ABSTRACT Mobile application recommendation has emerged as a pivotal domain within the realm of personalized recommendation systems. Traditional mobile application sequence recommendation approaches are predominantly dedicated to the pursuit of sophisticated sequence encoders to achieve more precise representations. However, existing sequence recommendation methods primarily consider the sequential order of historical App interactions, overlooking the time intervals between applications. This oversight hinders the model's capability to fully unearth the temporal correlations in user behavior, consequently limiting the accuracy and personalization of mobile application recommendations. Moreover, the interactions between users and mobile applications are typically sparse, which weakens the model's generalization capabilities. To address these issues, we propose a novel method for mobile application sequence recommendation, incorporating time interval‐aware attention and contrastive learning (called Ti‐CoRe). Specifically, this approach introduces a novel sequence augmentation strategy based on similarity replacement within a contrastive learning framework. By considering textual similarities between applications, this method selectively replaces applications that possess lower similarity scores to generate augmented sequences, increasing the diversity of the sample space and mitigating data sparsity. Furthermore, integrating a time interval‐aware mechanism into the BERT4Rec model, the paper presents a new T‐BERT encoder. It precisely assesses the influence of fluctuating time intervals on the prediction of the subsequent mobile application, thereby ensuring a more nuanced app representation. Experiments conducted on the 360APP real dataset demonstrate that Ti‐CoRe consistently outperforms various baseline models in terms of NDCG and HR metrics.

In industrial scenarios, noise interference and feature overlap often result in blurred classification boundaries, compromising the reliability of rolling bearing fault diagnosis. An adaptive distribution-aware discriminative loss (ADADL) is introduced, through which intraclass thresholds are dynamically adjusted and interclass boundaries are optimized, thereby enhancing compactness and separability in the feature space. By integrating it with the cross-entropy loss, ADADL yields marked gains in diagnostic accuracy on both the Case Western Reserve University benchmark and real-world datasets, particularly under conditions of class imbalance and high noise. Visualization analyses further confirm its ability to sharpen clustering boundaries, suppress feature overlap, and effectively mitigate blurred decision regions.

ABSTRACT The advantages of federated learning in collaborative computing of deep learning make it a crucial approach for distributed architectures in recommender systems. However, existing federated recommender systems typically share unified item embeddings across all clients, which fails to capture user‐specific characteristics of items. How to adaptively retain both the commonality and individuality meanings of item embeddings in the recommendation becomes a critical challenge, while simultaneously preventing personalized information leakage. Therefore, this paper proposes a novel federated recommendation method (named 2P‐FedRec) that constructs a privacy‐preserving personalized recommender system in an adaptive manner. Specifically, this method employs an adaptive attention module to generate item representation containing global item embeddings (capturing cross‐user commonalities) and personalized embeddings (capturing user‐specific preferences), and utilizes two regularizers to guide the optimization of independence between these two embeddings. To protect user privacy, we also apply local differential privacy (LDP) with noise injection to the uploaded parameters, preventing the reconstruction of sensitive data. Extensive experiments on Epinions and Yelp datasets demonstrate that 2P‐FedRec outperforms the state‐of‐the‐art baselines while maintaining privacy.

Unmanned aerial vehicles (UAVs)-assisted edge computing has emerged as an effective solution for providing contingency task offloading services when ground computing infrastructures are insufficient. However, UAVs face challenges in implementing efficient task offloading strategies due to their limited capabilities and the complexity of the privacy offloading problem. To address these challenges, this study constructs a digital twin (DT)-enabled UAV swarm-assisted secure computing model, which considers collaboration of devices, edges, and cloud resources. The model is designed to represent the three-tier computing environment as a DT virtual framework, allowing for the monitoring of network changes and the exploration of potential strategies. Furthermore, a joint optimization problem that considers time delay and energy consumption within encryption and decryption costs is formulated. To solve this problem, an entropy-enhanced proximal policy optimization-based multi-UAV assisted security-aware task offloading (EP-MUSTO) algorithm is proposed. In EP-MUSTO, the exploration capability is enhanced by utilizing an actor network with policy entropy, and the action cognition is improved through the parameterization of the hybrid action space. Experimental results demonstrate that compared with other advanced algorithms, EP-MUSTO achieves a reduction in security system costs and magnitude of convergence oscillations by at least 9.43% and 54.62%, respectively.

In the era of the Internet of Things, the significant increase in data volume, time, and space complexity presents great challenges to workflow scheduling in resource-constrained clouds. This study proposes an efficient hybrid algorithm, denoted white shark optimization (WSO) algorithm with budget constraints (BC-WSO), designed to adhere to budget constraints. The primary objective of BC-WSO is to optimize the scheduling length and cost. This objective is achieved by employing a heuristic algorithm that utilizes the predicted makespan matrix (PMMS) alongside the WSO algorithm as its foundation. The PMMS can minimize the scheduling length of a workflow application and satisfy the task prioritization dependencies. BC-WSO incorporates PMMS into the population initialization phase to improve the accuracy of WSO and accelerate the convergence process. Extensive experiments in two real-world scientific workflow applications show that BC-WSO outperforms current state-of-the-art meta-heuristic algorithms in simultaneously optimizing scheduling length and cost.

Cloud computing, as a cutting-edge computing paradigm, offers substantial data processing and storage capabilities. In a heterogeneous cloud environment, the diversity among cloud platforms results in varying task execution times, posing challenges in minimizing workflow makespan under budget constraints. On this basis, a novel meta-heuristic optimization algorithm, named snake optimizer (SO), is proposed for workflow scheduling in the cloud. Then, a negative offset mechanism is designed to dynamically guide the offset of individual positions during population update to prevent falling into local optimums, thereby optimizing the search for feasible solutions and improving the success rate. Finally, using the negative offset mechanism, a snake optimization budget-aware scheduling algorithm (NO-SO) is developed to schedule budget-constrained workflows in heterogeneous cloud computing environments and minimize the makespan. A series of comparative experiments conducted on real-world scientific workflows demonstrates that the NO-SO algorithm enhances the success rate in finding a feasible solution by 38.89% and 34.45% compared with the advanced MG-PRO algorithm and the original SO algorithm, respectively. Moreover, it achieves an average reduction in makespan of 30.30% and 32.19%.

Identifying compound-protein interactions (CPIs) is critical in drug discovery, as accurate prediction of CPIs can remarkably reduce the time and cost of new drug development. The rapid growth of existing biological knowledge has opened up possibilities for leveraging known biological knowledge to predict unknown CPIs. However, existing CPI prediction models still fall short of meeting the needs of practical drug discovery applications. A novel parallel graph convolutional network model for CPI prediction (ParaCPI) is proposed in this study. This model constructs feature representation of compounds using a unique approach to predict unknown CPIs from known CPI data more effectively. Experiments are conducted on five public datasets, and the results are compared with current state-of-the-art (SOTA) models under three different experimental settings to evaluate the model's performance. In the three cold-start settings, ParaCPI achieves an average performance gain of 26.75%, 23.84%, and 14.68% in terms of area under the curve compared with the other SOTA models. In addition, the results of the experiments in the case study show ParaCPI's superior ability to predict unknown CPIs based on known data, with higher accuracy and stronger generalization compared with the SOTA models. Researchers can leverage ParaCPI to accelerate the drug discovery process.

As the demand for Big Data analysis and artificial intelligence technology continues to surge, a significant amount of research has been conducted on cloud computing services. An effective workflow scheduling strategy stands as the pivotal factor in ensuring the quality of cloud services. Dynamic voltage and frequency scaling (DVFS) is an effective energy-saving technology that is extensively used in the development of workflow scheduling algorithms. However, DVFS reduces the processor's running frequency, which increases the possibility of soft errors in workflow execution, thereby lowering the workflow execution reliability. This study proposes an energy-aware reliability enhancement scheduling (EARES) method with a checkpoint mechanism to improve system reliability while meeting the workflow deadline and the energy consumption constraints. The proposed EARES algorithm consists of three phases, namely, workflow application initialization, deadline partitioning, and energy partitioning and virtual machine selection. Numerous experiments are conducted to assess the performance of the EARES algorithm using three real-world scientific workflows. Experimental results demonstrate that the EARES algorithm remarkably improves reliability in comparison with other state-of-the-art algorithms while meeting the deadline and satisfying the energy consumption requirement.

Mobile edge computing effectively enhances service quality and decreases system cost by processing resource-intensive tasks at the network edge. Today, unmanned aerial vehicles (UAVs) are increasingly being utilized for task offloading services in remote areas due to their convenient deployment and flexible mobility. However, the complex task environment when using UAVs brings great challenges to the optimization strategy’s capacity to solve and converge in a stable manner. To solve this issue, a differential rate rule (DRR) is proposed in this work with the goal of improving the update stability of the agent in the actor–critic reinforcement learning (RL). Second, a UAV-assisted task offloading algorithm called DSUTO is designed based on DRR and maximum entropy RL. Finally, a UAV-assisted mobile device-edge-cloud collaborative computing model is constructed with time-varying channel obstacles and user movement, thus solving a multi-objective joint optimization problem on the task completion cost (including delay and energy consumption) and UAV endurance under resource constraints. The experiment results demonstrate that DSUTO not only has excellent performance in terms of convergence and stability, but also significantly reduces the total system cost by 21.38% compared with the latest benchmark algorithms under complex environment conditions.

Aiming at the “bottleneck” problems of the traditional network security situation awareness model, such as large equipment limitations, single data source and poor integration ability, weak level of autonomous learning and data mining, a network security situation awareness framework suitable for big data is constructed. A gate recurrent unit (GRU) model is established to effectively extract features from the situation data set through the deep learning algorithm of big data. It is a method to automatically mine and analyze the hidden relationship and change trend of network security situation, realize the high-speed acquisition and fusion of massive multi-source heterogeneous data, and perceive the network security situation from an all-round perspective. The experimental results show that this method has a good awareness effect on network threats, and has strong representation ability in the face of network threats. It can effectively perceive the network threat situation without relying on data labels, which verifies that this method can effectively improve the efficiency and accuracy of security situation awareness.

Energy efficiency and system reliability are the two main measurements in modern high-performance computing. The majority of previous recent studies have focused on realizing parallel task scheduling with low energy consumption or fast execution time. These approaches were developed with the classic scheduling model. However, the contention model is gaining increasing recognition as a more practical tool to create accurate and efficient schedules. This study proposes a contention-aware reliability management with deadline and energy budget constraints (CARMEB) algorithm for parallel task scheduling in heterogeneous computing systems. CARMEB involves three phases, namely, task priority calculation, communication edge allocation, and slack reclaiming. Results are validated by conducting extensive experiments, including randomly generated task graphs and three types of task graphs in real-world applications. This study demonstrates that our algorithm significantly improves system reliability.

Scheduling for directed acyclic graph (DAG) tasks with the objective of minimizing makespan has become an important problem in a variety of applications on heterogeneous computing platforms, which involves making decisions about the execution order of tasks and task-to-processor mapping. Recently, the chemical reaction optimization (CRO) method has proved to be very effective in many fields. In this paper, an improved hybrid version of the CRO method called HCRO (hybrid CRO) is developed for solving the DAG-based task scheduling problem. In HCRO, the CRO method is integrated with the novel heuristic approaches, and a new selection strategy is proposed. More specifically, the following contributions are made in this paper. (1) A Gaussian random walk approach is proposed to search for optimal local candidate solutions. (2) A left or right rotating shift method based on the theory of maximum Hamming distance is used to guarantee that our HCRO algorithm can escape from local optima. (3) A novel selection strategy based on the normal distribution and a pseudo-random shuffle approach are developed to keep the molecular diversity. Moreover, an exclusive-OR (XOR) operator between two strings is introduced to reduce the chance of cloning before new molecules are generated. Both simulation and real-life experiments have been conducted in this paper to verify the effectiveness of HCRO. The results show that the HCRO algorithm schedules the DAG tasks much better than the existing algorithms in terms of makespan and speed of convergence.