The heterogeneous resources co-optimization in edge networks is essential to enhance the network throughput. Existing load-sensitive (re-)scheduling approaches mostly formulate the heterogeneous resources balancing as a single-objective optimization issue, omitting the balanced usage of heterogeneous resources on a given edge node. Moreover, these approaches are inadequate for the heterogeneous resources adaptive cooptimization, microservice dependency modeling at a more granular level, and multi-step online re-scheduling. Thus, a Dependency-aware Online Microservice re-Scheduling (DOMS) approach is introduced. In particular, we formulate the microservice re-scheduling as a multiple knapsack optimization issue, and solve it through the Double Dueling Deep Q-Network (D3QN) with prioritized experience replay. Our DOMS incorporates a heterogeneous resources adaptive balancing detection algorithm to enable adaptive co-optimization of heterogeneous resources. A fine-grained dependency graph of microservice performance metrics is built, upon which a multi-step scheduling partition algorithm is devised to facilitate multi-step online re-scheduling. Extensive experiments on a public dataset show that DOMS outperforms comparison approaches in terms of latency, energy consumption, balance degree, and throughput.

The Web 3.0 and metaverse can empower intelligent application of Connected Autonomous Vehicles (CAVs). The adoption of edge computing can contribute to the low latency interaction between CAVs and the metaverse. Microservices are widely deployed on edge networks and the cloud nowadays. User’s requests from CAVs are typically fulfilled through the composition of microservices, which may be hosted by contiguous edge nodes. Requests may differ on their required resources at runtime. Consequently, when requests are continuously injected into edge networks, the usage of heterogenous resources, including CPU, memory, and network bandwidth, may not be the same, or differ significantly, on certain edge nodes. This happens especially when burst requests are injected into the network to be satisfied concurrently. Therefore, the usage of heterogenous resources provided by edge nodes should be co-optimized through re-scheduling microservices. To address this challenge, this article proposes a Web 3.0-enabled M icroservice R e- S cheduling approach (called MRS ), which is a migration-based mechanism integrating a placement strategy. Specifically, we formulate the MRS task as a multi-objective and multi-constraint optimization problem, which can be solved through a penalty signal-integrated framework and an improved pointer network. Extensive experiments are conducted on two real-world datasets. Evaluation results show that our MRS performs better than the counterparts with improvements of at least 7.7%, 2.4%, and 2.2% in terms of network throughput, latency, and energy consumption, respectively.

The usage of heterogeneous resources provisioned by edge nodes can be co-optimized through re-scheduling microservices. Current (re-)scheduling approaches typically treat the task of co-optimization as a single-objective optimization problem, which cannot address the issue of imbalanced usage of heterogeneous resources (e.g., CPU, memory, bandwidth) on a single edge node. More importantly, these approaches are inadequate in handling: (i) the adaptive co-optimization of heterogeneous resources, (ii) the fine-grained construction of micro service dependencies, and (iii) multi-step online mi croservice re-scheduling. To address these challenges, this paper proposes a Dependency-aware Online Microservice re-Scheduling (DOMS) approach. DOMS formulates microservice re-scheduling as a multi-knapsack optimization problem and solves it using a Double Dueling Deep Q-Network (D3QN) with prioritized experience replay. Specifically, an adaptive heterogeneous resources balancing detection algorithm is developed, incorporating a dynamic detection threshold mechanism. A fine-grained microservice performance metrics dependency graph is constructed by capturing causal relationships to represent sequential execution dependency. Based on this graph, a microservice multi-step scheduling partition algorithm is devised. Extensive experiments are conducted upon publicly-available datasets, and evaluation results demonstrate that DOMS outperforms the state-of-the-art techniques with improvements of at least 1.85%, 6.45%, 0.56%, and 3.18% in terms of latency, energy consumption, balance degree, and throughput. These results highlight the effectiveness and superiority of DOMS in maintaining a balanced usage of heterogeneous resources and improving network throughput, while satisfying latency and energy consumption constraints.

In many fields, such as social networks and recommendation systems with high time requirements, fake news and false information are often released in real time, impacting on people’s daily life. Entity alignment (EA) in temporal knowledge graph (TKG) can fuse the information contained in entities by finding equivalent entities, thus helping to determine the regular pattern of disinformation under time change. The existing methods either ignore the use of temporal attributes’ information and structural information or the modeling of that is insufficient, which has become a major obstacle to the further and wider application of TKG EA. In this article, we put forward a new idea of training for the processing of time attributes and relational structure information, to further enhance the ability in the EA process of TKGs. By forming box embedding matrix and name embedding matrix, and adaptively fusing the above information, we propose a new TKG EA solution. We carry out comparative experiments on standard news media and social media datasets collected from the real world, which validates the effectiveness of our proposal.

As an emerging concept, the metaverse incorporates a range of advanced technologies and offers a great opportunity to enhance the experiences of healthcare in clinical practice and human health. However, many cyber security issues often occur in the metaverse healthcare analytics such as DDoS attack, probe attack, and port scanning attack. Fortunately, 6G-enabled intrusion detection can detect anomalous activities with the help of an anomaly detection algorithm for metaverse healthcare analytics. Nevertheless, different from static data, data streams in metaverse healthcare have the intrinsic characteristics of infiniteness, correlation, and distribution change. Traditional static data anomaly detection algorithms do not consider these characteristics, which may result in low accuracy and efficiency. In this paper, aDataStreamAnomalyDetection (DS_AD) approach driven by 6G network is proposed for metaverse healthcare analytics, which incorporates a sliding window and model update into LSHiForest. DS_AD uses a change detection mechanism to optimize the model update. The core design utilizes hash functions to partition data spaces to find anomalies. To validate the feasibility of DS_AD, multiple groups of experiments are designed and executed on SMTP and HTTP datasets. Experimental results show that compared with baselines, our proposal performs favorably for data streams in terms of accuracy and efficiency.

We address the problem of facial motion retargeting that aims to transfer facial motion from a 2D face image to 3D characters. Existing methods often formulate this problem as a 3D face reconstruction problem, which estimates the face attributes such as face identity and expression from face images. However, due to the lack of ground-truth labels for both identity and expression, most 3D-face reconstruction-based methods fail to capture the facial identity and expression accurately. As a result, these methods may not achieve promising performance. To address this, we propose an identity-consistent constraint to learn accurate identities by encouraging consistent identity prediction across multiple frames. Based on a more accurate identity, we are able to obtain a more accurate facial expression. Moreover, we further propose an expression-exclusive constraint to improve performance by avoiding the co-occurrence of contradictory expression units (e.g., ``brow lower'' vs. ``brow raise''). Extensive experiments on facial motion retargeting and 3D face reconstruction tasks demonstrate the superiority of the proposed method over existing methods. Our code and supplementary materials are available at https://github.com/deepmo24/CPEM.

Various cyber attacks often occur in logistics network of the Industry 4.0, which poses a threat to Internet security. Intrusion detection can intelligently detect anomalous activities and secure the Internet with the help of anomaly detection algorithms. Different from static data, intrusion detection data are a dynamic data form and have the following characteristics. First, it is multiaspect. Second, it contains point anomalies and group anomalies. Third, there are correlations between different attributes. Nevertheless, these properties pose a challenge on existing anomaly detection approaches. Thus, a novel anomaly detection approach MDS_AD is proposed in this article to deal with the challenges. It combines locality-sensitive hashing (LSH), isolation forest, and PCA techniques. MDS_AD has the following properties. 1) The introduced LSH can operate on multiaspect data. 2) MDS_AD can effectively catch group anomalies from the experimental results. 3) The PCA is utilized to reduce dimensionality for correlations between different attributes. 4) MDS_AD is a streaming approach, which can perform model update and process data in constant memory and time. To confirm the performance of MDS_AD, multiple experiments are designed and implemented on UNSW-NB15 dataset. Experimental results show that MDS_AD outperforms state-of-the-art baselines.

With the continuous accumulation of users' check-in data, we can gradually capture users' behavior patterns and mine users' preferences. Based on this, the next point-of-interest (POI) recommendation has attracted considerable attention. Its main purpose is to simulate users' behavior habits of check-in behavior. Then, different types of context information are used to construct a personalized recommendation model. However, the users' check-in data are extremely sparse, which leads to low performance in personalized model training using recurrent neural network. Therefore, we propose a category-aware gated recurrent unit (GRU) model to mitigate the negative impact of sparse check-in data, capture long-range dependence between user check-ins and get better recommendation results of POI category. We combine the spatiotemporal information of check-in data and take the POI category as users' preference to train the model. Also, we develop an attention-based category-aware GRU (ATCA-GRU) model for the next POI category recommendation. The ATCA-GRU model can selectively utilize the attention mechanism to pay attention to the relevant historical check-in trajectories in the check-in sequence. We evaluate ATCA-GRU using a real-world data set, named Foursquare. The experimental results indicate that our ATCA-GRU model outperforms the existing similar methods for next POI recommendation.

Nonrigid registration of diffusion magnetic resonance imaging (MRI) is crucial for group analyses and building white matter and fiber tract atlases. Most current diffusion MRI registration techniques are limited to the alignment of diffusion tensor imaging (DTI) data. We propose a novel diffeomorphic registration method for high angular resolution diffusion images by mapping their orientation distribution functions (ODFs). ODFs can be reconstructed using q-ball imaging (QBI) techniques and represented by spherical harmonics (SHs) to resolve intra-voxel fiber crossings. The registration is based on optimizing a diffeomorphic demons cost function. Unlike scalar images, deforming ODF maps requires ODF reorientation to maintain its consistency with the local fiber orientations. Our method simultaneously reorients the ODFs by computing a Wigner rotation matrix at each voxel, and applies it to the SH coefficients during registration. Rotation of the coefficients avoids the estimation of principal directions, which has no analytical solution and is time consuming. The proposed method was validated on both simulated and real data sets with various metrics, which include the distance between the estimated and simulated transformation fields, the standard deviation of the general fractional anisotropy and the directional consistency of the deformed and reference images. The registration performance using SHs with different maximum orders were compared using these metrics. Results show that the diffeomorphic registration improved the affine alignment, and registration using SHs with higher order SHs further improved the registration accuracy by reducing the shape difference and improving the directional consistency of the registered and reference ODF maps.

A novel single-shot trapezoidal-gradient-based Lissajous trajectory is described and implemented on a 3-tesla magnetic resonance (MR) scanner. A feature of this trajectory is that its sampling points are located on a nonequidistant rectangular grid, which permits the usage of one-dimensional optimal algorithms to increase the robustness and speed of image reconstruction. Another advantage of the trajectory is that two images with different effective echo times can be obtained within a single excitation, which might be used for fast T2* mapping, in functional MR imaging scanning of brain activity associated with mental processes. Potential artifacts in reconstructed images were investigated and methods for suppressing these artifacts were developed. Experiments on normal subjects at rest and during brain activation were performed to demonstrate the feasibility of the new sequence.