As user behavior data becomes increasingly scattered across different platforms, achieving cross-domain knowledge fusion while preserving privacy has become a critical issue in recommender systems. Existing Privacy-Preserving Cross-Domain Recommendation (PPCDR) methods usually rely on overlapping users or items as a bridge, making them inapplicable to non-overlapping scenarios. They also suffer from limitations in the collaborative modeling of global and local semantics. To this end, this paper proposes a Federated Cross-domain Recommendation method with deep knowledge Fusion (FedCRF). Using textual semantics as the cross-domain bridge, FedCRF achieves cross-domain knowledge transfer via federated semantic learning under the non-overlapping scenario. Specifically, FedCRF constructs global semantic clusters on the server side to extract shared semantic information, and designs a Fine-Grained Semantic Adaptation and Transfer (FGSAT) module on the client side to dynamically adapt to local data distributions and alleviate cross-domain distribution shift. Meanwhile, it builds a semantic graph based on textual features to learn representations that integrate both structural and semantic information, and introduces contrastive learning constraints between global and local semantic representations to enhance semantic consistency and promote deep knowledge fusion. In this framework, only item semantic representations are shared, while user interaction data remains locally stored, effectively mitigating privacy leakage risks. Experimental results on multiple real-world datasets show that FedCRF significantly outperforms existing methods in terms of Recall@20 and NDCG@20, validating its effectiveness and superiority in non-overlapping cross-domain recommendation scenarios.

Visual tracking is essential across numerous video analysis applications, surveillance systems, entertainment, and autonomous applications. However, most conventional state-of-the- art visual trackers are designed for constant-view scenarios with fixed camera viewpoints, and they only achieve satisfactory performance under stable visual features scenarios. In reality, visual tracking often encounters shift-view scenarios (e.g., sports broadcasting, ground-aerial surveillance), where cameras’ dynamic view transitions between ground and aerial views. These shifts lead to large variations in target scale and environmental complexity, resulting in inconsistent visual features that ultimately degrade the robustness of conventional visual trackers. Although developing a dedicated tracker for such shift-view scenarios is possible, it requires expensive temporal and computational costs. To address this challenge, we propose Shift-view Prompt Tuning, a cost-efficient method that enables conventional trackers to handle dynamic view transitions. We use sample pairs from different view datasets as prompts to guide the tracker’s adaptation. By embedding distinctive visual information from these prompts into training samples, we help the tracker learn about dynamic view transitions without requiring it to be relearned from scratch. This approach seamlessly transforms any constant-view trackers into shift-view trackers. Our extensive experiments on 14 datasets with 3 different view types show that our approach significantly enhances tracking performance. This advancement extends the application scope of current trackers and offers a robust solution for multimedia content production, sports analytics, and security monitoring in video analysis systems.

Cross-Project Defect Prediction (CPDP) utilizes the existing labeled data in the source project to assist with the prediction of unlabeled projects in the target dataset, which effectively improves the prediction performance and has become a research hotspot in software engineering. At present, CPDP can be categorized into homogeneous CPDP and heterogeneous CPDP (HDP), in which HDP doesn’t require that the source project and the target project have the same feature space, thus, it is more widely used in the actual CPDP. Most of current HDP methods map the original features to the latent feature space and reduce the inter-project variation by transferring domain-independent features, but the transferring process ignores the use of domain-related features, which affects the prediction performance of the model. Moreover, the mapped latent features are not conducive to the model’s interpretability. Based on these, this article proposes a Heterogeneous Defect Prediction method based on Feature Disentanglement (FD-HDP). We disentangle the features using domain-related and domain-independent feature extractors, respectively, to improve the interpretability of the model by maximizing the domain adversarial loss during training and guiding the feature extractors to produce accurate domain-related and domain-independent features. The weighted sum of the prediction results from domain-related and domain-independent predictors is used as the final prediction result of the project during the prediction process, which realizes the combination of domain-independent and domain-related features and effectively improves the prediction performance. In this article, we conducted experiments using four publicly available defect datasets to construct heterogeneous scenarios. The results demonstrate that the FD-HDP model shows significant advantages over state-of-the-art methods in six metrics.

Existing Salient Object Ranking (SOR) aims to infer ranking of salient objects based on their saliency degree. However, it tends to only focus on salient objects while neglecting non-salient ones. This coarse-grained ranking limits the performance of downstream tasks. For instance, in image retrieval tasks, focusing solely on the relationship between salient objects is insufficient for achieving fine-grained scene analysis, which may result in retrieved results that do not satisfy user requirements. High-quality retrieval requires fine-grained analysis, making it essential to rank non-salient objects. Based on this need, we propose a new task: Fine-grained Object Importance Ranking in 360 Scenes (FOIR-360), which focus on predicting the relative importance of "ALL objects'' at the instance-level. Our task takes into account all objects, allowing us to refine the original "coarse-grained'' to a "fine-grained'' level. Currently, the main challenge for this new task is the lack of supervised data for model training or even for model testing. Therefore, we propose a novel weakly supervised method to address the shortage of datasets. Furthermore, to the best of our knowledge, there is no existing suitable annotation protocol for this new task. The main reason is that annotating fine-grained rankings is extremely difficult, especially in panoramic scenes that contain numerous instances where even humans are unable to determine which one is more important than others. As the first attempt, we introduce a new annotation protocol designed to highlight the ranking of objects that are non-salient yet still important. Based on this protocol, we construct the first fine-grained 360Rank dataset. In summary, all these new task, weakly supervised method, annotation protocol, and dataset have the potential to drive advancements in the field.

Person re-identification (ReID) aims to identify the same individual across different cameras. Most existing researches focus on horizontal perspectives, where cameras and individuals are positioned at similar heights. However, in real-word applications, cameras are usually mounted at varying heights (e.g., either high-view or low-view) to achieve a broader field of view. Hence, some studies have explored high-view ReID, yet these rely heavily on manually annotating large datasets, which is extremely time-consuming and not publicly available. To improve, we propose a “controllable” data generation protocol that automatically generates omni-directional view data. This protocol can extend any common ReID dataset into an extensive omni-directional view one. By upgrading existing ReID SOTAs with the enhanced data, they can be made to handle ReID tasks with varying camera angles. B.t.w., to verify the effectiveness, we still need “real” data for testing. Thus, we constructed a small testing dataset containing diverse camera angles. Extensive quantitative results demonstrate that our solution is generic and can be applied to any SOTA ReID to achieve extensive performance promotions,e.g., 3%-12% improvement in mAP.

Video anomaly detection methods are mainly classified into two categories based on their primary feature types: appearance-based and action-based. Appearance-based methods rely on low-level visual features like color, texture, and shape, learning patterns specific to training scenes. While effective in familiar settings, they struggle with unknown or altered scenes due to poor generalization and limited understanding of action-scene relationships. In contrast, action-based methods focus on detecting action anomalies but often overlook contextual scene associations, leading to misjudgments (e.g., running on a street being deemed normal without considering scene context). To overcome these limitations, we propose a novel decoupling-based anomaly detection architecture (DecoAD). Its core lies in the decoupling and interweaving of scenes and actions, enabling explicit modeling of their complex relationships. By reconstructing these interactions using knowledge graphs, DecoAD achieves a deeper understanding of behaviors and contexts. This design ensures strong performance in both known and unknown scenarios, significantly enhancing generalization. To evaluate its effectiveness in dynamic scenes and its ability to handle scene-related anomalies, we introduce UFSR, the first video anomaly detection dataset featuring dynamic scenes and scene-related anomalies. DecoAD supports fully-supervised, weakly-supervised, and unsupervised settings, improving AUC on UBnormal by 1.1%, 3.1%, and 2.1% in fully-supervised, weakly-supervised, and unsupervised settings, and on UFSR by 1.2% and 8.2% in weakly-supervised and unsupervised settings. The source code and datasets are available at:https://github.com/liuxy3366/DecoAD.

Salient Object Detection (SOD) aims to identify the most attention-grabbing regions in an image and focuses on distinguishing salient objects from their backgrounds. Current SOD methods primarily use a discriminative approach, which works well for clear images but struggles in complex scenes with similar colors and textures between objects and backgrounds. To address these limitations, we introduce the diffusion-based salient object detection model (DiffSOD), which leverages a noise-to-image denoising process within a diffusion framework, enhancing saliency detection in both RGB and RGB-D images. Unlike conventional fusion-based SOD methods that directly merge RGB and depth information, we treat RGB and depth as distinct conditions, i.e., the appearance condition and the structure condition, respectively. These conditions serve as controls within the diffusion UNet architecture, guiding the denoising process. To facilitate this guidance, we employ two specialized control adapters: the appearance control adapter and the structure control adapter. Moreover, conventional denoising UNet models may struggle when handling low-quality depth maps, potentially introducing detrimental cues into the denoising process. To mitigate the impact of low-quality depth maps, we introduce a quality-aware filter. This filter selectively processes only high-quality depth data, ensuring that the denoising process is based on reliable information. Comparative evaluations on benchmark datasets have shown that DiffSOD substantially surpasses existing RGB and RGB-D saliency detection methods, improving average performance by 1.5% and 1.2% respectively, thus setting a new benchmark for diffusion-based dense prediction models in visual saliency detection.

In unsupervised graph anomaly detection, existing methods usually focus on detecting outliers by learning local context information of nodes, while often ignoring the importance of global context. However, global context information can provide more comprehensive relationship information between nodes in the network. By considering the structure of the entire network, detection methods are able to identify potential dependencies and interaction patterns between nodes, which is crucial for anomaly detection. Therefore, we propose an innovative graph anomaly detection framework, termed CoCo (Context Correlation Discrepancy Analysis), which detects anomalies by meticulously evaluating variances in correlations. Specifically, CoCo leverages the strengths of Transformers in sequence processing to effectively capture both global and local contextual features of nodes by aggregating neighbor features at various hops. Subsequently, a correlation analysis module is employed to maximize the correlation between local and global contexts of each normal node. Unseen anomalies are ultimately detected by measuring the discrepancy in the correlation of nodes’ contextual features. Extensive experiments conducted on six datasets with synthetic outliers and five datasets with organic outliers have demonstrated the significant effectiveness of CoCo compared to existing methods.

The purpose of Intrusion Detection Systems (IDS) is to identify security issues in data transmitted by various devices and communication protocols. For domains with sparse data, such as the Internet of Things (IoT), cross-domain models are applied to solve the sparse problem by transfer knowledge from the source domain with rich data to the target domain. However, most of the cross-domain intrusion detection methods map different explicit features in the source and target domains to implicit features in a common implicit space, which weakens the interpretability of these methods. To enhance the interpretability of cross-domain models, we propose a Cross-Domain Intrusion Detection Method Based on Nonlinear Augmented Explicit Features (NAEF). Specifically, we augment the feature space of the source and target domains as the combination of shared features, source domain specific features and target domain specific features. Moreover, we model the nonlinear mapping relationship from shared features to special features in the source and target domains separately. Then, the original features in the source and target domains are mapped to uniform explicit features in the augmented space by migration of the nonlinear mapping relationship. Additionally, a classifier based on ensemble learning and attention mechanism balances the data distribution and selects important features to enhance detection performance. Our experimental results demonstrate the effectiveness of the proposed NAEF method on four public datasets.

In the evolving landscape of recommender systems, the challenge of effectively conducting privacy-preserving Cross-domain Recommendation, especially under strict non-overlapping constraints, has emerged as a key focus. Despite extensive research has made significant progress, several limitations still exist: (1) Previous semantic-based methods fail to deeply exploit rich textual information, since they quantize the text into codes, losing its original rich semantics. (2) The current solution solely relies on the text-modality, while the synergistic effects with the ID-modality are ignored. (3) Existing studies do not consider the impact of irrelevant semantic features, leading to inaccurate semantic representation. To address these challenges, we introduce federated semantic learning and devise FFMSR as our solution. For Limitation 1, we locally learn items’ semantic encodings from their original texts by a multi-layer semantic encoder and then cluster them on the server to facilitate the transfer of semantic knowledge between domains. To tackle Limitation 2, we integrate both ID and Text modalities on the clients, and utilize them to learn different aspects of items. To handle Limitation 3, a Fast Fourier Transform-based filter and a gating mechanism are developed to alleviate the impact of irrelevant semantic information in the local model. We conduct extensive experiments on two real-world datasets, and the results demonstrate the superiority of our FFMSR method over other SOTA methods. Our source codes are publicly available at https://github.com/Sapphire-star/FFMSR .

Electrocardiogram(ECG) signal analysis is crucial in healthcare service computing. Ensuring accurate assessment of ECG signal quality is vital to prevent wastage of transmission bandwidth and ineffective analysis caused by noise. This enables the efficient utilization of service resources. However, existing ECG signal quality assessment(SQA) methods primarily focus on single-modal learning, overlooking the interrelation of ECG in a multimodal feature space and failing to effectively exploit available information for pattern mining. In this paper, we model the SQA for ECG as an anomaly detection problem and propose a multimodal unsupervised SQA method. It jointly explores the boundaries between high-quality ECG and noise in both the time and frequency domains by introducing time-frequency pseudo anomalies. Specifically, we first simulate real ECG noise from the time-domain using a combination of a series of noises and convert it to the frequency-domain to form time-frequency pseudo-anomalies. Next, we map the time-frequency pseudo anomalies onto hyperspheres and jointly refine the hyperspheres learned only from high-quality ECG samples in both feature spaces. Finally, the noise score is defined as the distance from the joint time-frequency features to the center of the hypersphere. Multiple experiments on various real-world ECG datasets validate the superior performance of our proposed method.

Multi-behavior recommendation systems enhance effectiveness by leveraging auxiliary behaviors (such as page views and favorites) to address the limitations of traditional models that depend solely on sparse target behaviors like purchases. Existing approaches to multi-behavior recommendations typically follow one of two strategies: some derive initial node representations from individual behavior subgraphs before integrating them for a comprehensive profile, while others interpret multi-behavior data as a heterogeneous graph, applying graph neural networks to achieve a unified node representation. However, these methods do not adequately explore the intricate patterns of behavior among users and items. To bridge this gap, we introduce a novel algorithm called Behavior Pattern mining-based Multi-behavior Recommendation (BPMR). Our method extensively investigates the diverse interaction patterns between users and items, utilizing these patterns as features for making recommendations. We employ a Bayesian approach to streamline the recommendation process, effectively circumventing the challenges posed by graph neural network algorithms, such as the inability to accurately capture user preferences due to over-smoothing. Our experimental evaluation on three realworld datasets demonstrates that BPMR significantly outperforms existing state-of-the-art algorithms, showing an average improvement of 268.29% in Recall@10 and 248.02% in NDCG@10 metrics. The code of our BPMR is openly accessible for use and further research at https://github.com/rookitkitlee/BPMR.

Abstract Generally, open source software (OSS) has a longer bug‐fixing time. If the bug‐fixing time can be predicted accurately as early as possible, it will be beneficial to the efficiency of bug fixing. Traditional bug‐fixing time prediction models are usually based on static features of bug report. It is difficult to go into service due to inappropriate feature extraction of data and low prediction accuracy of models. The HMM prediction model can predict the bug‐fixing time accurately according to earlier fixing activities. However, this method of temporal sequence feature selection results in a large number of inconsistent samples, and the HMM prediction model can only capture the adjacent activity behavior information of one sequence, and hence, it will reduce the performance of bug‐fixing time prediction. By incorporating the activity information and time information of bug activity transfer, the proportion of inconsistent samples is reduced significantly. In this paper, a double‐sequence input LSTM model (LSTM‐DA) is designed to capture both sequences interaction features and long‐distance‐dependent features. The results of the experiments show that the proposed model can improve the F‐measure and accuracy indicators by about 10% compared with the HMM model in all dimensions, which demonstrates the effectiveness of our method.

In order to improve traffic conditions and environmental pollution caused by automobile exhaust, it is critical and challenging to help numerous vehicle users to obtain ideal parking spaces in terms of quality of experience (QoE), security and efficiency. The parking assistance scheme using the consortium blockchain can get a tradeoff between security and efficiency, but it is a key issue to meet the preferences of parking space seekers and design a well-operated incentive sharing mechanism. This paper proposes a Parking Assistance Alliance (PAA) scheme based on double auction for vehicular blockchain. Through truthful incentives, the PAA encourages vehicles in the alliance to provide parking information for nearby vehicle users. Further, the vehicle user’s preference is fully considered when assigning the parking space. The matching priority is introduced by incorporating the parking space supply and demand, as a reference to provide prioritized service. Theoretical proofs and simulation results show that the PAA scheme can achieve individual rationality, budget balance, and truthfulness, with high user satisfaction, and the PAA scheme deployed on the IBM Hyperledger Fabric platform outperforms the existing method with an average of 7.42% higher successful allocation ratio and 35.4% less transaction confirmation time.

The explosive growth of Internet of Vehicle (IoV) applications has made information security a significant issue. Mobile IoV users are dynamic and the communication environment is very complex, which makes it very difficult to guarantee real-time secrecy communication performance. Thus, a reliable and effective evaluation and prediction of secrecy performance is critical. In this article, we have derived novel expressions for secrecy performance. A grey wolf optimization generalized regression (GWO-GR) algorithm is proposed to predict the secrecy performance and carry out the secrecy performance assessment. A generalized regression (GR) neural network is designed. Out of the input and output layers, the proposed GR network has a pattern layer and a summation layer, which can obtain a global convergence of network results. To further optimize the GR network, the grey wolf optimization algorithm is used to obtain the best spread factor for it, which can accelerate its rapid convergence. Through the simulated numerical results, we can obtain: 1) the proposed GWO-GR prediction algorithm is shown to provide better performance prediction results than other machine-learning-based methods; 2) in particular, the prediction accuracy is improved by 17.7%; and 3) the execution time has an 88.9% reduction.

The global healthcare industry and artificial intelligence have promoted the development of the diversified intelligent healthcare applications. Internet of Things (IoT) will play an important role in meeting the high throughput requirements of diversified intelligent healthcare applications. However, the mobile IoT-enabled healthcare networks are diverse and open, the healthcare big data transmission is vulnerable to a potential attack, which can cause network outages and serious healthcare security issues. To process the complex healthcare security event in real time, security performance prediction is critical for mobile IoT-enabled healthcare networks. In this article, we first analyze the security performance, and derive the novel expressions for the security performance in a closed form. Then, to analyze the security performance in real time, a security performance intelligent prediction algorithm is proposed. An improved convolutional neural network (CNN) model is designed, which combines the four-layer convolution and a four-branch inception block, and can adopt different convolution kernels in the same layer. The four-branch inception block can increase the width of the CNN while reducing the parameters. The improved CNN model can not only increases the width of the CNN, extract different sizes of healthcare data features, but also increases the adaptability to the nonlinear healthcare big data. Compared with different methods, the proposed intelligent algorithm can obtain better security performance prediction. In particular, for prediction precision, the proposed intelligent algorithm is increased by 20%.

Industry 5.0 has developed in full swing, and accelerated the process of the sixth-generation (6G) mobile communication. Physical layer security is important for complex 6G mobile communication networks. To process active complex events in 6G mobile cooperative networks, predicting secrecy performance in time is essential for the mobile communication quality evaluation. Using amplify-and-forward (AF) relaying, we propose a transmit antenna selection (TAS) based secrecy scheme in this article. To analyze the security of 6G mobile cooperative networks, signal-to-noise ratio of the end-to-end link is used to derive the novel expressions for secrecy outage probability (SOP). The theoretical results are confirmed by simulation results. Then, we use SOP as the important merit to evaluate the secrecy performance, and set up the dataset. To achieve secrecy performance prediction, a convolutional neural network (CNN) based SOP prediction algorithm is proposed. The designed CNN model has five convolution layers, which all use the same convolution in the padding and do not change data size. For this improved CNN structure, we adopt the idea of SqueezeNet, which belongs to the lightweight CNN. The improved CNN model can greatly reduce the parameters and network complexity on the premise of ensuring the prediction accuracy. We also examine the following state-of-the-art techniques, first, Elman, second, InceptionNet, third, deep neural network (DNN), and fourth, support vector machine methods. The proposed CNN algorithm can achieve better SOP prediction results than other existing methods. In particular, compared with DNN method, the prediction accuracy is increased by 66.7%.

With the advent of a fifth-generation mobile network and developments in technologies such as the Internet of Medical Things, smart healthcare systems are becoming ubiquitous in our daily lives. Patients, doctors, and other medical personnel rely on the safe and efficient storage, transmission, and analysis of electronic health records, particularly medical images, for successful diagnosis, treatment, and management of different ailments. In this study, a multiwatermarking scheme is proposed for medical images based on quantum random walk and the brain storm optimization algorithm. A logo image used to verify medical image integrity is embedded in regions of interest, and text data are embedded in regions of non-interest to conceal private hospital and patient information. This process improves the accuracy of medical image verification and helps to ensure authenticity. A series of experiments were conducted to validate the capacity, security, robustness, and imperceptibility of the proposed multiwatermarking scheme.

Recently, recommender systems are applied to provide personalized recomendation for healthcare wearables. However, due to the sparsity problem, traditional recommendation algorithms are difficult to achieve desired performance. Considering that consumers often buy and rate other types of items on E-commerce platforms, we can leverage significant information in the auxiliary domains to improve the recommendation performance of healthcare wearables, which can be regarded as cross-domain recommendation. However, traditional cross-domain recommendation model cannot fully represent user's characteristics and fail to consider the leaks of original auxiliary domain ratings during the information transfer process. To overcome the two shortcomings, this paper proposes a Privacy-Preserving Cross-Domain Healthcare Wearables Recommendation algorithm (PPCDHWRec). Firstly, user's characteristics are divided into domain-dependent features and domain-independent features, which complement each other and fully depict the user's characteristics. Secondly, inspired by the latent factor model, we factorize the original rating information of each auxiliary domain by Funk-SVD and Orthogonal Nonnegative Matrix Tri-Factorization (ONMTF) model, to obtain user's domain-dependent and domain-independent features, respectively. Finally, the Factorization Machine algorithm is used to fuse the obtained user's features with the target domain information to provide the recommendation results. By hiding the item latent factors obtained in the factorization process, PPCDHWRec ensures that the original information cannot be inferred from the transferred user hidden vector. Hence, PPCDHWRec is a privacy-preserving recommendation model. Experiments on two groups of auxiliary domains, having high and low correlations with target domain, show the effectiveness of PPCDHWRec.

With the help of the new generation information technology, the Internet of Vehicle (IoV) networks have become widespread. IoV can improve the automatic driving ability, and provide users with intelligence, comfort, safety, energy saving and efficiency traffic services. However, the IoV networks face serious challenges due to the complex wireless environment. The vehicles cannot obtain the real-time traffic condition and early warning information, which leads to the decrease of link quality and the failure of information transmission. To evaluate the communication quality of IoV networks, the outage probability (OP) is commonly employed as a metric. This paper considers mobile IoV networks, and investigates communication quality prediction. Novel OP expressions are derived, which can analyze the OP performance. Then, to predict OP in real time, an intelligent OP prediction approach with an Elman model is proposed. This is evaluated with data generated using the OP expressions. In terms of computational complexity and prediction accuracy, the results obtained show that the Elman-based approach provides better forecasting effect than other methods. For prediction accuracy, the proposed Elman approach is increased by 84.6%. For computational complexity, the execution time is reduced by 79.9%.

Abstract Aiming at the integrated scheduling problem of tree‐structured products with flexible machine selection, this article proposes a flexible integrated scheduling algorithm based on remaining work probability selection coding. The algorithm is based on the framework of a genetic algorithm. First, in order to ensure the diversity and goodness of the initial population, an encoding method based on remaining work probability selection is proposed. Second, two new different crossover and mutation methods are designed based on operation and position respectively, which ensure the legitimacy of the generated offspring individuals. Then, in order to enhance the searchability of the algorithm for an optimal solution of the problem, a local search strategy based on the machine is proposed. Finally, a simple and effective decoding method based on the idle period is given. The algorithm is tested by the existing instance and randomly generated instances. The experimental results show that the proposed algorithm's solving speed and solution quality outperform other comparison algorithms.

Aiming at the distributed integrated scheduling of complex products with tree structure, a memetic algorithm-based distributed integrated scheduling algorithm is proposed. Based on the framework of the memetic algorithm, the algorithm uses a distributed estimation algorithm for global search and performs a local search strategy based on the critical operation set for the current optimal solution obtained in each evolutionary generation. A bi-chain-based individual representation method is presented and a simple greedy insertion-based decoding method is given; two position-based probability models are built, which are used to describe the distribution of the operation priority and factory assignment, respectively. Based on the designed probability models, two learning-based updating mechanisms and an improved sampling method are given, which ensures that the population evolves towards a promising region. In order to enhance the searchability for the superior solutions, nine disturbance operators based on the critical operation set are presented. The parameters are determined by the design-of-experiment (DOE) test, and the effectiveness of the proposed algorithm is verified by comparative experiments.

Ensemble learning is an effective approach for improving the generalization ability of base classifiers. To generate a set of accurate and diverse base classifiers, different data perturbation schemes have been proposed. For instance, Bagging perturbs the training data via bootstrap sampling. However, when a stable learning algorithm (e.g., KNN, Naive Bayes) is used to train base classifiers, the sole perturbation on the training data may not produce diverse base classifiers. In this paper, by using the attribute reduction technology in rough sets, a multi-modal perturbation-based algorithm (called ‘E _ EARBS’) is proposed for the ensemble of base classifiers. E _ EARBS simultaneously perturbs the feature space, training data and learning parameters, where the relative decision entropy(RDE)-based approximate reducts are used to perturb the feature space, and bootstrap sampling is used to perturb the training data. Experimental results show that E _ EARBS can provide competitive solutions for ensemble learning.

Loneliness and isolation are on the rise worldwide, threatening human well-being and the wellness of different age groups and backgrounds. Notably, global social distancing measures during the COVID-19 crisis have exacerbated this problem, resulting in various psychological and physiological ailments. Within both the categories of social and medical robots, companion robots are capable of engaging emotionally with users and providing continuous monitoring and assessment of their health. In this study, we propose a framework for modeling the emotion space of companion robots to facilitate their emotion generation and transition based on Plutchik’s wheel of emotions and reversible quantum circuit schemes. Superposition encodings allow fewer computing resources for the generation and storage of emotional states, and by using unitary operations, they facilitate easier emotion transition and recovery over different intervals. Further, an encryption strategy is designed based on the emotion communication architecture to secure the emotion-related data in human-robot interaction. It is hoped that such an integrative framework and research agenda exploring the role of companion robots will be useful to care for users’ social health by mitigating their negative emotions, especially during difficult times.

The characteristic of multi-shop scheduling is that the processing equipment is scattered in multiple workshops in different geographical locations. To solve this problem, a multi-shop comprehensive scheduling algorithm considering migration dual-objective is proposed of migration of a single complex product in multi-shop equipment, and the workpiece is migrated from one device to another during the processing. The algorithm first uses the leaf nodes of the process tree as a set of schedulable operations; second, the pre-scheduled operation set is determined according to the long path first strategy and the short time strategy; third, the actual scheduling set of the same idle equipment is determined according to the same equipment procedure selection strategy in the workshop; finally, considering the migration time and cost of the process to be processed, a two-objective optimization strategy for process shop selection is proposed. The example results show that the algorithm in this paper consumes less time and cost during the migration process, and can obtain a relatively compromised solution of the total time and total cost of product scheduling.

This paper studies the flexible integrated scheduling problem, which is an extension of job-shop scheduling considering the assembly and the machining stages at the same time, with networked devices (FISND). The completion time of the entire product may be impacted by the uncertainty of the process constraint structure and flexible equipment, so we take machining structure evaluation (MSE) into account. We proposed an improved artificial bee colony algorithm considering MSE (ABC[Formula: see text]) with two new strategies: one is a dynamic perturbation step strategy and the other is double-chain similarity and migration time factor strategy to evaluate the product processing structure, and then we computed the selection probability of the followers by it. Finally, the experimental results show that ABC[Formula: see text] has a better performance and faster convergence than the algorithm ABC, hyABC, GA. It can not only solve the flexible integrated scheduling problem with networked devices, but also yield better solutions to the typical flexible integrated scheduling problem than the flexible equipment integrated scheduling algorithm based on device-driven.