Attention Deficit Hyperactivity Disorder (ADHD) is a common neurodevelopmental condition that can affect individuals across lifespan and is characterized by inattention, hyperactivity, and impulsivity. Traditional ADHD diagnostic methods commonly rely on subjective assessments, leading to potential inaccuracies. This study proposes a novel mobile game that utilizes a combined continuous screening paradigm (CCSP), integrating classical psychological measures such as the Continuous Performance, Simon, and Go/No-Go tasks to provide a 4-minute ADHD screening tool for children. The serious game screening design focuses on the two dimensions of sustained attention, and hyperactivity-impulsivity in ADHD and was verified using a test comparing children with ADHD and neurotypical children. The screening model achieved a sensitivity of 0.82. This study provides a methodological framework for leveraging serious games on mobile platforms to facilitate early ADHD screening.

This study addresses the increasing prevalence of emotional and behavioral problems among adolescents by designing and validating a game-based intervention model grounded in the Process Model of Emotion Regulation. We propose a novel integration of biofeedback therapy with Rational Emotive Behavior Therapy to establish a three-stage intervention framework: “emotional exposure - biofeedback - strategy reinforcement”. The research introduces Balance Masters, a serious game featuring a platform balancing mechanic as its core gameplay, connected to a Bluetooth heart rate sensor to implement a real-time biofeedback system. Through a self-controlled experiment with adolescents in elementary schools, results demonstrated significant improvements in cognitive reappraisal scores and significant reductions in expressive suppression scores post-intervention. Heart rate measurements showed a significant decreasing trend throughout gameplay sessions, with particularly notable differences between late and early phases. Game experience evaluations indicated high levels of immersion and positive affect ratings with strong usability metrics. This research contributes an accessible, highcompliance innovative solution for addressing emotion regulation challenges in adolescents, with implications for digital mental health interventions.

Promoting positive mental health and well-being, especially in adolescents, is a critical yet underexplored area in natural language processing (NLP). Most existing NLP research focuses on clinical therapy or psychological counseling for the general population, which does not adequately address the preventative and growth-oriented needs of adolescents. In this paper, we introduce DeepWell-Adol, a domain-specific Chinese dialogue corpus grounded in positive psychology and coaching, designed to foster adolescents’ positive mental health and well-being. To balance the trade-offs between data quality, quantity, and scenario diversity, the corpus comprises two main components: human expert-written seed data (ensuring professional quality) and its mirrored expansion (automatically generated using a two-stage scenario-based augmentation framework). This approach enables large-scale data creation while maintaining domain relevance and reliability. Comprehensive evaluations demonstrate that the corpus meets general standards for psychological dialogue and emotional support, while also showing superior performance across multiple models in promoting positive psychological processes, character strengths, interpersonal relationships, and healthy behaviors. Moreover, the framework proposed for building and evaluating DeepWell-Adol offers a flexible and scalable method for developing domain-specific datasets. It significantly enhances automation and reduces development costs without compromising professional standards—an essential consideration in sensitive areas like adolescent and elderly mental health. We make our dataset publicly available.

Technologically assisted methods have been extensively utilized in positive psychology interventions (PPIs), which aim to elevate the happiness levels of the widespread and diverse general public, a population that traditional methodologies struggle to comprehend and impact. Nevertheless, the literature provides insufficient insights into the effectiveness of chatbot-based PPIs (Chat-PPIs). This study endeavors to fill this void by employing both retrieval-based and generative pre-trained transformer (GPT)-based chatbots and executing three randomized controlled trials involving 326 participants to investigate the hypothesized effectiveness mechanisms. The statistical analysis affirms the effectiveness of Chat-PPI. Moreover, our results indicate that personalized PPI recommendations, adaptive multi-round dialogues, and real-time feedback significantly augment the efficacy of Chat-PPI. Besides exploring and confirming the mechanisms behind Chat-PPI, this study also endorses the use of generative chatbots, which, although less controllable, provide more natural interaction that can boost the efficacy of Chat-PPI.

In untrimmed video tasks, identifying temporal boundaries in videos is crucial for temporal video grounding. With the emergence of multimodal large language models (MLLMs), recent studies have focused on endowing these models with the capability of temporal perception in untrimmed videos. To address the challenge, in this paper, we introduce a multimodal large language model named MLLM-TA with precise temporal perception to obtain temporal attention. Unlike the traditional MLLMs, answering temporal questions through one or two words related to temporal information, we leverage the text description proficiency of MLLMs to acquire video temporal attention with description. Specifically, we design a dual temporal-aware generative branches aimed at the visual space of the entire video and the textual space of global descriptions, simultaneously generating mutually supervised consistent temporal attention, thereby enhancing the video temporal perception capabilities of MLLMs. Finally, we evaluate our approach on both video grounding task and highlight detection task on three popular benchmarks, including Charades-STA, ActivityNet Captions and QVHighlights. The extensive results show that our MLLM-TA significantly outperforms previous approaches both on zero-shot and supervised setting, achieving state-of-the-art performance.

Human emotion synthesis is a crucial aspect of affective computing. It involves using computational methods to mimic and convey human emotions through various modalities, with the goal of enabling more natural and effective human-computer interactions. Recent advancements in generative models, such as Autoencoders, Generative Adversarial Networks, Diffusion Models, Large Language Models, and Sequence-to-Sequence Models, have significantly contributed to the development of this field. However, there is a notable lack of comprehensive reviews in this field. To address this problem, this paper aims to address this gap by providing a thorough and systematic overview of recent advancements in human emotion synthesis based on generative models. Specifically, this review will first present the review methodology, the emotion models involved, the mathematical principles of generative models, and the datasets used. Then, the review covers the application of different generative models to emotion synthesis based on a variety of modalities, including facial images, speech, and text. It also examines mainstream evaluation metrics. Additionally, the review presents some major findings and suggests future research directions, providing a comprehensive understanding of the role of generative technology in the nuanced domain of emotion synthesis.

Psychological resilience refers to an individual's ability to adapt to adversity and stress. Education on psychological resilience during childhood can contribute to future mental health and well-being, such as reducing anxiety and depression [1] [2]. However, traditional psychosocial resilience training faces challenges with accessibility, heavily constrained by cost and spatiotemporal limitations. Recently, emerging large language models (LLMs) have demonstrated exceptional capabilities in conversational tasks, indicating new prospects for cultivating children's psychological resilience. In our work, 1) we conducted qualitative interviews with 10 Chinese children (aged 8-12) and their parents to understand their needs and current conditions; 2) based on the interview results and theories of psychological resilience, we summarized three pathways for developing children's psychological resilience using conversational agents (CAs) and identified six key challenges for designing child-centered CAs; 3) we designed and developed a web prototype using optimized LLMs (see Figure 1), which integrates personal and social support factors, to measure and foster children's psychological resilience through conversations; and 4) we invited 48 child volunteers in user testing and designed three sets of experiments to evaluate the effectiveness of system interventions, the effectiveness of measurements, and overall acceptability. Results indicate that the intervention tasks actively promoted psychological resilience in adolescents. Intelligent measurement scores were effectively consistent with traditional scales in objective scoring, while subjective evaluations, such as appeal and fun, significantly exceeded traditional scale scores.

The World Health Organization has reported a significant increase in mental health problems globally in 2022. In response, there has been a boom in the development of applications for physical and mental health regulation. However, many of these applications lack transparency in their dialogue process logic. To address this issue, this research proposes a communication flow based on the theory of psychological empathy and implemented it using positive psychology content and NLP techniques. Through a two-week pretest-posttest experiment with 25 participants, we found that this chatbot design not only improves the user's well-being but also makes the user feel understood.

Adolescents face much psychological stress in the current social environment, and effective emotional regulation is crucial to their mental health. This article introduces a paradigm of product-oriented psychological dialogue, that is, to study psychological problems first, determine user needs and the most effective way of action, and then develop tools based on this paradigm. We use the above paradigm to build an artificial intelligence-based adolescent emotion adjust the con-versational bot. Specifically, to explore adolescents' emotional regulation needs, this study collected the required data (n=317, 5,543 questionnaires) through the intensive tracking method. It revealed the mechanism of user needs and emotion regulation. Emotion regulation strategy weighting mechanism, and using the collected raw data and existing emotion support dialogue datasets (ESConv), a Chinese adolescent emotion regulation dia-logue dataset was constructed. After that, this paper fine-tunes the existing dialogue model (GPT-2 chitchat). Through these improvements, the dialogue model has dramatically improved its performance and can also provide more personalized and effective emotional regulation support according to the actual needs of adolescents. In summary, this study provides new ideas and methods for mental health support, and promotes the research and development of emotional regulation support for adolescents.

With the development of sensing technology, multiple sensors are widely used in Internet of Things (IoT) devices. A key challenge is to learn feature representations from multimodal sensor data to combine the information of different sensors. Although progress has been made by previous works, the correlation between different sensors is still not well exploited, which may limit the performance of representation learning. To address this problem, we propose a deep learning approach to learn representations from multimodal sensor data with maximally correlated autoencoders (MCA). It can efficiently capture high dependence between different modalities at different feature levels. The learned representations are further used for the recognition task. Experimental results on the real-world RGB- D dataset demonstrate the high effectiveness of MCA.

Missing data is a common problem in multimodal and multi-view learning. It raises a critical challenge for most multimodal algorithms, which are unable to deal with incomplete datasets. Rather than discarding entries with missing modalities, this paper aims to reconstruct the complete image-based multimodal data by imputing missing modalities. We solve the imputation problem as an image translation task, which transforms images in one domain to other domains. Existing image translation techniques either can not fully utilize the information contained in partially complete entries or are limited to the bimodal situation. We propose a semi-supervised algorithm for multimodal learning with missing data, namely Cyclic Autoencoder (CycAE). Specifically, a novel cyclical structure, as well as the correlation among modalities, is integrated to leverage infoπnation from complete entries to incomplete ones. Experiments on two multimodal datasets show that our model outperforms state-of-the-art models. Downstream tasks can also benefit from the completed datasets.

Network traffic classification, which can map network traffic to protocols in the application layer, is a fundamental technique for network management and security issues such as Quality of Service, network measurement, and network monitoring. Recent researchers focus on extracting features for traditional machine learning methods from flows or datagrams of the specific protocol. However, as the rapid growth of network applications, previous works cannot handle complex novel protocols well. In this paper, we introduce the recurrent neural network to network traffic classification and design a novel neural network, the Byte Segment Neural Network (BSNN). BSNN treats network datagrams as input and gives the classification results directly. In BSNN, a datagram is firstly broken into serval byte segments. Then, these segments are fed to encoders which are based on the recurrent neural network. The information extracted by encoders is combined to a representation vector of the whole datagram. Finally, we apply the softmax function to use this vector for predicting the application protocol of this datagram. There are several key advantages of BSNN: 1) no need for prior knowledge of target applications; 2) can handle both connection-oriented protocols and connection-less protocols; 3) supports multi-classification for protocols; 4) shows outstanding accuracy in both traditional protocols and complex novel protocols. Our thorough experiments on real-world data with different protocols indicate that BSNN gains average F1-measure about 95.82% in multi-classification for five protocols including QQ, PPLive, DNS, 360 and BitTorrent. And it also shows excellent performance for detection of novel protocols. Furthermore, compared with two recent state-of-the-art works, BSNN has superiority over the traditional machine learning-based method and the packet inspection method.

Real-time emotion detection has being attracted to human attention recently. Recognizing the inner emotion not only assists people to communicate and understand with each other, but also prevents the occurrence of the serious diseases (e.g., autism) and the emergency (i.e., child abuse, sexual invasion). Existing works usually adopt the professional and cumbersome devices to learn the emotions, and therefore limited in the daily usage. In this work, we design a pervasive and wearable device E-See that enables to recognize the emotion in real time. The prototype of the device is deployed in a microcomputer currently, and it can be resized as a small button worn on the collar or extend as a platform to detect the real-time emotion.

Speech unlocks the huge potentials in emotion recognition. High accurate and real-time understanding of human emotion via speech assists Human-Computer Interaction. Previous works are often limited in either coarse-grained emotion learning tasks or the low precisions on the emotion recognition. To solve these problems, we construct a real-world large-scale corpus composed of 4 common emotions (i.e., anger, happiness, neutral and sadness). We also propose a multi-task attention-based DNN model (i.e., MT-A-DNN) on the emotion learning. MT-A-DNN efficiently learns the high-order dependency and non-linear correlations underlying in the audio data. Extensive experiments show that MT-A-DNN outperforms conventional methods on the emotion recognition. It could take one step further on the real-time acoustic emotion recognition in many smart audio-devices.

Emotion recognition technologies have been widely used in numerous areas including advertising, healthcare and online education. Previous works usually recognize the emotion from either the acoustic or the visual signal, yielding unsatisfied performances and limited applications. To improve the inference capability, we present a multimodal emotion recognition model, EMOdal. Apart from learning the audio and visual data respectively, EMOdal efficiently learns the common and modality-specific information underlying the two kinds of signals, and therefore improves the inference ability. The model has been evaluated on our large-scale emotional data set. The comprehensive evaluations demonstrate that our model outperforms traditional approaches.