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In today’s society, with the continuous development of China’s economy, the power industry is also growing rapidly. As an important part of the logistics distribution system, the warehousing system plays an important role in the production and operation of the entire enterprise. Therefore, effective utilization of distributed generation is of great significance for improving energy supply efficiency and reducing operating costs. This paper mainly introduces the background and application status of distributed generation technology, analyzes and discusses the research and simulation of power supply network scheduling optimization methods based on DGS structure model and DGs topology mode, and gives the corresponding solutions. After the design of the system, this paper conducts a performance test on the system to determine whether the system can operate normally. The test results show that the performance of the distributed power storage system is basically stable at more than 80% under different power storage capacities, and the power utilization efficiency is also more than 90%. This shows that the performance of the power storage system can meet the needs of users.

Technical Q&A sites, such as Stack Overflow and Ask Ubuntu, have been widely utilized by software engineers to seek support for development challenges. However, not all the raised questions get instant feedback, and the retrieved answers can vary in quality. The users can hardly avoid spending much time before solving their problems. Prior studies propose approaches to automatically recommend answers for the question posts on technical Q&A sites. However, the lengthiness and the lack of background knowledge issues limit the performance of answer recommendation on these sites. The irrelevant sentences in the posts may introduce noise to the semantics learning and prevent neural models from capturing the gist of texts. The lexical gap between question and answer posts further misleads current models to make failure recommendations. From this end, we propose a novel neural network named TopicAns for answer selection on technical Q&A sites. TopicAns aims at learning high-quality representations for the posts in Q&A sites with a neural topic model and a pre-trained model. This involves three main steps: (1) generating topic-aware representations of Q&A posts with the neural topic model, (2) incorporating the corpus-level knowledge from the neural topic model to enhance the deep representations generated by the pre-trained language model, and (3) determining the most suitable answer for a given query based on the topic-aware representation and the deep representation. Moreover, we propose a two-stage training technique to improve the stability of our model. We conduct comprehensive experiments on four benchmark datasets to verify our proposed TopicAns's effectiveness. Experiment results suggest that TopicAns consistently outperforms state-of-the-art techniques by over 30% in terms of Precision@1. [ABSTRACT FROM AUTHOR]

Floating offshore wind platform (FOWP) has become the economically favored option for supporting wind turbines in deep waters. It is urgent to propose new concept designs for FOWPs that can be effectively deployed. Additionally, the extensive use of steel in such platforms significantly escalates costs, necessitating the optimization of steel utilization. Motivated by these challenges, a V-shaped floating semi-submersible platform equipped with NREL 5 MW wind turbine is designed and analyzed based on the potential flow theory and the blade element momentum theory. Fully coupled time-domain simulations are conducted using the F2A program, which couples NREL FAST and ANSYS AQWA via a Dynamic Link Library (DLL), to compare the hydrodynamic performance and stability of the V-shaped floating platform with the original triangle-shaped model of "Fuyao". Various sea conditions have been considered, including combined wind-wave action and wind-wave-current action at different incidence angles. The results show that the V-shaped floating platform has better economic and hydrodynamic performance (e.g., a reduction of 40.4% and 12.9%, respectively, in pitch and yaw motions, and a 17.4% reduction in maximum mooring tension), but lower stability than its triangle-shaped counterpart. [ABSTRACT FROM AUTHOR]

Federated learning is a distributed machine learning framework that has been widely applied in scenarios that require data privacy. To obtain a neural network model that performs well, when the model falls into a bug, existing solutions retrain it on a larger training dataset or the carefully selected samples from model diagnosis. To overcome this challenge, this paper presents Fed-DNN-Debugger, which can automatically and efficiently fix DNN models in federated learning. Fed-DNN-Debugger fixes the federated model by fixing each client model. Fed-DNN-Debugger consists of two modules for debugging a client model: nonintrusive metadata capture (NIMC) and automated neural network model debugging (ANNMD). NIMC collects the metadata with deep learning software syntax automatically. It does not insert any code for metadata collection into modeling scripts. ANNMD scores samples according to metadata and searches for high-quality samples. Models are retrained on the selected samples to repair their weights. Our experiments with popular federated models show that Fed-DNN-Debugger can improve the test accuracy by 8% by automatically fixing models. [ABSTRACT FROM AUTHOR]

In this paper, we study the problem of privacy-preserving data synthesis (PPDS) for tabular data in a distributed multi-party environment. In a decentralized setting, for PPDS, federated generative models with differential privacy are used by the existing methods. Unfortunately, the existing models apply only to images or text data and not to tabular data. Unlike images, tabular data usually consist of mixed data types (discrete and continuous attributes) and real-world datasets with highly imbalanced data distributions. Existing methods hardly model such scenarios due to the multimodal distributions in the decentralized continuous columns and highly imbalanced categorical attributes of the clients. To solve these problems, we propose a federated generative model for decentralized tabular data synthesis (HT-Fed-GAN). There are three important parts of HT-Fed-GAN: the federated variational Bayesian Gaussian mixture model (Fed-VB-GMM), which is designed to solve the problem of multimodal distributions; federated conditional one-hot encoding with conditional sampling for global categorical attribute representation and rebalancing; and a privacy consumption-based federated conditional GAN for privacy-preserving decentralized data modeling. The experimental results on five real-world datasets show that HT-Fed-GAN obtains the best trade-off between the data utility and privacy level. For the data utility, the tables generated by HT-Fed-GAN are the most statistically similar to the original tables and the evaluation scores show that HT-Fed-GAN outperforms the state-of-the-art model in terms of machine learning tasks. [ABSTRACT FROM AUTHOR]

Cloud platforms allow administrators or management applications with privileged accounts to remotely perform privileged operations for specific tasks, such as deleting virtual hosts. When privileged accounts are leaked and conduct dangerous privileged operations, severe security problems will appear on cloud platforms. To solve these problems, researchers focus on auditing privileged users' behaviors. However, it is difficult to automatically audit fine-grained privileged behaviors for graphical operating systems. Moreover, it is hard to prevent users from bypassing the audit system or to prevent hackers from attacking audit system. In this paper, we propose a Secure and Automatic Behavior Audit system named SA-UBA. It provides advanced deep learning models to automatically achieve fine-grained user behavior audits for graphical operating systems. Furthermore, it adopts cryptography-based account storage and sharing methods to securely manage privileged accounts. In particular, privileged accounts cannot be leaked even if SA-UBA is compromised by attackers. We built a threat model of a cloud platform to evaluate the security of the SA-UBA and conduct extensive experiments with SA-UBA in real scenarios. The results show SA-UBA introduces a small overhead on securely managing privileged accounts and accurately recognizes fine-grained user behaviors. [ABSTRACT FROM AUTHOR]

Objective: To determine the most influential data features and to develop machine learning approaches that best predict hospital readmissions among patients with diabetes. Methods: In this retrospective cohort study, we surveyed patient statistics and performed feature analysis to identify the most influential data features associated with readmissions. Classification of all-cause, 30-day readmission outcomes were modeled using logistic regression, artificial neural network, and EasyEnsemble. F1 statistic, sensitivity, and positive predictive value were used to evaluate the model performance. Results: We identified 14 most influential data features (4 numeric features and 10 categorical features) and evaluated 3 machine learning models with numerous sampling methods (oversampling, undersampling, and hybrid techniques). The deep learning model offered no improvement over traditional models (logistic regression and EasyEnsemble) for predicting readmission, whereas the other two algorithms led to much smaller differences between the training and testing datasets. Conclusions: Machine learning approaches to record electronic health data offer a promising method for improving readmission prediction in patients with diabetes. But more work is needed to construct datasets with more clinical variables beyond the standard risk factors and to fine-tune and optimize machine learning models. [ABSTRACT FROM AUTHOR]

Content-Defined Chunking (CDC) has been playing a key role in data deduplication systems recently due to its high redundancy detection ability. However, existing CDC-based approaches introduce heavy CPU overhead because they declare the chunk cut-points by computing and judging the rolling hashes of the data stream byte by byte. In this article, we propose FastCDC, a Fast and efficient Content-Defined Chunking approach, for data deduplication-based storage systems. The key idea behind FastCDC is the combined use of five key techniques, namely, gear based fast rolling hash, simplifying and enhancing the Gear hash judgment, skipping sub-minimum chunk cut-points, normalizing the chunk-size distribution in a small specified region to address the problem of the decreased deduplication ratio stemming from the cut-point skipping, and last but not least, rolling two bytes each time to further speed up CDC. Our evaluation results show that, by using a combination of the five techniques, FastCDC is 3-12X faster than the state-of-the-art CDC approaches, while achieving nearly the same and even higher deduplication ratio as the classic Rabin-based CDC. In addition, our study on the deduplication throughput of FastCDC-based Destor (an open source deduplication project) indicates that FastCDC helps achieve 1.2-3.0X higher throughput than Destor based on state-of-the-art chunkers. [ABSTRACT FROM AUTHOR]

The primary business challenge for the customers to use outsourced computation and storage is the loss of data control and security. So encryption will become a commodity in the near future. There is big diffusion with the above scenario: take advantage of current application’s full functionalities at the same time ensuring their sensitive data remains protected and under customers’ control. Prior works have achieved effective progress towards satisfying both sides. But there are still some technical challenges, such as supporting file or data-stream based applications and supporting full-text and advanced searches. In this paper, a novel security broker based encrypted data search scheme, called Enc-YUN, is proposed, which transparently builds a reverse index at the security broker when the data flow is transmitted to the cloud. And search firstly takes place on the index, in which the mapping structure corresponds to and retrieves the very encrypted data in the cloud on behalf of the client. With this scheme, updated-to-date full-text search techniques can be easily integrated to carry out the most advanced search functionalities, at the same time, maintaining the strongest levels of data protection from curious providers or third parties. Experimental results show that Enc-YUN is effective with broad categories of cloud applications, and the performance overhead induced is minor and acceptable according to user’s perceptual experience. [ABSTRACT FROM AUTHOR]

Summary: Cloud computing has become a new Internet application model, where network virtualization is recognized as an important technology for allowing multiple heterogeneous virtual networks (VNs) to coexist on a shared substrate network (SN). As demands in cloud computing increase, the scale of VN greatly increases as well, and providing an end‐to‐end SN to embed VNs in terms of scale is difficult. To utilize SN resources fully, we devise a topology‐aware Node‐Fusion algorithm, which is different from the traditional virtual network embedding (VNE) algorithms, for repeatable VNE over substrate nodes problem. We rank the resource of nodes through a novel solution by considering the CPU and bandwidth of adjacent link capacity and the number of adjacent links of each node as resources, and rank a node on the basis of resources. Furthermore, we embed several virtual nodes into the same substrate node together in accordance with Node‐Fusion interconnection value during the node mapping process, which can greatly improve the success ratio of the subsequent link mapping phase. Evaluation results confirm that Node‐Fusion outperforms traditional classical heuristics (Link‐opt, Node‐opt, and ORSTA), which are modified to fit into our model, with regard to acceptance ratio, long‐term revenue, long‐term cost, and revenue‐cost ratio. [ABSTRACT FROM AUTHOR]

The trust computing mechanism has an increasing role in the cooperative work of wireless sensor networks. However, the computing speed, resource overhead, and anti-collaborative attack ability of a trust mechanism itself are three key challenging issues for any open and resource-constrained wireless sensor networks. In this study, we propose a fast, resource-saving, and anti-collaborative attack trust computing scheme (FRAT) based on across-validation mechanism for clustered wireless sensor networks. First, according to the inherent relationship among three network entities (which are made up of three types of network nodes, namely base stations, cluster heads, and cluster members), we propose the cross-validation mechanism, which is effective and reliable against collaborative attacks caused by malicious nodes. Then, we adopt a fast and resource-saving trust computing scheme for cooperation between between cluster heads or cluster members. This scheme is suitable for wireless sensor networks because it facilitates resource-saving. Through theoretical analysis and experiments, the feasibility and effectiveness of the trust computing scheme proposed in this study are verified. [ABSTRACT FROM AUTHOR]

Inappropriate use of veterinary drugs can result in the presence of antibiotic residues in animal-derived foods, which is a threat to human health. A simple yet efficient antibiotic-sensing method is highly desirable. Programmable DNA amplification circuits have supplemented robust toolkits for food contaminants monitoring. However, they currently face limitations in terms of their intricate design and low signal gain. Herein, we have engineered a robust reciprocal catalytic DNA (RCD) circuit for highly efficient bioanalysis. The trigger initiates the cascade hybridization reaction (CHR) to yield plenty of repeated initiators for activating the rolling circle amplification (RCA) circuit. Then the RCA-generated numerous reconstituted triggers can reversely stimulate the CHR circuit. This results in a self-sufficient supply of numerous initiators and triggers for the successive cross-invasion of CHR and RCA amplifiers, thus leading to exponential signal amplification for the highly efficient detection of analytes. With its flexible programmability and modular features, the RCD amplifier can serve as a universal toolbox for the high-performance and accurate sensing of kanamycin in buffer and food samples including milk, honey, and fish, highlighting its enormous promise for low-abundance contaminant analysis in foodstuffs.

In this paper, we study the problem of privacy-preserving data synthesis (PPDS) for tabular data in a distributed multi-party environment. In a decentralized setting, for PPDS, federated generative models with differential privacy are used by the existing methods. Unfortunately, the existing models apply only to images or text data and not to tabular data. Unlike images, tabular data usually consist of mixed data types (discrete and continuous attributes) and real-world datasets with highly imbalanced data distributions. Existing methods hardly model such scenarios due to the multimodal distributions in the decentralized continuous columns and highly imbalanced categorical attributes of the clients. To solve these problems, we propose a federated generative model for decentralized tabular data synthesis (HT-Fed-GAN). There are three important parts of HT-Fed-GAN: the federated variational Bayesian Gaussian mixture model (Fed-VB-GMM), which is designed to solve the problem of multimodal distributions; federated conditional one-hot encoding with conditional sampling for global categorical attribute representation and rebalancing; and a privacy consumption-based federated conditional GAN for privacy-preserving decentralized data modeling. The experimental results on five real-world datasets show that HT-Fed-GAN obtains the best trade-off between the data utility and privacy level. For the data utility, the tables generated by HT-Fed-GAN are the most statistically similar to the original tables and the evaluation scores show that HT-Fed-GAN outperforms the state-of-the-art model in terms of machine learning tasks.