Automatic sleep staging plays a vital role in assessing sleep quality and diagnosing sleep disorders. Most existing methods rely heavily on long and continuous EEG recordings, which poses significant challenges for data acquisition in resource-constrained systems, such as wearable or home-based monitoring systems. In this paper, we propose the task of resource-efficient sleep staging, which aims to reduce the amount of signal collected per sleep epoch while maintaining reliable classification performance. To solve this task, we adopt the masking and prompt learning strategy and propose a novel framework called Mask-Aware Sleep Staging (MASS). Specifically, we design a multi-level masking strategy to promote effective feature modeling under partial and irregular observations. To mitigate the loss of contextual information introduced by masking, we further propose a hierarchical prompt learning mechanism that aggregates unmasked data into a global prompt, serving as a semantic anchor for guiding both patch-level and epoch-level feature modeling. MASS is evalutaed on four datasets, demonstrating state-of-the-art performance, especially when the amount of data is very limited. This result highlights its potential for efficient and scalable deployment in real-world low-resource sleep monitoring environments.

Human activity prediction is crucial for enabling intelligent smart home services, yet it is often hindered by the scarcity of high-quality, multi-dimensional datasets. Existing datasets are typically fragmented, capturing either long-term activity sequences or short-term device interactions, but rarely both in a unified manner. Traditional data collection methods are costly and time-consuming, while conventional simulation techniques struggle to generate diverse and logically coherent behavior sequences. To address these limitations, we propose SmartLLM, a novel Large Language Model (LLM)-based simulation framework for automated generation of multi-dimensional smart home datasets. SmartLLM simulates simulated agents with distinct profiles (e.g., old man, remote worker, holiday maker) performing daily activities within configurable home environments, generating temporally aligned sequences across Activity-Device-Sensor dimensions. We generate two months of simulated data for three user profiles and validated their plausibility through activity distribution visualization, statistical perplexity analysis, and case studies. Multi-dimensional feature validation experiments further demonstrate that our multi-dimensional data significantly enhances the accuracy of activity prediction models compared to using single-dimensional features. This work successfully addresses key bottlenecks in smart home data acquisition and provides a scalable, high-quality data foundation for advancing smart home algorithm research. The code is available at https://github.com/HuankeZheng/SmartLLM.

Transformers are widely utilized in the point cloud domain. However, existing methods tend to overburden Transformer with the dual task of local geometric perception and global feature extraction, limiting its ability to capture highlevel semantic knowledge. To address this issue, we present Representation Decoder (R-Decoder), a novel representation extraction module compatible with various point cloud Transformer methods, enabling the Transformer to focus on its excellent local perception. The R-Decoder iteratively extracts multiple global features from tokens generated by Transformer, refining them to construct an overall representation of point cloud. To ensure full adaptation of the R-Decoder to the knowledge of pre-trained Transformers, we design a cross-modal representation alignment task that leverages multimodal knowledge to specifically pre-train the R-Decoder. As a post-processing module, the R-Decoder seamlessly integrates with Transformers, while decoupling local perception and global representation. This design allows the Transformer to focus on the semantic encoding role for point tokens. Extensive experiments show that our RDecoder significantly boosts the capabilities of 3D representation learning in various point cloud Transformer methods. Notably, it achieves impressive classification accuracies of 95.1% on the ScanObjectNN dataset and 95.3% on the ModelNet40 dataset. Moreover, our method obtains new SOTA on all benchmarks of few-shot and zero-shot classification, while enhancing the multimodal task capabilities of pre-trained Transformers. Code and weights are available athttps://github.com/TangYuan96/RDecoder.

Recently, generative aspect-level sentiment quadruple prediction (ASQP) methods based on pre-trained language models have made significant progress. However, some challenges remain in extracting and recognizing complex sentiment elements from semantically rich sentences, limiting the generalization and adaptability of unidirectional generative models in aspect-level sentiment analysis. To overcome this limitation, this article proposes a Generative Aspect-Based Sentiment Quadruple Prediction Model based on Multi-Order Prompting (GenMOP). The model draws on the concept of prompt learning and introduces a multi-order prompting strategy, which breaks the traditional framework of a single generative order and enhances the flexibility and adaptability of the model. Furthermore, we integrate a quadruple quantity-aware module and a multi-view uncertainty-aware module based on a basic generative architecture, not only providing the model with more fine-grained information about the quadruple quantity but also improving the prediction accuracy through uncertainty estimation. The extensive experiments show that the GenMOP method achieves excellent performance in the ASQP task. On the four benchmark datasets including Rest15, Rest16, Rest and Lap, our model achieves F1 score improvements of 1.26%, 0.23%, 0.28%, and 2.07%, respectively, compared to existing state-of-the-arts, demonstrating its effectiveness and superiority in dealing with the joint extraction of multiple sentiment elements of the ASQP model.

With the increasing demand for artificial intelligence generated content (AIGC) services across diverse applications, AIGC service migration is essential to ensuring continuous service for mobile users in edge intelligence networks. However, AIGC service migration can lead to decreased inference accuracy due to the discarding of contextual memory. Furthermore, migrating large-scale AIGC models incurs high migration costs and latency. In this paper, we propose a context-aware AIGC service migration scheme to address the trade-off among inference accuracy, latency, and migration cost. Specifically, we focus on migrating historical AIGC context rather than large-scale AIGC models to achieve cost-efficient service provisioning. To improve service migration performance, we propose a Value of Context (VoC) metric to quantify the relevance and freshness of historical AIGC context. Based on the VoC, we formulate an optimization problem to jointly optimize inference accuracy, latency, and migration cost. To solve this problem, we develop a TransFormer-based Soft actor-critic algorithm for Context-aware AIGC service Migration (TFSCM) that leverages long-term dependencies in historical decisions for optimizing the migration process. Extensive experiments on real-world datasets demonstrate that the proposed TFSCM algorithm significantly enhances system performance compared to baseline solutions.

Automatic multi-modal depression recognition using artificial intelligence technology is crucial to advance early diagnosis and treatment. Existing methods suffer from a weak performance in detecting depression due to incomplete unimodal semantic information and insufficient fusion effects. To address these challenges, we propose a novel Semantic-Enhanced Dual Cross-modal Fusion Network (SE-DCFN) for multi-modal depression recognition, specifically designed for text-audio data. Firstly, we utilize a prompt learning-based text encoder and a language-audio pertaining-based audio encoder to capture specific information to enhance the semantic representation. Then, we introduce a dual cross-modal fusion module based on self-attention and cross-attention mechanisms to effectively explore linguistic and acoustic representation, facilitating inter-modal and intra-modal interaction and fusion. Additionally, a triplet contrastive loss is formulated to optimize the training process of the SE-DCFN. Experimental results on the EATD-Corpus dataset and AVEC-2017 dataset demonstrate the effectiveness and superiority of our proposed SE-DCFN on multi-modal depression recognition, outperforming existing methods.

On a passenger-freight collinear railway, the freight train operation level is comparatively low, frequently resulting in significant deviations from the original timetable and crew plan in the presence of various interferences. This article focuses on the problem of coordinated rescheduling of train timetable and crew scheme in the presence of disruptions on a double-track passenger-freight collinear railway. We develop a mixed-integer linear program (MILP) model considering the distinct priorities of passenger and freight trains, as well as crew operations, thereby surpassing the current practice of independently adjusting train timetable and crew plan to achieve a collaborative solution. The objective is to minimize delays for passenger trains and deviations in crew schedule, while maximizing the delivery rate of freight trains at railway Bureau boundary stations prior to the settlement time. Furthermore, for large-scale delays, we design a solution algorithm based on the rolling horizon approach to enhance computational efficiency. To validate the effectiveness of the proposed model, simulation experiments are conducted using actual running data from the Beijing–Shanghai railway. The experimental results illustrate that our coordinated model enhances the feasibility of adjustment outcomes during emergencies, in contrast to the model that neglects crew connections. Additionally, our proposed algorithm guarantees a solving error of under 5% and reduces solving time by over 60% compared with the results obtained by CPLEX. Moreover, three additional comparison experiments are conducted to further demonstrate the impact of crew activities on train operation adjustments, which also indicate that our approach can provide dispatchers with more feasible train operation adjustment schemes in terms of crew utilization.

Most researchers focus on designing accurate crowd counting models with heavy parameters and computations but ignore the resource burden during the model deployment. A real-world scenario demands an efficient counting model with low-latency and high-performance. Knowledge distillation provides an elegant way to transfer knowledge from a complicated teacher model to a compact student model while maintaining accuracy. However, the student model receives the wrong guidance with the supervision of the teacher model due to the inaccurate information understood by the teacher in some cases. In this paper, we propose a dual-knowledge distillation (DKD) framework, which aims to reduce the side effects of the teacher model and transfer hierarchical knowledge to obtain a more efficient counting model. First, the student model is initialized with global information transferred by the teacher model via adaptive perspectives. Then, the self-knowledge distillation forces the student model to learn the knowledge by itself, based on intermediate feature maps and target map. Specifically, the optimal transport distance is utilized to measure the difference of feature maps between the teacher and the student to perform the distribution alignment of the counting area. Extensive experiments are conducted on four challenging datasets, demonstrating the superiority of DKD. When there are only approximately 6% of the parameters and computations from the original models, the student model achieves a faster and more accurate counting performance as the teacher model even surpasses it.

Recently, the advancements in edge computing have boosted the deployment of video analysis systems based on deep learning, which breaks the limitation of the constrained communication and computing resources of local devices. However, processing multi-scene high-resolution video streams in crowd surveillance remains a significant challenge since it is difficult to formulate dynamic video content and communication environments to support offloading decisions. To bridge the gap between applications and modeling, this paper presents aReal-TimeCloud-edge-deviceCollaboration framework, which enables fast and accurateCrowd counting (RT3C) on the real dataset. RT3C comprises key frame detection, adaptive patch partition, patch encoder and decoder and computation offloading decision, designed to divide key frames into a minimum number of patches and determine the offloading location of patches. A Real-Time Multi-Agent Actor-Critic (RTMAAC) algorithm based on multi-agent reinforcement learning is proposed to decide whether to compute patches with a lightweight model on edge or a large model on cloud. Unlike traditional approaches ignoring the contents, RTMAAC is a dynamic online decision algorithm based on context of the network and video. Extensive experiments demonstrate that RT3C effectively discriminate the valid frames and optimizes offloading decisions in complex environments, outperforming other baseline algorithms on the two crowd counting datasets. In summary, RT3C provides a promising framework for multi-scene video streams, which can be extended to other applications to realize video computation based on deep models.

The crowd evacuation strategy seeks to arrange crowd evacuation in an orderly manner to protect people’s lives and reduce property damage in case of sudden emergencies in crowded and complex places. In recent years, there have been several works to apply deep learning to crowd evacuation to make evacuation strategies more intelligent. However, existing researches rarely consider the integration of scene perception and crowd evacuation, which leads to evacuation methods that are detached from the scene and also ignore the crowd collaboration in the evacuation process. To this end, we propose Intelligent Crowd Evacuation Architecture based on Visual features using Multi-Agent Reinforcement Learning (ICEA-VMARL). Subsequently, we present modeling analysis on the crowd grouping and group evacuation modules of the architecture. First, we propose the Population Grouping algorithm based on Continuous Spatiotemporal individual Similarity (PGCSS), which combines crowd features to group crowds. Then, we propose a Group Collaborative Evacuation algorithm based on Multi-Agent Reinforcement Learning (GCE-MARL), which considers group collaboration while evacuating to achieve global optimal evacuation. Finally, we build an experimental crowd simulation system, and the results demonstrate that the crowd grouping algorithm and group evacuation algorithm proposed have better performance compared with other methods.

Due to the expensive and laborious annotations of labeled data required by fully-supervised learning in the crowd counting task, it is desirable to explore a method to reduce the labeling burden. There exists a large number of unlabeled images in the wild that can be easily obtained compared to labeled datasets. Based on the characteristics of consistent spatial transformation with the annotations of heads and image, this paper proposes a self-supervised learning framework with unlabeled and limited labeled data for pre-training and fine-tuning crowd counting model (SSL-FT). It includes an online network and a target network that receive the same images but are randomly processed by two defined augmentation transformations. We leverage unlabeled data to pre-train the online network based on a self-supervised loss and small-scale labeled data to transfer the model to a specific domain based on a fully-supervised loss. We demonstrate the effectiveness of the SSL-FT on four public datasets including ShanghaiTech PartA, PartB, UCF-QNRF and WorldExpo'10 utilizing a classical counting model. Experimental results show that our approach performs better than state-of-art semi-supervised methods.

In recent years, depression has become an increasingly serious problem globally. Previous studies of automatic depression recognition based on functional near-Infrared spectroscopy (fNIRS) or other brain imaging techniques have shown potential to serve as auxiliary diagnosis methods that provide assistance to medical professionals. Recently, some studies have found that, besides directly using the data themselves (temporal data), the use of functional connectivity among channels (spatial data) also can be effective. In this paper, we propose a method based on Graph Neural Network (GNN) that combines both temporal and spatial features of fNIRS data for automatic depression recognition. Specifically, fNIRS data of 96 subjects were collected and pre-processed. Basic statistical metrics of each channel were extracted as temporal features, and channel connectivity (coherence and correlation) were calculated as spatial features. Point-biserial analysis was conducted on these features and depression labels as a data-driven motivation. For classification, we considered data of each subject as a graph, with temporal features as node features and spatial features as edge weights. The graphs were fed into GNNs for training and testing. Experimental results showed that our GNN-based methods realized the best depression recognition performance compared with classical machine-learning methods regarding accuracy, F1 score, and precision, especially in F1 score for over 10%.

The significant achievements have been made in crowd detection and tracking due to the advancement of artificial intelligence in the autonomous driving. However, the image-based methods have strict requirements for the collection conditions of video, and the development of the new generation of flexible fabrics has become potential sensors to perceive context. In this paper, an intelligent fabric space enabled by multi-sensing sensors is established to track the motion objects. We propose a behavior analysis pipeline including the modules of data preparation, trajectory coupling, motion scenario segmentation, and motion pattern measurement to capture the crowd information from micro-level and macro-level over the intelligent fabric space. After making preprocess for the multi-sensing data, a coupling mechanism is formulated to fuse the video-based trajectory and fabric-based trajectory. And an automatic motion scenario segmentation model divides the surrounding scenario into main-crowd, sub-crowd, and background according to the motion behavior. Further, we define measurement metrics to analyze the motion pattern for the different crowds. Extensive experiments prove that our proposed methods effectively fuse multiple trajectories and realize the crowd segmentation and the motion description. This will greatly help autonomous vehicles and control system perceive the surrounding pedestrians and the environment to make precise driving decisions.

The outbreak of the corona virus disease 2019 (COVID-19) has caused serious harm to people’s physical and mental health. Due to the serious situation of the epidemic, a lot of negative energy information increases people’s psychological burden. However, effective interventions against mental health problems are not in abundance. To address such challenges, in this article, we propose the concept of negative information to describe information that has a negative impact on people’s mental health. To achieve the measurement of negative information, the level of mental health inversely measures the degree of negative information. Specifically, we design a system to measure the negative information used to monitor the mental health state of the user under the impact of negative information. The cognition of mental health is realized based on the intelligent algorithm deployed on the edge cloud, and the needs of users can be responded to in real time in practical applications. Finally, we use real collected dataset to verify the influence of negative information. The experiments show that the system can achieve negative information measurement and provide an effective countermeasure for solving mental health problems during a pandemic situation.

The frequent occurrence of suicides in modern society constitutes a serious public health issue. While the motives, methods, and consequences of suicide are quite complicated, if people at risk of suicide can be identified and intervened in time, the loss of life can be reduced. Through analyses based on combining a large number of suicide texts and professional medical literature, a dictionary of potential suicide risk impact factors has been established in this article. Based on this dictionary, a novel medical-level suicide risk standard is proposed to monitor suicide risk from point-to-surface under the timeline baseline. In order to solve the problem of insufficient Chinese suicide data sets, the manually assisted method based on knowledge perception is adopted to annotate the data set with corresponding to risk level. At the same time, a Bert evaluation model based on knowledge perception was established for the classification of risk level. The experimental results showed that proposed method has a 56% recognition accuracy in the prediction of 10-Label suicide risk level proposed in this article, and the classification performance is better than traditional machine learning algorithms. Therefore, the results showed that the classification standard and evaluation model can be effectively used for the identification and early warning of suicide risk, which can discover high suicide risk groups to reduce the occurrence of suicide. It is of great significance to people’s emotion care monitoring.

Depression is the result of a complex interaction of social, psychological and physiological elements. Research into the brain disorders of patients suffering from depression can help doctors to understand the pathogenesis of depression and facilitate its diagnosis and treatment. Functional near-infrared spectroscopy (fNIRS) is a non-invasive approach to the detection of brain functions and activities. In this paper, a comprehensive fNIRS-based depression-processing architecture, including the layers of source, feature and model, is first established to guide the deep modeling for fNIRS. In view of the complexity of depression, we propose a methodology in the time and frequency domains for feature extraction and deep neural networks for depression recognition combined with current research. It is found that compared to non-depression people, patients with depression have a weaker encephalic area connectivity and lower level of activation in the prefrontal lobe during brain activity. Finally, based on raw data, manual features and channel correlations, the AlexNet model shows the best performance, especially in terms of the correlation features and presents an accuracy rate of 0.90 and a precision rate of 0.91, which is higher than ResNet18 and machine-learning algorithms on other data. Therefore, the correlation of brain regions can effectively recognize depression (from cases of non-depression), making it significant for the recognition of brain functions in the clinical diagnosis and treatment of depression.

With the advent of the Internet era, the trend of highly informed society has been becoming more and more obvious, and the requirement of society on communication is also increasing. flexible satellite communication mode has many advantages such as large communication load and no geographic restriction, which cannot be replaced by other communication modes. In the satellite communication system, the most important is the satellite earth station (SES). When receiving signals from the target satellite, the SES terminal must accurately point to the satellite and track it to obtain the maximum receiving signal and reduce the interference with other signals simultaneously. However, the motion of either satellite or terminal can cause a change in signal intensity, so it is necessary to adjust the pointing of the SES antenna in time to maintain optimal signal receiving conditions. In order to satisfy different satellite communication scenarios, in this paper, Artificial intelligent (AI) technology is applied to the satellite communication process, mainly to optimize the optimal antenna angle and time consumption reduction. Firstly, the process of antenna pointing is introduced, and the traditional antenna search algorithm Auto-Acqire algorithm (AA algorithm) is analyzed in detail. Considering that the satellite system needs to adapt to the communication requirements of different terminals, based on AI antenna pointing algorithms are proposed. In order to verify this research, we build an experimental platform and compare the traditional AA algorithm as a benchmark algorithm with FI-GRU and II-DRL algorithms. According to the experimental results, the two algorithms proposed in this paper can improve the efficiency of satellite pointing and tracking tasks.

With the development of software-defined network (SDN), there will be a large number of devices to access network, which may cause an incalculable burden to the communication network. In addition, due to the high bandwidth in the fifth-generation (5G) era, innovation will occur in different fields. There are not only strict requirements on the communication capability of SDN for these application scenarios but also a lot of computing resources. For massive access devices, it is difficult for the traditional service resource scheduling and the allocation system to meet user demand growth. To address the above-stated problems, an artificial intelligence agent (AI Agent) system is put forth in this article. AI Agents can be deployed in different layers of the SDN, thus realizing functions like network service prediction and resource scheduling. A brand new AI Agent framework is designed, and an AI algorithm is adopted to replace the traditional service prediction and resource scheduling strategies. In the meantime, a relevant agent deployment scheme is put forward. Finally, an AI Agent-based simulation experiment for resource scheduling is designed, and the accuracy in network service prediction and rationality in resource allocation based on this framework are tested. The experimental result showed that the operation efficiency of the SDN can be effectively improved, and the resource hit ratio and user service quality may be improved with AI-agent-based traffic prediction and resource allocation model.