In cloud computing, how to reasonably allocate computing resources for batch jobs to ensure the load balance of dynamic clusters and meet user requests is an important and challenging task. Most existing studies are based on deep Q network, which utilizes neural networks to estimate the expected value of cumulative return in the scheduling process. The value-based DQN algorithms ignore the complete information contained in the value distribution and lack strong adaptability to time-varying batch jobs and dynamic cluster resources. Therefore, to capture the inherent stochasticity of the scheduling process caused by environmental stochasticity, we utilize Distributional Reinforcement Learning to model the value distribution of the cumulative return. Specifically, we formalize the load balancing scheduling as a multi-objective optimization problem and construct a Distributional Reinforcement Learning model. Then we introduce quantile regression to learn the value distribution of the cumulative return during scheduling and propose a dynamic load balancing scheduling algorithm based on Distributional Reinforcement Learning. In addition, we develop a cluster environment for real-time processing of batch jobs to simulate the arrival of batch jobs and train the Distributional Reinforcement Learning-based scheduling agent. We conduct empirical experiments and detailed analysis by using the real Alibaba Cluster cluster traces v2018 and v2020. The results show that compared to the baseline algorithms, the proposed algorithm performs better in terms of cluster load balancing, success rate of instance creation and average completion time of the tasks. The experimental results on different trace datasets also indicate that the propsoed algorithm exhibits excellent scalability.

In clinical conditions limited by equipment, attaining lightweight skin lesion segmentation is pivotal as it facilitates the integration of the model into diverse medical devices, thereby enhancing operational efficiency. However, the lightweight design of the model may face accuracy degradation, especially when dealing with complex images such as skin lesion images with irregular regions, blurred boundaries, and oversized boundaries. To address these challenges, we propose an efficient lightweight attention network (ELANet) for the skin lesion segmentation task. In ELANet, two different attention mechanisms of the bilateral residual module (BRM) can achieve complementary information, which enhances the sensitivity to features in spatial and channel dimensions, respectively, and then multiple BRMs are stacked for efficient feature extraction of the input information. In addition, the network acquires global information and improves segmentation accuracy by putting feature maps of different scales through multi-scale attention fusion (MAF) operations. Finally, we evaluate the performance of ELANet on three publicly available datasets, ISIC2016, ISIC2017, and ISIC2018, and the experimental results show that our algorithm can achieve 89.87%, 81.85%, and 82.87% of the mIoU on the three datasets with a parametric of 0.459 M, which is an excellent balance between accuracy and lightness and is superior to many existing segmentation methods.

The traditional construction industry is characterized by high energy consumption and significant carbon emissions, primarily due to its reliance on on-site manual labor and wet operations, which are not only low in mechanization but also result in low material efficiency and substantial construction waste. Prefabricated construction offers a new solution with its efficient production methods, significantly enhancing material utilization and construction efficiency. This paper focuses on the production scheduling optimization of prefabricated components. The production scheduling directly affects the construction speed and cost of prefabricated buildings. Given the complex modeling and numerous constraints faced by the production of prefabricated components, we propose an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) for multi-objective optimization. The algorithm incorporates adaptive operators and greedy concepts for local search, enhancing solution exploration and diversity. We segment the production of prefabricated components into six stages, analyzing dependencies and constraints, and form a comprehensive scheduling model with objectives of minimizing contract penalties, storage costs, and production time. Extensive experiments demonstrate that the improved NSGA-II provides a more balanced and larger set of solutions compared to baseline algorithms, offering manufacturers a wider range of options. This research contributes to the optimization of production scheduling in the prefabricated construction industry, supporting coordinated, sustainable, automated, and transparent production environments.

Open-source systems help to manage the rapid development of software, while governing open-source systems properly can effectively promote software and software engineering. However, some significant problems, such as code controls, incentives, interaction and cooperation, automation, transparency and fairness of rights and responsibilities, cannot be properly solved by traditional methodologies. The decentralization, immutability, change in trust mode and smart contract programming of blockchain provide new solutions. In order to solve the problems of traditional centralized open-source governance, this paper proposes a decentralized open-source coordination management system using a novel triple-blockchain architecture. Through the analysis of traditional and blockchain-based research, the business and technical issues that need to be addressed in decentralized open-source governance systems have been emphatically studied. Combined with triple-blockchain architecture, smart contracts, oracles and continuous integration tools, we study the decentralization of open-source businesses and make them more trustworthy, automated and coordinated. An identity authentication mechanism is designed for permission control and inter-community collaboration. A decentralized open-source reputation is proposed for incentive and reference. We also improved the DPoS (Delegated Proof of Stake) consensus under triple-blockchain architecture to reduce repeated elections. By constructing the OSCMS prototype based on the proposed architecture model, many comparative experiments were conducted under different parameters and conditions and showed good feasibility, scalability, reliability and performance. The OSCMS not only solves the shortcomings of previous research but also provides a comprehensive and feasible reference for the decentralized practice of open-source governance.

Nowadays, microservice architecture has become mainstream way of cloud applications delivery. Distributed tracing is crucial to preserve the observability of microservice systems. However, existing trace representation approaches only concentrate on operations, relationships and metrics related to service invocations. They ignore service events that denotes meaningful, singular point in time during the service’s duration. In this paper, we propose iTCRL, a novel trace contrastive representation learning approach based on causal intervention. This approach first constructs a unified graph representation for each trace to describe the runtime status of service events in traces and the complex relationships between them. Then, Causal-intervention-based Trace Contrastive Learning is proposed, which learns trace representations from causal perspective based on the unified graph representations of traces. It uses causal intervention to generate contrastive views, heterogeneous graph neural network-based trace encoder to learn trace representations, and direct causal effect to guide the training of trace encoder. Experimental results on three datasets show that iTCRL outperforms all baselines in terms of trace classification, trace anomaly detection, trace sampling and noise robustness, and also validate the contribution of Causal-intervention-based Trace Contrastive Learning.

Dental diseases are among the most prevalent diseases globally, and accurate segmentation of dental calculus images plays a crucial role in periodontal disease diagnosis and treatment planning. However, the current methods are not stable and reliable enough due to the variable morphology of dental calculus and the blurring of the boundaries between the dental edges and the surrounding tissues; therefore, our hope is to propose an accurate and reliable calculus segmentation algorithm to improve the efficiency of clinical detection. We propose a multi-scale attention pyramid pooling network (MAPPNet) to enhance the performance of dental calculus segmentation. The network incorporates a multi-scale fusion strategy in both the encoder and decoder, forming a model with a dual-ended multi-scale structure. This design, in contrast to employing a multi-scale fusion scheme at a single end, enables more effective capturing of features from diverse scales. Furthermore, the attention pyramid pooling module (APPM) reconstructs the features on this map by leveraging a spatial-first and channel-second attention mechanism. APPM enables the network to adaptively adjust the weights of different locations and channels in the feature map, thereby enhancing the perception of important regions and key features. Experimental evaluation of our collected dental calculus segmentation dataset demonstrates the superior performance of MAPPNet, which achieves an intersection-over-union of 81.46% and an accuracy rate of 98.35%. Additionally, on two publicly available datasets, ISIC2018 (skin lesion dataset) and Kvasir-SEG (gastrointestinal polyp segmentation dataset), MAPPNet achieved an intersection-over-union of 76.48% and 91.38%, respectively. These results validate the effectiveness of our proposed network in accurately segmenting lesion regions and achieving high accuracy rates, surpassing many existing segmentation methods.