Suicide presents a global health challenge, prompting the development of diverse prevention strategies. Among them, timely identification of individuals at risk of suicide remains challenging. Although social media offers potential for tracking users’ mental status, harnessing collaboration between AI and human experts for real-time prediction of suicide risk is inadequately explored. This study presents a human-in-the-loop framework for real-time suicide risk prediction based on social media. Once a user made a new post on social media, the AI model assesses user’s suicide risk within the next month with explanation based on the historic and new posts plus domain knowledge. Human experts on the other side look into the explanation to confirm/clarify uncertain information as feedback, enabling consistent evolution of the model. Experiments on the constructed dataset, containing 66 suicidal users and 66 non-suicidal users, show that our method achieved 82.58% prediction accuracy, outperforming competitive baselines by 6.57%. Leveraging human feedback improved prediction accuracy by 4.12%. Consultation with 18 experts (including 6 medical staff and 12 psychologists) was conducted to examine the validity of our method. Ethics considerations, as well as potential and limitations of large language models in mental condition prediction, are also discussed at the end of the paper.

In this paper, we propose a hyperspectral anomaly detection approach based iForest (Isolation Forest) with band clustering. Instead of random selecting a feature from all bands in iForest algorithm, the proposed approach designed the following three main steps. Firstly, all bands were divided into several groups by use of the correlation among bands and optimal clustering. Then, one of the groups was randomly selected as a candidate. Finally, a band was randomly selected from the candidate group as an attribute for tree node splitting. Compared with other anomaly detection methods, our approach for attributes selection can not only handle high dimensional problem, but also reduce the probability that important information was ignored. The experiments demonstrate its robustness and competitive performance.

Increased health problems among adolescents caused by psychological stress have aroused worldwide attention. Long-standing stress without targeted assistance and guidance negatively impacts the healthy growth of adolescents, threatening the future development of our society. So far, research focused on detecting adolescent psychological stress revealed from each individual post on microblogs. However, beyond stressful moments, identifying teens' stressful periods and stressor events that trigger each stressful period is more desirable to understand the stress from appearance to essence. In this paper, we define the problem of identifying teens' stressful periods and stressor events from the open social media microblog. Starting from a case study of adolescents' posting behaviors during stressful school events, we build a Poisson-based probability model for the correlation between stressor events and stressful posting behaviors through a series of posts on Tencent Weibo (referred to as the microblog throughout the paper). With the model, we discover teens' maximal stressful periods and further extract details of possible stressor events that cause the stressful periods. We generalize and present the extracted stressor events in a hierarchy based on common stress dimensions and event types. Taking 122 scheduled stressful study-related events in a high school as the ground truth, we test the approach on 124 students' posts from January 1, 2012 to February 1, 2015 and obtain some promising experimental results: (stressful periods: recall 0.761, precision 0.737, and F1-measure 0.734) and (top-3 stressor events: recall 0.763, precision 0.756, and F1-measure 0.759). The most prominent stressor events extracted are in the self-cognition domain, followed by the school life domain. This conforms to the adolescent psychological investigation result that problems in school life usually accompanied with teens' inner cognition problems. Compared with the state-of-the-art top-1 personal life event detection approach, our stressor event detection method is 13.72% higher in precision, 19.18% higher in recall, and 16.50% higher in F1-measure, demonstrating the effectiveness of our proposed framework.

The rapid development of economy and society brings un-precedentedly intensive competition and adolescent psycho-logical pressures to current teenagers. If these psychological pressures could not be resolved properly, they will turn to mental problems, which will finally lead to serious conse-quences, such as suicide or aggressive behaviors. Tradition-al face-to-face psychological diagnosis and treatment cannot meet the demand of relieving teenagers ’ stress completely due to its lack of timeliness and diversity. With micro-blog becoming a popular media channel for teenagers ’ informa-tion acquisition, interaction, self-expression and emotion re-lease, we present a system called tHelper for sensing and easing teenagers ’ psychological pressures in study, commu-nication, affection, or self-recognition through micro-blog.

Long-term stress may lead to many severe physical and mental problems. Traditional psychological stress detection usually relies on the active individual participation, which makes the detection labor-consuming, time-costing and hysteretic. With the rapid development of social networks, people become more and more willing to share moods via microblog platforms. In this paper, we propose an automatic stress detection method from cross-media microblog data. We construct a three-level framework to formulate the problem. We first obtain a set of low-level features from the tweets. Then we define and extract middle-level representations based on psychological and art theories: linguistic attributes from tweets' texts, visual attributes from tweets' images, and social attributes from tweets' comments, retweets and favorites. Finally, a Deep Sparse Neural Network is designed to learn the stress categories incorporating the cross-media attributes. Experiment results show that the proposed method is effective and efficient on detecting psychological stress from microblog data.

It is of significant importance to detect and manage stress before it turns into severe problems. However, existing stress detection methods usually rely on psychological scales or physiological devices, making the detection complicated and costly. In this paper, we explore to automatically detect individuals' psychological stress via social media. Employing real online micro-blog data, we first investigate the correlations between users' stress and their tweeting content, social engagement and behavior patterns. Then we define two types of stress-related attributes: 1) low-level content attributes from a single tweet, including text, images and social interactions; 2) user-scope statistical attributes through their weekly micro-blog postings, leveraging information of tweeting time, tweeting types and linguistic styles. To combine content attributes with statistical attributes, we further design a convolutional neural network (CNN) with cross autoencoders to generate user-scope content attributes from low-level content attributes. Finally, we propose a deep neural network (DNN) model to incorporate the two types of user-scope attributes to detect users' psychological stress. We test the trained model on four different datasets from major micro-blog platforms including Sina Weibo, Tencent Weibo and Twitter. Experimental results show that the proposed model is effective and efficient on detecting psychological stress from micro-blog data. We believe our model would be useful in developing stress detection tools for mental health agencies and individuals.

As cloud computing increasingly enters the commercial domain, the resource management issues are becoming a major concern. The dynamics and complexity of cloud environments pose some challenges in managing the resources to ensure the quality of service (QoS) continuously met under fluctuating workloads. In this paper, we focus on the advance resource reservation management issues for the cloud environments. The architecture of virtualization-based resource reservation is first described and then the formulation of an optimization problem concerning the reservation acceptance decision is presented. To solve this problem, an adaptive QoS-aware reservation management approach is proposed, which calculates the reservation acceptance gain to make the final decision. Performance evaluation results of simulation experiments demonstrate that by using the approach we designed, the QoS of both types of applications could be guaranteed and thus the total revenue of the resource provider could stably keep rising. Detailed analysis shows that proper choices can be made to deal with resource reservation requests in a QoS-aware manner.