Sleep structure analysis plays a vital role in diagnosing and monitoring sleep disorders, with microarousal detection and sleep staging serving as essential components for evaluating sleep quality and identifying pathological events. While polysomnography (PSG) is the gold standard clinical sleep architecture analysis due to its high accuracy, its complex setup and need for professional supervision make it unsuitable for longterm home monitoring. Single-lead electrocardiogram (ECG) signals offer a promising alternative as they are easy to acquire and closely tied to autonomic nervous system activity. Recently, multi-task learning models have been explored to simultaneously perform microarousal detection and sleep staging using ECG signals. However, existing approaches often struggle with limited overall performance or imbalanced task effectiveness due to insufficient integration of multi-scale temporal features, inadequate cross-task knowledge sharing, and lack of dynamic coordination between tasks. To address these limitations, we introduce a model with Multi-Scale Feature fusion and Dynamic Task Coordination strategy for ECG-based sleep structure analysis (MSF-DTCNet). We employs an encoder-decoder architecture with a shared encoder for extracting multi-scale temporal features, a Mamba-based module for enhanced global sequence modeling, task-specific decoders with independent output layers for balanced representations, and a dynamic loss weighting strategy that adjusts task weights based on loss descent rates. Experiments on sleep datasets demonstrate that our approach achieves competitive performance in both microarousal detection and sleep staging tasks.

Sleep staging is imperative for evaluating sleep quality and diagnosing sleep disorders. Extant sleep staging methods with fusing multiple data-views of physiological signals have achieved promising results. However, they remain neglectful of the relationship among different data-views at different feature scales with view position-alignment. To address this, we propose a novel cross-view alignment network, termed cVAN, utilising scale-aware attention for sleep stages classification. Specifically, cVAN principally incorporates two sub-networks of a residual-like network which learn spectral information from time-frequency images and a transformer-like network which learns corresponding temporal information. The prime advantage of cVAN is to adaptively align the learned feature scales among the different data-views of physiological signals with a scale-aware attention by reorganizing feature maps. Extensive experiments on three public sleep datasets demonstrate that cVAN can achieve a new state-of-the-art result, which is superior to existing counterparts.

In light of the global proliferation of the COVID-19 pandemic, there is a notable surge in public interest towards Massive Open Online Courses (MOOCs) recently. Within the realm of personalized course-learning services, large amounts of online course recommendation systems have been developed to cater to the diverse needs of learners. However, despite these advancements, there still exist three unsolved challenges: 1) how to effectively utilize the course information spanning from the title-level to the more granular keyword-level; 2) how to well capture the sequential information among learning courses; 3) how to identify the high-correlated courses in the course corpora. To address these challenges, we propose a novel solution known asCascadedKnowledge-levelFusionNetwork (CKFN) for online course recommendation with incorporating a three-fold approach to maximize the utilization of course information: 1) two knowledge graphs spanning from the keyword-level to title-level; 2) a two-stage attention fusion mechanism; 3) a novel knowledge-aware negative sampling method. Experimental results on a real dataset of XuetangX demonstrate that CKFN surpasses existing baseline models by a substantial margin, thereby achieving the state-of-the-art recommendation performance. It means that CKFN can be potentially deployed into MOOCs platforms as a pivotal component to provide personalized course recommendation service.

Sleep apnea (SA) is a common sleep-related breathing disorder that tends to induce a series of complications, such as pediatric intracranial hypertension, psoriasis, and even sudden death. Therefore, early diagnosis and treatment can effectively prevent malignant complications SA incurs. Portable monitoring (PM) is a widely used tool for people to monitor their sleep conditions outside of hospitals. In this study, we focus on SA detection based on single-lead electrocardiogram (ECG) signals which are easily collected by PM. We propose a bottleneck attention based fusion network named BAFNet, which mainly includes five parts of RRI (R-R intervals) stream network, RPA (R-peak amplitudes) stream network, global query generation, feature fusion, and classifier. To learn the feature representation of RRI/RPA segments, fully convolutional networks (FCN) with cross-learning are proposed. Meanwhile, to control the information flow between RRI and RPA networks, a global query generation with bottleneck attention is proposed. To further improve the SA detection performance, a hard sample scheme with k-means clustering is employed. Experiment results show that BAFNet can achieve competitive results, which are superior to the state-of-the-art SA detection methods. It means that BAFNet has great potential to be applied in the home sleep apnea test (HSAT) for sleep condition monitoring.

Sleep stage classification is of great importance in human health monitoring and disease diagnosing. Clinically, visual-inspected classifying sleep into different stages is quite time consuming and highly relies on the expertise of sleep specialists. Many automated models for sleep stage classification have been proposed in previous studies but their performances still exist a gap to the real clinical application. In this work, we propose a novel multi-view fusion network named MVF-SleepNet based on multi-modal physiological signals of electroencephalography (EEG), electrocardiography (ECG), electrooculography (EOG), and electromyography (EMG). To capture the relationship representation among multi-modal physiological signals, we construct two views of Time-frequency images (TF images) and Graph-learned graphs (GL graphs). To learn the spectral-temporal representation from sequentially timed TF images, the combination of VGG-16 and GRU networks is utilized. To learn the spatial-temporal representation from sequentially timed GL graphs, the combination of Chebyshev graph convolution and temporal convolution networks is employed. Fusing the spectral-temporal representation and spatial-temporal representation can further boost the performance of sleep stage classification. A large number of experiment results on the publicly available datasets of ISRUC-S1 and ISRUC-S3 show that the MVF-SleepNet achieves overall accuracy of 0.821,$F_{1}$score of 0.802 and Kappa of 0.768 on ISRUC-S1 dataset, and accuracy of 0.841,$F_{1}$score of 0.828 and Kappa of 0.795 on ISRUC-S3 dataset. The MVF-SleepNet achieves competitive results on both datasets of ISRUC-S1 and ISRUC-S3 for sleep stage classification compared to the state-of-the-art baselines. The source code of MVF-SleepNet is available on Github (https://github.com/YJPai65/MVF-SleepNet).

Sleep stage classification is a critical concern in sleep quality assessment and disease diagnosis. Graph network based studies for sleep stages classification have achieved promising performance. However, these studies still ignored the importance of learning morphological feature information with the spatial-temporal relationship among multi-modal physiological signals. To address this issue, we propose a Spatial-temporal Adaptive Graph Network named StAGN for sleep stage classification. The main advantage of StAGN is to adaptively learn the time-dependent and channel-wise interdependent waveform morphological features in multimodal physiological signals. Such features will be extracted by a modified 1-dimensional ResNet with a projection shortcut connection and adjusted by a joint spatial-temporal attention, thereby best serving the followed brain topological connection graph network for sleep stage classification. Meanwhile, we leverage the contrastive learning scheme with label information to further improve classification accuracy without changing the signal morphology. Experiment results on two publicly available sleep datasets of ISRUC-S1 and ISRUC-S3 show that the proposed StAGN can achieve a competitive performance for sleep stage classification, which is superior to the state-of-the-art counterparts.

Many recent studies of Entity Alignment (EA) use Graph Neural Networks (GNNs) to aggregate the neighborhood features of entities and achieve better performance. However, aligned entities in real Knowledge Graphs (KGs) usually have non-isomorphic neighborhood structures due to the different data sources of KGs. Therefore, it is insufficient to simply compare the global direct neighborhood of aligned entities, which may also become a variable for the EA judgment. In this paper, we propose a Relation-based Adaptive Neighborhood Matching method (RANM), which matches larger range and higher confidence neighborhoods for aligned entities based on relation matching instead of alignment seeds.RANMfirst uses alignment seeds to construct the best relation matching set, and then performs local direct neighborhood matching and feature aggregation on the candidate alignments. To obtain high-quality entity embeddings, we design a variant attention mechanism based on heterogeneous graphs, which considers the heterogeneity of relations in KGs. We also adopt a bi-directional iterative co-training to further improve the performance. Extensive experiments on three well-known datasets show our method significantly outperforms 14 state-of-the-art methods, and is 3.01-11.5% higher than the best-performing baselines in [email protected] shows high performance on the long-tailed entities and the dataset with less alignment seeds.

Sleep stage classification is significant for sleep specialists to evaluate sleep quality and diagnose sleep disorders. Machine learning and deep learning technologies are widely employed to build automatic sleep stage classification models. However, how to make full use of and integrate multiple heterogeneous information such as unlabeled information, topological information, frequency information and neighboring information to improve classification is still an open problem. To address this issue, we propose a multi-stream fusion network named MSF-SleepNet for sleep stage classification, with contrastive learning to combine spatial, temporal, and spectral features. Firstly, we design a contrastive learning framework to learn general features from unlabeled information. On this basis, we apply graph structure learning, Chebyshev graph convolution and temporal convolution to capture spatial-temporal features from topological information of human body in non-Euclidean space and transition rules among sleep stages. Secondly, we utilize the short-time Fourier transform and Gate Recurrent Unit to gain spectral-temporal features from frequency information of different time series signals in Euclidean space and neighboring information of adjacent signal segments. Finally, fusing spatial-temporal features and spectral-temporal features can further enhance the performance of sleep stage classification. Experimental results on publicly available datasets of ISRUC-S3 show that our method is more effective in integrating heterogeneous information and achieves better performance than existing state-of-the-art methods.

Entity Alignment (EA) has attracted widespread attention in both academia and industry, which aims to seek entities with same meanings from different Knowledge Graphs (KGs). There are substantial multi-step relation paths between entities in KGs, indicating the semantic relations of entities. However, existing methods rarely consider path information because not all natural paths facilitate for EA judgment. In this paper, we propose a more effective entity alignment framework, RPR-RHGT, which integrates relation and path structure information, as well as the heterogeneous information in KGs. Impressively, an initial reliable path reasoning algorithm is developed to generate the paths favorable for EA task from the relation structures of KGs. This is the first algorithm in the literature to successfully use unrestricted path information. In addition, to efficiently capture heterogeneous features in entity neighborhoods, a relation-aware heterogeneous graph transformer is designed to model the relation and path structures of KGs. Extensive experiments on three well-known datasets show RPR-RHGT significantly outperforms 10 state-of-the-art methods, exceeding the best performing baseline up to 8.62% on Hits@1. We also show its better performance than the baselines on different ratios of training set, and harder datasets.

As a common vector-borne disease, dengue fever remains challenging to predict due to large variations in epidemic size across seasons driven by a number of factors including population susceptibility, mosquito density, meteorological conditions, geographical factors, and human mobility. An ensemble forecast system for dengue fever is first proposed that addresses the difficulty of predicting outbreaks with drastically different scales. The ensemble forecast system based on a susceptible-infected-recovered (SIR) type of compartmental model coupled with a data assimilation method called the ensemble adjusted Kalman filter (EAKF) is constructed to generate real-time forecasts of dengue fever spread dynamics. The model was informed by meteorological and mosquito density information to depict the transmission of dengue virus among human and mosquito populations, and generate predictions. To account for the dramatic variations of outbreak size in different seasons, the effective population size parameter that is sequentially updated to adjust the predicted outbreak scale is introduced into the model. Before optimizing the transmission model, we update the effective population size using the most recent observations and historical records so that the predicted outbreak size is dynamically adjusted. In the retrospective forecast of dengue outbreaks in Guangzhou, China during the 2011-2017 seasons, the proposed forecast model generates accurate projections of peak timing, peak intensity, and total incidence, outperforming a generalized additive model approach. The ensemble forecast system can be operated in real-time and inform control planning to reduce the burden of dengue fever.

Sleep apnea (SA) is a common sleep disorder that occurs during sleep and its symptom is the reduction or disappearance of respiratory airflow caused by upper airway collapse. The SA would cause a variety of diseases like diabetes, chronic kidney disease, depression, cardiovascular diseases, or even sudden death. Early detecting SA and intervention can help individuals to prevent malignant events induced by SA. In this study, we propose a multi-scaled fusion network named SEMSCNN for SA detection based on single-lead ECG signals acquired from wearable devices. The proposed SE-MSCNN mainly has two modules: multi-scaled convolutional neural network (CNN) module and channel-wise attention module. To utilize adjacent ECG segments information to facilitate the SA detection performance, the multi-scaled CNN module consists of three streams of shallow neural networks with segments with various length as inputs to extract different scaled features. To overcome the problem of feature information local concentration for feature fusion with concatenation, a channel-wise attention module with squeeze-to-excitation block is employed to fuse the different scaled features adaptively. Experiment results on PhysioNet Apnea-ECG dataset show that the proposed SE-MSCNN can achieve the best per-segment accuracy of 90.64 % and the best per-recording accuracy of 100 %, which is superior to state-of-the-art SA detection methods with a big margin. The SE-MSCNN with merits of quick response and lightweight parameters can be potentially embedded to a wearable device to provide a SA detection service for individuals in home sleep test.

There has been increasing interest in using Stackelberg game (known as a security game) to allocate limited security resources against different attacker types with a specific probability distribution. However, real problems of this kind often face ambiguous information, such as imprecise, unreliable and absent payoffs, and ambiguous assignments of these payoffs. To this end, based on decision theory and the Dempster–Shafer theory of evidence, this paper proposes a novel framework that can handle these common types of ambiguity. More specifically, this paper deploys the underlying principles of existing rules from decision theory, as a way to characterise different attitudes to ambiguity, during the transformation of ambiguous payoffs into point-valued payoffs. Hence, our framework holds some good properties: (i) it subsumes traditional security games without ambiguous payoffs, (ii) a uniform margin of error will not affect the results and (iii) the influence of complete ignorance can be minimised. Also, our framework is evaluated by using nine different transformation rules, under various conditions and constraints, against 73,000 randomly generated games (a first comprehensive empirical evaluation to date). The evaluation reveals the benefits of each transformation rule and confirms that different rules can model individuals' different attitudes to ambiguity.

The spatial scan statistic method has been widely used for detecting disease clusters. Its results may be affected by scales, including the aggregation level of the input data and the population threshold used in the detection. Previous studies offered inconsistent findings, and few had considered both types of scales at the same time. Using 24 simulated datasets and two real disease datasets, we investigated the method’s sensitivity to the two types of scales. We aggregated the individual-level data into areal units of three levels, including county, town, and a 900 m grid. We detected clusters with three population thresholds, including 10%, 25%, and 50%. We used two measurements, distance between cluster centres and the Jaccard index, to quantify the consistency of clusters detected with different scale settings. We find: (1) the method is not greatly sensitive to the data aggregation level when the cluster is strong and in a place with high population density; (2) the method’s sensitivity to the population threshold is determined by the actual size of the true cluster; and (3) a regular grid with fine resolution is advantageous over the subjectively defined areal units. The process and findings may have broader meanings to similar spatial analyses.

Closed-circuit television and sensor-based intelligent surveillance systems have attracted considerable attentions in the field of public security affairs. To provide real-time reaction in the case of a huge volume of the surveillance data, researchers have proposed event-reasoning frameworks for modeling and inferring events of interest. However, they do not support decision-making, which is very important for surveillance operators. To this end, this paper incorporate a function of decision-making in an event-reasoning framework, so that our model not only can perform event-reasoning but also can predict, rank, and alarm threats according to uncertain information from multiple heterogeneous sources. In particular, we propose a multiattribute decision-making model, in which an object being watched is modeled as a multiattribute event, where each attribute corresponds to a specific source, and the information from each source can be used to elicit a local threat degree of different malicious situations with respect to the corresponding attribute. Moreover, to assess an overall threat degree of an object being observed, we also propose a method to fuse the conflict threat degrees regarding all the relevant attributes. Finally, we demonstrate the effectiveness of our framework by an airport security surveillance scenario.

Some well-known paradoxes in decision making (e.g., the Allais paradox, the St. Peterburg paradox, the Ellsberg paradox, and the Machina paradox) reveal that choices conventional expected utility theory predicts could be inconsistent with empirical observations. So, solutions to these paradoxes can help us better understand humans decision making accurately. This is also highly related to the prediction power of a decision-making model in real-world applications. Thus, various models have been proposed to address these paradoxes. However, most of them can only solve parts of the paradoxes, and for doing so some of them have to rely on the parameter tuning without proper justifications for such bounds of parameters. To this end, this paper proposes a new descriptive decision-making model, expected utility with relative loss reduction, which can exhibit the same qualitative behaviours as those observed in experiments of these paradoxes without any additional parameter setting. In particular, we introduce the concept of relative loss reduction to reflect people's tendency to prefer ensuring a sufficient minimum loss to just a maximum expected utility in decision-making under risk or ambiguity.

In a matrix game, the interactions among players are based on the assumption that each player has accurate information about the payoffs of their interactions and the other players are rationally self-interested. As a result, the players should definitely take Nash equilibrium strategies. However, in real-life, when choosing their optimal strategies, sometimes the players have to face missing, imprecise (i.e., interval), ambiguous lottery payoffs of pure strategy profiles and even compound strategy profile, which means that it is hard to determine a Nash equilibrium. To address this issue, in this paper we introduce a new solution concept, called ambiguous Nash equilibrium, which extends the concept of Nash equilibrium to the one that can handle these types of ambiguous payoff. Moreover, we will reveal some properties of matrix games of this kind. In particular, we show that a Nash equilibrium is a special case of ambiguous Nash equilibrium if the players have accurate information of each player's payoff sets. Finally, we give an example to illustrate how our approach deals with real-life game theory problems.

In real life, decisions are often made under ambiguity, where it is difficult to estimate accurately the probability of each single possible consequence of a choice. However, this problem has not been solved well in existing work for the following two reasons. (i) Some of them cannot cover the Ellsberg paradox and the Machina Paradox. Thus, the choices that they predict could be inconsistent with empirical observations. (ii) Some of them rely on parameter tuning without offering explanations for the reasonability of setting such bounds of parameters. Thus, the prediction of such a model in new decision making problems is doubtful. To the end, this paper proposes a new decision making model based on D-S theory and the emotion of ambiguity aversion. Some insightful properties of our model and the validating on two famous paradoxes show that our model indeed is a better alternative for decision making under ambiguity.

In real life, sometimes multicriteria decision making (MCDM) problems are dealt with inevitably under cognitive limitations of human's minds. However, few existing models can directly solve MCDM problems of this kind. Thus, to address the issue, this paper proposes a novel approach, which can: (i) handle the cognitive limitations in MCDM problems by distinguishing the case of complete criteria (i.e., there are no hidden cognitive factors that can deviate rational decisions) from the case of incomplete criteria (i.e., there are some hidden cognitive factors that can deviate rational decisions); (ii) differentiate incomplete and complete relative ranking of the groups of decision alternatives (DAs) over a criterion; and (iii) solve the imprecise and uncertain evaluation of criterion weight as well as the ambiguous evaluations of the groups of DAs regarding a given criterion. Hence, we give a measure to consider the influence of cognitive limitations and give two methods to reduce the influence of cognitive limitations when a decision making needs more rational. Moreover, we illustrate our approach by solving a real-life problem of estate investment. Finally, we give some experimental results about the reduction of the required number of knowledge judgments in our method compared with the previous methods.

This paper presents an event recognition framework, based on Dempster-Shafer theory, that combines evidence of events from low-level computer vision analytics. The proposed method employing evidential network modelling of composite events, is able to represent uncertainty of event output from low level video analysis and infer high-level events with semantic meaning along with degrees of belief. The method has been evaluated on videos taken of subjects entering and leaving a seated area. This has relevance to a number of transport scenarios, such as onboard buses and trains, and also in train stations and airports. Recognition results of 78% and 100% for four composite events are encouraging.

Three issues usually are associated with threat prevention intelligent surveillance systems. First, the fusion and interpretation of large scale incomplete heterogeneous information; second, the demand of effectively predicting suspects' intention and ranking the potential threats posed by each suspect; third, strategies of allocating limited security resources (e.g., the dispatch of security team) to prevent a suspect's further actions towards critical assets. However, in the literature, these three issues are seldomly considered together in a sensor network based intelligent surveillance framework. To address this problem, in this paper, we propose a multi-level decision support framework for in-time reaction in intelligent surveillance. More specifically, based on a multi-criteria event modeling framework, we design a method to predict the most plausible intention of a suspect. Following this, a decision support model is proposed to rank each suspect based on their threat severity and to determine resource allocation strategies. Finally, formal properties are discussed to justify our framework.

This paper builds a multi-demand bargaining model based on fuzzy rules, and introduces its agreement concept, which satisfies four intuitive properties of consistency, collective rationality, disagreement and minimum concession. In the model, the fuzzy rules are used to calculate how much bargainers should change their preference during a bargaining. Moreover, the psychological experiment are used to elicit the fuzzy rules. In addition, we analyse how bargainers' risk attitude, patience and regret degree influence agreement of our bargaining game, and identify the existence conditions of bargaining agreement.

Bayesian games can handle the incomplete information about players' types. However, in real life, the information could be not only incomplete but also ambiguous for lack of sufficient evidence, i.e., a player cannot have a probability precisely about each type of the other players. To address this issue, we extend the Bayesian games to ambiguous Bayesian games. We also illustrate and analyse our game model.

Nowadays, decisions in estate investment are made by a group of investors with different demands and then how to find an agreement among them become an essential issue. Thus, this paper introduces a fuzzy logic based bargaining model to solve such problems. Moreover, we also do lots of simulation experiments to reveal how bargainers risk attitude, patience and regret degree influence the outcome of a game, and benchmark our model with the previous one. From these experiments, we can conclude that our model can reflect the human intuitions well, has a higher success rate, and bargains more efficiently than the previous one.

Bayesian games can handle the incomplete information about players' types. However, in real life, the information could be not only incomplete but also ambiguous for lack of sufficient evidence, i.e., a player cannot have a precise probability about each type of the other players. To address this issue, this paper firstly extends the Bayesian games to ambiguous Bayesian games. Then, we introduce the concept of a solution to this kind of games and discuss their properties, especially about solution existence, how the ambiguity degree and players' ambiguity attitude influence the outcomes of an ambiguous Bayesian game, the case of lower boundary probability, and the missing situation. We also illustrate our game model, especially in the public security domain.

In real-life multicriteria decision making (MCDM) problems, the evaluations against some criteria are often missing, inaccurate, and even uncertain, but the existing theories and models cannot handle such evaluations well. To address the issue, this paper extends the Dempster–Shafer (DS)/analytic hierarchy process (DS/AHP) approach of MCDM to handle three types of ambiguous evaluations: missing, interval-valued, and ambiguous lottery evaluations. In our extension, the aggregation of criteria's evaluation takes the following six steps: (i) calculate the expected evaluation interval and the ambiguity degree of each group of decision alternatives regarding each criterion, (ii) from them to obtain the preference degree of each group of decision alternatives, (iii) apply the DS/AHP method to obtain the mass function distribution of each group of decision alternatives, (iv) use the Dempster's rule of combination to get the overall mass function of each group of decision alternatives with respect to all criteria, (v) according to the overall mass function to count the belief function and the plausibility function of each decision alternative, and (vi) set the overall preference ordering of decision alternatives by our regret-avoid ambiguous principle and then find the optimal solution. Finally, we give an example of real estate investment to illustrate how our approach is employed to deal with real-life MCDM problems.

We present a novel design for managing competing user interests and privacy protection cooperation in a collaborative virtual environment for molecular modeling. Based on the user feedback, this design includes four levels of access control for collaborative sessions and provides dynamic action priority specification for manipulations on shared molecular models. Furthermore, we implement a messaging system that includes a text chatting tool and system broadcasting functions. Our design supports flexible user group formation and interaction. We have implemented our design on a set of Microsoft Windows PCs connected to a Linux molecule simulation server. A GUI interface developed in Python is also illustrated