Water level data obtained from telemetry stations typically contains large number of outliers. Anomaly detection and a data imputation are necessary steps in a data monitoring system. Anomaly data can be detected if its values lie outside of a normal pattern distribution. We developed a median-based statistical outlier detection approach using a sliding window technique. In order to fill anomalies, various interpolation techniques were considered. Our proposed framework exhibited promising results after evaluating with F1-score and root mean square error (RMSE) based on our artificially induced data points. The present system can also be easily applied to various patterns of hydrological time series with diverse choices of internal methods and fine-tuned parameters. Specifically, the Spline interpolation method yielded a superior performance on non-cyclical data while the long short-term memory (LSTM) outperformed other interpolation methods on a distinct tidal data pattern.

Anomaly Detection Using a Sliding Window Technique and Data Imputation with Machine Learning for Hydrological Time Series

Battery management solutions for li-ion batteries based on artificial intelligence

Past, present, and future of the application of machine learning in cryptocurrency research

Smart contract transactions are increasingly interleaved by cross-contract calls. While many tools have been developed to identify a common set of vulnerabilities, the cross-contract vulnerability is overlooked by existing tools. Cross-contract vulnerabilities are exploitable bugs that manifest in the presence of more than two interacting contracts. Existing methods are however limited to analyze a maximum of two contracts at the same time. Detecting cross-contract vulnerabilities is highly non-trivial. With multiple interacting contracts, the search space is much larger than that of a single contract. To address this problem, we present xFuzz, a machine learning guided smart contract fuzzing framework. The machine learning models are trained with novel features (e.g., word vectors and instructions) and are used to filter likely benign program paths. Comparing with existing static tools, machine learning model is proven to be more robust, avoiding directly adopting manually-defined rules in specific tools. We compare xFuzz with three state-of-the-art tools on 7,391 contracts. xFuzz detects 18 exploitable cross-contract vulnerabilities, of which 15 vulnerabilities are exposed for the first time. Furthermore, our approach is shown to be efficient in detecting non-cross-contract vulnerabilities as well&#x2014;using less than 20&#x0025; time as that of other fuzzing tools, xFuzz detects twice as many vulnerabilities.

xFuzz: Machine Learning Guided Cross-Contract Fuzzing

A systematic literature review on web service clustering approaches to enhance service discovery, selection and recommendation

The creative introduction of the smart contracts in Ethereum, which are Turing‐complete programs, boosted blockchain to the second generation. Meantime, the specifically designed young and fast‐evolving programming languages, such as Solidity, become the key factors behind smart contracts being the breeding ground of ubiquitous defects. As many contract defects occur within certain compiler versions, knowing the specific compiler version used to generate the contract's bytecode, facilitates the design of more targeted defect detection approaches, and provides ways to estimate the risks faced of invoking it. To this end, we propose VSmart (compiler Version identification for Smart contract), which takes in the bytecode of the smart contract to be analyzed and outputs the major compiler version used to produce it. The basic idea is to leverage deep neural networks to grasp version‐indicative features from contracts' normalized opcode sequences, and train classifiers on a data set consisting of 131,546 smart contracts with ground‐truth labels we collected from Etherscan. The performance evaluation conducted shows that VSmart achieves nearly 98% accuracy in identifying major Solidity compiler versions. Further, on the basis of VSmart, we perform an empirical study on the distribution of the Solidity compiler versions on a wild data set consisting of 15,326,672 nontrivial smart contracts actually deployed on the Ethereum blockchain. The landscape estimation results show that the Solidity version distribution is rather imbalanced, with Solidity 0.4 being the most popular one; and the developers' Solidity usage practices conflict with the official's suggestion of always using the latest version, while they tend to gradually switch to newer versions.

Landscape estimation of solidity version usage on Ethereum via version identification

Withthe rapid development of mobile Internet and wireless technologies, edge servers in mobile edge computing (MEC) provide a large number of mobile services to develop advanced and complex mobile applications. Quality of Service (QoS) is an essential indicator for recommendation and selection from such a huge number of candidate mobile services. However, QoS records are sparse in reality because users only invoke certain mobile services. How to predict missing QoS values is a key issue in operations related to mobile services. Some collaborative filtering (CF) methods have been used to solve QoS prediction problem of traditional Web services. Neverless, the high dimensionality, high sparsity and dynamic variability of QoS data in MEC limit the application of these methods. This paper proposes a temporal regularized tensor factorization method based on hybrid CF for QoS prediction of mobile services. The overall structure of QoS data is represented by constructing a user-service-time tensor model. As QoS data possesses certain characteristics of time series, a temporal regular term is introduced into CP decomposition to mine the relationship between QoS values in adjacent time intervals. The alternating least square (ALS) algorithm is used to optimize the approximate tensor iteratively. The low-rank estimation of the target tensor is obtaind to predict missing values. Extensive experimental studies on the large-scale QoS dataset WS-Dream show that our method has significantly better prediction accuracy than the other nine state-of-the-art methods under different data densities. Especially in the case of sparse data, the prediction effect is more prominent, and the accuracy increases up to 62.4% at most.

A hybrid tensor factorization approach for QoS prediction in time-aware mobile edge computing

We consider distributed Kalman filter for dynamic state estimation over wireless sensor networks. It is promising but challenging when network is under cyber attacks. Since the information exchange between nodes, the malicious attacks quickly spread across the entire network, which causing large measurement errors and even to the collapse of sensor networks. Aiming at the malicious network attack, a trust-based distributed processing frame is proposed. Which allows neighbor nodes to exchange information, and a series of trusted nodes are found using truth discovery. As a demonstration, distributed Cooperative Localization is considered, and numerical results are provided to evaluate the performance of the proposed approach by considering random, false data injection and replay attacks.

Trust-Based Distributed Kalman Filter Estimation Fusion under Malicious Cyber Attacks

Because of the short length of documents describing Web services on the Internet, the traditional modeling method is not ideal, which affects the clustering effect of Web services. To this end, we propose a word-embedded topic model, which can effectively solve the problem of data sparsity. A method based on embedded words and topic models. Firstly, Wikipedia is used as an external corpus to extend API service document, and LF-LDA model is used to model its topic distribution. The corpus data is extracted from Wikipedia by wikiextractor, and the corpus is trained with Word2vec tool. The data comes from its word vector model. Use the trained Wikipedia Word Vector Model to extend API service description documents. In the extended API service documents, LF-LDA topic modeling technology is used to mine the implicit topic information and determine the optimal number of topics, so as to accurately measure the semantic similarity between API service documents. Finally, K-means algorithm is used to cluster Web services, and the validity of this method is verified by the real data of API services on the basis of programmability. The experimental results show that this method has better accuracy, recall rate and F value than the traditional Web service clustering method based on topic model algorithm.

Research on Web Service Clustering Method Based on Word Embedding and Topic Model.

As the volume of data grows exponentially, big data brings an unprecedented burden to the current computing infrastructure How to deal with big data efficiently and concisely and reduce the burden of computing infrastructure has always been a big challenge Therefore, this paper proposes a high-quality core data extraction method in edge computing nodes Firstly, heterogeneous data are fused into a unified model, the data characteristics of the original data are retained Then, a Lanzcosbased incremental tensor decomposition method is proposed to extracted the high quality core tensor dynamically Finally, the model algorithm is verified using real data The experimental results show that the approximate tensor reconstructed from the tensor containing 15% of the core data can guarantee 90% accuracy At the same time, IncLHOSVD is significantly better than non-incremental HOSVD in execution time in guaranteeing the accuracy of approximate equal error

Multi-Source Heterogeneous Core Data Acquisition Method in Edge Computing Nodes

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