This research presents an enhanced deep learning-based approach for medical image segmentation by integrating multiple attention mechanisms into the U-Net architecture. Specifically, the proposed models incorporate seven advanced attention mechanisms, including Convolutional Block Attention Module, Attention Gate, Squeeze-and-Excitation, Halo, Coordinate, Spatial, and Triplet Attention strategies, to improve the model’s capability to capture both channel and spatial contextual information within medical images. These attention-enhanced U-Net variants are rigorously evaluated on multiple medical imaging datasets, demonstrating significant improvements in segmentation accuracy compared to benchmark models such as U-Net and DeepLabV3+. The results indicate superior performance, especially in complex scenarios with overlapping structures and fine organ/lesion details, showcasing the effectiveness of attention mechanisms for improving segmentation in medical imaging.

Automated patient monitoring solutions greatly benefit from audio emotion classification, although the considerable variance in individual expression and interpretation of emotions poses a challenge. Current approaches often employ standard Audio Spectrogram Transformer (AST) and deep learning models such as Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN)-based networks. However, their performance can be enhanced by integrating neural architecture search techniques using swarm optimisation algorithms. In this research, we explore AST with hyperparameter optimisation for speech emotion recognition. Three deep learning architectures with optimisable τ b -block structures and variable filter numbers, i.e. 1DCNN, bidirectional LSTM (BiLSTM) and CNN-BiLSTM, are also proposed, enabling the optimisation of network depth and width. A novel Cluster Search Optimisation (CSO) algorithm is introduced. It incorporates Cluster Centroid Search, a Cluster Distance Improvement metric and reinforcement learning to dispatch different search actions based on clustering convergence and Q -learning strategies, respectively. A novel Noise Tempered K-means (NTKM) clustering model is also proposed with the integration of Gaussian-based noise insertion and cluster compactness-separation measurement, to further fine-tune the cluster centriods obtained using OPTICS clustering. CSO is used for hyperparameter and architecture search for AST and aforementioned deep networks. Attention mechanisms are also integrated with CSO-optimised networks to further enhance feature learning. We evaluate the resulting models against those devised by other optimisation algorithms across the EMO-DB, SAVEE, and TESS datasets. The empirical results demonstrate that CSO-optimised AST and CNN-BiLSTM with attention mechanisms outperform other architectures and yield favourable comparison results against those from existing state-of-the-art audio emotion classification methods. • Evolving transformer and deep networks are devised for audio emotion recognition. • A Cluster Search Optimisation algorithm is proposed to adapt hyperparameters. • It incorporates Noise Tempered K-means clustering and Cluster Distance Improvement. • The Q-learning algorithm is used to optimise search behaviours. • Our study indicates CSO-optimised deep networks’ effectiveness across datasets.

The extraction of fine-grained spatial-temporal characteristics for emotion classification is a challenging task owing to the subtlety and ambiguity of emotional expressions through video and audio channels. In this research, we propose an audio-visual ensemble model, comprising a two-stream 3D Convolutional Neural Network (CNN) architecture with RGB and optical flow as inputs for video emotion classification, as well as a variant of Wav2Vec2 for audio emotion recognition. The Wav2Vec2 variant integrates additional recurrent and attention layers with each transformer block to extract long- and short-term dependencies. A new Particle Swarm Optimisation (PSO) algorithm is proposed to fine-tune hyper-parameters of 3D CNNs and the enhanced Wav2Vec2, and formulate audio-visual ensemble models with the smallest sizes. It integrates a reinforcement learning (RL) algorithm, i.e. Asynchronous Advantage Actor-Critic (A3C), for search parameter and hybrid leader construction, and another RL algorithm, Proximal Policy Optimisation (PPO), for search action selection, as well as hypotrochoid and super formula-based search operations. Evaluated using audio-visual emotion datasets, our evolving ensemble model outperforms those devised by other search methods and existing state-of-the-art deep networks, significantly.

Contrastive learning is a promising solution to the problem of data sparsity in the field of recommendation system since it is able to extract self-supervised signals from raw data. The traditional contrastive learning-based sequential recommendation algorithms generate augmentations of original item sequences by utilizing crop, mask and reorder operations. However, those augmentation schemes destroy the underlying semantics of item sequences, resulting in difficulty in accurately defining positive and negative samples. To address this issue, we propose a contrastive translation based sequential recommendation algorithm, namely, CT4Rec. Specifically, CT4Rec generates augmented views of item sequences by injecting noises into embeddings of users and items, which is able to guarantee that the underlying semantics of augmented views are consistent with those of original item sequence. Hence, CT4Rec is able to effectively learn the invariances among the augmented views. In addition, the personalized translation operations are utilized to model the third-order relationships among entities. Moreover, it is difficult for contrastive learning-based recommendation algorithms with static temperature to simultaneously capture the differences among individual users/items and among the clusters of users/items. Hence, we utilize a dynamic temperature strategy to enhance CT4Rec, which endows CT4Rec with the capabilities of group-wise discrimination and instance discrimination. Our validation on five benchmark datasets shows that CT4Rec outperforms SOTA sequential recommendation methods. Our code is released athttps://github.com/zar123123/CT4Rec.

Modeling users’ short-term dynamic and long-term static interests to enhance Point-of-Interests (POI) recommendation performance has shown lots of advantages. Since users’ check-in records can be viewed as a graph network, methods based on Graph Neural Networks (GNNs) have recently shown promising applicability for POI recommendation. However, existing GNN-based works have the following shortcomings: (1) ignoring the impact of complex higher-order relationships between user-POI dynamics over time; and (2) ignoring the difference in POI importance that cannot effectively capture the imbalances of geographical influence among POIs. To address these challenges, we propose a novel Self-supervised Long-and Short-term model (SLS-REC) for POI recommendation. Specifically, we first design a spatio-temporal Hawkes attention hypergraph neural network to capture the spatial dependence and temporal evolution in users’ short-term dynamic interests. Then we introduce a dynamic propagation mechanism of GNNs to learn the geographic influences underlying geographic imbalances among POIs. In addition, the contrastive learning framework over a fine-grained node dropout strategy is applied to maximize the mutual information of long and short-term interest representations. Finally, we adaptively unify the recommendation and self-supervised task with an attention-based mechanism to optimize the proposed SLS-REC model for POI recommendation. Experiments on real-world datasets show that the proposed model significantly outperforms state-of-the-art methods.

The recent breakthrough of deep learning based generative models has led to the escalated generation of photo-realistic synthetic videos with significant visual quality. Automated reliable detection of such forged videos requires the extraction of fine-grained discriminative spatial-temporal cues. To tackle such challenges, we propose weighted and evolving ensemble models comprising 3D Convolutional Neural Networks (CNNs) and CNN-Recurrent Neural Networks (RNNs) with Particle Swarm Optimization (PSO) based network topology and hyper-parameter optimization for video authenticity classification. A new PSO algorithm is proposed, which embeds Muller's method and fixed-point iteration based leader enhancement, reinforcement learning-based optimal search action selection, a petal spiral simulated search mechanism, and cross-breed elite signal generation based on adaptive geometric surfaces. The PSO variant optimizes the RNN topologies in CNN-RNN, as well as key learning configurations of 3D CNNs, with the attempt to extract effective discriminative spatial-temporal cues. Both weighted and evolving ensemble strategies are used for ensemble formulation with aforementioned optimized networks as base classifiers. In particular, the proposed PSO algorithm is used to identify optimal subsets of optimized base networks for dynamic ensemble generation to balance between ensemble complexity and performance. Evaluated using several well-known synthetic video datasets, our approach outperforms existing studies and various ensemble models devised by other search methods with statistical significance for video authenticity classification. The proposed PSO model also illustrates statistical superiority over a number of search methods for solving optimization problems pertaining to a variety of artificial landscapes with diverse geometrical layouts.

The goal of sequential recommendation algorithms is to predict personalized sequential behaviors of users (i.e., next-item recommendation). Learning representations of entities (i.e., users and items) from sparse interaction behaviors and capturing the relationships between entities are the main challenges for sequential recommendation. However, most sequential recommendation algorithms model relationships among entities in Euclidean space, where it is difficult to capture hierarchical relationships among entities. Moreover, most of them utilize independent components to model the user preferences and the sequential behaviors, ignoring the correlation between them. To simultaneously capture the hierarchical structure relationships and model the user preferences and the sequential behaviors in a unified framework, we propose a general hyperbolic translation-based sequential recommendation framework, namely HTSR. Specifically, we first measure the distance between entities in hyperbolic space. Then, we utilize personalized hyperbolic translation operations to model the third-order relationships among a user, his/her latest visited item, and the next item to consume. In addition, we instantiate two hyperbolic translation-based sequential recommendation models, namely Poincaré translation-based sequential recommendation (PoTSR) and Lorentzian translation-based sequential recommendation (LoTSR). PoTSR and LoTSR utilize the Poincaré distance and Lorentzian distance to measure similarities between entities, respectively. Moreover, we utilize the tangent space optimization method to determine optimal model parameters. Experimental results on five real-world datasets show that our proposed hyperbolic translation-based sequential recommendation methods outperform the state-of-the-art sequential recommendation algorithms.

Semantic segmentation is vital for many emerging surveillance applications, but current models cannot be relied upon to meet the required tolerance, particularly in complex tasks that involve multiple classes and varied environments. To improve performance, we propose a novel algorithm, neural inference search (NIS), for hyperparameter optimization pertaining to established deep learning segmentation models in conjunction with a new multiloss function. It incorporates three novel search behaviors, i.e., Maximized Standard Deviation Velocity Prediction, Local Best Velocity Prediction, and n -dimensional Whirlpool Search. The first two behaviors are exploratory, leveraging long short-term memory (LSTM)-convolutional neural network (CNN)-based velocity predictions, while the third employs n -dimensional matrix rotation for local exploitation. A scheduling mechanism is also introduced in NIS to manage the contributions of these three novel search behaviors in stages. NIS optimizes learning and multiloss parameters simultaneously. Compared with state-of-the-art segmentation methods and those optimized with other well-known search algorithms, NIS-optimized models show significant improvements across multiple performance metrics on five segmentation datasets. NIS also reliably yields better solutions as compared with a variety of search methods for solving numerical benchmark functions.

Existing works on session-based recommendation have shown the advantage in enhancing the prediction ability of recommendation with various deep learning techniques. However, the following challenges need to be addressed: (1) the hierarchy of item transition patterns is overlooked; (2) existing works fail to distinguish various factors of item transition within a single session for disentangling user intents. To cope with the above challenges, we propose a novel session based recommendation model called S elf-supervised D ual H ypergraph learning with I ntent D isentanglement model ( SDHID ). Specifically, we first propose a disentangled capsule hypergraph convolutional channel for ne-grained intent learning to capture the intra-session pattern. Accordingly, we introduce the hypergraph and capsule networks in disentangling to learn the item embedding for different factors, and then the representation of the intra-session pattern is obtained by aggregating item embedding with attention weights. Moreover, we build a novel dual–primal hypergraph convolutional channel by mapping the hypergraph to a dual–primal graph for learning the item transition pattern of inter-session. In addition, the above two channels are combined into a self-supervised contrastive learning framework by maximizing mutual information between the learned session representations. We unify the recommendation and the self-supervised tasks under a primary and auxiliary learning framework. The combined optimization of two tasks leads to a hierarchical joint learning item transition for intra- and inter-session. Extensive experiments on real datasets show that the proposed model outperforms several state-of-the-art models.

Human action recognition can be applied in a multitude of fully diversified domains such as active large-scale surveillance, threat detection, personal safety in hazardous environments, human assistance, health monitoring, and intelligent robotics. Owing to its high demands in real-world applications, it has drawn significant attention. In this research, we propose hybrid deep neural networks, i.e. Convolutional Long Short-Term Memory (ConvLSTM) Networks, Long-term Recurrent Convolutional Networks (LRCN), for tackling video action classification. In particular, for the LRCN model, different CNN encoder architectures such as VGG16, ResNet50, DenseNet121 and MobileNet, as well as several Long Short-Term Memory (LSTM) variant decoder architectures, such as LSTM, bidirectional LSTM (BiLSTM) and Gated Recurrent Unit (GRU), are used for spatial-temporal feature extraction to test model performance. We adopt diverse experimental settings including using different numbers of frames per video and learning configurations to optimize performance. The empirical results indicate the superiority of MobileNet in combination with a BiLSTM network over other hybrid network settings for the action classification using the UCF50 dataset. Owing to the lightweight MobileNet encoder, this LRCN model also achieves a better trade-off between performance and training and inference computational costs, while outperforming existing state-of-the-art methods.

Recommender systems have become a crucial intelligent tool, which provides users with personalized services. Graph learning-based recommendation methods treat user-item interactions and the item transitions as pairwise relations but ignore the complex, higher-order interaction information between nodes. Moreover, since most users often interact with few or even no items, graph learning-based recommendation methods suffer from the data sparsity problem, as well as the unbalanced distribution of edges and nodes. To tackle these issues, we propose a Dual Hypergraph-based Self-supervised Learning recommendation model, named DHSL-GM. Specifically, we derive two dual hypergraphs from the user-item bipartite graph, which models the complex high-order user-item interactions by using hypergraph convolution with spectral hypergraph convolution operator. Meanwhile, we design a gated network-based message passing mechanism to dynamically guide message propagation, addressing the problem of the unbalanced distribution of edges and nodes. In addition, to alleviate the data sparsity problem, we design another dual hypergraph convolutional network based on a node discard strategy, which innovatively integrates self-supervised learning into the training of the hypergraph convolutional network. Experimental results on several real datasets demonstrate the superiority and effectiveness of the proposed model.

Abstract Still image human action recognition (HAR) is a challenging problem owing to limited sources of information and large intra-class and small inter-class variations which requires highly discriminative features. Transfer learning offers the necessary capabilities in producing such features by preserving prior knowledge while learning new representations. However, optimally identifying dynamic numbers of re-trainable layers in the transfer learning process poses a challenge. In this study, we aim to automate the process of optimal configuration identification. Specifically, we propose a novel particle swarm optimisation (PSO) variant, denoted as EnvPSO, for optimal hyper-parameter selection in the transfer learning process with respect to HAR tasks with still images. It incorporates Gaussian fitness surface prediction and exponential search coefficients to overcome stagnation. It optimises the learning rate, batch size, and number of re-trained layers of a pre-trained convolutional neural network (CNN). To overcome bias of single optimised networks, an ensemble model with three optimised CNN streams is introduced. The first and second streams employ raw images and segmentation masks yielded by mask R-CNN as inputs, while the third stream fuses a pair of networks with raw image and saliency maps as inputs, respectively. The final prediction results are obtained by computing the average of class predictions from all three streams. By leveraging differences between learned representations within optimised streams, our ensemble model outperforms counterparts devised by PSO and other state-of-the-art methods for HAR. In addition, evaluated using diverse artificial landscape functions, EnvPSO performs better than other search methods with statistically significant difference in performance.

In recent years, a variety of deep learning techniques and methods have been adopted to provide AI solutions to issues within the medical field, with one specific area being audio-based classification of medical datasets. This research aims to create a novel deep learning architecture for this purpose, with a variety of different layer structures implemented for undertaking audio classification. Specifically, bidirectional Long Short-Term Memory (BiLSTM) and Gated Recurrent Units (GRU) networks in conjunction with an attention mechanism, are implemented in this research for chronic and non-chronic lung disease and COVID-19 diagnosis. We employ two audio datasets, i.e. the Respiratory Sound and the Coswara datasets, to evaluate the proposed model architectures pertaining to lung disease classification. The Respiratory Sound Database contains audio data with respect to lung conditions such as Chronic Obstructive Pulmonary Disease (COPD) and asthma, while the Coswara dataset contains coughing audio samples associated with COVID-19. After a comprehensive evaluation and experimentation process, as the most performant architecture, the proposed attention BiLSTM network (A-BiLSTM) achieves accuracy rates of 96.2% and 96.8% for the Respiratory Sound and the Coswara datasets, respectively. Our research indicates that the implementation of the BiLSTM and attention mechanism was effective in improving performance for undertaking audio classification with respect to various lung condition diagnoses.

Personalized tag recommender systems recommend a set of tags for items based on users' historical behaviors, and play an important role in the collaborative tagging systems. However, traditional personalized tag recommendation methods cannot guarantee that the collaborative signal hidden in the interactions among entities is effectively encoded in the process of learning the representations of entities, resulting in insufficient expressive capacity for characterizing the preferences or attributes of entities. In this paper, we proposed a graph neural networks boosted personalized tag recommendation model, which integrates the graph neural networks into the pairwise interaction tensor factorization model. Specifically, we consider two types of interaction graph (i.e. the user-tag interaction graph and the item-tag interaction graph) that is derived from the tag assignments. For each interaction graph, we exploit the graph neural networks to capture the collaborative signal that is encoded in the interaction graph and integrate the collaborative signal into the learning of representations of entities by transmitting and assembling the representations of entity neighbors along the interaction graphs. In this way, we explicitly capture the collaborative signal, resulting in rich and meaningful representations of entities. Experimental results on real world datasets show that our proposed graph neural networks boosted personalized tag recommendation model outperforms the traditional tag recommendation models.

Recommendation system plays an important role in online web applications. Sequential recommender further models user short-term preference through exploiting information from latest user-item interaction history. Most of the sequential recommendation methods neglect the importance of ever-changing item popularity. We propose the model from the intuition that items with most user interactions may be popular in the past but could go out of fashion in recent days. To this end, this paper proposes a novel sequential recommendation approach dubbed TRec, TRec learns item trend information from implicit user interaction history and incorporates item trend information into next item recommendation tasks. Then a self-attention mechanism is used to learn better node representation. Our model is trained via pairwise rank-based optimization. We conduct extensive experiments with seven baseline methods on four benchmark datasets, The empirical result shows our approach outperforms other state-of-the-art methods while maintains a superiorly low runtime cost. Our study demonstrates the importance of item trend information in recommendation system designs, and our method also possesses great efficiency which enables it to be practical in real-world scenarios.

The factorization machine models attract significant attention nowadays since they improve recommendation performance by incorporating context information into recommendation modeling. However, traditional factorization machine models often adopt the point-wise learning method for model parameter learning, as well as only model the linear interactions between features. They substantially fail to capture the complex interactions among features, which degrades the performance of factorization machine models. In this research, we propose a neural pairwise ranking factorization machine for item recommendation, namely NPRFM, which integrates the multi-layer perceptual neural networks into the pairwise ranking factorization machine model. Specifically, to capture the high-order and nonlinear interactions among features, we stack a multi-layer perceptual neural network over the bi-interaction layer, which encodes the second-order interactions between features. Moreover, instead of the prediction of the absolute scores, the pair-wise ranking model is adopted to learn the relative preferences of users. Since NPRFM does not take into account the importance of feature interactions, we propose a new variant of NPRFM, which learns the importance of feature interactions by introducing the attention mechanism . The empirical results on real-world datasets indicate that the proposed neural pairwise ranking factorization machine outperforms the traditional factorization machine models.

With the ever increasing popularity of social network services, social network platforms provide rich and additional information for recommendation algorithms. More and more researchers utilize the trust relationships of users to improve the performance of recommendation algorithms. However, most of the existing social-network-based recommendation algorithms ignore the following problems: (1) In different domains, users tend to trust different friends. (2) the performance of recommendation algorithms is limited by the coarse-grained trust relationships. In this paper, we propose a novel recommendation algorithm that integrates the social circles and the network representation learning for item recommendation. Specifically, we firstly infer the domain-specific social trust circles based on the original users’ rating information and the social network information. Next, we adopt the network representation technique to embed the domain-specific social trust circle into a low-dimensional space, and then utilize the low-dimensional representations of users to infer the fine-grained trust relationships between users. Finally, we integrate the fine-gained trust relationships with the domain-specific matrix factorization model to learn the latent user and item feature vectors. Experimental results on real-world datasets show that our proposed approach outperforms the traditional social-network-based recommendation algorithms.

The development of modern information technology and digitalization of our daily lives brings security database new challenges. It is necessary for a security database to provide access control and privacy protection mechanism to ensure the legal use of data and to prevent privacy breach. This paper introduces an integrated security model which can provide the functions of privacy protection and access control simultaneously by building the connection between the validity of query in parameterized authorization view model and the suspiciousness of a conjunctive select-project-join query in online query audit model, it also designs a polynomial time detecting algorithm and two incorporating frameworks for the integrated model. The integrated security model can provide higher performance and fine-grained access control in modern database systems.