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In the stochastic and dynamic edge-cloud collaborative environment, the computing resources of the host are limited, and the resource requirements of computing tasks are random and changeable. Therefore, how to efficiently schedule dynamic tasks and improve system performance becomes challenging. The scheduling algorithm based on deep reinforcement learning optimizes the delay and energy consumption of the system by dynamically interacting with the environment, which can solve the problem of dynamic and changeable environment to a certain extent, but there are still problems such as poor model adaptability, low training efficiency, and unbalanced system load. In this paper, aiming at optimizing the average response time of task scheduling and the average energy consumption of the system, a multi-objective task scheduling model is designed, and a task scheduling policy optimization algorithm based on improved asynchronous advantage actor-critic (A3C) is proposed. The residual convolutional neural network (RCNN) improves the network structure of A3C, using asynchronous multi-threaded training methods to interact with the edge-cloud collaborative environment and capturing the random dynamic characteristics of resources required for computing tasks and heterogeneous edge-cloud hosts resource change characteristics to better adapt to random dynamic environments, and it can quickly updating network parameters, improving training speed, and make full use of host resources to solve system load imbalance problems. Simulation results show that the scheduling algorithm proposed in this paper can effectively reduce task response time and system energy consumption in an edge-cloud collaborative environment. [ABSTRACT FROM AUTHOR]

Most existing Web service recommendation models based on machine learning do not fully consider the high-order features interaction between users and services and with poor interpretability. In this paper, an Interpretable Web Service Recommendation model based on Disentangled Representation Learning (WSR-DRL) is proposed. First of all, to make full use of the service description information to improve the accuracy of Web service recommendation, the features representation of service name is obtained by using BERT model, and the local and global features representation of service description information is further obtained by combining 2-D CNN and Bi-LSTM. Then the disentangled convolution neural network is used to generate the high-order interaction features between users and services, and the neighborhood routing algorithm is used to mine the latent factors in these features. That improves the accuracy of Web service recommendation and make it interpretable. Finally, in order to verify the effectiveness of the model, several groups of experiments are carried out on real data sets. The experimental results show that compared with latest models such as DMF, DeepFM, DKN, GCMC, NDCG model and WSR-MGAT model, the WSR-DRL model proposed in this paper shows better performance on Precision@10, Recall@10, F1@10 and NDCG@10 evaluation metrics. [ABSTRACT FROM AUTHOR]

Background and Objective: Although paracetamol (acetaminophen) combined with other analgesics can reduce pain intensity in some pain conditions, its effectiveness in managing low back pain and osteoarthritis is unclear. This systematic review investigated whether paracetamol combination therapy is more effective and safer than monotherapy or placebo in low back pain and osteoarthritis.Online database searches were conducted for randomised trials that evaluated paracetamol combined with another analgesic compared to a placebo or the non-paracetamol ingredient in the combination (monotherapy) in low back pain and osteoarthritis. The primary outcome was a change in pain. Secondary outcomes were (serious) adverse events, changes in disability and quality of life. Follow-up was immediate (≤ 2 weeks), short (> 2 weeks but ≤ 3 months), intermediate (> 3 months but < 12 months) or long term (≥ 12 months). A random-effects meta-analysis was conducted. Risk of bias was assessed using the original Cochrane tool, and quality of evidence using Grading of Recommendations Assessment, Development and Evaluation (GRADE).Twenty-two studies were included. Pain was reduced with oral paracetamol plus a non-steroidal anti-inflammatory drug (NSAID) at immediate term in low back pain (paracetamol plus ibuprofen vs ibuprofen [mean difference (MD) − 6.2, 95% confidence interval (CI) −10.4 to −2.0, moderate evidence]) and in osteoarthritis (paracetamol plus aceclofenac vs aceclofenac [MD − 4.7, 95% CI − 8.3 to − 1.2, moderate certainty evidence] and paracetamol plus etodolac vs etodolac [MD − 15.1, 95% CI − 18.5 to − 11.8; moderate certainty evidence]). Paracetamol plus oral tramadol reduced pain compared with placebo at intermediate term for low back pain (MD − 11.7, 95% CI − 19.2 to − 4.3; very low certainty evidence) and osteoarthritis (MD − 6.8, 95% CI − 12.7 to −0.9; moderate certainty evidence). Disability scores improved in half the comparisons. Quality of life was infrequently measured. All paracetamol plus NSAID combinations did not increase the risk of adverse events compared to NSAID monotherapy.Low-to-moderate quality evidence supports the oral use of some paracetamol plus NSAID combinations for short-term pain relief with no increased risk of harm for low back pain and osteoarthritis compared to its non-paracetamol monotherapy comparator.Methods: Although paracetamol (acetaminophen) combined with other analgesics can reduce pain intensity in some pain conditions, its effectiveness in managing low back pain and osteoarthritis is unclear. This systematic review investigated whether paracetamol combination therapy is more effective and safer than monotherapy or placebo in low back pain and osteoarthritis.Online database searches were conducted for randomised trials that evaluated paracetamol combined with another analgesic compared to a placebo or the non-paracetamol ingredient in the combination (monotherapy) in low back pain and osteoarthritis. The primary outcome was a change in pain. Secondary outcomes were (serious) adverse events, changes in disability and quality of life. Follow-up was immediate (≤ 2 weeks), short (> 2 weeks but ≤ 3 months), intermediate (> 3 months but < 12 months) or long term (≥ 12 months). A random-effects meta-analysis was conducted. Risk of bias was assessed using the original Cochrane tool, and quality of evidence using Grading of Recommendations Assessment, Development and Evaluation (GRADE).Twenty-two studies were included. Pain was reduced with oral paracetamol plus a non-steroidal anti-inflammatory drug (NSAID) at immediate term in low back pain (paracetamol plus ibuprofen vs ibuprofen [mean difference (MD) − 6.2, 95% confidence interval (CI) −10.4 to −2.0, moderate evidence]) and in osteoarthritis (paracetamol plus aceclofenac vs aceclofenac [MD − 4.7, 95% CI − 8.3 to − 1.2, moderate certainty evidence] and paracetamol plus etodolac vs etodolac [MD − 15.1, 95% CI − 18.5 to − 11.8; moderate certainty evidence]). Paracetamol plus oral tramadol reduced pain compared with placebo at intermediate term for low back pain (MD − 11.7, 95% CI − 19.2 to − 4.3; very low certainty evidence) and osteoarthritis (MD − 6.8, 95% CI − 12.7 to −0.9; moderate certainty evidence). Disability scores improved in half the comparisons. Quality of life was infrequently measured. All paracetamol plus NSAID combinations did not increase the risk of adverse events compared to NSAID monotherapy.Low-to-moderate quality evidence supports the oral use of some paracetamol plus NSAID combinations for short-term pain relief with no increased risk of harm for low back pain and osteoarthritis compared to its non-paracetamol monotherapy comparator.Results: Although paracetamol (acetaminophen) combined with other analgesics can reduce pain intensity in some pain conditions, its effectiveness in managing low back pain and osteoarthritis is unclear. This systematic review investigated whether paracetamol combination therapy is more effective and safer than monotherapy or placebo in low back pain and osteoarthritis.Online database searches were conducted for randomised trials that evaluated paracetamol combined with another analgesic compared to a placebo or the non-paracetamol ingredient in the combination (monotherapy) in low back pain and osteoarthritis. The primary outcome was a change in pain. Secondary outcomes were (serious) adverse events, changes in disability and quality of life. Follow-up was immediate (≤ 2 weeks), short (> 2 weeks but ≤ 3 months), intermediate (> 3 months but < 12 months) or long term (≥ 12 months). A random-effects meta-analysis was conducted. Risk of bias was assessed using the original Cochrane tool, and quality of evidence using Grading of Recommendations Assessment, Development and Evaluation (GRADE).Twenty-two studies were included. Pain was reduced with oral paracetamol plus a non-steroidal anti-inflammatory drug (NSAID) at immediate term in low back pain (paracetamol plus ibuprofen vs ibuprofen [mean difference (MD) − 6.2, 95% confidence interval (CI) −10.4 to −2.0, moderate evidence]) and in osteoarthritis (paracetamol plus aceclofenac vs aceclofenac [MD − 4.7, 95% CI − 8.3 to − 1.2, moderate certainty evidence] and paracetamol plus etodolac vs etodolac [MD − 15.1, 95% CI − 18.5 to − 11.8; moderate certainty evidence]). Paracetamol plus oral tramadol reduced pain compared with placebo at intermediate term for low back pain (MD − 11.7, 95% CI − 19.2 to − 4.3; very low certainty evidence) and osteoarthritis (MD − 6.8, 95% CI − 12.7 to −0.9; moderate certainty evidence). Disability scores improved in half the comparisons. Quality of life was infrequently measured. All paracetamol plus NSAID combinations did not increase the risk of adverse events compared to NSAID monotherapy.Low-to-moderate quality evidence supports the oral use of some paracetamol plus NSAID combinations for short-term pain relief with no increased risk of harm for low back pain and osteoarthritis compared to its non-paracetamol monotherapy comparator.Conclusions: Although paracetamol (acetaminophen) combined with other analgesics can reduce pain intensity in some pain conditions, its effectiveness in managing low back pain and osteoarthritis is unclear. This systematic review investigated whether paracetamol combination therapy is more effective and safer than monotherapy or placebo in low back pain and osteoarthritis.Online database searches were conducted for randomised trials that evaluated paracetamol combined with another analgesic compared to a placebo or the non-paracetamol ingredient in the combination (monotherapy) in low back pain and osteoarthritis. The primary outcome was a change in pain. Secondary outcomes were (serious) adverse events, changes in disability and quality of life. Follow-up was immediate (≤ 2 weeks), short (> 2 weeks but ≤ 3 months), intermediate (> 3 months but < 12 months) or long term (≥ 12 months). A random-effects meta-analysis was conducted. Risk of bias was assessed using the original Cochrane tool, and quality of evidence using Grading of Recommendations Assessment, Development and Evaluation (GRADE).Twenty-two studies were included. Pain was reduced with oral paracetamol plus a non-steroidal anti-inflammatory drug (NSAID) at immediate term in low back pain (paracetamol plus ibuprofen vs ibuprofen [mean difference (MD) − 6.2, 95% confidence interval (CI) −10.4 to −2.0, moderate evidence]) and in osteoarthritis (paracetamol plus aceclofenac vs aceclofenac [MD − 4.7, 95% CI − 8.3 to − 1.2, moderate certainty evidence] and paracetamol plus etodolac vs etodolac [MD − 15.1, 95% CI − 18.5 to − 11.8; moderate certainty evidence]). Paracetamol plus oral tramadol reduced pain compared with placebo at intermediate term for low back pain (MD − 11.7, 95% CI − 19.2 to − 4.3; very low certainty evidence) and osteoarthritis (MD − 6.8, 95% CI − 12.7 to −0.9; moderate certainty evidence). Disability scores improved in half the comparisons. Quality of life was infrequently measured. All paracetamol plus NSAID combinations did not increase the risk of adverse events compared to NSAID monotherapy.Low-to-moderate quality evidence supports the oral use of some paracetamol plus NSAID combinations for short-term pain relief with no increased risk of harm for low back pain and osteoarthritis compared to its non-paracetamol monotherapy comparator. [ABSTRACT FROM AUTHOR]

The construction industry is facing unique problems in accident prevention. The existing management method for detecting workers' unsafe behaviors and unsafe states of objects relies primarily on manual monitoring, which does not only consume large amounts of time and money but also cannot cover all workers in the entire construction site. Meanwhile, the workers' perception of being at risk of injury decreases when they are concentrated in a crowded and noisy environment. In this case, it is difficult for them to take essential measures to protect themselves in the face of danger. In view of the aforementioned issues, this study proposes a method of evaluating the collision safety level of construction workers based on computer vision and fuzzy inference. Specifically, the proposed model works via two modules: vision extraction and safety assessment. The vision extraction module identifies construction workers and equipment through computer vision; centroid pixel coordinates and crowdedness are then extracted from a detection box. Afterward, the spatial relationship between moving devices and workers is calculated by a pixel calibration process. In the safety assessment module, the collected status information is analyzed by evaluating the safety level of each worker and conducting accident prevention through a fuzzy inference system. The safety level, which indicates the comprehensive risk of collision between workers and equipment in a particular dynamic environment, will be displayed numerically, breaking through the limitations of conventional qualitative evaluation. Field experiments validate the feasibility of the proposed method of informing workers about potential danger situations in an objective way. Moreover, by setting a safety-level threshold, the onsite safety management personnel can take corresponding measures to avoid collision accidents when the worker's safety level is lower than the threshold. [ABSTRACT FROM AUTHOR]

Accurately detecting anomalies in microservice systems is crucial to avoid system failures and economic losses for users. Existing approaches detect anomalies by extracting sequential information from single-modal data, such as metrics or logs. However, they do not clearly analyze the temporal and spatial dependencies of each data type and ignore the correlation between different types of data, which can lead to a significant number of false positives. In this paper, we propose a novel Microservice system Anomaly Detection method via Multi-modal data and Multi-feature extraction (MADMM), which performs a joint analysis of metrics and logs from a spatial and temporal perspective. Specifically, we firstly construct separate feature graphs for metrics and logs in each time window. A graph convolution network is then utilized to capture the spatial correlation among different metrics, while a graph attention network is employed to analyze the contextual relationships among different log events. Then, we design a Cross-Modal Attention-based Gate Recurrent Unit (CMA-GRU) to capture the intricate temporal dependencies of each modal data and facilitate fulfilling cross-modal interactions. Finally, we introduce multi-grained contrastive learning methods to learn robust cross-modal features from both inter- and intra-modality aspects. Experimental results on real-world datasets demonstrate that MADMM outperforms existing baseline methods and exhibits better robustness. [ABSTRACT FROM AUTHOR]

Semantic segmentation of multi-modal remote sensing images is an important branch of remote sensing image interpretation. Multi-modal data has been proven to provide rich complementary information to deal with complex scenes. In recent years, semantic segmentation based on deep learning methods has made remarkable achievements. It is common to simply concatenate multi-modal data or use parallel branches to extract multi-modal features separately. However, most existing works ignore the effects of noise and redundant features from different modalities, which may not lead to satisfactory results. On the one hand, existing networks do not learn the complementary information of different modalities and suppress the mutual interference between different modalities, which may lead to a decrease in segmentation accuracy. On the other hand, the introduction of multi-modal data greatly increases the running time of the pixel-level dense prediction. In this work, we propose an efficient C3Net that strikes a balance between speed and accuracy. More specifically, C3Net contains several backbones for extracting features of different modalities. Then, a plug-and-play module is designed to effectively recalibrate and aggregate multi-modal features. In order to reduce the number of model parameters while remaining the model performance, we redesign the semantic contextual extraction module based on the lightweight convolutional groups. Besides, a multi-level knowledge distillation strategy is proposed to improve the performance of the compact model. Experiments on ISPRS Vaihingen dataset demonstrate the superior performance of C3Net with 15× fewer FLOPs than the state-of-the-art baseline network while providing comparable overall accuracy. [ABSTRACT FROM AUTHOR]

Automated system log anomaly detection plays a crucial role in ensuring service reliability. Existing methods incompletely utilize structured log entries, resulting in the loss of key information such as components and time. Besides, due to the limitations of labeled data, models trained by a single system are difficult to apply to other systems. Therefore, we propose a cross-system log anomaly detection method named LogGT, which simultaneously models log events, components and time, leveraging labeled system knowledge to achieve anomaly detection in unlabeled systems. Specifically, we firstly design a heterogeneous graph to accurately represent the interactions between different events and components in the log sequence. Then, in order to avoid noise interference and conduct cross-system semantic analysis, we employ BERT to extract log sentence vectors, and Normalizing Flow is used to optimize them for smoother node embedding. A Graph Transformer Architecture with Time Intervals (GTAT) is proposed to model heterogeneous graphs by integrating time feature, allowing for a comprehensive analyze of execution order and time anomalies. Additionally, we design a semantic weighting method and utilize a novel domain-adapted transfer learning technology to effectively transfer the heterogeneous graph features of the source system to the target system. Experimental results demonstrate that LogGT outperforms five log anomaly detection methods, achieving an average anomaly detection F1-score higher than 0.95. Moreover, the AUC value of GTAT exceeds the sequence model by more than 2.3%. • A novel log heterogeneous graph modeling method is proposed. • The Normalizing Flow-based BERT model is utilized to generate smooth log semantic. • A Graph Transformer Architecture with Time Interval for order and time anomalies. • A domain-adapted transfer learning with semantic weights for feature transfer. • The comprehensive evaluation of LogGT on three different system datasets. [ABSTRACT FROM AUTHOR]

Deep Reinforcement Learning (DRL) is widely used in path planning with its powerful neural network fitting ability and learning ability. However, existing DRL-based methods use discrete action space and do not consider the impact of historical state information, resulting in the algorithm not being able to learn the optimal strategy to plan the path, and the planned path has arcs or too many corners, which does not meet the actual sailing requirements of the ship. In this paper, an optimized path planning method for coastal ships based on improved Deep Deterministic Policy Gradient (DDPG) and Douglas–Peucker (DP) algorithm is proposed. Firstly, Long Short-Term Memory (LSTM) is used to improve the network structure of DDPG, which uses the historical state information to approximate the current environmental state information, so that the predicted action is more accurate. On the other hand, the traditional reward function of DDPG may lead to low learning efficiency and convergence speed of the model. Hence, this paper improves the reward principle of traditional DDPG through the mainline reward function and auxiliary reward function, which not only helps to plan a better path for ship but also improves the convergence speed of the model. Secondly, aiming at the problem that too many turning points exist in the above-planned path which may increase the navigation risk, an improved DP algorithm is proposed to further optimize the planned path to make the final path more safe and economical. Finally, simulation experiments are carried out to verify the proposed method from the aspects of plan planning effect and convergence trend. Results show that the proposed method can plan safe and economic navigation paths and has good stability and convergence. [ABSTRACT FROM AUTHOR]

Using semantic information can help to accurately find suitable services from a variety of available (different semantics) services, and the semantic information of Web services can be described in detail in a Web service knowledge graph. In this paper, a Web service recommendation algorithm based on knowledge graph representation learning (kg-WSR) is proposed. The algorithm embeds the entities and relationships of the knowledge graph into the low-dimensional vector space. By calculating the distance between service entities in low-dimensional space, the relationship information of services which is not considered in recommendation approaches using a collaborative filtering algorithm is incorporated into the recommendation algorithm to enhance the accurateness of the result. The experimental results show that this algorithm can not only effectively improve the accuracy rate, recall rate, and coverage rate of recommendation but also solve the cold start problem to some extent. [ABSTRACT FROM AUTHOR]

System logs are an important data source for performance monitoring and anomaly detection. Analyzing logs for anomaly detection can improve service quality. At present, although machine learning algorithms for anomaly detection can achieve high accuracy, they lack robustness. The detection model cannot dynamically adapt to changes of logs when system logs contain noises owing to the casualness of the operators or log templates update. In face of this challenge, the paper proposes a robust log anomaly detection method based on contrastive learning and multi-scale Masked Sequence to Sequence (MASS). First, a log feature extraction model integrating the BERT model with contrastive learning is proposed. It can extract effective features by pulling two related normal logs together and pushing apart normal and abnormal logs to ensure that the semantic similarity between normal and abnormal log templates is lower than that between normal log templates, effectively remove abnormal log templates and distinguish log categories to which normal log templates and normal noise log templates belong rather than rudely treating the noise log templates as anomalies, which enhances the robustness of anomaly detection. Then, a multi-scale Masked Sequence to Sequence (MSMASS) model is proposed, the Attention mechanism of the MASS model is replaced with multi-scale Attention to fully learn the context information of different scales of the log sequence, which improves the accuracy of anomaly detection. Contrast experiments are conducted with four baseline methods on common datasets, and the results show that the method proposed in this paper is superior to most existing log-based anomaly detection methods in terms of accuracy and robustness. [ABSTRACT FROM AUTHOR]

In this paper, we consider finding a sparse solution of nonnegative least squares problems with a linear equality constraint. We propose a projection-based gradient descent method for solving huge size constrained least squares problems. Traditional Newton-based methods require solving a linear system. However, when the matrix is huge, it is neither practical to store it nor possible to solve it in a reasonable time. We therefore propose a matrix-free iterative scheme for solving the minimizer of the captured problem. This iterative scheme can be explained as a projection-based gradient descent method. In each iteration, a projection operation is performed to ensure the solution is feasible. The projection operation is equivalent to a shrinkage operator, which can guarantee the sparseness of the solution obtained. We show that the objective function is decreasing. We then apply the proposed method to the inverse problem of constructing a probabilistic Boolean network. Numerical examples are then given to illustrate both the efficiency and effectiveness of our proposed method. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) services are directly deployed on resource-constrained smart devices. Smart devices are characteristic by spatial and temporal constraints and energy limitations. A single IoT service cannot meet the complex requirements of users, so multiple IoT services need to be combined to provide services to users. As more and more smart devices are deployed in IoT, how to select IoT services with lower energy consumption and better quality of service (QoS) for service composition becomes a challenging problem. Combined with the characteristics that the data information of IoT is closely related to geographical location, the GeoHash algorithm is used to locally screen services based on geographical location, so as to narrow the range of candidate services. For smart devices with energy constraints, this paper proposes a combined optimization model. The model considers not only the transmission energy consumption and switching energy consumption, but also the execution energy consumption when the device provides services. In order to balance QoS attributes and energy consumption, the composition problem is regarded as a multi-objective optimization problem and solved using a genetic algorithm (GA). The simulation results show that service composition scheme selected by this service composition optimization model has lower energy consumption and higher service quality. [ABSTRACT FROM AUTHOR]

Existing Web services recommendation models suffer from matrix sparsity and cold-start problems due to the limited number of user-service interactions and too little contextual information, so this paper introduces knowledge graph as auxiliary information to Web service recommendation to alleviate matrix sparsity and cold-start problems. And we propose the Web services recommendation based on Metapath-guided Graph Attention Network Model (WSR-MGAT) to fully exploit the structural information of the knowledge graph to improve the recommendation accuracy. Specifically, WSR-MGAT uses graph embedding method to obtain the initial embedding of entities and relationships. In order to obtain more closely related neighbors, we propose to use a distance-aware path sampling method to extract meta-path instances with closer relationships. Previous knowledge graph-based Web service recommendations do not make full use of the rich interaction information, which may lead to limited performance. To address this problem, this paper uses meta-paths to guide nodes to recursively aggregate higher-order neighbor information and use an attention mechanism to distinguish the importance of neighbors. Meanwhile, the semantic information of nodes under different meta-paths is fused to obtain a more comprehensive embedding of nodes. To verify the performance of the model, we use the real data crawled from the ProgrammableWeb platform to conduct multiple groups of experiments. The experimental results show that the WSR-MGAT improves over the strongest baselines w.r.t. Prec@10 by 4.6%; Recall@10 by 3.8%; NDCG@10 by 4.2% and F1@10 by 4.9%. [ABSTRACT FROM AUTHOR]

The log messages generated in the system reflect the state of the system at all times. The realization of autonomous detection of abnormalities in log messages can help operators find abnormalities in time and provide a basis for analyzing the causes of abnormalities. First, this paper proposes a log sequence anomaly detection method based on contrastive adversarial training and dual feature extraction. This method uses BERT (Bidirectional Encoder Representations from Transformers) and VAE (Variational Auto-Encoder) to extract the semantic features and statistical features of the log sequence, respectively, and the dual features are combined to perform anomaly detection on the log sequence, with a novel contrastive adversarial training method also used to train the model. In addition, this paper introduces the method of obtaining statistical features of log sequence and the method of combining semantic features with statistical features. Furthermore, the specific process of contrastive adversarial training is described. Finally, an experimental comparison is carried out, and the experimental results show that the method in this paper is better than the contrasted log sequence anomaly detection method. [ABSTRACT FROM AUTHOR]

Key performance indicator (KPI) anomaly detection is the underlying core technology in Artificial Intelligence for IT operations (AIOps). It has an important impact on subsequent anomaly location and root cause analysis. Variational auto-encoder (VAE) is a symmetry network structure composed of encoder and decoder, which has attracted extensive attention because of its ability to capture complex KPI data features and better detection results. However, VAE is not well applied to the modeling of KPI time series data and it is often necessary to set the threshold to obtain more accurate results. In response to these problems, this paper proposes a novel hybrid method for KPI anomaly detection based on VAE and support vector data description (SVDD). This method consists of two modules: a VAE reconstructor and SVDD anomaly detector. In the VAE reconstruction module, firstly, bi-directional long short-term memory (BiLSTM) is used to replace the traditional feedforward neural network in VAE to capture the time correlation of sequences; then, batch normalization is used at the output of the encoder to prevent the disappearance of KL (Kullback–Leibler) divergence, which prevents ignoring latent variables to reconstruct data directly. Finally, exponentially weighted moving average (EWMA) is used to smooth the reconstruction error, which reduces false positives and false negatives during the detection process. In the SVDD anomaly detection module, smoothed reconstruction errors are introduced into the SVDD for training to determine the threshold of adaptively anomaly detection. Experimental results on the public dataset show that this method has a better detection effect than baseline methods. [ABSTRACT FROM AUTHOR]

Identifying critical components are of great significance to the overall reliability of service-oriented systems (SOSs). As the size of the SOS increases, identifying critical components in the process of predicting the SOS reliability can reduce the number of components that need to be predicted and shorten the prediction time. Moreover, predicting the reliability of critical components can also ensure the stability of the SOS. Therefore, we transform the relationships among service components of the SOS into a service dependency graph. Then, an improved weighted LeaderRank algorithm (IW-LeaderRank) is proposed to measure the importance of components and obtain the sequence of critical components. Through experimental analysis, the method can accurately and efficiently identify critical components in SOSs. [ABSTRACT FROM AUTHOR]