencing students’ overall development, and much work has been done on investigating student learning outcomes from service-learning. A particular direction is to model students’ learning outcomes in the context of their learning experience, i.e., the various student, course, and pedagogical elements. It contributes to a better understanding of the learning process, a more accurate prediction of students’ attainments on the learning outcomes, and improvements in the design of learning activities to maximize student learning. However, most of the existing work in this area relies on statistical analysis that makes assumptions about attribute independence or simple linear dependence, which may not accurately reflect real-life scenarios. In contrast, the study described in this paper adopted a neural network-based approach to investigate the impact of students’ learning experience on different service-learning outcomes. A neural network with attention mechanisms was constructed to predict students’ service-learning outcomes by modeling the contextual information from their various learning experiences. In-depth evaluation experiments on a large-scale dataset collected from more than 10,000 students showed that this proposed model achieved better accuracy on predicting service-learning outcomes. More importantly, it could capture the interdependence between different aspects of student learning experience and the learning outcomes. We believe that this framework can be extended to student modeling for other types of learning activities.

The increasing number of suicide events in recent years has set off an alarm in society. To prevent suicides, it is important to develop platforms with associated intelligent algorithms, such as those for early suicide ideation detection (SID). In general, entering people’s lives to obtain indicative information for effective SID is prohibitive due to the possible risk of privacy invasion. Social media posts provide valuable information about users’ activities, indicating important hints toward SID in a non-intrusive manner. Although multiple datasets have been collected from social media platforms for efficient SID, they either neglect many suicidal posts by searching for pre-defined keywords, or contain limited information about suicide ideation. In this paper, a newly collected dataset, which expands the coverage of suicidal posts with more fine-grained annotations of suicide risk levels and suicide triggers compared with existing datasets, is presented. Benchmarking results by a popular deep-learning model are analysed to validate the reliability and potential of the collected dataset. Furthermore, multiple application scenarios of the dataset are discussed. The proposed dataset is expected to enhance the research on suicide ideation and behaviours, and have a strong impact on SID and more importantly, suicide prevention to save invaluable lives.

In recent years, there is a growing phenomenon with children playing mobile devices. Overplaying mobile devices, however, may not lead to a balanced childhood. Lack of physical touch-and-feel stimulus is detrimental to children. Can touch-and-feel stuffed toys be made more appealing to children for a wider variety on their playtime diet? We explore a stuffed toy controller which integrates elements from both digital and make-believe games in an attempt to enrich children interaction experience. A user study was conducted to evaluate the effectiveness of the proposed controller while another children-oriented user study shows that make-believe augmented reality games played by the toy controller can enhance children’s interest in stuffed toys.

Fire fighter fatalities and injuries in the U.S. remain too high and fire fighting too hazardous. Until now, fire fighters rely only on their experience to avoid life-threatening fire events, such as flashover. In this paper, we describe the development of a flashover prediction model which can be used to warn fire fighters before flashover occurs. Specifically, we consider the use of a fire simulation program to generate a set of synthetic data and an attention-based bidirectional long short-term memory to learn the complex relationships between temperature signals and flashover conditions. We first validate the fire simulation program with temperature measurements obtained from full-scale fire experiments. Then, we generate a set of synthetic temperature data which account for the realistic fire and vent opening conditions in a multi-compartment structure. Results show that our proposed method achieves promising performance for prediction of flashover even when temperature data is completely lost in the room of fire origin. It is believed that the flashover prediction model can facilitate the transformation of fire fighting tactics from traditional experience-based decision marking to data-driven decision marking and reduce fire fighter deaths and injuries.

Suicide ideation detection on social media is a challenging problem due to its implicitness. In this paper, we present an approach to detect suicide ideation on social media based on a BERT-LSTM model with Adversarial and Multi-task learning (BLAM). More specifically, BLAM combines BERT model with Bi-LSTM model to extract deeper and richer features. Furthermore, emotion classification is utilized as an auxiliary task to perform multi-task learning, which enriches the extracted features with emotion information that enhances the identification of suicide. In addition, BLAM generates adversarial noise by adversarial learning improving the generalization ability of the model. Extensive experiments conducted on our collected Suicide Ideation Detection (SID) dataset demonstrate the competitive superiority of BLAM compared with the state-of-the-art methods.